Nutrient Criteria

Technical Guidance Manual

Rivers and Streams

United States Office of Water EPA-822-B-00-002

Environmental Protection Office of Science and Technology July 2000

Agency Washington, DC 20460 www.epa.gov
iii

DISCLAIMER

This manual provides technical guidance to States, Tribes, and other authorized jurisdictions to establish

water quality criteria and standards under the Clean Water Act (CWA), in order to protect aquatic life

from acute and chronic effects of nutrient overenrichment. Under the CWA, States and Tribes are

required to establish water quality criteria to protect designated uses. State and Tribal decisionmakers

retain the discretion to adopt approaches on a case-by-case basis that differ from this guidance when

appropriate and scientifically defensible. While this manual constitutes EPA’s scientific

recommendations regarding ambient concentrations of nutrients that protect resource quality and aquatic

life, it does not substitute for the CWA or EPA’s regulations; nor is it a regulation itself. Thus, it cannot

impose legally binding requirements on EPA, States, Tribes, or the regulated community, and might not

apply to a particular situation or circumstance. EPA may change this guidance in the future.
THIS PAGE INTENTIONALLY 

LEFT BLANK

iv
v

CONTENTS

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

1. Introduction ..................................................................... 1

1.1 Purpose of the Document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Nutrient Enrichment Problems in Rivers and Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Water Quality Standards and Criteria ............................................. 9

1.4 Overview of the Criteria Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.5 The Criteria Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.6 Identify Needs and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.7 Document Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2. Stream System Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2 Classification Schemes Based on Physical Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Ecoregional Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Fluvial Geomorphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Rosgen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Stream Order . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Physical Factors Used to Classify Streams and Analyze Trophic State . . . . . . . . . . . . . . . . . . . 23

2.3 Classification Schemes Based on Nutrient Gradients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Classification by Nutrient Ecoregions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Classification by Trophic State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3. Select Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.2 Primary Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Algal Biomass as Chlorophyll a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Total Suspended Solids, Transparency, and Turbidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Flow and Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Secondary Response Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Sensitive Response Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Other Secondary Response Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4. Sampling Design for New Monitoring Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.2 Sampling Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Considerations for Sampling Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Where to Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

When to Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Approaches to Sampling Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
vi

Identifying and Characterizing Reference Stream Reaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Other Considerations for Monitoring Nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Involvement of Citizen Monitoring Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5. Building a Database of Nutrients and Algae-Related Water Quality Information . . . . . . . . . . 61

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.2 Databases and Database Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

National Nutrients Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.3 Collecting Existing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Potential Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Quality of Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Quality Assurance/Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

6. Analyze Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

6.2 Linking Nutrient Availability to Algal Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Defining the Limiting Nutrient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Establishing Predictive Nutrient-algal Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Analysis Methods for Establishing Nutrient-algal Relationships . . . . . . . . . . . . . . . . . . . . . . . . 80

Analysis of Algal Species Composition to Classify Stream Response to Nutrients . . . . . . . . . 81

Characterizing Nutrient Status with Algal Species Composition . . . . . . . . . . . . . . . . . . . . . . . . 83

Developing Multimetric Indices to Complement Nutrient Criteria . . . . . . . . . . . . . . . . . . . . . . 85

Assessing Nutrient-algal Relationships Using Experimental Procedures . . . . . . . . . . . . . . . . . 88

Other Issues to Keep in Mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

6.3 Statistical Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Frequency Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Correlation and Regression Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Tests of Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

6.4 Using Models as Management Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

7. Nutrient and Algal Criteria Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

7.2 Methods for Establishing Nutrient and Algal Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Using Reference Reaches to Establish Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Using Predictive Relationships to Establish Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Using Published Nutrient Thresholds or Recommended Algal Limits . . . . . . . . . . . . . . . . . . 100

Considerations for Downstream Receiving Waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

7.3 Evaluation of Proposed Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Guidance for Interpreting and Applying Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Sampling for Comparison to Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Criteria Modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Implementation of Nutrient Criteria into Water Quality Standards . . . . . . . . . . . . . . . . . . . . . 106

8. Management Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

8.2 Managing Streamflow Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Low Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

High Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
vii

8.3 Managing Point Source Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Water Quality Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

NPDES Permits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

Combined Sewer Overflows (CSOs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Stormwater Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

Total Maximum Daily Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

Look to the Future ... Pollutant Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

8.4 Managing Nonpoint Source Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Nonpoint Sources of Nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Efforts to Control Nonpoint Source Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

9. Monitoring and Reassessment of Nutrient Criteria Ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

9.2 Assessment of Process Through Monitoring and Periodic Review . . . . . . . . . . . . . . . . . . . . . 125

9.3 Completion and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

9.4 Continued Monitoring of the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Appendix A: Nutrient Criteria Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B: Methods of Analysis for Water Quality Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-65

Appendix C: Statistical Tests and Modeling Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-75

Appendix D: Acronym List and Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-81
THIS PAGE INTENTIONALLY 

LEFT BLANK

viii
ix

CONTRIBUTORS

Sharon Buck (U.S. Environmental Protection Agency)*

Gregory Denton (Tennessee Department of Environment and Conservation)

Walter Dodds (Kansas State University)*

Jen Fisher (U.S. Environmental Protection Agency)*

David Flemer (U.S. Environmental Protection Agency)

Debra Hart (U.S. Environmental Protection Agency)*

Amanda Parker (U.S. Environmental Protection Agency)*

Stephen Porter (U.S. Geological Survey)

Sam Rector (Arizona Department of Environmental Quality)

Alan Steinman (South Florida Water Management Districts)

Jan Stevenson (Michigan State University)*

Jeff Stoner (U.S. Geological Survey)

Danielle Tillman (U.S. Environmental Protection Agency)

Sherry Wang (Tennessee Department of Environment and Conservation)

Vicki Watson (University of Montana)*

Eugene Welch (University of Washington)*

*Denotes primary authors
THIS PAGE INTENTIONALLY 

LEFT BLANK

x
xi

ACKNOWLEDGMENTS

The authors wish to gratefully acknowledge the efforts and input of several individuals. These include

several additional members of our rivers and streams workgroup: Susan Davies (Maine Department of

Environmental Protection), Susan Holdsworth (USEPA), Susan Jackson (USEPA), and Latisha Parker

(USEPA). We also wish to thank Thomas Gardner (USEPA) and James Keating (USEPA) for their

reviews and comments on draft versions of the guidance; Jessica Barrera (Hispanic Association of

Colleges and Universities/University of Miami) for her assistance in compiling references; and Joanna

Taylor (CDM Group, Inc.) for her careful, editorial review.

This document was peer reviewed by a panel of expert scientists. The peer review charge focused on

evaluating the scientific validity of the process for developing nutrient and algal criteria described in the

guidance. The peer review panel comprised Drs. Elizabeth Boyer (State University of New York), Nina

Caraco (Institute of Ecosystem Studies), Gary Lamberti (University of Notre Dame), Judy Meyer

(University of Georgia), and Val Smith (University of Kansas). Edits and suggestions made by the peer

review panel were incorporated into the final version of the guidance.

Cover Photograph: South Umpqua River, Oregon. Photograph courtesy of Dr. E. B. Welch, University

of Washington.
THIS PAGE INTENTIONALLY 

LEFT BLANK

xii
xiii

EXECUTIVE SUMMARY

The purpose of this document is to provide scientifically defensible technical guidance to assist States

and Tribes in developing regionally-based numeric nutrient and algal criteria for river and stream

systems. The Clean Water Action Plan, a presidential initiative released in February 1998, includes an

initiative to address the nutrient enrichment problem. Building on this initiative, the EPA developed a

report entitled National Strategy for the Development of Regional Nutrient Criteria (USEPA 1998). The

report outlines a framework for development of waterbody-specific technical guidance that can be used

to assess nutrient status and develop regional-specific numeric nutrient criteria. This technical guidance

manual builds on the strategy and provides specific guidance for rivers and streams. Similar documents

are being prepared for lakes and reservoirs, estuaries and coastal marine waters, and wetlands.

A directly prescriptive approach to nutrient criteria development is not appropriate due to regional

differences that exist and the lack of a clear technical understanding of the relationship between

nutrients, algal growth, and other factors (e.g., flow, light, substrata). The approach chosen for criteria

development must be tailored to meet the specific needs of each State or Tribe. The criteria development

process described in this guidance can be divided into the following iterative steps.

1. Identify water quality needs and goals with regard to managing nutrient enrichment problems. 

2. Classify rivers and streams first by type, and then by trophic status. 

3. Select variables for monitoring nutrients, algae, macrophytes, and their impacts.

4. Design sampling program for monitoring nutrients and algal biomass in rivers and streams.

5. Collect data and build database.

6. Analyze data. 

7. Develop criteria based on reference condition and data analyses.

8. Implement nutrient control strategies.

9. Monitor effectiveness of nutrient control strategies and reassess the validity of nutrient criteria.

The components of each step is explained in detail in succeeding chapters of the document.

Chapter 1 addresses the necessity of defining water quality needs and goals for rivers and streams, and

gives a general overview of nutrient criteria development. Well-defined needs and goals help to assess

the applicability of the criteria development process and identify attainable water quality goals. This step

will be revisited throughout the criteria development process to assure defined needs and goals are met. 

Chapter 2 discusses classification of streams for water quality assessment and nutrient criteria

development. The intent of classification is to identify groups of rivers or streams that have comparable

characteristics (i.e., similar biological, ecological, physical, and/or chemical features). Classifying rivers

and streams reduces the variability of river-related measures (e.g., physical, biological, or water quality

attributes) within classes, maximizes variability among classes, and allows criteria to be identified on a

broader rather than site-specific scale. Hence, classification of stream systems will assist in setting

appropriate criteria for specific regions and stream system types and provide information used in

developing management and restoration strategies. 

Chapter 3 describes the candidate variables that can be used to evaluate or predict the condition or degree

of eutrophication in a water body. Variables that are required for nutrient criteria development are water

column nutrient concentrations (total nitrogen [TN] and total phosphorus [TP]), algal biomass (measured
xiv

as chlorophyll a [chl a]), and a measure of turbidity. Measurement of these variables provides a means to

evaluate nutrient enrichment and can form the basis for establishing regional and waterbody-specific 

nutrient criteria. This chapter provides an overview of the required variables and additional variables

that can be considered when setting criteria.

Chapter 4 provides technical guidance on designing effective sampling programs. Appropriate data

describing stream nutrient and algal conditions are lacking in many areas. Where available data are not

sufficient to derive criteria, it will be necessary to collect new data through existing or new monitoring

programs. New monitoring programs should be designed to assess nutrient and algal conditions with

statistical rigor while maximizing available management resources. 

Chapter 5 describes how to build a database of nutrient and algal information. A database of relevant

water quality information can be an invaluable tool to States and Tribes as they develop nutrient criteria. 

Databases can be used to organize existing information, store newly gathered monitoring data, and

manipulate data as criteria are being developed. This chapter discusses the role of databases in nutrient

criteria development and provides a brief review of existing data sources for nutrient-related water

quality information.

Data analysis, described in Chapter 6, is critical to nutrient criteria development. Proper analysis and

interpretation of data determines the scientific defensibility and effectiveness of the criteria. The

purpose of this chapter is to explore methods for analyzing data that can be used to derive nutrient

criteria. Included in this chapter are techniques that link cause and effect relationships between nutrient

loading and algal growth, statistical analyses to evaluate compiled data, and use of computer models.

Methods of statistical analyses and a review of relevant computer simulation models are provided in

appendices.

Chapter 7 presents several approaches that water quality managers can use to select numeric criteria for

the rivers and streams in their State/Tribal ecoregions. The approaches that are presented include: the

use of reference streams, applying predictive relationships to select nutrient concentrations that will

result in desirable levels of aquatic growth, and deriving criteria from thresholds established in the

literature. Considerations are also presented for those situations in which development of applicable

river and stream nutrient criteria might be driven by conditions that are deemed acceptable for

downstream receiving waters (i.e., the lake, reservoir, or estuary to which the river drains).

Chapter 8 provides information on regulatory and non-regulatory programs that may be affected by or 

utilize nutrient criteria. This chapter is intended to serve as an informational resource for water quality

managers and foster potential links among regulatory and non-regulatory watershed programs. 

Information on other agency programs that may assist in implementing criteria and maintaining water

quality is included. 

Chapter 9 discusses the continued monitoring of river and stream systems to reassess goals and

established nutrient criteria. This step should (1) evaluate the appropriateness of the nutrient criteria, (2)

ensure that river and stream systems are responding to management action, and (3) assess whether water

quality goals established by the resource manager are being met.

Appended to the guidance document are case studies; technical discussions of analytical methods,

statistical analyses, and computer modeling; a list of acronyms; and a glossary.
PAGE 1

1.1 PURPOSE OF THE DOCUMENT

The purpose of this document is to provide scientifically defensible technical guidance to assist States

and Tribes in developing regionally-based numeric nutrient, algal, and macrophyte criteria for river and

stream systems. Criteria are “elements of State water quality standards, expressed as constituent

concentrations, levels, or narrative statements, representing a quality of water that supports a particular

use. When criteria are met, water quality will generally protect the designated use” (USEPA 1994). 

Water quality criteria are based on scientifically-derived relationships among water constituents and

biological condition. “Water quality standards (WQS) are provisions of State or Federal law which

consist of a designated use or uses for the waters of the United States, water quality criteria for such

waters based upon such uses. Water quality standards are to protect public health or welfare, enhance the

quality of the water, and serve the purposes of the Act (40 CFR 131.3)” (USEPA 1994). Water quality

standards are comprised of three main components: criteria, which are scientifically based; designated

uses, which involve economic, social and political considerations including effects on downstream

receiving waters; and an anti-degradation policy, which protects the level of water quality necessary to

maintain existing uses (Figure 1).

Water quality can be affected when watersheds are modified by alterations in vegetation, sediment

balance, or fertilizer use from industrialization, urbanization, or conversion of forests and grasslands to

agriculture and silviculture (Turner and Rabalais 1991; Vitousek et al. 1997; Carpenter et al. 1998). 

Cultural eutrophication (human-caused inputs of excess nutrients in waterbodies) is one of the primary

factors resulting in impairment of U.S. surface waters (USEPA 1996). Both point and nonpoint sources

of nutrients contribute to impairment of water quality. Point source discharges of nutrients are fairly

constant and are controlled by USEPA National Pollutant Discharge Elimination System (NPDES)

permitting (see Section 8.3) [Source: http://www.epa.gov/owm/gen2.htm]. Nonpoint pollutant inputs

have increased in recent decades and have degraded water quality in many aquatic systems (Carpenter et

al. 1998). Nonpoint sources of nutrients are most commonly intermittent and are usually linked to

seasonal agricultural activity or other irregularly-occurring events such as construction or storm events. 

July 2000 Chapter 1. Introduction 

PAGE 3

Control of nonpoint source pollutants focuses on land management activities and regulation of pollutants

released to the atmosphere (Carpenter et al. 1998).

Control of nutrients is further complicated by the cycling of nitrogen (N) and phosphorus (P) in aquatic

systems. Nutrients can be re-introduced into a waterbody from the sediment, or by microbial

transformation, potentially resulting in a long recovery period even after pollutant sources have been

reduced. In flowing systems, nutrients may be rapidly transported downstream and the effects of nutrient

inputs may be uncoupled from the nutrient source, further complicating nutrient source control (Turner

and Rabalais 1991; Wetzel 1992; Vitousek et al. 1997; Carpenter et al. 1998). Recognizing cause-andeffect relationships between nutrient input and general waterbody response is the first step in mitigating

the effects of cultural eutrophication. Once relationships are established, nutrient criteria can be

developed to protect waterbodies. This document describes the process of developing numeric nutrient

criteria, a new initiative by the USEPA to address the problem of cultural eutrophication (USEPA

1998a).

The Clean Water Action Plan, a presidential initiative released in February 1998, provides a blueprint for

Federal agencies to work with States, Tribes and other stakeholders to protect and restore the Nation’s

water resources. The Clean Water Action Plan includes an initiative to address the nutrient enrichment

problem. Building on this initiative, the USEPA developed a report entitled National Strategy for the

Development of Regional Nutrient Criteria (USEPA 1998a). The report outlines a framework for

development of waterbody-specific technical guidance that can be used to assess nutrient status and

develop regional-specific numeric nutrient criteria. This technical guidance manual builds on the

strategy and provides specific guidance for rivers and streams. Similar documents are being prepared for

lakes and reservoirs, estuaries and coastal marine waters, and wetlands.

For the purposes of this document, river and stream systems are identified collectively as streams or

stream systems, unless otherwise noted. Information presented here will provide water quality managers

with an overview of the current state of the science, guidance on establishing and compiling a database,

and suggested methods for data analyses. The process for setting stream nutrient and algal criteria

ranges and a summary of appropriate regulatory and technical considerations are discussed. Diverse

geomorphic and climatologic conditions throughout the nation require nutrient and algal criteria

development to occur at the ecoregional, State, Tribal, or individual waterbody level to be scientifically

valid. The framework for nutrient and algal criteria development follows a logical iterative process that

begins with defining goals and needs for State and Tribal water quality. The steps of the process are

described in this chapter and detailed in succeeding chapters.

1.2 NUTRIENT ENRICHMENT PROBLEMS IN RIVERS AND STREAMS

Nutrient enrichment frequently ranks as one of the top causes of water resource impairment. Systems are

impaired when water quality fails to meet designated use criteria. The USEPA reported to Congress 

that of the systems surveyed and reported impaired, 40 percent of rivers, 51 percent of lakes, and 57

percent of estuaries listed and nutrients as a primary cause of impairment (USEPA 1996). The nutrient

enrichment issue is not new; however, traditional efforts at nutrient control have been only moderately

successful. Specifically, efforts to control nutrients in waterbodies that have multiple nutrient sources

(point and nonpoint sources) have been less effective in providing satisfactory, timely remedies for
July 2000 Chapter 1. Introduction 

PAGE 4

enrichment-related problems. The development of numeric criteria should aid control efforts by

providing clear numeric goals for nutrient and algal/macrophyte levels. Furthermore, numeric nutrient

criteria provide specific water quality goals that will assist researchers in designing improved best

management practices.

Nutrient impaired waters can cause problems that range from annoyances to serious health concerns

(Dodds and Welch 2000). Nuisance levels of algae and other aquatic vegetation (macrophytes) can

develop rapidly in response to nutrient enrichment when other factors (i.e., light, temperature, substrate,

etc.) are not limiting. High macrophyte growth can interfere with aesthetic and recreational uses of

stream systems (Welch 1992). Algae in particular can grow rapidly when the nutrients N and P (primary

nutrients that most frequently limit algal growth, see Section 6.2 Defining the Limiting Nutrient) are

abundant, often developing into single or multiple species blooms. Algal bloom development involves

complex relationships that are not always well understood. However, the relationship between nuisance

algal growth and nutrient enrichment in stream systems has been well-documented in the literature

(Welch 1992; Van Nieuwenhuyse and Jones 1996; Dodds et al. 1997; Chetelat et al. 1999). Taste and

odor problems in drinking water supplies are usually caused by algal blooms and actinomycete (nitrogenfixing filamentous bacteria) occurrence and other bacterial blooms that frequently follow (Silvey and

Watt 1971; Dorin 1981; Taylor et al. 1981). Algal blooms of certain cyanobacterial species produce

toxins that can affect animal and human health. Reports of livestock, waterfowl, and occasionally human

poisonings after drinking from waterbodies with blue-green algal blooms are not uncommon (Darley

1982; Carmichael 1986, 1994).

Human health problems can be attributed to nutrient enrichment. One serious human health problem

associated with nutrient enrichment is the formation of trihalomethanes (THMs). Trihalomethanes are

carcinogenic compounds that are produced when certain organic compounds are chlorinated and

bromated as part of the disinfection process in a drinking water treatment facility. Trihalomethanes and

associated compounds can be formed from a variety of organic compounds including humic substances,

algal metabolites, and algal decomposition products. The density of algae and the level of eutrophication

in the raw water supply has been correlated with the production of THMs (Oliver and Schindler 1980;

Hoehn et al. 1982).

Effects directly related to nutrients can also result in human health problems. A study of nitrate in

groundwater (the primary source of drinking water in the US) indicated that nitrate contamination

generally increased with high nitrogen input, greater proportions of well-drained soils, and low woodland

to cropland ratios (Nolan et al. 1997). The USEPA has an established maximum contaminant level of 10

mg/L because nitrates in drinking water can cause potentially fatal low oxygen levels in the blood when

ingested by infants (USEPA 1995). Nitrate concentrations as low as 4 mg/L in drinking water supplies

from rural areas have also been linked to an increased risk of non-Hodgkin lymphoma (Ward et al.

1996). A more detailed discussion of human health concerns related to eutrophication can be found in

Suess (1981).

Nutrient impairment can cause problems other than those related to human health. One of the most

expensive problems caused by nutrient enrichment is the increased treatment required for drinking water. 

Nutrient enriched waters commonly cause drinking water treatment plant filters to clog with algae or

macrophytes (Welch 1992) and can contribute to the corrosion of intake pipes (Nordin 1985). High algal
July 2000 Chapter 1. Introduction 

PAGE 5

biomass in drinking water sources require greater volumes of water treatment chemicals, increased backflushing of filters, and additional settling times to attain acceptable drinking water quality (Nordin 1985).

Adverse ecological effects associated with nutrient enrichment include reductions in dissolved oxygen

(DO) and the occurrence of HABs (harmful algal blooms). High algal and macrophyte biomass may be

associated with severe diurnal swings in DO and pH in some waterbodies (Wong et al. 1979; Welch

1992; Edmonson 1994; Correll 1998). Low DO can release toxic metals from sediments (Brick and

Moore 1996) contaminating habitats of local aquatic organisms. In addition, low DO can cause

increased availability of toxic substances like ammonia and hydrogen sulfide, reducing acceptable

habitat for most aquatic organisms, including valuable game fish. Decreased water clarity (increased

turbidity) can cause loss of macrophytes and creation of dense algal mats. Loss of macrophytes and

increased algal biomass may also reduce habitat availability for aquatic organisms. Thus, nutrient

enrichment may alter the native composition and species diversity of aquatic communities (Nordin 1985;

Welch 1992; Smith 1998; Carpenter et al. 1998; Smith et al. 1999).

A large area (6,000 to 7,000 square miles) of hypoxia–water which contains less than 2 parts per million

of DO–located off the Gulf of Mexico Texas-Louisiana Shelf is believed to be caused by a complicated

interaction of excessive nutrients transported to the Gulf of Mexico from the Mississippi River drainage;

physical changes to the river (e.g., channelization and loss of natural wetlands and vegetation along

riverbanks); and the interaction of riverine freshwater with Gulf marine waters (Turner and Rabalais

1994; Rabalais et al. 1996; Brezonik et al. 1999). Hypoxia can cause stress or death in bottom dwelling

organisms that cannot move out of the hypoxic zone. Abundant nutrients trigger excessive algal growth

which results in reduced sunlight, loss of aquatic habitat, and a decrease in DO. Depletion of DO for the

water column has resulted in virtually no biological activity in the hypoxic zone. Reductions in DO have

also been implicated in fish kills leading to significant economic impacts on local recreational and

commercial fisheries.

Harmful algal blooms (e.g., brown tides, toxic Pfiesteria piscicida outbreaks, and some types of red

tides) are also associated with excess nutrients. Evidence suggests that nutrients may directly stimulate

the growth of the toxic form of Pfiesteria, although more research is required to prove this conclusively

(Burkholder et al. 1992; Glasgow et al. 1995). Pfiesteria has been implicated as a cause of major fish

kills at many sites along the North Carolina coast and in several Eastern Shore tributaries of the

Chesapeake Bay.

The primary limiting nutrients in freshwaters are phosphorus and nitrogen. Phosphorus is a mineral

nutrient, i.e., it is introduced into the biological components of the environment by the breakdown of

rock and soil minerals. The breakdown of mineral phosphorus produces inorganic phosphate ions (PO4

3-)

that can be absorbed by plants from the soil or water. Phosphorus moves through the food web primarily

as organic phosphorus (after it has been incorporated into plant or algal tissue), where it may be released

as phosphate in urine or other waste (by heterotrophic consumers) and reabsorbed by plants or algae to

start another cycle (Figure 2a) (Nebel and Wright 2000).

The primary reservoir of nitrogen is the air. Plants and animals cannot utilize nitrogen directly from the

air, but require nitrogen in mineral form such as ammonium ions (NH4

+

) or nitrate ions (NO3

-

) for uptake. 

However, a number of bacteria and cyanobacteria (blue-green algae) can convert nitrogen gas to the 

July 2000 Chapter 1. Introduction 

PAGE 7

ammonium form through a process called biological nitrogen fixation. Mineral forms of nitrogen can be

taken up by plants and algae, and incorporated into plant or algal tissue. Nitrogen follows the same

pattern of food web incorporation as phosphorus, and is released in waste primarily as ammonium

compounds. The ammonium compounds are usually converted to nitrates by nitrifying bacteria, making

it available again for uptake, starting the cycle anew (Figure 2b) (Nebel and Wright 2000).

Nitrogen and P are transported to receiving waterbodies from rain, overland runoff, groundwater,

drainage networks, and industrial and residential waste effluents. Once nutrients have been received in a

waterbody they can be taken up by algae, macrophytes and micro-organisms (either in the water column

or in the benthos); sorbed to organic or inorganic particles in the water and sediment; accumulated or

recycled in the sediment; or transformed and released as a gas from the waterbody (denitrification).

Nitrogen and P have different chemical properties and therefore are involved in different chemical

processes. Nitrogen gas dissolved in the water column may be converted to ammonia (a usable form of

N) by nitrogen-fixing bacteria and algae when nitrate or ammonia are not readily available. However,

receiving waters can lose N through denitrification–anaerobic transformation of nitrate or nitrite into

gaseous N oxides (which are released into the air)–mediated by denitrifying bacteria (Atlas and Bartha

1993). Phosphorus is found primarily in two forms, organic and inorganic, in freshwater. The

biologically available form of inorganic P in water is orthophosphate (PO4

-3). Most P in surface water is

bound organically, and much of the organic P fraction is in the particulate phase of living cells, primarily

algae (Wetzel and Likens 1991). The remainder of the organic fraction is present as dissolved and

colloidal organic P. Phosphorus readily sorbs to clay particles in the water column reducing availability

for uptake by algae, bacteria and macrophytes. The exchange of P between the sediments and overlying

water involves net movement of P into the sediments. Exchanges across the sediment interface are

regulated by mechanisms associated with mineral-water equilibria, sorption processes, redox

interactions, and the activities of bacteria, fungi, algae, and invertebrates. Therefore, P in the sediment is

slow to recycle into the water column. Detailed discussions of N and P cycling in freshwater can be

found in Wetzel (1983); Goldman and Horne (1983); Atlas and Bartha (1993); and other limnology texts. 

Many lakes have been successfully treated for nutrient enrichment problems by an assortment of

techniques (Cooke et al. 1993). Lake Washington is a well-recognized example of nutrient diversion. 

Nutrients were diverted from Lake Washington by eliminating direct discharge from wastewater

treatment plants and other dischargers, effectively reducing nuisance algal blooms and improving water

clarity (Edmonson 1994). Although many cases have been documented for controlling organic waste

inputs to rivers (e.g., the Thames River, England [Goldman and Horne 1983]), nutrient control efforts to

correct algal and/or macrophyte problems in streams and rivers have been either minimal or

undocumented in the peer-reviewed, published literature. Two well-documented cases are described in

detail in Appendix A: the Clark Fork River, MT, and the Bow River, Alberta. Despite these and other

efforts, a greater percentage of stream systems surveyed are reported as being nutrient impaired (USEPA

1994; USEPA 1996).

Many States, Tribes, and Territories have adopted some form of nutrient criteria related to maintaining

natural conditions and avoiding nutrient enrichment. Most States and Tribes have narrative criteria with

no specific numeric criteria. Established criteria most commonly pertain to P concentrations in lakes. 

Nitrogen criteria, where they have been established, are usually in response to the toxic effects of

July 2000 Chapter 1. Introduction 

PAGE 9

ammonia and nitrates. In general, levels of nitrates (10 ppm for drinking water) and ammonia high

enough to be toxic (1.24 mg N/L at pH = 8 and 25°C) will also cause problems of enhanced algal growth

(USEPA 1986).

1.3 WATER QUALITY STANDARDS AND CRITERIA

States and authorized Tribes are responsible for setting water quality standards to protect the physical,

biological, and chemical integrity of their waters (Figure 1). “Water quality standards (WQS) are

provisions of State or Federal law which consist of a designated use or uses for the waters of the United

States, water quality criteria for such waters based upon such uses. Water quality standards are to protect

public health or welfare, enhance the quality of the water, and serve the purposes of the Act (40 CFR

131.3)” (USEPA 1994). A water quality standard defines the goals for a waterbody by designating its

specific uses, setting criteria to protect those uses, and establishing an antidegradation policy to protect

existing water quality. The three main components of water quality standards are based on different

concerns: criteria are scientifically based; specific uses involve economic, social and political

considerations including the protection of downstream receiving waters; and the anti-degradation policy

protects the level of water quality necessary to maintain designated uses (Figure 1). A waterbody can be

defined by an existing use (a use actually attained in the waterbody on or after November 28, 1975—the

date of the promulgation by USEPA of the first water quality standards regulations) or designated use (a

use specified in a water quality standard for each waterbody or segment, regardless of whether it is being

attained). An established use cannot be removed unless it is being replaced by one requiring more

stringent (protective) criteria. At a minimum, the uses must include recreation in and on the water, and

propagation of fish and wildlife (Clean Water Act, Section 101[a] and 303[c]). Other uses, such as

boating, cold water fisheries, or drinking water supply, may also be adopted.

Once designated uses of a waterbody have been established, the State or Tribe must adopt numeric or

narrative criteria to protect and support the specified uses. Narrative criteria are verbal expressions of

desired water quality conditions that are meant to describe the unimpaired condition of a waterbody. A

narrative criterion from Vermont is shown below:

There shall be no increase, in any waters, of total phosphorus above background conditions that

may contribute to the acceleration of eutrophication or the stimulation of the growth of aquatic

biota in a manner that has an undue adverse effect on any beneficial values or uses of any

adjacent or downstream waters. 

(Source: http://www.state.vt.us/wtrboard/rules/vwqs.htm#C1S1)

Numeric criteria, on the other hand, attempt to quantify this ideal by building on and refining narrative

criteria. Numeric criteria are values assigned to measurable components of water quality, such as the

concentration of a specific constituent that is present in the water column (e.g., average total phosphorus

[TP] concentration in a recreational stream shall not exceed 20 g/L during the growing season). In

addition to narrative and numeric criteria, some States and Tribes use numeric goals or assessment

levels, an intermediate step between numeric criteria and water quality standards, that are not written into

State or Tribal laws but are used internally by the State or Tribal agency for assessment and management

purposes. 
July 2000 Chapter 1. Introduction 

PAGE 10

Numeric criteria can be more useful than narrative criteria in a number of ways. Numeric criteria

provide distinct interpretations of acceptable and unacceptable conditions, form the foundation for

responsible measurement of environmental quality, and reduce ambiguity for management and

enforcement decisions. Despite these advantages, however, most of the Nation’s waterbodies do not

have numeric nutrient criteria. The lack of numeric criteria makes it difficult to assess the condition of

rivers and streams and develop protective water quality standards, hampering the water quality

manager’s ability to implement management strategies.

Setting numeric nutrient criteria can provide a variety of benefits. For example, information obtained

from compiling existing data and conducting new surveys can provide water quality managers and the

public a better perspective on the condition of State and Tribal waters. The compiled waterbody

information can be used to most effectively budget personnel and financial resources for the protection

and restoration of river and stream systems. In a similar manner, data collected in the criteria

development and implementation process can be compared before, during, and after specific

management actions. Analyses of these data can determine the response of the waterbody and the

effectiveness of management endeavors.

Nutrient criteria also support watershed-protection activities. Nutrient criteria can be used in conjunction

with State/Tribal and Federal biocriteria surveys, National Estuary Program and Clean Lakes projects,

and in development of TMDLs (Total Maximum Daily Loads) to improve resource management at local,

State, Tribal, and national levels. 

1.4 OVERVIEW OF THE CRITERIA DEVELOPMENT PROCESS

This section describes the five general elements of nutrient criteria development outlined in the National

Strategy (USEPA 1998a) and is followed by a detailed overview of the steps taken to derive nutrient

criteria for river and stream systems. A prescriptive approach is not appropriate due to regional

differences that exist and the scientific community’s limited technical understanding of the relationship

between nutrients, algal growth, and other factors (e.g., flow, light, substrata). The approach chosen for

criteria development must be tailored to meet the specific needs of each State or Tribe.

The USEPA has adopted the following principal elements as part of its National Strategy for the

Development of Regional Nutrient Criteria (USEPA 1998a). This document can be downloaded in PDF

format at the following website: www.epa.gov/OST/standards/nutrient.html. 

1. Ecoregional nutrient criteria will be developed to account for the natural variation existing

within various parts of the country. Different waterbody processes and responses dictate that

nutrient criteria be specific to the waterbody type. No single criterion will be sufficient for each

waterbody, therefore we anticipate system classification within waterbody type for appropriate

criteria derivation (see Section 1.5, item 2). 

2. Guidance documents for nutrient criteria will provide methodologies for developing nutrient

criteria for four primary variables (total nitrogen [TN], TP, chlorophyll a [chl a], and a measure

of turbidity) by ecoregion and waterbody type. 
July 2000 Chapter 1. Introduction 

PAGE 11

3. Regional Nutrient Coordinators will lead State/Tribal technical and financial support operations

used to compile data and conduct environmental investigations. A team of agency specialists

from USEPA Headquarters will provide technical and financial support to the Regions, and will

establish and maintain communications between the Regions and Headquarters.

4. Nutrient criteria numeric ranges, developed at the national level from existing databases and

additional environmental investigations, will be used to derive specific criterion values. Criteria

values will be implemented into water quality standards by States and Tribes within three years

of criteria publication. Ecoregional nutrient criteria will be used by States and Tribes either as a

point of departure for the development of more refined criteria, or as numeric criteria. The

USEPA will promulgate nutrient criteria in the absence of State or Tribal criteria development

initiatives. 

5. Nutrient and algal criteria will serve as benchmarks for evaluating the relative success of any

nutrient management effort, whether protection or remediation. Criteria will be re-evaluated

periodically to assess whether refinements or other improvements are needed.

Nutrient criteria will form the basis for regulatory values such as standards, NPDES permit limits, and 

TMDL values. Nutrient criteria will also be valuable as decision making benchmarks for management

planning and assessment. The development of TMDLs may serve as an intermediate step between

criteria development and watershed-based management planning. 

The USEPA Strategy envisions a process by which State/Tribal waters are initially measured, reference

conditions are established, individual waterbodies are compared to reference waterbodies, and

appropriate management measures are implemented. This process is outlined in detail below.

1.5 THE CRITERIA DEVELOPMENT PROCESS

Figure 3 presents a flow chart of the nine key steps involved in the criteria development process. A brief

discussion of each of the steps involved, and what ideally is accomplished at each stage, is given below:

1. Identify water quality needs and goals with regard to managing nutrient enrichment problems. State

and Tribal water quality managers should define the water quality needs and goals for their rivers and

streams. Well-defined needs and goals will help in assessing the success of the criteria development

process, and will identify attainable water quality goals. This step should be revisited throughout the

criteria development process to assure defined needs and goals are addressed. 

2. Classify rivers and streams first by type, and then by trophic status. The intent of classification is to

identify groups of stream systems that have comparable characteristics (i.e., biological, ecological,

physical, chemical features). Classifying rivers and streams reduces the variability of stream-related

measures (e.g., physical, biological, or water quality attributes) within classes and maximizes variability

among classes. Classification will allow criteria to be identified on a broader rather than site-specific

scale.

July 2000 Chapter 1. Introduction 

PAGE 13

3. Select variables for monitoring nutrients. Variables, in the context of this document, are measurable

attributes that can be used to evaluate or predict the condition or degree of eutrophication in a water

body. Four primary water quality variables that must be addressed are TN, TP, chl a as an estimate of

algal biomass, and turbidity (see Section 3.2). Measurement of these variables provides a means to

evaluate nutrient enrichment and can form the basis for establishing regional and waterbody-specific

nutrient criteria. Additional secondary variables may also be monitored. 

4. Design a sampling program for monitoring nutrients and algal biomass in rivers and streams. New

monitoring programs should be designed to identify statistically significant differences in nutrient and

algal conditions while maximizing available management resources (see Section 4.2). Initial monitoring

efforts should focus on targeting reference stream reaches that can be used to assess impairment by

nutrients and algae.

5. Collect data and build database. Potential sources of additional data for nutrient criteria development

include current and historical water quality monitoring data from Federal, State, and local water quality

agencies; university studies; and volunteer monitoring information. Databases can be used to organize

existing data, store newly gathered monitoring data, and manipulate data as criteria are being developed. 

The USEPA is developing a national relational database for State/Tribal users to store, screen, and

manipulate nutrient-related data.

6. Analyze data. Statistical analyses are used to interpret monitoring data for criteria development. 

Nutrient criteria development should relate nutrient concentrations in streams, algal biomass, and

changes in ecological condition (e.g., nuisance algal accrual rate and deoxygenation). In addition, the

relative magnitude of an enrichment problem can be determined by examining total nutrient

concentration and chl a frequency distributions for stream classes. These analyses provide water quality

managers with a tool for measuring the potential extent of overenrichment. 

7. Develop criteria based on reference conditions and data analyses. Criteria selected must first meet

the optimal nutrient condition for that stream class and second be reviewed to ensure that the level

proposed does not result in adverse nutrient loadings to downstream waterbodies.

Three general approaches for criteria setting are discussed in this manual: (1) identification of

reference reaches for each stream class based on best professional judgement (BPJ) or percentile

selections of data plotted as frequency distributions, (2) use of predictive relationships (e.g., trophic

state classifications, models, biocriteria), and (3) application and/or modification of established

nutrient/algal thresholds (e.g., nutrient concentration thresholds or algal limits from published

literature).

Initial criteria should be verified and calibrated by comparing criteria in the system of study to nutrients,

chl a, and turbidity values in waterbodies of known condition to ensure that the system of interest

operates as expected. A weight of evidence approach that combines any or all of the three

approaches above will produce criteria of greater scientific validity. Selected criteria and the data

analyzed to identify these criteria will be comprehensively reviewed by a panel of specialists in each

USEPA Region. Calibration and review of criteria may lead to refinements of either derivation
July 2000 Chapter 1. Introduction 

PAGE 14

techniques or the criteria themselves. In some instances empirical and simulation modeling, or data sets

from adjacent States/Tribes with similar systems may assist in criteria derivation and calibration.

8. Implement nutrient control strategies. Much of the management work done by USEPA and the States

and Tribes is regulatory. Nutrient criteria can be incorporated into State/Tribal standards as enforceable

tools to preserve water quality. As an example, nutrient criteria values can be included as limits in

NPDES permits for point source discharges. The permit limits for N, P and other trace nutrients emitted

from wastewater treatment plants, factories, food processors and other dischargers can be appropriately

adjusted and enforced in accordance with the criteria. 

In addition, watershed source reduction responsibilities, especially with respect to nonpoint sources, can

be established on the basis of nutrient criteria. Resource managers can use nutrient criteria to help define

source load allocations for a watershed. Once sources have been identified, resource managers can

begin land use improvements and other activities necessary to maintain or improve the system. System

improvements from a watershed perspective must proceed on a reasonable scale so that protection and

restoration can be achieved.

9. Monitor effectiveness of nutrient control strategies and reassess the validity of nutrient criteria. 

Nutrient criteria can be applied to evaluate the relative success of management activities. Measurements

of nutrient enrichment variables in the receiving waters preceding, during, and following specific

management activities, when compared to criteria, provide an objective and direct assessment of the

success of the management project.

Throughout the continuing process of problem identification, response and remediation, and evaluation

to protect and enhance our water resources, States, Tribes, and the USEPA are called upon by the U.S.

Congress to periodically report on the status of the Nation’s waters (Section 305 [b] of the Clean Water

Act as amended). Establishment of nutrient criteria will add two causal and two response parameters

(see Sections 3.2 - 3.3) to the measurement process required for the biannual Report to Congress. These

measurements can be used to document change and monitor the progress of nutrient reduction activities.

The chapters that follow present detailed information that elaborates upon this outline of nutrient criteria

development. For some water quality managers, components of certain criteria development steps may

already be completed for existing stream monitoring programs (e.g., sampling design for specific stream

systems). Thus, some steps can be excluded as the manager advances further through the process. 

However, should new or revised monitoring programs be envisioned, review by the water quality

manager of each of the steps outlined in this guidance is recommended.

Although this document is meant to provide the best available scientific procedures and approaches for

collecting and analyzing nutrient-related data, including examination of nutrient and algal relationships,

a comprehensive understanding of nutrient and algal dynamics within all types of stream systems is

beyond the current state of scientific knowledge. The National Nutrient Program represents a new effort

and approach to criteria development that, in conjunction with efforts made by State and Tribal water

quality managers, will ultimately result in a heightened understanding of nutrient/algal relationships. As

the proposed process is put into use to set criteria, program success will be gauged over time through

evaluation of management and monitoring efforts. A more comprehensive knowledge base pertaining to
July 2000 Chapter 1. Introduction 

PAGE 15

nutrient and algal relationships will be expanded as new information is gained and obstacles overcome,

justifying potential refinements to the criteria development process described here. 

1.6 IDENTIFY NEEDS AND GOALS

The overarching goal of developing nutrient criteria is to ensure the quality of our national waters. 

Ensuring water quality may include restoration of impaired systems, conservation of high quality waters,

and protection of systems at high risk for future impairment. The goals of a State or Tribal water quality

program will be defined differently based on the needs of each State or Tribe, but should, at a minimum,

protect established designated uses for the waterbodies within State or Tribal lands. Once goals and

objectives are defined, they should be revisited regularly to evaluate progress and assess the need for

refinements or revisions. 

The first task of a water quality manager is to set a water quality goal, such as “no nuisance algal blooms

such that swimming is restricted during summer months.” After such a goal is established, managers

must develop a timeline, budget, and plan of action for accomplishing this goal. Needs of the program,

such as funding, acquiring relevant data, and assigning employee responsibilities must be addressed. 

Well-defined needs and goals will help in assessing the success of the criteria development process and

will identify attainable water quality goals.

1.7 DOCUMENT STRUCTURE

This manual comprises nine chapters that formulate the steps recommended for nutrient criteria

development. The first step of the process, identifying goals and objectives, is unique to each water

quality manager and should be revisited regularly to evaluate progress and assess the need for goal

and/or objective refinements or revisions. The next step entails stream classification based on physical

and nutrient gradient factors (Chapter 2). Sampling variables, including primary and appropriate

secondary variables (Chapter 3), should be selected for monitoring efforts. Once these variables are

determined, sampling designs for new monitoring programs can be developed (Chapter 4). Chapter 5

discusses potential data sources that can be used by water quality managers to develop criteria and

addresses the usefulness of databases in compiling, storing, and analyzing data. A variety of data

analysis methods and techniques used to derive criteria are presented in Chapters 6 and 7, respectively. 

These two chapters are meant to provide water quality managers with a range of options that may be

useful for deriving criteria. Nutrient management programs (including nutrient control strategies for

point and nonpoint sources) and points of contact or references that may be useful to water quality

managers are provided in Chapter 8. Chapter 9 concludes the criteria development process with a brief 

discussion of continued monitoring and reassessment of goals and the established criteria. 

It should be noted that completion of each previously described step may not be required by all water

quality managers. Many State or Tribal water quality agencies already have established stream classes,

monitoring programs, and/or databases for their programs and therefore can bypass those steps. This

manual is meant to be comprehensive in the sense that all of the criteria development steps are described;

however, the process can be adapted to suit existing water quality programs. 
July 2000 Chapter 1. Introduction 

PAGE 16

Appendix A of the manual contains five case studies: (1) Tennessee ecoregion streams (southeastern

U.S.), (2) Clark Fork River (western forested mountains), (3) upper Midwest river basins (prairieagricultural river systems), (4) Bow River (northern Rockies), and (5) desert streams (arid western U.S.). 

These case studies are meant to characterize some of the variability observed within North American

stream systems and region-specific issues that should be considered when developing nutrient criteria. 

Appendices B and C provide water quality managers with information and additional references for

laboratory/field methods and statistical tests/modeling tools, respectively. Appendix D defines

frequently used acronyms and technical terms found throughout the document.
PAGE 17

2.1 INTRODUCTION

This chapter discusses classification of streams for water quality assessment and nutrient criteria

development. The purpose of classification is to identify groups of rivers or streams that have

comparable characteristics (i.e., similar biological, ecological, physical, and/or chemical features) so that

data may be compared or extrapolated within stream types. This chapter focuses on providing water

quality managers with a menu of tools that can be used to classify the stream system of interest, resulting

in different aggregations of physical parameters that correlate with water quality variables. 

Classifying rivers and streams reduces the variability of stream-related measures (e.g., physical,

biological, or water quality variables) within identified classes and maximizes inter-class variability. 

Classification schemes based on non-anthropogenic factors such as parent geology, hydrology, and other

physical and chemical attributes help identify variables that affect nutrient/algal interactions. 

Classification can also include factors that are useful when creating nutrient control strategies such as

land use characteristics, bedrock geology, and identification of specific point and nonpoint nutrient

sources. Grouping streams with similar properties will aid in setting criteria for specific regions and

stream system types, and can provide information used in developing management and restoration

strategies. 

A two-phased approach to system classification is prescribed here. Initially, stream classification is

based primarily (though not exclusively) on physical parameters associated with regional and sitespecific characteristics, including climate, geology, substrate features, slope, canopy cover, retention time

of water, discharge and flow continuity, system size, and channel morphology. The second phase

involves further classifying stream systems by nutrient gradient (based upon measured nutrient

concentrations and algal biomass). Trophic state classification, in contrast, focuses primarily on

chemical and biological parameters including concentrations of nutrients, algal biomass as chlorophyll a,

and turbidity, and may also include land use and other human disturbance parameters. The additional
July 2000 Chapter 2. Stream System Classification

PAGE 18

sub-classification of streams by nutrient condition, in conjunction with an understanding of doseresponse relationships between algae and nutrients, helps define the goals for establishing nutrient

criteria.

The physical and nutrient characterization discussed above can often be complemented by designated use

classifications. These are socially-based classifications developed in accordance with EPA policy and

based on the predominant human uses that a State or Tribe has concluded are appropriate for a particular

stream or river. Water quality standards, predicated on criteria, are applied to these designated use

classifications and are enforceable to protect specified uses. Uses are designated in accordance with

relative water quality condition and trophic state. For more information on designated use classifications

and their relationship to water quality criteria and standards, see the USEPA Water Quality Standards

Handbook (USEPA 1994).

Stream classification requires consideration of stream types at different spatial scales. Drainage basins

can be delineated and classified at multiple spatial scales ranging from the size of the Mississippi River

basin to the few square meters draining into a headwater stream. The general approach is to establish

divisions at the largest spatial scale (river basins of the continent), and then to continue stratification at

smaller scales to the point at which variability of algal-nutrient relationships is limited within specific

stream classes.

The highest level of classification at the national level is based on geographic considerations. The

Nation has been divided into 14 nutrient ecoregions (Omernik 2000) based on landscape-level geographic

features including climate, topography, regional geology and soils, biogeography, and broad land use

patterns (Figure 4). The process of identifying geographic divisions (i.e., regionalization) is part of a

hierarchical classification procedure that aggregates similar stream systems together to prevent grouping

of unlike streams. The process of subdividing the 14 national ecoregions should be undertaken by the

State(s) or Tribe(s) within each of those ecoregions. Classification of State/Tribal lands invariably

involves the professional judgement of regional experts. Experts familiar with the range of conditions in

a region can help define a workable system that clearly separates different ecosystem types, yet does not

consider each system a special case.

The usefulness of classification is determined by its practicality within the region, State, or Tribal lands

in which it will be applied; local conditions determine the appropriate classes. In this Chapter, a

regionalization system derived at the national level is presented. This system provides the framework

from which State and Tribal water resource management agencies can work to establish appropriate

subdivisions. In addition, different classification schemes are presented to provide resource managers

with information to use in choosing a stream classification system. It is the intent of this document to

provide adequate flexibility to States and Tribes in identifying State and Tribal-specific subregions. 

The following sections describe specific examples of first-phase physical classification based on

variation in natural characteristics and secondly, nutrient gradient classification schemes for identifying

similarities within stream system types. Each classification method is presented and the rationale for its

use is provided.

July 2000 Chapter 2. Stream System Classification

PAGE 20

2.2 CLASSIFICATION SCHEMES BASED ON PHYSICAL FACTORS

The classification systems described in the following sections (including ecoregional, fluvial

geomorphological, and stream order classification schemes) are based on physical stream and watershed

characteristics. Stream systems are characterized by the continual downstream movement of water,

dissolved substances, and suspended particles. These components are derived primarily from the land

area draining into a given channel or the drainage basin (watershed). The climate, geology, and

vegetational cover of the watershed are reflected in the hydrological, biological, and chemical

characteristics of the stream. Therefore, factors such as general land use, climate, geology and general

hydrological properties must be considered regardless of the method of classification used. As described

above, the initial classification should be based on physical characteristics of parent geology, elevation,

slope, hydrology and channel morphology. Hydrologic disturbance frequency and magnitude are also

important when classifying stream systems. 

In addition to classification of stream systems, factors contributing to trophic state and macrophyte and

algal growth should be considered. Table 1 presents several factors that affect periphyton and plankton

biomass levels in stream systems. Macrophyte-dominated systems could occur under conditions similar

to those favorable for high periphyton biomass (Table 1), if the velocity is low and the substrate includes

organic sediment. Macrophytes are generally unlikely to develop in systems where the stream bottom is

composed primarily of gravel or other large substrata (Wong and Clark 1979). The following section

specifically addresses the potential effects of hydrology and channel morphology, flow, and parent

geology on algal and macrophyte growth within stream systems.

River and stream types (and reaches within these waterbodies) are too diverse to set one criterion for all

stream/river types. However, it is not necessarily feasible or recommended to develop site-specific

criteria for every stream reach within the U.S. Morphological and fluvial characteristics of a stream

influence many facets of its behavior. Streams with similar morphologies may have similar nutrient

capacities or similar responses to nutrient loadings. Rivers and streams are very diverse within

ecoregions. Reaches within one stream can have a distinct morphology. The geomorphology of a river

or stream – its shape, depth, channel materials – affects the way that waterbody receives, processes, and

distributes nutrients. Nutrient cycling processes that occur upstream affect communities and processes

downstream by altering the form and concentration of nutrients and organic matter in transport (nutrient

spiraling); these effects can be further intensified by patch dynamics (Mulholland et al. 1995). The

spatial scales which most influence upstream-downstream linkages are the geomorphology-controlled

patterns observed at the landscape scale and the nutrient-cycling-controlled patterns observed at the

stream reach scale (Mulholland et al. 1995). Therefore, to set appropriate criteria for rivers and streams

in an ecoregion, streams must be classified by their morphological characteristics at both the landscape

and stream reach scale, with an emphasis on those characteristics most likely to affect nutrient cycling. 

ECOREGIONAL CLASSIFICATION

Ecoregions are based on geology, soils, geomorphology, dominant land uses, and natural vegetation

(Omernik 1987; Hughes and Larsen 1988) and have been shown to account for variability of water

quality and aquatic biota in several areas of the United States (e.g., Heiskary et al. 1987; Barbour et al.

1996). On a national basis, individual streams and rivers are affected by varying degrees of development,

and user perceptions of acceptable water quality can differ even over small distances. 
July 2000 Chapter 2. Stream System Classification

PAGE 21

Table 1. Geological, physical, and biological habitat factors that affect periphyton and phytoplankton

biomass levels in rivers and streams given adequate to high nutrient supply and non-toxic conditions. 

Note that only one factor is sufficient to limit either phytoplankton or periphyton biomass.

Phytoplankton-Dominated Systems Periphyton-Dominated Systems

High Phytoplankton Biomass

 low current velocity(< 10 cm/s)/long 

 detention time (>10 days) and

 low turbidity/color and

 open canopy and

 greater stream depth and

 greater depth to width ratio

High Periphyton Biomass

 high current velocity (>10 cm/s) and

 low turbidity/color and

 open canopy and

 shallow stream depth and

 minimal scouring and

 limited macroinvertebrate grazing and

 gravel or larger substrata and

 smaller depth to width ratio

Low Phytoplankton Biomass

 high current velocity (>10 cm/s)/short 

 detention time (<10 days) and/or

 high turbidity/color and/or

 closed canopy and/or

 shallow stream depth

Low Periphyton Biomass

 low current velocity (< 10 cm/s) and/or

 high turbidity/color and/or

 closed canopy and/or

 greater stream depth and/or

 high scouring and/or

 high macroinvertebrate grazing and/or

 sand or smaller substrata

Ecoregions are generally defined as relatively homogeneous areas with respect to ecological systems and

the interrelationships among organisms and their environment (Omernik 1995). Ecoregions can occur at

various scales; broad-scale ecoregions may include the glaciated corn belt of the central and upper

Midwest or the arid to semi-arid basin and desert regions of the southwest. At more refined scales,

regions within the broader regions can be identified.

Ecoregions serve as a framework for evaluating and managing natural resources. The ecoregional

classification system developed by Omernik (1987) is based on multiple geographic characteristics (e.g.,

soils, climate, vegetation, geology, land use) that are believed to cause or reflect the differences in the

mosaic of ecosystems. Omernik’s original compilation of national ecoregions was based on a fairly

coarse (1:7,500,000) scale that has subsequently been refined for portions of the southeast, mid-Atlantic,

and northwest regions, among others (Omernik 1995). The process of defining subregions within an

ecoregion requires collaboration with State/Tribal scientists and resource managers. Once appropriate

subregions are delineated, reference sites can be identified (see Section 4.2). Similar to the process

described for ecoregion refinement, reference site selection involves interactions with scientists and

water quality managers that understand local conditions. Field verification techniques, methods for

selecting reference sites for small and/or disjunct subregions can be found in Omernik (1995).
July 2000 Chapter 2. Stream System Classification

PAGE 22

FLUVIAL GEOMORPHOLOGY

Fluvial geomorphology mechanistically describes river and slope processes on specific types of

landforms, i.e., the explanation of river and slope processes through the application of physical and

chemical principles. The morphology of the present-day channel is governed by the laws of physics

through observable stream channel features and related fluvial processes. Stream pattern morphology is

directly influenced by eight major variables including channel width, depth, velocity, discharge, channel

slope, roughness of channel materials, sediment load and sediment size (Leopold et al. 1964). A change

in one variable causes a series of channel adjustments which lead to changes in the other variables,

resulting in channel pattern alterations. Many stream classification systems, have a fluvial

geomorphologic component. 

ROSGEN

The stream classification method devised by David Rosgen is a comprehensive guide to river and stream

classification (see Rosgen 1994 or 1996). The Rosgen classification system is currently utilized by

several States. This system integrates fluvial geomorphology with other stream characteristics. 

Specifically, Rosgen combines several methods of stream classification into one complete, multi-tiered

approach. Rosgen’s method has four levels of detail: broad morphological (geomorphic)

characterization, morphological description (stream types), stream “state” or condition, and verification. 

Level I classification, geomorphic characterization, takes into account channel slope (longitudinal

profile), shape (plan view morphology, cross-sectional geometry), and patterns. Level I streams are

divided into seven major categories and labeled A-G. The Level II morphological delineative criteria

include landform/soils, entrenchment ratio, width/depth ratio, sinuosity, channel slope, and channel

materials. The 42 subcategories of Level II streams are labeled with a letter and a number, A1-G6 (see

Rosgen 1994, 1996). Level III designations are primarily used in specific studies or in restoration

projects to assess the quality and/or progress of a specific reach. Level IV classifications may be used to

verify results of specific analyses used to develop empirical relationships (such as a roughness

coefficient) (Rosgen 1996).

Rivers and streams are complicated systems. A classification scheme is an extreme simplification of the

geomorphic and fluvial processes. However, the Rosgen system of classification is a useful frame of

reference to :

1. Predict a river’s behavior from its appearance;

2. Develop specific hydraulic and sediment relations for a given morphological channel type and

state;

3. Provide a mechanism to extrapolate site-specific data collected on a given stream reach to those of

similar character; and

4. Provide a consistent and reproducible frame of reference of communication for those working with

river systems in a variety of professional disciplines (Rosgen 1994).

Classification of streams and rivers allows comparisons and extrapolation of data from different streams

or rivers in an ecoregion. Comparing similar streams may help to predict the behavior of one stream

based data and observations from another. Applied River Morphology (Rosgen 1996) contains in-depth

descriptions of each Level II stream type (A1-G6) and includes photographs and illustrations. Rosgen
July 2000 Chapter 2. Stream System Classification

PAGE 23

discusses theoretical characterizations and variables and provides field methods for delineating stream

types. The Rosgen classification system may be more detailed than needed for many States and Tribes. 

For more information on the Rosgen classification system, see Rosgen (1996).

STREAM ORDER

Identifying stream orders in a given delineated watershed can provide a classification system for

monitoring streams. A variety of methods have been proposed for ordering drainage networks for stream

classification and monitoring. The Horton-Strahler method (Horton 1945; Strahler 1952) is most widely

used in the US. Each headwater stream is designated as a first order stream. Two first order streams

combine to produce a second order stream, two second order streams combine to produce a third order

stream and so on (Figure 5). Only when two streams of the same order are combined does the stream

order increase. Numerous lower order streams may enter a main stream without changing the stream

order. As a result, utilizing this method for classification may lead to problems of disparity in

hydrological and ecological conditions among same order streams even within the same region. 

Resource managers using stream order as a classification system should ensure that topographic maps

used to identify watershed boundaries all utilize the same scale. The inclusion or exclusion of perennial

headwater streams should be decided before ordering drainage networks of interest.

Stream order (Strahler 1952) is used to classify streams in the EPA Environmental Monitoring and

Assessment Program (EMAP). Sample sites were selected using a randomized sampling design with a

systematic spatial component. The survey in the mid-Atlantic region was restricted to wadeable streams

defined as 1st, 2nd, or 3rd order as delineated using USGS 1:100,000 scale USGS hydrologic maps that

were incorporated into EPA’s River Reach File (Version 3). Sample probabilities were set so that

approximately equal numbers of 1st, 2nd, and 3rd order stream sites would appear in the sample population. 

Data were collected at 368 different sites representing 182,000 km of wadeable streams in the midAtlantic region (Herlihy et al. 1998). 

PHYSICAL FACTORS USED TO CLASSIFY STREAMS AND ANALYZE TROPHIC STATE

The following sections focus on physical characteristics of streams that can be used to sub-classify

stream systems. Physical characteristics that can be used for stream classification include system

hydrology and morphology, flow conditions, and underlying geology.

Hydrology and Morphology

Hydrologic and channel morphological characteristics are often important determinants of algal biomass. 

Unidirectional flow of water sets up longitudinal patterns in physical and chemical factors that may also

affect macrophyte growth when light and substrate conditions are adequate. Channel morphology or

shape of a river or stream channel at any given location is a result of the flow, the quantity and character

of the sediment moving through the channel, and the composition of the streambed and banks of the

channel including riparian vegetation characteristics (Leopold et al. 1964). Frequent disturbance from

floods (monthly or more frequently) and associated movement of bed materials can scour algae from the

surface rapidly and often enough to prevent attainment of high biomass (Peterson 1996). In areas with

less stable substrata, such as sandy bottomed streams, only slight increases in flow may lead to bed

movement and scouring. Scouring by movement of rocks has been directly linked to reduction in algal

biomass and subsequent recovery from floods (Power and Stewart 1987). Larger, more stable rocks can

have higher periphyton biomass (Dodds 1991; Cattaneo et al. 1997). Thus, in cases where
July 2000 Chapter 2. Stream System Classification

PAGE 24

Figure 5. Stream ordering of a watershed basin network using the Strahler method. (Adapted from

Strahler [1964]). 
July 2000 Chapter 2. Stream System Classification

PAGE 25

there is frequent movement of substrata, high nutrients may not necessarily translate into excessive algal

biomass (Biggs et al. 1998a,b).

Consideration of both geology and hydrologic disturbance can provide important insights into factors

influencing algal biomass. Research done in New Zealand identified geology, land use patterns, and

stream conductivity (as a surrogate for total nutrients) as important determinants of algal biomass

because these factors affected nutrient inputs and flood disturbance (Biggs 1995). The effects of

disturbance by floods can be complex and complicated by biological factors; very stable stream beds may

be associated with an active grazing community and have less biomass than more unstable systems. This

notwithstanding, flow regime, channel morphology and bed composition (such as sand versus large

boulders) appear to be major controlling factors and should be considered when managing eutrophication

in a particular watershed.

Flow Conditions

Low and stable flow conditions should be considered in addition to frequency and timing of floods when

physically classifying stream systems. Flood frequency and scouring may be greater in steep-gradient

(steep slope) and/or channelized streams and in watersheds subject to intense precipitation events or

rapid snow melt. Periods of drying can also reduce algal biomass to low levels (Dodds et al. 1996). A

stream may flood frequently during certain seasons, but also remain stable for several months at a time. 

The effects of eutrophication may be evident during stable low flows. Also, stable flow periods are

generally associated with low flow conditions, resulting in the highest nutrient concentration from point

source loading. Hence, low-flow periods often present ideal conditions for achieving maximum algal

biomass. For these reasons, nutrient control plans may require strategies that vary seasonally (e.g.,

criteria for a specific system may differ with season or index period).

Underlying Geology

Streams draining watersheds with phosphorus-rich rocks (such as from sedimentary or volcanic origin)

may be naturally enriched and the control of algal biomass by nutrient reduction in such systems may be

difficult. Bedrock composition has been related to algal biomass in some systems (e.g., Biggs 1995). In

addition, nutrient content, and hence algal biomass, often naturally increases as elevation decreases,

especially in mountainous areas (Welch et al. 1998). Some naturally phosphorus-rich areas include

watersheds draining some volcanic soils, and other areas have high weathering of nitrate from bedrock

(Halloway et al. 1998). Review of geologic maps and consultation with a local Natural Resources

Conservation Service (NRCS) agent or soil scientist may reveal such problems.

2.3 CLASSIFICATION SCHEMES BASED ON NUTRIENT GRADIENTS

Nutrient loading is the factor most likely to be controlled by humans, but the ability to control algal

biomass within the stream itself may be influenced by additional factors. Factors that may control algal

biomass in streams include bedrock type and elevation (because they determine the natural or

background nutrient supply), physical disturbance (flooding and drying), light, sediment load, and

grazing. Many of these factors will be accounted for in the physical classification of stream systems. 

However, characterization of nutrient gradients in stream systems will be influenced by land use

practices as well as point source discharges (Carpenter et al. 1998). The nutrient ecoregions defined by

Omernik (2000) separate the country into large ecoregions with common land use characteristics. These

ecoregions should be further subdivided for use at the State, Tribal, or local scale. 
July 2000 Chapter 2. Stream System Classification

PAGE 26

Changes in the natural processes that control algal production and biomass in a stream or river as one

moves downstream through a watershed are obviously an important consideration. The River Continuum

Concept (RCC) (Vannote et al. 1980) provides one general model for predictions of stream size effects

on algal-nutrient relations. The RCC predicts, among other things, that benthic algal biomass will

increase with stream size to a maximum for intermediate stream orders (i.e., third and fourth order stream

reaches) as stream width increases and canopy cover consequently decreases. The RCC also suggests

that (1) sestonic (suspended) chlorophyll will become more important in larger, slow-moving rivers and

(2) turbidity in deep, high order streams causes light attenuation, which tends to prohibit high benthic

algal biomass. The RCC may not hold for unforested watersheds (e.g., Dodds et al. 1996) or those with

excessive human impacts such as impoundments or severe sediment input from logging. For example,

Rosenfield and Roff (1991) observed that stream primary productivity in Ontario streams was largely

independent of stream size. However, the RCC is valuable for identifying variables that change with

stream size and affect algal-nutrient relations. 

CLASSIFICATION BY NUTRIENT ECOREGIONS

The draft nutrient aggregations map of level III ecoregions for the conterminous United States (Figure 4;

Omernik 2000) defines broad areas that have general similarities in the quantity and types of ecosystems

as well as natural and anthropogenic characteristics of nutrients. As such, ecoregions are intended to

provide a spatial framework for the National Nutrient Criteria Program. In general, the variability in

nutrient concentrations in streams, lakes, and soils should be less in those ecoregions having higher

hierarchical levels, i.e., nutrient concentrations found in level III ecoregions (84 ecoregions delineated for

the mainland U.S.) (Omernik 1987), than those of waterbodies located in draft aggregations of Level III

ecoregions.

CLASSIFICATION BY TROPHIC STATE

The primary response variable of interest for stream trophic state characterization is algal biomass. Algal

biomass is usually concentrated in the benthos of fast-flowing, gravel/cobble bed streams (i.e.,

periphyton dominated) and measured as benthic chl a per unit area of stream substrate. In slow-moving,

sediment-depositing rivers (i.e., plankton dominated), algal biomass is suspended in the water column

and measured as sestonic chl a per unit water volume. Trophic classifications for lakes and reservoirs

may be appropriately applied to seston in slow-moving rivers as these classifications are based primarily

on chl a per unit volume (e.g., OECD 1982). However, lake classification schemes have limited value

for fast-flowing streams dominated by benthic periphyton because the limited areal planktonic

chlorophyll data available for lakes reveal little differentiation between oligotrophic and eutrophic

systems (Dodds et al. 1998). 

Nitrogen and phosphorus are important variables for classification of trophic state because they are the

nutrients most likely to limit aquatic primary producers and are expressed per unit volume in both fastflowing streams and slow-flowing rivers. Concentrations of total nutrients and suspended algal biomass are

well-correlated in lakes and reservoirs (Dillon and Rigler 1974; Jones and Bachmann 1976; Carlson 1977). 

Developing predictive relationships between nutrient and algal levels in fast-flowing streams may be

difficult considering that most available nutrients are in the water column and most chl a is in the benthos. 

Therefore, trophic state classification for periphyton-dominated stream systems is more appropriately based

on benthic or areal algal biomass (e.g., mg/m2

 chl a) than on concentrations of N and P.
July 2000 Chapter 2. Stream System Classification

PAGE 27

As stated above, classification of trophic state in stream systems is most appropriately based on algal

biomass and secondarily on nutrients. When trophic state classification is based upon nutrients, total

water column concentrations (TP and TN) are more appropriate than dissolved inorganic nitrogen (DIN)

or soluble reactive phosphorus (SRP). Inorganic nutrient pools are depleted and recycled rapidly. Most

monitoring programs will not be able to closely track soluble nutrients in a stream system and should

therefore focus on total water column concentration rather than soluble nutrient species. 

Additional factors also confound the interpretation of dissolved nutrient data. Algae are able to directly

utilize inorganic nutrient pools (DIN and SRP) and deplete these pools if algal biomass is high enough

relative to stream size and nutrient load. Thus, moderately low levels of DIN and SRP do not necessarily

result in low algal biomass. This seeming contradiction is because the supply rate of inorganic nutrients

may still be high even if a large biomass of algae has removed a significant portion of the DIN or SRP

from the water column. Algal growth rate (including diatoms and filamentous greens) can be saturated at

low dissolved inorganic nutrient concentrations (Bothwell 1985, 1989; Watson et al. 1990; Walton et al.

1995). Total phosphorus and TN may better reflect stream trophic status compared to inorganic P and N

because algal drift increases with benthic algal biomass. Thus, as soluble nutrient depletion increases

with benthic algal biomass, that depletion can be partially compensated for by increases in particulate

fractions of TP and TN resulting from benthic algal drift and suspension in the water column.

A trophic classification scheme for streams and rivers, based on chlorophyll a and nutrients, was recently

developed by Dodds et al. (1998). The approach used by Dodds et al. was based upon establishing

statistical distributions of trophic state-related variables. The data were viewed in two ways: 1) three

trophic state categories were constructed based on the lower, middle, and upper thirds of the distributions

and were assigned to oligotrophic, mesotrophic and eutrophic categories respectively; and 2) the actual

distributions (Table 2) were used to determine the proportion of streams in each trophic category. It

should be stressed that this approach proposes 

Table 2. Suggested boundaries for trophic classification of streams from cumulative frequency

distributions. The boundary between oligotrophic and mesotrophic systems represents the lowest third of

the distribution and the boundary between mesotrophic and eutrophic marks the top third of the

distribution.

Variable (units)

Oligotrophicmesotrophic 

boundary

Mesotrophic-eutrophic 

boundary Sample size

(N)

mean benthic chlorophyll (mg m-2)

+ 20 70 286

maximum benthic chlorophyll (mg m-2)

+ 60 200 176

sestonic chlorophyll 

(g L-1)

++ 10 30 292

TN (g L-1)

+,+++ 700 1500 1070

TP (g L-1)

+,++,+++ 25 75 1366

+

Data from Dodds et al. (1998); ++data from Van Nieuwenhuyse and Jones (1996); +++data from Omernik 

(1977).
July 2000 Chapter 2. Stream System Classification

PAGE 28

trophic state categories based on the current distribution of algal biomass and nutrient concentrations

which may be greatly changed from pre-human settlement levels. These distributions were determined

using data for benthic and sestonic chlorophyll and water column TN and TP from a wide variety of

previously published studies. The data were gathered from temperate stream sites located in North

America and New Zealand. The data for TN and TP used in this analysis were not taken from the same

sources as the data for benthic and sestonic chlorophyll a. Hence, the distributions should only be used

to link nutrient concentrations and algal biomass in a very general sense.

Management Applications

Classifying streams by trophic state can assist water quality managers in setting criteria and identifying

those systems most at risk for impairment by nutrient enrichment. For example, an understanding of

stream trophic state and ambient nutrient concentrations allows the manager to determine if the system of

interest is eutrophic due to nutrient inputs that are natural or cultural. Comparisons with streams in the

same local area that have similar physical characteristics will help clarify this issue prior to making

management decisions. Management options may be limited if the condition of the stream is caused by

high background levels of nutrient enrichment. However, if nutrient sources are largely cultural,

establishing nutrient control strategies may realistically result in improvements in stream trophic state

and therefore be useful in managing the stream system.
PAGE 29

3.1 INTRODUCTION

Candidate variables, in the context of this document, are measurable water quality variables that can be

used to evaluate or predict the condition or degree of eutrophication in a water body. Data that are most

useful in determining river and stream trophic status are water column nutrient concentrations and algal

biomass. Benthic and/or planktonic biomass can reach nuisance levels in many stream systems. 

Measurement of these variables provides a means to evaluate the current degree of nutrient enrichment,

and can form the basis for establishing regional and waterbody-specific nutrient criteria. Numerous

variables can potentially be used as part of nutrient surveys or eutrophication assessments including

measures of water column nutrient concentrations (e.g., TP, SRP, orthophosphate, TN, total Kjeldahl

nitrogen [TKN], NO3

-

, ammonia [NH3]); dissolved organic carbon (DOC); water column and

algal/macrophyte tissue N:P nutrient ratios; and algal biomass surrogates (e.g., chl a, ash-free dry mass

[AFDM], turbidity, percent of benthic algal coverage, species composition).

Criteria development at the EPA Regional and National level will begin with nutrient data gleaned

from EPA’s STORET (STOrage and RETrieval) database. Primary nutrient parameters to be

considered include water column concentrations of TN, TP, algal biomass as chl a, and turbidity or

transparency. These four variables are considered a starting point for criteria development and their

efficacy in controlling nutrient enrichment will be re-evaluated over time. Inorganic nutrient species

(PO4 and NO3) are usually more biologically available, and may need to be considered in instances where

small scale effects from specific sources are an important issue (e.g., point source impacts from outfall

pipes, and non-point source impacts during rain events immediately following inorganic fertilizer

application). STORET data on the primary parameters are the foundation of the dataset used at National

and Regional levels for developing nutrient criteria. Supplemental data from other Federal agencies,

State/Tribal agencies, and university studies will also be included as available. Sources of available data,

the parameters included in the primary datasets, and the minimum data requirements for criteria

development are discussed in Chapter 5.

Interpretation of parameter values and their cause-and-effect relationships depends on whether the data

are from stream segments that are slow-moving with a depository substratum and plankton-dominated, or
July 2000 Chapter 3. Select Variables

PAGE 30

that are fast-moving with an eroding (gravel/cobble) substratum and periphyton-dominated. Criteria for

streams with intermediate characteristics, i.e., in which the bottom is not generally visible in slowmoving segments and is not likely to have algal biomass problems, may need to be developed primarily

for fast moving stream segments. Hence, significance of each individual or group of variables is

discussed for each extreme stream/river type; the reader, of course, realizes that flowing waters can be

found along all points on the trophic continuum and parameter values can vary even within a stream

reach. This chapter lists and describes (1) primary response variables that will be used by EPA to set

default criteria and (2) secondary response variables (including sensitive variables, i.e., those likely to be

most sensitive to enrichment as influenced by increased primary producer biomass and metabolic

activity) that can be used to predict the enrichment status of stream systems. 

3.2 PRIMARY VARIABLES

The primary variables considered for nutrient criteria development are water column concentrations of

TN, TP, benthic and planktonic algal biomass as chl a, and turbidity or transparency. These variables

will be used to set criteria ranges for each EPA ecoregion at the National level (see section 1.5). The

primary causal variables, TN and TP, are closely related to the response variables, algal biomass as chl a

and turbidity or transparency, although the relationships between these variables are not as tightly

coupled in rivers and streams as they are in lakes. Concentrations of nutrients and algal biomass and

measures of turbidity/transparency are more highly variable in rivers and streams because of fluctuating

flow conditions. Therefore, knowledge of the flow conditions in the waterbody of concern will be used

to help define the nutrient condition of that waterbody, and will be used in criteria development. Criteria

will not be established for flow as a variable. Stream sampling should be conducted during periods of

peak algal biomass or periods when problems related to algae may be greatest (e.g., low-flow or

following rain events with high nonpoint source nutrient inputs). Subsequent sections of the chapter

discuss other potential variables that may be useful in developing nutrient criteria. Methods for

measuring and analyzing many of the variables discussed in this Chapter can be found in Appendix B. 

NUTRIENTS

Nitrogen and phosphorus are the primary macro-nutrients that enrich streams and rivers and cause

nuisance levels of algae. Conditions that allow periphyton/plankton biomass to accumulate (i.e.,

adequate light, optimum current velocity [periphyton], sufficient water detention time [plankton], as well

as low loss to grazing) will not result in high biomass without sufficient nutrient supply. Nutrients,

especially P, are frequently the key stimulus to increased and high algal biomass. 

Phosphorus is the key nutrient controlling productivity and causing excess algal biomass in many

freshwaters worldwide. However, nitrogen can become important in waters receiving agricultural runoff

and/or wastewater with a low N/P ratio and in waters with naturally phosphorus-rich bedrock (Welch

1992). Nitrogen may have more importance as a limiting element of biomass in streams than in lakes

(Grimm and Fisher 1986; Hill and Knight 1988; Lohman et al. 1991; Chessman et al. 1992; Biggs 1995;

Smith et al. 1999). Lohman et al. (1991) reported low NO3-N causing N limitation at sixteen sites in ten

Ozark Mountain streams and cited sources for N limitation in northern California and the Pacific

Northwest. Nitrogen was clearly the limiting nutrient in the upper Spokane River, Washington (Welch et

al. 1989). Chessman et al. (1992) observed that N was more often limiting than P in Australian streams. 
July 2000 Chapter 3. Select Variables

PAGE 31

Analyses of data from 200 rivers suggests that TN is more closely correlated to mean benthic algal

biomass than TP, and DIN is more closely correlated to biomass than SRP (Dodds et al. unpublished).

The directly available forms of N and P are mainly inorganic (NO3

-

, NH4

+

 and PO4

3-), although many

algae are able to use organic forms (Darley 1982). Total N and TP include these soluble fractions, as

well as the particulate and dissolved organic fractions. Particulate and dissolved organic fractions are not

immediately available and portions may be relatively refractory. Because soluble inorganic fractions are

directly available, soluble inorganic N, P, or both may be low during active growth periods when demand

is high and, therefore, may not be good predictors of biomass (Welch et al. 1988). Total N and TP are

often good predictors of algal biomass in lakes and reservoirs, to a large extent because much of the

particulate fraction is live algal biomass. That is not the case in fast-flowing, gravel/cobble bed streams

where the total nutrient concentration includes detritus but not the living periphytic algae where biomass

measurements are taken. In fast-flowing systems, water column nutrients flow past the living periphyton

biomass before they can be completely assimilated. Therefore, the relationship between benthic

chlorophyll and water column nutrients is weaker in fast-flowing versus standing water systems (Dodds

et al. 1998).

ALGAL BIOMASS AS CHLOROPHYLL a

Algae are either the direct or indirect cause of most problems related to excessive nutrient enrichment;

e.g., algae are directly responsible for excessive, unsightly periphyton mats or surface plankton scums,

and may cause high turbidity, and algae are indirectly responsible for diurnal changes in DO and pH. Chl

a is a photosynthetic pigment and sensitive indicator of algal biomass. It can be considered the most

important biological response variable for nutrient-related problems. The following discussion of chl a

as a primary variable includes information for both benthic and planktonic chl a. Benthic chl a can be

difficult to measure reliably due to its patchy distribution and occurrence on non-uniform stream

bottoms. Periphyton is often analyzed for AFDM, which includes non-algal organisms. Additional

factors that can be used to determine which type of chlorophyll (benthic or planktonic) is most important

in the system of interest can be found in Table 1, Section 2.2.

Unenriched, light-limited, or scour-dominated stream systems typically have benthic chl a values much

less than 50 mg/m2

. Biggs (1995) reported the following range of chl a values from monthly

observations over a one year period in 16 New Zealand streams: 1) unenriched streams in forested

catchment (0.5-3 mg/m2

), 2) moderately enriched streams in catchments with moderate agricultural use

(3-60 mg/m2

), and 3) enriched streams in catchments highly developed for agriculture and/or underlain

with nutrient-rich bedrock (25-260 mg/m2

). Lohman et al. (1992) reported a range of 42 to 678 mg/m2

chl a from over two years of spring to fall biweekly observations at 22 sites on 12 Missouri Ozark

Mountain streams, with higher levels occurring at more enriched sites. Unenriched sites exhibited mean

biomass values that did not exceed 75 mg/m2

. However, highly and moderately enriched sites exceeded a

nuisance level mean biomass (150 mg/m2

) within 3 or 4 weeks, respectively, following flood-scour

events. The highest maximum value observed at ten sites in late summer 1987 in the Clark Fork River,

Montana, was approximately 600 mg/m2 (Watson and Gestring 1996). Furthermore, values for benthic

chl a as high as 1200 mg/m2

 have been observed in gravel/cobble bottom bed streams (Welch et al.

1992).
July 2000 Chapter 3. Select Variables

PAGE 32

Planktonic chl a in deep, slow-moving rivers will have an upper limit determined by light attenuation,

which increases with the suspended chl a concentration. Maximum chl a can be low (<10 g/L) even if

slow-moving systems are nutrient enriched because most flowing systems disperse phytoplankton before

high algal biomass develops. However, under low flow conditions (accompanied by low mixing and

shallow depth), large planktonic algal blooms often develop in slow-moving, nutrient enriched rivers. 

The theoretical maximum attainable before light limits photosynthesis in lakes (assuming light is

attenuated by algae only) is about 250 mg/m2

. This theoretical maximum is equivalent to 25 mg/m3

(g/L) in a 10-m depth water column or 125 g/L in a 2 m deep lake. Van Nieuwenhuyse and Jones

(1996) compiled summer mean suspended chl a values for rivers, and found no values greater than 180

g/L. Mixing and light attenuation from non-algal particulate matter, which are typical in deep, slowmoving rivers, may further limit light availability for photosynthesis. 

A conceptual distribution of algal biomass in the euphotic zone over a range of water detention times was

suggested by Rickert et al. (1977) (see Welch 1992). For example, the lower Duwamish River,

Washington estuary typically contained around 2 g/L chl a during summer, even though it was heavily

enriched with secondary treated sewage effluent. However, when the water detention time increased and

mixing decreased as a combined result of minimum range tidal conditions and low river flow in August,

chl a reached a maximum of 70 g/L (Welch 1992).

Algal biomass data in fast-flowing, gravel/cobble bed streams and deep, slow-moving, turbid rivers must

be interpreted in light of the physical constraints that determine the potential for nutrient utilization (see

Chapter 2). Relatively low biomass can be observed in highly enriched waters, if physical (light,

temperature, current) or grazing constraints are severe. Relatively high algal biomass can occur with low

enrichment if physical constraints approach the optima for algal growth. However, chl a concentrations

near the maximum values cited above will not occur without nutrient enrichment.

TOTAL SUSPENDED SOLIDS, TRANSPARENCY, AND TURBIDITY

Total suspended solids (particulate matter suspended in the water column) attenuate light and reduce

transparency, whether the source is algae, algal detritus or inorganic sediment. Streams may also have

high concentrations of light-absorbing dissolved compounds (e.g., blackwater streams). The

concentration of total suspended solids can be determined directly or as an effect on light transmission or

scattering. Quantitative relationships have been developed for individual and/or groups of waters to

predict transparency from particulate matter and/or chl a (Reckhow and Chapra 1983; Welch 1992). 

However, relationships of chl a and transparency (as an effect of nutrients) are not prevalent in fastmoving streams systems; most likely because of interference from time- and flow-variable inorganics and

large diameter suspended solids. Total suspended solids may increase due to algae and detritus sloughed

from large algal mats, but caution should be exercised in interpreting these data. During high flow, the

concentration of suspended solids (and water clarity) will likely be more strongly influenced by inputs of

inorganic sediment or channel erosion in streams, especially in urbanized and agricultural watersheds. 

Turbidity, as NTUs (Nephelometric Turbidity Units), measures suspended matter in the water column

whether of organic (i.e., chl a) or inorganic origin. Turbidity correlated with rain-event sampling may

help identify non-point source loadings. Although turbidity is not commonly used as an index of

eutrophication in either lakes or streams, it nonetheless should increase in streams with increasing algal

biomass due to nutrient enrichment. 
July 2000 Chapter 3. Select Variables

PAGE 33

Periphyton are directly affected by suspended solids (as turbidity) due to light attenuation. Quinn et al.

(1992) found that waters with turbidity measurements that range between 7-23 NTUs have reduced

abundance and diversity of benthic invertebrates. They attributed the reduction in benthic invertebrates

to turbidity, largely because of its adverse effect on periphyton production as an invertebrate food source

(Quinn et al. 1992). In Illinois, the turbidity of agricultural streams (NTU 10-19) had more effect on

periphyton accrual than did nutrient enrichment (Munn et al. 1989). Total suspended solids ranging from

about 22 to 30 mg/L increased the loss rate of periphyton (mixture of filamentous blue-green and

diatoms) tenfold, although increased velocity with and without solids caused more loss (Horner et al.

1990). 

The vertical water column in relatively clear-water, gravel/cobble bed streams/rivers is usually

insufficient to determine Secchi disk depth. However, the white Secchi disk routinely used in lakes and

reservoirs to determine transparency is appropriate for slow-moving streams and rivers (Welch 1992). 

Transparency, as influenced by low concentrations of particulate matter in shallow, fast-flowing streams

systems, can also be determined with a black disk (Davies-Colley 1988). The path length for

transparency is measured horizontally in such shallow streams, as opposed to vertically in lakes,

reservoirs and deep rivers/estuaries. As periphyton biomass increases, particulate matter sloughed and/or

eroded from the substratum also increases, reducing transparency. 

FLOW AND VELOCITY

The rate of discharge or flow in a stream system can be separated into two primary components, baseflow

and storm or direct runoff. Baseflow comprises the regular groundwater inputs to a stream. This water

typically reaches the stream through longer flow paths than direct runoff and sustains streamflow during

rainless periods. Direct runoff is hillslope or overland flow runoff that reaches a stream channel during

or shortly after a precipitation event. Both components of flow are reflected in a hydrograph (a graph of

the rate of discharge plotted against time) of the stream segment. Runoff processes (including stream

discharge and groundwater recharge), seasonal variation of flow, and methods to calculate average

stream velocity, the annual probability hydrograph and flow duration curves are discussed at length in

Dunne and Leopold (1978).

The flow of a river or stream affects the concentration and distribution of nutrients. Generally, point

source concentrations are higher during low flow conditions due to reduced water volumes; in contrast,

nutrients from non-point sources may be more highly concentrated during high flow conditions due to

increased flow paths through the upper soil horizons and overland flow. There is also a rough correlation

of total dissolved solids concentration with climate and hydrology. Streams in arid regions tend to have

high concentrations of total dissolved solids (though the total annual solute transport is low because of

low runoff), whereas in humid regions, concentrations tend to be lower with higher total annual solute

transport (Dunne and Leopold 1978). However, the complexity of the interactions of nutrient

concentration and flow make it important to examine both point sources and non-point sources of

nutrients and wet weather (high flow) and dry weather (low flow) stream conditions to verify nutrient

sources and concentrations in multiple flow conditions (Dunne and Leopold 1978). 

Brandywine Creek, Pennsylvania, provides an example of how stream flow can affect nutrient

concentrations in a stream system (Dunne and Leopold 1978). The Brandywine Creek watershed drains

portions of the Piedmont plateau and Atlantic coastal plain into the Delaware River. The watershed land
July 2000 Chapter 3. Select Variables

PAGE 34

use is a mix of urban, agricultural and suburban uses, and includes both point and non-point pollution

sources. Brandywine Creek was sampled during periods of storm runoff and dry-weather flow for P and

stream discharge. Point discharges of P were diluted as stream discharge increased following storm

events. As storm runoff occurred, concentrations of P increased dramatically at sampling sites not

dominated by point discharges. At sites not dominated by point discharges, runoff from forested and

cultivated hillslopes washed large amounts of P into the Brandywine Creek in both solid organic form

and sorbed to soil particles. 

Hydrologic variability is an important consideration in the development of nutrient and algal criteria for

all streams; nonetheless, there is often a higher degree of variability for specific types of regional stream

systems. In particular, the spatial and temporal heterogeneity found in arid regions, the stark contrast

between wet and dry, can be dramatic (see Desert Streams Case Study, Appendix A). When viewing

desert catchments from above, the observer is often presented with a dry landscape of high relief bisected

by the string of glistening beads that is the spatially intermittent stream. The dry arroyos or quiet,

disconnected pools and short reaches of wetted stream that characterize desert streams during dry periods

are in complete contrast to the raging torrents that they can become at flood stage. This hydrologic

variability and the unique chemical and biological characteristics of arid lands aquatic ecosystems make

the use of broad generalizations to explain nutrient regimes difficult.

In arid landscapes, stream ecosystems are dynamically linked with the surrounding upland ecosystem. In

addition, surface discharge regimes may vary from completely dry, to flows as much as three to five

orders of magnitude greater than mean annual flow, all within a period of hours or days. In comparison

to streams in more mesic regions, the coefficient of variation of annual flow is 467% greater in arid lands

streams (Davies et al. 1994). The aquatic ecosystems structured by these chaotic flow regimes (Thoms

and Sheldon 1996) may require different techniques for nutrient criteria development than those used in

more homogeneous environments.

Drying disturbance, or more specifically the contraction and fragmentation of a stream ecosystem, is a

critical component of the hydrologic regime of desert streams. Drying occurs as a spatially or temporally

intermittent stream recedes after a wet period. In streams where the dry period and extent may be

greater than the wet, drying is likely to be an important determinant of biological pattern and process

(Stanley et al. 1997; Stanley and Boulton 1995). 

In order to properly characterize the nutrient regime of a stream ecosystem, the flow of water, surface

and subsurface, flood or base flow, wet or dry must be considered at ecologically significant temporal

and spatial scales. It is also important that the manager address this hydrologic regime at the scale of the

question to be answered. If a stream is dry for 75% of the average year, or for 75% of its length, is it

correct to characterize it from surface water data alone? If 50% of the entire annual load of a limiting

nutrient passes through a stream ecosystem in three discrete storm events, what is the effect of that

nutrient on the stream ecosystem itself? What is the effect to downstream ecosystems? Due to the

spatial and temporal variability of flow patterns, the characterization of desert stream nutrient dynamics

is an intricate undertaking. However, stream complexities will only be understood through appropriate

assessment and evaluation.
July 2000 Chapter 3. Select Variables

PAGE 35

3.3 SECONDARY RESPONSE VARIABLES

The following sections describe additional variables that may be useful in criteria development. These

variables comprise chemical, physical, and biological parameters, some of which exhibit heightened 

response to nutrient enrichment. 

SENSITIVE RESPONSE VARIABLES

The variables discussed below that are apt to be most sensitive to nutrient enrichment, via increased algal

productivity and biomass are: 1) DO and pH, 2) benthic community metabolism, and 3) autotrophic

index. These variables should vary directly with algal productivity and detect relatively small changes in

nutrient condition. While other variables such as total suspended solids, macroinvertebrate indices,

dissolved organic matter, and secondary production may be directly affected by algal productivity and

biomass, they may also be strongly dependent on other natural factors and/or sources/types of pollutants.

Dissolved Oxygen and pH

Periphyton algal biomass above nuisance levels often produces large diurnal fluctuations in DO and pH. 

Photosynthesis/respiration by dense periphyton mats commonly causes water quality violations

(Anderson et al. 1994; Watson et al. 1990; Wong and Clark 1976). These water quality impairments

occur in stream systems as a result of nutrient-produced excessive algal biomass in fast-flowing,

gravel/cobble bed streams as well as sluggish stream systems. Excessive macrophyte biomass can

produce similar swings in DO and pH (Wong and Clark 1979; Wong et al. 1979).

The extent of diurnal swings in DO and pH will depend on several factors, such as turbulence (which

affects reaeration), light, temperature, buffering capacity, and the amount and health of algal and/or

macrophyte biomass. Sluggish streams and rivers may show a greater range in DO and pH per unit

biomass compared to faster streams due to less turbulence and associated atmospheric exchange of CO2

and O2 (Odum 1956; Welch 1992). Light limitation may also be a common feature of algae in enriched

streams, and therefore, light is likely an important control on diurnal DO and pH swings (Jasper and

Bothwell 1986; Boston and Hill 1991; Hill 1996). Higher temperatures tend to enhance algal growth in

many streams and may increase photosynthesis and respiration in many systems resulting in greater

variation in diurnal DO and pH values. Streams with low buffering capacity will show greater diurnal

swings in pH. Furthermore, biomass-specific metabolic rate (especially respiration–see

photosynthesis/respiration discussion) tends to be greater in fast-flowing waters because periphytic

growth is stimulated by velocity. The influence of the above factors on DO concentration and pH value

reduce the specificity and potentially reduce the reliability of these variables to indicate response from

nutrient enrichment. Therefore, direct measures of algal biomass, such as chl a, are preferred response

variables. 

Aquatic animals are affected most by maximum pH and minimum DO, rather than by the daily means for

these variables (Welch 1992). Hence, monitoring for water quality should include pre-dawn hours to

observe the diurnal minimum DO and afternoon hours for maximum pH. Routine grab samples in

monitoring programs usually do not include such strict protocols. It may be possible to estimate

minimum DO from equilibrated average and maximum DO (Slack 1971) which occurs during mid-day to

afternoon, along with maximum pH.
July 2000 Chapter 3. Select Variables

PAGE 36

Metabolism

Photosynthetic rate, or primary productivity, is often considered a more sensitive variable of response to

nutrients than algal biomass. Biomass is a net result of gains (productivity) minus losses (algae lost due

to death, scour, etc.) (see discussion in Stevenson 1996). Productivity is essentially growth, and

therefore is a more direct measure of nutrient effects. Productivity can be determined for whole stream

reaches by monitoring diurnal DO concentrations (see methods section, Appendix B) or alternatively,

productivity and respiration may be measured using light/dark chambers. Whole-stream metabolism

measurements are integrative over all components of the stream system and eliminate artifacts of

enclosure that commonly confound results in chamber experiments. Marzolf et al. (1994, 1998) detail

the methods for measuring whole-stream metabolism. Productivity and respiration in light/dark

chambers may vary on an hourly and daily basis with temperature, light, and nutrients; short-term

measurements must be corrected for those factors (Welch et al. 1992). The necessity of normalizing

measurements and the greater analytical difficulty of productivity, has made algal biomass the preferred

variable to indicate nutrient effects on periphyton and phytoplankton as evidenced by the generally

established trophic state criteria for lakes and reservoirs (Welch 1992), and proposed for streams/rivers

(Dodds et al. 1998). The rate at which maximum biomass is attained is dependent mostly on nutrient

availability, minus losses to grazing and scouring, or washout in the case of phytoplankton. While

integrated daily productivity is usually directly related to biomass as chl a (Boston and Hill 1991), there

can be considerable variability in the relationship due to the variables discussed above, as shown by the

ratio of productivity to biomass as chl a (Figure 6). The ratio of productivity to biomass as chl a is an

index of growth rate. If there is no variability in productivity:biomass, the relationship will be constant

and will not vary on a day-to-day basis.

Gross photosynthesis/respiration ratios (P/R ratios) can be useful indicators of trophic characteristics. 

P/R ratios have long been recognized to indicate the relative autotrophic (P/R >1) or heterotrophic (P/R

<1) character of streams and rivers. Measurement of P/R and interpretation of results is dependent on the

scale at which the measurements are made, and the point in the annual cycle when the measurements are

taken. For example, low-order streams that flow through forested watersheds tend to be heterotrophic

with photosynthesis limited by light due to shading; mid-order streams and rivers flowing through areas

with minimal riparian vegetation, or largely unshaded due to width, are usually autotrophic (unless

organic waste inputs are significant); high order rivers tend to return to a heterotrophic character due to

light limitation brought on by increased depth and turbidity (Vannote et al. 1980; Bott et al. 1985). 

Furthermore, the P/R ratio for a short-term measurement (24-72 hours) in the spring may indicate an

autotrophic stream, while on an annual basis the stream is heterotrophic (Hall and Moll 1975; Wetzel

1975; Wetzel and Ward 1992). 

There are problems with interpreting P/R ratios, however. Photosynthesis/respiration ratios can vary

seasonally and could actually reflect a temporary heterotrophic condition during a period of low

periphyton biomass, due to scouring or low light, while otherwise it would be autotrophic. Decreased

velocity can also decrease stream/river P/R, because mat thickness of periphytic diatoms can increase

while the depth of active photosynthesis remains relatively constant (Biggs and Hickey 1994). Thus,

photosynthesis is limited by light attenuation in the mat, but respiration is stimulated by movement of

organic materials to heterotrophic organisms in the mat.
July 2000 Chapter 3. Select Variables

PAGE 37

Figure 6. Integrated daily productivity related to biomass as chlorophyll a (data compiled by Dodds

from published literature; many of the data from Bott et al. 1985).
July 2000 Chapter 3. Select Variables

PAGE 38

Autotrophic Index

The ratio of AFDM to chl a is termed the autotrophic index for periphyton and is used to distinguish the

relative response of inorganic (N and P) and organic (BOD) enrichment. Periphyton growing in surface

water that is relatively free of organic matter contain approximately one to two percent chl a by weight. 

Surface water that is high in particulate organic matter may support large populations of bacteria, fungi

and other non-chlorophyll bearing microorganisms, and have a larger ratio of AFDM to chl a. Increased

ratios indicate that heterotrophs utlizing organic substances comprise a larger percentage of AFDM than

autotrophic periphyton that rely largely on inorganic nutrients to increase biomass (Weber 1973). Ratios

of AFDM/chl a can vary over three orders of magnitude, with values >400 indicating organically

polluted conditions (Collins and Weber 1978). Ratios of AFDM/chl a around 250 are more typical for

streams enriched with inorganic nutrients that are likely to have existing or potential eutrophication

problems (Watson and Gestring 1996; Biggs 1996). The autotrophic index should be used with caution,

because non-living organic detrital material may artificially inflate the ratio.

Interpretation of Sensitive Response Variables

High algal productivity can cause supersaturated DO and high pH during the day, P/R ratios >1, and

unusually low autotrophic indices. Unfortunately, broad predictive relationships do not exist between

nutrient concentration and algal/macrophyte biomass, DO, or pH. However, relationships could be

developed for individual streams and rivers. Nevertheless, without inclusion of other factors that affect

DO and pH (such as exchange with the atmosphere for specific stream systems), a biomass limit to

prevent low DO (e.g., <5 mg/L) cannot be determined from any existing relationship, such as the chl a -

TP relationships discussed earlier (Lohman et al. 1992; Dodds et al. 1997). As concentrations of

nutrients and algae increase, diel fluctuations in DO and pH also increase (see Dissolved Oxygen and pH

discussion above). However, established relationships observed in lakes and reservoirs, such as TP

loading and hypolimnetic DO deficit (Welch 1992), do not exist for streams and rivers.

OTHER SECONDARY RESPONSE VARIABLES

Additional chemical, physical, and biological attributes may be useful when evaluating nutrient and algal

relationships. Descriptions for several potential useful variables are provided below. 

Chemical Waterbody Characteristics

Conductivity

Specific conductance (typically measured as conductivity) has also been used as an indicator of nutrient

enrichment (Biggs and Price 1987; Biggs 1996). Conductance reflects the concentrations of macro-ions,

so nutrients dissolved from bedrock are assumed to increase proportionately with increases in total ions. 

Conductance at low flow was found to increase proportionately with urbanization in 23 western

Washington streams and was hypothesized to be a loose surrogate for soluble nutrient supply during

summer when residual soluble nutrient concentration was low due to algal demand (May et al. 1997). 

However, conductance may be a poor indicator of nutrient availability in calcareous regions or those with

high concentrations of dissolved salts that are not typically limiting nutrients. 

Dissolved Organic Carbon

DOC is an important energy source that drives the heterotrophic community and can alter a river’s

response to algal growth problems. DOC can originate as allochthonous inputs naturally from the
July 2000 Chapter 3. Select Variables

PAGE 39

watershed through decomposition of terrestrial primary production, or from cultural waste production. 

The heterotrophic community will dominate the periphyton in gravel/cobble bed streams and rivers that

have high inputs of labile DOC. 

Inflow and in-stream DOC should be related to the autotrophic index, as discussed previously. Streams

and rivers enriched with DOC will have high autotrophic indices, and may be more prone to low oxygen

events that can be exacerbated by excessive periphyton biomass. High rates of autochthonous DOC

production` is usually a result of inorganic nutrient enrichment. Such eutrophication-caused DOC

production can be an important source of decomposition by-products (e.g., tri-halomethane precursors

and other sources of taste and odor problems) which is a concern for drinking water supplies.

Physical Waterbody Characteristics

Temperature

Algal metabolic rate, at a given biomass and growth phase (relative cell health), is controlled by

temperature (DeNicola 1996), water movement, nutrients and light. In general, the response to

enrichment will be faster at higher than lower temperature; e.g., twice as fast at 20°

C as at 10°C (McIntire

and Phinney 1965; Welch 1992). However, the maximum biomass will depend on nutrient availability;

temperature will determine only the rate at which the maximum is reached (Welch 1992).

Temperature, as it interacts with light and nutrients, will determine which taxa dominate the algal

biomass. The various algal taxa have individual thermal optima. In general blue-greens have higher

optima than greens which have higher optima than diatoms (Rodhe 1948; Cairns 1956; Hutchinson

1967). For example, the nuisance filamentous green, Cladophora, apparently has an optimum around

18°

C and its growth stops at 25°C (Storr and Sweeney 1971). As a result of differing thermal optima,

seasonal succession of taxa is often observed, with diatom dominance during spring low temperature and

greens and blue-greens dominating in summer. However, nutrients often override temperature effects,

with diatoms dominating the periphyton throughout the spring-summer period at low nutrient

concentrations and greens (and/or blue-greens) dominating for the whole period at high nutrient

concentrations (Welch 1992).

Biological Attributes

Algal Biomass as Ash-Free Dry Mass

Algal biomass or standing crop is often expressed as AFDM. However, the weight of particulate detritus

in fresh water frequently exceeds that of the algae. No reasonable method currently exists to separate

algae from detrital material in the water. Therefore, chl a is usually the primary biomass indicator

because it is specific to algae, while AFDM can include other living or non-living organic matter (Darley

1982; Wetzel 1975).

Algal Biomass - % Cover of Bottom by Nuisance Algae

Extent of periphyton coverage of a stream bed can be an important indicator of algal biomass problems. 

As enrichment increases, the fraction of periphyton biomass composed of filamentous greens increases,

as does the percent of stream bed covered with algae (Welch et al. 1988; Lohman et al. 1992; Biggs

1996). However, there may be an uncoupling between percent cover and total biomass depending on the

thickness of the algal mat, e.g. , a system might have 100% algal cover, but if the algal growth was very
July 2000 Chapter 3. Select Variables

PAGE 40

thin (e.g., “sheets” of Oscillatoria filaments), the total biomass could be far less than a system with 50%

cover of Cladophora. Nevertheless, estimates of percent cover are often a useful indicator of the

intensity of algal proliferation in gravel/cobble-bed streams, and as an index of aesthetic appeal. The

occurrence of floating blue-green algae scums in slow-moving rivers, lakes, and reservoirs is likewise an

aesthetic nuisance, but there has been no attempt to quantify scum intensity/surface-cover similarly to

periphyton in fast-flowing streams, largely due to the variable, diurnal nature of floating blue-green

scums.

Pigment Ratios

Two pigment ratios are commonly used in periphyton assessments. One is the chl a:AFDM ratio, which

is a modified version of the autotrophic index (Weber 1973; Stevenson 1996; Stevenson and Bahls 1999)

and indicates the relative importance of autotrophy versus heterotrophy in streams. Values of the

autotrophic index increase when algae (chl a) become a greater proportion of benthic biomass. The

second is the chl a:phaeophytin ratio, which is an indicator of periphyton health. Phaeophytin is a

degradation product of chlorophyll. Relatively low values of phaeophytin, thus relatively high values of

the chl a:phaeophytin index, indicate periphyton is actively growing.

Chemical Composition of Algae (N:P Stoichiometry)

Phosphorus and N concentrations in periphyton increase with nutrient concentrations and trophic status

of streams (Humphrey and Stevenson 1992; Biggs 1995). Periphyton can be analyzed for P and N

content, as well as chl a or AFDM. Then P and N concentrations in periphyton can be expressed as a

fraction of algal biomass as indicated by chl a or AFDM (μg P/μg chl a or μg P/mg AFDM). This metric

can be another valuable complement to assessments of P and N availability, especially when P and N

concentrations are variable in the stream. 

Nutrient ratios in periphyton may provide a line of evidence to indicate whether N or P is limiting algal

growth. The range of ambient or cellular N:P ratios has been used as to define the transition between N

and P limitation for benthic algae (Schanz and Juon 1983). If ambient N:P ratios are greater than 20:1,

then P can be assumed to be in limiting supply. If the ambient N:P ratio is less than 10:1, then N can be

assumed to be in limiting supply. The distinction of the limiting nutrient when ambient N:P ratios are

between 10 and 20 to 1 is not precise. Nutrient enrichment studies have supported these transition ratios

in broad terms (e.g., Grimm and Fisher 1986a; Peterson et al. 1993). However, the accuracy of ambient

nutrient ratio analysis decreases when greater amounts of detritus occur in periphyton samples. In

streams, N:P ratios of periphyton can be different than N:P ratios in the water column (Humphrey and

Stevenson 1992). Periphyton N:P ratios may better indicate relative nutrient availability to the

periphyton than ratios based on water column nutrient concentrations. In addition, ambient ratios may

not reflect the cellular ratio relevant to physiological growth processes when nutrients are abundant. 

Cellular nutrient ratios are a more direct measurement of nutrient limitation (Borchardt 1996). Even so,

nutrient ratios only suggest limitation–bioassays are required to establish cause and effect relationships. 

Phosphatase Activity

Alkaline phosphatase is an enzyme excreted by algae in response to P limitation. Alkaline phosphatase

hydrolyzes phosphate ester bonds, releasing PO4 from organic P compounds (Steinman and Mulholland

1996). Concentration of alkaline phosphatase in the water column can be used to evaluate P limitation. 

Alkaline phosphatase activity (APA), monitored over time in a waterbody, can be used to assess the

influence of P loads on the growth limitation of algae (Smith and Kalff 1981). Artificial stream channel
July 2000 Chapter 3. Select Variables

PAGE 41

experiments by Klotz (1992) support the hypothesis that stream N:P ratio is the important factor in

determining periphyton APA. In this study, APA varied seasonally, and shading of the stream channel

resulted in lower APA. Results from studies of cultured algae appear to indicate that phosphatase levels

above 0.003 mmol (micromoles) mg chl a-1 h-1 indicate moderate P deficiency, and phosphatase levels

above 0.005 mmol mg chl a-1 h-1 indicate severe P deficiency (Steinman and Mulholland 1996). 

Algal Species Composition 

Assessment of algal species composition can indicate that nutrient related problems exist or that

conditions are right for such problems to develop (Kelly and Whitton 1995; Pan et al. 1996). Since algae

are often the problem associated with nutrient contamination, assessments of algal species composition

can show whether nuisance algae are present or whether biotic integrity of this target community has

changed. Assessment of algal species composition is more time consuming than simpler measurements

of water chemistry or chl a measurement, however algal species composition may provide more reliable

indicators of trophic status in streams and rivers than one-time sampling and assessment of water

chemistry and benthic algal biomass (Stevenson, unpublished data). Assessment of algal species

composition is an element of periphyton programs in all States that monitor periphyton. One of the

reasons for relying on species composition is periphyton biomass is so variable spatially and temporally,

and challenging to measure accurately. In addition, species composition is highly informative, especially

when linked to the ecology of a species in relation to the environment, i.e., the autecological information

about the species (Stevenson and Bahls 1999).

Many attributes of algal species composition can be used as metrics or indicators of nutrient conditions,

trophic status, and biotic integrity (Stevenson and Bahls 1999). Indicators of nutrient status based on

algal taxa fall in three categories: diversity, deviations in species composition from reference conditions,

and weighted-average autecological indices. Diversity is comprised of two components: 1) the variety of

species (species richness), and 2) the relative abundance of species (eveness). Shannon diversity (a

measure of diversity which combines the components of diversity [Pielou 1975]) usually decreases with

increasing trophic status because evenness decreases. Weighted-average autecological indices based on

pollution tolerance, or more specifically, nutrient requirements can be used to infer nutrient status or

trophic conditions in a habitat (Steinberg and Scheifele 1988; Schiefele and Schreiner 1991; Van Dam et

al. 1994; Kelly and Whitton 1995; Pan et al. 1996). Dissimilarity in species composition between test

and reference sites can be used to determine whether water quality is similar in test and reference sites. 

A more complete review of metrics and how algae can be used in environmental assessment of rivers and

streams can be found in McCormick and Cairns (1994), Stevenson and Pan (1999) or Stevenson and

Bahls (1999).

Grazers and Secondary Production

Dense populations of algae-consuming grazers may lead to negligible algal biomass in spite of high

levels of nutrients (Steinman 1996). The existence of a “trophic cascade” (control of algal biomass by

community composition of grazers and their predators) has been demonstrated for some streams (e.g.,

Power 1990). Grazer biomass was related more strongly with P concentration in 12 Quebec streams than

was periphytic algal biomass, which was considered controlled by grazing in spite of TP concentrations

ranging from 5 to 60 g/L (Bourassa and Cattaneo 1998). The potential for manipulations of foodwebs

to control eutrophication certainly warrants more investigation, but there is not currently enough

information on trophic cascades in streams to allow for use of foodweb dynamics as a management

option. Managers still should realize the potential control of algal biomass by grazers, but also be aware
July 2000 Chapter 3. Select Variables

PAGE 42

that populations of grazers may fluctuate seasonally or unpredictably, and fail to control biomass at

times. Consideration of grazer populations may at least explain why some stream systems with high

nutrients have low algal biomass.

Phytoplankton losses in slow-moving rivers due to filter-feeding grazers can also be significant. Bivalve

communities can filter large volumes of water on a daily basis (as much as 10-100% of the water column,

depending on population density) (Strayer et al. 1999). The amount of particulate matter grazed from

this filtration may exceed losses to pelagic filter-feeders or downstream advection. Significant losses of

pelagic phytoplankton have been observed in large rivers. Strayer et al. (1999) describe a zebra mussel

invasion of the Hudson River ecosystem that drastically reduced phytoplankton (and zooplankton)

biomass by 80-90%, as well as a 50% reduction in phytoplankton biomass in a reach of the Potomac

River following colonization by the bivalve Corbicula fluminea. Ecosystem response to severe biomass

reduction by filter-feeding grazers is often characterized by an increase in dissolved nutrients like SRP,

reduced turbidity, and proliferation of macrophytes. Inherent qualities of the waterbody (e.g., mixing,

sediment stability, and light attenuation) are a factor in determining whether phytoplankton biomass is

permanently reduced, regardless of increases in nutrient concentration, or temporarily reduced and then

replenished with a shift in dominant phytoplankton species (Caraco et al. 1997). 

Production and biomass of consumers is expected to be greater in streams/rivers enriched with N and P. 

At some point, however, productivity and biomass will cease to increase at all or the rate of increase per

unit nutrient will be greatly reduced. One feature of highly enriched lakes and reservoirs is the switch to

grazer-resistant filamentous/colonial blue-green algae, which reduces the efficiency of nutrient utilization

and energy conversion to higher trophic levels (Welch 1992). Although not well documented, the same

phenomenon may be expected in enriched streams and rivers resulting in increased biomass and percent

coverage of filamentous green algae. On the other hand, low-level enrichment of oligotrophic streams

and rivers may result in pronounced increases in benthic invertebrates and fishes in addition to increased

algal biomass. For example, continuous enrichment of the P-limited Keogh River and Grilse Creek on

Vancouver Island, British Columbia, led to substantial increases in secondary producers, but did not

produce nuisance biomass levels of periphyton (Perrin et al. 1987; Slaney and Ward 1993). Enrichment

of the Keogh River and Grilse Creek with 5-10 and 5 μg/L SRP, respectively, produced maximum

periphyton biomass (chl a) levels of 100-150 and 50-100 mg/m2

. Consequently, benthic invertebrate

biomass increased from 2-7 fold and fish size 1.4-2 fold. Phosphorus fertilization (10 μg/L) of a tundra

river led to increased fish and algae production, but negligible increases in invertebrate production

(Peterson et al. 1993). In some cases, enrichment of oligotrophic waters may result in increased grazer

biomass with little or no change in periphyton biomass (Biggs and Lowe 1994).

Even if nuisance levels of periphyton are produced, secondary production will probably be higher than in

unenriched waters in spite of reduced efficiency of conversion. Enrichment of Berry Creek, Oregon,

with sucrose (1-4 mg/L) produced large, nuisance mats of filamentous bacteria, but benthic invertebrate

biomass increased 4.5 fold and fish (cutthroat trout) increased 6.3 fold with enrichment (Warren et al.

1964). Although adverse effects of periphytic mats and water quality were apparently not evaluated, fish

growth obviously prospered from the large biomass of chironomids that consumed the filamentous

bacteria.

Secondary production can clearly respond to enrichment and the response may be more efficient and

beneficial in oligotrophic than eutrophic streams systems. A transition region in enrichment from
July 2000 Chapter 3. Select Variables

PAGE 43

beneficial to detrimental effects has not been defined to the extent that it has for lakes and reservoirs

(Welch 1992), but probably exists for different physical types of streams and rivers. Two recent studies

have provided independent estimates of target streamwater nutrient concentrations that should be

maintained in order to assure acceptable water quality needed for fish growth (Smith et al. 1999). 

McGarrigle (1993) concluded that maintaining a mean annual SRP concentration <47 mg m-3 was

necessary to prevent the nuisance growth of attached algae and to preserve water quality suitable for

salmonid fishes in Irish rivers. Similarly, Miltner and Rankin (1998) observed deleterious effects of

eutrophication on fish communities in low order Ohio streams when total inorganic nitrogen (TIN) and

SRP concentrations exceeded 610 mg m-3 and 60 mg m-3, respectively.

Invertebrate and fish biomass are considered very useful variables, albeit more demanding to measure

than other indices discussed above. Measuring such variables could prove useful because: 1) both may

respond to enrichment, 2) fish are of direct economic and recreational importance, and 3) case studies are

needed to develop guidelines for regions of enrichment that represent a transition between beneficial and

detrimental effects of enrichment.

Macrophytes

Macrophyte is a general term of no taxonomic significance that is applied to many species of aquatic

vegetation. Aquatic plants (macrophytes) can be classified into four groups: emergent, floating-leaved,

submersed, and freely floating and are large enough to be observed by the naked eye. Aquatic

macrophytes represent a taxonomically diverse group of aquatic plants and include flowering vascular

plants, mosses, ferns, and macroalgae (USEPA 1973; Wetzel 1975). Macrophytes are found in most

waterbodies and play an important role in the aquatic community providing food for other aquatic

organisms, processing nutrients or toxic elements in the water column, and aiding in the stabilization of

river/stream sediments (Davis 1985).

The four categories of macrophytes are defined by their connection or anchor to the waterbody substrate: 

free-floating, emergent (rooted but breaking the water surface), floating leaf anchored, and immersed

floating mat anchored (USEPA 1973). The type of growth form plays an important role in the effects of

eutrophication on macroscopic plant communties in rivers and streams. For example, the large surface

area provided by the thin narrow leaves of Potamogeton pectinatus (sago pondweed) allow this species to

persist in flowing water with high turbidity (Hynes 1969; Goldman and Horne 1983). Emergent

macrophytes grow on the banks of rivers and streams in depths of water less than a meter and are

typically rooted in the sediment, have their basal portions submersed in water and have their upper

structural biomass growing in the air. Most emergent macrophytes are perennials (living for more than

one year). Common emergent macrophytes include plants such as reeds (Phragmites spp.), bulrushes

(Scirpus spp.), cattails (Typha spp.), and wild rice (Zizania spp.). Floating-leaved macrophytes are

rooted to the river bottom with leaves that float on the surface of the water such as waterlilies (Nymphaea

spp.) and spatterdock (Nuphar spp). Submersed macrophytes are a diverse group that grow completely

under the water and include mosses (Fontinalis spp.), muskgrasses (Chara spp.), stoneworts (Nitella

spp.) and numerous native vascular plants such as various pondweeds (Potamogeton spp.), tape-grass

(Vallisneria spp.), and exotic species including hydrilla and Eurasian watermilfoil. Free-floating

macrophytes typically float on or just under the water surface with their roots suspended in the water

column. These unattached macrophytes range in size from small duckweeds (Lemna spp.) and water fern

(Salvinia spp.) to larger surface floating plants such as water hyacinth (Eichhornia crassipes). Freefloating species are entirely dependent on the water for their nutrient supply. The distribution and
July 2000 Chapter 3. Select Variables

PAGE 44

abundance of free-floating macrophytes in streams is affected by current velocity and wind. Thus, they

are most frequently found in backwaters and embayments (Goldman and Horne 1983).

The most important environmental factors affecting the abundance and distribution of aquatic

macrophytes in rivers are light availability (Spence 1975; Chambers and Kalff 1985; Canfield et al.

1985), nutrients and water chemistry (Hutchinson 1975; Beal 1977; Kadono 1982; Hoyer et al. 1996),

substratum characteristics (sediment texture, nutrient content) (Pearsall 1920; Barko et al. 1986; Nichols

1992), and current velocity. Aquatic plants require light for growth, thus light availability is often

considered the single most crucial environmental factor regulating the maximum depth of plant growth

(Pearsall 1920; Spence 1975; Chambers and Kalff 1985). Light availability is directly linked to water

clarity; as water depth increases or water clarity decreases, both the amount and spectral quality of light

for photosynthesis decreases (Canfield et al. 1985; Chambers and Kalff 1985). Light availability in

rivers is controlled by riparian canopy cover and water clarity, which can be due to both organic and

inorganic suspended particles (Vannote et al. 1980). Thus, shaded, turbid, and deep rivers will have

fewer aquatic macrophytes.

There are few reports of nutrient-related growth limitation for aquatic plants; nutrients supplied from

sediments combined with those in solution are usually adequate to meet nutritional demands of rooted

aquatic plants, even in oligotrophic systems (Barko et al. 1986). There are exceptions, however. Barko

et al. (1991) showed that interstitial ammonia limited the growth of hydrilla in the Potomac estuary.

Nutrient enrichment of nutrient poor waters will increase plant production if no other factors constrain

growth. However, the effects of enrichment for macrophytes are confounded by competition with

planktonic and epiphytic algae that may reduce underwater light penetration of submerged macrophytes

and negate any direct effects of nutrient enrichment (Chambers et al. 1999). Bottom sediments act as the

primary nutrient source for macrophytes, and for the most part, water column nutrients must be

incorporated into the sediments before they become available for uptake by macrophytes (Chambers et al.

1999).

The physical aspects of sediment texture and as an anchoring point for aquatic plants are also important

to the success of macrophytes in stream systems. Some bottom types (e.g., rocks or cobble) are so hard

that plant roots cannot penetrate them and fast flowing gravel/cobble bottom stream systems rarely

contain enough sediment to support rooted macrophytes. Other sediments are too soft or unstable to

anchor rooted macrophytes well enough to endure changes in velocity. In addition, extremely coarsetextured sediment (sand) can be nutritionally poor and therefore require accumulation of organic matter

from plant growth or erosion to provide suitable substrate for macrophyte growth (Goldman and Horne

1983). 

Macrophytes affect the water quality and human uses of water, other resident organisms, and nutrient

cycling. In turn, the above factors influence the growth and abundance of the macrophyte community.

To obtain the desired biological integrity of an aquatic community, macrophytes should be present and

healthy. However, excess natural or cultural enrichment may yield an overabundance or nuisance growth

of macrophytes (USEPA 1973). Macrophytes can inhibit phytoplankton growth by competing for

nutrients and sunlight, and by limiting light penetration and therefore photosynthetic processes below the

surface (Wetzel 1975). Macrophytes affect the DO and carbon dioxide (CO2) concentrations, alkalinity,

pH, and nutrient supply of a water body through primary production and respiration. Overgrowth of

macrophytes in rivers and streams may decrease sediment transport by lowering the flow velocity. 
July 2000 Chapter 3. Select Variables

PAGE 45

Current velocity, sediment type, and light availability to a large extent determine the plant types that

occur in rivers (Hynes 1969; Goldman and Horne 1983; Chambers et al. 1999).

Macrophytes can be an important index of biological health in a waterbody. Their abundance or shortage

may be an indicator of excess or deficient nutrient supply. By monitoring macrophytes over a long

period of time (along with other parameters), relationships may be developed between macrophyte

productivity and nutrients, nutrient cycling, eutrophication, sediment, and other biota (USEPA 1973). 

Depending on natural nutrient conditions or waterbody trophic state, N or P may be the limiting nutrient

in algal/macrophyte biomass accumulation (USEPA 1973; Smart 1990). Phosphorus in particular, but

also N and other nutrients, may be taken up by submerged macrophytes from sediment, uncoupling

macrophyte growth from water column nutrient concentrations (Welch 1992). Hence, water column

measurements of total N and P (or soluble N and P) are usually not indicative of macrophyte growth

potential. However, macrophyte growth has been shown to be responsive to sediment pore-water

ammonia content. As noted in the Bow River case study (see Appendix A), macrophytes declined in the

Bow River following N removal from point source wastewater plants. This decline was hypothesized to

have resulted from reductions in sediment N. 

Macroinvertebrate Multi-Metric Indices

Indices employing macroinvertebrates as indicators of nutrient pollution have great potential because

they are the most reliable and frequently used organisms to indicate the quality of water. 

Macroinvertebrates are 1) highly sensitive to changes in water quality and disturbance, 2) relatively

immobile, long-lived and easy to sample, and 3) an important food supply for fish and therefore

economically important. While the productivity and biomass of macroinvertebrates, as secondary

producers, readily respond to enrichment as noted above, the individual taxa also respond. Some

macroinvertebrates are particularly sensitive to nutrient enrichment, but local metrics of

macroinvertebrates must be developed to reliably use macroinvertebrates as indicators of nutrient

enrichment. The peer-reviewed stream ecology literature describing nutrient and macroinvertebrate

interactions is extensive. Wallace and Webster (1996) provide a review of the literature. Specific

methods for sampling macroinvertebrates and developing metrics for different stressors are described in

Barbour et al. (1999). Further discussion of macroinvertebrate multi-metric index development can be

found in Resh and Rosenberg (1984) and Resh et al. (1996). This type of metric development could be

used to derive macroinvertebrate indices of nutrient enrichment in wadeable streams and rivers. In

addition, Norton et al. (2000) describes procedures to use biological assessments, including multi-metric

indices, for identifying nutrient stress on both macroinvertebrates and fish.
July 2000 Chapter 3. Select Variables

PAGE 46
PAGE 47

4.1 INTRODUCTION

The purpose of this chapter is to provide technical guidance on designing effective sampling programs

for reconnaissance. Appropriate data describing stream nutrient and algal conditions are lacking in many

places. Where available data are not sufficient to derive criteria, it will be necessary to collect new data

through existing or new monitoring programs. New monitoring programs should be designed to assess

nutrient and algal conditions with statistical rigor while maximizing available management resources. 

Nutrient monitoring programs are used to better define nutrient and algal relationships within stream

systems. At the broadest level, monitoring data should detect:

1. Seasonal patterns in nutrient levels and their relationship to algal biomass levels; 

2. The assimilation capacity of the system for nutrients: i.e., how much nutrient loading can be

assimilated without causing unacceptable changes in water quality or the algal community

(biomass and composition);

3. Whether nutrient concentrations are increasing, decreasing, or staying the same over time.

This Chapter provides discussion on issues to consider with regard to monitoring nutrients and their

effects in stream systems. The various forms of nutrients to consider for sampling are discussed in

Chapter 3. Field sampling and laboratory methods for nutrient assessment are described in Appendix B. 

Monitoring programs are often poorly and inconsistently funded or are improperly designed and carried

out, making it difficult to collect a sufficient number of samples over time and space to identify changes

in water quality or estimate average conditions with statistical rigor. This Chapter provides a procedural

approach for assessing water quality condition and identifying impairment by nutrients and algae in

stream reaches. The approaches described below present sampling designs that allow one to obtain a

significant amount of information with relatively minimal effort. Probabilistic and stratified random
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 48

sampling begin with large-scale random monitoring designs that are reduced as nutrient and algal

conditions are characterized. The tiered approach to monitoring begins with coarse screening and

proceeds to more detailed monitoring protocols as impaired and high-risk systems are identified and

targeted for further investigation. 

Water quality variables other than the primary variables discussed in Chapter 3, e.g., DO, pH, TSS, etc.,

should be critically selected in a monitoring design to obtain the most cost-effective information required

to assess river system nutrient and algal conditions. Sampling should be designed to answer questions

such as: how, when, where and at what levels do nutrient concentration and algal biomass contribute to

unacceptable water quality conditions (e.g., offensive odors, aesthetic impairment, degraded habitat for

aquatic life, diurnal decreases in DO and pH increases)? These questions are interrelated, and a welldesigned program that monitors the primary variables (TN, TP, chl a, turbidity) with other water quality

variables can contribute to answering them. 

4.2 SAMPLING PROTOCOL

CONSIDERATIONS FOR SAMPLING DESIGN

Developing nutrient criteria and monitoring the success of nutrient management programs involve

important considerations for sampling design. Initially, the relationships between critical response

variables and nutrient concentrations need to be established. Next, reference reaches should be sampled

and assessed for specific classes of streams. Nutrient concentrations and algal biomass levels in

reference reaches should define the ecological state that could be attained if impaired reaches were

restored. In some streams and rivers, nutrient levels may be naturally high if bedrock, soils, or wetlands

are nutrient-rich sources in the region. However, human actions can exacerbate nutrient enrichment

regardless of the natural nutrient condition. 

Reach/stream selection for establishing causal relationships between nutrients and algal biomass is based

on the need to sample a relatively large number of streams with nutrient concentrations distributed along

the entire nutrient gradient for each class of streams in a specific regional setting. Cause-response 

relationships can also be identified using large sample sizes and streams with low as well as medium and

high nutrient concentrations. All ranges of responses should be observed along the gradient from

reference condition to high levels of human disturbance. Therefore, streams should be selected based on

land-use in the region so that watersheds range from minimally impaired with expected low nutrient

runoff to high levels of development (e.g., agriculture, forestry, or urban) with expected high runoff.

Assessing watershed characteristics through aerial photography and the use of geographical information

systems (GIS) linked to natural resource and land-use databases, can aid in identifying reference and

impaired streams. Some examples of watershed characteristics which can be evaluated using GIS and

aerial photography include land-use, land-cover (including riparian vegetation), soils, bedrock,

hydrography, infrastructure (e.g., roads, public sewerage systems, private septic systems), and climate. 

Watersheds with little or no development that receive minimal anthropogenic inputs could potentially

contain streams that would serve as reference sites (see section below). Watersheds with a high

percentage of their area occupied by nutrient-rich soils, heavily fertilized agricultural land, and extensive

unsewered development in coarse soils are likely to contain streams receiving high nutrient loads that

could potentially be considered ‘at risk’ for developing nutrient and algal problems. The USDA
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 49

agricultural census provides information on agricultural land use (crops, livestock, irrigation, chemicals

used) at the national, state, and county levels. Data are available on their website at:

http://www.nass.usda.gov/census/.

Once the watershed level has been considered, a more stream-specific investigation can be initiated to

better evaluate nutrient and algal conditions. Rivers and streams need adequate light and nutrients to

develop and maintain high levels of algal biomass. In addition, attached algae (periphyton) require

coarse substrata (cobbles, boulders) and a flow regime that provides sufficient periods between scouring

floods (at least one month) to accumulate high levels of biomass. The condition of the riparian zone

needs to be considered. Riparian buffer zones may mediate the effects of nonpoint sources of nutrients

and turbidity and, depending on the slope of the system, may reduce the velocity of overland runoff to a

stream. Riparian wetlands may serve as both sources and sinks for nutrients varying with wetland type,

seasonal flows, and degree of disturbance. The presence or absence of streamside trees can affect light

limitation in a stream. Light is unlikely to limit algal growth where streamside trees have been removed

or the stream is wide, shallow and clear enough to permit sufficient light to reach much of the bottom. 

Shaded streams may have high nutrient concentrations with no correlative response in algal growth,

though the nutrient load may stimulate algal growth further downstream. The relative risk to develop

nutrient and algal problems could be assessed by noting how many of the above factors that permit higher

algal levels and/or nutrient concentrations are common to a stream or reach.

WHERE TO SAMPLE

Nutrient inputs can occur at a myriad of points along a river system resulting in highly variable

concentrations of nutrients throughout the system. System variability and multiple nutrient input points

require numerous sampling sites for assessing the nutrient condition of a river system. Monitoring

stations for nutrients in streams and rivers should be located upstream and downstream from major

sources of nutrients or diluting waters (e.g., discharges, development, tributaries, areas of major

groundwater inputs) to quantify sources and loads. 

WHEN TO SAMPLE

Nutrient and algal problems are frequently seasonal in streams and rivers, so sampling periods can be

targeted to the seasonal periods associated with nuisance problems. Nonpoint sources may cause

increased nutrient concentrations and turbidity or nuisance algal blooms following periods of high runoff

during spring and fall, while point sources of nutrient pollutants may cause low-flow plankton blooms

and/or increased nutrient concentrations in pools of streams and in rivers during summer. In most state

monitoring programs, sampling is only conducted once during the season when greatest impacts are

expected. If only a one-time sampling is possible, then sampling between two to four (2-4) weeks after a

storm or high flow event has disturbed algal assemblages (Stevenson and Bahls 1999) is recommended. 

Two to four weeks will allow sufficient time for algal biomass recovery in streams where algal biomass 

predominantly consists of diatoms or micro-algae. Alternatively, sampling should be conducted during

the growing season at the mean time after flooding for the system of interest. In streams where macroalgae or macrophytes comprise the dominate photosynthetic biomass, recovery of photosynthetic biomass

may take one or more growing seasons following a major high-flow event. However, if a high-flow event

does not move anchoring substrata, the flow event will only have a nominal effect on photosynthetic

biomass. High flow events late in the growing season when algal and macrophyte filaments and fronds
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 50

are more prone to slough, may cause a reduction in the photosynthetic biomass. A one-time sampling

approach may be adequate for indicators of nutrient status, designated use, and biotic integrity. 

However, criteria and biological or ecological indicator development (see Assessing Algal Biomass

below) may require more frequent sampling to observe nutrient conditions that relate to peak algal

biomass (Biggs 1996; Stevenson 1996; Stevenson 1997b). 

Nutrient concentrations vary with climate-driven changes in flow. Algal blooms, both benthic and

planktonic, can develop rapidly and then may dissipate as nutrient supplies are depleted or flow

increases. Thus sampling through the season of potential blooms may be necessary to observe peak algal

biomass and to characterize the nutrient conditions that caused the bloom. Sampling through the season

of potential problems is important for developing cause-response relationships (with which biological

and ecological indicators can be developed) and for characterizing reference conditions. Keep in mind

that there is a time-lag between nutrient enrichment and algal response. Therefore to characterize algal

response to a specific enrichment event, nutrient sampling should be conducted prior to algal sampling. 

Samples for nutrients should also be collected during the season of lowest algal levels (at least 3

samplings spread over the period) to determine current background levels of algal biomass; avoid the

problem of algal uptake attenuating nutrient concentrations, and help provide an estimate of maximum

nutrient concentration. Many nutrient monitoring programs are based on quarterly sampling. However,

quarterly samples are usually inadequate to detect long-term trends due to year-to-year variation in the

window of high flows, the period of high nutrient uptake and algal growth, and the period of algal

sloughing at the end of the growing season.

If few nutrient and algal data exist, then multi-year surveys on a twice monthly or monthly basis may be

necessary to determine if nuisance algal problems occur. Frequent sampling is necessary because algal

blooms may develop and dissipate rapidly with residual adverse effects, such as fish kills and impaired

aquatic habitat. Multi-year sampling is necessary because unusually large annual variability can occur

annually in the intensity of nutrient/algal problems, due to timing of weather (primarily scouring storm

events or persistent low flow events with long residence time) and seasonality of algal blooms.

Ideally, water quality monitoring programs produce long-term datasets compiled over multiple years, to

capture the natural, seasonal and year-to-year variations in waterbody constituent concentrations (e.g.,

Dodds et al. 1997; Tate 1990). Multiple-year datasets can be analyzed with statistical rigor to identify

the effects of seasonality and unusual flow years (Miltner and Rankin 1998). Once the pattern of natural

variation has been described, the data can be analyzed to determine the water quality conditions that

degrade the ecological state of the waterbody or effect downstream receiving waters. Long-term data

sets have also been extremely important in determining the cost-effectiveness of management techniques

for lakes and reservoirs (Cooke et al. 1993). The same should be true for streams and rivers, if not more

so (due to greater constituent variability), although management of nutrients to improve quality in

streams and rivers has not been as well documented.

In spite of the documented value of long-term data sets, there is a tendency even in lake/reservoir

management to intensively study a waterbody for one year before and one year after treatment. A more

cost-effective approach would be to measure only the most essential indices, but to double or triple the

monitoring period. Two or more years of data are needed to identify the effects of years with extreme

climatic or flow conditions. Low periphyton biomass has often been observed during high-flow summers

as well as the reverse, i.e., high biomass-low flow. The cause for that is not entirely clear; high flows
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 51

may reduce biomass through scouring and/or dilute inputs of ground water nutrients. Whatever the

cause, the effect will be “averaged out” enough to discern the overall effect of treatment (e.g., nutrient

reduction or diversion) if several years of data are available to minimize the effect of the unusual flow

year(s). At the very minimum, two years of data before and two after implementing nutrient

management, but preferably three or more each, are recommended to evaluate treatment costeffectiveness with some degree of certainty. If funds are limited, restricting sampling frequency and/or

numbers of constituents analyzed should be considered to preserve a longer-term data set. This will

allow for effectiveness of management approaches to be assessed against the high annual variability that

is common in most streams. High hydrological variation in a stream from year to year, requires more

years of sampling before and after mitigation procedures.

Characterizing Precision of Estimates

Estimates of dose-response relationships, nutrient and biological conditions in reference reaches, and

stream conditions of a region are based on sampling. Therefore, precision and accuracy must be

assessed. Determining precision of measurements for one-time assessments from single samples in a

reach is often necessary. The variation associated with one-time assessments from single samples in a

reach can often be determined by re-sampling a specific number of reaches during the survey. 

Measurement variation among replicate samples can then be used to establish the expected variation for

one-time assessment of single samples. Re-sampling does not establish the precision of the assessment

process, but rather identifies the precision of an individual measurement. Re-sampling frequency is often

conducted for one stream reach in every block of ten reaches. However, investigators should adhere to

the objectives of re-sampling (often considered an essential element of QA/QC) to establish an

assessment of the variation in a one-time/sample assessment. The larger the sample size the better

(smaller) will be the estimate of that variation. Often, more than one in ten samples need to be replicated

in monitoring programs to provide a reliable estimate of measurement precision.

APPROACHES TO SAMPLING DESIGN

The following sections discuss two different approaches to sampling design, probabilistic and goaloriented. Both approaches have advantages and disadvantages that under different circumstances warrant

the choice of one approach over the other (Table 3). The decision as to the best approach for sample

design in a new monitoring program must be made by the water quality resource manager or management

team after carefully considering different approaches.

Probabilistic Sampling

Probability sampling, where randomness is required, can be used to determine the variability of nutrient

and algae levels in streams and rivers across a state or a region. Random sampling is a generic type of

probability sampling where randomness can enter at any stage of the sampling process. Probabilistic

sampling – a sampling process wherein randomness is a requisite (Hayek 1994) – can be used to

characterize the status of nutrient conditions and biotic integrity in a region’s streams and rivers. 

Probabilistic designs are often modified by stratification (such as classification [Chapter 2]), by deleting

"redundant" reaches, or by adding important sites. Stratification or stratified random sampling is a type

of probability sampling where a target population is divided into relatively homogenous groups or classes

(strata) prior to sampling based on factors that influence variability in that population (Hayek 1994). 

Analysis of variance can be used to identify statistically different parameter means among the sampling 
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 52

Table 3. Comparison of probabilistic and goal-oriented sampling designs.

Probabilistic Sampling Goal-oriented Sampling

• random selection of streams from entire population

within a region

• requires no prior knowledge of streams within the

sample population

• may require more resources (time and money) to

randomly sample stream classes because more streams

may be sampled

• nutrient condition characterization for a class of

streams is more statistically robust

• potentially best for regional characterization of

stream classes, especially if water quality conditions

are not known

• targeted selection of streams based on problematic

(reaches known to have nutrient/algal problems) and

reference reaches

• requires prior knowledge of streams within the

sample population

• utilizes fewer resources because only targeted

streams are sampled

• nutrient condition characterization for a class of

streams is less statistically robust, though

characterization of a targeted stream or reach may be

statistically robust 

• potentially best for site-specific and watershedspecific criteria development when water quality

conditions for the reach of interest are known 

• selection of sites that represent a range of nutrient

conditions will facilitate establishment of nutrient-algal

relationships for the systems of interest

strata or classes. The strata are then used as the analysis of variance treatments (Poole 1972). Goaloriented sampling as described in the tiered approach in this Chapter, is not as easily analyzed by

rigorous statistical analyses. Goal-oriented monitoring may be better suited to statistical analyses using

basic descriptive statistics and correlational analyses.

Streams are selected for probabilistic sampling by random selection of a sample of streams from the

entire population of streams within a region. Thus, all stream reaches within a region must be identified

to establish the statistical population of streams; then a sample of all possible streams is selected from

that population. The results of collecting and assessing water quality and biotic responses with a

probabilistic sample is, presumably, an unbiased estimate of the descriptive statistics (e.g., means,

variances, modes, and quartiles) of all streams in a region. Probabilistic sampling designs are commonly

modified by stratifying by stream size and stream classes. Otherwise, sample statistics would be most

characteristic of the numerous small streams of the dominant stream types in a region.

Many state 305b and watershed monitoring programs utilize modified probabilistic sampling designs. 

Stratification in many of these programs is based on identifying all stream reaches in a region (or

watershed) and then selecting an "appropriate" sample of reaches from the defined population. The

sample population is often modified by deleting stream reaches that are too close to other reaches to be

different, thereby reducing redundant collection efforts. The selected sample of streams may also be

modified by adding sites that are near known sources of impact. Estimates of ecological conditions from

these kinds of modified probabilistic sampling designs can be used to characterize the nutrient status, and

over time, to distinguish trends in stream nutrient condition within a region. Estimates of regional

conditions are best when sites near known sources of impact are removed from the analysis and later

compared to the distribution of regional nutrient conditions.
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 53

Goal-Oriented Sampling

A goal-oriented approach to sampling design may be more appropriate when resources are limited. The

tiered approach described here focuses the greatest efforts on identifying and characterizing rivers and

streams likely to have nutrient problems, and on relatively undisturbed streams, often called reference

streams or reaches, that can serve as regional or sub-regional examples of natural biological integrity. 

Choosing sampling stations that best allow comparison of nutrient concentrations at reference stream or

river sites of known condition can conserve financial resources. Goal-oriented sampling also includes

some elements of randomness. However, the identification of systems with nutrient problems and

reference conditions eliminates the need for selecting a random sample of the population for monitoring. 

Goal-oriented sampling assumes some knowledge of the systems sampled. Systems with evidence of

impairment are compared to reference systems that are similar in their physical structure. Sites chosen to

represent a range of nutrient conditions will facilitate development of nutrient concentration-algal

biomass relationships. Goal-oriented sampling requires that the reaches be characterized according to

assessed nutrient and algal levels. Comparison of the monitoring data to data collected from reference

stream reaches will allow characterization of the sampled streams. Reaches identified as ‘at risk’ should

be evaluated through a sampling program to characterize the degree of impairment. An impaired reach is

simply a reach of any length where nutrient concentrations exceed acceptable levels, or algae interfere

with beneficial uses. Once characterized, the reaches should be placed in one of the following

categories:

1. Impaired reaches – reaches in which nutrients or algal biomass levels interfere with designated

uses;

2. High-risk reaches – reaches where nutrient concentrations are high but do not significantly impair

designated uses. In high-risk streams impairment is prevented by one or a few factors that could be

changed by human actions, though water quality characteristics (e.g., DO, turbidity) are already

marginal;

3. Low-risk reaches – reaches where many factors contain nutrient concentrations and algal biomass

levels are below problem levels and/or no development is contemplated that would change these

conditions.

4. Reference reaches – reaches where nutrient concentrations and algal biomass levels most closely

represent the pristine or minimally impaired condition.

Once stream reaches have been classified based on their physical structure (see Chapter 2) and placed

into the above categories, specific reaches need to be selected for monitoring. At this point, randomness

is introduced; stream reaches should be randomly selected within each class and risk category for

monitoring.

Monitoring efforts are often prioritized to best utilize limited resources. Impaired and high-risk streams

should be monitored more intensively than low-risk streams. Impaired streams should be monitored to

evaluate, implement, and assess management activities to reduce algal biomass and improve water

quality. High-risk streams should be monitored to assure that no further degradation takes place. Lowrisk streams can be monitored less frequently, but should be monitored frequently enough to identify any
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 54

increase in nutrients or algae, and/or change of water quality. Reference reaches should be monitored

frequently enough to make robust comparisons with impaired and high-risk stream reaches. In addition,

monitoring of changes in the watershed can help identify areas where changes are likely to result in

degradation of nutrient condition. Human activities within a watershed that can increase the risk of

nutrient and/or algal problems include 1) stabilization of flows (reduces scour); 2) reduction of flows

(increases light, reduces dilution of nutrients); 3) removal of streamside vegetation (increases light, may

decrease depth of stream; and increases the flux of nutrients from the stream hillslope due to reduced

uptake from plant roots); 4) discharge of nutrient rich waste water; 5) construction of unsewered

residential development (especially in thin coarse soils); 6) over fertilization of agricultural land; 7)

development that increases the percent of impervious surface in the watershed; and hence nutrient runoff;

and 8) discharge of toxins or release of exotic species that reduce grazer populations.

IDENTIFYING AND CHARACTERIZING REFERENCE STREAM REACHES

Potential reference streams should be characterized to allow for the identification of appropriate

reference streams and reference stream reaches. Classification of streams, as discussed in Chapter 2, 

will allow appropriate reference reaches to be identified for specific regions and stream types. Stream

classification should be supplemented with information on return frequency of flows. Reference streams 

or reaches may not be available for all stream classes. In this case, data from systems that are as close as

possible to the assumed unimpaired state of rivers and streams in that class should be sought from States

or Tribes within the same nutrient ecoregion.

The identification of reference reaches as opposed to reference streams is an important distinction (see

Chapter 7, Section 7.2). Identification of impaired and reference streams would be relatively simple if an

entire stream had all the same physical characteristics and risk factors. However, only one specific

portion of a stream length, a reach, may have all the characteristics necessary to produce algal problems. 

It may not be possible to find an entire stream that has little or no impacts anywhere in its watershed. 

Therefore, stream reaches should be targeted, but their watersheds should also be kept in mind. The

stream bed, banks, and riparian zone of a reference reach should be in a fairly natural state, and its

watershed as undeveloped as possible. States/Tribes should endeavor to protect such reference reaches

from future development.

Streams for reference-reach sampling should be selected based on low levels of human alteration in their

watersheds and aquatic habitat. Selecting reference reaches usually involves assessment of land-use

within watersheds, and visits to streams to ground-truth expected land-use and check for unsuspected

impacts. Sometimes ecological impairment that was not apparent from land-use and local habitat

conditions may be identified. Again, sufficient sample size is important to characterize the range of

conditions that can be expected in the least impacted systems of the region (see TN case study in

Appendix A).

Reference reaches should be identified for each nutrient ecoregion in the State or Tribal lands and then

characterized with respect to nutrient concentrations, algal biomass levels, algal community composition

and associated environmental conditions including turbidity, light, and substrata as well as factors that

are affected by algae, such as DO and pH. For each ecoregion in a state, a minimum of three low impact

reference systems should be identified for each stream class. Highest priority should be given to

identifying reference streams for those stream types considered to be at the greatest risk from nutrients
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 55

and algae. Reference stream reaches are often less accessible than reaches adversely affected by nutrient

and algal impairments. However, sampling need not be as frequent in reference reaches, except to

validate models of algal response to nutrient loads for such reaches.

Continuation of Less Intensive Monitoring of High-Risk Reaches

The continuation of monitoring of high-risk reaches should focus on factors likely to increase nutrient

concentrations or limit algal growth and on any actions that might alter those factors. For example, if

light is limiting, it may be most appropriate to evaluate the potential impact of the removal of streamside

trees or of the manipulation of water levels which may kill streamside trees. Stabilization of flows

results in the decline of flood-dependent vegetation. Increased grazing levels can reduce streamside trees 

degrade banks, altering the depth and width of the stream. State/Tribal water quality agencies should

encourage adoption of local riparian protection plans where light is limiting to minimize nutrient-caused

water quality problems. 

If scouring flows limit algal accrual and significantly dilute nutrient loading, a closer evaluation of plans

that could manipulate flows (by diversion, damming or altering management at existing structures) is

warranted. State/Tribal water quality agencies should inform agencies that regulate water development

of the potential impacts of flow manipulation.

Development plans in the watershed should be evaluated where nutrients are limiting (see Defining the

Limiting Nutrient, Section 6.2). Changes in point sources can be monitored through the NPDES permit

program. Changes in nonpoint sources can be evaluated through the identification and tracking of

wetland loss and/or degradation, increased residential development, increased tree harvesting, and shifts

to more intensive agriculture with greater fertilizer use or increases in livestock numbers. Local planning

agencies should be informed of the risk of increased nutrient loading and encouraged to guide

development accordingly. Nutrient levels often gradually increase due to many growing nonpoint

sources. Hence, in-stream nutrient monitoring is warranted in nutrient-limited, high-risk reaches if

sufficient resources remain after meeting the needs of impaired reaches. Seasonal nutrient levels should

be more stable in streams with low algal biomass than in streams with high algal biomass because

nutrient concentrations would not be depleted in such streams. Sampling during growing season

baseflow and nongrowing season baseflow should provide a limited, yet useful, assessment of trends in

nutrient levels from year-to-year. 

Whenever development plans appear likely to alter factors that were limiting algae growth in a high-risk

reach, instream monitoring should be initiated at a level similar to that described for impaired reaches in

order to enhance the understanding of baseline conditions. 

OTHER CONSIDERATIONS FOR MONITORING NUTRIENTS

Assimilative Capacity

The assimilative capacity of a stream for nutrients depends on its physical and biological nature. 

Assimilative capacity is the load of nutrients entering a river system at which nutrient and algal biomass

levels remain low enough such that excessive diurnal fluctuations of DO concentrations and pH levels

will not occur, recreation and aesthetics will not be negatively impacted, irrigation ditches will not be

clogged with algae, and biotic criteria will be consistently met. Such nutrient loads are difficult to

predict because nutrients are stored in many forms and released under a variety of conditions, and
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 56

because the levels of nutrients and algae causing impaired conditions may vary from system to system. 

The simplest model applied has been to apply an exponential decline in instream nutrient concentrations

below point sources and tributaries, with the rate of decline derived from monitored data. This approach

does not quantify mechanisms (such as sedimentation, uptake, dilution by groundwater and

denitrification), that can lead to nutrient losses. Such an approach was applied on the Clark Fork River

(Dodds et al. 1997) to model the influence of lowered inputs from point sources on instream nutrient

concentrations.

Nutrient Load Attenuation

A given nutrient load may produce a few kilometers (km) containing unacceptable algal biomass

followed by a section of river containing acceptable levels because a river’s load is attenuated by

retention in algae and sediment. The total length of river containing unacceptable algae biomass levels

may change from year-to-year due to changing nutrient loads or changes in other factors (e.g., flow,

dilution) that may limit algae growth (see Section 6.2). This phenomenon was illustrated following

nutrient control in the Bow River, Alberta, where TDP remained high (25 μg/L) for several km

downstream from the treated wastewater source. High TDP in the portions of the stream closest to the

point source release resulted in no change in algal biomass, while algae decreased farther downstream as

TDP decreased (see Bow River case study, Appendix A). The length of river containing unacceptable

algal biomass levels may be hypothetically estimated by the following equation described in Welch et al.

(1989).

Dc = Q*r*(SRPi

 - SRPc)/[(P/chl a day)*Bn*T*W*103 m/km])

where SRP is in μg/L (mg/m3

) producing the threshold nuisance biomass (150 mg chl/m2) in the growth

period (nominally ~ 1-4 mg/m3

 in channel experiments [Walton et al. 1995]); Q is the daily flow in

m3

/day; r accounts for the recycle (~ 1.5, after Newbold et al. 1981); SRPi is the influent concentration

(ambient river and groundwater in mg/m3

) to the segment; SRPc is the critical concentration, above which

nuisance algal growth occurs; P/chl a-day is the average uptake by periphyton with nominal value of 0.2;

Bn is the nuisance threshold biomass of 150 mg chl a/m2

; T is the factor for trophic (consumer) retention

(~ 1.2 representing a 20% conversion); and W is average stream width in meters.

This equation is simply the ratio of SRP mass available for uptake in excess of the critical level and the

expected demand for SRP by periphyton in an enriched stream reach in which the threshold nuisance

biomass is attained. The basis of the formulation is that periphytic biomass will not be reduced unless

SRP is less than the critical concentration (SRPc) during low-flow, maximum growth conditions, which

has been shown to be quite low in channel experiments (Walton et al. 1995). Low values for the critical

P concentration were supported by the Bow River case study (see Appendix A). The length of river with

unacceptable algal biomass levels increases as the criterion decreases. The important recycle rate in the

equation is a nominal value taken from uptake studies in a natural stream and could be highly variable. 

More definite predictions of limiting nutrient content and algal biomass changes downstream from a

point source requires a dynamic model for algal biomass, such as:

dB/dt = (u * L * Bi) - (S + G)
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 57

where u = nutrient uptake rate in 1/day, L = dimensionless light factor, Bi = periphyton biomass from

previous time step in mg chl/m2

, S = sloughing loss in mg chl/m2-day and G = grazing loss in mg chl/m2-

day (after Elswick 1998).

Estimating nutrient loads to a stream is at least as complex as a detailed nutrient source study for a lake

and requires the tracking of nutrient sources upstream and upgradient. In some cases, loading estimates

of stream and river systems may be back calculated from the loading estimate for the receiving

waterbody. That is, the partition of the nutrient load to a receiving waterbody (lake or estuary) identified

as belonging to a particular stream may be used as an estimate of the total load for that stream or reach. 

Loading is often estimated using a calibrated model that predicts nutrient loads from hydrologic inputs or

other parameters if nutrient data are inadequate to calculate load.

The USGS has developed a set of spatially referenced regression models for evaluating nutrient loading

in a watershed. The modeling approach is referred to as SPARROW (SPAtially Referenced Regressions

On Watershed attributes), a statistical modeling approach that retains spatial referencing for illustrating

predictions, and for relating upstream nutrient sources to downstream nutrient loads (Preston and

Brakebill 1999) (See Appendix C). Stream-load estimates at gaged monitoring sites are generated from

stream-discharge and water quality data by utilizing a log-linear regression model called ESTIMATOR. 

The ESTIMATOR model estimates daily concentration values based on flow, season, and temporal trend

terms (Preston and Brakebill 1999) (see Appendix C). 

Better Assessment Science Integrating Point and Nonpoint Sources, or BASINS, is a tool developed by

EPA to facilitate water quality analysis on a watershed level for specific waterbodies or stream segments. 

BASINS was designed to integrate national water quality data, modeling capabilities, and (GIS) so that

regional, State, local and Tribal agencies can easily address the effects of both point and nonpoint source

pollution and perform sophisticated environmental assessments (http://www.epa.gov/ost/BASINS/).

Models should be used with caution. Models can be used incorrectly and, therefore, can be less accurate

than loads calculated from data. Regardless of the method used for calculating loads, subsequent

changes in the watershed may alter the relationship between hydrologic and nutrient inputs requiring

loads to be re-calculated to reflect those changes.

Assessing Algal Biomass

This section focuses on assessing attached algal biomass and how to obtain a meaningful, representative

algal biomass sample. Sampling strategies will vary with objectives of programs. Algal sample

collection techniques for streams and laboratory methods for the analysis of chlorophyll, AFDM, and

other measures of biomass are discussed in Appendix B.

If the goal of sampling is to develop a relationship between nutrients and algal problems for the rivers of

a region or to assess status and trends in nutrient-related problem areas of a region (i.e. probabilistic

sampling), then one representative estimate of algal assemblage characteristics is all that can be used in

an analysis. In most cases, the desired estimate is a mean algal biomass measure for a reach that can be

obtained with composite sampling (explained below). However, spatial extent and temporal duration of

blooms or nuisance growths may also be important parameters to characterize. More than one sample (or

estimate) from a site would result in pseudoreplication (Hurlburt 1984) and would be unacceptable for

data analyses which require independent observations of conditions (biotic and nutrient) at each site. 
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 58

Variability in attached algal biomass estimates due to spatial variability can be reduced by collecting

composite samples and by sampling in targeted habitats where algal biomass is relatively uniform (e.g.,

riffles). Composite sampling calls for combining subsamples from many substrata into a single sample,

thus incorporating spatial variability into the one sample. The targeted habitat is usually defined as the

habitat in which nuisance problems are greatest, typically the riffles during higher flow seasons and pools

during low-flow seasons. Variability in algal biomass assessments should decrease with increasing

numbers of riffles and area of stream assessed. Therefore, composite samples should be collected over

the entire study reach. 

Large scale assessments are particularly important for patchy filamentous algae, which may be best

assessed using rapid periphyton surveys (in-stream, visual assessments of periphyton biomass; see

Stevenson and Bahls 1999). Streams and rivers shallow enough to be wadeable during the period when

nuisance problems are greatest may be sampled randomly across the entire width of the stream. If

variability is still too great, the focus of assessments could be reduced to an indicator zone (an area

having a high potential for nuisance algal growth) with a narrow range of water velocity, depth, and

substratum size. For rivers with unwadeable depths, sampling attached algae is commonly confined to

the wadeable portions because deeper portions may not have enough light for dense benthic algal growth. 

However, SCUBA has been used to sample benthic algae in large rivers (Lowe 1974). 

In streams and rivers where nuisance algal problems arise from planktonic algal blooms during low-flow

conditions, sources of variability in algal biomass (and related factors like low DO) tend to be due to

temporal as opposed to spatial variability. Repeated plankton sampling during the low-flow period is

strongly recommended to relate nutrients to peak plankton biomass and potential problems of low DO or

noxious (toxic, taste, and odor causing) algal blooms. If the goal of estimating algal biomass at a

problem site is to compare estimates of biomass to a criterion, then replicate sampling of at least four

samples at that site is recommended to characterize the mean and variance in observations. If the goal of

sampling is to develop a relationship between nutrients and algal problems for the rivers of a region, or to

assess status and trends for nutrient-related problems, then replicate sampling is not as important as

accounting for temporal variability and sampling more sites. 

Relating nuisance algal problems to nutrient concentrations during stream low-flow conditions can be

complicated by a number of factors. Algal problems may be due to a combination of planktonic algae

blooming throughout pools and benthic algae along margins of pools. Planktonic algae may settle into

sediments of pools and may generate oxygen demand from those sediments. Thus, thorough sampling

designs should be employed that consider both spatial and temporal variability in algal biomass and

associated nutrients to ensure development of accurate and precise relationships between nuisance algal

problems and nutrients.

Attached algal biomass can vary greatly in time as well as space within the same stream. Temporal

variability in algal biomass can be addressed by repeated sampling during periods when high algal

biomass is most likely a problem. Alternatively, algal biomass can be sampled during periods of peak

biomass following flood disturbances. This period of peak biomass may endure from one week to two

months, depending upon nutrient concentrations in streams and the severity of flood events. Repeated

assessment of algal biomass in streams can be facilitated by using rapid periphyton surveys to reduce

sampling and laboratory assay costs (see Stevenson and Bahls 1999). Even though many measurements

are being made through time, only one measurement per site can be used to develop biomass-nutrient
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 59

relationships because of site-specific dependence and problems of repeated measures from the same site 

(Green 1979; Sokal and Rohlf 1998). 

In some cases, the goal of assessment might be to estimate algal biomass at a problem site to compare

estimates of biomass to a criterion. In this case, replicate sampling of at least four or many more samples

at a site is recommended to characterize the mean and variance in the mean with replicate samples from a

site. If the variability in algal biomass is similar to that in the Clark Fork River (see Appendix A case

study), as many as 20 replicate samples may be required to detect small changes, which may be important

to monitor restoration efforts.

INVOLVEMENT OF CITIZEN MONITORING PROGRAMS

Citizen input can be used to assist in identifying and prioritizing potential problem streams. For

example, citizens can be asked (through the use of surveys) to identify streams in which they have

observed algal biomass levels that interfere with human uses or impair aesthetic enjoyment. They can

also be asked to provide their evaluation of which streams have been affected most and which uses have

been impaired to the greatest degree.

While state water quality agencies will likely take the lead in monitoring impaired reaches, citizen

monitors may provide much of the monitoring on high-risk reaches. If properly trained and directed,

citizen volunteers can be valuable in algal and nutrient monitoring. Citizens, with training, can visually

assess algal levels, collect algal samples and freeze them for analysis by an approved laboratory, and may

also help in the initial characterization of streams. Citizen monitors can frequently provide more

complete flow records by visiting gauges more often than state personnel. Once advised that a stream is

high-risk and that the limiting factors have been identified, citizens can help monitor development plans

that might affect those factors. Involvement in monitoring programs may lead citizens to effective

participation in local planning. 

Many excellent resources are available for training citizen monitors. EPA has a volunteer monitoring

coordinator (Alice Mayio—E-mail: alice.mayio@epamail.epa.gov) and a web site that lists many

resources http://www.epa.gov/OWOW/monitoring/volunteer/spring94/ppresf04.html). Numerous nongovernmental organizations, such as the Izaak Walton League, have developed citizen monitoring

manuals. One of the best is the Streamkeeper’s Guide by the Adopt-a-Stream Foundation (600-128th St.

SE, Everett, WA 98208, phone 206-316-8592; web site: http://www.streamkeeper.org/).
July 2000 Chapter 4. Sampling Design for New Monitoring Programs

PAGE 60
PAGE 61

5.1 INTRODUCTION

A database of relevant water quality information can be an invaluable tool to States and Tribes as they

develop nutrient criteria. Existing data may provide considerable information that is specific to the

region where criteria are to be set. First the data must be located, then the suitability of the data (type

and quality) ascertained before they are to be used for historical reconstruction of water quality

parameters. It is also important to determine how the data were collected to make future monitoring

efforts compatible with earlier approaches. 

Databases operate much like spreadsheet applications, but have greater capabilities. While spreadsheets

analyze and graphically display small quantities of data, databases store and manage large quantities of

data and allow viewing and exporting of data sorted in a variety of ways. Databases can be used to

organize existing information, store newly gathered monitoring data, and manipulate data as criteria are

being developed. Databases can sort data for export into statistical analyses programs, spreadsheets, and

graphics programs. This chapter will discuss the role of databases in nutrient criteria development, and

provide a brief review of existing sources of nutrient-related water quality information for streams and

rivers.

5.2 DATABASES AND DATABASE MANAGEMENT

This section describes general database structure and provides detailed information on relational and GIS

databases. A database is a collection of information related to a particular subject or purpose. Databases

are arranged so that they divide data into separate electronic repositories called tables. Data in tables can

be viewed and edited, and new data can be added. A single datum is stored in only one table, but can be

viewed from multiple locations. Updating one view of a datum will update it in all the various viewable

forms. Each table should contain a specific type of information. Data from different tables can be

viewed simultaneously according to the user-defined table relationships. That is, the relationship among

data in different tables can be defined so that more than one table can be queried or reported, and

accessed in a single view. Data stored in tables can be located and retrieved using queries. A query

allows the user to find and retrieve only the data that meets user-specified conditions. Queries can also
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 62

be used to update or delete multiple records simultaneously and to perform built-in or custom

calculations of data. Data in tables can be analyzed and printed in specific layouts using reports. Data

can be analyzed or presented in a specific way in print by creating a report. 

To facilitate data manipulation and calculations, it is highly recommended that historical and present-day

data be transferred to a relational database. A relational database is a collection of data items organized

as a set of formally-described tables from which data can be accessed or reassembled in many different

ways without having to reorganize the database tables. Each table (which is sometimes called a relation)

contains one or more data categories in columns. Each row contains a unique instance of data for the

categories defined by the columns. The organization of data into relational tables is known as the logical

view of the database. In other words, the logical view is the form in which a relational database presents

data to the user and the programmer (www.whatis.com/relation.htm). Relational databases are powerful

tools for data manipulation and initial data reduction. They allow selection of data by specific, multiple

criteria, and definition and redefinition of linkages among data components. 

GISs are geo-referenced relational databases that have a geographical component (i.e., spatial platform)

in the user interface. Spatial platforms associated with a database allow geographical display of sets of

sorted data. Databases with spatial platforms are becoming more common. The system is based on

premises that "pictures are worth thousands of words" and most data can be related to a map or other

easily understood graphic. GIS platforms such as ArcViewTM, ArcInfoTM, and MapInfoTM are frequently

used to integrate spatial data with monitoring data for watershed analysis.

NATIONAL NUTRIENTS DATABASE

The Nutrient Criteria Program has initiated development of a national relational database application that

will be used to store and analyze nutrient data. The ultimate use of these data will be to derive

ecoregion- and waterbody-specific numeric nutrient criteria ranges. Initially, EPA is developing a

Microsoft AccessTM application which will ultimately be populated with STORET Legacy Data, USGS

NAWQA, NASQAN and Benchmark data, and other relevant nutrient data from universities,

States/Tribes, and additional data rich entities. EPA is also developing a compatible, interactive system

in an OracleTM environment which allows for easy web-accessibility, geo-referencing/GIS compatibility,

and data analysis on both a State/Tribal, regional, and national basis. The total amount of existing

nutrient data nationally is large (>20 gigabytes), and it is anticipated that more data will be entered into

the system. The OracleTM application can easily manage large quantities of data and will provide ample

room for expansion as more data are collected. Both the AccessTM and the OracleTM database

applications are being designed for compatibility with EPA’s latest edition of STORET to avoid

duplication of effort for users of STORET and the Nutrients database application. Considerable efforts

are also being made to assure compatibility with other database systems (e.g., WQS and RAD) currently

being developed in EPA’s Office of Water. The Microsoft AccessTM application will be available in

January 2000; the OracleTM application will be online in the spring of 2000.

5.3 COLLECTING EXISTING DATA

In some States/Tribes, historical data on streams and rivers are already available. These data can be used

to identify reference streams and begin development of potential nutrient criteria. Data should be
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 63

compiled in a format that is easily imported into database and spreadsheets. Ideally, data will be

compiled in the Nutrients Database described above. Potential data sources for river and stream nutrient

data that will be useful for developing criteria are discussed below. These data sources contain extensive

water quality data, however, data collection should not be limited to these sources. Collection of

scientifically sound water quality data from any reliable source is encouraged.

POTENTIAL DATA SOURCES

Potential sources of data include water quality monitoring data from Federal, State, Tribal and local

water quality agencies; university studies; and volunteer monitoring information. The data sources

described in this section do not encompass the full extent of available data sources. Many State/Tribal,

and Federal programs that are regional or site-specific are excellent data sources, but are not included in

this discussion.

EPA Water Quality Data

EPA has many programs of national scope that focus on collection and analysis of water quality data. 

The following presents information on several of the databases and national programs that may be useful

to water quality managers as they compile data for criteria development.

STORET

STOrage and RETrieval system (STORET) is EPA’s national database for water quality and biological

data. EPA’s original STORET System, operated continuously since the 1960s, was historically the

largest repository of water quality data in the nation. This legacy mainframe-based system will cease to

exist in the year 2000. In its place, EPA will support two independent, web-accessible databases. The

older database, called the STORET Legacy Data Center (LDC) is the repository of all data held in EPA’s

original STORET System as of the end of 1998. The newer, modernized database, simply called

STORET, is designed as a replacement for the original STORET System. It is the repository for more

current data, and offers major improvements in database content and quality control documentation.

Interested parties may view both databases on the World-Wide-Web, where the capability will exist to

produce printed reports and download data files. Queries for data via the web will be designed for use by

the general public and will require no special training or software. The web site will be announced in the

first quarter of FY2000.

STORET (the new STORET system) is a compendium of data supplied by Federal, State, and local

organizations which evaluates environmental conditions in the field. The data in STORET is organized

by both geographic location and data ownership. Every field study site is identified by at least one

latitude/longitude and, where appropriate, also by State/Province, County, drainage basin, and stream

reach. Monitoring activities recorded include field measurements, habitat assessments, water and

sediment samples, and biological population surveys. Records cover the complete spectrum of physical

properties, concentrations of substances, and abundance and distribution of species observed during

biological monitoring. STORET is designed for maximum compatibility with commercial software,

including Geographic Information Systems such as the ESRI ArcView package, and statistical packages

such as PC SAS. STORET download files import easily into all standard spread sheet packages.
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 64

Further information about STORET may be obtained by e-mailing STORET@epa.gov, or telephoning

toll-free at 1-800-424-9067.

National Surface Water Survey (NSWS)

EPA’s National Surface Water Survey consists of two parts: the National Lake Survey and the National

Stream Survey. The purpose of the National Lake Survey is to quantify, with known statistical

confidence, the current status, extent, and chemical and biological characteristics of lakes in regions of

the United States that are potentially sensitive to acidic deposition. The purpose of the National Stream

Survey (NSS) is to determine the percentage, extent, and location of streams in the United States that are

presently acidic or have low-acid neutralizing capacity and may, therefore, be susceptible to future

acidification, as well as to identify streams that represent important classes in each region for possible

use in more intensive studies or long-term monitoring. The NSS provides an overview of stream water

quality chemical characterisitics in regions of the United States that are expected, on the basis of

previous alkalinity data, to contain predominantly low-acid neutralizing capacity waters (EPA website

http://www.epa.gov/ceisweb1/ceishome/ceisdocs/usguide/prog(56).htm). 

Environmental Monitoring and Assessment Program (EMAP)

The Environmental Monitoring and Assessment Program is an EPA research program designed to

develop the tools necessary to monitor and assess the status and trends of national ecological resources

(see EMAP Research Strategy on the EMAP website: www.epa.gov/emap). EMAP's goal is to develop

the scientific understanding for translating environmental monitoring data from multiple spatial and

temporal scales into assessments of ecological condition and forecasts of future risks to the 

sustainability of the Nation’s natural resources. EMAP's research supports the National Environmental

Monitoring Initiative of the Committee on Environment and Natural Resources (CENR)

(www.epa.gov/emap/). Data from the EMAP program can be downloaded directly from the EMAP

website (www.epa.gov/emap/). The EMAP Data Directory contains information on available data sets

including data and metadata (language that describes the nature and content of data). Current status of

the data directory as well as composite data and metadata files are available on this website.

Clean Lakes Program (CLP)

The EPA Clean Lakes Program was initiated to assess water quality in impaired public lakes and

reservoirs and to restore these systems where appropriate. CLP included a monitoring and assessment

component to identify the efforts needed to restore water quality. Lakes in this program were selected

because they were perceived to have water quality impairment. Major tributaries into lakes and

reservoirs included in this program were sampled on a regular basis. EPA encouraged States in its May

1996 section 319 nonpoint source guidance to use section 319 funds to fund eligible activities that might

have been funded in previous years in the CLP under Section 314. Data from this program may be useful

for positioning river and stream systems on a nutrient gradient continuum, but are unlikely to provide

data for reference stream reaches. Information about EPA’s CLP can be found at the website: 

http://www.epa.gov/owowwtr1/lakes/lakes.old.html.

Ecological Data Application System (EDAS)

EDAS is EPA’s program-specific counterpart to STORET. EDAS was developed by EPA’s Office of

Water to manipulate data obtained from biological monitoring and assessment and to assist States/Tribes

in developing biocriteria. It contains built-in data reduction and recalculation queries that are used in
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 65

biological assessment. The EDAS database is designed to enable the user to easily manage, aggregate,

integrate, and analyze data to make informed decisions regarding the condition of a water resource.

Biological assessment and monitoring programs require aggregation of raw biological data (lists and

enumeration of taxa in a sample) into informative indicators. EDAS is designed to facilitate data

analysis, particularly the calculation of biological metrics and indexes. Pre-designed queries that

calculate a wide selection of biological metrics are included with EDAS. Future versions of EDAS will

include the capability to upload data to, and download data from, the distributed version of modernized

STORET. EDAS is not a final data warehouse, but is a program or project-specific customized data

application for manipulating and processing data to meet user requirements. The EDAS application is

currently under development; more information will be available at a later date through the EPA website.

USGS (U.S. Geological Survey) Water Data

The USGS has national and distributed databases on water quantity and quality for waterbodies across

the nation. Much of the data for rivers and streams are available through the National Water Information

System (NWIS). These data are organized by state, Hydrologic Unit Codes (HUCs), latitude and

longitude, and other descriptive attributes. Most water quality chemical analyses are associated with an

instantaneous streamflow at the time of sampling and can be linked to continuous streamflow to compute

constituent loads or yields. The most convenient method of accessing the local data bases is through the

USGS State representative. Every State office can be reached through the USGS home page on the

Internet at URL http://www.usgs.gov/wrd002.html.

HBN and NASQAN

USGS data from several national water quality programs covering large regions offer highly controlled

and consistently collected data that may be particularly useful for nutrient criteria analysis. Two

programs, the Hydrologic Benchmark Network (HBN) and the National Stream Quality Accounting

Network (NASQAN) include routine monitoring of rivers and streams during the past 30 years. The

HBN consisted of 63 relatively small, minimally disturbed watersheds. HBN data were collected to

investigate naturally-induced changes in streamflow and water quality and the effects of airborne

substances on water quality. The NASQAN program consists of 618 larger, more culturally influenced

watersheds. NASQAN data provides information for tracking water-quality conditions in major U.S.

rivers and streams. The watersheds in both networks include a diverse set of climatic, physiographic,

and cultural characteristics. Data from the networks have been used to describe geographic variations in

water-quality concentrations, quantify water-quality trends, estimate rates of chemical flux from

watersheds, and investigate relations of water quality to the natural environment and anthropogenic

contaminant sources. Since 1995, the NASQAN Program has focused on monitoring the water quality of

four of the Nation's largest river systems—the Mississippi (including the Missouri and Ohio), the

Columbia, the Colorado, and the Rio Grande. NASQAN currently operates a network of 40 stations in

which the concentration of a broad range of chemicals—including pesticides and trace elements—and

stream discharge are measured.

Alexander and others (1996) assembled much of the historical water-quality and streamflow data

collected by the NASQAN and HBN on two CD-ROMs, including supporting documentation and quality

assurance information (see Internet URL http://wwwrvares.er.usgs.gov/wqn96/). These data are

collectively referred to as Water-Quality Networks (WQN). The CD-ROMs are designed to allow users

to efficiently browse text files and retrieve data for subsequent use in user-supplied software including
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 66

spreadsheet, statistical analysis, or geographic information systems. The data may be extracted from one

of the CD-ROMs (the "DOS disc") using the supplied DOS-based software, and output in a variety of

formats. This software allows the user to search, retrieve, and output data according to user-specified

requirements. Alternatively, the ASCII form of the WQN data may be accessed on a second CD-ROM

(the "ASCII disc") from user-supplied software including a Web browser, spreadsheet, or word

processor. 

A comprehensive review of sources, concentrations, and loads of nutrients in the Mississippi River Basin

was completed by USGS under the Committee of Environmental Natural Resources. The review focused

on analyzing issues related to the Gulf of Mexico hypoxia. Much of Topic 3, Flux and sources of

nutrients in the Mississippi-Atchafalaya River Basin, includes data and analysis that could be useful for

the development of nutrient criteria in large river systems, such as the Mississippi River. Results of this

effort, which was led by the National Oceanic and Atmospheric Administration, have been published and

can also be found at the Internet site http://www.nos.noaa.gov/products/pubs_hypox.html.

NAWQA

The USGS National Water-Quality Assessment (NAWQA) Program is building a third national database

of stream quality information from data collected and analyzed for more than 50 river basins and aquifer

systems, called Study Units, across the Nation. NAWQA studies are based on a complex sampling

design that targets specific land uses and hydrologic conditions in addition to assessing the most

important aquifers and large streams and rivers in each area studied. Gilliom and others (1995) describe

the NAWQA sampling design in detail. A comprehensive data screening, computer retrieval, and review

of existing data on nutrients in streams was completed for each of the first 20 Study Units (Mueller et al.

1995). A major component of the sampling design for streams is to target specific watersheds influenced

primarily by a single dominant land use (agricultural or urban) that is important in a particular area of the

United States. Some of the watersheds were selected as undeveloped areas relative to the rest of the

Study Unit to use in comparative analysis of land-use effects on water quality. Water-quality data

collection during 1992-1996 include analyses of eight nutrient species from about 8,500 samples of

streams and rivers in the first 20 Study Units. A data set used for national synthesis of water quality has

been compiled and can be viewed and downloaded via the Internet URL

http://wwwrvares.er.usgs.gov/nawqa/nutrient.html. Mueller and others (1997) describe quality control of

the NAWQA stream data and Mueller (1998) provides a rigorous assessment of the quality of these data.

WEBB

The Water, Energy, and Biogeochemical Budgets (WEBB) program was developed by USGS to study

water, energy, and biogeochemical processes in a variety of climatic/regional scenarios. Five

ecologically diverse watersheds, each with an established data history, were chosen. This program may

prove to be a rich data source for ecoregions in which the five watersheds are located. Many

publications on the WEBB project are available. See the USGS website for more details

(http://water.usgs.gov/nrp/webb/about.html).

USDA

Agricultural Research Service (ARS)

ARS houses Natural Resources and Sustainable Agricultural Systems, which has seven national

programs to examine the effect of agriculture on the environment. The program on Water Quality and
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 67

Management addresses the role of agriculture in nonpoint source pollution through research on

Agricultural Watershed Management and Landscape Features, Irrigation and Drainage Management

Systems, and Water Quality Protection and Management Systems. Research is conducted across the

country and several models and databases have been developed. Information on research and program

contacts is listed on the website (http://www.nps.ars.usda.gov/programs/nrsas.htm).

Forest Service

The Forest Service has designated research sites across the country, many of which are Long Term

Ecological Research (LTER) sites. Many of the data from these experiments are available in the USFS

databases located on the website (http://www.fs.fed.us/research/). Most of the data are forest-related, but

may be of use for determining land uses and questions on silviculture runoff.

National Science Foundation (NSF) 

The National Science Foundation funds projects for the LTER Network. The Network is a collaboration

of over 1,100 researchers investigating a wide range of ecological topics at 24 different sites nationwide. 

The LTER research programs are not only an extremely rich data source, but also a source of data

available to anyone through the Network Information System (NIS), the NSF data source for LTER sites. 

Data sets from sites are highly comparable due to standardization of methods and equipment. Data can

be accessed from the website http://www.lternet.edu/research/data/nis/.

U.S. Army Corps of Engineers (COE)

The U.S. Army Corps of Engineers is responsible for more than 750 reservoirs. Many have extensive

monitoring data that could contribute to the development of nutrient criteria for tributaries to those

reservoirs. The COE focuses more on water quantity issues than on water quality issues. As a result,

much of the river and stream system data collected by the COE does not include nutrient or algal

constituents. Nonetheless, the COE does have a large water sampling network and supports USGS and

EPA monitoring efforts in many programs. A list of the water quality programs that the COE actively

participates in was compiled in 1997. This information can be found at the website:

http://cw71.cw-wc.usace.army.mil/wqinfo/wq98sem/ANNWQMGT.HTM. 

U.S. Department of the Interior, Bureau of Reclamation (BuRec)

The Bureau of Reclamation manages many irrigation and water supply reservoirs in the West, some of

which may have operational data available. These data focus on water supply information and limited

water quality data. However, real time flow data are collected for rivers supplying water to BuRec,

which may be useful for the flow component of criteria development. These data can be gathered on a

site-specific basis from the BuRec website: www.usbr.gov. Extensive remote sensing data are available

from the website: http://wais.rsgis.do.usbr.gov/html/rsgig_wq.html.

State/Tribal Monitoring Programs

Most states monitor some subset of stream and river systems within their borders for algal and nutrient

variables. Data collected by State/Tribal water quality monitoring programs can be used for nutrient

criteria development. These data should be available from the agencies responsible for monitoring. 
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 68

Volunteer Monitoring Programs

Many States have volunteer water quality monitoring programs. Some programs are state-sponsored,

while others are independent organizations such as Adopt-A-Stream. Citizens in many areas donate their

time, money, or experience to aid State, Tribal, and local governments in collecting water quality data. 

Volunteers analyze water samples for DO (dissolved oxygen), nutrients, pH, temperature, and a host of

other water constituents; evaluate the health of stream habitats and aquatic biological communities; note

stream-side conditions and land uses that may affect water quality; catalogue and collect beach debris;

and restore degraded habitats. 

State and local agencies may use volunteer data to screen for water quality problems, establish

trends in waters that would otherwise be unmonitored, and make planning decisions. Volunteers

benefit from learning more about their local water resources and identifying what conditions or

activities might contribute to pollution problems. As a result, volunteers frequently work with clubs,

environmental groups, and State/Tribal or local governments to address problem areas. 

The EPA supports volunteer monitoring and local involvement in protecting our water resources. EPA

support takes many forms including: sponsoring national and regional conferences to encourage

information exchange among volunteer groups, government agencies, businesses, and educators;

publishing sampling methods manuals for volunteers; producing a nationwide directory of volunteer

programs; and providing technical assistance (primarily on quality control and lab methods) and

Regional coordination through the ten EPA Regional offices. In addition, grants to States/Tribes that can

be used to support volunteer monitoring in lakes and for nonpoint source pollution control are managed

by the EPA Regions (http://www.epa.gov/OWOW/monitoring/volunteer/epavm.html).

Adopt-A-Stream

The Adopt-A-Stream Foundation (AASF) is a non-profit organization that works to increase public

awareness and involvement in water quality issues, stream enhancement, and environmental education. 

Their two main areas of focus are Environmental Education and Habitat Restoration. AASF seeks to

protect streams through volunteer work, encouraging school and community groups, sports clubs, civic

organization, and individuals to become “Streamkeepers.” “Adoption” of a stream requires that

volunteers provide long-term care of the stream and establish stream monitoring, restoration, and

community-wide environmental education activities. AASF provides education materials, classes, and

tools for monitoring. Data collected through the volunteer monitoring associated with Adopt-A-Stream

is usually site-specific, focusing on a single stream. However, if volunteers have been properly trained,

the data collected may be useful in helping identify streams at risk for nutrient problems. The AASF

website contains additional information on this organization and data they may be able to provide

(http://www.streamkeeper.org/).

American Heritage Rivers

The American Heritage Rivers Initiative is a program launched by President Clinton to help communities

restore their local waters and waterfront areas. Participation is voluntary and must be initiated by the

community. To date, fourteen rivers have been designated on the basis of historical, economic, and

environmental considerations. One goal of the program is to develop additional information that can be

used by communities to improve any river system. Through the American Heritage Rivers website

(http://www.epa.gov/OWOW/heritage/rivers.html), valuable information about our nation's rivers is
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 69

easily available to everyone. Information organized geographically on flood events, population change,

road networks, condition of water resources, and partnerships already at work in the area is available.

Additionally, customized maps and environmental and educational assessment models will be made

available through this initiative.

Electric Utilities

Many electric utilities own reservoirs for hydroelectric power generation, and are required to monitor the

reservoirs’ water quality. The largest of these, the Tennessee Valley Authority (TVA), has extensive

chemical and biological monitoring data from most of its reservoirs from the early 1980s to the present. 

Data collected in conjunction with hydroelectric reservoirs must be gathered from the facility owners or

managers. 

Drinking Water Facilities

Many local drinking water facilities are supplied from river systems. These facilities continuously

monitor some water quality parameters at the intake pipe. Nutrients are infrequently monitored by most

of these facilities, but supplemental data, i.e., turbidity, pH, and flow are usually measured. These data

may not provide the necessary parameters for deriving criteria, but may be very useful in combination

with State/Tribal water quality monitoring data to develop criteria. Data from these facilities should be

accessed locally for the waterbody of concern. 

Academic and Literature Sources

Many research studies are conducted by academic institutions that may provide data useful for

developing nutrient criteria. Much of the research conducted by the academic community concentrates

on unimpaired or minimally impaired systems. While data collected from these sources may not be

directly representative of the population of stream systems within an ecoregion, they could be useful for

identifying reference conditions. Academic research also tends to be site-specific and span a limited

number of years, although data for some systems may span 20 years or more. Academic research data

should be available from researchers and the scientific literature. 

QUALITY OF HISTORICAL DATA

The value of older historical data sets is a recurrent problem because data quality is often unknown. 

Knowledge of data quality is also problematic for long-term data repositories such as STORET and longterm State databases, where objectives, methods, and investigators may have changed many times over

the years. The most reliable data tend to be those collected by a single agency, using the same protocol,

for a limited number of years. Supporting documentation should be examined to determine the

consistency of sampling and analysis protocols. Investigators must determine the acceptability of data

contained in large, heterogeneous data repositories. Considerations and requirements for acceptance of

these data are described below.

Location

STORET and USGS data are geo-referenced with latitude, longitude, and Reach File 3 (RF3) codes. 

Geo-reference data can be used to select specific locations, or specific USGS Hydrologic Units. In

addition, STORET often contains a site description. Knowledge of the rationale and methods of site 
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 70

selection from the original investigators may supply valuable information. Metadata of this type, when

known, is frequently stored within large long-term databases. 

Variables and Analytical Methods

Thousands of variables are recorded in database records. Each separate analytical method yields a

unique variable. For example, five ways of measuring TP results in five unique variables. We do not

recommend mixing analytical methods in data analyses because methods differ in accuracy, precision,

and detection limits. Data analyses should concentrate on a single analytical method for each parameter

of interest. Selection of a particular “best” method may result in too few observations, in which case it

may be more fruitful to select the most frequently used analytical method in the database. Data may

have been recorded using analytical methods under separate synonymous names, or analytical methods

incorrectly entered when data were first added to the database. Review of recorded data and analytical

methods recorded by knowledgeable personnel is necessary to correct these problems. 

Laboratory Quality Control (QC)

Laboratory QC data (blanks, spikes, replicates, known standards, etc.) are infrequently reported in larger

data repositories. Records of general laboratory quality control protocols and specific quality control

procedures associated with specific datasets are valuable in evaluating data quality. However, premature

elimination of lower quality data can be counterproductive, because the increase in variance caused by

analytical laboratory error may be negligible compared to natural variability or sampling error, especially

for nutrients and related water quality parameters. However, data of uncertain and undocumented 

quality should not be accepted.

Data Collecting Agencies

Selecting data from particular agencies with known, consistent sampling and analytical methods and

known quality will reduce variability due to unknown quality problems. Requesting data review for

quality assurance from the collecting agency will reduce uncertainty about data quality.

Time Period

Long-term records are critically important for establishing trends. Determining if trends exist in the time

series database is also important for characterizing reference conditions for nutrient criteria. Length of

time series data needed for analyzing nutrient data trends is discussed in Chapter 4.

Index Period

An index period for estimating average concentrations can be established if nutrient and water quality

variables were measured through seasonal cycles. The index period may be the entire year or the

summer growing season. The best index period is determined by considering stream characteristics for 

the region, the quality and quantity of data available, and estimates of temporal variability (if available). 

Additional information and considerations for establishing an index period are discussed in Chapter 4.

Representativeness

Data may have been collected for specific purposes. Data collected for toxicity analyses, effluent limit

determinations, or other pollution problems may not be useful for developing nutrient criteria. Further,

data collected for specific purposes may not be representative of the region or stream classes of interest. 

The investigator must determine if stream systems or a subset of the stream systems in the database are 
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 71

representative of the population of stream systems to be characterized. If a sufficient sample of

representative systems cannot be found, then a new survey will be necessary (see Chapter 4).

Gather New Data

New data should be gathered following the sampling design protocols discussed in Chapter 4. New data

collection activities for developing nutrient criteria should focus on filling in gaps the database and

collecting regional monitoring data. Data gathered under new monitoring programs should be imported

into databases or spreadsheets and merged with existing data for criteria development. 

Data Reduction

Data reduction requires a clear idea of the analysis that will be performed and a clear definition of the

sample unit for the analysis. For example, a sample unit might be defined as “a watershed during JulyAugust”. For each variable measured, a mean value would then be estimated for each watershed in each

July-August index period on record. Analyses are then done with the observations (estimated means) for

each sample unit, not with the raw data. Steps in reducing the data include:

• Selecting the long-term time period for analysis;

• Selecting an index period;

• Selecting relevant chemical species;

• Identifying the quality of analytical methods;

• Identifying the quality of the data recorded; and

• Estimating values for analysis (mean, median, minimum, maximum) based on the reduction

selected.

QUALITY ASSURANCE/QUALITY CONTROL

The validity and usefulness of data depend on the care with which they were collected, analyzed and

documented. The EPA provides guidance on data quality assurance (QA) and quality control (USEPA

1998b) to assure the quality of data. Factors that should be addressed in a QA/QC plan are elaborated

below. The QA/QC plan should state specific goals for each factor and should describe the methods and

protocols used to achieve the goals. The five factors discussed below are: representativeness,

completeness, comparability, accuracy and precision. 

Representativeness

Sampling program design (when, where, how you sample) should produce samples that are

representative or typical of the environment being described. Sampling designs for developing nutrient

criteria are addressed in Chapter 4. 

Completeness

Data sets are often incomplete because of spilled samples, faulty equipment, and/or lost field notebooks. 

A QA/QC plan should describe how complete the data set must be in order to answer the questions posed

(with a statistical test of given power and confidence) and the precautions being taken to ensure that

completeness. Data collection procedures should document the extent to which these conditions have

been met. Incomplete data sets may not invalidate the collected data, but may reduce the rigor of

statistical analyses. Therefore, precautions should be taken to ensure data completeness. Precautions to
July 2000 Chapter 5. Building a Database of Nutrient- and Algae-Related Water Quality Information

PAGE 72

ensure completeness may include collecting extra samples, having back-up equipment in the field,

installing alarms on freezers, copying field notebooks after each trip, and/or maintaining duplicate sets of

data in two locations. 

Comparability

In order to compare data collected under different sampling programs or by different agencies, sampling

protocols and analytical methods must demonstrate comparable data. The most efficient way to produce

comparable data is to use sampling designs and analytical methods that are widely used and accepted.

Accuracy

To assess the accuracy of field instruments and analytical equipment, a standard (a sample with a known

value) must be analyzed and the measurement error or bias determined. Internal standards should

periodically be checked with external standards provided by acknowledged sources. At Federal, State,

Tribal, and local government levels, the National Institute of Standards and Technology (NIST) provides

advisory and research services to all agencies by developing, producing, and distributing standard

reference materials. For calibration sevices and standards see:

http://ts.nist.gov/ts/htdocs/230/233/calibration/home.html.

Standards and methods of calibration are typically included with turbidity meters, pH meters DO meters,

and DO testing kits. The USGS, the EPA and some private companies provide reference standards or

QC samples for nutrients. Reference standards for chlorophyll are also available from the EPA and some

private companies, although chlorophyll standards are time and temperature sensitive because they

degrade over time.

Variability

Natural variability rather than imprecision in the method used, is usually the greatest source of error in

the constituent measured. The variability in field measurements and analytical methods should be

demonstrated and documented to identify the source of variability when possible. EPA QA/QC guidance

provides an explanation and protocols for measuring sampling variability (USEPA 1998b). Methods for

creating a chlorophyll standard to determine if the spectrophotometer is measuring chlorophyll

consistently from one year to the next or from the beginning to the end of an analytical run are described

in Wetzel and Likens (1991). In addition, a large number of replicates for each sample time and site

must be collected because the largest source of variation is likely to be natural (i.e., in the samples).
PAGE 73

6.1 INTRODUCTION

Data analysis is critical to nutrient criteria development. Proper analysis and interpretation of data 

determines the scientific defensibility and effectiveness of the criteria. Therefore, it is important to reevaluate short and long-term goals for stream systems within the ecoregion of concern. These goals

should be addressed when analyzing and interpreting nutrient and algal data. Specific objectives to be

accomplished through use of nutrient criteria should be identified and revisited regularly to ensure that

goals are being met. The purpose of this chapter is to explore methods for analyzing data that can be used

to develop nutrient criteria. Included are techniques that link relationships between nutrient loading and

algal biomass, statistical analyses to evaluate compiled data, and a discussion of computer simulation

models.

The difficulty associated with understanding predictive relationships between nutrient loading and algal

biomass is perhaps the biggest challenge to establishing meaningful nutrient criteria. Several relatively

simple methods of making this link for a variety of stream systems are discussed in this chapter. This

chapter also presents more in-depth methods to use when simpler techniques prove inadequate.

Macrophytes depend primarily on sediments for nutrient uptake, and are relatively unaffected by nutrient

water column concentrations. However, attempts to relate macrophyte growth or biomass with sediment

nutrient content have been largely unsuccessful (Chambers et al. 1999). Links between macrophytes and

nutrient enrichment are more indirect than with algae, and are therefore not considered here. A review of

macrophytes and the current state of the science can be found in Chambers et al. (1999).

Statistical analyses are used to interpret monitoring data for criteria development. Statistical methods are

data-driven, and range from very simple descriptive statistics to more complex statistical analyses. The

type of statistical analysis required for criteria development will be determined by the source, quality, and

quantity of data being analyzed. Concerns to be aware of during statistical analyses are discussed in this

chapter. Specific statistical tests that may be useful in criteria development are described in Appendix C.
July 2000 Chapter 6. Analyze Data

PAGE 74

Models are abstractions designed to represent something real. In this sense, models can be anything from

a representation of the human form in plaster, or a statistical equation expressing assumed relationships

between parameters of interest. This chapter discusses modeling as mathematical abstractions for the

purposes of analyzing data to derive nutrient criteria. Mathematical models can be categorized as

process-based or empirical, and are used for different purposes. This guidance focuses on empirical

models that serve to illuminate the relationship between the behavior of the system and measurements of

one or many attributes of the system. Empirical models identify patterns but do not explain them. In

contrast, process-based models are explanatory, and are built of equations that contain directly definable,

observable parameters. The rules used for process-based models invoke levels of organization other than

the components being modeled (Wiegert 1993).

Empirical models can be simple, statistical models or more complex simulation models. A linear

regression of chlorophyll and P (phosphorus) data from a population of streams is a simple empirical

model, in that it elucidates the relationship between chlorophyll and P in a single equation represented by

a line. A more complex empirical model is the computer simulation model CE-QUAL-RIV1, which is

comprised of a set of equations that predicts a constituent concentration over time. Prediction by both

linear regression and computer simulation are based on empirical observations of a stream or population

of streams. The linear regression described above is an example of a static model; static models do not

represent changes over time. Dynamic models, such as CE-QUAL-RIV1, represent changes in system

constituents over time (Wiegert 1993).

6.2 LINKING NUTRIENT AVAILABILITY TO ALGAL RESPONSE

When evaluating the relationships among nutrients and algal response within stream systems, it is

important to first understand which nutrient is limiting. Once the limiting nutrient is defined, critical

nutrient concentrations can be specified and nutrient and algal biomass relationships can be examined to

identify potential criteria to avoid nuisance algal levels. This section will discuss defining the limiting

nutrient, establishing predictive nutrient-algal relationships, analysis methods for establishing nutrientalgal relationships, analysis of algal species composition for system response to nutrients, characterizing

biotic integrity and response to nutrients, developing a multimetric index of trophic status, assessing

nutrient-algal relationships using experimental procedures, and a few other issues to keep in mind while

analyzing data.

DEFINING THE LIMITING NUTRIENT

Defining the limiting nutrient is the first step in identifying nutrient-algal relationships. Nuisance levels

of algal biomass are common in areas with strong nutrient enrichment, ample light, and stable flow

regime. Experimental data have demonstrated that given optimum light, non-scouring flow, and modest

to low grazing, enrichment of an oligotrophic stream will usually increase algal biomass and even

secondary production (Perrin et al. 1987; Slaney and Ward 1993; Smith et al. 1999). Identification of the

limiting nutrient is the first step in controlling nutrient enrichment and algal growth (Smith 1998; Smith et

al. 1999). Criteria will be set for both TN and TP, but it is often more cost-effective to reduce the loading

of one nutrient (N or P) to achieve reduction of nuisance algal growths.
July 2000 Chapter 6. Analyze Data

PAGE 75

Nitrogen frequently limits algal growth in streams and some have argued that this might be more common

in streams than it is in lakes (Grimm and Fisher 1986; Hill and Knight 1988; Lohman et al. 1991;

Chessman et al. 1992; Biggs 1995; Smith et al. 1999). However, there is evidence that P still often limits

stream algae (Dodds et al. 1998; Welch et al. 1998; Smith et al. 1999). If nonpoint sources of nutrients

predominate (assuming relatively high background levels of N), then N control may be a more important

issue than control of P. However, if N limits growth in a stream due to point source discharges such as

wastewater with low N:P, then the logical, cost-effective measure to control nuisance biomass is to reduce

P input, because N:P should then increase and cause P limitation (see Section 3.3 Secondary Response

Variables). If N and P are co-limiting, increasing the concentration of one nutrient will result in the other

nutrient becoming limiting (e.g., an increase in N concentrations will result in P becoming limiting). The

most prudent approach to controlling nutrient enrichment, regardless of the limiting nutrient, is to set

criteria for maxima of N and P, and try to limit inputs of both. 

Nitrogen usually becomes more limiting as enrichment increases because (1) wastewater N:P ratios are

low, (2) N is increasingly lost through denitrification; (3) P is more easily sorbed to sediment particles

than N and, thus, tends to be deposited in the sediment (in a waterbody with enough residence time to

allow sedimentation) more effectively than does N (Welch 1992); and (4) P is released from high Pyielding bedrock. However, N lost through anaerobic denitrification may be limited by streamflow

aeration, although denitrification rates may still be relatively high if the subsurface (hyporheic and

parafluvial) components of the stream ecosystem are considered (see Holmes et al. 1996). Furthermore, P

dissolved from bedrock or soil, whether complexed or not, is apt to remain in the water until it reaches a

waterbody with enough residence time to allow sedimentation, therefore loss of nutrients via

sedimentation is not usually important in most streams.

Although N may be a relatively more important controlling factor for growth in streams than lakes, there

is evidence that P can limit stream algae. For instance, ratios of soluble N:P averaged 90:1 (by weight) in

seven western Washington streams draining both forested and urbanized watersheds (Welch et al. 1998). 

Soluble N:TP ratios averaged 13:1 in three other western Washington streams (Welch et al. in press). 

Even more convincing evidence for a greater prevalence for P limitation in streams comes from the large

data set discussed later in this chapter (Dodds et al. 1998). These data show that: 1) TN:TP ratios are

nearly all >10:1, and 2) TN:TP ratios declined as enrichment increased from 24:1 (10% of data; TN = 316

and TP = 13 μg/L) to 20:1 (50% of data; TN = 1000 and TP = 50 μg/L) to 12.6:1 (90% of data; TN =

2512 and TP = 100 μg/L). The second point indicates that TN:TP in streams behaves similarly to that in

lakes as enrichment increases, i.e., as enrichment increases, the ratio of water column TN:TP declines. 

An important cause for this may be the high concentration of P in wastewater (N:P = 3:1; Welch 1992)

and in the runoff from applied animal manure (N:P < 3:1; Daniel et al. 1997). As an in-stream example,

DIN to SRP ratios in seven New Zealand streams receiving wastewater averaged 57:1 upstream and 13:1

downstream from effluent inputs (Welch et al. 1992). 

Many experimental procedures are used to determine which nutrient (N, P, or carbon) limits algal growth. 

Algal growth potential (AGP) bioassays are very useful for determining the limiting nutrient and

revealing the presence of chemical inhibitors (USEPA 1971). Yet, results from such assays usually agree

with what would have been predicted from N:P ratios in the water or, especially N:P in biomass. While

limiting nutrient-potential biomass relationships from AGP bottle tests are useful in projecting maximum

potential biomass in standing or slow-moving water bodies, they are not as useful in fast-flowing, and/or
July 2000 Chapter 6. Analyze Data

PAGE 76

gravel or cobble bed environments. Also, the AGP bioassay utilizes a single species which may not be

representative of the natural species assemblage response.

Limitation may be detected by other means, such as alkaline-phosphatase activity, to determine if N is

actually limiting in spite of a high N:P ratio. Alkaline phosphatase is an enzyme excreted by some algal

species in response to P limitation. This enzyme hydrolyzes phosphate ester bonds, releasing

orthophosphate (PO4) from organic phophorus compounds (Steinman and Mulholland 1996). Therefore,

the concentration of alkaline phosphatase in the water column can be used to assess the degree of P

limitation. Alkaline phosphatase activity, monitored over time in a waterbody, can be used to assess the

influence of P loads on the growth limitation of algae (Smith and Kalff 1981).

Periphyton biomass accrual experiments using nutrient-diffusing substrata (Pringle and Triska 1996) are

useful for determining the limiting nutrient for a mixed species assemblage in running water and include

the important factors of velocity-enhanced, nutrient uptake as well as constraints imposed by mat

thickness that are nonexistent with bottle tests (Grimm and Fisher 1986b; Lohman et al. 1991; Pringle and

Triska 1996). However, the existing ambient nutrient concentrations produced from the nutrient diffusing

substrata and available for algal uptake are largely unknown with such tests.

Another experimental technique to determine ambient nutrient-maximum periphyton biomass potential in

running water is with constructed channels, either with controlled light and temperature in the laboratory

(Horner et al. 1983) or with natural light and temperature outdoors, along side natural streams (Stockner

and Shortreed 1976; Bothwell 1985, 1989; Pringle and Triska 1996). Pringle and Triska (1996) describe

methodologies for both nutrient diffusing substrata and in-stream channels.

Correlations between algal biomass and TN and TP (Dodds et al. 1997) indicate that N explains more of

the variance than does P, although P may frequently be the limiting nutrient in stream systems. However,

these results may be biased by the stream data used in correlation analyses. That is, the systems where

nuisance algal biomass has been measured may be primarily N limited, although this may not be a

reflection of a tendency for N limitation in all stream systems generally. In addition, sediment-bound

particulate P may remain suspended in streams, confounding the relationship between P and algal

biomass. Finally, the nutrient that limits growth in the short term may not always be the most costeffective nutrient to control. Therefore, careful evaluation of nutrient limitation should be undertaken

prior to criteria development and restoration efforts.

ESTABLISHING PREDICTIVE NUTRIENT-ALGAL RELATIONSHIPS

Once the limiting nutrient has been identified, the data need to be analyzed to characterize nutrient-algal

relationships and patterns that clarify those relationships. Data analyses can provide mathematical

approximations of the relationships that will allow prediction of algal biomass as a function of nutrient

concentration. Predictive relationships between nutrients and periphyton (or phytoplankton) biomass are

required to identify the critical or threshold concentrations that produce a nuisance algal biomass.

Relationships between TP and/or TN and periphytic biomass in streams have relatively low r2

 values on

the order of 0.4-0.6 (Lohman et al. 1992; Dodds et al. 1997). Therefore, the following considerations

need to be taken into account when establishing predictive nutrient-algal relationships. Critical and
July 2000 Chapter 6. Analyze Data

PAGE 77

highly variable factors other than nutrients – shading, type of attachment surfaces, scour, water level

fluctuations that result in dessication, grazing intensity – have major effects on algal biomass levels and

may provide an explanation for the weakness of the predictive relationships in streams. In addition, TP in

the stream water column contains more sediment-and detrital-bound P than observed in lakes, and

sediment-bound P is not necessarily available for algal uptake. The high detritus level in streams is

indicated by TP versus chl a per volume (i.e., seston) relationships in streams where chl a/TP ratios

ranged from 0.08 to 0.22 (Van Nieuwenhuyse and Jones 1996). These ratios suggest that the high detritus

levels in streams are indicative of high proportions of water-column P bound to sediment or heterotrophic

components of detrital material. Finally, inorganic nutrient species (PO4 and NO3) are frequently more

available for uptake, and may need to be considered in instances where small scale effects from specific

point and nonpoint sources are an important issue. 

There are few existing relationships that predict algal biomass as a function of TN and TP. Dodds et al.

(1997) compiled and analyzed the largest and broadest dataset (approximately 200 sites) in the literature

that predicts relationships for benthic algal biomass. The best general approach for predicting mean

suspended chlorophyll was developed using data from 292 temperate streams (Van Nieuwenhuyse and

Jones 1996). The equations derived from these analyses are presented for use with periphyton-dominated

and plankton-dominated systems, respectively. 

The equations suggested by Dodds et al. (1997) are recommended to predict benthic algal biomass if more

local, ecoregion-specific relationships are unavailable:

log (mean chl a) = 1.091 + log (TP) * 0.2786 (r2

 = 0.089)

log (mean chl a) = 0.01173 + log (TN) * 0.5949 (r2

 =0.35)

log (maximum chl a) = 1.4995 + log(TP) * 0.28651 (r2

 = 0.071)

log (maximum chl a) = 0.47022 + log (TN) * 0.60252 (r2

 = 0.28)

where seasonal mean and maximum benthic chlorophyll are in mg/m2

 and TN and TP are in μg/L. The

above equations are fairly simple and, although they have low r2

 values, are best suited for use with data

having high TN and TP concentrations. Note that the graphical illustration of the relationships from

which these equations were derived, shows a broad distribution of the data (Figure 7). This distribution

suggests that periphytic algae tend to respond in a similar fashion to nutrients, regardless of location. 

A second set of equations, also derived by Dodds et al. (1997), combines TN and TP measures resulting in

higher r2

 values, but may be inaccurate in some high nutrient situations.

log (mean chl) = -3.233 + 2.826(log TN) – 0.431(log TN)2

 + 0.255(log TP) (r2 = 0.43)

log (max chl) = -2.702 + 2.786(log TN) – 0.433(log TN)2

 + 0.306(log TP) (r2 = 0.35).
July 2000 Chapter 6. Analyze Data

PAGE 78

Figure 7. Relationships of log-transformed mean chlorophyll a as a function of TN and TP. 

Data points are represented by abbreviations identifying the State or country of origin: AK- Alaska, IDIdaho, MI- Michigan, MO-Montana, NH-New Hampshire, NC-North Carolina, OR-Oregon, PAPennsylvania, WA-Washington, QU-Quebec, EU-Europe, NZ-New Zealand.
July 2000 Chapter 6. Analyze Data

PAGE 79

It should be kept in mind that there is considerable variance in these relationships, and if extensive data

for a single system are available, tighter predictive relationships may be constructed. More local,

ecoregion-specific data sets should produce tighter relationships.

The equation suggested by Van Nieuwenhuyse and Jones (1996) is recommended to predict mean

suspended chlorophyll if more local, ecoregion-specific relationships are unavailable:

log Chl = -1.65 + 1.99(log TP) – 0.28(log TP)2

 (r2 = 0.67)

Where chl = summer mean chlorophyll and TP are expressed in mg/m3

.

Yields of algal biomass from given nutrient concentrations derived from regression models differ from

the yield observed in controlled channel experiments. This discrepancy creates a problem when

attempting to predict nutrient-periphyton chl a relationships in streams. For example, to produce a mean

chl a of 100 mg/m2

 would require approximately 100-200 μg/L TP according to regression models of

Lohman et al. (1992) and Dodds et al. (1997). Brezonik et al. 1999 used the equation from Van

Nieuwenhuyse and Jones (1996) that includes the catchment size (basin area) to predict likely

improvements in concentrations of growing season mean chl a that would occur with corresponding

reductions in growing season mean TP.

log Chl = -1.92 + 1.96(log TP) - 0.30(log TP)2

 + 0.12(log Ac) (r2 = 0.73, n = 292) 

Where Ac = stream catchment area.

They predicted that a reduction of streamwater TP from 125 to 100 μg/L would result in a chl a reduction

of 18%, and a TP reduction from 50 to 25 μg/L would result in a chlorophyll a reduction of 52%. 

However, in-channel experiments have produced 600 to 1000 mg/m2 chl a in a mixed algal assemblage

using in-channel SRP and TP concentrations of 10-15 and 20-50 μg/L , respectively, a yield of ~10-50 chl

a/TP (Horner et al. 1983, 1990; Walton et al. 1995; unpublished data). This seeming discrepancy may

result from the nutrient demand by heterotrophic organisms in the detritus of natural streams. Residence

time was short (16 minutes or less) in the above cited channel experiments, nutrient input was controlled

to low levels, and velocity was usually constant with little sloughing during the growth period (Horner et

al. 1990). Such characteristics would generate little detritus and low ambient TP and, hence, higher inchannel chl a/TP ratios than in natural streams sampled throughout the year. 

The discrepancy in algal biomass yield between regression models and channel experiments may partly

justify the use of regression models generated from large field data sets in recommending nutrient criteria. 

Channel data are not significantly confounded by the sloughed biomass that produces detrital material in

natural streams and is unavailable for uptake and algal biomass increase. Although the correlation

between chl a and nutrients in natural streams may be weakened (from the cause-effect standpoint) due to

interference with detritus, the relations may nonetheless be useful for extrapolation and management

because nutrient criteria must be applied where high detritus levels do exist.

Soluble nutrient concentrations determine periphytic growth rate and biomass; uptake is clearly saturated

at very low (<10 μg/L SRP) concentrations (Bothwell 1985, 1989; Walton et al. 1995) and is independent
July 2000 Chapter 6. Analyze Data

PAGE 80

of TP concentrations. However, soluble nutrients are usually lowest when biomass is highest, due to

depletion by algal uptake, similar to the situation in lakes. Therefore, estimates of inflow nutrient

concentrations, in-stream concentrations during non-growth periods or at least annual mean

concentrations are required to use soluble nutrients to set critical levels and relate soluble nutrients to

algal biomass. These data/relationships are not currently available, but should be pursued in order to

develop more direct, stronger nutrient-biomass relationships for streams.

ANALYSIS METHODS FOR ESTABLISHING NUTRIENT-ALGAL RELATIONSHIPS

The following analysis methods are suggested to develop predictive nutrient-biomass relationships in

stream systems. These methods were primarily developed for gravel/cobble bed streams, but should

function for other stream types with modifications. Intermittent and effluent-dominated streams will

benefit from supplemental analysis methods specific to those stream types as the seasonal sampling

discussed here may not be possible (see Appendix A). Samples for soluble and/or total N and P should be

collected for at least one, preferably two or more years at sites with high as well as low summer biomass. 

Ideally, samples for periphyton biomass should be collected weekly or biweekly during summer low flow,

beginning immediately after spring runoff or any subsequent high water, scouring event. Monthly data

collection may be sufficient to define algal-nutrient relationships if supporting long-term trend data is

available. Data can be analyzed using one or all of the following methods to establish predictive nutrientbiomass relationships in stream systems. 

1. Relate the total concentration of a limiting nutrient (e.g., TN, TP) with the mean and maximum

algal biomass as chl a; both data sets should be collected at the same time during summer (or

season of maximum algal biomass). Such data were used by Dodds et al. (1997) to develop the

relationship between nutrients and algal biomass discussed in the previous section. Relate the

low/non-growing period mean concentration of limiting nutrient to summer maximum biomass as

chl a. 

2. It may also be possible to relate the pre-maximum growth period (usually spring, immediately

following runoff) mean soluble limiting nutrient concentration to maximum algal biomass. 

Inorganic soluble N (ammonium and nitrate) should be used as the limiting nutrient if the N:P

(soluble) is <10 (by weight) and SRP should be used if N:P >10. The threshold of 10 is chosen to

simplify the assessment protocol, although N and P are known to be co-limiting over a rather wide

range in N:P ratio (7-15) (Smith 1982; Welch 1992). Data should be stratified into discrete ranges

of N:P ratios, if this approach does not produce sound relationships, in a manner similar to the

methods used by Prairie et al. (1989). 

This analysis selects data that would most closely represent an “inflow concentration” of dissolved

inorganic limiting nutrient because it utilizes the available form of the designated limiting nutrient

during a period when algal nutrient uptake is minimal. The pre-growth period nutrient

concentration should be analogous to the inflow limiting nutrient concentration (including

groundwater) entering a continuous algal culture system, whether planktonic or periphytic, that

yields a maximum steady-state biomass. Analysis of N and P loading could be used for this

assessment in stream systems, though it has not been tested. However, because rivers, streams,

lakes, and estuaries form a linked system in the context of a watershed, load analysis becomes
July 2000 Chapter 6. Analyze Data

PAGE 81

crucial at watershed scales. Relationships can be sought for TP and TN using this method and in

method 3 below, which may be more appropriate for criteria throughout an ecoregion, although

less specific for given streams.

3. Relate annual mean soluble nutrient concentration to the 75th percentile mean algal biomass. This

approach does not provide sound continuous culture rationale like inflow concentration-maximum

biomass relationships, but annual mean values for nutrients were used in the cellular N and flood

frequency versus chl a relationship discovered by Biggs (1995), as well as soluble N and P

concentrations versus maximum chl a for different accrual times (Biggs 2000). In instances where

nutrient data are inadequate to provide distinct and reliable values used in method 2 above, an

annual mean approach may offer a reasonable approximation of nutrient availability.

4. Another possibility for developing strong predictive relationships is the use of cellular

concentrations of limiting nutrient (same ratio criterion used in 2 above) determined during the

summer growth period, related to maximum algal biomass. This approach estimates the available

nutrient directly from physiologically relevant data, as opposed to using the pre-growth soluble

fractions in water to infer what is available for uptake. The validity of this approach is supported

by a multiple relationship among cellular N, chl a, and flood frequency, in which cellular N

content varied over a range of four-fold (Biggs 1995). A sound relationship between cellular

nutrient content and periphytic algal biomass would, however, still require a link to the respective

limiting nutrient concentration in water for management purposes. That could be accomplished by

developing a relationship between cellular nutrient and ambient nutrient concentrations (either

soluble or total) using constant flow laboratory channel experiments.

As further evidence for the potential of this approach, Wong and Clark (1976) described a direct

relationship (r2

=0.80) between cellular P and ambient TP in six rubble-bed streams in Ontario, such that;

TPw = 0.05 Pt

 - 0.02

where Pt

 is tissue content, and TPw is ambient water column TP. They determined further that

photosynthetic rate of Cladophora at optimum light availability, decreased below 1.6 mg P/g dry weight,

which was equivalent to 60 mg/L TP in the water. Nevertheless, this had no predictive value for

maximum biomass. Development of a relation between cellular limiting nutrient and biomass, instead of

productivity, would be necessary to back- calculate to ambient nutrient content, either soluble nutrient as

in methods two or three above, or total nutrient as from method one and Wong and Clark (1976).

ANALYSIS OF ALGAL SPECIES COMPOSITION TO CLASSIFY STREAM RESPONSE TO NUTRIENTS

Differences in algal species composition among streams can identify important regional environmental

gradients that may affect algal-nutrient relationships. Algal species composition should be used in data

analysis to validate stream classification and enable development of indicators of nutrient conditions and

the likelihood of nuisance algal blooms. Different classes of streams may require different nutrient

criteria, depending upon algal responses to nutrients in different stream classes. For example, algalnutrient problems may be related to proliferation of filamentous green algae Cladophora or Spirogyra,

benthic or planktonic diatoms, dinoflagellates, or blue-green algae. Each of these problems may occur at
July 2000 Chapter 6. Analyze Data

PAGE 82

different nutrient concentrations, but will probably only occur in certain classes of streams during specific

seasonally-optimum conditions (Biggs et al. 1998b).

Cluster analysis is used to identify groups of streams with similar algal assemblages. TWINSPAN (Two

Way INdicator SPecies Analysis; Hill 1979) and UPGMA (Unweighted Pair Group Method using

Arithmetic averages; Sneath and Sokal 1973) represent two examples of cluster analysis that are

commonly used and differ in how results are generated. TWINSPAN employs a divisive approach in

which all algal assemblages are initially grouped in one cluster and then that cluster is divided into two

groups based on the greatest dissimilarities between the groups. Subsequently, each cluster is divided into

two more clusters so that one cluster becomes two, two becomes four, four becomes eight, and eight

becomes 16, etc. In contrast, UPGMA is an aggregational technique that begins with all algal

assemblages separated into single assemblage clusters and builds clusters by aggregation of the most

similar clusters. So N clusters becomes N-1 clusters, and N-1 clusters becomes N-2 clusters, and so on. 

At each step, one algal assemblage is grouped with another assemblage or group of assemblages. Results

of both techniques can be used together by identifying groups of assemblages (and associated streams)

that cluster the same in both analyses. These groups can be designated as core clusters. Assemblages that

are not grouped in the same clusters in both analyses can be associated with core clusters based on some

simple evaluation, such as percent similarity to assemblages in the core cluster. 

Cluster analysis of algal assemblages can be used as one step in classifying streams based on their

response to nutrients (e.g., Pan et al. in press). Habitat classification is based on assemblages in reference

conditions, because human impacts may constrain species membership in assemblages and mask diversity

among stream classes and impacts that nutrients have on that diversity. In addition, algal assemblages in

different classes of streams may respond differently to nutrient addition (Biggs et al. 1998b). The number

of stream classes that should be used depends on many factors, but the number should be limited based on

practicality, utility in explaining algal responses to nutrient enrichment, and utility in explaining algal

responses to remediation. In addition, statistical significance of clusters, based on discriminate analysis

for example, can also form the basis for determining the number of stream classes. Algal assemblage

clusters can be related to the physical classification (described in Chapter 2), to predict responses of

similar stream classes to further enrichment or remediation (Biggs et al. 1998b).

The form of species composition data used in classification of stream algal assemblage, and other

analyses as well, has a substantial effect on resolution of patterns that are related to the phenomena with

which we are concerned. Algal species composition data based on species densities (cells/cm2

), relative

abundance (% of assemblage), and presence/absence differ successively in sensitivity to diurnal and daily

changes in environmental conditions. Both theoretically and in practice, species composition data based

on species densities are more sensitive to small-scale spatial and temporal variability than are data based

on species relative abundances and presence/absence data (Stevenson unpublished data). Most stream

classification analyses should be done with relative abundances because they integrate over space and

time and most results in the literature are presented in this form.

Ordination helps to visualize differences in species assemblages among classes of streams. When species

composition is combined with environmental data or algal autecological characteristics, the important

environmental factors affecting species composition in a region can be deduced. These environmental

factors may be important for constraining algal response to nutrient concentration and may therefore be
July 2000 Chapter 6. Analyze Data

PAGE 83

important for identifying confounding factors in the relationship between algal assemblages and nutrient

conditions. Caution should be exercised in using ordination to develop attributes of algal assemblages for

use in establishing nutrient criteria. Ordination scores for taxa and classifications will change as new data

are added and ordinations are recalculated. Therefore, ordinations should not be recalculated after a

standard classification system or assessment system has been established. Species scores based on the

original ordination should be used in subsequent classifications and assessments (Barbour et al. 1999).

CHARACTERIZING NUTRIENT STATUS WITH ALGAL SPECIES COMPOSITION

Theory and empirical evidence indicate that algal species composition may be a more precise indicator of

nutrient status and the potential for nuisance algal problems than one-time sampling and assessment of

nutrient concentrations and algal biomass. Shifts in algal species composition may be more sensitive to

changes in nutrient concentrations and may therefore help define nutrient criteria. Many monitoring

programs utilize multiple lines of evidence to increase the certainty of assessments. Algal species

composition, as well as growth form and mat chemistry, can provide evidence of nutrient condition and a

greater certainty of assessing nutrient conditions. This topic has been the subject of many recent reviews

(McCormick and Cairns 1994; Kelly et al. 1995; Whitton and Kelly 1995; Lowe and Pan 1996; Stevenson

1998; McCormick and Stevenson 1998; Wehr and Descy 1998; Kelly et al. 1998; Ibelings et al. 1998;

Stevenson and Pan 1999; Stevenson and Bahls 1999; Stoermer and Smol 1999; Stevenson in press).

Species composition and autecological characteristics of algae are commonly used to evaluate

environmental conditions, ranging from organic (sewage) contamination to pH and nutrient conditions

(Kolkwitz and Marsson 1908; Zelinka and Marvan 1961; Renberg and Hellberg 1982; Charles and Smol

1988; Whitmore 1989; Kelly and Whitton 1995; Pan et al. 1996). With diurnal and weekly variability in

environmental concentrations within streams due to metabolic and weather-related factors or periodic

releases of pollution from point sources, it is assumed that the biological assemblages that develop over

longer periods of time are adapted to the average conditions in those habitats and tolerant to the

environmental maxima and minima. Thus, if environmental tolerances and sensitivities of organisms are

known, the physical, chemical, and potentially biological conditions for a habitat can be inferred if

environmental effects differed among species.

Autecological characteristics, the environmental preferences for specific taxa, are frequently documented

in the literature, particularly for diatoms (see van Dam et al. [1994] or Stevenson and Bahls [1999] for a

literature list). Autecological characteristics have been compiled and summarized in several publications

(Lowe 1974; Beaver 1981; Van Dam et al. 1994). Accuracy of the autecological characterizations in

these compilations is limited to multi-category classification systems. For example, a categorical

characterization of nutrient sensitivity might vary with the integers from 1-5, where 1 would be assigned

to species least sensitive to low nutrients and 5 would indicate taxa most sensitive to low nutrients (van

Dam et al. 1994). Thus, high abundance in a habitat of taxa classified as 5 would indicate highly

eutrophic conditions. In contrast, more accurate characterizations of algal taxa have been achieved

recently by using weighted averages of species relative abundances and a quantitative assessment of the

environmental conditions in which they are observed (e.g., ter Braak and van Dam 1989; Birks 1988). 

The result is an accurate assessment of the specific environmental conditions in which a species will have

its highest relative abundance (environmental optima). The weighted average approach assumes that

species have optima along environmental gradients if each gradient (nutrients, pH, salinity, organic
July 2000 Chapter 6. Analyze Data

PAGE 84

contamination) includes a broad range of conditions that includes most of a species range. These

weighted average descriptions of species autecologies have been developed for optimal total phosphorus

concentrations in streams (Pan et al. 1996).

 A trophic status indicator (TSI) can be calculated by summing the products of species relative

(proportional) abundances (pi

, ranging from 0-1) and their autecological characterization for trophic status

(i

) for all i species:

TSI = i=1,spi

i

If all i species do not have autecological characteristics, normalize the index by adjusting description of

the community to only those taxa that have autecological characteristics:

TSI = i=1,spi

Θi

/i=1,spi

Weighted average indices can be calculated easily with a spreadsheet. The weighted average formula can

be used with categorical or weighted average autecological characterizations; see Kelly and Whitton

(1995) and Pan et al. (1996) respectively. When indices are used with the highly accurate environmental

optima determined by weighted average regression, they actually infer the phosphorus concentration or

nitrogen concentration in the stream (Pan et al. 1996). Comparisons of precision of inferring TP

concentrations with weighted average indicators and one-time measurement of TP concentration in a

stream show that diatom indices are more precise (Stevenson and Smol in press).

Kelly and Whitton (1995) make several adjustments to sample processing and index calculation that make

processing easier while maintaining index performance and distinguishing between organic and inorganic

nutrients. They make sample processing easier by only counting 200 diatoms and a single set of diatom

taxa that are easy to identify and that are good indicators of nutrient condition (Kelly 1996). Weights of

species can be added to this formula to decrease the importance of taxa that have a broad tolerance to

trophic status (see formula in Kelly and Whitton 1995), but they may not improve precision of the indices

(Pan et al. 1996). Finally, autecological information is also available for assessing organic (sewage)

contamination in waters. This information can be used with a TSI to distinguish enrichment effects due to

inorganic and organic pollution Kelly and Whitton (1995).

Most autecological characteristics of diatom taxa have been described from European populations. 

Further testing will be important to determine how well autecological characterizations of taxa found in

Europe compare to those in North America. However, these autecological indices should be useful for

general classification of relative trophic status in streams when reference conditions and relations between

changes in species composition and nutrient concentrations have not been established. The relative

benefits of more accurately defining autecological characteristics with weighted averages versus coarse

scale categories have not been thoroughly evaluated. Investigations have shown that inferences of

environmental conditions based on indices using weighted average autecologies are more precise than

those using categorical autecologies (ter Braak and van Dam 1989; Agbeti 1992). Tradeoffs may exist

between greater precision for indices that are calculated with weighted average autecologies when they

are used in conditions similar to those where the autecologies were developed versus less error associated

with categorical autecologies when indices are used across broad diverse regions. Details and references
July 2000 Chapter 6. Analyze Data

PAGE 85

to development of algal indices of environmental conditions can be found in recent reviews (Birks 1998;

Stoermer and Smol 1999, Stevenson and Pan 1999; Stevenson and Smol in press).

DEVELOPING MULTIMETRIC INDICES TO COMPLEMENT NUTRIENT CRITERIA

Multimetric indices are valuable for summarizing and communicating results of environmental

assessments and may be developed as an alternative to numeric criteria. Furthermore, preservation of the

biotic integrity of algal assemblages, as well as fish and macroinvertebrate assemblages, may be an

objective for establishing nutrient criteria. Multimetric indices for macroinvertebrates and fish are

common (e.g., Kerans and Karr 1994; Barbour et al. 1999), and multimetric indices with benthic algae

have recently been developed and tested on a relatively limited basis (Kentucky Division of Water 1993;

Hill et al. 2000). However, fish and macroinvertebrates do not directly respond to nutrients, and therefore

may not be as sensitive to changes in nutrient concentrations as algal assemblages. It is recommended

that relations between biotic integrity of algal assemblages and nutrients be defined and then related to

biotic integrity of macroinvertebrate and fish assemblages in a stepwise, mechanistic fashion. This

section provides an overview for developing a multimetric index that will indicate algal problems that are

associated with trophic status in streams.

The first step in developing a multimetric index of trophic status is to select a set of ecological attributes

that respond to human changes in nutrient concentrations or loading in streams. Attributes that respond to

an increase in human disturbance are referred to as metrics. Six to ten metrics should be selected for the

index based on their sensitivity to human activities that increase nutrient availability (loading and

concentrations), their precision, and their transferability among regions and habitat types. Selected

metrics should also respond to the breadth of biological responses to nutrient conditions (see discussion of

metric properties in McCormick and Cairns 1994; Stevenson and Smol in press).

Many structural and functional attributes of algal assemblages can be used to characterize the biotic

integrity of algae (McCormick and Cairns 1994; Stevenson 1996; Kelly et al. 1998; Stevenson and Pan

1999). Biomass, species composition, species diversity, chemical composition, productivity, respiration,

and nutrient turnover rates (spiraling distance) are examples of these attributes. All of these attributes are

important and respond with different lag times to spatial and temporal variability in environmental

conditions. Most monitoring programs measure structural attributes because structural characteristics

vary less than functional characteristics on diurnal and daily time scales. For example, state monitoring

programs (e.g., KY, MT) rely on changes in species composition, rather than biomass and chemical

composition, to assess ecological conditions in streams because species composition is hypothesized to

vary less. However, the relationship between all algal attributes, if characterized for an appropriate time 

and space, can be related to nutrient concentrations to determine the effect of nutrients on algal

assemblages in streams.

Many algal metrics can be used to characterize the valued ecological attributes that we want to protect in a

habitat or the nuisance problems that may develop as a result of nutrient enrichment. These are

"response" or "condition" metrics (Paulsen et al. 1991; Barbour et al. 1999) and they should be

distinguished from "stressor" or "causal" indicators, such as nutrient concentrations (water chemistry or

periphyton chemistry) and biological indicators of nutrient concentrations. While both "response" and

"stressor" metrics could be used in a single multimetric index, we recommend that separate multimetric
July 2000 Chapter 6. Analyze Data

PAGE 86

indices be used for "response" and "stressor" assessment. Distinguishing between "response" and

"stressor" indices can be accomplished utilizing a risk assessment approach with separate hazard and

exposure assessments that are linked with response-stressor relationships (USEPA 1996; Stevenson 1998;

Barbour et al. 1999; Stevenson and Smol in press). A multimetric index that specifically characterizes

"responses" can be used to clarify goals of management (maintainance or restoration of valued ecosystem

attributes) and to measure whether goals have been attained with nutrient management strategies.

Measurements of nutrient concentrations and algal indicators of nutrients could be combined to develop a

multimetric "stressor" index specifically for nutrient conditions. Metrics of nutrient concentrations such as

water and mat chemistry (μg P/mg AFDM, μg N/mg AFDM) are described in Appendix C and are

relatively straight forward. Biological indicators of nutrient concentrations are described in the above

section, Characterizing Nutrient Status with Algal Species Composition. The following paragraphs

discuss algal metrics that characterize valued ecological attributes and nuisances.

Algal metrics can be distinguished with respect to types of designated use that is being impaired. Algal

biomass can be measured as percent cover by filamentous algae, turbidity, mg chl a/m2

, g AFDM/m2. 

Determining when biomass becomes a nuisance will require relating biomass to designated uses, such as

support of aquatic life (biotic integrity), or potability. Effects of nutrients on algal biomass and effects of

algae on the biotic integrity of macroinvertebrates and fish should be characterized to aid in developing

nutrient criteria that will protect designated uses related to aquatic life (e.g., Miltner and Rankin 1998). 

Potability can be impaired by algae that cause taste and odor problems and whose growth may be

stimulated by nutrients. Thus, relationships should be developed between nutrients and taste and odor

producing algae or nutrients and the frequency of taste and odor complaints to develop management plans

and criteria to support potability as a designated use. Relative abundance or biomass of taste and odor

algae (Palmer 1962) may be good indicators of the potential for potability problems. Percent toxic algae

could provide indicators of potential for toxic algal blooms in streams at low flow in which wildlife and

livestock could be endangered, although little is known about the effects of toxic algae in streams.

Biotic integrity of algal assemblages may be indicated by many quantitative attributes of algal

assemblages (Stevenson 1996; Stevenson and Pan 1999). Attributes of species composition can be

characterized at different levels of resolution, e.g., actual biomass (biovolume/cm2

), relative biovolume

relative abundances, cell density, or presence/absence at each taxonomic level. Relative biovolume is

usually used to characterize changes in functional groups (as defined by physiognomy and taxonomic

division) of algae in assemblages because cell sizes vary so much among functional groups (e.g.,

filamentous cyanobacteria, colonial cyanobacteria, diatoms, and large cells of filamentous green algae). 

Relative abundances are usually used to characterize changes in species composition of specific groups of

taxa, such as diatoms. Many environmental programs only evaluate diatom assemblages for species level

indicators (e.g., Kentucky Division of Water 1993; Pan et al. 1996; Kelly et al. 1998).

Even though many taxonomic attributes of algal assemblages would be expected to change in response to

increasing nutrient concentrations, analyses should be focused to some extent on variables that have

intrinsic value. Thus, changes in relative biovolume from non-nuisance algae (e.g., diatoms) to

filamentous green algae with nutrient addition may be an indicator of loss in biotic integrity, because

habitat structure and food availability for invertebrates (e.g., Holomuzki et al. 2000). Loss of species may
July 2000 Chapter 6. Analyze Data

PAGE 87

be an issue: such as some macroalgae that are relatively sensitive to nutrient enrichment and overgrowth

by diatoms (e.g., filamentous red algae or some nitrogen-fixing, blue-green algae such as Nostoc). 

Another approach for characterizing biotic integrity of algal assemblages as a function of trophic status in

streams is to calculate the deviation in species composition or algal growth forms at assessed sites from

composition in the reference condition. Multivariate similarity or dissimilarity indices need to be

calculated for multivariate attributes such as taxonomic composition (Stevenson 1984; Raschke 1993) as

defined by relative abundance of different algal growth forms or species, or species presence/absence. 

One standard form of these indices is percent community similarity (PSc, Whittaker 1952): 

PSc =i=1,s min(ai

,bi)

Here ai

 is the percentage of the ith species in sample a, and bi is the percentage of same ith species in

sample b. A second common community similarity measurement is based on a distance measurement

(which is actually a dissimilarity measurement, rather than similarity measurement, because the index

increases with greater dissimilarity, Stevenson 1984; Pielou 1984). Euclidean distance (ED) is a standard

distance dissimilarity index, where:

ED = (i=1(ai

-bi)

2

)

log-transformation of species relative abundances in these calculations can increase precision of metrics

by reducing variability in the most abundant taxa. Theoretically and empirically, we expect to find that

multivariate attributes based on taxonomic composition more precisely and sensitively respond to nutrient

conditions than do univariate attributes of algal assemblages (see discussions in Stevenson and Smol

accepted). High precision and sensitivity argues for including assessments of algal species composition

and its response to nutrient conditions in the process of developing nutrient criteria. The response of algal

species composition to increases in nutrient concentrations can be used as another line of evidence to

develop a rationale for specific nutrient criteria in specific classes of streams.

To develop the multimetric index, metrics must be selected and their values normalized to a standard

range such that they all increase with trophic status. Criteria for selecting metrics can be found in

McCormick and Cairns (1994) or many other references. Basically, sensitive and precise metrics should

be selected for the multimetric index and selected metrics should represent a broad range of impacts and

perhaps, designated uses. Values can be normalized to a standard range using many techniques. For

example, if 10 metrics are used and the maximum value of the multimetric index is defined as 100, all ten

metrics should be normalized to the range of 10 so that the sum of all metrics would range between 0 and

100. The multimetric index is calculated as the sum of all metrics measured in a stream. A high value of

this multimetric index of trophic status would indicate high impacts of nutrients in a stream and should be

a robust (certain and transferable) and moderately sensitive indicator of nutrient impacts in a stream. A 1-

3-5 scaling technique is commonly used with aquatic invertebrates (Barbour et al. 1999; Karr and Chu

1999) and could be used with a multimetric index of trophic status as well.

Arguments have been made for limiting membership of metrics in a mulitmetric index to only biological

metrics and only biological metrics from one assemblage of organisms (Karr and Chu 1999). We
July 2000 Chapter 6. Analyze Data

PAGE 88

generally concur with that recommendation. More detailed descriptions of this multimetric index

development can be found in Karr and Chu (1999), Barbour et al. (1999), and Hill et al. (2000)

ASSESSING NUTRIENT-ALGAL RELATIONSHIPS USING EXPERIMENTAL PROCEDURES

Management of nutrients to ensure high stream quality is greatly strengthened by examining relationships

between the limiting nutrient and maximum algal biomass (i.e., potential) that will occur if/when other

factors are optimum. Relationships between ambient nutrient content and existing biomass may not

adequately predict maximum biomass potential for any single stream because other factors, such as light,

high-flow scouring, and grazing often limit biomass accrual in natural streams. Experimental procedures

are valuable for determining the maximum biomass potential of a system. However, physical constraints

imposed in experimental setups are often unrealistic. Thus, the value of extrapolating results from

laboratory experiments to natural conditions is often uncertain. There are many more experimental results

reported to determine which nutrient (N, P, or carbon) limits algal growth, than to determine nutrientbiomass relationships. Experimental procedures to determine the limiting nutrient/s for algal growth are

discussed earlier in this section (see Defining the Limiting Nutrient).

As indicated previously, biomass levels up to 1000 mg/m2

 chl a were accrued on stones of in-stream

channels receiving as little as 10 mg/L SRP (Walton et al. 1995). Although Cladophora has not been

grown in channels, other filamentous green algae (FGA) (Mougeotia, Stigeoclonium, Ulothrix) have

dominated in such experiments. In contrast, bottle tests with unattached Cladophora have shown that

growth/biomass is not saturated at such low SRP concentrations (Pitcairn and Hawkes 1973), indicating

results from flowing-water channel experiments more closely represent natural systems. Nevertheless,

Bothwell (1989) did show added accrual of diatom films from about 250 mg/m2

 chl a at an SRP of 5

μg/L, increasing to 350 mg/m2

 at about 50 μg/L.

There may be problems with achieving a species assemblage in channel experiments that is representative

of the natural stream(s) in question. In fact, accurate prediction or even chatacterization of ambient

assemblages in dynamic systems may be challenging. Cladophora has been difficult, if not impossible, to

establish in such systems, and other FGA have not established on Styrofoam substrata (used by Bothwell

1985), even when abundant in the source stream. Diatoms are usually first to establish, with more time

required for FGA to colonize due to their more complex reproduction requirements. Natural stones seem

to be the most effective substratum for colonizing either diatoms or FGA in these systems, but resulting

dominant taxa in channels may not replicate exactly as in natural streams, even though channels are

inoculated from stream rocks. Moreover, diatoms may, in fact, dominate the biomass in channels even

though FGA establishes and appears most abundant to the eye. Correctly predicting community

composition in future stages of succession is very difficult, even in simple systems. Given the complexity

inherent in dynamic ecosystems, only excessively broad predictions may be possible. Data gathered from

channel experiments may be little better at characterizing process than a grab sample is at characterizing

water chemistry. Only simple extrapolations can be made employing data gathered from simple systems.

Caution is recommended in applying nutrient-biomass relationships developed in channel experiments to

natural streams, primarily for two reasons: (1) TP and TN content required to produce a maximum

biomass will probably be higher in natural streams than in channels, as previously discussed, because

more detrital TP and TN will accumulate in enriched natural streams than in short-detention time
July 2000 Chapter 6. Analyze Data

PAGE 89

channels. Hence, the yield (i.e., slope of regression line) of chl a/TP or TN in channels will be greater. 

(2) The more or less continual input of soluble nutrients from groundwater to the natural stream is usually

unknown, so inflow soluble nutrient-maximum biomass relations from short-detention time channels may

not be applicable to natural streams where in-stream soluble nutrients are low as a result of algal uptake

during long travel times, yet may have a relatively high inflow concentration of soluble nutrients.

OTHER ISSUES TO KEEP IN MIND

Changes in certain physical factors including: (1) riparian vegetation; (2) total suspended solids (TSS);

(3) reduced flow following scouring-flood conditions; (4) greatly reduced summer flow due to prolonged

drought (somewhat common); or (5) reduced grazing may cause nuisance algal growths in stream

systems. Identifying the controlling physical constraint(s), should be rather straightforward. If the stream

is shaded, available light at the streambed should be measured to determine the extent to which

photosynthesis is inhibited (Jasper and Bothwell 1986; Boston and Hill 1991). Shading can substantially

reduce production (Welch et al. 1992), even though photosynthesis of periphyton is usually saturated at

relatively low intensities (<25% full sunlight; Boston and Hill 1991). Turbidity can inhibit periphyton at

relatively low levels (>10 NTU) (Quinn et al. 1992).

Biggs (1996) argued that flood disturbance is “perhaps the fundamental factor” determining the physical

suitability for algal accrual in unshaded streams. Floods act as a “reset” mechanism, initiating a new

cycle of accrual, succession, and loss due to grazing. Post-flood (scour) accrual rates are related to

enrichment level (Lohman et al. 1992). The role of scouring high flow should be readily discernible from

flow records and the seasonal pattern of periphyton accrual (Biggs 1996). 

Flow can also regulate biomass. For example, Cladophora was observed to reach high biomass followed

by senescence and detachment from substrata in enriched, unregulated northern California rivers, which

experienced winter flooding and scour (Power 1992). In regulated rivers, where the flood, scour, and regrowth phenomenon did not occur, low biomass levels of Cladophora were maintained through grazing.

6.3 STATISTICAL ANALYSES

Statistical analyses are used to identify variability in data and to elucidate relationships among sampling

parameters. Several statistical approaches for analyzing data are mentioned here. We advocate simple

descriptive statistics for initial data analyses, i.e., calculating the mean, median, mode, ranges and

standard deviation for each parameter in the system of interest. The National Nutrients Database

discussed in Chapter 5 will calculate simple descriptive statistics for queried data. Creating a histogram

or frequency distribution of the data for the class of streams of concern can identify the nutrient condition

continuum for that class of streams. Specific recommendations for setting criteria using frequency

distributions are discussed in Chapter 7, although the basis for the analysis is discussed here. Methods of

statistical analyses are included in Appendix C to provide relevant references for the investigator if

additional analyses are needed to understand and interpret data for criteria derivation. 
July 2000 Chapter 6. Analyze Data

PAGE 90

FREQUENCY DISTRIBUTION

Frequency distributions can be used to aid in the setting of criteria. Frequency distributions do not require

prior knowledge of individual stream condition prior to setting criteria. Criteria are based on and, in a

sense, developed relative to the population of stream systems in the Region, State, or Tribe.

Data plotted on a scale of mean nutrient concentration versus frequency of occurrence in a specific stream

class produces a frequency distribution of mean nutrient concentration. Plots of frequency distributions of

mean TP, mean TN, mean chl a, and turbidity for the index period (discussed in Chapter 4) should be

examined to determine the normalcy of the data in the distribution and to locate patterns for the class of

streams being investigated. A sample size of thirty streams within a stream class is recommended for 

developing nutrient criteria. Smaller sample sizes will require more reference streams, more complete

knowledge of the stream systems being investigated, more in-depth statistical analyses, and/or modeling

to complete criteria derivation. Sample sizes smaller than thirty may be highly affected by extreme values

in the dataset. Data that are not normally distributed are often transformed into a distribution more

approximating the normal distribution by taking the logarithm of each value. Analysis of outliers may

assist in explaining variability in small data sets. Additional analysis can be conducted to identify the

statistical significance of population differences.

CORRELATION AND REGRESSION ANALYSES

The relationship between two variables may be of use in analyzing data for criteria derivation. 

Correlation and regression analyses allow the relationship to be defined in statistical terms. A correlation

coefficient, usually identified as r, can be calculated to quantitatively express the relationship between

two variables. The appropriate correlation coefficient is dependent on the scale of measurement in which

each variable is expressed (whether the distribution of data is continuous or discrete) and, whether there is

a linear or non-linear relationship. Results of correlation analyses may be represented by indicating the

correlation coefficient, and represented graphically as a scatter diagram which plots all of the collected

data, not just a measure of central tendency. The statistical significance of a calculated correlation

coefficient can be determined with the t test. The t test is used to determine if there is a true relationship

between two variables. Therefore, the null hypothesis states that there is no correlation between the data

variables measured within the population. A critical value is chosen as a criterion for determining

whether to reject the null hypothesis. If the null hypothesis is rejected, the alternate hypothesis states that

the correlation at the calculated r value between the two variables is significant.

Regression analyses provides a means of defining a mathematical relationship between two variables that

permits prediction of one variable if the value of the other variable is known. In contrast to correlation

analyses, there should be a true independent variable (a variable under the control of the experimenter) in

regression analyses. Regression analyses establishes a relationship between two variables that allows

prediction of the dependent variable (predicted variable) for a given value of an independent variable

(predictor variable). However, scientists (other than statisticians) apply regression analyses to field data

when a relationship is known to exist, even when there is no true independent variable (e.g., cell counts of

algae and chlorophyll concentration; nutrient concentrations and chlorophyll concentration) (Ott 1988,

1995; Campbell 1989; Atlas and Bartha 1993).
July 2000 Chapter 6. Analyze Data

PAGE 91

TESTS OF SIGNIFICANCE

Various statistical tests are used to assess the hypotheses being tested. Statistical tests of significance

differ in their applicability to the dataset of interest, and the power of the test (the ability of the test to

detect a false null hypothesis). A parametric test of significance assumes a normal distibution of the

population. Non-parametric analyses are valid for any type of distribution (normal, log-normal, etc.) and

can be used if the data distribution is not normal or unknown. A parametric test has more power than a

non-parametric test when its assumptions are satisfied. Two types of errors can be made when testing

hypotheses: Type I–where a correct null hypothesis is mistakenly rejected, and Type II–when there is a

failure to reject a false null hypothesis. The parametric test is less likely to make a Type II error, when

the assumptions are met, than a non-parametric test. Therefore, if given a choice, the parametric test

should be used rather than the non-parametric test when the assumptions of the parametric test are

fulfilled. Less powerful, non-parametric tests of significance must be used in cases where the data do not

fit the assumption of a normal distribution (Ott 1988; Campbell 1989; Atlas and Bartha 1993). Parametric

tests include: the student t test, analysis of variance, multivariate analysis of variance, and multiple range

tests. Non-parametric tests include: chi square, Mann Whitney U test; and the Kruskal - Wallis test (Ott

1988; Campbell 1989; Atlas and Bartha 1993) Detailed descriptions of these and other relevant statistical

tests can be found in Appendix C.

6.4 USING MODELS AS MANAGEMENT TOOLS

Computer simulation modeling and probability testing can be used to predict responses to candidate

criteria (i.e., numeric nutrient concentrations). Models that have been calibrated and verified can be used

to extrapolate to a projected nutrient condition where existing data are either insufficient or unavailable. 

Data from the same system that is far removed from the present can be used if parameters can be adjusted

to the present conditions. The model output can be compared to data from a similar stream system of the

same class and in the same ecoregion for validation. Data from a similar system may also be used to

extrapolate the nutrient condition when data for the system of interest are unavailable. In both cases, data

are complemented by a set of clearly stated assumptions developed from data representing one point in

time to estimate conditions in the future. In some instances, surrogate information such as turbidity and

chl a concentration can be used to estimate nutrient concentrations.

Site-specific simulation models can also be developed for a system of interest, although this is frequently

a time-consuming, expensive process. Site-specific computer simulation models should be solicited from

the regional academic community, because they are more accurate for predicting specific waterbody

concentrations and loadings. This section will not discuss site-specific model development, although

several ecological and water quality modeling texts and articles can assist the investigator in developing

such a model (see Fry [1993] and McIntire et al. [1996]). Appendix C provides information on several

relevant stream water quality models.
July 2000 Chapter 6. Analyze Data

PAGE 92
PAGE 93

7.1 INTRODUCTION

This chapter addresses the details of developing scientifically defensible criteria for nutrients and algae. 

Three approaches are presented that water quality managers can use to derive numeric criteria for

streams in their State/Tribal ecoregions. The approaches that are presented include: (1) the use of

reference streams, (2) applying predictive relationships to select nutrient concentrations that will result in

appropriate levels of algal biomass, and (3) developing criteria from thresholds established in the

literature. Considerations are also presented for deriving criteria based on the potential for effects to

downstream receiving waters (i.e., the lake, reservoir, or estuary to which the stream drains). The

chapter concludes with the process for evaluating proposed criteria including the role of the Regional

Technical Assistance Group (RTAG) in reviewing criteria, guidance for interpreting and applying

criteria, considerations for sampling for comparison to criteria, potential revision of criteria, and final

implementation of criteria into water quality standards.

The most rational approach for deriving criteria is to determine nutrient values in the absence of nonnutrient related factors that influence growth of algal biomass (e.g., light availability, flow). Then,

refinements and exceptions to the criteria can be made based on the extent to which non-nutrient related

factors are present for specific streams in an ecoregion or subecoregion. Thus, for both periphyton- and

plankton-dominated systems, criteria should be set with the goal of reaching an acceptable algal biomass

in streams with little or no light limitation, during periods of stable, post-flood/runoff, and moderate

numbers of grazing invertebrates. For periphyton-dominated streams, substrata for attachment is

assumed to be adequate and stable.

Expert evaluations are important throughout the criteria development process. The data upon which

criteria are based and the analyses performed to arrive at criteria must be assessed for veracity and

applicability. The EPA RTAGs are responsible for these assessments. The RTAG is composed of State,

Tribal, and Regional specialists that will help the Agency and States/Tribes establish nutrient criteria for

adoption into State/Tribal water quality standards. The RTAG is tasked with conducting an objective
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 94

and exhaustive evaluation of regional nutrient information to establish protective nutrient criteria for the

ecoregional waterbodies located in their EPA Region.

7.2 METHODS FOR ESTABLISHING NUTRIENT AND ALGAL CRITERIA

The following discussions focus on three methods that can be used in developing nutrient and algal

criteria ranges. The first method requires identification of reference reaches for each established stream

class based on either best professional judgement (BPJ) or percentile selections of data plotted as

frequency distributions. The second method advocates refinement of trophic classification systems, use

of models, and/or examination of system biological attributes to assess the relationships among nutrient

and algal variables. The two methods described above should be based on data for the selected index

period (see Chapter 4). Finally, the third method provides several published nutrient/algal thresholds

that may be used (or modified for use) as criteria. A weight of evidence approach that combines one or

more of the three approaches described below will produce criteria of greater scientific validity. This

section also discusses how to develop criteria for streams that feed into standing receiving waters. 

USING REFERENCE REACHES TO ESTABLISH CRITERIA

One approach that may be used in developing criteria is the reference reach approach. Reference reaches

are relatively undisturbed stream segments that can serve as examples of the natural biological integrity

of a region. There are three ways of using reference reaches to establish criteria. 

1. Characterize reference reaches for each stream class within a region using best professional

judgement and use these reference conditions to develop criteria.

2. Identify the 75th percentile of the frequency distribution of reference streams for a class of streams

and use this percentile to develop the criteria (see Figure 8 and the Tennessee case study, 

Appendix A).

3. Calculate the 5th to 25th percentile of the frequency distribution of the general population of a class

of streams and use the selected percentile to develop the criteria (Figure 8). 

Identification of reference streams allows the investigator to arrange the streams within a class in order

of nutrient condition (i.e., trophic state) from reference, to at risk, to impaired. Defining the nutrient

condition of streams within a stream class allows the manager to identify protective criteria and

determine priorities for management action. Criteria developed using reference reach approaches may

require comparisons to similar systems in States or Tribes that share the ecoregion so that criteria can be

validated, particularly when minimally-disturbed systems are rare. 

Best professional judgement-based reference reaches may be identified for each class of streams within a

State or Tribal ecoregion and then characterized with respect to algal biomass levels, algal community

composition, and associated environmental conditions (including factors that affect algal levels such as

nutrients, light, and substrate). The streams classified as reference quality by best professional

judgement may be verified by comparing the data from the reference systems to general population data

for each stream class. Reference systems should be minimally disturbed and should have primary

parameter (i.e., TN, TP, chl a, and turbidity) values that reflect this condition. Factors that are affected
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 95

by algae, such as DO and pH, should also be characterized. At least three minimally impaired reference

systems should be identified for each stream class (see Chapter 2). Highest priority should be given to

identifying reference streams for stream types considered to be at the greatest risk from impact by

nutrients and algae, such as those with open canopy cover, good substrata, etc. [Conditions at the

reference reach (e.g., algal biomass, nutrient concentrations) can be used in the development of criteria

that are protective of high quality, beneficial uses for similar streams in the ecoregion.]

Alternatively, a reference condition for a stream class may be selected using either of two frequency

distribution approaches. In both of the following approaches, an optimal reference condition value is

selected from the distribution of an available set of stream data for a given stream class.

In the first frequency distribution approach, a percentile is selected (EPA generally recommends the 75th

percentile) from the distribution of primary variables of known reference systems (i.e., highest quality or

least impacted streams for that stream class within a region). As discussed in Chapter 3, primary

variables are TP, TN, chl a, and turbidity or TSS. It is reasonable to select a higher percentile (i.e., 75th

percentile) as the reference condition, because reference streams are already acknowledged to be in an

approximately ideal state for a particular class of streams (Figure 8).

The second frequency distribution approach involves selecting a percentile of (1) all streams in the class

(reference and non-reference) or (2) a random sample distribution of all streams within a particular class. 

Due to the random selection process, an upper percentile should be selected because the sample

distribution is expected to contain some degraded systems. This option is most useful in regions where

the number of legitimate “natural” reference water bodies is usually very small, such as highly developed

land use areas (e.g., the agricultural lands of the Midwest and the urbanized east or west coasts). The

EPA recommendation in this case is usually the 5th to 25th percentile depending upon the number of

“natural” reference streams available. If almost all reference streams are impaired to some extent, then

the 5th percentile is recommended.

Both the 75th percentile for reference streams and the 5th to 25th percentile from a representative sample

distribution are only recommendations. The actual distribution of the observations should be the major

determinant of the threshold point chosen. Figure 8 shows both options and illustrates the presumption

that these two alternative methods should approach a common reference condition along a continuum of

data points. In this illustration, the 75th percentile of the reference stream data distribution produces a

TP reference condition of 20 μg/L. The 25th percentile of the random sample distribution produces a

value of 25 μg/L. Because there is little distinction in this case, the Agency may select either 20 μg/L, 25

μg/L, or the intermediate 23 μg/L value as illustrated in Figure 8.

Each State or Tribe should similarly calculate its reference condition initially using both approaches to

determine which method is most protective. The more conservative approach is recommended for

subsequent reference condition calculations. A State or Tribe may choose to draw one single line

vertically through the data distribution to set their criterion (the equivalent of the line drawn at the

23μg/L TP concentration shown in Figure 8). The obvious difficulty is choosing where the line is drawn. 

If drawn to the left of the central tendency point, most streams are in unacceptable condition and

significant restoration management should occur. If the line is drawn to the right of the central tendency

point, then most streams would be in acceptable condition and far less effort would be needed for 
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 96

Figure 8. Selecting reference values for total phosphorus concentration (g/L) using percentiles from

reference streams and total stream populations.
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 97

restoration. The establishment of a reference condition helps to set the position of the line as objectively

as possible.

It is important to understand that any line drawn through the data has certain ramifications; streams in

unacceptable condition (on the right) should be dealt with through restoration. The streams to the left of

the line are in acceptable condition, and should not be allowed to increase their nutrient concentrations. 

These streams should be protected according to the State’s or Tribe’s approved antidegradation policy,

and through continued monitoring to assure that no future degradation occurs. 

If a State or Tribe desires greater flexibility in setting their criteria, the frequency distribution can be

divided into more than two segments (Figure 9). Using this approach, a criterion range is created and a

greater number of stream systems fall within the criterion range. This approach divides systems into

those that are of reference quality, currently in acceptable condition, or impaired. In this case, emphasis

may be shifted from managing stream systems based on a central tendency (as shown above when a

single line is drawn through the frequency distribution) to managing systems based on the level of

impairment. This approach will also aid in prioritizing systems for protection and restoration. Stream

data plotted to the right represent an increasingly degraded condition. Use of this approach requires that

subsequent management efforts focus on improving stream conditions so that, over time, stream data

plots shift to the left of their initial position. 

State or Tribal water quality managers may also consider analyzing stream data based on designated use

classifications. Using this approach, frequency distributions for specific designated uses could be

examined and criteria proposed based on maintenance of high quality systems that are representative of

each designated use.

In summary, frequency distributions can be used to aid in setting criteria. The number of divisions used

has significant implications with respect to system management. A single criterion forces the manager to

make decisions about the number of streams that will be in unacceptable condition, with considerable

ramifications from that decision. If the distribution is divided into three segments, the majority of

streams will be in acceptable condition (assuming that these streams are meeting their specified

designated uses and do not contribute to downstream degradation of water quality), which will minimize

management requirements. The method that is used may depend on the goals of the individual State or

Tribe; some may wish to set criteria that encourage all State/Tribal stream systems to be preserved or

restored to reference conditions. Other managers may consider additional options, such as developing

criteria specific to protect the designated uses established for local streams.

USING PREDICTIVE RELATIONSHIPS TO ESTABLISH CRITERIA

The following section provides several options that can be used to evaluate nutrient and algal

relationships in stream systems. These options include use of trophic state classifications, models, and

biocriteria. 

Trophic State Classification

One challenge associated with setting criteria is defining the relative trophic state of a stream. It is

difficult to determine whether a stream is excessively eutrophic if its trophic state is not known relative 
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 98

Figure 9. Frequency distribution divided into three segments that represent (from left to right) 

high-quality reference streams, acceptable quality streams, and impaired streams.
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 99

to other streams. There is no generally accepted system for classifying the trophic states of streams

(Dodds et al. 1998). The only proposed system divides data plotted as cumulative frequency diagrams

into oligotrophic (lower third), mesotrophic (middle third), and eutrophic (upper third) categories (see

Chapter 2) (Dodds et al. 1998). This approach is similar to the reference reach method described in the

previous section. More data are necessary to determine the applicability of such a classification scheme

to streams from different ecoregions.

Models

A few models establish correlations between TN/TP and benthic algal biomass in streams (e.g., Lohman

et al. 1992; Dodds et al. 1997; Bourassa and Cattaneo 1998; Chételat et al. 1999; Biggs 2000). Such

models estimate algal biomass as a function of water column nutrients (as has often been done for lakes

and reservoirs).

A regression model linking TP to river phytoplankton has been published (Van Nieuwenhuyse and Jones

1996). This model can be used to set TP criteria. The TP levels can in turn be used to calculate

corresponding TN concentrations using the Redfield ratio (Harris 1986). This model captures additional

variance when watershed area is considered (as discussed in Chapter 6).

Finally, it is necessary to relate instream TN and TP concentrations to nonpoint and point sources of

nutrients. Models allowing prediction of nutrient loading in streams are needed. A method for

determining instream TN and TP concentrations based on loading from point sources has been developed

for use in the Clark Fork River (Dodds et al. 1997). Simple correlation techniques using data available

from various regions may yield a nutrient and chlorophyll relationship that can be used to predict what

management strategies are necessary to bring nutrients from point sources, and consequently algal

biomass, to target levels.

Biocriteria

Biocriteria involve the use of biological parameters to establish nutrient impairment in streams. There

are two ways to use biocriteria to establish water quality criteria. The first approach involves the

protection and restoration of ecosystem services, which is almost exclusively related to biological

features and functions in aquatic ecosystems. Although it is recognized that chemical and physical

factors play a critical role in the algal-nutrient relationship, it is felt that the effect of nutrients on algae

and other components of aquatic ecosystems is critical. This is why ecoregional and waterbody-specific

nutrient criteria are recommended and chl a and Secchi depth/turbidity, arguably biocriteria, are

required. The second approach is based on the concept that attributes of biological assemblages vary less

in space and time than most physical and chemical characteristics. Thus, fewer mistakes in assessment

may occur if biocriteria are employed in addition to physical and chemical criteria.

Multimetric indices are a special form of biocriteria in which many metrics are used to summarize and

communicate in one number the state of a complex ecological system. Multimetric indices for

macroinvertebrates and fish are used successfully as biocriteria in many States. A multimetric index of

trophic status could be developed to complement N, P, and chl a criteria (see Section 6.2, Developing

Multimetric Indices to Complement Nutrient Criteria).

The same approaches used to establish nutrient and algal criteria could be employed to establish criteria

for other biological attributes, such as a Diatom Index of Trophic State (DITS). Frequency distributions
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 100

of reference conditions or a random sample of streams would provide a target for management and

restoration efforts. Alternatively, dose-response relations (predictive models) between biocriteria and

nutrients could be used to set nutrient and biocriteria, based on a desired level of biotic integrity or other

valued ecosystem component.

A fourth approach is also possible when characterizing the responses of many biological attributes to

nutrients. Some of these factors change linearly with increasing nutrient concentrations, for a number of

reasons, and some factors change non-linearly. Non-linear changes in metrics indicate thresholds along

environmental gradients where small changes in environmental conditions cause relatively great changes

in a biological attribute. These thresholds are valuable for setting nutrient criteria, but changes in these

metrics are not necessarily the best indicators of biotic integrity. They can for example, remain

relatively constant as human disturbance increases until a stress threshold is reached. Alternatively,

during restoration, they may not respond to remediation until a lower threshold is reached. Thus, metrics

or indices that change linearly (typically higher-level community attributes such as diversity or a

multimetric index) provide better variables for establishing biocriteria because they respond to

environmental change along the entire gradient of human disturbance. However, parameters changing

non-linearly along environmental gradients are valuable for determining where along the environmental

gradient the physical and chemical criteria should be set and, correspondingly, where to establish other

biocriteria.

USING PUBLISHED NUTRIENT THRESHOLDS OR RECOMMENDED ALGAL LIMITS

In addition to using the ‘reference reach’ concept or applying predictive relationships to establish criteria

for trophic state variables, other methods to consider include using thresholds and criteria already

recommended in the literature. These approaches might be used as limits if identifying reference reaches

proves difficult or as temporary measures until reference reaches can be adequately described. The

following text describes potential criteria for several nutrient-related variables. Because most of the

following threshold concentrations were derived primarily for northern to mid-temperate cobble-bottom

streams, caution should be exercised when applying them to streams found in other geographic areas

such as southern temperate and subtropical regions. The nutrient/algal relationships described below

may not be valid for sandy streams of the southeast and southwest and should be tested on intermittent

and effluent-dominated systems. Literature values may be used as criteria if a strong rationale is

presented that demonstrates the suitability of the threshold value to the stream of interest (i.e., the system

of interest should share characteristics with the systems used to derive the threshold, published values).

Nutrients

Criteria for nutrients in streams have been set or suggested by various agencies and investigators (Table

4). However, in contrast to lake management schemes, there is much less agreement on whether to use

total nutrient concentrations, soluble nutrient concentrations, or nutrient concentrations that might

produce a given biomass level or an undesirable effect in gravel-bed streams. Although much of the total

nutrient concentrations in the water column of streams is not immediately available (due to a high

fraction of detritus, as discussed previously), total concentrations probably have more general

applicability than soluble fractions. While soluble fractions are more available, they also may be held at

low levels during high-biomass periods due to uptake (Dodds et al. 1997). Nevertheless, some 

investigators have had considerable success relating soluble nutrients to algal biomass if annual mean or

seasonal values are used for nutrient concentrations. Using the Bow River as an example, mean TDP

during summer was more useful than TP (Table 4).
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 101

Table 4. Nutrient (μg/L) and algal biomass criteria limits recommended to prevent nuisance conditions

and water quality degradation in streams based either on nutrient-chlorophyll a relationships or

preventing risks to stream impairment as indicated.

PERIPHYTON Maximum in mg/m2

TN TP DIN SRP Chlorophyll a Impairment Risk Source

100-200 nuisance growth Welch et al.

1988, 1989

275-

650

38-90 100-200 nuisance growth Dodds et al.

1997 

 1500 75 200 eutrophy Dodds et al.

1998

 300 20 150 nuisance growth Clark Fork

River Tri-State

Council, MT

 20 Cladophora

nuisance growth

Chetelat et al.

1999

 10-20 Cladophora

nuisance growth

Stevenson

unpubl. data

 430 60 eutrophy UK Environ.

Agency 1988

 1001 101 200 nuisance growth Biggs 2000

 25 3 100 reduced invertebrate

diversity

Nordin 1985

15 100 nuisance growth Quinn 1991

1000 102 ~100 eutrophy Sosiak pers.

comm.

PLANKTON Mean in μg/L

TN TP DIN SRP Chlorophyll a Impairment Risk Source

 3003 42 8 eutrophy Van

Nieuwenhuyse

and Jones 1996

70 15 chlorophyll action

level

OAR 2000

 2503 35 8 eutrophy OECD 1992

(for lakes) 1

30-day biomass accrual time

2

Total Dissolved P

3

Based on Redfield ratio of 7.2N:1P (Smith et al. 1997)
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 102

Notwithstanding the sparse set of cases, there is an indication of some consistency for total and soluble P

criteria (Table 4). In two separate data sets, the tendency for Cladophora to begin dominating the

periphyton was observed at TP concentrations of 10-20 μg/L (Chetelat et al. 1999; Stevenson pers.

comm.). This general range was also selected by the Clark Fork Tri-State Council to limit maximum

biomass to levels below 150 mg chl a/m2

. Setting a criterion equivalent to ‘no filamentous green algae’,

even if chl a levels exceed 150 mg/m2

, would protect aesthetic use and still may not limit fisheries

production.

Using a criterion for periphytic or planktonic biomass to initially judge if nutrient concentrations are

excessive, may have a practical management and enforcement appeal. Advantages are several: (1) there

is general agreement among some investigators and agencies on a biomass level that minimizes risk to

recreational and aquatic life uses (see Table 4), (2) problems of algal control that result in poor doseresponse relationships of nutrients versus biomass (due to shading by riparian canopies or suspended

sediment and grazing) are averted, and (3) TMDLs and resultant controls would be required only for

situations in which biomass criteria were exceeded. However, criteria for nutrients (specifically TN and

TP) will ultimately be required for all stream classes within an ecoregion.

Algal Biomass

Criteria for levels of periphyton algal biomass that present a nuisance condition in streams and impact

aesthetic use have been recommended by several investigators. There is surprising consistency in these

values, with a maximum of about 150 mg/m2

 chl a being a generally agreed upon criterion (Table 4). As

objective support for that criterion, percent coverage by filamentous forms was less than 20 percent, but

increased with increased biomass and noticeably affected aesthetic quality (Welch et al. 1988). At this

level, there were no apparent effects on DO, pH, or benthic invertebrates, which, as described earlier,

occur at higher biomass levels. 

Furthermore, a literature review of 19 cases indicated biomass levels greater than 150 mg/m2

 tended to

occur with enrichment and when filamentous forms were more prevalent (Horner et al. 1983). As noted

earlier, Lohman et al. (1992) observed that biomass rapidly recovered following flood-scour events in 12

Ozark streams when biomass exceeded the 150 mg/m2

 level at moderately to highly enriched sites. Predisturbance biomass did not recover as rapidly when initial levels did not exceed approximately 75

mg/m2 at unenriched sites.

A provisional guideline of a maximum 100 mg/m2

 chl a and 40 percent coverage of filamentous forms

was proposed for New Zealand streams to “protect contact recreation”. There was insufficient evidence

for protection of other uses that require specific DO and pH thresholds, which in turn vary due to

atmospheric exchange (area:volume ratio) and buffering capacity (Quinn 1991).

While the 150 mg/m2

 level cannot be supported as an absolute threshold above which adverse effects on

water quality and benthic habitat readily occur, it nonetheless is a level below which an aesthetic quality

use will probably not be appreciably degraded by filamentous mats or any other of the adverse effects

attributed to dense mats of filamentous algae (e.g., objectionable taste and odors in water supplies and

fish flesh, impediment of water movement, clogging of water intakes, restriction of intra-gravel water

flow and DO replenishment, DO/pH flux in the water column, or degradation of benthic habitat) (Welch

1992). Avoidance of these problems in many stream systems may be achieved with a maximum 150

mg/m2

 chl a criterion. As an example, control strategies were developed for the Clark Fork River,
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 103

Montana, using a 100-150 mg/m2 maximum as a criterion (see Appendix A case studies) (Watson and

Gestring 1996; Dodds et al. 1997).

CONSIDERATIONS FOR DOWNSTREAM RECEIVING WATERS

More stringent nutrient criteria may be required for streams that feed into lentic or standing waters. For

example, it is proposed that 35 g/L TP concentration and a mean concentration of 8 g/L chl a

constitute the dividing line between eutrophic and mesotrophic lakes (OECD 1982). In contrast, data

from Dodds et al. (1997) suggest that seasonal mean chlorophyll a values within stream systems of 100

mg/m2

 are likely at concentrations of 221 μg/L TP. Thus, unacceptable levels of chlorophyll may occur

in lakes at much lower nutrient concentrations compared to streams (Dodds and Welch 2000).

7.3 EVALUATION OF PROPOSED CRITERIA

During criteria derivation, the RTAG will provide expert assessment of any proposed criteria or criteria

ranges and their applicability to all streams within the class of interest. Criteria will need to be verified

in many cases by comparing criteria values for a stream class within an ecoregion across State and Tribal

boundaries. In addition, prior to recommending any proposed criterion, the RTAG must consider the

potential for the proposed criterion to cause degradation of downstream receiving waters. In developing

criteria, States/Tribes must consider the designated uses and standards of downstream waters and ensure

that their water quality standards provide for the attainment and maintenance of water quality standards

in downstream waters. Criteria recommended by the RTAG can be adopted by the State or Tribe as

approved by EPA if there is documented evidence that no adverse effects will result downstream. 

However, if downstream waters are not adequately protected at the concentration level associated with

the proposed criteria, then the criteria should be adjusted accordingly. Load estimating models, such as

those recommended by EPA (USEPA 1999), can assist in this determination (see Section 4.2, Nutrient

Load Attenuation). Water quality managers responsible for downstream receiving waters should also be

consulted. 

GUIDANCE FOR INTERPRETING AND APPLYING CRITERIA

After evaluating criteria proposed for each stream class, determining streams condition in comparison

with nutrient criteria can be made by following the steps: 

1. Calculate duration and frequency of criteria violations as well as associated consequences. This

can be done using modeling techniques or correlational analysis of existing data.

2. Develop and test hypothesis to determine agreement with criteria. Analyze for alpha and beta

(Type I and II) errors (see Appendix C).

3. Reaffirm appropriateness of criteria for protecting designated uses and meeting water quality

standards. 

The goal is to identify protective criteria and standards. Criteria should be based on ecologically

significant changes as well as statistically significant differences in compiled data. Although criteria are

developed exclusively on scientifically defensible methods, assignation of designated uses requires
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 104

consideration of social, political, and economic factors. Thus, it is imperative that some thought be

given during the criteria development process of how realistically the criteria can be implemented into

standards that are accepted by the local public.

SAMPLING FOR COMPARISON TO CRITERIA

Once criteria have been selected for each indicator variable, a procedural rule to assess stream

concurrence with criteria should be established. The four primary criteria variables include two causal

variables (TN and TP) and two response variables (chl a and Secchi depth or a similar indicator of

turbidity). Failure to meet either of the causal criteria should be sufficient to require remediation and

typically the biological response, as measured by chl a and turbidity, will follow the nutrient trend.

Should the causal criteria be met, but some combination of response criteria are not met, then a

decisionmaking protocol should be in place to resolve the issue of whether the stream in question meets

the proposed nutrient criteria.

Sampling to evaluate agreement with the standards implemented from nutrient and algal criteria will

have to be carefully defined to ensure that State or Tribal sampling is compatible with the procedures

used to establish the criteria. If State or Tribal observations are averaged over the year, balanced

sampling is essential and the average should not exceed the criterion. In addition, no more than ten

percent of the observations contributing to that average value should exceed the criterion.

A load estimating model (e.g., BASINS [see Appendix C]) may be applied to a watershed to backcalculate the criteria concentration for an individual stream from its load allocation. This approach to

criteria determination may also be applied on a seasonal basis and should help States/Tribes relate their

stream reach criteria with their lake or estuarine criteria. It may also be particularly important for criteria

developed for streams and rivers that cross State/Tribal boundaries.

Algal Sampling for Comparison to Criteria

Once criteria for algal biomass have been established, certain sampling considerations must be addressed

to obtain meaningful samples. This section discusses some of the more relevant considerations, using

several questions as the basis for determining stream condition with respect to nutrients and algae.

1. How can algal criteria be applied to samples that come from only certain depths of the stream?

Aesthetic criteria should be applied to the wadeable portion of large rivers, as has been done in British

Columbia (Nordin 1985; see Table 4). The level necessary to protect aquatic life is likely to be systemspecific and is best evaluated by determining how algal biomass affects DO, pH, and aquatic

communities. 

2. How large an area must exceed an algal criterion (e.g., 150 mg chl a/m2

) to be considered

unacceptable? The area must be large enough to interfere with aesthetics and recreation or to cause

undesirable water quality changes. Obviously, regional and site-specific testing of criteria will be

necessary. The related sampling question is: how large an area should be characterized when assessing

whether a reach exceeds a quantitative criterion? To ensure that a reasonably representative portion of a

reach is sampled, replicate samples should be distributed over a reach at least 100 m long. Before

selecting a point for sampling, a walk upstream and downstream a few hundred meters should be

conducted to ensure that the preferred sampling point is not atypical of the reach being characterized.
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 105

Low altitude aerial photos taken on a sunny day in mid-to-late growing season can be used to determine

the longitudinal extent of conditions similar to those at the sampling site. Floating the stream by boat

can serve a similar purpose.

3. For how long must algal biomass exceed criteria to be considered unacceptable?

Attached algal biomass does not change as rapidly as water column parameters. Hence, one sample a

month(from June to September) may be adequate to assess algal biomass, though weekly or bi-weekly

sampling is ideal. If only two samplings can be afforded, the likely period containing the highest biomass

levels should be bracketed. However, such a sampling scheme may be regarded as unacceptable if both

sample values exceed aesthetic criteria. If algal biomass is high enough to cause excessive DO and pH

fluctuations that violate water quality standards or that release toxins at unacceptable levels, then the

time frames for those water quality violations should be used to judge the acceptability of algal biomass

levels. As an example, some States or Tribes might regard the exceedance of algal biomass criteria once

in 10 years (i.e., only during the 10-year low-flow) as acceptable, but more frequent exceedances may be

deemed unacceptable.

4. How many replicate samples at a site are needed to obtain acceptable precision of data in order

to detect differences between sites and changes over time? This depends on the variability in algal

biomass in the particular system. The Kendall test with Sen slope estimate (Hirsch et al. 1982) allows the

determination of the number of replicate samples needed to detect a certain percent change in annual

means of a variable or a certain percent trend over a period such as 10 years (see Clark Fork River case

study, Appendix A).

CRITERIA MODIFICATIONS

There may be specific cases identified by States or Tribes that require modification of established

criteria, either due to unique stream system characteristics or specific designated uses approved for a

stream or stream reach. Two examples of acceptable criteria modifications are presented below. 

Site Specific Criteria

If a State/Tribe has additional information and data which indicate a different value or set of values is

more appropriate for specific stream systems than ecoregionally-derived criteria, a scientifically

defensible argument should be prepared that a “site specific” criteria modification is required. Once

approved by EPA, this value can be incorporated into State or Tribal water quality standards. If no

action is taken by the State or Tribe involved, EPA may propose to promulgate criteria based on the

regional values and best available supporting science at the time.

Designated Use Approaches

Once a regional criterion has been established, it is subject to periodic review and calibration. Any State

or Tribe in the region may elect to use the criterion as the basis for developing its own criteria to protect

designated uses for specific stream classes. This is entirely appropriate as long as the criteria are as

protective as the basic EPA criterion for that region. This ecoregional criterion represents EPA’s

“304(a)” recommendation for protection of an aquatic life use.

The Clean Water Act as amended (Pub. L. 92-500 (1972), 33 U.S.C. 1251, et seq.) requires all States to

establish designated uses for their waters (Section 303[c]). Designated uses are set by the State. EPA’s
July 2000 Chapter 7. Nutrient and Algal Criteria Development

PAGE 106

interpretation of the Clean Water Act requires that wherever attainable, standards should provide for the

protection and propagation of fish, shellfish, and wildlife and provide for recreation in and on the water

(Section 101[a]). Other uses identified in the Act include industrial, agricultural, and public water

supply. However, no waters may be designated for use as repositories for pollutants (see 40 CFR

131.10[a]). Each water body must have legally applicable criteria or measures of appropriate water

quality that protect and maintain the designated use of that water. It is therefore proper for States and

Tribes to set nutrient criteria appropriate to each of their designated uses in so far as they are as

protective as the regional nutrient criteria established for those classes of waters.

IMPLEMENTATION OF NUTRIENT CRITERIA INTO WATER QUALITY STANDARDS

Criteria, once developed and adopted into water quality standards by a State or Tribe, are submitted to

EPA for review and approval (see 40 CFR 131). EPA reviews the criteria (40 CFR 131.5) for

consistency with the requirements of the Clean Water Act and 40 CFR 131.6, which requires that water

quality criteria be sufficient to protect the designated use (40 CFR 131.6[c] and 40 CFR 131.11). The

procedures for State/Tribal review and revision of water quality standards, EPA review and approval of

water quality standards, and EPA promulgation of water quality standards (upon disapproval of

State/Tribal water quality standards) are found at 40 CFR 131.20 -22 (see Figure 1, Chapter 1). The

Water Quality Standards Handbook (EPA 1994) provides guidance for the implementation of these

regulations.
PAGE 107

8.1 INTRODUCTION

This chapter provides information on regulatory and non-regulatory programs that may utilize or be

affected by nutrient criteria, as well as management solutions for problems associated with varying

streamflow conditions. This chapter is intended to inform resource managers and foster potential links

among regulatory and non-regulatory programs to best manage watersheds. Information about other

agency programs that may assist in implementing criteria and maintaining water quality is also included. 

The information provided by nutrient surveys of stream systems in a region will permit the resource

manager to rank stream systems by trophic state; i.e., the manager should be able to classify systems

according to the degree of nutrient enrichment. Stream systems can be selected for priority attention for

management action. Documented stream nutrient and algal conditions and an understanding of regional

public preferences regarding limits of productivity can be used to establish three categories of streams:

1. Systems with algal and/or nutrient problems. The most severely degraded waterbodies requiring

extensive, expensive restoration.

2. Systems with a strong potential for developing algal problems (factors other than nutrients are

unlikely to be limiting). The intermediate streams in need of remedial management to improve

conditions requiring various levels of expense and manpower depending on the characteristics and

problems identified in each case. 

3. Systems with a low potential for developing algal problems that do not contribute to degraded

nutrient conditions in downstream waterbodies. The systems in excellent condition requiring no

restoration and for which management is essentially the protection of this resource through careful

watershed land use planning and diligent observation of conditions. This is usually a relatively

low cost option allowing for the protection of many such waterbodies with little expenditure of

budget or personnel.
July 2000 Chapter 8. Management Programs

PAGE 108

Systems with high nutrient loading but low potential for developing algal problems due to other limiting

factors should be prioritized based on the potential for degradation of downstream receiving waters. The

management strategies required for nutrient reduction within streams and those for lakes and estuaries

are not different, so these processes should be linked when management plans are being formulated.

The next logical action is the design of management plans to enhance collective water body resources. 

The initial categorization helps set priorities for the best use of limited personnel and funds by selecting

some optimal combination of many low cost but effective projects combined with some important

restoration projects, and perhaps long range planning to begin to address major restoration of one or two

important stream systems on an incremental basis.

This chapter is separated into discussions of point source and nonpoint source programs. Each program

is discussed and a list of source information or contacts is provided. This chapter is intended to aid the

resource manager in identifying programs that may assist in implementation of nutrient criteria. These

programs include regulatory and non-regulatory programs that address both point and nonpoint sources

of nutrients. Consultation with these programs is recommended for watershed and development planning

activities. Linking with other programs may allow maximization of resources for addressing water

quality concerns.

8.2 MANAGING STREAMFLOW CONDITIONS

LOW FLOWS

Maintaining flow is often essential to habitat protection. In many regions of the United States, stream

segments periodically lose water due to irrigation, industrial and municipal withdrawals; and/or diversion

for hydroelectric power; evaporation; and groundwater infiltration. Additionally, during low-flow

conditions, impacts from point source discharges of chemical stressors are typically greatest, because

effluent constitutes a larger percentage of (or sometimes all) stream water at low flow, with increased

pollutant concentration. National Pollutant Discharge Elimination System (NPDES) permits based on

low flow conditions (e.g., 7Q10) often cannot antic-ipate various combinations of climatic conditions

and water demand that lead to exceedingly low flows.

Impacts attributable to low flows caused by human actions can be mitigated by several in-stream

restoration techniques, including: 

• Reducing channelization, 

• Restoring wetlands for conservation and storage purposes thereby restoring natural hydrologic

regimes, 

• Controlling evaporation through restoration of the riparian canopy, 

• Replacing exotic riparian plant species that have high evapotranspiration rates with native

species that have lower transpiration rates, 

• Constructing drop structures to create pools that provide protection for aquatic life during

low-flow periods,
July 2000 Chapter 8. Management Programs

PAGE 109

• Increasing channel depth and undercut banks to provide protective areas for fish and other

species during periods of low flow, and 

• Increasing groundwater recharge to streams through increased infiltration (e.g., reduced 

imperviousness in recharge areas).

Minimum flows can also be addressed by applying techniques in the surrounding watershed, such as

managing watershed land use to prevent excessive dewatering. Restoration practices to mitigate low

velocity/low-flow conditions often require close collaboration with other resource management agencies

(e.g., USDA Forest Service), zoning authorities (e.g., county governments), and agricultural extension

agencies. Several agricultural activities contribute to low velocity/low flow conditions. Agricultural

extension agencies have developed specific techniques to modify the practices that result in low-flow

impact to streams. For example, irrigation plans can be optimized to reduce the demand for water that is

diverted directly from the stream. Changing crop rotations and using less water-intensive crop

alternatives are other tools that have been used effectively to address low velocity/low-flow situations. 

Source: [http://www.epa.gov/owowwtr1/NPS/Ecology/chap3.html]

HIGH FLOWS

High-energy flows can erode substrate and bank materials, destabilize the physical structure of aquatic

habitats, eradicate resident aquatic organisms, and destroy eggs located in the benthic environment. 

Seasonal cycles of high-energy flow events (e.g., spring floods) are typical in most aquatic systems. 

Habitat alteration and degradation, however, may exacerbate impacts of high-energy flows and

contribute to impairment of designated uses. For instance, in a channelized stream with minimal riparian

vegetation, flow velocity and volume will likely be much greater than would be expected in a "natural

stream," thereby increasing its erosive potential.

Two aspects of flooding are considered here. It has recently been recognized that water retention

structures remove the natural flooding that is part of a normal stream ecosystem (the flood pulse

concept). Such floods are known to reduce levels of algae and macrophytes and may be beneficial to

stream communities otherwise. The floods appear destructive on the short term, but most stream

organisms are adapted to some level of flooding.

Alternatively, channel alteration and watershed modification can lead to abnormally high water

velocities through the stream channel and amplify the effects of floods. For example, channelization can

reduce the amount of refugia used by stream organisms to escape floods. Removal of riparian

vegetation, urbanization, and deforestation of watersheds can lead to much greater peak flows during

floods for a given amount of rain. Watershed disturbance can also lead to increases in sedimentation,

which will scour away excessive algal biomass and, if deposited, make it difficult for periphyton to

become established. However, such sediment will compromise the ecological integrity by harming fish

and invertebrates in the stream channels.

In-stream and riparian techniques that can mitigate high flow impacts include: 

• Restoring natural stream meander and channel complexity; 

• Increasing substrate roughness; 

• Promoting growth of riparian vegetation, which serves as a drag on flows; 
July 2000 Chapter 8. Management Programs

PAGE 110

• Modifying land use along buffers and other source areas; and 

• Creating plunge pools and flow baffles to decrease the high energy of discharged waters. 

These in-stream practices may need to be accompanied by techniques applied in the surrounding

watershed, such as upland revegetation or the establishment of nonpoint source best management

practices (BMPs).

Resource management agencies, for example, can encourage or allow beavers to colonize stream

segments; beaver dams create wetlands and retain water that supplements low flow during dry periods.

Restored wetlands can have the same effect as a beaver dam. In areas below dams where flow is very

stable and excessive growths of macrophytes and periphyton are common, water releases to mimic

natural floods may be considered. Local zoning authorities have also begun to encourage impervious

area reduction in watersheds through land-use ordinances. Increased infiltration and reduced peak flows

from rapid runoff contributes to a more sustained base flow to the stream from groundwater discharge. 

Source: [http://www.epa.gov/owowwtr1/watershed/wacademy/acad2000/river/]

8.3 MANAGING POINT SOURCE POLLUTION

The term "point source" means any discernible, confined, and discrete conveyance, including but not

limited to any pipe, ditch, channel, tunnel, conduit, well, discrete fissure, container, rolling stock,

concentrated animal feeding operation, or vessel or other floating craft, from which pollutants are or may

be discharged. This term does not include agricultural storm water discharges and return flows from

irrigated agriculture. This section describes some of the regulatory programs that permit point source

discharges into rivers and streams. The regulatory programs discussed here apply to federal

requirements of the Clean Water Act (Section 303). State, Tribal, and local governments frequently have

regulatory programs that operate on agency specific requirements. These agencies should be considered

in management planning activities.

WATER QUALITY STANDARDS

Anti-degradation

Water quality standards include an anti-degradation policy and methods through which the State or Tribe

implements the anti-degradation policy. Anti-degradation is a policy required in State water quality

standards to protect waters from degradation. At a minimum, States must maintain and protect the

quality of waters to support existing uses. Anti-degradation was originally based on the spirit, intent,

and goals of the Clean Water Act, especially the clause “...restore and maintain the chemical, physical,

and biological integrity of the Nation's waters" (USEPA 1994). The water quality standards regulation

sets out a three-tiered anti-degradation approach for the protection of water quality. 

Tier 1

Maintains and protects existing uses and the water quality necessary to protect these uses (40 CFR

131.12[a][1]). An existing use can be established by demonstrating that fishing, swimming, or other uses

have actually occurred since November 28, 1975, or that the water quality is suitable to allow such uses

to occur, whether or not such uses are designated uses for the water body in question. 
July 2000 Chapter 8. Management Programs

PAGE 111

Tier 2

Protects the water quality in waters whose quality is better than that necessary to protect

"fishable/swimmable" uses of the water body (40 CFR 131.12[a][2]). The water quality standards

regulation requires that certain procedures be followed and certain showings be made (an "antidegradation review") before lowering water quality in high quality waters. In no case may water quality

for a tier 2 water body be lowered to a level at which existing uses are impaired.

Tier 3

Preserves outstanding national resource waters (ONRWs), which are provided the highest level of

protection under the anti-degradation policy (40 CFR 131.12[a][3]). ONRWs generally include the

highest quality waters of the United States. However, the ONRW anti-degradation classification also

offers special protection for waters of "exceptional ecological significance," i.e., those water bodies

which are important, unique, or sensitive ecologically, but whose water quality, as measured by the

traditional parameters such as dissolved oxygen or pH, may not be particularly high. Waters of

exceptional ecological significance also include waters whose characteristics cannot adequately be

described by traditional parameters (such as wetlands and estuaries). 

Anti-degradation implementation procedures address the measures used by States and Tribes to ensure

that permits and control programs meet water quality standards and anti-degradation requirements. 

General Policies

The water quality standards regulation allows States and Tribes to include implementation in their

standards policies and provisions, such as mixing zones, variances, and low-flow exemptions. Such

policies are subject to EPA review and approval. These policies and provisions should be specified in

the State or Tribe's water quality standards document. The rationale and supporting documentation

should be submitted to EPA for review during the water quality standards review and approval process. 

Mixing Zones

States and Tribes may, at their discretion, allow mixing zones for dischargers. The water quality

standards should describe the methodology for determining the location, size, shape, outfall design, and

in-zone quality of mixing zones. Careful consideration must be given to the appropriateness of a mixing

zone where a substance discharged is bioaccumulative, persistent, carcinogenic, mutagenic, or

teratogenic.

Low-Flow Provisions

State and Tribal water quality standards should protect water quality for the designated and existing uses

in critical low-flow situations. States and Tribes may, however, designate a critical low-flow below

which numerical water quality criteria do not apply. When reviewing standards, States and Tribes should

review their low-flow provisions for conformance with EPA guidance. 

Water Quality Standards Variances

As an alternative to removing a designated use, a State or Tribe may wish to include a variance as part

of a water quality standard, rather than changing the entire standard, especially if the State or Tribe

believes that it can ultimately be attained. By maintaining the standard rather than changing it, the State

or Tribe will assure that further progress is made in improving water quality and attaining the standard. 

Variances are temporary, subject to review every three years, and may be extended upon expiration. If a
July 2000 Chapter 8. Management Programs

PAGE 112

variance specifies an interim criterion applicable for the duration of the variance for a particular

pollutant, a long-term underlying goal criterion is also specified that is adequate to protect the designated

use. EPA has approved variances in the past and will continue to do so if:

• The variance is included as part of the water quality standard; 

• The variance is subjected to the same public review as other changes in water quality

standards; 

• The variance is granted based on a demonstration that meeting the standard is not feasible due

to the presence of any of the same conditions as if a designated use were being removed (these

conditions are listed in section 131.10(g) of the water quality standards regulation); and 

• Existing uses will be fully protected.

For additional information, see http://www.epa.gov:80/ostwater/econ/chaptr5.pdf.

NPDES PERMITS

The Clean Water Act requires wastewater dischargers to have a permit establishing pollution limits, and

specifying monitoring and reporting requirements. More than 200,000 sources are regulated by the

NPDES permits nationwide. These permits regulate household and industrial wastes that are collected in

sewers and treated at municipal wastewater treatment plants. Permits also regulate industrial point

sources and concentrated animal feeding operations that discharge into other wastewater collection

systems or that have the potential to discharge directly into receiving waters. Permits regulate discharges

with the goals of 1) protecting public health and aquatic life, and 2) assuring that every facility treats

wastewater. Typical pollutants regulated by NPDES are “conventional pollutants” such as fecal

coliforms or oil and grease from the sanitary wastes of households, businesses, and industries and “toxic

pollutants” including pesticides, solvents, polychlorinated biphenyls (PCBs), dioxins, and heavy metals

that are particularly harmful to animal or plant life. “Non-conventional pollutants” are any additional

substances that are not conventional or toxic that may require regulation, including nutrients such as N

and P. [Source: http://www.epa.gov/owm/gen2.htm].

Discharge monitoring data for pollutants limited and/or monitored pursuant to NPDES permits issued by

States, Tribes, or EPA are required to be stored in the central EPA Permit Compliance System (PCS). 

The assessment of point source loadings is not a simple process of assessing PCS data, even though PCS

is an important data source. The PCS database does not provide complete information for important N

sources. Most PCS N data is generated by water quality-based permit limitations on ammonia, often

applied in discharges to smaller streams. Few data exist in PCS on other forms of N, or TN; and data for

TP is not frequently found in PCS. This situation exists largely because most permits do not include

limits and/or monitoring requirements for N or P. The lack of nutrient limits and/or monitoring

requirements in permits is due to a general lack of State water quality standards for these parameters. 

[Source: http://www.epa.gov/msbasin/protocol.html]

The NPDES Storm Water Permitting Program

Storm water runoff is one of the remaining causes of contaminated lakes, streams, rivers, and estuaries

throughout the country. Pollution in storm water runoff is responsible for closing beaches and shellfish

harvesting areas, contaminating fish, and reducing populations of water plants and other aquatic life. 

High flows of storm water runoff cause flooding, property damage, erosion and heavy siltation. The
July 2000 Chapter 8. Management Programs

PAGE 113

Clean Water Act requires EPA and States/Tribes to implement a national storm water control program to

correct these problems. In the first phase of the program, discharges of storm water from municipal

separate storm sewers serving populations of over 100,000 and from industrial facilities are illegal unless

controlled by an NPDES storm water permit. Phase II of the program required that EPA, in consultation

with the States, conduct a study identifying additional sources of storm water contamination and

establish procedures and methods to control these discharges.

Source: [http://www.epa.gov/owmitnet/pipes/wetlib/disc_pap.txt]

Construction Permits

The 1987 Congressional Amendments to the Clean Water Act required EPA to control pollution from

storm water discharges. Phase I storm water regulations were finalized by EPA in 1990, and NPDES

permit coverage was required for construction sites disturbing five or more acres beginning in 1992. 

General permits provide EPA with an effective mechanism to regulate these discharge from tens of

thousands of construction sites, thus protecting and improving surface water quality across the nation.

EPA Regions 1, 2, 3, 7, 8, and 9 have reissued the general permit which authorizes the discharge of storm

water associated with construction activity disturbing five or more acres (Phase I sources) and smaller

Phase II sources that are designated by the Agency on a case-by-case basis. This multi-regional permit is

know as the "Construction General Permit" (CGP). As used in the permit, the term "storm water

associated with construction activity" refers to category (x) of the definition of "discharge of storm water

associated with industrial activity" which includes construction sites and common plans of development

or sale that disturb five or more acres (See 40 CFR 122.26 [b][14]). This permit replaces the Baseline

Construction General Permit issued by EPA in September 1992. Issuance of the new CGP will not affect

areas where the State is the NPDES permitting authority.

Region 4 has issued a separate construction general permit for the State of Florida and Indian Country

lands in Florida, Mississippi, Alabama, and North Carolina. Region 6 is also issuing its own

construction general permit for the States of Texas and New Mexico; Indian Country lands in Texas,

New Mexico, Oklahoma and Louisiana; and construction activity at oil, gas, and pipeline facilities in

Oklahoma in the near future. [Source: http://www.epa.gov/owmitnet/cgp.htm]

COMBINED SEWER OVERFLOWS (CSOS)

Combined sewer overflows, or CSOs, are a significant water pollution and public health threat. EPA's 

1994 CSO Control Policy addresses CSOs in a flexible, cost-effective manner that provides for local

decision-making and negotiation to achieve compliance with the Clean Water Act. CSOs contain not

only storm water but also untreated human and industrial waste, toxic materials, and debris. This is a

major water pollution concern for cities with combined sewer systems. CSOs are among the major

sources responsible for beach closings, shellfishing restrictions, and other water body impairments. 

During dry weather, these "combined sewer systems" transport wastewater directly to sewage treatment

plants. In periods of rainfall or snowmelt, however, the wastewater volume in a combined sewer system

can exceed the capacity of the sewer system or treatment plant. For this reason, combined sewer systems

are designed to overflow occasionally and discharge excess wastewater directly to nearby streams, rivers,

lakes, or estuaries.
July 2000 Chapter 8. Management Programs

PAGE 114

EPA's CSO Control Policy published April 19, 1994, is a national framework for control of CSOs

through the NPDES permitting program. The Policy resulted from negotiations among municipal

organizations, environmental groups, and State agencies. It provides guidance to municipalities and State

and Federal permitting authorities on meeting pollution control goals of the Clean Water Act in a

flexible, cost-effective manner. Information on EPA’s CSO Control Policy can be found on the

following Website. Source: [http://www.epa.gov/OWM/cso.htm]

STORMWATER PLANNING

The Watershed Management Institute, Inc. recently published a new manual entitled Operation,

Maintenance, and Management of Stormwater Management Systems (1998). This manual presents a

comprehensive review of the technical, educational, and institutional elements needed to assure that

stormwater management systems are designed, built, maintained and operated properly during and after

their construction. The manual was developed in cooperation with the U.S. EPA Office of Water to

assist individuals responsible for designing, building, maintaining, or operating stormwater management

systems. It will also be helpful to individuals responsible for implementing urban stormwater

management programs.

The book includes fact sheets on 13 common stormwater treatment best management practices (BMPs).

These summarize operation, maintenance, and management needs and obligations, along with

construction recommendations. Other chapters review planning and design considerations,

programmatic and regulatory aspects, considerations for facility owners, construction inspection,

inspection and maintenance after construction, costs and financing, and disposal of stormwater

sediments. Forms for inspecting BMPs during construction and determining maintenance needs

afterwards are included in the book and in a separate supplement. 

Source: [http://www.epa.gov/owowwtr1/NPS/wmi/index.html]

Additional information: [http://www.epa.gov/owowwtr1/NPS/ordinance/osm6.htm] and

[http://www.epa.gov/owowwtr1/info/NewsNotes/issue05/nps05sto.html]

TOTAL MAXIMUM DAILY LOAD

States, territories, and authorized Tribes establish section 303(d) lists of impaired waters based on

information contained in their 305(b) reports as well as other relevant and available water quality data. 

The section 303(d) list is a prioritized list of waters not meeting water quality standards. The USEPA

has 30 days in which to approve the lists or add waters to the State's lists, if the Agency determines the

list is not complete. Once a waterbody is placed on the 303(d) list, a TMDL must be prepared for the

system.

A TMDL is a written, quantitative plan and analysis for attaining and maintaining water quality

standards in all seasons for a specific waterbody and pollutant. Specifically, a TMDL is the sum of the

allowable loads of a pollutant from all contributing point, nonpoint, and background sources. Total

maximum daily loads may be established on a coordinated basis for a group of waterbodies in a

watershed. Total maximum daily loads must be established for waterbodies on the list of impaired

waterbodies and must include the following 11 elements:
July 2000 Chapter 8. Management Programs

PAGE 115

1. The name and geographic location of the impaired waterbody;

2. Identification of the pollutant and the applicable water quality standard;

3. Quantification of the pollutant load that may be present in the waterbody and still ensure

attainment and maintenance of water quality standards;

4. Quantification of the amount or degree by which the current pollutant load in the waterbody,

including the pollutant load from upstream sources that is being accounted for as background

loading, deviated from the pollutant load needed to attain and maintain water quality standards;

5. Identification of source categories, source subcategories or individual sources of pollutant;

6. Wasteload allocations;

7. Load allocations;

8. A margin of safety;

9. Consideration of seasonal variations;

10. Allowance for reasonably foreseeable increases in pollutant loads including future growth; and

11. An implementation plan.

Both the 1996 and 1998 section 303(d) lists, as well as more recent 305(b) reports reflect similar

patterns: sediments, nutrients, and pathogens are the top three causes of waterbody impairment.

Source: [http://www.epa.gov/owowwtr1/tmdl/faq.html]

Waste Load Allocation

A waste load allocation (WLA) is the proportion of a receiving water’s total maximum daily load that is

allocated to point sources of pollution. Water quality models are often utilized by regulatory agencies in

conducting an assessment to determine a WLA. Models establish a quantitative relationship between a

waste load and its impact on water quality. WLAs are used by permit writers to establish Water Quality

Based Effluent Limits (WQBELs).

Source: [http://www.epa.gov:80/owmitnet/permits/pwcourse/chapt_06.pdf]

Continuing Planning Process (CPP)

Each State is required to establish and maintain a continuing planning process (CPP) as described in

section 303(e) of the Clean Water Act. A State's CPP contains, among other items, a description of the

process that the State uses to identify waters needing water quality-based controls, a priority ranking of

these waters, the process for developing TMDLs, and a description of the process used to receive public

review of each TMDL. Descriptions may be as detailed as the Regional office and the State determine is

necessary to describe each step of the TMDL development process. This process may be included as

part of the EPA/State Agreement for TMDL development.

[Source: http://www.epa.gov/owowwtr1/tmdl/decisions/dec4.html]

LOOK TO THE FUTURE ... POLLUTANT TRADING

Point and nonpoint source pollutant trading involves financing reductions in nonpoint source pollution in

lieu of undertaking more expensive point source pollution reduction efforts. A trading program is

intended to produce cost savings for point source dischargers while improving water quality. 

Implementing a trading program requires a waterbody identifiable as a watershed or segment, as well as

a measurable combination of point sources and controllable nonpoint sources. There must be significant

load reductions for which the cost per pound reduced for nonpoint source controls is lower than the cost

for upgrading point source controls. Lastly, point source dischargers must face requirements to either
July 2000 Chapter 8. Management Programs

PAGE 116

upgrade facility treatment capabilities or trade for nonpoint source reductions in order to meet water

quality goals.

Such a program allows the private sector to allocate its resources to reduce pollutants in the most

cost-effective manner, and it encourages the development of a watershed-wide or basin-wide

approach to water quality protection. A pollutant trading program also requires cooperation between

agencies, and requires a system to arrive at trading ratios between point and nonpoint source controls.

For example, in a North Carolina watershed, the Tar-Pamlico Basin Association (a coalition of point

source dischargers) and State and regional environmental groups have proposed a two-phased nutrient

management strategy that incorporates point and nonpoint source pollutant trading. The plan requires

association members to finance nonpoint source reduction activities in the basin if their nutrient

discharges exceed a base allowance.

Source: [http://www.epa.gov/OWOW/NPS/MMGI/funding.html#9]

8.4 MANAGING NONPOINT SOURCE POLLUTION

During the first 15 years of the national program to abate and control water pollution, EPA and the States

have focused most of their water pollution control activities on traditional "point sources," such as

discharges through pipes from sewage treatment plants and industrial facilities. These point sources

have been regulated by EPA and the States through the NPDES permit program established by section

402 of the Clean Water Act. Discharges of dredged and fill materials into wetlands have also been

regulated by the U.S. Army Corps of Engineers and EPA under section 404 of the Clean Water Act.

The Nation has greatly reduced pollutant loads from point source discharges and has made considerable

progress in restoring and maintaining water quality as a result of the above activities. However, the

gains in controlling point sources have not solved all of the Nation's water quality problems. Recent

studies and surveys by EPA and by State/Tribal water quality agencies indicate that the majority of the

remaining water quality impairments in our nation's rivers, streams, lakes, estuaries, coastal waters, and

wetlands result from nonpoint source pollution and other nontraditional sources, such as urban storm

water discharges and combined sewer overflows.

Nonpoint source pollution generally results from land runoff, precipitation, atmospheric deposition,

drainage, seepage, or hydrologic modification. Technically, the term "nonpoint source" is defined to

mean any source of water pollution that does not meet the legal definition of "point source" in section

502(14) of the Clean Water Act, defined in the preceding section. Although diffuse runoff is generally

treated as nonpoint source pollution, runoff that enters and is discharged from conveyances such as those

described above is treated as a point source discharge and hence is subject to the permit requirements of

the Clean Water Act. In contrast, nonpoint sources are not subject to Federal permit requirements.

The pollution of waters by nonpoint sources is caused by rainfall or snowmelt moving over and through

the ground. As the runoff moves, it picks up and carries away natural pollutants and pollutants resulting

from human activity, finally depositing them into lakes, rivers, wetlands, coastal waters, and ground

waters. Nonpoint source pollution can also be caused by atmospheric deposition of pollutants onto

waterbodies. Furthermore, hydrologic modification is a form of nonpoint source pollution that often
July 2000 Chapter 8. Management Programs

PAGE 117

adversely affects the biological and physical integrity of surface waters. A more detailed discussion of

the range of nonpoint sources and their effects on water quality and riparian habitats is provided in

subsequent chapters of this guidance. A summary of State laws related to nonpoint source pollution can

be found in the Almanac of Enforceable State Laws to Control Nonpoint Source Water Pollution (ELI

1988). This report can be accessed on the internet at http://www.eli.org/bookstore/research.htm.

NONPOINT SOURCES OF NUTRIENTS

Guidance Specifying Management Measures for Sources of Nonpoint Pollution in Coastal Waters

(USEPA 1993a) was developed by EPA for the planning and implementation of Coastal Nonpoint

Pollution Programs. The guidance focuses on controlling five major categories of nonpoint sources that

impair or threaten waters nationally. Management measures are specified for (1) agricultural runoff; (2)

urban runoff (including developing and developed areas); (3) silvicultural (forestry) runoff; (4) marinas

and recreational boating; and (5) hydromodification (e.g., channelization and channel modification,

dams, and streambank and shoreline erosion). EPA guidance also includes management measures for

wetlands, riparian areas, and vegetated treatment systems that apply generally to various categories of

sources of nonpoint pollution. Management measures are defined in the Coastal Zone Act

Reauthorization Amendments of 1990 as economically achievable measures to control the addition of

pollutants to waters, which reflect the greatest degree of pollutant reduction achievable through the

application of the best available nonpoint pollution control practices, technologies, processes, siting

criteria, operating methods, or other alternatives.

The following section outlines some of the management measures specified in the CZARA guidance for

the various types of nonpoint sources. These measures should be considered when implementing

programs targeting nutrient releases into waters of the U.S.

Agricultural Runoff

• erosion and sediment control

• control of facility wastewater and runoff from confined animal facilities

• nutrient management planning on cropland

• grazing management systems

• irrigation water management

Urban Runoff

• control of runoff and erosion from existing and developing areas

• construction site runoff and erosion control

• construction site chemical control (includes fertilizers)

• proper design, location, installation, operation, and maintenance of on-site disposal systems

• pollution prevention education (e.g., household chemicals, lawn and garden activities, golf

courses, pet waste, on-site disposal systems, etc.)

• planning, siting, and developing roads, highways, and bridges (including runoff management)

Silvicultural Runoff

• streamside management

• road construction and management

• forest chemical management (includes fertilizers)
July 2000 Chapter 8. Management Programs

PAGE 118

• revegetation

• preharvest planning, harvesting management

Marinas and Recreational Boating

• siting and design

• operation and maintenance

• storm water runoff management

• sewage facility management

• fish waste management

• pollution prevention education (e.g., proper boat cleaning, fish waste disposal, and sewage

pump out procedures)

Hydromodification (i.e., channelization, channel modification, dams)

• minimize changes in sediment supply and pollutant delivery rates through careful planning and

design

• erosion and sediment control

• chemical and pollutant control (includes nutrients)

• stabilization and protection of eroding streambanks or shorelines

Wetlands, Riparian Areas, Vegetated Treatment Systems

• protect the NPS abatement and other functions of wetlands and riparian areas through

vegetative composition and cover, hydrology of surface and ground water, geochemistry of the

substrate, and species composition

• promote restoration of preexisting function of damaged and destroyed wetlands and riparian

systems

• promote the use of engineered vegetated treatment systems if they can serve a NPS pollution

abatement function

EFFORTS TO CONTROL NONPOINT SOURCE POLLUTION

Efforts to control nonpoint source pollution include nonpoint source management programs, the National

Estuary Program, atmospheric deposition, coastal nonpoint pollution control programs, and Farm Bill

conservation provisions. These efforts are described below.

Nonpoint Source Management Programs

In 1987, in view of the progress achieved in controlling point sources and the growing national

awareness of the increasingly dominant influence of nonpoint source pollution on water quality,

Congress amended the Clean Water Act to focus greater national efforts on nonpoint sources. In the

Water Quality Act of 1987, Congress amended section 101, "Declaration of Goals and Policy," to add the

following fundamental principle:

It is the national policy that programs for the control of nonpoint sources of

pollution be developed and implemented in an expeditious manner so as to

enable the goals of this Act to be met through the control of both point and

nonpoint sources of pollution.
July 2000 Chapter 8. Management Programs

PAGE 119

More importantly, Congress enacted section 319 of the Clean Water Act, which established a national

program to control nonpoint sources of water pollution. Under section 319, States address nonpoint

pollution by assessing nonpoint source pollution problems and causes within the State, adopting

management programs to control the nonpoint source pollution, and implementing the management

programs. While not required, many States have incorporated the management measures specified in the

1993 CZARA guidance into their State Nonpoint Source Management Programs. 

Section 319 also authorizes EPA to issue grants to States to assist them in implementing those

management programs or portions of management programs which have been approved by EPA. As of 

FY 2000, over $1 billion in grants have been given to States, Territories, and Tribes for the

implementation of nonpoint source pollution control programs.

For additional information on the Nonpoint Source Management Program and distribution of Section 319

grants in your State, contact your State’s designated nonpoint source agency. For many states, the

nonpoint source agency is the State Water Quality Agency. However, in several instances, other

agencies or departments are given nonpoint source responsibility (see Table 5).

National Estuary Program 

EPA also administers the National Estuary Program under section 320 of the Clean Water Act. This

program focuses on point and nonpoint pollution in geographically targeted, high-priority estuarine

waters. Under this program, EPA assists State, regional, and local governments in developing

comprehensive conservation and management plans that recommend priority corrective actions to restore

estuarine water quality, fish populations, and other designated uses of the waters. For additional

information, contact your local estuary program. The following estuaries are currently enrolled in the

program:

• Albemarle-Pamlico Sounds, NC

• Barataria-Terrebonne Estuarine Complex, LA

• Barnegat Bay, NJ

• Buzzards Bay, MA

• Casco Bay, ME

• Charlotte Harbor, FL

• (Lower) Columbia River Estuary, OR and WA

• Corpus Christi Bay, TX

• Delaware Estuary, DE, NJ, and PA

• Delaware Inland Bays, DE

• Galveston Bay, TX

• Indian River Lagoon, FL

• Long Island Sound, NY and CT

• Maryland Coastal Bays, MD
July 2000 Chapter 8. Management Programs

PAGE 120

Table 5. States for which the nonpoint source agency is not the water quality agency.

State State Nonpoint Source Agency

Arkansas State Department of Soil and Water Conservation

Delaware State Department of Soil and Water Conservation

Oklahoma State Department of Soil and Water Conservation

Tennessee State Department of Agriculture

Texas Department of Soil and Water Conservation (for agriculture)

Texas Water Quality Board (all other nonpoint sources)

Vermont State Department of Agriculture

Virginia State Department of Soil and Water Conservation

• Massachusetts Bays, MA

• Mobile Bay, AL

• Morro Bay, CA

• Narragansett Bay, RI

• New Hampshire Estuaries, NH

• New York-New Jersey Harbor, NY and NJ

• Peconic Bay, NY

• Puget Sound, WA

• San Francisco Estuary, CA

• San Juan Bay, PR

• Santa Monica Bay, CA

• Sarasota Bay, FL

• Tampa Bay, FL

• Tillamook Bay, OR

Atmospheric Deposition

While runoff from agricultural and urban areas may be the largest sources of nonpoint pollution, growing

evidence suggests that atmospheric deposition may have a significant influence on nutrient enrichment,

particularly from nitrogen (Jaworski et al. 1997). Gases released through fossil fuel combustion and

agricultural practices are two major sources of atmospheric N that may be deposited in waterbodies

(Carpenter et al. 1998). Nitrogen and nitrogen compounds formed in the atmosphere return to the earth

as acid rain or snow, gas, or dry particles (http://www.epa.gov/acidrain/effects/envben.html). EPA has

several programs that address the issue of atmospheric deposition, including the National Ambient Air

Quality Standards, the Atmospheric Deposition Initiative, and the Great Waters Program. 
July 2000 Chapter 8. Management Programs

PAGE 121

National Ambient Air Quality Standards

The Clean Air Act provides the principal framework for national, State, and local efforts to protect air

quality. Under the Clean Air Act, national ambient air quality standards (NAAQS) for pollutants which

are considered harmful to people and the environment are established.

The Clean Air Act established two types of national air quality standards. Primary standards set limits

to protect public health, including the health of "sensitive" populations such as asthmatics, children, and

the elderly. Secondary standards set limits to protect public welfare, including protection against

decreased visibility, damage to animals, crops, vegetation, and buildings

(http://www.epa.gov/airs/criteria.html).

Atmospheric Deposition Initiative

In 1995, EPA's Office of Water established an "Air Deposition Initiative" to work with the EPA Office

of Air and Radiation to identify and characterize air deposition problems with greater certainty and

examine solutions to address them. The Air and Water Programs are cooperating to assess the

atmospheric deposition problem, conduct scientific research, provide innovative solutions to link Clean

Air Act and Clean Water Act tools to reduce the of these pollutants, and communicate the findings to the

public. To date, most efforts have focused on better understanding of the links between nitrogen and

mercury emissions and harmful effects on water quality and the environment. Significant work has also

been done towards quantifying the benefits to water quality of reducing air emissions and developing

sensible, cost effective approaches to reducing the emissions and their ecosystem and health effects

(http://www.epa.gov/owowwtr1/oceans/airdep/index.html).

Great Waters Program

On November 15, 1990, in response to mounting evidence that air pollution contributes to water

pollution, Congress amended the Clean Air Act and included provisions that established research and

reporting requirements related to the deposition of hazardous air pollutants to the "Great Waters." The

waterbodies designated by these provisions are the Great Lakes, Lake Champlain, and Chesapeake Bay. 

As part of the Great Waters Program, Congress requires EPA, in cooperation with the National Oceanic

and Atmospheric Administration, to monitor hazardous pollutants by establishing sampling networks, 

investigate the deposition of these pollutants, improve monitoring methods, monitor for hazardous

pollutants in fish and wildlife, determine the contribution of air pollution to total pollution in the Great

Waters, evaluate any adverse effects to public health and the environment, determine sources of

pollution, and provide a report to Congress every 2 years. These reports provide an information base that

can be used to establish whether air pollution is a significant contributor to water quality problems of the

Great Waters, determine whether there are significant adverse effects to humans or the environment,

evaluate the effectiveness of existing regulatory programs in addressing these problems, and assess

whether additional regulatory actions are needed to reduce atmospheric deposition to the Great Waters.

For more detail, the Great Waters biennial Reports to Congress discuss current scientific understanding

of atmospheric deposition (http://www.epa.gov/airprogm/oar/oaqps/gr8water/xbrochure/program.html).

Coastal Nonpoint Pollution Control Programs

In November 1990, Congress enacted the Coastal Zone Act Reauthorization Amendments of 1990. 

These Amendments were intended to address several concerns, a major one of which is the impact of

nonpoint source pollution on coastal waters.
July 2000 Chapter 8. Management Programs

PAGE 122

To address more specifically the impacts of nonpoint source pollution on coastal water quality, Congress

enacted section 6217, "Protecting Coastal Waters," which was codified as 16 U.S.C. -1455b. This

section provides that each State with an approved coastal zone management program must develop and

submit a Coastal Nonpoint Pollution Control Program for EPA and the National Oceanic and

Atmospheric Administration (NOAA) approval. The purpose of the program "shall be to develop and

implement management measures for nonpoint source pollution to restore and protect coastal waters,

working in close conjunction with other State and local authorities."

States with Coast Nonpoint Pollution Control Programs are required to include measures in their

programs that are “in conformity” with the 1993 CZARA guidance, as discussed previously. A listing of

States with Coastal Nonpoint Pollution Control Programs is presented in Table 6. For additional

information on the programs in these States, contact the State water quality agency.

Farm Bill Conservation Provisions

Technical and financial assistance for landowners seeking to preserve soil and other natural resources is

authorized by the Federal Government under provisions of the Food Security Act (Farm Bill). 

Provisions of the 1996 Farm Bill relating directly to installation and maintenance of BMPs are

summarized in the following sections. Contact your Natural Resources Conservation Service (NRCS)

State Conservationist’s office for State-specific information.

Environmental Conservation Acreage Reserve Program (ECARP)

ECARP is an umbrella program established by the 1996 Farm Bill which contains the conservation

Reserve Program (CRP), Wetlands Reserve Program (WRP), and Environmental Quality Incentives

Program (EQIP). It authorizes the Secretary of Agriculture to designate watersheds, multi-state areas, or

regions of special environmental sensitivity as conservation priority areas which are eligible for

enhanced Federal assistance. Assistance in priority areas is to be used to help agricultural producers

comply with NPS pollution requirements of the Clean Water Act and other State or Federal

environmental laws. The ECARP is authorized through 2002.

Conservation Reserve Program (CRP)

First authorized by the Food Security Act of 1985 (Farm Bill), this voluntary program offers annual

rental payments, incentive payments, and cost-share assistance for establishing long-term, resourceconserving cover crops on highly erodible land. CRP contracts are issued for a duration of 10 to 15 years

for up to 36.4 million acres of cropland and marginal pasture. Land can be accepted into the CRP

through a competitive bidding process through which all offers are ranked using an environmental

benefits index, or through continuous sign-up for eligible lands where certain special conservation

practices will be implemented.

The Conservation Reserve Enhancement Program (CREP) is a new initiative of CRP authorized under

the 1996 Federal Agricultural Improvement and Reform Act. CREP is a joint, State-federal program

designed to meet specific conservation objectives. CREP targets State and Federal funds to achieve

shared environmental goals of national and state significance. The program uses financial incentives to

encourage farmers and ranchers to voluntarily protect soil, water, and wildlife resources.
July 2000 Chapter 8. Management Programs

PAGE 123

Table 6. An alphabetical list of States and Territories with Coastal Nonpoint Pollution Control

Programs.

States and Territories with Coastal Nonpoint Pollution Control Programs

Alabama Maine Oregon

Alaska Maryland Pennsylvania 

American Samoa Massachusetts Puerto Rico 

California Michigan Rhode Island

Connecticut Mississippi South Carolina

Delaware New Hampshire Virgin Islands

Florida New Jersey Virginia 

Guam New York Washington

Hawaii North Carolina Wisconsin 

Louisiana Northern Mariana Islands

Wetlands Reserve Program (WRP)

The WRP is a voluntary program to restore and protect wetlands and associated lands. Participants may

sell a permanent or 30-year conservation easement or enter into a 10-year cost-share agreement with

USDA to restore and protect wetlands. The landowner voluntarily limits future use of the land, yet

retains private ownership. The NRCS provides technical assistance in developing a plan for restoration

and maintenance of the land. The landowner retains the right to control access to the land and may lease

the land for hunting, fishing, and other undeveloped recreational activities.

Environmental Quality Incentives Program

The EQIP was established by the 1996 Farm Bill to provide a voluntary conservation program for

farmers and ranchers who face serious threats to soil, water, and related natural resources. EQIP offers

financial, technical, and educational help to install or implement structural, vegetative, and management

practices designed to conserve soil and other natural resources. Current priorities for these funds dictate

that one half of the available monies be directed to livestock-related concerns. Cost-sharing may pay up

to 75% of the costs for certain conservation practices. Incentive payments may be made to encourage

producers to perform land management practices such as nutrient management, manure management,

integrated pest management, irrigation water management, and wildlife habitat management.

Wildlife Habitat Incentives Program (WHIP)

This program is designed for parties interested in developing and improving wildlife habitat on private

lands. Plans are developed in consultation with NRCS and the local Conservation District. USDA will

provide technical assistance and cost-share up to 75% of the cost of implementing the wildlife
July 2000 Chapter 8. Management Programs

PAGE 124

conservation practices. Participants generally must sign a 5- to 10-year contract with USDA which

requires that they maintain the improvement practices.

Forestry Incentives Program (FIP)

Originally authorized in 1978, the FIP allows cost sharing of up to 65% (up to a maximum of $10,000

per person per year) for tree planting, timber stand improvement, and related practices on nonindustrial

private forest land. The FIP is administered by NRCS and the U.S. Forest Service. Cost share funds are

restricted to individuals who own no more than 1,000 acres of eligible forest land.

Conservation of Private Grazing Land

This program was authorized by the 1996 Farm Bill for the purpose of providing technical and

educational assistance to owners of private grazing lands. It offers opportunities for better land

management, erosion reduction, water conservation, wildlife habitat, and improving soil structure.

Cooperative Extension

State land grant universities and Cooperative Extension play an important role in management

implementation. They have the expertise to research, transfer, and implement agriculture management

systems that will be needed to meet nutrient criteria. In addition, they have developed models and other

predictive management tools that will aid in selecting the most appropriate management activities. 

Contact your local Cooperative Extension Agent, or the Agriculture Department at a State land grant

university for more information on the services they can provide.
PAGE 125

9.1 INTRODUCTION

After criteria are set, compliance determinations made, and management plans implemented, resource

managers should continue to monitor river and stream systems while reassessing goals and nutrient

criteria. This step should (1) evaluate the appropriateness of established nutrient criteria, (2) ensure that

river and stream systems are responding to management action, and (3) assess whether water quality

goals established by the resource manager are being met.

Those streams selected for management may be approached using a rational course of action beginning

with a statement of major problems or symptoms and progressing logically to a final assessment to

determine the relative success of the effort. Throughout this process, the water quality manager must reexamine (1) the initial goals identified for the stream system(s) prior to criteria development and (2)

subsequent management actions taken to evaluate the effectiveness of criteria and management plans. 

The manager should assess the efficacy of management actions and potentially re-evaluate the

appropriateness of established criteria if monitoring data indicate that goals are not being met.

9.2 ASSESSMENT OF PROCESS THROUGH MONITORING AND PERIODIC

REVIEW

The management plan should always include “before,” “during,” and “after” water resource quality

monitoring to demonstrate the relative response of the system to management efforts, thus the

recommendation that initial survey stations should generally be maintained and expanded. Availability

of continuous, year-to-year monitoring data is critical and can be used as a bench mark for evaluating

progress. If monitoring data indicate that water quality is improved, monitoring should continue to

validate the progress made. Should water quality decline, the criteria development process should be revisited and potentially revised. At a minimum, monitoring data should be reevaluated every five years to

gauge progress. The reevaluation of monitoring data should include seasonality and periodic data

assessment intervals for management review to provide the opportunity for responses to changing

circumstances, modifications of methods, schedules, and changes of emphasis as needed. Control of

point source nutrients may result in fairly quick system recovery from cultural eutrophication (Edmonson
July 2000 Chapter 9. Monitoring and Reassessment of Nutrient Criteria Ranges

PAGE 126

1994), although nutrient cycling mechanisms and changes in food web dynamics may result in a

persistent eutrophic state in many systems (Carpenter et al. 1999). Therefore, continued monitoring and

reevaluation of nutrient control strategies is of particular importance.

9.3 COMPLETION AND EVALUATION

Management projects are frequently planned, initiated, and concluded with new initiatives undertaken to

meet pressing schedules without sufficiently evaluating what was or was not initially accomplished. 

Review of progress, original objectives or goals, and monitoring data will reveal whether the river or

stream trophic state was successfully protected or improved. Just as important, this evaluation will

provide the documentation necessary to determine if methods and techniques attempted in this instance

can be applied, perhaps with modification, elsewhere. Alternatively, it will also reveal if mistakes were

made which should be noted and avoided in future projects and if perhaps a sequel to the current project

is required to fully accomplish that which was intended.

9.4 CONTINUED MONITORING OF THE SYSTEM

Monitoring programs initiated and expanded in the course of the project can now be reduced to the

periodic measuring of key variables at critical times and locations. At this stage, the purpose of

monitoring is to keep sufficiently informed of the status of the river or stream to ensure that the

protection or remediation achieved is maintained. Intervention should be possible at an early point to

minimize the costs of remediation if periodic maintenance monitoring indicates a return of trophic

decline. The evaluation and periodic monitoring steps of this process essentially close the loop. If new

issues arise, the manager returns to step one with a new problem statement.
PAGE 127

REFERENCES

Agbeti, M. D. 1992. The relationship between diatom assemblages and trophic variables: A

comparison of old and new approaches. Can. J. Fish. Aquat. Sci. 49:1171-1175.

Alexander, R. B., J.R. Slack, A.S. Ludtke, K.K. Fitzgerald, and T.L. Schertz. 1996. Data from selected

U.S. Geological Survey National Stream Water-Quality Monitoring Networks: USGS Digital

Data Series DDS-37, 2 compact disks.

Allan, J. D. 1995. Stream Ecology: Structure and Function of Running Waters. Chapman and Hall, 

U. K. 400 pp.

Ameel, J. P., R. P. Axler, and C. S. Owen. 1993. Persulfate digestion for determination of total nitrogen

and phosphorus in low-nutrient waters. Am. Environ. Lab. 10(93):1-11.

Anderson, C. W., D. Q. Tanner, and D. B. Lee. 1994. Water-quality data for the South Umpqua River

Basin, Oregon, 1990-1992. U.S. Geological Survey Open-File Rept. 94-40, Portland, OR.

APHA. 2000. Standard Methods for Examination of Water and Wastewater. 21st ed. Eaton, A. D., L.

C. Clesceri, and A. E. Greenberg (eds.). American Public Health Association, Washington, DC. 

Atlas, R. M. and R. Bartha. 1993. Microbial Ecology Fundamentals and Applications. The

Benjamin/Cummings Publishing Company, Inc., Redwood City, CA. 

Baker, W. L. 1990. Climatic and hydrologic effects on the regeneration of Populus angustifolia James

along the Animas River, Colorado. J. Biogeog. 17:59-73.

Barbour, M. G., J. H. Burk, and W. D. Pitts. 1980. Terrestrial Plant Ecology. Benjamin/Cummings

Publishing Company, Menlo Park, CA.

Barbour, M. T., J. Gerritsen, G. E. Griffith, R. Frydenborg, E. McCarron, J. S. White, and M. L. Bastian. 

1996. A framework for biological criteria for Florida streams using benthic macroinvertebrates. 

J. N. Am. Benthol. Soc. 15:185-211.

Barbour, M. T., J. Gerritsen, B. D. Snyder, and J. B. Stribling. 1999. Rapid Bioassessment Protocols

for Use in Wadeable Streams and Rivers: Periphyton, Benthic Macroinvertebrates, and Fish. 

2nd ed. U.S. Environmental Protection Agency, Office of Water, Washington, DC. EPA 841-B99-002. 

Barko, J. W. and R. M. Smart. 1986. Sediment-related mechanisms of growth limitation in submersed

macrophytes. J. Ecol. 67(5):1328-1340.

Barko, J. W., D. Gunnison, and S. R. Carpenter. 1991. Sediment interactions with submersed

macrophyte growth and community dynamics. Aquat. Bot. 41:41-65.
July 2000 REFERENCES

PAGE 128

Beal, Ernest O. 1977. A manual of marsh and aquatic vascular plants of North Carolina with habitat data. 

Technical Bulletin No. 247. North Carolina Agricultural Experiment Station, North Carolina

State University, Raleigh, NC. 298 pp. 

Beaver, J. 1981. Apparent ecological characteristics of some common freshwater diatoms. Ontario

Ministry of the Environment. Technical Support Section, Central Region, Don Mills.

Bicknell, B. R., J. C. Imhoff, J. L. Kittle, Jr., A. S. Donigian, Jr., and R. C. Johanson. 1997. Hydrological

Simulation Program—Fortran: User's manual for version 11: U.S. Environmental Protection

Agency, National Exposure Research Laboratory, Athens, GA. EPA/600/R-97/080. 755 pp.

Bicknell, B. R., J. C. Imhoff, J. L. Kittle, A. S. Donigian, and R. C. Johanson. 1993. Hydrological

Simulation Program - FORTRAN (HSPF): User's manual for release 10.0. Environmental

Research Laboratory, U.S. Environmental Protection Agency, Athens, GA. EPA 600/3-84-066.

Biggs, B. J. F. 2000. Eutrophication of streams and rivers: Dissolved nutrient - chlorophyll relationships

for benthic algae. J. N. Am. Benthol. Soc. 19:17-31.

Biggs, B. J. F. 1995. The contribution of disturbance, catchment geology and land use to the habitat

template of periphyton in stream ecosystems. Freshwater Biol. 33:419-438.

Biggs, B. J. F. 1996. Patterns in benthic algae of streams. In: Algal Ecology. Stevenson, J., M. L.

Bothwell, and R. L. Lowe (eds.). Academic Press, San Diego, CA. pp. 31-51.

Biggs, B. J. F. and M. E. Close. 1989. Periphyton biomass dynamics in gravel bed rivers: The relative

effects of flows and nutrients. Freshwater Biol. 22:209-231.

Biggs, B. J. F. and C. W. Hickey. 1994. Periphyton responses to a hydraulic gradient in a regulated

river, New Zealand. Freshwater Biol. 32:49-59.

Biggs, B. J. F., C. Kilroy, and R. L. Lowe. 1998a. Periphyton development in three valley segments of a

New Zealand grassland river: Test of a habitat matrix conceptual model within a catchment. 

Arch. Hydrobiol. 143:147-177.

Biggs, B. J. F. and R. L. Lowe. 1994. Responses of two trophic levels to patch enrichment along a New

Zealand stream continuum. N. Z. J. Mar. Freshwater Res. 28:119-134.

Biggs, B. J. F. and G. M. Price. 1987. A survey of filamentous algal proliferations in New Zealand

rivers. N. Z. J. Mar. Freshwater Res. 21:175-191.

Biggs, B. J. F., R. J. Stevenson, and R. L. Lowe. 1998b. A habitat matrix conceptual model for stream

periphyton. Arch. Hydrobiol. 143:21-56.
July 2000 REFERENCES

PAGE 129

Birks H. H., H. J. B. Birks, P. E. Kaland, and D. Moe (eds.). 1988. The Cultural Landscape: Past,

Present and Future. Cambridge University Press, Cambridge, England. 521 pp.

Birks, H. J. B. 1998. Numerical tools in palaeolimnology—Progress, potentialities, and problems. J.

Paleolimnol. 20:307-332.

Blaney, H. and W. Criddle. 1962. Determining Consumptive Use and Irrigation Water Requirements. 

USDA Technical Bulletin Number 1275. 59 pp.

Borchardt, M. A. 1996. Nutrients. In: Algal Ecology: Freshwater Benthic Ecosystems. Stevenson, R.

J., M. L. Bothwell, and R. L. Lowe (eds.). Academic Press, San Diego, CA.

Boston, H. L. and W. R. Hill. 1991. Photosynthesis-light relations of stream periphyton communities.

Limnol. Oceanogr. 36:644-656.

Bothwell, M. L. 1985. Phosphorus limitation of lotic periphyton growth rates: An intersite comparison

using continuous-flow troughs (Thompson River System, British Columbia). Limnol. Oceanogr.

30:527-542.

Bothwell, M. L. 1989. Phosphorus-limited growth dynamics of lotic periphytic diatom communities: 

Areal biomass and cellular growth rate responses. Can. J. Fish. Aquat. Sci. 46:1293-1301.

Bourassa, N. and A. Cattaneo. 1998. Control of periphyton biomass in Laurentian streams (Québec). J.

N. Am. Benthol. Soc. 17:420-429.

Braico, R. D. 1973. Dissolved Oxygen and Temperature Diurnal Variations in the Clark Fork River,

August 1973. Report to the MT Dept. Health and Env. Sciences (now the Dept. Env. Quality). 

Brezonik. P. L., K. W. Easter, L. Hatch, D. Mulla, and J. Perry. 1999. Management of diffuse pollution

in agricultural watersheds: Lessons learned from the Minnesota River Basin. Water Sci. Tech.

39(12):323-330.

Brick, C. M. and J. N. Moore. 1996. Diel variation of trace metals in the upper Clark Fork River,

Montana. Environ. Sci. Technol. 30:1953-1960.

Brown, L. C. and T. O. Barnwell. 1987. The enhanced stream water quality model QUAL2E and

QUAL2E-UNCAS: Documentation and user manual. U.S. Environmental Protection Agency, 

Athens, GA. EPA 600/3-87/007. 

Burkholder, J. M., E. J. Noga, C. H. Hobbs, and H. B. Glasgow, Jr. 1992. New ‘phantom’ dinoflagellate

is the causative agent of major estuarine fish kills. Nature 358:407-410.

Busch, D. E. and S. G. Fisher. 1981. Metabolism of a desert stream. Freshwater Biol. 11:301-308.
July 2000 REFERENCES

PAGE 130

Cairns Jr., J. 1956. Effects of increased temperatures on aquatic organisms. Ind. Waste 1:150-152.

Campbell, R.C. 1989. Statistics for Biologists. 3rd ed. Cambridge University Press, Cambridge.

Canfield, D. E. G., K. A. Langeland, S. B. Linda, and T. W. Haller. 1985. Relations between water

transparency and maximum depth of macrophyte colonization in lakes. J. Aquat. Plant Manage.

23:25-28.

Caraco, N. F., J. J. Cole, P. A. Raymond, D. L. Strayer, M. L. Pace, S. E. G. Findlay, and D. T. Fischer. 

1997. Zebra mussel invasion in a large, turbid river: Phytoplankton response to increased

grazing. Ecology 78(2):588-602.

Carlson, R. E. 1977. A trophic state index for lakes. Limnol. Oceanogr. 22:361-368.

Carmichael, W. W. 1986. Algal toxins. Adv. Bot. Res. 12:47-101.

Carmichael, W. W. 1994. The toxins of cyanobacteria. Sci. Am. Jan. 1994:78-84.

Carpenter, S. R., W. A. Brock, and P. C. Hanson. 1999. Ecological and social dynamics in simple models

of ecosystem management. Conserv. Ecol. 3(2):4. www.consecol.org/vol3/iss2/art4. 

Carpenter, S. R., N. F. Caraco, D. L. Correll, R. W. Howarth, A. N. Sharpley, and V. H. Smith. 1998. 

Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8(3):559-568.

Cattaneo, A., T. Kerimian, M. Roberge, and J. Marty. 1997. Periphyton distribution and abundance on

substrata of different size along a gradient of stream trophy. Hydrobiologia 354:101-110

Chambers, P. A., R. E. DeWreede, E. A. Irlandi, and H. Vandermeulen. 1999. Management issues in

aquatic macrophyte ecology: A Canadian perspective. Can. J. Bot. 77:471-487.

Chambers, P. A. and J. Kalff. 1985. Depth distribution and biomass of submerged aquatic macrophyte

communities in relation to Secchi depth. Can. J. Fish. Aquat. Sci. 42:701-709.

Charles, D. F. and J. P. Smol. 1988. New methods for using diatoms and chrysophytes to infer past pH

of low-alkalinity lakes. Limnol. Oceanogr. 33:1451-62.

Charlton, S. E. D., H. R. Hamilton, and P. M. Cross. 1986. The Limnological Characteristics of the

Bow, Oldman and South Saskatchewan Rivers (1979-82). Alberta Environment, Edmonton, AL.

Chauvet, E. and H. Decamps. 1989. Lateral interactions in a fluvial landscape: The River Garonne,

France. J. N. Am. Benthol. Soc. 8(1):9-17.

Chessman, B. C., P. E. Hutton, and J. M. Burch. 1992. Limiting nutrients for periphyton growth in subalpine, forest, agricultural and urban streams. Freshwater Biol. 28:349-361.
July 2000 REFERENCES

PAGE 131

Chetelat, J., F. R. Pick, and A. Morin. 1999. Periphyton biomass and community composition in rivers

of different nutrient status. Can. J. Fish Aquat. Sci. 56(4):560-569.

Chorley, R. J. and B. A. Kennedy. 1971. Physical Geography: A Systems Approach. Prentice-Hall,

London.

Collins, G. B. and C. I. Weber. 1978. Phycoperiphyton (algae) as indicators of water quality. Trans.

Am. Microscop. Soc. 97:36-43.

Cockrum, D. K. and J. J. Warwick. 1994. Assessing the impact of agricultural activities on water

quality in a periphyton dominated stream using the Water Quality Analysis Program (WASP). 

In: Proceedings of the Symposium on the Effects of Human-Induced Changes on Hydrologic

Systems. American Water Resources Association. Jackson Hole, WY, June 26-29, 1994. 

Cooke, G. D., E. B. Welch, S. A. Peterson, and P. R. Newroth. 1993. Restoration and Management of

Lakes and Reservoirs. Lewis Publ., Boca Raton, FL.

Correll, D. L. 1998. The role of phosphorus in the eutrophication of receiving waters: A review. J.

Environ. Qual. 27:261-266.

Coupe, R. H., D. A. Goolsby, J. L. Iverson, D. J. Markovichick, and S. D. Zaugg. 1995. Pesticide,

Nutrient, Water-discharge and Physical-Property Data for the Mississippi River and Some of its

Tributaries, April 1991-September 1992. U.S. Geological Survey Open-File Report 93-406. 66

pp.

Dahm, C. N., N. B. Grimm, P. Marmonier, H. M. Valett, and P. Vervier. 1998. Nutrient dynamics at the

interface between surface waters and ground waters. Freshwater Biol. 40:427-451.

Dahm, C. N., E. H. Trotter, and J. R. Sedell. 1987. Role of anaerobic zones and processes in stream

ecosystem productivity. In: Chemical Quality of Water and the Hydrologic Cycle. Averett, R.

A. and D. M. McKnight (eds.). Lewis Publ., Chelsea, MI. pp. 157-178.

Daniel, T. C., A. N. Sharpley, J. L. Lemunyon, and J. T. Sims. 1997. Agricultural Phosphorus and

Eutrophication: An Overview. Department of Agronomy, University of Arkansas, Fayetteville,

AR. 

Darley, W. M. 1982. Algal Biology: A Physiological Approach. Blackwell Scientific Publications,

Oxford, UK. 

Davies, B. R., M. C. Thoms, K. F. Walker, J. H. O’Keeffe, and J. A. Gore. 1994. Dryland rivers: Their

ecology, conservation and management. In: The Rivers Handbook, Vol. 2. Calow, P. and G. E.

Pets (eds.). Blackwell Scientific, Oxford. pp. 484-512.

Davies-Colley, R. J. 1988. Measuring water clarity with a black disk. Limnol. Oceanogr. 33:616-623.
July 2000 REFERENCES

PAGE 132

Davis, F. W. 1985. Historical changes in submerged macrophyte communities of upper Chesapeake

Bay. Ecology 66(3):981-993.

DeNicola, D. 1996. Periphyton responses to temperature. In: Algal Ecology: Freshwater Benthic

Ecosystems. Stevenson, R. J., M. L. Bothwell, and R. L. Lowe (eds.). Academic Press, San

Diego. pp. 149-181.

Dent, C. L. and N. B. Grimm. (In press). Spatial heterogeneity in stream water nutrient concentrations

over successional time. Ecology.

Dillon, P. J. and F. H. Rigler. 1974. The phosphorus-chlorophyll relationship in lakes. Limnol.

Oceanogr. 19:767-773. 

Dodds, W. K. 1991. Factors associated with dominance of the filamentous green alga Cladophora

glomerata. Water Res. 25:1325-1332.

Dodds, W. K. 1995. Availability, uptake and regeneration of phosphate in mesocosms with varied

levels of P deficiency. Hydrobiologia 297:1-9.

Dodds W. K. and J. Brock. 1998. A portable flow chamber for in situ determination of benthic

metabolism. Freshwater Biol. 39:49-59.

Dodds, W. K., J. M. Blair, G. M. Henebry, J. K. Keolliker, R. Ramundo, and C. M. Tate. 1996. 

Nitrogen transport from tallgrass prairie watersheds. J. Environ. Qual. 25:973-981.

Dodds, W. K., J. R. Jones, and E. B. Welch. 1998. Suggested classification of stream trophic state: 

Distributions of temperate stream types by chlorophyll, total nitrogen, and phosphorus. Water

Res. 32:1455-1462.

Dodds, W. K., V. H. Smith, and B. Zander. 1997. Developing nutrient targets to control benthic

chlorophyll levels in streams: A case study of the Clark Fork River. Water Res. 31:1738-1750.

Dodds, W. K. and E. B. Welch. 2000. Establishing nutrient criteria in streams. J. N. Am. Benthol. Soc.

19:186-196.

Dorin, G. 1981. Organochlorinated compounds in drinking water as a result of eutrophication. In: 

Restoration of Lakes and Inland Waters. U.S. Environmental Protection Agency, Washington,

DC. EPA 440/5-83-001. pp. 373-378.

Dunne, T. and L. B. Leopold. 1978. Water in Environmental Planning. W. H. Freeman, San Francisco.

Edmonson, W. T. 1994. Sixty years of Lake Washington: A curriculum vitae. Lake Reservoir Manage.

10:75-84.
July 2000 REFERENCES

PAGE 133

ELI (Environmental Law Institute). 1988. Almanac of Enforceable State Laws to Control Nonpoint

Source Water Pollution, ELI Project # 970301, Washington, DC. 293 pp.

Elswick, D. A. 1998. Spatial Prediction of Phosphorus and Algal Biomass in Cobble/Gravel-bed Rivers

During Summer Conditions. Ph.D. Dissertation, Department of Civil and Environmental

Engineering, University of Washington, Seattle, WA.

Fisher, S. G. 1983. Succession in streams. In: Stream Ecology: Application and Testing of General

Ecological Theory. Barnes, J. and G.W. Minshall (eds.). Plenum Press, New York. pp. 7-27.

Fisher, S. G. 1986. Structure and dynamics of desert streams. In: Pattern and Process in Desert

Ecosystems. W. Whitford (ed.). University of New Mexico Press, Albuquerque. pp. 114-139.

Fisher, S. G., L. J. Gray, N. B. Grimm, and D. E. Busch. 1982. Temporal succession in a desert stream

following flash flooding. Ecol. Monogr. 52:93-110.

Fisher, S. G. and N. B. Grimm. 1983. Water Quality and Nutrient Dynamics of Arizona Streams. OWRT

Project Completion Report A-106-ARIZ. Office of Water Research and Technology.

Fisher, S. G. and N. B. Grimm. 1988. Disturbance as a determinant of structure in a Sonoran Desert

stream ecosystem. Internationale Vereinigung für Theoretische und Angewandt Limnologie,

Verhandlungen 23:1183-1189.

Fisher, S. G. and N. B. Grimm. 1991. Streams and disturbance: Are cross-ecosystem comparisons

useful? In: Comparative Analyses of Ecosystems: Patterns, Mechanisms and Theories. Cole, 

J. C., G. M. Lovett, and S. E. G. Findlay (eds.). Springer-Verlag, New York, New York, US. pp.

196-221.

Fisher, S. G., N. B. Grimm, E. Marti, and R. Gomez. 1998a. Hierarchy, spatial configuration, and

nutrient cycling in streams. Austral. J. Ecol. 23:41-52.

Fisher, S. G., N. B. Grimm, E. Martí., J. B. Jones, Jr., and R. M. Holmes. 1998b. Material spiraling in

river corridors: A telescoping ecosystem model. Ecosystems 1:19-34.

Fisher, S. G. and W. L. Minckley. 1978. Chemical characteristics of a desert stream in flash flood. J.

Arid Environ. 1:25-33.

Fitzpatrick, F. A., I. R. Waite, P. J. D’Arconte, M. R. Meador, M. A. Maupin, and M. E. Gurtz. 1998.

Revised Methods for Characterizing Stream Habitat in the National Water-Quality Assessment

Program. U.S. Geological Survey Water-Resources Investigations Report 98-4052. 67 pp.

Fry, J. C. (ed.). 1993. Biological Data Analysis: A Practical Approach. Oxford University Press,

Oxford, England. 
July 2000 REFERENCES

PAGE 134

Fuhrer, G. J., R. J. Gilliom, P. A. Hamilton, J. L. Morace, L. H. Nowell, J. F. Rinella, J. D. Stoner, and D.

A. Wentz. 1999. The Quality of Our Nation’s Waters. Nutrients and Pesticides. U.S.

Geological Survey Circular 1225. 82 pp.

Fuller, W. H. 1975. Soils of the Desert Southwest. University of Arizona Press. 102 pp.

Gilbert, R. O. 1987. Statistical Methods for Environmental Pollution Monitoring. Van Nostrand

Reinhold, New York.

Gilliom, R. J., W. M. Alley, and M. E. Gurtz. 1995. Design of the National Water-Quality Assessment

Program: Occurrence and Distribution of Water-Quality Conditions. U.S. Geological Survey

Circular 1112. 33 pp. [URL: http://water.usgs.gov/pubs/circ1112/]

Glasgow, H. B., J. M. Burkholder, D. E. Schmechel, P. A. Tester, and P. A. Rublee. 1995. Insidious

effects of a toxic estuarine dinoflagellate on fish survival and human health. J. Toxicol. Environ.

Health 46:501-522.

Goldman, C. R. and A. J. Horne. 1983. Limnology. McGraw-Hill, New York.

Goolsby, D. A., R. C. Coupe, and D. J. Markovchick. 1991. Distribution of Selected Herbicides and

Nitrate in the Mississippi River and its Major Tributaries, April Through June 1991. U.S.

Geological Survey Water-Resources Investigations Report 91-4163. 35 pp.

Graff, W. L. 1988. Fluvial Processes in Dryland Rivers. Springer-Verlag, New York.

Gray, L. J. 1981. Species composition and life histories of aquatic insects in a lowland Sonoran Desert

stream. Am. Midland Naturalist 106:229-242.

Green, R. H. 1979. Sampling Design and Statistical Methods for Environmental Biologists. John

Wiley, New York. 257 pp.

Grimm, N. B. 1987. Nitrogen dynamics during succession in a desert stream. Ecology 68:1157-1170.

Grimm, N. B. 1988. Role of macroinvertebrates in nitrogen dynamics of a desert stream. Ecology

69:1884-1893.

Grimm, N. B. 1992. Biogeochemistry of nitrogen in arid-land stream ecosystems. J. Arizona-Nevada

Acad. Sci. 26:130-146.

Grimm, N. B. and S. G. Fisher. 1986a. Nitrogen limitation potential of Arizona streams and rivers. J.

Arizona-Nevada Acad. Sci. 21:31-43.

Grimm, N. B. and S. G. Fisher. 1986b. Nitrogen limitation in a Sonoran Desert stream. J. N. Am.

Benthol. Soc. 5:2-15.
July 2000 REFERENCES

PAGE 135

Grimm, N. B. and S. G. Fisher. 1989. Stability of periphyton and macroinvertebrates to disturbance by

flash floods in a desert stream. J. N. Am. Benthol. Soc. 8:293-307.

Grimm, N. B., S. G. Fisher, and W. L. Minckley. 1981. Nitrogen and phosphorus dynamics in hot desert

streams of Southwestern U.S.A. Hydrobiologia 83:303-312.

Grimm, N. B. and K. C. Petrone. 1997. Nitrogen fixation in a desert stream ecosystem.

Biogeochemistry 37:33-61.

Hall, C. A. S. and R. Moll. 1975. Methods of assessing aquatic primary productivity. In: Primary

Productivity of the Biosphere. H. Leith and R. H. Whittaker (eds.). Springer-Verlag, New York. 

 pp. 19-54.

Halloway, J. M., R. A. Dahlgren, B. Hansen, and W. H. Casey. 1998. Contribution of bedrock nitrogen

to high nitrate levels in stream water. Nature 395:785-788.

Harris, M.A. and S.D. Porter. (Unpublished manuscript). Relating Epidendric Macroinvertebrate

Communities to Physical and Chemical Factors in Upper Midwest Streams. U.S. Geological

Survey Water-Resources Investigations Report. 

Hauer, F. R. and V. H. Resh. 1996. Benthic macroinvertebrates. In: Methods in Stream Ecology.

Hauer, F. R. and G. A. Lamberti (eds.). Academic Press, San Diego, CA. pp. 339-369.

Hayek, L. C. 1994. Research design for quantitative amphibian studies. In: Measuring and Monitoring

Biological Diversity, Standard Methods for Amphibians. Smithsonian Institution Press,

Washington, DC. pp. 21-38.

Heintz, A. J. 1970. Low-Flow Characteristics of Iowa Streams Through 1966. Iowa Natural Resources

Council Bulletin No. 10. 176 pp.

Heiskary, S. A., C. B. Wilson, and D. P. Larsen. 1987. Analysis of regional patterns in lake water

quality: Using ecoregions for lake management in Minnesota. Lake Reservoir Manage. 3:337-

344. 

Herlihy, A. T., J. L. Stoddard, and C. B. Johnson. 1998. The relationship between stream chemistry and

watershed land cover data in the Mid-Atlantic region, U.S. Water, Air, and Soil Pollution

105:377-386.

Hickey, C. W. 1987. Benthic chamber for use in rivers: Testing against oxygen mass balance. J.

Environ. Eng. 114:828-845.

Hilborn, R. and M. Mangel. 1997. The Ecological Detective: Confronting Models with Data. Princeton

University Press, Princeton, NJ.
July 2000 REFERENCES

PAGE 136

Hill, M. O. 1979. TWINSPAN-A FORTRAN Program for Detrended Correspondence Analysis and

Reciprocal Averaging. Cornell University, Ithaca, New York, USA.

Hill, B. H., A. T. Herlihy, P. R. Kaufmann, R. J. Stevenson, F. H. McCormick, and C. B. Johnson. 2000.

The use of periphyton assemblage data as an index of biotic integrity. J. N. Amer. Benthol. Soc.

19:50-67.

Hill, W. 1996. Effects of light. In: Algal Ecology: Freshwater Benthic Ecosystems. Stevenson, R. J.,

M. L. Bothwell, and R. L. Lowe (eds.). Academic Press, San Diego. pp. 121-148.

Hill, W. R. and A. K. Knight. 1988. Concurrent grazing effects of two stream insects on periphyton.

Limnol. Oceanogr. 33:15-26.

Hirsch, R. M., J. R. Slack, and R. A. Smith. 1982. Techniques in trend analysis for monthly water

quality data. Water Res. 18:107-121.

Holmes, R. M., S. G. Fisher, and N. B. Grimm. 1994. Parafluvial nitrogen dynamics in a desert stream

ecosystem. J. N. Am. Benthol. Soc. 13:468-478.

Holmes, R. M., J. B. Jones, Jr., S. G. Fisher, and N. B. Grimm. 1996. Denitrification in a

nitrogen-limited stream ecosystem. Biogeochemistry 33:125-146.

Horner, R. R., E. B. Welch, M. R. Seeley, and J. M. Jacoby. 1990. Responses of periphyton to changes

in current velocity, suspended sediment and phosphorus concentration. Freshwater Biol. 24:215-

232.

Horner, R. R., E. B. Welch, and R. B. Veenstra. 1983. Development of nuisance periphytic algae in

laboratory streams in relation to enrichment and velocity. In: Periphyton of Freshwater

Ecosystems: Proceedings of the First International Workshop on Periphyton of Freshwater

Ecosystems. R. G. Wetzel (ed.). Developments in Hydrobiology Series, Vol. 17. Kluwer,

Boston. pp. 21-134. 

Horton, R. E. 1945. Erosional development of streams and their drainage basins; hydrophysical

approach to quantitative morphology. Geol. Soc. Am. Bull. 56:275-370.

Hoyer, M. V., D. E. Canfield, Jr., C. A. Horsburgh, K. Brown. 1996. Florida Freshwater Plants. A

Handbook of Common Aquatic Plants in Florida Lakes. University of Florida, Institute of Food

and Agricultural Sciences. Publication SP 189.

Hughes, R. M. and D. P. Larsen. 1988. Regional, chemical and biological goals for surface waters. J.

Water Pollut. Control Fed. 60:486-493. 

Humphrey, K. P. and R. J. Stevenson. 1992. Responses of benthic algae to pulses in current and

nutrients during simulations of subscouring spates. J. N. Am. Benthol. Soc. 11:37-48.
July 2000 REFERENCES

PAGE 137

Hurlburt, S. H. 1984. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr.

54:187-211.

Hutchinson, G. E. 1967. A Treatise on Limnology. Vol. 2. John Wiley, New York.

Hutchinson, G. E. 1975. A Treatise on Limnology. Vol. 3, Limnological Botany. John Wiley, New

York. 660 pp. 

Hynes, H. B. N. 1969. The enrichment of streams. In: Eutrophication: Causes, Consequences,

Correctives-Proceedings of a Symposium. National Academy of Sciences, Washington, DC. pp.

188-196.

Ibelings, B., W. Admiraal, R. Bijker, T. Letswaart, and H. Prins. 1998. Monitoring of algae in Dutch

rivers: Does it meet its goals? J. Appl. Phycol. 10:171-181.

Jasper, S. and M. L. Bothwell. 1986. Photosynthetic characteristics of lotic periphyton. Can. J. Fish.

Aquat. Sci. 43:1960-1969.

Jaworski, N. A., R. W. Howarth, and L. J. Hetling. 1997. Atmospheric deposition of nitrogen oxides

onto the landscape contributes to coastal eutrophication in the northeast United States. Environ.

Sci. Technol. 31:1995-2004.

Johnson, H. E. and C. L. Schmidt. 1988. Clark Fork Basin Status Report & Action Plan. Montana

Governor’s Office, Helena, MT.

Jones, Jr., J. B. 1995. Factors controlling hyporheic respiration in a desert stream. Freshwater Biol.

34:101-109. 

Jones, Jr., J. B., S. G. Fisher, and N. B. Grimm. 1995. Nitrification in the hyporheic zone of a desert

stream ecosystem. J. N. Am. Benthol. Soc. 14:249-258.

Jones, J. R. and R. W. Bachmann. 1976. Prediction of phosphorus and chlorophyll levels in lakes. J.

Water Pollut. Control Fed. 48:2176-2182. 

Kadono, Y. 1980. Photosynthetic carbon sources in some Potamogeton species. Botanical Magazine

Tokyo 93:185-193.

Kaplan, L. A. 1992. Comparison of high-temperature and persulfate oxidation methods for

determination of dissolved organic carbon in freshwaters. Limnol. Oceanogr. 37:1119-1125.

Karr, J. R. and E. W. Chu. 1999. Restoring Life in Running Waters. Island Press, Washington, DC.

Kelly, M. G. and B. A. Whitton. 1995. The trophic diatom index: A new index for monitoring

eutrophication in rivers. J. Appl. Phycol. 7:433-444.
July 2000 REFERENCES

PAGE 138

Kelly, M. G., C. J. Penny, and B. A Whitton. 1995. Comparative performance of benthic diatom indices

used to assess river water quality. Hydrobiologia 302:179-88.

Kelly, M. G., A. Cazaubon, E. Coring, A. Dell’Uomo, L. Ector, B. Goldsmith, H. Guasch, J. Hürlimann,

A. Jarlman, B. Kawecka, J. Kwandrans, R. Laugaste, E.-A. Lindstrøm, M. Leitao, P. Marvan, J.

Padisák, E. Pipp, J. Prygiel, E. Rott, S. Sabater, H. van Dam, and J. Viznet. 1998. 

Recommendations for the routine sampling of diatoms for water quality assessments in Europe. 

J. Appl. Phycol. 10:215-224.

Kentucky Division of Water. 1993. Methods for Assessing Biological Integrity of Surface Waters. 

Kentucky Natural Resources and Environmental Protection Cabinet, Frankfort, KY. 

Kerans, B. L. and J. R. Karr. 1994. A benthic index of biotic integrity (B-IBI) for rivers of the

Tennessee Valley. Ecol. Appl. 4:768-785.

Klotz, R. L. 1992. Factors influencing alkaline phosphatase activity of stream epithilon. J. Freshwater

Ecol. 7(2):233-242.

Kolkwitz, R. and M. Marsson. 1908. Ökologie der pflanzliche Saprobien. Ber. Deutsche Bot. Ges.

26:505-519.

Land & Water Consulting. 1996. Water Quality Status and Trends Monitoring System for the Clark

Fork-Pend Oreille Watershed. Report to Montana Dept. Env. Quality.

Leopold, L. B., M. G. Wolman, and J. P. Miller. 1964. Fluvial Processes in Geomorphology. W. H.

Freeman, San Francisco, CA. 

Lohman, K., J. R. Jones, and C. Baysinger-Daniel. 1991. Experimental evidence for nitrogen limitation

in an Ozark stream. J. N. Am. Benthol. Soc. 10:13-24.

Lohman, K., J. R. Jones, and B. D. Perkins. 1992. Effects of nutrient enrichment and flood frequency on

periphyton biomass in northern Ozark streams. Can. J. Fish. Aquat. Sci. 49:1198-1205.

Lowe, R. L. 1974. Environmental Requirements and Pollution Tolerance of Freshwater Diatoms. U.S.

Environmental Protection Agency, Cincinnati, OH. EPA-670/4-74-005.

Lowe, R. L. and G. D. LaLiberte. 1996. Benthic Stream Algae: Distribution and Structure. In: 

Methods in Stream Ecology. Hauer, F. R. and G. A. Lamberti (eds.). Academic Press, San

Diego, CA. pp. 269-293. 

Lowe, R. L., and Y. Pan. 1996. Benthic algal communities and biological monitors. In: Algal Ecology:

Freshwater Benthic Ecosystems. Stevenson, R. J., M. L. Bothwell, and R. L. Lowe (eds.). 

Academic Press, San Diego, CA. pp. 705-739.
July 2000 REFERENCES

PAGE 139

Lowrance, R., R. Todd, J. Fail, Jr., O. Hendrickson Jr., R. Leonard, and L. Asmussen. 1984. Riparian

forests as nutrient filters in agricultural watersheds. Bioscience 34(6):374-377. 

Lung, W. and K. Larson. 1995. Water quality modeling of the Upper Mississippi River and Lake Pepin. 

J. Env. Engineering 121:691-699.

Martí, E., S. G. Fisher, J. J. Schade, and N. B. Grimm. (In press). (b) Effect of flood frequency on

hydrological and chemical linkages between streams and their riparian zones: An intermediate

disturbance model. In: Surface-Subsurface Interactions in Streams. Jones, Jr., J. B. and P. J.

Mulholland (eds.). 

Martí, E., S. G. Fisher, J. J. Schade, J. R. Welter, and N. B. Grimm. (n press). (a) Hydrological and

chemical linkages between streams and their riparian zones: An intermediate disturbance model.

Internationale Vereinigung fur Theoretische und Angewandt Limnologie, Verhandlungen 27.

Marti, E., N. B. Grimm, and S. G. Fisher. 1997. Pre- and post-flood nutrient retention efficiency in a

desert stream ecosystem. J. N. Am. Benthol. Soc. 16:805-819.

Marzolf, E. R., P. J. Mulholland, and A. D. Steinman. 1994. Improvements to the diurnal upstreamdownstream dissolved oxygen change technique for determining whole-stream metabolism in

small streams. Can. J. Fish. Aquat. Sci. 51:1591-1599.

Marzolf, E. R., P. J. Mulholland, and A. D. Steinman. 1998. Reply: Improvements to the diurnal

upstream-downstream dissolved oxygen change technique for determining whole-stream

metabolism in small streams. Can. J. Fish. Aquat. Sci. 55:1786-1787.

May, C. W., E. B. Welch, R. R. Horner, J. R. Karr, and B. W. Mar. 1997. Quality Indices for

Urbanization Effects in Puget Sound Lowland Streams. Department of Civil Engineering,

University of Washington, Water Res. Series Tech. Rep. No. 154.

McCormick, P. V. and J. Cairns, Jr. 1994. Algae as indicators of environmental change. J. Appl.

Phycol. 6:509-526.

McCormick, P. V. and R. J. Stevenson. 1998. Periphyton as a tool for ecological assessment and

management in the Florida Everglades. J. Appl. Phycol. 34:726-733.

McGarrigle, M. L. 1993. Aspects of river eutrophication in Ireland. Ann. Limnol. 29:355-364.

McIntire, C. D., S. V. Gregory, A. D. Steinman, and G. A. Lamberti. 1996. Modeling benthic algal

communities: An example from stream ecology. In: Algal Ecology: Freshwater Benthic

Ecosystems. Stevenson, R. J., M. L. Bothwell, and R. L. Lowe (eds.). Academic Press, San

Diego, CA. pp. 669-704.
July 2000 REFERENCES

PAGE 140

McIntire, C. D. and H. K. Phinney. 1965. Laboratory studies of periphyton production and community

metabolism in lotic environments. Ecol. Monogr. 35:237-258.

Miltner, R. J. and E. T. Rankin. 1998. Primary nutrients and the biotic integrity of rivers and streams. 

Freshwater Biol. 40:145-158.

Molles, M. C., Jr. and C. N. Dahm. 1990. A perspective on El Niño and La Niña: Global implications

for stream ecology. J. N. Am. Benthol. Soc. 9:68-76.

Moore, L. W., C. Y. Chew, R. H. Smith, and S. Sahoo. 1992. Modeling of best management practices

on North Reelfoot Creek, Tennessee. Water Environ. Res. 64:241-247. 

Mueller, D. K. 1998. Quality of Nutrient Data from Streams and Ground Water Sampled During 1993-

95. National Water-Quality Assessment Program: U.S. Geological Survey Open-File Report 98-

276. 25 pp.

Mueller, D. K., P. A. Hamilton, D. R. Helsel, K. J. Hitt, and B. C. Ruddy. 1995. Nutrients in Ground

Water and Surface Water of the United States--Analysis of Data Through 1992. U.S. Geological

Survey Water-Resources Investigations Report 95-4031. 74 pp.

Mueller, D. K., J. D. Martin, and T. J. Lopes. 1997. Quality-Control Design for Surface-Water

Sampling in the National Water-Quality Assessment Program. U.S. Geological Survey OpenFile Report 7-223. 17 pp.

Mulholland, P. J., E. R. Marzolf, S. P. Hendricks, R. V. Wilkerson, and A. K. Baybayan. 1995. 

Longitudinal patterns of nutrient cycling and periphyton characteristics in streams: A test of

upstream-downstream linkage. J. N. Am. Benthol. Soc. 14(3):357-370.

Munn, M. D., L. L. Osborne, and M. J. Wiley. 1989. Factors influencing periphyton growth in

agricultural streams of central Illinois. Hydrobiologia 174:89-97.

Myers, R. 1990. Classical and Modern Regression with Applications. PWS-Kent, Boston, MA. 

Nebel, B. J. and R. T. Wright. 2000. Environmental Science: The Way the World Works. 7th ed. 

Prentice-Hall, Upper Saddle River, NJ. 

Newbold, J. D., J. W. Elwood, R. V. O’Neill, and W. Van Winkle. 1981. Measuring nutrient spiraling

in streams. Can. J. Fish. Aquat. Sci. 38:860-863.

Nolan, B. T., B. C. Ruddy, K. J. Hitt, and D. R. Helsel. 1997. Risk of nitrate in groundwaters of the

United States – A national perspective. Environ. Sci. Technol. 31(8):2229-2236.

Nordin, R. N. 1985. Water Quality Criteria for Nutrients and Algae (Technical Appendix). British

Columbia Ministry of the Environment, Victoria, BC. 104 pp. 
July 2000 REFERENCES

PAGE 141

Nordin, R. N. 1995. Personal communication. Water Management Branch, British Columbia Ministry

of the Environment, Victoria, British Columbia. 

OAR (Oregon Administrative Rules). 2000. Water Quality Program Rules, 340-041-0150, Nuisance

Phytoplankton Growth.

Odum, H. T. 1956. Primary production in flowing waters. Limnol. Oceanogr. 1:102-117.

OECD. 1982. Eutrophication of Waters: Monitoring, Assessment and Control. OECD, Paris. 154 pp.

O’Hearn, M. O. and J. P. Gibb. 1980. Groundwater Discharge to Illinois Streams. Illinois Institute of

Natural Resources, State Water Survey Division, Groundwater Section, SWS Contract Report

246, Champaign, IL. 31 pp. 

Oliver, B. G. and D. B. Schindler. 1980. Trihalomethanes from the chlorination of aquatic algae. 

Environ. Sci. Technol. 14:1502-1505. 

Olsen, D. S. and J. P. Potyondy (Eds.). 1999. Wildland Hydrology. American Water Resources

Association. Herndon, VA. TPS-99-3. 536 pp. 

Omernik, J. A. 1977. Nonpoint Source-Stream Nutrient Level Relationships: A Nationwide Study. U.S.

Environmental Protection Agency, Corvallis, OR. 151 pp. EPA 600/3-77-105. (Map scale

1:7,500,000)

Omernik, J. A. 1987. Ecoregions of the conterminous United States. Ann. Assoc. Am. Geographers

77:118-125.

Omernik, J. A. 1995. Ecoregions: A spatial framework for environmental management. In: Biological

Assessment and Criteria: Tools for Water Resource Planning and Decision Making. Davis, W.

S. and P. S. Thomas (eds.). Lewis Publishers, Boca Raton, FL. pp. 49-66.

Omernik, J. A. 2000. Draft Aggregations of Level III Ecoregions for the National Nutrient Strategy. 

[http://www.epa.gov/ost/standards/ecomap.html].

Omernik, J. M. 1986. Ecoregions of the United States. U.S. Environmental Protection Agency,

Corvallis Environmental Research Laboratory. 1 p.

O’Neill, R. V., D. L. DeAngelis, J. B. Waide, and T. F. H. Allen. 1986. A Hierarchical Concept of

Ecosystems. Princeton University Press, Princeton, NJ.

Ott, L. 1988. An Introduction to Statistical Methods and Data Analysis. 3rd ed. PWS Publishing

Company, Boston, MA. 

Ott, W. R. 1995. Environmental Statistics and Data Analysis. Lewis Publishers, Boca Raton, FL.
July 2000 REFERENCES

PAGE 142

Palmer, C. M. 1962. Algae in Water Supplies. U.S. Department of Health, Education and Welfare.

Washington, DC.

Pan, Y., R. J. Stevenson, B. H. Hill, A. T. Herlihy, and G. B. Collins. 1996. Using diatoms as indicators

of ecological conditions in lotic systems: A regional assessment. J. N. Am. Benthol. Soc.

15:481-495.

Pan, Y., R. J. Stevenson, B. H. Hill, and A. T. Herlihy. (In press). Ecoregions and benthic diatom

assemblages in the Mid-Atlantic Highland streams, USA. J. N. Am. Benthol. Soc.

Paulsen, S. G., D. P. Larsen, P. R. Kaufmann, T. R. Whittier, J. R. Baker, D. V. Peck, J. McGue, R. M.

Hughes, D. McMullen, D. Stevens, J. L. Stoddard, J. Larzorchak, W. Kinney, A. R. Selle, and R.

Hjort. 1991. Environmental Monitoring and Assessment Program (EMAP) Surface Waters

Monitoring and Research Strategy – Fiscal Year 1991.

Pearsall, W. H. 1920. The aquatic vegetation of the English Lakes. J. Ecol. 8:163-199.

Perdue, E. M., F. Mantoura, J. Ertel, C. Lee, K. Mopper, G. Peyton, E. Tanoue, P. M. Williams, O.

Zafirou, and P. Coble. 1993. Mechanisms subgroup report. Mar. Chem. 41:51-60.

Perrin, C. J., M. L. Bothwell, and P. A. Slaney. 1987. Experimental enrichment of a coastal stream in

British Columbia: Effects of organic and inorganic additions on autotrophic periphyton

production. Can. J. Fish. Aquat. Sci. 44:1247-1256.

Peterjohn, W. T. and D. L. Correll. 1984. Nutrient dynamics in an agricultural watershed: Observations

on the role of a riparian forest. Ecology 65(5):1466-1475. 

Peterson, B. J., L. Deegan, J. Helfrich, J. E. Hobbie, M. Hullar, B. Moller, T. E. Ford, A. Hershey, A.

Hiltner, G. Kipphut, M. A. Lock, D. M. Fiebig, V. McKinley, M. C. Miller, J. R. Vestal, R.

Ventullo, and G. Volk. 1993. Biological responses of a tundra river to fertilization. Ecology

74:653-672.

Peterson, C. G. 1996. Mechanisms of lotic microalgal colonization following space-clearing

disturbances at different spatial scales. Oikos 77:417-435. 

Pickett, S. T. A., J. J. Kolasa, and S. L. Collins. 1989. The ecological concept of disturbance and its

expression at various hierarchical levels. Oikos 54:129-136.

Pielou, E. C. 1975. Ecological Diversity. Wiley, New York.

Pielou, E. C. 1984. The Interpretation of Ecological Data, A Primer on Classification and Ordination.

John Wiley, New York. 263 pp.
July 2000 REFERENCES

PAGE 143

Pinay, G. and H. Decamps. 1988. The role of riparian woods in regulating nitrogen fluxes between the

alluvial aquifer and surface water: A conceptual model. Regulated Rivers; Research and

Management 2:507-516.

Pitcairn, E. R. and H. A. Hawkes. 1973. The role of phosphorus in the growth of Cladophora. Water

Res. 7:159-171.

Poff, N. L. and J. V. Ward. 1989. Implications of streamflow variability and predictability for lotic

community structure: A regional analysis of streamflow patterns. Can. J. Fish. Aquat. Sci.

46:1805-1818.

Poole, R. W. 1972. An Introduction to Quantitative Ecology. McGraw-Hill, New York.

Power, M. E. 1990. Effects of fish in river food webs. Science 250:811-814.

Power, M. E. 1992. Hydrologic and trophic controls of seasonal algal blooms in northern California

rivers. Arch. Hydrobiol. 125:385-410.

Power, M. E. and A. J. Stewart. 1987. Disturbance and recovery of an algal assemblage following

flooding in an Oklahoma stream. Am. Midland Naturalist 117:333-345. 

Prairie, Y. T., C. M. Duarte, and J. Kalff. 1989. Unifying nutrient-chlorophyll relations in lakes. Can. J.

Fish. Aquat. Sci. 46:1176-1182.

Preston, S. D. and J. W. Brakebill. 1999. Application of Spatially Referenced Regression Modeling for

the Evaluation of Total Nitrogen Loading in the Chesapeake Bay Watershed. U.S. Geological

Survey Water Resources Investigation Report 99-4054. 12 pp.

Pringle, C. M. and F. J. Triska. 1996. Effects of nutrient enrichment on periphyton. In: Methods in

Stream Ecology. Hauer, F. R. and G. A. Lamberti (eds.). Academic Press, San Diego. pp. 607-

623.

Puckridge, J. T., F. Sheldon, K. F. Walker, and A. J. Boulton. 1998. Flow variability and the ecology of

large rivers. Marine Freshwater Res. 49(1):55-72.

Quinn, J. M. 1991. Guidelines for the Control of Undesirable Biological Growths in Water. National

Inst. Water and Atmos. Res., Consultancy Report No. 6213/2.

Quinn, J. M., R. J. Davies-Colley, C. W. Hickey, M. L. Vickers, and P. A. Ryan. 1992. Effects of clay

discharges on streams 2. Benthic invertebrates. Hydrobiologia 248:235-247.

Rabalais, N. N., R. E. Turner, Q. Dortch, W. J. Wiseman, Jr., and B. K. S. Gupta. 1996. Nutrient

changes in the Mississippi River and system responses on the adjacent continental shelf. 

Estuaries 19(2B):386-407.
July 2000 REFERENCES

PAGE 144

Raschke, R. 1993. Guidelines for assessing and predicting eutrophication status of small southeastern

piedmont impoundments. EPA Region IV. Environmental Services Division, Ecological

Support Branch, Athens, GA.

Reckhow, K. H. and S. C. Chapra. 1983. Data Analysis and Empirical Modeling; Engineering

Approaches for Lake Management Volume I. Butterworths, Boston, MA.

Remington, R. D. and M. A. Schork. 1985. Statistics with Applications to the Biological and Health

Sciences. Prentice Hall, Englewood Cliffs, NJ.

Renberg, I. and T. Hellberg. 1982. The pH history of lakes in Southwestern Sweden, as calculated from

the subfossil diatom flora of the sediments. Ambio 11:30-33.

Resh, V. H., M. J. Myers, and M. J. Hannaford. 1996. Macroinvertebrates as biotic indicators of

environmental quality. In: Methods in Stream Ecology. Hauer, F. R. and G. A. Lamberti (eds.). 

Academic Press, San Diego. pp. 647-667.

Resh, V. H. and D. M. Rosenberg (eds.). 1984. The Ecology of Aquatic Insects. Praeger Special

Studies-Praeger Scientific, New York.

Rickert, D. A., R. R. Petersen, S. W. McKenzie, W. G. Hines, and S. A. Wille. 1977. Algal conditions

and the potential for future algal problems in the Willamette River, Oregon. U.S. Geological

Circular 715-G. 39 pp.

Rodhe, W. 1948. Environmental requirements of freshwater plankton algae. Experimental Studies in

the Ecology of Phytoplankton. Symbol. Bot. Upsalien 10. 149 pp.

Rosen, B. H. and R. L. Lowe. 1984. Physiological and ultrastructural responses of Cyclotella

meneghiniana (Bacillariophyta) to light intensity and nutrient limitation. J. Phycol. 20:173-183.

Rosenfield, J. and J. C. Roff. 1991. Primary production and potential availability of autochthonous

carbon in southern Ontario streams. Hydrobiologia 224:99-109.

Rosgen, D. L. 1994. A classification of natural rivers. Catena 22:169-199.

Rosgen, D. 1996. Applied River Morphology. Wildland Hydrology, Pagosa Springs, CO. 380 pp.

Sartory, D. P. and J. U. Grobbelaar. 1984. Extraction of chlorophyll a from freshwater phytoplankton

for spectrophotometric analysis. Hydrobiologia 114:117-187.

Schanz, F. and H. Juon. 1983. Two different methods of evaluating nutrient limitations of periphyton

bioassays using water from the River Rhine and eight of its tributaries. Hydrobiologia 102:187-

195.
July 2000 REFERENCES

PAGE 145

Scheckenberger, R. B., and A. S. Kennedy. 1994. The use of HSPF in subwatershed planning. In: 

Current Practices in Modelling the Management of Stormwater Impacts. W. James (ed.). Lewis

Publ., Boca Raton, FL. pp. 175-187.

Schiefele, S. and C. Schreiner. 1991. Use of diatoms for monitoring nutrient enrichment, acidification,

and impact of salt in rivers in Germany and Austria. In: Use of Algae for Monitoring Rivers. 

Whitton, B. A., E. Rott, and G. Friedrich (eds.). Institut für Botanik, Universität Innsbruck,

Austria. pp. 103-110.

Schade, J. D. and S. G. Fisher. 1997. The influence of leaf litter on a Sonoran Desert stream ecosystem.

J. N. Am. Benthol. Soc. 16:612-626.

Sheath, R. G. and J. M. Burkholder. 1985. Characteristics of soft water streams in Rhode Island. II.

Composition and seasonal dynamics of macroalgal communities. Hydrobiologia 128:109-118.

Shelton, L.R. 1994. Field Guide for Collection and Processing Stream-Water Samples for the National

Water Quality Assessment Program. U.S. Geological Survey Open-File Report 94-455. 42 pp.

Silvey, J. K. G. and J. T. Watt. 1971. The interrelationship between freshwater bacteria, algae, and

actinomycetes in Southwestern reservoirs. In: The Structure and Function of Freshwater

Microbial Communities. J. Cairns, Jr. (ed.). American Microscopical Society Symposium. 

Research Division monograph 3. Virginia Polytechnic Institute and State University,

Blacksburg, VA. 

Slack, K. V. 1971. Average dissolved oxygen-measurement and water quality significance. J. Water

Pollut. Control Fed. 43:433-446.

Slaney, P. A. and B. R. Ward. 1993. Experimental fertilization of nutrient deficient streams in British

Columbia. In: Le Development du Saumon Atlantique Au Quebec: Connaitre Les Regles du Jeu

Pour Reussir. Shooner G. and S. Asselin (eds.). Colloque international de la Federation

quebecoise pour le saumon atlantique, Quebec, Canada.

Smart, R. M. 1990. Effects of Water Chemistry on Submersed Aquatic Plants: A Synthesis.

Miscellaneous Paper A-90-4. U.S. Army Engineer Waterways Experiment Station, Vicksburg,

MS.

Smith, R. A., G. E. Schwarz, and R. B. Alexander. 1997. Regional interpretation of water-quality

monitoring data. Water Res. 33(12):2781-2798.

Smith, R. E. H. and J. Kalff. 1981. The effect of phosphorus limitation on algal growth rates: Evidence

from alkaline phosphatase. Can. J. Fish. Aquat. Sci. 38:1421-1427. 

Smith, V. H. 1982. The nitrogen and phosphorus dependence of algal biomass in lakes: An empirical

and theoretical analysis. Limnol. Oceanogr. 27:1101-1112. 
July 2000 REFERENCES

PAGE 146

Smith, V. H. 1998. Cultural eutrophication of inland, estuarine, and coastal waters. In: Successes,

Limitations and Frontiers in Ecosystem Science. Pace, M. L. and P. M. Groffman (eds.). 

Springer-Verlag, New York. pp. 7-49.

Smith, V. H., G. D. Tilman, and J. C. Nekola. 1999. Eutrophication: Impacts of excess nutrient inputs

on freshwater, marine, and terrestrial ecosystems. Environ. Pollut. 100:179-196.

Sneath, P. H. A. and R. R. Sokal. 1973. UPGMA (Unweighted Pair-Group Method Using Arithmetic

averages). Numerical Taxonomy. W. H. Freeman, San Francisco, CA.

Sokal, R. R. and F. J. Rohlf. 1998. Biometry: The Principles and Practice of Statistics in Biological

Research. W. H. Freeman, New York. 887 pp.

Sorenson, S. K., S. D. Porter, K. K. B. Akers, M. A. Harris, S. J. Kalkhoff, K. E. Lee, L. R. Roberts, and

P. J. Terrio. 1999. Water Quality and Habitat Conditions in Upper Midwest Streams Relative to

Riparian Vegetation and Soil Characteristics, August 1997: Study Design, Methods, and Data.

U.S. Geological Survey Open-File Report 99-202. 53 pp.

Sosiak, A. J. Personal communication. Long-term response of periphyton and macrophytes to reduced

municipal nutrient loading to the Bow River (Alberta, Canada). Alberta Environment Protection,

Calgary, Alberta. 

Spence, D. H. N. 1975. Light and plant responses in freshwater. In: Light as an Ecological Factor. 

Evans, G. C., R. Bainbridge, and O. Rackham (eds.). Blackwell Scientific Publishers, Oxford. 

pp. 93-134.

Squillace, P. J., J. P. Caldwell, P. M. Schulmeyer, and C. A. Harvey. 1996. Movement of Agricultural

Chemicals Between Surface Water and Ground Water, Lower Cedar River Basin, Iowa. U.S.

Geological Survey Water-Supply Paper 2448. 59 pp.

Stanley, E. H. 1999. personal communication. Department of Zoology, University of Wisconsin,

Madison, WI.

Stanley, E. H. and A. J. Boulton. 1995. Hyporheic processes during flooding and drying in a Sonoran

Desert stream. I. Hydrologic and chemical dynamics. Arch. Hydrobiol. 134:1-26.

Stanley, E. H., S. G. Fisher, and N. B. Grimm. 1997. Ecosystem expansion and contraction in streams.

BioScience 47:427-436.

Stanley, E. H. and H. M. Valett. 1992. Interaction between drying and the hyporheic zone of a desert

stream ecosystem. In: Climate Change and Freshwater Ecosystems. Firth, P. and S. G. Fisher

(eds.). Springer-Verlag, New York. pp. 234-249.
July 2000 REFERENCES

PAGE 147

Steinberg, C. and S. Schiefele. 1988. Indication of trophy and pollution in running waters. Zeitschrift

für Wasser-Abwasser Forschung 21:227-234.

Steinman, A. D. 1996. Effects of grazers on freshwater benthic algae. In: Algal Ecology: Freshwater

Benthic Ecosystems. Stevenson, R. J., M. L. Bothwell, and R. L. Lowe (eds.). Academic Press,

San Diego, CA. pp. 341-373. 

Steinman, A. D. and G. A. Lamberti. 1996. Biomass and pigments of benthic algae. In: Methods in

Stream Ecology. Hauer, F. R. and G. A. Lamberti (eds.). Academic Press, San Diego, CA. pp.

295-313.

Steinman, A. D. and P. J. Mulholland. 1996. Phosphorus limitation, uptake, and turnover in stream

algae. In: Methods in Stream Ecology. Hauer, F. R. and G. A. Lamberti (eds.). Academic

Press, San Diego, CA. pp. 161-189.

Stevenson, J. Personal communication. Michigan State University, East Lansing, MI.

Stevenson, R. J. 1984. Epilithic and epipelic diatoms in the Sandusky River, with emphasis on species

diversity and water quality. Hydrobiologia 114:161-175.

Stevenson, R. J. 1996. An introduction to algal ecology in freshwater benthic habitats. In: Algal

Ecology: Freshwater Benthic Ecosystems. Stevenson, R. J., M. Bothwell, and R. L. Lowe (eds.). 

Academic Press, San Diego, CA. pp. 3-30.

Stevenson, R. J. 1997. Scale dependent determinants and consequences of benthic algal heterogeneity. 

J. N. Am. Benthol. Soc. 16(1):248-262.

Stevenson, R. J. 1998. Diatom indicators of stream and wetland stressors in a risk management

framework. Environ. Monitor. Assess. 51:107-118.

Stevenson, R. J. (In press). Using algae to assess wetlands with multivariate statistics, multimetric

indices, and an ecological risk assessment framework. In: Biomonitoring and Management of

North American Freshwater Wetlands. Batzger, D., R. Rader, and S. Wissinger (eds.). John

Wiley, New York.

Stevenson, R. J. and L. L. Bahls. 1999. Periphyton protocols. In: Revision to Rapid Bioassessment

Protocols for Use in Streams and Rivers: Periphyton, Benthic Macroinvertebrates, and Fish.

Barbour, M. T., J. Gerritsen, and B. D. Snyder (eds.). U.S. Environmental Protection Agency,

Washington, DC.

Stevenson, R. J. and Y. Pan. 1999. Assessing ecological conditions in rivers and streams with diatoms. 

In: The Diatoms: Applications to the Environmental and Earth Sciences. Stoermer, E. F. and J.

P. Smol (eds.). Cambridge University Press, Cambridge, UK. pp. 11-40.
July 2000 REFERENCES

PAGE 148

Stevenson, R. J. and J. P. Smol. (In press). Use of algae in environmental assessment. In: Freshwater

Algae in North America: Classification and Ecology. Wehr, J. D. and R. G. Sheath (eds.).

Academic Press, San Diego, CA.

Stockner, J. G. and K. R. S. Shortreed. 1976. Autotrophic production in Carnation Creek, a coastal

rainforest stream on Vancouver Island, British Columbia. J. Fish. Res. Board Can. 33:1553-

1563.

Stoermer, E. F. and J. P. Smol (eds.). 1999. The Diatoms: Applications for the Environmental and

Earth Sciences. Cambridge University Press, Cambridge. 484 pp.

Storr, J. F. and R. A. Sweeney. 1971. Development of a theoretical seasonal growth response curve of

Cladophora glomerata to temperature and photoperiod. Proc. 14th Conf. Gt. Lakes Res. 14:119-

127.

Strahler, A. N. 1964. Quantitative geomorphology of drainage basins and channel networks. In Chow,

V. T. (ed.). Handbook of Applied Hydrology. McGraw-Hill, New York. pp. 439-476.

Strayer, D. L., N. F. Caraco, J. J. Cole, S. Findlay, and M. L. Pace. 1999. Transformation of freshwater

ecosystems by bivalves. Bioscience 49(1):19-27.

Stromberg, J. C., D. T. Patten, and B. D. Richter. 1991. Flood flows and dynamics of Sonoran riparian

forests. Rivers 2(3):221-235.

Stumm, W. G. and J. J. Morgan. 1981. Aquatic Chemistry. John Wiley, New York.

Suess, M .J. 1981. Health aspects of eutrophication. Water Qual. Bull. 6:63-64. 

Tate, C. M. 1990. Patterns and controls of nitrogen in tallgrass prairie streams. Ecology 71:2007-2018.

Taylor, W. D., L. R. Williams, S. C. Hern, V. W. Lambou, C. L. Howard, F. A. Morris, and M. K.

Morris. 1981. Phytoplankton Water Quality Relationships in U.S. Lakes, Part VIII: Algae

Associated with or Responsible for Water Quality Problems. Environmental Protection Agency,

Las Vegas, NV. Report EPA-600/S3-80-100 or NTIS PB-81-156831. 

ter Braak, C. J. F. and H. van Dam. 1989. Inferring pH from diatoms: A comparison of old and new

calibration methods. Hydrobiologia 178:209-223.

Thoms, M. C. and F. Sheldon. 1996. The importance of channel complexity for ecosystem processing: 

An example of the Barwon-Darling River. In: Stream Management in Australia. Rutherfurd, I.

(ed.). CRC for Catchment Hydrology, Melbourne, Australia. pp. 111-118.

TNDEC (Tennessee Department of Environment and Conservation). 1996. Standard Operating

Procedure for Modified Clean Technique Sampling Protocol. Tennessee Department of

Environment and Conservation, Division of Water Pollution Control, Nashville, TN.
July 2000 REFERENCES

PAGE 149

Turner, R. E. and N. N. Rabalais. 1991. Changes in Mississippi River water quality this century: 

Implications for coastal food webs. Bioscience 41(3):140-147.

Turner, R. E. and N. N. Rabalais. 1994. Coastal eutrophication near the Mississippi River delta. Nature

368:619-621.

USEPA. 1971. Algal Assay Procedure: Bottle Test. National Eutrophication Research Program,

Corvallis, OR.

USEPA. 1973. Water Quality Criteria - 1972. Tech. Adv. Comm., Nat. Acad. Sci. Engr. U.S.

Government Printing Office.

USEPA. 1986. Quality Criteria for Water - 1986. Office of Water, U.S. Environmental Protection

Agency, Washington, DC. EPA 440/5-86-001.

USEPA. 1993a. Guidance Specifying Management Measures for Sources of Nonpoint Pollution in

Coastal Waters. Office of Water, U.S. Environmental Protection Agency, Washington, DC. 

EPA 840-B-92-002.

USEPA. 1993b. Clark Fork-Pend Oreille Basin Water Quality Study. EPA 910/R-93-006.

USEPA. 1994. Water Quality Standards Handbook. 2nd ed. Office of Water, U.S. Environmental

Protection Agency. EPA 823-B-94-005a.

USEPA. 1995. Drinking water regulations and health advisories. Office of Water, U.S. Environmental

Protection Agency, Washington, DC. EPA 822-R-95-001. 

USEPA. 1996. National Water Quality Inventory 1996 Report to Congress. Office of Water, U.S. 

Environmental Protection Agency. EPA 841-R-97-008.

USEPA. 1998a. National Strategy for the Development of Regional Nutrient Criteria. Office of Water,

U.S. Environmental Protection Agency. EPA 822-R-98-002.

USEPA. 1998b. Guidance for Quality Assurance Project Plans. Office of Research and Development,

U.S. Environmental Protection Agency. EPA/600/R-98/018.

USEPA. 1999. Protocol for Developing Nutrient TMDLs. Office of Water (4503F), U.S.

Environmental Protection Agency. EPA 841-B-99-007.

UK Environment Agency. 1998. Aquatic Eutrophication in England and Whales. Environmental Issues

Series Consultative Report.

Valett, H. M., S. G. Fisher, and E. H. Stanley. 1990. Physical and chemical characteristics of the

hyporheic zone of a Sonoran Desert stream. J. N. Am. Benthol. Soc. 9:201-215.
July 2000 REFERENCES

PAGE 150

Valett, H. M., S. G. Fisher, N. B. Grimm, and P. Camill. 1994. Vertical hydrologic exchange and

ecological stability of a desert stream ecosystem. Ecology 75:548-560.

Van Dam, H., A. Mertenes, and J. Sinkeldam. 1994. A coded checklist and ecological indicator values

of freshwater diatoms from the Netherlands. Netherlands J. Aquat. Ecol. 28:117-133.

Van Nieuwenhuyse, E. E. and J. R. Jones. 1996. Phosphorus-chlorophyll relationship in temperate

streams and its variation with stream catchment area. Can. J. Fish. Aquat. Sci. 53:99-105.

Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell, and C. E. Cushing. 1980. The river

continuum concept. Can. J. Fish. Aquat. Sci. 37:130-137.

Vitousek, P. M., J. D. Aber, R. W. Howarth, G. E. Likens, P. A. Matson, D. W. Schindler, W. H.

Schlesinger, and D. G. Tilman. 1997. Human alteration of the global nitrogen cycle: Sources

and consequences. Ecol. Appl. 7(3):737-750.

Wallace, J. B. and J. R. Webster. 1996. The role of macroinvertebrates in stream ecosystem function. 

Ann. Rev. Entomol. 41:115-139. 

Walton, S. P., E. B. Welch, and R. R. Horner. 1995. Stream periphyton response to grazing and changes

in phosphorus concentration. Hydrobiologia 302:31-46.

Walton, W. C. 1965. Ground Water Recharge and Runoff in Illinois. Report of Investigation 48,

Illinois State Water Survey, Urbana, IL. 55 pp.

Ward, M. H., S. D. Mark, K. P. Cantor, D. D. Weisenburger, A. Correa-Vilasenore, and S. H. Zahm. 

1996. Drinking water nitrate and the risk of non-Hodgkin's lymphoma. Epidemiology 7(5):465-

471.

Warren, C. E., J. H. Wales, G. E. Davis, and P. Douderoff. 1964. Trout production in an experimental

stream enriched with sucrose. J. Wildlife Manage. 28:617-660.

Watershed Management Institute, Inc. 1998. Operation, maintenance, and management of stormwater

management systems. Technical report with USEPA.

(http://www.epa.gov/OWOW/NPS/wmi/wmi.html)

Watson, V. J. 1989a. Dissolved oxygen levels in the middle Clark Fork River, summer 1987. Proc.

Mont. Acad. Sci. 49:147-156.

Watson, V. J. 1989b. Dissolved oxygen levels in the upper Clark Fork River, summer 1987. Proc.

Mont. Acad. Sci. 49:157-162.

Watson, V. J. and B. Gestring. 1996. Monitoring algae levels in the Clark Fork River. Intermountain J.

Sci. 2:17-26.
July 2000 REFERENCES

PAGE 151

Watson, V. J., P. Perlind, and L. Bahls. 1990. Control of algal standing crop by P and N in the Clark

Fork River. Proc. Clark Fork River Symposium, Montana Academy of Sci., Missoula, MT.

Weber, C. I. 1973. Recent developments in the measurement of the response of plankton and periphyton

to changes in their environment. In: Bioassay Techniques and Environmental Chemistry. 

Glass, G. (ed.). Ann Arbor Science Publishers, Ann Arbor, MI. pp. 119-138.

Wehr, J. D. and J. P. Descy. 1998. Use of phytoplankton in large river management. J. Phycol. 34:741-

749.

Welch, E. B. 1992. Ecological Effects of Wastewater. Chapman and Hall, London.

Welch, E. B., R. R. Horner, and C. R. Patmont. 1989. Prediction of nuisance periphytic biomass: A

management approach. Water Res. 23:401-405.

Welch, E. B., J. M. Jacoby, R. R. Horner and M. R. Seeley. 1987. Nuisance biomass levels of periphytic

algae in streams. Hydrobiologia 157:161-168.

Welch E. B., J. M. Jacoby, and C. W. May. 1998. Stream quality. In: River Ecology and Management: 

Lessons from the Pacific Coastal Ecoregion. Naiman, R. J. and R. E. Bilby (eds.). SpringerVerlag. pp. 69-94.

Welch, E. B., C. L. May, and J. M. Jacoby. (In press). Stream quality. In: River Ecology and

Management: Lessons from the Pacific Coastal Ecoregion. Bilby, R. E. and R. J. Naiman (eds.).

Springer-Verlag.

Welch, E. B., J. M. Quinn, and C. W. Hickey. 1992. Periphyton biomass related to point-source

enrichment in seven New Zealand streams. Water Res. 26:669-675.

Welschmeyer, N. A. 1994. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and

phaeopigments. Limnol. Oceanogr. 39:1985-1992.

Wetzel, R. G. 1983. Limnology. 2nd ed. Saunders College Publishing, Philadelphia. 860 pp.

Wetzel, R. G. 1992. Clean water: A fading resource. Hydrobiologia 243/244:21-30.

Wetzel, R. G. and G. E. Likens. 1991. Limnological Analyses. 2nd ed. Springer-Verlag, New York.

Wetzel, R. G. and A. K. Ward. 1992. Primary production. In: Rivers Handbook. P. Calow and G. E.

Petts (eds.). Blackwell Scientific Publications, Oxford, England. pp. 354-369.

Whitmore, T. J. 1989. Florida diatom assemblages as indicators of trophic state and pH. Limnol.

Oceanogr. 34:882-895.

Whittaker, R. H. 1952. A study of summer foliage insect communities in the Great Smoky Mountains. 

Ecol. Monogr. 22:1-44.
July 2000 REFERENCES

PAGE 152
PAGE A-1

APPENDIX A. NUTRIENT CRITERIA CASE STUDIES

The following five case studies are meant to capture some of the variability of stream systems located

throughout the country. Although these case studies exhibit varying levels of complexity, they are meant

to provide the reader with real-world examples of how criteria can be developed on a practical level and

several region-specific issues that may be encountered as one works through the criteria development

process. The ecoregional nutrient criteria process discussed in the Tennessee case study involves

refinement of the Level III ecoregions found within the State; identification and monitoring of reference

stream systems; and correlational analyses of nutrient levels, conventional water chemistry parameters,

and biological indices to derive criteria. In contrast, the Clark Fork, Montana, case study delineates a

process for setting target nutrient and algal levels based on a combination of modified established

criteria, literature values, and observed thresholds for nuisance algal growth. The Upper Midwest river

systems case study describes the results of a cooperative effort among three USGS NAWQA projects in

the upper Midwest Corn Belt region that evaluated algal and macroinvertebrate response to nonpoint

agricultural sources relative to naturally-occurring factors (e.g., riparian vegetation, hydrology). The

Bow River, Canada, case study details the reduction of nuisance biomass (both periphyton and

macrophytes) over a 16-year period through decreases in nitrogen (~50%) and phosphorus (80%) from

domestic wastewater effluent. Finally, the desert stream case study discusses several of the determinants

of nutrient regimes in desert streams that should be considered when developing nutrient criteria for

these, as well as other, complex, highly variable stream systems. 
July 2000 Appendix A. Case Studies

PAGE A-2
July 2000 Appendix A. Case Studies

PAGE A-3

TENNESSEE ECOREGIONAL NUTRIENT CRITERIA

In 1992, the Tennessee DWPC (Division of Water Pollution Control) faced an important decision on how

water quality assessment would be done in the future. When program status was assessed, there were

problems that were likely to be amplified in the future. For example:

• The “one-size-fits-all” statewide numeric criteria approach provided stability, but lacked

regional flexibility. Statewide criteria were clearly overprotective in parts of the state, but

arguably underprotective in other areas.

• Narrative criteria were based on a verbal description of water quality, rather than a number. 

Thus, they provided flexibility, but lacked an objective means of interpretation. As an

example, the narrative criterion for biological integrity states “waters shall not be modified to

the extent that the diversity and/or productivity of aquatic biota within the receiving waters is

substantially reduced”. However, an interpretation of the word “substantially” was not

provided.

• Unlike biological integrity, nutrients did not have specific narrative criteria. Nutrients were

assessed under the more generic “free from” statements found in toxicity sections of the fish

and aquatic life criteria and under “aesthetic” sections of the recreational criteria. Thus, before

any stream could be assessed as impacted by nutrients, the existence of a “problem” had to be

established.

• Tennessee was encouraged by EPA to convert to a watershed approach for issuance of water

quality permits. Without a sense of regional variability in water quality, there was a distinct

disadvantage in goal setting for these watersheds. Additionally, the rigors of 303(d) listing and

TMDL development required accurate interpretation of Tennessee’s narrative water quality

criteria. The specter of lawsuits by citizens and members of the regulated community required

that assessments be defensible. 

A method was needed for comparing the existing conditions found in a stream to unimpacted conditions. 

This reference condition varied across the state. The reference condition established should be within a

similar area, to avoid “apples and oranges” comparisons. It was determined that ecoregions were the best

geographic basis upon which to make this assessment.

An ecoregion is a relatively homogeneous area defined by similarity of climate, landform, soil,

potential natural vegetation, hydrology, and other ecologically relevant variables.

The “Ecoregions of the United States” map (Level III) developed in 1986 by James Omernik of EPA's

Corvallis Laboratory delineated eight ecoregions in Tennessee. The DWPC arranged for Omernik and

Glenn Griffith to sub-regionalize and update state ecoregions. 

The Tennessee Ecoregion Project began in 1993 and was envisioned to occur in three phases: 
July 2000 Appendix A. Case Studies

PAGE A-4

PHASE I: DELINEATE SUB-ECOREGION BOUNDARIES

Phase I of the project involved geographic data gathering, development of a draft sub-regionalization

scheme, and ground-truthing of the draft into a final product. This product included new maps and

digitized coverages for use in the DWPC GIS system. This part of the project began in 1993 and was

completed in 1995. This refinement resulted in a total of 14 ecoregions for the state (Figure A-1).

PHASE II: REFERENCE STREAM SELECTION

EPA and DWPC staff identified potential reference streams. Reference streams selected were located in

relatively unimpacted watersheds typical for that ecoregion (Figure A-2). When possible, watersheds

within state or federally protected areas were selected.

A reference stream is a least impacted waterbody within an ecoregion that can be monitored to establish

a baseline to which other waters can be compared. Reference streams are not necessarily pristine or

undisturbed by humans.

Division staff visited each candidate stream. Chemical and benthic macroinvertebrate samples were used

to cull the list of streams down to a final list. Three reference streams per sub-ecoregion were considered

the minimum requirement.

PHASE III: INTENSIVE MONITORING OF REFERENCE STREAMS

Since August 1996, final selected reference sites have been monitored quarterly. During the first year of

the project, water chemistry was monitored using grab samples collected on three consecutive days (if

possible). Chemical sampling procedures followed modified clean technique methodology as outlined in

the Division’s Chemical Standard Operating Procedure: Modified Clean Technique Sampling Protocol

(TNDEC 1996).

Chemical sampling at reference sites generally included all the parameters historically included by the

Division in its long-term ambient monitoring network. As a concession to resource constraints, certain

parameters, such as mercury, were dropped after they were never detected the first year of sampling. 

Additional parameters such as chlorophyll a were considered to have value, but were not sampled due to

the need make the best use of program funding. Division staff were recently trained in algal assessment

techniques and will likely incorporate rapid biological assessment protocols in future sampling efforts.

Macroinvertebrate samples were collected at ecoregional reference sites beginning in August 1996. 

Habitat and flow were also assessed. Outside expertise was sought to analyze the monitoring data to

determine how sub-ecoregions aggregate by aquatic habitat and biological community to form

ecosystems or bioregions. This step was essential for assessing benthic communities accurately and

consistently.
July 2000 Appendix A. Case Studies

PAGE A-5

Figure A-1. Tennessee Level IV ecoregions and locations of reference streams.

July 2000 Appendix A. Case Studies

PAGE A-7

How Are Reference Stream Data Being Used?

For the first time, the DWPC has regionally-based chemical, physical, and biological data representing

least impacted conditions in Tennessee. These data are important to our program and have multiple

applications.

For some time, it was known that an ecoregion-specific approach to certain water quality standards

would provide greater accuracy. This ecoregion project has provided the data necessary to initiate

nutrient criteria discussions.

Figures A-3 and A-4 illustrate the levels of total phosphorus (TP) and nitrate-nitrite (NO3-NO2),

respectively, documented at reference streams within each ecoregion. The box and whisker plot shows

median measured concentrations and ranges. Based on the data collected, TP at less impacted streams is

generally higher in West Tennessee than Middle and East Tennessee.

Finalizing the Ecoregion Reference Stream Nutrient Database

Additional steps are needed to finalize the ecoregion nutrient database:

• Incorporate data from other States. If reference streams in neighboring States are located

within shared ecoregions and are selected and sampled in a similar manner to those in

Tennessee, the nutrient data can be added into our database.

• Review the database for quality assurance. Data will be checked for outliers that may

represent data entry errors. Outliers that indicate degrading conditions in reference streams

will be identified. The Division considered eliminating outliers based on a consistent

rationale, such as values more than two standard deviations from the mean, but decided against

such an approach.

Development of Regional Interpretations of Narrative Nutrient Criteria

Division staff will propose ecoregion-specific interpretations of the narrative nutrient criteria for TP and

nitrate-nitrite for the year 2000 triennial water quality standards review. These numeric goals will be

used primarily for water quality assessment purposes.

The specific goals will likely be based on the establishment of the nutrient concentration for each

ecoregion or subecoregions database at the 90th percentile of the reference stream data. (However, the

Division has not ruled out the possibility of setting the criteria at the 75th percentile.) As an important

part of the process, Division staff will statistically analyze nutrient levels and their ranges at each subecoregion. Where significant differences exist between sub-ecoregions, the nutrient criteria will be

established at the sub-ecoregion level. Where no significant difference is found between sub-ecoregions,

the data will be aggregated back to the ecoregion level.

These numeric goals will provide the means to assess nutrient levels at similar streams within the same

ecoregion. Streams with nutrient levels less than the 90th (or 75th) percentile of the reference stream

database will be considered to meet the narrative criteria. Streams with nutrient levels higher than the

reference stream database range will be considered in violation of the narrative criteria. These streams 
July 2000 Appendix A. Case Studies

PAGE A-8

Figure A-3. Total phosphorus concentrations (g/L) for reference streams within each ecoregion.

Key: 1 = Mississippi Alluvial Plain, 2 = Mississippi Valley Loess Plains, 3 = Southeastern Plains, 

4 = Interior Plateau, 5 = Southeastern Appalachians, 6 = Central Appalachians, 7 = Ridge and Valley, 

8 = Blue Ridge Mountains.
July 2000 Appendix A. Case Studies

PAGE A-9

Figure A-4. Total nitrate-nitrite concentrations (mg/L) for reference streams within each ecoregion.

Key: 1 = Mississippi Alluvial Plain, 2 = Mississippi Valley Loess Plains, 3 = Southeastern Plains, 

4 = Interior Plateau, 5 = Southeastern Appalachians, 6 = Central Appalachians, 7 = Ridge and Valley, 

8 = Blue Ridge Mountains.
July 2000 Appendix A. Case Studies

PAGE A-10

will be added to the 303(d) list for future TMDL generation. Additionally, the regional interpretation of

the narrative criteria will provide the goal for TMDL control strategies.

Data Relationships

Division staff have taken a preliminary look at the reference stream data in an attempt to investigate

relationships between sampled parameters. Examination of these relationships has three facets: (1)

consideration of possible nutrient data surrogates, (2) exploring relationships between nutrient levels and

biological indices, and (3) comparison of reference stream data to EPA’s regional nutrient database.

1. The initial investigation was whether there was a relationship between nutrient levels and other

chemical constituents in the water column. If a strong correlational relationship could be

established, these other values could be used as data surrogates if nutrient data were unavailable or

as a less costly substitute for nutrient sampling.

Relationships were investigated primarily for turbidity, total organic carbon (TOC), and suspended

solids. We found numerous positive correlations, but the large number of data points at the

detection level caused relationships to be suspect. For example, Figures A-5 and A-6 illustrate the

relationship between total phosphorus and turbidity (r2

 value = 0.282) as well as total phosphorus

and TOC (r2

 value = 0.163) in ecoregion 67g.

We intend to do the same type analysis with regional data from EPA’s national nutrient database. 

At least in theory, this database would contain fewer observations below detection level.

2. If the correlation between either TP or nitrate+nitrite levels and the quality of biological

communities can be established, a stronger rationale for ecoregion-specific numerical nutrient

criteria can be provided. However, it should be noted that even where correlation is strong,

identifying a numeric nutrient criteria is dependent on knowing the biological integrity score above

which, the community is considered impaired. Fortunately, as in the case of nutrients, this

biological integrity goal can be established from the reference stream data.

In sub-ecoregion 71h (Outer Nashville Basin), a preliminary comparison was done. Nitrate-nitrite

levels were compared to two biological indices frequently used by the Division, the North Carolina

Biotic Index (NCBI) and the Hilsenhoff Biotic Index (EPA Rapid Bioassessment Protocols, 1999). 

While there was some scatter in the dataset, a relationship was suggested which was slightly

stronger for the Hilsenhoff index (Figure A-7) than the NCBI. (Figure A-8).

An additional test was done with the appearance of a relationship between nitrate-nitrite and NCBI

scores. According to the reference stream database for sub-ecoregion 71h, the 75th percentile of the

NCBI data is a score of approximately 5.0. Presuming that an NCBI score of 5.0 is the biological

goal for sub-ecoregion 71h, then according to the above chart, nitrate-nitrite levels should not

exceed approximately 1.2 mg/L. Following the same approach with the Hilsenhoff scores also

produced a similar nitrate-nitrite level, approximately 1.2 mg/L. It is interesting to note that the

90th percentile of the reference stream nitrate-nitrite data for 71h is approximately 1.0 mg/L.
July 2000 Appendix A. Case Studies

PAGE A-11

Figure A-5. Relationship between total phosphorus and TOC (r2

 value = 0.163) in ecoregion 67g.
July 2000 Appendix A. Case Studies

PAGE A-12

Figure A-6. Relationship between total phosphorus and turbidity (r2

 value = 0.282) in ecoregion 67g.
July 2000 Appendix A. Case Studies

PAGE A-13

Figure A-7. Relationship between nitrate-nitrite levels and the Hilsenhoff Biotic Index.
July 2000 Appendix A. Case Studies

PAGE A-14

Figure A-8. Relationship between nitrate-nitrite levels and the North Carolina Biotic Index (NCBI).
July 2000 Appendix A. Case Studies

PAGE A-15

While the two values, 1.2 and 1.0 mg/L, are not exactly the same, clearly these two methods of

criteria development can be used to strengthen the rationale for a final criteria recommendation or

to justify a “margin of safety”. It also demonstrates that should the Division set the nitrate+nitrite

goal for 71h at 1.0 mg/L, that level should generally be protective of biological integrity.

3. Another potential methodology for nutrient criteria development was examined. According to EPA

draft guidance, the reference conditions may be compared to all other nutrient data to potentially

provide a range for criteria selection. EPA suggests that the range is established by comparing the

reference stream data at the 75th percentile with the 25th percentile of all other data. We were

curious to see if this approach would work and if so, would it provide values similar to those we

had already identified.

To assist in this effort, EPA provided us with the nutrient databases from STORET for the three

large nutrient regions in Tennessee. (For purposes of this initial test, only Tennessee STORET

data were included.) Nutrient Region XI in east Tennessee is a combination of Level III

ecoregions 66, 67, 68, and 69. Nutrient Ecoregion IX in middle Tennessee is composed of

Ecoregions 71, 65, and 74. Ecoregion 73 in west Tennessee is Nutrient Ecoregion X.

The EPA nutrient database was primarily data collected by the Division of Water Pollution

Control, the Tennessee Valley Authority (TVA), and the U.S. Geological Survey (USGS). As we

were familiar with TVA’s monitoring program, we were concerned that some percentage of their

data was from lakes or embayments. Since we were developing stream nutrient criteria, rather than

lake or embayment criteria, we did not consider it appropriate to include non-stream data. Lacking

the time to identify and cull only the embayment or lakes data from the database, we decided to

exclude all TVA data.

Figure A-9 illustrates a comparison of the National nutrient database for Nutrient Ecoregion

Region XI and the reference stream database for the same geographic area. The 75th percentile of

the reference stream data and the 25th percentile of the National Nutrient database lined up well for

some ecoregions (68, 69, & 66), but not for the Central Appalachian Ridge and Valley Region (67).

We also looked at EPA draft Nutrient Aggregate Ecoregion IX in West Tennessee (Figure A-10). 

Data for total phosphorus were elevated nearly an order of magnitude higher than the reference

stream data. We discovered that a few stations provided a sizable number of data points within the

database. It is possible that some of these data represent “storm chasing” sampling events designed

to quantify worst case nutrient loadings. Another possibility is that sampling in the phosphorusrich soils of ecoregion 71 biased the database. If we can identify these sites and determine that

these data are not representative of the ambient water quality in the ecoregion, these data could be

excluded and the database re-formed.

SUMMARY

With the assistance of EPA, the Tennessee Division of Water Pollution Control subdelineated ecoregions

from Level III to Level IV. Reference streams were identified in each sub-ecoregion to establish a 

July 2000 Appendix A. Case Studies

PAGE A-17

1E-3

.01

.1

1

00630 00665

TN Ecoregion 71 Interior Plateau

TN Ecoregion 65 Southeastern Plains

TN Ecoregion 74 Mississippi Valley Loess Plains

EPA Level III Nutrient Ecoregion IX Southeastern

Nitrate+Nitrite (mg/l) Total Phosphorus (mg/l)

1

.1

.01

1E-3

Figure A-10. Comparison of EPA Nutrient Ecoregion Region IX data to the Tennessee reference stream

database for the same geographic area.
July 2000 Appendix A. Case Studies

PAGE A-18

database of least-impacted conditions. These databases will be used to develop nutrient criteria based on

either the 75th or 90th percentile of the data. 

Attempts to identify a relationship between nutrient levels and other parameters such as turbidity, TOC,

and suspended solids were confounded by the amount of data below the detection level. While data

relationships were indicated, they were not strong. Further investigations might include similar

comparisons using the national nutrient database values.

Relationships between nutrient data and biological indices were explored to see if positive correlations

could be established. Such correlations could be used to strengthen a criteria justification and to insure

that potential criteria values will be protective of biological integrity. The preliminary results are

promising.

Tennessee’s reference stream data were also compared to values from the national nutrient database. In

several ecoregions, the 75th percentile of the reference data corresponded well with the 25th percentile of

the national database. However, certain ecoregions did not correspond well, possibly suggesting that

there are distinct differences within the EPA nutrient ecoregions. States would be well advised to

consider these differences in setting nutrient goals.

Additionally, states should examine the national nutrient database carefully and use local knowledge to

identify stormwater or embayment stations. Data from specific event sampling and reservoir or

embayment stations may not be representative of the ambient water quality in the region. Such data

could inappropriately bias results.

REFERENCES

TNDEC (Tennessee Department of Environment and Conservation). 1996. Standard operating

procedure for modified clean technique sampling protocol. Tennessee Department of Environment and

Conservation, Division of Water Pollution Control, Nashville, Tennessee.

Contact: Jim Harrison, Region 4 Nutrient Coordinator

United States Environmental Protection Agency

61 Forsyth Street, SW Atlanta, GA 30303-3104

harrison.jim@epa.gov
July 2000 Appendix A. Case Studies

PAGE A-19

CLARK FORK RIVER—SCIENTIFIC BASIS OF A NUTRIENT TMDL FOR A RIVER

OF THE NORTHERN ROCKIES1

V.J. Watson,2 Gary Ingman,3 and Bruce Anderson4

ABSTRACT: In recent decades, river bottom algal levels have interfered with beneficial uses of western

Montana’s Clark Fork of the Columbia. The total maximum daily load analysis (TMDL) required by the

Clean Water Act was addressed through a voluntary nutrient reduction plan developed by a stakeholder

group with the aid of scientists. Targets for acceptable nutrient and algal levels were set using

modifications of established criteria, literature values, and levels observed in the Clark Fork where algae

problems did and did not occur. These targets were considered starting points that would be refined as

more long-term data on the Clark Fork become available. Nutrient load reductions needed to meet

instream targets were estimated using a model that diluted loads in the 30 day 10 year low flows. It

appeared possible to achieve instream targets in most of the river with reductions that the main

dischargers considered reasonably achievable, if other small dischargers and nonpoint sources were also

controlled. Hence 4 local governments and one large industry signed the VNRP, and a VNRP coordinator

was hired to obtain the participation of other sources. 

KEY TERMS: nutrients, TMDL, benthic algae, benthic chlorophyll

INTRODUCTION

River bottom algal levels were first recognized as a water quality problem in the Clark Fork River of

western Montana in the 1970’s when it was found to lower dissolved oxygen levels below state standards

on warm summer nights (Braico 1973). Massive algae growths and low oxygen levels were noted

through the low flow summers of the 1980’s (Watson 1989a; Watson and Gestring 1996) and identified

as a critical problem by the Montana governor’s office (Johnson and Schmidt 1988). In 1987, the

reauthorization of the Clean Water Act called for a study and action plan to address nutrients and

associated nuisance growths in the Clark Fork basin from Montana to Washington. The act also

established the Tristate Implementation Council to carry out the study and plan. The resulting study

(USEPA 1993b) documented that nuisance levels of algae were interfering with beneficial uses in 250

miles of river in Montana. The Council convened a group of stakeholders (dischargers, local

governments, and conservation groups) which spent 4 years developing a voluntary nutrient reduction

plan or VNRP to restore the river’s integrity. The plan was signed in August, 1998, and EPA accepted

the VNRP as a TMDL because it had a rational, scientific basis and provided a margin of safety. The

VNRP will continue to serve as a TMDL as long as reasonable progress is shown toward its goals.

Unlike a TMDL, the VNRP did not require that effluent limits be written into permits, rather permits

simply reference the VNRP which states the instream targets for algae and nutrient levels and timetables 

1

 This paper was published previously and is used with permission of the publisher. Olsen, D. S. and J. P. Potyondy

(Eds.). 1999. Wildland Hydrology. American Water Resources Association. Herndon, VA. TPS-99-3. 536 pp.

2

 Professor, Environmental Studies, University of Montana, Missoula, MT 

3

 Chief, Data Mgt. & Monitoring, MT Dept. Env. Quality, Helena, MT

4

 Hydrologist, Land & Water Consulting, Missoula, MT
July 2000 Appendix A. Case Studies

PAGE A-20

to achieve these, suggests likely loading reductions needed to achieve these, and lists some methods

signatories agree to pursue to achieve reductions. By signing the VNRP, stakeholders agree to implement

certain efforts to achieve loading reductions, to monitor and evaluate results and to pursue additional

efforts if needed to reach targets. The VNRP also recognizes that the targets and reductions pursued are

based on the best information currently available and are subject to renegotiation as more information

becomes available. The VNRP references a long-term monitoring plan aimed at gathering more

information and evaluating the effect of load reduction efforts. The VNRP also explains the scientific

basis for the targets, load reductions, monitoring plan, margin of safety and areas of uncertainty. This

paper discusses the scientific basis of the VNRP by addressing a series of questions. 

QUESTIONS ADDRESSED BY THE VNRP

What Are Current and Desired Algae Levels in the River?

Summer algal levels in the Clark Fork vary dramatically in time and space, from highs of over 500 mg of

chlorophyll a/sq. m. in the upper river in the 1980’s to lows of 3 mg/sq. m. at some sites in recent years

(Watson and Gestring 1996; Watson unpublished data).

Currently, EPA is developing guidance to assist states in developing nutrient and algal criteria. It is likely

that this guidance will direct the states to develop criteria based on little-impacted reference water bodies

in each ecoregion. The Clark Fork VNRP committee found little guidance in the literature on what algal

levels were natural to this region or what levels were associated with water quality problems. 

The British Columbia Ministry of the Environment considers that recreation and aesthetics are protected

when algal levels are below 50 mg chlorophyll a/sq. m. and that undesirable changes in aquatic life will

be avoided at levels below 100 mg/sq. m. (Nordin 1985). Although these criteria were developed for

small, shallow streams, Nordin agrees that it is reasonable to apply them to the shallow parts of large

rivers (Nordin pers. comm.). Welch et al. (1988) demonstrated that filamentous algae tend to dominate

stream communities when chlorophyll levels exceed 100 mg/sq. m. and proposed that nuisance levels

exist above 100-150 mg/sq. m. The VNRP committee decided to adopt these target algal levels: less than

100 mg chlorophyll a/sq. m. when averaged over the growing season and 150 mg/sq. m. as the maximum

acceptable peak.

The committee agreed that these algal targets might be revised in time as more information becomes

available concerning what levels appear associated with water quality problems. In the mid 1980’s, river

algal levels contributed to violations of the state dissolved oxygen standard. However, that standard has

since been raised and is no longer exceeded, changing this view of what constitutes nuisance levels. But

it was recently discovered that river algae lower dissolved oxygen and pH sufficiently on summer nights

to release toxic heavy metals from old mine wastes in the river bed, violating water quality standards.

Further studies are needed to determine what algal levels would avoid this and other water quality

problems.

What Actions Seem Most Likely to Reduce Algal Levels?

Many factors affect river algal levels, including scouring, shading, grazing, toxic chemicals and available

nutrients. The VNRP committee agreed that the factor that can best be managed to reduce algal levels in

the Clark Fork is available nutrients.
July 2000 Appendix A. Case Studies

PAGE A-21

How Much Must Nutrients Be Reduced to Achieve Algal Targets?

This question raised many others. What form of nutrients should be assessed, total or soluble? Which

nutrient is most limiting, nitrogen or phosphorus? At what levels do nutrients become limiting? Should

we focus on nutrient levels or loads?

Based on N:P ratios in the river, Watson (1989b) found that both N and P appear to be limiting at some

times in some river reaches. Hence, the committee concluded that both nutrients should be reduced if

possible. Artificial stream studies by Bothwell (1989) and Watson (1989) indicated what levels of

soluble nutrients are low enough to reduce algal levels in artificial streams. However, using a 200 river

database, Dodds et al. (1997) pointed out that total nutrient levels are better correlated with algal levels

than are soluble nutrient levels. So the VNRP committee opted to focus on total nutrients (while

monitoring soluble nutrients to insure they did not rise). A variety of approaches suggested targets

ranging from 250-350 total N and 20-45 total P, so the committee adopted 300 ppb total N and 39 ppb

total P in the middle river and 20 ppb total P in the upper river (where a higher N:P ratio was desired to

discouraged the filamentous alga Cladophora).

What Are Major Nutrient Sources and How Much Reduction Is Needed?

The basin wide study called for in the 1987 Clean Water Act bill found that both point and nonpoint

sources accounted for significant portions of nutrient loading, hence both must be reduced (USEPA

1993b). However, the largest sources were found to be three municipal discharges (Butte, Deer Lodge

and Missoula), a pulp mill and a county (Missoula) with large areas of unsewered development. Hence

these 4 local governments and one private industry were initial signatories to the VNRP. Ultimately, the

VNRP committee hopes to convince smaller point sources and nonpoint sources (other developing

counties and large landowners) to agree to certain efforts to control nutrients and to sign the VNRP. 

To estimate the amount of load reductions needed, the Montana Department of Environmental Quality

(DEQ) modified a model provided by EPA that estimates instream concentrations from loads, flows and

historic percent losses within each river reach. This model allowed DEQ to estimate how much loads

would need to be reduced from various sources to meet instream targets. Once again, the committee

recognized that this simple model did not include all the gains and losses and so provided only a rough

estimation of likely concentrations resulting from given loads. The model predicted that reductions the

committee felt were reasonably possible would achieve instream targets in almost all the impaired

reaches. The model suggested reaching targets in the few remaining miles of river would require

reductions of questionable feasibility. The committee agreed to use the model only as a general guide and

not to set required reductions. It was pointed out that algal uptake might reduce nutrient levels lower than

the model predicted.

How Was a Margin of Safety Incorporated in the VNRP?

A margin of safety is provided by using instream nitrogen targets that are more protective than those

recommended by Dodds et al. (1997). In addition, needed load reductions were estimated using the

river’s dilution capacity at very low flows—the 30 day 10 year low flow (the lowest 30 day average flow

likely to be observed in one of 10 summers). Hence, targets will likely be met in almost all the river, in

all but one month out of 10 years.
July 2000 Appendix A. Case Studies

PAGE A-22

What Actions Are Expected to Achieve Needed Load Reductions?

All the municipalities in the area have adopted a phosphate detergent ban which has reduced P loads. The

city of Deer Lodge agreed to land apply its wastewater. The city of Butte agreed to augment stream flows

and pursue various land application options. The city of Missoula has reduced nutrient loading by

operating its activated sludge plant like a biological nutrient removal plant. It also plans to construct a

biological nutrient removal plant or use a combination of wetland treatment and land application in the

future. The pulp mill will reduce summer discharge, store its water so as to reduce seepage, and increase

use of a color removal process that also reduces nutrients. Missoula County will reduce and control

loading from septic systems through land use planning and controls. 

The VNRP committee has hired a VNRP coordinator to work with small discharges, local governments

and land owners to identify ways these can reduce or at least control nutrient loads. These efforts are

needed to avoid losing ground given the rapid population growth occurring in the area. 

How Will Progress Towards the Targets Be Determined?

The TriState Implementation Council contracted with Land & Water, Inc., to develop and carry out a

long term monitoring plan (Land & Water 1996) that will provide reliable information on nutrient related

water quality status and trends in the basin. The monitoring plan uses a statistically rigorous sampling

scheme designed to be able to detect trends in algal and nutrient levels in the Clark Fork and to assess

compliance with instream targets. Using a seasonal Kendall with Sen slope estimate, the monitoring plan

is intended to be able to detect a 50% change in nutrient levels over a 10 year period with 95%

confidence and 90% power. In addition it can detect a 35% change in algal levels over a 10 year period

with 90% confidence and 80% power. Compliance with instream targets will be evaluated annually using

excursion analysis.

Monitoring consists of sampling 32 stations on the mainstem and major tributaries for total and soluble

nutrients monthly (with biweekly sampling in summer). Algal levels are sampled at 7 mainstem stations

twice a summer. Because of the high spatial variability in algal distributions, 10-20 replicates are

collected. Details of the algal sampling scheme appear in Watson and Gestring (1996).

Timelines in the VNRP focus on timing of actions. However, the goal of the VNRP is to reduce algal

level to the point that beneficial uses are fully supported by the end of the 10 year plan. Hence, the plan

should be regarded as successful if a significant downward trend in nutrient and algal levels is detected 5

years into the plan, and if targets are no longer exceeded by the end of the 10 year plan. Of course, it will

be necessary to evaluate changes in these parameters in light of the flows observed over this 10 year

period.

REFERENCES 

Bothwell, M. L. 1989. Phosphorus-limited growth dynamics of lotic periphytic diatom communities:

areal biomass and cellular growth rate responses. Can. J. Fish. Aqua. Sci. 46:1293-1301.

Braico, R. D. 1973. Dissolved oxygen and temperature diurnal variations in the Clark Fork River, August

1973. Rep. to MT Dept. Health and Env. Sciences (now the Dept. Env. Quality).
July 2000 Appendix A. Case Studies

PAGE A-23

Brick, C. & J. Moore. 1996. Diel variation of trace metals in the upper Clark Fork River, MT. Env. Sci.

Tech. 30(6):1953-60. 

Dodds, W. K., V. H. Smith & B. Zander. 1997. Developing nutrient targets to control benthic chlorophyll

levels in streams: a case study of the Clark Fork River. Water Res. 31(7):1738-50.

Ingman, G. 1992. A rationale and alternatives for controlling nutrients and eutrophication problems in the

Clark Fork River basin. Mt Dept. Health and Env. Sciences, Helena, MT. 

Johnson, H. E. & C. L. Schmidt. 1988. Clark Fork basin status report & action plan. MT. Governor’s

Office, Helena, MT.

Land & Water Consulting. 1996. Water quality status and trends monitoring system for the Clark ForkPend Oreille Watershed. Rep. To MT Dept. Env. Quality.

Nordin, R. N. 1985. Water quality criteria for nutrients and algae (technical appendix). British Columbia

Ministry of the Environment. Victoria, BC. 104 pp.

USEPA. 1993b. Clark Fork-Pend Oreille Basin Water Quality Study. EPA 910/R-93-006. 

Watson, V. J. 1989a. Dissolved Oxygen levels in the Clark Fork River. Proc. Mont. Acad. Sci. 49:146-

162.

________. 1989b. Control of attached algae by nitrogen and phosphorus in the Clark Fork River. Proc.

Symp. Headwaters Hydrology, Amer. Water Res. Assn. Missoula, MT pp 287-97. 

________ and B. Gestring. 1996. Monitoring algae levels in the Clark Fork River. Intermountain J. Sci.

2(2):17-26. 

Welch, E. B., J. M. Jacoby, R. R. Horner and M. R. Seeley. 1988. Nuisance biomass levels of periphytic

algae in streams. Hydrobiologia 157:161-8.

Contact: Debbi Hart (4304), Headquarters Nutrient Program

United States Environmental Protection Agency

Ariel Rios Building 1200 Pennsylvania Avenue, NW 

Washington, DC 20460

hart.debra@epa.gov
July 2000 Appendix A. Case Studies

PAGE A-24
July 2000 Appendix A. Case Studies

PAGE A-25

UPPER MIDWEST RIVER SYSTEMS—ALGAL AND NUTRIENT CONDITIONS IN

STREAMS AND RIVERS IN THE UPPER MIDWEST REGION DURING SEASONAL

LOW-FLOW CONDITIONS

Stephen D. Porter, U.S. Geological Survey, Water Resources Division, Denver, CO 80225

INTRODUCTION

Extensive agricultural practices in the Midwestern Corn Belt region over the past 100 years have

contributed to nonpoint source degradation of water quality and biological integrity in many streams and

rivers. For example, intensive row-crop production and confined animal-feeding operations in Iowa,

Illinois, and southern Minnesota have resulted in accelerated nutrient and organic enrichment in tributary

streams, as well as in the Mississippi River (Goolsby et al. 1991; Coupe et al. 1995) and the Gulf of

Mexico (Turner and Rabalais 1994; Rabalais et al. 1996). When ambient light and other algal-growth

factors are favorable, nutrient enrichment can promote excessive productivity and respiration in streams

and rivers, resulting in aesthetic and recreational impairments, departures from water quality criteria, and

adverse effects to aquatic life. The U.S. Environmental Protection Agency (USEPA) has been charged

with developing guidance for establishing regional water quality criteria to protect streams and rivers

from accelerated eutrophication processes (http://www.cleanwater.gov). Results from State water quality

(305[b]) reports to Congress indicate that over 40% of streams and rivers in the U.S. are contaminated by

nutrient runoff and resultant indicators of excessive algal productivity.

Despite the prevalence of eutrophication, no implicit standards or criteria have been proposed to protect

beneficial water uses (e.g., no significant ecological changes) in streams and rivers, apart from drinkingwater standards for nitrate and chronic aquatic life criteria for elemental phosphorus in estuarine/marine

waters. Although predictive algal-nutrient relations have been established for classifying the trophic

status of lakes and reservoirs (Carlson 1977; Reckhow and Chapra 1983), there is no generally accepted

system for classifying streams and rivers (Dodds et al. 1998; Dodds and Welch 2000). Recent

approaches for classifying algal-nutrient relations in lotic systems have focused on constructing

frequency distributions of total nutrients and periphyton (Biggs 1996; Dodds et al. 1998) or seston

(suspended algae or phytoplankton) (Van Niewenhuyse and Jones 1996), and establishing boundaries

between oligotrophic–mesotrophic and mesotrophic-eutrophic conditions, similar to trophic criteria

established for lakes. Results from these investigations have suggested criteria for total nitrogen

(TN>1500 g/L), total phosphorus (TP>75 g/L), seston chlorophyll a (chl a>30 g/L), and periphyton

(chl a>100-200 mg/m2

) to avoid adverse effects of stream eutrophication. Periphyton results from these

and other such studies (Welch et al. 1988; Biggs and Close 1989; Lohman et al. 1992; Watson and

Gestring 1996; Dodds et al. 1997) are representative of streams with gravel or rock substrates that were

characterized by nuisance growths of filamentous green algae. Relatively little is known about nutrient

and algal-productivity relations in low-gradient streams with unstable, sand, or silt bottoms. Even less is

known about natural and human factors that contribute to the predominance of seston or periphyton in

streams, relations with landscape factors such as agricultural intensity and riparian zones, and how

differences in algal-nutrient relations influence stream metabolism and biological integrity.

To provide better understanding of eutrophication conditions and processes in streams and rivers in the

upper Midwest Corn Belt region, the USGS National Water-Quality Assessment (NAWQA) Program
July 2000 Appendix A. Case Studies

PAGE A-26

conducted a large water quality study in the Minnesota, Wapsipinicon, Cedar, Iowa, Skunk, and Illinois

River basins during seasonal low-flow conditions in August 1997. The study was a cooperative effort

among three NAWQA projects: the Upper Mississippi River basin, Eastern Iowa basins, and Lower

Illinois River basin study units. The objective of the study was to evaluate algal and macroinvertebrate

responses to nutrient, herbicide, and organic enrichment from nonpoint agricultural sources relative to

natural factors such as riparian vegetation, soil-drainage characteristics, and hydrology. This paper

summarizes the status of algal and nutrient conditions in portions of the Central and Western Corn Belt

Plains ecoregions (Omernik 1986), which could serve as a starting point for USEPA and State/Tribal

agencies to establish regional nutrient criteria in rivers and streams in relation to low-flow conditions.

METHODS

Water chemistry and biological samples were collected from 70 streams and rivers in southern

Minnesota, eastern Iowa, and western Illinois during seasonal low-flow conditions in August 1997. The

study area is one of the most intensive and productive agricultural regions in the world; average row-crop

production of corn and soybeans in stream watersheds accounts for over 90 percent of land cover

(Sorenson et al. 1999). The density of riparian vegetation was quantified at two spatial scales: stream

reach and segment. The length of a stream reach was approximately 20 times the mean wetted channel

width (Fitzpatrick et al. 1998). The length of a stream segment was defined as the log10 of the basin area

upstream from each sampling location, ranging from approximately 3 km to 4.9 km. Basin soil-drainage

characteristics were quantified using information from the U.S. Soil Conservation Service STATSGO

database. Water chemistry samples were collected for total and dissolved nutrients, dissolved herbicides

and metabolites, and suspended and dissolved organic carbon (Shelton 1994). Stream productivity and

respiration were estimated from continuous measurements of dissolved oxygen (DO) concentrations and

pH over a 48-hour period. Phytoplankton (algal seston) samples were collected in conjunction with

water-chemistry sampling, and quantitative samples of periphyton (benthic algae) and macroinvertebrates

were collected from submerged woody debris. Water clarity was quantified using a light meter and

submersible quantum sensor; the depth of the euphotic zone was measured or estimated by comparing

subsurface photosynthetically-active radiation (PAR) with PAR measurements at the bottom of the

deepest pool in the stream reach. Stream flow and velocity were measured using standard USGS

procedures. Land-use and cover information was determined for each basin using ARC-INFO GIS

procedures with the most-recent (1996-97) agricultural data that were available. A summary of the study

design and methods, and data discussed in this report is presented by Sorenson et al. (1999;

http://wwwrcolka.cr.usgs.gov/nawqa).

NUTRIENT INDICATORS OF TROPHIC CONDITION

Nutrient concentrations in many streams in the upper Midwest region are relatively higher than in other

areas of the country, exceeding criteria proposed generally for temperate streams and rivers. For

example, median concentrations of total nitrogen (TN; NH4+NO2+NO3+organic N) and total phosphorus

(TP; dissolved orthophosphate + particulate phosphorus) (Table A-1) exceeded the mesotrophiceutrophic boundaries of 1500 g/L (TN) and 75 g/L (TP) proposed for temperate streams (Dodds et al.

1998). Average stream concentrations of dissolved nitrite+nitrate nitrogen (NO2+NO3-N) and total

organic nitrogen (TON) were significantly (p<0.05) higher in the Minnesota River basin; nitrate

concentrations exceeded 8 mg/L in nearly one third of these streams. Although concentrations and
July 2000 Appendix A. Case Studies

PAGE A-27

annual loads of TN increase with the intensity of nitrogen sources (e.g., fertilizer application and other

land-use practices) nationally (Fuhrer et al. 1999), concentrations in Midwestern streams during seasonal,

low-flow conditions were not related to rates of fertilizer application or the number of livestock in

agricultural watersheds. Instead, NO2+NO3-N concentrations increased significantly with stream flow,

corresponding with differences in rainfall and runoff in the region during the months prior to the study,

and TON concentrations were correlated with the abundance of phytoplankton (seston), as indicated by

chl a concentrations. Concentrations of NO2+NO3-N decreased significantly with increases in seston chl

a concentrations. Particulate phosphorus (total phosphorus as P; Table A-1) concentrations did not differ

significantly relative to human or natural factors; however, concentrations of dissolved orthophosphate

(DoP) varied in relation to the importance of ground-water discharge and the abundance of benthic algae

(periphyton) in Midwestern streams and rivers. Dissolved orthophosphate (available directly for algal

growth) accounted for about 28 percent of the concentration of TP.

NATURAL FACTORS THAT INFLUENCE NUTRIENT INDICATORS OF TROPHIC CONDITION

Soil drainage and landform characteristics in the upper Midwest region were influenced profoundly by

patterns of glacial advance and retreat during the late Pleistocene era. For example, soils on the

Wisconsin glacial lobe in north-central Iowa and southern Minnesota are characterized by fine-grained

materials through which water drains very poorly, whereas soils in eastern Iowa and western Illinois

contain relatively larger proportions of sand and coarser grained materials that constitute moderately-well

drained soils. The proportion of stream water that is derived from ground-water inflow is substantially

less in streams on the Wisconsin lobe than in streams located to the southeast of the Wisconsinan glacial

advance (Winter et al. 1998). Land-surface runoff, via tile drains, is probably an important contributor to

nutrient fluxes in streams that drain low-gradient, prairie-pothole landscapes. In contrast, ground-water

inflow contributes appreciably to stream flow, particularly during low-flow periods, in areas with

moderately-well drained soils such as the Wapsipinicon, Cedar, and Illinois River 

Table A-1. Distribution of nutrient concentrations (in μg/L) in Midwestern agricultural streams and

rivers.

Water quality

constituent

10th percentile 25th

percentile

50th

percentile

(median)

75th percentile 90th percentile Maximum

value

Total Nitrogen1 948 1364 2627 4205 8550 13679

Total Phosphorus2 61 114 175 235 523 1092

Dissolved NH4-N <15 <15 17 46 105 1308

Dissolved

NO2+NO3-N

103 278 1320 3415 8145 12730

Total Organic N 356 570 934 1258 1560 2899

Dissolved ortho-P <10 19 41 72 161 409

Particulate

Phosphorus3

39 68 139 185 378 778

1

 Sum of dissolved NH4-N + dissolved NO2+NO3-N + total organic N 2

 Sum of dissolved ortho-PO4 + particulate phosphorus 3

 Total phosphorus as P (USGS WATSTORE code 00665)
July 2000 Appendix A. Case Studies

PAGE A-28

basins (Walton 1965; Heintz 1970; O’Hearn and Gibb 1980; Squillace et al. 1996). Figure A-11 shows

soil-drainage relations among stream and river basins in the study (U.S. Soil Conservation Service

STATSGO data normalized to watershed area; Sorenson et al. 1999) and the correspondence with the

Wisconsinan glacial advance.

Concentrations of TN and TP varied in relation to soil-drainage and riparian-zone conditions in the upper

Midwest region (Figure A-12). Average TN concentrations were significantly higher in stream basins

with very-poorly drained soils, such as those in the Minnesota River basin. In basins with moderatelywell drained soils, concentrations of TN were significantly lower in streams with well-developed riparian

zones, suggesting that the presence of riparian trees may beneficially influence water quality conditions

in streams with appreciable ground-water discharge. Average TP concentrations were relatively lower in

streams with moderately- or poorly-drained basins and well-developed riparian zones (Figure A-12), but

concentrations of TP did not differ significantly in relation to riparian conditions. Average TP

concentrations were significantly less in streams with a low percentage of riparian trees and very-poorly

drained basins; however, average TP concentrations were generally (but not significantly) lower in

streams with well-developed riparian zones (Figure A-13).

Dissolved nutrient concentrations differed in relation to basin soil-drainage properties and riparian-zone

conditions, but nutrient conditions were more related to algal abundance and productivity in streams and

rivers than physical factors. Average concentrations of dissolved ammonia-nitrogen (NH4-N) were

significantly higher in streams that drain basins with moderately well-drained soils, whereas average

dissolved NO2+NO3-N concentrations were significantly higher in streams with very-poorly drained soils,

such as those on the Wisconsin lobe. Similarly, average DoP concentrations were relatively higher in

highly-shaded streams with moderately well-drained basins, whereas concentrations of DoP were

significantly lower in poorly-shaded, poorly-drained stream systems on the Wisconsin lobe. The

combination of very-poorly drained soils, high rainfall and land-surface runoff relations, and extensive

tile drainage in the Minnesota River basin may account for the higher-than-expected concentrations of

TN and dissolved NO2+NO3-N concentrations in these streams. Relatively larger concentrations of NH4-

N and DoP in streams with moderately-well drained basins may indicate ground-water fluxes of these

constituents that reflect both present and past agricultural intensity. The time of constituent transport

along local and regional ground-water flow paths can range from months to years. Integration of these

results could indicate an interaction among land-use practices, stream hydrology, riparian shading, and

algal-nutrient relations.

ALGAL INDICATORS OF TROPHIC CONDITION

Algal indicators of eutrophication in streams and rivers of the upper Midwest region are related to

agricultural intensity (fertilizer application and livestock in stream basins), soil-drainage conditions,

hydrology, and riparian-zone conditions along stream segments. Median and inter-quartile seston chl a

concentrations (Table A-2) are similar to those reported from mesotrophic-to-eutrophic lakes and

reservoirs (e.g., Carlson 1977), and seston (but not periphyton) chl a concentrations were significantly

higher in poorly-shaded than well-shaded streams (Figures A-13 and A-14). These results likely indicate

that ambient light conditions influence the development of large phytoplankton populations in

Midwestern streams and rivers. Seston chl a values indicative of eutrophic conditions (greater than 30

g/L) were found in streams that drain basins with poor soil drainage, high rates of fertilizer application, 

July 2000 Appendix A. Case Studies

PAGE A-30

Figure A-12. Total nitrogen and phosphorus concentrations relative to soil drainage and riparian

conditions in Midwestern streams and rivers.
July 2000 Appendix A. Case Studies

PAGE A-31

Figure A-13. Average concentrations of total nitrogen, total phosphorus, seston chlorophyll a, and

periphyton chlorophyll a in relation to riparian-zone conditions.
July 2000 Appendix A. Case Studies

PAGE A-32

Table A-2. Distribution of algal seston, periphyton, ash-free dry mass, stream productivity and

respiration, suspended and dissolved carbon, total suspended solids, and water clarity (euphotic zone

depth) in Midwestern agricultural streams and rivers.

Water quality

constituent

10th percentile 25th percentile 50th percentile

(median)

75th percentile 90th percentile Maximum

value

Seston

chlorophyll a

(mg/L)

6.40 11.0 18.4 38.7 71.7 175

Periphyton

chlorophyll a

(g/m2

)

3.67 13.1 25.1 42.2 72.6 102

Periphyton ashfree dry mass

(g/m2

)

15.7 19.3 25.4 31.5 39.5 57.8

Stream

productivity

(g O2/m3

/hr)

0.113 0.242 0.398 0.697 0.998 1.46

Stream

respiration 

(g O2/m3

/hr)

0 0 -0.044 -0.159 -0.226 -0.804

Suspended

organic carbon

(mg/L)

0.5 0.7 1.3 2.5 3.6 5.0

Dissolved

organic carbon

(mg/L)

2.4 3.6 4.3 5.8 7.2 11

Total

Suspended Solids

(mg/L)

19 41 72 128 158 330

Estimated

euphotic zone

depth (m)

0.32 0.42 0.56 0.68 0.84 1.4
July 2000 Appendix A. Case Studies

PAGE A-33

Figure A-14. Seston and periphyton chlorophyll a values relative to soil drainage and riparian

conditions in Midwestern streams and rivers.
July 2000 Appendix A. Case Studies

PAGE A-34

and relatively large populations of hogs and other livestock. Stream productivity (Pmax) and respiration

(Rmax) values increased significantly with seston chl a concentrations. Concentrations of NO2+NO3-N

decreased significantly with increases in stream productivity, which is probably correlated with algal

uptake of dissolved nutrients. Seston chl a concentrations were positively correlated with concentrations

of suspended organic carbon (SOC), TON, particulate phosphorus, and total suspended sediment (TSS),

which suggests that total nutrient and organic enrichment in Midwestern streams is reflected by large

populations of algal seston. Seston chl a concentrations were negatively correlated with euphotic zone

depth, indicating that water clarity decreases with increases in the abundance of suspended algae

(phytoplankton).

Periphyton chl a values were significantly larger in streams with high water clarity and riparian shading,

and above-average stream velocity. Concentrations of total and dissolved nutrients and seston chl a in

periphyton-dominated streams were generally moderate to low; however, productivity (Pmax) was about

average for Midwestern streams, suggesting that periphyton (rather than seston) influences the

productivity of streams with high riparian shading and appreciable ground-water discharge. Large

populations of diatoms and blue-green algae were observed growing on sand (near the hyporheic zone) in

these streams. Although concentrations of NH4-N and DoP were larger in streams that drain basins with

moderately-well drained soils, regionally, periphyton uptake of dissolved nutrients from ground-water

discharges might account for the lower-than-expected concentrations of these constituents in the

Wapsipinicon and Cedar River basins of eastern Iowa. While Pmax rates in periphyton-dominated streams

were near average regionally, rates of stream respiration (Rmax) were generally low, and early-morning

concentrations of DO appeared to be favorable for aquatic life. In contrast, rates of Rmax were relatively

high in seston-dominated streams; DO concentrations during early-morning hours were low and benthic

macroinvertebrate community structure was poor (Harris and Porter in review).

Periphyton chl a and ash-free dry mass (AFDM) values were positively correlated; however, chl a and

AFDM relations (refer to Table A-2) differed with respect to precedent stream-flow conditions, water

clarity, and non-algal sources of carbon. Ratios of chl a to AFDM were relatively low (less than one) in

over half the streams in the Minnesota River basin, where the organic content of soils is relatively high

and soil drainage is very poor. In addition, above-average stream flow and water turbidity, as well as a

higher frequency of hydrologic disturbances associated with summer storms during the months prior to

the study (Figure A-15) probably limited the growth of algal periphyton in the Minnesota River basin. In

contrast, chl a/AFDM ratios were larger (greater than one) in streams with relatively stable stream flow

and good water clarity.

Periphyton samples were analyzed for species composition and abundance (cells/cm2

), and the biovolume

of each algal taxon was determined by measuring cell dimensions and calculating the average volume of

the cell (m3

) in relation to the nearest geometric shape (e.g., sphere, cylinder, etc.). Biovolume

(m3

/cm2) for each species was calculated by multiplying the volume of one cell by the abundance of the

species in the sampling reach. Total algal biovolume (cm3

/m2) was estimated by summing biovolumes

for all species present in the sample. Total algal biovolume (TAB) is positively correlated with

periphyton chl a and AFDM, and periphyton chl a can be estimated from TAB using the following

regression relation:

chl a = (4.229 + 2.733*log10(TAB))2 adjusted R2

=0.570; p<0.001; n=67
July 2000 Appendix A. Case Studies

PAGE A-35

Figure A-15. Total and base flow discharge for selected Midwestern streams and rivers in relation to

collection date of water quality samples.
July 2000 Appendix A. Case Studies

PAGE A-36

where: chl a = periphyton chlorophyll a (mg/m2)

TAB = total algal biovolume (cm3

/m2)

Unexplained variance associated with the regression is probably attributable to differences in chl a

content among algal species, differences in riparian shading or other factors that influence ambient light

conditions (e.g., Darley 1982; Rosen and Lowe 1984), and challenges with distinguishing live and dead

cells during taxonomic enumeration.

Periphyton communities were dominated by eutrophic microalgae (diatoms, blue-green algae, and green

algae). Filamentous red algae (Audouinella hermanii) were abundant in streams with above average

water clarity, velocity, and riparian-tree density (i.e., periphyton-dominated streams). Filamentous green

algae were relatively uncommon on submerged woody debris; however, sparse to moderate growths of

Cladophora glomerata were observed in flowing streams with bedrock or boulders, and moderate

growths of Spirogyra spp. were present in pools and slow-flowing sections of streams with sand or silt

bottoms. The predominance of fine streambed materials (sand and silt) in many Midwestern streams

probably precluded the establishment and growth of nuisance filamentous algal species. These factors

probably account for the relatively lower periphyton chl a values observed in this study when compared

with those associated with eutrophic streams in the western U.S., that are dominated by filamentous

green algae (Welch et al. 1988; Watson and Gestring 1996; Dodds et al. 1997).

FACTORS ASSOCIATED WITH SESTON OR PERIPHYTON DOMINANCE IN MIDWESTERN STREAMS AND

RIVERS

Streams were classified relative to the abundance of algae (seston, periphyton, or both) as indicated by

chl a values, and analysis of variance (ANOVA) and Tukey multiple range tests were used to determine

whether water quality conditions differed significantly among stream classifications. Approximately 30

percent of the streams contained above-average (relative to this data set) concentrations of seston chl a

and below-average values for periphyton chl a (seston-dominated streams). Concentrations of TON,

dissolved organic carbon (DOC), and SOC were significantly higher in seston-dominated streams,

suggesting organic enrichment from autotrophic (in-stream) processes. About 28 percent of streams

contained below-average concentrations of seston chl a and above-average values for periphyton chl a

(periphyton-dominated streams). Water clarity in periphyton-dominated streams was good, as indicated

by significantly lower TSS concentrations and significantly larger euphotic-zone depths.

Stream productivity (Pmax) was moderate to high in all streams with above-average amounts of algae;

however, high rates of stream respiration (Rmax) were associated primarily with large populations of

seston. High Rmax conditions were associated with low DO concentrations during early morning hours, at

levels that can adversely affect aquatic fauna. Stream respiration was significantly higher in streams with

above-average chl a values for both seston and periphyton (algal-eutrophic streams; 22 percent of

streams in the study). Concentrations of dissolved (but not total) nutrients were relatively lower in algaleutrophic streams than in streams with below-average seston and periphyton chl a (nutrient-eutrophic

streams; 20 percent of streams in the study). Stream productivity (Pmax) was significantly higher in algaleutrophic and seston-dominated streams than in nutrient-eutrophic and periphyton-dominated streams. 

However, periphyton chl a decreased significantly with modest increases in the relative abundance of

macroinvertebrate scraper organisms (Harris and Porter, in review); therefore, monitoring of algal-
July 2000 Appendix A. Case Studies

PAGE A-37

nutrient relations in Midwestern streams should probably consider the abundance of grazer organisms

that consume benthic algae.

The abundance of algal tychoplankton (species that are loosely associated with, but not attached to,

submerged benthic surfaces) in the periphyton community was a primary factor in identifying differences

in community structure among Midwestern streams. These species, including Microcystis, Anabaena,

other blue-green algae, and centric diatoms, are found commonly in eutrophic lakes, reservoirs, and other

warm, slow-flowing water bodies such as large impounded rivers. The abundance of blue-green algae

increased with the concentration of triazine herbicides (atrazine, cyanazine, and degradation products). 

The predominance of tychoplankton in periphyton communities in algal-eutrophic and seston-dominated

streams was associated with large populations of these species in the seston, probably indicating that they

had settled from the water column. Indicators of organic enrichment (SOC, DOC, TON) and stream

metabolism (Pmax and Rmax) are consistent with the large abundance of algae in these streams, whereas

concentrations of dissolved nutrients were relatively low. The highest rates of stream respiration were

found in algal-eutrophic streams; benthic macroinvertebrate indicators of biological integrity (e.g., EPT

richness) indicated poor water quality conditions in algal-eutrophic and seston-dominated streams (Harris

and Porter in review).

In contrast, algal communities in periphyton-dominated and nutrient-eutrophic streams were dominated

by diatoms, blue-green algae, and red algae that grow attached to benthic surfaces. These species are

found commonly in cool, flowing streams and rivers. A secondary factor in classifying differences in

algal community structure in the region relates to the age of the periphyton community as inferred by the

presence or dominance of certain algal species. For example, periphyton communities in streams of the

Minnesota and upper Iowa River basins were characterized by diatoms (e.g., Fragilaria vaucheriae and

Achnanthidium minutissimum) that are typically found in abundance on bare or recently-scoured

substrates. Algae that are associated with soils (e.g., Luticola mutica, Chlorococcum sp., and

Protococcus sp.) were also common in these streams. Periphyton community structure in these streams

is consistent with recent hydrologic disturbance as indicated by relatively high rainfall, surface-water

runoff, and elevated streamflow in the region (Figure A-15). Water quality in these streams is influenced

by relatively low rates of stream metabolism and high concentrations of nutrients (notably TN,

NO2+NO3-N and TP). In contrast, periphyton communities in streams of the Wapsipinicon and upper

Cedar River basins consisted of species found commonly in diverse, mature algal communities (e.g.,

Audouinella hermanii, Navicula spp. and Gyrosigma spp.), which is consistent with relatively stable

hydrologic conditions, ground-water discharge, and seasonally-typical streamflow (Figure A-15).

SUMMARY AND IMPLICATIONS FOR ESTABLISHING AND MONITORING ALGAL-NUTRIENT CRITERIA

Nutrient concentrations and the abundance of algae during low-flow conditions were not related directly

to rates of fertilizer application or the number of livestock in Midwestern stream basins; however, rates

of stream metabolism (Pmax and Rmax) increased significantly with indicators of agricultural intensity. 

Algal-nutrient relations during August 1997 were more a function of landscape characteristics (riparian

zones and soil properties), hydrology (ground-water and surface-water relations), and rainfall-runoff

characteristics than agricultural land use, which is relatively homogeneous throughout the region. For

example, average nutrient concentrations were significantly higher in the Minnesota River basin despite

relatively lower agricultural intensity. Above-average rainfall and runoff from poorly drained soils,

discharged through tile drains, probably explains the higher-than-expected nutrient concentrations in
July 2000 Appendix A. Case Studies

PAGE A-38

these streams. Average rates of stream metabolism were relatively lower in streams in the Minnesota

River basin, which is consistent with relatively higher concentrations of suspended solids and lower

water clarity. Over half of these streams contained above-average seston chl a concentrations, which

corresponds with relatively less riparian shading in Minnesota than in Illinois or Iowa. However, seston

and periphyton communities were dominated by species associated with soils or those with high rates of

colonization and reproduction. Benthic invertebrate and periphyton communities contained relatively

fewer species; however, reduced species richness was more indicative of hydrologic disturbance (high,

flashy stream flow and velocity) than organic enrichment. 

In contrast, average dissolved nitrate concentrations in the Illinois River basin were significantly lower,

even though agricultural intensity in those stream basins was among the highest in the region. Belowaverage rainfall (near drought conditions), resulting in significantly lower (surface water) nutrient yields

from stream watersheds, lower stream velocities, and high rates of stream metabolism, probably explain

the lower-than-expected dissolved nutrient and DO concentrations. However, concentrations of

dissolved NH4-N were relatively higher, probably attributable (in part) to ground-water fluxes in basins

with moderately well-drained soils. Water quality conditions in Illinois streams during August 1997

were relatively degraded, as revealed by relatively high concentrations of SOC, DOC, and TON

(indicators of organic enrichment), low minimum dissolved-oxygen concentrations, high rates of stream

respiration, and poor macroinvertebrate communities (low taxa and EPT richness).

Water quality in Iowa streams differed in relation to basin soil properties and riparian shading. Overall

water quality was best in streams that drain basins with moderately well-drained soils and a high

percentage of riparian trees (Wapsipinicon and upper Cedar River basins). These periphyton-dominated

streams were characterized by low to moderate concentrations of nutrients and average stream

productivity. Seston chl a values and rates of respiration were relatively low, and macroinvertebrate

communities (e.g., EPT richness) indicated good water quality and habitat conditions.

Although phytoplankton chl a criteria are available to classify the trophic status of lakes and reservoirs

(e.g., Carlson 1977), comparable criteria have not been established for seston or periphyton in lotic water

bodies. Average chlorophyll values in the upper Midwest region are considerably lower than criteria

proposed by Dodds et al. (1998) for temperate streams and rivers, whereas proposed criteria for total

nutrients (TN>1500 g/L; TP>75 g/L) are exceeded in 74 percent (TN) to 89 percent (TP) of the

streams in this study. Periphyton chl a values exceeded 70 mg/m2

 (proposed minimum eutrophic

criterion) in only 13 percent of the streams, and seston chl a values exceeded 30 g/L (proposed

eutrophic criterion) in about one-third of the streams in this study. The higher recommended criteria for

periphyton chl a (100 mg/m2

 to 200 mg/m2) (Welch et al. 1988; Watson and Gestring 1996; Dodds et al.

1998) was intended to protect streams and rivers from nuisance growths of filamentous algae such as

Cladophora glomerata, other macroalgae, or other aquatic plants. These taxa require stable benthic

surfaces (e.g., submerged rocks or bedrock) on which to colonize and grow to nuisance proportion. Sand

and silt bottom streams of the Midwest, and submerged woody debris in these streams, do not generally

provide suitable habitat to sustain nuisance filamentous algal growths. However, dense growths of

microalgae (primarily diatoms and blue-green algae) on sand or woody snags in Midwestern streams

could provide visible evidence of stream eutrophication during low-flow periods; the proposed minimum

eutrophic criterion may be appropriate for indicating that condition.
July 2000 Appendix A. Case Studies

PAGE A-39

Results from this study suggest that the abundance and composition of algal seston (phytoplankton) may

be one of the better indicators of trophic conditions in streams and rivers of the upper Midwest region. 

Because of the highly significant correspondence between the standing crop (e.g., chl a) of algal seston

and concentrations of total nutrients and carbon, criteria established for seston (evaluated during stable,

low-flow conditions) is likely to represent total nutrient concentrations in the water and the extent to

which organic enrichment is a problem for maintaining biological integrity in streams and rivers. Seston

criteria would also provide an index for evaluating the clarity of streams and rivers, an important

consideration relative to the public perception of trophic conditions, and water quality in general. 

However, criteria for total nutrients (and perhaps total suspended solids) cannot be abandoned for

streams where algal growth is limited by inorganic turbidity or dense riparian-canopy shading. For

example, if best-management practices (BMPs) are applied in watersheds to reduce adverse effects of

sedimentation without consideration given to commensurate reductions in nitrogen or phosphorus loads

to streams, excessive algal growths could ensue when productivity is no longer limited by the availability

of light (e.g., in nutrient eutrophic streams and rivers). A consideration of water quality variables for

establishing and monitoring the trophic condition of temperate streams and rivers is presented in 

Table A-3.

Table A-3. Summary of water quality variables for establishing criteria and monitoring the trophic

condition of temperate streams and rivers.

Variable Media (and frequency) Relevance Risk

Total nutrients Water chemistry (monthly & in relation

to hydrology)

chemical indicator eutrophy

Dissolved nutrients Water chemistry (monthly & in relation

to hydrology)

chemical indicator 

algal-nutrient relations

eutrophy

Seston Water samples (growing season & in

relation to hydrology)

biological indicator

organic enrichment

food-web relations

eutrophy

aquatic life

biocriteria

Periphyton Natural substrates (growing season & in

relation to hydrology and aquatic

herbivores)

biological indicator

organic enrichment

food-web relations

eutrophy;

aquatic life;

biocriteria

Stream metabolism Estimates of system productivity &

respiration (low flow conditions with

chemical & biological measures)

biological indicator

understanding

direct measure of

process; aquatic

life; biocriteria

Water clarity Euphotic zone depth; water

transparency; secchi depth (seasonal;

with chemical & biological measures)

physical indicator

understanding

aesthetic properties

light availability for

algal growth

Aquatic fauna Natural substrates (low-flow 

conditions)

biological indicator 

understanding 

response to organic enrichment

receptor biocriteria 

TMDL process
July 2000 Appendix A. Case Studies

PAGE A-40

Improved understanding of natural factors and algal-nutrient relations that contribute to chemical and

biological indicators of eutrophication in lotic systems could enhance the development of water quality

criteria within and among ecoregions in the U.S. (e.g., Level III; Omernik 1986). For example, results

from this study indicate larger variance within the Western Corn Belt Plains ecoregion than between the

Central and Western Corn Belt Plains ecoregions. Differences in soil drainage, ground-water/surfacewater relations, and precedent rainfall-runoff conditions account for part of this variance. Improved

understanding of dissolved nutrient relations with the abundance of seston and periphyton, rates of

stream metabolism, and organic enrichment processes in streams could assist water managers with

decisions concerning BMPs, total maximum daily load (TMDL) allocations, and the establishment of

appropriate biocriteria relative to natural and human factors that contribute to the quality of streams and

rivers.

LITERATURE CITED

Biggs, B.J.F. and Close, M.E., 1989, Periphyton biomass dynamics in gravel bed rivers: the relative

effects of flows and nutrients. Freshwater Biology, v. 22, p. 209-231.

Biggs, B.J.F., 1996, Patterns in benthic algae in streams. In: Algal Ecology—Freshwater benthic

ecosystems, Stevenson, R.J., Bothwell, M.L., and Lowe, R.L., eds., Academic Press, San Diego, CA, p.

31-56.

Carlson, R.E., 1977, A trophic state index for lakes. Limnology and Oceanography, v. 22, p. 361-369.

Coupe, R.H., Goolsby, D.A., Iverson, J.L., Markovichick, D.J., and Zaugg, S.D., 1995, Pesticide,

nutrient, water-discharge and physical-property data for the Mississippi River and some of its tributaries,

April 1991-September 1992. U.S. Geological Survey Open-File Report 93-406, 66 p.

Darley, W.M., 1982, Algal biology: A physiological approach. Blackwell Scientific Publications,

Oxford, U.K., 168 p.

Dodds, W.K., Jones, J.R., and Welch, E.B., 1998, Suggested classification of stream trophic state:

Distributions of temperate stream types by chlorophyll, total nitrogen, and phosphorus. Water Resources,

v. 32, no. 5 p. 1455-1462.

Dodds, W.K., Smith, V.H., and Zander, B., 1997, Developing nutrient targets to control benthic

chlorophyll levels in streams: A case study of the Clark Fork River. Water Resources, v. 31, no. 7, p.

1738-1750.

Dodds, W.K., and Welch, E.B., 2000, Establishing nutrient criteria in streams. Journal of the North

American Benthological Society, v. 19, p. 186-196.

Fitzpatrick, F.A., Waite, I.R., D’Arconte, P.J., Meador, M.R., Maupin, M.A., and Gurtz, M.E., 1998,

Revised methods for characterizing stream habitat in the National Water-Quality Assessment Program.

U.S. Geological Survey Water-Resources Investigations Report 98-4052, 67 p.
July 2000 Appendix A. Case Studies

PAGE A-41

Fuhrer, G.J., Gilliom, R.J., Hamilton, P.A., Morace, J.L., Nowell, L.H., Rinella, J.F., Stoner, J.D., and

Wentz, D.A., 1999, The quality of our Nation’s waters. Nutrients and pesticides. U.S. Geological Survey

Circular 1225, 82 p.

Goolsby, D.A., Coupe, R.C., and Markovchick, D.J., 1991, Distribution of selected herbicides and nitrate

in the Mississippi River and its major tributaries, April through June 1991. U.S. Geological Survey

Water-Resources Investigations Report 91-4163, 35 p.

Harris, M.A., and Porter, S.D., (unpublished manuscript), Relating epidendric macroinvertebrate

communities to physical and chemical factors in upper Midwest streams. U.S. Geological Survey WaterResources Investigations Report.

Heintz, A.J., 1970, Low-flow characteristics of Iowa streams through 1966: Iowa Natural Resources

Council Bulletin No. 10, 176 p.

Lohman, K., Jones, J.R., and Perkins, B.D., 1992, Effects of nutrient enrichment and flood frequency on

periphyton biomass in Northern Ozark streams. Canadian Journal of Fisheries and Aquatic Sciences, v.

49, p. 1198-1205.

O’Hearn, M.O., and Gibb, J.P., 1980, Groundwater discharge to Illinois streams. Illinois Institute of

Natural Resources, State Water Survey Division, Groundwater Section, SWS Contract Report 246,

Champaign, Illinois, 31 p. 

Omernik, J.M., 1986, Ecoregions of the United States. U.S. Environmental Protection Agency, Corvalis

Environmental Research Laboratory, 1 p.

Rabalais, N.N., Turner, R.E., Justic, D., Dortch, Q., Wiseman, W.J., and Sen Gupta, B.K., 1996, Nutrient

changes in the Mississippi River and system responses on the adjacent continental shelf. Estuaries, v. 19,

no. 2B, p. 386-407.

Reckhow, K.H., and Chapra, S.C., 1983, Engineering approaches for lake management, Volume 1.

AnnArbor Science, Butterworth Publishing Company, Woburn, Mass., 340 p.

Rosen, B.H., and Lowe, R.L., 1984, Physiological and ultrastructural responses of Cyclotella

meneghiniana (Bacillariophyta) to light intensity and nutrient limitation. Journal of Phycology, v. 20., p.

173-183.

Shelton, L.R., 1994, Field guide for collection and processing stream-water samples for the National

Water Quality Assessment Program, U.S. Geological Survey Open-File Report 94-455, 42 p.

Sorenson, S.K., Porter, S.D., Akers, K.K.B., Harris, M.A., Kalkhoff, S.J., Lee, K.E., Roberts, L.R., and

Terrio, P.J., 1999, Water quality and habitat conditions in upper Midwest streams relative to riparian

vegetation and soil characteristics, August 1997: Study design, methods, and data. U.S. Geological

Survey Open-File Report 99-202, 53 p.
July 2000 Appendix A. Case Studies

PAGE A-42

Squillace, P.J., Caldwell, J.P., Schulmeyer, P.M., and Harvey, C.A., 1996, Movement of agricultural

chemicals between surface water and ground water, lower Cedar River basin, Iowa, U.S. Geological

Survey Water-Supply Paper 2448, 59 p.

Turner, R.E., and Rabalais, N.N., 1994, Coastal eutrophication near the Mississippi River delta. Nature,v.

368, p. 619-621.

Van Niewenhuyse, E.E., and Jones, J.R., 1996, Phosphorus-chlorophyll relationships in temperate

streams and its variation with stream catchment area. Canadian Journal of Fisheries and Aquatic

Sciences, v. 53, p. 99-105.

Walton, W.C., 1965, Ground water recharge and runoff in Illinois. Report of Investigation 48, Illinois

State Water Survey, Urbana, Illinois, 55 p.

Watson, V., and Gestring, B., 1996, Monitoring algae levels in the Clark Fork River. Intermountain

Journal of Sciences, v. 2, no. 2, p. 17-26.

Welch, E.B., Jacoby, J.M., Horner, R.R., and Seeley, M.R., 1988, Nuisance biomass levels of periphytic

algae in streams. Hydrobiologia, v. 157, p. 161-168.

Winter, T.C., Harvey, J.W., Franke, O.L., and Alley, W.M., 1998, Ground water and surface water—A

single resource. U.S. Geological Survey Circular 1139, 79 p.

Contact: Dave Pfeifer, Region 5 Nutrient Coordinator

United States Environmental Protection Agency

77 West Jackson Boulevard Chicago, IL 60604-3507

pfeifer.david@epa.gov
July 2000 Appendix A. Case Studies

PAGE A-43

BOW RIVER, ALBERTA

The Bow River is a documented case of recovery from point source nutrient loading rather than one of

setting criteria. In contrast to lakes, cases in which the recovery of streams or rivers from nutrient

reduction was thoroughly evaluated are scarce. The Bow River, Alberta, is an exception; it has been

monitored for over 16 years to evaluate the effect of a reduction in first phosphorus (80%) and later

nitrogen (~ 50%) from two domestic wastewater plants in Calgary (Sosiak pers. comm.). Algae and

macrophytes had caused problems in the river by clogging irrigation water intakes, interfering with

boating and angling, and causing low DO at night. Nitrogen removal was for the purpose of minimizing

risk of ammonia toxicity rather than control of algae or macrophytes. Both periphyton and macrophytes

decreased downstream in response to nutrient reduction, but the distribution and timing of the decreases

were to some extent unexpected. The river’s response to nutrient reduction offers pertinent implications

and guidance for setting nutrient criteria in large fast-flowing, gravel-bed rivers. Median April to

October flow in the Bow River over the sampling period ranged from approximately 75 to 130 m3

/s.

Prior to P reduction, periphyton biomass consisted mostly of diatoms, although filamentous green algae

(including Cladophora) were also present (Charlton et al. 1986). Biomass reached summer maximums

downstream averaging approximately 300-400 mg chlorophyll a/m2

, but occasionally up to 600 mg

chlorophyll a/m2

. Such maxima have persisted within 10 km of the effluent input since P reduction in

1983, but decreased markedly farther downstream over an approximately 90-330 km reach (Table A-4;

note that data from two stations between km 304 and 533 are not shown). The decrease in periphyton

occurred rather gradually over 13 years following P reduction as total dissolved P (TDP) declined to very

low levels (median 10 μg/L) downstream (Sosiak pers. comm). Within 10 km downstream of the effluent

input, however, TDP declined initially from a mean summer value of 111 g/L to 19-24 g/L and

periphyton biomass exhibited no change from the high pre-treatment levels of 300-400 mg chlorophyll

a/m2

. The data upstream and downstream demonstrated that if summer TDP consistently averaged < 10

μg/L, maximum periphyton biomass typically averaged less than 100 mg chorophyll a/m2

. Maximum

summer biomass averaged approximately 1.4 times mean values.

TDP and periphyton biomass decreased gradually over the 13-year period following treatment with the

largest decline occurring after 1989, although this is not apparent from the data summary in Table A-4. 

The delayed decrease in TDP may have been due to declining recycling from sediments, the TP content

of which declined downstream, but not upstream of Calgary (Sosiak pers. comm.).

This extensive data base also indicates that TDP was linked much closer to periphytic biomass than TP,

which decreased markedly following treatment upstream (Stier’s Ranch). The change was only slight

downstream, in contrast to the 50% decrease in TDP (Table A-4). Note that average maximum biomass

varied from 77 to 428 over a range of summer mean TP of only 40 to 59 μg/L. Periphytic biomass was

also correlated with TDP (r values of -.61 to .70), but not with TP (Sosiak pers. comm.). Sosiak

concluded that TDP was a much better indicator of periphytic biomass throughout the river than TP.

An interesting contrast for this case study in comparison with the Clark Fork River involves the reduced

frequency of filamentous green algae and lower maximum biomass levels in the Bow River. Cladophora

was the dominant taxa that extensively covered the bottom substrata and created the nuisance condition

interfering with recreational use in the Clark Fork River. In the Bow River, the periphyton was

dominated by diatoms, which can be highly visible if biomass is high. Although Cladophora was present
July 2000 Appendix A. Case Studies

PAGE A-44

downstream from Calgary prior to nutrient reduction (Charlton et al. 1986), there was apparently not the

high percent cover of filamentous greens that interferes with recreation. Part of the difference in

nuisance conditions between the two rivers may be related to the higher summer flows in the Bow. 

Nevertheless, Cladophora and some other filamentous greens did largely disappear after nutrient

reduction (Sosiak pers. comm.).

These data indicate that: 1) periphyton biomass in streams and rivers does respond to nutrient reduction,

2) biomass levels below nuisance levels (~150 mg chlorophyll a/m2

) can be attained if P can be

sufficiently reduced, 3) sufficient reductions are defined by levels approaching ~10-15 μg/L TDP, and 4)

response to nutrient reductions may not occur quickly even in rivers where water exchange is immediate. 

The gradual reduction in river TDP suggests that there is a long-term, slow release of P stored in (or

adsorbed to) bottom sediments, even in rubble-bottom rivers. 

In addition, macrophytes (mostly pond weeds) reached biomass levels of > 2000 g/m2

 within 30 km

downstream of discharges prior to effluent treatment in 1987, but declined soon after N reduction,

reaching levels in 1995-1996 of < ~200 mg/m2

. The cause for macrophyte decline in response to N

reduction is not clear, but was hypothesized to be due to increased N limitation at plant roots (Sosiak

pers. comm., citing Barko et al. 1991). Nitrogen in the water was never considered limiting to

macrophytes or algae, because DIN:TDP ratios were always well above 20:1 by weight even after N

removal. 

The downstream change in periphyton biomass was simulated with a model that predicts spatial and

temporal biomass and nutrient (in this case SRP) concentrations in cobble/gravel-bed rivers during

summer low-flow conditions. SRP was not determined in the Bow River, but TDP was converted to SRP

using 0.65 X TDP. Of five years suitable for model calibration, a 4-week period in October 1997 was

selected (Elswick et al. 2000). Sloughing loss was assumed negligible during this period, as has been

observed in laboratory channel experiments during which periphyton is actively growing to a maximum

biomass (Horner et al. 1990; Anderson et al. 1999). The model was not verified, because there was

insufficient data for some processes, such as grazing, for which a constant value was used (10% of

existing biomass/day).

Model simulation compared favorably with actual data (Figure A-16). The largest discrepancy was at

Carsland (56 km) where biomass was overestimated by 100%. Biomass was also overestimated at all

other sites, but by an average of only 25%. Part of this difference may have been related to P retention in

run-of-the-river impoundments located upstream from Carsland and not included in the model. Also,

grazing may have been greater than the assumed rate. Grazing rates per unit grazer biomass are available

in the literature and could be used in this model if grazer biomass were available. Nevertheless, the

model demonstrates the phenomenon of biomass reduction downstream following nutrient reduction at a

point source. That is, while P concentrations were still too high to reduce periphyton biomass below the

nuisance level (~150 mg chl/m2

), P concentrations and biomass declined downstream and the extent of

the decline can be estimated with this model.

LITERATURE CITED

Anderson, E .L., J. M. Jacoby, G. M. Schimek, E .B. Welch, and R .R. Horner. 1999. Periphyton removal

related to phosphorus and grazer biomass level. Freshwater Biol. 41:633-651.
July 2000 Appendix A. Case Studies

PAGE A-45

Barko, J. W., D. Gunnison, and S. R. Carpenter. 1991. Sediment interactions with submersed

macrophyte growth and community dynamics. Aquatic Botany 41:41-65. 

Charlton, S. E. D., H. R. Hamilton, and P. M. Cross. 1986. The Limnological Characteristics of the Bow,

Oldman and South Saskatchewan Rivers (1979-82). Alberta Environment, Edmonton, AL.

Elswick, D. A., B. W. Mar, and E. B. Welch. 2000. The use of dynamic modeling to predict periphyton

algal biomass in the Bow River, Canada. Department of Civil and Environmental Engineering, University

of Washington, Seattle.

Horner, R. R., E. B. Welch, M. R. Seeley, and J. M. Jacoby. 1990. Responses of periphyton to changes

in current velocity, suspended sediment and phosphorus concentration. Freshwater Biol. 24:215-232.

Sosiak, A. J., Alberta Environment Protection, Calgary, Alberta. Personal communication (unpublished

manuscript, “Long-term response of periphyton and macrophytes to reduced municipal nutrient loading

to the Bow River [Alberta, Canada].”)
Table A-4. Summary of Phosphorus and Periphytic Algal Data from the Bow River, Canada

Station Distance from

Headwaters, km

Data type Data years Average summer

periphytic biomass,

mg/m2

Average

maximum

summer

periphytic

biomass, mg/m2

Mean sum

phosphor

85th St. bridge 254.53 post-treatment 1984-87 27 49 8.

post-treatment 1988-96 71 97 15

STP 279.14STP 294.03

Stier’s Ranch 304.81 pre-treatment 1981-82 248 294 147

post-treatment 1984-87 189 369 52post-treatment 1988-96 225 428 59

Bow City 533.78 pre-treatment 1980-82 83 196 54

post-treatment 1984-87 51 77 40

post-treatment 1988-96 57 92 37

Ronalane Bridge 625.61 pre-treatment 1980-82 94 148 40

post-treatment 1984-86 74 111 39

post-treatment 1996-98 59 111 16

1Data from Sosiak, Alberta Environment Protection, personal communication

Contact: Debbi Hart (4304), Headquarters Nutrient Program

United States Environmental Protection Agency

Ariel Rios Building

1200 Pennsylvania Avenue, NW 

Washington, DC 20460

hart.debra@epa.gov
0

100200300400500600700

Stier's Ranch (11) Carseland (56) Cluny (133) Bow City (240) Ronal

Station and Downstream Distance (km)

Periphyton Biomass (mg chl 

a

m

-2

)

ModelPredicActual 

Figure A-16. Model simulation of periphyton biomass during low-flow conditions in the Bow River downstream from the Creek STP, Calgary, Alberta, compared with actual 1997 data.
July 2000 Appendix A. Case Studies

PAGE A-48
July 2000 Appendix A. Case Studies

PAGE A-49

NUTRIENT CRITERIA DEVELOPMENT FOR DESERT STREAMS—

DETERMINANTS OF NUTRIENT DYNAMICS IN SOUTHWESTERN HOT DESERT

STREAM ECOSYSTEMS

As a class of pollutants, nutrients are unique from toxicants such as mercury or DDT, in that they have

known biological functions. Macronutrients such as nitrogen and phosphorus, are at once biological

necessities and, in excess quantity, agents of change to community and ecosystem attributes. As such,

great care must be taken in the characterization of nutrient regimes in stream ecosystems. Streams are by

their very nature dynamically changing ecosystems that must be studied at ecologically meaningful

temporal and spatial scales (O’Neill et al. 1986). The characterization of “ambient” conditions with a

few grab samples is inappropriate, if not reckless. The researcher must first learn what limits autotrophic

productivity, the major nutrient sources and sinks and how and where nutrient transformations take place

in order to make informed decisions and avoid the adoption of water quality standards that may allow or

even cause shifts in stream community structure or ecosystem process. 

The nutrient regime of streams in general can be complex, however, desert streams present particular

complexities not found in more homogeneous, mesic landscape stream ecosystems. Spatial and temporal

variability in physical structure, community composition, materials availability and the interactions

between these elements strongly control nutrient processes in desert streams. Dent and Grimm (in press)

found a high coefficient of variability (as high as 145%) in the spatial distribution of nutrients in

Sycamore Creek, Arizona, with coefficients of variation increasing over successional time. Part of this is

due to hydrologic variability, in all its temporal, spatial and amplitude scales. However, stream

ecosystems are complex in time, space, composition and process, and extend beyond the limit of the

wetted surface stream. These ecosystems must be considered as a whole, temporally and spatially, and

not as disconnected, stand alone components in order for accurate characterization to take place.

The following discusses a number of the many determinants of nutrient regimes in desert streams. As the

subject matter of this section deals with desert streams, and in particular, southwestern hot desert

streams, the literature cited will concentrate on research done in those ecosystems. A hierarchical

structure (sensu Stevenson 1997) will be used to organize determinants into ultimate, intermediate, and

proximate categories. These hierarchical levels operate as interconnected units, with a particular level

being limited by constraints imposed by higher levels with processes structured at lower levels (Pickett et

al. 1989). Because of the interconnectedness of the differing hierarchical scales, it will be sometimes

necessary to “mix” scales in the discussion that follows. Hydrologic variability and its effects on desert

stream nutrient regimes is also discussed.

ULTIMATE DETERMINANTS

Ultimately, the stream is a product of its parent geology, catchment configuration and climate. In desert

streams, these structural determinants combine to organize processes at lower hierarchical levels forming

ecosystems unique from more mesic ecosystems. These desert stream ecosystems consist of four

interconnected and interacting subsystems, the surface stream, the hyporheic zone (zone of subsurface

flow), the parafluvial zone (lateral sandbars within the active channel) and the riparian zone. These

subsystems interact with and are ultimately a product of the geology and the climate/precipitation

regime. 
July 2000 Appendix A. Case Studies

PAGE A-50

Parent geology can play a large role in the availability of nutrients to aquatic ecosystems. Because of the

impermeability of desert soils and the low biomass per unit area found in terrestrial desert ecosystems,

materials entrained by precipitation events readily move into aquatic ecosystems, assuring an ample

supply of nutrients associated with the parent material. Soils of the arid southwest are rich in calcium

phosphate (Fuller 1975) and transfer that nutrient readily to stream ecosystems. A survey of 196 sites on

157 streams in Arizona found soluble reactive phosphorus (SRP) and total dissolved phosphorus did not

differ significantly among stream types (Fisher and Grimm 1983; Grimm and Fisher 1986a). This

uniformity in concentration may be due to solubility equilibria (Stumm and Morgan 1981) and indicate

physical rather than biological control of this nutrient. The ample supply of phosphorus coupled with the

low overall input of nitrogen from the surrounding landscape has lead to the condition in which nitrogen,

rather than phosphorus, is the nutrient that limits primary productivity (Grimm and Fisher 1986b).

The size of the soil particles moving into and staying within the channel can also have a large impact on

the nutrient dynamics of streams (Jones 1995). In Arizona desert streams, the unglaciated terrain

provides little silt and clay to the stream (Fisher 1986) and the flashy hydrologic regime may cause what

little sediment that makes it into the stream to be deposited either laterally or in unconstrained reaches. 

Valett et al. (1990) found that in Sycamore Creek, Arizona, most sediment ranged in size from coarse

sand to fine gravel (0.5 -5.0mm). This paucity of fine sediments allows a relatively high rate of

hydrologic conductivity within the hyporheic (Valett et al. 1990), parafluvial (Holmes et al. 1994), and

riparian zone (Marti et al. in press [a]).

The hydrologically conductive sediments beneath and lateral to the wetted stream are an important zone

of biologically mediated nutrient processes. Jones et al. (1995) observed significant rates of nitrification

(mineralization of organic nitrogen to ammonia and a subsequent transformation to nitrate) within the

hyporheic zone of Sycamore Creek. This nitrate rich hyporheic water may then exchange with nitrogen

poor surface water (Dahm et al. 1987; Valett et al. 1990; Stanley and Valett 1992) where it is an

important nutrient source for primary producers and may strongly influence biomass and community

composition (Valett et al. 1994).

The parafluvial zone consists of sediments within the active channel but outside the wetted stream. 

Results from Holmes et al. (1994) indicate that the parafluvial zone can be an area of net nitrification, 

and increased algal productivity has been observed on the downstream edges of parafluvial sandbars. 

However, Holmes et al. (1996) found significant denitrification (the reduction of nitrate to nitrous oxide

or dinitrogen) potential existed in hyporheic and parafluvial sediments. Conservative estimations by

these authors indicated that 5-40% of nitrate produced by nitrification may be consumed by this process.

The riparian zone can also be an important area of nutrient storage and transformation. Denitrification

and uptake by vegetation within riparian areas may constitute a significant sink of nutrients within a

watershed (Peterjohn and Correll 1984; Pinay and Decamps 1988). Chauvet and Decamps (1989) found

denitrification and nutrient retention to be important processes in these areas. Marti et al. (in press [b])

found retention of nutrients in riparian areas to be affected by the length of the interflood period rather

than by the magnitude of the flood. 

The overall geomorphological structure of the stream, which is a product of geology and climate,

determines the pattern of the four subsystems; surface stream, hyporheic, parafluvial, and riparian. This
July 2000 Appendix A. Case Studies

PAGE A-51

spatial mosaic can have a strong influence on the retention, transformation, uptake, and emission of

nutrients (Fisher et al. 1998a). 

The climate, while a contributing determinant of desert stream physical structure, is also an ultimate

determinant of biological structure and function. Desert streams receive high rates of insolation due to

the low amount of shading contributed by the relatively open riparian canopy. This open canopy itself is

a product of the low precipitation rates and high pan evaporation rates found in southwestern deserts. 

Although precipitation rates are low in desert landscapes, this sparse precipitation may contribute

substantial amounts of nitrate and ammonium to desert stream ecosystems (Grimm 1992).

INTERMEDIATE DETERMINANTS

Community structure and function are shaped in part by the constraints imposed from the higher

geological and climatic scale. Species composition and life history are ultimately products of the

physical components of aquatic ecosystems and, in turn, alter physical structure and chemical processing

at the ecosystem level. These interactions can significantly affect nutrient dynamics in aquatic

ecosystems.

The change in the biotic community that occurs over time after a flood disturbance shapes and changes

nutrient processing. The concept of temporal succession has been put forward to conceptualize this

temporal change that occurs after a flood disturbance. Fisher et al. (1982) described the recovery of

community and ecosystem attributes after flooding disturbance in Sycamore Creek, Arizona, as a model

of temporal succession. In these ecosystems, biologically driven nutrient transformation, uptake, and

emission can vary significantly over spatial and temporal scales. 

Changes in nutrient processing and species composition over successional time can drastically alter the

nutrient regime of streams in general, and, due to their open, autotrophic nature, in desert streams in

particular. A flood disturbance of sufficient magnitude can scour and shuffle hyporheic and parafluvial

sediments, removing attached biomass and essentially homogenizing ecosystem structure and function at

the reach level. The emergent physical structure confers organization to the biotic community

recolonizing the reach post flood. During the interflood period, biological processes become a

progressively more prominent organizational force which, given a sufficient interdisturbance interval,

can then confer organization to higher hierarchical levels.

The complexity of nitrogen processing interactions increases with successional time. Dent and Grimm

(in press) found nutrient spatial heterogeneity in the surface stream to increase with time post flood. Over

successional time, nitrogen uptake length decreases due to increased biological uptake (Fisher et al.

1982; Marti et al. 1997) and retention of nitrogen in the surface stream increases from early to mid

successional stages and then declines during late succession (Grimm 1987). The changes seen in nutrient

processing length due to disturbance may vary according to the relative resistance of the individual

stream subsystem and recovery may vary over successional time due to relative resilience (Fisher et al.

1998b). 

Streams by their very nature are spatially heterogeneous. In desert streams, the spatial heterogeneity of

nutrients and nutrient processing is manifested in several ways. Hydrologic linkages between the surface
July 2000 Appendix A. Case Studies

PAGE A-52

stream and the parafluvial and hyporheic ecosystem components vary spatially due to the underlying

geomorphological structure of the stream ecosystem. As stated earlier, these are areas where important

nutrient processing occurs. The extent and intensity of hyporheic upwelling and downwelling can change

or even reverse in response to flooding or drying (Stanley and Valett 1992; Valett et al. 1994)

considerably affecting concentrations of nitrate in the surface stream and influencing algal community

composition (Valett et al. 1994).

One of the most striking successional events in desert streams is the drying and contraction of the surface

stream ecosystem. This phenomena can take place either at large spatial and temporal scales, or at the

scale of the reach during a 24 hour period. In Sycamore Creek, as the quantity of water delivered to the

surface stream ecosystem begins to diminish after an extended wet period or post flood, drying begins to

occur. 

During dry periods, the wetted surface stream area can contract as much as eight fold at the scale of the

entire basin. (Stanley et al. 1997). At the scale of the individual run, drying may begin at the downstream

terminus and continue upstream to the area of hyporheic upwelling at the head of the run (Stanley et al.

1997). This contraction of the surface water ecosystem can strongly affect algal community composition.

Nitrogen fixing cyanobacteria species inhabiting the downstream extremity of the drying run are exposed

to the atmosphere long before upstream mats of filamentous green algae situated closer to the source of

nitrogen rich hyporheic upwelling, potentially altering nitrogen cycling (Stanley et al. 1997). At all

scales, drying of the surface stream increases the relative contribution of hyporheic processes to overall

ecosystem function (Stanley and Valett 1992).

The increase in the relative proportion of the wetted stream ecosystem occupied by subsurface

subsystems could have a profound effect on nitrogen processing and retention. With the decline of

surface stream area available to autotrophic production and nitrogen fixation, the nitrogen

transformations associated with subsurface flowpaths, nitrification, and denitrification will become

proportionally more prevalent (Stanley and Valett 1992). This, coupled with the ample organic carbon

available from decaying algae (Jones et al. 1995) and oxygen depletion due to respiration over extended

hyporheic flowpaths, could possibly cause subsurface subsystems to become net nitrogen emitters. This

emission of nitrogen gas would represent a real loss of nitrogen from the stream ecosystem.

The riparian component of the desert stream ecosystem, while ultimately a product of climate, geology,

and catchment configuration, also modifies environmental factors within the ecosystem of which it is a

part. At successional and reach scales, the presence of riparian vegetation is a strong determinant of the

shape of the surface stream channel. Given a long period between stand destructive floods and ample

surface or near surface water, fast growing woody species such as seep willow (Baccharis salicifolia) will

progressively impinge on the surface stream. As the surface stream narrows, parafluvial ecosystem

components gradually convert to riparian, considerably changing nutrient processing pathways.

The high pan evaporation rate (>300 cm year -1) found in hot desert ecosystems coupled with high

consumptive water use by phreatophytes (water-loving riparian plants) (1514 L day -1) (Blaney and

Criddle 1962) can significantly influence the size of a surface stream reach on a diel basis. Large

changes in reach length during a 24 hour period have been observed on Sycamore Creek (Stanley pers.

com.). This type of short term stress can effectively eliminate desiccation intolerant organisms from the

benthic community of large portions of a stream reach.
July 2000 Appendix A. Case Studies

PAGE A-53

In contrast to more mesic streams, many desert stream riparian zones occupy a relatively narrow band

lateral to an underfit surface stream, providing minimal shading to the surface stream itself. The resultant

high rate of insolation to the surface stream favors high rates of primary productivity, high relative water

temperatures, and attendant increases in metabolic rates (Busch and Fisher 1981). The elevated rates of

primary productivity and metabolism are controlling factors in the short uptake lengths for nitrogen

covered earlier in this document.

PROXIMATE DETERMINANTS

The nitrogen cycle, as it occurs within a desert stream, is essentially a biologically driven process. Given

the physical organization conferred from higher hierarchical levels, the resultant biotic communities of

the surface stream, riparian, parafluvial, and hyporheic control fixation, mineralization, nitrification and

denitrification.

Nitrogen fixation by heterocystous cyanobacteria such as Nostoc or Calothryx and diatoms with

phycoendosymbionts such as Epithemia sorex may be significant in nitrogen poor desert stream

ecosystems. Grimm and Petrone (1997) measured in-situ N2 fixation rates as high as 51mg N2 m-2 h-1. 

These rates were high in comparison to published values from more mesic inland systems. In this study,

as much as 85% of the net nitrogen flux to the benthos was accounted for by N2 fixation on five dates for

which nitrogen input/output budgets were constructed. Nitrogen fixation may be an extremely important

vector of nitrogen into desert stream ecosystems, as between precipitation events, little fixed nitrogen

from the surrounding uplands is transported to the stream ecosystem (Grimm and Petrone 1997).

As stated earlier, nitrification, or the biologically mediated oxidation of ammonium to nitrate takes place

within hyporheic and parafluvial sediments. Jones et al. (1995) reported mean nitrification rates of 13.1

mg NO3 

.

 L sediments-1 h-1 in downwelling zones in Sycamore Creek and Holmes et al. (1994) reported 

increases in nitrate concentrations in water moving through parafluvial flowpaths. In both studies, the

highest rates of biotic activity occurred at the interface where the surface stream infiltrated into

hyporheic/parafluvial sediments. This effect suggests the importance of dispersed interfaces in a

heterogeneous system (Dahm et al. 1998)

Denitrification is well documented in anoxic environments such as riparian soils (Peterjohn and Correll

1984; Lowrance et al.1984), but in well oxygenated environments such as the coarse sand/gravel

hyporheic/parafluvial subsystems found in desert streams, the occurrence of denitrification is somewhat

of a conundrum. Holmes et al. (1996) investigated denitrification potential in hyporheic, parafluvial and

riparian sediments and found field measured rates in excess of 150 mg N.

 m2 . h-1 at the stream/parafluvial

interface. This study also found the highest rates of biotic activity (denitrification) at the point of

infiltration at the surface-water-sediment interface.

Despite the low overall availability of the most probable limiting nutrient in southwestern hot desert

streams, nitrogen, high mid-summer instantaneous standing crops of algae (191 mg -1 m-2 chlorophyll a)

have been measured in Sycamore Creek (Busch and Fisher 1981). The spatial distribution of algal

standing crop has been linked with areas of hyporheic upwelling and downwelling. Valett et al. (1994)

found significantly higher areal concentrations of chlorophyll a in upwelling zones when compared to

areas of downwelling. These high quantities of algal biomass and the associated autotrophic uptake of

nitrogen are the most probable cause for the declines in nitrogen concentrations of surface water found
July 2000 Appendix A. Case Studies

PAGE A-54

downstream of spring sources (Grimm et al., 1981) and the lower concentrations found at points of

downwelling (Valett et al., 1994).

Organic carbon released as a result of autochthonous primary production has been hypothesized as the

energy source utilized in nitrification (Holmes et al., 1994; Jones et al., 1995) and denitrification

(Holmes et al., 1996). However, allochthonous input of organic matter from riparian leaf litter was found

to play an insignificant role in nitrogen dynamics in Sycamore Creek (Schade and Fisher 1997).

Macroinvertebrates may also significantly affect nitrogen processing in desert streams. Grimm (1988)

found that during a 20 day successional period, collector gatherer invertebrate standing stock increased

from 32,000 to 108,000 individuals . m2

 . Twenty seven percent of the nitrogen ingested by the collector

gatherers during this period was converted to biomass, of which only 26% (7% of total ingested nitrogen)

remained in the stream as macroinvertebrate biomass. One percent of collector gatherer biomass was lost

to the surrounding upland ecosystem due to the emergence of adults, 19% was lost to mortality and 9-

31% was excreted as ammonia. The transformation of organic nitrogen to ammonia may be particularly

significant, as the ammonia form of nitrogen is readily taken up and utilized by primary producers or

available for utilization in nitrification/denitrification transformations.

HYDROLOGIC VARIABILITY IN ARID LANDS STREAMS

While hydrologic variability is an important consideration in the development of nutrient standards for

all streams, the spatial and temporal heterogeneity found in arid regions, the stark contrast between wet

and dry, brings this variability into sharper relief. When viewing desert catchments from above, the

observer is often presented with a dry landscape of high relief bisected by the string of glistening beads

that is the spatially intermittent stream. The dry arroyos or quiet, disconnected pools and short reaches of

wetted stream that characterize desert streams during dry periods are in complete contrast to the raging

torrents that they can become at flood stage. This hydrologic variability and the unique chemical and

biological characteristics of arid lands aquatic ecosystems may make the use of broad generalizations to

explain nutrient regimes impossible.

When analyzing stream nutrient regimes in the context of hydrologic variability, there is a continuum of

spatial and temporal scale (sensu Pickett et al. 1989; Fisher and Grimm 1991) beyond and including

discreet disturbance flows which must be considered. Ecologically important spatial and temporal scales

can vary from that of a discreet patch at a single point in time, to the fluvial geomorphological and

climatic factors determining the physical structure of an entire catchment. These spatial and temporal

scales exist as nested hierarchies, with structure at smaller scales being influenced by higher scales

(Pickett et al. 1989).

Use of a coherent hierarchical schema can confer useful organization to the analysis of nutrients and

primary productivity. The heterogeneity of benthic algal assemblages is determined at several

hierarchical levels, with proximate and intermediate determinants such as nutrient regime and flow

stability being governed by the ultimate determinants of climate and geology (Stevenson 1997). It is

important to consider the determinants of structure and function at different scales when designing

ecological studies.
July 2000 Appendix A. Case Studies

PAGE A-55

In arid landscapes, stream ecosystems are more dynamically linked with the surrounding upland

ecosystem than streams in more mesic regions. This close linkage is due to the higher percentage of

uninterrupted vectors of runoff and entrained materials from the surrounding uplands to the aquatic

ecosystem. The extensive riparian buffers and dense upland terrestrial vegetation found in more mesic

ecosystems are largely absent in spatially intermittent and ephemeral watercourses. The sparse

vegetative cover (5-50%; Barbour et al. 1980) and high orographic relief found in the upland terrestrial

catchments promote increased rates of short-term, sheetflow runoff during intense precipitation events,

leading to larger, more rapid movements of precipitation and entrained materials into watercourses (see

Graff 1988). This “spiky” oscillation in the hydrograph is then transferred downstream to the more

perennial sections of a stream. 

In desert streams, surface discharge regimes may vary from completely dry, to flows as much as three to

five orders of magnitude greater than mean annual flow, all within a period of hours or days. In

comparison to streams in more mesic regions, the coefficient of variation of annual flow is 467% greater

in arid land lands streams (Davies et al. 1994). The aquatic ecosystems structured by these often

catastrophic and always chaotic flow regimes exhibit spatially and temporally heterogeneous structures

and functions (sensu Thoms and Sheldon 1996) which may not allow the application of nutrient criteria

derivation techniques applicable to more homogeneous environments. 

Short-term disturbance of small spatial extent may cause considerable alteration in the chemical and

biological structure of a stream. Flooding may scour the benthic surface, reset the stream ecosystem to

an earlier successional stage (Fisher 1983) and transport large, short-term pulses of nutrients (Fisher and

Minckley 1978). Drying of a surface stream reach due to diel changes in evapotranspiration can strand

algal mats (Stanley pers. com.) causing a stress disturbance (sensu Pickett et al. 1989). Recovery from

these types of small scale disturbances may be rapid in ecosystems where the biota is disturbance adapted

(Gray 1981; Grimm and Fisher 1989) and when observed in the context of larger spatial and temporal

scales, these types of disturbances may represent normal oscillations in a steady state equilibrium.

Often, hydrologic regimes that effect a particular ecological structure or function may exist at spatial and

temporal scales that can only be measured using multiple measurements over space and time. While the

flood pulse itself may cause considerable disturbance to a stream ecosystem, the entire hydrologic regime

must be considered biologically significant (Poff and Ward 1989). Variability in rates of rise and fall,

timing, duration, magnitude and frequency of the flood pulse can have a significant effect on the biota of

a stream (Puckridge et al. 1998). At slightly longer temporal scales, it is the relatively short interval (1.5

year) bankfull discharge that forms and maintains the physical structure of the wetted channel (Dunne

and Leopold 1978) rather than the catastrophic long return interval flood.

During interflood periods, flow regimes are comparatively stable as precipitation stored within the

watershed moves into the stream. The stable flow allows the control of ecosystem state variables, such as

primary productivity, to shift from disturbance to morphometric/biotic controls. If the interflood period is

of sufficient duration, a phase shift from wetted surface to dry occurs as flow from the watershed

diminishes (Fisher and Grimm 1991). During this interval, primary productivity is a partial function of

the number of days post flood (Fisher 1986; Fisher and Grimm 1988). Characterization of the interflood

period is an important tool which may allow the researcher to locate the point in successional time when

indexing biological data for inter or intra-stream comparison. 
July 2000 Appendix A. Case Studies

PAGE A-56

One portion of the hydrologic regime that is often overlooked is drying. Drying disturbance, or more

specifically the contraction and fragmentation of a stream ecosystem, occurs as a spatially or temporally

intermittent stream recedes after a wet period. Differing reach types (e.g., riffles, runs, constrained,

unconstrained) respond to this contraction and fragmentation differentially (Stanley et al. 1997) and

hyporheic, or subflow processes may come to dominate as a larger portion of the wetted volume of a

stream is subsurface. (Stanley and Valett 1992; Valett et al. 1990). Drying is likely to be an important

determinant of biological pattern and process (Stanley et al. 1997; Stanley and Boulton 1995), especially

in streams where the dry period and extent may be greater than the wet.

Longer-term hydrologic/disturbance regimes are also an important consideration. Decadal climate

variability such as the El Niño and La Niña phenomena can cause large, prolonged fluctuations in stream

flow (Molles and Dahm 1990). This long return interval climate variability and the attendant change in

short-term weather patterns can significantly affect the structure and function of aquatic ecosystems. The

establishment, maintenance and species composition of riparian associations are strongly dependant on

the seasonality, periodicity, duration, sequentiality and magnitude of storm events and subsequent flow

regimes (Baker 1990; Stromberg et al. 1991).

Large, long return interval disturbances can also greatly alter the physical structure and pattern of

watercourses at greater than reach scales. These large alterations may affect the physical equilibrium of

the watercourse in several ways. If the system is stable or dynamically stable, internal feedback

mechanisms will cause physical values such as bed load transport to return to original following a

disturbance. If a system is unstable or metastable, the system may adjust to a new value causing changes

in channel pattern and shape (sensu Chorley and Kennedy 1971). 

In the event a large scale, destructive flood event significantly restructures a stream, changes may occur

in mean particle size, pattern of reach types or the ratio of the different stream ecosystem components

(surface, riparian, parafluvial, hyporheic). These changes in physical structure may significantly alter the

prevailing nutrient regime (sensu Fisher et al. 1998a).

In order to properly characterize the nutrient regime of a stream ecosystem, the flow of water, surface or

subsurface, flood or base flow, wet or dry must be considered at ecologically significant temporal and

spatial scales. It is also important that the researcher address this hydrologic regime at the scale of the

question to be answered. If a stream is dry for 75% of the average year, or 75% of its length, is it correct

to characterize it from surface water data alone? If 50% of the entire annual load of a limiting nutrient

passes through a stream ecosystem in three discreet storm events, what is the effect of that nutrient on the

stream ecosystem itself? What is the effect to downstream ecosystems? Due to the spatial and temporal

variability of flow patterns, the characterization of desert stream nutrient dynamics is an intricate

undertaking. However, it is important to recognize that all stream ecosystems posses complexities that

will only yield to proper inquiry.

CONCLUSIONS

The characterization of nutrient dynamics in streams with high temporal and spatial variability, such as

southwestern hot desert streams, may prove to be difficult without a commitment to addressing questions

at the appropriate ecological scale. Variability in the products of climate and geology; precipitation, flow

regime and physical structure, define the limits of community composition and nutrient processing. The
July 2000 Appendix A. Case Studies

PAGE A-57

predictive power of any model designed to characterize nutrient dynamics without considering the

ultimate determinants is extremely limited. Conversely, relying on coarse generalizations generated at

the spatial and temporal scale of the ecozone to predict process at the scale of the reach is also

inappropriate. Nutrient dynamics must be characterized and nutrient water quality standards developed

considering the constraints and processes dictated at the different and interacting scales.

STEPS FOR CHARACTERIZING THE NUTRIENT REGIME OF DESERT STREAM SYSTEMS

Selecting Index Sites and Periods

As in any investigation, the researcher must remove or account for as many of the differing sources of

variability as possible prior to gathering nutrient data. In streams with high spatial and temporal

variability, the best case scenario would be to characterize the entire stream, source to terminus, in space

and time. While this may be the most scientifically sound methodology, it is infeasible in all but the

smallest basins. An alternative is to carefully choose and compare index sites (and periods) from which

reasonable extrapolations can be made. This can be done using a similar hierarchical approach to that

outlined above, however, extrapolation beyond the specific index is risky. The number of sights required

to accurately characterize the nutrient regime in a stream type will vary with the complexity of that

nutrient regime.

First, at the largest spatial scale, the position of the stream reach within the watershed must be

determined. The areal extent of the basin above the sample reach, the watershed aspect (orientation to

weather patterns), mean stream gradient, parent material(s) and stream order are all attributes that should

be considered. At the largest temporal scale, the time since the last flood that restructured all of the

stream compartments (surface, hyporheic, parafluvial, and near stream riparian) should be determined as

well as any long-term fluctuations or trends in the hydrograph.

At the scale of the sample reach, the researcher should consider the landscape setting surrounding and

upstream of the sampling point. It is important that the sample reach and surrounding landscape be

consistent with that found for a reasonable distance upstream. This distance will depend on stream

velocity, with greater distances being required in faster flowing streams. The following is a list of the

major elements of a sample reach that should be addressed to characterize nutrient regime and increase

data conformity between sampling sights:

Physical/Structural Elements

• altitude

• terrestrial vegetation association

• terrestrial land use 

• Rosgen stream type

• physical setting - constrained or unconstrained? (canyon or open plane) 

• reach gradient

• solar aspect - is the sun blocked by canyon walls at times during the day or year?

• riparian association - including understory plants

• riparian cover percent

• riparian canopy density - stream shading

• stream discharge and velocity 
July 2000 Appendix A. Case Studies

PAGE A-58

• substrate particle size distribution 

• estimated subsurface compartment volume (hyporheic, riparian and parafluvial)

• location of upwelling and downwelling zones

• water temperature

Temporal Elements

• season

• photoperiod

• time since last flood

• flow regime the previous 30 days

• temperature regime the previous 30 days (air and water)

Chemistry (other than TN and TP)

• NH3/NH4, NO3, SRP

• CO2

• potassium (K), calcium (Ca), magnesium (Mg), sulphur (S), boron (B), chlorine (Cl), copper

(Cu), iron (Fe) manganese (Mn), molybdenum (Mo) and zinc (Zn)

• O2 - dissolved and % saturation

• pH - field measured

• electrical conductivity

• total dissolved solids

• total organic carbon

• turbidity - field measured

• total suspended solids

• volatile suspended solids

Biological Elements

• algal community composition

• benthic chlorophyl a

• benthic organic matter

• benthic community productivity and respiration

When taking physical, chemical or biological samples, it is extremely important to choose the sampling

point(s) and times carefully in order to accurately characterize the element in question for a particular

reach at a particular time. Multiple samples taken within the reach and analyzed separately is the

preferred method, however composite samples, or carefully taken grab samples can work well. The

researcher should avoid or account for samples taken in areas of the stream that differ from the main

body. Anoxic backwaters, upwelling or downwelling zones, highly aerated areas below waterfalls and

other sections that differ physically, chemically, or biologically from the main stream, usually account

for only a small portion of total stream area but may contribute significantly to materials processing. 

Rather than characterizing these sections individually, a point can be chosen that integrates these areas

into the greater flow. A fast “run” with relatively uniform flow, biological, and bank characteristics for

20 meters that has neutral subsurface hydraulic head may be a good selection. Insolation rate (solar
July 2000 Appendix A. Case Studies

PAGE A-59

energy per unit area per unit time) and diel curve should also be considered, although a viable alternative

would be careful consideration of time of day, time of year, riparian shading, and cloud cover.

It is extremely important that enough data be gathered to characterize a nutrient regime. While the

ancillary data requirement may seem large, lack of one or more of these data points may preclude

accurate interpretation of the nutrient data.

REFERENCES

Baker, William L. 1990. Climatic and hydrologic effects on the regeneration of Populus angustifolia

James along the Animas River, Colorado. Journal of Biogeography. 17(1): 59-73.

Barbour, M. G., J. H. Burk, and W. D. Pitts. 1980. Terrestrial Plant Ecology. The

Benjamin/Cummings Publishing Company, Menlo Park, CA.

Blaney, H. and W. Criddle, 1962. Determining consumptive use and irrigation water requirements. 

USDA Technical Bulletin Number 1275, 59 pages.

Busch, D.E., and S.G. Fisher. 1981. Metabolism of a desert stream. Freshwater Biology 11:301-308.

Chauvet, E. and H. Decamps. 1989. Lateral interactions in a fluvial landscape: the River Garonne,

France. Journal of the North American Benthological Society. 8(1) p 9-17.

Chorley, R. J. and B. A. Kennedy. 1971. Physical geography: a systems approach. Prentice-Hall,

London.

Dahm, C.N., N.B. Grimm, P. Marmonier, H.M. Valett, and P. Vervier. 1998. Nutrient dynamics at the

interface between surface waters and ground waters. Freshwater Biology 40:427-451.

Dahm, C. N., E. H. Trotter and J. R. Sedell, 1987. Role of anarobic zones and processes in stream

ecosystem productivity. P 157-178 in R. A. Averett and D. M. McKnight, editors. Chemical quality of

waterand the hydrologic cycle. Lewis, Chelsea, Michigan.

Davies, B. R., Thoms, M. C., Walker, K. F., O’Keeffe, J. H., and Gore, J. A. 1994. Dryland rivers: their

ecology, conservation and management, in Calow, P. and Pets, G. E. (Eds) The Rivers Handbooy, Vol. 2,

Blackwell Scientific, Oxford. 484-512

Dent, C.L., and N.B. Grimm. Spatial heterogeneity in stream water nutrient concentrations over

successional time. Ecology: in press.

Dunne, T. and L. B. Leopold, 1978. Water in environmental planning. W. H. Freeman and Co. San

Francisco.
July 2000 Appendix A. Case Studies

PAGE A-60

Fisher, S.G. 1983. Succession in streams. Pages 7-27 in J. Barnes and G.W. Minshall, editors. Stream

ecology: Application and testing of general ecological theory. Plenum Press, New York, New York,

U.S.A.

Fisher, S.G. 1986. Structure and dynamics of desert streams. Pages 114-139. IN: W. Whitford (ed.).

Pattern and Process in Desert Ecosystems. University of New Mexico Press, Albuquerque.

Fisher, S.G., L.J. Gray, N.B. Grimm, and D.E. Busch. 1982. Temporal succession in a desert stream

following flash flooding. Ecological Monographs 52:93-110.

Fisher, S.G., and N.B. Grimm. 1983. Water quality and nutrient dynamics of Arizona streams. OWRT

Project Completion Report A-106-ARIZ. Office of Water Research and Technology.

Fisher, S. G., and N. B. Grimm. 1988. Disturbance as a determinant of structure in a Sonoran Desert

stream ecosystem. Internationale Vereinigung für Theoretische und Angewandt Limnologie,

Verhandlungen 23:1183-1189. 

Fisher, S.G., and N.B. Grimm. 1991. Streams and disturbance: are cross-ecosystem comparisons useful?

pp 196-221 in J.C. Cole, G.M. Lovett, and S.E.G. Findlay, editors. Comparative analyses of ecosystems:

patterns, mechanisms and theories. Springer-Verlag, New York, New York, U.S.A.

Fisher, S.G., N.B. Grimm, E. Marti, and R. Gomez. 1998a. Hierarchy, spatial configuration, and nutrient

cycling in streams. Australian Journal of Ecology 23: 41-52.

Fisher, S.G., N.B. Grimm, E. Martí., J.B. Jones, Jr., and R.M. Holmes. 1998 (b). Material spiralling in

river corridors: a telescoping ecosystem model. Ecosystems 1:19-34.

Fisher, S.G., and W.L. Minckley. 1978. Chemical characteristics of a desert stream in flash flood. Journal

of Arid Environments 1:25-33.

Fuller, W. H. 1975. Soils of the desert southwest. University of Arizona Press. 102p

Graff, W. L. 1988. Fluvial Processes in Dryland Rivers. Springer-Verlag, New York.

Gray, L.J. 1981. Species composition and life histories of aquatic insects in a lowland Sonoran Desert

stream. American Midland Naturalist 106:229-242.

Grimm, N.B. 1987. Nitrogen dynamics during succession in a desert stream. Ecology 68:1157-1170.

Grimm, N. B. 1988. Role of macroinvertebrates in nitrogen dynamics of a desert stream. Ecology 69:

1884-1893.

Grimm, N.B. 1992. Biogeochemistry of nitrogen in arid-land stream ecosystems. Journal of the

Arizona-Nevada Academy of Science 26:130-146.
July 2000 Appendix A. Case Studies

PAGE A-61

Grimm, N.B., and S.G. Fisher. 1986a. Nitrogen limitation potential of Arizona streams and rivers.

Journal of the Arizona-Nevada Academy of Science 21:31-43.

Grimm, N.B., and S.G. Fisher. 1986b. Nitrogen limitation in a Sonoran Desert stream. Journal of the

North American Benthological Society 5:2-15.

Grimm, N.B., and S.G. Fisher. 1989. Stability of periphyton and macroinvertebrates to disturbance by

flash floods in a desert stream. Journal of the North American Benthological Society 8:293-307.

Grimm, N.B., S.G. Fisher, and W.L. Minckley. 1981. Nitrogen and phosphorus dynamics in hot desert

streams of Southwestern U.S.A. Hydrobiologia 83:303-312.

Grimm, N.B., and K.C. Petrone. 1997. Nitrogen fixation in a desert stream ecosystem. Biogeochemistry

37:33-61.

Holmes, R.M., S.G. Fisher, and N.B. Grimm. 1994. Parafluvial nitrogen dynamics in a desert stream

ecosystem. Journal of the North American Benthological Society 13:468-478.

Holmes, R.M., J.B. Jones, Jr., S.G. Fisher, and N.B. Grimm. 1996. Denitrification in a nitrogen-limited

stream ecosystem. Biogeochemistry 33:125-146.

Jones Jr., J.B. 1995. Factors controlling hyporheic respiration in a desert stream. Freshwater Biology

34:101-109. 

Jones, J.B., Jr., S.G. Fisher, and N.B. Grimm. 1995. Nitrification in the hyporheic zone of a desert stream

ecosystem. Journal of the North American Benthological Society 14:249-258.

Lowrance, R., R. Todd, J. Fail Jr., O. Hendrickson Jr., R. Leonard and L. Asmussen. 1984. Riparian

forests as nutrient filters in agricultural watersheds. Bioscience 34(6) p 374-377. 

Martí, E., S.G. Fisher, J.J. Schade, and N.B. Grimm. in press (b) Effect of flood frequency on

hydrological and chemical linkages between streams and their riparian zones: an intermediate disturbance

model. J.B. Jones, Jr., and P.J. Mulholland, editors. Surface-subsurface interactions in streams. Book

chapter.

Martí, E., S.G. Fisher, J.J. Schade, J.R. Welter, and N.B. Grimm. in press (a) Hydrological and chemical

linkages between streams and their riparian zones: an intermediate disturbance model. Internationale

Vereinigung fur Theoretische und Angewandt Limnologie, Verhandlungen 27.

Marti, E., N.B. Grimm, and S.G. Fisher. 1997. Pre- and post-flood nutrient retention efficiency in adesert

stream ecosystem. Journal of the North American Benthological Society 16:805-819.

Molles, M. C., Jr., and C. N. Dahm. 1990. A perspective on El Nino and La Nina: global implications for

stream ecology. Journal of the North American Benthological Society. 9:68-76.
July 2000 Appendix A. Case Studies

PAGE A-62

O’Neill, R. V. , D. L. DeAngelis, J. B. Waide and T. F. H. Allen, 1986. A hierarchical concept of

ecosystems. Princton University Press, Princton, New Jersey.

Peterjohn, W. T. and D. L. Correll. 1984. Nutrient dynamics in an agricultural watershed: observations

on the role of a riparian forest. Ecology 65(5) p 1466-1475. 

Peterson, C.G., and N.B. Grimm. 1992. Temporal variation in enrichment effects during periphyton

succession in a nitrogen-limited desert stream ecosystem. Journal of theNorth American Benthological

Society 11:20-36.

Pickett, S. T. A., J. J Kolasa, and S. L. Collins. 1989. The ecological concept of disturbance and its

expression at various hierarchical levels. Oikos 54: 129-136.

Pinay, G. and H. Decamps. 1988. The role of riparian woods in regulatimg nitrogen fluxes between the

alluvial aquifer and surface water: a conceptual model. Regulated Rivers; Research and Management

Volume 2. p 507-516.

Poff, N.L., and J.V. Ward. 1989. Implications of streamflow variability and predictability for lotic

community structure: a regional analysis of streamflow patterns. Canadian Journal of Fisheries and

Aquatic Sciences. 46:1805-1818.

Puckridge, J.T., Sheldon, F., Walker, K.F. & Boulton, A.J. 1998. Flow variability and the flood pulse

concept in river ecology. Australian Journal of Marine and Freshwater Research. 

Schade, J.D., and S.G. Fisher. 1997. The influence of leaf litter on a Sonoran Desert stream ecosystem.

Journal of the North American Benthological Society 16:612-626.

Stanley, E.H. 1999. Personal Communication Department of Zoology, University of Wisconsin,

Madison, WI

Stanley, E.H. and A.J. Boulton. 1995. Hyporheic processes during flooding and drying in a Sonoran

Desert stream. I. Hydrologic and chemical dynamics. Archiv fur Hydrobiologie 134:1-26.

Stanley, E.H., S.G. Fisher, and N.B. Grimm. 1997. Ecosystem expansion and contraction: a desert stream

perspective. BioScience 47:427-435.

Stanley, E.H. and H.M. Valett. 1992. Interaction between drying and the hyporheic zone of a desert

stream ecosystem. pp 234-249 in P. Firth and S.G. Fisher, editors. Climate Change and Freshwater

Ecosystems. Springer-Verlag, New York, New York, U.S.A. 

Stevenson, R. J. 1997. Scale dependent determinants and consequences of benthic algal heterogeneity. 

Journal of the North American Benthological Society. 16(1):248-262

Stromberg, J. C., D. T. Patten and B. D. Richter. 1991. Flood flows and dynamics of Sonoran riparian

forests. Rivers 2(3):221-235.
July 2000 Appendix A. Case Studies

PAGE A-63

Stumm, W. G. and J. J. Morgan, 1981. Aquatic chemistry, John Wiley and Sons, New York.

Thoms, M.C. and Sheldon, F. 1996. The importance of channel complexity for ecosystem processing: An

example of the Barwon-Darling River. Pp:111-118 in Rutherfurd, I. (ed) Stream Management in

Australia. CRC for Catchment Hydrology, Melbourne.

Valett, H.M., S.G. Fisher and E.H. Stanley. 1990. Physical and chemical characteristics of the hyporheic

zone of a Sonoran Desert stream. Journal of the North American Benthological Society 9:201-215.

Valett, H.M., S.G. Fisher, N.B. Grimm, and P. Camill. 1994. Vertical hydrologic exchange and

ecological stability of a desert stream ecosystem. Ecology75:548-560.

Contact: Suesan Saucerman, Region 9 Nutrient Coordinator

United States Environmental Protection Agency

75 Hawthorne Street San Francisco, CA 94105

saucerman.suesan@epa.gov
July 2000 Appendix A. Case Studies

PAGE A-64
PAGE A-65

APPENDIX B. METHODS OF ANALYSIS FOR WATER QUALITY VARIABLES

Several methods used to analyze certain water quality variables are discussed briefly in this section. 

Additionally, relevant publications are referenced as appropriate. Some tips on analysis will also be

presented to help users to be efficient in determinations. As with any environmental analysis, the most

efficient strategy when learning a new technique is to visit a laboratory where it is routinely performed. 

The methods that will be discussed include those for light transmission; total suspended solids; total

nitrogen, total phosphorus, and dissolved inorganic nitrogen and phosphorus; conductivity; chlorophyll a

and ash free dry mass (AFDM) for algal biomass; and microscopic identification to determine the algal

taxa present. Brief discussion of secondary indicators of eutrophication (algal production, dissolved

oxygen concentrations, limiting nutrients and macroinvertebrates) will also be presented. As discussed

above, determination of other factors such as hydrology, geology, soil characteristics may also be

necessary.

PHYSICAL WATERBODY CHARACTERISTICS

LIGHT TRANSMISSION

Total suspended solids and dissolved humic compounds can absorb light and limit algal biomass. As

periphyton biomass increases, particulate matter sloughed and eroded from the periphyton also increases,

reducing transparency. Light transmission measurements may be required. Light transmission can be

measured using turbidity meters (transmissometers or turbidometers). Use of these meters is described in

Standard Methods (APHA 2000). A quick method for determining light transmission is use of a black

disk and an underwater periscope (Davies-Colley 1988). The path length for transparency is measured

horizontally in shallow streams, as opposed to vertically in lakes, reservoirs and deep rivers or estuaries. 

The vertical water column in relatively clear-water, gravel/cobble bed streams/rivers is usually

insufficient to determine Secchi disk depth.

LIGHT AVAILABILITY 

Light availability can be measured directly with a light meter as photon flux density (mole quanta m-2

s

-1), but such measurement vary temporally. Measures of % canopy cover, TSS and average water depth,

light transmission with a black disk or periscope, and stream direction provide measures of relative

availability of light which can be related to a regional average. Light intensity varies so much during a

day or with weather from day to day that indicators of relative light intensity may be a more precise

indicators of light availability than one-time measurements of light intensity.

Light availability for photosynthesis can be reduced by the amount of total suspended sediment (TSS) in

the water column, light attenuation caused by dissolved compounds, river depth, and channel shading. In

addition to scouring algae, TSS also attenuates light to benthic algae. Dissolved organic humic

compounds can absorb light, and if they are present in high enough concentrations, they can prohibit

algal growth. Similarly, forest canopies can shade stream channels (Dodds et al. 1996). This shading

can lead to rivers with relatively high nutrient concentrations, but with negligible sestonic or benthic

algal biomass. In such cases, nutrient control may have few immediate benefits.

If there is seasonally high TSS or shading (e.g., deciduous forests), the high nutrients may cause

excessive periphyton algal biomass only during certain times of the year. An example of this would be
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-66

streams where snow melt is common in the spring; this could lead to high levels of TSS and low algal

biomass, but during stable flows in summer, low TSS and high algal biomass. Finally, very deep channels

will not usually have excessive algal biomass except at the margins, since limited amounts of light reach

most of the bottom (Allan 1995), and sestonic algae are mixed frequently throughout the water column,

which reduces available light while increasing respiration (Welch 1992). Therefore, net productivity

(gross production minus respiration) decreases with depth of mixing.

FLOW AND VELOCITY

Flow and velocity measurements are important for determining nutrient loadings, concentrations, and

distributions. Flow volume or discharge is easily calculated based on stream channel area and velocity. 

Velocity is typically measured with a stream gauge or current meter. See 

http://water.usgs.gov/pubs/circ1123/collection.html for more details.

TOTAL SUSPENDED SOLIDS AND VOLATILE SOLIDS

It can be useful to quantify total suspended solids because of their effect on light attenuation, and the

determination of volatile solids may be of interest to determine if the total suspended solids are from

organic sources. The methods for total suspended solids and volatile solids are presented in Standard

Methods (APHA 2000).

TEMPERATURE

Temperature can be an important variable in determining alkalinity, saturation, and rates of chemical and

biological reactions. It is a simple but useful measurement to include in a sampling regime. Methods for

temperature measurement in the field and laboratory are described in Standard Methods (APHA 2000).

CHEMICAL WATERBODY CHARACTERISTICS

NUTRIENT ANALYSES

Nutrient analyses are the most important indicators for determining sources of nutrients and for

monitoring the effectiveness of control programs. The analyses for soluble reactive phosphorus and

dissolved inorganic nitrogen are mentioned first because they are the forms available for algal uptake and

because they are the forms determined (after digestion) for total nitrogen and total phosphorus. In

general, determinations of nutrient concentrations by field kits are only adequate to identify potential

problems. If many nutrient assays are required to define the problem accurately, laboratory procedures

are more cost effective and have greater sensitivity.

In nutrient-poor systems, levels of dissolved inorganic nutrients are generally near the limits of detection

of the assays used. For example, phosphate levels in excess of 30 μg/L saturate uptake by algae, but this

is the lower limit of detection in many laboratories. Care must be taken that the assay procedure used

matches the question being asked.
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-67

The assay for dissolved or soluble reactive phosphorus from Standard Methods (APHA 2000) should be

followed. A common source of contamination that causes problems with soluble reactive phosphorus

analysis is the use of phosphate containing detergents to wash laboratory equipment. It is good practice

to use phosphate-free detergents in the laboratory for this reason. Another important problem is the

source of low-phosphorus water for dilution and blanks. Absorbance should be very low (0.001-0.003

absorbance units per cm) for such purposes.

The soluble reactive phosphorus assay does not determine only phosphate, because the chemicals in the

assay react with some dissolved organic compounds that contain phosphorus other than orthophosphorus. It has been demonstrated that increased phosphorus deficiency in algae in natural systems

leads to a lower percentage of biologically available phosphate in the chemically determined soluble

reactive phosphorus (e.g., Dodds 1995). Unfortunately, the identity of the remaining fraction of soluble

reactive phosphorus is unclear, so soluble reactive phosphorus values from natural waters are difficult to

interpret, unless the values are fairly high (e.g., above 10 mg/L). In some such cases (e.g., groundwater

or wastewater input), a large portion of the soluble reactive phosphorus may actually be in the form of

phosphate, and the assay will provide a fairly accurate measure of the phosphate immediately available

for algal consumption. The soluble reactive phosphorus assay is particularly useful to determine

phosphate in sewage (where most soluble reactive phosphorus is phosphate) and to analyze digested

samples for total phosphorus. A method for the analysis of PO4

3- (orthophosphate) is also available in

Standard Methods (APHA 2000).

Analysis of ammonium is straightforward with the phenate method (APHA 2000). Note that ammonium

(NH4

+

) is the ion that identifies the available nutrient, and ammonia (NH3) is the gas, known as unionized

ammonia, which is the fraction that can cause toxicity. Contamination of ammonium assays can occur

from scratched glassware and airborne ammonia gas, which can come from smoke (tobacco and

otherwise), cleaning products with ammonia, and newly cut grass. Care should be taken to avoid these

potential sources of contamination. 

Nitrate is commonly measured by reduction to nitrite in a copper-cadmium reduction column (APHA

2000). Nitrite can be analyzed alone to correct estimates of nitrate, but in most studies of streams, nitrite

is assumed to be a relatively small fraction of nitrate and as such is not accounted for. Cadmium is toxic

and difficult to handle and dispose. Some packaged nitrate kits use cadmium pillows that are added to

the sample. Appropriate precautions for handling and disposing of samples are recommended if these

kits are used. Other (e.g., hydrazine) nitrate techniques may be more prone to interference or reduced

efficiency. Automated analysis methods with segmented flow autoanalyzers are commonly used to speed

processing and maintain sensitivity. Ion chromatography can be used successfully for nitrate

determinations, but it should be kept in mind that this method is not sensitive enough for nitrate values

typical of many moderately productive systems.

Total nitrogen and total phosphorus require digestion to dissolved inorganic forms before analysis. 

There are a number of available techniques. An important point is that the efficiency of digestion of

organic materials varies with procedures and waters being analyzed. Regardless of the procedure chosen,

solutions with known concentrations of organic compounds (e.g., urea for nitrogen, ATP for phosphorus)

should be added to natural water samples in known concentrations and analyzed to check for complete

digestion.
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-68

Persulfate digestion is commonly used for total phosphorus. This procedure can be modified to oxidize

organic phosphorus to phosphate, as well as organic nitrogen to nitrate (Ameel et al. 1993). Careful

attention to pH of the samples is necessary in these digestions (the digest must remain alkaline for

nitrogen digestion, but if too much persulfate is used, it may not become acidic later in the digestion and

incompletely decompose the phosphorus) and appropriate concentrations of fresh reagents should be

used to allow for complete digestion of both organic nitrogen and phosphorus.

Persulfate digestion converts all forms of nitrogen except N2 gas to nitrate. If nitrate analysis is not

easily accomplished in the laboratory, it may be desirable to use a Kjeldahl digestion procedure (APHA

2000) for total nitrogen analysis. In this procedure, all nitrogen forms but nitrate, nitrite and nitrogen gas

are digested to ammonium. If this procedure is employed, it is still necessary to analyze for nitrate and

nitrite to determine total nitrogen. Because the sensitivity and accuracy of the cadmium reduction method

for nitrate are greater than analyses for ammonium, and the toxicity and corrosiveness of the digestion

procedure are less, persulfate digestion and nitrate analysis is usually preferred to Kjeldahl.

Analyses for TN, TP, phosphate, and nitrate can also be used to calculate water column N:P ratios.

CONDUCTIVITY AND PH

Conductivity may serve as a first indicator of total nutrients (although it indicates total ions which are

much more abundant than, and not always closely correlated to, nutrients), and pH may be of interest as a

variable indicating impairment. Both of these analyses are most easily accomplished with electronic

probes. Refer to Standard Methods (APHA 2000) for the particulars of the analysis.

DISSOLVED OXYGEN

Analyses of dissolved oxygen, for measurements of primary production and determination of low oxygen

demand should be done with a titrametric method or polarographic sensor. The titrametric is more

accurate, but more time consuming. Standard Methods should be consulted for these analyses (APHA

2000). If diurnal measurements of oxygen are performed, procedures outlined by Marzolf et al. (1994)

should be followed for small streams.

ORGANIC CARBON

Analysis of organic carbon (dissolved) may be problematic because incomplete digestion of dissolved

organic carbon is common. This has been most thoroughly investigated for marine samples (Perdue et al.

1993). However, similar problems have been documented for freshwater samples (Kaplan 1992). High

temperature catalyzed analyzers provide more complete digestion and generally yield reliable results.

ALGAL AND PLANT ATTRIBUTES

COLLECTION OF ALGAL SAMPLES

The choice of methods for sample collection is dependent upon the intent of algal sample analysis. 

These methods are reviewed in the Revised Rapid Bioassessment Protocols for Streams and Rivers

(Stevenson and Bahls 1999), so only a brief overview will be presented in this document. Sampling for
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-69

assessments of the biotic integrity of algal assemblages should be more thorough and extensive spatially

than sampling for algal assessments of water quality. Thorough assessments of biotic integrity would

call for multihabitat sampling over large reaches of the stream to find as many species and habitats within

the stream or river as possible. Targeted habitat sampling (most commonly samples of algae from rocks

in riffles) can provide collections that provide indications of biotic integrity or water quality. A third

major alternative is to the use of artificial substrata that have the advantage of controlling variability

among streams due to substratum type, but the disadvantage of having to visit the field two times (to

place and retrieve substrata) and the concern that non-natural assemblages are being sampled. Targeted

habitat sampling is usually recommended, is employed by most State programs, and is known to be

successful. Efforts should be made to sample more than one riffle, particularly if an important goal of

sampling is to assess benthic algal biomass in a stream.

The collection of algal samples can be a complex exercise due to the variability of stream features such

as depth, substrata, flow velocity, and bottom characteristics. Holding some of these variables relatively

constant by selecting a habitat zone with a narrow range for these variables was suggested earlier.

Another approach is using artificial substrata which are easier to sample than natural substrata but which

have several drawbacks. Artificial substrata are more likely to be vandalized, and they often tend to alter

the flow regime around them resulting in silt deposition. The use of artificial substrata limits the ability

to move to a different area where conditions are more acceptable, as can be done when using natural

substrata. Perhaps the biggest drawback of artificial substrata is their inability to promote colonization

by certain forms of algae, especially the massive filamentous forms. This issue is discussed further below

in connection with the best methods of sampling various algal growth forms.

While there is a great variety of algal taxa, there are two main growth forms of algal communities: thin

biofilms and long filaments. Many single-celled and colonial forms of attached algae appear to the naked

eye as a biofilm of slippery, gelatinous material (often referred to technically as slime) on river rocks.

This material can be easily sampled by using a template method.

Template Method

A template is a piece of flat, flexible, waterproof material in which a window of about 2.5 cm to 5 cm per

side is cut. This template is placed in the center of the upper surface of a rock collected from the sample

site, and a razor blade is used to scrape together all the material in the window. The material is then

placed in a small water tight container (snap-shut plastic petri dishes, vials, or a piece of aluminum foil),

and stored on ice in the dark until frozen.

This procedure is greatly facilitated by selecting smooth rocks. To avoid bias, sample points should be

selected randomly. Then, rocks are selected blindly until one is chosen that is between 10 and 20 cm (in

some regions of the country one is allowed to take a quick look for snakes first). If the rock’s surface is

too rough to sample, it should be replaced and the process continued until a rock of the right size and

smoothness is selected.

The biofilms sampled by the above method form fairly quickly on artificial substrata and often the

thickness and composition of this film is quite similar to that on nearby natural substrata in a matter of

weeks (Watson unpublished). However, some of the more complex attached algae, most noticeably

Cladophora glomerata, attach to rocks using a basal holdfast cell which supports a long filament. 

Cladophora holdfasts often survive short exposure and drying out and the scour that removes the
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-70

filament. The holdfasts spread over the rock and support more massive growths in subsequent years.

After several years of flows that are too low to dislodge and roll the river rocks over, Cladophora may

take the form of massive tangled branched filaments streaming several meters long. Since the massive

growths take several years to develop, they can not be produced on artificial substrata which are likely to

wash away during spring floods. Hence, such growth forms must be sampled from natural substrata. An

example of a method for sampling from natural substrates is the hoop method.

Hoop Method

The template method does not work well for sampling the massive growth form (long string filaments)

mentioned above. It is possible to be standing in a sea of waving Cladophora and pick up a random rock

that has no Cladophora on it, or that has a tangled mass hanging by a few threads a few inches from the

rock. The preferred way to sample such a growth form is to place a heavy metal hoop about 0.3 to 0.5

meter in diameter on the bottom of the stream (at the randomly selected point) and collect all the

filamentous material inside the hoop. This often involves cutting the filaments around the hoop and

picking up the filaments and rocks inside the hoop. The collection should be brought to shore in a tub

where the filaments should be removed from the rocks. Razor blades and paint scrapers work well; hack

saws are generally unnecessary. Wrapping these large samples in aluminum foil will facilitate the

drying, weighing and ashing process. The collection of 10 to 20 replicates of such samples at a series of

high biomass sites will represent a large volume.

Freezing samples not only helps preservation, but cells are ruptured, facilitating chlorophyll extraction.

Samples collected by templates should be frozen at -10°C on return to lab, and analyzed for chlorophyll

a and AFDM within 2 weeks to a month. Laboratory methods for analyzing algal biomass for

chlorophyll and ash free dry weight (AFDM) are discussed below. The same sample can be analyzed for

both chlorophyll a and AFDM. After chlorophyll analysis, the extracted sample is poured into an

aluminum weigh boat, the solvent is evaporated, and AFDM analysis is performed on the boat. This

facilitates determining the chlorophyll to AFDM ratio on these samples.

Due to the abundance of material collected using the hoop method, it is not possible to extract all the

chlorophyll from these samples. Hence, only small subsamples of each large sample are analyzed for

chlorophyll and AFDM, and the large samples are analyzed for AFDM only. Their chlorophyll content is

estimated using the chlorophyll to AFDMratio determined from the subsamples. These samples are

handled as follows: 1) before freezing the samples collected by the hoop method, take each sample and

spread it out; 2) collect many tiny subsamples from all over this bulk sample; 3) chop and mix the

subsamples; 4) make at least 4 replicate composite samples from this well mixed pile; 5) place these in

small containers and process as you do the template samples; 6) analyze the remaining bulk sample for

AFDM; and 7) use the chlorophyll to AFDM ratio of the small composite samples to estimate the

chlorophyll in the bulk sample (consider the variability in the chlorophyll/AFDM ratio of the composite

subsamples as well as the variability in biomass of the large samples).

COLLECTION OF MACROPHYTE SAMPLES

Macrophyte sampling is commonly performed 1) to qualitatively assess the distribution of vegetation in

an area or 2) to quantitatively measure primary productivity (gauged by changes in biomass). Caution

must be taken when sampling macrophytes for biomass determination to ensure that the appropriate

portions of macrophytes (above and below ground) are collected. (Macrophytes may have up to 90%
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-71

underground biomass.) After collection, macrophyte samples may be dried and combusted to determine

AFDM in a manner comparable to that for algal samples. Macrophyte sampling and biomass

determinations are discussed in Wetzel and Likens (1991). 

ALGAL BIOMASS - % COVER OF BOTTOM BY NUISANCE ALGAE

Methods have been described in the literature (e.g., Sheath and Burkholder 1985) to estimate algal

biomass in the stream by visual observation. In the Revised Rapid Bioassessment Protocols for Rivers

and Streams (Stevenson and Bahls 1999), a rapid periphyton survey is described that provides an instream assessment of algal biomass. The technique is simple and can be used by professionals or

volunteers with little training. Two steps are involved as the stream bottom is observed at multiple sites

(usually >9) through a viewing bucket (clear-bottom bucket submerged in stream for clear observation of

the stream bottom). First, percent cover of filamentous algae over the stream bottom is assessed. Then

thickness and percent cover of microalgae is assessed. A ranking system is used to quantify thickness of

microalgal mats. The advantages of this rapid periphyton survey are that it allows for rapid assessment of

algal biomass, particularly filamentous algal green biomass, and it covers large regions of the stream

(thus accounting for the great spatial variability in algal biomass).

CHLOROPHYLL a

The most commonly used determinant of benthic algal biomass is chlorophyll a. Chlorophyll a is often a

superior indicator of biomass compared to determination of AFDM because non-algal material can

contribute to biomass. Chlorophyll a is used because it occurs in all common photosynthetic organisms. 

Other forms of chlorophyll can inflate estimates of algal biomass, because the amount per cell can be

more variable. In addition, counts of algal cells and biovolume are often used as a determinant of

biomass. These counts are time consuming and require taxonomic expertise, and thus are rarely done and

will not be considered here. The general methods for biomass determination are well described by

Steinman and Lamberti (1996) and Stevenson (1996); the interested reader should consult these

references and others cited herein.

Chlorophyll is determined in seston on filtered material and from benthic material either from cores,

artificial substrata, or scraped and extracted substrata. In general, artificial substrata yield higher

chlorophyll a/AFDM values than natural substrata (Dodds et al., unpublished), and this should be kept in

mind when selecting the method to be used. However, measurement of area and extraction of pigment is

easier with artificial substrata. 

Chlorophyll analyses without an acidification step to correct for chlorophyll degradation products

(phaeophytin correction) are occasionally encountered. This acidification is essential for periphyton

because dead cells that contain phaeophytin can remain in the assemblage, and lead to biomass

overestimates. A fluorometric method with narrow band filters that correct for phaeophytin but omit the

acidification step was recently introduced (Welschmeyer 1994).

Determination of phaeophytin concentrations may be useful not only for correcting chlorophyll a

concentrations, but also as an indicator of periphyton degradation. Wetzel and Likens (1991) give a

method for determining both chlorophyll a and phaeophytin concentrations. The ratio of chlorophyll a to

phaeophytin gives an indication of periphyton growth and activity.
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-72

Generally, a ratio of 9:1, acetone:water, is used as an extractant. We have found that hot 90% ethanol

extraction (Sartory and Grobbelaar 1984) offers some advantages. Primarily, material need not be

scraped from the substratum, and grinding of the sample is not required. Rather, the entire sample of

substratum and periphyton is placed in a heat resistant (autoclavable) plastic bag with extractant and

heated to 80 o

C for 5 min. Ethanol fumes are also less noxious than acetone fumes.

The preferred procedure is to use a spectrophotometer to read absorbance, because the relatively dense

solutions of extracted chorophyll are common for periphyton samples. Very dense solutions of chl must

be avoided for spectrophotometry and fluorometry to prevent analytical errors; the problem is of greater

concern in fluorometry. In spectrophotometry, solutions of greater than 1.5 absorbance units per cm at

665 nm should not be analyzed. Fluorometric analysis should not be attempted with samples having

more than 0.5 absorbance units per cm at 665 nm. Dilution with extractant can bring samples to within

the appropriate absorbance range.

AFDM AND ALGAL CELL BIOVOLUME

Methods for AFDM and algal cell biovolume are covered by Steinman and Lamberti (1996) and Wetzel

and Likens (1991), respectively. Ash-free dry-weight values have been used in conjunction with

chlorophyll a as a means of determining the trophic status (autotrophic vs. heterotrophic) of streams

(Weber 1973). The Autotrophic Index (AI) is calculated as: 

AI = AFDM (mg/m2) / chlorophyll a (mg/m2). 

As suggested before, these should be relied upon as supplementary methods, and the large degree of time

required for biomass determinations by cell counts and biovolume estimates should be considered.

ALKALINE PHOSPHATASE ACTIVITY

Analysis of alkaline phosphatase activity (APA) is used to determine phosphorus limitation in algae. 

Alkaline phosphatases are enzymes produced by algae to break down organic phosphorus compounds and

release bioavailable (PO4) P (Steinman and Mullholland 1996). Studies have shown that lower levels of

P result in higher levels of APA and vice versa (Klotz 1992). The most common method for APA

analysis is a fluorometric method described by Hill et. al. (1968).

ALGAL SPECIES COMPOSITION

Different methods can be used to assess algal species composition depending upon the objective of the

assessment (Whitton et al. 1991; Whitton and Rott 1996; Lowe and Pan 1996; Stevenson 1998;

Stevenson and Pan 1999; Stevenson and Bahls 1999). For example, if the objective of the assessment is

to determine if nutrient conditions meet a drinking water use, then analysis of all algae in samples may be

desirable to determine if taste and odor algae are present. If the objective is to get an indication of

nutrient conditions, trophic status, or biotic integrity, then analysis of species composition of diatoms

only may be sufficient. The latter is less time consuming that an analysis of all algae in samples. The

methods for analysis of algal species composition in samples can be found in Standard Methods (APHA

2000), the Revised Rapid Bioassessment Protocols (Stevenson and Bahls 1999) or in Lowe and

LaLiberte (1996).
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-73

Although time consuming, it may be desireable to determine the types of algae present that are thought to

be creating problems. The methods necessary for such determinations are described in Lowe and

LaLiberte (1996) and in Standard Methods (APHA 2000). In general, taxa determination, especially to

species, requires expertise, similar to that required for precise water chemistry and macroinvertebrate

assays. Such fine level determinations may be useless if not conducted by experienced taxonomists. 

Some companies provide algal identification and analysis services that may be useful for those lacking

such expertise. The reputation of prospective companies should be verified. In general, total algal

biomass is of greater concern than taxonomic composition to those wishing to control eutrophication and

its effects.

MACROINVERTEBRATE ANALYSIS

Macroinvertebrates may indicate water quality problems and some monitoring programs may want to

evaluate biomass and diversity of macroinvertebrates. There is little precedence for this in stream

eutrophication studies, and the analysis of macroinvertebrates to species is time consuming. Methods to

assess stream macroinvertebrates have recently been reviewed (Hauer and Resh 1996). Generally,

identification of most animals to species is required for accurate indices to be constructed, so it is

important that such analyses be carried out by individuals with taxonomic expertise.

COMMUNITY METABOLISM ANALYSES

Productivity/respiration (P/R) ratios can be determined by the upstream-downstream method with

dissolved oxygen data and estimates of atmospheric reaeration (Odum 1956; Marzolf et al. 1994) or

light/dark, flow-through chambers (Hickey 1987; Dodds and Brock 1998). P/R ratios measured using

chambers are generally higher than those measurements obtained from upstream-downstream methods. 

Even in streams with heavy algal growths, it is rare to find P/R ratios in excess of one (1) using upstreamdownstream methodology. Both methods convert the diel changes in dissolved oxygen into actual rates

of productivity. The diel range in dissolved oxygen indicates the magnitude of gross productivity and

can be used to monitor ecological integrity in streams and rivers of similar velocity, depth, and

turbulence. 

REFERENCES

Hill, D., G. K. Summer, and M.D. Waters. 1968. An automated fluorometric assay for alkaline

phosphatase using 3-0-methylfluorescein phosphate. Anal. Biochem. 24:9-17.

R. L. Klotz. 1992. Factors influencing alkaline phosphatase activity of stream epithilon. Journal of

Freshwater Ecol. 7(2):233-242.

APHA. 2000. Standard Methods for the Examination of Water and Wastewater. 21st ed. Eaton, A. D.,

L. C. Clesceri, and A. E. Greenberg (eds.). American Public Health Association, Washington, DC.

Steinman, A. D., and P. J. Mulholland. 1996. Phosphorus limitation, uptake, and turnover in stream

algae. Methods in Stream Ecology. Academic Press, Inc.

Wetzel, R. G., and G. E. Likens. 1991. Limnological Analyses. 2nd Edition. Springer-Verlag. New

York. Flow and velocity: http://water.usgs.gov/pubs/circ1123/collection.html.
July 2000 Appendix B. Laboratory and Field Methods and Analyses

PAGE A-74
PAGE A-75

APPENDIX C. STATISTICAL TESTS AND MODELING TOOLS

STATISTICAL ANALYSES

In order to use parametric tests, (Student t test, ANOVA, MANOVA, etc.) assumptions about the

population distribution must be made. When the data are not normally distributed, transformations of the

data to obtain a normal distribution are commonly made (e.g., log transformation). Less powerful, nonparametric tests of significance must be used in cases where the data do not fit the assumption of a

normal distribution (Atlas and Bartha 1993).

STUDENT t TEST

The validity of hypotheses is frequently tested using the Student t test. There is a family of distributions 

for the t statistic that vary as a function of degrees of freedom. The t distributions are symmetrical about

a mean of 0, as are normal distributions, though the t distributions are more spread out than on the

normal curve. As degrees of freedom increase, the t distribution more closely approximates the normal

curve. There are published tables of critical values for t that allow one to compare a calculated t value

from one’s own data with a t value determined by the level of significance. This comparison allows one

to decide whether or not to reject the null hypothesis (Atlas and Bartha 1993). An in-depth discussion of

the use of the t statistic for analyzing environmental data can be found in Ott (1995). Most statistics texts

include the published tables of the t statistic and information on it’s application. 

ANOVA

The analysis of variance (ANOVA) method is used to determine the significance or validity of data

when information is collected from different populations. ANOVA is generally used to confirm that

there are not significant differences in sample population means. ANOVA determines whether there is

greater variability among sample populations or within population groups. ANOVA is performed by

summing the variance of all sample points and comparing it to the sum of the variance of all the sample

means (Remington and Schork 1985). ANOVA is useful for calculating the unbiased variance of

samples that have been composited or parts of samples (such as a 10 mL water sample analyzed for TP

taken from a 50 mL total sample) (Gilbert 1987).

CHI SQUARE TEST

A common non-parametric statistical test is the 2 (chi square) test. When attempting to analyze the

apportionment of a characteristic within a population, the chi square test is valuable for determining the

independence of categorical variables. The raw data for a chi square test should be on a scale for which

data are placed into discrete groups (nominal scale) (Atlas and Bartha 1993).

MANN WHITNEY U TEST

The Mann Whitney U test is one of the most powerful non-parametric statistical tests. This test may be

employed in place of the t test when data are on an ordinal scale. This test is used with two independent

groups. The null hypothesis is that both samples are drawn from populations with the same distributions. 

The alternative hypothesis is that the parent populations from which the samples are taken have different

medians. This test assumes that the distributions have the same form, but have different medians. This
July 2000 Appendix C. Statistical Tests and Modeling Tools

PAGE A-76

test ranks scores from lowest to highest while retaining the identity of the group from which they came

(control or experimental group), to determine the distribution of the U statistic. U represents the number

of times the n1 value precedes the n2 value. U is large if the n1 population is located below the n2

population.

LINEAR REGRESSION

In regression analysis a relationship of best fit is used to describe the data. The experimenter must

decide the type of relationship that best describes the data. If the relationship is linear, a linear regression

may be appropriate. When the data are not linear, they may be log transformed to fit the linear

assumption. The slope of the regression line is called the regression coefficient. In constructing a

regression line of best fit, it is necessary to define the slope of the line and the intercept of an axis. 

Regression analysis minimizes the variance, though a residual variance remains. The statistical

significance of the regression coefficient using the student t test described above. The null hypothesis in

such a test, is that there is no difference between the calculated regression coefficient and a true

population regression coefficient 0. In other words, the population regression coefficient indicates that

no prediction of y can be made from x, nor of x from y (Atlas and Bartha 1993). 

MULTIPLE REGRESSION

Multiple regression is based on the same principle as linear regression (where y=mx+b), but involves

more than one regression variable (i.e., multiple sets of x values). Multiple regression is often performed

using matrices and least squares approximations (Myers 1990). Applications may include developing

relationships between response variables for various indicators.

BAYESIAN ANALYSIS

Bayesian analysis is most useful when incorporating historical data or comparing probabilities of various

competing hypotheses. It allows use of all available data from various studies and weighing of different

outcomes. A discussion of the uses of Bayes Theorem in statistical analysis can be found in Hilborn and

Mangel (1997).

MODELS

The models discussed in this appendix may be used in criteria derivation when data are not sufficient. 

However, only the WASP model predicts periphyton biomass. The other models described here use

periphyton as a forcing function for predicting nutrients or DO.

BETTER ASSESSMENT SCIENCE INTEGRATING POINT AND NONPOINT SOURCES (BASINS)

Better Assessment Science Integrating Point and Nonpoint Sources, or BASINS, is a tool developed by

EPA to facilitate water quality analysis on a watershed level and for specific waterbodies or stream

segments. BASINS was designed to integrate national water quality data, modeling capabilities, and

geographic information systems (GIS) so that regional, State, local and Tribal agencies can easily address

the effects of both point and nonpoint source pollution and perform sophisticated environmental

assessments.
July 2000 Appendix C. Statistical Tests and Modeling Tools

PAGE A-77

BASINS is made up of five components: (1) national databases; (2) assessment tools (TARGET,

ASSESS, and Data Mining) for evaluating water quality and point source loadings at a variety of scales;

(3) utilities including local data import, land-use and DEM (Digital Elevation Model) reclassification,

watershed delineation, and management of water quality observation data; (4) watershed and water

quality models including NPSM (Nonpoint Source Model), HSPF (Hydrologic Simulation Program

Fortan), TOXIROUTE, and QUAL2E; and (5) post processing output tools for interpreting model

results.

The three analytical tools (TARGET, ASSESS, and Data Mining) within BASINS allow the user a range

of environmental assessment options. TARGET examines large area watersheds on a State/Tribal or

regional level to analyze point source loads or general water quality. ASSESS gives information about

specific water bodies and the monitoring stations or discharge points near them. Data Mining integrates

historical, geographic, and water quality data using maps and tables. In addition, models such as the

NPSM, QUAL2E, and TOXIROUTE can be used to predict the fate, transport, and effects of loadings

from various sources. The BASINS package can be used for many water quality management analyses,

particularly the development of total maximum daily loads (TMDLs). In addition, the GIS component of

BASINS allows the user to virtually traverse the watershed.

BASINS is a software package that is installed on the user’s computer. It may be downloaded from the

EPA website (http://www.epa.gov/ost/BASINS/download.htm) or ordered on CD-ROM from the

National Service Center for Environmental Publications (NSCEP). A printed copy of BASINS version

2.0 Users' Manual is also available through NSCEP. BASINS training courses are available in some

areas of the country. For more information on BASINS, see the BASINS website

(http://www.epa.gov/ost/BASINS/).

HYDROLOGICAL SIMULATION PROGRAM - FORTRAN (HSPF)

HSPF is a comprehensive package developed by EPA for simulating water quantity and quality for a

wide range of organic and inorganic pollutants from agricultural watersheds (Bicknell et al. 1993). The

model uses continuous simulations of water balance and pollutant generation, transformation, and

transport. Time series of the runoff flow rate, sediment yield, and user-specified pollutant concentrations

can be generated at any point in the watershed. The model also includes instream quality components for

nutrient fate and transport, biochemical oxygen demand (BOD), dissolved oxygen (DO), pH,

phytoplankton, zooplankton, and benthic algae. Statistical features are incorporated into the model to

allow for frequency-duration analysis of specific output parameters. Data requirements for HSPF are

extensive, and calibration and verification are recommended. The program is maintained on IBM

microcomputers and DEC/VAX systems. Because of its comprehensive nature, the HSPF model requires

highly trained personnel. It is recommended that its application to real case studies be carried out as a

team effort. The model has been extensively used for both screening-level and detailed analyses. Moore

et al. (1992) describe an application to model BMP effects on a Tennessee watershed. Scheckenberger

and Kennedy (1994) discuss how HSPF can be used in subwatershed planning. The HSPF model can be

downloaded at EPA’s BASINS website given above.
July 2000 Appendix C. Statistical Tests and Modeling Tools

PAGE A-78

QUAL2E

The Enhanced Stream Water Quality Model (QUAL2E), originally developed in the early 1970s, is a

one-dimensional water quality model that assumes steady-state flow but allows simulation of diurnal

variations in temperature or algal photosynthesis and respiration (Brown and Barnwell 1987). QUAL2E

represents the stream as a system of reaches of variable length, each of which is subdivided into

computational elements that have the same length in all reaches. The basic equation used in QUAL2E is

the one-dimensional advection-dispersion mass transport equation. An advantage of QUAL2E is that it

includes components that allow quick implementation of uncertainty analysis using sensitivity analysis,

first-order error analysis, or Monte Carlo simulation. The model has been widely used for stream waste

load allocations and discharge permit determinations in the United States and other countries. EPA's

Office of Science and Technology recently developed a Microsoft Windows-based interface for

QUAL2E that facilitates data input and output evaluation, and QUAL2E is one of the models included in

EPA’s BASINS tool. More information on QUAL2E, including downloadable program files, can be

found at EPA’s website (www.epa.gov/docs/QUAL2E_WINDOWS/index.html).

CE-QUAL-RIV1

The one-dimensional Hydrodynamic and Water Quality Model for Streams (CE-QUAL-RIV1) was

developed through the Waterways Experiment Station of the Corps of Engineers. The model was

designed to simulate water quality conditions associated with the highly unsteady flows that can occur in

regulated rivers (e.g., storm water flows and streams below peaking hydropower dams). The model has

two submodels for hydrodynamics (RIV1H) and water quality (RIV1Q). Output from the hydrodynamic

solution is used to drive the water quality model. Water quality constituents modeled include

temperature, dissolved oxygen, carbonaceous biochemical oxygen demand, organic nitrogen, ammonia

nitrogen, nitrate nitrogen, and soluble reactive phosphorus. The effects of algae and macrophytes on

water quality can also be included as external forcing functions specified by the user. A limitation of CEQUAL-RIV1 is that it is only applicable to situations where flow is predominantly one-dimensional. 

Currently, this model can only be downloaded for USCOE use. More information on CE-QUAL-RIV1

can be found at the WES website (www.wes.army.mil/el/elmodels/riveinfo.html).

CE-QUAL-W2

CE-QUAL-W2 is a two-dimensional, longitudinal/vertical water quality model that can be applied to

most waterbody types. It includes both a hydrodynamic component (dealing with circulation, transport,

and deposition) and a water quality component. The hydrodynamic and water quality routines are

directly coupled, although the water quality routines can be updated less frequently than the

hydrodynamic time step to reduce the computational burden in complex systems. Water quality

constituents that can be modeled include algae, dissolved oxygen, ammonia-nitrogen, nitrate-nitrogen,

phosphorus, total inorganic carbon, and pH. http://www.wes.army.mil/el/elmodels/w2info.html

WASP5

The Water Quality Analysis Simulation Program is a general-purpose modeling system for assessing the

fate and transport of conventional and toxic pollutants in surface waterbodies. Its EUTRO5 submodel is

designed to address eutrophication processes and has been used in a wide range of regulatory and water
July 2000 Appendix C. Statistical Tests and Modeling Tools

PAGE A-79

quality management applications. The model may be applied to most waterbodies in one, two, or three

dimensions and can be used to predict time-varying concentrations of water quality constituents. The

model reports a set of parameters, including dissolved oxygen concentration, carbonaceous biochemical

oxygen demand (BOD), ultimate BOD, phytoplankton, carbon, chlorophyll a, TN, total inorganic

nitrogen, ammonia, nitrate, organic nitrogen, total inorganic nitrogen, organic phosphorus, and inorganic

phosphorus. Although zooplankton dynamics are not simulated in EUTRO5, their effect can be

described by user-specified forcing functions. Lung and Larson (1995) used EUTRO5 to evaluate

phosphorus loading reduction scenarios for the Upper Mississippi River and Lake Pepin, while Cockrum

and Warwick (1994) used WASP to characterize the impact of agricultural activities on instream water

quality in a periphyton-dominated stream. http://www.epa.gov/earth100/records/wasp.html
July 2000 Appendix C. Statistical Tests and Modeling Tools

PAGE A-80
PAGE A-81

APPENDIX D. ACRONYM LIST AND GLOSSARY

ACRONYMS

 AASF Adopt-A-Stream Foundation

 AFDM Ash-Free Dry Mass

 AFDW Ash-Free Dry Weight

 AGP

 AI 

 ANOVA

 Algal Growth Potential

 Autotrophic Index

 Analysis of Variance

 APA Alkaline Phosphatase Activity

 B-IBI Benthic Macroinvertebrate Index of Biological Integrity

 BMP

 BOD

 BPJ

 BuRec

 Best Management Practice

 Biochemical Oxygen Demand

 Best Professional Judgement

 U.S. Department of the Interior, Bureau of Reclamation

 CENR

 CE-QUAL-RIV1

 Committee on Environment and Natural Resources

 Hydrodynamic and Water Quality Model for Streams

 CFR Code of Federal Regulations

 CGP Construction General Permit

 CLP Clean Lakes Program 

 COE Corps of Engineers

 CPP Continuing Planning Process

 CREP Conservation Reserve Enhancement Program

 CRP Conservation Reserve Program

 CSO Combined Sewer Overflow

 CZARA

 DDT

 Costal Zone Act Reauthorization Amendment

 Dichlorodiphenyltrichloroethane

 DEQ Department of Environmental Quality

 DIN Dissolved Inorganic Nitrogen

 DITS Diatom Index of Trophic Status

 DO Dissolved Oxygen

 DOC Dissolved Organic Carbon

 DWPC Division of Water Pollution Control

 ECA Ecological Community Analysis

 ECARP

 EDAS

 Environmental Conservation Acreage Reserve Program

 Ecological Data Application System

 EMAP

 EPT

 Environmental Monitoring and Assessment Program 

 Ephemeroptera (mayflies), Plecoptera (stoneflies), and

 Trichoptera (caddisflies)

 EQIP Environmental Quality Incentives Program

 FGA Filamentous Green Algae

 FIP Forestry Incentives Program

 GIS Geographical Information Systems

 HAB Harmful Algal Bloom

 HBN

 HSFP

 Hydrologic Benchmark Network

 Hydrologic Simulation Project FORTAN
July 2000 Appendix D: Acronym List & Glossary

PAGE A-82

 HUC Hydrologic Unit Code

 IBI Index of Biological Integrity

 LDC Legacy Data Center

 MITS Multimetric Index of Trophic Status

 N Nitrogen

 NASQAN National Stream Quality Accounting Network 

 NAWQA National Water-Quality Assessment

 NIST

 NOAA

 National Institute of Standards and Technology

 National Oceanic and Atmospheric Association

 NPDES

 NPS

 NPSM

 National Pollutant Discharge and Elimination System

 Nonpoint Source

 Nonpoint Source Model

 NRCS

 NSCEP

 Natural Resources Conservation Service

 National Service Center for Environmental Publications

 NSS National Stream Survey

 NSWS National Surface Water Survey

 NTU Nephelometric Turbidity Units

 NWIS National Water Information System

 ONRW Outstanding National Resource Waters

 P

 PAR

 Phosphorus

 Photosynthetically-active Radiation

 PCS Permit Compliance System

 P/R Productivity/Respiration

 QA Quality Assurance

 QC

 QUAL2E

 Quality Control

 Enhanced Stream Water Quality Model

 RAD Reach Address Database 

 RCC

 RF3

 River Continuum Concept

 Reach File 3

 RTAG Regional Technical Assistance Groups

 SAV Submerged Aquatic Vegetation

 SRP Soluble Reactive Phosphorus

 STORET

 TAB

 Storage and Retrieval

 Total Algal Biomass

 TDP Total Dissolved Phosphorus 

 THM trihalomethane

 TIA Total Impervious Area

 TKN Total Kjeldahl Nitrogen

 TMDL Total Maximum Daily Load

 TN Total Nitrogen

 TP Total Phosphorus

 TSS Total Suspended Solids

 TVA

 TWINSPAN

 USGMA

 Tennessee Valley Authority

 Two Way Indicator Species Analysis

 Unweighted Pair Group Method Using Arithmetic Averages
July 2000 Appendix D: Acronym List & Glossary

PAGE A-83

 USGS United States Geologic Survey 

 VNRP

 WASP

 WES

 Voluntary Nutrient Reduction Plan

 Water Analysis Simulation Program

 Waterways Experiment Station

 WHIP Wildlife Habitat Incentives Program

 WLA

 WQBEL

 WQN

 Waste Load Allocation

 Water Quality Based Effluent Limits

 Water Quality Networks

 WQS Water Quality Standards

 WRS

2

 Wetlands Reserve Program

 Chi Square
July 2000 Appendix D: Acronym List & Glossary

PAGE A-84

GLOSSARY

algal biomass

The weight of living algal material in a unit area at a given time (Wetzel 1983). 

allochthonus

An organism or substance foreign to a given ecosystem (Atlas and Bartha 1993); describes organic

matter reaching an aquatic community from the outside in the form of organic detritus or organic matter

adsorbed to sediment (Wetzel 1983). 

ash-free dry weight

An algal biomass measurement that measures the standing crop of algae to estimate net production (see

Appendix B) (APHA 2000).

autochthonus

Microorganisms and/or substances indigenous to a given ecosystem; the true inhabitants of an

ecosystem; refering to the common nicrobiota of the body or soil microorganisms that tend to remain

constant despite fluctuations in the quantity of fermentable organic matter (Atlas and Bartha 1993);

describes organic matter originating within a waterbody / aquatic community (Wetzel 1983).

autotrophic index (AI)

A means of determining the trophic nature of the periphyton community; calculated by dividing the

biomass (ash-free weight of organic matter) by chlorophyll a. High AI values indicate heterotrophic

associations or poor water quality (APHA 2000).

benthos/benthic

The assemblage of organisms associated with the bottom, or the solid-liquid interface of the aquatic

system. Generally applied to organisms in the substrata (Wetzel 1983).

biocriteria

(biological criteria) Narrative or numeric expressions that describe the desired biological condition of

aquatic communities inhabiting particular types of waterbodies and serve as an index of aquatic

community health. (USEPA 1994).

BOD

Biochemical Oxygen Demand. Oxygen required to break down organic matter and to oxidize reduced

chemicals (in water or sewage) (APHA 2000).

chlorophyll a

A complex molecule composed of four carbon-nitrogen rings surrounding a magnesium atom; constitutes

the major pigment in most algae and other photosynthetic organisms; is used as a reliable index of algal

biomass (Darley 1982).

Cladophora

A common nuisance filamentous green alga (Dodds et al. 1997).
July 2000 Appendix D: Acronym List & Glossary

PAGE A-85

community metabolism

The relationship between gross community production and total community respiration (Odum 1963).

criteria

Elements of State water quality standards, expressed as constituent concentrations, levels, or narrative

statements, representing a quality of water that supports a particular use. When criteria are met, water

quality will generally protect the designated use (USEPA 1994).

cultural enrichment

Human activities that result in increased nutrient loads to a waterbody.

designated uses

Uses defined in water quality standards for each water body or segment whether or not the use is being

attained (USEPA 1994).

detritus

Unconsolidated sediments comprised of both inorganic and dead and decaying particulate organic matter

inhabited by decomposer microorganisms (Wetzel 1983).

eutrophic

Abundant in nutrients and having high rates of productivity frequently resulting in oxygen depletion

below the surface layer (Wetzel 1983).

eutrophication

The increase of nutrients in [waterbodies] either naturally or artificially by pollution (Goldman and

Horne 1983).

existing uses

The use that has been achieved for a waterbody on or after November 28, 1975 (USEPA 1994).

flowpath

Conveys water between points in the stream system. Examples of flow paths are a stream channel, canal,

storm sewer, or reservoir (http://il.water.usgs.gov/proj/feq/feqdoc/chap3_1.html).

heterotrophic

Describes organisms that need organic compounds to serve as a source of energy for growth and

reproduction (Atlas and Bartha 1993).

hypolimnetic

Characteristic of the hypolimnion, the deep, cold, relatively undisturbed stratum of a lake (Wetzel 1983).

hydrologic unit codes (HUC)

An 8-digit code, determined by the U.S. EPA, that is used as a standard method for watershed

identification throughout the United States.
July 2000 Appendix D: Acronym List & Glossary

PAGE A-86

hyporheic zone

The subsurface zone where stream water flows through short segments of its adjacent bed and banks

(Winter et al. 1998). 

lentic

Relatively still-water environment (Goldman and Horne 1983).

lotic

Running-water environment (Goldman and Horne 1983).

macrophyte (also known as SAV-Submerged Aquatic Vegetation)

Larger aquatic plants, as distinct from the microscopic plants, including aquatic mosses, liverworts,

angiosperms, ferns, and larger algae as well as vascular plants; no precise taxonomic meaning (Goldman

and Horne 1983).

macroinvertebrate

Small benthic organisms which are retained on sieves with a mesh size >2 mm (Thorp and Covich 1991).

mesotrophic (2-4)

Having a nutrient loading resulting in moderate productivity (Wetzel 1983).

morphological characteristics (2-2)

The morphological characteristics of a waterbody are the characteristics that comprise the shape of the

waterbody. In stream systems, morphology usually refers to the shape of the stream channel.

NPDES

National Pollutant Discharge Elimination System. The EPA program that regulates point source

discharges through the issuance of permits to discharges and enforcement of the terms and conditions of

those permits.

oligotrophic (2-4)

Trophic status of a waterbody characterized by a small supply of nutrients (low nutrient release from

sediments), low production of organic matter, low rates of decomposition, oxidizing hypolimnetic

condition (high DO) (Wetzel 1983).

parafluvial

Sediments within the active channel, outside the wetted stream; lateral sandbars (Holmes et al. 1994).

periphyton

Associated aquatic organisms attached or clinging to stems and leaves of rooted plants or other surfaces

projecting above the bottom of a water body (USEPA 1994).

primary production

Quantity of new organic matter created by photosynthesis or chemosynthesis, or stored energy which that

material represents (Wetzel 1983).
July 2000 Appendix D: Acronym List & Glossary

PAGE A-87

probability sampling

A sampling process wherein randomness is a requisite (Hayek 1993).

production/respiration ratio

The primary production to respiration ratio is a measure of community or whole system metabolism. 

This measurement can be used to assess ecosystem health and determine if the system is

heterotrophically or autotrophically dominated.

Q10

The estimated discharge of ten year flood (USEPA 1994).

random sampling

Generic type of probability sampling, randomness can enter at any stage of the sampling process (Hayek

1993).

RTAG (Regional Technical Assistance Group)

Group of technical experts assembled at the EPA Regional level to assist in establishing criteria for

States, Tribes and nutrient ecoregions.

reference conditions

Describe the characteristics of water body segments least impaired by human activities. As such,

reference conditions can be used to describe attainable biological or habitat conditions for water body

segments with common watershed/catchment characteristics within defined geographical regions.

riparian

Riverside, usually referring to vegetation (riparian vegetation) (Goldman and Horne 1983).

Secchi disk

A white or black and white disk used to measure transparency of a waterbody. The Secchi disk

transparency is measured as the mean depth of the point where a weighted white (or black and white)

disk, 20 cm in diameter, disappears when viewed from the shaded side of a vessel, and that point where

the disk reappears upon raising it after it has been lowered beyond visibility (Wetzel 1983).

secondary production

New organic material created by an organism that uses organic substrates (i.e. uses material from

primary producers) (Wetzel 1983)

seston/sestonic

organic matter suspended in the water column generally comprised of phytoplankton, bacteria and fine

detritus (Thorp and Covich 1991).

STORET

EPA’s computerized water quality database that includes physical, chemical, and biological data

measured in water bodies throughout the United States (USEPA 1994).
July 2000 Appendix D: Acronym List & Glossary

PAGE A-88

Stratification, stratified random sampling

Type of probability sampling where a target population is divided into relatively homogenous groups or

classes (strata) prior to sampling based on factors that influence variability in that population (Hayek

1993). In stratified sampling, a heterogenous environment is divided into homogenous strata or parts. 

Analysis of variance can be used to identify statistically different parameter means among the sampling

strata or classes. The strata are the analysis of variance treatments (Poole 1972).

TMDLs

Total maximum daily loads (TMDLs) are defined by calculating the assimilative capacity of a waterbody

for a substance (e.g. total phosphorus) and identifying the sources to determine the maximum load the

waterbody is capable of carrying without causing detrimental effects.

trophic state

The trophic status of a waterbody (Carlson 1977).

TSS (total suspended solids)

Particulate matter suspended in the water column.

turbidity

Cloudiness or opaqueness of a suspension. In our context, refers to the amount of suspended matter in

the water column, usually measured in nephelometric turbidity units (Atlas and Bartha 1993).

TVSS (total volatile suspended solids)

Volatile particulate matter suspended in the water column.

watershed

The area of land that drains water, sediment, and dissolved materials to a common outlet at some point

along a stream channel. In American usage, watershed is synonymous with the terms drainage basin

and catchment (Dunne and Leopold 1978).