U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2007-5206

In cooperation with the New Jersey Department of Environmental Protection

Development of the Hydroecological Integrity Assessment 

Process for Determining Environmental Flows for 

New Jersey Streams
Cover. Dunnfield Creek in the Delaware Water Gap National Recreation Area, Warren County, 

New Jersey. Photograph by Jason Lewis, U.S. Geological Survey. 
Development of the Hydroecological 

Integrity Assessment Process for 

Determining Environmental Flows for 

New Jersey Streams

By Jonathan G. Kennen, James A. Henriksen, and Steven P. Nieswand

In cooperation with the New Jersey Department of Environmental Protection

Scientific Investigations Report 2007-5206

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2007

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Suggested citation:

Kennen, J.G., Henriksen, J.A., and Nieswand, S.P., 2007, Development of the Hydroecological Integrity Assessment 

Process for Determining Environmental Flows for New Jersey Streams: U.S. Geological Survey Scientific Investigations Report 2007-5206, 55 p.
iii

Contents

Abstract...........................................................................................................................................................1

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

Background............................................................................................................................................2

Purpose and scope...............................................................................................................................2

Study area description.........................................................................................................................2

Technical Advisory Committee Activities .........................................................................................4

Comparison of 10 Environmental Flow Methods ....................................................................5

Examination of Two Environmental Flow Methods ................................................................5

Development of the Hydroecological Integrity Assessment Process ..................................................9

Conceptualization .................................................................................................................................9

Software.................................................................................................................................................9

Methods................................................................................................................................................10

Hydrologic Index Tool................................................................................................................10

Stream Classification Processes ............................................................................................10

Selection of Streams and Period of Record.................................................................12

Clustering of Streams.......................................................................................................12

Development of the New Jersey Stream Classification Tool (NJSCT)....................13

Application of the Hydroecological Integrity Assessment Process ...................................................13

Establishing a Hydrologic Baseline .................................................................................................14

Examples of New Jersey Hydroecological Assessment Tool (NJHAT) Application ...............15

Example A....................................................................................................................................15

Example B....................................................................................................................................19

Developing Environmental Flow Standards ............................................................................................24

Summary and Conclusions.........................................................................................................................25

Acknowledgments.......................................................................................................................................25

References Cited..........................................................................................................................................25

Appendix 1. Members of the Technical Advisory Committee...........................................................29

Appendix 2. Review of seminal ecological concepts.........................................................................30

Appendix 3. Distinctive characteristics of the four stream classes in New Jersey.....................37

Appendix 4. Stream class and characteristics of gaging stations representing relatively 

unimpaired basins used to classify New Jersey streams.......................................................38

Appendix 5. Verification results for the 171 hydrologic indices .......................................................40

Appendix 6. Hydrologic indices with the largest absolute loading for each of the 

statistically significant principal component axes for each stream type in the nine 

sub-components of the flow regime ...........................................................................................47

Appendix 7. Definitions of the 171 hydrologic indices. ......................................................................48
iv

Figures

1. Maps showing location of physiographic regions and streamflow sites used 

to classify the primary stream classes in New Jersey ..........................................................3

2. Graph showing time series of daily flow, minimum flow, and 25th- and 75th-percentile 

flow ranges for the South Branch Raritan River (USGS station 01396500), 

New Jersey, with flows for three environmental flow methods: Aquatic Base Flow 

(ABF), Range of Variation Approach (RVA), and 7-day, 10-year low flow (7Q10)...............8

3. Diagram showing steps taken to develop and apply the Hydroecological Integrity 

Assessment Process (HIP)..........................................................................................................9

4–19. Graphs showing—

4. Monthly maximum daily mean streamflow for time periods before and after

regionalization of a wastewater facility, water years 1964–2003..............................15

5. Monthly mean streamflow for time periods before and after regionalization 

of a wastewater facility, water years 1964–2003..........................................................16

6. Monthly minimum daily mean streamflow for time periods before and after

regionalization of a wastewater facility, water years 1964–2003..............................16

7. Maximum monthly streamflow for time periods before and after regionalization 

of a wastewater facility, water years 1964–2003..........................................................17

8. Median monthly streamflows for time periods before and after regionalization 

of a wastewater facility, water years 1964–2003..........................................................18

9. Minimum monthly streamflows for time periods before and after regionalization 

of a wastewater facility, water years 1964–2003..........................................................18

10. The calculated value and the 25th- and 75th-percentile values of selected

hydrologic indices pre- and post-regionalization ........................................................19

11. Monthly maximum daily mean streamflow for water years 1924–2003....................19

12. Monthly mean streamflow for water years 1924–2003................................................19

13. Monthly minimum daily mean streamflow for water years 1924–2003.....................19

14. Hydrograph showing two diversion scenarios with baseline daily flow 

conditions for water year 2000 (diversions of 5 and 50 ft3 with an

environmental passing flow of 25 ft3) .............................................................................20

15. Hydrograph showing two diversion scenarios with baseline daily flow 

conditions for water year 2000 (diversions of 25 and 50 ft3 with environmental

passing flows of 25 and 10 ft3, respectively) .................................................................21

16. Maximum monthly flows for four flow-diversion scenarios for 

water years 1924–2003......................................................................................................21

17. Median monthly flows for four flow-diversion scenarios for 

water years 1924–2003......................................................................................................22

18. Minimum monthly flows for four flow-diversion scenarios for 

water years 1924–2003......................................................................................................22

19. Mean, 25th, and 75th percentiles of 10 principal hydrologic indices for baseline

conditions and four streamflow-diversion scenarios..................................................23
v

Tables

1. Land-use changes in the State of New Jersey, 1972 to 2001................................................4

2. Information used by Technical Advisory Committee to evaluate and compare 10 

instream flow methods for potential application in New Jersey..........................................6

3. Hydrologic indices with the largest absolute loading on each of the four or five 

significant principal component axes for each group in each of the 10 subcomponents of flow for four New Jersey stream classes...................................................11

4. Comparison of pre- and post-regionalization values of selected hydrologic indices ....17

5. Comparison of the 10 principal hydrologic indices for baseline conditions and four 

water-diversion scenarios ........................................................................................................23
vi

Acronyms and abbreviations used in this report:

Abbreviation Description

ABF Aquatic Base Flow 

DFA Discriminant Function Analysis

EFG Ecological Flow Goals 

GIS Geographic Information System

HIP Hydroecological Integrity Assessment Process 

HIT Hydrologic Indices Tool

HAT Hydrologic Assessment Tool

HRIs Hydroecologically Relevant Indices

IDH Intermediate-Disturbance Hypothesis

IHA Indicators of Hydrologic Alteration

NJDEP New Jersey Department of Environmental Protection

NJHAT New Jersey Hydrologic Assessment Tool

NJSCT New Jersey Stream Classification Tool

NWIS National Water Information System

PCA Principal Components Analysis

POR Period of Record

RCC River Continuum Concept

RVA Range of Variability Approach

SCT Stream Classification Tool

TAC Technical Advisory Committee

UPGMA Unweighted Pair Group Method using Arithmetic Averages

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

7Q10 7-day, 10-year low-flow statistic

ft feet

ft3/s cubic feet per second

> greater than

< less than

≥ greater than or equal to

≤ less than or equal to
Development of the Hydroecological Integrity Assessment 

Process for Determining Environmental Flows for 

New Jersey Streams

By Jonathan G. Kennen, James A. Henriksen, and Steven P. Nieswand

Abstract

The natural flow regime paradigm and parallel stream 

ecological concepts and theories have established the benefits 

of maintaining or restoring the full range of natural hydrologic 

variation for physiochemical processes, biodiversity, and the 

evolutionary potential of aquatic and riparian communities. 

A synthesis of recent advances in hydroecological research 

coupled with stream classification has resulted in a new 

process to determine environmental flows and assess hydrologic alteration. This process has national and international 

applicability. It allows classification of streams into hydrologic 

stream classes and identification of a set of non-redundant 

and ecologically relevant hydrologic indices for 10 critical 

sub-components of flow. Three computer programs have been 

developed for implementing the Hydroecological Integrity 

Assessment Process (HIP): (1) the Hydrologic Indices Tool 

(HIT), which calculates 171 ecologically relevant hydrologic 

indices on the basis of daily-flow and peak-flow streamgage data; (2) the New Jersey Hydrologic Assessment Tool 

(NJHAT), which can be used to establish a hydrologic baseline 

period, provide options for setting baseline environmentalflow standards, and compare past and proposed streamflow 

alterations; and (3) the New Jersey Stream Classification 

Tool (NJSCT), designed for placing unclassified streams 

into pre-defined stream classes. Biological and multivariate 

response models including principal-component, cluster, and 

discriminant-function analyses aided in the development of 

software and implementation of the HIP for New Jersey. A 

pilot effort is currently underway by the New Jersey Department of Environmental Protection in which the HIP is being 

used to evaluate the effects of past and proposed surfacewater use, ground-water extraction, and land-use changes on 

stream ecosystems while determining the most effective way 

to integrate the process into ongoing regulatory programs. 

Ultimately, this scientifically defensible process will help to 

quantify the effects of anthropogenic changes and development on hydrologic variability and help planners and resource 

managers balance current and future water requirements with 

ecological needs.

Introduction

Maintaining and restoring the ecological integrity of 

streams—that is, native biodiversity and physiochemical 

processes that result in self-sustaining productivity—is an 

elusive goal for State water- and land-use regulatory and planning programs. Although many State water-quality programs 

are well developed, most existing State laws, regulations, and 

policies addressing the quantity of water in a stream are insufficient to enable meaningful flow management (Annear and 

others, 2004).

Furthermore, State agencies with authority and responsibility to protect and manage stream resources are confronted 

with four troublesome issues when they attempt to develop 

or apply standards or requirements for environmental flow 

(that is, a flow regime of a particular magnitude, duration, 

frequency, timing, and rate of change which is necessary to 

ensure that a river system remains ecologically, environmentally, economically, and socially healthy; also called instream 

flow). Proponents of water development may assume that:

•	 The environmental flow necessary to protect stream 

resources, commonly described using terms such as 

aquatic habitat, fishery resources, aquatic communities, 

or ecological integrity, is known or easily quantified 

by the State regulatory agency for every stream reach 

within its authority.

•	 Regardless of the extent of previous hydrologic 

alterations of the flow (that is, cumulative impact on 

streamflow), a sufficient quantity remains available for 

an additional water use.

•	 The environmental-flow standard developed by a State 

regulatory agency is simple and compliance with the 

standard can easily be attained.

•	 Streamflow alteration results primarily from direct 

diversion (for example, municipal water supply) or 

regulation of flow (for example, hydropower), not 

from land-use change. In other words, water develop-
2 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

ment and land use are not perceived to be competing 

for the same water resources.

Most such assumptions are incorrect.

Background

The New Jersey Department of Environmental Protection 

(NJDEP) has broad responsibilities through its various regulatory and planning programs for managing water and land 

resources, while concurrently protecting and restoring stream 

resources. Several divisions within the NJDEP have regulatory, management, and (or) monitoring responsibilities that 

directly or indirectly concern streamflow: Division of Land 

Use Regulation, Fish and Wildlife, Water Supply, Water Quality, and Watershed Management. The primary streamflow regulatory standard that historically has been used by the Division 

of Water Supply has been a “minimum passing-flow” requirement as a condition for a water-use permit. This environmental-flow requirement typically is a low-flow statistic, such as 

the 7-day, 10-year low flow (7Q10); however, other low-flow 

statistics are occasionally used. The NJDEP recognized that 

a flow requirement that focuses on only the low-flow portion 

of the stream hydrograph does not adequately address the full 

scope of stream resources and fails to protect the ecological 

integrity of streams. There is no direct relation between the 

7Q10 and aquatic-life protection, and many suggest that this 

statistic should not be used as a basis for flow recommendations (Annear and others, 2004). Equally important was the 

recognition by the NJDEP that other regulatory agencies were 

not specifically establishing environmental-flow requirements 

for activities that could potentially alter streamflows.

To address these concerns, the U.S. Geological Survey 

(USGS) in cooperation with the NJDEP undertook a project—to identify or develop a methodology for establishing 

environmental-flow standards for regulatory and planning 

purposes that will sustain stream communities in New Jersey. This project was called the Ecological Flow Goals (EFG) 

project. Due to the broad scope and potential significance of 

the task, the agreement required the creation and implementation of a Technical Advisory Committee (TAC) to direct and 

manage the efforts to achieve this goal.

Purpose and scope

This report:

1. Describes the initial goal of the Ecological Flow Goals 

project and the approach the TAC took to help achieve 

that goal.

2. Compares some established environmental flow methods, 

reviews important stream ecosystem concepts, and examines the critical hydrologic research that represents the 

framework for the environmental flow process presented 

herein.

3. Describes the development of a new environmental 

flow assessment process—the Hydroecological Integrity Assessment Process (HIP). Application of the HIP 

includes a hydrologic classification of streams in New 

Jersey, and the development of software tools for classifying unclassified streams (the New Jersey Stream Classification Tool (NJSCT)) and for establishing a baseline (reference) time period, comparing and contrasting past and 

proposed streamflow alterations, and providing options 

for setting environmental-flow standards at the reach scale 

(the New Jersey Hydrologic Assessment Tool (NJHAT)).

4. Provides example management applications using the 

NJSCT and the NJHAT for an analysis of land-use 

changes that are known to alter streamflow, and a series 

of scenarios of direct surface-water withdrawals from an 

unregulated stream.

Study area description

New Jersey covers approximately 8,100 mi2. This area 

has a population of more than 8.6 million people and includes 

some of the most densely populated metropolitan areas in the 

United States (U.S. Census Bureau, 2005). New Jersey is composed of four physiographic provinces–the Valley and Ridge, 

New England, Piedmont (all north of the Fall Line), and the 

Coastal Plain (which is south of the Fall Line) (fig. 1).

In general, the provinces north of the Fall Line consist 

of sedimentary rock (for example, shale and sandstone) and 

crystalline rock. South of the Fall Line is unconsolidated 

gravel, sand, silt, and clay. The Valley and Ridge physiographic province is characterized by a series of parallel ridges 

and valleys trending northeast-southwest, with mountainous 

topography that commonly reaches elevations of more than 

1,600 ft. The New England physiographic province consists 

of broad, flat-topped highlands and long, narrow valleys that 

range in elevation from 500 to 1,500 ft. The Piedmont physiographic province consists of northwestward-dipping sedimentary rocks that form broad, gently sloping lowlands and rolling 

hills, where elevations typically reach only 400 ft. About 55 

percent of the study area is in the Coastal Plain physiographic 

province and is characterized by flat to gently rolling topography and unconsolidated sedimentary deposits. (For a detailed 

description of New Jersey’s physiography, refer to Wolfe 

(1977).) Streamflow characteristics differ greatly among 

the physiographic provinces. Streamflow in the Piedmont 

province tends to be highly variable (flashy) due to minimal 

ground-water recharge. In the Coastal Plain, however, groundwater recharge is high and streamflow is relatively stable. In 

the New England and Valley and Ridge provinces, streamflow tends to fall somewhere between the flashy flows of the 

Piedmont and the more stable ground-water-supported flows 

of the Coastal Plain. Ground water contributes from 65 to 

95 percent of the base flow in the Coastal Plain. North of the 

Fall Line, the ground-water contribution to base flow ranges 
Introduction 3

Figure 1. Location of physiographic regions and streamflow sites used to classify the primary stream classes in New Jersey. (Each 

symbol represents a U.S. Geological Survey streamflow-gaging station and is color coded to indicate a stream class as referenced in 

appendix 4.)

A B

C D

0 20 40 MILES

PENNSYLVANIA

NEW YORK

DELAWARE

ATLANTIC OCEAN

PENNSYLVANIA

NEW YORK

DELAWARE

ATLANTIC OCEAN

PENNSYLVANIA

NEW YORK

DELAWARE

ATLANTIC OCEAN

PENNSYLVANIA

NEW YORK

DELAWARE

ATLANTIC OCEAN

EXPLANATION

Physiographic Province

COASTAL PLAIN

PIEDMONT

NEW ENGLAND

VALLEY AND RIDGE

Stream Class

A

B

C

D

CONN.

CONN.

CONN.

CONN.

0 20 40 KILOMETERS

41°

40°

39°

75° 74°

41°

40°

39°

75° 74°

41°

40°

39°

75° 74°

41°

40°

39°

75° 74°

Philadelphia Philadelphia

Philadelphia Philadelphia

New York City New York City

New York City New York City

FALL LINE

FALL LINE

FALL LINE

FALL LINE
4 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

from approximately 22 to 88 percent (Watt, 2001). In general, 

streamflow in the northern part of the study area is dominated 

by surface runoff; in the Coastal Plain, it is dominated by flow 

from ground-water sources.

Since the 1970s, the landscape in the study area has experienced extensive disturbances associated with the conversion 

of forested and agricultural land to urban and suburban developments and concomitant changes in the hydrologic regime 

(Kennen and Ayers, 2002). As a consequence of these largescale changes in population and land use, very few streams 

remaining are not affected by some form of flow regulation or 

water development (for example, water diversions, wastewater 

discharges, dams, and reservoirs). Estimated land use in the 

study area over the 30-year period from 1972 to 2001 is shown 

in table 1. During this period, developed land has increased 

more than 66 percent, with a concurrent decrease in forest 

and wetlands. As a result of these changes, most of northeastern New Jersey and the corridor between New York City 

and Philadelphia consist of heavily urbanized and, therefore, 

relatively impervious areas that yield runoff rapidly. Surfacewater and ground-water withdrawals have reduced base flow 

in streams in some areas. Water-supply systems in the study 

area are highly connected and transfer of water across drainage divides and among basins is common. For example, nearly 

100 million gallons per day is transferred from the Delaware 

River to the Raritan River Basin via the Delaware and Raritan 

Canal (Ayers and others, 2000).

Technical Advisory Committee Activities

A TAC was assembled by the NJDEP to direct the effort 

to address the project’s goals. The TAC consisted of 40 members representing six agencies: NJDEP, USGS, New Jersey 

Pinelands Commission, New Jersey Water Supply Authority, 

Philadelphia Academy of Natural Sciences, and Pennsylvania 

Fish and Boat Commission. Individual members of the TAC 

and their affiliations are listed in appendix 1.

The initial goal of the EFG project was to identify or 

develop a method that will provide environmental streamflow 

standards necessary to sustain or restore ecological integrity of 

the stream communities. Ecological integrity—that is, native 

biodiversity and physiochemical processes—results in selfsustaining productivity. In addition, three guidelines regarding 

the goal of the project were established by the cooperative 

agreement: (1) application of the method selected or developed to determine environmental flows would not require 

long-term project or stream-specific field studies; (2) the 

development of the method would utilize, if possible, the 

results of NJDEP’s Watershed Indicator Study (Kennen and 

others, in press), which focused on a State-wide benthic invertebrate community impairment assessment; and (3) the method 

would, ideally, be useable by all regulatory and planning 

programs within the NJDEP. The TAC was directed not to 

consider any consequences or policy issues potentially related 

to any new method developed as part of the EFG project. This 

directive allowed the TAC to select the most effective method 

for managing streamflows regardless of implementation or 

policy considerations.

The TAC undertook two primary tasks. First, a review 

of existing environmental-flow methods was conducted, and 

10 documented environmental-flow methods were chosen for 

comparison. Second, based on the results of this review, the 

TAC identified two applicable methods that potentially would 

meet the goal of the EFG project. The two selected methods 

were compared to the established 7Q10 environmental-flow 

standard that historically has been used by the NJDEP in the 

regulatory process. A third task, undertaken after compleTable 1. Land-use changes in the State of New Jersey, 1972 to 2001.

Land use

Acres Percent change 

1972–2001 1972 1984 1995 2001

Developed - urban 888,848 1,204,958 1,427,359 1,483,205 66.9

Cultivated/grassland 999,372 1,007,012 883,618 850,027 -14.9

Upland forest 1,673,164 1,465,727 1,421,105 1,388,984 -17.0

Bare land 29,840 38,452 45,531 58,984 97.7

Coastal wetland 220,726 208,287 201,575 200,172 -9.3

Inland wetland 925,329 788,896 737,033 734,051 - 20.7

Unconsolidated shore 12,311 47,162 45,882 46,811 280.2

Water 517,716 516,587 514,975 514,859 - 0.6

Total 5,267,006 5,277,080 5,277,080 5,277,092 0.2

Data source: Classification and analysis of Landsat satellite image data performed at the Grant F.Walton Center for Remote Sensing & Spatial Analysis, 

Rutgers, The State University of New Jersey, New Brunswick, New Jersey. Data are available on the World Wide Web at http://www.crssa.rutgers.edu/ 

projectN/lc/
Introduction 5

tion of tasks 1 and 2, was a general review of seminal stream 

ecological concepts and recent hydroecological research. This 

information is presented in appendix 2.

Comparison of 10 Environmental Flow Methods

The TAC reviewed Annear and others (2002), which was 

published by the Instream Flow Council, an organization that 

represents the interests of state and provincial fish and wildlife 

management agencies in the United States and Canada and 

is dedicated to improving the effectiveness of instream-flow 

programs. The comprehensive list of environmental-flow 

methods presented was evaluated and a subset was identified 

that potentially could meet the goal of the EFG project. Specifically, the 29 “Instream Flow Assessment Tools” (methods) 

presented in Annear and others (2002, chap. 7) were evaluated 

to determine which, if any, met the EFG project guidelines 

presented above. Based on this review, 10 methods were 

selected for a thorough examination and comparison to the 

historically used 7Q10 standard.

Twenty-eight criteria in four categories were selected 

to aid in the comparison of the 10 flow methods (table 2). 

The “Background” category in table 2 summarizes where the 

method was developed and geographically where the method 

has been or is being implemented. “Beecher Elements” are 

from Beecher (1990), which indicates that an unambiguous 

instream-flow standard requires addressing five critical elements to: (1) establish a goal, such as maintenance of or no 

net loss in a resource; (2) identify the resource(s) of value, 

such as fish or recreation; (3) use a unit of measurement that 

is restricted to the resource itself, for example flow or habitat; 

(4) set a benchmark time period for the resource level, typically current conditions (historic conditions can be used as a 

benchmark time period if current conditions are unacceptable); 

and (5) derive a “protection statistic.” Beecher (1990) points 

out that using a mean or median as a protection statistic may 

not protect the resource at its historical mean or median level. 

Beecher (1990) also notes that the “upper extremes of a distribution are important to maintaining an average condition” 

and that “it may be necessary to protect an upper extreme in 

order to maintain an average condition.” The “Components” 

category (table 2) identifies physical (for example, hydrology) 

and biological (for example, fish or invertebrates) stream components that the method purportedly addresses. The “Application” category (table 2) addresses size (drainage area or 

stream order), what the method is applicable to, level of effort 

required to apply the method, and the type of method (incremental, standard, or planning). See Annear and others (2004, 

p. 133, table 6-3) for a summary of assessment tools.

The TAC reached a consensus that no single method was 

sufficiently holistic to specifically meet the stated goal of or 

guidelines for the EFG project. This conclusion was based 

primarily on three points. First, most of the methods (8 of 10) 

addressed only one or two of the components explicitly identified in the goal statement for the EFG project. Second, some 

methods appeared to arbitrarily select hydrologic measures 

or unintentionally select redundant measures and typically 

failed to address all of the five critical components of flow 

(magnitude, frequency, duration, timing, and rate of change) 

identified by Poff and others (1997) as being important to 

maintaining biological integrity in stream systems. Third, 

some of the methods were applicable only to specific streams 

(for example, Pennsylvania trout streams) or applicable to all 

the streams without differentiating among stream types (for 

example, Range of Variability Approach (RVA)). Some of the 

flow methods presented in table 2, however, had attributes 

that could be used as building blocks for a method that met 

the goals of the EFG project. Therefore, the TAC undertook 

a more through examination of two well-established flow 

methods: the New England Aquatic Base Flow (ABF) standard (Larsen, 1981; Lang, 1999), which is based on hydrology in the New England area; and the Range of Variability 

Approach (Richter and others, 1997), which is an extension of 

the Indicators of Hydrologic Alteration (IHA) method (Richter 

and others, 1996).

Examination of Two Environmental Flow Methods

The Aquatic Base Flow (ABF) standard was developed as 

part of the U.S. Fish and Wildlife Service’s (FWS) New England Flow Policy (Larsen, 1981). The method uses measures 

of central tendency (for example, median monthly flows) as a 

surrogate for the amount of available aquatic habitat. Specifically, the ABF standard recommends the median August 

flow (calculated from a period of record with minimum 

anthropogenic alteration) as a minimum instantaneous flow 

requirement unless additional, seasonal flows are needed (for 

example, to protect fish spawning and incubation). During the 

spring and fall/winter periods, the recommendations are the 

April/May and February median flow, respectively. Median 

monthly flows are derived from a period of record (POR) with 

minimum anthropogenic alteration or from established ratios 

of cubic feet per second per square mile of drainage area in 

representative unregulated streams. The relation of aquatic 

habitat to seasonal flows was established by professional opinion and typically focuses on the low-flow summer period and 

general seasonal needs for spawning and incubation in spring 

and fall. The principal underlying assumption of the ABF 

standard is that because aquatic life evolved and adapted to 

the natural flow regime, emulating critical parts (for example, 

seasonal high and low flow pulses) of that flow regime should 

provide an adequate level of protection. The intended purpose 

of the ABF method, however, is to protect fish spawning and 

incubation. There is no stated objective to address the dynamic 

physical processes, especially the movement of water and 

sediment within the channel and between the channel and 

the floodplain (that is, stream and floodplain connectivity), 

typically associated with rejuvenation and creation of aquatic 

habitat or processes related to the maintenance of biodiversity.

The Range of Variability Approach (RVA) developed 

by The Nature Conservancy addresses all five critical components of the flow regime by using 67 hydrologic attributes 
6 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams Table 2. Information used by Technical Advisory Committee to evaluate and compare 10 instream flow methods for potential application in New Jersey. [PA, Pennsylvania; MD, Maryland; 7Q10, 7-day, 10-year low flow; RVA, Range of Variation Approach, %, percent; ADF, Average Daily Flow; Adm., Administration; Max., Maximum; (ft3/s)/mi2, cubic feet per second per square mile; Q, discharge; NS, not specified; Sept., September; SRBC, Susquehanna River Basin Commission; TNC, The Nature Conservancy; AAD, Average Annual Discharge; ADF, Average Daily Flow; Dev., Development; Dept. Nat. Res., Department of Natural Resources; OR, Oregon, MT, Montana; US, United States; #, number; >, greater than; <, less than] Evaluation criterion INSTREAM FLOW METHOD PA Wetted perimeter 7Q10 MD Most com- mon flow Aquatic base flow RVA Texas method Georgia method Georgia method Georgia method Background Reference Denslinger and others, 1998 Collings, 1974 Unknown Water Resource Adm. US Fish & Wildlife Richter and others, 1997 Water Dev. Board Dept. Nat. Res. Dept. Nat. Res. Dept. Nat. Res. Development location PA Unknown Unknown Maryland New England US Texas Georgia Georgia Georgia Current use PA OR, MT Several states Maryland New England Many locations Texas Georgia Georgia Georgia Beecher Elements1 Resource Trout Invertebrates Water quality Stream biota Aquatic habitat Biodiversity/ integrity Aquatic resources Aquatic life Aquatic life Aquatic life Goal Protect Protect Protect Protect Protect Protect Protect Protect Protect Protect Unit of measurement Habitat Wetted area Flow Flow Flow Flow Flow Flow Flow Flow Benchmark time period Past Present Present Present Past Past Past Past Past Present Protection statistic % ADF Max. wetted area 7Q10 50 to 85% Exceedence (ft3/s)/mi2 67 Flow parameters % Median monthly 30% AAD Sept. median Q 30 to 60% AAD Components Explicitly Addressed Hydrology No No No No No Yes Yes No No No Channel form No No No No No Yes No No No No Sediment transport No No No No No Yes No No No No Water quality No No Yes No No Yes No No No No Temperature No No No No No Yes No No No No Connectivity No No No No No Yes No No No No Riparian zone No No No No No Yes No No No No Fish species Yes No No No No Yes Yes No Trout No Fish community No No No No No Yes Yes No No No Invertebrate species No No No No No Yes No No No No Invertebrate community No Yes No No No Yes No No No No Aquatic “health” No No No Yes Yes Yes No Yes Yes Yes # Components addressed 1 1 2 1 1 12 3 1 2 1 Application Stream order 

(Drainage area)

<100 mi2 NS NS NS >50 mi2 NS Below reservoirs NS - Warm NS - Trout NS - All

Office analysis Yes No Yes Yes Yes Yes Yes Yes Yes YesField data required No Yes No No No No No No No NoImpact analysis Yes No No No No Yes No No No NoLevel of effort2 Low Moderate Low Low Low Moderate Low Low Low LowScale3 Segment Segment Segment NS NS Segment Segment Segment Segment SegmentCategory (type of 

method)

Incremental Standard setting Planning Standard setting Standard setting Standard setting Standard setting/

planning

Standard setting Standard setting Standard setting

1Beecher, 19902Low, < 5 staff weeks; moderate, >2 <4 staff weeks; high = months3Any length of stream with homogeneous flow regime and channel geometry
Introduction 7

(33 hydrologic indices and 34 environmental flow components) to assess the degree of hydrologic alteration attributable to human influence (The Nature Conservancy, 2006). 

The degree of alteration is evaluated by comparing the values 

of the hydrologic indices calculated from daily flow records 

for any pre-project condition to a post-project condition (for 

example, flow prior to and after installation of a hydroelectric dam). A range calculated around the mean index value of 

either ±1 standard deviation or the 25th to 75th percentile of 

flow is used to define flow-management targets. If a postproject flow alteration moves the mean outside the range of 

the pre-project flow condition for any of the flow indices, it is 

considered a significant change. The objective of the method 

is “conservation of native aquatic biodiversity and protection 

of natural ecosystem functions” (Richter and others, 1997). 

The critical element of this approach is that it is derived from 

aquatic ecological theory, which recognizes the importance 

of hydrologic variability and all five critical components of 

the flow regime (magnitude, frequency, duration, timing, and 

rate of change) in sustaining aquatic ecosystem integrity (Poff, 

1996; Richter and others, 1997). Poff and others (1997) established that hydrologic variability is strongly correlated with 

many physiochemical characteristics of rivers and is considered a “master variable” that affects, directly and indirectly, 

the distribution and abundance of riverine species and thus, 

the ecological integrity of rivers.

Two concerns arose regarding the RVA. First, there 

appeared to be a potential for some level of redundancy among 

the hydrologic parameters. For example, 3 of the 67 parameters are based on the annual minimum 1-day mean, annual 

minimum 3-day mean, and annual minimum 7-day mean. 

Many of these parameters tend to be highly intercorrelated. 

The RVA was not explored, however, beyond identifying 

parameter redundancy, nor was an evaluation conducted to 

determine whether any of the implicit redundancy affected 

the interpretation of the hydrologic relations. Olden and Poff 

(2003) provide a comprehensive evaluation of this redundancy. Most flow methods utilizing hydrologic indices that 

include annual minima and maxima are highly likely to 

include some level of implicit redundancy. More importantly, 

however, it was recognized that the RVA did not specifically 

address differences among stream types. Streamflow regimes 

are known to show distinct regional patterns (Poff and Ward, 

1989; Poff, 1996; Olden and Poff, 2003), which are driven by 

geographic variation in climate, geology, topography, and vegetation. NJDEP water managers routinely recognize general 

hydrogeomorphic differences in stream characteristics among 

the four physiographic provinces of New Jersey (Valley and 

Ridge, New England, Piedmont, and Coastal Plain). Any 

methodology that does not explicitly consider differences in 

streams based on known geophysical characteristics including 

geology, geography, or physiography would be of limited use 

in New Jersey.

Data for several pilot streams were selected to compare 

the ABF and the RVA to the 7Q10. Because the comparisons 

were similar among all of the pilot streams identified for this 

study, however, only one example is presented here. The South 

Branch of the Raritan River near High Bridge in north-central 

New Jersey (USGS streamflow-gaging station 01396500) was 

selected because anthropogenic alteration of flow in the upper 

part of the basin is limited. A time-series plot of median daily 

flows, minimum daily flows, and the 25th- to 75th-percentile 

range for the daily flows for a 23-year POR (water years 

1978–2000) are shown in figure 2. Values calculated using 

the ABF, RVA (mean monthly flow), and 7Q10 methods are 

superimposed on the daily-flow values. The 7Q10, applied as 

a minimum-flow standard, allows a flow less than the minimum daily flow ever recorded for approximately 150 days of 

the year, which the TAC found to be unacceptable. Similarly, 

implementing the ABF as a minimum-flow standard would 

allow complete utilization of the available water resource for 

nearly 2.5 months of the year. The TAC concluded that the 

7Q10 and the ABF produced unrealistically low standards for 

some periods and would probably fail to adequately protect 

some critical stream-resource components, such as invertebrates, the stream bank, and riparian habitat.

Application of the RVA to the South Branch Raritan 

River raised additional methodological concerns beyond that 

noted for the ABF method. The RVA is described by Richter 

and others (1997) as an impact analysis tool in application, in 

contrast to the ABF, which was developed to establish an environmental-flow standard. The RVA analysis is accomplished 

by comparing pre- and post-project hydrologic conditions. A 

typical application requires that all 67 pre-project hydrologic 

indices be compared to the 67 post-project indices using the 

same POR. Alternatively, the analysis could be done using a 

simulated post-project hydrograph. Of the 67 indices, only 

those for which the mean moves outside ±1 standard deviation, or the 25th- to 75th-percentile range, are considered as 

significant alterations for which “management targets” (moving the post-project mean back into the pre-project standard 

deviation or percentile range) would be developed. Twelve of 

the parameters used in RVA—that is, the mean monthly value 

for each calendar month—are plotted in figure 2. Although 

this interpretation was not proposed by Richter and others 

(1997), a user could infer that the 25th-percentile values for all 

67 RVA indices could be applied as the maximum allowable 

alteration.

The TAC determined that neither of these two methods 

was sufficiently comprehensive to meet the goal of the EFG 

project. Consequently, the focus was shifted from examining environmental-flow methods to a review of seminal 

stream-ecology concepts as a fundamental building block to 

understanding the relation between flow variation and stream 

ecosystem integrity (see appendix 2 for a general review of 

seminal stream-ecology concepts).
8 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Figure 2. Time series of daily flow, minimum flow, and 25th- and 75th-percentile flow ranges for the South Branch Raritan River (USGS streamflow-gaging station 01396500), New Jersey, with flows for three environmental flow methods: Aquatic Base Flow (ABF), Range of Variation Approach (RVA), and 7-day, 10-year low flow (7Q10).

0

755025

400375350325300

275

250225

200175150125100

1 31 61 91 121 151 181 211 241 271 301 331 361

DAY IN WATER YEAR

DISCHARGE, IN CUBIC FEET PER SECOND

25th percentile

75th percentile

Monthly Median (RVA)

Minimum

7Q10

ABF

EXPLANATION
Development of the Hydroecological Integrity Assessment Process 9

Development of the Hydroecological 

Integrity Assessment Process

Conceptualization

The Hydroecological Integrity Assessment Process (HIP) 

is based on the large body of research linking hydrologic 

variability and aquatic ecosystem integrity. This research 

strongly supports the natural flow paradigm—that is, “the full 

range of natural intra- and inter-annual variation of hydrological regimes, and associated characteristics of magnitude, 

frequency, duration, timing, and rate of change, are critical in 

sustaining the full diversity and integrity of aquatic ecosystems” (Poff and others, 1997). Streamflow is strongly related 

to many critical physiochemical components of rivers, such as 

dissolved-oxygen concentration, channel geomorphology, and 

water temperature, and can be considered a “master variable” 

that limits the distribution, abundance, and diversity of many 

aquatic plant and animal species (Resh and others, 1988; Poff 

and others, 1997). The need for applicable management tools 

that incorporate this connection and assist in the establishment 

of flow standards protective of aquatic ecosystem integrity 

was one of the primary reasons the HIP was developed.

The HIP can be used by any federal, state, provincial, 

regional, national, or international agency or a non-governmental entity that has a responsibility for or interest in the 

management and (or) regulation of streams with an objective 

to address ecological integrity at the reach scale. In addition, 

the HIP can assist researchers by identifying critical streamtype-specific hydrologic indices that adequately characterize 

the five critical components of the flow regime by using 10 

non-redundant indices (see Olden and Poff, 2003).

Development of the Hydroecological Integrity Assessment Process requires four major steps (fig. 3):

1. Perform a hydrologic classification of streams in a geographic area using long-term gaging-station records for 

relatively unmodified streams. Calculate 171 hydroecologically relevant indices (HRIs) for each station (Olden 

and Poff, 2003). Employ an unweighted pair group 

method using arithmetic averages (UPGMA) (McCune 

and Medford, 1999; McCune and Grace, 2002), or a comparable hierarchical-clustering method, to group streams 

into area-specific stream classes.

2. Identify statistically significant, non-redundant HRIs (surrogate indices are also identified) associated with the five 

critical flow components (a total of 10 primary indices) 

of the flow regime for each stream class using principal 

components analysis (SAS Institute Inc., 1989). This suite 

of HRIs should adequately characterize the flow regime 

for each stream class (Olden and Poff, 2003).

3. Develop an area-specific Stream Classification (computer 

software) Tool (SCT) for placing unclassified streams 

(that is, streams not placed into a specific stream class as 

part of the initial classification analysis) into one of the 

identified stream classes. The SCT software uses multiple 

discriminant function analyses (SYSTAT Software Inc., 

2004) to match a stream with one of the four specified 

stream classes based on the level of concordance among a 

suite of significant hydrologic parameters identified during the initial classification analysis.

4. Develop an area-specific Hydrologic Assessment Tool 

(HAT). This software can be used to (a) establish a hydrologic baseline (that is, a reference time period), (b) provide options for setting environmental-flow standards, and 

(c) evaluate past and proposed hydrologic modifications 

for a stream reach. The HAT software uses 10 streamclass-specific indices chosen from an available 171.

Software

Three computer software tools were developed for implementation of the New Jersey HIP:

•	 Hydrologic Index Tool–Version 1.0 (HIT)–The HIT is 

used to calculate the 171 HRIs for the stream-classification analysis. The program is designed to import 

USGS daily-mean and peak-flow discharges from the 

Figure 3. Steps taken to develop and apply the Hydroecological 

Integrity Assessment Process (HIP).

Identify Classification Study Streams

Calculate 171 Indices - HIT

Classify Streams and

Identify 10 Primary Flow Indices

Develop NJ HAT and NJ SCT

Assign Stream to Class

Conduct Analyses

using NJ HAT

Development Application
10 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

National Water Information System (NWIS) database 

(http://waterdata.usgs.gov/nwis/sw). Simulated data 

can also be used; however, the format should be consistent with the USGS NWIS format. HIT can be used 

for any gaging site and is not specific to New Jersey.

•	 New Jersey Stream Classification Tool-Version 1.0 

(NJSCT)-The NJSCT classifies a stream as belonging 

to one of four stream classes by comparing specific 

HRI values for an unclassified stream with indices 

defining each of the four previously identified stream 

classes. The four classes of streams (table 3) identified 

in New Jersey are characterized by the relative degree 

of skewness of daily flows (low = stable flow, high = 

flashy flow) and frequency of low-flow events (low = 

high base flow, high = low base flow). Thus, streams 

belonging to stream class A are semiflashy with moderately low base flow, class B streams are stable with 

high base flow, class C streams are moderately stable 

with moderately high base flow, and class D streams 

are flashy with low base flow. The characteristics of 

the four classes of streams in New Jersey are shown in 

appendixes 3 and 4.

•	 New Jersey Hydrologic Assessment Tool-Version 3.0 

(NJHAT)-The NJHAT is used to evaluate the hydrologic baseline (that is, a reference period of flow 

used as a basis for comparison), provide options for 

establishing environmental-flow standards, and assess 

past and proposed hydrologic modifications of streams 

in New Jersey. It accomplishes this by using flow 

statistics, trend assessment (that is, evaluating possible 

changes in streamflow over a specified period), and 10 

primary stream-class-specific indices (that is, the first 

hydrologic index listed in table 3 for each of the flow 

components) chosen from the available 171 HRIs.

A national HAT–Version 3.0 (NATHAT) has also been 

developed. This tool is based on a hydrologic classification 

conducted by Poff (1996) using 420 sites across the contiguous United States. NATHAT has the same capabilities as 

NJHAT, but contains six stream classes. Until such time that 

area-specific classification results in greater discrimination 

among stream classes, and correspondingly, a refined identification of critical HRI values in other states, provinces, 

or regions, NATHAT can be used to establish a hydrologic 

baseline (reference period), to establish environmental-flow 

standards, and to evaluate past and proposed hydrologic modification for the six stream classes.

Methods

Hydrologic Index Tool

HIT is a stand-alone program that calculates 171 HRIs by 

using daily-mean and peak-flow discharge values. The USGS 

conducted tests to verify that the computer code in the Hydroecological Integrity Assessment Process computer programs 

(HIT and NJHAT) correctly applies the definitions and the formulas for the calculation of the 171 HRIs. The results of these 

tests are presented in appendix 5. The HIT is primarily used in 

conjunction with the classification analysis of any geographic 

area; however, it is not specifically used in conjunction with 

NJSCT or NJHAT. Prior to using the HIT, a researcher would 

select all stream-gaging stations within a geographic area of 

interest using the POR that provides the least altered streamflow record. These station records should have a POR that 

is a minimum of 10 years in length, with 25 years preferred 

(Interagency Advisory Committee on Water Data, 1981). 

Daily-mean discharge and peak-flow data (if available) would 

be processed using the HIT; that is, the program would calculate the 171 indices for each stream. If peak-flow data are not 

available, then eight HIT indices (FH11, DH22, DH23, DH24, 

TA3, TH3, TL3, and TL4) are not calculated. The daily-mean 

discharge values could also be acquired by simulating dailyflow data (for example, Kennen and others (in press)).

Daily-mean discharges and peak annual flows are 

necessary to run a complete HIT analysis. These data can be 

downloaded from the USGS web site NWIS Web Data for the 

Nation at http://waterdata.usgs.gov/nwis/. Simulated data can 

also be used, but the format has to be consistent with that of 

USGS continuous streamflow records.

Stream Classification Processes

A series of procedural steps can be followed to identify a 

set of minimally impaired New Jersey streams and group these 

streams into distinct stream classes based on hydroecologically relevant indices. This approach includes the calculation 

of 171 HRIs for a minimally impaired period of record for 

94 streams and a statistical process to reduce the amount of 

redundancy among the hydrologic indices. The reduced set of 

indices is used to cluster the streams into four primary stream 

classes, and principal components analysis (PCA) is used to 

identify the most significant hydrologic indices for each of the 

four stream classes. The HRIs characterize the magnitude of 

flow events, frequency of flow events, duration of flow events, 

timing of flow events, and rate of change of flow events (for 

example, table 3). The primary indices from these stream 

classes are then used to build a discriminant model that is used 

to predict the class in which an unclassified stream would be 

placed based on a level of concordance with HRIs of a specific 

stream class.
Development of the Hydroecological Integrity Assessment Process 11

Table 3. Hydrologic indices with the largest absolute loading on each of the four or five significant principal component axes for each group in each of the 10 subcomponents of flow for four New Jersey stream classes. (Refer to appendix 7 for definitions of hydrologic indices.)

Flow component

CLASS

All streams A B C D

Magnitude of flow events:

Average flow conditions MA18, MA39, MA26, MA37 MA9, MA15, MA33, MA32 MA24, MA11, MA43, MA40, 

MA45

MA39, MA13, MA44, MA40, 

MA9

MA20, MA37, MA34, MA40

Low−flow conditions ML6, ML13, ML13, ML16 ML20, ML4, ML21, ML16 ML3, ML19, ML20, ML3, 

ML13

ML20, ML20, ML13, ML15, 

ML21

ML8, ML19, ML13, ML15

High−flow conditions MH5, MH16, MH20, MH18 MH24, MH4, MH18, MH26 MH14, MH17, MH12, MH13, 

MH16

MH16, MH2, MH21, MH3, 

MH1

MH20, MH3, MH20, MH23

Frequency of flow events:

Low−flow conditions FL3, FL3, FL1, FL1 FL3, FL2, FL1, FL1 FL1, FL3, FL3, FL2, FL3 FL3, FL1, FL1, FL2, FL3 FL3, FL3, FL1, FL2High−flow conditions FH4, FH3, FH1, FH9 FH4, FH10, FH1, FH10 FH7, FH3, FH3, FH4, FH11 FH3, FH9, FH5, FH10, FH11 FH7, FH3, FH9. FH2

Duration of flow events:

Low−flow conditions DL4, DL12, DL16, DL6 DL15, DL1, DL16, DL12 DL16, DL14, DL5, DL9, DL17 DL4, DL16, DL16, DL11, DL7 DL3, DL12, DL16, DL6High−flow conditions DH2, DH13, DH20, DH8 DH12, DH2, DH20, DH24 DH11, DH14, DH1, DH9, DH23 DH14, DH2, DH17, DH12, 

DH23

DH11, DH2, DH15, DH8

Timing of flow events:

Low−flow conditions TL4, TL4, TL1, TL1 TL4, TL3, TL2, TL1 TL3, TL3, TL2, TL1, TL2 TL4, TL2, TL2, TL1, TL4 TL4, TL3, TL3, TL1High−flow conditions TH2, TH2, TH2, TH3 TH2, TH2, TH1, TH3 TH2, TH2, TH3, TH4, TH3 TH1, TH3, TH2, TH3, TH3 TH3, TH2, TH2, TH3Rate of change in flow 

events:

RA3, RA7, RA8, RA5 RA7, RA1, RA6, RA2 RA6, RA3, RA1, RA2, RA4 RA7, RA3, RA8, RA1, RA6 RA6, RA3, RA8, RA2

1Some indices can appear more than once in a table cell because they may be the most highly loaded variable on PCA axis one, two, three, and so forth, for a given index.
12 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Selection of Streams and Period of Record

The steps used to select New Jersey streams for use in 

the HIP eliminated streams that either were not representative 

of “least impaired” conditions or did not have a continuous 

period of record long enough to be considered appropriate 

for analysis. These steps included a site-selection process 

that incorporated visual and statistical procedures as well as 

professional judgment. First, all candidate New Jersey streams 

(N=114) with a minimum POR of 20 years were identified. 

Second, the flow records were used to establish a minimum 

background flow profile based on the available POR. The 

flow profile was used to visually identify any flow-related 

anomalies and determine whether any major changes in flow 

processes occurred over the established POR. Least impaired 

sites—that is, sites that had minimal flow regulation or a POR 

reflecting a time period prior to major flow alteration—were 

identified. This included New Jersey streams with a POR that 

in many cases did not include the 1960s drought of record 

and had minimal (<25 percent) catchment urbanization. In 

addition, streams with a catchment size exceeding 400 mi2

were eliminated to reduce the influence of dams and reservoirs 

commonly established on larger river systems (for example, 

the Delaware River) in New Jersey. Professional judgment 

based on more than 40 years of accumulated knowledge and 

experience was used to identify some New Jersey streams 

that initially did appear to meet the minimum requirements 

for inclusion, but later were found to be anomalous due to 

changes in flow associated with water withdrawals, inter-basin 

water transfers, or other flow-regulation processes. In many 

cases, trend analysis between split periods of the flow record 

was necessary to fully establish whether differences in flow 

occurred over a given timeframe and whether these changes 

were a result of water-development processes in the basin 

or of long-term climatic variation (for example, Watson and 

others (2005)). This process reduced the number of streams 

suitable for inclusion in the HIP from 114 to 94.

Clustering of Streams

Stream classes in New Jersey were defined by classifying 

the 94 stream sites that met the minimum criteria established 

above—that is, a sufficiently long POR (about 20 years) with 

minimal anthropogenic disturbance in the catchment. In this 

analysis, the 94 streams chosen were assumed to represent the 

“least impaired” portion of the gaging record and were subsequently analyzed using the HIP to generate 171 ecologically 

relevant indices (Richter and others, 1997; Olden and Poff, 

2003) for each stream. By focusing on streams with a least 

impaired POR, the resulting HRIs are thought to be indicative 

of what would be expected for a relatively unmodified aquatic 

system. The results of these HIT analyses were validated 

against available published USGS streamflow records and by 

a series of validation techniques outlined in Henriksen and 

others (2006). (See also appendix 5.) All 171 HRIs were calculated for the 94 streams.

Principal components analysis (SAS Institute Inc., 1989) 

in combination with collinearity assessment (Spearman’s 

rho) was used to reduce the number of HRIs and to isolate a 

subset of indices that accounted for the greatest proportion of 

variance while minimizing redundancy. PCA is well suited 

to decreasing the dimensionality of complex data sets (Digby 

and Kempton, 1987; Manly, 1994) and was used to minimize 

HRI intercorrelation. Distributions of all HRIs were evaluated for normality and standardized when necessary. PCA was 

conducted on the correlation matrix and the significance of 

principal components was evaluated using the broken stick 

method (Jackson, 1993). The broken stick method is used to 

determine statistically significant principal component axes by 

comparing the observed eigenvalues to the eigenvalues from 

random data. In addition, use of the correlation matrix ensured 

that all HRIs contributed equally to the PCA and that the contributions were scale-independent (Legendre and Legendre, 

1998; Olden and Poff, 2003). Loadings (the level of correlation between the HRIs and principal components) of the HRIs 

on each significant principal component were used to identify 

indices explaining the dominant patterns of variation among a 

full suite of intercorrelated indices. Indices with the strongest 

loadings (minimum cutoff was set at 0.6000) along significant 

primary components were retained for additional analysis. 

Spearman’s correlations were then used to further diminish 

redundancy and the combination of these two approaches 

reduced the number of significant HRIs from 171 to 70.

The reduced set of HRIs was then used to classify the 

94 streams using the unweighted pair group method using 

arithmetic averages (UPGMA), a clustering technique that is 

also known as average linkage or group average, into distinct 

stream classes. UPGMA is a hierarchical clustering technique 

in which the similarity between clusters is calculated using the 

average of all Euclidean distances for all pairs of individuals 

(McCune and Grace, 2002). Prior to the cluster analysis, HRIs 

were normalized to reduce the effect of scale and the UPGMA 

was carried out using PRIMER 6 (Clarke and Gorley, 2006). 

The UPGMA cluster analysis separated the 94 streams based 

on the strength of the associations among the 70 indices into 

four distinct stream classes (app. 3; fig. 1). A fifth group that 

represented the largest catchment areas (for example, the Delaware River sites) was found to be an outlier and was omitted 

from further analysis. The remaining four stream classes were 

found to be highly distinct and the sensitivity of the clusters 

was validated using a jackknifing procedure (for example, 

Ibarra and Stewart, 1989; Kennen and others, 2002). This 

analysis requires a sequential deletion of sites and calculation 

of percent persistence of each cluster division. This analysis 

indicated 80- to 95-percent persistence for the four primary 

divisions.

Following the cluster analysis, PCA was conducted to 

identify the HRIs that best exemplify the 10 sub-components 

of the flow regime (magnitude–low, average, high; frequency–

low, high; duration–low, high; timing–low, high; rate of 

change–average (table 3)) for each of the four stream classes. 
Application of the Hydroecological Integrity Assessment Process 13

A matrix was produced by identifying, for each stream class, 

the indices that are most significant for each of the 10 critical components of the flow regime. Significant indices were 

derived by assessing the loading pattern on significant principal components. Loadings of the hydroecological indices on 

each significant principal component were used to identify 

indices that explain dominant patterns of hydrologic variation. 

Because principal component axes by definition are orthogonal, indices from significant secondary and tertiary principal 

component axes also were selected to ensure that the chosen 

indices are relatively independent from one another and to 

identify surrogate indices for subsequent comparisons (Olden 

and Poff, 2003). (Surrogate indices represent other indices 

within each component that are collinear with the indices 

of interest (Henriksen and others, (2006).) The primary and 

surrogate indices for each stream class are listed in table 3. 

The four stream classes in New Jersey are referred to here as 

stream classes A, B, C, and D (fig. 1; app. 3). Each of these 

stream classes has predominant hydrologic traits. For example, 

class A streams are semiflashy with moderately low base 

flow and are typically located north of the Fall Line; class B 

streams are generally more stable, have high base flow, and 

are located primarily in the Coastal Plain; class C streams 

are moderately stable with moderately high base flow and 

are commonly in glaciated areas; and class D streams are 

smaller, flashy streams (catchments generally <10 mi2) with 

low base flow, and are distributed throughout New Jersey 

(fig. 1). Additional information on the distinguishing characteristics of the four stream classes in New Jersey is found in 

appendixes 3 and 4.

Development of the New Jersey Stream Classification 

Tool (NJSCT)

Linear discriminant function analysis (DFA) was used 

to derive parsimonious multivariate models that best separated the means of the four New Jersey stream classes based 

on the calculated flow indices. Several steps were taken to 

select variables for the final model. (1) Backward elimination 

of variables (based on an F-ratio P-value of 0.15) in separate 

DFAs was performed using the 70 variables identified from 

the PCA for each of the five critical flow components (magnitude, frequency, duration, timing, and rate of change) (table 3). 

(2) This analysis produced a reduced set of variables that were 

then considered simultaneously in another DFA using backward elimination of variables. The philosophy of this approach 

was to try to further reduce variable redundancy while allowing all five critical flow components to potentially contribute 

to the final model. The final model included 20 flow-magnitude variables (MA9, MA13, MA18, MA24, MA37, MA40, 

ML4, ML6, ML13, ML15, ML16, ML20, ML21, MH4, MH5, 

MH13, MH14, MH16, MH18, MH20), 7 frequency-of-flow 

variables (FL1, FL2, FL3, FH1, FH3, FH5, FH7), 8 durationof-flow variables (DL4, DL5, DL7, DL15, DL16, DH13, 

DH14, DH17), and 2 timing-of-flow variables (TA1, TL2). 

This model had an overall jackknifed classification accuracy 

of 63 percent (raw optimistic classification accuracy was 98 

percent) based on the final DFA with the 37 variables on 94 

streams. Prior probabilities in the classification function were 

based on relative sample sizes of the four stream classes.

To validate the DFA model, three test cases based on 

minimums, means, and maximums for these 37 variables were 

constructed in the statistical package SYSTAT version 11 

(SYSTAT Software Inc., 2004) to compare the statistical 

model classification of these new observations with the classification functions included in this software. They agreed. 

The NJSCT software was programmed based on the discriminatory power of this model, and any given stream representing 

flow attributes that are in concordance with a specific stream 

class would be automatically placed into that stream class (see 

Henriksen and others, 2006).

During the development of the software tools it became 

apparent that the process could concurrently be developed 

for the six national stream types classified by Olden and Poff 

(2003) (app. 6). Olden and Poff’s (2003) national classification identified only two stream types in New Jersey (see also 

Poff, 1996), whereas when the HIP was developed for New 

Jersey, four stream classes were identified. Identification of 

additional stream classes is expected to occur as a refinement 

in the classification when the HIP is developed and implemented specifically for other geographic areas (for example, 

states, provinces, or sub-regions). Finally, Olden and Poff 

(2003, p. 109, Table III) (app. 6) identified nine sub-components of flow for each stream type in the national classification. Our analysis indicated that more specific comparisons 

could be made by dividing the timing of flow events into two 

sub-components of flow, average timing of low- and high-flow 

events (table 3).

Application of the Hydroecological 

Integrity Assessment Process

The natural flow regime paradigm (Poff and others, 

1997) is the conceptual basis for the HIP (Henriksen and 

others, 2006). As implemented here, it evaluates the degree 

of alteration of 10 or more statistically significant hydrologic 

indices (out of a total of 171) that address the five critical 

components of streamflow (Richter and others, 1997; Olden 

and Poff, 2003). Commonly, however, the “natural flow 

regime” is assumed to require a lengthy flow record pre-dating 

any anthropogenic water or land-use alteration. Such a period 

of record is rarely available, and usually is not necessary to 

apply the HIP tools. In most cases, the user will be limited to 

either an existing streamflow record or a synthesized period 

of record (which can be derived through flow modeling; for 

example, Kennen and others, 2007) documenting the flow history. In addition, regardless of what POR is recommended (for 

example, Poff and others, 1997), the user should determine 

whether a stream’s biotic integrity can be considered “healthy” 

or “acceptable” in its current condition or at some prior point 
14 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

in time for the available POR. If stream biotic integrity (user 

defined) is found to be acceptable, then the recent historic 

POR (alternatively, a timeframe in the POR when the stream’s 

integrity was considered acceptable) can be used as the baseline (reference) condition. Ultimately, the biotic condition of a 

stream reach should be derived from biological metrics from 

monitoring programs (for example, New Jersey’s Ambient 

Biological Monitoring Network–see also Kennen and Ayers, 

2002) or rapid stream assessment tools (for example, the U.S. 

Environmental Protection Agency’s Rapid Assessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, 

Benthic Macroinvertebrates, and Fish–see Barbour and others, 

1999). Hereafter, the term “baseline hydrologic condition” will 

be used as a surrogate for the natural flow regime.

The primary purpose of the NJHAT (or any version 

developed for a specific state, province, or geographic area) 

is to assist decision makers (water-resource managers, planners, or regulatory agencies) with the comparison of baseline 

conditions to either current conditions or proposed hydrologic 

changes through the evaluation of HRIs. HRIs account for 

characteristics of streamflow variability that are known to be 

“biologically relevant”—that is, they are important in shaping 

ecological processes in streams (Olden and Poff, 2003; Richter 

and others, 1997). The basic premise is that if one or more 

stream-class-specific indices is significantly altered due to past 

or proposed streamflow alterations, the alteration will have 

a significant impact, directly or indirectly, on biodiversity, 

physical processes (habitat), and (or) production. It is anticipated that this process will be used by regulatory agencies to 

establish ecologically based environmental-flow standards and 

criteria that will be used to protect, maintain, or restore stream 

biotic integrity.

NJHAT can be applied to an unlimited number of flow 

scenarios, but all have one thing in common–that the flow 

regime of the stream system being assessed has been or is 

going to be altered. The most likely application of NJHAT 

would be the assessment of a proposed or existing diversion (for example, water-treatment-facility intake, reservoir, 

or ground-water extraction) in a watershed. For a proposed 

diversion, the goal would be to establish environmental-flow 

standards that address all 10 sub-components of flow. In the 

case of an existing diversion, the purpose would be to determine whether or not the agency-established standard is being 

violated for any of the stream-class-specific flow indices (for 

example, table 3). For either case, adaptive management (a 

type of natural-resource management that implies making 

decisions as part of an ongoing process) of environmental 

flows can be effectively applied for determining the ecologically compatible withdrawal rate necessary to protect biotic 

integrity (Richter and others, 2006).

NJHAT can be used to compare a variety of waterdevelopment or hydrologic-infrastructure scenarios by directly 

varying the streamflow and (or) the project’s operating procedures. For example, if a proposed water-development project 

consists of an intake on stream (x) and the facilities require 

(y) amount of water per day, an environmental-flow standard 

could be established to meet the most stringent level (worstcase scenario) or be adjusted monthly or seasonally to set 

flows during the most ecologically sensitive time of the year 

(for example, during fish spawning or migration periods). The 

proposed diversion could also be adjusted to meet seasonal 

demands or the project could be revised to include storage 

that reduces reliance on diversions during specific times of the 

year.

Another application of the NJHAT would be to evaluate 

the effects of anthropogenic changes that have occurred over a 

long period of time for a given stream. If a streamflow POR is 

available or can be developed for a fairly long period of time 

(for example, >20 years), NJHAT can be used to evaluate the 

effects that historical hydrologic alteration associated with 

land-use changes may have had during that timeframe. This 

would provide managers and decision makers with the ability 

to compare the effects of differing approaches for proposed 

water-development projects, which may facilitate optimal 

management choices if restoration is warranted. NJHAT also 

can be used to evaluate trends in streamflow for the entire 

POR or for segments of the record. Again, such an analysis 

can give the user a more comprehensive understanding of the 

variability in flow characteristics for the stream being investigated.

Establishing a Hydrologic Baseline

It is important to determine the appropriate baseline 

hydrologic conditions so that the extent of alteration to the 

hydrology is accurate and that environmental-flow standards 

can be established. A hydrologic baseline represents the 

“relatively unimpaired” or “natural” conditions that embody 

natural flow variability (Poff and others, 1997) and represent 

the foundation of environmental-flow management. In this 

document we take a two-pronged approach that incorporates 

visual and statistical evaluation of stream hydrographs to identify least impaired streams or portions of the hydrograph that 

represent the least impaired time period. In addition, professional judgment was used to identify specific time periods or 

overlooked water-development processes (withdrawals, dams, 

diversions, inter-basin transfers, etc.) in the POR that would 

affect the baseline conditions or skew the results of statistical 

comparisons. This method worked well for this study although 

many other statistical processes and hydrologic criteria can be 

used to establish baseline hydrographs. Such approaches could 

include simulating flow-duration curves (Seelbach and Wiley, 

1997; Wiley and others, 1998), quantile regression (Cade and 

Noon, 2003), hydrologic trends, the use of simulated hydrographs (Kennen and others, 2007), or regression approaches 

(Sanborn and Bledsoe, 2006; Stuckey, 2006) to identify 

hydrograph anomalies, hydrologic inflection points, statistical differences in trend-line slopes, back-projected baseline 

conditions using hydrologic modeling, or predicted stream 

baselines, respectively.
Application of the Hydroecological Integrity Assessment Process 15

Examples of New Jersey Hydroecological 

Assessment Tool (NJHAT) Application

Two examples are presented in this section to illustrate 

some of the basic functions of NJHAT. The data for each 

example are taken from USGS stream-gaging stations located 

in New Jersey. The first example is an analysis that characterizes the hydrologic changes that have occurred in a watershed 

over a 40-year period; the second is an analysis based on a 

proposed water diversion from a relatively unaltered stream. 

Although based on real streams, these examples are for illustration purposes only. Although example A represents historical data, example B should not be misinterpreted to represent 

any specific current or ongoing water-development project 

in New Jersey. Therefore, in an effort to avoid any potential 

misunderstanding or confusion, the names of the streams are 

not revealed.

Example A

Example A is an analysis of a watershed that had undergone continuous urban development, resulting in the discharge 

of increasing amounts of treated wastewater from several 

wastewater-treatment facilities to the stream. The watershed is 

17 mi2 and is in the Coastal Plain. In the late 1980s, a regional 

wastewater facility that aggregated all of the sewage from 

within the watershed and discharged to a location outside 

the watershed began operation. To evaluate the subsequent 

changes in flow, the streamflow record was divided into 

two periods, one before and one after sewer regionalization. 

Monthly maximum daily mean streamflow, monthly mean 

streamflow, and monthly minimum daily mean streamflow 

for pre-and post-regionalization are shown in figures 4, 5, 

and 6, respectively. These figures were generated with the 

NJHAT software based on a specific stream class (table 3). A 

visual inspection of the graphs of monthly mean (fig. 5) and 

monthly minimum (fig. 6) streamflow shows a slight decrease 

in streamflow at the time of regionalization of the sewerage 

system (1988). There is no discernible difference, however, 

between the two periods for monthly maximum (fig. 4). This 

is a predictable result given that the sewage flow comprises a 

much smaller percentage of high flows than of low or mean 

flows.

Three additional indices—median of monthly flows 

(MA12–23), median of minimum monthly flows (ML1–12), 

and median of maximum monthly flows (MH1–12)—were 

calculated by determining the respective monthly value for the 

Oct-63 Oct-68 Oct-73 Oct-78 Oct-83 Oct-88 Oct-93 Oct-98 Oct-03

FLOW, IN CUBIC FEET PER SECOND

Pre-Regionalization - October 1963 - September 1988

Post-Regionalization - October 1988 - September 2003

0

200

400

600

800

1,000

1,200

1,400

1,600

WATER YEAR

EXPLANATION

Figure 4. Monthly maximum daily mean streamflow for time periods before and after regionalization of a wastewater facility, water 

years 1964–2003.
16 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Pre-Regionalization - October 1963 - September 1988

Post-Regionalization - October 1988 - September 2003

0

20

40

60

80

100

120

Oct-63 Oct-68 Oct-73 Oct-78 Oct-83 Oct-88 Oct-93 Oct-98 Oct-03

FLOW, IN CUBIC FEET PER SECOND

EXPLANATION

WATER YEAR

Pre-Regionalization - October 1963 - September 1988

Post-Regionalization - October 1988 - September 2003

5

10

15

20

25

30

35

40

45

Oct-63 Oct-68 Oct-73 Oct-78 Oct-83 Oct-88 Oct-93 Oct-98 Oct-03

FLOW, IN CUBIC FEET PER SECOND

EXPLANATION

WATER YEAR

0

Figure 5. Monthly mean streamflow for time periods before and after regionalization of a wastewater facility, water years 1964–2003.

Figure 6. Monthly minimum daily mean streamflow for time periods before and after regionalization of a wastewater facility, water 

years 1964–2003.
Application of the Hydroecological Integrity Assessment Process 17

flow record and then determining the median for each month 

(app. 7). For example, ML1 is the median of the minimum 

daily mean flows for all Januarys for each flow record (preregionalization and post-regionalization). These indices are 

shown for the two flow records in figures 7, 8, and 9. These 

figures also show the 25th and 75th percentiles of the preregionalization range of the indices. As above, the maximum 

monthly flows for both flow records are similar and do not 

fall outside the pre-regionalization 25th or 75th percentiles, 

whereas the median and minimum monthly flows show differences between the pre- and post-wastewater-regionalization 

periods and, in some cases, fall outside the 25th- and 75thpercentile ranges (figs. 8 and 9).

The 10 principal HRIs are shown for pre- and postregionalization of the watershed in table 4 (NJHAT-calculated 

values) and figure 10 (values normalized to pre-regionalization indices). For some indices, the 25th- and 75th-percentile 

values are based on data for stream class C (app. 3); in those 

cases, the calculated value of the baseline or, in this case, the 

two periods of record could fall outside the established range 

(for example, figure 10, index TH3).

For this stream, however, a synthesized daily flow record 

would be required because no pre-alteration (baseline) period 

of record exists. The POR for this site began in 1964, after 

development and operation of wastewater-treatment facilities. 

In this example, indices ML3, MH14, FH7, DH11, and TL2 

show a distinct difference between the two time periods. The 

values for ML3, MH14, FH7, and DH11 for the post-regionalization period clearly fall outside the respective 25th- and 

75th-percentile pre-regionalization values (table 4), whereas 

the value for TL2 falls between the pre-regionalization 25thand 75th-percentile values. Regionalization of the sewerage 

system may not be the sole cause of the hydrologic changes 

that occurred in this watershed; however, a significant difference in flow processes between these two time periods clearly 

exists. Additional investigation is always warranted, as other 

factors including climate, increased water diversions, and 

increases in impervious cover due to urban development may 

have influenced these changes as well.

0

50

100

150

200

250

300

350

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

FLOW, IN CUBIC FEET PER SECOND

Pre-regionalization 25 percent (1964-88)

Pre-regionalization median (1964-88)

Pre-regionalization 75 percent (1964-88)

Post-regionalization median (1989-2003)

EXPLANATION

Figure 7. Maximum monthly streamflow for time periods before and after regionalization of a wastewater facility, water years 

1964–2003.

Table 4. Comparison of pre- and post-regionalization values of 

selected hydrologic indices.

[Indices outside the 25th- to 75th-percentile range are shown in bold; refer 

to appendix 7 for index definitions]

Index 

 Pre-regionalization Postregionalization 25th 

percentile Value 75th percentile

MA24 48.716 71.491 108.631 101.849

ML3 18.5 22 24.5 15

MH14 12.899 16.857 24.454 27.133

FL1 8 16 23 14

FH7 4 6 9 12

DL16 3.607 5.333 6.167 4.826

DH11 12.396 19.855 26.208 31.964

TL2 29.362 42.497 40.797 35.823

TH3 0.108 1.003 0.431 1.003

RA6 0.074 0.204 0.647 0.369
18 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Pre-regionalization 25 percent (1964-88)

Pre-regionalization median (1964-88)

Pre-regionalization 75 percent (1964-88)

Post-regionalization median (1989-2003)

EXPLANATION

0

10

20

30

40

50

60

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

FLOW, IN CUBIC FEET PER SECOND

Figure 8. Median monthly streamflows for time periods before and after regionalization of a wastewater facility, water years 1964–

2003.

0

5

10

15

20

25

30

Jan. Feb. Mar. Apr. May June July Aug. Oct. Nov. Dec.

FLOW, IN CUBIC FEET PER SECOND

Pre-regionalization 25 percent (1964-88)

Pre-regionalization median (1964-88)

Pre-regionalization 75 percent (1964-88)

Post-regionalization median (1964-2003)

Sept.

EXPLANATION

Figure 9. Minimum monthly streamflows for time periods before and after regionalization of a wastewater facility, water years 

1964–2003.
Application of the Hydroecological Integrity Assessment Process 19

NORMALIZED HYDROLOGIC

INDEX VALUE

0

1

2

3

4

5

6

MA24 ML3 MH14 FL1 FH7 DL16 DH11 TL2 TH3 RA6

Pre-regionalization, 75th percentile

Post-regionalization, 75th percentile

Pre-regionalization, 25th percentile

Post-regionalization, 25th percentile

Median

Figure 10. The calculated value and the 25th- and 75th-percentile values of selected hydrologic indices pre- and post-regionalization. 

(Indices have been normalized to the pre-regionalization indices for easy comparison.)

Example B

Example B is an analysis of a watershed that has an 

extensive streamflow record and is considered to be relatively 

unaltered hydrologically. This watershed is 64 mi2 in size and 

is in northwestern New Jersey. Because the streamflow and the 

watershed are relatively unaltered, the entire stream-gaging 

record (1924–2003) was used as a baseline for this evaluation. 

A theoretical surface-water diversion and several alternatives 

are examined to illustrate the use of NJHAT to analyze a new 

water-diversion project. The amount of the diversion and the 

required environmental flow (amount of streamflow required 

before the diversion would be allowed) were varied to determine the amount of water that hypothetically could be diverted 

without violating the 25th- and 75th-percentile range standard 

applied to the baseline flow indices for stream class A (table 3, 

fig. 1).

The monthly maximum, monthly mean, and monthly 

minimum daily flows for the period 1924–2003 are shown in 

figures 11, 12, and 13, respectively. No changes are discern0

100

200

300

400

500

600

Oct.

1923

Oct.

1933

Oct.

1943

Oct.

1953

Oct.

1963

Oct.

1973

Oct.

1983

Oct.

1993

Oct.

2003

FLOW, IN CUBIC FEET PER SECOND

Trend

WATER YEAR

Figure 12. Monthly mean streamflow for water years 1924–2003.

Oct.

1923

Oct.

1933

Oct.

1943

Oct.

1953

Oct.

1963

Oct.

1973

Oct.

1983

Oct.

1993

Oct.

2003

Trend

10

100

1,000

10,000

FLOW, IN CUBIC FEET PER SECOND

WATER YEAR

Figure 11. Monthly maximum daily mean streamflow for water 

years 1924–2003.

0

50

100

150

200

250

Oct.

1923

Oct.

1933

Oct.

1943

Oct.

1953

Oct.

1963

Oct.

1973

Oct.

1983

Oct.

1993

Oct.

2003

FLOW, IN CUBIC FEET PER SECOND

Trend

WATER YEAR

Figure 13. Monthly minimum daily mean streamflow for water 

years 1924–2003.
20 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Baseline flow

Diversion = 5 ft /s; passing = 25 ft /s

Diversion = 50 ft /s; passing = 25 ft /s

10

100

1,000

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

WATER YEAR 2000

FLOW, IN CUBIC FEET PER SECOND

3

3

3

3

able for the entire POR, which would be expected for this relatively unaltered watershed. Visible in these hydrographs are 

typical high and low flows that are related to annual variability 

in climate, and some longer term variations related to droughts 

(in the mid 1960s) and a fairly wet decade (the 1970s); there 

is, however, no evidence of anthropogenic change that was 

sufficiently significant to affect the natural hydrologic regime 

of this watershed. (Note that NJHAT generates the trend line 

shown in figures 11, 12, and 13 by a linear regression calculation, but does not currently provide a significance test for the 

line. This analysis can be done outside the NJHAT program 

using an appropriate inferential statistical test.)

Four diversion scenarios are considered. The scenarios 

consist of flow diversions of 5, 50, 25, and 50 ft3/s with environmental passing flows of 25, 25, 25, and 10 ft3

/s, respectively. The stream hydrographs for water year 2000 (October 

1, 1999, through September 30, 2000) for the first two scenarios are shown in figure 14; the hydrographs for the second 

two scenarios are shown in figure 15. Flat sections of the 

hydrographs represent periods during which the diversion was 

decreased to meet the environmental flow requirement. If the 

baseline flow falls below the established environmental flow 

standard, the diversion would cease and the streamflow would 

be the natural unaltered flow; however, this condition did not 

occur in water year 2000. If the diversion or the environmental 

flow were increased, the amount of time that the diversion 

could operate would decrease.

The maximum monthly (MH 1–12), mean monthly (MA 

12–23), and minimum monthly flows (ML 1–12) are shown 

in figures 16, 17, and 18, respectively, with the 25th and 

75th percentiles for the baseline streamflow record. Each of 

the diversion alternatives shown falls below the natural flow 

median for each flow condition, and the 50-ft3/s alternatives 

show several months in which the streamflow falls below the 

25th percentile for the mean and minimum monthly flows 

(figs. 17 and 18). The 5-ft3/s diversion has the smallest overall 

impact on streamflow and is consistently within the 25th- and 

75th-percentile range for all months and all flow conditions 

(figs. 16, 17, and 18).

Statistics for the 10 principal HRIs (based on stream classification A, table 3, fig. 1) for the four diversion scenarios are 

listed in table 5 and shown in figure 19. The values of two or 

more of the HRIs for the latter three flow-diversion scenarios 

are outside the 25th- to 75th-percentile range of the baseline 

streamflow, whereas those for the 5-ft3/s diversion appear to 

be, for the most part, within the 25th- to 75th-percentile range 

of the baseline flow indices, with the exception of indices TL1 

and TH1, whose values closely match the baseline. This is 

Figure 14. Hydrograph showing two diversion scenarios with baseline daily flow conditions for water year 2000. (In this example, 5 and 

50 ft3/s are removed from the stream with an environmental passing flow of 25 ft3/s.)
Application of the Hydroecological Integrity Assessment Process 21

1

10

100

1,000

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

WATER YEAR 2000

FLOW, IN CUBIC FEET PER SECOND

Baseline flow

Diversion = 50 ft /s; passing flow = 10 ft /s

Diversion = 25 ft /s; passing flow = 25 ft /s 3

3

3

3

Figure 15. Hydrograph showing two diversion scenarios with baseline daily flow conditions for water year 2000. (In this example, 25 

and 50 ft3/s are removed from the stream with environmental passing flows of 25 and 10 ft3/s, respectively.)

0

100

200

300

400

500

600

700

800

900

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

FLOW, IN CUBIC FEET PER SECOND

Baseline flow - 25 percent

Baseline flow - median

Baseline flow - 75 percent

Diversion = 5 ft /s; passing flow = 25 ft /s

Diversion = 25 ft /s; passing flow = 25 ft /s

Diversion = 50 ft /s; passing flow = 25 ft /s

Diversion = 50 ft /s; passing flow = 10 ft /s

EXPLANATION

3 3

3 3

3 3

3 3

Figure 16. Maximum monthly flows for four flow-diversion scenarios for water years 1924–2003.
22 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

0

50

100

150

200

250

300

FLOW, IN CUBIC FEET PER SECOND

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov.

Baseline flow - 25 percent

Baseline flow - median

Baseline flow - 75 percent

EXPLANATION

Diversion = 5 ft /s; passing flow = 25 ft /s

Diversion = 25 ft /s; passing flow = 25 ft /s

Diversion = 50 ft /s; passing flow = 25 ft /s

Diversion = 50 ft /s; passing flow = 10 ft /s

3 3

3 3

3 3

3 3

0

20

40

60

80

100

120

140

Jan. Mar. May July Sept. Nov.

FLOW, IN CUBIC FEET PER SECOND

Feb. Apr. June Aug. Oct.

Baseline flow - 25 percent

Baseline flow - median

Baseline flow - 75 percent

EXPLANATION

Diversion = 5 ft /s; passing flow = 25 ft /s

Diversion = 25 ft /s; passing flow = 25 ft /s

Diversion = 50 ft /s; passing flow = 25 ft /s

Diversion = 50 ft /s; passing flow = 10 ft /s

3 3

3 3

3 3

3 3

Figure 17. Median monthly flows for four flow-diversion scenarios for water years 1924–2003.

Figure 18. Minimum monthly flows for four flow-diversion scenarios for water years 1924–2003.
Application of the Hydroecological Integrity Assessment Process 23

because the range of some indices is based on using a mean 

or median value for all streams in the stream class (Henriksen 

and others, 2006); therefore, the calculated values for these 

two indices for the baseline and for all diversion scenarios 

appear to fall outside the 25th to 75th percentiles. Depending 

upon the management criteria selected, the 5-ft3/s diversion 

could be the only scenario considered to be acceptable because 

of its minimal alteration of the natural flow regime and 

assumed concomitant minimal effect on the stream biota. The 

results for the alternatives with a 25-ft3/s and 50-ft3/s diversion and a 25-ft3

/s environmental flow did not meet the criteria 

necessary to have no minimal effect on the flow regime, as the 

FH4 value is clearly outside the 25th- to 75th-percentile range 

of the baseline (fig. 19). Diversion scenario four (50-ft3/s 

diversion and a 10-ft3/s environmental flow) had the overall 

greatest impact on the flow regime, as the HRI values MA18, 

ML6, FL1, FH4, and DL4 all fell outside the 25th- to 75thpercentile range of the baseline (table 5, fig. 19).

Table 5. Comparison of the 10 principal hydrologic indices for baseline conditions and four water-diversion scenarios.

[Values in bold are those outside the 25th- to 75th-percentile range for a given withdrawal scenario; refer to appendix 7 for index definitions; ft3/s, cubic feet 

per second]

Index 

Baseline 5-ft3

/s diversion

25-ft3/s passing flow

25-ft3/s diversion

25-ft3/s passing flow

 50-ft3/s diversion

25-ft3/s passing flow

 50-ft3/s diversion

10-ft3 25th percentile Value 75th percentile /s passing flow

MA18 26.21 40.145 68.855 36.742 29.226 26.048 13.274

ML6 24 35.5 46 30.5 25 25 10

MH5 202.25 287.5 553 282.5 262.5 237.5 237.5

FL1 5 6 8 6 6 6 0

FH4 3 6 11.75 8 14.5 36.6 47

DL4 12.028 16.283 22.133 16.268 16.168 16.168 10

DH2 563.833 830.167 1035.583 825.167 805.167 780.167 780.167

TL1 246.837 264.792 262.492 264.792 266.588 265.863 271.866

TH1 42.796 71.355 70.441 71.355 71.355 71.355 71.355

RA3 -19 -7 -3 -7 -9 -10 -13

0

1

2

3

4

5

6

7

8

9

10

MA18 FH4 TH1

NORMALIZED HYDROLOGIC INDEX VALUE

Baseline

EXPLANATION

ML6 MH5 FL1 DL4 DH2 TL1 RA3

INDEX

Value

25th percentile

75th percentile

Symbol Diversion

(ft /s)

Passing flow

(ft /s)

5

25

50

50

25

25

25

10

3 3

Figure 19. Mean, 25th, and 75th percentiles of 10 principal hydrologic indices for baseline conditions and four streamflow-diversion 

scenarios. (Indices have been normalized to the baseline indices for easy comparison; ft3/s, cubic feet per second)
24 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Developing Environmental Flow 

Standards

Research in the field of environmental flows has resulted 

in more than 207 methods that have been grouped into four 

categories: hydrologic rules, hydraulic rating methods, habitat 

simulation methods, and holistic methodologies (Tharme, 

2003). Many of these methods address arbitrary “minimum” 

flows and are recognized as being inadequate to protect freshwater biodiversity and maintain essential goods and services 

(Naiman and others, 2002; Postel and Richter, 2003). The HIP 

is established on the basis of the principles of hydroecology 

and the natural flow regime paradigm (Poff and others, 1997), 

and to maintain biodiversity and overall stream integrity, it 

takes into account natural flow variability. This is achieved 

by addressing the magnitude, frequency, duration, timing, and 

rate of change of streamflow. However, translating hydrologicecological principles and knowledge into specific environmental-flow standards remains a challenge (Poff and others, 2003).

Adaptive management is an example of a collaborative 

approach that has been used to address scientific uncertainties 

by implementing carefully planned long-term adaptive-management experiments and developing appropriate management 

actions (for example, Grand Canyon flow release (Rubin and 

others, 2002); Snowy River flow-restoration program (Pigram, 

2000)). In addition, adaptive-management procedures have 

been successfully used in environmental-flow restoration of 

impounded rivers with substantial flow control (Richter and 

others, 2006; The Nature Conservancy, 2007). It is yet to be 

seen, however, whether adaptive-management processes can 

be used for management of short-term water-development 

projects or whether there is enough time in such circumstances 

to implement strategic, incremental actions to reduce policy 

uncertainties. Water managers are more commonly faced with 

a situation in which no additional experimentation is possible 

(due to funding limitations), because regulatory control of 

most flow processes except low flow is limited, and, once the 

proposed water-development project is approved, the possibility of retracting or amending the decision through either 

incremental revision or stakeholder participation is impractical. Due to the high risk of being unable to restore stream 

biodiversity and stream integrity once management options 

are foregone, and because in most situations time is limited, 

the implementation of scientifically defensible environmentalflow standards could maximize the likelihood of maintaining 

or restoring stream integrity. Fortunately, a few approaches 

have been put forth to bridge the gap between simplistic 

hydrologic low-flow standards and long-term, empirically 

developed environmental-flow assessments.

As discussed in the “Application of the Hydroecological 

Integrity Assessment Process” section of this report, the first 

step for instituting environmental-flow standards is to establish whether the stream reach of interest is in a non-impaired 

“healthy” or impaired condition. Ideally, stream biotic 

condition can be evaluated by using long-term biomonitoring 

or rapid assessment methods that address the status of key 

aquatic communities, instream habitat, and geomorphological 

processes. Once established, the management objective would 

be to protect, maintain, or restore the stream to a desired 

condition. Accordingly, the manager would use the 10 primary 

(or secondary) HRIs from the HIP that are associated with a 

specific stream class. The 10 primary indices, however, may 

be supplemented or substituted with surrogate hydrologic 

indices that have been identified and in some cases validated 

with empirical biological data. For example, Kennen and 

Ayers (2002) established that hydrologic instability associated 

with urban development was significantly related to impairment of fish, invertebrate, and algal communities in New 

Jersey streams across the four stream classes identified in New 

Jersey. Specifically, the 2-year peak-flow event was identified 

as accounting for a significant proportion of the variability in 

aquatic community structure (Kennen and Ayers, 2002). Thus, 

index FH11 (frequency of high-flow events) could potentially 

be substituted for one of the 10 evaluation indices for stream 

classes A and B even though it was not previously listed in 

NJHAT as a primary or secondary index (table 3). Likewise, 

FH11 is identified as a secondary index for stream classes C 

and D in NJHAT (table 3) and, therefore, based on empirical 

results, could justifiably be used to replace the primary indices 

FH7 and FH3, respectively.

Richter and others (1997) recommend that environmental flow standards address hydrologic variability (using 

ecologically relevant hydrologic indices) and require that 

mean or median flow (depending on the index in question) 

remain within the 25th- to 75th-percentile range for all of 

the selected “standard” indices. The 25th- to 75th-percentile 

range is viewed as allowing some management flexibility to 

accommodate human uses while still maintaining stream biotic 

integrity. A water regulatory agency, however, has the option 

of using more stringent standards to provide a higher level of 

protection for streams classified as being of “high value” (for 

example, streams with threatened or endangered species). For 

example, a 40th- to 60th-percentile range would be narrower 

and, therefore, would represent a more restrictive standard. 

Some HRIs available in NJHAT may have even broader 

regulatory applicability if the standards take into consideration dry, average, and wet water seasons or years. Hoffman 

and Rancan (2007) recommend a variety of possible statistics 

generated by the NJHAT as guidelines for setting passing 

flows. For example, the statistics associated with MA12-23 

(median monthly flow values) provide information on median 

monthly flows. The 25th-percentile value represents a median 

flow that occurs in one year out of four. These values could 

be used to set monthly flow standards. A flow standard based 

on median monthly flows would be very protective of streamflows and thus require frequent reduction in withdrawals. 

In contrast, the statistics associated with ML1-12 (monthly 

low-flow values) provide information on lowest monthly 

flows in that stream. Using one of these statistics as a standard 

(for example, the 25th-percentile value of ML1-12) would be 

less protective of streamflows but would require less frequent 
References Cited 25

withdrawal reductions. The NJHAT provides for novel and 

innovative approaches to selecting alternative indices (beyond 

the 10 primary indices identified in NJHAT) that allow water 

managers to apply meaningful regulatory standards that take 

into consideration inherent natural hydrologic variability. This 

application requires, however, that the water managers analyze 

tradeoffs between reliability of the stream as a water source 

and protection of streamflows.

Recently, a holistic flow-management approach that 

incorporates essential aspects of the natural flow variability 

shared among stream classes was published by Arthington and 

others (2006). This approach can be used to validate thresholds for individual ecologically relevant hydrologic indices 

using empirical biological data from natural or “reference” 

streams and flow-altered streams. Flow-ecological response 

relations are developed for each ecological index across the 

gradient of reference flow regimes to modified flow regimes 

for each streamflow variable and stream class. The HIP 

approach described in this report is based on the identification 

of specific stream classes in New Jersey and is highly consistent with the recommendations proposed by Arthington and 

others (2006) for classifying streams based on key attributes 

of flow variability. The HIP approach attempts to balance 

the need for managing streams based on the unique hydrologic variability of specific stream classes and on generalized 

ecological attributes (described herein as stream-class-specific 

HRIs). Arthington and others (2006) describe a series of 

steps for characterizing streams and provide two critical “risk 

levels” or “benchmarks” that can be used to establish or guide 

the setting of environmental flow standards. This approach 

shows much promise in (1) validating the indices used to 

assess hydrologic alteration, and (2) refining the threshold and 

benchmark (risk-level) standard.

Summary and Conclusions

This report documents the development of the Hydroecological Integrity Assessment Process (HIP), which includes a 

hydroecological classification of streams in New Jersey and 

the development of three software tools: the Hydrologic Indices Tool (HIT), for calculating 171 hydroecologically relevant 

indices (HRIs) to aid in stream classification; the New Jersey 

Stream Classification Tool (NJSCT), for classifying unclassified streams; and the New Jersey Hydrologic Assessment 

Tool (NJHAT) for evaluating baseline (reference) periods, 

conducting hydrologic-alteration analyses, evaluating past and 

proposed hydrologic modifications of streams, and establishing environmental-flow standards. The HIP was specifically 

designed to assist state and watershed resource planners in 

making sound and scientifically defensibly management decisions by providing reference points that can be used as a basis 

for comparing pre- and post-watershed conditions or evaluating the effects of planned water-development projects.

Two hypothetical examples taken from USGS streamgaging stations located in New Jersey are presented to highlight some of the basic functions and flexibility of the HIP: (1) 

an analysis that characterizes the hydrologic changes that have 

occurred in a watershed over a 40-year period after implementation of a regional wastewater facility; and (2) an analysis 

based on a proposed water diversion from a relatively unaltered stream. In the latter example, the amount of the diversion 

and the required environmental flow were varied to determine 

the amount of water that hypothetically could be diverted 

without violating the 25th- and 75th-percentile range standard applied to the baseline flow indices for a selected stream 

class. The HIP methodology is robust and can be applied in 

any state, region, or province where improved flow-based 

management processes are needed; however, this methodology 

relies on a hydroecological stratification of stream classes and 

the recognition that hydrology is the “master variable” that 

directly and (or) indirectly affects the distribution and abundance of riverine species.

Acknowledgments

The authors thank Julian Olden and LeRoy Poff for 

providing the data sets and formulations used to verify index 

calculations and for the essential role their research played 

in the development of the software and approach used in the 

Hydroecological Integrity Assessment Process. Scientific 

guidance from numerous members of the Technical Advisory Committee, especially Jeffrey Hoffman, Andrew Didun, 

Robert Kecskes, Marjorie Kaplan, Michelle Putnam, and 

Kevin Berry of the NJDEP and Robert Schopp of the USGS, 

was greatly appreciated. Thanks also to Ward Hickman of the 

USGS for his assistance with the index validation process. 

The authors thank David Hamilton and Murphy Laubhan of 

the USGS for their continued support of this project. Ward 

Hickman and Dale Simmons of the USGS and Jeff Hoffman 

of the NJDEP provided many helpful suggestions that greatly 

improved this report.

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Appendix 1 29

Appendix 1. Members of the Technical Advisory Committee.

Name Agency Division/Discipline

Jeffrey Hoffman (chairperson) NJDEP New Jersey Geological Survey

Helen Rancan NJDEP New Jersey Geological Survey

Andrew Didun NJDEP Fish and Wildlife

Patricia Hamilton NJDEP Fish and Wildlife

Jeanette Bowers-Altman NJDEP Fish and Wildlife

Thomas Belton NJDEP Science, Research & Technology

Marjorie Kaplan NJDEP Science, Research & Technology

Karen Schaffer NJDEP Science, Research & Technology

Flavian Stellerine NJDEP Water Quality

Jan Gheen NJDEP Water Supply

Michael Bleicher NJDEP Water Supply

Michelle Putnam NJDEP Water Supply

Fred Sickels NJDEP Water Supply

Robert Kecskes NJDEP Water Supply

Kevin Berry NJDEP Watershed Management

Thomas Brand NJDEP Watershed Management

Ambrosia Collier NJDEP Watershed Management

James Gaffney NJDEP Watershed Management

Barbara Hirst NJDEP Watershed Management

Joseph Mattle NJDEP Watershed Management

Donna Milligan NJDEP Watershed Management

Harold Nebling NJDEP Watershed Management

Elizabeth Semple NJDEP Watershed Management

Steven Nieswand USGS Water Resources

Jonathan Kennen USGS Water Resources

Robert Schopp USGS Water Resources

David Steadfast USGS Water Resources

Kara Watson USGS Water Resources

Robert Reiser USGS Water Resources

Mark Ayers USGS Water Resources

Steven Tessler USGS Water Resources

Jacob Gibs USGS Water Resources

Pierre Lacombe USGS Water Resources

James Henriksen USGS Biological Resources

Nicholas Procopio NJPC New Jersey Pinelands Commission

Robert Zampella NJPC New Jersey Pinelands Commission

Daniel Van Abs NJWSA New Jersey Water Supply Authority

Richard Horwitz ANSP Academy of Natural Sciences, Philadelphia

Camille Finders ANSP Academy of Natural Sciences, Philadelphia

Leroy Young PADEP Fish and Boat Commission
30 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Appendix 2. Review of seminal ecological concepts.

Figure 2-1. Conceptual view of the major interrelated factors that influence the structure and function of freshwater ecosystems. 

(From Baron and others, 2002)

Flow Regime

Functional Aquatic 

Ecosystems

Water Quality

Short-term

goods and services

Long-term

sustainability and 

adaptive capacity

Chemical/Nutrient

flux

Thermal/Light

inputs

Sediment

flux

Biotic

assemblage

Conceptual models

The TAC decided to review important stream-ecology 

concepts that have been presented in the literature over the 

past 25 years. There was no attempt to identify and review all 

stream ecological theories and concepts; rather, the review was 

limited to several well-known and highly regarded concepts 

with applicability to the current study. This task began with the 

examination of a series of conceptual models illustrating the 

generalized view of a stream ecosystem and the many biotic 

and abiotic factors that influence stream communities and ecological integrity. These conceptual models (for example, figs. 

2-1 and 2-2) emphasize the importance of flow regime, and 

the associated inter- and intra-annual hydrologic variability in 

influencing distribution, abundance, and diversity of stream 

communities (stream biotic integrity).

According to these and other conceptual models (figs. 2-1 

through 2-6), the structure and function of streams are based 

on five highly interrelated components: hydrology, geomorphology, biology, water quality, and connectivity. The TAC 

reviewed five conceptual models of stream biotic integrity:

•	 River Continuum Concept.

•	 Intermediate-Disturbance Hypothesis.

•	 Flood Pulse Concept.

•	 Hierarchical Framework for Stream Habitat.

•	 Natural Flow Regime Paradigm.

Each is described briefly below.
Appendix 2 31

The River Continuum Concept (RCC) describes fluvial systems as a continuously integrating series of physical 

gradients and associated biotic adjustments as a river flows 

from the headwaters to the mouth (Vannote and others, 1980; 

fig. 2-3). The physical foundation from which the RCC is 

derived indicates that the size of the stream and location along 

the gradient from the headwaters to the mouth influences the 

type and distribution of the aquatic fauna. It is well established 

that stream order, discharge, and watershed area are highly 

correlated. Furthermore, energy inputs along this gradient 

have profound consequences on the structure and function of 

the consumer communities, especially those organisms that 

rely on allochthonous (pertaining to energy sources that were 

derived from outside the stream—for example, leaves and 

sticks) and autochthonous (energy sources that are derived 

from within the lotic system—for example, photosynthesis) 

inputs and are responsible for processing (breaking down into 

smaller particles) organic matter in the flowing water along 

the river continuum. In the context of environmental flows, 

hydrologic variability and, subsequently, the stream biota 

are highly dependent on their longitudinal location along the 

continuum.

One conclusion that can be drawn from the RCC (fig. 

2-3) is that as the physical environment and aquatic-community structure and function change from the headwaters 

Biotic Communities

Distribution, abundance,

diversity of aquatic species

Geomorphology

Channel and 

floodplain

Land use

Water quality

Connectivity

Climate

Hydrologic

variability

Figure 2-2. Conceptual view of the interrelations between the physical environment and biotic communities.

(stream orders 1 and 2), to the middle reaches (stream orders 

3 through 5), to the lower reaches (stream orders 6 through 

10), there is a concomitant change in the flow regime. Consequently, quantifying the extent of hydrologic change is important in estimating its effect on the structural and functional 

integrity of aquatic ecosystems. Therefore, it is necessary 

to identify which components of the hydrologic regime (for 

example, magnitude, frequency, duration) are the most influential and what hydrologic measures (statistics and indices) 

are best suited to evaluate the degree of alteration.

The Intermediate-Disturbance Hypothesis (IDH) predicts 

that biotic diversity is greatest in communities subjected to 

moderate levels of disturbance (for example, floods). This 

hypothesis is consistent with patterns of diversity observed 

in natural and altered lotic stream ecosystems (Ward and 

Stanford, 1983; Pickett and White, 1985; Resh and others, 

1988). The general view is that diversity is enhanced by the 

spatial-temporal heterogeneity resulting from the intermediate 

disturbance, which maintains the communities in a nonequilibrium state. The disturbance can be biotic or abiotic or both, 

and can fluctuate from severe to moderate to no disturbance. 

Furthermore, even the sequence of these disturbances influences diversity. Ultimately, community structure is shaped 

by a myriad of physical, chemical, and biological processes 

acting synergistically (Ward and Stanford, 1983).
32 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Figure 2-3. Generalized view of the River Continuum Concept. (From Vannote and others, 1980)
Appendix 2 33

If a moderate level of disturbance is necessary to maintain biodiversity, applying this concept in a way that assists 

with ecological flow evaluation requires determining what a 

moderate level of disturbance represents for a stream or class 

of streams and how it can be measured in terms of magnitude, frequency, and (or) duration of flow events. In addition, 

one could ask how frequently a moderate-disturbance event 

occurs, whether the timing of the event is important in terms 

of life-cycle cues (see Lytle and Poff, 2004), and how such a 

disturbance affects the life history of longer lived species.

The Flood Pulse Concept (Junk and others, 1989) is 

further summarized by Bayley (1991). Other authors recognized as contributing to the concept include Brinson and 

others (1980), Gosselink and Turner (1978), Odum (1984), 

Ward and Stanford (1989), and Wood (1951). The Flood 

Pulse Concept recognizes the importance of lateral exchange 

of water, nutrients, and organisms between the stream channel and the connected floodplain. It focuses on how pulsing 

hydrology affects the organisms and specific processes in the 

floodplain. Hydrologic pulsing enhances biological productivity, efficiency of nutrient use, and movement of detritus and 

sediments, and maintains biodiversity in aquatic systems. 

Bayley (1991) presents the idea that the flood pulse should not 

be viewed as a disturbance; rather, only significant departures 

from the average hydrologic regime, such as the prevention of 

floods, should be regarded as a disturbance. This perspective 

is consistent with Ward and Stanford’s (1983) suppositions 

regarding the Intermediate-Disturbance Hypothesis. Most 

apparent to the TAC, however, was that floods are inextricably 

part of the natural hydrologic process and are integral to the 

development of any ecologically based flow method.

Figure 2-4. Hierarchical organization of a stream system and its habitat subsystem. (From Frissell and others, 1986)

 The Hierarchical Framework for stream-habitat classification presented by Frissell and others (1986) indicates 

that structure, operation, and other aspects of the organization 

and development of stream communities are largely determined by the physical stream habitat, together with the pool of 

species available for colonization (fig. 2-4). The hierarchical 

framework itself entails an organized view of the spatial and 

temporal variation among and within stream systems. Stream 

systems can be defined as hierarchically organized systems 

successively related to lower levels—stream segment, reach, 

pool/riffle, and microhabitat (fig. 2-4).

At each level in the hierarchy, systems can develop and 

persist predominantly at a specified spatiotemporal scale. 

According to Frissell and others (1986), “by viewing stream 

systems as hierarchically organized systems, the framework 

focuses on a small set of variables at each level that must 

determine system behaviors within the relevant spatiotemporal 

frame.” This framework represents an integral part of the EFG 

project because it emphasizes the role of physical processes 

in determining watershed characteristics at different scales, 

and how the flow regime determines the relative suitability of 

habitats for different organisms, which ultimately affects their 

distribution, abundance, and diversity.

The final concept examined, the Natural Flow Regime 

Paradigm (Poff and others, 1997), synthesizes existing scientific knowledge to argue that the natural flow regime plays 

a critical role in sustaining native biodiversity and ecosystem 

integrity in rivers. Decades of observation of the effects of 

human alteration of natural flow regimes have resulted in 

well-established scientific findings indicating that altering the 

hydrologic regimes in rivers can be ecologically deleterious 

(for example, Arthington and others, 1991; Castleberry and 
34 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

others, 1996; Hill and others, 1991; Johnson and others, 1976; 

Richter and others, 1997; Sparks, 1995; Stanford and others, 1996; Toth, 1995; Tyus, 1990). These authors argue that 

streamflow quantity and timing are critical components affecting the ecological integrity of river systems. Furthermore, 

streamflow, which is strongly correlated with many critical 

physiochemical characteristics of rivers, can be considered a 

“master variable” that limits the distribution and abundance 

of riverine species (fig. 2-5). In addition, river flow regimes 

show regional patterns determined by the size of the river and 

geographic variation in climate, geography, and topography. 

Thus, all five critical components of the flow regime—magnitude, frequency, duration, timing, and rate of change—must 

be considered explicitly to characterize the entire range of 

flows and specific hydrologic phenomena that are critical to 

maintaining the integrity of river ecosystems. Many studies 

have documented ecological responses to alterations of the 

natural flow regime; examples include fish life-cycle disruption, encroachment of vegetation, loss of sensitive invertebrate 

species, and loss of fish access to backwaters and wetlands 

(Poff and others, 1997).

The Natural Flow Regime Paradigm approach to flow 

variability effectively incorporates several concepts that are 

vital to preserving hydroecological integrity. These concepts 

are:

•	 The structure and function of riverine ecosystems varies spatially (longitudinally and laterally) and temporally and is strongly influenced by hydrologic variability in terms of magnitude, frequency, duration, timing, 

and rate of change.

Flow Regime

Magnitude

Frequency

Duration

Timing

Rate of change

Water

quality

Energy

sources

Physical

habitat

Biotic

interactions

Ecological Integrity

Figure 2-5. Conceptual view of the direct and indirect influence 

of the five critical components of the flow regime (magnitude, 

frequency, duration, timing, and rate of change) on the physical 

environment and biotic communities. (From Poff and others, 1997)

•	 River ecosystems consist of three templates—physical, 

chemical, and biological—emphasizing the dynamic 

spatial and temporal interactions between abiotic and 

biotic factors.

•	 Natural flow regimes show regional patterns that represent a gradient of ecological processes that influence 

the structure and function of the aquatic, plant, and 

animal communities.

•	 Magnitude, frequency, duration, timing, and rate of 

change of flow can be used to characterize streams, 

the entire range of flows, and the specific hydrologic 

events critical to maintaining the integrity of river 

ecosystems.

Critical Hydroecological Research

Questions that arose as a result of the review of seminal 

ecological concepts include (1) what specific statistics, indices, and (or) parameters best characterize the hydrologic variability of an unaltered flow regime (that is, what is the natural 

flow regime)? (2) Alternatively, because streamflow regimes 

show regional patterns that are determined largely by stream 

size and variation in climate, geology, topography, vegetation, 

and land use, which statistics, indices, and (or) parameters best 

characterize hydrologic variability?

These questions were addressed by detailed examination of several specific studies. According to Poff and Ward 

(1989), who investigated streamflow variability and community structure at the regional level, patterns of diversity of all 

major lotic assemblages, including fish, invertebrates, algae, 

and macrophytes, are related to patterns of temporal variation in flow. Furthermore, a substantial body of evidence (for 

example, Ward and Stanford (1983)) indicates that high-flow 

and low-flow disturbances have a central role in structuring 

stream communities. Different combinations of streamflow 

variation result in different degrees of physical control over 

biotic organizations.

Poff and Ward’s (1989) research develops an objective 

and a general quantitative characterization of streamflow 

variability and predictability. They use 11 summary statistics 

(3 addressing overall flow variability, 6 addressing the pattern of the flood regime, and 2 addressing the degree of flow 

intermittency) of long-term daily-discharge records from 78 

streams across the continental United States to classify streams 

into nine hydrologically distinct stream types. Thus, the 

analysis assesses hydrologic similarity among streams using 

components of the flow regime with ecological significance. 

They use flow variability, flood patterns, and extent of flow 

intermittency to develop their conceptual model (fig. 2-6) of 

stream types. They also recognize that benign or predictable 

flow environments are more conducive to strong biotic interactions than are unpredictable flow environments. The authors 

note, however, that in most lotic systems, streamflow regimes 
Appendix 2 35

are intermediate between these extremes and, consequently, 

abiotic and biotic factors influence community structure at 

various degrees at various times. They conclude that long-term 

daily-flow records are valuable sources of information with 

which to evaluate temporal and spatial patterns of lotic environmental variability and disturbances across physiographic 

and ecographic regions. Thus, long-term patterns in flow variability need to be evaluated as part of the development of any 

hydrologically based flow methodology.

Poff (1996) expanded on this research using ecologically 

relevant hydrologic measures (that is, streamflow indices) to 

classify streams and examine relations between hydrologic 

variability and population- and community-level processes 

and patterns. He used long-term daily- and peak-flow gaging 

records (>36 years) from 420 relatively undisturbed streams in 

the conterminous United States to classify streams according 

to variation in 10 ecologically relevant hydrologic characteristics. Cluster analysis was used to identify 10 distinct stream 

types which included seven permanent and three intermittent 

streams. This study established that there are distinct patterns 

in the hydrological regimes of streams across the United States 

Intermittent

Streams Perennial Streams

Intermittency

High Low None

Harsh

 Intermittent

 Abiotic

Intermittent

 Flashy

 Abiotic

Intermittent

 Runoff

 Abiotic

Perennial Flashy

Low Flow Predictability

Abiotic

Winter + Rain

High Flow Predictability

Seasonal

Biotic

Snow + Rain

Low Flow Predictability

Seasonal

Biotic

Perennial Flashy

Low Flow Predictability

Abiotic

Snowmelt

High Flow Predictability

Seasonal

Biotic

Mesic Ground Water

High Flow Predictability

Biotic

Low High 

 Flood Predictability

 Low High 

Flood Predictability

 Low High

 Flood Predictability Low High

Flood Predictability

Figure 2-6. Conceptual model of classification of stream clusters based on hierarchical ranking of four temporal components of 

discharge regime. (From Poff and Ward, 1989)

based on geographic distribution that can be used to identify 

similar streams for the purpose of broad-scale, comparative 

ecological research.

Poff and Ward (1989) selected 11 ecologically relevant 

flow measures to characterize flow variability, flow predictability, flood-regime patterns, and degree of intermittency. The 

classification analysis (n = 78) identified nine stream types. 

Poff (1996) selected 10 ecologically relevant hydrologic indices to classify streams, only 5 of which differed from those of 

Poff and Ward (1989). This classification (n = 420), however, 

resulted in the identification of 10 distinct stream types. Many 

studies have used pre-selected hydrologic variables and flow 

data from either unclassified or classified streams to seek out 

biological relations. For example, Clausen and Biggs (1997) 

selected 34 hydrologic variables and biological data from 

83 New Zealand streams. Four of the 34 hydrologic variables were significantly correlated with periphyton biomass, 

whereas 24 were correlated with periphyton diversity. In 

contrast, 24 of the hydrologic variables were correlated with 

total invertebrate density, whereas only 4 were correlated with 

diversity. Monk and others (2006) classified 83 river basins 
36 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

in England and Wales into five classes using a suite of 201 

flow-regime descriptors. They found significant correlations 

with macroinvertebrate community metrics, primarily with 

two of the variables associated with magnitude of the flow 

regime. This relation was consistent for all sites and a subset 

of the flow-regime classes. Poff and Allan (1995) evaluated 

fish assemblages and hydrologic data from 34 Wisconsin and 

Michigan streams. They found strong hydrologic-assemblage 

relations, which indicated that hydrologic factors significantly 

affect fish assemblage structure.

These examples illustrate many of the issues facing 

managers attempting to maintain or restore stream integrity 

through flow management. Hydroecological studies such as 

those discussed above indicate that flow managers need to be 

able to account for the connection between hydrologic variability and aquatic-community structure for the three ecosystem templates (physical, chemical, and biological), while 

simultaneously addressing the dynamic spatial and temporal 

interactions known to occur between abiotic and biotic factors. 

It is questionable whether this is actually an attainable goal. 

Poff (Colorado State University, oral commun., 2006) also recognizes that species have differing, and commonly opposing, 

environmental requirements. Therefore, it may not be possible 

to determine a single environmental optimum for all individual 

species or a community. In addition, the sequence of inter- and 

intra-annual hydrologic events influences the significance of 

these relations. Consequently, fluctuations between favorable 

and unfavorable environmental conditions (that is, hydrologic 

variability) though space and time are required to sustain the 

ecological integrity of a stream ecosystem.

In the third study examined, Olden and Poff (2003) 

recognized that the overarching goal of streamflow characterization and classification is to select hydrologic indices 

that account for characteristics of streamflow variability that 

are “biologically relevant.” Researchers, however, have used 

many different ways to characterize streamflow, generally 

taking a multivariable approach. Moreover, “the use of single 

indices has been criticized as being overly simplified and lacking biological relevance, and that stream ecologists must now 

choose from an excess of available hydrologic indices many of 

which are intercorrelated” (Olden and Poff, 2003).

One of Olden and Poff’s (2003) primary goals was to 

address the question: “Which minimum subset of available 

hydrologic indices is required to adequately describe the 

main aspects of the flow regime?” The authors addressed this 

question by (1) reclassifying the same 420 stream gages (>20 

years, unregulated, flow records) analyzed in the Poff (1996) 

study by using 171 published hydrologic indices that were 

found to be biologically relevant. This reclassification identified 6 distinctive stream types; identified patterns of redundancy among hydrologic indices; and provided a number of 

statistically and ecologically based recommendations for the 

selection of a reduced set of indices that adequately represent all five critical elements of the flow regime (magnitude, 

frequency, duration, timing, and rate of change) by stream 

type. Olden and Poff (2003) state that their research provides 

a statistically based framework that can guide researchers in 

the selection of non-redundant hydrologic indices that fully 

characterize the flow regime. Thus, one can reduce the population of indices to a minimal set that incorporates all critical 

components of the flow regime (for example, app. 6) for all 

6 stream types. Appendix 6 represents the quantification of 

the “natural flow regime,” defined in Poff and others (1997) 

as follows: “A number of ecologically important streamflow 

characteristics constitute the natural flow regime, including 

the seasonal patterning of flows; timing of extreme flows; the 

frequency, predictability, and duration of floods, droughts, and 

intermittent flows; daily and seasonal, and annual flow variability; and rates of change. These hydrologic indices must be 

derived from an adequate period of record. Consequently, if, 

using biological metrics, a stream reach’s integrity is declared 

to be ‘healthy’ or ‘acceptable’ currently, or at some previous 

point in time, it can be attributed in large part to the preceding 

historic flow regime.”

After thorough review, the TAC concluded that: to 

address stream integrity, one must recognize the importance of 

stream ecosystem theory and hydrologic-ecologic principles 

and streams should be classified, at a minimum, based on 

ecologically relevant indices that incorporate all dimensions of 

hydrologic variability including magnitude, frequency, duration, timing, and rate of change.
Appendix 3 37

High Low Skewness of Daily Flows

*(MA5 - Dimensionless)

High Base

Flow

Low Base

Flow

Frequency of Low Flow Events

*(FL3 - events/yr) Low High

Stable B (1.25) C (1.62) A (1.74) D (2.09) Flashy

B (0.0) C (0.7) A (1.3) D (2.8)

Stream Type

Sq. Miles

750

600

450

300

150

0 B C A D 

max 141 37 786 23 

min 47 8 25 1

mean 86 21 165 6

*(mean value)

-

Drainage Area for Stream Types A, B, C, and D

Appendix 3. Distinctive characteristics of the four stream classes in 

New Jersey.
38 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Appendix 4. Stream class and characteristics of gaging stations representing 

relatively unimpaired basins used to classify New Jersey streams. 

[Da, drainage area, in square miles]

Class Station number Station name Da Record Years

A 01368000 Wallkill River near Unionville NY 140 1938-1981 44

A 01369000 Pochuck Creek near Pine Island NY 98 1939-1978 39

A 01379000 Passaic River near Millington NJ 55.4 1979-2001 23

A 01379500 Passaic River near Chatham NJ 100 1938-1960 23

D 01379773 Green Pond Brook at Picatinny Arsenal NJ 7.65 1983-2001 19

A 01380500 Rockaway River above Reservoir at Boonton NJ 116 1938-1960 23

C 01381500 Whippany River at Morristown NJ 29.4 1922-1960 39

C 01383500 Wanaque River at Awosting NJ 27.1 1919-2001 82

A 01384000 Wanaque River at Monks NJ 40.4 1935-1985 50

C 01384500 Ringwood Creek near Wanaque NJ 19.1 1935-2001 59

D 01385000 Cupsaw Brook Nr Wanaque NJ 4.37 1936-1958 22

C 01386000 West Brook near Wanaque NJ 11.8 1935-1978 43

D 01386500 Blue Mine Brook Nr Wanaque NJ 1.01 1935-1958 24

A 01387000 Wanaque River at Wanaque NJ 90.4 1920-1928 9

C 01387450 Mahwah River near Suffern NY 12.3 1959-1995 37

A 01387500 Ramapo River near Mahwah NJ 120 1923-1960 38

A 01388000 Ramapo River at Pompton Lakes NJ 160 1922-1950 29

A 01389500 Passaic River at Little Falls NJ 762 1898-1927 30

C 01390500 Saddle River at Ridgewood NJ 21.6 1955-1975 20

C 01391000 Hohokus Brook at Ho-Ho-Kus NJ 16.4 1955-1974 19

A 01391500 Saddle River at Lodi NJ 54.6 1923-1965 43

D 01392000 Weasel Brook at Clifton NJ 4.45 1938-1950 13

C 01392210 Third River at Passaic NJ 11.8 1977-1997 21

C 01392500 Second River at Belleville NJ 11.6 1938-1964 28

A 01396500 South Branch Raritan River near High Bridge NJ 65.3 1919-2002 84

C 01396580 Spruce Run at Glen Gardner NJ 11.3 1979-2001 19

C 01396660 Mulhockaway Creek at Van Syckel NJ 11.8 1977-2002 26

A 01397000 South Branch Raritan R at Stanton NJ 147 1920-1963 44

D 01397500 Walnut Brook near Flemington NJ 2.24 1937-1961 25

A 01398000 Neshanic River at Reaville NJ 25.7 1931-1960 30

D 01398045 Back Brook Tributary near Ringoes NJ 1.98 1978-1988 11

D 01398107 Holland Brook at Readington NJ 9 1979-1996 18

C 01398500 North Branch Raritan River near Far Hills NJ 26.2 1922-1975 54

D 01399190 Lamington (Black) River at Succasunna NJ 7.37 1977-1987 11

D 01399200 Lamington (Black) River near Ironia NJ 10.9 1976-1987 12

C 01399500 Lamington (Black) River near Pottersville NJ 32.8 1922-2001 80

D 01399510 Upper Cold Brook near Pottersville NJ 2.18 1973-1996 23

D 01399525 Axle Brook near Pottersville NJ 1.22 1978-1988 11

C 01399670 South Branch Rockaway Creek at Whitehouse Station NJ 12.3 1977-2002 25

A 01400000 North Branch Raritan River near Raritan NJ 190 1924-1962 39

D 01400350 Macs Brook at Somerville NJ 0.77 1983-1995 13

A 01400500 Raritan River at Manville NJ 490 1922-1963 41

A 01400730 Millstone River at Plainsboro NJ 65.8 1964-1975 10

A 01401000 Stony Brook at Princeton NJ 44.5 1954-2001 48
Appendix 4 39

Class Station number Station name Da Record Years

A 01401500 Millstone River near Kingston NJ 171 1934-1949 16

D 01401650 Pike Run at Belle Mead NJ 5.36 1981-2002 21

A 01402000 Millstone River at Blackwells Mills NJ 258 1922-1960 39

A 01403060 Raritan River Below Calco Dam at Bound Brook NJ 785 1945-1963 18

D 01403400 Green Brook at Seeley Mills NJ 6.23 1980-2001 21

D 01403500 Green Brook at Plainfield NJ 9.75 1939-1984 45

D 01403535 East Branch Stony Brook at Best Lake at Watchung NJ 1.57 1981-2000 20

D 01403540 Stony Brook at Watchung NJ 5.51 1975-2001 26

A 01405030 Lawrence Brook at Weston Mills NJ 44.9 1989-2001 13

A 01405300 Matchaponix Brook at Spotswood NJ 43.9 1958-1967 10

D 01407000 Matawan Creek at Matawan NJ 6.11 1933-1955 22

A 01408000 Manasquan River at Squankum NJ 44 1932-1960 29

B 01408500 Toms River near Toms River NJ 123 1929-1966 37

B 01409000 Cedar Creek at Lanoka Harbor NJ 53.3 1933-1958 25

D 01409095 Oyster Creek near Brookville NJ 7.43 1966-1984 18

D 01409280 Westecunk Creek at Stafford Forge NJ 15.8 1974-1988 14

B 01409400 Mullica River near Batsto NJ 46.7 1958-2001 43

B 01409500 Batsto River at Batsto NJ 67.8 1940-2001 62

B 01409810 West Branch Wading River near Jenkins NJ 84.1 1975-1996 21

B 01410000 Oswego River at Harrisville NJ 72.5 1931-2001 70

D 01410150 East Branch Bass River near New Gretna NJ 8.11 1979-2001 22

B 01411000 Great Egg Harbor River at Folsom NJ 57.1 1926-1960 35

C 01411300 Tuckahow River at Head Of River NJ 30.8 1971-2001 30

D 01411456 Little Ease Run near Clayton NJ 9.77 1989-2001 13

B 01411500 Maurice River at Norma NJ 112 1933-2001 68

D 01412000 Menantico Creek near Millville NJ 23.2 1932-1957 25

C 01412800 Cohansey River at Seeley NJ 28 1978-1988 10

A 01437500 Neversink River at Godeffroy NY 307 1938-1954 17

A 01440000 Flat Brook near Flatbrookville NJ 64 1924-2001 77

A 01443500 Paulins Kill at Blairstown NJ 126 1922-1975 54

C 01445000 Pequest River at Huntsville NJ 31 1940-1962 22

B 01445500 Pequest River at Pequest NJ 106 1922-2001 80

C 01446000 Beaver Brook near Belvidere NJ 36.7 1923-1961 38

C 01455200 Pohatcong Creek at New Village NJ 33.3 1960-1969 9

B 01456000 Musconetcong River near Hackettstown NJ 68.9 1922-1973 51

B 01457000 Musconetcong River nears Bloomsbury NJ 141 1922-1960 39

A 01464000 Assunpink Creek at Trenton NJ 90.6 1924-1954 30

A 01464500 Crosswicks Creek at Extonville NJ 81.5 1941-2001 60

B 01465850 South Branch Rancocas Creek at Vincentown NJ 64.5 1962-1975 13

D 01466000 Mb Mount Misery Brook In Lebanon State Forest NJ 2.82 1953-1964 11

D 01466500 Mcdonalds Branch In Lebanon State Forest NJ 2.35 1954-2001 47

B 01467000 North Branch Rancocas Creek at Pemberton NJ 118 1922-2001 79

C 01467081 South Branch Pennsauken Creek at Cherry Hill NJ 8.98 1967-2001 34

D 01475000 Mantua Creek at Pitman NJ 6.05 1942-1976 34
40 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Appendix 5. Verification results for the 171 hydrologic indices

Implementation of mathematical or statistical relations as computer code should be verified as to its proper representation 

of the relations being computerized. Verification was done for the HIP software (Hydrologic Indices Tool–HIT; New Jersey 

Hydrologic Assessment Tool–NJHAT; and the National Hydrologic Assessment Tool-NATHAT) by (1) comparing the computer 

code with the definitions being implemented and (2) comparing the results of applying the same input data set(s) to alternative 

implementations of the definitions. In this case, inputting the data to alternative, commercially available software − MATLAB,

SAS, and EXCEL.

Statistical tests were conducted to verify that the computer code is representative of the formulas being computerized by:

1. Reviewing the computer code and the definitions.

2. Comparing results using the HIP implementation for some indices to results of implementing the index formulation in 

Microsoft Excel.

3. Comparing the results using the HIP implementation for the remaining indices to results previously generated by Julian D. 

Olden (Department of Biology, Colorado State University, Fort Collins, Colo.) using MATLAB and SAS.

The criterion for verification was set to a 5-percent difference in index values for the two implementations. For those 

indices for which the difference was greater than 5 percent, the computer code was again compared to the index definitions. The 

indices were compared using the formulation:

Absolute percent difference = average(index HIP, index CAS) * 100

index valueHIP - index valueCAS

where CAS is commercially available software.

Table 5-1 presents the results for the 171 indices. Figure 5-1 shows that there is less than a 5-percent difference for 168 of 

the indices. For the three remaining indices, subjective adjustment of the index formulation most likely accounted for the larger 

differences.

Results using the NJHAT implementation for 64 indices were compared with the results of implementing the definitions for 

those indices using the commercial available software SAS. The index values were calculated using data sets from 19 streamgage sites obtained from the USGS National Water Information System (NWIS) using the formulation:

Percent difference = (index valueCAS) * 100

index valueNJHAT - index valueCAS

Table 5-2 lists the indices used and summarizes the results of the comparison. In the initial comparison, the results for four 

indices were considered problematic. For two indices, FH3 and FH6, the ratios varied from 106 percent to 164 percent for three 

gage sites. Indices for all other gage sites were within 2 percent. Because NJHAT uses water year (October through September) 

data and the SAS analysis uses calendar year (January through December) data, the SAS calculations for FH3 and FH6 were 

recalculated using the water year and the ratios for all stream-gage sites were then within 2 percent. The ratios for ML22 varied 

from 103 percent to 133 percent, with most values in the 106 percent to 115 percent range. The ratios for DL16 varied from 105 

percent to 126 percent, with most being between 110 percent and 126 percent. The definitions for both ML22 and DL16 were 

checked and the input data reviewed. ML22 was recalculated using the water year and all results were within 2 percent. For 

DL16, the definition was changed so that the median was used in the calculation rather than the mean. When the ratios were 

recalculated using the median, 16 of the ratios were within 3 percent; 1 difference was 12.8 percent.

One of the differences between the NJHAT and SAS calculations of the indices was how missing daily streamflow values 

were handled in the input data. In NJHAT, all available data are used and missing daily values are not used in the calculations. In 

the SAS calculations, the data for an entire year were not used if there was more than one missing value. This likely explains the 

small differences between the indices determined using NJHAT and those calculated using SAS.

Because none of the problems in the analysis was associated with the NJHAT software and the issues found using independent calculations of the indices were related to the input data structure, it was concluded that the NJHAT software was performing satisfactorily.
Appendix 5 41

Table 5-1. Verification results for 171 indices calculated in the Hydrologic Indices Tool.—Continued[USGS, U.S. Geological Survey; CAS, commercially available software; CSU, Colorado State University. Refer to appendix 7 for index definitions]

Index Verification method1,2,3 HIT index value Verification index value Percent difference4 Comments

MA1 USGS-Microsoft Excel (CAS) 126.29 126.29 0.00MA2 USGS-Microsoft Excel (CAS) 126.29 126.29 0.00MA3 CSU-CAS 101.70 102.20 0.12MA4 USGS-Microsoft Excel (CAS) 15.49 15.08 0.68MA5 USGS-Microsoft Excel (CAS) 1.49 1.49 0.00MA6 CSU-CAS 7.27 7.24 0.08MA7 CSU-CAS 3.87 3.87 0.00MA8 CSU-CAS 2.98 2.98 0.00MA9 CSU-CAS 0.45 0.45 0.00MA10 CSU-CAS 0.30 0.30 0.00MA11 CSU-CAS 0.25 0.25 0.00MA12 USGS-Microsoft Excel (CAS) 146.52 146.52 0.00MA13 USGS-Microsoft Excel (CAS) 167.71 167.71 0.00MA14 USGS-Microsoft Excel (CAS) 201.94 201.94 0.00MA15 USGS-Microsoft Excel (CAS) 209.95 209.95 0.00MA16 USGS-Microsoft Excel (CAS) 147.21 147.21 0.00MA17 USGS-Microsoft Excel (CAS) 101.53 101.53 0.00MA18 USGS-Microsoft Excel (CAS) 78.39 78.39 0.00MA19 USGS-Microsoft Excel (CAS) 73.37 73.37 0.00MA20 USGS-Microsoft Excel (CAS) 68.81 68.81 0.00MA21 USGS-Microsoft Excel (CAS) 73.25 73.25 0.00MA22 USGS-Microsoft Excel (CAS) 106.67 106.67 0.00MA23 USGS-Microsoft Excel (CAS) 143.16 143.16 0.00MA24 USGS-Microsoft Excel (CAS) 57.23 57.23 0.00MA25 USGS-Microsoft Excel (CAS) 71.08 71.08 0.00MA26 USGS-Microsoft Excel (CAS) 54.05 54.05 0.00MA27 USGS-Microsoft Excel (CAS) 46.48 46.48 0.00MA28 USGS-Microsoft Excel (CAS) 51.30 51.30 0.00MA29 USGS-Microsoft Excel (CAS) 43.91 43.91 0.00MA30 USGS-Microsoft Excel (CAS) 54.06 54.06 0.00MA31 USGS-Microsoft Excel (CAS) 60.55 60.55 0.00MA32 USGS-Microsoft Excel (CAS) 53.34 53.34 0.00MA33 USGS-Microsoft Excel (CAS) 58.13 58.13 0.00MA34 USGS-Microsoft Excel (CAS) 64.71 64.71 0.00MA35 USGS-Microsoft Excel (CAS) 59.84 59.84 0.00
42 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams Table 5-1. Verification results for 171 indices calculated in the Hydrologic Indices Tool.—Continued [USGS, U.S. Geological Survey; CAS, commercially available software; CSU, Colorado State University. Refer to appendix 7 for index definitions] Index Verification method1,2,3 HIT index value Verification index value Percent difference4 Comments MA36 CSU-CAS 4.94 4.94 0.00 MA37 CSU-CAS 1.07 1.07 0.09 MA38 CSU-CAS 2.12 2.11 0.08 MA39 CSU-CAS 66.45 66.45 0.00 MA40 CSU-CAS 0.23 0.23 0.00 MA41 CSU-CAS 0.75 0.75 0.00 MA42 CSU-CAS 1.30 1.30 0.00 MA43 CSU-CAS 0.60 0.57 1.03 HIT percentile calculations are more accurate MA44 CSU-CAS 0.91 0.75 4.66 HIT percentile calculations are more accurate MA45 CSU-CAS 0.04 0.04 0.00 ML1 USGS-Microsoft Excel (CAS) 71.56 71.56 0.00 ML2 USGS-Microsoft Excel (CAS) 77.69 77.69 0.00 ML3 USGS-Microsoft Excel (CAS) 102.06 102.06 0.00 ML4 USGS-Microsoft Excel (CAS) 110.14 110.14 0.00 ML5 USGS-Microsoft Excel (CAS) 80.39 80.39 0.00 ML6 USGS-Microsoft Excel (CAS) 56.28 56.28 0.00 ML7 USGS-Microsoft Excel (CAS) 42.17 42.17 0.00 ML8 USGS-Microsoft Excel (CAS) 37.89 37.89 0.00 ML9 USGS-Microsoft Excel (CAS) 37.75 37.75 0.00 ML10 USGS-Microsoft Excel (CAS) 40.42 40.42 0.00 ML11 USGS-Microsoft Excel (CAS) 52.17 52.17 0.00 ML12 USGS-Microsoft Excel (CAS) 68.78 68.78 0.00 ML13 USGS-Microsoft Excel (CAS) 55.92 55.92 0.00 ML14 CSU-CAS 0.34 0.35 0.36 ML15 CSU-CAS 0.24 0.23 0.94 ML16 CSU-CAS 0.32 0.34 1.43 ML17 CSU-CAS 0.27 0.26 0.47

ML18 CSU-CAS 26.44 24.54 1.86 Slight variation in standard deviation formula accounts for this differenceML19 CSU-CAS 24.32 24.32 0.00ML20 CSU-CAS 0.61 0.61 0.12ML21 CSU-CAS 31.39 31.39 0.00ML22 CSU-CAS 0.17 0.18 0.14MH1 USGS-Microsoft Excel (CAS) 536.39 536.39 0.00MH2 USGS-Microsoft Excel (CAS) 629.00 629.00 0.00MH3 USGS-Microsoft Excel (CAS) 621.14 621.14 0.00
Appendix 5 43

Table 5-1. Verification results for 171 indices calculated in the Hydrologic Indices Tool.—Continued[USGS, U.S. Geological Survey; CAS, commercially available software; CSU, Colorado State University. Refer to appendix 7 for index definitions]

Index Verification method1,2,3 HIT index value Verification index value Percent difference4 Comments

MH4 USGS-Microsoft Excel (CAS) 612.25 612.25 0.00MH5 USGS-Microsoft Excel (CAS) 466.83 466.83 0.00MH6 USGS-Microsoft Excel (CAS) 300.67 300.67 0.00MH7 USGS-Microsoft Excel (CAS) 328.47 328.47 0.00MH8 USGS-Microsoft Excel (CAS) 319.78 319.78 0.00MH9 USGS-Microsoft Excel (CAS) 267.69 267.29 0.04MH10 USGS-Microsoft Excel (CAS) 276.50 276.50 0.00MH11 USGS-Microsoft Excel (CAS) 409.86 409.86 0.00MH12 USGS-Microsoft Excel (CAS) 519.69 519.69 0.00MH13 USGS-Microsoft Excel (CAS) 97.90 97.90 0.00MH14 CSU-CAS 12.89 12.89 0.00MH15 CSU-CAS 8.11 8.02 0.28MH16 CSU-CAS 2.91 2.88 0.19MH17 CSU-CAS 1.79 1.77 0.30MH18 CSU-CAS 6.70 6.70 0.00MH19 CSU-CAS -0.01 -0.01 0.00MH20 CSU-CAS 7.79 7.79 0.00MH21 CSU-CAS 17.50 17.30 0.28MH22 CSU-CAS 5.73 5.71 0.07MH23 CSU-CAS 5.57 5.56 0.06MH24 CSU-CAS 3.65 3.63 0.16MH25 CSU-CAS 6.36 6.37 0.03MH26 CSU-CAS 11.57 11.57 0.00MH27 CSU-CAS 4.91 4.87 0.22FL1 CSU-CAS 9.25 9.00 0.69FL2 CSU-CAS 46.18 46.18 0.00FL3 CSU-CAS 0.00 0.00 0.00FH1 CSU-CAS 16.33 16.33 0.00FH2 CSU-CAS 33.53 33.53 0.00FH3 CSU-CAS 33.92 33.14 0.58FH4 CSU-CAS 5.28 5.14 0.67FH5 CSU-CAS 14.28 14.14 0.24FH6 CSU-CAS 13.36 13.03 0.63FH7 CSU-CAS 3.75 3.64 0.75FH8 CSU-CAS 16.33 16.22 0.17
44 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams Table 5-1. Verification results for 171 indices calculated in the Hydrologic Indices Tool.—Continued [USGS, U.S. Geological Survey; CAS, commercially available software; CSU, Colorado State University. Refer to appendix 7 for index definitions] Index Verification method1,2,3 HIT index value Verification index value Percent difference4 Comments FH9 CSU-CAS 9.53 8.63 2.48 HIT percentile calculations are more accurate FH10 CSU-CAS 3.33 2.28 9.40 Computer code checked-OK FH11 CSU-CAS 0.86 0.76 3.15 Threshold has subjective elements DL1 CSU-CAS 29.53 29.53 0.00 DL2 CSU-CAS 30.35 30.25 0.08 DL3 CSU-CAS 31.95 31.77 0.14 DL4 CSU-CAS 40.04 38.48 0.99 DL5 CSU-CAS 61.24 55.04 2.67 Computer code checked-OK DL6 CSU-CAS 49.17 49.17 0.00 DL7 CSU-CAS 30.58 30.44 0.12 DL8 CSU-CAS 31.94 31.75 0.15 DL9 CSU-CAS 37.78 36.21 1.06 DL10 CSU-CAS 49.07 43.85 2.81 Computer code checked-OK DL11 CSU-CAS 0.35 0.35 0.00 DL12 CSU-CAS 0.38 0.38 0.00 DL13 CSU-CAS 0.47 0.47 0.00 DL14 CSU-CAS 0.60 0.60 0.00 DL15 CSU-CAS 0.40 0.40 0.00 DL16 CSU-CAS 8.40 8.83 1.26 DL17 CSU-CAS 65.30 65.30 0.00 DL18 CSU-CAS 0.00 0.00 0.00 DL19 CSU-CAS 0.00 0.00 0.00 DL20 CSU-CAS 0.00 0.00 0.00 DH1 CSU-CAS 1318.92 1318.92 0.00 DH2 CSU-CAS 838.54 838.54 0.00 DH3 CSU-CAS 553.62 553.62 0.00 DH4 CSU-CAS 303.16 303.16 0.00

DH5 CSU-CAS 216.84 218.58 0.20DH6 CSU-CAS 49.17 49.17 0.00DH7 CSU-CAS 41.07 41.07 0.00DH8 CSU-CAS 38.71 38.71 0.00DH9 CSU-CAS 31.78 31.78 0.00DH10 CSU-CAS 31.97 31.11 0.68DH11 CSU-CAS 15.52 15.52 0.00DH12 CSU-CAS 6.51 6.51 0.00
Appendix 5 45

Table 5-1. Verification results for 171 indices calculated in the Hydrologic Indices Tool.—Continued[USGS, U.S. Geological Survey; CAS, commercially available software; CSU, Colorado State University. Refer to appendix 7 for index definitions]

Index Verification method1,2,3 HIT index value Verification index value Percent difference4 Comments

DH13 CSU-CAS 3.57 3.57 0.00DH14 CSU-CAS 0.24 0.24 0.42DH15 USGS Manual Calculation 4.63 4.63 0.00DH16 USGS Manual Calculation 56.72 56.72 0.00DH17 USGS Manual Calculation 15.82 15.82 0.00DH18 CSU-CAS 2.53 2.54 0.11DH19 CSU-CAS 1.43 1.41 0.26DH20 CSU-CAS 5.56 5.59 0.12DH21 USGS Manual Calculation 45.40 45.40 0.00DH22 CSU-CAS 93.98 83.00 3.10 Threshold has subjective elementsDH23 CSU-CAS 1.70 1.53 2.63 Threshold has subjective elementsDH24 USGS Manual Calculation 116.89 116.89 0.00TA1 Colwell (1974) table 1 0.10 0.10 0.00TA2 Colwell (1974) table 1 0.29 0.29 0.00TA3 CSU-CAS 0.21 0.30 8.65 Threshold has subjective elementsTL1 CSU-CAS 261.50 260.90 0.06TL2 CSU-CAS 28.44 28.30 0.12TL3 CSU-CAS 0.46 0.68 9.95 Threshold has subjective elementsTL4 USGS Manual Calculation 0.04 0.04 0.00TH1 USGS Manual Calculation 62.82 62.82 0.00TH2 USGS Manual Calculation 61.45 61.45 0.00TH3 CSU-CAS 0.08 0.07 3.59 Threshold has subjective elementsRA1 CSU-CAS 70.15 70.15 0.00RA2 USGS Manual Calculation 233.73 233.73 0.00RA3 CSU-CAS -31.35 -30.66 0.56RA4 USGS Manual Calculation -290.07 -290.07 0.00RA5 CSU-CAS 0.28 0.31 2.37RA6 CSU-CAS 0.15 0.15 0.00RA7 CSU-CAS -0.08 -0.08 0.00RA8 CSU-CAS 118.08 117.94 0.03RA9 CSU-CAS 8.16 8.09 0.21

1U.S. Geological Survey (Fort Collins Science Center) compared HIT results to results using Microsoft Excel.2Julian D. Olden (Colorado State University) provided previously calculated results using commercially available software − Excel, MATLAB, SAS.3Colwell (1974).4Absolute percent difference = HIP index value-CAS index value/average (HIP value, CAS value) * 100 
46 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Figure 5-1. Verification results: distribution of percent difference between Hydrologic Indices Tool index values and values calculated 

using commercially available software.

Table 5-2. Verification results for differences between the New Jersey Hydrologic Assessment Tool index values and values 

calculated using commercially available software for 64 indices at 19 selected stream-gage sites.

Index Results

MA1 All values within 2 percent of NJHAT values

MA2 All values within 3 percent of NJHAT values

MA12-MA23 All values within 2 percent of NJHAT values

MA24-MA35 All values within 3 percent of NJHAT values

MA36 All values within 3 percent of the NJHAT values

MA41 All values within 2 percent of NJHAT values

ML1-ML12 223 values within 2 percent of NJHAT values. For August, September, and October, the months with lowest flows, 5 values 

within 6.2 percent of NJHAT values

ML22 All values within 2 percent of NJHAT values

MH1-MH12 All values within 3 percent of NJHAT values

MH14 All values within 3 percent of NJHAT values

MH15 All values within 3 percent of NJHAT values

MH25 All values within 3 percent of NJHAT values

FL1 17 of the values within 2 percent of NJHAT values and 2 values 5-6 percent different from NJHAT values

FH3 All values within 2 percent of NJHAT values

FH6 All values within 2 percent of NJHAT values

DL3 All of values within 2 percent of NJHAT values

DL14 All of values within 2 percent of NJHAT values

DL16 16 values within 3 percent of NJHAT values, 2 values within 5 percent of NJHAT values, and 1 value 12.8 percent different

DH3 All values within 2 percent of NJHAT values.

DH8 17 values within 2 percent of NJHAT values and, 2 values within 4 percent of NJHAT values

RA1 All values within 3 percent of NJHAT values

RA3 All values within 2 percent of NJHAT values

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

0

10

20

30

40

50

60

70

80

90

100

110

120

111

43

5 5 3 1 0 0 0 1 2

NUMBER OF INDICES

PERCENT DIFFERENCE
Appendix 6 47

Appendix 6. Hydrologic indices with the largest absolute loading for each of the statistically significant principal component axes for each stream type in the nine sub-components of the flow regime. (From Olden and Poff, 2003)

Flow component

Stream classification

All streams

Intermittent Perennial

Harsh intermittent Intermittent flashy or runoff Snowmelt Snow and rain

Superstable or stable 

ground water

Perennial flashy or 

runoff

Magnitude of flow events

Average flow conditions MA34, MA22, MA16 MA37, MA18, MA21, MA9 MA29, MA40 MA3, MA44 MA3, MA41, MA8 MA26, MA41, MA10 MA5, MA41, MA3, MA11Low flow conditions ML13, ML15, ML1 ML16, ML6, ML22, ML15 ML13, ML22 ML13, ML14 ML18, ML14, ML16 ML17, ML14, ML16 ML17, ML4, ML21, ML18High flow conditions MH23, MH14, MH9 MH23, MH4, MH14, MH7 MH1, MH20 MH17, MH20 MH17, MH19, MH10 MH23, MH8, MH14 MH16, MH8, MH10, MH14

Frequency of flow events

Low flow conditions FL2, FL3, FL1 FL3, FL2, FL1 FL3, FL2 FL3, FL2 FL3, FL1, FL2 FL3, FL2, FL3 FL3, FL2, FL3, FL1 High flow conditions FH2, FH5, FH7 FH3, FH7, FH2, FH10 FH8, FH11 FH3, FH5 FH3, FH6, FH11 FH4, FH6, FH7 FH3, FH6, FH7, FH2

Duration of flow events

Low flow conditions DL13, DL1, DL2 DL18, DL16, DL13, DL1 DL5, DL16 DL6, DL13 DL9, DL11, DL16 DL10, DL17, DL6 DL18, DL17, DL16, DL13High flow conditions DH10, DH5, DH22 DH13, DH15, DH12, DH23 DH19, DH16 DH12, DH24 DH11, DH20, DH15 DH13, DH16, DH24 DH13, DH16, DH20, DH15

Timing of flow events

TH1, TL2, TH2 TA1, TA2, TL1, TH3 TA1, TA3 TA1, TL1 TA1, TH1, TL2 TA1, TA3, TH3 TA1, TH3, TA1, TL2

Rate of change of flow events

RA4, RA1, RA5 RA9, RA6, RA5, RA7 RA1, RA8 RA9, RA8 RA9, RA8, RA5 RA9, RA7, RA6 RA9, RA8, RA6, RA5
48 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Appendix 7. Definitions of the 171 hydrologic indices.

MA# Magnitude, average flow conditions

ML# Magnitude, low flow conditions

MH# Magnitude, high flow conditions

FL# Frequency, low flow conditions

FH# Frequency, high flow conditions

DL# Duration, low flow conditions

DH# Duration, high flow conditions

TA# Timing, average flow conditions

TL# Timing, low flow conditions

TH# Timing, high flow conditions

RA# Rate of change, average conditions

The following information for the 171 hydrologic indices 

is from Olden and Poff (2003). The USGS revised a limited 

number of the formulae and (or) definitions when deemed 

appropriate. These changes are also documented in Henriksen 

and others (2006). Olden and Poff (2003) contains 12 additional references from which the indices were derived. Two of 

these articles (Colwell, 1974; Poff, 1996) are referenced here 

because they provide examples and additional explanation for 

complex indices.

The alphanumeric code preceding each definition refers 

to the category of the flow regime (magnitude, frequency, 

duration, timing, or rate of change) and type of flow event 

(A, average; L, low; and H, high) the hydrologic index was 

developed to describe. Indices are numbered successively 

within each category. For example, MA1 is the first index 

describing magnitude of the average flow condition.

Following each definition, in parentheses, are (1) the 

units of the index and (2) the type of data (temporal or spatial), from which the upper and lower percentile limits (for 

example, 75th and 25th) are derived. Temporal data are from 

a multiyear daily-flow record from a single stream gage. For 

example, index MA1—mean for the entire flow record—uses 

365 mean daily flow values for each year in the flow record to 

calculate the mean for the entire flow record. Consequently, 

there are 365 values for each year to calculate upper and lower 

percentile limits. However, formulas for 60 of the indices do 

not produce a range of values from which percentile limits 

can be calculated. MA5 (skewness), for example—the mean 

for the entire flow record divided by the median for the entire 

record—results in a single value; therefore, upper and lower 

percentile limits cannot be calculated. NJHAT uses spatial 

data, values for each stream gage for all the streams within 

a stream class, to compute limits. Upper and lower percentile limits are calculated from the 31 MA5 values from the 

31 stream gages that were identified from the classification 

analysis as stream class A.

Exceedence and percentile are used in the calculation 

for a number of indices. A 90-percent exceedence means 

that 90 percent of the values are equal to or greater than the 

90-percent exceedence value, whereas a 90th percentile means 

that 10 percent of the values are equal to or greater than the 

90th-percentile value.

Code Definition

MA1 Mean of the daily mean flow values for the entire flow record (cubic feet per second-temporal).

MA2 Median of the daily mean flow values for the entire flow record (cubic feet per second-temporal).

MA3 Mean (or median-Use Preference option) of the coefficients of variation (standard deviation/mean) for each year. Compute the coefficient of variation for each year of daily flows. Compute the mean of the annual coefficients of variation 

(percent-temporal).

MA4 Standard deviation of the percentiles of the logs of the entire flow record divided by the mean of percentiles of the logs. 

Compute the log10 of the daily flows for the entire record. Compute the 5th, 10th, 15th, 20th, 25th, 30th, 35th, 40th, 

45th, 50th, 55th, 60th, 65th, 70th, 75th, 80th, 85th, 90th, and 95th percentiles for the logs of the entire flow record. Percentiles are computed by interpolating between the ordered (ascending) logs of the flow values. Compute the standard 

deviation and mean for the percentile values. Divide the standard deviation by the mean (percent-spatial).

MA5 The skewness of the entire flow record is computed as the mean for the entire flow record (MA1) divided by the median 

(MA2) for the entire flow record (dimensionless-spatial).

MA6 Range in daily flows is the ratio of the 10-percent to 90-percent exceedence values for the entire flow record. Compute 

the 5-percent to 95-percent exceedence values for the entire flow record. Exceedence is computed by interpolating between the ordered (descending) flow values. Divide the 10-percent exceedence value by the 90-percent value 

(dimensionless–spatial).

MA7 Range in daily flows is computed in the same way as MA6 except using the 20-percent and 80-percent exceedence values. Divide the 20-percent exceedence value by the 80-percent value (dimensionless-spatial).
Appendix 7 49

Code Definition

MA8 Range in daily flows is computed in the same way as MA6 except using the 25-percent and 75-percent exceedence values. Divide the 25-percent exceedence value by the 75-percent value (dimensionless–spatial).

MA9 Spread in daily flows is the ratio of the difference between the 90th and 10th percentile of the logs of the flow data to the 

log of the median of the entire flow record. Compute the log10 of the daily flows for the entire record. Compute the 5th, 

10th, 15th, 20th, 25th, 30th, 35th, 40th, 45th, 50th, 55th, 60th, 65th, 70th, 75th, 80th, 85th, 90th, and 95th percentiles 

for the logs of the entire flow record. Percentiles are computed by interpolating between the ordered (ascending) logs 

of the flow values. Compute MA9 as (90th–10th) /log10 (MA2) (dimensionless–spatial).

MA10 Spread in daily flows is computed in the same way as MA9 except using the 20th and 80th percentiles (dimensionless–

spatial).

MA11 Spread in daily flows is computed in the same way as MA9 except using the 25th and 75th percentiles (dimensionless–

spatial).

MA12–MA23 Means (or medians-Use Preference option) of monthly flow values. Compute the means for each month over the entire 

flow record. For example, MA12 is the mean of all January flow values over the entire record (cubic feet per second–

temporal).

MA24–MA35 Variability (coefficient of variation) of monthly flow values. Compute the standard deviation for each month in each 

year over the entire flow record. Divide the standard deviation by the mean for each month. Average (or compute the 

median-Use Preference option) these values for each month across all years (percent–temporal).

MA36 Variability across monthly flows. Compute the minimum, maximum, and mean flows for each month in the entire flow 

record. MA36 is the maximum monthly flow minus the minimum monthly flow divided by the median monthly flow 

(dimensionless–spatial).

MA37 Variability across monthly flows. Compute the first (25th percentile) and the third (75th percentile) quartiles (every month 

in the flow record). MA37 is the third quartile minus the first quartile divided by the median of the monthly means 

(dimensionless–spatial).

MA38 Variability across monthly flows. Compute the 10th and 90th percentiles for the monthly means (every month in the flow 

record). MA38 is the 90th percentile minus the 10th percentile divided by the median of the monthly means (dimensionless–spatial).

MA39 Variability across monthly flows. Compute the standard deviation for the monthly means. MA39 is the standard deviation 

times 100 divided by the mean of the monthly means (percent–spatial).

MA40 Skewness in the monthly flows. MA40 is the mean of the monthly flow means minus the median of the monthly means 

divided by the median of the monthly means (dimensionless–spatial).

MA41 Annual runoff. Compute the annual mean daily flows. MA41 is the mean of the annual means divided by the drainage 

area (cubic feet per second/square mile–temporal).

MA42 Variability across annual flows. MA42 is the maximum annual flow minus the minimum annual flow divided by the 

median annual flow (dimensionless–spatial).

MA43 Variability across annual flows. Compute the first (25th percentile) and third (75th percentile) quartiles and the 10th and 

90th percentiles for the annual means (every year in the flow record). MA43 is the third quartile minus the first quartile 

divided by the median of the annual means (dimensionless–spatial).

MA44 Variability across annual flows. Compute the first (25th percentile) and third (75th percentile) quartiles and the 10th and 

90th percentiles for the annual means (every year in the flow record). MA44 is the 90th percentile minus the 10th percentile divided by the median of the annual means (dimensionless–spatial).

MA45 Skewness in the annual flows. MA45 is the mean of the annual flow means minus the median of the annual means divided by the median of the annual means (dimensionless–spatial).

ML1–

ML12

Mean (or median-Use Preference option) minimum flows for each month across all years. Compute the minimum daily 

flow for each month over the entire flow record. For example, ML1 is the mean of the minimums of all January flow 

values over the entire record (cubic feet per second–temporal).

ML13 Variability (coefficient of variation) across minimum monthly flow values. Compute the mean and standard deviation for 

the minimum monthly flows over the entire flow record. ML13 is the standard deviation times 100 divided by the mean 

minimum monthly flow for all years (percent–spatial).

ML14 Compute the minimum annual flow for each year. ML14 is the mean of the ratios of minimum annual flows to the median 

flow for each year (dimensionless–temporal).

ML15 Low flow index. ML15 is the mean of the ratios of minimum annual flows to the mean flow for each year (dimensionless–temporal).
50 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Code Definition

ML16 Median of annual minimum flows. ML16 is the median of the ratios of minimum annual flows to the median flow for 

each year (dimensionless–temporal).

ML17 Base flow. Compute the mean annual flows. Compute the minimum of a 7-day moving average flow for each year and 

divide them by the mean annual flow for that year. ML17 is the mean (or median-Use Preference option) of those ratios 

(dimensionless–temporal).

ML18 Variability in base flow. Compute the standard deviation for the ratios of 7-day moving average flows to mean annual 

flows for each year. ML18 is the standard deviation times 100 divided by the mean of the ratios (percent–spatial).

ML19 Base flow. Compute the ratios of the minimum annual flow to mean annual flow for each year. ML19 is the mean (or 

median-Use Preference option) of these ratios times 100 (dimensionless–temporal).

ML20 Base flow. Divide the daily flow record into 5-day blocks. Find the minimum flow for each block. Assign the minimum 

flow as a base flow for that block if 90 percent of that minimum flow is less than the minimum flows for the blocks on 

either side. Otherwise, set it to zero. Fill in the zero values using linear interpolation. Compute the total flow for the 

entire record and the total base flow for the entire record. ML20 is the ratio of total base flow to total flow (dimensionless–spatial).

ML21 Variability across annual minimum flows. Compute the mean and standard deviation for the annual minimum flows. 

ML21 is the standard deviation times 100 divided by the mean (percent–spatial).

ML22 Specific mean annual minimum flow. ML22 is the mean (or median-Use Preference option) of the annual minimum flows 

divided by the drainage area (cubic feet per second/square mile–temporal).

MH1–MH12 Mean (or median-Use Preference option) maximum flows for each month across all years. Compute the maximum daily 

flow for each month over the entire flow record. For example, MH1 is the mean of the maximums of all January flow 

values over the entire record (cubic feet per second–temporal).

MH13 Variability (coefficient of variation) across maximum monthly flow values. Compute the mean and standard deviation 

for the maximum monthly flows over the entire flow record. MH13 is the standard deviation times 100 divided by the 

mean maximum monthly flow for all years (percent–spatial).

MH14 Median of annual maximum flows. Compute the annual maximum flows from monthly maximum flows. Compute the ratio of annual maximum flow to median annual flow for each year. MH14 is the median of these ratios (dimensionless–

temporal).

MH15 High flow discharge index. Compute the 1-percent exceedence value for the entire data record. MH15 is the 1-percent 

exceedence value divided by the median flow for the entire record (dimensionless–spatial).

MH16 High flow discharge index. Compute the 10-percent exceedence value for the entire data record. MH16 is the 10-percent 

exceedence value divided by the median flow for the entire record (dimensionless–spatial).

MH17 High flow discharge index. Compute the 25-percent exceedence value for the entire data record. MH17 is the 25-percent 

exceedence value divided by the median flow for the entire record (dimensionless–spatial).

MH18 Variability across annual maximum flows. Compute the logs (log10) of the maximum annual flows. Find the standard 

deviation and mean for these values. MH18 is the standard deviation times 100 divided by the mean (percent–spatial).

MH19 Skewness in annual maximum flows. Use the equation:

 N2 x sum(qm3) - 3N x sum(qm) x sum(qm2) + 2 x (sum(qm))3

 N x (N - 1) x (N - 2) x stddev3 MH19 =

where N = Number of years

qm = Log10 (annual maximum flows)

stddev = standard deviation of the annual maximum flows

(dimensionless–spatial).

MH20 Specific mean annual maximum flow. MH20 is the mean (or median-Use Preference option) of the annual maximum 

flows divided by the drainage area (cubic feet per second/square mile–temporal).

MH21 High flow volume index. Compute the average volume for flow events above a threshold equal to the median flow for the 

entire record. MH21 is the average volume divided by the median flow for the entire record (days–temporal).

MH22 High flow volume. Compute the average volume for flow events above a threshold equal to three times the median flow 

for the entire record. MH22 is the average volume divided by the median flow for the entire record (days-temporal).

MH23 High flow volume. Compute the average volume for flow events above a threshold equal to seven times the median flow 

for the entire record. MH23 is the average volume divided by the median flow for the entire record (days-temporal).
Appendix 7 51

Code Definition

MH24 High peak flow. Compute the average peak-flow value for flow events above a threshold equal to the median flow for 

the entire record. MH24 is the average peak flow divided by the median flow for the entire record (dimensionless–

temporal).

MH25 High peak flow. Compute the average peak-flow value for flow events above a threshold equal to three times the median 

flow for the entire record. MH25 is the average peak flow divided by the median flow for the entire record (dimensionless–temporal).

MH26 High peak flow. Compute the average peak-flow value for flow events above a threshold equal to seven times the median 

flow for the entire record. MH26 is the average peak flow divided by the median flow for the entire record (dimensionless–temporal).

MH27 High peak flow. Compute the average peak-flow value for flow events above a threshold equal to the 75th-percentile 

value for the entire flow record. MH27 is the average peak flow divided by the median flow for the entire record 

(dimensionless–temporal).

FL1 Low flood pulse count. Compute the average number of flow events with flows below a threshold equal to the 25thpercentile value for the entire flow record. FL1 is the average (or median-Use Preference option) number of events 

(number of events/year–temporal).

FL2 Variability in low pulse count. Compute the standard deviation in the annual pulse counts for FL1. FL2 is 100 times the 

standard deviation divided by the mean pulse count (percent–spatial).

FL3 Frequency of low pulse spells. Compute the average number of flow events with flows below a threshold equal to 5 percent of the mean flow value for the entire flow record. FL3 is the average (or median-Use Preference option) number of 

events (number of events/year–temporal).

FH1 High flood pulse count. Compute the average number of flow events with flows above a threshold equal to the 75thpercentile value for the entire flow record. FH1 is the average (or median-Use Preference option) number of events 

(number of events/year–temporal).

FH2 Variability in high pulse count. Compute the standard deviation in the annual pulse counts for FH1. FH2 is 100 times the 

standard deviation divided by the mean pulse count (number of events/year–spatial). 

FH3 High flood pulse count. Compute the average number of days per year that the flow is above a threshold equal to three 

times the median flow for the entire record. FH3 is the mean (or median–Use Preference option) of the annual number 

of days for all years (number of days/year–temporal).

FH4 High flood pulse count. Compute the average number of days per year that the flow is above a threshold equal to seven 

times the median flow for the entire record. FH4 is the mean (or median-Use Preference option) of the annual number 

of days for all years (number of days/year–temporal).

FH5 Flood frequency. Compute the average number of flow events with flows above a threshold equal to the median flow 

value for the entire flow record. FH5 is the average (or median-Use Preference option) number of events (number of 

events/year–temporal).

FH6 Flood frequency. Compute the average number of flow events with flows above a threshold equal to three times the 

median flow value for the entire flow record. FH6 is the average (or median-Use Preference option) number of events 

(number of events/year–temporal).

FH7 Flood frequency. Compute the average number of flow events with flows above a threshold equal to seven times the 

median flow value for the entire flow record. FH6 is the average (or median-Use Preference option) number of events 

(number of events/year–temporal).

FH8 Flood frequency. Compute the average number of flow events with flows above a threshold equal to the 25-percent 

exceedence value for the entire flow record. FH8 is the average (or median-Use Preference option) number of events 

(number of events/year–temporal).

FH9 Flood frequency. Compute the average number of flow events with flows above a threshold equal to the 75-percent 

exceedence value for the entire flow record. FH9 is the average (or median-Use Preference option) number of events 

(number of events/year–temporal).

FH10 Flood frequency. Compute the average number of flow events with flows above a threshold equal to the median of the 

annual minima for the entire flow record. FH10 is the average (or median-Use Preference option) number of events 

(number of events/year–temporal).

FH111 Flood frequency. Compute the average number of flow events with flows above a threshold equal to flow corresponding 

to a 1.67-year recurrence interval. FH11 is the average (or median-Use Preference option) number of events (number of 

events/year–temporal).
52 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Code Definition

DL1 Annual minimum daily flow. Compute the minimum 1-day average flow for each year. DL1 is the mean (or median-Use 

Preference option) of these values (cubic feet per second–temporal). 

DL2 Annual minimum of 3-day moving average flow. Compute the minimum of a 3-day moving average flow for each year. 

DL2 is the mean (or median-Use Preference option) of these values (cubic feet per second–temporal). 

DL3 Annual minimum of 7-day moving average flow. Compute the minimum of a 7-day moving average flow for each year. 

DL3 is the mean (or median-Use Preference option) of these values (cubic feet per second–temporal). 

DL4 Annual minimum of 30-day moving average flow. Compute the minimum of a 30-day moving average flow for each year. 

DL4 is the mean (or median-Use Preference option) of these values (cubic feet per second–temporal). 

DL5 Annual minimum of 90-day moving average flow. Compute the minimum of a 90-day moving average flow for each year. 

DL5 is the mean (or median-Use Preference option) of these values (cubic feet per second–temporal).

DL6 Variability of annual minimum daily average flow. Compute the standard deviation for the minimum daily average flow. 

DL6 is 100 times the standard deviation divided by the mean (percent–spatial).

DL7 Variability of annual minimum of 3-day moving average flow. Compute the standard deviation for the minimum 3-day 

moving averages. DL7 is 100 times the standard deviation divided by the mean (percent-spatial).

DL8 Variability of annual minimum of 7-day moving average flow. Compute the standard deviation for the minimum 7-day 

moving averages. DL8 is 100 times the standard deviation divided by the mean (percent-spatial).

DL9 Variability of annual minimum of 30-day moving average flow. Compute the standard deviation for the minimum 30-day 

moving averages. DL9 is 100 times the standard deviation divided by the mean (percent-spatial).

DL10 Variability of annual minimum of 90-day moving average flow. Compute the standard deviation for the minimum 90-day 

moving averages. DL10 is 100 times the standard deviation divided by the mean (percent-spatial).

DL11 Annual minimum daily flow divided by the median for the entire record. Compute the minimum daily flow for each year. 

DL11 is the mean of these values divided by the median for the entire record (dimensionless–temporal).

DL12 Annual minimum of 7-day moving average flow divided by the median for the entire record. Compute the minimum of a 

7-day moving average flow for each year. DL12 is the mean of these values divided by the median for the entire record 

(dimensionless–temporal).

DL13 Annual minimum of 30-day moving average flow divided by the median for the entire record. Compute the minimum 

of a 30-day moving average flow for each year. DL13 is the mean of these values divided by the median for the entire 

record (dimensionless–temporal).

DL14 Low exceedence flows. Compute the 75-percent exceedence value for the entire flow record. DL14 is the exceedence 

value divided by the median for the entire record (dimensionless–spatial).

DL15 Low exceedence flows. Compute the 90-percent exceedence value for the entire flow record. DL14 is the exceedence 

value divided by the median for the entire record (dimensionless–spatial).

DL16 Low flow pulse duration. Compute the average pulse duration for each year for flow events below a threshold equal to the 

25th-percentile value for the entire flow record. DL16 is the median of the yearly average durations (number of days–

temporal).

DL17 Variability in low pulse duration. Compute the standard deviation for the yearly average low pulse durations. DL17 is 100 

times the standard deviation divided by the mean of the yearly average low pulse durations (percent–spatial).

DL18 Number of zero-flow days. Count the number of zero-flow days for the entire flow record. DL18 is the mean (or medianUse Preference option) annual number of zero flow days (number of days/year–temporal).

DL19 Variability in the number of zero-flow days. Compute the standard deviation for the annual number of zero-flow days. 

DL19 is 100 times the standard deviation divided by the mean annual number of zero-flow days (percent–spatial).

DL20 Number of zero-flow months. While computing the mean monthly flow values, count the number of months in which 

there was no flow over the entire flow record (percent–spatial).

DH1 Annual maximum daily flow. Compute the maximum of a 1-day moving average flow for each year. DH1 is the mean (or 

median-Use Preference option) of these values (cubic feet per second–temporal).

DH2 Annual maximum of 3-day moving average flows. Compute the maximum of a 3-day moving average flow for each year. 

DH2 is the mean (or median-Use Preference option) of these values (cubic feet per second–temporal).

DH3 Annual maximum of 7-day moving average flows. Compute the maximum of a 7-day moving average flow for each year. 

DH3 is the mean (or median-Use Preference option) of these values (cubic feet per second–temporal).
Appendix 7 53

Code Definition

DH4 Annual maximum of 30-day moving average flows. Compute the maximum of 30-day moving average flows. Compute 

the maximum of a 30-day moving average flow for each year. DH4 is the mean (or median-Use Preference option) of 

these values (cubic feet per second–temporal).

DH5 Annual maximum of 90-day moving average flows. Compute the maximum of a 90-day moving average flow for each 

year. DH5 is the mean (or median-Use Preference option) of these values (cubic feet per second–temporal).

DH6 Variability of annual maximum daily flows. Compute the standard deviation for the maximum 1-day moving averages. 

DH6 is 100 times the standard deviation divided by the mean (percent–spatial).

DH7 Variability of annual maximum of 3-day moving average flows. Compute the standard deviation for the maximum 3-day 

moving averages. DH7 is 100 times the standard deviation divided by the mean (percent–spatial).

DH8 Variability of annual maximum of 7-day moving average flows. Compute the standard deviation for the maximum 7-day 

moving averages. DH8 is 100 times the standard deviation divided by the mean (percent–spatial).

DH9 Variability of annual maximum of 30-day moving average flows. Compute the standard deviation for the maximum 30-

day moving averages. DH9 is 100 times the standard deviation divided by the mean (percent–spatial).

DH10 Variability of annual maximum of 90-day moving average flows. Compute the standard deviation for the maximum 90-

day moving averages. DH10 is 100 times the standard deviation divided by the mean (percent–spatial).

DH11 Annual maximum of 1-day moving average flows divided by the median for the entire record. Compute the maximum 

of a 1-day moving average flow for each year. DL11 is the mean of these values divided by the median for the entire 

record (dimensionless–temporal).

DH12 Annual maximum of 7-day moving average flows divided by the median for the entire record. Compute the maximum daily average flow for each year. DL12 is the mean of these values divided by the median for the entire record 

(dimensionless–temporal).

DH13 Annual maximum of 30-day moving average flows divided by the median for the entire record. Compute the maximum 

of a 30-day moving average flow for each year. DL13 is the mean of these values divided by the median for the entire 

record (dimensionless–temporal).

DH14 Flood duration. Compute the mean of the mean monthly flow values. Find the 95th percentile for the mean monthly 

flows. DH14 is the 95th-percentile value divided by the mean of the monthly means (dimensionless–spatial).

DH15 High flow pulse duration. Compute the average duration for flow events with flows above a threshold equal to the 

75th-percentile value for each year in the flow record. DH15 is the median of the yearly average durations (days/year–

temporal).

DH16 Variability in high flow pulse duration. Compute the standard deviation for the yearly average high pulse durations. DH16 

is 100 times the standard deviation divided by the mean of the yearly average high pulse durations (percent–spatial).

DH17 High flow duration. Compute the average duration of flow events with flows above a threshold equal to the median flow 

value for the entire flow record. DH17 is the average (or median-Use Preference option) duration of the events (days–

temporal).

DH18 High flow duration. Compute the average duration of flow events with flows above a threshold equal to three times the 

median flow value for the entire flow record. DH18 is the average (or median-Use Preference option) duration of the 

events (days–temporal).

DH19 High flow duration. Compute the average duration of flow events with flows above a threshold equal to seven times the 

median flow value for the entire flow record. DH19 is the average (or median-Use Preference option) duration of the 

events (days–temporal).

DH20 High flow duration. Compute the 75th-percentile value for the entire flow record. Compute the average duration of flow 

events with flows above a threshold equal to the 75th-percentile value for the median annual flows. DH20 is the average (or median-Use Preference option) duration of the events (days–temporal).

DH21 High flow duration. Compute the 25th-percentile value for the entire flow record. Compute the average duration of flow 

events with flows above a threshold equal to the 25th-percentile value for the entire set of flows. DH21 is the average 

(or median-Use Preference option) duration of the events (days–temporal). 

DH221 Flood interval. Compute the flood threshold as the flow equivalent for a flood recurrence of 1.67 years. Determine the 

median number of days between flood events for each year. DH22 is the mean (or median-Use Preference option) of 

the yearly median number of days between flood events (days–temporal).

DH231 Flood duration. Compute the flood threshold as the flow equivalent for a flood recurrence of 1.67 years. Determine the 

number of days each year that the flow remains above the flood threshold. DH23 is the mean (or median-Use Preference option) of the number of flood days for years in which floods occur (days–temporal).
54 Development of the Hydroecological Integrity Assessment Process for New Jersey Streams

Code Definition

DH241 Flood-free days. Compute the flood threshold as the flow equivalent for a flood recurrence of 1.67 years. Compute the 

maximum number of days that the flow is below the threshold for each year. DH24 is the mean (or median-Use Preference option) of the maximum yearly no-flood days (days–temporal).

TA1 Constancy. Constancy is computed via the formulation of Colwell (see example in Colwell, 1974). A matrix of values is 

compiled in which the rows are 11 flow categories and the columns are 365 (no February 29th) days of the year. The 

cell values are the number of times that a flow falls into a category on each day. The categories are:

log(flow) < 0.1 x log(mean flow),

0.1 x log(mean flow) ≤ log(flow) < 0.25 x log(mean flow)

0.25 x log(mean flow) ≤ log(flow) < 0.5 x log(mean flow)

0.5 x log(mean flow) ≤ log(flow) < 0.75 x log(mean flow)

0.75 x log(mean flow) ≤ log(flow) < 1.0 x log(mean flow)

1.0 x log(mean flow) ≤ log(flow) < 1.25 x log(mean flow)

1.25 x log(mean flow) ≤ log(flow) < 1.5 x log(mean flow)

1.5 x log(mean flow) ≤ log(flow) < 1.75 x log(mean flow)

1.75 x log(mean flow) ≤ log(flow) < 2.0 x log(mean flow)

2.0 x log(mean flow) ≤ log(flow) < 2.25 x log(mean flow)

log(flow) ≥ 2.25 x log(mean flow)

The row totals, column totals, and grand total are computed. Using the equations for Shannon information 

theory parameters, constancy is computed as:

 (uncertainty with respect to state)

log (number of state) 1 -

(dimensionless–spatial).

TA2 Predictability. Predictability is computed from the same matrix as constancy (see example in Colwell, 1974). It is computed as: 

 (uncertainty with respect to interaction of time and state - uncertainty with respect to time)

log (number of state) 1 -

(dimensionless–spatial).

TA31 Seasonal predictability of flooding. Divide years into 2-month periods (that is, Oct-Nov, Dec-Jan, and so forth). Count the 

number of flood days (flow events with flows > 1.67-year flood) in each period over the entire flow record. TA3 is the 

maximum number of flood days in any one period divided by the total number of flood days (dimensionless–temporal).

TL1 Julian date of annual minimum. Determine the Julian date that the minimum flow occurs for each water year. Transform 

the dates to relative values on a circular scale (radians or degrees). Compute the x and y components for each year and 

average them across all years. Compute the mean angle as the arc tangent of y-mean divided by x-mean. Transform the 

resultant angle back to Julian date (Julian date–spatial).

TL2 Variability in Julian date of annual minima. Compute the coefficient of variation for the mean x and y components and 

convert to a date (Julian date–spatial).

TL32 Seasonal predictability of low flow. Divide years into 2-month periods (that is, Oct-Nov, Dec-Jan, and so forth). Count 

the number of low flow events (flow events with flows ≤ 5-year flood threshold) in each period over the entire flow

record. TL3 is the maximum number of low flow events in any one period divided by the total number of low flow 

events (dimensionless–spatial).

TL42 Seasonal predictability of non-low flow. Compute the number of days that flow is above the 5-year flood threshold as the 

ratio of number of days to 365 or 366 (leap year) for each year. TL4 is the maximum of the yearly ratios (dimensionless–spatial).

TH1 Julian date of annual maximum. Determine the Julian date that the maximum flow occurs for each year. Transform the 

dates to relative values on a circular scale (radians or degrees). Compute the x and y components for each year and 

average them across all years. Compute the mean angle as the arc tangent of y-mean divided by x-mean. Transform the 

resultant angle back to Julian date (Julian date–spatial).

TH2 Variability in Julian date of annual maxima. Compute the coefficient of variation for the mean x and y components and 

convert to a date (Julian date-spatial).

TH31 Seasonal predictability of nonflooding. Computed as the maximum proportion of a 365-day year that the flow is less than 

the 1.67-year flood threshold and also occurs in all years. Accumulate nonflood days that span all years. TH3 is maximum length of those flood-free periods divided by 365 (dimensionless–spatial).

RA1 Rise rate. Compute the change in flow for days in which the change is positive for the entire flow record. RA1 is the 

mean (or median-Use Preference option) of these values (cubic feet per second/day–temporal).
Appendix 7 55

Code Definition

RA2 Variability in rise rate. Compute the standard deviation for the positive flow changes. RA2 is 100 times the standard 

deviation divided by the mean (percent–spatial).

RA3 Fall rate. Compute the change in flow for days in which the change is negative for the entire flow record. RA3 is the 

mean (or median–Use Preference option) of these values (cubic feet per second/day–temporal).

RA4 Variability in fall rate. Compute the standard deviation for the negative flow changes. RA4 is 100 times the standard 

deviation divided by the mean (percent–spatial).

RA5 Number of day rises. Compute the number of days in which the flow is greater than it was the previous day. RA5 is the 

number of positive-gain days divided by the total number of days in the flow record (dimensionless–spatial).

RA6 Change of flow. Compute the log10 of the flows for the entire flow record. Compute the change in log of flow for days 

in which the change is positive for the entire flow record. RA6 is the median of these values (cubic feet per second–

temporal).

RA7 Change of flow. Compute the log10 of the flows for the entire flow record. Compute the change in log of flow for days in 

which the change is negative for the entire flow record. RA7 is the median of these log values (cubic feet per second/

day–temporal).

RA8 Number of reversals. Compute the number of days in each year when the change in flow from one day to the next 

changes direction. RA8 is the average (or median-Use Preference option) of the yearly values (days-temporal).

RA9 Variability in reversals. Compute the standard deviation for the yearly reversal values. RA9 is 100 times the standard 

deviation divided by the mean (percent–spatial).

1

Note–1.67-year flood threshold (Olden and Poff, 2003)-For indices FH11, DH22, DH23, DH24, TA3, and TH3, compute the log10 of the peak annual 

flows. Compute the log10 of the daily flows for the peak annual flow days. Calculate the coefficients for a linear regression equation for logs of peak annual 

flow versus logs of average daily flow for peak days. Using the log peak flow for the 1.67-year recurrence interval (60th percentile) as input to the regression 

equation, predict the log10 of the average daily flow. The threshold is 10 to the log10 (average daily flow) power (cubic feet per second).

2

Note–5-year flood threshold (Olden and Poff, 2003)–For TL3 and TL4, compute the log10 of the peak annual flows. Compute the log10 of the daily flows 

for the peak annual flow days. Calculate the coefficients for a linear regression equation for logs of peak annual flow versus logs of average daily flow for 

peak days. Using the log peak flow for the 5-year recurrence interval (80th percentile) as input to the regression equation; predict the log10 of the average 

daily flow. The threshold is 10 to the log10 (average daily flow) power (cubic feet per second).
For additional information, write to:

Director

U.S. Geological Survey

New Jersey Water Science Center

810 Bear Tavern Road, Suite 206

West Trenton, NJ 08628

or visit our Web site at:

http://nj.usgs.gov/

Document prepared by the West Trenton Publishing Service Center
Kennen and others—Development of the Hydroecological Integrity Assessment Process for New Jersey Streams—Scientific Investigations Report 2007-5206

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