Using Water-Quality Profiles to

Characterize Seasonal Water Quality

and Loading in the Upper Animas River

Basin, Southwestern Colorado

By Kenneth J. Leib, M. Alisa Mast, and Winfield G. Wright

Denver, Colorado

2003

Prepared in cooperation with the

BUREAU OF LAND MANAGEMENT

U.S. GEOLOGICAL SURVEY

Water-Resources Investigations Report 02–4230
District Chief

U.S. Geological Survey

Box 25046, Mail Stop 415

Denver Federal Center

Denver, CO 80225–0046

For additional information write to:

District Chief

U.S. Geological Survey

Box 25046, Mail Stop 415

Denver Federal Center

Denver, CO 80225–0046

U.S. DEPARTMENT OF THE INTERIOR

GALE A. NORTON, Secretary

U.S. GEOLOGICAL SURVEY

Charles G. Groat, Director

The use of firm, trade, and brand names in this report is for identification purposes only and does 

not constitute endorsement by the U.S. Geological Survey.

Copies of this report can be purchased 

from:

U.S. Geological Survey

Information Services

Box 25286

Denver Federal Center

Denver, CO 80225
CONTENTS III

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

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

Purpose and Scope....................................................................................................................................................... 3

Description of Study Area ........................................................................................................................................... 3

Hydrology .......................................................................................................................................................... 4

Geology.............................................................................................................................................................. 5

Mine-Site Remediation...................................................................................................................................... 5

Acknowledgments ....................................................................................................................................................... 7

Methods of Data Collection................................................................................................................................................... 7

Water-Quality Sampling Sites...................................................................................................................................... 8

Sample Collection and Laboratory Analysis ............................................................................................................... 9

Streamflow Regression Models ................................................................................................................................... 11

Water-Quality Regression Models............................................................................................................................... 12

Model Selection ................................................................................................................................................. 13

Trend Analysis................................................................................................................................................... 15

Water-Quality Profiles ................................................................................................................................................. 16

Limitations of Data Analysis ....................................................................................................................................... 17

Seasonality of Water Quality in the Upper Animas River Basin........................................................................................... 18

Water-Quality Summary Statistics............................................................................................................................... 18

Water-Quality Profiles ................................................................................................................................................. 21

Cement Creek .................................................................................................................................................... 22

Mineral Creek .................................................................................................................................................... 27

Upper Animas River .......................................................................................................................................... 30

Application of Water-Quality Profiling in Mineral Creek..................................................................................................... 35

Summary................................................................................................................................................................................ 36

References Cited.................................................................................................................................................................... 38

Appendix—State of Colorado table value standards for trace metals affected by hardness concentration .......................... 43

FIGURES

1. Map showing streamflow-gaging stations and water-quality sampling sites in the upper Animas 

River Basin.................................................................................................................................................................. 2

2–16. Graphs showing:

2. Daily mean streamflow and maximum, minimum, mean daily streamflow at site A72, 

water years 1992 to 1999 ................................................................................................................................... 4

3. (A) Dissolved zinc concentration related to daily mean streamflow; and (B) daily mean 

streamflow, sampling site A72, water year 1997 ............................................................................................... 6

4. Measured streamflow at ungaged sampling site M27 related to daily mean streamflow at 

gaged sampling site M34 showing the variation about the regression line of streamflow 

measurements from differing water years.......................................................................................................... 12

5. Plot of hyperbolically transformed streamflow related to zinc concentration at site C20 

showing the presence of outliers ........................................................................................................................ 14

6. Residuals scatterplot and LOWESS smooth line showing a downward trend in dissolved 

zinc concentration at site M34 ........................................................................................................................... 16

7. Zinc concentration graphs at sampling site C18 that relate: (A) Measured streamflow to 

measured zinc concentration, (B) hyperbolically transformed streamflow to measured zinc 

concentration, (C) hyperbolically transformed streamflow to estimated zinc concentration, 

and (D) estimated zinc to measured zinc concentration..................................................................................... 17

8. Streamflow profile for Cement Creek subbasin................................................................................................. 25

CONTENTS
IV CONTENTS

9. Streamflow data before and after the American Tunnel bulkhead closure in September of 1996..................... 25

10. Water-quality profiles for hardness and dissolved zinc at Cement Creek sampling sites.................................. 26

11. Hardness data at site C20 before and after the American Tunnel bulkhead closure in September 

of 1996 ............................................................................................................................................................... 27

12. Streamflow profile for Mineral Creek subbasin................................................................................................. 28

13. Water-quality profiles for hardness and dissolved cadmium, copper, and zinc at Mineral Creek 

sampling sites..................................................................................................................................................... 29

14. Trace-metal concentrations at site M34 before and after the Longfellow/Koehler project was 

completed in October of 1997............................................................................................................................ 31

15. Streamflow profile for the Animas River........................................................................................................... 32

16. Water-quality profiles for hardness and dissolved cadmium, copper, and zinc at Animas River 

sampling sites..................................................................................................................................................... 33

TABLES

1. Summary of major mined-land remediation projects in the upper Animas River Basin ........................................ 7

2. Descriptive information for sampling sites ............................................................................................................. 8

3. Methods of sample collection for water-quality data collected in the upper Animas River Basin......................... 9

4. Analytical methods and minimum reporting limits for water-quality data collected in the upper 

Animas River Basin ................................................................................................................................................ 10

5. Summary of equation forms used to simulate constituent concentrations at sampling sites .................................. 15

6. Summary statistics for selected constituents and properties at sampling sites ....................................................... 19

7. Summary of streamflow regression model coefficients and diagnostics at ungaged sampling sites...................... 22

8. Summary of water-quality regression model coefficients and diagnostics at all 15 sampling sites ....................... 23

CONVERSION FACTORS, DEFINITIONS, AND ABBREVIATIONS

Temperature in degrees Celsius (C) may be converted to degrees Fahrenheit (F) as follows:

F = 1.8 (C) + 32

Water Year: A 12-month period beginning October 1 and ending September 30 of the following year. Water year is defined by the 

calendar year in which it ends.

Multiply By To obtain

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

square mile (mi2) 2.590 square kilometer (km2)

gallon (gal) 3.785 liter (L)

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

cubic foot per second per square mile [(ft3/s)/mi2] or cfsm 0.01093 cubic meter per second per square kilometer [(m3/s)/km2]

pound per day (lb/d) 0.4536 kilogram per day (kg/d)
CONTENTS V

Additional Abbreviations

the regression coefficient that is the intercept in the regression model 

 constant beta used in hyperbolic transformation 

B and Bn coefficient of explanatory variables in multiple regression 

CQ constituent concentration in milligrams per liter

DV1 dummy variable 

JC first-order julian date transformation 

JD second-order julian date transformation

JE third-order julian date transformation 

JF fourth-order julian date transformation 

XQ streamflow in cubic feet per second

Xh transformed streamflow 

r

2

 the coefficient of determination

g/L micrograms per liter

m micrometer

S/cm microsiemens per centimeter at 25 degrees Celsius

mg/L milligrams per liter

Acronyms

USGS U.S. Geological Survey 

CDMG Colorado Division of Minerals and Geology 

CDPHE Colorado Department of Public Health and Environment

BOR Bureau of Reclamation 

CRW Colorado River Watch Program 

SGC Sunnyside Gold Corporation

EDTA ethylenediaminetetraacetate 

ICP-AES inductively coupled atomic emission spectroscopy 

GFAA graphite furnace atomic absorption spectroscopy

AAS atomic absorption spectroscopy 

MRL minimum reporting level
INTRODUCTION 1

Using Water-Quality Profiles to Characterize 

Seasonal Water Quality and Loading in the Upper 

Animas River Basin, Southwestern Colorado

By Kenneth J. Leib, M. Alisa Mast, and Winfield G. Wright

Abstract

One of the important types of information 

needed to characterize water quality in streams 

affected by historical mining is the seasonal 

pattern of toxic trace-metal concentrations and 

loads. Seasonal patterns in water quality are 

estimated in this report using a technique called 

water-quality profiling. Water-quality profiling 

allows land managers and scientists to assess 

priority areas to be targeted for characterization 

and(or) remediation by quantifying the timing 

and magnitude of contaminant occurrence.

Streamflow and water-quality data 

collected at 15 sites in the upper Animas River 

Basin during water years 1991–99 were used to 

develop water-quality profiles. Data collected at 

each sampling site were used to develop ordinary 

least-squares regression models for streamflow 

and constituent concentrations. Streamflow was 

estimated by correlating instantaneous streamflow 

measured at ungaged sites with continuous 

streamflow records from streamflow-gaging 

stations in the subbasin. Water-quality regression 

models were developed to estimate hardness and 

dissolved cadmium, copper, and zinc concentrations based on streamflow and seasonal terms. 

Results from the regression models were used 

to calculate water-quality profiles for streamflow, 

constituent concentrations, and loads.

Quantification of cadmium, copper, 

and zinc loads in a stream segment in Mineral 

Creek (sites M27 to M34) was presented as an 

example application of water-quality profiling. 

The application used a method of mass 

accounting to quantify the portion of metal 

loading in the segment derived from uncharacterized sources during different seasonal 

periods. During May, uncharacterized sources 

contributed nearly 95 percent of the cadmium 

load, 0 percent of the copper load (or uncharacterized sources also are attenuated), and about 

85 percent of the zinc load at M34. During 

September, uncharacterized sources contributed 

about 86 percent of the cadmium load, 0 percent 

of the copper load (or uncharacterized sources 

also are attenuated), and about 52 percent 

of the zinc load at M34. Characterized sources 

accounted for more of the loading gains estimated in the example reach during September, 

possibly indicating the presence of diffuse inputs 

during snowmelt runoff. The results indicate that 

metal sources in the upper Animas River Basin 

may change substantially with season, regardless 

of the source.

INTRODUCTION

The upper Animas River Basin (the Animas 

Basin) (fig. 1) is a mineralized region of the San Juan 

Mountains in southwestern Colorado where gold, 

silver, and other base metals were mined from the late 

1800’s to 1992. As a result of historical mining activities, many streams in the Animas Basin have high 

concentrations of dissolved metals, which have caused 

a reduction or elimination of fish and invertebrate 

communities (Besser and Leib, 1999).
2 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

Since 1991, various government agencies have 

established initiatives which, through collaborative 

efforts, seek to assess the effects of historical mining 

on water quality in the basin. Included in these efforts 

is the U.S. Department of the Interior Abandoned 

Mine Lands Initiative. As part of this initiative, the 

U.S. Geological Survey (USGS) has provided technical assistance in support of actions by Federal 

land-management agencies to improve water quality 

in contaminated stream systems associated with 

inactive hard-rock mining activities. These efforts are 

important because cost-efficient remediation requires 

a thorough understanding of the extent and cause of 

water-quality degradation and because instream waterquality standards are being proposed by the Colorado 

Department of Public Health and Environment to 

comply with the 1977 Clean Water Act.

One of the important types of information 

needed to characterize water quality in streams 

affected by historical mining are seasonal and spatial 

patterns of toxic metal concentrations and loads. Typically, the concentrations of most dissolved constituents in streams decrease during periods of high 

streamflow and increase during base flow (Anderson 

and others, 1997). An understanding of when tracemetal concentrations are highest improves the ability 

EXPLANATION

Streamflow-gaging station 

and water-quality sampling site

(number from table 2)

A72

M13

Road

Water-quality sampling site

(number from table 2)

Gulch

Eureka

Maggie

Minnie

Boulder Gulch

Gulch

Gulch

Ouray 12 mi.

SAN MIGUEL CO

HINSDALE CO

SAN JUAN CO

CONTINENTAL 

DIVIDE

OURAY CO

SAN JUAN CO

Grand Junction

SOUTH

PLATTE RIVER

DENVER

Study area

COLORADO 

GUNNISON RIVER

COLORADO R

Pueblo

550

110

 River

West For

Cinnamon Cr

k

South Fork

Animas

North Fork

Lake

Emma

Arrastra

Creek

Silver

Lake

Highland

Mary

South 

Fork

Creek

Bear

Creek

Mineral 

Clear

 Cr 

Clear

Lake

Fuller 

Lake

Ice

Lake

Columbine 

Lake

Mill Cr

Lakes

Cunningham Cr

Cement Creek

South

Fork

Fairview Gulch

Illinois Gulch

37°45'

107°37'30"

107°45'

37°52'30"

0

0

1

1

2

2

3

3

4 MILES

4 KILOMETERS 

Base from U.S. Geological Survey 1:100,000 Digital Line Graphs

Albers Equal-Area Conic projection 

Standard parallels 37°30' and 40°30' Central meridian 37°

Silverton

Howardsville

Middleton

Gladstone

Vermilion Peak

elevation

13,894 feet

Chattanooga

Burro

Bridge

Basin boundary

C48

A68

A72

M34

M07

M13

M27

C43

C31

A60

A53

A33

A72

Middle Fk Mineral

C18

M02 C20

Cr

Figure 1. Streamflow-gaging stations and water-quality sampling sites in the upper Animas River Basin.
INTRODUCTION 3

of biologists to evaluate the potential changes to 

the health of aquatic ecosystems. Biologists are 

interested in periods of highest trace-metal concentrations because this is when aquatic species are most 

vulnerable, particularly those in sensitive life-cycle 

development stages. Land managers may be more 

interested in knowing what areas contribute the largest 

trace-metal loads in order to assess where remediation 

would be most effective. Because the largest tracemetal loads generally occur during periods of highest 

streamflow and the highest concentrations occur 

during periods of low streamflow, it is important 

to characterize seasonal concentration patterns over 

an entire annual cycle.

Several years of sample collection often are 

needed to adequately characterize an annual waterquality cycle because wet, dry, and normal years may 

have dissimilar water-quality patterns. A long-term 

water-quality database exists for the Animas Basin and 

was used as a starting point for quantifying seasonal 

water-quality patterns at gaged and ungaged sites in 

the drainage basin. This database, although extensive, 

could not provide accurate estimates of annual waterquality cycles due to the gaps between sampling 

periods. Some continuous estimate of water quality, 

including streamflow and concentration derived from 

the existing data, was needed. Therefore, selected 

data sets were expanded using statistical inference 

to provide estimated data during periods when no 

data were collected. In doing this, a more complete 

estimate of the annual range and timing of constituent 

concentrations and loads could be reported because 

more information was made available. This report 

summarizes how statistical modeling is used to define 

water quality in main-stem streams of the Animas 

Basin as part of a technique for presenting watershedscale information in areas affected by historical 

mining.

Purpose and Scope

The purpose of this report is to characterize 

spatial and seasonal variability of water quality in the 

Animas Basin using a technique called water-quality 

profiling. Water-quality profiling is similar to traditional 

seasonal water-quality characterization techniques in 

which solute concentrations and loads are estimated 

with regression modeling (Searcy, 1959; Steele, 2000). 

In addition, water-quality profiling combines data 

from multiple sites to provide information about 

concentrations and loads along the elevational 

gradient of a stream. Hence, water-quality profiles 

depict seasonal differences in water quality at multiple 

sampling locations in one concise and informative 

graph. Water-quality profiles quantify the timing and 

extent of contaminant occurrence and can aid land 

managers and scientists in determining priority areas 

to be targeted for characterization and possible remediation. Thus, the water-quality profile presents a useful 

way to package information from large and complicated 

data sets in a format that is easily interpreted. A description of how this technique has been applied to waterquality data from the Animas Basin study is included. 

The objectives of this report are to (1) describe 

the methods used to derive water-quality profiles; 

(2) compute water-quality profiles for 15 sampling 

sites in the Animas Basin for streamflow, hardness, 

and dissolved cadmium, copper, and zinc concentrations that represent current water-quality conditions 

in the basin; (3) discuss results of water-quality 

profiling; and (4) present an example application 

of results from water-quality profiling. 

Methodology and interpretation contained in 

this report are intended to guide future water-quality 

studies that seek to quantify effects from multiple 

contamination sources under varying hydrologic 

conditions. In addition, an understanding of the 

seasonal patterns of hardness should improve estimates of metals toxicity because hardness is used in 

numerous equations to determine regulatory standards 

(see Appendix). Hardness is included in many standard calculations because it possesses properties that 

decrease the toxic effects of certain constituents 

including cadmium, copper, and zinc.

Description of Study Area

The upper Animas River Basin encompasses 

about 146 mi2 of rugged terrain in the San Juan 

Mountains of southwestern Colorado (U.S. Geological 

Survey, 2000). Elevations in the basin range from 

9,200 ft near the town of Silverton, Colo., to nearly 

13,900 ft at the summit of Vermilion Peak (fig. 1). 

The upper part of the river has a channel length of 

about 15.5 mi upstream from streamflow-gaging 

station 09359020 (site A72) and an average gradient of 

about 184 ft/mi. Major tributaries to the upper Animas 

River are Mineral and Cement Creeks, which represent 
4 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

about 50 percent of the Animas Basin drainage 

area. Climate in the area is characterized by long, 

cold winters and short (3 to 4 months), cool summers. 

Average monthly air temperature at Silverton ranges 

from 16F in January to 55.3F in July. Precipitation 

averages 45 inches annually, of which about 

70 percent accumulates in a seasonal snowpack 

between November and April (Colorado Climate 

Center, 2000). Most of the remaining precipitation 

falls during monsoonal thundershowers in late 

summer and early fall. The Animas Basin lies in 

the Southern Rocky Mountain ecoregion (Bailey 

and others, 1994), with much of the area lying above 

treeline. Areas below treeline typically are vegetated 

by dense stands of subalpine Engelmann spruce and 

subalpine fir.

Hydrology

Flow in the upper Animas River originates 

primarily from melting snowpack. Snowpack ranges 

from 10 to 20 ft in depth and averages 10 to 50 percent 

water content depending on season (Colorado Climate 

Center, 2000). Variations in annual snowpack are 

reflected in the magnitude and duration of snowmelt 

runoff each year. Figure 2 shows how the annual peak 

and duration of snowmelt runoff at sampling site A72 

varied during the study period (water years 1992 to 

1999). Streamflow patterns at site A72 are representative of streamflow patterns at the other gaging stations 

in the study area. Peak flows in the Animas Basin 

generally occur from late May to early July. Maximum 

daily mean streamflow for the study period occurred

WATER YEAR

DAILY MEAN STREAMFLOW, 

IN CUBIC FEET PER SECOND

MEAN DAILY STREAMFLOW, 

IN CUBIC FEET PER SECOND

0 

500 

1,000 

1,500 

2,000 

2,500 

1992 1993 1994 1995 1996 1997 1998 1999

0 

500 

1,000 

1,500 

2,000 

2,500 

Mean

Minimum

Maximum

OCT DEC JAN FEB MAR APR MAY JUNE JULY AUG SEPT NOV

MONTH

Figure 2. Daily mean streamflow and maximum, minimum, mean daily streamflow at site A72, water 

years 1992 to 1999.
INTRODUCTION 5

on June 10, 1995, at 2,350 ft3/s. Minimum daily 

mean streamflow for the study period occurred on 

January 2, 1992, at 35 ft3/s. This low streamflow 

condition was caused by freezing temperatures in 

winter, when the main source of streamflow is ground 

water. Streamflow derived from monsoonal thunderstorms in late summer and fall typically do not exceed 

peak snowmelt volumes in the spring. However, the 

USGS (2000) suggests that historical peak flooding 

at site A72 occurred October 5, 1911, as a result 

of sustained high-intensity thunderstorms in the 

region.

A pattern measured in many streams is a 

tendency for water during a rising stage to have a 

considerably higher concentration than water passing 

a sampling point at an equal flow rate after peak 

discharge has passed (Hem, 1985). This condition is 

referred to as “hysteresis” and has been documented 

in the Animas Basin by Wirt and others (1999) for 

localized storm runoff events in Cement Creek. 

Besser and Leib (1999) also observed annual hysteresis in the upper Animas River and found that 

concentrations of dissolved zinc and copper, during 

the rising limb of the snowmelt hydrograph, can 

be two or three times those that occur at similar flow 

rates during the receding portion of the snowmelt 

hydrograph. In addition, concentrations of zinc and 

copper tended to increase during periods of base flow 

(November–March) with little variation in streamflow. Figure 3 illustrates the hysteretic pattern at A72 

for dissolved zinc in water year 1997. This plot illustrates the clockwise progression that dissolved zinc 

concentration takes throughout the water year, where 

dissolved zinc is operationally defined as the concentration remaining in 0.45-m filtrate. During base 

flow, concentrations increase despite little or no 

decrease in streamflow. During the rising limb of the 

snowmelt hydrograph (April–June), concentrations 

are diluted; however, concentrations during the rising 

limb are not as dilute as concentrations on the falling 

limb of the snowmelt hydrograph (July–October). 

The pattern for zinc is typical for other trace metals 

as well as for other sampling sites in the Animas 

Basin. Because a hysteretic cycle is typical of 

concentration patterns in the Animas Basin, it is 

possible to correlate not only streamflow discharge to 

constituent concentration but also time (periodicity). 

These correlations form the foundation from which 

water quality will be estimated in this report.

Geology

The rocks of the upper Animas River Basin 

are mineralized as a result of the emplacement of the 

ancient Silverton Caldera, which was the second of 

two volcanoes that collapsed and formed cylindrical pits 

(or calderas) about 26 million years ago (Varnes, 1963; 

Luedke and Burbank, 1996). Lava was deposited within 

and around the caldera, and volcanic ashes accumulated 

in thick deposits throughout the region. Doming and 

collapse of the Silverton Caldera were accompanied by 

the development of numerous faults and fracture zones, 

which acted as a plumbing system for later circulation 

of hot, acidic fluids that contained large amounts of 

dissolved copper, gold, lead, manganese, silica, sulfur, 

and zinc (Casadevall and Ohmoto, 1977). As these 

ore fluids cooled near the land surface, minerals were 

precipitated in the faults, forming veins. The ore fluids 

also altered and leached the surrounding host rocks. 

Some of the rocks in the basin were highly altered 

and mineralized by a combination of intrusive magma 

bodies (molten rock that never breached the land 

surface) and the circulation of hot, mineral-rich fluids.

Within the caldera boundary, veins of disseminated pyrite are present throughout the volcanic rocks. 

Water draining from the mineralized bedrock can be 

acidic and can have high concentrations of dissolved 

minerals (Mast and others, 2000). Outside the caldera 

boundary, veins are less common and the rocks usually 

contain greater amounts of calcium carbonate, which 

tends to buffer the water in streams and mine drainage.

Mines and prospects also can affect the water 

quality of streams: mining leads to an increase in 

weathering rates because minerals in the mines are 

exposed to oxygen on freshly broken rock surfaces 

as air moves through the mines. The exposure to 

the oxygen changes the chemical makeup of some 

of the minerals into forms that more readily dissolve 

in water; therefore, high concentrations of dissolved 

minerals are present in water that drains from some 

mines. Mines also can divert ground water from its 

premining flow path, and the water can be collected 

into a single discharge at the mine entrance. Hence, the 

water flow and quality in the vicinity of a mine can be 

affected.

Mine-Site Remediation

Mining in the San Juan Mountains has left a 

legacy of abandoned mine sites as sources of acidic 

mine drainage. The majority of sites in the Animas 

Basin have been inventoried and some priority mines 
6 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

are slated for remediatory action. This remediation 

work has the potential to alter water quality at downstream sampling sites, thus complicating any interpretation of the data. For this reason, it was necessary to 

document the completion date and location of remediation projects in the Animas Basin. Later in the report, 

this information will be used for trend analysis to 

determine which data sets or portions of data sets 

represent stable water-quality conditions.

Based on the limited number of remediation 

projects completed at the time this report was written 

(2000), trends (if any) in water quality were assumed to 

be related to mined-land remediation done by Sunnyside 

Gold Corporation (SGC). SGC, through an agreement 

signed in May 1996 with the Colorado Department of 

Public Health and Environment, Water-Quality Control 

Division, closed the valves on bulkhead seals placed in 

mines located in Cement Creek and tributaries to the

0 

500 

1,000 

1,500 

2,000 

2,500 

0 

0.2 

0.4 

0.6 

0.8 

1.0 

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 

DAILY MEAN STREAMFLOW, IN CUBIC FEET 

PER SECOND

DISSOLVED ZINC CONCENTRATION, 

IN MILLIGRAMS PER LITER

DAILY MEAN STREAMFLOW, IN 

CUBIC FEET PER SECOND

DATE

EXPLANATION

Water-quality data collected November–March

Water-quality data collected July–October

Water-quality data collected April–June

A

B

OCT NOV DEC JAN FEB MAR APR MAY JUNE JULY AUG SEPT

Figure 3. (A) Dissolved zinc concentration related to daily mean streamflow; and 

(B) daily mean streamflow, sampling site A72, water year 1997.
METHODS OF DATA COLLECTION 7

Animas River and also removed and(or) covered 

selected mine wastes (Colorado Department of Public 

Health and Environment, 1995). Beginning in 1996, 

SGC also treated the majority of streamflow in Cement 

Creek during periods of base flow between sites C18 and 

C20 by using settling ponds to precipitate metals in solution. Most of these projects and many other smaller ones 

were completed in the summer of 1997; however, other 

maintenance work and mined-land remediation is 

ongoing in the Animas Basin by SGC and an increasing 

number of Federal and private parties. Completion dates 

for Federal and private party projects (excluding SGC) 

were not compiled and considered in this study because 

of the small number and small size of each project relative to the SGC projects. Table 1 lists dates and locations 

for mined-land remediation projects that were performed 

by SGC (Larry Perino, Sunnyside Gold Corporation, 

written commun., 2000) and were used in this study 

to correlate to possible trends in water quality.

Acknowledgments

The authors thank Sunnyside Gold Corporation, 

the Animas River Stakeholders Group, and the Bureau 

of Reclamation for data contributed for this analysis. The 

authors also thank David Nimick and Richard Hooper 

for technical reviews; Mary Kidd for editorial review; 

Michael Karlinger, Briant Kimball, and Kernell Ries for 

technical assistance; Jonathan Evans, Jedd Sondergard, 

David Grey, and Mark Gress for field assistance; and 

James Bennett, David Grey, and Mark Gress for maintaining the streamflow-gaging stations.

METHODS OF DATA COLLECTION

Water-quality data presented in this report 

were collected by seven groups and government agencies. During water years 1991 to 1999, water-quality 

data were collected by the Colorado Department of 

Health and Environment (CDPHE), Sunny Side Gold 

Corporation (SGC), Colorado Division of Minerals 

and Geology (CDMG), Colorado River Watch (CRW), 

and the Bureau of Reclamation (BOR). Water-quality 

data also were collected by the USGS during water 

years 1997 to 1999, in cooperation with the Bureau 

of Land Management (BLM), as part of a national 

interdepartmental initiative to provide essential information for mine cleanups planned by land-management 

agencies in the Western United States (Nimick and 

von Guerard, 1998). Beginning in November 1996, 

all data-collection efforts by the USGS were conducted 

in collaboration with the Animas River Stakeholders 

Group (ARSG), which consists of citizens, landowners, 

members of the mining industry, and representatives 

from State and Federal agencies. The ARSG compiles 

and stores all available water-quality data in the Animas 

Basin for purposes of scientific interpretation and landmanagement decision making. The ARSG database for 

the Animas Basin, which was the main source of data 

for this study, resides on the World Wide Web (Animas 

River Stakeholders Group, 2000). Data collected by 

the USGS are reported in Mast and others (2000). 

Continuous streamflow data used in this report were 

obtained by the USGS. This streamflow information is 

also available on the Internet (U.S. Geological Survey, 

1991–2000, URL: http://waterdata.usgs.gov/co/nwis/).

Table 1. Summary of major mined-land remediation projects in the upper Animas River Basin

Project Completion

date

Downstream

sampling sites Subbasin

American Tunnel bulkhead closure September 9, 1996 C20, C31, C43, C48, A72 Cement Creek 

Cement Creek treatment Ongoing C20, C31, C43, C48, A72 Cement Creek 

Terry Tunnel bulkhead closure July 17, 1996 A53, A60, A68, A72 Upper Animas River

Lead Carbonate and American Tunnel mine 

waste dump removal

Fall 1995 C31, C43, C48, A72 Cement Creek

Eureka mill tailings removal Fall 1996 A53, A60, A68, A72 Upper Animas River

Ransom Tunnel bulkhead June 1997 A53, A60, A68, A72 Upper Animas River

Boulder Creek tailings removal June 1997 A68, A72 Upper Animas River

Gold Prince project September 1997 A33, A53, A60, A68, A72 Upper Animas River

Longfellow/Koehler project Fall 1997 M02, M07, M13, M27, M34, A72 Mineral Creek

Pride of the West tailings removal Fall 1997 A53, A60, A68, A72 Upper Animas River

Mayflower mill diversion project Fall 1999 A68, A72 Upper Animas River

Tailings pond surface and ground-water 

drainage project

December 1999 A68, A72 Upper Animas River
8 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

Water-Quality Sampling Sites 

Fifteen sampling sites on the Animas River 

or its two major tributaries (Cement and Mineral 

Creeks) were selected in 1997 for inclusion in this 

study (table 2). Four of these sites were gaging stations 

with continuous streamflow record. The sites were 

in the downstream portion of the Animas Basin; 

two were at the mouths of Cement and Mineral 

Creeks and two were on the Animas River—one 

upstream from Cement Creek and the other downstream from Mineral Creek. These gaged sites were 

selected because the continuous streamflow record 

was needed to estimate streamflow at ungaged sites. 

These sites also had an extensive record of waterquality data (32 to 112 samples). Eleven ungaged sites 

in the middle and upper parts of the Animas Basin also 

were selected because their locations bracketed major 

mining-related sources of metals and because some 

water-quality data were already available. Additional 

water-quality data (Mast and others, 2000; Animas 

River Stakeholders Group, 2000) were collected at 

some sites during water years 1997–99 to ensure that 

sufficient data for statistical modeling were available 

for all sites.

Table 2. Descriptive information for sampling sites

[mi2, square miles]

Sampling

site name

Sampling

site number

(fig. 1)

Latitude

and

longitude

Drainage

area

(mi2)

Percentage of

upper Animas

River drainage

basin

River mile

(distance

downstream

from respective

subbasin

headwaters)

Streamflowgaging

station

number

Cement Creek upstream from 

Gladstone

C18 3753'26''

10738'55''

3.0 2.1 2.7 Ungaged

Cement Creek near Gladstone C20 3753'23''

10739'08''

3.1 2.1 2.9 Ungaged

Cement Creek downstream from 

Fairview Gulch

C31 3752'31''

10740'17''

9.44 6.5 4.5 Ungaged

Cement Creek downstream from 

Illinois Gulch

C43 3750'50''

10740'38''

14.8 10.1 6.3 Ungaged

Cement Creek at Silverton C48 3749'11''

10739'47''

20.1 13.8 9.2 09358550

Mineral Creek near headwaters M02 3753'40''

10742'48''

0.10 0.1 0.15 Ungaged

Mineral Creek at Chattanooga M07 3752'27''

10743'26''

4.4 3.0 1.8 Ungaged

Mineral Creek at Burro Bridge M13 3751'02''

10743'31''

10.4 7.1 3.5 Ungaged

Mineral Creek upstream from 

South Fork Mineral Creek

M27 3749'16''

10713'08''

20.0 13.7 5.9 Ungaged

Mineral Creek at Silverton M34 3748'10''

10740'20''

52.4 35.9 9.0 09359010

Animas River at Eureka A33 3752'45''

10733'55''

18.2 12.5 6.2 Ungaged

Animas River at Howardsville A53 3750'07''

10735'52''

57.7 39.5 10.1 Ungaged

Animas River near Arrastra Gulch A60 3749'38''

10737'34''

60.0 41.1 12.1 Ungaged

Animas River at Silverton A68 3748'40''

10739'31''

70.6 48.4 14.3 09358000

Animas River downstream from 

Silverton

A72 3747'25''

10740'01''

146 100 16.0 09359020
METHODS OF DATA COLLECTION 9

Sample Collection and Laboratory 

Analysis

Sample collection protocols and laboratory 

analytical methods differed slightly among agencies 

and groups; however, there was a concerted effort, 

coordinated by the ARSG, to adopt common sample 

collection protocols, compare laboratory analytical 

methods, and collect concurrent replicate samples 

for comparison of results among laboratories. Brief 

descriptions of the different methods for collection 

of water-quality samples are listed in table 3. Most 

stream samples were collected with depth-integrated 

samplers using the equal-width-increment method 

(EWI), except during high-flow conditions when 

grab sampling was occasionally used for safety 

reasons (Edwards and Glysson, 1988). Each waterquality sample was collected at approximately 

the same time the streamflow measurements were 

taken.

Water-quality samples were filtered with 

0.45-m filters by all groups. Polyethylene bottles 

were used by all participants for sample collection 

and shipment to the laboratory. Concentrated nitric 

acid was used by all participants for preservation 

of water-quality samples for cation and trace-metal 

analyses. Powderless surgical gloves were worn 

by all participants during collection of water-quality 

samples. A detailed description of USGS watersample collection methods is presented in Mast 

and others (2000). 

Laboratory methods used for analysis of waterquality samples and the analytical detection limits 

differed among agencies and groups. The different 

analytical methods and detection limits for each 

element are listed in table 4. Quality-assurance procedures used by all participants included rigorous cleaning 

of sampling equipment and sample bottles and collection 

of quality-control samples (field-equipment blanks and 

replicate samples). Comparison of selected qualitycontrol samples collected by the USGS is presented in 

Mast and others (2000). The results indicate that the 

analytical precision for the majority of replicate samples 

collected by the USGS was ±8 percent. In addition, 

field-equipment blanks did not show any constituent 

contamination from the methods used in this study. 

Agencies and groups who contributed data to the 

ARSG for use in this analysis were quality assured by 

the participating agency as well as the ARSG watershed 

coordinator. No major problems with water-quality 

collection and precision were reported. Concurrent replicates were collected on two occasions by the USGS and 

ARSG groups as a check for any possible differences in 

constituent concentrations resulting from the differences 

in collection method and(or) laboratory procedures 

(Wilde and Radtke, 1997). Results of concurrent replicate sampling indicated that constituent concentrations 

reported by any given party were within 10 percent. 

Quality-assurance information for the various other 

agencies and groups that collected data in coordination 

with the ARSG, and for use in this study, is available on 

the Internet (Animas River Stakeholders Group, 2000).

Table 3. Methods of sample collection for water-quality data collected in the upper Animas River Basin

[USGS, U.S. Geological Survey; BOR, Bureau of Reclamation; CDMG, Colorado Division of Minerals and Geology; CDPHE, Colorado Department 

of Public Health and Environment; CRW, Colorado River Watch Program; SGC, Sunnyside Gold Corporation; EWI, equal-width-increment method; 

m, micrometer]

Participant Method of water-sample collection Streamflow determination Filtration method

USGS EWI wading during low flow

EWI from cableway or bridge during high flow

Wading the stream during low flow; from 

cableway or bridge during high flow

0.45-m plate filter, cartridge 

filter

BOR EWI wading during low flow

EWI from cableway or bridge during high flow

Streamflows obtained from streamflowgaging stations0.45-m plate filter

CDMG EWI wading during low flow

Grab sample from bridge during high flow

Wading the stream during low flow; from 

cableway or bridge during high flow

0.45-m syringe filter

CDPHE EWI wading during low flow

Grab sample from bridge during high flow

Wading the stream during low flow 0.45-m syringe filter

CRW Grab sample from bridge during low and

 high flow; bucket in centroid of flow

Streamflows obtained from streamflowgaging stations0.45-m syringe filter

SGC EWI wading during low flow

Grab sample from bridge during high flow

Streamflows obtained from streamflowgaging stations, flumes, and wading 

during low flow

0.45-m plate filter 
10 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado Table 4. Analytical methods and minimum reporting limits for water-quality data collected in the upper Animas River Basin

[USGS, U.S. Geological Survey; CDMG, Colorado Division of Minerals and Geology; CDPHE, Colorado Department of Public Health and Environment; BOR, Bureau of Reclamation; CRW, Colorado River Watch Program; SGC, Sunnyside Gold Corporation; EDTA, ethylenediaminetetraacetate; ICP-AES, inductively coupled atomic emission spectroscopy; GFAA, graphite furnace atomic absorption spectroscopy; AAS, atomic absorption spectroscopy; Ca+Mg, summation method in terms of calcium carbonate; titration, gravimetric titration; g/L, micrograms per liter; mg/L, milligrams per liter;CaCO3, calcium carbonate]

Analyte

Method Minimum reporting level Units

USGS CDMG CDPHE BOR CRW SGC USGS CDMG CDPHE BOR CRW SGC

Cadmium ICP-AES,

GFAA

ICP-AES ICP-AES GFAA ICP-AES GFAA 2, 0,

0.02

3 3 0.3 3 1 g/L

Calcium ICP-AES,

AAS

ICP-AES ICP-AES GFAA ICP-AES ICP-AES, 

AAS

0.15, 0.1

0.2 1 0.2 0.2 1 mg/L

Copper ICP-AES,

GFAA

ICP-AES ICP-AES GFAA AAS GFAA 4 1 4 5 1 3 g/L

Magnesium ICP-AES,

AAS

ICP-AES ICP-AES GFAA, AAS Titration ICP-AES, 

AAS

0.01 1 1 0.2 1 1 mg/L

Hardness (Ca+Mg) (Ca+Mg) Titration

(EDTA)

(Ca+Mg) (Ca+Mg) Titration

(EDTA)

1 1 1 0.2 1 1 mg/L

(as CaCO3)

Zinc ICP-AES ICP-AES ICP-AES GFAA AAS ICP-AES, 

AAS

20 20 8 10 10 10 g/L
METHODS OF DATA COLLECTION 11

Hardness values reported by each group are 

defined as the total concentration of calcium and 

magnesium ions in units of milligrams per liter 

as calcium carbonate. Analytical methods used 

to determine hardness values are listed in table 4. 

Streamflow Regression Models

Streamflow regression models were developed 

to provide estimated values of daily mean streamflow 

at ungaged sampling sites. The streamflow models 

were developed by regressing daily mean streamflow 

at gaged sampling sites with instantaneous streamflow 

measured at ungaged sites. Sites C18, C20, C31, and 

C43 in Cement Creek were correlated to site C48; 

sites M02, M07, M13, and M27 in Mineral Creek 

were correlated to site M34; and sites A33, A53, 

A60, and A68 in the Animas River were correlated 

to site A72. Sites A33, A53, and A60 were correlated 

to site A72 rather than site A68 (a gaged site on the 

Animas) because the correlation was better at A72. 

During water years 1991–99, 10 to 20 streamflow measurements at each ungaged site were obtained 

during different streamflow conditions. Up to four 

measurements at each of the ungaged sites were 

obtained from 1991 to 1996. The rest of the measurements were obtained from 1997 to 1999 with the 

exception of sites A60, C31, M07, and M13, which 

were all obtained in 1999. When possible, these 

measurements were taken monthly during fall and 

winter and more frequently during snowmelt.

The reader is cautioned when using the streamflow regression models to estimate response from 

localized rainfall events; the assumption of a consistent correlation at gaged and ungaged sites is no longer 

valid because localized rainfall events may have 

varying effects on streamflow at different sites. Also, 

it was assumed that streamflow correlations between 

gaged and ungaged sites were constant from year to 

year unless there was a change in streamflow caused 

by an impoundment or diversion. To test this assumption, the relation between streamflow at gaged and 

ungaged sites was examined at all sampling sites for 

the entire period of record. Figure 4 shows the variation about the regression line when measured streamflow at ungaged site M27 is related to daily mean 

streamflow at gaged site M34 for 5 years of record. 

The majority of streamflow measurements in figure 4, 

regardless of water year, fall within the 95-percent 

confidence interval for the regression line. Other 

sampling sites generally showed little variation in 

correlation from year to year as well.

Streamflow regression models were derived 

using ordinary least-squares (OLS) regression with 

SYSTAT 7.0 software (SPSS Inc., 1997a). Each model 

was evaluated for significance based on the coefficient 

of determination (r2

), p values, residual plots, and the 

standard error of estimate. Values of r2 less than 0.6 

and p values greater than 0.05 were generally considered an indication of a poor correlation. Plots of residuals (where a residual is an estimated value minus its 

corresponding observed value) related to prediction 

estimates and time were used to identify the presence of 

serial correlations. The presence of a serial correlation 

indicates that sampling bias or a trend may exist in the 

data set. When data showed evidence of serial correlation, tests were performed to determine if the correlation was due to trend or sampling bias. A combination 

of parametric and nonparametric tests for trend were 

used when serial correlations existed. If tests for trends 

were found to be inconclusive, it was determined that 

the serially correlated data set was biased and that the 

data set would not be used to derive a streamflow 

regression model. References and a brief description 

of trend testing used for streamflow and water-quality 

model calibration are described in the following 

sections. The final parameter, standard error, is an estimate of the standard deviation of the residuals about the 

regression. The smaller the standard error of estimate, 

the more representative will be the predictions (Driver 

and Tasker, 1990). All diagnostics were considered 

collectively to determine the most appropriate streamflow regression model at ungaged sampling sites.

The form of the regression equation used for 

estimating streamflow at ungaged sampling sites is:

(1)

where

YQ is the streamflow at the ungaged site, in 

cubic feet per second;

B is the slope of the regression line;

XQ is the streamflow at the gaged site, in cubic 

feet per second; and

is the y intercept of the regression line.

In equation 1, the explanatory variable (XQ) is 

daily mean streamflow at the gaged site, which is the 

average streamflow over a 24-hour period. The response 

variable (YQ) estimated using equation 1 is returned as

YQ B XQ =  +
12 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

an instantaneous streamflow value for the ungaged site 

of interest because instantaneous streamflow was used 

to calibrate the models. Because no daily mean streamflow data were available at ungaged sites and because 

instantaneous streamflow data were not available for all 

the samples at gaged sites, the instantaneous streamflow 

value returned by equation 1 is assumed to be similar to 

the daily mean streamflow. This assumption is based on 

streamflow regressions between sites A68 and A72, 

where both instantaneous and daily mean streamflow 

were used to calibrate the surface-water model at A68. 

Both the instantaneous and daily mean models tended 

to underestimate streamflow by an average of about 

8–9 percent. However, the mean daily model tended 

to estimate values that were on average approximately 

4 percent lower than those of the instantaneous model.

Water-Quality Regression Models

Water-quality regression models were developed 

to estimate daily values of hardness and concentrations 

of dissolved cadmium, copper, and zinc at gaged and 

ungaged sites. Water-quality data used for regression 

modeling were obtained during different streamflow 

regimes at all sampling sites. Constituent concentrations 

below minimum reporting levels were assigned one-half 

the value of the minimum reporting level. Alternative 

methods of dealing with data below minimum reporting 

levels, such as tobit or logistic regression (Helsel and 

Hirsch, 1992), are available but were considered beyond 

the scope of this report and were generally not needed 

at the majority of sites due to the high levels of trace 

metals present in the water.

20 

40 

60 

80 

100 

120 

140 

INSTANTANEOUS STREAMFLOW AT M27, 

IN CUBIC FEET PER SECOND 

Ordinary least-squares estimate of mean response

95-percent confidence interval for mean response estimate

1992

1995 1998

1996 1999

DAILY MEAN STREAMFLOW AT M34,

IN CUBIC FEET PER SECOND

0

0

50 100 150 200 250 300 350 400 450 500

EXPLANATION

Figure 4. Measured streamflow at ungaged sampling site M27 related to daily mean streamflow at 

gaged sampling site M34 showing the variation about the regression line of streamflow measurements from differing water years.
METHODS OF DATA COLLECTION 13

Model Selection

Explanatory variables considered for the 

regression included streamflow and seasonal terms. 

A variety of physical and chemical variables such 

as conductivity, alkalinity, sulfate, and colloidal 

iron also may help explain variability in a data set

(Wetherbee and Kimball, 1989); however, continuous data for these parameters were not available 

for consideration in this study. A continuous record 

is needed because estimates of seasonally defined 

(monthly or multimonthly) loads are generated 

using daily values as input (explanatory) variables 

in the regression models. The use of daily values 

will better define monthly loads as compared to

estimates obtained using periodic record from 

random site visits. Instantaneous values also could 

be used as input variables; however, some historical 

data from the streamflow gages used graphic 

recorders, which store continuous record in analog 

format, thus making this data extremely difficult to 

compile and use in electronic format. Therefore, 

for purposes of consistency, all historical daily 

values that could be retrieved electronically and 

compiled easily in spreadsheet form were used in 

this study.

Derivation of water-quality models at gaged 

and ungaged sampling sites differed because of the 

type of streamflow data available at each site. At 

gaged sampling sites, daily mean streamflow was 

used to derive the water-quality models because 

an instantaneous streamflow value was not available 

for every water-quality sample. At ungaged sites, 

the regression models were developed from instantaneous streamflow, which was measured at the time 

of water-quality sampling. As a check for comparability between models derived from daily mean and 

instantaneous streamflow, zinc and copper data were 

regressed against the two streamflow types for each 

gaged site. Results indicated that there was little 

difference in the slope and intercept of each model; 

therefore, it was assumed that gaged and ungaged 

water-quality models were comparable.

In addition to streamflow, the other explanatory variable used for model derivation was a 

seasonal term. Because solute concentrations tend 

to follow a hysteretic pattern (fig. 3) in the Animas 

Basin, it is useful to relate a seasonal component to 

solute concentration. This relation was established 

using methods suggested by Aulenbach and Hooper 

(1994), where sine and cosine pairs are used to simulate constituent concentration as a function of julian 

date. This approach, however, has limited utility for 

smaller data sets. Because data sets were small at 

most of the ungaged sites (7–21 samples), an additional variable was tested in water-quality models. 

This variable also is based on julian date and is often 

referred to as a “flushing-dummy variable” (herein 

referred to as a “flushing variable”). The flushing 

variable assigns a value of 1 to sample data collected 

during periods of interest and a value of 0 to the rest, 

where periods of interest were those associated with 

outliers observed in the summary statistics. An 

example plot of outliers is shown in figure 5. Most 

samples collected at site C20 during the period of 

April 15–May 31 in any given year (shaded symbols) 

do not generally follow correlation patterns measured 

in other months. These outliers were thought to indicate the presence of a hysteretic pattern that could 

not be completely defined by the existing data set; 

however, factors driving these processes are not 

fully understood.

Models were calibrated using interactivestepwise regression to determine which combination 

of explanatory variables composed the most suitable 

regression models (SPSS Inc., 1997b). If too many 

unnecessary variables are included in a regression 

model, a decrease in model significance results 

from lower degrees of freedom (Helsel and Hirsch, 

1992). Interactive-stepwise regression allows the user 

to test different combinations of variables and select 

the most appropriate model based on diagnostics 

such as adjusted r2, standard error of estimate, 

p values for individual variables, and the F statistic. 

Following stepwise regression, each model was 

evaluated for significance using basic statistical 

diagnostics as evaluation tools. These parameters 

included r2

, model p values, and residual plots. 

All diagnostics were considered collectively by using 

the same guidelines used to evaluate the streamflow 

regression models. 

The utility of a water-quality model typically 

increases after a data set is linearized. A hyperbolic 

transformation of streamflow suggested by Johnson 

and others (1969) was used to linearize the streamflow variable. Transformations are typically 

performed to improve the linear fit of the data set, 

where a linear fit of the data will better approximate
14 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

the assumption of normality, which is a requirement 

for parametric statistical techniques like OLS regression. This transformation is defined by the equation:

(2)

where

 is beta, a constant typically in the range 10–3

to 102 ; and

Q is streamflow, in cubic feet per second.

Beta is fit to linearize a given data set. Prior to the 

inclusion of other variables, beta was fit visually using 

a spreadsheet program.

Inverse and logarithmic transformations 

suggested by Ott (1993) were used to linearize the 

response variable. Some iteration is required when 

trying to determine the transformations that would 

constitute the best regression model. Variable transformations and equation forms for water-quality models 

are listed in table 5.

When logarithmic transformations are used, a 

transformation bias is produced when logarithms of 

the estimated mean response (log of the response variable) are retransformed. This transformation usually 

results in the underestimation of the estimated mean 

response. The major portion of this translation bias 

may be eliminated by multiplying the estimated mean 

response by a correction factor (Duan, 1983):

where

BCF is the bias correction factor;

n is the number of observations in the data set; 

and

ei is the least-squares residual for observation i

from the calibration data set, in log units.

Xh 1 1  XQ =    +   

BCF 1 n 10ei

i = 1

n

= 

DISSOLVED ZINC CONCENTRATION, IN 

 MILLIGRAMS PER LITER

HYPERBOLICALLY TRANSFORMED STREAMFLOW

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 

0 

1 

2 

3 

4 

5 

EXPLANATION

Water-quality sample collected during the period

 of June 1–April 14 in any given year for the period

 of record

Water-quality sample collected during the period

 of April 15–May 31 in any given year for the period

 of record

Water-quality sample collected during the period

 of April 15–May 31 in any given year for the period

 of record which was designated as an outlier

Figure 5. Plot of hyperbolically transformed streamflow related to zinc concentration at site C20 showing 

the presence of outliers.
METHODS OF DATA COLLECTION 15

Trend Analysis

If variable transformation did not improve 

the significance of a given model, a second measure 

was taken that tests for trend in a given data set. 

Testing for trend in a data set is useful for determining 

which data points are representative of the condition 

of interest. The condition of interest for this report 

is postremediation (table 1). Trends in a data set also 

may result from climatic shifts or anthropogenic influences. This study focused on trends that resulted from 

anthropogenic influences such as impoundments, 

streamflow diversions, and mined-land remediation. 

It was assumed the effect of trend resulting from a 

change in climate would be negligible on the basis 

of a period of record less than 10 years. 

Three techniques were used to evaluate for 

trends at sampling sites with 30 or more samples. 

Data sets with fewer than 30 samples generally did 

not contain sufficient data to establish a representative 

preremediation sample population. Therefore, it was 

assumed that trends in data sets at sites with fewer than 

30 samples existed if they were downstream from an 

anthropogenic influence and upstream from a site 

where a trend was detected. 

LOWESS smooth curves were calculated 

using SYSTAT 7.0 (SPSS Inc., 1997a) software for 

plots that related time-series data to raw constituent 

concentrations and residuals. Upward or downward 

trending LOWESS curves (fig. 6) indicated the 

presence of a trend in the data set. Next, a parametric 

test that uses a dummy variable (different from the 

flushing variable) to indicate the significance of preand postcondition samples was used. Pre- and postcondition samples were categorized and assigned a 

value of 0 or 1 and included as an explanatory variable in a given water-quality model. A trend was 

detected with this test if the dummy variable was 

retained in the regression model after stepwise 

regression at a 5-percent level of significance. 

The final test was the nonparametric KolmogorovSmirnov two-sample test, which compares the 

cumulative distribution functions for pre- and postcondition residual values (SPSS Inc., 1997b). A 

trend was detected with this test when the alternative 

hypothesis (the cumulative distribution functions 

differ) was accepted at a 5-percent level of significance. An advantage to the Kolmogorov-Smirnov 

two-sample test is that sample sizes for pre- and postconditions were not required to be equal in size. Note 

that if no regression models are considered significant for a given constituent after interactive-stepwise 

regression, no residual-based trend analysis should 

be performed (Crawford and others, 1983).

Following trend analysis, if it was concluded 

that a trend existed in a given data, the data set was 

divided into pre- and postcondition groups and 

regressions were derived using the postcondition 

data set.

Table 5. Summary of equation forms used to simulate constituent concentrations at sampling sites

[hyperbolic transformation constant; , the regression coefficient that is the intercept in the regression model; B and Bn, estimated coefficient of explanatory variables in multiple regression; DV1..n, flushing variable(s); JC, first-order julian date transformation; JD, second-order julian date transformation; 

JE, third-order julian date transformation; JF, fourth-order julian date transformation; XQ, streamflow in cubic feet per second; Xh, transformed streamflow; 

CQ, constituent concentration in milligrams per liter]

Equation

form number

 Response variable Explanatory variables Equation form

Variable Transformation Variable Transformation

 3 CQ None XQ None CQ = B(XQ) + 

 4 CQ Inverse Xh

DV1..n

Hyperbolic1

None

1/CQ = B(Xh) + DV1..n

 5

CQ

None Xh

JC, JD, JE, JF

DV1..n

Hyperbolic1

Trigonometric2

None

CQ = B(Xh) + B1(JC) + B2(JD) 

+ B3(JE) + B4(JF) + DV1..n + 

 36 CQ

Logarithmic Xh

JC, JD, JE, JF

DV1..n

None

Trigonometric2

None

logCQ = B(Xh) + B1(JC) 

+ B2(JD) + B3(JE) + B4(JF) 

+ DV1..n + 

1

Hyperbolic transformation is Xh = 1/{1+(XQ)]}. 2Trigonometric transformation, first order; JC = sin[((julian date))/365], second order; JD = cosine[((julian date))/365], third order; 

JE = sin[((julian date))/182.5], fourth order; JF = cosine[((julian date))/182.5]. 3

Equation form uses a logarithmic transformation that requires a bias correction factor when estimating mean response.
16 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

An example of how a water-quality model is 

derived for zinc at site C18 using the aforementioned 

methods is given in figure 7. Graph A shows streamflow related to measured zinc concentration. Regression diagnostics and graph A indicate that the data 

may benefit from a hyperbolic transformation of 

streamflow or additional explanatory variables, or 

both. Graph B shows how a hyperbolic transformation of streamflow helps to linearize the data set. 

To test for hysteresis in the data set, the additional 

explanatory variables related to date (sin cosine 

pairs and the dummy variable) are added to the model 

(graph C). After stepwise regression, an evaluation of 

regression diagnostics indicated that model significance had improved. Trend testing was then done 

using the model, and residuals from the model and 

results indicated no trend was detectable. Concentrations estimated from the model are checked in 

graph D, which illustrates an approximate 1 to 1 relation (slope 0.95) and residuals variance (data scatter 

about the 1 to 1 line) of 0.89 mg/L between estimated 

zinc related to measured zinc. Deviation in the 1:1 

line in figure 7D is thought to have arisen from some 

logistical problems encountered while collecting 

winter samples (typically higher concentration 

samples) at alternate sites than summer samples 

because of avalanche danger. This deviation was 

not observed at other sites.

Water-Quality Profiles

 Using the streamflow and water-quality regression models, water-quality profiles were calculated 

for the 15 sampling sites. Profiles depicting seasonal 

fluctuations in streamflow, constituent concentration, 

and constituent load were calculated. Streamflow 

profiles were developed by first quantifying daily 

mean streamflow at each sampling site. At ungaged 

sites, the streamflow regression models were used to 

Sept 1991 July 1993 July 1996 Sept 1997 Aug 1999

–0.4

–0.3

–0.2

–0.1

0

0.1

0.2

RESIDUAL

DATE

Figure 6. Residuals scatterplot and LOWESS smooth line showing a downward 

trend in dissolved zinc concentration at site M34.
METHODS OF DATA COLLECTION 17

estimate daily mean streamflows, and at gaged sites, 

the continuous streamflow record was used. The daily 

mean streamflows were used to calculate mean 

monthly flows that were then plotted as streamflow 

profiles by subbasin. Daily mean streamflow values 

were used in the water-quality models to estimate 

daily mean constituent concentration. The daily mean 

constituent concentrations were used to calculate averaged monthly concentrations and results were then 

plotted at each sampling site as a concentration profile. 

Daily mean constituent loads were calculated from the 

product of the daily mean constituent concentration 

and daily mean streamflow. The resulting loads were 

then averaged to produce mean-monthly loads which 

were plotted as loading profiles. Concentration and 

loading profiles were not developed at sites where 

water-quality regression models were not statistically 

significant (p > 0.05).

Limitations of Data Analysis

Use of the statistical regression models provided 

in this report requires the adherence to specific guidelines and an understanding of the limitations inherent 

in each model. Estimates from streamflow and waterquality regression models in this report were only 

provided after careful consideration of the following 

guidelines and limitations:

1 

2 

3 

4 

5 

6 

7 

8 

9 

0 0.2 0.4 0.6 0.8 1.0 

C

A

TRANSFORMED STREAMFLOW

1/[1+β(XQ)]

1 

2 

3 

4 

5 

6 

7 

8 

9 

0 5 10 15 20 25 30 35 

MEASURED DISSOLVED ZINC

CONCENTRATION, IN

MILLIGRAMS PER LITER

MEASURED DISSOLVED ZINC

CONCENTRATION, IN

MILLIGRAMS PER LITER

 MEASURED STREAMFLOW,

IN CUBIC FEET PER SECOND

1 

2 

3 

4 

5 

6 

7 

8 

9 

0 0.2 0.4 0.6 0.8 1.0 

TRANSFORMED STREAMFLOW

1/[1+β(XQ)]

1 

2 

3 

4 

5 

6 

7 

8 

9 

1 2 3 4 5 6 7 8 9 

MEASURED DISSOLVED ZINC

CONCENTRATION, IN MILLIGRAMS

PER LITER

D

B

ESTIMATED DISSOLVED ZINC

CONCENTRATION, IN

MILLIGRAMS PER LITER

ESTIMATED DISSOLVED ZINC

CONCENTRATION, IN 

MILLIGRAMS PER LITER

Figure 7. Zinc concentration graphs at sampling site C18 that relate: (A) Measured streamflow to measured zinc concentration, (B) hyperbolically transformed streamflow to measured zinc concentration, (C) hyperbolically transformed streamflow to 

estimated zinc concentration, and (D) estimated zinc to measured zinc concentration.
18 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

1. Models are site specific.

2. Models with considerable standard error values are 

less accurate than models having low values for 

standard error. 

3. Model output represents water-quality conditions 

during the period sampled and do not necessarily 

represent past or future conditions. 

4. Models represent a specific range of estimations 

which, when exceeded, may decrease a model’s

 usefulness. 

5. Negative values returned by a water-quality model 

can occur at the extremes of the estimation range 

if a model’s calibration data set contains extensive data below a constituent’s minimum 

reporting level (MRL). Loading values for negative concentration estimates were reported as 

one-half the value of the minimum estimate in 

one annual cycle. 

6. Model verification (using sample data collected but 

not included in model derivations) was done at 

sites M27 and M34 only for September because 

of the availability of data at the time this report 

was written (2000). 

Although the correlation for gaged and 

ungaged streamflow is assumed to be constant 

(fig. 4) for the period of record, values estimated 

using the streamflow regression models will not 

always fall within a model’s standard error. This 

situation can occur under certain environmental 

conditions or if the range of estimation is not considered. Because snow depth, snowpack temperature, 

and rainfall can vary in a basin with varying elevation, aspect, and vegetation cover, the onset of runoff 

from these zones will vary (Dunne and Leopold, 

1978). This is especially true when estimating 

streamflow at ungaged sites during periods of intense 

localized rainfall because one or more of the ungaged 

subbasins may not experience any rainfall runoff 

from a rainfall that has affected streamflow at a 

gaged site. When this occurs, the statistical correlation between gaged and ungaged sites is no longer 

meaningful. Caution should be used when estimating 

streamflow at ungaged sites during rainfall. The 

authors were aware of this problem and therefore 

chose to report only monthly loads, thus avoiding 

possibly sizable errors that may result from estimating daily loads during the rainy season. Also, 

the timing of snowmelt runoff (lag time) will differ 

at each ungaged sampling site because snowpack 

at higher elevations or on northern aspects is usually 

the last to melt and south-facing, low-elevation 

basins will melt early. Estimates made for ungaged 

sites using the water-quality models could not be 

adjusted to account for differences in timing of snowmelt runoff because continuous streamflow data were 

not available.

SEASONALITY OF WATER QUALITY IN 

THE UPPER ANIMAS RIVER BASIN

This section contains a summary and analysis of 

seasonal variations in water quality in Cement Creek, 

Mineral Creek, and the upper Animas River using 

methods described previously for constructing waterquality profiles. The summary lists some basic statistics that describe the range of water quality in the 

Animas Basin data set and also gives a brief account of 

where and for which constituents water-quality trends 

are present, as well as what factors may have initiated 

these trends. The analysis contains concentration and 

loading profiles for hardness and dissolved cadmium, 

copper, and zinc at all locations where water-quality 

models were significant. Coefficients and equation 

forms for streamflow and water-quality models are 

provided.

Water-Quality Summary Statistics

A summary of water-quality and streamflow 

data for each sampling site (fig. 1) is presented in 

table 6. Minimum, maximum, median, and mean 

values for dissolved cadmium, dissolved copper, 

dissolved zinc, and hardness were calculated for each 

site. Also included are the results of water-quality 

trend analysis at sites with 30 or more water-quality 

samples. Trend analysis is important for determining 

which portion of a water-quality data set is representative of current conditions. If a water-quality trend 

was detected, the direction of trend also is given as 

upward (U) or downward (D). Where no trend was 

detected, it is assumed that no trend exists; however, 

subtle changes in stream chemistry over time may 

not be detectable using the methods described in 

this report.
SEASONALITY OF WATER QUALITY IN THE UPPER ANIMAS RIVER BASIN 19

Table 6. Summary statistics for selected constituents and properties at sampling sites

[MRL, minimum reporting level; ft3/s, cubic feet per second; mg/L, milligrams per liter; D, downward trend detected; U, upward trend detected; nm, no 

significant model; no, no trend detected]

Summary

statistic

Streamflow

(ft3/s)

Cadmium,

dissolved

(mg/L)

Copper,

dissolved

(mg/L)

Zinc,

dissolved

(mg/L)

Hardness

as CaCO3

(mg/L)

Cement Creek at Gladstone (C18)

Number of samples 47 84 84 85 86

Minimum 0.05 MRL 0.020 1.44 37.0

Maximum 30.9 0.120 3.190 8.40 327

Median 1.04 0.012 0.340 3.53 150

Mean 3.99 0.015 0.396 3.84 167

Trend no no no no no

Cement Creek near Gladstone (C20)

Number of samples 47 85 85 85 85

Minimum 1.5 MRL MRL MRL 151

Maximum 32.6 0.100 0.970 4.58 1,660

Median 3.76 0.003 0.010 0.82 981

Mean 6.87 0.006 0.090 1.11 899

Trend D nm nm no D

Cement Creek near Fairview Gulch (C31)

Number of samples 13 13 13 13 13

Minimum 7.23 MRL 0.020 0.870 75.4

Maximum 98 0.016 0.260 1.75 538

Median 22.9 0.006 0.150 1.18 224

Mean 33.1 0.008 0.130 1.15 286

Cement Creek near Yukon Mine (C43)

Number of samples 9 7 7 9 7

Minimum 20.2 MRL 0.08 0.768 124

Maximum 95.5 0.130 0.186 1.20 486

Median 28.2 0.004 0.114 0.926 182

Mean 43.0 0.006 0.121 0.946 235

Cement Creek at Silverton (C48)

Number of samples 82 170 170 166 113

Minimum 12.4 MRL MRL 0.462 16

Maximum 329 0.011 0.250 1.57 1,460

Median 32.3 0.003 0.055 0.738 291

Mean 57.9 0.002 0.063 0.768 319

Trend no nm nm nm D

Mineral Creek near Headwaters (M02)

Number of samples 18 18 18 18 17

Minimum 0.02 MRL 1.91 7.84 70

Maximum 2.08 1.12 82.0 230 423

Median 0.12 0.256 2.92 80.0 244

Mean 0.46 0.353 2.96 85.3 239

Mineral Creek at Chattanooga (M07)

Number of samples 17 17 17 17 16

Minimum 0.17 MRL 0.020 0.180 24.5

Maximum 65.5 0.028 1.90 8.90 120

Median 11.8 0.005 0.299 1.39 42.2

Mean 20.2 0.008 0.537 2.41 50.4
20 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

Mineral Creek at Burro Bridge (M13)

Number of samples 18 18 18 18 14

Minimum 3.48 MRL 0.020 0.210 26.1

Maximum 123 0.006 0.220 1.80 318

Median 26.8 0.003 0.058 0.719 99.2

Mean 39.1 0.003 0.075 0.868 120

Mineral Creek upstream from South Fork Mineral Creek (M27)

Number of samples 16 21 21 21 14

Minimum 9.09 MRL 0.013 0.181 59.9

Maximum 134 0.010 0.190 1.50 446

Median 27.5 0.002 0.056 0.605 156

Mean 47.8 0.002 0.069 0.589 188

Mineral Creek at Silverton (M34)

Number of samples 207 207 207 207 207

Minimum 14.8 MRL MRL 0.070 45.2

Maximum 863 0.002 0.137 0.750 341

Median 77.0 0.001 0.011 0.234 150

Mean 157 0.001 0.021 0.265 150

Trend no no D D no

Animas River at Eureka (A33)

Number of samples 17 17 17 17 17

Minimum 1.16 0.002 MRL 0.260 30.4

Maximum 177 0.003 0.032 0.573 133

Median 16.6 0.002 0.011 0.458 53.9

Mean 44.6 0.002 0.014 0.442 63.8

Animas River at Howardsville (A53)

Number of samples 17 17 17 17 17

Minimum 14.3 0.001 MRL 0.197 47.7

Maximum 505 0.002 0.010 0.366 149

Median 73.9 0.001 0.002 0.270 92.0

Mean 140 0.001 0.003 0.267 92.2

Animas River near Arrastra Gulch (A60)

Number of samples 11 11 11 11 11

Minimum 26.2 0.001 MRL 0.059 47.5

Maximum 591 0.001 0.015 0.320 138

Median 166 0.001 0.002 0.230 72.2

Mean 182 0.001 0.004 0.246 85.3

Animas River at Silverton (A68)

Number of samples 70 70 70 70 66

Minimum 8.48 0.008 MRL 0.226 20.0

Maximum 883 0.033 0.023 1.24 192

Median 176 0.001 0.005 0.378 80.0

Mean 234 0.002 0.006 0.456 70.0

Trend no no no no no

Table 6. Summary statistics for selected constituents and properties at sampling sites—Continued

[MRL, minimum reporting level; ft3/s, cubic feet per second; mg/L, milligrams per liter; D, downward trend detected; U, upward trend detected; nm, no 

significant model; no, no trend detected]

Summary

statistic

Streamflow

(ft3/s)

Cadmium,

dissolved

(mg/L)

Copper,

dissolved

(mg/L)

Zinc,

dissolved

(mg/L)

Hardness

as CaCO3

(mg/L)
SEASONALITY OF WATER QUALITY IN THE UPPER ANIMAS RIVER BASIN 21

Dissolved cadmium concentrations ranged 

from the minimum reporting level (MRL) to 1.12 mg/L 

(M02). Mean cadmium concentrations were highest 

(0.353 mg/L) at site M02 and lowest (0.001 mg/L) 

at sites M34, A53, and A60. Dissolved copper concentrations ranged from the MRL to 82.0 mg/L (M02). 

Mean copper concentrations were highest (2.96 mg/L) 

at site M02 and lowest (0.003 mg/L) at site A53. 

Dissolved zinc concentrations at sampling sites ranged 

from the MRL to 230 mg/L (M02). Mean zinc concentration levels were highest (85.3 mg/L) at site M02 and 

lowest (0.246 mg/L) at site A60. The only site with zinc 

concentrations less than the MRL was site C20. Hardness concentrations ranged from 16.0 mg/L (C48) to 

1,660 mg/L (C20). Mean hardness concentrations were 

highest at site C20 (899 mg/L) and lowest at site M07 

(50.4 mg/L).

Measured streamflow at the sampling sites 

ranged from 0.02 ft3

/s at site M02 to 2,100 ft3/s 

at site A72. Mean streamflow at the farthest downstream sampling site (A72) was 501 ft3

/s and median 

streamflow was 266 ft3

/s.

Water-Quality Profiles

Streamflow regression model coefficients for 

each ungaged site are listed in table 7. Each streamflow model uses equation 1 (“Streamflow Regression 

Models” section). Water-quality regression model 

coefficients and the appropriate equation form 

number from table 5 are listed in table 8. All streamflow and water-quality regression models used to 

estimate water-quality profiles were significant at 

the 5-percent level (p < 0.05) and generally showed 

constant variance throughout the range of prediction 

in residual plots. Water-quality regression models 

were not considered significant enough for estimation for cadmium and copper at all sampling sites 

in Cement Creek subbasin and for zinc at sites C31, 

C43, and C48.

The range of estimation provided for each 

site in tables 7 and 8 refers to the range of streamflow 

at the time samples were collected. Estimates made 

using the streamflow and water-quality regression 

models outside the range of estimation may be less 

reliable than those made within the limits of model 

calibration.

Water-quality profiles for selected months 

(February, May, June, July, and September) were 

plotted to illustrate seasonal increases or decreases 

in concentration and load between sampling sites 

in an average year. February was selected to represent base flow; May, June, and July were selected 

to represent snowmelt runoff (spring and summer); 

and September was selected to represent the 

monsoonal season (late summer and fall). Note 

that the following sections use the monthly means 

(figs. 10, 13, and 16) for May, June, and July to 

represent snowmelt runoff, while the base flow 

and monsoonal periods (February and September, 

respectively) use only one monthly mean each from 

the water-quality profiles. This was done to account 

for the extreme variability in water quality during 

the runoff period.

Animas River downstream from Silverton (A72)

Number of samples 114 114 114 114 106

Minimum 55.5 MRL MRL 0.160 39.0

Maximum 2,100 0.005 0.029 0.940 340

Median 266 0.001 0.006 0.400 134

Mean 501 0.002 0.007 0.430 146

Trend no no no no no

Table 6. Summary statistics for selected constituents and properties at sampling sites—Continued

[MRL, minimum reporting level; ft3/s, cubic feet per second; mg/L, milligrams per liter; D, downward trend detected; U, upward trend detected; nm, no 

significant model; no, no trend detected]

Summary

statistic

Streamflow

(ft3/s)

Cadmium,

dissolved

(mg/L)

Copper,

dissolved

(mg/L)

Zinc,

dissolved

(mg/L)

Hardness

as CaCO3

(mg/L)
22 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

Cement Creek

Estimates of seasonal water quality in Cement 

Creek subbasin were calculated for sites C18, C20, 

C31, C43, and C48. Regression models were statistically significant for streamflow and hardness at all 

sampling sites. Models at two sites (C18 and C20) 

were statistically significant for zinc, whereas no 

models were significant for cadmium and copper 

at any sites in Cement Creek. Significant models for 

hardness and zinc had r2 values ranging from 0.61 

to 0.95.

The streamflow profile for sites in Cement 

Creek is shown in figure 8. The profile indicates that 

base flow in Cement Creek ranged among sampling 

sites from less than 1.0 ft3/s at C18 to about 13 ft3/s 

at C48. Peak snowmelt runoff generally occurs in 

June and ranged from about 24 ft3/s at site C18 to 

150 ft3/s at C48. Estimated streamflow at the mouth 

of Cement Creek subbasin (C48) is generally about 

10 to 22 percent of the total streamflow at the mouth 

of the Animas Basin (A72) for any given month. 

About 30 to 40 percent of the streamflow at site C48 

originates between sites C20 and C31 during snowmelt runoff. Average annual streamflow at C48 is 

about 42 ft3

/s, or a yield of about 2.1 ft3/s/mi2 (cfsm) 

from the Cement Creek subbasin.

A trend was detected at site C20 that 

indicated that a decrease in streamflow occurred 

in 1996. This change may have resulted from the 

September 1996 closure of the American Tunnel 

bulkhead near Gladstone, Colo. The trend was most 

apparent during base-flow conditions (fig. 9). A trend 

during high flow was not apparent, possibly due to 

streamflow measurement error, which can be more 

prevalent during high stage. No trends in streamflow 

were detected at C48; therefore, all streamflow 

measurements were utilized in streamflow model 

development at sites C31 and C43. Data for samples 

collected before September 1996, however, were 

not used to derive models at site C20. 

Table 7. Summary of streamflow regression model coefficients and diagnostics at ungaged sampling sites

[, the regression coefficient that is the intercept in the regression model; r2, the coefficient of determination; XQ, explanatory variable streamflow, in cubic 

feet per second, at streamflow-gaging station]

Sampling site

(fig. 1)

Range of

estimation1

(ft3/s)



Coefficients for 

explanatory variable r

2

Standard error

of estimate

(ft3/s) XQ

(ft3/s)

 Cement Creek2

C18 8.5–239 –1.26 0.15 0.75 3.5

C20 8.5–202 –1.14 0.206 0.87 2.35

C31 12–174 –2.41 0.53 0.98 4.4

C43 17–123 –0.12 0.76 0.99 2.23

Mineral Creek3

M02 19–903 0.0 0.002 0.83 0.27

M07 19–550 0.0 0.099 0.84 8.3

M13 32–550 0.0 0.21 0.94 8.8

M27 19–400 6.13 0.28 0.96 7.8

Upper Animas River4

A33 57–1,500 –7.4 0.12 0.96 10.7

A53 57–1,500 0.0 0.35 0.95 31.3

A60 83–1,500 0.0 0.40 0.94 40.7

A68 56–2,040 0.0 0.42 0.99 24.2

1Estimation range applies to the explanatory variable of streamflow (XQ) from corresponding streamflow-gaging station. 2

The explanatory variable (XQ) is obtained from streamflow-gaging station number 09358550. 3

The explanatory variable (XQ) is obtained from streamflow-gaging station number 09359010. 4The explanatory variable (XQ) is obtained from streamflow-gaging station number 09359020.
SEASONALITY OF WATER QUALITY IN THE UPPER ANIMAS RIVER BASIN 23

Table 8. Summary of water-quality regression model coefficients and diagnostics at all 15 sampling sites[constant used in hyperbolic transformation; , the regression coefficient that is the intercept in the regression model; B and Bn, coefficient of explanatory variables in multiple regression; DV1, dummy 

variable; JC, first-order julian date transformation; JD, second-order julian date transformation; JE, third-order julian date transformation; JF, fourth-order julian date transformation; XQ, streamflow in cubic 

feet per second; Xh, transformed streamflow; r2, the coefficient of determination; --, indicates explanatory variable was not used in the regression model or was not applicable]

Sampling

site

Equation

form from

table 5

Range of

estimation

(ft3/s)

 

B..Bn Standard 

error of 

estimate

(mg/L)

Bias

correction

factor

XQ

(ft3/s)

Xh JC JD JE JF DV1 r

2

Hardness—Cement Creek

C18 5 0.047–32 0.55 43.3 -- 171 -- -- -- -- c93.9 0.82 36.7 --C20 5 1.5–35.3 10 154 -- 19,429 -- -- -- -- -- 0.90 120 --C31 5 7.2–98.0 1 46.1 -- 4,485 -- -- -- -- -- 0.95 40.3 --C43 5 12.6–95.5 0.1 30.6 -- 900.5 -- -- -- -- -- 0.91 43.2 --C48 5 12.0–329 10 64.8 -- 62,397 -- -- -- -- -- 0.91 49.4 --

Dissolved zinc—Cement Creek

C18 5 0.047–32 1.33 2.55 -- 2.86 0.32 0.65 -- -- b0.62 0.61 0.95 --C20 5 1.5–35.3 0.24 1.66 -- –1.39 0.061 –0.40 -- -- a1.44 0.73 0.538 --

Dissolved cadmium—Mineral Creek

M02 5 0.02–2.1 100 0.0 -- 2.23 -- -- -- -- -- 0.92 0.115 --M07 5 0.17–65.5 0.41 0.0 -- 0.03 -- -- -- -- -- 0.90 0.002 --M13 5 3.5–123 0.05 0.0 -- 0.0071 -- -- -- -- -- 0.95 0.001 --M27 5 9.1–134 0.07 0.0007 -- 0.0041 -- -- -- -- -- 0.64 0.0005 --M34 5 15–863 0.019 0.0005 -- 0.0012 0.0003 0.0000 0.0001 -0.0002 -- 0.65 0.0003 --

Dissolved copper—Mineral Creek

M02 5 0.02–2.1 10 –1.07 -- 60.4 -- -- -- -- -- 0.93 5.25 --M07 5 17–65.5 0.50 0.0 -- 1.19 -- -- -- -- -- 0.81 0.095 --M13 5 3.5–123 1.0 0.020 -- 0.452 -- -- -- -- -- 0.78 0.018 --M27 5 9.1–134 0.05 –0.002 -- 0.173 -- -- -- -- -- 0.80 0.019 --M34 5 15–964 0.05 –0.005 -- 0.087 0.0096 0.0002 -- -- -- 0.76 0.01 --

Hardness—Mineral Creek

M02 5 0.02–2.1 10 75.9 -- 382.7 -- -- -- -- -- 0.80 60.3 --M07 5 17–65.5 0.31 23.05 -- 59.4 -- -- -- -- -- 0.92 5.5 --M13 5 3.5–123 1.5 37.7 -- 1,552 -- -- -- -- -- 0.91 25.5 --M27 5 9.1–134 1.1 34.3 -- 4,196 -- -- -- -- -- 0.94 32.6 --M34 5 15–752 0.05 38.9 -- 417 -- -- -- -- -- 0.95 17.0 --

Dissolved zinc—Mineral Creek

M02 5 0.02–2.1 12 0.0 -- 176 -- -- -- -- -- 0.98 4.06 --M07 5 17–65.5 0.25 –0.014 -- 4.44 -- -- -- -- -- 0.98 0.15 --M13 5 3.5–123 0.07 0.005 -- 1.88 -- -- -- -- -- 0.94 0.120 --M27 5 9.1–134 0.04 0.038 -- 0.985 -- -- -- -- -- 0.91 0.070 --M34 5 15–863 0.025 0.078 -- 0.510 0.049 0.037 –0.024 –0.038 -- 0.91 0.051 --
24 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern ColoradoDissolved cadmium—Upper Animas River

A33 5 1.15–177 0.005 0.0 -- 0.0026 -- -- -- -- -- 0.95 0.0005 --A53 3 14.3–505 -- 0.0007 2.36–E6 -- -- -- -- -- -- 0.66 0.0003 --A60 4 26.2–680 100 1089 -- 505,000 -- -- -- -- f–310 0.60 0.006 --A68 6 8.5–880 0.004 –3.34 -- 0.822 0.064 –0.009 -- -- f0.147 0.64 0.0014 1.0A72 5 55.5–2340 0.004 0.001 -- 0.0013 0.00048 0.00005 –0.00006 –0.0002 b0.0003 0.74 0.0003 --

Dissolved copper—Upper Animas River

A33 5 1.15–177 0.2 0.0051 -- 0.01711 0.0062 –0.0015 -- -- d0.017 0.83 0.005 --A53 5 14.3–505 0.01 0.0 -- 0.0032 -- -- -- -- f0.005 0.88 0.0015 --A60 5 26.2–680 0.000001 –28.42 -- 28.42 –0.0011 –0.0061 -- -- f0.015 0.92 0.0019 --A68 5 8.5–880 -- 0.003 -- -- 0.001 –0.0003 –0.001 –0.0001 b0.0033 0.62 0.002 --A72 5 55.5–2340 -- 0.011 -- -- 0.0075 0.0057 0.0028 –0.00126 -- 0.80 0.003 --

Hardness—Upper Animas River

A33 5 1.15–177 0.1 57.2 -- 49.3 –0.187 21.0 13.0 –1.34 -- 0.98 4.6 --A53 5 14.3–505 0.01 27.2 -- 122 -- -- -- -- -- 0.95 6.6 --A60 5 26.2–680 0.0078 23.3 -- 122 -- -- -- -- -- 0.91 9.0 --A68 5 8.5–880 0.013 45.1 -- 144 7.24 10.0 -- -- -- 0.97 8.0 --A72 5 55.5–2,340 0.02 48.8 -- 520 8.77 9.61 –1.22 –6.58 -- 0.93 23.2 --

Dissolved zinc—Upper Animas River

A33 5 1.15–177 0.035 0.307 -- 0.253 -- -- -- -- -- 0.65 0.051 --A53 5 14.3–505 0.0009 0.0 -- 0.291 -- -- -- -- d0.042 0.98 0.038 --A60 5 26.2–680 0.002 0.0 -- 0.303 -- -- -- -- f0.076 0.97 0.044 --A68 5 8.5–1,050 0.007 0.0 -- 0.834 0.188 –0.028 -- -- e0.140 0.90 0.188 --A72 5 55.5–2,340 0.01 0.249 -- 0.598 0.152 0.0298 –0.0267 –0.0592 -- 0.87 0.07 --

aA value of 1 is assigned to the flushing variable during the period April 15 to May 31.bA value of 1 is assigned to the flushing variable during the period April 1 to May 31.cA value of 1 is assigned to the flushing variable during the period January 1 to March 31.dA value of 1 is assigned to the flushing variable during the period May 1 to May 31.eA value of 1 is assigned to the flushing variable during the period April 1 to June 30.fA value of 1 is assigned to the flushing variable during the period May 1 to June 30.

Table 8. Summary of water-quality regression model coefficients and diagnostics at all 15 sampling sites—Continued[constant used in hyperbolic transformation; , the regression coefficient that is the intercept in the regression model; B and Bn, coefficient of explanatory variables in multiple regression; DV1, dummy 

variable; JC, first-order julian date transformation; JD, second-order julian date transformation; JE, third-order julian date transformation; JF, fourth-order julian date transformation; XQ, streamflow in cubic 

feet per second; Xh, transformed streamflow; r2, the coefficient of determination; --, indicates explanatory variable was not used in the regression model or was not applicable]

Sampling

site

Equation

form from

table 5

Range of

estimation

(ft3/s)

 

B..Bn Standard 

error of 

estimate

(mg/L)

Bias

correction

factor

XQ

(ft3/s)

Xh JC JD JE JF DV1 r

2
SEASONALITY OF WATER QUALITY IN THE UPPER ANIMAS RIVER BASIN 25

Snowmelt runoff

Base flow

Monsoon season

SITE

EXPLANATION

February

May

June

July

September

RIVER MILE

STREAMFLOW, IN CUBIC 

FEET PER SECOND

0

20

40

60

80

100

120

140

160

C18 C20 C31 C43 C48

2.7 2.9 4.5 6.3 9.2

STREAMFLOW AT C20, IN CUBIC FEET PER SECOND

6 

8 

10 

4 

2 

0

5 10 15 20 25 30 35 40

Data collected before September 1996

Data collected after September 1996

EXPLANATION

STREAMFLOW AT C48, IN CUBIC FEET PER SECOND

Figure 8. Streamflow profile for Cement Creek subbasin.

Figure 9. Streamflow data before and after the American Tunnel bulkhead closure in 

September of 1996.
26 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

Concentration and loading profiles for hardness 

and zinc estimated using the Cement Creek waterquality models are shown in figure 10. The highest 

monthly concentration of hardness (1,275 mg/L) was 

estimated at site C20 during base flow and the lowest 

(55 mg/L) was estimated at site C18 during snowmelt 

runoff. The largest increases in hardness concentration 

were estimated between sites C18 and C20 throughout 

the year, with an average increase during base flow of 

about 1,000 mg/L and an increase during snowmelt 

runoff of about 130 mg/L. Hardness concentrations in 

Cement Creek were lowest at site C18 and highest at 

site C20 year-round. 

The largest hardness load in Cement Creek 

was estimated at site C48 during snowmelt runoff 

(87,000 lb/d), and the smallest was estimated 

at site C18 during base flow (1,000 lb/d). Among 

the sites, the largest increase in hardness load to 

Cement Creek during base flow occurred between 

sites C18 and C20 (about 10,000 lb/d); however, 

loading between sites was fairly consistent regardless 

of location at this time. During snowmelt runoff, the 

largest increase in hardness loading to Cement Creek 

also occurred between sites C18 and C20 (about 

29,300 lb/d).

A flushing variable was retained in the hardness water-quality model after stepwise regression 

at site C18 for the period of January 1 to March 31 

(DV, table 8). When estimating concentrations at this 

site during this period, a value of 1 is assigned to the 

flushing variable. The reason for the increase in hardness concentration during this period is not known, but 

outliers were apparent in XY plots of the measured 

sample data much like the outliers for zinc were 

apparent at C20 (fig. 5).

Figure 10. Water-quality profiles for hardness and dissolved zinc at Cement Creek sampling sites.

HARDNESS,

AS CACO3

HARDNESS,

AS CACO3

ZINC ZINC

CONCENTRATION, IN MILLIGRAMS PER LITER

LOAD, IN POUNDS PER DAY

20,000 

40,000 

60,000 

80,000 

100,000 

50 

100 

150 

200 

250 

300 

350 

3.0 

4.0 

5.0 

6.0 

Snowmelt runoff

Base flow

Monsoon season

EXPLANATION

February

May

June

July

September

2.7

0

1.0

2.0

2.9 4.5 6.3 9.2 2.7

0

2.9 4.5 6.3 9.2

RIVER MILE RIVER MILE

0 0

200

400

600

800

1,000

1,200

1,400

C18 C20 C31 C43 C48

SITE

C18 C20 C31 C43 C48

SITE
SEASONALITY OF WATER QUALITY IN THE UPPER ANIMAS RIVER BASIN 27

A downward trend in hardness concentration 

was detected at sites C20 and C48. This trend correlates to the period when the American Tunnel bulkhead was closed in September of 1996 (table 1). 

Figure 11 shows the apparent shift in the correlation of 

streamflow with hardness concentration that occurred 

during this time period. As a result of trend detection, 

data collected before September 1996 were not used to 

derive water-quality regression models for hardness at 

sites C20, C31, C43, and C48.

The highest monthly concentration of zinc 

(5.4 mg/L) was estimated at site C18 during base flow 

and the lowest (0.40 mg/L) was estimated at site C20 

during the same period (fig. 10). Concentrations at 

site C18 were higher during base flow than snowmelt 

runoff, but concentrations at site C20 tended to be 

lower during base flow relative to snowmelt runoff 

concentrations. This may be a result of the watertreatment plant located between sites C18 and C20; 

the plant treats most of the water in Cement Creek 

during the base flow (winter) but does not have the 

capacity to treat all the streamflow during snowmelt 

runoff. Both sampling sites retained a flushing variable 

for either April through May or May (DV, table 8).

The largest and smallest monthly loads of 

zinc were estimated at site C20 during May (330 lb/d) 

and February (2.4 lb/d). The largest increase in zinc 

loading between the two sites occurred in May (about 

50 lb/d); however, this amount is small relative to 

the loading that occurred upstream from C20, which 

ranged from 90 to 100 percent (depending on the 

season) of the total load at C20.

Mineral Creek

Streamflow and water-quality profiles in 

Mineral Creek subbasin were calculated for sites M02, 

M07, M13, M27, and M34. Regression models at all 

sites in Mineral Creek were statistically significant for 

streamflow, hardness, cadmium, copper, and zinc and 

had r2

 values ranging from 0.64 to 0.98 (table 8). The

HYPERBOLICALLY TRANSFORMED STREAMFLOW

HARDNESS, IN MILLIGRAMS PER LITER AS CACO3

0

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0.01 0.02 0.03 0.04 0.05 0.06 0.07

Data collected before September 1996

Data collected after September 1996

EXPLANATION

Figure 11. Hardness data at site C20 before and after the American Tunnel bulkhead closure in 

September of 1996. 
28 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

M02 M07 M13 M27 M34

SITE

Snowmelt runoff

Base flow

Monsoon season

EXPLANATION

February

May

June

July

September

RIVER MILE

STREAMFLOW, IN CUBIC FEET 

PER SECOND

0.15 1.8 3.5 5.9 9.0

0

100

200

300

400

500

streamflow profile for Mineral Creek subbasin is 

shown in figure 12, and concentration and loading 

profiles for hardness and metals are shown in 

figure 13.

The streamflow profile for Mineral Creek indicates that base flow ranged from less than 0.1 ft3

/s at 

M02 to about 21 ft3

/s at M34. Peak snowmelt runoff 

generally occurs in June and ranges from about 1.0 ft3

/s 

at site M02 to 460 ft3

/s at M34. Streamflow at the 

mouth of Mineral Creek (M34) accounts for about 32 

to 52 percent of the total streamflow at A72, of which 

about 43–70 percent originates between sites M27 and 

M34. Average annual streamflow draining Mineral 

Creek subbasin is about 120 ft3

/s or 2.2 cfsm.

The highest monthly concentration of hardness (335 mg/L) was estimated at site M02 during 

February, and the lowest (26 mg/L) was estimated 

at site M07 in June. Hardness values were highest 

at site M02 throughout the year with concentrations 

that ranged from 100 to 335 mg/L; however, concentrations at site M27 approached 335 mg/L during 

base flow.

The largest monthly hardness load in 

Mineral Creek was estimated at site M34 during 

June (139,000 lb/d), and the smallest was estimated at site M02 in February (86 lb/d). The largest 

increase in hardness loading to Mineral Creek during 

base flow occurred between sites M13 and M27 

(16,500 lb/d), and the largest increase in loading 

during snowmelt runoff occurred between sites M27 

and M34 (71,000 lb/d). Hardness loads from Mineral 

Creek are generally about 27 to 37 percent of the 

hardness load at A72 for any given month.

The highest monthly concentration of cadmium 

(0.39 mg/L) was estimated at site M02 in February, 

and the lowest (0.0006 mg/L) was estimated at site M34 

in July. Cadmium concentrations were highest at M02 

throughout the year with concentrations that ranged 

from 0.020 to39 mg/L. Concentrations decreased 

considerably at downstream sampling sites where estimates ranged from 0.0006 mg/L at M34 to 0.016 mg/L 

at M07. 

The largest monthly load of cadmium in 

Mineral Creek was estimated at site M34 during June 

(1.8 lb/d), and the smallest (0.06 lb/d) was estimated

Figure 12. Streamflow profile for Mineral Creek subbasin. 
SEASONALITY OF WATER QUALITY IN THE UPPER ANIMAS RIVER BASIN 29

CADMIUM CADMIUM

COPPER COPPER

ZINC ZINC

0.15 1.8 3.5 5.9 9.0

M02 M07 M13 M27 M34

0.15 1.8 3.5 5.9 9.0

0.15 1.8 3.5 5.9 9.0

M02 M07 M13 M27 M34

M02 M07 M13 M27 M34

M02 M07 M13 M27 M34

0.15 1.8 3.5 5.9 9.0

0.15 1.8 3.5 5.9 9.0

M02 M07 M13 M27 M34

M02 M07 M13 M27 M34

0.15 3.5 5.9 9.0 1.8

Snowmelt runoff

Base flow

Monsoon season

EXPLANATION

February

May

June

July

September

RIVER MILES RIVER MILES

0.15

0.01 0

50

100

150

200

250

0

5

10

15

20

25

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

0

0.5

1

1.5

2

0.1

1

10

100

1,000

0.0001

0.001

0.01

0.1

1

0.0001

0.001

0.01

0.1

1

10

100

1,000

10

100

1.8 3.5 5.9 9.0 0.15 1.8 3.5 5.9 9.0 LOAD, IN POUNDS PER DAY

CONCENTRATION, IN MILLIGRAMS PER LITER

(LOG SCALE)

M02 M07 M013 M027 M034 M02 M07 M013 M027 M034

HARDNESS,

AS CACO3

HARDNESS,

AS CACO3

SITE SITE

Figure 13. Water-quality profiles for hardness and dissolved cadmium, copper, and zinc at Mineral Creek sampling 

sites.
30 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

at site M02 in February. The largest increase in 

cadmium loading to Mineral Creek during base flow 

occurred upstream from site M02 (0.10 lb/d) and 

between sites M02 and M07 (0.08 lb/d). The largest 

increase in loading during snowmelt runoff occurred 

between sites M27 and M34 (0.87 lb/d). The estimates 

indicate that seasonal cadmium loads at the mouth of 

Mineral Creek subbasin (M34) account for about 15 

to 25 percent of the total cadmium load at A72. 

The highest monthly concentration of copper 

(39.8 mg/L) was estimated at site M02 in February, 

and the lowest (0.0004 mg/L) was estimated at 

site M34 in August (not shown in fig. 13). Copper 

concentrations were highest at M02 throughout 

the year with concentrations that ranged from 3.0 

to 39.8 mg/L. Concentrations decreased considerably 

at downstream sampling sites where estimates ranged 

from 0.0004 mg/L at M34 to 0.6 mg/L at M07. 

The largest monthly load of copper in 

Mineral Creek was estimated at site M02 during 

July (24.5 lb/d), and the smallest (2.4 lb/d) was 

estimated at site M13 in January. The largest increase 

in copper loading to Mineral Creek during base flow 

occurred upstream from site M02 (10 lb/d) and 

between sites M13 and M27 (4.5 lb/d). The largest 

increase of loading during snowmelt runoff primarily 

occurred upstream from site M02 (23 lb/d) and 

between sites M13 and M27 (3.3 lb/d). Stream reaches 

M02 through M13 and M27 to M34 showed no net 

loading increases during any part of the year. This 

does not imply that no loading is occurring in these 

sections but may indicate an attenuation of copper 

load that exceeds the rate of loading inputs from 

sources draining into these stream segments. Estimates 

indicate that seasonal copper loads at M34 account 

for about 31 to 65 percent of the load at A72 for any 

given month.

The highest monthly concentration of zinc 

(112 mg/L) was estimated at site M02 during February, 

and the lowest (0.05 mg/L) was estimated at site M34 

in June. Zinc concentrations were highest at M02 

throughout the year with estimates that ranged from 

13.0 to 112 mg/L. Concentrations decreased considerably at downstream sampling sites where estimates 

ranged from 0.05 mg/L at M34 to 2.9 mg/L at M07.

The largest monthly load of zinc in Mineral 

Creek was estimated at site M34 during May (220 lb/d), 

and the smallest was estimated at site M02 in February 

(28 lb/d). The largest increase in zinc loading to Mineral 

Creek during base flow occurred between sites M13 

and M27 (11.2 lb/d), and the largest increase in loading 

during snowmelt runoff occurred between sites M27 

and M34 (39.5 lb/d). Models indicate that seasonal zinc 

loads at M34 account for about 10 to 27 percent of the 

load at A72 for any given month.

Downward trends in trace-metal concentrations were measured for zinc and copper at site M34 

(fig. 14). These trends correlate to the period when the 

Longfellow/Koehler project was completed in fall 1997 

(table 1). The project consisted of the removal of dump 

material near sampling site M02 that was exposed to 

runoff during spring snowmelt. As a result of trend 

detection, data collected before October 1997 were not 

used to derive water-quality regression models for zinc 

and copper at sites M02, M07, M13, and M27.

Upper Animas River 

Streamflow and water-quality profiles in the 

upper Animas River subbasin were calculated for 

sites A33, A53, A60, A68, and A72 (fig. 1). Site A72, 

which is downstream from sites C48 and M34, was 

included so the combined loading from Cement 

and Mineral Creeks could be quantified and compared 

to sites upstream from A68. Inclusion of A72 also 

provided an indirect estimate of the effects of Cement 

Creek to the Animas River. Direct estimates of Cement 

Creek loading were not calculated because of the insignificance of the trace-element water-quality models.

Regression models at all sites in the upper 

Animas River subbasin (including A72) were statistically significant for streamflow, hardness, cadmium, 

copper, and zinc. These models had r2

 values that 

ranged from 0.60 to 0.98. The streamflow profile for 

the Animas River is shown in figure 15, and concentration and loading profiles for metals are shown in 

figure 16.

Two sites in the Animas River (A68 and A72) 

had large enough data sets to test for trends; however, 

no trends were detected for hardness or trace metals. 

This finding indicates that site A72 does not show 

any measurable effect from the various remediation 

projects performed in Mineral and Cement Creeks. 

This does not indicate that no effect has resulted 

from remediation but only that no effect has been 

measured. Further water-quality monitoring of this 

site may be necessary to detect subtle trends in 

concentration that may be occurring over an extended 

period of time.
SEASONALITY OF WATER QUALITY IN THE UPPER ANIMAS RIVER BASIN 31

The streamflow profile for the Animas River 

indicates that base flow ranged among sampling sites 

from about 0.5 ft3

/s at A33 to about 63 ft3/s at A72. 

Peak snowmelt runoff generally occurred in June and 

ranged from about 140 ft3

/s at site A33 to 1,200 ft3/s at 

A72. Simulated streamflow at the mouth of the upper 

Animas River subbasin (A68) accounted for about 41 

to 42 percent of the total streamflow at A72, of which 

about 55 to 83 percent originated between sites A33 

and A53. Average annual streamflow draining the 

upper Animas River subbasin (A68) is about 126 ft3/s, 

or 1.8 cfsm.

The highest monthly concentration of hardness 

(291 mg/L) was estimated at site A72 in January (not 

shown), and the lowest (30.0 mg/L) was estimated 

at site A33 in June (fig. 16). Water-quality models 

estimated hardness concentrations that showed dilution during snowmelt runoff and concentration 

during base flow. At site A72 in January, estimated 

concentrations of hardness were approximately 150 

to 180 mg/L higher than concentrations at other 

sampling sites in the upper Animas River subbasin 

despite the increase in streamflow; however, in May, 

concentrations at all sampling sites were within

EXPLANATION

HYPERBOLICALLY TRANSFORMED STREAMFLOW

CONCENTRATION, IN MILLIGRAMS PER LITER

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 0.1 0.2 0.3 0.4 0.5 0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

0.7 0.8

ZINC

COPPER

Data collected before October 1997 

Data collected after October 1997 

Figure 14. Trace-metal concentrations at site M34 before and after the 

Longfellow/Koehler project was completed in October of 1997.
32 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

35.0 mg/L of one another. This finding indicates the 

presence of hardness sources in Mineral and Cement 

Creeks, which could be of vital importance to aquatic 

habitat during base flow. The decrease in hardness 

detected at C20 did not noticeably alter the correlation of hardness and streamflow at A72 based on 

results from the trend testing performed for this 

study; however, additional portal closures in Mineral 

or Cement Creeks could theoretically increase stream 

toxicities in receiving waters if additional hardness 

sources are eliminated.

The largest monthly load of hardness in the 

Animas River was estimated at site A72 during June 

(375,000 lb/d), and the lowest (1,450 lb/d) was estimated at site A33 in February. The largest increase 

in loading in the Animas River during base flow and 

during snowmelt runoff occurred between sites A68 

and A72 at 75,500 lb/d (base flow) and 187,000 lb/d 

(snowmelt runoff). Sources in the A68 to A72 stream 

segment contribute about 59 to 76 percent of the 

average monthly hardness load estimated at A72. 

Approximately two-thirds of the load in the A68 

to A72 stream reach originates from Mineral Creek 

subbasin during snowmelt runoff, and two-thirds 

originates from Cement Creek during base flow. 

The highest monthly concentration of 

cadmium (0.0028 mg/L) was estimated at site A68 

during the month of March (not shown in fig. 16), 

and the lowest (0.0007 mg/L) was estimated at 

site A53 in January and February. Detection limits 

reported by the USGS for 1997–99 samples in the 

upper Animas River for cadmium were lower than 

those of Cement and Mineral Creek because analysis 

was done using GFAA (table 4). The lower detection 

limit improved the significance of the cadmium 

models because the majority of cadmium concentrations would have otherwise been below detection 

limits and therefore estimated. This decision was 

based on information gained in Mineral Creek and 

the awareness of low levels from preexisting data. 

A33 A53 A60

SITE

A68 A72

Snowmelt runoff

Base flow

Monsoon season

EXPLANATION

February

May

June

July

September

RIVER MILE

STREAMFLOW, IN CUBIC FEET 

PER SECOND

6.2

0

200

400

600

800

1,000

1,200

10.1 12.1 14.3 16.0

Figure 15. Streamflow profile for the Animas River.
SEASONALITY OF WATER QUALITY IN THE UPPER ANIMAS RIVER BASIN 33

A33 A53 A60 A68 A72

SITE SITE

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

RIVER MILE RIVER MILE

0.2

0

0

50

100

150

200

250

300

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.005

0.010

0.015

0.020

0.025

0.030

0

0

0

2

4

6

8

0

100,000

200,000

300,000

400,000

5

10

15

20

25

30

35

500

1,000

1,500

2,000

6.2 10.1 12.1 14.3 16.0

6.2 10.1 12.1 14.3 16.0

6.2 10.1 12.1 14.3 16.0

6.2 10.1 12.1 14.3 16.0

6.2 10.1 12.1 14.3 16.0

6.2 10.1 12.1 14.3 16.0

6.2 10.1 12.1 14.3 16.0

0.3

0.4

0.5

0.6

0.7

0.8

0.9

ZINC

COPPER

CADMIUM

6.2 10.1 12.1 14.3 16.0

ZINC

COPPER

CADMIUM

LOAD, IN POUNDS PER DAY

CONCENTRATION, IN MILLIGRAMS PER LITER

Snowmelt runoff

Base flow

Monsoon season

EXPLANATION

February

May

June

July

September

HARDNESS,

AS CACO3

HARDNESS,

AS CACO3

Figure 16. Water-quality profiles for hardness and dissolved cadmium, copper, and zinc at Animas River sampling sites.
34 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

A flushing variable was retained in the 

cadmium water-quality model after stepwise regression for May 1 through June 30 at sites A60 and A68 

and for the period of April 1 to May 31 at site A72 

(DV, table 8). When estimating cadmium at these sites 

during these months, a value of 1 is assigned to the 

flushing variable.

The cadmium model at site A53 estimated a 

positive relation of cadmium to streamflow which 

showed the highest cadmium concentration in June 

(0.0017 mg/L) and lowest in February (0.0007 mg/L). 

Ore mills formerly located between sites A33 and A53 

supplied huge quantities of tailings to the river at rates 

50 to 4,700 times greater than the natural production 

of sediments from hill slopes (Vincent and others, 

1999). The presence of mill tailings in the flood 

plain may explain why there is a positive relation of 

cadmium concentration to streamflow because higher 

streamflow will tend to flush areas of the flood plain 

that are dry during base flow. Water-quality models 

for other sites estimated cadmium concentrations 

that showed dilution during snowmelt runoff, which 

was preceded by an initial flush of higher cadmium 

concentration.

The largest load of cadmium in the Animas 

River was estimated at site A72 during June (7.0 lb/d), 

and the lowest (0.03 lb/d) was estimated at site A33 in 

February. The largest increases in cadmium loading in 

the Animas River during base flow and during snowmelt runoff occurred between sites A68 and A72 at 

0.36 lb/d (base flow) and 4.3 lb/d (snowmelt runoff). 

Loss of cadmium load between sites A53 and A60 

may be a result of attenuation in this segment. Models 

indicate that cadmium loads at the mouth of the upper 

Animas River subbasin (A68) account for about 38 to 

62 percent of the load at A72 for any given month.

The highest monthly concentration of copper 

(0.024 mg/L) was estimated at site A33 during 

February, and the lowest (0.001 mg/L) was estimated 

at site A60 in February. Estimated copper concentrations showed dilution during snowmelt runoff with 

the exception of site A72, which were preceded by 

an initial flush of higher copper concentration in May. 

The highest concentrations of copper in the upper 

Animas River typically occurred at the same sampling 

sites year-round. During base flow, the models 

estimated the highest concentrations of copper at 

site A33 (0.024 mg/L) and A72 (0.023 mg/L), during 

snowmelt runoff, the highest concentrations of copper 

(0.013 mg/L) occurred at site A33. During the 

monsoon season, models at site A60 estimated 

concentrations (0.0044 mg/L) that approached those 

of A33 (0.0047). The higher concentrations at A60 

during the monsoon season may be occurring as a 

result of rainfall runoff from Arrastra Creek, which 

drains an area with several historical mill sites and 

mines where copper ore was processed.

Flushing variables were retained in several 

copper water-quality models after stepwise regression 

for the month of May at site A33, the period of May 1 

to June 30 at sites A53 and A60, and April 1 to May 31 

at site A68 (DV, table 8).

The largest load of copper in the Animas River 

was estimated at site A72 during May (33 lb/d), and 

the lowest (0.25 lb/d) was estimated at site A33 in 

February. The largest increase in copper loading in the 

Animas River during base flow and during snowmelt 

runoff occurred between sites A68 and A72 at 7.5 lb/d 

(base flow) and 16.7 lb/d (snowmelt runoff). Moderate 

gains in load (5.2 lb/d) during snowmelt runoff also 

occurred between A33 and A60. Estimates from 

models indicate that seasonal copper loads at A68 are 

generally about 5 to 39 percent that of the load at A72 

for any given month and that the lowest percentages 

occur during base flow.

The highest monthly concentration of zinc 

(0.90 mg/L) was estimated at site A68 during April 

(not shown), and the lowest (0.21 mg/L) was estimated 

at site A60 in July. Models estimated zinc concentrations that showed dilution during snowmelt runoff and 

concentration during base flow.

The largest load of zinc in the upper Animas 

River was estimated at site A72 during June (1,750 lb/d), 

and the smallest (1.1 lb/d) was estimated at site A33 in 

February. The largest increase in zinc loading among 

sites during base flow and snowmelt runoff occurred 

between A68 and A72 at 125 lb/d (base flow) and 

570 lb/d (snowmelt runoff). However, about 40 to 

60 percent of the average monthly zinc load estimated 

at site A72 for any given month originates upstream 

from site A68. Thus, roughly one-half of the zinc 

loading at site A72 appears to be coming from Cement 

and Mineral Creeks and the other one-half comes from 

sources upstream from site A68. The largest increases 

in zinc loading upstream from A68 occurred between 

either A33 to A53 or A60 to A68 throughout the year.
APPLICATION OF WATER-QUALITY PROFILING IN MINERAL CREEK 35

APPLICATION OF WATER-QUALITY 

PROFILING IN MINERAL CREEK

The application presented in this section demonstrates that the results from water-quality profiling not 

only provide information about seasonal streamflow, 

concentration, and load on a watershed scale but also 

provide information from which to compare contaminant source loads to instream loads. This comparison 

is important because undetected contaminant sources 

(such as diffuse inputs) may be indicated when quantified contaminant sources do not account for the total 

instream load. This technique is generally referred to 

as “mass accounting;” however, this example includes 

a seasonal component, which quantifies the portion 

of source contaminants accounted for in the stream 

during different seasonal periods. Therefore, in addition to the information obtained from a loading profile, 

this application requires characterization data from 

contaminant sources affecting water quality.

The example stream segment was bracketed by 

sampling sites M27 and M34 (fig.1) in the Mineral 

Creek subbasin. Selection of this segment was based 

on the availability of source characterization data and 

the large downstream loading increases in May and 

June for cadmium and zinc (fig. 13). Changes in load 

were calculated from the Mineral Creek loading 

profiles and then compared to loading calculations 

from contaminant sources, which were obtained from 

seasonal mine-site characterization studies (Animas 

River Stakeholders Group, 2000; Mast and others, 

2000). Monthly samples from Mast and others were 

used to calculate instantaneous loading values from 

the characterized sources in the example reach. 

Loading values are calculated by taking the product 

of concentration, flow, and a conversion constant to 

obtain a loading value measured in pounds per day 

(lb/d). These values were assumed to represent mean 

monthly loads from each characterized source because 

these were the only data available. Loading changes at 

main-stem sites were calculated by subtracting the 

constituent load at site M27 from the constituent load 

at M34. The resulting number is called the “net load.” 

A positive value of net load indicates an overall gain in 

load for the selected stream reach. A negative value of 

net load indicates that the metal load is attenuated 

along the stream segment, typically because of adsorption and coprecipitation processes (Schemel and 

others, 2000). Characterized source loads were then 

compared to the net load. The difference between 

characterized source load and net load (where net load 

is subtracted from characterized source load) in the 

stream segment is referred to as the “load discrepancy.” The load discrepancy indicates the portion of 

load at the downstream sampling site (M34) contributed by uncharacterized sources in the selected stream 

segment, as well as the attenuation of trace metals. 

Load discrepancies near 0 indicate that there are 

no uncharacterized sources or that a portion of the 

instream and(or) characterized source load is being 

attenuated and uncharacterized sources contribute 

additional loadings, which happen to equal the net 

load. A positive load discrepancy indicates that the net 

load is being attenuated and a negative load discrepancy indicates that uncharacterized sources exist. 

Net loads for cadmium, copper, and zinc in the 

example segment between sites M27 and M34 were 

calculated for May and September. Loading discrepancies were evaluated by comparing all available source 

loading data for the reach to the net load. This stream 

segment had source characterization data collected at 

five mine portals during high streamflow and the relatively lower flows of the monsoon season (May and 

September). These portals represent the majority of 

mining effects in the stream segment; however, other 

diffuse sources such as in-stream waste-rock piles and 

ground-water inputs may exist that have not been characterized. The presence of these additional sources 

is assumed if the load discrepancy in this example 

is determined to be negative for a given metal.

From the Mineral Creek loading profile, on 

average during May, net loads of cadmium, copper, 

and zinc were 1.0 (+0.49) lb/d, –3.1 (+1.52) lb/d, and 

111 (+55.4) lb/d (fig. 13), indicating a net gain for 

each metal, with the exception of copper, along the 

stream segment. Error estimates for the net loading 

values are reported as + one standard deviation 

of the estimated net loading for the given month. 

Total characterized source loads in May, computed 

using the source characterization data for each 

constituent, indicated that the load discrepancy 

was about: (1) –0.44 to –1.42 lb/d for cadmium, 

(2) 5.17 to 2.14 lb/d for copper, and (3) –40.6 to 

–151 lb/d for zinc. The negative load discrepancies 

indicate that other sources contribute possibly substantial amounts of cadmium and zinc in the M27 to M34 

stream segment during May, whereas the positive load 

discrepancy for copper indicates that attenuation is 
36 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

taking place. Taking the average loading-discrepancy 

estimate from the above ranges for each metal indicates that the uncharacterized sources contribute 

nearly 95 percent of the cadmium load, 0 percent of 

the copper load (or uncharacterized sources also are 

attenuated), and about 85 percent of the zinc load at 

M34. 

For September, net mean monthly loads 

of cadmium, copper, and zinc were estimated 

to be 0.22 (+0.12) lb/d, –9.3 (+0.27) lb/d, and 

13.8 (+10.5) lb/d (fig. 13), indicating a net gain 

in cadmium and zinc load and a net loss of copper 

along the stream segment. Total characterized source 

loads in September indicated that the load discrepancy ranged from (1) –0.07 to –0.31 lb/d for 

cadmium, (2) 9.6 to 9.06 lb/d for copper, and 

(3) 5.01 to –16.0 lb/d for zinc. The negative load 

discrepancy range for cadmium indicates that other 

sources are contributing metals along the stream 

segment during September. The positive loadingdiscrepancy range for copper indicates an attenuation 

of metals from characterized sources. Zinc is more 

difficult to define based on the positive and negative 

range; however, an average value for the loading 

discrepancy (–5.49) indicates that additional sources 

may be contributing in the stream reach during 

September. Taking the average loading-discrepancy 

estimate for each constituent indicates that the 

uncharacterized sources contribute about 86 percent 

of the cadmium load, 0 percent of the copper load 

(or uncharacterized sources also are attenuated), 

and about 52 percent of the zinc load at M34. 

Further characterization of the M27 to M34 

segment may help explain where uncharacterized 

loading sources of cadmium and zinc emanate from, 

especially during snowmelt runoff. The results indicate these inputs may occur diffusely because the 

majority of surface-water sources were thought to 

have been accounted for. Other surface-water sources 

may exist, however, and there is likely some degree of 

flushing of attenuated metals. It also appears likely 

that diffuse sources of copper are not problematic in 

the M27 to M34 stream reach where water-column 

chemistry is concerned. Regardless of where these 

metals emanate from, mass accounting with loading 

profiles indicates that metal sources in the upper 

Animas River Basin may change substantially with 

season, as well as the need for further characterization 

studies in the example segment. 

SUMMARY

Because instream water-quality standards are 

being proposed for the Animas River as part of the 

1977 Clean Water Act (Public Law 92–500), multiagency collaborative efforts are being made to assess 

the effects of historical mining on water quality in the 

upper Animas River Basin. Included in these efforts 

is the U.S. Department of the Interior Abandoned 

Mine Lands Initiative. As part of this initiative, 

the U.S. Geological Survey has provided technical 

assistance in support of Federal land-management 

agencies’ actions to improve water quality in contaminated stream systems associated with abandoned 

hard-rock mining activities. One of the important 

types of information needed to characterize water 

quality in streams affected by historical mining is the 

seasonal pattern of toxic trace-metal concentrations 

and loads. These patterns were estimated with a technique called water-quality profiling.

Streamflow and water-quality data collected 

at 15 sites in the upper Animas River Basin during 

water years 1991–99 were used to develop waterquality profiles. Data collected at each sampling site 

were used to develop ordinary least-squares regression models for streamflow and water quality. 

Streamflow was estimated by correlating instantaneous streamflow measured at ungaged sites with 

continuous streamflow records from streamflowgaging stations in the subbasin. Water-quality regression models were developed to estimate hardness and 

dissolved cadmium, copper, and zinc concentrations 

based on streamflow and seasonal terms. Results 

from the regression models were used to calculate 

water-quality profiles for streamflow and select 

constituent concentrations and loads.

Trends in streamflow and water quality were 

detected at three locations in the Animas Basin. A 

downward trend in streamflow was detected at 

site C20, which may have resulted when the American 

Tunnel bulkhead was closed in September 1996. Data 

at sites C20 and C48 indicated a decrease in hardness 

concentrations, which also correlates to the period 

when the American Tunnel was closed. Data collected 

at site M34 indicated a decrease in copper and zinc 

concentrations, which correlated to the period when 

the Longfellow/Koehler project was completed in 

fall 1997. 
SUMMARY 37

Hardness estimates at all sampling sites in the 

Animas Basin indicated that concentrations were generally highest in Cement Creek and lowest in the upper 

Animas River. At sampling sites in each subbasin, 

models estimated hardness concentrations that varied 

substantially during base flow and moderately during 

snowmelt runoff. 

Inputs of hardness loading to Cement Creek were 

highest between sites C18 and C20 during snowmelt 

runoff; however, loading inputs were fairly proportional 

the rest of the year among sites. The largest increases of 

hardness load in Mineral Creek were estimated to be 

occurring between sites M27 and M34 during snowmelt 

runoff and between sites M13 and M27 during base 

flow. The largest increases of hardness loads in the 

upper Animas River occurred between sites A68 and 

A72 during base flow and snowmelt runoff. The largest 

increases of hardness load estimated upstream from 

A68 occurred between A33 and A53 throughout the 

year.

A summary of results from the water-quality 

profiles indicates that cadmium concentrations and loads 

estimated in Mineral Creek and the upper Animas River 

were generally higher at Mineral Creek sites; however, 

concentrations at site A68 exceeded those estimated 

at site M34 throughout the year. Model estimates at 

site A53 showed a positive relation of cadmium concentration to streamflow. Flushing of cadmium at sampling 

sites in the Animas River was readily apparent from the 

models and raw data, whereas flushing of cadmium in 

Mineral Creek was less extensive. 

The largest increase of cadmium load in Mineral 

Creek was estimated between sites M27 and M34 

during snowmelt runoff. Cadmium loads in the upper 

Animas River were largest between sites A68 and A72 

throughout the year. 

Copper concentrations estimated for Mineral 

Creek and the upper Animas River indicated that 

concentrations were significantly higher at Mineral 

Creek sites. Estimates of copper concentration at 

upper Animas River sampling sites were generally 

highest in May as a result of flushing, but concentrations in Mineral Creek were highest during the later 

part of the base-flow period. 

The largest increases of copper load in Mineral 

Creek were estimated to be occurring upstream from 

site M02 and between sites M13 and M27 throughout 

the year. The largest increases of copper load in the 

upper Animas River occurred between sites A68 and 

A72 and to a lesser extent between A33 and A60 

during snowmelt runoff. 

In Cement Creek subbasin (C18 and C20), the 

highest zinc concentrations were estimated at site C18 

throughout the year. Concentrations at site C18 were 

highest during base flow, whereas concentrations at 

site C20 were lowest during this period. This may 

be a result of the water-treatment plant located between 

sites C18 and C20. The plant treats most of the water in 

Cement Creek during base flow (winter), but does not 

have the capacity to treat all the streamflow during 

snowmelt runoff. Zinc concentrations estimated for 

Mineral Creek and the upper Animas River indicated 

that concentrations in Mineral Creek decrease in a downstream fashion, whereas in the upper Animas River, 

concentrations increased substantially between sites A60 

and A68 despite increases estimated by the streamflow 

profile. Zinc concentrations in Mineral Creek were not 

elevated during spring snowmelt by flushing events, but 

zinc concentrations in the Animas River showed this 

effect.

The largest zinc load in Cement Creek was 

estimated to be occurring upstream from site C18 

throughout the year. Between C18 and C20, decreases 

in zinc load were estimated during base flow. The largest 

increases of zinc load in Mineral Creek were between 

sites M27 and M34 and upstream from site M02 during 

snowmelt runoff. The largest increases of zinc load 

during base flow and snowmelt runoff in the upper 

Animas River occurred between sites A68 and A72; 

however, loading of zinc upstream from A68 accounts 

for approximately 40 to 60 percent of zinc load at A72.

Quantification of cadmium, copper, and zinc 

loads in a stream segment in Mineral Creek (M27 to 

M34) was presented as an example application of waterquality profiling. The application used a method of mass 

accounting to quantify the portion of metal loading in the 

segment derived from uncharacterized sources during 

different seasonal periods. During May, uncharacterized 

sources contributed nearly 95 percent of the cadmium 

load, 0 percent of the copper load (or uncharacterized 

sources also are attenuated), and about 85 percent of the 

zinc load at M34. During September, uncharacterized 

sources contributed about 86 percent of the cadmium 

load, 0 percent of the copper load (or uncharacterized 

sources also are attenuated), and about 52 percent of the 

zinc load at M34. Characterized sources accounted for 

more of the loading gains estimated in the example reach 

during September, possibly indicating the presence of 

diffuse inputs during snowmelt runoff. Regardless of 

where the sources emanate from, the results indicate 

that metal sources in the upper Animas River Basin 

may change substantially with season.
38 Using Water-Quality Profiles to Characterize Seasonal Water Quality and Loading in the Upper Animas River Basin, 

Southwestern Colorado

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APPENDIX
APPENDIX 43

Appendix. State of Colorado table value standards for trace metals affected by hardness concentration

[Modified from Colorado Department of Public Health and Environment, 1995, classification and numeric standards for San Juan and Dolores River 

Basins: Water Quality Control Commission, regulation notice 3.4.0, 65 p.; g/L, microgram per liter; dis, dissolved]

Metal1

Aquatic life1,3,4,D

Agriculture2

Drinkingwater 

supply2

Acute

(g/L)

Chronic

(g/L)

Cadmium (1.13667–[(ln hardness)

(0.04184)]) e(1.128[ln(hardness)]

–3.6867)

(Trout)=(1.13667–[(ln 

hardness)(0.04184)])

e(1.128[ln(hardness)]–3.828)

(1.10167–[(hardness)

(0.04184)]) e(0.7852[ln(hardness)]–2.715)

10A

(30-day)

5.0B

(1-day)

Chromium III5 e(0.819[ln(hardness)]+2.5736) e(0.819[ln(hardness)]+0.5340) 100A

(30-day)

50B

(1-day)

Copper e(0.9422[ln(hardness –1.7408) e(0.8545[ln(hardness)]–1.7428) 200A 1,000B

(30-day)

Lead (1.46203–[(ln hardness)

(0.145712)]) e(1.273[ln(hardness)]–1.46)

(1.46203–[(ln hardness)

(0.145712)]) e(1.273[ln(hardness –

4.705)

100A

(30-day)

50B

(1-day)

Manganese e(0.3331[ln(hardness)]+6.4676) e(0.3331[ln(hardness)]+5.8743) 200A

(30-day)

50(dis)C

(30-day)

Nickel e(0.846[ln(hardness)]+2.253) e(0.846[ln(hardness)]+0.0554) 200A

(30-day)

100B

(30-day)

Silver 1⁄2e(1.72[ln(hardness)]–6.52) e(1.72[ln(hardness)]–9.06)

(Trout) = e(1.72[ln(hardness)]–10.51)

100C

(1-day)

Uranium e(1.1021[ln(hardness)]+2.7088) e(1.102[ln(hardness)]+2.2382)

Zinc e(0.8473[ln(hardness)]+0.8618) e(0.8473[ln(hardness)]+0.8699) 2,000A

(30-day)

5,000C

(30-day)

1

Metals for aquatic-life use are stated as dissolved unless otherwise specified.

2

Metals for agricultural and domestic uses are stated as total recoverable unless otherwise specified.

3

Hardness values to be used in equations are in milligrams per liter as calcium carbonate and shall be no greater than 400 mg/L. The hardness 

values used in calculating the appropriate metal standard should be based on the lower 95-percent confidence limit of the mean hardness value at the 

periodic low-flow criterion as determined from a regression analysis of site-specific data. Where insufficient site-specific data exist to define the mean 

hardness value at the periodic low-flow criterion, representative regional data shall be used to perform the regression analysis. Where a regression analysis is not appropriate, a site-specific method should be used. In calculating a hardness value, regression analysis should not be extrapolated past the 

point that data exist.

4Both acute and chronic numbers adopted as stream standards are levels not to be exceeded more than once every 3 years on the average.

5Unless the stability of the chromium valence state in receiving waters can be clearly demonstrated, the standard for chromium should be in terms 

of chromium VI. In no case can the sum of the in-stream levels of hexavalent and trivalent chromium exceed the water-supply standard of 50 g/L total 

chromium in those waters classified for domestic water use.

AEPA—Water Quality Criteria 1972, Ecological Research Series, National Academy of Sciences, National Academy of Engineering, 

EPA–R3–73–033, March 1973, Washington, D.C., 594 p.

BEPA National Interim Primary Drinking Water Regulations, 40 Code of Federal Regulations, Part 141.

CEPA, March 1977, Proposed National Secondary Drinking Water Regulation, Federal Register, v. 42, no. 62, p. 17143–17147.

DFinal Report of the Water Quality Standards and Methodologies Committee to the Colorado Water Quality Control Commission, June 1986.