Characterization of Mainstem Streams 

Using Water-Quality Profiles

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

Chapter E11 of

Integrated Investigations of Environmental Effects of Historical 

Mining in the Animas River Watershed, San Juan County, Colorado

Edited by Stanley E. Church, Paul von Guerard, and Susan E. Finger

Professional Paper 1651

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract .......................................................................................................................................................547

Introduction.................................................................................................................................................547

Purpose and Scope ..........................................................................................................................548

Description of Study Area ...............................................................................................................548

Mine Site Remediation ............................................................................................................548

Data Collection and Data Synthesis .......................................................................................................550

Water-Quality Sampling Sites .........................................................................................................550

Sample Collection and Laboratory Analysis ................................................................................550

Streamflow Regression Models .....................................................................................................551

Water-Quality Regression Models .................................................................................................552

Water-Quality Hysteresis........................................................................................................552

Model Selection .......................................................................................................................552

Trend Analysis ..........................................................................................................................552

Limitations of Data Analysis ............................................................................................................552

Seasonality of Water Quality in the Animas River Watershed Study Area ......................................555

Water-Quality Profiles ......................................................................................................................555

Cement Creek ...........................................................................................................................558

Mineral Creek ...........................................................................................................................560

Upper Animas River .................................................................................................................562

Application of Water-Quality Profiling in Mineral Creek .....................................................................567

Summary......................................................................................................................................................568

References Cited........................................................................................................................................569

Figures

 1. Map showing streamflow-gauging stations and water-quality sampling sites 

in the Animas River watershed study area ..........................................................................549

 2. Graphs showing dissolved zinc concentration related to daily-mean streamflow, 

and daily-mean streamflow, sampling site A72, water year 1997 .......................................553

 3. Graph of transformed streamflow related to zinc concentration at site C2 

showing presence of outliers .................................................................................................554

 4. Streamflow profile for Cement Creek basin .........................................................................558

 5. Graph of streamflow data before and after American tunnel bulkhead closure 

in September 1996 ....................................................................................................................559

 6. Water-quality profiles for hardness and dissolved zinc at Cement Creek 

sampling sites ............................................................................................................................560

 7. Graph of hardness data at site C20 before and after American tunnel bulkhead 

closure in September 1996 ......................................................................................................561

 8. Streamflow profile for Mineral Creek basin .........................................................................562

 9. Water-quality profiles for hardness and dissolved cadmium, copper, and zinc 

at Mineral Creek sampling sites .............................................................................................563

 10. Graph of trace-metal concentrations at site M34 before and after Longfellow/

Koehler project was completed in October 1997 ................................................................564

 11. Streamflow profile for upper Animas River ..........................................................................565

 12. Water-quality profiles for hardness and dissolved cadmium, copper, and zinc 

at Animas River sampling sites ..............................................................................................566
Tables

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

watershed study area ..............................................................................................................550

 2. Descriptive information for sampling sites ...........................................................................551

 3. Summary of equation forms used to simulate constituent concentrations 

at sampling sites .......................................................................................................................554

 4. Summary of streamflow regression model coefficients and diagnostics 

at sampling sites .......................................................................................................................555

 5. Summary of water-quality regression model coefficients and diagnostics 

at sampling sites .......................................................................................................................556

Conversion Factors, Definitions, and Abbreviations

Multiply By To obtain

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

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)

0.01093 cubic meter per second per square 

kilometer ((m3/s)/km2)

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

Temperature in degrees Celsius (°C) is converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Water year is defi ned as the 12-month period October 1 through September 30. The water year 

is designated by the calendar year in which it ends.

Additional Abbreviations

α the regression coeffi cient that is the intercept in the regression model

β constant beta used in hyperbolic transformation

B and Bn

 coeffi cient of explanatory variables in multiple regression

CQ constituent concentration in milligrams/liter

DV1

 fl ushing variable

JC

 fi rst order Julian date transformation

JD second order Julian date transformation

JE third order Julian date transformation

JF

 fourth order Julian date transformation

XQ streamfl ow in cubic feet

Xh

 transformed streamfl ow

r2

 coeffi cient of determination

μg/L micrograms per liter

μS/cm microsiemens per centimeter at 25° Celsius

mg/L milligrams per liter

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 Animas River watershed study area 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 ungauged sites with 

continuous streamflow records from streamflow-gauging 

stations in the basins. Water-quality regression models were 

developed to estimate concentrations of hardness and dissolved cadmium, copper, and zinc based on streamflow and 

seasonality. 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 

selected stream segment in Mineral Creek (sites M27 to M34) 

provides 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 (sites where no water-quality data 

have been collected) during different seasonal periods. During 

May, uncharacterized sources contribute nearly 95 percent of 

the cadmium load, 0 percent of the copper load (or uncharacterized sources are also attenuated), and about 85 percent 

of the zinc load at M34. During September, uncharacterized 

sources contribute about 86 percent of the cadmium load, 

0 percent of the copper load (or uncharacterized sources are 

also 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 inflows during snowmelt 

runoff. Regardless of the sources of the loads, the results indicate that metal sources in the Animas River watershed study 

area may change substantially with season.

Introduction

Seasonal and spatial patterns of toxic metal concentrations and loads are one of the important types of information 

needed to characterize water quality in streams affected by 

historical mining. Typically, the concentrations of most dissolved constituents in streams decline during high streamflow 

and increase during base flow (Anderson and others, 1997). 

An understanding of when trace-metal concentrations are 

highest improves the ability of biologists to evaluate the health 

of aquatic ecosystems. Biologists are interested in periods of 

highest trace metal concentrations, as aquatic species would 

be highly vulnerable to acute exposures (hours to a few days) 

under these field conditions. In contrast, land managers generally are most interested in knowing what areas contribute the 

largest trace metal loads in order to assess where remediation 

would be most effective. Because the largest trace metal loads 

generally occur during periods of high streamflow, and high 

concentrations and toxicities (Besser and Leib, this volume, 

Chapter E19) occur during periods of base flow, it is important 

to characterize seasonal concentration patterns over an entire 

annual cycle.

Several years of sample collection are needed to adequately characterize an annual water-quality cycle, because 

wet, dry, and normal years can have dissimilar water-quality 

patterns. A long-term water-quality database that exists for the 

Animas River watershed was used as a starting point for quantifying seasonal water-quality patterns at gauged and ungauged 

sites in the watershed. This database, although extensive, could 

not provide accurate estimates of annual water-quality cycles 

due to the gaps between sampling periods. A continuous estimate of water quality, including streamflow and concentration 

derived from the existing data, was needed. Therefore, select 

Chapter E11

Characterization of Mainstem Streams Using 

Water-Quality Profiles

By Kenneth J. Leib, M. Alisa Mast, and Winfield G. Wright
data sets were expanded using linear regression to estimate 

periods when no data were collected. By this means, a more 

accurate estimate of the annual range and timing of constituent 

concentrations and loads could be reported, because uncertainty was reduced when the data set was expanded to include 

the modeled data. This report summarizes how statistical 

modeling is used to characterize water-quality conditions in 

mainstem streams of the Animas River watershed study area 

as part of a technique for presenting watershed-scale information about 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 

River watershed study area in southwestern Colorado, using 

a technique known herein as water-quality profiling. Waterquality profiling is similar to traditional seasonal water-quality 

characterization techniques in which solute concentrations and 

loads are estimated with regression modeling (Searcy, 1959; 

Cohn and others, 1989; Crawford and others, 1983). In addition, water-quality profiling combines data from multiple sites 

to provide information about concentrations and loads along 

the 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 water-quality data from the 

Animas River watershed study is included.

The objectives of this report are the following:

• Describe the methods used to derive water-quality 

profiles

• Compute water-quality profiles for 15 sampling sites 

in the Animas River watershed for streamflow and 

concentrations of hardness and dissolved cadmium, 

copper, and zinc, which are representative of current 

water-quality conditions in the Animas River watershed study area

• Discuss results of water-quality profiling

• Present an example application of results from waterquality profiling.

Methodology and interpretation in this report are 

intended to guide future water-quality studies that seek to 

quantify effects from multiple sources of contamination in 

varying hydrologic conditions. An understanding of seasonal 

patterns of hardness could also improve estimates of metal 

toxicity, because hardness is used to determine regulatory 

standards for selected constituents. Hardness of water, calculated by summing equivalents of polyvalent cations (primarily 

calcium and magnesium) and expressed as equivalent concentration of calcium carbonate (CaCO3

), is included in many 

calculations of instream water-quality standards because it 

can lessen the toxic effects of certain dissolved constituents 

including cadmium, copper, and zinc.

Description of Study Area

The Animas River watershed study area encompasses 

about 146 mi2 of rugged terrain in the San Juan Mountains of 

southwestern Colorado (U.S. Geological Survey, 1991–2000). 

Elevations 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 Animas River has a channel 

length of about 15.5 mi upstream from streamflow-gauging 

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 about 50 percent 

of the Animas River watershed study area drainage. Climate in 

the area is characterized by long, cold winters and short (3 to 

4 month), cool summers (von Guerard and others, this volume, 

Chapter B). Average monthly air temperature at Silverton, 

Colo., ranges from 16° F in January to 55.3° F in July. Precipitation averages 45 in. 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 River watershed study 

area lies in the Southern Rocky Mountain ecoregion province 

(Bailey and others, 1994), with much of the area existing 

above treeline. Areas below treeline typically are vegetated by 

dense stands of subalpine Engelmann spruce and subalpine fir.

Mine Site Remediation

Mining in the San Juan Mountains has left a legacy of 

inactive mines as sources of acidic mine drainage (Church, 

Mast, and others, this volume, Chapter E5). The majority of 

sites in the Animas River watershed study area have been 

inventoried, and some priority mines are slated for remediation. Remediation has the potential to change water quality at 

downstream sampling sites. For this reason, it was necessary 

to document the completion date and location of remediation 

projects in the study area. 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 minedland remediation done by Sunnyside Gold Corporation. 

Sunnyside Gold Corporation, through an agreement signed 

in May 1996 with the Colorado Department of Public Health 

548 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 549 EXPLANATION Streamflow-gauging station and water-quality sampling site (number from table 2) Water-quality sampling site (number from table 2) Road A72 M13 37O 45'

107O37'30" 107O45'

37O

52'30"

0

0

1

1

2

2

3

3

4 MILES

4 KILOMETERS 

Base from U.S. Geological Survey1:100,000 Digital Line GraphsAlbers Equal-Area Conic projectionStandard parallels 37o30' and 40o30'

Central meridian 37o00'

Gulch

Eureka

Maggie

Minnie

Boulder

ch Gul

Gulch

Gulch

Ouray 12 mi

SAN MIGUEL CO

HINSDALE CO

SAN JUAN 

CO

CON NEN TI

L TA D IVIDE

OURAY CO

550

110

 River

Cinnamon Cr

West 

Fork

South

Fork

Animas

North Fork

Former LakeEmma

Arrastra

Cr

SilverLake

HighlandMary

South 

Fork

Cr

Bear

Cr

Mineral 

Clear Cr Clear Lake

Fuller Lake

IceLake

Columbine Lake

Mill Cr

Lakes

Cement Cr

k or F South

Fairview Gulch

Illinois Gulch

Cunningham Cr Silverton

Howardsville

Middleton

Gladstone

Vermilion Peak

elev. 13,894

Chattanooga

BurroBridge

Watershed study area boundary

C48

A68

A72

M34

M07

M13

M27

C43

C31

A60

A53

A33

A72

Middle Fk M ni eral Cr

C18

C20 M02

Animas Riverwatershed study area

C O L O R A D O

Durango

Figure 1. Streamflow-gauging stations and water-quality sampling sites in the Animas River watershed study area.
and Environment, Water-Quality Control Division, closed the 

valves on bulkhead seals placed in inactive mines in Cement 

Creek and tributaries to the Animas River and also removed 

and (or) covered select mine wastes (Colorado Department 

of Public Health and Environment, 1995). Beginning in 

1996, Sunnyside Gold Corporation also treated the majority of streamflow in Cement Creek during periods of base 

flow between sites C18 and C20 (fig. 1), 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 River watershed study 

area by both Sunnyside Gold Corporation and an increasing 

number of Federal and private parties. Completion dates for 

Federal and private party projects (excluding Sunnyside Gold 

Corporation) were not compiled and considered in this study 

because of the small number and small size of each project relative to the Sunnyside Gold Corporation projects. Table 1 lists 

dates and locations for mined-land remediation projects done 

by Sunnyside Gold Corporation (Larry Perino, Sunnyside 

Gold Corporation, written commun., 2000), used in this study 

to interpret possible trends in water quality.

Data Collection and Data Synthesis

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), 

Sunnyside Gold Corporation (SGC), Colorado Division of 

Minerals and Geology (CDMG), Colorado River Watch 

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

data were also collected by the USGS during water years 1997 

to 1999, in cooperation with the Bureau of Land Management 

(BLM), as part of this study. Data from each group reside 

collectively in the Animas River Stakeholders Group (ARSG) 

website located at www.wateinfo.org/arsg/main.html#data.

Water-Quality Sampling Sites

In 1997, fifteen sampling sites on the upper Animas 

River and its two major tributaries (Cement and Mineral 

Creeks) were selected for inclusion in this study (table 2). 

Four of these sites were streamflow-gauging stations with continuous streamflow record (fig. 1). The sites were in the downstream portion of the Animas River watershed study area; two 

were near the mouths of Cement (C48) and Mineral Creeks 

(M34) and two were on the Animas River—one upstream from 

Cement Creek (A68) and the other downstream from Mineral 

Creek (A72). Continuous streamflow records from these 

gauged sites were used to estimate streamflow at ungauged 

sites. These sites also had an extensive record of water-quality 

data (from 32 to 112 samples). Also, 11 ungauged sites in the 

middle and upper parts of the Animas River watershed study 

area were selected because their locations bracketed major 

mining-related sources of metals and because some waterquality 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.

Sample Collection and Laboratory Analysis

Sample-collection protocols and laboratory analytical methods differed slightly among agencies and groups; 

however, there was a concerted effort to adopt common 

Table 1. Summary of major mined-land remediation projects in the Animas River watershed study area.

[-, not applicable]

Project Mine No.1 Completion date Downstream sampling sites Basin

American tunnel bulk-head closure 96 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 120 July 17, 1996 A53,A60,A68,A72 Upper Animas River.

Lead Carbonate and American tunnel mine 

waste dump removal.

95 Fall 1995 C31,C43,C48,A72 Cement Creek.

Sunnyside Eureka mill tailings removal 164 Fall 1996 A53,A60,A68,A72 Upper Animas River.

Ransom tunnel bulkhead 504 June 1997 A53,A60,A68,A72 Upper Animas River.

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

Gold Prince project 49 September 1997 A33,A53,A60,A68,A72 Upper Animas River.

Longfellow/Koehler project 75, 77 Fall 1997 M02,M07,M13,M27, M34,A72 Mineral Creek.

Pride of the West tailings removal 233 Fall 1997 A53, A60, A68, A72 Upper Animas River.

Mayflower Mill diversion project 221, 510 Fall 1999 A68,A72 Upper Animas River.

Tailings pond surface and ground-water 

drainage project.

510 December 1999 A68,A72 Upper Animas River.

1

Church, Mast, and others, this volume, Chapter E5.

550 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 551

sample-collection protocols, compare laboratory analytical 

methods, and collect concurrent replicate samples for comparison of results among laboratories. A complete description of 

the methods used for laboratory analysis, data collection, and 

quality assurance is available in Mast and others (2000) and 

Leib and others (2003).

Streamflow Regression Models

Streamflow regression models were developed to provide 

estimated daily-mean streamflow at ungauged sampling sites. 

The streamflow models were developed by regressing dailymean streamflow at gauged sampling sites with instantaneous 

streamflow measured at ungauged 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 gauged 

site on the Animas River) because the correlation was better 

at A72.

During water years 1991–99, between ten and twenty 

streamflow measurements at each ungauged site were obtained 

during different streamflow conditions. As many as four 

measurements at each of the ungauged sites were obtained in 

1991–96. 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.

Streamflow regression models were derived using 

ordinary least-squares (OLS) regression. 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 >0.05 

were generally considered an indication of a poor correlation. 

Table 2. Descriptive information for sampling sites. 

[mi2, square miles]

Sampling site name

Sampling

site No.

(fig. 1)

Lat N.; 

long W.

Drainage 

area

(mi2

)

Percentage of 

Animas River 

watershed 

study area

River mile

(distance downstream 

from respective 

subbasin headwaters)

Streamflowgauging 

station number

Cement Creek upstream from Gladstone C18 37°53'26";

107°38'55"

3.0 2.1 2.7 ungauged

Cement Creek near Gladstone C20 37°53'23";

107°39'08"

3.1 2.1 2.9 ungauged

Cement Creek downstream from Georgia Gulch C31 37°52'31";

107°40'17"

9.44 6.5 4.5 ungauged

Cement Creek downstream from Illinois Gulch C43 37°50'50";

107°40'38"

14.8 10.1 6.3 ungauged

Cement Creek at Silverton C48 37°49'11";

107°39'47"

20.1 13.8 9.2 09358550

Mineral Creek near headwaters M02 37°53'40";

107°42'48"

0.10 0.1 0.15 ungauged

Mineral Creek at Chattanooga M07 37°52'27";

107°43'26"

4.4 3.0 1.8 ungauged

Mineral Creek at Burro Bridge M13 37°51'02";

107°43'31"

10.4 7.1 3.5 ungauged

Mineral Creek upstream from South Fork 

Mineral Creek.

M27 37°49'16";

107°13'08"

20.0 13.7 5.9 ungauged

Mineral Creek at Silverton M34 37°48'10";

107°40'20"

52.4 35.9 9.0 09359010

Animas River at Eureka A33 37°52'45";

107°33'55"

18.2 12.5 6.2 ungauged

Animas River at Howardsville A53 37°50'07";

107°35'52"

57.7 39.5 10.1 ungauged

Animas River near Arrastra Creek A60 37°49'38";

107°37'34"

60.0 41.1 12.1 ungauged

Animas River at Silverton A68 37°48'40";

107°39'31"

70.6 48.4 14.3 09358000

Animas River downstream from Silverton A72 37°47'25";

107°40'01"

146 100 16.0 09359020
Additional information regarding streamflow model selection, 

including statistical diagnostics, equation form, and model 

significance, is discussed in Leib and others (2003).The form 

of the regression equation used for estimating streamflow at 

ungauged sampling sites is:

YQ = B(XQ) + α (1)

where

YQ is the streamflow at the ungauged site, in ft3

/s;

 B is the slope of the regression line;

 XQ is the streamfl ow at the gauged site in ft3

/s;

and

α is the y intercept of the regression line.

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 gauged and ungauged 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.

Water-Quality Hysteresis

A pattern measured in many streams is a tendency for 

water of a rising stage to have a considerably higher constituent 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 River watershed study area 

by Wirt and others (1999) for localized storm runoff events 

in Cement Creek. Besser and Leib (1999) also observed 

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 2 or 3 times those that 

occur at similar flow rates during the receding portion of the 

snowmelt hydrograph. In addition, concentrations of dissolved 

zinc and copper tended to increase during periods of base 

flow (November–March) with little variation in streamflow. 

Figure 2 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. 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 River watershed 

study area. Because of hysteresis, it is preferable to correlate 

constituent concentration with seasonal terms (periodicity) 

as well as streamflow. Leib and others (2003) have included 

a discussion of seasonal terms, including transformed 

Julian date and dummy variables used to characterize outliers which were thought to be related to snowmelt flushing. 

Outliers, like those shown in figure 3, were characterized 

with a dummy variable at sites where Julian date transformations were not significant in the regression analysis. These 

correlations form the foundation from which water quality 

was estimated.

Model Selection

Variable transformations and equation forms for 

water-quality models are listed in table 3. Several models 

contain transformations that were used to improve model 

utility. For an extensive discussion related to how and 

why specific transformations are used, see Leib and others (2003). Additional information that addresses the theory 

and practice of regression analysis is available in Helsel and 

Hirsch (1992).

Trend Analysis

If transformation of an independent variable did not 

improve the significance of a given model, a second measure 

was taken to test 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 post-remediation (table 1). Trends in 

a data set may also 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 that 

the effect of trend resulting from a change in climate would 

be negligible on the basis of a period of record of less than 

10 years. For the specifics of how trends were analyzed, see 

Leib and others (2003).

Limitations of Data Analysis

Use of the statistical regression models provided in this 

report requires adherence to specific guidelines and an understanding of the limitations inherent in each model. Estimates 

from streamflow and water-quality regression models in this 

report were only provided after careful consideration of the 

following guidelines and limitations:

1. Models are site specific.

2. Models with considerable standard error values are less 

accurate.

3. Data represent water-quality conditions during the period 

sampled and do not necessarily represent past or future 

conditions.

552 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 553

4. Models represent a specific range of estimates which, 

when exceeded, decreases 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.

Although the correlation for gauged and ungauged 

streamflow is assumed to be constant for the period of 

record (Leib and others, 2003), 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 elevation, aspect, and vegetation cover, the onset of runoff from these zones will vary 

(Dunne and Leopold, 1978). This is especially true in estimation of streamflow at ungauged sites during periods of intense 

localized rainfall because one or more of the ungauged basins 

may not experience any rainfall runoff from a rain event that 

has affected streamflow at a gauged site. When this occurs, 

Figure 2. Graphs showing A, dissolved zinc concentration related to daily-mean 

streamflow; B, daily-mean streamflow, sampling site A72, water year 1997.

0 

500 

1,000 

1,500 

2,000 

2,500 

OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

0 

0.2 

0.4 

0.6 

0.8 

1 

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 in November–March

Water-quality data collected in July–October

Water-quality data collected in April–June

A

B
0 

1 

2 

3 

4 

5 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

 DISSOLVED ZINC CONCENTRATION, IN  MILLIGRAMS PER LITER

HYPERBOLICALLY TRANSFORMED STREAMFLOW

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 and designated as an outlier

Figure 3. Transformed streamflow related to zinc concentration at site C20 showing presence of outliers.

Table 3. Summary of equation forms used to simulate constituent concentrations at sampling sites.

[β, hyperbolic transformation constant; α, 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 (ft3/s); Xh, transformed streamflow; CQ, constituent concentration, in milligrams/liter] 

Equation

form number

Response variable Explanatory variables Equation form

Variable Transformation Variables Transformation

3 CQ

None XQNone CQ = B(XQ) + α

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 + α

3

6 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 + α

1

Hyperbolic transformation is Xh = 1/(1+β{XQ}). 

2

Trigonometric transformation, first order, JC = sin[(π(Julian date))/365]; second order, JD =cosine[(π(Julian date))/365]; third order, 

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

3

Equation form uses a logarithmic transformation that requires a bias correction factor when estimating mean response.

554 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 555

the statistical correlation between gauged and ungauged sites 

is no longer meaningful. Caution should be used in estimation of streamflow at ungauged sites during rainfall events. 

The authors were aware of this problem and therefore chose 

to report only monthly loads, thus avoiding possibly sizable 

errors that could result from their estimating daily loads during 

periods of rainfall runoff. Also, the timing of snowmelt runoff 

(lag time) will differ at each ungauged sampling site because 

snowpack at higher elevations or on northern aspects is usually 

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

melt early. Estimates made for ungauged sites using the waterquality 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 Animas River Watershed 

Study Area

Water-Quality Profiles

Using the streamflow and water-quality regression 

models, the authors calculated water-quality profiles for the 

15 sampling sites. Three profiles were calculated depicting 

seasonal fluctuations in streamflow, constituent concentration, and constituent load. Streamflow profiles were developed by, first, an estimation of daily-mean streamflow at each 

sampling site. At ungauged sites, the streamflow regression 

models were used to estimate daily-mean streamflows, and at 

gauged 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 basin. 

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 

average-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 and 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).

Streamflow regression-model coefficients for each 

ungauged site are listed in table 4. Each streamflow model 

uses equation 1 (“Streamflow Regression Models” section). 

Water-quality regression-model coefficients and the appropriate equation from table 3 are listed in table 5. 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 of cadmium and copper at all sampling sites in 

Cement Creek basin and for zinc at sites C31, C43, and C48.

The range of estimation provided for each site in tables 4 

and 5 refers to the range of streamflow encountered when 

samples were collected. Estimates made using the streamflow 

and water-quality regression models outside the range of estimation will 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. 6, 9, and 12) for May, June, and July to represent snowmelt runoff, whereas 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.

Table 4. Summary of streamflow regression model coefficients 

and diagnostics at sampling sites.

[α, 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 (ft3/s), at streamflow-gauging station]

Sampling 

site

(fig. 1)

Range of 

estimation1

(ft3/s)

α

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 .206 .87 2.35

C31 12–174 –2.41 .53 .98 4.4

C43 17–123 –0.12 .76 .99 2.23

Mineral Creek3

M02 19–903 0.0 0.002 0.83 0.27

M07 19–550 0.0 .099 .84 8.3

M13 32–550 0.0 .21 .94 8.8

M27 19–400 6.13 .28 .96 7.8

Upper Animas River4

A33 57–1,500 –7.4 0.12 0.96 10.7

A53 57–1,500 0.0 .35 .95 31.3

A60 83–1,500 0.0 .40 .94 40.7

A68 56–2,040 0.0 .42 .99 24.2

1

Estimation range applies to the explanatory variable of streamflow 

(XQ) from corresponding streamflow-gauging station.

2

The explanatory variable (XQ) is obtained from streamflow-gauging 

station number 09358550.

3

The explanatory variable (XQ) is obtained from streamflow-gauging 

station number 09359010.

4

The explanatory variable (XQ) is obtained from streamflow-gauging 

station number 09359020.
Table 5. Summary of water-quality regression model coefficients and diagnostics at sampling sites.

[β, constant used in hyperbolic transformation; 

α, the regression coefficient that is the intercept in the regression model; B and 

Bn, coefficient of explanatory variables in multiple regression; DV1, flushing 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; mg/L, milligrams per liter; 

XQ, 

streamflow, in cubic feet per second (ft3/s); 

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

Sampling 

site

Equation form

from table 3

Range of

estimation

(ft3/s)

β α

B..Bn

r 2

Standard error 

of estimate

(mg/L)

Bias 

correction

factor

XQ

(ft3/s)

Xh JC JD JE JF DV1

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 -- -- -- -- -- .90 120 --C31 5 7.2–98.0 1 46.1 -- 4,485 -- -- -- -- -- .95 40.3 --C43 5 12.6–95.5 0.1 30.6 -- 900.5 -- -- -- -- -- .91 43.2 --C48 5 12.0–329 10 64.8 -- 62,397 -- -- -- -- -- .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 .061 –0.40 -- -- a1.44 .73 0.538 --

Dissolved cadmium—Mineral Creek

M02 5 0.02–2.1 100 0.0 -- 2.23 -- -- -- -- -- 0.92 0.115 --M07 5 .17–65.5 0.41 .0 -- 0.03 -- -- -- -- -- .90 .002 --M13 5 3.5–123 .05 .0 -- .0071 -- -- -- -- -- .95 .001 --M27 5 9.1–134 .07 .0007 -- .0041 -- -- -- -- -- .64 .0005 --M34 5 15–863 .019 .0005 -- .0012 0.0003 0.0000 0.0001 –0.0002 -- .65 .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 -- -- -- -- -- .81 0.095 --M13 5 3.5–123 1.0 .020 -- 0.452 -- -- -- -- -- .78 .018 --M27 5 9.1–134 .05 –.002 -- .173 -- -- -- -- -- .80 .019 --M34 5 15–964 .05 –.005 -- .087 0.0096 0.0002 -- -- -- .76 .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 -- -- -- -- -- .92 5.5 --

M13 5 3.5–123 1.5 37.7 -- 1,552 -- -- -- -- -- .91 25.5 --M27 5 9.1–134 1.1 34.3 -- 4,196 -- -- -- -- -- .94 32.6 --M34 5 15–752 .05 38.9 -- 417 -- -- -- -- -- .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 –.014 -- 4.44 -- -- -- -- -- .98 0.15 --M13 5 3.5–123 .07 .005 -- 1.88 -- -- -- -- -- .94 .120 --M27 5 9.1–134 .04 .038 -- 0.985 -- -- -- -- -- .91 .070 --M34 5 15–863 .025 .078 -- .510 0.049 0.037 –.024 –0.038 -- .91 .051 --

556 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 557

Table 5. Summary of water-quality regression model coefficients and diagnostics at sampling sites.—Continued

Sampling

site

Equation form

from table 3

Range of 

estimation

(ft3/s)

β α

B..Bn

r 2

Standard error 

of estimate

(mg/L)

Bias

 correction 

factor

XQ

(ft3/s)

Xh JC JD JE JF DV1

Dissolved cadmium—Upper Animas River

A33 5 1.15–177 0.005 0.0 -- 0.0026 -- -- -- -- -- 0.95 0.0005 --A53 3 14.3–505 -- 7.0E-04 2.36E-06 -- -- -- -- -- -- .66 .0003 --

A60 4 26.2–680 100 1,089 -- 5.05E+04 -- -- -- -- f–310 .60 .006 --

A68 6 8.5–880 .004 –3.34 -- 0.822 0.064 –0.009 -- -- f0.147 .64 .0014 1.0

A72 5 55.5–2,340 .004 .001 -- .0013 .00048 .00005 –0.00006 –0.0002 b3.0E–4 .74 .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 -- .0032 -- -- -- -- f.005 .88 .0015 --

A60 5 26.2–680 1.0E-06 –28.42 -- 28.42 –.0011 –.0061 -- -- f.015 .92 .0019 --A68 5 8.5–880 -- .003 -- -- .001 –.0003 –0.001 –0.0001 .0033 .62 .002 --A72 5 55.5–2,340 -- .011 -- -- .0075 .0057 .0028 –.00126 -- .80 .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 .01 27.2 -- 122 -- -- -- -- -- .95 6.6 --

A60 5 26.2–680 .0078 23.3 -- 122 -- -- -- -- -- .91 9.0 --A68 5 8.5–880 .013 45.1 -- 144 7.24 10.0 -- -- -- .97 8.0 --A72 5 55.5–2,340 .02 48.8 -- 520 8.77 9.61 –1.22 –6.58 -- .93 23.2 --

Dissolved zinc—Upper Animas River

A33 5 1.15–177 0.035 0.307 -- 0.253 -- -- -- -- -- 0.65 0.051A53 5 14.3–505 .0009 .0 -- .291 -- -- -- -- d0.042 .98 .038 --

A60 5 26.2–680 .002 .0 -- .303 -- -- -- -- f.076 .97 .044 --

A68 5 8.5–1,050 .007 .0 -- .834 0.188 –0.028 -- -- e.140 .90 .188 --

A72 5 55.5–2,340 .01 .249 -- .598 .152 .0298 –0.0267 –0.0592 -- .87 .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.
Cement Creek

Estimates of seasonal water quality in Cement Creek 

basin 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 (table 5).

The streamflow profile for sites in Cement Creek is 

shown in figure 4. The profile indicates that base flow in 

Cement Creek ranges 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 ranges from about 24 ft3

/s at 

site C18 to 150 ft3

/s at C48. Estimated streamflow from 

Cement Creek basin is generally from about 10 to 22 percent of the total streamflow at site A72 for any given month. 

About 30–40 percent of the streamflow at site C48 originates 

between sites C20 and C31 during snowmelt runoff. Averageannual streamflow from Cement Creek basin is about 42 ft3

/s 

or a yield of about 2.1 ft3

/s/mi2.

A trend detected at site C20 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. (table 1). The trend was most 

apparent during base-flow conditions (fig. 5). A trend during high flow was not apparent, possibly due to streamflow 

measurement error, which is often more prevalent during high 

stage. No trends in streamflow were detected at C48; therefore, all streamflow measurements were used 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.

Concentration and loading profiles for hardness and zinc 

estimated using the Cement Creek water-quality models are 

shown in figure 6. The highest monthly concentration of hardness (1,280 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. The large increase in hardness concentration in 

this reach most likely results from lime applications at the 

Sunnyside Gold Corporation water-quality treatment facility 

(Larry Perino, Sunnyside Gold Corporation, written commun., 

1999). Hardness concentrations in Cement Creek were lowest 

at site C18 and highest at site C20 year around.

Figure 4. Streamflow profile for Cement Creek basin.

0 

20 

40 

60 

80 

100 

120 

140 

160 

C18

 STREAMFLOW, IN CUBIC FEET PER SECON

D

Snowmelt runoff

Monsoon season

C20 C31 C43 C48

2.7 2.9 4.5 6.3 9.2

C20 C31 C43 C48

2.7 2.9 4.5 6.3 9.2

 SITE AND RIVER MILE

February

May

June

July

September

EXPLANATION

Base flow

558 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 559

The largest hardness load in Cement Creek basin 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 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 waterquality model after stepwise regression at site C18 for the 

period of January 1 to March 31 (DV1

, table 5). Estimation 

of concentrations at this site during this period used a value 

of 1 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. 3).

A decreasing 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 1996 (table 1). Figure 7 shows the apparent shift in 

the correlation of streamflow with hardness concentration that 

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

for hardness, 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. 6). Concentrations at site C18 were higher during 

base flow than during snowmelt runoff, but concentrations at 

site C20 tended to be lower during base flow relative to snowmelt runoff concentrations. The decrease most likely results 

from the effect of the water-quality 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. Both sampling sites retained a flushing variable for either 

April through May or May (DV1

, table 5).

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 ranges from 90 to 100 percent (depending on the 

season) of the total load at C20.

Figure 5. Streamflow data before and after American tunnel bulkhead closure in September 1996.

0 

2 

4 

6 

8 

10 

5 10 15 20 25 30 35 40 

STREAMFLOW AT C20, IN CUBIC FEET PER SECOND

 STREAMFLOW AT C48, IN CUBIC FEET PER SECOND

EXPLANATION

Data collected before September 1996

Data collected after September 1996

 
Mineral Creek

Streamflow and water-quality profiles in Mineral Creek 

basin 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; r2

 values ranged from 0.64 to 0.98 (table 5). 

Streamflow profiles for Mineral Creek basin are shown in 

figure 8, and concentration and loading profiles for hardness 

and metals are shown in figure 9.

The streamflow profile for Mineral Creek indicates that 

base flow ranges 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. 

Estimated streamflow from Mineral Creek basin is generally 

about 32–52 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 basin is 

about 120 ft3

/s (yield of 2.2 ft3/s/mi2).

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 

are highest at site M02 throughout the year with concentrations 

that ranged from 100 to 335 mg/L; however, concentrations at 

site M27 approach 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 

20,000 

0 

40,000 

60,000 

80,000 

100,000 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

C18 C20 C31 C43 C48 0 

1.0 

2.0 

3.0 

4.0 

5.0 

6.0 

2.7 2.9 4.5 6.3 9.2

CONCENTRATION, IN MILLIGRAMS PER LITER

LOAD, IN POUNDS PER DAY

HARDNESS HARDNESS

C18 C20 C31 C43 C48

2.7 2.9 4.5 6.3 9.2

0 

50 

100 

150 

200 

250 

300 

350 

C18 C20 C31 C43 C48

2.7 2.9 4.5 6.3 9.2

ZINC

C18 C20 C31 C43 C48

2.7 2.9 4.5 6.3 9.2

ZINC

Snowmelt runoff

Monsoon season

SITE AND RIVER MILE

February

May

June

July

September

EXPLANATION

Base flow

July

July July

July

Figure 6. Water-quality profiles for hardness and dissolved zinc at Cement Creek sampling sites.

560 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 561

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 27–37 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 to 0.39 mg/L. 

Concentrations decrease 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 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 basin 

(M34) account for about 15–25 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). Copper concentrations were highest at M02 

throughout the year with concentrations that ranged from 

3.0 to 39.8 mg/L. Concentrations decrease 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 (1.1 lb/d) was estimated at site M34 in July. 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 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 

HARDNESS, IN MILLIGRAMS PER LITER

HYPERBOLICALLY TRANSFORMED STREAMFLOW

EXPLANATION

Data collected before September 1996

Data collected after September 1996

Figure 7. Hardness data at site C20 before and after American tunnel bulkhead closure in September 1996.
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 from sources draining into these stream segments. Estimates indicate that seasonal copper loads at M34 

account for about 31–65 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 mg/L to 112 mg/L. Concentrations 

decrease 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 10–27 percent of 

the load at A72 for any given month.

Decreasing trends in trace-metal concentrations were 

measured for zinc and copper at site M34 (fig. 10). 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 basin were calculated for sites A33, A53, A60, 

A68, and A72 (figs. 11, 12). Site A72, located downstream of 

sites C48 and M34, was included so that 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. Recall that direct estimates of Cement 

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

Figure 8. Streamflow profile for Mineral Creek basin.

0 

100 

200 

300 

400 

500 

M02 M07 M13 M27 M34

 STREAMFLOW, IN CUBIC FEET PER SECON

D

0.15 1.8 3.5 5.9 9.0

Snowmelt runoff

Monsoon season

 SITE AND RIVER MILE

February

May

June

July

September

EXPLANATION

Base flow

562 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 563

Figure 9. Water-quality profiles for hardness and dissolved cadmium, copper, and zinc at Mineral Creek sampling sites.

Jn, June; Jl, July.

10

100

1,000

M02 M07 M13 M27 M34 0 

20,000 

40,000 

60,000 

80,000 

100,000 

120,000 

140,000 

0.0001 

0.001 

0.01 

0.1 

1 

M02 M07 M13 M27 M34

0 

0.5 

1 

1.5 

2 

M02 M07 M13 M27 M34

10

100

1,000

M02 M07 M13 M27 M34

0 

50 

100 

150 

200 

250 

0.15 1.8 3.5 5.9 9.0

0.15 1.8 3.5 5.9 9.0

0.15 1.8 3.5 5.9 9.0

0.15 1.8 3.5 5.9 9.0

0.15 1.8 3.5 5.9 9.0

0.15 1.8 3.5 5.9 9.0

HARDNESS HARDNESS

CADMIUM CADMIUM

COPPER COPPER

 CONCENTRATION, IN MILLIGRAMS PER LITER

 LOAD, IN POUNDS PER DAY

0.0001 

0.001 

0.01 

0.1 

1 

10 

100 

M02 M07 M13 M27 M34

0 

5 

10 

15 

20 

25 

M02 M07 M13 M27 M34

ZINC

M02 M07 M13 M27 M34

0.15 1.8 3.5 5.9 9.0

ZINC

LOG SCALE

M02 M07 M13 M27 M34

0.15 1.8 3.5 5.9 9.0

1

0.1

0.01

Snowmelt runoff

Monsoon season

 SITE AND RIVER MILE

February

May

June

July

September

EXPLANATION

Base flow

Jl

Jn

Jl

Jn

Jl

Jl

Jn

Jn Jl

Jl

Jn

Jl

Jn

Jl

Jn

Jl

Jn
Regression models at all sites in the upper Animas River 

basin (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 (table 5). Streamflow profiles for the upper Animas River basin are shown in 

figure 11, and concentration and loading profiles for metals 

are shown in figure 12.

Two sites on the upper 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 was measured. Further water-quality monitoring of this site may be necessary to detect subtle trends in 

concentration that are occurring over an extended period 

of time.

The streamflow profile for the upper Animas River indicates that base flow ranges among sampling sites from about 

0.5 ft3

/s at A33 to about 63 ft3/s at A72. Peak snowmelt runoff 

generally occurs in June and ranges from about 140 ft3

/s at 

site A33 to 1,200 ft3/s at A72. Estimated streamflow from the 

upper Animas River basin is generally about 41–42 percent 

of the total streamflow at A72, of which about 55–83 percent 

originates between sites A33 and A53. Average-annual streamflow draining the upper Animas River basin (A68) is about 

126 ft3

/s (yield of 1.8 ft3/s/mi2).

CONCENTRATION, IN MILLIGRAMS PER LITER

HYPERBOLICALLY TRANSFORMED STREAMFLOW

EXPLANATION

Data collected before October 1997

Data collected after October 1997

ZINC

COPPER

0 

0.02 

0.04 

0.06 

0.08 

0.1 

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 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 

Figure 10. Trace-metal concentrations at site M34 before and after Longfellow/

Koehler project was completed in October 1997.

564 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 565

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. 12). Water-quality models estimated hardness concentrations that showed dilution during snowmelt runoff and concentration during base flow. At site A72 in January (not shown), 

estimated concentrations of hardness were approximately 

150–180 mg/L higher than concentrations at other sampling 

sites in the upper Animas River basin despite the increase in 

streamflow; however, in May, concentrations at all sampling 

sites were within 35.0 mg/L of one another. This finding indicates the presence of hardness sources in Mineral and Cement 

Creeks, which could be important 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 along 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 upper 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 from 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 basin during snowmelt runoff, and two-thirds 

originates from Cement Creek during base flow.

The highest monthly concentration of cadmium 

(0.0031 mg/L) was estimated at site A72 during the month of 

April (not shown), 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 

Creeks because analysis was done using graphite furnace 

atomic absorption spectroscopy. 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 concentrations from preexisting data.

A flushing variable was retained in the cadmium waterquality 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 (DV1

, table 5). Estimation of concentrations at this site during this period used a value of 1 assigned 

to the flushing variable.

0 

200 

400 

600 

800 

1,000 

1,200 

 STREAMFLOW, IN CUBIC FEET PER SECOND

A33 A53 A60 A68 A72

6.2 10.1 12.1 14.3 16.0

Snowmelt runoff

Monsoon season

 SITE AND RIVER MILE

February

May

June

July

September

EXPLANATION

Base flow

Figure 11. Streamflow profile for upper Animas River.
0 

50 

100 

150 

200 

250 

300 

A33 A53 A60 A68 A72

0 

100,000 

200,000 

300,000 

400,000 

0.0005 

0.001 

0.0015 

0.002 

0.0025 

0.003 

0 

2 

4 

6 

8 

0 

0.005 

0.015 

0.02 

0.025 

0.03 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

0 

500 

1,000 

1,500 

2,000 

6.2 10.1 12.1 14.3 16.0

HARDNESS

CADMIUM

CONCENTRATION, IN MILLIGRAMS PER LITER

 LOAD, IN POUNDS PER DAY

0.01 

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

6.2 10.1 12.1 14.3 16.0

A33 A53 A60 A68 A72

10.1 12.1 14.3 16.0

A33 A53 A60 A68 A72

A33 A53 A60 A68 A72

6.2 10.1 12.1 14.3 16.0

A33 A53 A60 A68 A72

6.2 10.1 12.1 14.3 16.0

0 

5 

10 

15 

20 

25 

30 

35 

COPPER COPPER

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

ZINC

HARDNESS

CADMIUM

ZINC

6. 3

Snowmelt runoff

Monsoon season

 SITE AND RIVER MILE

February

May

June

July

September

EXPLANATION

Base flow

Jl

Jn

Jn

Jn

Jn

Jn

Jn

Jn

Jn

Jl

Jl

Jl

Jl

Jl

Jl

Jl

Figure 12. Water-quality profiles for hardness and dissolved cadmium, copper, and zinc at Animas River sampling sites.

Jn, June; Jl, July.

566 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 567

The cadmium model at site A53 (and to a lesser extent 

A60) indicates a positive relation between cadmium and 

streamflow, which produced the highest cadmium concentration (0.0017 mg/L) in June and lowest (0.0007 mg/L) 

in February. The Sunnyside Eureka Mill downstream from 

site A33 (AMLI mine site # 55; Church, Mast, and others, 

this volume), located between sites A33 and A53, supplied 

huge quantities of tailings to the river at rates from 50 to 

4,700 times greater than the natural production of sediments 

from hillslopes (Vincent and others, 1999). The presence of 

mill tailings in the flood plain may explain the 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 were more dilute during 

snowmelt runoff than during base flow.

The largest load of cadmium in the upper 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 0.45 lb/d (base flow) and 7.6 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 basin (A68) account for about 25–53 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. Copper 

models at all sites showed a dilution of copper concentration 

during snowmelt. The dilution at each site, with the exception 

of site A72, was preceded by an initial flush of higher copper 

concentrations in May. The highest concentrations of copper in 

the upper Animas River typically occurred at the same sampling 

sites year around. 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 (0.023 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, the location of several historical mill sites and mines 

where copper ore was processed.

Flushing variables were retained in several copper waterquality 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 (DV1

, table 5).

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 exist between A33 and A60. Estimates from models 

indicate that seasonal copper loads at A68 are generally about 

5–39 percent 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 Animas River watershed 

study area 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 40–60 percent of the average-monthly 

zinc load estimated at site A72 for any given month originated 

upstream from site A68. Thus, roughly half of the zinc loading 

at site A72 appears to be coming from Cement and Mineral 

Creeks and the other half from sources upstream of site A68. 

The largest increases in zinc loading upstream from A68 

occurred either between A33 and A53, or between A60 and 

A68 throughout the year.

Application of Water-Quality Profiling 

in Mineral Creek

Water-quality profiles 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. To demonstrate this, an example application from Leib and others 

(2002), which illustrates how contaminant loading can be 

characterized, is summarized herein.

The example stream segment is defined by sampling 

sites M27 and M34 (fig. 1) in the Mineral Creek basin. This 

segment was selected based on the availability of source characterization data and because of the large downstream loading increases in May and June for cadmium and zinc (fig. 9). 

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 (2000) were used to calculate instantaneous 

loads from the characterized sources in the example reach.

The example stream segment had source characterization data collected at five mine adits during high streamflow 

(May) and the relatively lower flows of the monsoon season 

(September). These mine adits represent the majority of mining sources in the stream segment; however, other diffuse 

sources such as in-stream waste-rock piles and ground-water 

contributions exist (Kimball and others, this volume).
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. 9), indicating a net gain for cadmium and zinc, and a 

net loss of copper, along the stream segment. Error estimates 

for the net loading values were 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 indicated

that other sources contributed possibly substantial amounts 

of cadmium and zinc in the M27 to M34 stream segment 

during May, whereas the positive load discrepancy for copper 

indicated that attenuation was taking place. Taking the average 

loading discrepancy estimate from the above ranges for each 

metal indicated that the uncharacterized sources contributed 

nearly 95 percent of the cadmium load, 0 percent of the copper load (or uncharacterized sources are also 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. 9), 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 indicated that other sources 

were contributing metals along the stream segment during 

September. The positive loading-discrepancy range for copper 

indicated 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) indicated that additional sources were present in 

the stream reach during September. Taking the average loading 

discrepancy estimate for each constituent indicated that the 

uncharacterized sources contributed about 86 percent of the 

cadmium load, 0 percent of the copper load (or uncharacterized sources were also attenuated), and about 52 percent of the 

zinc load at M34.

Further characterization of the M27 to M34 segment may 

help explain where uncharacterized loads of cadmium and zinc 

emanate from, especially during snowmelt runoff. The results 

indicate that these contributions may be diffuse, 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 the main source type for copper in the M27 to M34 stream 

reach. Regardless of where these metals emanate from, mass 

accounting with loading profiles indicates that metal sources 

in the Animas River watershed study area may change substantially with season.

Summary

Multi-agency collaborative efforts are being made to 

assess the effects of historical mining on water quality in 

the Animas River watershed. Included in these efforts is the 

U.S. Department of the Interior’s Abandoned Mine Lands 

Initiative. As part of this initiative, the U.S. Geological Survey 

has provided technical assistance in support of Federal landmanagement agencies actions to improve water quality in 

contaminated stream systems associated with former 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 Animas River watershed study area 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 

water quality. 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. A decreasing 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 corresponds to the period when the American 

tunnel was closed. Data collected at site M34 indicated a 

decreasing trend for concentrations of copper and zinc, which 

correspond to the period when the Longfellow/Koehler project 

was completed in fall 1997.

Hardness estimates at all sampling sites in the Animas 

River watershed study area indicated that concentrations were 

generally highest in Cement Creek and lowest in the upper 

Animas River. At sampling sites in each basin, estimated hardness concentrations varied dramatically during base flow and 

moderately during snowmelt runoff.

Contributions of hardness loading to Cement Creek were 

highest between sites C18 and C20 during snowmelt runoff; 

however, loading contributions 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 sites M13 to 

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.

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 

568 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Stream Characterization Using Water-Quality Profiles 569

cadmium at sampling sites in the upper 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 basin (C18 and C20), the highest 

concentrations of zinc 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 water-quality treatment at 

the water-quality 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 increase substantially between sites A60 and 

A68, despite increases estimated by the streamflow profile. 

Zinc concentrations in Mineral Creek were not elevated during 

snowmelt by flushing events, but zinc concentrations in the 

upper Animas River showed this effect.

The largest zinc load in Cement Creek was estimated 

to be occurring upstream of 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 40–60 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 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 were also 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 

were also 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 inflows during snowmelt runoff. Regardless of where the metals emanate from, 

the results indicate that metal sources in the Animas River 

watershed study area may change substantially with season.

References Cited

Animas River Stakeholders Group, 2000, Animas River basin 

database: Available at URL http://www.waterinfo.org/arsg/

main.html. Accessed 1997–2000.

Anderson, S.P., Dietrich, W.E., Torres, R., Montgomery, D.R., 

and Loague, K., 1997, Concentration discharge relationships in runoff from a steep, unchanneled catchment: Water 

Resources Research, v. 33, p. 211–225.

Bailey, R.G., Avers, P.E., King, T., and McNab, W.H., eds., 

1994, Ecoregions and subregions of the United States with 

supplementary table of map unit descriptions: Washington, D.C., U.S. Department of Agriculture Forest Service, 

scale 1:7,500,000; also at URL http://www.fs.fed.us/land/

ecosysmgmt/ecoreg1_home.html.

Besser, J.M., and Leib, K.J., 1999, Modeling frequency 

of occurrence of toxic concentrations of zinc and copper in the Upper Animas River, in Morganwalp, D.W., 

and Buxton, H.T., eds., U.S. Geological Survey Toxic 

Substances Hydrology Program—Proceedings of the 

technical meeting, Charleston, S.C., March 8–12, 1999: 

U.S. Geological Survey Water-Resources Investigations 

Report 99–4018A, p. 75–81.

Cohn, T.A., Delong, L.L., Gilroy, E.J., Hirsch, R.M., and 

Wells, D.K., 1989, Estimating constituent loads: Water 

Resources Research, v. 25, no. 5, p. 937–942.

Colorado Climate Center, 2000, Annual climatology data: 

Available at URL http://ulysses.atmos.colostate.edu/. 

Accessed May 2000.

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.

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