Using the OTIS Solute-Transport Model 

to Evaluate Remediation Scenarios in 

Cement Creek and the Upper Animas River

By Katherine Walton-Day, Suzanne S. Paschke, Robert L. Runkel, and 

Briant A. Kimball

Chapter E24 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

Prepared in cooperation with the

U.S. Bureau of Land Management
Contents

Abstract .......................................................................................................................................................979

Introduction.................................................................................................................................................979

Purpose and Scope ..........................................................................................................................980

Methods.......................................................................................................................................................982

Mass-Loading Studies .....................................................................................................................982

Solute-Transport Modeling with OTIS ...........................................................................................982

Determination of Physical Parameters ................................................................................983

Model Calibration for Conservative Solutes .......................................................................984

Model Calibration for Reactive Solutes ...............................................................................984

Simulation of Remediation .....................................................................................................985

OTIS Solute-Transport Modeling .............................................................................................................985

Upper Cement Creek Subbasin ......................................................................................................985

Determination of Physical Parameters and Boundary Conditions ..................................987

Model Calibration for Simulation of the Bromide Tracer Profile......................................987

Model Calibration for Copper, Iron, and Zinc Simulations ................................................988

Remediation Simulations ........................................................................................................992

Lower Cement Creek Subbasin ......................................................................................................994

Determination of Physical Parameters and Boundary Conditions ..................................996

Model Calibration for Simulation of the Lithium Tracer Profile ........................................996

Model Calibration for Copper, Iron, and Zinc Simulations ................................................998

Remediation Simulations ......................................................................................................1002

Simulation of the Effect of Changed Conditions at the Mogul Mine .............................1005

Animas River, Eureka to Howardsville .........................................................................................1008

Determination of Physical Parameters and Model Calibration for Simulation 

of the Chloride Tracer Profile .................................................................................1010

Model Calibration for Zinc Simulations ..............................................................................1010

Remediation Simulations ......................................................................................................1012

Animas River, Howardsville to Silverton .....................................................................................1014

Determination of Physical Parameters and Model Calibration for Simulation 

of the Chloride Tracer Profile .................................................................................1016

Model Calibration for Zinc Simulations ..............................................................................1016

Remediation Simulations ......................................................................................................1016

Significance of Metal Removal Processes in Cement Creek and the Upper Animas River ...........1019

Comparison of Remediation Using OTIS and a Mass-Loading Model ............................................1021

Summary and Conclusions .....................................................................................................................1022

References Cited......................................................................................................................................1023

Appendix. Calibration of the Solute-Transport Model OTIS ..........................................................1025

Determination of Physical Parameters .......................................................................................1025

Model Calibration for Conservative Solutes ..............................................................................1025

Model Calibration for Reactive Solutes ......................................................................................1027

Physical Parameters for OTIS Models ........................................................................................1027
Figures

 1. Map showing location of study area and study reaches, Animas River 

watershed study area, Colorado ............................................................................................981

 2. Graph showing variation in dissolved bromide concentration with time 

at transport sites T1, T2, and T3, upper Cement Creek tracer injection, 

September 1999 .........................................................................................................................983

 3. Map showing detail of upper Cement Creek study reach .................................................986

 4–7. Graphs showing:

 4. Upper Cement Creek, September 1999: variation in bromide concentration 

 in synoptic samples, variation in streamflow discharge calculated from 

 bromide concentrations in synoptic samples, distances corresponding to 

 model reaches in calibrated OTIS model ......................................................................988

 5. Upper Cement Creek, September 1999: variation in OTIS lateral-inflow 

 concentrations and observed inflow concentrations for dissolved copper, 

 iron, and zinc with distance from injection site ............................................................990

 6. Upper Cement Creek, September 1999: OTIS results for conservative and 

 reactive simulations for dissolved copper, iron, and zinc ..........................................991

 7. Upper Cement Creek, September 1999: OTIS results for five remediation 

 scenarios for dissolved copper, iron, and zinc .............................................................993

 8. Map showing detail of lower Cement Creek study reach ..................................................995

 9–15. Graphs showing:

 9. Lower Cement Creek, September 1996: variation in lithium concentration 

 in synoptic samples, variation in streamflow discharge calculated from 

 lithium concentrations in synoptic samples, distances corresponding to 

 model reaches in calibrated OTIS model ......................................................................998

 10. Lower Cement Creek, September 1996: variation in OTIS lateral-inflow 

 concentrations and observed inflow concentrations for filtered copper, 

 total iron, and filtered zinc with distance from injection site .....................................999

 11. Lower Cement Creek, September 1996: OTIS results for conservative and 

 reactive simulations for filtered copper, total iron, and filtered zinc ......................1001

 12. Lower Cement Creek, September 1996: OTIS remediation simulations for 

 filtered copper, total iron, and filtered zinc .................................................................1004

 13. Lower Cement Creek, September 1996: OTIS results for simulation of 1999 

 conditions in Cement Creek and South Fork Cement Creek .....................................1006

 14. Gauge at mouth of Cement Creek (CC48): record of observed 

 streamflow, filtered (0.45 μm) copper, total iron, and filtered (0.45 μm) 

 zinc concentrations and filtered (0.1 μm) copper, total iron, and filtered 

 (0.1 μm) zinc concentrations simulated for 1999 conditions using calibrated 

 OTIS model ........................................................................................................................1007

 15. Lower Cement Creek, residual concentrations of copper and zinc at 

 Cement Creek gauge versus time for regression models constructed 

 using environmental data collected between 1996 and 2001 ..................................1008

 16. Map showing detail of upper Animas River, Eureka to Howardsville study reach ........1009

 17–20. Graphs showing:

 17. Animas River, Eureka to Howardsville, August 1998: variation in chloride 

 concentration in synoptic samples, variation in streamflow discharge 

 calculated from chloride concentrations in synoptic samples, distances 

 corresponding to model reaches in calibrated OTIS model ....................................1011
 18. Animas River, Eureka to Howardsville, August 1998: variation in OTIS 

 lateral-inflow concentrations and observed inflow concentrations for 

 dissolved zinc with distance along study reach ........................................................1012

 19. Animas River, Eureka to Howardsville, August 1998: OTIS results for 

 conservative and reactive simulations for zinc ..........................................................1013

 20. Animas River, Eureka to Howardsville, August 1998: OTIS results for 

 two remediation scenarios ............................................................................................1014

 21. Map showing detail of upper Animas River, Howardsville to Silverton study 

reach .........................................................................................................................................1015

 22–25. Graphs showing:

 22. Animas River, Howardsville to Silverton, September 1997: variation in 

 chloride concentration in synoptic samples, variation in streamflow 

 discharge calculated from chloride concentrations in synoptic samples, 

 distances corresponding to model reaches in calibrated OTIS model ..................1017

 23. Animas River, Howardsville to Silverton, September 1997: variation in 

 OTIS lateral-inflow concentrations and observed inflow concentrations 

 for dissolved zinc with distance along study reach ..................................................1018

 24. Animas River, Howardsville to Silverton, September 1997: OTIS results 

 for conservative and reactive simulations for zinc ....................................................1019

 25. Animas River, Howardsville to Silverton, September 1997: OTIS results 

 for two remediation scenarios ......................................................................................1020

 26. Flow chart indicating actions necessary to calibrate the solute transport 

model OTIS ...............................................................................................................................1026

Tables

 1. Summary of mass-loading studies used for OTIS simulations ..........................................982

 2. Upper Cement Creek, reach number, distance at end of reach, and description 

of reaches used in OTIS modeling of synoptic samples collected for upper 

Cement Creek (September 1999) ............................................................................................987

 3. Upper Cement Creek, samples and methods used to estimate lateral-inflow 

concentrations (CL

) for copper, iron, and zinc in calibrated OTIS model ........................989

 4. Upper Cement Creek, first-order removal coefficients (λ) by metal and reach 

for calibrated OTIS model .......................................................................................................992

 5. Simulated concentration at the downstream end of the upper Cement Creek 

mass-loading study reach resulting from various remediation scenarios .....................994

 6. Lower Cement Creek, reach number, distance at end of reach, and description 

of reaches used in OTIS modeling of synoptic samples collected for lower 

Cement Creek (September 1996) ............................................................................................997

 7. Lower Cement Creek, samples and methods used to estimate lateral-inflow 

concentrations (CL

) for copper, iron, and zinc in calibrated OTIS model ......................1000

 8. Lower Cement Creek, first-order removal coefficients (λ) by metal and reach 

for calibrated OTIS model .....................................................................................................1002

 9. Lower Cement Creek, simulated concentrations at the mouth of lower Cement 

Creek resulting from five remediation scenarios, and from changed conditions 

at the Mogul mine (scenario 6) .............................................................................................1003
 10. Animas River, Eureka to Howardsville, reach number, distance at end of 

reach, and description of reaches used in OTIS modeling of synoptic samples 

(August 1998) ...........................................................................................................................1010

 11. Animas River, Howardsville to Silverton, reach number, distance at end of 

reach, and description of reaches used in OTIS modeling of synoptic samples 

(September 1997) ....................................................................................................................1017

 12. Damkohler number (Dn

) for upper Animas River OTIS solute-transport study 

reaches that have first-order removal coefficients ..........................................................1021

 13. Summary of maximum simulated reductions in zinc load for all four study areas 

using OTIS and using a simple mass-loading model ........................................................1022

 14. Model reaches, reach lengths, and hydraulic parameters for upper Cement 

Creek OTIS simulations ..........................................................................................................1028

 15. Model reaches, reach lengths, and hydraulic parameters for lower Cement 

Creek OTIS simulations ..........................................................................................................1028

Conversion Factors, Terms, and Abbreviations

Multiply By To obtain

cubic foot (ft3

) 0.02832 cubic meter

foot (ft) 0.3048 meter

foot per second (ft/s) 0.3048 meter per second

cubic foot per second (ft3

/s) 0.0283 cubic meter per second

gallon (gal) 3.785 liter

mile (mi) 1.609 kilometer

square foot (ft2

) 0.09290 square meter

Degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=9/5(°C)+32

The following terms and abbreviations also are used in this report:

cubic feet per second (ft3

/s)

cubic meters per second (m3/s)

cubic meters per second per meter (m3

/s-m)

inverse second (s–1)

kilogram (kg)

kilogram per second (kg/s)

kilogram per day (kg/d)

liters per minute (L/min)

liters per second (L/s)

meters (m)

meters per second (m/s)

micrometer (μm)

milligrams per liter (mg/L)

natural logarithm (ln)

square meters per second (m2

/s)

Abstract

The OTIS (One-dimensional Transport with Inflow and 

Storage) solute-transport model was used to simulate ambient 

water-quality conditions and effects of hypothetical remediation scenarios on water quality for four subbasins of the 

Animas River watershed study area in southwestern Colorado. 

Samples collected during metal-loading studies conducted in 

upper and lower Cement Creek, and the Animas River from 

Eureka to Howardsville and from Howardsville to Silverton, 

provided data to calibrate the models and simulate ambient 

conditions. Hypothetical remediation targets in each subbasin 

included either the largest sources of loading, the largest 

sources of loading that occurred on Federal land, or sources 

of mining-related loading. Results of the simulations indicate 

that remediation is likely to effect the greatest water quality improvement in areas where loading is limited to a small 

number of distinct inflows, such as in upper Cement Creek. 

Simulations conducted by removing all copper and zinc loading from the Queen Anne and Grand Mogul mines, the Mogul 

mine, and North Fork Cement Creek indicated reduction of 

copper and zinc concentrations at the downstream end of the 

study reach by 90 and 82 percent. Remediation will have a 

smaller effect in streams where a significant proportion of the 

load is supplied from diffuse sources of seepage and groundwater inflow such as lower Cement Creek. Remediation 

scenarios in lower Cement Creek that reduced copper and zinc 

loading by as much as 75 percent from Prospect Gulch, Ohio 

Gulch, and the May Day mine area only reduced copper and 

zinc concentrations at the lower end of the study reach by 19 

and 9 percent. Ambient conditions in Cement Creek (low pH 

and elevated metal concentrations) complicate any remediation 

scheme: even though simulated remediation in upper Cement 

Creek projected the greatest water quality improvement of all 

the remediation scenarios considered in the four subbasins, 

simulated post-remediation metal concentrations exceeded 

levels favorable for aquatic life, as represented by hardnessbased toxicity standards. Remediation scenarios considered 

for the two Animas River subbasin study reaches simulated 

post-remediation concentrations of zinc near levels considered 

favorable for aquatic life (0.18 mg/L). Ambient water quality 

in the Animas River had near-neutral pH values, and metal 

concentrations were much lower than those in Cement Creek. 

Although loading was not confined to distinct sources such 

as in upper Cement Creek, remediation simulations affected 

water-quality improvement sufficient to improve aquatic habitat in some portions of the stream because of the lower initial 

metal concentrations and higher pH. These results indicate that 

remediation will be most successful, in terms of improving 

aquatic habitat, if applied to areas where degradation is less 

severe. Severely impaired areas, and areas where significant 

proportions of the load are supplied from non-mining-affected 

acid drainage, may continue to negatively affect water quality 

even when remediation of other large sources is attempted.

Additional simulations conducted for lower Cement 

Creek indicated that the model successfully predicts trends 

in dissolved copper, total iron, and dissolved zinc concentrations that have resulted from changes in metal loading in upper 

Cement Creek.

Introduction

Planning remedial actions in streams affected by acid 

mine drainage is a complex process. One must first understand 

the location and magnitude of sources of metal loading to the 

stream. Sources can then be ranked by the severity of their 

effect on stream-water quality. Generally, those sources having 

the greatest effect will be chosen for remediation. However, 

if a stream receives acid and metal loading from many diffuse 

sources, or sources that cannot be readily remediated (such as 

ground-water inflow to the stream), it may be difficult to significantly improve stream-water quality through remediation. 

In the context of multiple sources of water-quality degradation 

to a stream and limited resources available for remediation, 

tools are needed to understand the effects of various remedial 

actions. Solute-transport modeling is one such tool.

Chapter E24

Using the OTIS Solute-Transport Model to Evaluate 

Remediation Scenarios in Cement Creek and the 

Upper Animas River

By Katherine Walton-Day, Suzanne S. Paschke, Robert L. Runkel, and Briant A. Kimball
Multiple metal-loading studies that combined tracer 

dilution with synoptic sampling were conducted on the upper 

Animas River and its primary tributaries Cement Creek and 

Mineral Creek from 1996 through 2000 (Kimball and others, 

this volume, Chapter E9). The tracer-dilution method provides 

estimates of streamflow that were used to quantify the amount 

of water entering the stream through tributaries and groundwater inflow. Synoptic sampling of mainstem and inflow 

chemistry provided a spatially detailed snapshot of streamwater quality. When used together, these techniques provide a 

description of the watershed that includes both streamflow and 

water quality, which can be used to construct mass-loading 

profiles for the stream. These profiles can be used to identify 

and quantify the greatest sources of mass loading to a stream 

(Kimball and others, this volume). Sources having the greatest mass loads may then be the target of remedial actions. The 

results from mass-loading studies can also be used to calibrate 

OTIS (One-dimensional Transport with Inflow and Storage), a 

solute-transport model (Runkel, 1998). The calibrated model 

can be modified to simulate the results of various remediation 

scenarios, providing a guide for remediation decisions.

OTIS is used to describe the hydrologic processes controlling solute transport in streams. In particular, OTIS quantifies the effects of storage zones on solute transport. Storage 

zones in streambeds delay the transport of a constituent and 

contribute to dispersion. OTIS also can account for chemical 

reaction using first-order decay or removal. First-order decay 

means that the rate of decrease in concentration of a constituent follows first-order kinetics and is directly proportional to 

the concentration of the constituent. This approach for considering chemical reactions is an alternative to equilibrium-based 

approaches used in models such as WATEQ and MINTEQ 

(Allison and others, 1991; Ball and Nordstrom, 1991). OTIS 

has been used to quantify hydrologic processes in a mountain 

stream (Broshears and others, 1993), to quantify the combined 

effects of hydrologic and geochemical processes on manganese transport (Harvey and Fuller, 1998), and more recently, to 

examine the effects of remedial alternatives in streams affected 

by acid mine drainage (Paschke and others, 2005; Walton-Day 

and others, 1999). OTIS-P is a modified version of OTIS that 

couples OTIS with a nonlinear regression package to automate 

the parameter-estimation process (Runkel, 1998).

Using OTIS to understand the effects of remedial 

alternatives is a fairly straightforward process. The model is 

calibrated using data collected during synoptic sampling of the 

stream. Model calibration entails adjusting variables (parameters) in the model so that the model generally reproduces (simulates) the downstream profile of various chemical constituents observed during synoptic sampling (ambient conditions). 

The primary data used in the model include the concentration 

of constituents at the upstream end of the profile (known as the 

upstream boundary condition), the location and concentration 

of constituents that are flowing into the stream at various locations along the profile (known as lateral-inflow concentrations, 

or CL

); and the rate of inflow of water at various locations 

along the profile (known as the lateral inflow, or qL

). Once the 

model is calibrated, it allows rapid simulation and comparison 

of the effects of multiple remedial alternatives for a stream. 

The effects of remediation are simulated by adjusting lateralinflow concentrations (CL

) to mimic the effects of the hypothetical remediation, and the resulting downstream profile of 

constituent concentration is compared to the profile constituent concentration from the calibrated model.

The use of OTIS to model ambient conditions and the 

effects of remediation has several advantages. First, once the 

model is calibrated, simulation and comparison of the effects 

of multiple remediation scenarios is quick and easy. Second, 

the model accounts for chemical reactions by using first-order 

removal coefficients. Mass-balance approaches, which are 

commonly used to examine the effects of removal of various 

sources of load to a stream, do not account for the instream 

reactivity of metals and may overestimate or underestimate 

the effects of remediation by not accounting for secondary changes that result from the removal of the source to the 

stream. Third, if temporal variations in streamflow and chemistry are known, the effects of remediation under differing 

hydrologic scenarios (variable flow) can readily be examined.

Purpose and Scope

The purpose of this chapter is:

• To demonstrate the utility of a solute-transport modeling approach to describe ambient stream conditions 

and investigate the effects of hypothetical remediation 

scenarios on trace-metal concentrations in the streams.

Specifically, the objectives of this chapter are to:

• Apply the solute-transport model OTIS to results of 

four of the mass-loading studies conducted in Cement 

Creek and the Animas River (Kimball and others, this 

volume). Four stream reaches in the Animas River 

watershed study area were used for this demonstration: upper and lower Cement Creek, the Animas River 

from Eureka to Howardsville, and the Animas River 

from Howardsville to Silverton (fig. 1; table 1). These 

reaches demonstrate the range in chemical conditions 

in the Animas River watershed study area: from low 

pH and elevated metal concentrations throughout much 

of Cement Creek, to near-neutral pH values and lower 

concentrations of metals in the upper Animas River 

reaches.

• Construct solute-transport models that simulate the 

ambient hydrologic and geochemical characteristics 

of each stream reach (also referred to as “calibrated 

models”)

980 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 981

Figure 1. Location of Animas River watershed study area and four study reaches used in solute-transport 

modeling. Reach A, upper Cement Creek; reach B, lower Cement Creek; reach C, upper Animas River, from Eureka 

to Howardsville; reach D, upper Animas River, from Howardsville to Silverton. Map shows alteration assemblages 

mapped by Bove and others (this volume, Chapter E3).

Houghton Mt

Kendall Mt

Silverton

Red Mtn. 3

Ohio Pk.

Mt. Moly

Howardsville

Gladstone

Eureka

Red Mtn 3

Cunningham 

Creek

Ohio Pk

Anvil Mt

EXPLANATION

Hydrothermal alteration assemblages

Vein-related quartz-sericite-pyrite (V-QSP)

Weak sericite-pyrite (WSP)

Quartz-sericite-pyrite (QSP)

Acid-sulfate 

Regional propylitic 

Location of mass-loading study used for

 OTIS solute-transport model

MILES

KILOMETERS

1

1

2

0 2

0

37°57' 18"

107°41' 06" 107°35' 02"

37°53' 25"

37°50' 08"

DENVER

COLORADO

Study area

Mineral

Creek

Cement Creek

Animas

River

A

D

B

A

C

3,792 m

California Mt

Eureka Gulch
• Evaluate the consistency of the models with ambient 

stream conditions by comparing inflow concentrations 

and first-order removal used in the model with environmental data and observations

• Evaluate the possible effects of remediation scenarios by 

decreasing or eliminating the loads of different sources 

of metal to the streams within the models. Although the 

models do not address all possible effects of remediation on stream chemistry, the model results indicate possible changes to instream concentrations resulting from 

the simulated remediation.

Methods

Data collected during mass-loading studies were used to 

construct the solute-transport models for each stream reach. 

A brief overview of mass-loading studies and tracer-injection 

methods is followed by an explanation of the solute-transport 

model OTIS, and the steps employed to construct, calibrate, 

and investigate the effects of remediation using the model.

Mass-Loading Studies

Application of mass-loading studies to identify sources 

of trace-metal loading in areas affected by inactive miningrelated features has been demonstrated by Bencala and 

McKnight (1987), Kimball and others (1994), and Kimball 

(1997). The approach includes using tracer injections to 

quantify streamflow by tracer dilution (Kilpatrick and Cobb, 

1985) and synoptic sampling to provide spatial profiles of pH 

and constituent concentrations. The tracer-dilution method 

entails continuous injection of a tracer that is geochemically 

nonreactive into a stream at a constant rate and concentration. 

Given sufficient time, all portions of the stream including side 

pools and the hyporheic zone become saturated with tracer, 

and instream concentrations will plateau (Kimball, 1997). 

Decreases in plateau concentration with stream length indicate 

dilution of tracer by additional water entering the channel 

from surface- and ground-water inflows. Consideration of 

this dilution allows the calculation of streamflow at each 

stream site. Streamflow and concentration can be combined 

to construct load profiles (Kimball and others, this volume). 

Analysis and interpretation of load profiles helps to rank the 

sources that have the greatest effects on stream chemistry and 

also helps to identify targets for remedial activities.

The four mass-loading studies used for the modeling 

exercise were conducted over a 3-year period (table 1). During 

this time, filtration methods varied as more efficient equipment 

was incorporated into the sample-processing scheme. Consequently, the earliest mass loading study (lower Cement Creek) 

used 0.1 μm filtration. Subsequent studies used ultrafiltration 

(10,000 Dalton molecular weight nominal pore size). Thus, the 

filtered samples for lower Cement Creek represent a larger filter size than those for the other three mass-loading studies presented here, and could contain substantial amounts of colloidal 

material relative to ultrafiltered samples. To distinguish this 

difference in filtration techniques, concentrations in figures 

and text from the lower Cement Creek samples are referred to 

as “filtered,” whereas concentrations in figures and text from 

the other three studies are referred to as “dissolved.”

Solute-Transport Modeling with OTIS

The data generated from mass-loading studies can also 

be used to construct solute-transport models that reproduce, or 

simulate, the ambient hydrologic and geochemical characteristics of the stream. These models can then be used to examine 

changes in instream concentrations that might result from 

remediation.

Like most solute-transport models, OTIS is based on 

the advection dispersion equation that describes downstream 

movement of solute mass. Additional terms are included to 

simulate lateral inflow and first-order removal, leading to the 

following governing equation (Runkel, 1998):

Q

A

--- wC

wx

------ 1

A

--- w

wx

----- ADwC

wx

------ © 1 § · qL

A

----- CL = +  O – C – C (1)

where

C is solute concentration in the main channel 

(mg/L),

 CL

 is the lateral infl ow solute concentration 

(mg/L),

 x is distance along the stream reach (meters),

 t is time (seconds),

 Q is the volumetric fl ow rate (m3/s),

 D is the longitudinal dispersion coeffi cient 

(m2

/s),

 A is the main channel cross-sectional area (m2

),

 qL

 is the lateral infl ow or fl ow added to the 

stream along each reach which is usually 

in units of volume per time (in this case, 

the infl ow is distributed along the length 

of a stream reach, so the appropriate units 

are cubic meters per second per meter, 

m3

/s-m),

Table 1. Summary of mass-loading studies used for OTIS 

simulations.

[LiBr, lithium bromide; LiCl, lithium chloride; NaCl, sodium chloride]

Location of 

tracer injection

Date of massloading studyLength of

study reach

Tracer 

used

Upper Cement Creek September 20, 1999 4,133 m LiBr

Lower Cement Creek September 20, 1996 10,548 m LiCl

Animas River, Eureka 

to Howardsville.

August 14, 1998 7,250 m NaCl

Animas River, 

Howardsville 

to Silverton.

September 14, 1997 7,858 m NaCl

982 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 983

and

λ is the main channel fi rst-order removal 

coeffi cient (this term is a rate constant, 

and is reported in units of reciprocal 

time, in this case, inverse seconds, s–1).

Four steps were used in the solute-transport modeling 

process:

1. Determination of physical parameters

2. Model calibration for conservative solutes

3. Model calibration for reactive solutes

4. Simulation of remediation scenarios.

All four steps were used for the simulations on lower Cement 

Creek and both reaches of the Animas River. For the upper 

Cement Creek data set, however, physical parameters determined for lower Cement Creek were used. This shortcut is permissible because in steady-state conditions (when constituent 

concentration and streamflow are constant with time), simulations are mostly insensitive to variations in dispersion (D) and 

cross-sectional area (A) (Runkel and Kimball, 2002). However, 

the steps are included for the reader interested in time-variable 

transport.

Determination of Physical Parameters

Physical parameters (dispersion (D), and stream crosssectional area (A)) were determined using data collected at 

transport sites. Transport sites are where periodic samples 

were collected and analyzed for tracer concentration. Profiles 

of tracer concentration through time (fig. 2) are influenced by 

the hydrologic characteristics of a stream reach. Sites located 

directly downstream from the tracer injection demonstrate 

an abrupt concentration increase and decrease caused by the 

beginning and end of the tracer injection (site T1, fig. 2). 

Data collected from sites moving downstream (sites T2 and 

9/19/1999 1500

9/19/1999 2100

9/20/1999 0300

9/20/1999 0900

9/20/1999 1500

9/20/1999 2100

9/21/1999 0300

9/21/1999 0900

-1

0

1

2

3

4

5

6

7

Plateau concentration at T3

Plateau concentration at T2

Plateau concentration at T1

Travel time from T1 to T2

DISSOLVED BROMIDE CONCENTRATION, 

IN MILLIGRAMS PER LITER

DATE AND TIME

 Site T1 (507 meters)

 Site T2 (2,885 meters)

 Site T3 (3,844 meters)

Figure 2. Variation in dissolved-bromide concentration with time at transport sites T1, T2, and T3, upper Cement Creek 

tracer injection, September 1999. The difference in travel time for the tracer between sites T1 and T2 is used to calculate 

stream cross-sectional area. Plateau concentrations are used to calculate streamflow.
T3, fig. 2) from the injection site exhibit several features 

that are affected by the physical transport of the tracer from 

the injection site to the transport site. First, the arrival of the 

tracer at downstream sites occurs later than at T1 because of 

the greater transport distance. Second, the plateau concentrations of tracer will be lower at downstream sites as the tracer 

is diluted by surface- and ground-water inflow. Finally, the 

arrival and departure of the tracer pulse will be less distinct 

indicating the effects of storage and dispersion as distance 

from the injection increases. The shapes of these curves were 

used to fit the hydrologic (or physical) parameters representing dispersion (D), and stream cross-sectional area (A) using 

the solute-transport models OTIS and OTIS-P (Runkel, 1998). 

The appendix provides details of the steps to accomplish this 

process. Once the physical parameters were determined, the 

next process was to calibrate the model in steady-state (where 

streamflow and constituent concentration were constant at 

each site through time) for each constituent of interest using 

data from the synoptic sampling. The calibration procedure 

varied depending on whether the constituent was conservative 

or reactive. A conservative constituent is one that is not 

removed from the water column by chemical reactions, so 

that its mass is conserved during downstream transport. There 

may be additions to mass from inflows, but there are no losses 

of mass from the water column. A reactive constituent is 

removed from the water column by geochemical reactions so 

that constituent mass in the water column may decrease during 

downstream transport.

Model Calibration for Conservative Solutes

The first step in calibration for conservative solutes was 

to use the tracer data to estimate streamflow for each stream 

site where synoptic samples were collected (Appendix and 

Kimball and others, this volume). Each modeled reach was 

then divided into subreaches (known as model reaches) chosen 

to bracket the major surface-water and ground-water inflows 

to the stream, including mine drainage from adits or tributaries. Lateral inflow (qL

) was then estimated for each model 

reach (Appendix) using the tracer estimates of streamflow.

The next step was estimation of lateral-inflow concentrations (CL

) for each model reach. The lateral-inflow concentration used in each model reach depended on whether the model 

reach contained any inflows that were sampled during the synoptic sampling event, and whether there was any increase in 

streamflow along the reach. For model reaches that contained 

no inflow, both lateral inflow (qL

) and lateral-inflow concentration (CL

) were zero. In model reaches that contained no 

sampled inflow, but where flow and constituent load increased 

along the reach, lateral-inflow concentration was approximated using the concentration from an inflow sample in an 

adjacent reach, or by using the effective inflow concentration 

(CI

E ) calculated from:

CI

E CCQC CBQB –

QC QB   – = ------------------------------------ (2)

where

CC is concentration of the constituent of interest 

at the downstream site,

 QC is streamfl ow at the downstream site,

 CB is concentration of the constituent of interest 

at the upstream site,

and

B

In model reaches that contained one sampled inflow, the 

chemistry of that inflow was used as a first-estimate of lateralinflow concentration. For model reaches that contained more 

than one inflow, lateral-inflow concentration was derived from 

a flow-weighted average concentration of the inflow samples, 

or the concentration of the most representative inflow sample 

was used.

After comparing concentration profiles produced by the 

model to profiles of data from the synoptic sampling, values 

of lateral-inflow concentration (CL

) were adjusted using an 

iterative process (Appendix) until the model results generally 

matched the data.

Model Calibration for Reactive Solutes

Calibration of the model for reactive solutes requires 

all of the previous steps noted for conservative solutes. The 

distinction between conservative and reactive solutes becomes 

important during calibration of lateral-inflow concentrations. 

For a conservative solute, when appropriate values were 

chosen for the inflows, the simulation matched the data. For 

a reactive solute, when appropriate values were chosen for 

the inflows, the simulation overestimated the data. It seems 

somewhat counterintuitive to use lateral-inflow concentrations during the calibration process that caused the simulations 

to overestimate the data. However, elevated lateral-inflow 

concentrations (CL

) were used to match large, incremental 

increases in stream concentration in some model reaches for 

some constituents. Downstream from these model reaches, all 

possible choices for lateral-inflow concentrations (CL

) (including CL

 = 0) sometimes resulted in simulations that overestimated the observed data. Therefore, removal of the constituent 

(using first-order removal coefficients) was needed for the 

simulations to match the observed data.

First-order removal coefficients (λ) were used to offset 

the large concentration increases in certain model reaches and 

were estimated using OTIS-P (Appendix) (Runkel, 1998). 

Simulations resulting from initial estimates of first-order 

removal coefficients (λ) were compared to the data. Generally, 

good agreement was quickly obtained; however, agreement 

sometimes required multiple modeling efforts. In this study, 

first-order removal coefficients (λ) for iron were estimated 

first. Then first-order removal coefficients (λ) for solutes that 

might sorb to or coprecipitate with iron, such as copper or 

zinc, were estimated so that the resulting model simulated 

their removal in reaches having favorable geochemical conditions (that is, coincident removal of iron).

984 Environmental Effects of Historical Mining, Animas River Watershed, Colorado

 Q is streamfl ow at the upstream site.
OTIS and Remediation in Cement Creek and Upper Animas River 985

Simulation of Remediation

The calibrated model contained appropriate lateral 

inflow (qL), lateral-inflow concentrations (CL), and first-order 

removal coefficients (λ) to generally reproduce, or simulate, 

the data collected during synoptic sampling. Simulation of 

remediation entailed changing lateral-inflow concentrations 

(CL) to mimic potential remediation scenarios. For instance, 

if one model reach had a tributary inflow where remediation 

could occur, then post-remediation lateral-inflow concentrations were estimated. The resulting lateral-inflow concentrations replaced the calibrated lateral-inflow concentrations 

in the model. The revised model estimated new instream 

concentrations for the constituent downstream from the point 

of remediation. For a conservative solute, the result was the 

same as might be obtained using a mass-loading model. For a 

reactive (nonconservative) solute, the result incorporated any 

first-order removal that occurred downstream from the point of 

remediation. Thus, the resulting improvement in water quality 

was more than what would be predicted from a mass-loading 

model. An advantage of the OTIS modeling approach was 

that once a model was calibrated, rapid simulation of multiple 

remediation scenarios for multiple elements was possible. A 

disadvantage of the approach was that the first-order removal 

coefficients oversimplify the interaction between constituents. 

Remediation of an inflow would likely increase the pH conditions of the inflow that could, in turn, cause instream pH to 

increase. Greater instream pH would result in greater removal 

of reactive constituents than predicted by the first-order 

removal coefficients in the calibrated model. To model such 

pH-dependent processes would require use of a model that 

incorporates chemical equilibrium such as OTEQ (Runkel and 

Kimball, 2002). However, herein, such an approach was not 

utilized because preliminary modeling with OTEQ in Cement 

Creek indicated that remediation simulations were not likely 

to significantly change instream pH; numerous non-miningaffected inflows supply low-pH water to the stream and offset 

the pH benefits of simulated remediation. Similarly, in the two 

modeled reaches in the upper Animas River basin, pH values 

were near neutral and would increase slightly with simulated 

remediation, but probably not enough to make a significant 

difference to instream pH. Finally, underestimating the effects 

of remediation was an advantage because it was desirable to 

produce a conservative estimate of the effects of remediation.

OTIS Solute-Transport Modeling

Four subbasins (upper and lower Cement Creek, and the 

Animas River, Eureka to Howardsville and Howardsville to 

Silverton) were separately modeled using the OTIS solutetransport model. Results of mass-loading analyses (Kimball 

and others, this volume) identified locations of surface- and 

ground-water sources of acid and metal to the rivers, and the 

extent of attenuation occurring for certain constituents. These 

results were used to help divide each of the four synoptic 

sample study basins into model reaches, and to guide choices 

about appropriate lateral-inflow concentrations (CL

) and 

first-order removal coefficients (λ) for each modeled reach 

and stream. This section presents a description of each of the 

study reaches along with a brief summary of the chemical and 

loading conditions for each, and then presents the results of 

calibration and remediation simulations.

Upper Cement Creek Subbasin

The September 1999 upper Cement Creek study reach 

covered 4,133 m of stream starting downstream from Ross 

Basin and continuing to just downstream from the confluence 

with South Fork Cement Creek near Gladstone (figs. 1 and 3). 

This stream reach included inflows from the Queen Anne and 

Grand Mogul mines at 271 and 298 m and the Mogul mine 

at 1,318 and 1,348 m, as well as several others. North Fork 

Cement Creek, which included drainage from several historical mines, joins Cement Creek at 3,076 m (table 2). Treated 

effluent from the American tunnel (site # 96)1

 enters the 

stream at 3,853 m. This effluent contains mine drainage that 

is collected from underground workings associated with the 

Sunnyside mine, and is treated and released to Cement Creek 

under a National Pollutant Discharge Elimination System 

(NPDES) permit. This source will be referred to as “Sunnyside 

mine effluent.” South Fork Cement Creek enters Cement 

Creek at 4,001 m. Numerous additional historical mines and 

prospects exist in the watershed (see Church, Mast, and others, this volume, Chapter E5). Stream elevation ranges from 

approximately 3,595 m at the injection site to 3,170 m at the 

downstream end of the study reach. Average stream gradient 

is approximately 12 percent (Blair and others, 2002).

A continuous injection of lithium bromide solution 

occurred from September 19 through September 20, 1999 

(Kimball and others, this volume). Synoptic sampling occurred 

during the injection from approximately 0900 to 1500 hours on 

September 20, 1999. A total of 36 stream sites and 23 inflow 

sites were sampled. A Parshall flume located at the Sunnyside 

mine effluent provided an independent measure of that inflow.

The water quality in upper Cement Creek changed from 

the injection site to the end of the study reach. At the upstream 

end of the study reach, pH was 6.8; sulfate and zinc concentrations were 70 and 0.5 mg/L, respectively. At the downstream 

end of the study reach, pH was 4.0 and sulfate and zinc concentrations were 400 and 4.0 mg/L. Along the study reach, pH 

values were as low as 3.3, and sulfate and zinc concentrations 

were as high as 600 and 12 mg/L, respectively. The results of 

the loading analysis in the upper Cement Creek subbasin indicated that, during the 1999 sampling, there were four primary 

1

Mine, mill, and mill-tailings deposit names in this chapter are identified 

by their AML_MINE_ID number in the Animas River watershed database. 

See Church, Mast, and others, this volume, Chapter E5, especially table 1, 

which contains names in numerical order as well as latitude and longitude 

of each feature.
41m

927m 827m

345m

259m

3,931 m

3,1 m

2,976 m

2,885 m

1,367 m

1,315 m

1,142 m

4

4,133 m

end of 

study reach

3,844 m

,200 m

 

1,292 m

07

107°39' 107 8'

37°54'

107°39'30" 107 39' 107°38'30" 10 3 '

37°53'30"

'

37°54'30"

0 0 0 .1 5 MILES .3 0 .6

0 0 .1 5 KILOMETERS 0 .3 0 .6

EXPLANATION

BONITA FAULT

ROSS BASIN FAULT

Mogul mine

Grand 

Mogul mine

Queen Anne mine

Fracturecontrolled

canyon

Start of 

massloading 

study reach

Gladstone

Cement 

Creek

Sampled stream sites

Fault

Sampled and labeled stream sites 

 and end of OTIS model reach

Sampled inflow sites

OTIS model downstream boundary

Hydrography

Mines, mills, and mine tunnels

Pride of Bonita mine

Red and Bonita mine

Red and Bonita Mill

American tunnel level of Sunnyside mine

(Gold King Mill level tunnel)

S outh

Fork Cement Creek

North Fork Cement Creek

Middle Fork Cement Creek

Minnehaha Creek

3,844 m

°

986 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 987

sources of loading to the stream: the Mogul mine, North Fork 

Cement Creek, and South Fork Cement Creek accounted 

for from 63 to 97 percent of the copper, iron, and zinc loads 

(Kimball and others, this volume), whereas the Sunnyside 

mine effluent and South Fork Cement Creek accounted for 87 

and 76 percent of the calcium and sulfate loads. Combined, the 

Grand Mogul and Queen Anne mines accounted for less than 

5 percent of the calcium, copper, iron, sulfate, and zinc loads.

Determination of Physical Parameters 

and Boundary Conditions

The upper Cement Creek study reach was divided into 

15 model reaches for the steady-state simulations (table 2; 

fig. 4). The model reaches bracketed locations of all major 

and some of the minor sources of load to the stream (Kimball 

and others, this volume). The model included 4,200 m of the 

stream with the last model reach containing the last synoptic 

sampling site at 4,133 m. The modeled reach was slightly 

longer than the synoptic sampling reach (fig. 3) to account 

for the fact that in the model, the concentration gradient at 

the downstream boundary condition is zero (Runkel, 1998).

Required model parameters include the dispersion 

coefficient (D), the cross-sectional area of the stream (A), an 

upstream-boundary condition (Ub

) for each solute of interest, and the lateral inflow (qL

) and concentration (CL) for each 

model reach. In addition, some model reaches used first-order 

removal coefficients (λ). The appendix contains discussion of 

the values of D and A. Determination and values of Ub, qL, CL, 

and λ are presented herein.

OTIS simulations used streamflow estimates from the 

bromide tracer data and ultrafiltered concentrations of copper, 

iron, and zinc. The upstream boundary condition (Ub) for simulating the bromide profile was 27.2 mg/L, the concentration 

at the 41 m site during the synoptic sampling. The upstream 

boundary condition was 0.016 mg/L for copper, 0.005 mg/L 

for iron, and 0.490 mg/L for zinc. These values represent 

the ultrafiltered concentrations of the metals at the site 41 m 

downstream from the injection site.

Model Calibration for Simulation of the Bromide 

Tracer Profile

Several problems complicated the calculation of streamflow from the tracer data. Some portions of stream water and 

tracer were probably lost to ground water in the far upstream 

portion of the study reach. Streamflow along the study reach 

was therefore calculated by calibrating the bromide concentrations to streamflow measurements made using a velocity meter 

and extrapolating the bromide mass-flux upstream (Kimball 

and others, this volume). The resulting simulated bromide 

profile matched the data downstream from 260 m (fig. 4). 

The simulation underestimated the data in model reach 2. 

Figure 3 (facing page). Location of stream reach for upper 

Cement Creek mass-loading study (September 1999) and 

OTIS solute-transport model. Stream- and inflow-sample data 

provided in Sole and others (this volume, Chapter G). Mining 

features shown (and inventory numbers, from Church, Mast, and 

others this volume, Chapter E5) include American tunnel level 

of Sunnyside mine (# 96); Grand Mogul mine (# 35); Mogul mine 

(# 31); Pride of Bonita mine (# 101); Red and Bonita mine (# 99) and 

Red and Bonita Mill (# 97); and Queen Anne mine (# 34). Mining 

features shown limited to those discussed in the text. See Church, 

Mast, and others (this volume) for a complete inventory. Location 

of Bonita fault from Yager and Bove (this volume, Chapter E1, 

pl. 1). Base from Digital Orthophoto Quads, Transverse Mercator, 

–105.00 central meridian, 1927 datum.

Table 2. Upper Cement Creek, reach number, distance at end of reach, and description of reaches used in OTIS modeling of synoptic 

samples collected for upper Cement Creek (September 1999).

[Distance is downstream from arbitrary stream datum. Reach numbers correspond to those in figures 4–6; m, meter]

Model 

reach

Distance

(m) Brief description of reach and significant inflows

1 41 Cement Creek downstream from tracer-injection site.

2 259 Cement Creek downstream from tracer-injection site, upstream from Queen Anne and Grand Mogul mines.

3 345 Cement Creek downstream from Queen Anne and Grand Mogul mines.

4 827 Cement Creek upstream from fracture-controlled canyon.

5 927 Cement Creek downstream from fracture-controlled canyon.

6 1,142 Cement Creek where canyon ends and stream flows in more open valley.

7 1,292 Cement Creek between canyon and start of Mogul mine inflows.

8 1,315 Cement Creek upstream from Mogul mine inflows.

9 1,367 Cement Creek downstream from Mogul mine inflows.

10 2,885 Cement Creek downstream from area with the most ferricrete and iron-bog inflows.

11 2,976 Cement Creek upstream from North Fork Cement Creek.

12 3,107 Cement Creek downstream from North Fork Cement Creek.

13 3,844 Cement Creek upstream from Sunnyside mine tunnel inflow.

14 3,931 Cement Creek downstream from Sunnyside mine tunnel inflow and upstream from South Fork Cement Creek.

15 4,200 Cement Creek downstream from South Fork Cement Creek.
This pattern occurred because very little flow (approximately 

0.1 L/s) was coming into the stream reach except for a visible 

surface inflow at 237 m. In the simulations, the inflow volume 

was distributed evenly along the entire model reach from 41 to 

259 m. The simulation and data matched well at the upstream 

and downstream ends of model reach 2 (41 to 259 m) indicating that the lateral-inflow (qL

) value was appropriate for that 

reach. Downstream, the match between the simulation and the 

data indicated that the streamflow and qL values were appropriate. The discrepancy in reach 2 was inconsequential as there 

were no inflows of interest in that reach. Streamflow at the 

upper end of the study reach was 9 L/s. Streamflow downstream from the confluence with South Fork Cement Creek at 

the end of the study reach (4,200 m) was 299 L/s. Six distinct 

inflow areas (the Queen Anne mine, the Grand Mogul mine, 

the Mogul mine, North Fork Cement Creek, the Sunnyside 

mine effluent at the American tunnel (site # 96), and South 

Fork Cement Creek) accounted for 79 percent of the increased 

streamflow along the study reach. The remaining 21 percent of 

the streamflow originated in smaller tributaries, seeps, springs, 

and diffuse subsurface inflow that discharged to the stream.

Model Calibration for Copper, Iron, and 

Zinc Simulations

Simulations for upper Cement Creek utilized a variety 

of estimation techniques for lateral-inflow concentration (CL) 

values (table 3; fig. 5). In model reaches 1 and 4, CL

 for copper, iron, and zinc was zero. In model reach 11, CL

 for zinc 

was zero. CL values for model reaches 3 and 7 were flowweighted average values calculated from two inflows. Model 

reaches 2, 5, 6, and 12 through 15 used representative inflow 

concentrations for CL

 values from tributaries that were located 

within each reach. Model reaches 8 through 11 used effective 

inflow concentrations (equation 2) for CL

 values, except for 

iron in model reach 10, where a flow-weighted average value 

was used, and zinc in model reach 11, where CL

 was zero. The 

use of effective inflow for these four model reaches is justified 

because for most elements listed, prior analyses of mass loading showed the reaches to be locations of diffuse, subsurface 

inflow (Kimball and others, this volume). Iron is the exception. Because iron load decreased in the course of model reach 

10, an effective inflow concentration could not be calculated. 

-500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

0

5

10

15

20

25

30

15

13 14 12 1 2 3 4 5 6 7 8 9 10 11

South Fork Cement Creek

Mogul mine inflow

North Fork Cement Creek

Sunnyside mine

Queen Anne and Grand Mogul inflows

 Dissolved bromide concentration in stream samples

 Dissolved bromide concentration in inflow samples

 Simulated bromide concentration

 Streamflow

 Labeled significant inflow

 Model reach boundary and number

DISTANCE ALONG STUDY REACH, IN METERS

DISSOLVED BROMIDE CONCENTRATION,

IN MILLIGRAMS PER LITER

0

100

200

300

400

Model 

reach 

number

STREAMFLOW,

IN LITERS PER SECOND

Figure 4. Variation in dissolved bromide concentration, streamflow, and simulated bromide concentration with distance, showing 

location of OTIS model reaches and significant features, upper Cement Creek mass-loading study (September 1999) and OTIS 

solute-transport model. 

988 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 989

Therefore, a flow-weighted average concentration of the seven 

inflows was used for CL

. This flow-weighted average value 

is large relative to iron concentrations measured in inflows 

in model reach 10 (fig. 5B). In addition, in model reach 10, 

the relative value of CL

 for iron compared to the distribution 

of sampled inflow concentrations is similar to the relative 

values of CL

 for copper and zinc compared to sampled inflow 

concentrations (fig. 5 A–C). This similarity adds confidence to 

the value of CL

 used for iron in model reach 10.

The wide variation in lateral-inflow concentration (CL) 

values is consistent with and similar to the wide variation in 

concentrations of the sampled inflow values for each element 

along the study reach (fig. 5). In some cases the effective inflow 

values chosen for CL

 are similar to or within the range of inflow 

values in a model reach (for example, model reaches 9 and 10; 

fig. 5).

First-order removal coefficients (λ) were used to improve 

the fit between the data and the simulations for some model 

reaches. Conservative simulations of copper, iron, and zinc 

(fig. 6) indicated that simulations slightly overestimated the 

ambient data. Simulations with OTIS-P were used to estimate 

first-order removal coefficients for some of the model reaches. 

Simulations including first-order removal coefficients indicated 

a better fit to the data (fig. 6). Large first-order removal coefficients (ranging from 1.0 × 10–4 to 1.0 × 10–1s–1) were used in 

reaches 1 through 3 for copper, iron, and zinc, and for iron in 

model reaches 14 and 15 (table 4). Smaller first-order removal 

coefficients (approximately 1.0 × 10–4s–1 or less) were used in 

model reaches 10 through 12 for iron. The use of large firstorder removal coefficients in the upstream model reaches is 

consistent with the high pH values (5.7 to 6.8) of the stream 

and indicates possible precipitation of amorphous iron minerals and coprecipitation or sorption of copper and zinc with the 

iron minerals. Simulated removal of iron from the water column 

in model reach 10 is consistent with the results of the loading 

analysis that indicated removal of iron in the stream reach from 

1,367 to 2,885 m (Kimball and others, this volume). Simulated removal of iron in model reach 12 is consistent with the 

large iron load supplied by North Fork Cement Creek, which 

accounted for approximately 50 percent of the total iron load 

reported by the end of the study reach (Kimball and others, this 

volume). Iron removal in model reach 11 could be caused by 

excess iron loads to the stream similar to model reach 10, or by 

contributions of iron in the subsurface upstream from the North 

Fork Cement Creek tributary. The large amount of iron removal 

required in model reaches 14 and 15 is consistent with the addition of high-pH water (7.9) from the Sunnyside mine effluent in 

model reach 14, and with the addition of pH 5.5, relatively iron 

rich water in model reach 15 from South Fork Cement Creek. 

Overall, except for the model reaches described, first-order 

removal coefficients were not required to achieve a fit between 

the simulations and the data. The relatively small size and 

limited distribution of removal coefficients indicate that, except 

in a few model reaches, reactive processes are operating only 

to a minimal extent in the study reach. This result is consistent 

with low pH values that occurred in the stream throughout most 

of the upper Cement Creek study reach. At these low pH values 

(3–4), iron removal can occur, but sorption of metals to precipitating iron would be minimal.

Table 3. Upper Cement Creek, samples and methods used to estimate lateral-inflow concentrations (CL) for copper, iron, and zinc in 

calibrated OTIS model.

[Synoptic samples collected September 1999. Numeric values represent distances (in meters, m) of inflow samples used in estimation technique (inflow samples 

are in project database, Sole and others, this volume). Alpha characters indicate method used to estimate lateral inflow: NI, no lateral-inflow concentration in 

that reach; R, representative inflow concentration used for lateral-inflow concentration; F, flow-weighted average value used for lateral-inflow concentration; 

E, effective inflow concentration used for lateral-inflow concentration. Lateral-inflow concentration values shown in figure 5]

Model reach Distance

(m) Dissolved copper Dissolved iron Dissolved zinc

1 41 NI NI NI

2 259 R, 237 R, 237 R, 237

3 345 F, 271, 298 F, 271, 298 F, 271, 298

4 827 NI NI NI

5 927 R, 880 R, 880 R, 880

6 1,142 R, 933 R, 933 R, 933

7 1,292 F, 1,237, 1,287 F 1,237, 1,287 F, 1,237, 1,287

8 1,315 E E E

9 1,367 E E E

10 2,885 E F, 1,411, 1,679, 1,719, 1,884, 

2,407, 2,761, 2,763

E

11 2,976 E E NI

12 3,107 R, 3,076 R, 3,076 R, 3,076

13 3,844 R, 3,518 R, 3,518 R, 3,518

14 3,931 R, 3,853 R, 3,853 R, 3,853

15 4,200 R, 4,001 R, 4,001 R, 4,001
Figure 5. Variation in calibrated lateral-inflow concentrations and observed inflow concentrations (September 1999) with distance 

for A, dissolved copper; B, dissolved iron; C, dissolved zinc showing OTIS model reaches, upper Cement Creek OTIS model.

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

1E-4

1E-3

0.01

0.1

1

10

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

1E-4

1E-3

0.01

0.1

1

10

100

1,000

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

0.01

0.1

1

10

100

1,000

13 14 15 12 9 10 11 8 2 3 4 5 6 7

DISSOLVED COPPER CONCENTRATION, 

IN MILLIGRAMS PER LITER

13 14 15 12 9 10 11

8

2 3 4 5 6 7

DISSOLVED IRON CONCENTRATION, 

IN MILLIGRAMS PER LITER

C

B

A

13 14 15 12 11

10

9

8

2 3 4 5 6 7

DISSOLVED ZINC CONCENTRATION, 

IN MILLIGRAMS PER LITER

DISTANCE ALONG STUDY REACH, IN METERS

 Sampled inflow concentration

 Representative inflow concentration

 Flow-weighted average inflow concentration

 Effective inflow concentration

 Model reach boundary and number

990 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 991

Figure 6. Variation in concentrations of A, dissolved copper; B, dissolved iron; C, dissolved zinc with distance in stream and inflow 

samples and in OTIS conservative and calibrated reactive simulations, showing model reach and number and location of significant 

features, upper Cement Creek, September 1999. Reactive OTIS simulation included first-order removal coefficients (λ).

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

0

10

20

30

150

160

170

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

0

5

10

15

20

25

85

90

95

100

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

0

1

2

3

4

5

6

12

13

14

13 14 15

12

9 10 11 8 2 3 4 5 6 7

South Fork Cement Creek

Sunnyside mine

North Fork Cement Creek

Mogul mine inflow

Queen Anne and Grand Mogul inflows

DISSOLVED IRON CONCENTRATION, 

IN MILLIGRAMS PER LITER

C

B

A

9 10 11 12 13 14 15 8 2 3 4 5 6 7

DISSOLVED ZINC CONCENTRATION, 

IN MILLIGRAMS PER LITER

DISTANCE ALONG STUDY REACH, IN METERS

13 14 15

12

9 10 11 8 2 3 4 5 6 7

DISSOLVED COPPER CONCENTRATION, 

IN MILLIGRAMS PER LITER

 Stream samples

 Inflow samples

 Conservative OTIS simulation

 Reactive OTIS simulation with first-order coefficient

 Labeled significant inflow

 Model reach boundary and number
Table 4. Upper Cement Creek, first-order removal coefficients 

(λ) by metal and reach for calibrated OTIS model.

[All values in second–1]

Model

reach

Distance

(m)

Dissolved

copper

Dissolved

iron

Dissolved

zinc

1 41 3.0×10–3 1.04×10–1 1.19×10–4

2 259 3.0×10–3 1.04×10–1 1.19×10–4

3 345 3.0×10–3 1.04×10–1 9.98×10–4

4 827 0.00 0.00 0.00

5 927 0.00 0.00 0.00

6 1,142 0.00 0.00 0.00

7 1,292 0.00 0.00 0.00

8 1,315 0.00 0.00 0.00

9 1,367 0.00 0.00 0.00

10 2,885 0.00 1.07×10–4 0.00

11 2,976 0.00 9.19×10–5 0.00

12 3,107 0.00 9.19×10–5 0.00

13 3,844 0.00 0.00 0.00

14 3,931 0.00 1.26×10–3 0.00

15 4,200 0.00 1.26×10–3 0.00

Remediation Simulations

Remediation scenarios tested using the calibrated 

model included eliminating metal loading individually from 

the Queen Anne and Grand Mogul mines, from the two inflows 

to the stream from the Mogul mine, from North Fork Cement 

Creek, and then from all of these inflows together (fig. 7; 

table 5). For demonstration purposes, this approach assumed 

that total remediation of the inflows could be accomplished and 

that concentrations of copper, iron, and zinc from these sources 

would be negligible following remediation. However, given the 

extensive hydrothermal alteration mapped in this area (fig. 1; 

Bove and others, this volume, Chapter E3), total elimination 

of the metals from these sources is not realistic. For remediation of the Queen Anne and Grand Mogul mines, lateral-inflow 

concentrations (CL

) of copper, iron, and zinc in model reach 3 

were set to zero. For remediation of contributions from the 

Mogul mine, lateral-inflow concentrations in model reaches 8 

and 9 were set to zero. Model reach 8 was included in the 

Mogul mine remediation because analysis of the loading 

results (Kimball and others, this volume) indicated that some 

subsurface flow from the Mogul mine was probably entering 

the stream in this reach, which is directly upstream from the 

reach containing the primary inflow from the Mogul mine. For 

remediation of North Fork Cement Creek, lateral-inflow concentrations (CL

) of copper, iron, and zinc in model reaches 11 

and 12 were set to zero. Reach 12 contains North Fork Cement 

Creek. Similar to the Mogul mine, there was possibly some 

subsurface flow from North Fork Cement Creek that entered 

Cement Creek upstream from the tributary. We assumed 

that this flow was contributed to the subsurface from surface 

flow in North Fork Cement Creek during transport across the 

alluvial cone of North Fork Cement Creek, and represented 

surface water that was only in the subsurface for a short time 

and distance before entering Cement Creek. Therefore, we 

assumed that remediation of surface flow upstream in North 

Fork Cement Creek also would clean up this subsurface flow. 

The final remediation simulation combined the results of the 

three previous simulations. Percent changes in load resulting 

from the simulated remediations were calculated relative to the 

simulated concentration value in the calibrated model at the 

last stream sampling site, 4,133 m.

The remediation simulations indicate that the largest 

changes in copper and zinc concentrations would result from 

remediation of the Mogul mine inflows to the stream, whereas 

the largest changes in iron concentration would result from 

elimination of iron from North Fork Cement Creek (table 5). 

These results are consistent with the results of the loading 

analysis that indicated that the Mogul mine was the largest 

source of copper and zinc loading to the stream, whereas 

North Fork Cement Creek was the largest source of iron 

(Kimball and others, this volume). Simulation results indicate 

that remediation of all the sources together could result in a 

90 percent reduction in copper concentration, a 62 percent 

reduction in iron concentration, and an 82 percent reduction in 

zinc concentration at the last synoptic sampling site (4,133 m) 

at the downstream end of the study reach near Gladstone. 

Realistically, remediation of these sources would probably 

not accomplish 100 percent metal reduction as was simulated 

here. Particularly in North Fork Cement Creek, hydrothermally altered rocks that generate acid water are present (fig. 1 

and Bove and others, this volume) and would likely continue 

to generate acid water after remediation of mining-related 

sources in the basin. Thus, the reductions presented here are 

the maximum possible from elimination of the mining-related 

sources identified in the basin.

The simulated concentration profiles for the aggregate 

remediation scenario indicate that even though the major 

sources of metal loading are removed, there are other sources 

of metal loading to the stream that cause elevated concentrations at the downstream end of the study reach (fig. 7). There 

is a distinct increase in iron concentration near the downstream end of the study reach that is caused by the confluence with South Fork Cement Creek (4,001 m). There is a 

gradual increase in copper, iron, and zinc concentrations in 

the stream between the Mogul mine and North Fork Cement 

Creek. This increase is probably caused by diffuse subsurface 

flow along that zone. During the synoptic sampling event and 

other field activities, we observed some small, distinct inflows 

along that reach that were probably related to mine waste 

and drainage originating from historical mining and milling activity at the Pride of Bonita and Red and Bonita mines 

(# 101 and # 99). However, these inflows were generally not 

traceable back to the mines as distinct surface flow, but rather 

emerged as springs near the stream. In addition, this reach of 

stream contains abundant ferricrete deposits (Yager and Bove, 

this volume, Chapter E1 and pl. 2). These ferricrete deposits indicate previous and possibly current discharge of iron 

from metal-rich ground water in the stream reach (Wirt and 

others, this volume, Chapter E17; Verplanck and others, this 

volume, Chapter E15) over the last 10,000 years. Therefore, 

the simulated elevated concentrations of iron and zinc that 

992 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 993

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

0

1

2

3

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

0

5

10

15

20

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

0

5

10

15

20

DISSOLVED COPPER CONCENTRATION, 

IN MILLIGRAMS PER LITER

DISSOLVED IRON CONCENTRATION, 

IN MILLIGRAMS PER LITER

 Simulation with no remediation

 Simulation of Queen Anne and 

 Grand Mogul mines remediation

 Simulation of Mogul mine remediation

 Simulation of North Fork Cement Creek remediation

 Simulation of remediation of 

 all major inflows

 Labeled significant features

C

B

A

End of study reach, 4,133 meters

North Fork Cement Creek

Mogul mine inflow

DISSOLVED ZINC CONCENTRATION, 

Queen Anne and Grand Mogul inflows

IN MILLIGRAMS PER LITER

DISTANCE ALONG STUDY REACH, IN METERS

Figure 7. Variation in concentrations of A, dissolved copper; B, dissolved iron; C, dissolved zinc with distance in OTIS simulations of 

remediation scenarios, showing location of some significant features, upper Cement Creek. Details of remediation scenarios are in table 5.
remain following remediation probably result from groundwater discharge to the stream and would most likely remain 

following remediation of the distinct mining sources simulated 

in the model. In addition, note that the concentration of zinc 

was 0.49 mg/L at the upstream end of the study reach indicating that sources for that metal lie upstream from the study 

reach. Dissolved zinc concentrations greater than 2 mg/L were 

reported in one spring upstream from the study reach (Mast 

and others, 2000), and vein-related quartz, sericite, and pyrite 

alteration that can cause elevated metal concentrations in water 

crossing through it was mapped in the drainage basin upstream 

from the study reach (fig. 1; Bove and others, this volume; 

Mast and others, this volume, Chapter E7). In addition, Bove 

and others (this volume) hypothesized that high backgroundmetal concentrations noted in water samples in the area were 

from interaction of the water with mineralized, unmined veins. 

Although the aggregate remediation scenario achieved metal 

reduction of as much as 90 percent for copper, elevated metal 

concentrations remained at the downstream end of the study 

reach from discharge of metal-rich ground water to the stream 

along the study reach, and from inflow of metals upstream 

from the study reach.

Lower Cement Creek Subbasin

The study reach for lower Cement Creek covered 

approximately 10,500 m of stream starting on South Fork 

Cement Creek, just upstream from the confluence with Cement 

Creek, and continued to the U.S. Geological Survey streamflow gauging station (CC48) near the mouth of Cement Creek 

in Silverton at 11,558 m (figs. 1 and 8). The arbitrary start for 

measuring distance along the stream was located upstream from 

North Fork Cement Creek. The study reach included inflows 

from Cement Creek (1,080 m), Prospect Gulch (2,795 m), 

Tiger Gulch (3,670 m), Georgia Gulch (3,793 m), Fairview 

Gulch (4,493 m), Cascade Gulch (5,100 m), Minnesota Gulch 

(5,490 m), the Anglo-Saxon mine (# 183; 5,817 m), Porcupine 

Gulch (5,907 m), Ohio Gulch (6,781 m), Illinois Gulch 

(7,006 m), Topeka Gulch (7,548 m), Niagara Gulch (8,422 m), 

and Hancock Gulch (8,735 m). Numerous inactive mines and 

prospects exist in the subbasin (Church, Mast, and others, this 

volume). Iron bogs and springs and alluvial ferricrete deposits 

occur throughout the watershed but are particularly plentiful in the stream reach that contains the inflow from Prospect 

Gulch from 2,500 to 3,300 m (Stanton, Yager, and others, this 

volume, Chapter E14; Wirt and others, this volume; Verplanck 

and others, this volume; Yager and Bove, this volume, pl. 2). 

Stream elevation ranges from 3,185 m at the injection site on 

South Fork Cement Creek to 2,860 m at the downstream end of 

the stream reach. Stream gradient ranges from 1 to 5.3 percent 

(Blair and others, 2002) and averages about 3 percent.

During the 1996 mass-loading study, all of the flow in 

Cement Creek was collected just downstream from the confluence of North Fork Cement Creek and diverted into the treatment system at the Sunnyside mine. Part of the treated mine 

drainage and stream water was returned to the Cement Creek 

through a conduit located just upstream from the confluence 

with South Fork Cement Creek (1,080 m). The chemical composition of water discharged from the conduit reflected the net 

result of treatment of the Sunnyside mine effluent and upper 

Cement Creek.

From September 19 through 21, 1996, a continuous 

injection of lithium chloride solution took place on South 

Fork Cement Creek approximately 40 m upstream from the 

confluence with Cement Creek (1,080 m). Synoptic sampling 

was done on September 20, 1996. The sampling divided 

the 10,518-m study reach into 54 segments. The segments 

bracketed major and minor tributary inflows and areas of 

likely subsurface inflow. Stream-sampling sites were located 

sufficiently downstream from inflows to capture both visible 

tributary inflow and any additional subsurface inflow that 

Table 5. Simulated concentration at the downstream end of the upper Cement Creek mass-loading study reach resulting from various 

remediation scenarios.

[Dissolved copper, iron, and zinc concentration values in milligrams per liter; m, meter]

Remediation Copper Iron Zinc

Simulated concentration at end of study 

reach (4,133 m) in calibrated model.

0.650 2.65 4.48

Total remediation of Queen Anne and 

Grand Mogul mine sources.

Simulated concentration at end of study reach 

(4,133 m).

0.621 2.65 4.19

Percent reduction 4.5 0 6.5

Total remediation of Mogul mine sources Simulated concentration at end of study reach 

(4,133 m).

0.275 2.52 1.82

Percent reduction 58 5 59

Total remediation of North Fork Cement 

Creek sources.

Simulated concentration at end of study reach 

(4,133 m).

0.469 1.13 3.79

Percent reduction 28 57 15

Simulation of remediation of all major 

inflows.

Simulated concentration at end of study reach 

(4,133 m).

0.064 1.0 0.830

Percent reduction 90 62 82

994 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 995

Figure 8. Location of stream reach of lower Cement Creek mass-loading study (September 1996) 

and OTIS solute-transport model. Stream- and inflow-sample data provided in Sole and others 

(this volume). Mining features shown (and inventory numbers, from Church, Mast, and others, this 

volume) include American tunnel level of Sunnyside mine (# 96); Anglo Saxon mine (# 183); May 

Day mine (# 181); and Yukon Mill (# 184) and tunnel (# 186). Mining features shown are limited to 

those discussed in the text. See Church, Mast, and others (this volume) for a complete inventory. 

Base from Digital Orthophoto Quads, Transverse Mercator, –105.00 central meridian, 1927 datum.

9,905 m

9,360 m

9,060 m

7,698 m

7,50 m1

7,331 m

7,131 m

6,907 m

6,447 m

5,957 m

5

Anglo Saxon 

mine

,897 m 5,767 m

5,652 m

5,365 m 5,215 m

5,050 m

4,586 m

4,483 m 4,260 m

3,893 m

3,655 m 3,317 m

3,125 m 2,785 m

2,425 m

1,050 m 1,106 m

10, 0 13 m

 

11,740 m Animas

River

Cement Creek 

ra 

Cre

Boulder Gulch

Blair Gulch

Hancock Gulch

Swansea Gulch

lch

Prospect Gulch

Zuni Gulch

IllinoisGulch

CascadeGulch

Idaho Gulch

Niagara Gulch

Topeka Gulch

Niagara Gulch

Topeka Gulch

B

Minnesota Gulch

F

Tiger Gulch

Georgia Gulch

airviewGulch

O

Porcupine Gulch

Mineral Creek

hioGulch

th F

South Fork

Cement Creek

Soda Gulch

2,970 m

107°39'51"W

50'45N"

0 1 0 5 MILES .

0 1 0.5 KILOMETERS

0 1 0 5 MILES .

0 1 0 .5 KILOMETERS

Silverton

11,558 m

End of study reach.

US. Geological Survey

stream gauge CC 48

U.S. Geological Survey

May Day mine

 Lower bog

Yukon tunnel and mill

Gladstone American tunnel level 

of Sunnyside mine

American tunnel level 

Start of massloading study reach

Red and Bonita Mill

EXPLANATION

Sampled stream sites

Sampled and labeled stream sites 

 and end of OTIS model reach

Sampled inflow sites

OTIS model downstream boundary

Hydrography

Mines, mills, and mine tunnels

10,130 m

107°41'53" 107°39'51" 107°37'49"

37°48'43"

37°50'45"

37°52'47"
might have been associated with the surface flow. In addition 

to the 54 stream samples, 44 inflow sites were sampled to 

characterize watershed contributions to the stream.

Water quality in lower Cement Creek deteriorated somewhat from the site of the injection to the end of the study reach 

as evidenced by lower pH and greater loads of metals. In 1996, 

all of the flow from upper Cement Creek was being diverted 

into the treatment system for the Sunnyside mine effluent. 

Consequently, Cement Creek upstream from the South Fork 

Cement Creek confluence had a near-neutral pH (7.5). South 

Fork Cement Creek was acidic (pH was 3.7). Below the confluence, at 1,106 m, Cement Creek had a pH of 6.5 that decreased 

to 3.7 at the U.S. Geological Survey’s streamflow gauging 

station at 11,558 m. Similarly, total (unfiltered) copper, iron, 

and zinc concentrations in the mainstem of Cement Creek 

upstream from South Fork Cement Creek were approximately 

0.03, 0.5, and 0.8 mg/L and were 0.04, 3.7, and 0.9 mg/L in 

South Fork Cement Creek. These streams mixed to yield water 

downstream from the confluence (at 1,106 m) having 0.04, 2.7, 

and 0.9 mg/L copper, iron, and zinc. At the end of the study 

reach (11,558 m), total concentrations of copper, iron, and 

zinc were 0.04, 7.8, and 0.8 mg/L. As the flow had increased 

approximately 8-fold from the upstream end of the study reach 

to the downstream end, the downstream concentrations, though 

similar to the upstream concentrations, represent much larger 

metal loads.

The results of the lower Cement Creek basin-loading 

analysis (Kimball and others, this volume; Kimball and others, 2002) indicated that during the 1996 sampling, more 

than 50 percent of the load of copper, iron, and zinc was from 

diffuse subsurface flow to the stream. Distinct areas where 

significant loading occurred for one, two, or all three of the 

metals include areas upstream from the study reach in the 

mainstem of Cement Creek and South Fork Cement Creek; the 

Prospect Gulch and upper iron bog area; the Minnesota Gulch 

area; Ohio Gulch; the Yukon tunnel (# 186) at the mouth of 

Illinois Gulch; and the lower bog (fig. 8).

Determination of Physical Parameters 

and Boundary Conditions

Lower Cement Creek (from Gladstone to Silverton) was 

divided into 30 model reaches (table 6). Model parameters 

required for these simulations include the dispersion coefficient 

(D), the cross-sectional area of the stream (A), an upstreamboundary condition (Ub

), and the lateral inflow rate (qL) and 

concentration (CL) for each reach. A uniform dispersion coefficient of 1.0 m was used for all reaches. The coefficient A was 

estimated using OTIS-P (Appendix). Determination and values 

of Ub

, qL, CL, and λ are presented in this section.

The 30 model reaches were chosen to bracket inflows 

shown to be significant during the loading analysis (Kimball 

and others, this volume). Simulations were constructed for 

the lithium tracer, and for filtered (0.1 μm) concentrations of 

copper and zinc, and unfiltered (total) concentrations of iron. 

A large proportion of iron in lower Cement Creek is colloidal, 

and the ratio of total iron to colloidal iron shifts as the pH 

changes in the creek. When pH decreases enough that colloids 

dissolve, the dissolved iron load increases. Conversely, when 

pH increases enough that iron colloids precipitate, the dissolved iron load decreases. If dissolved iron concentration is 

used in the model, these decreases and increases in dissolved 

iron load can be mistaken as sinks and sources of iron in the 

stream, rather than as shifts in iron speciation. The use of total 

iron concentration in the model avoids this misinterpretation of 

the data. But then, the loss of total iron from the stream is due 

to settling, or precipitation and settling of iron mineral phases, 

not simply chemical precipitation of iron phases. (Note, in 

upper Cement Creek, colloidal iron concentrations were low, 

so dissolved iron concentrations were used in the OTIS solutetransport model—see Kimball and others, this volume, fig. 9). 

The upstream boundary of the model was at 990 m, on South 

Fork Cement Creek, just upstream from the confluence with 

Cement Creek and approximately coinciding with the location of the start of the tracer injection (fig. 8). Cement Creek 

upstream from South Fork Cement Creek was treated as a 

tributary that was sampled at 1,050 m distance along the study 

reach. In presentation of the results, samples collected from 

this site on Cement Creek were shown as the most upstream 

inflow. South Fork Cement Creek upstream from the confluence was treated as the mainstem for plots of the data and 

modeling results. The most upstream sampling point on South 

Fork Cement Creek was located upstream from the confluence, but downstream from the tracer injection at 1,041 m. In 

the model, the confluence between South Fork Cement Creek 

and Cement Creek occurred in model reach 2 (table 6) and the 

streams were assumed mixed by the end of that model reach 

(1,106 m). The upstream boundary conditions were as follows: lithium 2.96 mg/L, filtered copper 0.04 mg/L, total iron 

3.75 mg/L, and filtered zinc 0.87 mg/L. These concentrations 

were measured in the stream at site 1,041 m, the first sampling 

site downstream from the injection.

Model Calibration for Simulation 

of the Lithium Tracer Profile

The simulated steady-state lithium profile is consistent 

with the data (fig. 9) and indicates that calculated streamflow was consistent with the dilution of lithium observed 

at the synoptic-sampling sites. Flow at the upstream end of 

the study reach was 91 L/s. Flow at the downstream end of 

the study reach was 703 L/s. Lithium concentrations were 

3.0 mg/L at the beginning of the study reach and were diluted 

by inflows to 0.39 mg/L at the end of the study reach. Visible 

and sampled surface-water inflow and ground-water seepage 

accounted for 74 percent of the inflow. The principal inflows 

to the study reach were the main fork of Cement Creek at 

1,050 m that contributed 91 L/s to streamflow;

Prospect Gulch, 2,795 m, 9.7 L/s;

Georgia Gulch, 3,793 m, 16.3 L/s;

Minnesota Gulch, 5,512 m, 14.3 L/s;

Porcupine Gulch, 5,907 m, 22.7 L/s;

996 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 997

Ohio Gulch, 6,781 m, 9.7 L/s;

Illinois Gulch, 7,006 m, 52.3 L/s;

Topeka Gulch, 7,548 m, 12.9 L/s;

Niagara Gulch, 8,422 m, 13.5 L/s; and

Hancock Gulch, 8,735 m, 14.1 L/s.

Tiger Gulch (3,670 m) and Cascade Gulch contributed less 

than 5 L/s each. Dry Gulch and Fairview Gulch contained 

no surface inflow during the synoptic sampling. Two adits 

discharged flow directly into Cement Creek: an unnamed adit 

at 4,493 m contributed 15.2 L/s, and the Anglo Saxon mine 

(# 183) at 5,817 and 5,857 m contributed a total of 14 L/s. 

Twenty-six percent of the flow represented unsampled groundwater inflow that was dispersed along the length of the study 

reach (Kimball and others, 2002; Kimball and others, this volume). Some of this unsampled inflow was spatially associated 

with alluvial ferricrete deposits, sedge bogs, and springs in 

the stream reach from 9,360 to 9,905 m (Yager and Bove, this 

volume, pl. 2). These ferricrete deposits and iron-rich springs 

were similar to those prevalent along the Prospect Gulch 

reach. The springs in this area are referred to as the lower 

bog and are indicative of ground-water inflow. Slickensides 

were also observed on rock outcrops along this reach, indicating that faulting has occurred in the area; the faults may 

be conduits for ground-water flow into the stream. Minor 

adjustments were made to streamflow in the model reach 

from 9,060 to 9,360 m relative to the streamflow presented in 

Kimball and others (this volume) and Sole and others (this volume, Chapter G, project database). In Kimball and others (this 

volume), no increase occurred in streamflow for this reach. 

However, iron data showed significant concentration increases 

in the model reach that could not be simulated without the 

inclusion of some increase in flow along the model reach. For 

the simulations, flow at 9,060 m was 636 L/s; flow at 9,360 m 

was 648 L/s versus 636 L/s at both sites in Kimball and others 

(this volume). At all other locations, streamflow was the same 

as that used by Kimball and others (this volume).

Table 6. Lower Cement Creek, reach number, distance at end of reach, and description of reaches used in OTIS modeling of synoptic 

samples collected for lower Cement Creek (September 1996).

[Distance is downstream from arbitrary stream datum; m, meter. Reach numbers correspond to those in figures 9–11]

Model 

reach

Distance

(m) Brief description of reach and significant inflows

1 1,050 South Fork Cement Creek upstream from confluence with Cement Creek.

2 1,106 Cement Creek downstream from confluence with South Fork Cement Creek.

3 2,245 Cement Creek downstream from Dry Gulch inflow. Some iron bog inflow on right bank.

4 2,785 Cement Creek upstream from Prospect Gulch, some iron bog inflow on right bank.

5 2,970 Cement Creek downstream from Prospect Gulch inflow (2,795 m).

6 3,125 No sampled inflows in this reach.

7 3,317 Cement Creek downstream from numerous iron bog inflows on both banks.

8 3,655 Cement Creek upstream from Tiger Gulch.

9 3,893 Cement Creek downstream from Tiger (3,670 m) and Georgia (3,793 m) Gulches.

10 4,260 Cement Creek at 4,260 m.

11 4,483 Cement Creek upstream from Fairview Gulch.

12 4,586 Cement Creek downstream from Fairview Gulch (4,493 m).

13 5,050 Cement Creek upstream from Cascade Gulch.

14 5,215 Cement Creek downstream from Cascade Gulch (5,100 m).

15 5,365 Cement Creek upstream from Minnesota Gulch. 

16 5,652 Cement Creek downstream from Minnesota Gulch (5,490 and 5,512 m).

17 5,767 Cement Creek upstream from Anglo-Saxon mine.

18 5,897 Cement Creek downstream from Anglo-Saxon mine inflows (5,817 and 5,857 m).

19 5,957 Cement Creek downstream from Porcupine Gulch (5,907 m).

20 6,447 Cement Creek upstream from Ohio Gulch.

21 6,907 Cement Creek downstream from Ohio Gulch (6,781 m).

22 7,131 Cement Creek downstream from Illinois Gulch (7,006 m).

23 7,331 Cement Creek downstream from the Yukon tunnel (no visible surface inflow).

24 7,501 Cement Creek downstream from the May Day mine and dump (no visible surface inflow).

25 7,698 Cement Creek downstream from Topeka Gulch (7,548 m).

26 9,060 Cement Creek downstream from Niagara (8,422 m) and Hancock (8,735 m) Gulches.

27 9,360 Cement Creek upstream from lower iron bog.

28 9,905 Cement Creek downstream from lower iron bog inflow (9,543 m).

29 10,130 Cement Creek downstream from unnamed mine (no surface inflow).

30 11,740 Cement Creek downstream from the gauging station (11,558 m).
Model Calibration for Copper, Iron, and 

Zinc Simulations

Simulations for lower Cement Creek used a variety of 

estimation techniques for lateral-inflow concentrations (CL). 

In general, the techniques were not nearly as consistent across 

lower Cement Creek model reaches or between metals as for 

upper Cement Creek. Effective or representative inflow concentrations were used most often in the copper, iron, and zinc 

simulations (fig. 10; table 7). Only two model reaches used 

flow-weighted average concentrations: model reach 16 for 

copper, and model reach 26 for iron. Thirteen model reaches 

showed no inflow concentration for copper; 7 model reaches 

showed no inflow concentration for iron; and 5 model reaches 

showed no inflow concentration for zinc (table 7). In these 

reaches streamflow increased, but no increase in metal load 

occurred in the reach. For copper, one reach (model reach 17) 

used the CL

 value from the adjacent upstream reach; and one 

reach (model reach 26) used a CL

 value that was greater than 

the value from the representative inflow in the reach, but less 

than the calculated effective inflow. For iron, one reach (model 

reach 8) used a CL

 value that was the average of the value 

used in the adjacent upstream and downstream reaches, and 

two reaches (model reaches 17 and 27) used CL values from 

the adjacent downstream model reaches. For zinc, two reaches 

(model reaches 6 and 17) used CL

 values from the adjacent 

downstream reaches; a value greater than the representative 

inflow but less than the calculated effective inflow was used 

in model reach 20, and in model reaches 27 and 28, CL values 

were greater than calculated effective inflow. For all metals, 

the highest CL

 value is greater than the highest concentration 

of the observed inflows (fig. 13). In particular, in the reaches 

including Ohio Gulch (model reach 21), the Yukon tunnel 

(# 186; model reach 23), and the tailings at May Day mine 

(# 181; model reach 24), some of the CL values for copper and 

zinc were greater than any sampled inflow values. Additional 

sampling within the May Day mine (# 181) identified water 

samples having extremely elevated concentrations of copper 

and zinc (almost as high as 70 mg/L copper and approximately 

200 mg/L zinc—Wright, Kimball, and Runkel, this volume, 

Chapter E23). In addition, a low-pH, metal-rich seep along 

the west bank of Cement Creek in the vicinity of the May Day 

Figure 9. Variation in lithium concentration, streamflow, and simulated lithium concentration with distance, showing location 

of OTIS model reaches and significant features, lower Cement Creek mass-loading study (September 1996) and OTIS solutetransport model.

0 2,000 4,000 6,000 8,000 10,000 12,000

0

1

2

3

4

25 26 27 28 29

24 23

22 20 21

19

18 30

17

16

15

14 13

12

3 4 5 6 7 8 9 10 11

2

1

Cement Creek/South Fork Cement Creek confluence

Topeka Gulch

Illinois Gulch Ohio Gulch

Minnesota Gulch

Inflow from

lower bog

 Filtered lithium concentration in stream samples

 Filtered lithium concentration in inflow samples

 Simulated lithium concentration

 Streamflow 

 Labeled significant inflow

 Model reach boundary and number

DISTANCE ALONG STUDY REACH, IN METERS

FILTERED LITHIUM CONCENTRATION,

IN MILLIGRAMS PER LITER

0

100

200

300

400

500

600

700

800

Prospect 

Gulch and

iron bog 

inflow reach

STREAMFLOW,

IN LITERS PER SECOND

998 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 999

Figure 10. Variation in calibrated lateral-inflow concentrations and observed inflow concentrations (September 1996) with distance 

for A, filtered copper; B, total iron; C, filtered zinc showing OTIS model reaches, lower Cement Creek OTIS model.

0 2,000 4,000 6,000 8,000 10,000 12,000

0.01

0.1

1

10

100

1,000

0 2,000 4,000 6,000 8,000 10,000 12,000

1E-3

0.01

0.1

1

0 2,000 4,000 6,000 8,000 10,000 12,000

0.01

0.1

1

10

13 26 27 28 29 30 25

24

23 22 21 20 19 18

17

16 15 12 14

11

7 8 9 10 2 3 4 5 6

TOTAL IRON CONCENTRATION,

IN MILLIGRAMS PER LITER

 Sampled inflow concentration

 Effective inflow concentration

 Representative inflow concentration

 Flow-weighted average inflow

 concentration

 Used inflow value from adjacent reach, or used other inflow value (see table 7)

 Model reach boundary and number

29 19 20 21 22 23 24 25 26 27 28 30

18

17

16

15

14

13 12 2 3 4 5 6 7 8 9 10 11

FILTERED COPPER CONCENTRATION, 

IN MILLIGRAMS PER LITER

C

B

A

30 23 24 25 26 27 28 29 22 19 20 21 18

17 16 15 14 2 3 4 5 6 7 8 9 10 11 12 13

FILTERED ZINC CONCENTRATION, 

IN MILLIGRAMS PER LITER

DISTANCE ALONG STUDY REACH, IN METERS
tailings pile contained copper concentrations of approximately 

0.3 mg/L and zinc concentrations of approximately 5 mg/L 

(Wright, Kimball, and Runkel, this volume). Geophysical 

surveys showed a high conductivity zone in the area that 

indicated near-surface ground-water flow paths as a source 

of water containing high dissolved solids (Smith and others, 

this volume, Chapter E4); they were probably the source for 

this seep. The seep was not sampled during the 1996 massloading study, but the concentrations cited previously are 

similar to those used for CL

 in that reach. Therefore, it was not 

unrealistic to use elevated values in the Ohio Gulch to Topeka 

Gulch reach of the stream that includes the May Day mine and 

tailings.

Steady-state, conservative simulations of iron, copper, and 

zinc (fig. 11) overestimated the ambient concentrations. Therefore, simulations with OTIS-P were conducted to estimate firstorder removal coefficients (λ) for some of the model reaches 

(table 8). Simulations including first-order removal coefficients 

fit the data well (fig. 14). First-order removal coefficients 

(greater than 1.0 × 10–5s–1) were used in model reaches 7, 18, 

19, 22, 24, and 30 for all three metals. First-order removal 

coefficients were used for two of the metals in model reaches 

3, 8, 9, 10, and 12 (table 8). First-order removal coefficients 

were used for iron only in model reaches 11 and 15. Stream pH 

decreased from a high of 6.45 downstream from the confluence 

with South Fork Cement Creek (1,106 m), to 5.25 upstream 

Table 7. Lower Cement Creek, samples and methods used to estimate lateral-inflow concentrations (CL) for copper, iron, and zinc 

in calibrated OTIS model.

[Synoptic samples collected September 1996. Numeric values represent distances (in meters, m) of inflow samples used in estimation technique (inflow samples 

are in project database, Sole and others, this volume). Bold indicates the end of an adjacent model reach whose value was used for lateral-inflow concentration. 

Alpha characters indicate method used to estimate lateral inflow: NI, no lateral-inflow concentration; R, representative inflow concentration used for lateralinflow concentration; F, flow-weighted average value used for lateral-inflow concentration; E, effective inflow concentration used for lateral-inflow concentration. 

Lateral-inflow concentration values shown in figure 10]

Model reach Distance (m) Dissolved copper Total iron Dissolved zinc

1 1,050 NI NI NI

2 1,106 NI E E

3 2,425 E E E

4 2,785 E E E

5 2,970 E R, 2,795 E

6 3,125 NI E 3,317

7 3,317 NI R, 3,255 R, 3,255

8 3,655 E R, 3,425 R, 3,425

9 3,893 NI 3,655, 4,260a E

10 4,260 R, 4,110 R, 3,968 R, 4,110

11 4,483 NI NI NI

12 4,586 NI R, 4,493 R, 4,493

13 5,050 NI NI E

14 5,215 R, 5,100 R, 5,100 R, 5,100

15 5,365 E NI NI

16 5,652 F 5,490, 5,512 R, 5,512 R, 5,512

17 5,767 5,652 5,897 5,897

18 5,897 E R, 5,857 R, 5,857

19 5,957 NI R, 5,907 R, 5,907

20 6,447 NI R, 6,422 0.75 mg/Lb

21 6,907 E E E

22 7,131 NI R, 7,006 R, 7,006

23 7,331 E, 0.270 E E

24 7,501 E, 0.202 NI E

25 7,698 R, 7,548 R, 7,548 R, 7,548

26 9,060 0.083 (<E, >R) F, 8,422, 8,735, 8,860 E

27 9,360 E, 0.060 9,905 1.8 mg/L (>E)

28 9,905 E R, 9,543 1.8 mg/L (>E)

29 10,130 NI NI NI

30 11,740 NI NI NI

a

In model reach 9, the lateral-inflow concentration for total iron was the average value used in the adjacent upstream and downstream model reaches.

b

The lateral-inflow concentration value for dissolved zinc in model reach 20 was less than the effective inflow concentration and greater than the 

representative inflow concentration.

1000 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1001

0 2,000 4,000 6,000 8,000 10,000 12,000

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.28

0.30

0.32

0 2,000 4,000 6,000 8,000 10,000 12,000

0

10

20

30

40

50

0 2,000 4,000 6,000 8,000 10,000 12,000

0.0

0.5

1.0

1.5

2.0

2.5

25 26 27 29 28 30

24

23

22

20 21

19

18

17

16

15

14

13

12

11

10

9

8

7

6

5

2 3 4

FILTERED COPPER CONCENTRATION, 

IN MILLIGRAMS PER LITER

2 3 4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27 28 29 30

TOTAL IRON CONCENTRATION, 

IN MILLIGRAMS PER LITER

 Stream samples

 Inflow samples

 Conservative OTIS simulation

 Reactive OTIS simulation 

 with first-order coefficient

 Labeled significant inflow

 Model reach boundary and number

C

B

A

2 3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26 27 28 29 30

Illinois 

Gulch 

inflow

Lower 

bog 

area

Topeka 

Gulch 

inflow

Minnesota Gulch inflow

Ohio Gulch inflow

Prospect Gulch inflow

Cement Creek/South Fork confluence

 DISTANCE ALONG STUDY REACH, IN METERS

FILTERED ZINC CONCENTRATION, 

IN MILLIGRAMS PER LITER

Figure 11. Variation in A, filtered copper; B, total iron; C, filtered zinc concentrations with distance in stream and inflow samples 

(September 1996) and in OTIS conservative and calibrated reactive simulations, showing model reach and number and location of 

significant features, lower Cement Creek. Reactive OTIS simulation included first-order removal coefficients (λ).
Table 8. Lower Cement Creek, first-order removal coefficients 

(λ) by metal and reach for calibrated OTIS model.

[All values in second–1]

Model 

reach

Distance

(m)

Dissolved

copper

Total

iron

Dissolved

zinc

1 1,050 0.00 0.00 0.00

2 1,106 0.00 0.00 0.00

3 2,245 3.40×10–5 3.72×10–5 0.00

4 2,785 0.00 0.00 0.00

5 2,970 0.00 0.00 0.00

6 3,125 0.00 0.00 0.00

7 3,317 1.90×10–4 2.47×10–4 1.45×10–5

8 3,655 0.00 2.02×10–5 5.96×10–5

9 3,893 4.06×10–5 2.02×10–5 0.00

10 4,260 2.06×10–5 2.02×10–5 0.00

11 4,483 0.00 2.87×10–4 0.00

12 4,586 0.00 9.90×10–5 1.61×10–4

13 5,050 0.00 0.00 0.00

14 5,215 0.00 0.00 0.00

15 5,365 0.00 9.82×10–5 0.00

16 5,652 0.00 0.00 0.00

17 5,767 0.00 0.00 0.00

18 5,897 5.84×10–5 5.66×10–5 1.30×10–5

19 5,957 5.84×10–5 2.51×10–5 4.50×10–4

20 6,447 0.00 0.00 0.00

21 6,907 0.00 0.00 0.00

22 7,131 2.70×10–4 2.54×10–4 3.8×10–4

23 7,331 0.00 0.00 0.00

24 7,501 3.70×10–4 6.62×10–5 5.50×10–4

25 7,698 0.00 0.00 0.00

26 9,060 0.00 0.00 0.00

27 9,360 0.00 0.00 0.00

28 9,905 0.00 0.00 0.00

29 10,130 0.00 0.00 0.00

30 11,740 2.00 × 10–5 3.14×10–5 6.0×10–5

from Prospect Gulch (2,785 m), and to 4.23 downstream from 

Prospect Gulch (3,125 m); pH remained at less than 4.5 for 

the downstream extent of the study reach. First-order removal 

coefficients generally were located in model reaches downstream from inflows that had elevated concentrations of metals 

or downstream from inflows that had high pH. Examples of 

model reaches having a first-order removal coefficient and 

located downstream from inflows having elevated metal concentrations are model reach 3 downstream from the upper iron 

bog inflows; model reaches 7 through 10, which included areas 

downstream from Prospect Gulch (2,795 m), iron bog inflows, 

Tiger Gulch (3,670 m), and Georgia Gulch (3,793 m); model 

reach 18 downstream from the Anglo-Saxon mine inflow 

(# 183); model reach 19 downstream from Porcupine Gulch; 

and model reach 24 downstream from the May Day mine 

(# 181). Examples of model reaches having first-order removal 

coefficients and located downstream from high-pH inflows are 

model reach 12 downstream from an adit inflow (pH = 7.25); 

model reach 22 downstream from Illinois Gulch (pH = 7.6); 

and model reach 30, where all inflows along the reach had 

pH values greater than 6.90. Metal removal downstream from 

areas that receive metal loading probably occurred because 

stream water was supersaturated with respect to iron oxyhydroxide phases such as schwertmannite or ferrihydrite, which 

precipitate out of solution. Cement Creek is colored orange 

from precipitating iron minerals for much of the study reach, 

and schwertmannite has been identified in streambed-sediment 

samples (Desborough and others, 2000). Coprecipitation or 

sorption of the copper and zinc with the iron phase accounts for 

the accompanying removal of these metals. Removal of metals 

downstream from high-pH inflows probably occurs in the mixing zones of the high-pH tributary and the creek. In this case, 

solubility of iron phases such as schwertmannite or ferrihydrite 

is probably exceeded because of the elevated pH in the mixing 

zone, rather than because of an increase in metal concentrations. In each case, the net effect is similar; amorphous iron 

minerals precipitate and contain sorbed or coprecipitated copper and zinc. The relatively small size (less than 10–3 s–1) and 

limited distribution of removal coefficients, however, indicate 

that reactive processes are operating only to a minimal extent 

for the metals considered in the study reach.

Removal coefficients in model reaches 3 (1,106 to 

2,245 m) and 15 (5,215 to 5,365 m) are more difficult to 

explain as neither reach fits the two scenarios just described. 

However, model reach 3 was an area of the stream that experienced elevated metal loading and where pH also was elevated. 

So, precipitation of iron minerals is possible. Model reach 15 

is 265 m downstream from the confluence with Cascade Gulch 

(5,100 m), which had a pH of 7.13. Removal in reach 15 may 

be due to the effects of the high-pH inflow or to some iron 

loading that occurred associated with the Minnesota Gulch 

area inflows described in Kimball and others (2002).

Remediation Simulations

Five remediation scenarios were simulated using the 

calibrated OTIS model for lower Cement Creek (table 9)2

. 

The maximum remediation that could be assigned to mining 

was chosen for each target location. Whereas the large contributions attributed to mining in some of the scenarios may 

be debatable, even though most are derived from published 

sources, these large numbers were used to illustrate the 

maximum effect that might occur from cleanup of miningassociated effects on water quality.

The first scenario simulated remediation of inflows to 

Prospect Gulch associated with historical mining activity on 

Federal land. These inflows were quantified in a metal-loading 

study conducted during low-flow conditions in September 1999 

(Wirt and others, 2001). The study concluded that inflows 

2

Table 9 describes the five remediation scenarios and scenario 6, which 

is a simulation of instream effects of the changed conditions at the Mogul 

mine—which is covered in the following section.

1002 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1003

associated with inactive mines located on Federal land caused 

59 percent of the copper loading, 12 percent of the iron loading, and 37 percent of the zinc loading observed at the mouth 

of Prospect Gulch. Remediation scenario 1 reduced loads in 

Prospect Gulch by these amounts. The second remediation 

scenario reduced all mining-related inflows (those on both private and Federal land) in Prospect Gulch to negligible concentrations. Wirt and others (2001) indicated that mining-related 

contribution from private and Federal land caused 73 percent 

of the copper load, 12 percent of the iron load, and 57 percent 

of the zinc load observed at the mouth of Prospect Gulch. 

Remediation scenario 2 reduced loads in Prospect Gulch by 

these amounts. Scenario 3 reduced mining-related inflows from 

three mines in Ohio Gulch. Detailed mass-loading studies were 

not conducted in Ohio Gulch. However, the three largest mining contributors were sampled and caused approximately 3, 5, 

and 21 percent of the copper, iron, and zinc loading observed at 

the mouth of the gulch (W.G. Wright, written commun., 2002). 

Scenario 4 considered the reach of the stream affected by the 

May Day mine (# 181) and tailings. Detailed mass-loading and 

geochemical studies along that reach indicated that, at most, 

75 percent of the load to the stream was related to mining 

activity at the May Day site (Wright, Kimball, and Runkel, 

this volume). Accordingly, remediation scenario 4 reduced 

copper and zinc concentrations in the model reach containing 

the May Day mine by 75 percent each. The calibrated lateralinflow concentration (CL

) value for iron was 0.00 in that model 

reach, so the simulation of a reduced iron concentration was 

not possible. Remediation scenario 5 combined remediation 

scenarios 2, 3, and 4.

The results of the remediation scenarios (fig. 12; table 9) 

indicated that the combined remediation (remediation scenario 5) reduced copper, iron, and zinc concentrations by 

approximately 17 percent, 0.5 percent, and 8.3 percent at the 

mouth of Cement Creek. Remediation scenario 2 (the larger 

of the Prospect Gulch remediations) had the greatest effect 

on copper concentrations and reduced them by 12 percent at 

the mouth of Cement Creek. The May Day remediation had 

the largest effect on zinc concentrations and reduced zinc 

concentrations at the mouth of Cement Creek by 4.4 percent. 

Iron concentrations were not reduced by any of the remediation scenarios. Although the maximum possible reductions 

Table 9. Lower Cement Creek, simulated concentrations at the mouth of lower Cement Creek resulting from five remediation 

scenarios, and from changed conditions at the Mogul mine (scenario 6).

[Concentration values in milligrams per liter. NA, not applicable]

Remediation Filtered

copper

Total

iron

Filtered

zinc

Simulated concentration at end of study reach 

(11,558 m) in calibrated model.

0.042 8.45 0.793

Scenario 1: Prospect Gulch: 59 percent reduction 

in copper, 12 percent reduction in iron, 

37 percent reduction in zinc. 

Simulated concentration 

at end of study reach 

(11,558 m).

0.038 8.44 0.786

Percent reduction 10 0.1 0.9

Scenario 2: Prospect Gulch: 73 percent reduction 

in copper, 12 percent reduction in iron, 

57 percent reduction in zinc.

Simulated concentration 

at end of study reach 

(11,558 m).

0.037 8.44 0.783

Percent reduction 12 0.1 1.3

Scenario 3: Ohio Gulch: 3 percent reduction in 

copper, 5 percent reduction in iron, 21 percent 

reduction in zinc.

Simulated concentration 

at end of study reach 

(11,558 m).

0.042 8.43 0.772

Percent reduction 0 0.2 2.6

Scenario 4: May Day mine: 75 percent reduction 

in copper, 75 percent reduction in zinc.

Simulated concentration 

at end of study reach 

(11,558 m).

0.041 NA 0.758

Percent reduction 2.4 NA 4.4

Scenario 5: Combined result of scenarios 2, 3, 

and 4.

Simulated concentration 

at end of study reach 

(11,558 m).

0.035 8.41 0.727

Percent reduction 17 0.5 8.3

Scenario 6: Simulating changes in the mainstem 

of Cement Creek due to changes at the Mogul 

mine. Upper boundary conditions of the OTIS 

model were copper, 1.22 mg/L; iron, 5.7 mg/L; 

zinc, 7.27 mg/L.

Simulated concentration 

at end of study reach 

(11,558 m).

0.180 8.83 1.46

Percent increase 329 5 84
0 2,000 4,000 6,000 8,000 10,000 12,000

0

2

4

6

8

10

12

14

16

0 2,000 4,000 6,000 8,000 10,000 12,000

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0 2,000 4,000 6,000 8,000 10,000 12,000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

TOTAL IRON CONCENTRATION, 

IN MILLIGRAMS PER LITER

 OTIS simulation with no remediation

 Scenario 1, 12 percent reduction in Prospect Gulch 

 Scenario 2, 12 percent reduction in Prospect Gulch

 Scenario 3, 5 percent reduction in Ohio Gulch

 Scenario 5, combination of scenarios 2 and 3

 Labeled significant inflow

2

4

5

1

3

FILTERED COPPER CONCENTRATION, 

IN MILLIGRAMS PER LITER

 OTIS simulation with no remediation

 Scenario 1, 59 percent reduction in Prospect Gulch 

 Scenario 2, 73 percent reduction in Prospect Gulch

 Scenario 3, 3 percent reduction in Ohio Gulch

 Scenario 4, 75 percent reduction in May Day inflow

 Scenario 5, combination of scenarios 2, 3, and 4

 Labeled significant inflow 

C

B

A

Reach containing

May Day inflow

Topeka Gulch inflow

End of study reach, 11,558 meters

Lower bog 

 area

Illinois Gulch 

 inflow

Ohio Gulch 

inflow

Minnesota Gulch

 inflow

Prospect Gulch

 inflow

Cement Creek/South Fork confluence

FILTERED ZINC CONCENTRATION, 

IN MILLIGRAMS PER LITER

DISTANCE ALONG STUDY REACH, IN METERS

 OTIS simulation with no remediation

 Scenario 1, 37 percent reduction in Prospect Gulch 

 Scenario 2, 57 percent reduction in Prospect Gulch

 Scenario 3, 21 percent reduction in Ohio Gulch

 Scenario 4, 75 percent reduction in May Day inflow

 Scenario 5, combination of scenarios 2, 3, and 4

 Labeled significant inflow

1004 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1005

for mining sources were chosen for the areas targeted in the 

simulations, the resulting metal reductions were relatively 

small. These modest reductions resulted because metal sources 

to lower Cement Creek were spread over much of the length 

of the study reach, both in tributaries that were not considered in the remediation scenarios (such as Minnesota Gulch 

and related inputs in the area near 5,500 m) and in diffuse 

ground-water inflows (such as in the iron bog area in the vicinity of Prospect Gulch, and the lower iron bog area). These 

results differ from those for upper Cement Creek, where the 

metal sources were largely confined to four major inflows, 

and where remediation simulations showed larger effects on 

metal concentrations.

Simulation of the Effect of Changed Conditions 

at the Mogul Mine

In early September 1996, a bulkhead was placed in 

the American tunnel (# 96, Finger and others, this volume, 

Chapter F). Results of synoptic studies performed in Cement 

Creek indicated that the chemistry and flow from the Mogul 

mine changed between the lower Cement Creek metal-loading 

study in 1996 and the upper Cement Creek metal-loading 

study in 1999 (Kimball and others, this volume; Bove and 

others, this volume). The result of this change was that metal 

concentrations in upper Cement Creek downstream from the 

Mogul mine were greater in 1999 than in 1996 (Kimball and 

others, this volume). The calibrated OTIS model for lower 

Cement Creek was used to simulate the potential effects of 

this changed chemistry on metal concentrations at the mouth 

of Cement Creek.

Two changes were made in the upstream-most reaches 

of the calibrated model to simulate the changed conditions 

in upper Cement Creek in 1999. First, the calibrated model 

used metal concentrations and streamflow measured in 1996 

at the mainstem Cement Creek upstream from South Fork 

Cement Creek for lateral-inflow concentrations (CL

) and 

lateral inflow (qL

) in model reach 2. To simulate concentration 

changes resulting from change upstream at the Mogul mine, 

the CL

 and qL values for this reach were adjusted to match 

the values measured in 1999 during the upper Cement Creek 

mass-loading study. Second, the 1999 upper Cement Creek 

data set also provided concentration and flow information for 

South Fork Cement Creek just upstream from the confluence. 

The boundary conditions (streamflow and concentrations of 

filtered copper, total iron, and filtered zinc) of the 1996 model 

were changed to represent the 1999 conditions at this point. 

The simulation was then performed with no other changes to 

observe the effect at the mouth of Cement Creek. This simulation is referred to as “Scenario 6” in table 9.

Agreement is good between the streamflow predicted by 

the model in 1999 and the streamflow measured at the gauge 

on September 20, 1999. In the simulation, streamflow at 

the bottom of the study reach (11,558 m) was 820 L/s compared to 703 L/s for the calibrated 1996 model. This increase 

was due to the increased flow used at the boundary condition (91 L/s in 1996, 147 L/s in 1999) and the inflow reach 

(model reach 2) for the mainstem of Cement Creek (91 L/s 

1996; 152 L/s 1999). The simulated streamflow value was 

only 3 percent greater than the average daily streamflow on 

September 20, 1999 (793 L/s; Crowfoot and others, 1997), the 

day that the model attempted to recreate. The good agreement 

between flow values in the model and the environmental data 

increases confidence in the modeling results.

The results of the simulation of Scenario 6 indicate that 

at the gauge at Cement Creek, filtered copper and zinc concentrations increased 329 percent and 84 percent. Total iron 

concentration increased 5 percent (table 9; fig. 13).

It is possible to compare the simulated increases in 

filtered copper and zinc concentrations to data collected at 

the mouth of Cement Creek to assess whether the simulation 

approximated environmental conditions observed at the mouth 

of Cement Creek. In the course of several sampling programs, 

samples were routinely collected at the mouth of Cement 

Creek from 1996 to 2001 (Leib and others, 2003). The results 

for filtered copper, total iron, and filtered zinc concentrations (using 0.45-μm filtration) seem to show a shift to higher 

copper and zinc concentrations sometime between July 1999 

and August 1999 (fig. 14). Dates where flow conditions 

approximate those observed on September 20, 1996 and 1999 

(approximately 20 to 30 ft3

/s, or 566 to 850 L/s) are indicated 

on the graph (fig. 14). On the six dates indicated, copper and 

zinc concentrations are greater on the three later dates than on 

the earlier dates. The shift to higher copper and zinc concentrations beginning around July 1999 is more apparent with a 

rigorous analysis of the relations between filtered copper and 

zinc concentrations and streamflow.

Linear regression models were constructed between filtered copper and zinc and streamflow using the data presented 

in figure 14. For each model, copper and zinc concentrations 

were log-transformed. The streamflow data were transformed 

using a hyperbolic transformation:

 Qtrans = 1/(1+(beta * Q)) (3)

where

Qtrans is the transformed streamfl ow value,

 Q is the streamfl ow data,

and

 beta is a parameter that can vary from 10–3 * Qm

–1

to 102

 * Qm

–1 (where Qm is the mean value 

of the measured streamfl ow) to obtain 

the most linear representation of the data 

(Crawford and others, 1983).

Figure 12 (facing page). Variation in A, filtered copper; 

B, total iron; C, filtered zinc concentrations with distance in OTIS 

simulations of remediation scenarios, showing location of significant 

features, lower Cement Creek. Details of remediation scenarios, 

table 9. In view A, OTIS simulation with no remediation is upper line 

and is overprinted by remediation scenarios 3 and (or) 4 until just 

upstream from Topeka Gulch inflow. Simulations all overprint each 

other in view B.
0 2,000 4,000 6,000 8,000 10,000 12,000

0.0

0.2

0.4

0.6

0.8

0 2,000 4,000 6,000 8,000 10,000 12,000

0

2

4

6

8

10

12

14

16

0 2,000 4,000 6,000 8,000 10,000 12,000

0

1

2

3

4

C

B

A

FILTERED COPPER CONCENTRATION, 

IN MILLIGRAMS PER LITER

 Calibrated OTIS model simulating conditions in 1996

 Simulation of 1999 conditions using model calibrated with 1996 data

 Labeled significant features

TOTAL IRON CONCENTRATION, 

IN MILLIGRAMS PER LITER

Lower bog area

End of study reach, 11,558 meters

 Topeka Gulch inflow

Ohio Gulch inflow

Illinois Gulch inflow

Minnesota Gulch inflow

Prospect Gulch inflow

Cement Creek/South Fork confluence

FILTERED ZINC CONCENTRATION, 

IN MILLIGRAMS PER LITER

DISTANCE ALONG STUDY REACH, IN METERS

Figure 13. Variation in A, filtered copper; B, total iron; C, filtered zinc with distance in calibrated OTIS simulations and OTIS simulations 

of 1999 conditions in Cement Creek and South Fork Cement Creek, lower Cement Creek.

1006 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1007

For copper, beta = 1.0/67 ft3

/s and for zinc beta = 10–3/67 ft3/s. 

(The regressions were conducted on the raw data obtained 

from Leib and others (2003), where streamflow data were 

presented in ft3/s.)

The regression model for copper was log (Cu) = –0.11 

(Qtrans) – 0.95 with r2 (the coefficient of determination) = 

0.53 (p-value, p < 0.0001). For zinc, the regression model 

was log (Zn) = 6.7 (Qtrans) – 6.7 with r2 = 0.24 (p < 0.0001). 

These regression models are not particularly strong, partly 

because factors other than streamflow affect instream metal 

concentrations, and possibly because of changing conditions 

in the basin. Plots of the residuals of the regression models 

indicate a greater scatter for zinc residuals than for copper 

residuals, which reflects the smaller r2

 value for zinc (fig. 15). 

In addition, plots of the residuals of the regression models also 

show when changes may have occurred. The distinct scattering in the residuals for copper and zinc shows a positive 

bias beginning in late July to early August 1999. The timing 

of the beginning of this high bias is consistent with the 1999 

mass-loading study in upper Cement Creek (September 1999) 

and indicates that since August 1999, concentrations of copper and zinc generally were greater at the stream gauge at the 

mouth of Cement Creek than they were before August 1999.

Although the data indicated that higher concentrations of 

copper and zinc have occurred at the mouth of Cement Creek 

since summer 1999, simulations of these values using the 1996 

calibrated OTIS model with minor adjustments to streamflow, 

inflow, and boundary conditions, as previously described, 

overestimated the observed environmental conditions at the 

gauge. This overestimation may have occurred for several 

reasons. First, although the simulation attempted to adjust for 

higher streamflow and concentration at the start of the modeled 

reach using the 1999 data, inflow throughout the rest of the 

model remained at 1996 levels. Increased inflow from tributaries and ground water containing lower concentrations of copper 

and zinc than the mainstem of Cement Creek upstream from 

South Fork Cement Creek would help dilute simulated concentrations. In addition, the overestimation of environmental 

7/2/1996 7/2/1997 7/2/1998 7/2/1999 7/2/2000 7/2/2001

1E-3

0.01

0.1

1

10

100

Simulated total iron concentration: 8.83 mg/L

Simulated filtered zinc concentration: 1.46 mg/L

Simulated filtered copper concentration: 0.18 mg/L

8/21/2001: 24 ft 3 /s

7/17/2000: 23 ft3 /s

6/28/2000: 27 ft3 /s

4/29/1999: 22 ft 3 /s

10/22/1997: 27 ft 3 /s

3/25/1997: 22 ft 3 /s

 Filtered copper

 Total iron

 Filtered zinc

 Streamflow

DATE

CONCENTRATION, IN MILLIGRAMS PER LITER

0

200

400

600

800

1,000

STREAMFLOW, IN CUBIC FEET PER SECOND

Figure 14. Variation in observed streamflow, filtered (0.45-μm filtration) copper, total iron, and filtered (0.45-μm filtration) zinc 

concentrations with time at the gauge (CC48) at the mouth of Cement Creek. Filtered (0.1 μm) copper, total iron, and filtered 

(0.1 μm) zinc concentrations simulated for 1999 conditions using calibrated OTIS model are horizontal lines. Vertical lines indicate 

environmental streamflow conditions (20 to 30 ft3

/s; data from Crowfoot and others, 1998, 1999, 2000, 2001, and 2002) similar to 

those modeled using OTIS. Environmental chemistry data at CC48 from various sources described in Leib and others (2003).
conditions by the model may indicate that chemical reactions that would remove copper and zinc from solution, such 

as coprecipitation and sorption, were underestimated in the 

calibrated model. In spite of these shortcomings, however, it is 

noteworthy that, at low flow, the model simulated increases in 

copper and zinc concentrations as opposed to little change for 

iron, and that the environmental data generally recorded these 

trends. The ability of the calibrated model to estimate these 

trends increases confidence in the model results.

Animas River, Eureka to Howardsville

The Eureka to Howardsville mass-loading study was 

conducted on a 7,250 m reach of the upper Animas River 

extending from near the historical mining town of Eureka to 

downstream from Howardsville (figs. 1, 16). The study reach 

included inflows from Eureka Gulch (347 m), the Forest Queen 

mine (# 195 at 2,090 m), Minnie Gulch (2,465 m), Maggie 

Gulch (3,450 m), Cunningham Creek (6,558 m), and Hematite 

Gulch (6,633 m). Unique features of this stream reach included 

two intensely braided reaches (from 786 to 2,030 m, and from 

3,150 to 3,435 m), the Kittimack mine (# 201), Kittimack Mill 

(# 194), and tailings (# 192) near 3,165 m, and the mill and 

tailings at Howardsville (# 233, 234) with inflow to the stream 

occurring at 6,438 m.

A continuous injection of sodium chloride was pumped 

into the river from August 12 through August 14, 1998. On 

August 14, 1998, synoptic samples were collected from 

35 stream sites and 18 inflow sites. Chloride concentrations 

were used to calculate streamflow (Kimball and others, this volume; Paschke and others, 2005). Dissolved zinc (using ultrafiltration, 10,000 Dalton molecular weight nominal pore size) was 

the only metal modeled using OTIS, due to the low concentrations of other metals (Paschke and others, 2005).

Water-quality conditions at the time of the mass-loading 

study indicated better water quality in this reach of the Animas 

River than in Cement Creek. Values of pH were near neutral 

throughout the study reach and showed no clear downstream 

trend. Zinc concentrations ranged from 0.55 mg/L near the 

upstream end of the study reach to 0.28 mg/L near the downstream end of the study reach. The greater pH in the Animas 

River was likely due to lesser amounts of acid-altered rock in 

Figure 15. Lower Cement Creek, variation in residual concentrations of filtered copper and zinc at the Cement Creek 

gauge (11,558 m) with time for regression models constructed using environmental data collected between 1996 and 

2001 (Leib and others, 2003) and presented in figure 14.

7/2/1996 7/2/1997 7/2/1998 7/2/1999 7/2/2000 7/2/2001

-0.2

0.0

0.2

0.4

0.6

0.8

RESIDUAL (PREDICTED - OBSERVED) CONCENTRATION,

IN MILLIGRAMS PER LITER

DATE

 Residual copper concentration

 Residual zinc concentration

1008 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1009

Figure 16. Location of stream reach of the Animas River, Eureka to Howardsville 

mass-loading study (August 1998) and OTIS solute-transport model. Stream- and 

inflow-sample data provided in Sole and others (this volume). Mining features shown 

(and inventory numbers, from Church, Mast, and others, this volume) include Forest 

Queen mine (# 195); Kittimack mine (# 201), Kittimack Mill (# 194), and tailings (# 192); 

and the Pride of the West Mill #4 (# 233) and tailings (# 234) at Howardsville. Mining 

features shown are limited to those discussed in the text. See Church, Mast, and 

others (this volume) for a complete inventory. Base from Digital Orthophoto Quads, 

Transverse Mercator, –105.00 central meridian, 1927 datum.

the upper Animas River drainage as compared to the Cement 

Creek drainage, and a higher proportion of propylitically 

altered rock that helped buffer the effects of acidic mine drainage and acidic alteration (fig. 1; Bove and others, this volume). 

The near-neutral pH in the stream limited the solubility of 

acid-soluble metals such as aluminum, copper, and iron, and 

helped account for their lower concentrations in this stream 

compared to Cement Creek.

Although zinc concentrations decreased through 

the study reach, zinc loads increased from 10 kg/d at the 

upstream end of the study to 60 kg/d downstream. The greatest sources of zinc load to the study reach were unsampled 

inflows to the river downstream from the braided reach 

near the Forest Queen mine at 1,940 m (13 percent of the 

zinc load); inflows near the Kittimack tailings (at 3,165 and 

3,405 m) that contributed 12 percent of the zinc load; and 

5

Forest Queen mine

Start of mass- loading 

study reach

Start of mass- loading 

study reach

Kittimack Mill

Pride of the West Mill tailings

Pride of the West Mill #4 (Dixilyn/P&G/Howardsville)

Kittimack tailings

 Eureka

Townsite

86 m

282 m

7

7,350 m 

 End of 

study reach

,250 m

6,993 m

6 , 618 m 6

Howardsville

,528 m

5,467 m

4,670 m

3,435 m 3,400 m 3,150 m

2,240 m

1,918 m

 

 

MaggieGulch

Roc

re

r 

reka G

C

Hematite Gulch

ataractGulch

Braided reach

Braided reach

O

Cunningham Creek

Eureka Gulch

ttoGulch

r

Niagara Gulch

lch

107°35'47" 7°34' 46 10 " 107°33'45"

37°49'44"

37°50'45 "

37°51'46 "

37°52'47

0 1.2 KILOMETERS

0 0.4 0.8 MILES

Sampled and labeled stream sites 

 and end of OTIS model reach

Sampled inflow sites

Sampled stream sites

OTIS model downstream boundary

Hydrography

Mines, mills, and mine tunnels

EXPLANATION

3,844 m
the inflow near Howardsville (6,438 m) that contributed 

19 percent of the zinc load. Undifferentiated upstream sources 

represented 14 percent of the zinc load, and the inflow from 

Eureka Gulch (347 m) contributed 6 percent of the load 

(Paschke and others, 2005).

Determination of Physical Parameters 

and Model Calibration for Simulation 

of the Chloride Tracer Profile

The Eureka to Howardsville study reach (fig. 16) was 

divided into 14 model reaches (table 10) based on streamflow 

calculations (fig. 17) and mass-loading graphs (Kimball and 

others, this volume; Paschke and others, 2005). Model parameters required for the OTIS simulations include the dispersion 

coefficient (D), the cross-sectional area of the stream (A), an 

upstream-boundary condition (Ub

), and the lateral-inflow rate 

(qL

) and concentration (CL) for each reach. In addition, some 

model reaches used first-order removal coefficients (λ). The 

parameter A was determined using OTIS-P simulations of 

the transport-site data (Paschke and others, 2005). A uniform 

dispersion coefficient of 1.0 m was used for all model reaches. 

For each model reach, lateral inflow (qL

) was computed based 

on the streamflow profile. The upstream boundary conditions 

for the model were 13.3 mg/L chloride and 0.468 mg/L zinc. 

Calibration and estimation of CL

 and λ values are discussed 

following.

Simulation of the chloride profile represented the data 

well (fig. 17) and indicated that boundary conditions and 

lateral-inflow values (qL) used in the model were consistent with the dilution of chloride observed at the synoptic 

sampling sites. Streamflow at the upstream end of the study 

reach (0 m) was 260 L/s. Streamflow at the downstream 

end of the study reach (7,250 m) was 2,320 L/s. Chloride 

concentrations were 14.2 mg/L at the top of the study reach 

and were diluted by inflows to 1.8 mg/L at the downstream 

end of the study reach.

Stream segments containing sampled surface-water 

inflow or seeps accounted for 70 percent of the inflow. The 

principal surface-water inflows to the study reach were 

Cunningham Creek (6,558 m), which contributed 445 L/s or 

22 percent of the flow increase; Eureka Gulch (347 m), which 

contributed 161 L/s or 8 percent of the flow increase; Minnie 

Gulch (2,465 m), which contributed 90 L/s or 4 percent of the 

flow increase; and Maggie Gulch (3,450 m), which contributed 80 L/s or 4 percent of the flow increase. Ground-water 

inflow along the braided reach (from 786 to 2,030 m) contributed 11 percent of the flow increase (230 L/s). Additional 

ground-water inflow (72 L/s or approximately 3 percent of 

the inflow) occurred along the stream reach ending at 4,670 m 

that contained abundant willow vegetation and beaver ponds.

Model Calibration for Zinc Simulations

Using lateral-inflow concentrations (CL) representative 

of measured inflow concentrations (fig. 18), the conservative simulation had greater instream zinc concentrations than 

measured instream concentrations downstream from Eureka 

Gulch (fig. 19). Consistent with the mass-loading graphs 

(Kimball and others, this volume), this simulation result 

indicates removal of dissolved zinc from the stream. The 

loss of dissolved zinc probably results from zinc adsorption 

onto iron oxyhydroxides or streambed sediment (Church and 

others, 1997) and was represented as a first-order process 

using OTIS. First-order removal coefficients (λ) ranging from 

2.5 × 10–5 to 1.0 × 10–4s–1 were assigned to model reaches 3 

(586 to 1,918 m), 5 (2,240 to 3,150 m), 8 (3,435 to 4,670 m), 

and 13 (6,993 to 7,250 m) to represent the decrease of sampled 

Table 10. Animas River, Eureka to Howardsville, reach number, distance at end of reach, and description of reaches used in OTIS 

modeling of synoptic samples (August 1998).

[Distance in meters (m) downstream from arbitrary stream datum. Reach numbers correspond to those in figures 17–20]

Model 

reach

Distance

(m) Brief description of reach and significant inflows

1 282 Animas River downstream from tracer-injection site.

2 586 Animas River downstream from Eureka Gulch.

3 1,918 Animas River downstream from most of braided reach.

4 2,240 Animas River downstream from Forest Queen mine (# 195).

5 3,150 Animas River upstream from Kittimack tailings (# 192).

6 3,400 Animas River downstream from Kittimack tailings.

7 3,435 Animas River downstream from flow increase at 3,405 m.

8 4,670 Animas River downstream from flow increase associated with willows and beaver ponds.

9 5,467 Animas River upstream from Howardsville.

10 6,528 Animas River downstream from Howardsville and upstream from Cunningham Creek.

11 6,618 Animas River downstream from Cunningham Creek.

12 6,993 Animas River at 6,993 m.

13 7,250 Animas River at end of study reach.

14 7,350 Animas River at downstream OTIS boundary.

1010 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1011

instream zinc load through these model reaches noted in the 

results of the loading analysis (Paschke and others, 2005). The 

OTIS result for instream zinc concentrations using representative lateral-inflow concentrations (CL

) and first-order removal 

(fig. 19) provided a reasonable match to the measured instream 

zinc concentrations and represented the calibrated model used 

for simulation of remediation scenarios. Similar to results in 

the Cement Creek basins, the limited distribution and the small 

magnitude of first-order removal coefficients indicate that reactive processes were operating only to a minimal extent in the 

study reach.

The instream zinc concentrations were generally greater 

than the model simulations in model reach 3 indicating that 

some zinc sources may exist in the model reach that were not 

represented in the model. This model reach contained one of 

the intensely braided reaches (fig. 17). Stream samples were 

collected along an individual, continuous channel throughout the reach, but because of the multiple channels, how 

representative each sample was of processes occurring along 

each stream segment was not known. Therefore, a conservative approach was chosen wherein the model was designed to 

lump the processes in the braided reach into one model reach 

in order not to emphasize small, possibly anomalous processes 

occurring within the reach. The difference between the conservative and reactive simulations at the end of the model reach 

(fig. 19) indicates that some zinc inflow to the model reach 

was removed using first-order removal coefficients. Therefore, 

the model generally represents what was occurring within the 

model reach.

Removal of zinc onto streambed sediment in reaches 3, 

5, 8, and 13 is geochemically consistent with other features 

of those reaches. Reach 3 contained the braided reach where 

ground water entered the stream. Because of multiple surface 

channels in this reach, and very coarse streambed sediment 

(gravel to cobble-sized), good interaction likely occurred 

between surface water and ground water that would increase 

sediment/water contact times and promote sorption. In addition, the fluctuation between high and low zinc concentrations in instream samples in this reach indicates that zinc was 

entering and being removed within the reach. Reach 5 was 

downstream from the Forest Queen mine (# 195). A reasonable 

interpretation is that mixing between mine drainage from 

the Forest Queen and the cleaner water of the Animas River 

caused formation and settling of some colloidal material. 

Reach 8 contained inflow from Maggie Gulch and from ground 

water. Mixing between the stream water and tributary surface 

Figure 17. Variation in chloride concentration, streamflow, and simulated chloride concentration with distance, 

showing location of OTIS model reaches and significant features, Animas River, Eureka to Howardsville mass-loading 

study (August 1998) and OTIS solute-transport model.

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

-2

0

2

4

6

8

10

12

14

Beaver ponds 

and braided reach

containing

Kittimack tailings

Beaver ponds 

and braided 

reach

Hematite Gulch

Maggie Gulch

12 13 14

11

10

3 4 5 6 7 8 9

1 2

Cunningham 

Creek

Inflow near Howardsville

Minnie Gulch

Forest Queen mine

Eureka Gulch

 Dissolved chloride concentration in stream samples

 Dissolved chloride concentration in inflow samples

 Simulated chloride concentration

 Streamflow 

 Labeled significant inflows

 Model reach boundary and number

DISTANCE ALONG STUDY REACH, IN METERS

DISSOLVED CHLORIDE CONCENTRATION, IN MILLIGRAMS PER LITER

0

500

1,000

1,500

2,000

2,500

STREAMFLOW, IN LITERS PER SECOND
and ground water probably promoted formation of colloidal 

material that settled to the streambed. Reach 13 contained 

inflows that were enriched in zinc (fig. 18) and iron (Paschke 

and others, 2005). Mixing of this water with the Animas River 

likely promoted formation and settling of iron-rich colloidal 

material.

Though the modeling results indicated that reactive 

processes were minimal in Cement Creek and in the Animas 

River reach from Eureka to Howardsville, the reasons for 

minimal reactivity differ between the streams. In Cement 

Creek, reaction was minimal because the pH was too low 

to promote sorption of copper and zinc to precipitating iron 

phases that were present in the water column. In the Animas 

River, precipitation of iron minerals in the water column was 

not a major process, probably because of limited sources of 

dissolved iron to the river. Consequently, most surfaces (colloidal iron and aluminum minerals) available for zinc sorption 

were on the streambed. Interaction between zinc in the water 

column and streambed material was probably limited due to 

relatively fast flow conditions that limited the amount of sorption that occurred. Although this interaction was minimal in 

that the first-order removal coefficients were relatively small, 

the reaction was sufficient to lower instream zinc concentrations (fig. 19) and metals accumulated in streambed sediment 

during the spring and summer: during low flow, colloidal 

streambed sediment that has accumulated since spring runoff 

can have zinc concentrations exceeding 1,500 ppm (Church 

and others, 1997). From the perspective of a solute-transport 

model that examines instantaneous processes, reaction was 

minimal. But, over time, this minimal reaction is sufficient to 

account for elevated concentrations of metals in streambed 

sediment. This interpretation is consistent with the conclusions of Church and others (1997), who reported that one of 

the major differences between Cement Creek and the Animas 

River upstream from Cement Creek was that in Cement Creek, 

metals were concentrated in the water column, whereas in the 

Animas River, metal concentrations were greater in colloids 

stored in the streambed sediment.

Remediation Simulations

Two remediation scenarios were evaluated for the Eureka 

to Howardsville study reach. The first remediation scenario 

evaluated the effects of remediating unsampled inflow near 

Figure 18. Variation in dissolved zinc calibrated lateral-inflow concentrations and observed inflow concentrations 

(August 1998) with distance and OTIS model reach, Animas River, Eureka to Howardsville OTIS model.

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

0

1

2

3

7

8

(4,670 m)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 Increase in flow (3,435 m) Upstream from Kittimack tailings (3,150 m) End of study reach (7,250 m) Downstream from Cunningham Creek (6,618 m) Downstream from Howardsville (6,528 m) Downstream from Kittimack tailings (3,435 m) Downstream from braided reach and upstream from Forest Queen (1,918 m) Downstream from Eureka Gulch (586 m)

Inflow near

Howardsville

Mill

(6,438 m)

DISTANCE ALONG STUDY REACH, IN METERS

DISSOLVED ZINC CONCENTRATION, IN MILLIGRAMS PER LITER

 Sampled inflow concentration

 Lateral-inflow concentration

 Model reach boundary, number, 

 and labeled features

1012 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1013

1,940 m and the Forest Queen mine (model reach 4), and 

removing contributions from the Kittimack tailings (model 

reach 6). The second remediation scenario simulated the 

effects of remediating unsampled inflow near 1,940 m and 

the Forest Queen mine (model reach 4), the Kittimack tailings 

(model reach 6), and the various mills, both historical and 

active, near Howardsville (model reach 10).

To simulate remediation of stream reaches, the lateralinflow concentrations (CL

) were reduced from the measured 

inflow concentrations to concentrations representative of 

inflow after remediation. Because these post-remediation 

concentrations are uncertain, the remedial simulations for 

the Eureka to Howardsville study reach assumed a 75 percent 

reduction in zinc lateral-inflow concentrations (CL

) for remediated model reaches.

Simulation results for the first remediation scenario 

indicated that downstream from the Kittimack tailings and 

upstream from Howardsville, instream zinc concentrations 

would approach and sometimes be less than 0.18 mg/L if zinc 

inflow concentrations were reduced by 75 percent in model 

reaches 4 and 6 (fig. 20). An instream zinc concentration of 

0.18 mg/L is the upper limit of acute toxicity for some sensitive aquatic species (Besser and Brumbaugh, this volume, 

Chapter E18).

Simulation results for the second remediation scenario 

(fig. 20) indicated that downstream from the Kittimack tailings 

and downstream from Howardsville, instream zinc concentrations would approach and sometimes be less than 0.18 mg/L 

if zinc inflow concentrations were reduced by 75 percent in 

the inflows to model reaches 4, 6, and 10. Paschke and others 

(2005) indicated that simulated instream zinc concentrations were greater than the State of Colorado hardness-based 

chronic and acute toxicity standards. Zinc sources that cause 

zinc concentrations to remain elevated in spite of the simulated 

remediations include sources upstream from the study reach, 

slight zinc loading along model reaches 8 and 9, the remaining 

25 percent of the zinc loads at the Forest Queen, Kittimack, 

and Howardsville locations, and some loading that occurs in 

model reach 13.

Figure 19. Variation in dissolved zinc concentrations with distance in stream and inflow samples (August 1998) and in OTIS conservative 

and calibrated reactive simulations, showing model reach and number and location of significant features, Animas River, Eureka to 

Howardsville.

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2

4

6

8

14

Howardsville

Forest Queen 

and unsampled inflow

12

11 8 9 10

Kittimack tailings

7

1 2 3 4 5 6 13

DISTANCE ALONG STUDY REACH, IN METERS

DISSOLVED ZINC CONCENTRATION, IN MILLIGRAMS PER LITER

 Measured instream zinc concentration

 Measured inflow zinc concentration

 OTIS result; conservative simulation

 OTIS result with first-order coefficient in reaches 3, 5, 8, and 13

 Model reach boundary, number, and labeled features
Animas River, Howardsville to Silverton

The Howardsville to Silverton mass-loading study was 

conducted on a 7,858 m reach of the upper Animas River 

starting near the historical mining town of Howardsville and 

extending to just upstream from Silverton at USGS gauging station number 09358000, now operated by the State of 

Colorado, gauging station A68 (figs. 1, 21).

The sampled reach included inflows from Cunningham 

Creek (1,075 m); Hematite Gulch (1,150 m); Arrastra 

Creek (4,186 m); Boulder Gulch (4,951 m), and Blair Gulch 

(5,221 m). Other notable locations on the stream reach 

include the town and mill at Howardsville (site # 233; 965 m), 

the Mayflower Mill (# 221) and reclaimed tailings ponds 

(sites # 507–510) from 4,500 to about 6,500 m, and the 

Lackawanna Mill (# 287; 7,103 m).

A continuous injection of sodium chloride was conducted 

on the river from September 13 through September 15, 1997. 

On September 14, 1997, synoptic samples were collected 

at 38 stream sites and 34 inflow sites. Chloride concentrations 

were used to calculate streamflow (Kimball and others, this 

volume; Paschke and others, 2005). Dissolved zinc (using 

ultrafiltration, 10,000 Dalton molecular weight nominal pore 

size) was the only metal modeled using OTIS due to nearneutral instream pH and low concentrations of other metals 

(Paschke and others, 2005).

Water quality in this reach of the Animas River was 

better than in Cement Creek. Values of pH were near neutral 

throughout the stream reach and showed no clear downstream 

trend. In contrast to the Eureka to Howardsville study reach, 

dissolved zinc concentrations generally increased through 

the study reach, ranging from approximately 0.3 mg/L at the 

upstream end of the study reach to approximately 0.45 mg/L 

at the downstream end of the study reach. Zinc load increased 

from 10 to 80 kg/d through the study reach. Zinc load at the 

downstream end of the Eureka to Howardsville study reach 

was approximately 37 kg/d during the 1997 study in contrast 

to 60 kg/d during the 1998 study. Lower flow in 1997 compared to 1998 caused this difference.

The greatest sources of zinc load to the Animas River 

in the study reach were downstream from Howardsville 

(inflow at 965 m; downstream stream site at 1,059 m) which 

accounted for 9 percent of the load, and downstream from 

the tailings repositories near the Mayflower Mill (no sampled 

Figure 20. Variation in dissolved zinc concentrations with distance in calibrated OTIS model and in simulations of two remediation 

scenarios, showing location of some significant features, Animas River, Eureka to Howardsville.

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.18 mg/L = Upper limit of acute toxicity for sensitive aquatic species

14

Howardsville

Forest Queen 

and unsampled inflow

12

11 8 9 10

Kittimack tailings

7

1 2 3 4 5 6 13

DISTANCE ALONG STUDY REACH, IN METERS

DISSOLVED ZINC CONCENTRATION, IN MILLIGRAMS PER LITER

 Measured instream zinc concentration

 Calibrated OTIS model 

 OTIS result, 75 percent reduction in zinc inflow in reaches 4 and 6

 OTIS result, 75 percent reduction in zinc inflow in reaches 4, 6, and 10

 Model reach boundary, number, and labeled features

1014 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1015 Figure 21. Location of stream reach of the Animas River, Howardsville to Silverton mass-loading study (September 1997) and OTIS solute-transport model. Stream- and inflow- sample data provided in Sole and others (this volume). Mining features shown (and inventory numbers, from Church, Mast, and others, this volume) include Pride of the West Mill #4 (# 233) and tailings (# 234) at Howardsville; Mayflower Mill (# 221); Mayflower Mill tailings repositories #1 (# 507), #2 (# 508), #3 (# 509), and #4 (# 510); William Crooke Mill (# 215); and Lackwanna Mill (# 287). Mining features shown are limited to those discussed in the text. See Church, Mast, and others (this volume) for a complete inventory. Base from Digital Orthophoto Quads, Transverse Mercator, –105.00 central meridian, 1927 datum.

955

595

m

m

7

End of study reach, A68 stream gauge

,958

m

7,858 m

7

,

28

Lackawanna Mill

3 m

6

,

,288

m

5

,

536

m

5,

,

131

m

1,135

m

1

 Pride of the West Mill #4 

,059

m

An

mas i River

An

ma i s River

Arrastra Creek

Blair Gulch

Swansea Gulch

 Gulch

Swansea G

Boulder

ulch

Gulch

Idaho Gulch

Brendel Gulch

Cement Creek 

HematiteGulch

CunninghamCr

6,038

m

4,916 m

Mayflower Mill 

4

Mayflower Mill tailings repository #1

Mayflower Mill tailings repository #2

Mayflower Mill tailings repository #3

Mayflower Mill tailings repository #4

,310

m

4

2,800 m

,166

m

Pride of the West Mill tailings

Start of mass-loading study reach

107°38'50" 107°37'49" 107°36'48" 107°35'47"

37°47'42"N

37°49'44"37°48'43"

William Crooke Mill

Silverton

0 0 .5 1 0 .2 5 MILES

0 0 .7 1.4 0 .3 5 KILOMETERS

c

Mines, mills, and mine tunnelsHydrographyOTIS model downstream boundarySampled inflow sitesSampled stream sites

Sampled and labeled stream sites  and end of OTIS model reach

EXPLANATION

3,844 m

Howardsville
inflows in the reach, downstream site at 6,288 m, fig. 21), 

which represented 15 percent of the load. Additional sources 

of load are presented in Paschke and others (2005) and 

Kimball and others (this volume).

Determination of Physical Parameters 

and Model Calibration for Simulation 

of the Chloride Tracer Profile

The Howardsville to Silverton study reach (fig. 21) 

was divided into 15 model reaches (table 11; fig. 22) based 

on streamflow calculations and mass-loading graphs (Kimball 

and others, this volume; Paschke and others, 2005). Model 

parameters required for the OTIS simulations include the 

dispersion coefficient (D), the cross-sectional area of the 

stream (A), an upstream-boundary condition (Ub

), and the 

lateral-inflow rate (qL

) and concentration (CL) for each reach. 

In addition, some model reaches used first-order removal coefficients (λ). The parameter A was determined using OTIS-P 

simulations of the transport-site data (Paschke and others, 

2005). A uniform dispersion coefficient of 1.0 m was used for 

all model reaches. For each model reach, lateral inflow (qL

) 

was computed based on the streamflow profile. The upstream 

boundary conditions for the model were 2.19 mg/L chloride 

and 0.281 mg/L zinc. Model calibration and estimation of CL

and λ values are discussed following.

Simulation of the chloride profile represented the data 

well (fig. 22) and indicates that boundary conditions and 

lateral-inflow values (qL

) used in the model are consistent 

with the dilution of chloride observed at the synoptic sampling 

sites. Chloride concentrations were 2.2 mg/L at the top of the 

study reach and were diluted by inflows to 1.1 mg/L at the 

downstream end of the study reach. Elevated chloride values 

in some inflows (fig. 22) resulted from precipitation runoff 

that flowed into the stream during the synoptic sampling 

and that contained magnesium chloride used to reduce road 

dust. The elevated chloride concentration in inflow samples 

complicated the calculation of streamflow values. However, 

streamflow values computed from the synoptic sampling 

results compare well with streamflow measured independently 

at the six transport sites (Paschke and others, 2005).

Streamflow at the injection site was 1,107 L/s and 

increased by 1,157 L/s over the course of the study reach to 

2,264 L/s at the downstream end of the study reach (fig. 22). 

Stream segments containing sampled surface-water inflow or 

seeps accounted for 93 percent of the inflow along the reach. 

The principal sampled inflows along the study reach were 

Cunningham Creek (1,075 m), which contributed 386 L/s or 

33 percent of the flow increase; Arrastra Creek (4,186 m) and 

a pipe discharging water diverted from Arrastra Creek3

 and 

located just downstream from Arrastra Creek (4,190 m), which 

each contributed 110 L/s or 10 percent of the flow increase; 

Boulder Gulch (4,951 m), which contributed 78 L/s or 7 percent of the flow increase; and Hematite Gulch (1,150 m), 

which contributed 57 L/s or 5 percent of the flow. Small 

surface inflows and ground-water seeps caused the remaining 

streamflow increase along the study reach.

Model Calibration for Zinc Simulations

Methods used to compute lateral-inflow concentrations (CL

) varied with model reach. Upstream from Arrastra 

Creek, lateral-inflow concentrations were assigned to each 

model reach based on inflow concentrations measured during 

synoptic sampling (fig. 23). Downstream from Arrastra Creek, 

lateral-inflow concentrations were adjusted to obtain a reasonable match between simulated and measured instream zinc 

concentrations. Paschke and others (2005) provided details 

supporting the choices of lateral-inflow concentrations and 

first-order removal coefficients.

The conservative simulation overestimated instream 

conditions (fig. 24), and indicated loss of zinc in some model 

reaches. Mass-loading results (Paschke and others, 2005) 

indicated loss of zinc in model reaches 11 (5,536 to 6,038 m) 

and 13 (6,288 to 7,283 m). Consequently, first-order removal 

coefficients were applied in those model reaches (reach 11, 

4.0×10–4s–1; reach 13, 1.0×10–4s–1) for OTIS simulations. The 

resulting calibrated model exhibited a good fit with measured instream conditions (fig. 24). The limited distribution 

and magnitude of removal coefficients indicate that reactive 

processes are operating only to a minimal extent in the study 

reach. Throughout much of the study reach, the streambed is 

relatively free from iron, aluminum, and manganese flocculent material. The limited amount of iron, aluminum, and 

manganese phases available in the water column and on the 

streambed relative to other areas limits sorption reactions. 

In addition, the near-neutral pH of the Animas River in the 

study reach limits mobility of many solutes (for example, 

iron and aluminum), so that if they do enter the river in acidic 

tributary inflow, they are quickly removed to the streambed. 

Near-neutral pH in the upper Animas River is a result of a 

large proportion of propylitically altered rocks in the basin 

that generated water having near-neutral pH and some buffering capacity compared to conditions in the Cement Creek 

basin, where a larger proportion of rocks have been affected 

by acid alteration and thus generate acid waters (fig. 1; Bove 

and others, this volume).

Remediation Simulations

Two remediation scenarios were evaluated for the 

Howardsville to Silverton study reach. The first remediation 

scenario examined the effects of remediating contributions 

from historical and active mills at Howardsville (model 

reach 3). This model reach accounted for 9 percent of the zinc 

load in the study reach (Paschke and others, 2005). The second 

remediation scenario evaluated the effect of remediating 

3

The pipe contained water from Arrastra Creek that was diverted from the 

creek to supply process water to the Mayflower Mill when it was operating. 

At the time of the study, the water flowed from Arrastra Creek through the 

pipeline to the mill and back to the Animas River. It was not used or altered 

at the mill.

1016 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1017

Figure 22. Variation in chloride concentration, streamflow, and simulated chloride concentration with distance, 

showing location of OTIS model reaches and significant features, Animas River, Howardsville to Silverton mass-loading 

study (September 1997) and OTIS solute-transport model.

Table 11. Animas River, Howardsville to Silverton, reach number, distance at end of reach, and description of reaches used in OTIS 

modeling of synoptic samples (September 1997).

[Distance is downstream from arbitrary stream datum; m, meters. Reach numbers correspond to those in figures 22–25]

Model 

reach

Distance

(m) Brief description of reach and significant inflows

1 595 Animas River downstream from tracer-injection site.

2 955 Animas River upstream from Howardsville.

3 1,059 Animas River downstream from Howardsville.

4 1,135 Animas River downstream from Cunningham Creek.

5 2,800 Animas River at 2,800 meters.

6 4,166 Animas River upstream from Arrastra Creek.

7 4,310 Animas River downstream from Arrastra Creek.

8 4,916 Animas River downstream from right bank acid inflows and upstream from Boulder Gulch.

9 5,131 Animas River downstream from Boulder Gulch.

10 5,536 Animas River downstream from Blair Gulch.

11 6,038 Animas River at 6,038 m.

12 6,288 Animas River downstream from zinc concentration increase.

13 7,283 Animas River downstream from zinc loss.

14 7,858 Animas River at end of study reach.

15 7,958 Animas River at downstream OTIS boundary.

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

0

1

2

3

4

5

6

7

8

20

25

30

Inflow from Mayflower tailings

Lackawanna inflow

15 End of study reach

Boulder Gulch

1 2 3 4 5 6 7 8 9 10 11 12 13 14 Blair Gulch Arrastra Creek Cunningham Creek and downstream from Howardsville

Ditch draining Howardsville ponds

 Chloride concentration in stream samples

 Chloride concentration in inflow samples

 Simulated chloride concentration

 Streamflow 

 Model reach boundary and number

 Labeled significant features

DISTANCE ALONG STUDY REACH, IN METERS

CHLORIDE CONCENTRATION, IN MILLIGRAMS PER LITER

0

500

1,000

1,500

2,000

2,500

3,000

STREAMFLOW, IN LITERS PER SECOND
contribution from the mills at Howardsville and from the 

Mayflower Mill (model reaches 3 and 12). Model reach 12 

contributed 15 percent of the total zinc load observed throughout the Howardsville to Silverton study reach during the 1997 

synoptic study (Paschke and others, 2005).4 Remediation 

simulations were accomplished through the assumption that 

remediation would reduce zinc concentrations in the inflows 

by 75 percent. In addition, both of these remediation scenarios 

were implemented with the assumption that upstream remediation (Eureka to Howardsville section) had already been accomplished. The upstream zinc load accounted for 26 percent 

of the load in the study reach (Paschke and others, 2005). 

Accordingly, for both remediation simulations, the upstream 

boundary concentration was reduced from 0.28 to 0.19 mg/L, 

which represented the simulated zinc concentration upstream 

from the inflow at Howardsville and reflects 75 percent reduction in the zinc load from the vicinity of the Forest Queen mine 

(including unsampled inflow at 1,940 m in the 1998 Eureka to 

Howardsville study) and Kittimack tailings inflows from our 

previous results (fig. 20).

The first remediation scenario is similar to remediation 

scenario 1 completed for the Eureka to Howardsville 1998 

study. It is repeated here for the 1997 data set to examine 

the effects of remediation on stream reaches downstream 

from Howardsville. The simulation result (fig. 25) indicates 

that downstream from the Kittimack tailings to upstream 

from Arrastra Creek instream zinc concentrations would 

approach 0.18 mg/L if zinc concentrations were reduced by 

75 percent in the inflows to the stream reaches containing 

the Forest Queen mine, the Kittimack tailings, and the mills 

downstream from Howardsville. An instream zinc concentration of 0.18 mg/L is the upper limit of acute toxicity for 

some sensitive aquatic species (Besser and Brumbaugh, this 

volume). State of Colorado hardness-based acute and chronic 

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

1E-3

0.01

0.1

1

10

100

14 15

Downstream from zinc loss (7,283 m)

Downstream from zinc concentration increase (6,288 m)

Downstream from Blair Gulch (5,536 m)

12 13

Upstream from Boulder Gulch (4,916 m)

Downstream from Boulder Gulch (5,131 m)

Upstream from Arrastra Creek (4,166 m)

Downstream from Arrastra Creek (4,310 m)

6,038 mEnd of study reach (7,858 m)

Downstream from Cunningham Creek (1,135 m)

Downstream from Howardsville (1,059 m)

Upstream from Howardsville (955 m)

11

9 10

8

1 2 3 4 5 6 7 595 m 2,800 m

 Sampled inflow concentration

 Lateral-inflow concentration

 Model reach boundary, number, 

 and labeled features

DISTANCE ALONG STUDY REACH, IN METERS

DISSOLVED ZINC CONCENTRATION, IN MILLIGRAMS PER LITER

Figure 23. Variation in dissolved zinc calibrated lateral-inflow concentrations and observed inflow concentrations 

(September 1997) with distance and OTIS model reach, Animas River, Howardsville to Silverton OTIS model.

4

Note that zinc concentrations increased along model reach 12 and 

remained elevated downstream (fig. 24). However, no inflows having high 

zinc concentrations were sampled in the reach, which indicates that unsampled 

inflows contributed zinc load to the stream (Paschke and others, 2005). When 

load increases along a stream reach having no sampled inflows to account for 

the load increase, the increased loads are generally attributed to unsampled 

ground-water discharge along the reach (Kimball and others, 2002).

1018 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1019

zinc toxicity standards are less than 0.13 mg/L throughout the 

study reach (Paschke and others, 2005). However, as the zincrich inflows downstream from Arrastra Creek (including some 

near the Mayflower Mill) contributed to the Animas River, 

instream concentrations increased to greater than 0.18 mg/L.

For the second remediation scenario, the effects of remediating inflow to the reach containing the Mayflower Mill and 

tailings (model reach 12) in addition to the upstream model 

reaches were evaluated. Remediation of these inflows was represented using an upstream boundary condition of 0.19 mg/L 

zinc, and 75 percent reductions in zinc lateral-inflow concentration (CL

) for model reaches 3 and 12. These simulations 

represented the effects of remediating inflow from the Forest 

Queen mine, Kittimack tailings, inflow downstream from 

Howardsville, and presumed ground-water discharge to the 

stream in model reach 12. Simulation results are shown in figure 25 and indicate that instream zinc concentrations remained 

higher than 0.30 mg/L downstream from Arrastra Creek due to 

sampled and unsampled inflows having elevated zinc concentrations in the lower portion of the study reach (for example, 

model reaches 8, 10, 12, and 14, fig. 24).

Significance of Metal Removal 

Processes in Cement Creek and 

the Upper Animas River

The first-order removal coefficients (λ) estimated by the 

model are a measure of the rate of metal removal and may be 

compared across model reaches in each model, or between 

modeled streams. The comparison can be instructive, but it 

is necessary to also compare the metal-removal rates (firstorder removal coefficients) to the residence time of water in 

each model reach to assess where removal reactions are most 

important. Using a method similar to that presented in Kimball 

and others (1994), we calculated a reaction half-life (t

r

, the 

amount of time the reaction used to consume half of the metal 

present in the water column) for each first-order removal coefficient (λ):

t

r

1n 2

O = ---------- (4)

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

0.0

0.2

0.4

0.6

0.8

10

100

Downstream from Boulder Gulch

Downstream from Arrastra Creek

Downstream from Blair Gulch

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

DISTANCE ALONG STUDY REACH, IN METERS

DISSOLVED ZINC CONCENTRATION, IN MILLIGRAMS PER LITER

 Measured instream zinc concentration

 Measured inflow zinc concentration

 OTIS result; conservative simulation

 OTIS result; first-order coefficient 

 in reaches 11 and 13

 Model reach boundary, number, and 

 labeled features 

Figure 24. Variation in dissolved zinc concentrations with distance in stream and inflow samples (September 1997) 

and in OTIS conservative and calibrated reactive simulations, showing model reach and number and location of 

significant features, Animas River, Howardsville to Silverton.
In addition, a convective travel time (t

c

) can be calculated 

for each reach:

t

c

L

u = -- (5)

where L is the reach length in meters and u is the velocity (m/s) in the reach determined by dividing streamflow by 

stream cross-sectional area (A). A dimensionless parameter, 

the Damkohler number (Bahr and Rubin, 1987; Kimball 

and others, 1994), relates travel time to the half-reaction 

time:

Dn

t

c

t

r

= --- (6)

If the travel time is large compared to the half-reaction 

time (Dn

 > 1), stream water resides in the model reach long 

enough for more than half of the solutes to be removed from 

the water column by the reaction. Conversely, if the travel time 

is less than the half-reaction time (Dn

 < 1), less than half of the 

solutes are removed by the reaction during transport through 

the model reach. The larger the value of Dn

, the more complete 

the removal reaction.

Damkohler numbers for the OTIS model reaches were 

greater than 1 only in the upstream model reaches of Cement 

Creek (table 12). Elsewhere, Dn was less than 1, indicating 

that travel times through the reaches were fast relative to the 

half-reaction rates: reactions progressed less than halfway to 

completion. In many cases, Dn

 was less than 0.10: reactions 

removed less than 5 percent of the solute from the water column and were not very significant.

If removal reactions were not significant, how can we 

account for streambed metal concentrations for some metals 

that exceed 1,500 parts per million (Church and others, 1997) 

in some reaches of the upper Animas River and Cement Creek? 

Although the reactions may not appear significant on the time 

scale of the OTIS solute-transport model, when integrated 

through time by the continuous flow of the streams, the total 

amount of metal transferred from the water column to the 

streambed by removal reactions can be large. Using data from 

the OTIS model for the upper Animas River, Howardsville 

to Silverton reach, we can calculate the magnitude of daily 

metal removal. For instance, the difference in zinc concentrations between the conservative and reactive OTIS models at 

the downstream end of the upper Animas River, Howardsville to Silverton study reach was 0.15 mg/L (fig. 24). This 

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

0.0

0.2

0.4

0.6

0.8

0.18 mg/L = Upper limit of acute toxicity for sensitive aquatic species Downstream from Boulder Gulch Downstream from Arrastra Creek Downstream from Blair Gulch

7 8 9 10 11 12 13 14 15 1 2 3 4 5 6

DISTANCE ALONG STUDY REACH, IN METERS

DISSOLVED ZINC CONCENTRATION, IN MILLIGRAMS PER LITER

 Measured instream zinc concentration

 Calibrated OTIS model

 OTIS result, 75 percent reduction in zinc inflow reach 3

 and upstream boundary condition = 0.19 mg/L zinc

 OTIS result, 75 percent reduction in zinc inflow reaches 3 and 12 

 and upstream boundary condition = 0.19 mg/L zinc

 Model reach boundary, number, and labeled features 

Figure 25. Variation in dissolved zinc concentrations with distance in calibrated OTIS model and in simulations 

of two remediation scenarios, showing model reach and number and location of some significant features, Animas 

River, Howardsville to Silverton.

1020 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1021

concentration difference represented a load of 29 kg/d removed 

by instream processes. Kimball and others (this volume) report 

removal of 17 kg/d zinc in this reach. The difference between 

the two removal estimates arises from the different calculation 

methods—Kimball and others (this volume) integrated net 

decreases in load between each stream site and the next in the 

Animas River along the entire study reach to estimate the total 

amount of zinc removed. This method can underestimate load 

losses, because in some reaches a net gain in load may be the 

result of a larger gain offset by some loss. The net calculation misses this smaller loss. In contrast, within OTIS, individual model reaches can experience a gain in load through 

contribution from inflow in the model reach, and a loss in load 

through implementation of a first-order removal coefficient. 

The gain in load is recorded at the end of the reach in the 

conservative simulation. The loss in load and the net change are 

recorded in the reactive simulation. The difference between the 

two simulations approximates metal removal through the entire 

modeled reach and provides a different estimate of the net loss 

than the mass-balance calculation used by Kimball and others (this volume). Nevertheless, the estimates of zinc removed 

throughout the extent of the reach differ only by a factor of 2 

between the solute-transport model presented herein and the 

mass-balance calculations of Kimball and others (this volume) 

and indicate that, although zinc transport may be considered 

relatively conservative based on the Damkohler numbers, 

metal removal is substantial when integrated through time. The 

amount of metal stored on streambed sediment (Church and 

others, 1997) is consistent with the results of the OTIS solutetransport simulations.

Comparison of Remediation Using 

OTIS and a Mass-Loading Model

A comparison of concentrations resulting from remediation at the end of the OTIS model reaches with concentrations estimated by the same remediation using a mass-loading 

model is also instructive. The mass-loading calculations were 

estimated by reducing the load of each remediated inflow by 

the amount of the hypothetical remediation. The load of each 

inflow was the load estimated by environmental sampling of 

the inflow which was different than the load estimated by the 

OTIS model for those inflow reaches where calibrated lateralinflow concentrations differed from observed-inflow concentrations. The load at the end of the study reach was reduced by the 

amount of load reduction caused by remediation of the inflows. 

The resulting concentration at the end of the study reach was 

calculated by dividing the resulting load by streamflow.

Differences in concentrations ensuing from remediation estimated using OTIS and using a simple mass-loading 

model increase with the amount of load reduction caused by 

remediation (table 13). The mass-loading model estimated a 

zinc concentration 100 percent greater than the OTIS model 

in upper Cement Creek, and 23 percent greater than the OTIS 

model in the upper Animas River, Eureka to Howardsville 

study reach (table 13). In contrast, in lower Cement Creek and 

in the upper Animas River, Howardsville to Silverton reach, 

the mass-loading and OTIS models estimated comparable 

zinc concentrations at the end of the study reaches (table 13). 

The differences between the OTIS and mass-loading models arise because (1) lateral-inflow concentrations used for 

some reaches of the OTIS model were different than ambient 

(observed) concentrations and (2) first-order removal reactions 

that occurred downstream from remediation in the OTIS model 

may change instream concentrations. For constituents where 

inflow concentrations are representative of the load increase 

occurring in the stream, and where reactive removal is trivial, 

there may be no advantage to using OTIS. However, one may 

need to go through construction of an OTIS model or a detailed 

loading analysis such as presented in Kimball and others (this 

volume) before knowing which model will be more applicable 

and expedient for the situation at hand.

Table 12. Damkohler number (Dn) for upper Animas River OTIS 

solute-transport study reaches that have first-order removal 

coefficients.

[Leaders (--), model reaches and constituents not having a first-order removal 

coefficient (λ)]

Model reach 

number Copper Iron Zinc

Upper Cement Creek

1 3.9 130 0.15

2 12 400 0.46

3 2.1 72 0.69

10 -- 1.2 --

11 -- 0.06 --

12 -- 0.08 --

14 -- 0.49 --

15 -- 0.74 --

Lower Cement Creek

3 0.2 0.22 --

7 0.12 0.16 0.01

8 -- 0.02 0.06

9 0.04 0.02 --

10 0.03 0.03 --

11 -- 0.25 --

12 -- 0.04 0.06

15 -- 0.05 --

18 0.03 0.03 0.01

19 0.01 0.005 0.09

22 0.16 0.15 0.23

24 0.16 0.03 0.24

30 0.08 0.13 0.25

Upper Animas River, Eureka to Howardsville

3 -- -- 0.18

5 -- -- 0.05

8 -- -- 0.17

13 -- -- 0.24

Upper Animas River, Howardsville to Silverton

11 -- -- 0.41

13 -- -- 0.31
Summary and Conclusions

The solute-transport model OTIS was used to simulate 

ambient conditions in four study reaches in the Animas River 

watershed study area and to simulate a variety of remediation 

scenarios for the study reaches. Lateral-inflow concentrations (CL

) and first-order removal coefficients (λ) used in the 

models were consistent with geochemical conditions observed 

in the four modeled reaches. Simulations for lower Cement 

Creek indicated that the model was successful in simulating 

increases in concentration at the mouth of the stream that have 

resulted from increased loading from the Mogul mine (# 13) 

after plugging of the American tunnel (# 96) in 1996. Except 

for some model reaches in upper Cement Creek, comparison 

of first-order removal coefficients with travel time indicated 

that removal reactions do not proceed to completion on the 

time scale of the OTIS simulations. However, when integrated 

through time, reactions that remove metal from the water 

column to the streambed explain observed elevated concentrations of metals in streambed sediment in the Animas River and 

Cement Creek (Church and others, 1997).

The results of the simulations reemphasize some of the 

conclusions from the mass-loading analysis (Kimball and others, this volume).

1. Remediation likely will be more successful in reducing 

instream concentrations of metals where metal loading is 

dominated by a few large sources.

2. Loading from diffuse seepage and ground-water inflow 

along some streams sustains and elevates metal concentrations and loads along some stream reaches.

3. In streams where metal concentrations and pH are not 

extreme, remediation may decrease metal concentrations 

sufficient to improve water quality and support more 

aquatic biota.

The largest percent reductions from remediation were 

achieved where a small number of distinct sources dominated 

loading for a particular metal, as, for example, in upper 

Cement Creek, where three mining sources (combined inflow 

from the Queen Anne and Grand Mogul mines, the Mogul 

mine, and North Fork Cement Creek) dominated the loading for copper and zinc. Simulation of complete remediation 

of these three sources achieved 90 and 82 percent reductions in copper and zinc concentrations. In contrast, lower 

Cement Creek has more distinct and diffuse sources of metal 

loads, and remediation of three of the larger point sources of 

mining-related metal loading achieved only 17 and 8.3 percent 

reduction in copper and zinc loads at the mouth of the creek. 

In lower Cement Creek, more than 50 percent of the copper 

and zinc loads were from unsampled ground-water discharge 

that occurred along the study reach (Kimball and others, this 

volume; Smith and others, this volume). The model results 

agree with loading analyses that indicate that remediation will 

be less effective in streams where ground-water sources of 

metal loading contribute a significant proportion of the metal 

loading to the stream.

Loading from diffuse seepage and ground water along 

the study reaches sustains elevated metal concentrations (and 

load) along some study reaches. This pattern is evident in 

the remediation simulations where concentrations increase 

in some nonremediated model reaches. Examples are copper and zinc concentrations in the Mogul mine to North Fork 

Cement Creek reach of upper Cement Creek; copper, iron, 

and zinc concentrations in the reaches just upstream from 

Prospect Gulch and in the lower bog area in lower Cement 

Creek; zinc in model reach 13 in the Animas River, Eureka 

to Howardsville study reach; and zinc in model reaches 8, 10, 

and 14 in the Animas River, Howardsville to Silverton study 

reach. Remediation of these reaches is not considered because 

most of this seepage results from the weathering of exposed 

hydrothermally altered rock and is not related to mining 

Table 13. Summary of maximum simulated reductions in zinc load for all four study areas using OTIS and using a simple mass-loading 

model.

[Concentration values in milligrams per liter. Mass-loading model remediation estimates used the same percentage reductions and the same inflow sites as used 

in the OTIS model remediation simulations. Data for the mass-loading calculations from Sole and others (this volume)]

Study reach

Calibrated zinc 

concentration at 

downstream end 

of study reach

Simulated remediated 

zinc concentration at 

downstream end of study 

reach, using maximum 

remediation possible 

and OTIS model

Percent 

reduction 

(OTIS model 

versus ambient 

conditions)

Simulated remediated 

zinc concentration at 

downstream end of study 

reach, using maximum 

remediation possible and 

mass-loading model

Percent 

difference

(OTIS model 

versus 

mass-loading 

model)

Upper Cement Creek 4.52 0.83 82 1.69 104

Lower Cement Creek 0.793 0.727 8.3 0.715 2

Upper Animas River, Eureka 

to Howardsville.

0.295 0.191 35 0.235 23

Upper Animas River, 

Howardsville to Silverton.

0.417 0.321 23 0.330 3

1022 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1023

activity. However, if some of the seepage were traced to mining activity (such as the presumed ground-water discharge 

in model reach 12 of the upper Animas River, Howardsville 

to Silverton OTIS model), remediation is likely to be cost 

prohibitive considering current (2004) available technologies. 

Although much of the diffuse seepage contributing to waterquality degradation is likely related to non-mining affected 

weathering (as opposed to weathering accelerated by mining 

activities) of acid-generating mineral assemblages, locations of diffuse seepage that may be mining related represent 

opportunities to explore innovative and emerging treatment 

technologies such as reactive barriers or sulfate-reducing 

injection systems (Benner and others, 1999; Saunders and 

others, 2001).

Streams containing lower initial metal concentrations and 

higher pH values probably represent the best targets for remediation. In spite of the large percent reductions simulated by 

the most extensive hypothetical remediation in upper Cement 

Creek, the simulated post-remediation zinc concentration was 

still much greater than concentrations that were simulated with 

remediation in both Animas River study reaches. In the upper 

Animas River, the simulated post-remediation concentrations 

approached concentrations that might promote improvement 

in conditions for aquatic life. Therefore, remediation seems 

more likely to improve the aquatic ecosystem in streams like 

the Animas River, than in streams that initially contain low pH 

and high metal concentrations such as Cement Creek. The pH 

and metal concentrations in Cement Creek, however, represent 

challenges that can be studied to increase our understanding 

of the natural processes that operate in such systems, and the 

limits of remediation in watersheds where non-mining affected 

weathering of acid-generating mineral assemblages degrades 

water quality.

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U.S.A.: Applied Geochemistry, v. 17, p. 1183–1207.

Kimball, B.A., 1997, Use of tracer injections and synoptic 

sampling to measure metal loading from acid mine drainage: U.S. Geological Survey Fact Sheet FS–245–96, 4 p.

Kimball, B.A., Broshears, R.E., Bencala, K.E., and McKnight, 

D.M., 1994, Coupling of hydrologic transport and chemical reactions in a stream affected by acid mine drainage: 

Environment Science and Technology, v. 28, p. 2065–2073.

Leib, K.J., Mast, M.A., and Wright, W.G., 2003, Using waterquality profiles to characterize seasonal water quality and 

loading in the upper Animas River basin, southwestern 

Colorado: U.S. Geological Survey Water-Resources Investigations Report 02–4230, 43 p.

Mast, M.A., Evans, J.B., Leib, K.J., and Wright, W.G., 2000, 

Hydrologic and water-quality data at selected sites in the 

upper Animas River watershed, Southwestern Colorado, 

1997–99: U.S. Geological Survey Open-File Report 00–53, 

20 p. plus Appendix and 1 plate.

Paschke, S.S., Kimball, B.A., and Runkel, R.L., 2005, 

Quantification and simulation of metal loading to the 

upper Animas River, Eureka to Silverton, San Juan County, 

Colorado, September 1997 and August 1998: U.S. Geological Survey Scientific Investigations Report 2005–5054, 

82 p.

Runkel, R.L., 1998, One-dimensional transport with inflow 

and storage (OTIS)—A solute transport model for streams 

and rivers: U.S. Geological Survey Water-Resources 

Investigations Report 98–4018, 73 p. At URL http://

co.water.usgs.gov/otis.

Runkel, R.L., and Kimball, B.A., 2002, Evaluating remedial 

alternatives for an acid mine drainage stream—Application 

of a reactive transport model: Environmental Science and 

Technology, v. 36, p. 1093–1101.

Saunders, J.A., Lee, M.K., Whitmer, J.M., and Thomas, 

R.C., 2001, In situ bioremediation of metals-contaminated 

groundwater using sulfate-reducing bacteria—A case history, in Leeson, A., Peyton, B.M., Means, J.L., and Magar, 

V.S., eds., Bioremediation of inorganic compounds, v. 6, 

no. 9: Battelle Press, p. 105–112.

Walton-Day, Katherine, Runkel, R.L., Kimball, B.A., and 

Bencala, K.E., 1999, Application of the solute-transport 

models OTIS and OTEQ and implications for remediation 

in a watershed affected by acid mine drainage, Cement 

Creek, Animas River Basin, Colorado, in Morganwalp, 

D.W., and Buxton, H.T., eds., U.S. Geological Survey 

Toxic Substances Hydrology Program—Proceedings of the 

technical meeting, Charleston, South Carolina, March 8–12, 

1999—Volume 1 of 3, Contamination from hardrock mining: U.S. Geological Survey Water-Resources Investigations 

Report 99–4018A, p. 37–46.

Wirt, Laurie, Leib, K.J., Melick, Roger, and Bove, D.J., 2001, 

Metal loading assessment of a small mountainous sub-basin 

characterized by acid drainage—Prospect Gulch, Upper 

Animas River watershed, Colorado: U.S. Geological Survey 

Open-File Report 01–0258, online version 1.1, 36 p. URL 

http://pubs.usgs.gov/of/2001/ofr-01-0258/.

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R.M., and Kimball, B.A., 1988, Practical aspects of tracer 

experiments in acidic, metal enriched streams, in Mallard, 

G.E., ed., U.S. Geological Survey Toxic Substances Hydrology Program—Surface-Water Contamination—Proceedings 

of the technical meeting, Denver, Colo., February 2–4, 

1987: U.S. Geological Survey Open-File Report 87–764, 

p. 125–130.

1024 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1025

Calibration of the solute transport model OTIS involved 

three steps:

• Determination of physical parameters

• Model calibration for conservative solutes

• Model calibration for reactive solutes.

The preceding text provided a general overview of how to 

accomplish these steps. This appendix provides step-by-step 

details of model construction and calibration for the interested 

reader.

Determination of Physical Parameters

Steps 1a through 1e (fig. 26) were used to determine 

physical parameters (dispersion (D) and stream cross-sectional 

area (A)) for OTIS unless otherwise noted in the chapter. First, 

the average concentration value of samples collected during 

the plateau period (fig. 2) was calculated to represent the plateau concentrations (step 1a, fig. 26). Streamflow (Q, step 1b, 

fig. 26) at each transport site was calculated by dividing the 

constant and known mass flux of the injected tracer by the 

plateau concentration of the tracer at a transport site (Kimball 

and others, this volume). Arrival time (t

50) at a site (step 1c, 

fig. 26) is defined as the time at which the instream tracer 

concentration reaches one-half of the plateau concentration 

(C50) (Zellweger and others, 1988). Travel time (t

t

) between 

two sites is simply the difference in arrival times between 

two transport sites (fig. 2). Stream cross-sectional area (A) 

(step 1d, fig. 26) was initially calculated from:

A streamflow

velocity ----------------------------- Qi Qi–1   + /2

xi xi–1   – /t

t

= = --------------------------------- (7)

where

 Qi and Qi–1 are streamfl ow (m3

/s) between two transport 

sites

and

xi

 and xi–1 are the distances (m) of the transport sites 

downstream from an arbitrary stream 

reference point.

This initial estimate of stream cross-sectional area was 

later refined using OTIS-P (step 1e, fig. 26). The remaining 

parameter (dispersion (D)) needed to simulate ambient conditions at each transport site was adjusted to obtain a best-fit of 

simulated conditions to the data using the model OTIS-P in 

transient mode (step 1e, fig. 26). Once the physical parameters 

were determined, the next step was to calibrate the model in 

hydrological steady-state (where streamflow and constituent 

concentration are constant at each site through time) for each 

constituent of interest using data from the synoptic sampling 

(steps 2a–3f, fig. 26). There were two types of calibration: one 

for conservative constituents (steps 2a–2g, fig. 26), and one for 

reactive constituents (steps 2a–3f, fig. 26).

Model Calibration for Conservative Solutes

The first step in model calibration for conservative solutes 

was to use OTIS to simulate the tracer concentration to verify 

that the simulation matched the data and that lateral-inflow values (qL

) were correct. To accomplish this task (step 2a, fig. 26), 

the streamflow was calculated for each stream site using the 

tracer concentrations measured in the synoptic sample collected at each site. This process can be somewhat involved, 

and is described in detail in Kimball and others (this volume). 

But, briefly, streamflow at each site was calculated by dividing the constant and known mass flux of the injected tracer by 

the concentration of tracer in the synoptic sample, after first 

adjusting for ambient concentration of the tracer. Next (step 2b, 

fig. 26), the study reach was divided into subreaches (known as 

model reaches) chosen on the basis of knowledge of the major 

physical and chemical inflows to the stream derived from the 

loading calculations (Kimball and others, this volume). Model 

reaches were chosen to bracket the major surface- and groundwater inflows to the stream, including locations of mine drainage such as inflow from adits or tributaries. An effort was made 

to minimize the total number of model reaches to expedite 

parameter estimation and model computation times but still 

have a sufficient number of model reaches to accommodate 

the variations in constituent profiles. Lateral inflow (qL

) was 

then calculated for each model reach (step 2c, fig. 26). Lateral 

inflow (qL

) has units of m2/s, and was calculated as the difference in flow between the downstream ends of adjacent model 

reaches divided by the distance between the downstream ends 

of the model reaches. Lateral inflow (qL

) does not have units of 

volume per time as expected for a variable that relates to flow, 

but rather units of area per time because the inflow is distributed along the length of a model reach.

Next, preliminary estimates of lateral-inflow concentrations (CL

) were made for each model reach (step 2d, fig. 26). 

For some tracers (such as lithium and bromide), these concentrations typically were negligible. For other tracers (such as 

sodium and chloride) and other constituents, several methods 

were used to assign lateral-inflow concentrations (CL). If there 

was no gain in flow or load along a model reach, the lateralinflow concentration (CL

) and the lateral inflow (qL) used in 

the model were zero. For model reaches that contained inflow, 

three potential situations exist: (1) No visible inflows were 

observed or no inflow samples were collected. (2) The model 

reach contained one sampled inflow. (3) The model reach contained several sampled inflows. In case (1), inflow concentrations from an adjacent model reach can be used, or an effective 

inflow concentration, that was calculated from the change in 

load observed along the model reach, was used (equation 2). 

Effective inflow concentration may actually be lower than an 

actual inflow concentration because the calculated effective 

inflow concentration incorporates reactions that may have 

occurred between the two sites and removed solute from the 

water column.

Appendix. Calibration of the Solute-Transport Model OTIS
1a.Define plateau concentrations

at transport sites

1b. Calculate Q at each

transport site

1c. Calculate arrival time (t50) at 

each transport site

1d. Calculate stream cross-sectional

area (A) for each transport site

1e. OTIS-P in transient mode 

to estimate A and D

2a. Calculate Q for each stream site

2b. Divide study reach into model reaches

for

OTIS steady-state simulations

2c. Calculate qL for each model reach

2d. Estimate CL for constituent in each

model reach 

2e. OTIS steady-state, conservative

2f. Do data match simulation?

Yes, go to No

step 4 

2g. Have CL values been calibrated?

No, go to Yes

Step 2d 

3a. Estimate using

OTIS-P

3b. Do data match simulations?

No, go to Yes

step 2d 

3c. Do values make geochemical

sense?

Yes, go to No 

step 4 

3d. Adjust values

3e. OTIS steady-state with values

3f. Do data match simulations and

do values make geochemical

sense?

Yes No, go to 

 Step 3d 

4. OTIS steady-state

 with remediation

1026 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
OTIS and Remediation in Cement Creek and Upper Animas River 1027

In case (2), the chemistry of the sampled inflow was used 

as a first estimate for lateral-inflow concentration (CL). In case 

(3), the flow-weighted average value of inflow concentrations 

or the value from one of the inflows judged to be most representative of the inflows in that model reach was used as a first 

estimate for lateral-inflow concentration (CL

).

After the model is constructed and constituent profiles 

produced by the model are examined (step 2e, fig. 26), first 

estimates of lateral-inflow concentration (CL

) may not produce 

a simulation that fits the measured data (answer to step 2f is 

“no”). At this point two potential actions exist: lateral-inflow 

concentrations (CL

) either have or have not been calibrated. If 

lateral-inflow concentrations (CL

) have been calibrated, then 

move to step 3a (fig. 26) to begin estimation of first-order 

removal coefficients (λ). If lateral-inflow concentrations have 

not been calibrated, then calibration proceeds as follows. 

Calibration of lateral-inflow concentrations (CL

) uses an iterative approach (steps 2d–2g, fig. 26) wherein representative 

concentrations are used as the initial values for lateral-inflow 

concentrations (CL). Starting from the upstream end of the 

study section and working in a downstream direction, if the 

change in load within a model reach is underestimated using 

the representative concentration, then effective inflow concentration (equation 2) is used to attempt to increase the change 

in load. In some cases, a value less than the effective inflow 

concentration was used if the effective inflow concentration 

seemed much greater than sampled inflow concentrations. 

This procedure was continued for each model reach working 

in a downstream direction until the simulation either generally 

fit the observed data (conservative solute) or yielded greater 

concentrations than the observed data (reactive solute) at 

which point the lateral-inflow concentrations (CL

) have been 

calibrated. Then, in the case of a reactive solute, the answer to 

step 2f (fig. 26) is “no” (the simulation does not fit the data), 

but the answer to step 2g (fig. 26) is “yes” (lateral-inflow 

concentrations (CL

) have been calibrated), and the next step 

is to estimate first-order removal coefficients (λ).

Model Calibration for Reactive Solutes

Calibration of the model for reactive solutes requires 

all of the previous steps noted for conservative solutes 

(steps 2a–2g, fig. 26). The distinction between conservative 

and reactive solutes becomes important during calibration of 

lateral-inflow concentration (CL

). For a conservative solute, 

when appropriate lateral-inflow concentrations (CL

) have been 

chosen, the simulation matches the data. For a reactive solute, 

the simulation will overestimate the data. To use lateral-inflow 

concentrations (CL

) that cause the simulations to overestimate 

the data seems somewhat counterintuitive. However, during 

the iterative process used in steps 2d–2g (fig. 26), it sometimes 

becomes apparent that the elevated lateral-inflow concentrations (CL

) needed to match large, incremental increases in 

stream concentration in some model reaches must be offset 

by removal in downstream model reaches where all possible 

choices for lateral-inflow concentrations (CL

) (sometimes 

including CL

 = 0) result in simulations that overestimate the 

observed data.

First-order removal coefficients (λ) are used to offset 

the large concentration increases in certain model reaches. 

First-order removal coefficients (λ) are estimated using 

OTIS-P (step 3a, fig. 26) (Runkel, 1998). Simulations resulting from initial estimates of first-order removal coefficients 

(λ) were compared to the data. Generally, good agreement 

between simulations and data was quickly obtained. However, 

an examination of the distribution of first-order removal coefficients (λ) is instructive in light of the general geochemistry 

of the stream and coincident behavior of other solutes. For 

example, if first-order removal coefficients (λ) are large in 

one model reach for zinc only, and pH values are not sufficiently high to indicate removal of zinc by formation of zinc 

minerals, then a likely mechanism for zinc removal is sorption to or coprecipitation with iron or manganese minerals. 

However, if there is no first-order removal coefficient (λ) for 

iron or manganese in that model reach, then the removal of 

zinc may need to be transferred to an adjacent or nearby model 

reach where iron or manganese removal is occurring. Therefore, if the estimated first-order removal coefficients (λ) do 

not relate to the geochemistry of the stream, then an iterative 

approach is recommended for determining first-order removal 

coefficients (λ) whereby the values are adjusted model reach 

by model reach moving in a downstream direction (fig. 26, 

steps 3b–3f). Generally, first-order removal coefficients (λ) for 

iron and (or) manganese are estimated first. Then solutes that 

might sorb to or coprecipitate with iron or manganese, such 

as copper or zinc, are estimated so that the resulting model 

simulates their removal in reaches having favorable geochemical conditions (that is, coincident removal of either iron or 

manganese).

Physical Parameters for OTIS Models

In the upper Cement Creek study reach, a dispersion 

coefficient of 0.6 was used for the simulations. Values for 

stream cross-sectional area (A) were calculated from the timevariant tracer data collected at sites T1 (501 m), T2 (2,885 m), 

and T3 (3,844 m) as previously described (fig. 2). The change 

in cross-sectional area between T1 and T2 was large. Therefore, cross-sectional area for model reaches between the two 

sites was calculated dividing the average velocity of T1 and T2 

into the streamflow value determined using the tracer for the 

site at the end of each model reach. The cross-sectional area 

for T3 was used for all modeled reaches between T2 and T3, 

and downstream from T3. Values of A, D, and qL

 are shown 

in table 14. Physical parameters used for the OTIS models for 

lower Cement Creek are in table 15.

Figure 26 (facing page). Actions necessary to calibrate OTIS 

solute transport model. Q, streamflow; D, dispersion coefficient; 

qL

, lateral inflow; CL, lateral-inflow concentration; λ, first-order 

removal coefficient. Oval shapes indicate actions or calculations. 

Diamond shapes indicate execution of the OTIS or OTIS-P model. 

Rectangular shapes indicate decision points.
Table 14. Model reaches, reach lengths, and hydraulic parameters for upper Cement Creek OTIS simulations.

[m, meters; m2, square meters; m2/s, square meters per second; m3/s-m, cubic meters per second per meter]

Model reach

number

Distance from

injection site

(m)

Model reach

length

(m)

Dispersion

coefficient (D)

(m2

/s)

Stream cross-sectional 

area (A)

(m2)

Lateral inflow (qL

)

(m3

/s-m)

1 41 41 0.6 0.195 0.00

2 259 218 0.6 0.195 3.25×10–5

3 345 86 0.6 0.195 2.24×10–4

4 827 482 0.6 0.198 6.91×10–6

5 927 100 0.6 0.199 2.99×10–6

6 1,142 215 0.6 0.212 1.17×10–5

7 1,292 150 0.6 0.231 2.46×10–5

8 1,315 23 0.6 0.27 3.31×10–4

9 1,367 52 0.6 0.314 1.64×10–4

10 2,885 1,518 0.6 0.437 1.69×10–5

11 2,976 91 0.6 0.455 2.21×10–5

12 3,107 131 0.6 0.455 5.15×10–5

13 3,844 737 0.6 0.455 7.78×10–6

14 3,931 87 0.6 0.455 5.24×10–4

15 4,200 269 0.6 0.455 5.67×10–4

Table 15. Model reaches, reach lengths, and hydraulic parameters for lower Cement Creek OTIS simulations.

[m, meters; m2, square meters; m2/s, square meters per second; m3/s-m, cubic meters per second per meter]

Model reach

number

Distance from 

arbitrary datum

(m)

Model reach

length

(m)

Dispersion

coefficient (D) 

(m2

/s)

Stream crosssectional area (A)

(m2)

Lateral inflow (qL

)

(m3

/s-m)

1 1,050 58 1.0 0.212 0.00

2 1,106 56 1.0 0.301 2.63×10–3

3 2,425 1,319 1.0 0.620 1.11×10–5

4 2,785 360 1.0 0.620 7.28×10–5

5 2,970 185 1.0 0.620 5.23×10–5

6 3,125 155 1.0 0.620 1.27×10–4

7 3,317 192 1.0 0.620 1.07×10–4

8 3,655 338 1.0 0.620 3.43×10–5

9 3,893 238 1.0 0.827 8.14×10–5

10 4,260 367 1.0 0.827 1.92×10–5

11 4,483 223 1.0 0.827 0.00

12 4,586 103 1.0 0.827 1.47×10–4

13 5,050 464 1.0 0.827 6.55×10–5

14 5,215 165 1.0 0.827 2.92×10–5

15 5,365 150 1.0 0.827 1.02×10–4

16 5,652 287 1.0 0.827 9.93×10–5

17 5,767 115 1.0 0.827 1.68×10–4

18 5,897 130 1.0 1.051 1.07×10–4

19 5,957 60 1.0 1.051 3.78×10–4

20 6,447 490 1.0 1.051 5.13×10–5

21 6,907 460 1.0 1.051 6.09×10–5

22 7,131 224 1.0 1.051 2.33×10–4

23 7,331 200 1.0 1.051 4.26×10–5

24 7,501 170 1.0 1.051 3.67×10–5

25 7,698 197 1.0 1.277 6.55×10–5

26 9,060 1,362 1.0 1.277 3.11×10–5

27 9,360 300 1.0 1.277 4.00×10–5

28 9,905 545 1.0 1.277 6.79×10–5

29 10,130 225 1.0 1.277 0.00

30 11,743 1,613 1.0 1.277 1.26×10–5

1028 Environmental Effects of Historical Mining, Animas River Watershed, Colorado