Multisite Assessment of Hydrologic Processes inSnow-Dominated Mountainous River Basins in

Colorado Using a Watershed ModelCaleb Foy1; Mazdak Arabi2; Haw Yen, Aff.M.ASCE3; Jorge Gironás, A.M.ASCE4;

and Ryan T. Bailey, A.M.ASCE5

Abstract: Hydrologic fluxes in mountainous watersheds are particularly important as these areas often provide a significant source offreshwater for more arid surrounding lowlands. The state of Colorado in the United States comprises a principal snow catchment area, withall major headwater river basins in Colorado providing substantial water flows to surrounding western and midwestern states. The ability torepresent and quantify hydrologic processes controlling the generation and movement of water in headwater basins of Colorado thereforehas significant implications for effective management of water resources in the western United States under varying climatic and land-useconditions. In the research reported in this paper, hydrologic modeling was applied to four snow-dominated, mountainous basins of Colorado[i.e., the river basins of (1) Cache la Poudre, (2) Gunnison, (3) San Juan, and (4) Yampa] to evaluate the relevance of specific hydrologiccomponents (i.e., evapotranspiration, snow processes, groundwater processes, surface runoff, and so on) in the complex, high-elevationwatersheds. The soil and water assessment tool (SWAT) model was calibrated and tested for multiple river locations within each basin usingmonthly naturalized flows over the 1990–2005 period. The model was able to adequately simulate streamflows at all locations within the fourbasins. Monthly patterns of precipitation, snowfall, evapotranspiration (ET), and total water yield were similar for all the basins, whilesubsurface lateral flow was the dominant hydrologic pathway, contributing between 64 and 82% to gross basin water yields on an averageannual basis. Overall, results indicated the strong influence of snowmelt and groundwater processes on amounts and timing of streamflows inthe study basins. Hence, enhanced representation of these processes may be essential to improve hydrological estimation using computersoftware in snowmelt-driven mountainous basins. In particular, examination of monthly streamflow residuals indicated that the normalityand independence of model residuals, which are often assumed in parameter estimation and uncertainty analysis, were not always satisfied.DOI: 10.1061/(ASCE)HE.1943-5584.0001130. © 2015 American Society of Civil Engineers.

Author keywords: Watershed modeling; Hydrological processes; Mountainous watersheds; Snow processes; Soil and water assessmenttool (SWAT).

Introduction

Hydrologic fluxes in mountainous watersheds, often headwaterbasins that exhibit a steep gradient in elevation, are particularly

important as these areas typically provide a significant source offreshwater for more arid surrounding lowlands. For example, inthe mountainous regions of western North America, where 50–70% of the precipitation may fall in the form of snow (Serreze et al.1999), the seasonal snowmelt of the spring and early summer mayaccount for 50–80% of the total annual runoff (Stewart et al. 2004).The state of Colorado comprises a principal snow catchmentarea, with all major headwater river basins in Colorado providingsubstantial water flows to surrounding western and midwesternstates of the United States, including Arizona, California, Kansas,Nebraska, Nevada, New Mexico, Oklahoma, Texas, Utah, andWyoming. Understanding and quantifying the hydrologic proc-esses that control generation and movement of water in headwatercatchments of Colorado therefore has significant implications formanagement of scarce water resources in the western United States.The comprehension and ability to represent such processes is vitalfor the current and future effective management of water resources.

Hydrologic models have increasingly been recognized as impor-tant tools for improving understanding of the processes involvedin generation of freshwater resources, as well as prediction of po-tential impacts on these resources from changing climate and landuse (Praskievicz and Chang 2009). Many models have been devel-oped in recent decades for such purposes [e.g., Leavesley et al.1983; Bicknell et al. 1996; Arnold et al. 1998; Ewen et al.2000; Danish Hydraulic Institute (DHI) 2004], capable of simulat-ing a multitude of hydrologic processes in watersheds. However,

1Water Resources Engineer, Colorado Division of Water Resources,1313 Sherman St., Room 818, Denver, CO 80203.

2Associate Professor, Dept. of Civil and Environmental Engineering,Colorado State Univ., Fort Collins, CO 80523 (corresponding author).E-mail: Mazdak.Arabi@ColoState.Edu

3Research Associate, Blackland Research & Extension Center, TexasA&M AgriLife Research, 720 East Blackland Rd., Temple, TX 76502;and Postdoctoral Researcher, Grassland, Soil & Water ResearchLaboratory, USDA-ARS, 808 East Blackland Rd., Temple, TX 76502.

4Associate Professor, Departamento de Ingeniería Hidráulica y Am-biental, Pontificia Universidad Catolica de Chile, Av. Vicuna Mackenna4860, Santiago, Chile; Centro Nacional de Investigacion para la GestionIntegrada de Desastres Naturales CONICYT/FONDAP/15110017, Aveni-da Vicuna Mackenna, Santiago, Chile; and Centro Interdisciplinario deCambio Global, Pontificia Universidad Catolica de Chile, Avenida VicunaMackenna 4860, Santiago, Chile.

5Assistant Professor, Dept. of Civil and Environmental Engineering,Colorado State Univ., Fort Collins, CO 80523.

Note. This manuscript was submitted on August 20, 2013; approved onOctober 21, 2014; published online on February 17, 2015. Discussion per-iod open until July 17, 2015; separate discussions must be submitted forindividual papers. This paper is part of the Journal of Hydrologic Engi-neering, © ASCE, ISSN 1084-0699/04015017(12)/$25.00.

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representing and simulating hydrological processes accurately inmountainous watersheds is often inhibited by scarcity and inad-equate distribution of high-elevation meteorological stations(Stonefelt et al. 2000), poor resolution of available climatic data(Marks et al. 1992), the need to accurately represent orographiceffects on precipitation and temperature (Hjermstad 1970; Barryand Chorley 1976), and the occurrence of snow accumulationand snowmelt processes (Luce et al. 1998). In addition, in areaswhere storage and redistribution of snowmelt-driven flows areimportant (e.g., the headwater basins of Colorado), modelingefforts may be complicated by the intricate system of reservoirs,diversions, transfers, and other artificial structures.

Studies involving the development, calibration, and testing ofnumerical models to simulate hydrologic processes in mountainousbasins have been conducted in regions worldwide. Hartman et al.(1999) applied a version of the Regional Hydroecologic SimulationSystem (RHESS) to the Loch Vale watershed, a 6.6-km2 basin lo-cated in Rocky Mountain National Park, Colorado, to investigatesnow distribution; Anderton et al. (2002) used Systeme Hydrolo-gique Europeen-TRANsport (SHETRAN) to study subsurface flowin a small 0.56-km2 catchment in the foothills of the Pyrenees innortheastern Spain; Krecek et al. (2006) investigated water balanceand runoff processes in four small alpine catchments (from 0.023to 1.1 km2) in Slovakia and Poland; and numerous studies havebeen conducted across the United States using the soil and waterassessment tool (SWAT; Arnold et al. 1998), including in the UpperWind River basin, Wyoming (Fontaine et al. 2002; Stonefelt et al.2000); Cannonsville Reservoir Watershed, New York (Tolson andShoemaker 2007); Blue River Watershed, Colorado (Lemonds andMcCray 2007); Tenderfoot Creek basin, Montana (Ahl et al. 2008);Dry Creek Experimental Watershed, Idaho (Stratton et al. 2009);and Reynolds Creek Experimental Watershed, Idaho (Sridharand Nayak 2010). However, to the knowledge of the writers nostudy has investigated hydrologic processes in the major headwaterbasins in Colorado at the basin scale. Past studies in the region haveapplied SWAT to only one watershed and focused solely on theperformance of the model at a single site (watershed outlet).

This paper is aimed at identifying critical hydrologic processesthat control the generation of streamflow and movement of water atvarying spatial scales within four snow-dominated, mountainouswatersheds of Colorado [i.e., the river basins of the (1) Cache laPoudre, (2) Gunnison, (3) San Juan, and (4) Yampa]. To this end,the following objectives are defined: (1) exploratory analysis of aprocess-based watershed model applied to major snow-dominatedriver watersheds, through incorporation of detailed watershed char-acteristics and necessary modifications for mountainous basins;and (2) use the corresponding calibrated models to evaluate the rel-evance of specific hydrologic components [e.g., evapotranspiration(ET), snow processes, groundwater processes, and so on] occurringin the complex, high-elevation watersheds of Colorado.

Materials and Methods

The comprehensive watershed model SWAT was calibrated andtested for mountainous watersheds in the headwaters of Coloradoto assess and quantify the dominant hydrologic processes occurringat the watershed-scale. Four watersheds were included to accountfor a diversity of landscapes, land uses, soils, and climatic condi-tions. The watersheds, watershed data, model setup, and calibrationand testing procedures are described in this section.

Study Watersheds

Four headwater river basins in Colorado, United States, wereconsidered, including the river basins of (1) Cache la Poudre,(2) Gunnison, (3) San Juan, and (4) Yampa (Fig. 1). Drainage areasof these basins are 2,732, 10,284, 8,375, and 8,760 km2, respec-tively. River basins 2–4 are located on the western side of thecontinental divide, making them tributaries of the Upper ColoradoRiver, while River basin 1 is located on the eastern side of thecontinental divide and is a tributary of the South Platte River, even-tually flowing into the Missouri River. River basins 1–4 exhibit awide variety of characteristics related to geology, climate, and land

Fig. 1. Location of the basins of the research reported in this paper, and associated USGS surface water gauges and meteorological stations (SNOTELand NCDC) considered during analysis

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cover. Such variability is mainly attributable to complex terrainand high relief of elevation within each of the basins.

The Cache la Poudre River basin spans two states [(1) Colorado,and (2) Wyoming; Fig. 1], located primarily in northern Coloradowith a small portion extending into southeastern Wyoming, andflows into the South Platte River. The Gunnison River basin restson the west side of the Continental Divide, located centrally in thestate of Colorado, and flows to the Colorado River. The San JuanRiver basin flanks the west side of the Continental Divide, locatedprimarily in southwestern Colorado with a sizable portion locatedin northwestern New Mexico, and flows to the Colorado River.The Yampa River basin sits on the west side of the ContinentalDivide in the northwest corner of Colorado and parts of southernWyoming, and flows to the Green River, which feeds the ColoradoRiver. For this paper, the basin outlets are defined at the mouth ofthe Cache la Poudre Canyon (USGS Gage 06752000); just up-stream of Black Canyon of Gunnison National Park (USGS Gage09128000); near Archuleta, New Mexico (USGS Gage 09355500);and near the town of Maybell, Colorado (USGS Gage 09251000),for the Cache la Poudre, Gunnison, San Juan, and Yampa Riverbasins, respectively.

Monthly naturalized streamflows at the outlet of the watershedsand sites within the watersheds (Fig. 1) were obtained fromColorado’s Decision Support Systems (Colorado Water Con-servation Board 2010) for the Gunnison (January 1990 throughDecember 2005), San Juan (January 1990 through December2005), and Yampa (January 1990 through December 2004) Riverbasins. Naturalized flow data for the Cache la Poudre River basin(January 1990 through December 2005) were obtained from thenorthern Colorado Water Conservancy District (NorthernWater 2009).

Climate

Daily precipitation (P), maximum temperature (Tmax), and mini-mum temperature (Tmin) from stations in and around the watershedsof the research reported in this paper were obtained from theNational Climatic Data Center (NCDC) archives (NCDC 2009)and the NRCS Snowpack Telemetry (SNOTEL) Data Network(NRCS 2009; Fig. 1). Thirty-one meteorological stations (includ-ing six, nine, six, and 10 stations for the Cache le Poudre, Yampa,Gunnison, and San Juan River basins, respectively) were selectedbased on proximity to the watersheds, the type of data provided,and the length and completeness of the record (Table 1).

The substantial differences in elevation within the basins(Table 1) yield high variability in the amount and form of precipi-tation and overall topographically driven climate. For example, fortwo stations in the Cache la Poudre River basin [i.e., the (1) Dead-man Hill site, and (2) Rustic site], separated by a distance of ap-proximately 12 km and a 768-m difference in elevation, averageannual precipitation over the 1998–2004 period were 721 and308 mm, respectively. For the same period, the average annual

temperature at Deadman Hill and Rustic were 0.3 and 6°C respec-tively. Basin-averaged annual precipitation amounts of 476, 543,and 596 mm were computed for the Cache la Poudre, Gunnisonand San Juan River basins for 1990–2005, whereas 657 mm werecomputed for the Yampa River basin (1990–2004). The lowest an-nual precipitation occurring in the Cache la Poudre watershed isdue to its location on the eastern side of the Continental Divide.

Lapse rates typically are used to represent influential effects oftemperature and precipitation across different elevations withinmountainous regions. Due to the limited number of meteorologicalstations and the scarcity of data, single values for surface temper-ature and precipitation lapse rates were calculated (Table 1). Lapserates were calculated from mean annual values averaged over aperiod of at least 5 years for precipitation and temperature, of whichthe period was chosen based on the most complete and reliable setof continuous years on record. All meteorological stations (Fig. 1)were utilized for the calculations with the exception of the RedFeather Lakes Station in the Cache la Poudre basin, due to lackof contemporary data. Precipitation and temperature lapse rates var-ied from 353.9 mm=km and −2.7°C=km in the Gunnison Riverbasin, to 658.4 mm=km and −5.5°C=km in the Cache la PoudreRiver basin. These values are comparable to those determinedby Fontaine et al. (2002) in the Upper Wind River basin inWyoming, Lemonds and McCray (2007) in the Blue River water-shed (Colorado), and Ahl et al. (2008) in the Rocky Mountainsin Montana. The lapse rates are also similar to those reported bySanadyha et al. (2014) for the same basins between 1979 and 1998,except for the Gunisson River basin, whose outlet is located furtherdownstream. Thus, the different time period for the computationand the use of more information from lower regions when comput-ing the lapse rates can explain this difference.

Land Cover and Soil

The land use/land cover attributes for each watershed (Fig. 2) aredescribed using National Land Cover Data (NLCD) from 1992(NLCD1992; Vogelmann et al. 2001) and 2001 (NLCD2001;Homer et al. 2007). NLCD1992 was used for model simulationsduring the years 1990–1997 (calibration), while NLCD2001 wasused for model simulations during the years 1998 to present (test-ing). A thorough description of the types of land use/land coverpresent in each of the NLCD datasets are available elsewhere(Homer et al. 2004; Vogelman et al. 2001). From the analysis ofthe data, it is observed that at the highest elevations, land coverconsists mainly of alpine tundra. Below tree line at moderate ele-vations, land cover consists mainly of subalpine coniferous forestsand deciduous forests. At the lowest elevations, land cover consistsof shrub and scrublands, herbaceous grasslands, and pasture/hay.

The distribution of soil within each basin (Fig. 2) is describedusing a combination of the state soil geographic (i.e., STATSGO2)and the soil survey geographic (i.e., SSURGO) databases. Becauseof the more detailed inventory of soils in the database, theSSURGO soils were utilized wherever possible. AvailableSSURGO data encompassed 90.6, 76.6, 55.0, and 69.5% of theCache la Poudre, Gunnison, San Juan, and Yampa watersheds, re-spectively. A preprocessing tool developed by Sheshukov et al.(2009) was used to represent SSURGO soils in the watershedmodel. Thus, it was possible to differentiate 164 soil types inthe Cache la Poudre watershed, 248 in the Gunnison watershed,348 in the San Juan watershed, and 317 in the Yampa Watershed.In general, the soils in the watersheds have very low to moderateinfiltration rates, mainly due to the high content of clay, accordingto the soil classification proposed by the USDA Natural ResourcesConservation Service (NRCS).

Table 1. Temperature and Precipitation Lapse Rates RepresentingOrographic Effects, and R2 Values Associated with Each Fit

Study watershed

Precipitationlapse rate

ðmm=kmÞ=R2

Temperaturelapse rate

ð°C=kmÞ=R2

Periodof

recordElevation

(m)

Cache la Poudre 658.4=0.87 −5.5=0.93 1998–2004 1,594–4,132Gunnison 353.9=0.58 −2.7=0.57 1996–2001 1,982–4,359San Juan 434.9=0.79 −5.4=0.83 2000–2004 1,724–4,280Yampa 497.8=0.62 −3.4=0.78 2002–2007 1,804–3,764

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Hydrologic Model

Watershed Model

The SWAT is a comprehensive process-based watershed model thatsimulates water and chemical transport at a catchment scale in acontinuous mode. The model is semidistributed and uses readilyavailable input data (Neitsch et al. 2005). Physically based equa-tions are used in the model to represent hydrologic processes andsimulations are based on transient water balances. The SWAT sim-ulates important hydrologic processes such as surface runoff,groundwater return flow, percolation, evapotranspiration, snowaccumulation, snowmelt, and sediment movement. In the model,the watershed is divided into subbasins linked together by mainchannel segments. Each of the subbasins is an individual unit withunique climate and hydrologic properties that are further parti-tioned into hydrologic response units (HRUs) possessing uniquecombinations of land use, management, and soil attributes. TheSWAT lumps together identical HRUs within a subbasin into sin-gle, noninteracting units of response (Neitsch et al. 2005). Loadingsof water, sediment, nutrients, and so on are aggregated at the HRUlevel and transferred to the respective subbasin.

From the subbasin level, constituents are delivered to the mainchannel and then routed to the outlet of the watershed. In the re-search reported in this paper the Soil Conservation Service (SCS)curve number (USDA 1993) and the variable storage method wereadopted to simulate surface runoff and channel routing, respec-tively. For potential evapotranspiration, the Penman-Monteith(P-M) method is used, except for the Cache la Poudre watershed,wherein the Hargreaves ET method (Hargreaves and Samani 1985)yielded better results. For more details on the mathematical formu-lation of each process simulated in SWAT, the reader is referredto the model documentation (Neitsch et al. 2005). However, snowaccumulation, snowmelt processes, and baseflow (groundwater)processes are described in further detail due to their importancein snow-dominated river basins.

The SWAT represents several snow processes (i.e., snow accu-mulation, snow distribution/cover, and snowmelt) of high relevancein the watersheds of the research reported in this paper. Precipita-tion is simulated as snowfall when the mean daily air temperature is

less than the snowfall threshold temperature, and snowfall is storedon the ground in the form of a snowpack, which is described by itssnow water equivalent. The snowpack of a subbasin is rarely uni-form in distribution, and is affected by processes such as driftingand shading in complex topography, with the snow coveragedecline defined using an areal depletion curve. Variations in thesnowpack depth are simulated with a mass-balance equation, whichincorporates a snowmelt term, controlled by the air and snowpacktemperatures, the snow areal coverage, and the melting rate. Themelting rate is linearly related to the difference between the averagesnowpack-maximum temperature and the threshold temperature forsnowmelt. Melted snow then is treated as constant rainfall withinthe time step, with no associated energy available for erosion.

Groundwater discharge is the primary mechanism for the trans-fer of snowmelt runoff to mountain streams during the recessionlimb of hydrographs (e.g., Martinec 1975; Flerchinger et al. 1992;Ohte et al. 2004). In SWAT, soil water not removed by ET can be-come either subsurface lateral flow or recharge to the underlyingwater table. The SWAT simulates subsurface flow using a kin-ematic storage model, from which net daily discharge within eachHRU is generated. In large subbasins where not all subsurfacelateral flow will reach the main channel in 1 day, a portion of theflow is lagged for later release. Upward water movement from theshallow aquifer to the overlying unsaturated zone occurs whenthe overlying layer is dry.

Model Setup

Model input data was prepared and modified as described next.Orographic effects in the watersheds of the research reported in thispaper are taken into account through the definition of elevationbands (EBs; Fontaine et al. 2002). Each subbasin was topographi-cally discretized into a set of 10 EBs. For each band, the averageelevation and the percentage of the subbasin area within that bandwas specified. The elevation bands were created by dividing theoverall relief in each subbasin by 10 equal-interval bands. The tem-perature TEB and precipitation PEB of each elevation band in a sub-basin are computed as

TEB ¼ T þ ðZEB − ZÞ dTdZ

ð1Þ

(a) (b) (c) (d)

Fig. 2. Distribution of land cover and soil (as represented by Hydrologic Soil Groups A–D) in the following basins (land cover is computed fromNLCD2001): (a) Cache la Poudre; (b) Gunnison; (c) San Juan; (d) Yampa

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PEB ¼ Pþ ðZEB − ZÞ dPdZ

ð2Þ

where T and P = meteorological station temperature (°C) and pre-cipitation (mm), respectively; ZEB = elevation of the center of theelevation band (m); Z = elevation of the meteorological station (m);and dT=dZ and dP=dZ = temperature (°C=m) and precipitationlapse rates (mm=m; Table 1), respectively.

Watershed delineation for each of the four basins was performedusing a 30-m resolution digital elevation model (DEM) from theUSGS National Elevation Dataset (Gesch 2007; Gesch et al.2002). Basins were defined by their respective outlets, while sub-basins were defined by outlets created at stream junctions, of whichthe extent is defined by a threshold area for stream initiation. Thenumber and spatial distribution of HRUs in subbasins within eachbasin was determined by unique combinations of soil and land use.Thresholds of 10 and 5% were applied to land use and soils, re-spectively, which limited the number of HRUs without oversimpli-fying the complex distribution of soil and land use within thewatersheds. Thus, all land uses that cover less than 10% of a sub-basin are neglected, of which the area is reapportioned to all re-maining land uses. Similarly, all soils covering less than 5% ofa given land use also are eliminated. Overall, threshold levels avoiddistribution of very small HRUs with little effect on model simu-lations. Table 2 summarizes the number of subbasins, land use andsoil types, and HRUs in each watershed.

Previous applications of SWAT in Pennsylvania (Peterson andHamlett 1998) and in upstate New York (Tolson and Shoemaker2004, 2007) have shown that the model does not always sim-ulate streamflow accurately in basins with freezing temper-atures wherein snow accumulation and snowmelt processesdominate. Thus, the following modifications were incorporatedinto the SWAT model source code as described in Tolson andShoemaker (2004):• Algorithms for partitioning water into percolate and subsurface

lateral flow were modified to allow the simulation of subsurfacelateral flow in frozen soils,

• Amendments were made so that average monthly Tmax andTmin values of a subbasin were adjusted using temperature lapserate and difference (in elevation between the subbasin and themeteorological station) with available data [this allows the oro-graphic effects to be considered by the internal weather genera-tor (WXGEN; Sharpley and Williams 1990) in adjustingtemperature values], and

• Amendments were made to allow both snowfall and snowmeltto occur on the same day under the EB formulation.

Model Calibration and Testing

Parameter Estimation

For each watershed, calibration to monthly naturalized flow wasperformed for a period of 8 years (January 1, 1990, throughDecember 31, 1997) after a 2-year warmup period (January 1,

1988, through December 31, 1989), implemented to adjust the ini-tial storage conditions of the watersheds. The calibration processwas performed for multiple sites within each of the watersheds(except for the Cache la Poudre basin, which only had naturalizedflow for a single site) based on minimization of the objective func-tion (fk) defined as

fk ¼ 1 − ðENSÞk ¼ 1 − 1

Mk

XMk

j¼1

� Pni¼1 ðQi;j − Qi;jÞ2P

ni¼1 ðQi;j −Qmean;jÞ2

�ð3Þ

where k = watershed index; and Mk = number of calibration siteswithin each watershed (Mk ¼ 1 in the Cache la Poudre watershedand Mk ¼ 3 for all other basins). ENS is the Nash-Sutcliffe effi-ciency coefficient. The calibration run that resulted in the lowestrelative error (RE; see the “Rating Model Performance” section),in addition to an ENS comparable to the best calibrated ENS,was chosen as the optimal parameter set. This ensured that the errorbetween simulated and observed streamflows was minimized whilestill maintaining a correlation comparable to the optimal objectivefunction.

A combination of automated and manual calibration methodswere used to achieve the optimal parameter set, with 30 SWATparameters targeted (Table 3) and allowed to be adjusted betweendefined ranges selected from the SWAT literature (Neitsch et al.2005). Several parameters (indicated in Table 3) have multiple val-ues in subbasins with more than one HRU and hence percentage-based variations were assigned to maintain spatial variability. Forthe automated calibration procedure, a combined approach usingboth the shuffled complex evolution of the University of Arizona(SCE-UA; Duan et al. 1992, 1993, 1994) and the Gibbs sampleralgorithm (GSA) methods is used to take advantage of their respec-tive strengths (Lin and Radcliffe 2006). Whereas SCE-UA is aglobal optimization algorithm, proven to be successful for SWATapplications (e.g., Eckhardt and Arnold 2001; Eckhardt et al. 2005;Pohlert et al. 2005; Zhang et al. 2007), GSA is a local optimizationalgorithm that can handle higher-dimenionsality parameter spacewithout slowing convergence. The combined approach has beenapplied previously (Lin and Radcliffe 2006). The SCE-UA methodis applied for the first 1,250 model runs, with results used as a start-ing point for an additional 1,250 model runs using the GSAmethod, with both methods considering the objective function[Eq. (3)]. Finally, if necessary, the obtained parameters values weremanually adjusted to ensure the best parameter set with physicalmeaning, according to the ranges defined in Table 3.

Model Testing

Testing of each model was performed by applying the set of param-eters resulting from the calibration period to each model for asubsequent number of years. In the Cache la Poudre, Gunnison,and San Juan River basins the model was tested for a period of8 years (January 1, 1998, through December 31, 2005), whereasdue to unavailability of naturalized streamflow for the latter halfof 2005, the Yampa River basin model was tested for a period of7 years (January 1, 1998, through December 31, 2004). For thetesting period, the models used NLCD2001 as an input for landuse/land cover rather than NLCD1992 and therefore required a2-year warm-up period (January 1, 1996, through December 31,1997) before testing.

Rating Model Performance

Five error statistics were used to assess the performance of SWATduring the calibration and testing periods through comparison ofobserved and simulated streamflows in each basin, as follows:

Table 2. Number of Subbasins, Types of Land Use and Soil, and HRUswithin Each of the Study Basins

Study watershedArea(km2) Subbasins

Land usetypes

Soiltypes HRUs

Cache la Poudre 2,732 33 6 72 350Gunnison 10,284 39 7 125 518San Juan 8,375 25 8 110 316Yampa 8,760 29 5 122 336

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(1) RE, (2) bias, (3) RMS error, (4) coefficient of correlation (R2),and (5) Nash-Sutcliffe efficiency coefficient (ENS). The RE mea-sures the goodness of fit between simulated and observed stream-flows, and is expressed as a percentage by

RE ¼P

ni¼1ðQi − QiÞP

ni¼1 Qi

· 100 ð4Þ

where Qi and Qi = observed and simulated streamflows, respec-tively, at month i; and n = number of months under analysis.The bais is a measure of whether or not simulated streamflows tendto overestimate (negative bias) or underestimate (positive bias)observed values, calculated as

bias ¼ 1

n

Xni¼1

ðQi − QiÞ ð5Þ

RMS error measures the error between simulated and observedstreamflows with respect to both magnitude and timing, and iscalculated as

RMSerror ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

n

Xni¼1

ðQi − QiÞ2s

ð6Þ

R2 measures the correlation between observed and simulatedstreamflows, and is defined as

R2 ¼ 1

n − 1

Xni¼1

�Qi −Qmean

sQ

��Qi − Qmean

sQ

�ð7Þ

where Qmean and Qmean = averages of the observed and simulateddischarges, respectively; and sQ and sQ = SDs of the observed andsimulated discharges, respectively. ENS measures how well the plotof observed versus simulated streamflows fits the 1:1 line. ENS iscalculated as (Nash and Sutcliffe 1970)

ENS ¼ 1 −P

ni¼1 ðQi − QiÞ2P

ni¼1 ðQi −QmeanÞ2

ð8Þ

The ENS measure ranges between −∞ and 1, with 1 corre-sponding to perfect agreement. Out of these five error statistics, REand ENS are considered the primary measures and are used to ratemodel performance. A review of published literature revealedperformance ratings of very good (ENS > 0.75, jREj < 10%), good(0.65 < ENS < 0.75, 10% < jREj < 15%), satisfactory (0.5 < ENS <0.65, 15% < jREj < 20%), and unsatisfactory (ENS < 0.5, jREj >25%) for monthly simulations of streamflow (Moriasi et al. 2007;Santhi et al. 2001).

Results and Discussion

Calibration and Testing Results

Table 4 presents the evolution of ENS values at increments of 500model evaluations using the combined SCE-UA/GSA method.Overall, high values (0.78–0.87) are obtained and convergenceis observed for the number of model runs adopted. The optimalparameter sets found for each basin of the research reported inthis paper during the calibration period result in simulations withnearly all satisfactory performance over the calibration and testing

Table 3. List of SWAT Calibration Parameters

Number Input parameter Range Definition Process

x1 ALPHA_BF 0–1 Base flow recession constant (days) Groundwaterx2 CANMX 0–10 Maximum canopy storage Runoffx3 CH_KI 0–300 Effective hydraulic conductivity of tributary channel (mm=h) Channel flowx4 CH_KII −0.01 to 500 Effective hydraulic conductivity of main channel (mm=h) Channel flowx5 CH_NI 0.008–0.3 Manning’s n for tributary channel Channel flowx6 CH_NII 0.01–0.3 Manning’s n for main channel Channel flowx7 CH_SIIa −0.05 to 0.05 Average channel slope along channel length (m=m) Channel flowx8 CN_Fa −0.15 to 0.15 Curve number Runoffx9 DEPIMP_BSN 0–6,000 Depth to impervious layer (mm) Groundwaterx10 EPCO 0.01–1 Plant evaporation compensation factor Evaporationx11 ESCO 0.01–1 Soil evaporation compensation factor Evaporationx12 GW_DELAY 0–500 Groundwater delay (days) Groundwaterx13 GW_REVAP 0.02–0.2 Groundwater Revap coefficient Groundwaterx14 GW_SPYLDa −0.5 to 1 Specific yield of the shallow aquifer (m=m) Groundwaterx15 GWHT 0–25 Initial groundwater height Groundwaterx16 GWQMN 0–5,000 Threshold water level in shallow aquifer at which base flow occurs (mm) Groundwaterx17 OV_N 0.01-0.3 Manning’s n for overland flow Runoffx18 RCHRG_DP 0–1 Groundwater recharge to deep aquifer, fractional value Groundwaterx19 REVEP_MN 0–500 Threshold water level in shallow aquifer at which revap occurs (mm) Groundwaterx20 SFTMP −5 to 5 Snowfall temperature (°C) Snow coverx21 SLOPEa −0.1 to 0.1 Slope of HRU (m/m) Geomorphologyx22 SMFMN 0–10 Melt factor on June 21 (mm=°C=day) Snowmeltx23 SMFMX 0–10 Melt factor on Dec 21(mm=°C=day) Snowmeltx24 SMTMP −5 to 5 Threshold temperature for snowmelt (°C) Snowmeltx25 SNO50COV 0–1 Fraction of snow volume represented by SNOCOVMX corresponding to

50% snow coverSnow cover

x26 SNOCOVMX 0–650 Minimum snow water content corresponding to 100% snow cover. Snow coverx27 SOL_AWCa −0.1 to 2 Available water capacity of the soil layer (mm=mm) Soil percolationx28 SOL_Ka −0.5 to 5 Soil conductivity (mm=h) Soil percolationx29 SURLAG 1–24 Surface runoff lag coefficient Runoffx30 TIMP 0.0–1.0 Snow temperature lag factor SnowmeltaThese parameters were varied as a percentage of their default values to maintain their relative spatial variability.

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periods. Fig. 3 displays the simulated and observed monthlystreamflows for the Cache la Poudre basin outlet, as well as averagemonthly simulated and observed streamflow for each of the10 gaging stations. Simulated and observed monthly streamflowsfor each stream gage, along with other summary statistics, arepresented in the Supplemental Data. Simulated versus observedmonthly streamflows for each of the watershed outlets is shownin Fig. 4.

Table 5 presents a summary of error statistics, with nearly allconsidered satisfactory. At the outlet of each watershed, ENS variesbetween 0.86 and 0.95 during the calibration period, and between0.70 and 0.90 during the testing period. Overall, results comparefavorably with those from other SWAT modeling studies in moun-tainous watersheds (Fontaine et al. 2002; Lemonds and McCray2007; Stratton et al. 2009; Sanadyha et al. 2014). Moreover, theyalso compare favorably with those reported by Debele et al. (2010)for mountainous basins in Montana and China; they used bothprocess-based and temperature-index snowmelt models in SWAT.Calibration and testing results from the research reported in thispaper are also consistent with the finding of Ahl et al. (2008),who concluded that SWAT is able to predict hydrologic proc-esses in snow-dominated mountain watersheds with performancelevels similar to those from the agriculture-dominated basins forwhich the model was originally designed. Finally, results were con-sistent with those reported by Rango and van Katwijk (1990)and Martinec et al. (2008), who used the Snowmelt-Runoff Model

Fig. 3. Observed versus simulated average monthly streamflows during the simulation period for each of the gage sites of the Cache la Poudre,Gunnison, San Juan, and Yampa watersheds; station number (Fig. 1) is indicated; comparison of the observed versus simulated monthly streamflow isalso shown for the Cache la Poudre watershed

Fig. 4. Plot of simulated versus observed monthly streamflows for the stations (STs) at the outlet of each watershed of the research reported inthis paper

Table 4. Evolution of ENS Values during Calibration Process

Study watershed500Runs

1,000Runs

15,00Runs

2,000Runs

2,500Runs

Cache la Poudre 0.566 0.732 0.839 0.872 0.874Gunnison 0.587 0.773 0.827 0.854 0.863San Juan 0.724 0.802 0.854 0.860 0.860Yampa 0.753 0.753 0.776 0.781 0.782

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(SRM; Martinec et al. 2008) and year-dependent modified snowcover depletion curves to simulate snowmelt runoff hydrographs inthe Rio Grande basin, also located in Colorado. The SRM methodfor the simulation of the recession limb of hydrographs differsgreatly from the physically based approach used in SWAT. TheSRM uses a conceptual method based on a variable recessioncoefficient that increases with decreasing streamflows.

In basins with multiple calibration points, a range of perfor-mance between sites was observed. Typically, the outlet of the en-tire basin had better error statistics than sites within the watershed,as shown in the Yampa basin with ENS ¼ 0.95 at the outlet andENS ¼ 0.85 and 0.71 at two interior sites. One reason for these dis-crepancies is the rectification of spatial scales within the frameworkof a watershed model, as parameter values often are applied overthe entire watershed regardless of the size and characteristics ofthe computational unit. This is the case for all parameters, exceptthose designated in Table 3, which are given different values de-pending on the soil and land-use characteristics of the respectiveHRU (e.g., curve number). The application of a single parametervalue represented over an entire watershed will not necessarilybe appropriate for the smaller-scale, heterogeneous HRU and sub-basin areas.

Quantification of Hydrologic Processes

Model results are analyzed on a monthly basis to determine andquantify dominant hydrologic processes occurring in the moun-tainous headwater catchments of Colorado. Results were takenfrom simulations over the calibration and testing periods (years1990–2005 for Cache la Poudre, Gunnison, and San Juan Riverbasins, and years 1990–2004 for the Yampa River basin).

Dominant Processes in Mountain Watersheds

Fig. 5 displays the average annual proportion of precipitationcredited to ET, gross water yield, and other losses during the periodof the research reported in this paper. Annual average percentages ofprecipitation that went to ET range between 55% in the GunnisonRiver basin to 65% in the Cache la Poudre River basin. Other lossesaccount for between 3 and 12% of annual precipitation, and includesoil water storage and contribution to the deep aquifer, which is as-sumed not to contribute to streamflow within the watershed and istherefore lost to the system. Typical gross water yield in each basinwas on average between 25 and 32% of annual precipitation.However, gross water yield was quantified before transmission

Table 5. Error Statistics between Observed and Simulated Monthly Streamflows for Both the Calibration and Validation Periods

Study watershed USGS identifier

Calibration period Validation period

RE Bias RMS error R2 ENS RE Bias RMS error R2 ENS

Cache la Poudre 6752000 −4.82 −0.55 4.55 0.97 0.93 −2.99 −0.27 7.17 0.85 0.70Gunnison 9128000 −3.88 −2.31 24.36 0.99 0.89 −15.92 −6.85 24.62 0.94 0.70

9110000 1.59 0.16 5.28 0.96 0.83 −8.12 −0.57 4.73 0.97 0.469126500 2.26 0.10 1.78 0.95 0.90 −3.65 −0.13 2.20 0.89 0.68

San Juan 9355500 9.66 4.79 20.14 0.93 0.86 8.35 3.15 15.65 0.95 0.889341500 −1.27 −0.06 2.23 0.94 0.88 −12.10 −0.47 2.56 0.92 0.749339900 −10.13 −0.28 1.41 0.94 0.85 −10.22 −0.22 0.87 0.97 0.92

Yampa 9251000 −5.11 −2.61 16.44 0.98 0.95 −6.28 −2.61 17.94 0.96 0.909249750 −32.560 −2.11 3.87 0.95 0.85 −24.71 −1.35 3.56 0.92 0.799237500 22.23 0.79 1.72 0.90 0.71 26.13 0.78 1.39 0.89 0.63

Note: Performance ratings include very good (ENS ≥ 0.75, jREj ≤ 10%), good (0.65 ≤ ENS < 0.75, 10% < jREj ≤ 15%), satisfactory (0.5 ≤ ENS < 0.65,15% < jREj ≤ 20%), and unsatisfactory (ENS < 0.5, jREj > 25%).

Fig. 5. Hydrologic budgets displaying the fate of precipitation (ET, water yield, and other) for the following river basins, with the average annualcontribution of three major hydrologic processes (surface runoff, lateral flow, and baseflow) to the gross water yield [each hydrologic componentand process was averaged over an annual basis from 1990–2005 (1990–2004 for the Yampa River watershed)]: (a) Cache la Poudre; (b) Gunnison;(c) San Juan; (d) Yampa

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losses were removed. Transmission losses represent the movementof water from the stream channel to the shallow aquifer, and are typ-ically larger in areas with ephemeral and/or intermittent streams, andlower groundwater tables. For each watershed, the average annualcontribution of surface runoff, lateral flow, and baseflow to the grosswater yield also are shown. As seen in Fig. 5, subsurface lateral flowis the dominant process in governing annual gross yield withineach watershed, providing between 64 and 82% of the gross basinyield on an annual basis.

An assessment of model sensitivity performed by Sanadyha et al.(2014) further verifies that the most influential parameters in termsof streamflow include those governing groundwater and snow proc-esses. Table 6 displays the optimized value from a selection of theseparameters in each basin. The first four parameters in Table 6 cor-respond to snow-related processes, and are the (1) snowfall temper-ature (SFTMP), (2) threshold temperature for snowmelt (SMTMP),(3) snow water equivalent corresponding to 100% snow cover(SNOCOVMX), and (4) fraction of SNOCOVMX resulting in50% snow cover (SNO50COV). The relative values of these param-eters are mostly consistent between the basins, suggesting a similardominance of these hydrologic processes within each of the basins.This consistency also underlines the prospect for regionalization ofprocess-based hydrologic modeling in the area, as suggested bySanadyha et al. (2014). Two notable exceptions are (1) SFTMP,which was approximately twice as high in the Gunnison and Yampabasins than in the Cache la Poudre and San Juan basins; and(2) SNOCOVMX, which was significantly lower in the Cache laPoudre basin than the other three. Sanadyha et al. (2014) deter-mined that flow discharges and hydrograph timing in the area ofthe research reported in this paper are very sensitive to theseparameters, which have an impact both independently and globally(i.e., through interaction with other parameters). This sensitivitymay explain the variability of SFTMP and SNOCOVMX amongthe basins once the model has been calibrated.

Additionally, the last three parameters in Table 6 correspond tothe flow of water either on the surface or subsurface, and includethe (1) baseflow recession constant (ALPHA_BF), (2) effective hy-draulic conductivity of the alluvium in the main channel reach(CH_KII), and (3) effective hydraulic conductivity of the tributarychannels (CH_KI). The values obtained for ALPHA_BF withineach watershed indicate relatively slow groundwater flow re-sponses to aquifer recharge, as expected in large and geologicallycomplex watersheds. The values of CH_KII and CH_KI are rela-tively high and correspond to channels with higher loss rates oftencomposed of coarse materials, present in the headwater streams ofColorado.

Annual Hydrologic Cycle

The long-term monthly distributions of precipitation, snowfall, ET,and total water yield are presented in Fig. 6 for each of the basins inColorado over the period 1990–2005 (1990–2004 for the Yampa

basin). The monthly patterns appear to be similar within all water-sheds and begin with the majority of precipitation falling as snow inthe winter months (November–April), with little water yield antici-pated during this time. Peak yield was estimated to occur in May inall four watersheds, most likely due to the rapid temperature in-crease and resulting spring snowmelt. Summer months are charac-terized by lower amounts of precipitation and decreasing yields,although ET continues to increase with increasing temperatures.Precipitation in the fall months was moderate, except in the SanJuan River basin, where on average a significant amount of rainfell during August–October, coinciding with the monsoon season.Both ET and water yield decreased over fall months, and precipi-tation began to fall as snow at the end of the year.

Residual Analysis

Simulation results also reveal important implications regardinguncertainty analysis, sensitivity analysis, and automatic calibrationfor hydrologic models applied to mountainous watersheds. Theseanalyses typically assume that residuals are normally and inde-pendently distributed (NID) with mean equal to zero, and unknownbut constant SD (Stedinger et al. 2008). If residuals in hydrologicmodel simulations are not normally distributed, independent, andhomoscedastic, suitable transformations must be applied to providea statistically correct formulation of the likelihood function. Re-sults of the simulations performed in the research reported in thispaper show that, in general, monthly residuals are normally distrib-uted (i.e., the hypothesis that residuals are normally distributedcannot be rejected), hence satisfying the first criterion. For exam-ple, the frequency distribution of monthly streamflow residuals atthe outlets for the four study watersheds [Fig. 7(a)] were normallydistributed based on the chi-square test for normality (χ2 ¼ 144.3,43.7, 26.1, and 31.4, respectively; P < 0.001). However, model

Table 6. Groundwater and Snow Processes SWAT Parameters for Each ofthe Study Basins

Parameter Cache la Poudre Gunnison San Juan Yampa

SFTMP (°C) 2.48 4.91 2.01 4.97SMTMP (°C) 3.33 3.42 3.70 3.13SNOCOVMX (mm) 299.45 625.83 646.68 554.76SNO50COV 0.57 0.30 0.32 0.44ALPHA_BF (days) 0.25 0.29 0.52 0.57CH_KII (mm=h) 264.35 102.83 430.09 428.69CH_KI (mm=h) 299.40 299.38 299.62 299.84

Fig. 6. Long-term monthly distribution of precipitation, evapotran-sporation, and total water yield (all in millimeters) in the followingwartersheds [during the 1990–2005 (1990–2004 in Yampa) period ofthe research reported in this paper]: (a) Cache la Poudre; (b) Gunnison;(c) San Juan; (d) Yampa

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residuals for smaller catchments within each watershed may not benormally distributed (see the Supplemental Data). Thus, cautionmust be used when applying uncertainty and sensitivity analyseswithin a multisite calibration framework to mountain catchmentswith areas similar to those of the small subcatchments studiedin this research (i.e., < 1,000 km2).

Results show that monthly residuals also were highly autocor-related [i.e., have a temporal persistence; Fig. 7(b)], although an-nual residuals do not display the same degree of autocorrelation(see the Supplemental Data). This autocorrelation likely is due toan overestimation of the recession of the snowmelt hydrograph dur-ing the summer months (May–November) and underestimation ofstreamflow during the winter months (December–April; Fig. 3).The underestimation during the winter months is repeatable onan annual basis in each study basin due to the model not holdingenough of the water from snowmelt in the aquifer for long enough.The error is most pronounced in the Gunnison River basin (Fig. 3;Stations 2–4). It is suggested that future work be concentrated onthe representation of snowmelt and groundwater processes inmountainous watersheds. Overall, although performance measures(Table 5) indicate high model confidence, uncertainty analysis andsensitivity analysis must be conducted with a careful and thoroughexamination of the structure of streamflow residuals for mountain-ous watersheds.

Summary and Concluding Remarks

Dominant hydrologic processes in mountainous headwater basinswere identified by applying the process-based, hydrologic modelSWAT to four headwater basins in Colorado. The model for eachbasin was developed using detailed sets of geospatial data repre-senting terrain, land use, soil, and climate. To alleviate the need torepresent the complex systems of diversions, reservoirs, and irriga-tion canals within the basins, the models were calibrated to natu-ralized streamflow data. Parameter estimation was accomplished bya method combining the automated SCE-UA and GSA optimiza-tion algorithms, with the method applied to one or more streamflowgage sites within each watershed to ensure accurate parameteriza-tion representative of the entire watershed. Error statistics were cal-culated to rate model performance in regards to observed monthlystreamflows. Results indicate a high level of confidence in the

models, with ENS varying between 0.86 and 0.95 during the cal-ibration period, and between 0.70 and 0.90 for the testing period,for streamflows at the outlet of each basin. However, the recessionof the snowmelt hydrograph was systematically overestimatedduring the summer months, and streamflow was systematicallyunderestimated during the winter months, particularly for theGunnison River basin, likely due to deficiencies in the representa-tion of aquifer storage and flow processes in the model. Similardifficulties to reproduce the hydrograph recessions were reportedby Sanadyha et al. (2014) for the same area, and Ahl et al. (2008)for a 22.5-km2 watershed in Montana. Moreover, simulations usingSRM (Rango and van Katwijk 1990; Martinec et al. 2008) alsoslightly overestimated the observed values for the baseflow seasonin the Rio Grande basin.

Several hydrologic components were quantified over the simu-lation period (1990–2005), as follows: (1) precipitation, (2) snow-fall, (3) ET, and (4) resulting gross water yield. Subsurface lateralflow was a dominant process in all the basins, and contributed be-tween 64 and 82% to gross basin water yields on an average annualbasis. Monthly patterns of precipitation, snowfall, ET, and totalwater yield are similar for all the basins, with precipitation fallingas snow in the winter months, followed by peak water yield in latespring due to snowmelt, and lower amounts of precipitation anddecreasing water yields during the summer months.

Residual analysis shows that independence of monthly stream-flow residuals, a criterion for common techniques of uncertaintyanalysis, sensitivity analysis, and automatic calibration, is in gen-eral not observed, with a pronounced autocorrelation occurring forall the watersheds. Although normality of monthly streamflow re-siduals, another criterion for analyses, is observed for stream hy-drographs at the outlet of all the watersheds, nonnormality occursfor several of the small catchments within the watersheds. Analysesand calibration methods therefore must be conducted with a carefuland thorough examination of the structure of streamflow residualsfor mountainous watersheds.

Overall, results reported in this paper indicate an overall satis-factory performance of the SWAT model in simulating monthlystreamflows and in identifying dominant hydrologic componentsin mountainous headwater basins in semiarid areas, but also dem-onstrate a need for improvement in representation of snowmelt andgroundwater processes for mountainous watersheds.

Fig. 7. Results of monthly residual analysis for each watershed, showing the following for the stations at the outlet of each study watershed:(a) frequency distribution of monthly residuals; (b) autocorrelation of monthly residuals

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Acknowledgments

The research reported in this paper was supported by grants fromthe Agriculture and Food Research Initiative of the USDA Nationalinstitute of Food and Agriculture (NIFA), Grant No. 2012-67003-19904, and Colorado Agricultural Experiment Station Project Nos.COL-2008-00689 and COL-2011-00652. The writers would alsolike to thank the funding from the CONICYT/FONDAP Grant15110017, and the Direccion de Relaciones Académicas Interna-cionales, Pontificia Universidad Catolica de Chile, for supportingthe preparation of the paper.

Supplemental Data

Figs. S1–S10 are available online in the ASCE Library (www.ascelibrary.org).

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