gis-based water quality modeling in the sandusky...

ABSTRACT: This study focused on the Sandusky Watershed(SW) in Ohio, located within the Great Lakes Basin, withemphasis on two of its subwatersheds, namely Honey Creek(HC) and Rock Creek (RC). The goal was to assess the capabili-ties of the Soil and Water Assessment Tool (SWAT) to simulatesuspended sediment (SS), phosphorus (P) and nitrogen (N)yield in the SW that contribute major sediment and nutrientloads into Lake Erie. The model was calibrated using water flowand water quality parameters for water years 1998 to 1999 andvalidated model simulations covering the period of water years2000 to 2001 for monthly conditions. The validation of SSshowed correlation coefficients of 0.29 (SW), 0.75 (HC) and0.69 (RC). Correlation coefficients for P were 0.68 (SW), 0.78(HC) and 0.37 (RC); for NO2-N 0.84 (HC) and 0.38 (RC); forNO3-N 0.27 (HC) and 0.76 (RC); for NH3-N 0.57 (SW), 0.49(HC), and 0.13 (RC). In addition, mean errors, root meansquare errors, Nash-Sutcliffe coefficients, and graphs wereused to compare simulated to measured data. Simulation suc-cess was variable with poor and good simulations, but in mostcases, simulated water quality values followed the trend ofmeasured data except for extreme (or intense) rainfall/runoffevents. Reviews of 17 applications indicated that the SWAT issuitable for long term continuous simulations but not for stormevents. A spatially distributed modeling approach generatedmaps showing the spatial distribution of SS, P, and N for eachsimulation element across the Sandusky Watershed.(KEY TERMS: Geographic Information System (GIS); SWAT;nutrients; simulation; sediment transport; nonpoint source pol-lution.)

Grunwald, Sabine and Chen Qi, 2006. GIS-Based Water QualityModeling in the Sandusky Watershed, Ohio, USA. Journal ofthe American Water Resources Association (JAWRA) 42(4):957-973.

INTRODUCTION

The Great Lakes Basin contains one-fifth of all thefresh surface water resources on Earth. More than 40million people live in the basin, including nearly 20percent of the United States’ population. The GreatLakes provide drinking water for about 11 millionpeople, and support a US$1 billion fish and recre-ational industry, infusing more than US$2 billion intothe regional economy each year (USEPA, 1995), yetstresses due to urbanization and agriculture posemajor threats on this ecosystem. Surface runoffenriched in agrichemicals and sediment is a majornonpoint source impacting the water quality of thebasin.

The Sandusky Watershed, located in Ohio with adrainage area at Fremont of 3,240 km2, is part of theGreat Lakes Basin contributing major loads of agri-chemicals and suspended sediment into Lake Erie(Figure 1). Monitoring showed that unit area loads inthe Sandusky Watershed are highest for total phos-phorus (P), suspended sediments (SS), and nitrate-nitrogen (NO3-N) out of seven major watersheds inOhio and higher than most other locations (Richards,2000). According to Richards and Baker (1993,1997),point sources of total P averaged 5.5 percent and donot constitute more than 15 percent of the annualloads in any year of the period 1975 to 1995 in theSandusky Watershed. Since the load estimationmethod provides information on the lumped responseof the entire watershed, it does not facilitate thetracking of sources of agrichemicals transported within the watershed. Spatially distributed computer

1Paper No. 04082 of the Journal of the American Water Resources Association (JAWRA) (Copyright © 2006). Discussions are open untilFebruary 1, 2007.

2Respectively, Assistant Professor, Soil and Water Department, University of Florida, 2169 McCarty Hall, P.O. Box 110290, Gainesville,Florida 32611-0290; and Graduate Student, Department of Civil and Coastal Engineering, University of Florida, Gainesville, Florida 32611(E-Mail/Grunwald: [email protected]).

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 957 JAWRA

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONAUGUST AMERICAN WATER RESOURCES ASSOCIATION 2006

GIS-BASED WATER QUALITY MODELING INTHE SANDUSKY WATERSHED, OHIO, USA1

Sabine Grunwald and Chen Qi2

simulation models describe processes such as surfacerunoff, peak flow, transport of sediment, phosphorus,and nitrogen (N), enabling geographic tracking ofnonpoint sources.

Spatially distributed computer simulation modelssuch as the Soil and Water Assessment Tool (SWAT)have been suggested to assess water quality in largebasins (Arnold et al., 1998). Due to the nature of spa-tial and temporal variability in topography, land use,soils, meteorological characteristics, and nonpointsources within a basin, it is essential to model trans-port processes of sediment and nutrients in space andtime. Geographic information systems (GIS) providethe capability of preprocessing and post-processing ofspatial and temporal input and output of distributedwater quality models. The main motivation of GISintegrated watershed water quality models is touncover and evaluate the crucial areas within awatershed whose land use or management practicescan be improved to control sediment and nutrientloading to water bodies.

The objective of this study was to assess the capa-bilities of SWAT to simulate SS, P, and N yield in theSandusky Watershed in Ohio and two of its subwater-sheds, namely Honey Creek and Rock Creek, which

contribute major sediment and nutrient loads intoLake Erie. Specific objectives were to calibrate andvalidate the model for SS, P, and N loads at thedrainage outlet of the Sandusky Watershed as well asthe two subbasins.

In this paper, the SWAT model and its applicationsto the simulations of watershed hydrological cycleand, sediment and nutrient transport are firstreviewed in detail. Then, a general description of thestudy area and model input is presented, followed bythe results of the calibration and validation of waterquality components. Model shortcomings and con-straints are critically discussed. A section of summaryand conclusions complete this paper.

SWAT FORMULATION ANDAPPLICATION REVIEWS

The SWAT model is a continuous time, quasi physi-cally-based, semidistributed computer simulationmodel designed to simulate water, sediment, and agri-cultural chemical transport for large watersheds and basins (Arnold et al., 1998). It represents the

JAWRA 958 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

GRUNWALD AND QI

Figure 1. Elevations, Stream Network, and Boundaries in the Sandusky Watershed.

hydrological cycle by interception, evapotranspiration,surface runoff, infiltration, percolation, lateral flow,ground water flow, and channel routing processes. Adetailed description of all SWAT model componentscan be found in Neitsch et al. (2002). Since this studyfocuses on sediment and nutrient transport specialattention is given to the implementation of these pro-cesses in SWAT.

In the SWAT model, suspended sediment yield iscomputed based on the Modified Universal Soil LossEquation (MUSLE) (Williams and Berndt, 1977) andsediment routing is rooted in Bagnold’s (1977) streampower concept as modified by Williams (1980). Themaximum amount of sediment that can be transport-ed from a reach segment is a function of the peakchannel velocity. Available stream power is used to re-entrain loose and deposited material until all of thematerial is removed. Excess stream power causes beddegradation. Bed degradation is adjusted for streambed erodibility and cover. A sediment lag factoraccounts for the fact that in large subbasins with atime of concentration greater than one day, only a por-tion of the surface runoff will reach the main channelon the day it is generated. The SWAT model incorpo-rates a surface runoff storage feature to lag a portionof the surface runoff release to the main channel. Sed-iment and nutrients in the surface runoff are laggedas well. The model allows the lateral and groundwater flow to contribute sediment to the main chan-nel.

Plant use of N is estimated using the supply anddemand approach. In addition to plant use, nitrateand organic N may be removed from the soil via massflow of water. Amounts of NO3-N contained in runoff,lateral flow, and percolation are estimated as productsof the volume of water and the average concentrationof nitrate in each layer. Organic N transport with sed-iment is calculated with a loading function developedby McElroy et al. (1976) and modified by Williams andHann (1978) for application to individual rainfall-runoff events. The loading function estimates thedaily organic N runoff loss based on the concentrationof organic N in the top soil layer, the sediment yield,and the enrichment ratio which accounts for the selec-tive transport of N associated with different soil parti-cle sizes. The enrichment ratio is the concentration oforganic N in the sediment divided by that in the soil.

Plant use of P is estimated using the supply anddemand approach. The primary mechanism of phos-phorus movement in the soil is by diffusion. Due tothe low mobility of solution phosphorus, surfacerunoff will only partially interact with the solution Pstored in the top 10 mm of soil. The amount of solubleP removed in runoff is predicted using solution P con-centration in the top 10 mm of soil, the runoff volume,and the partitioning factor, which is the ratio of the

soluble phosphorus concentration in the surface 10mm of soil to the concentration of soluble phosphorusin surface runoff. Sediment transport of P is simulat-ed with a loading function similar to the one used fororganic N transport. Organic and mineral P attachedto soil particles may be transported by surface runoffto the main channel using a loading function devel-oped by McElroy et al. (1976) and modified byWilliams and Hann (1978). For P transport with thesediment during storm events a relationship devel-oped by Menzel (1980) is used in which the enrich-ment ratio is logarithmically related to sedimentconcentration.

Nutrient transformations in the stream are con-trolled by the instream water quality component ofthe model. The instream kinetics used in SWAT fornutrient routing are adapted from The EnhancedWater Quality Model, QUAL2E (Brown and Barnwell,1987). The model tracks nutrients dissolved in thestream and nutrients adsorbed to the sediment. Dis-solved nutrients are transported with the water whilethose adsorbed to sediments are allowed to be deposit-ed with the sediment on the bed of the channel.

The SWAT model was designed to simulate longterm, continuous processes of flow, sediment yields,and nutrient transport in ungaged watersheds ofdiverse hydrologic, geologic, and climate conditions(Borah and Bera, 2003,2004) and has been employedin numerous ‘Total Maximum Daily Loads’ and waterquality assessment studies. For example, Saleh et al.(2000) used SWAT in the Upper North Bosque RiverWatershed (93,250 ha), which included 94 dairies. TheNash-Sutcliffe coefficient evaluating model efficiencyfor predicting average monthly flow, sediment andnutrient loading at 11 stream sites over the validationperiod ranged from 0.54 to 0.94, indicating reasonablepredicted values except for NO3-N, which had a lowvalue of 0.27. Nutrient loadings were consistentlyhighest in the subwatersheds with most of the dairyoperations. Kirsch et al. (2002) focused on predictionsof flow, sediment and phosphorus loads using SWATin the Rock River Basin in Wisconsin. Validation atJackson Creek showed a Nash-Sutcliffe coefficient of -1.64 for SS and -1.37 for P, suggesting severe discrep-ancies between simulated and measured values. Inthe same study, suspended sediment and P validationin water year 1999 showed either underestimations oroverestimations at different stations. Srinivasan etal. (1998) calibrated SS in the Richland and ChamberCreek watersheds (5,080 km2) in Texas. In a subwa-tershed (Mill Creek Watershed) 84 percent (calibra-tion phase) and 65 percent (validation phase) of thevariability in streamflow could be explained by theSWAT model. The authors experienced difficultiessimulating streamflows in spring/summer monthswhen the spatial variability of rainfall was high,


GIS-BASED WATER QUALITY MODELING IN THE SANDUSKY WATERSHED, OHIO, USA

which impacted sediment yield calculations. TheSWAT model underpredicted SS loads slightly duringthe period 1965 to 1968 and matched SS loads toobserved data during the period 1968-1975. Benamanet al. (2001) found that SWAT severely underpredict-ed sediment yield during high flow months in theCannonsville Reservoir Watershed (1,178 km2). Vachéat al. (2002) employed SWAT in the Buck Creek (88.2km2) and Walnut Creek watersheds (51.3 km2). Thecoefficient of determination based on monthly dis-charge data in Buck Creek was 0.64 and 0.67 in Wal-nut Creek Watershed. A semiquantitative graphicalvalidation method in Buck Creek was used where theauthors, due to a lack of measured SS data for theentire seven-year simulation period, used averagemeasured SS stream concentration of 150 mg/L col-lected during a low flow period in 1997 and 1998(Vaché et al., 2002). Such an ambiguous validationmethod has major limitations to evaluate the perfor-mance of the sediment routine in SWAT. Santhi et al.(2001) compared monthly predicted and measuredsediment yield in the Bosque River Watershed inTexas (4,277 km2), yielding coefficients of determina-tion and Nash-Sutcliffe coefficients above 0.81 and0.69, respectively, suggesting SWAT is well suited topredict suspended sediment for large watersheds.

Fewer studies attempted to validate the nutrientroutines of the SWAT model. Shirmohammadi et al.(2001) found major discrepancies between SWAT sim-ulated and measured NO3-N load in the WarnerCreek Watershed (3.5 km2) in Maryland with a coeffi-cient of determination of 0.27. Santhi et al. (2001)used a 12-month period for the validation of SWATsimulations of SS and nutrients in the Bosque RiverWatershed, Texas, at two gage stations. They foundvery high coefficient of determinations of 0.98 and0.95 at the Hico and Valley Mills stations for SS dur-ing the validation period, respectively, suggestingSWAT performs well to predict nutrient loads. Thecoefficients of determination at both stations were allabove 0.72 for organic and mineral N and P. A graphi-cal comparison between measured and predicted NO3-N for Walnut Creek Watershed was presented byVaché at al. (2002), which showed major discrepanciesin the timing and magnitude. The authors contributethe model uncertainties to a lack of field specific inputdata describing the timing and magnitude of fertilizerapplications.

Overall, most SWAT applications showed betterperformance for streamflow when compared to simu-lations of SS and nutrients. Limited validationdatasets for SS, P, and N exist because water qualitymonitoring programs are costly and typically areoperated over limited time periods. Water quality simulations of SS and nutrients were confoundingwith major underpredictions and overpredictions, in

particular, for the latter parameter. Uncertainties innutrient simulations can be attributed to (1) lack ininput data (e.g. site specific fertilizer applicationrates); (2) a simplified model structure, which is inad-equate to represent the complexity of sediment trans-port, nitrogen, and phosphorus dynamics, and otherprocesses; (3) the spatial discretization of a watershedinto simulation elements; (4) the allocation of water-shed characteristics (e.g., land use, soil properties) toeach simulation element; and (5) errors propagatingfrom one model routine into another routine (e.g.,peak flow calculated from watershed characteristics isused to calculate sediment yield).

WATERSHED DESCRIPTIONAND MODEL INPUT

The Sandusky Watershed is located within theGreat Lakes Basin (Figure 1), which drains into LakeErie with a drainage area at Fremont of 3,240 km2.Figure 1 shows two subwatersheds within the San-dusky Watershed: Honey Creek and Rock Creek, withdrainage areas of 388.2 km2 at Melmore and 90.3 km2

at Tiffin, respectively. Baker and Ostrand (2000) pro-vided a comprehensive characterization of the water-shed, summarized in this paragraph. Bedrockunderlying the watershed is primarily Silurian dolo-stone and Devonian limestone. In the eastern portionof the watershed, Devonian shale and Mississippiansandstone are present. Surface features of the San-dusky Watershed reflect the effects of the Wisconsini-an glaciers, which retreated approximately 13,000years ago. This resulted in two physiographic regionsin the watershed, the Lake Plains in the northernportion and the Till Plains in the central and south-ern portions. The landscape of the Lake Plains is anextremely flat plain of fine clay sediments, formed bywave action of glacial meltwater lakes that precededLake Erie. The Till Plains consists of flat to gentlyrolling plains with heavy till soils. Most of the reliefwithin the Till Plains is located in three end morainesthat lie in an east-west orientation. The majority ofthe Till Plains consists of flatter, ground morainesthat lie between the end moraines. Besides glacial till,lacustrine sediments and alluvial deposits along thedrainage system of the Sandusky River are found.Dominant soils are Hapludalfs, Ochraqualfs, Fra-giaqualfs, Medisaprists, Fluvaquents, and Argiaquolls(NRCS, 2001). The soils typically have silt loam andsilty clay loam textures.

Analysis of 1994 LANDSAT data indicated that 84percent of the watershed was used for agriculture,12.6 percent was wooded, 1.2 percent was urban and 1.1 percent was nonforested wetlands. Land use


GRUNWALD AND QI

mapping performed in 2000 to 2001 by the Universityof Toledo (Czajkowski, 2001) identified the major landuse as agriculture (61.2 percent) and woody vegeta-tion (27.9 percent). The major crops were cabbage,soybean, and corn. Major crops based on county levelestimates in 1985 were corn with 35.6 percent, soy-beans with 44.9 percent, and wheat with 19.5 percent.Crop production was similar in 1995 with 32.1 per-cent in corn, 49.1 percent in soybeans, and 18.7 per-cent in wheat. Tillage practices shifted from 86percent conventional management in 1985 to 50.5percent in 1995, as farmers replaced conventionalwith conservation tillage practices. Tile drainage isused extensively throughout the watershed. Urbanareas within the Sandusky Watershed are Bucyrus,Fremont, Tiffin, and Upper Sandusky, and numeroussmaller communities. A pie chart of land use (timeperiod: 2000) in the Sandusky Watershed is shown inFigure 2. Average annual precipitation ranges from881 mm at Fremont to 964 mm at Bucyrus. Annualmean discharge for the Sandusky Watershed at Fre-mont is 29.1 m3/s; Honey Creek at Melmore, 3.8 m3/s;and Rock Creek at Tiffin, 0.88 m3/s (Baker andOstrand, 2000).

Statistics of the measured water quality data in theSandusky Watershed at the Fremont station fromwater years 1998 to 2000 are summarized in Table 1,while Table 2 lists the discharge and loads of sedi-ment and major nutrients in the Sandusky Watershedfor water years 1998 to 2001.

Sediment (and nutrient loads) was calculated inmass per time according to the following equation(Maidment, 1992).

Qs = Q x C

where Qs is sediment load in mass per time, Q is flowdischarge in volume per time, and C is sediment con-centration in mass per volume.

In International System of Units (SI) , with Q inm3/s and C in kg/m3, and Qs in units of metricton/unit time, then

Qs = 86.4 QC (metric tons/day)

The total sediment (nutrient) load is defined as thetotal amount of sediment (nutrients) passing througha given stream cross section (Maidment, 1992).



Figure 2. Land Use (2000) in the Sandusky Watershed (source: Czajkowski, 2001).

(1)

(2)

The AVSWAT ArcView (Version 3.3., Environmen-tal Systems Research Institute, Redlands, California)GIS interface (Di Luzio et al., 2002) was used to pre-pare the input data files for SWAT. A digital elevationmodel (DEM), hydrography, soil maps, land use/cover,management, climate, water use, and pond data wereacquired for the Sandusky Watershed. A DEM fromthe National Elevation Dataset with cell size of 30 mby 30 m from the U.S. Geological Survey (USGS,2002) was used. A digital land cover/land use map

derived from Landsat ETM+ imagery of years 1994and 2000 was provided by the Department of Geogra-phy and Planning, University of Toledo (Czajkowski,2001). A digital soil map from the State Soil Geo-graphic Database (STATSGO) developed by the Natu-ral Resources Conservation Service U.S. Departmentof Agriculture (USDA) was used (NRCS, 2001). [SoilMaps for STATSGO Database are produced by gener-alizing the detailed soil survey data. The mappingscale for STATSGO is 1:250,000 (with the exception of


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TABLE 1. Statistics of Measured Water Quality Parameters in the Sandusky Watershed at Fremont.

SS Total P Nitrite + Nitrate NH3(mg/l) (mg/l) (mg/l) (mg/l)

Water Years 1998 to 1999

Minimum 0.00 0.00 0.00 0.00Mean 47.93 0.14 3.44 0.82Median 21.11 0.09 2.50 0.75Maximum 843.61 1.02 19.06 3.84Variance 6,074.54 0.02 11.57 0.34Standard Deviation 77.94 0.14 3.40 0.58

Water Year 2000

Minimum 1.00 0.00 0.00 0.00Mean 52.92 0.16 5.38 0.97Median 25.35 0.12 4.83 0.85Maximum 621.49 0.80 18.34 3.05Variance 5,766.59 0.02 20.29 0.24Standard Deviation 75.94 0.13 4.50 0.49

TABLE 2. Discharge and Loads of Sediment and Nutrients in the SanduskyWatershed and Two Subbasins (data derived from Richards, 2002).

Nitrate-Q Sediment Total P Nitrogen

Watershed Year (million m3) (ton) (ton) (ton)

Sandusky 1998 1,159 207,946 518 5,961

1999 545 52,683 138 3,838

2000 719 94,217 219 6,169

2001 618 N/A N/A N/A

Honey Creek 1998 152 22,736 66 728

1999 60 2,395 12 331

2000 106 11,841 46 692

2001 60 3,256 14 358

Rock Creek 1998 40 8,839 16.7 105

1999 13 1,084 3.4 50

2000 22.1 4,170 11.4 102

2001 14.3 1,442 4.0 51

Q: Total water flow

Alaska, which is 1:1,000,000). The level of mapping isdesigned to be used for broad planning and manage-ment uses covering state, regional, and multistateareas.]

Watershed boundaries were delineated usingAVSWAT. The drainage area threshold of 1,000 ha, aminimum area to maintain a stream, was chosenaccording to a previous sensitivity study conducted inthe Honey Creek Watershed. Multiple hydrologicalresponse units (HRUs) were delineated to discretizethe Sandusky Watershed into simulation elements.The threshold values of land use and soil were bothset at 10 percent (i.e., land use that covered less than10 percent of the subwatershed area and soil thataccounted for less than 10 percent of the subwater-shed were not considered in the simulation).

The meteorological data of daily precipitation andtemperature time series were obtained from sevenrain gage stations and 10 climate stations locatedwithin/around the Sandusky Watershed (NOAA,2001). Four reservoirs including the St. John’s Dam(OH02392), Bacon Low Head Dam (OH00801), a lowhead dam (OH00802), and Ballville Dam (OH00809)were treated as reservoirs in the model while all other26 reservoirs were considered as large ponds in thesimulation. The rainfall distribution was calculatedby the skewed normal method (Neitsch et al., 2002);potential evapotranspiration was estimated by theHargreaves method, which requires precipitationamount and temperature as input; the variable-stor-age method was used to route water in the channel.Twenty three major point pollution sources, includingSS, P, NO3-N, nitrite-nitrogen (NO2-N), and ammo-nia-nitrogen (NH3-N), distributed in 16 subwater-sheds were identified in the Sandusky Watershed andincorporated into the model (Eric Pineiro, Ohio Envi-ronmental Protection Agency, written communication,November 2001). Observed daily flows at USGS stations and water quality parameters (SS, NO3-N,NO2-N, NH3-N, and total P) measured by the WaterQuality Laboratory, Heidelberg College for wateryears 1998 and 1999 were used for calibration andwater years 2000 and 2001 were utilized for valida-tion (Water Quality Laboratory, 2001). Detailed infor-mation about water quality observations and trendsin watersheds in northern Ohio can be found inRichards and Baker (2002) and Richards et al. (2002).

The model was initialized and run for water year1997 which was not included in the analysis andinterpretation. The stations have almost continuousdaily water flow and water quality observations fromwater year 1998 to 2001.

RESULTS AND DISCUSSION

Summary of Surface and Ground Water Calibrationand Validation

A detailed description of the calibration and valida-tion of streamflow, ground water flow and total flow inthe Sandusky, Honey Creek and Rock Creek water-sheds was presented in Qi and Grunwald (2005).Water flow was simulated, calibrated, and validatedon a daily basis and presented in forms of graphs/statistics on a monthly basis (Qi and Grunwald,2005). Monthly measured and simulated flows wereused to evaluate the performance of SWAT in thethree watersheds. In this study, for the two-year vali-dation period, the Nash-Sutcliffe coefficient was 0.58(Sandusky), 0.65 (Honey Creek), and 0.73 (RockCreek) for total streamflow. For the same period thecorrelation coefficients were 0.80 (Sandusky), 0.90(Honey Creek), and 0.86 (Rock Creek) and the meanerrors in m3/s were 4.00, 0.68, and 0.03, respectively.A few rainfall/runoff events during the winter periodshowed large discrepancies between simulated andmeasured water flow. During most other seasons,total water flow matched closely with observed valuesin all watersheds.

Water Quality Calibration and Validation

Suspended Sediment. Sediment calibration ofthe SWAT model was performed for water years 1998and 1999. To calibrate the model parameters that hadsignificant effects on SS yield, they were adjusteduntil the optimal simulated sediment outputs at theFremont station of the Sandusky Watershed, HoneyCreek, and Rock Creek were obtained for the period.The MUSLE P-factor for land use categories forestdeciduous, cabbage, and corn was adjusted to 0.6 andremained at 1 for all other land use/land cover. Theexponential factor for the stream power equation(SPEXP) was adjusted to 2.0. Both the peak rateadjustment factor for sediment routine in the mainchannel (PRF) and the peak rate adjustment factorsfor sediment routine in the tributary channels (APM)were fixed at 0.8.

Sediment validation (water years 2000 to 2001)was conducted using the same parameters as identi-fied in the calibration process. Statistics of the simu-lated monthly SS output compared with observedmonthly sediment yield at Fremont, Honey Creek andRock Creek stations are summarized in Table 3.

Figure 3 shows the comparisons of the simulatedand recorded monthly sediment yield for the threeUSGS stations for water years 1998 to 2001. Missing



data were represented as hollow squares in Figures 3,5, and 7. The simulated SS generally followed thetrend of observed data. Overall, sediment results werein agreement with the simulated surface water in theSandusky Watershed. Since the model had difficultiesdealing with rainfall/runoff simulation for the winterwhen heavy snowfalls accounted for most of the annu-

al total precipitation in the watershed, a similar prob-lem existed for the sediment prediction.

Figure 4 illustrates the average monthly SS ratedistribution in the Sandusky Watershed for wateryears 1998 to 2001. The darker the color, the higherthe SS rate per area, which suggests a higher risk ofsoil erosion. The regions located upstream of Honey


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TABLE 3. Statistics of Simulated Monthly Suspended Sediment, Total Phosphorus,Nitrite-Nitrogen, Nitrate-Nitrogen, and Ammonia-Nitrogen Yields.

Suspended TotalStation Statistics Sediment Phosphorus NO2-N NO3-N NH3-N

Calibration (1998 to 1999)

Fremont Mean Error1 -3,248.6 3,841.4 2,843.0 5,099.1 -48,989.6

RMSE2 2,349.5 8,669.2 2,407.3 76,005.2 28,244.1

Correlation Coefficient 0.48 0.57 0.27 0.71 0.54

Nash-Sutcliffe Coefficient 0.08 -0.89 -4.64 0.29 -0.44

Honey Mean Error 506.6 -1,175.5 -127 -22,411.8 -7,064.7

RMSE 215.2 813.2 214.7 11,185.8 3,315.6


Nash-Sutcliffe Coefficient 0.2 -0.03 0.19 -0.03 -0.24

Rock Mean Error 963.7 -658.3 -106.7 1,129.5 -3,716.9

RMSE 542.6 315.7 100 4,577.8 1,395.3


Nash-Sutcliffe Coefficient -5.1 0.07 0.02 -0.12 -0.3

Validation (2000 to 2001)

Fremont Mean Error -1,520.7 8,691.5 N/A N/A -64,050.0

RMSE 2,672.7 4,800.5 N/A N/A 33,833.9

Correlation Coefficient 0.29 0.68 N/A N/A 0.57

Nash-Sutcliffe Coefficient 0.02 0.28 N/A N/A -0.21

Honey Mean Error 953.6 -624.8 -53.4 -14,656.9 -8,236.1

RMSE 268.7 425.7 89.4 13,250.4 2,761.5


Nash-Sutcliffe Coefficient -1 0.45 0.48 -0.1 -0.44

Rock Mean Error 164.8 -213.9 -37.6 -58.9 -2,411.6

RMSE 63.9 178.2 26.6 1,023.9 1,083.1


Nash-Sutcliffe Coefficient -0.26 0.08 -0.16 0.57 -0.24

1value, and n is the number of observations.

Note: N/A = No measurements available.

1Mean Error = Units in tons for SS and kilograms for others; Mean Error

z z

nz

i ii

n

i=−( )

=∑ ˆ

ˆ ,1 in which is the predicted value zi is the measured

2RMSE (Root Mean Square-Error) = Units in tons for SS and kilograms for others; RMSE

z z

n

i ii

n

=−( )

=∑ ˆ

.

2

1



(a)

(b)

(c)

Figure 3. Simulated and Observed Monthly Suspended Sediment at(a) Fremont (Sandusky Watershed), (b) Honey Creek, and (c) Rock Creek.

Creek and Rock Creek subwatersheds showed largerSS yield experiencing elevated soil erosion due tolarge slopes, and regions downstream of Tiffin showedaccumulated SS loads from upstream drainage areas.

The simulated monthly SS at three stations wasmoderately close to the observed values. Simulationat all three stations had difficulties predicting SS dur-ing winter periods, resembling the same difficultiesexperienced for streamflow simulations. Suspendedsediment was underestimated at the Fremont stationin June 2000. The opposite was found at the HoneyCreek station, where SS was overestimated Februaryto June 2000 and in August 2000. The Nash-Sutcliffecoefficient is sensitive to outliers and few deviationsbetween observed and calculated values of large rain-fall runoff events had severe impact on the coefficient.Validation showed negative Nash-Sutcliffe coefficientsin Honey Creek and Rock Creek watersheds and acoefficient of 0.02 in the Sandusky Watershed. Thecorrelation coefficients were relatively high in HoneyCreek and Rock Creek watersheds, with 0.75 and0.69, respectively, suggesting that simulations

matched measured SS reasonably well. The correla-tion coefficient was low with 0.29 at the Fremont sta-tion, and also showed a large mean error of -1,521tons, underestimating measured SS.

Total Phosphorus. The SWAT model was cali-brated against observed monthly total P at the Fre-mont, Honey Creek, and Rock Creek stations forwater years 1998 and 1999, and validation was car-ried out for water years 2000 and 2001. Parametersthat had significant effects on total P were adjusteduntil the optimal P output at the three USGS stationswas obtained during calibration and the same set ofparameters was adopted for validation. During cali-bration, phosphorus fertilizer application fraction forthe first layer (FRT_LY1) was modified to 0.2. TheResidue Decomposition Coefficient (RSDCO) factordescribing the fraction of residue that will decom-pose in a day assuming optimal moisture, temper-ature, C:N ratio, and C:P ratio, was adjusted to 0.02.The phosphorus percolation coefficient (PPERCO) inthe basin input file (.bsn) was changed to 17.5; the


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Figure 4. Average Monthly Suspended SedimentDistribution in the Sandusky Watershed Based

on Subbasins (simulation elements).

fraction of algal biomass that is phosphorus (AI2) wasset to 0.01; the phosphorus uptake distributionparameter (UBP) was 100; the phosphorus availabili-ty index (PSP) was chosen as 0.7; and the phosphorussoil partitioning coefficient (PHOSKD) was adjustedto 200.

Figure 5 shows the comparisons of the simulatedand recorded monthly total P yield for the threeUSGS stations. Each hollow square in Figure 5 indi-cates that data were unavailable for one month. Forexample for the large rainfall/runoff event in January1999, no measured data at the Fremont station wereavailable. For that time period, the model simulated a



Figure 5. Simulated and Observed Monthly Total Phosphorus Yield at(a) Fremont (Sandusky Watershed), (b) Honey Creek, and (c) Rock Creek.

(a)

(b)

(c)

large total P yield following a large rainfall/runoffevent. Though the simulated monthly total P at threestations generally followed the trends of observed sea-sonal changes, simulation fell short of reproducing thetotal P yield for extremely high rainfall/runoff eventsand during winter months, similar to what happenedin the sediment calibration and validation processes.During the period April to July 2000, precipitationacross the Sandusky Watershed was high (averageprecipitation per month was 150 mm) and the modelresponded with high total P yield. In contrast, for thesame period, the observations of total P were muchlower. For example, total P observed at the RockCreek station was more than five times less than sim-ulated P for the period April 2000.

Figure 6 illustrates the average monthly total Pdistribution in the Sandusky Watershed for wateryears 1998 to 2001. The darker the color, the higherthe total P yield rate per area, suggesting elevated Pin surface water. The regions located downstream ofTiffin, upstream of Rock Creek and around Meekerwere likely to be affected by larger P concentrationsin the water bodies.

Table 3 presents the statistics of the simulatedmonthly total P yield compared with observed month-ly total P yield at Fremont, Honey Creek, and RockCreek.

Nitrogen. The SWAT simulation output was cali-brated against observed monthly measured NO2-N,NO3-N, and NH3-N yield at Fremont, Honey Creek,and Rock Creek stations for water years 1998 and1999. The model validation was performed for wateryears 2001 and 2001. Parameters that had significanteffects on nitrogen yields were adjusted until the bestpredictions at the three USGS gage stations wereobtained in calibration.

Nitrogen fertilizer application fraction for the firstlayer (FRT_LY1) was set at 0.2; the RSDCO factorwas adjusted to 0.02; the nitrogen percolation coeffi-cient (NPERCO) in the basin file (.bsn) was changedto 1; and the fraction of algal biomass that is nitrogen(AI1) was set at 0.07.

Statistics of the simulated monthly NO2-N, NO3-N,and NH3-N yields compared with correspondingobserved yields at Fremont, Honey Creek and Rock


GRUNWALD AND QI

Figure 6. Average Monthly TotalPhosphorus Distribution in theSandusky Watershed Based on

Subbasins (simulation elements).

Creek stations are presented in Table 3. Figure 7shows the comparisons of the simulated and recordedmonthly nitrate-nitrogen yields at the three USGSstations. Each hollow square in Figure 7 indicatesthat observed data were unavailable for one month.

Figure 8 shows the average monthly nitrate-nitrogenyield distribution in the Sandusky Watershed forwater years 1998 to 2001. The regions with darkercolor correspond to zones with a higher nitrate-nitrogen yield rate per area. The regions located



Figure 7. Simulated and Observed Monthly Nitrate-Nitrogen Yield at(a) Fremont (Sandusky Watershed), (b) Honey Creek, and (c) Rock Creek.

(a)

(b)

(c)

downstream of Tiffin, upstream of Rock Creek andaround Meeker were likely to be affected, showinghigher nitrate yields.

Simulations at the Fremont station showed under-estimations of NO3-N and NH3-N for winter events inFebruary and April 1998 (calibration phase) andFebruary and April 2000 (validation phase). Simula-tions in Honey Creek and Rock Creek showed similarproblems. In Rock Creek, the highest measured NH3-N yield was about 23,000 kg (April 2000) andhighest measured NO3-N was about 80,000 kg (June1998), which was much lower when compared tonitrogen measured at the Honey Creek and Fremontstations. Correlations coefficients showed weak tomoderate relationships between simulated and mea-sured NO2-N, NO3-N, and NH3-N yields, whereas theNash-Sutcliffe coefficients were quite poor for all sta-tions. Nitrogen simulations were impacted by errorsfrom the hydrology routine, which propagated into thenitrogen simulations. As simulations suggest, the nitrogen routines in SWAT might not be adequateto describe processes of nitrate leaching, mineraliza-tion, transport and transformation. Nitrogen and its

components are soluble and their behavior within awatershed is dynamic. Transport processes (e.g.,leaching of nitrogen) can occur within short time peri-ods. Therefore, a daily time step to simulate theseprocesses might not be adequate. This is a major limi-tation of the SWAT model.

The land use pattern in the Honey Creek, RockCreek, and Sandusky watersheds is different, show-ing a higher percentage of cabbage in Honey Creek(19 percent), and Sandusky (14 percent) when com-pared to 4 percent in Rock Creek Watershed. The per-centage of agriculture/row crops was 19 percent inRock Creek Watershed, 11 percent in the SanduskyWatershed, and 10 percent in Honey Creek Water-shed. Typically, agricultural land uses, in particularcabbage and row crops, receive higher fertilizer appli-cation rates increasing the risk to find elevated NO3-N in surface and ground water. The percentageof forest deciduous is similar in Rock Creek Water-shed with 23 percent when compared to the SanduskyWatershed with 19 percent, and Honey Creek Water-shed with 18 percent. Different land uses within thethree watersheds are suggested to cause different


GRUNWALD AND QI

Figure 8. Average Monthly total Nitrate-Nitrogen Yield Distribution in the

Sandusky Watershed Based onSubbasins (simulation elements).

NO3-N and NH3-N spatial patterns. Measured andobserved NO3-N in Honey Creek was higher whencompared to Rock Creek Watershed. The maps in Fig-ure 7 show relatively higher NO3-N in Honey CreekWatershed (mean monthly average of 0.32 kg/ha)when compared with the remaining Sandusky Water-shed.

Different soils are suggested to explain differencesin N simulations in Honey Creek, Rock Creek, andSandusky watersheds. In Sandusky Watershed andRock Creek Watershed, the Blount-Pewamo-Glynwoodsoil association is dominant with 33 percent and 69percent coverage, respectively, followed by the Tiro-Pandora-Bennington soil association with 12 percentcoverage in the Sandusky Watershed and the Blount-Glynwood-Morley soil association with 18.5 percentcoverage in the Rock Creek Watershed.

The Blount Soil Series is a fine, illitic, mesic AericEpiaqualfs; the Pewamo Soil Series is a fine, mixed,active, mesic Typic Argiaquolls; and the GlynwoodSoil Series is a fine, illitic, mesic Aquic Hapludalfs.Pewamo soils are very poorly drained, with clay loamtexture in the A horizon, silty clay texture in the Btghorizons, and silty clay loam texture in the Cg hori-zon. Poorly drained soils show higher denitrificationwhen compared to moderately and well drained soils.Leaching of N is expected to be higher in well drainedsoils when compared to poorly drained soils. TheGlynwood soils are moderately well drained soils withsilt loam texture in the A and E horizons, silty clayloam texture in the BE, silty clay, clay, and silty clayloam in the Bt, BC, and clay loam in the C horizons.The Morley Series (fine, illitic, mesic Oxyaquic Haplu-dalfs) consists of very deep, moderately well drainedsoils formed in clay loam or silty clay loam till. Inthese soils permeability is moderate to slow in thesolum and very slow in the substratum. The horizonsare A, Bt, and a Cd at a shallow depth of about 70 cm.

In Honey Creek Watershed, the Tiro-Pandora-Bennington association is dominant (50 percent cover-age), which is fine silty, mixed (illitic), mesic Aeric(Typic) Epiaqualfs. Epiaqualfs are soils that areformed by episaturation, which means that there isperching of water over a more slowly permeable layerof horizon in the soil. These soils have silt loam tex-ture in the upper horizon (Ap), silt loam and silty clayloam texture in the B horizons (BE, Bt and Btg), andsilty clay loam, loam, and clay loam texture in thelower horizons (BC, BCg, and C). Different soils inHoney Creek, Sandusky, and Rock Creek watershedsare suggested to account for differences in infiltration,percolation, and lateral transport of water, solublesand sediment, mineralization, and denitrification.

DISCUSSION

Overall the validation of SS, total P, NO2-N, NO3-N, and NH3-N showed moderate to low Nash-Sutcliffe coefficients, correlation coefficients, and rela-tively high mean errors and root mean square errorssuggesting shortcomings in model predictions. Sincethe validation of total streamflow in the Sandusky,Honey Creek, and Rock Creek watersheds showed agood match between simulated and measured valuesexcept for a few extreme winter storm-runoff events,it was unexpected that water quality simulationsshowed moderate to high uncertainties.

There are numerous explanations for those short-comings. For a large watershed, such as the San-dusky, there are limitations to describe watershedcharacteristics in detail. To improve predictions of SSand nutrients using SWAT, the model structureand/or input datasets could be improved. In particu-lar, shortcomings in the nutrient routines were foundin this study documented by poor to moderate perfor-mance in modeling N and P components. Datasets ofhigher quality and spatial resolution would be desir-able to possibly improve model output. Measurementsare also associated with errors. For example, in thisstudy, for select rainfall/runoff events no measure-ments were available and in few other instances rain-fall patterns did not seem to coincide well with themeasured flow, SS, and nutrient data. The STATSGOsoil data at scale 1:250,000 were used because of theunavailability of higher resolution digital soil data.Since land use management data including site spe-cific fertilizer application rates were unavailable, thestudy relied on generic extension datasets for Ohio.Higher resolution soil data (e.g., Soil Data Martdatabase at scale 1:24,000) are being developed by theNatural Resources Conservation Service that providemore detailed information about soil characteristics.Because of the sensitivity of numerous soil character-istics that impact water flow and transport processes,even higher resolution soil datasets would be desir-able for SWAT-based simulation modeling of SS andnutrients. The information on actual fertilizer appli-cations in the Sandusky Watershed was minimal. TheSWAT model inputs partially were organized basedon Tri-State Fertilizer Recommendations, Bulletin E-2567. The land use map derived from remote sens-ing imagery showed a high percentage of cabbage andtomato, which might have been overestimated accord-ing to Ohio county statistics.

Due to the unavailability of the solar radiation,wind speed, and relative humidity data, the potentialevapotranspiration was estimated by the Hargreavesmethod, which might contribute to the errors in simu-lating water yields. This further complicated the



calibration and validation of the model for simulatingsediment and nutrients.

SUMMARY AND CONCLUSIONS

The SWAT model was used to simulate SS, P, andN transport in the Sandusky Watershed and two sub-watersheds in Ohio. Two years of calibration were fol-lowed by an independent validation phase. Thespatially distributed modeling approach generatedmaps showing the spatial distribution of SS andnutrients for each simulation element across the San-dusky Watershed. Maps facilitate regional compar-isons of SS and nutrients across the watershed (e.g.,identifying areas that show elevated and low phos-phorus). The quality of simulations differed geograph-ically. Though uncertainties were identifiedcomparing simulated to measured SS, N, and P at thedrainage outlets of all three watersheds, maps pro-vide a useful planning instrument to guide restora-tion efforts focusing on relative differences insimulated values across the Sandusky Watershed.

This study showed moderate to high uncertainty insimulating SS and nutrients using SWAT in the San-dusky, Rock Creek, and Honey Creek watersheds. Theresults are consistent with other studies (Kirsch,2000; Benaman et al., 2001; Shirmohammadi et al.,2001; Vaché et al., 2002). Though the model providesregional spatially distributed output that describesthe water quality at the watershed scale, careful cali-bration and validation are required. This study indi-cated that simulations were constrained by usingavailable regional datasets and the model structure ofSWAT.

ACKNOWLEDGMENTS

This research was supported by the Florida Agricultural Experi-ment Station and approved for publication as Journal Series No. R-10250. The authors thank T.F.A. Bishop for editing and assemblingSWAT input datasets. The Water Quality Laboratory at HeidelbergCollege provided the water quality measurements. The authors arethankful for the support from the Ohio Lake Erie Commission thatfunded this project.

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