application of the soil and water assessment tool for six watersheds in lake erie model parameterization and calibration

Journal of Great Lakes Research 37 (2011) 263–271 Contents lists available at ScienceDirect Journal of Great Lakes Research j o u r n a l h o m e p a g e : w w w e l s e v i e r c o m / l o c a t e / j g l r Application of the Soil and Water Assessment Tool for six watersheds of Lake Erie: Model parameterization and calibration Nathan S Bosch a,⁎, J David Allan b,1, David M Dolan c,2, Haejin Han d,3, R Peter Richards e,4 a Department of Science and Mathematics, Grace College, Winona Lake, IN 46590, USA School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109, USA Natural and Applied Sciences, University of Wisconsin — Green Bay, Green Bay, WI 54311, USA d Korea Adaptation Center for Climate Change, Korea Environmental Institute, Seoul 122-706, Republic of Korea e National Center for Water Quality Research, Heidelberg University, Tiffin, OH 44883, USA b c a r t i c l e i n f o Article history: Received 30 June 2010 Accepted 15 February 2011 Available online April 2011 Communicated by Joseph DePinto Index words: SWAT Great Lakes Nutrients Sediments Catchment Model confirmation a b s t r a c t The Soil and Water Assessment Tool (SWAT), a physically-based watershed-scale model, holds promise as a means to predict tributary sediment and nutrient loads to the Laurentian Great Lakes In the present study, model performance is compared across six watersheds draining into Lake Erie to determine the applicability of SWAT to watersheds of differing characteristics After initial model parameterization, the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand SWAT models were calibrated (1998–2001) and confirmed, or validated (2002–2005), individually for stream water discharge, sediment loads, and nutrient loads (total P, soluble reactive P, total N, and nitrate) based on available datasets SWAT effectively predicted hydrology and sediments across a range of watershed characteristics SWAT estimation of nutrient loads was weaker although still satisfactory at least two-thirds of the time across all nutrient parameters and watersheds SWAT model performance was most satisfactory in agricultural and forested watersheds, and was less so in urbanized settings Model performance was influenced by the availability of observational data with high sampling frequency and long duration for calibration and confirmation evaluation In some instances, it appeared that parameter adjustments that improved calibration of hydrology negatively affected subsequent sediment and nutrient calibration, suggesting trade-offs in calibrating for hydrologic vs water quality model performance Despite these considerations, SWAT accurately predicted average stream discharge, sediment loads, and nutrient loads for the Raisin, Maumee, Sandusky, and Grand watersheds such that future use of these SWAT models for various scenario testing is reasonable and warranted © 2011 International Association for Great Lakes Research Published by Elsevier B.V All rights reserved Introduction Nutrient delivery to the Laurentian Great Lakes through tributary loading has long been identified as a major contributor to the trophic status of the lakes Eutrophication in the Great Lakes first attracted attention in the 1960s and 70s as Lake Erie experienced benthic anoxia and other water quality problems associated with nutrient enrichment (Boyce et al., 1987; Rosa and Burns, 1987) The 1972 Great Lakes Water Quality Agreement brought about reductions in point ⁎ Corresponding author Tel.: + 574 372 5100x6447 E-mail addresses: boschns@grace.edu (N.S Bosch), dallan@umich.edu (J.D Allan), doland@uwgb.edu (D.M Dolan), hanhj@kei.re.kr (H Han), prichard@heidelberg.edu (R.P Richards) Tel.: + 734 764 6553 Tel.: + 920 465 2986 Tel.: + 82 6922 7803 Tel.: + 419 448 2240 source loadings of phosphorus (P) that dramatically lowered annual P loads from tributaries and reversed many of the effects of cultural eutrophication in the lakes (DePinto et al., 1986) Despite this major reduction, point-source control programs alone were not adequate to reduce tributary nutrient loading, and attention shifted to nonpoint sources (DePinto et al., 1986; Dolan, 1993; Richards, 1985) Much research in the Great Lakes basin has provided insight into the relationship between riverine nutrient export and characteristics of watersheds, including land use and nutrients inputs (Baker and Richards, 2002; Bosch and Allan, 2008; Dolan and McGunagle, 2005; Han and Allan, 2008; Han et al., 2011; Robertson, 1997) Dolan and McGunagle (2005) estimated that nonpoint sources often account for more than 70% of total tributary loadings in Lake Erie Watershed-scale nutrient budgets have confirmed this observation and refined it to show specifically that fertilizer application is the largest input of nitrogen (N) and P in agricultural watersheds, while atmospheric deposition of N is an important input to forested watersheds (Baker and Richards, 2002; Bosch and Allan, 2008; Han and Allan, 2008; Han et al., 2011) In urban watersheds, by contrast, point source P inputs may continue to be significant (Nemery et al., 2005) 0380-1330/$ – see front matter © 2011 International Association for Great Lakes Research Published by Elsevier B.V All rights reserved doi:10.1016/j.jglr.2011.03.004 264 N.S Bosch et al / Journal of Great Lakes Research 37 (2011) 263–271 Watershed characteristics and source inputs determine riverine nutrient export For 18 well monitored tributaries of Lake Michigan and Lake Superior, topography and surficial deposits were top predictors of P and sediment yields, followed by land use (Robertson, 1997) Cropland has larger P losses per unit area than pasture or forest (Alvarez-Cobelas et al., 2009; Castillo et al., 2000; Cooke and Prepas, 1998; Pieterse et al., 2003) Crop land use is associated with P fertilization and increased soil erosion caused by tillage practices (Sharpley, 1999) Mass balance approaches have demonstrated strong relationships between source inputs to the watershed's landscape and river export for both N and P (Boyer et al., 2002; Han and Allan, 2008; Han et al., 2011) Clearly, it is important to incorporate point and nonpoint sources of N and P into models as well as other watershed characteristics such as surficial geology and stream gradients, in order to accurately predict tributary export of nutrients to the Great Lakes The Soil and Water Assessment Tool (SWAT), a semi-empirical hydrologic and water quality model that is calibrated to the conditions of individual watersheds, may be used to predict changes in tributary nutrient loads based on nutrient application and management choices SWAT is a continuous-time, watershed-scale model that runs at a daily time step (Gassman et al., 2007) SWAT models are internally organized in a nested spatial hierarchy, including Hydrologic Response Units (HRUs) within subwatersheds within watersheds Watersheds are defined by a main outlet point for the drainage area of interest A variable number of subwatersheds are delineated in SWAT based on interior outlet points located on the stream channel and spatially linked to each other through the adjoining stream channel network Within each subwatershed a variable number of HRUs are defined as areas with unique combinations of land cover, soil type, and slope It is important to note that these HRUs are not spatially referenced except that they are within a specific subwatershed Processes modeled within each HRU are aggregated up to the subwatershed scale by a weighted average based on land area Thus as a mechanistic model, SWAT includes spatially explicit parameterization at the subwatershed spatial scale and partially lumped parameterization within each subwatershed SWAT hydrology, sediment, and nutrient processes are modeled in both upland and water-routing phases (Neitsch et al., 2005) Various forms of N and P, including mineral P (soluble reactive P (SRP)), other P (total P - SRP), organic N, nitrate, nitrite, and ammonia are modeled explicitly with a thorough representation of transport, uptake, loss, and transformation mechanisms (more detail on SWAT is provided in Appendix A) The SWAT model was applied to six watersheds draining into Lake Erie to model scenarios of riverine nutrient export as part of a larger investigation of the recent resurgence of anoxic waters in Lake Erie (Burns et al., 2005) and the contributions of tributary runoff We include six of the largest U.S watersheds of Lake Erie, which receive some of the highest nutrient inputs of any in the Great Lakes due largely to intensive agriculture in the region Previous application of SWAT for two of the watersheds utilized some similarities in methodology (Bosch, 2008), but the approach has been improved and a newer version of the SWAT model has been employed In the present study, model performance was compared across these diverse watersheds to determine the performance of SWAT within watersheds of differing characteristics Care was taken to apply SWAT similarly to all six watersheds to allow comparison of model performance There is growing interest in restoring the Great Lakes, in which nonpoint source nutrient loads are widely considered one of the most important threats It is our hope that this study will inform future studies of the suitability of SWAT to predict tributary sediment and nutrient loads under scenarios of agricultural best management practices, nutrient source reductions, and future climates Methods Study area The Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand watersheds drain into the western and central basins of Lake Erie (Fig 1) from southeastern Michigan, northeastern Indiana, and northern Ohio Primary differences among watersheds are reflected in land cover (Table 1), with the Raisin, Maumee, and Sandusky being predominantly agricultural The Huron and Cuyahoga are the most urbanized watersheds The Grand watershed is mostly forested with relatively little agriculture and urban land Average precipitation increases slightly from west to east due to more lake-effect precipitation in the Cuyahoga and Grand watersheds Model input data sources The Geographic Information System (GIS) interface created for SWAT, called ArcSWAT (version 2.1.5), was used to develop inputs for the six watershed models Elevation, stream network, land cover, soil type, weather, point source discharges, impoundment (reservoir, lake, or pond) characteristics, atmospheric N deposition, and land management practices were included Specific data descriptions, sources, and scales are included in Appendix B Model setup and parameterization For each of the six watershed models we first delineated subwatersheds and distributed HRUs within subwatersheds (Appendix C) The delineation process resulted in 31, 32, 203, 39, 23, and 22 subwatersheds for the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand, respectively, which was the desired level of spatial detail for the study This was an average subwatershed size of 85 km2 across all six watersheds There were 441, 468, 2341, 567, 302, and 297 HRUs distributed in the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand watershed models, respectively, based on unique combinations of land cover and soil type and leading to an average HRU size of km2 Measured data were used for all weather parameters including daily rainfall, minimum and maximum air temperature, windspeed, relative humidity, and solar radiation Weather data were collected for the time period January 1, 1995 through December 31, 2005 Data from the nearest station were used to fill in missing data whenever data records were incomplete Loading data for point source dischargers were entered into the models as constant average daily loadings For parameters that were not measured by individual municipal wastewater treatments plants, we used average estimated values (Table 2) obtained from measurements during 1995–2005 at eight Midwestern U.S wastewater treatment plants where all parameters were measured For larger wastewater treatment plants, parameters in addition to discharge and TP were often reported, such that directly measured data could be used Discharge loading information was entered for 36, 23, 83, 13, 25, and dischargers in the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand, respectively Tile drainage was implemented in the six watershed models following the approach of Green et al (2006; Appendix C) Average denitrification rates in upland crop land over various soil types and N application rates are near 15 kg N ha− y− (Hofstra and Bouwman, 2005), while rates in well-drained, clay loam, forested soils average 18 kg N ha− y− (Groffman et al., 1992) Since these six watersheds are dominated with agriculture and forest land cover (Table 1), model parameters were adjusted to approximate these denitrification rates (Appendix C) Agricultural land management practices were generalized for each of the six watersheds based on most common management practices in each watershed, and used to define operation schedules in SWAT N.S Bosch et al / Journal of Great Lakes Research 37 (2011) 263–271 265 Fig The Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand watersheds draining into western and central Lake Erie as delineated in SWAT models for both row-crop and hay agricultural lands Most land classified as hay in these watersheds was managed for hay production or used for grazing and was modeled by SWAT accordingly Row-crop agriculture in these six watersheds consisted of various rotational schedules and combinations utilizing mostly corn, soybean, and wheat crop production SWAT operation schedules were defined according to these rotation patterns for each watershed individually as well as for corresponding practices such as tillage, fertilizer application, crop planting, manure application, and crop harvesting The operation schedules were adjusted to accurately represent fertilizer and manure application rates based on county estimates (Ruddy et al., 2006) In addition, each of the multi-year operation schedules differed based on the starting crop in the rotation pattern This ensures that the single year of wheat production in the 6-year rotation of the Maumee watershed occurs in a staggered manner across the watershed rather Table Characteristics of the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand watersheds for the modeled areas, determined by the watershed outlet location Huron Raisin Maumee Sandusky Cuyahoga Grand Watershed Size (km2) Precipitation (mm/y) Landcover (%) Agriculture Urban Forested 2379 2784 17030 3455 2100 1896 896 861 934 962 1039 1093 27 72 81 83 17 37 37 16 8 35 52 34 11 11 47 10 than all subwatersheds producing wheat in the same year These land management schedules were distributed among subwatersheds such that each version was equally represented and applied uniformly across the watershed Other land cover types (residential, industrial, range, forest, wetlands, and water) were given general operation schedules based on most common management practices across the six watersheds as a whole High, medium, and low density residential as well as Table Point source discharge input data by parameter Average estimated concentrations were based on measured data during 1995–2005 at eight Midwestern WWTPs CBOD refers to chemical/biological oxygen demand Parameter Units Notes Water flow Sediment m3 Mg Organic nitrogen Organic phosphorus Nitrate Ammonia Nitrite Mineral phosphorus CBOD Dissolved oxygen kg kg kg kg kg kg kg kg Directly measured data used Based on average total suspended solid concentration of 7.2 mg/L Based on average organic nitrogen concentration of 2.2 mg N/L Assumed to be 30% of directly measured total phosphorus concentration Based on average nitrate concentration of 11.3 mg N/L Based on average ammonia concentration of 3.2 mg N/L Based on average nitrite concentration of 0.6 mg N/L Assumed to be 70% of directly measured total phosphorus concentration Based on average CBOD concentration of 4.9 mg/L Based on average dissolved oxygen concentration of 6.8 mg/L 266 N.S Bosch et al / Journal of Great Lakes Research 37 (2011) 263–271 industrial land covers were set to a plant type of Kentucky Bluegrass and scheduled to grow from May to September 29 Fertilizer application equal to the watershed-specific, non-farm fertilizer estimates (Ruddy et al., 2006) was applied to low, medium, and high density residential in four equal applications scheduled on April 1, June 1, August 15, and September 30 Most land classified as range in these watersheds is left fallow as conservation reserve areas, so the plant type was set to Tall Fescue with a growing season from April to October 31 with no fertilizer or manure application operations Operations for forest and wetland land covers were scheduled to correspond with leaf-out in the spring and leaf senescence in the fall, with the growing season beginning on May and ending on October 10 of each year The water land cover was kept at its default settings Model calibration and confirmation The six SWAT models were calibrated and confirmed individually for stream flow discharge, sediment loads, and nutrient loads (total P (TP), SRP, total N (TN), and nitrate) based on available datasets Daily mean stream discharge measurements were available for 1995–2005 from USGS gage stations near the river mouth of each watershed, including the Huron River at Ann Arbor, MI (04174500), the River Raisin at Monroe, MI (04176500), the Maumee River at Waterville, OH (04193500), the Sandusky River at Fremont, OH (04198000), the Cuyahoga River at Independence, OH (04208000), and the Grand River at Painesville, OH (04212100) For the Raisin, Maumee, Sandusky, Cuyahoga, and Grand rivers, near-daily sediment, TP, SRP, TN, and nitrate loads were available for sampling sites located near the USGS gage stations provided by the National Center for Water Quality Research at Heidelberg College for the entire time period of 1995– 2005 In order to produce observed monthly loads for later calibration and confirmation tests, missing values for daily loads were determined using the USGS Estimator protocol (Richards, 1998) For the Huron River, fewer sediment and nutrient data were available Approximately biweekly TP, SRP, TN, and nitrate concentration data were collected during 2003–2005 near the mouth of the river (Bosch, 2007) No sediment data were available for the Huron watershed As with the other five watersheds, missing daily values for TP, SRP, TN, and nitrate loads were generated for the Huron River using the USGS Estimator protocol in order to produce observed monthly loads Observed data were then compared to simulated SWAT output The first years (1995–1997) were used for model spin-up, in order to minimize the impact of initial model parameter values which may be suspect, and this model output was not used in calibration or confirmation The next years of observed data were used for calibration (1998–2001), and the remaining years (2002–2005) for model confirmation Model confirmation (using the terminology of Reckhow and Chapra, 1983 and Oreskes et al., 1994; others have used the term “validation”) consisted of comparing model predictions with observations using a data set for years and conditions distinct from those represented by the calibration data For the Huron watershed only, 2003 and 2004 observed loads were used for calibration, and 2005 loads were used for confirmation To evaluate model performance during calibration and confirmation, statistical measures at the monthly time-step were used as well as visual graphical comparison at the daily time-step The monthly statistical measures used for calibration and confirmation evaluation for stream discharge, sediment, TP, SRP, TN, and nitrate included: coefficient of determination (R2), Nash–Sutcliffe simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to the standard deviation of the observations (RSR) Moriasi et al (2007) thoroughly explain the utility of each of these evaluation statistics, how they are calculated, and what range of values are satisfactory for watershed hydrology and water quality modeling with models such as SWAT (Appendix D) Model calibration included several sequential stages for each individual watershed, including hydrology sensitivity analysis, hydrology manual calibration, hydrology autocalibration, sediment manual calibration, and nutrient manual calibration First, an automated sensitivity analysis (Van Griensven et al., 2006) is carried out through the ArcSWAT interface with hydrologic model parameters in order to identify the parameters to be adjusted during the autocalibration procedure as well as to give some insight into parameters to adjust during manual hydrology calibration The sensitivity analysis procedure uses the Latin hypercube one factor at a time design, and identifies the top 15 most sensitive parameters Next, the hydrology was roughly calibrated manually by changing sensitive hydrologic parameters as described in Santhi et al (2001) Once simulated stream discharge roughly fit the observed discharge for the calibration time period, a second sensitivity analysis was performed The top 15 parameters of both the pre- and post-manual calibration sensitivity analysis runs were then used for the hydrology autocalibration The hydrology autocalibration employed the PARASOL calibration procedure included in the ArcSWAT interface (Van Griensven and Meixner, 2007) The PARASOL method applied a shuffled complex evolution optimization scheme to select the optimal parameter value set for the 15 sensitive hydrologic parameters after several thousand model runs The calibration was based on monthly observed USGS daily mean stream discharge data (1998–2001) and SWAT simulated stream discharge Hydrologic model parameter values were then adjusted to reflect the optimal value set chosen by the autocalibration process After model hydrology calibration was completed, manual calibration of sediments and nutrients was completed, followed by model confirmation SWAT sediment parameters were calibrated following the procedure of Santhi et al (2001) based on monthly observed sediment loads from 1998 to 2001 Since no observed sediment data were available for the Huron River, the optimized sediment parameter values from the adjacent Raisin watershed were used for the Huron SWAT model as well After sediment calibration was completed, TP, SRP, TN, and nitrate calibration were done based on monthly observed nutrient data (Santhi et al., 2001) SWAT output included mineral P and other P, so SWAT mineral P was compared to observed SRP data and observed TP was compared to the sum of SWAT mineral P and other P After model nutrient parameters were optimized for all six watershed models, calibration was complete and model confirmation was initiated During model confirmation, evaluation statistics were calculated for stream discharge, sediments, TP, SRP, TN, and nitrate and for each of the six SWAT models, but no further model parameter changes were made Results and discussion Stream discharge SWAT predicted measured stream discharge well for all six watersheds during both the calibration and confirmation time periods (Table 3) In fact, NSE, PBIAS, and RSR statistic values were mostly “very good” with a few categorized as “good” according to Moriasi et al (2007) Somewhat surprisingly, SWAT prediction of discharge was slightly more accurate during the confirmation period than the calibration time period overall Though all SWAT models performed well, across the six watersheds, stream discharge was best predicted for the Maumee, and least well predicted for the Huron Successful modeling of stream discharge is expected given the extensive calibration data sets available from stations located near the mouth of each watershed, and the effectiveness of the PARASOL autocalibration method This success indicates that the hydrologic mechanisms included in the model are fit uniquely and well to each of these watersheds and are reliable for future model simulations Stream discharge calibration is critical for subsequent water quality calibration success The less satisfactory hydrologic performance of the Huron model likely is due to specific characteristics of the Huron watershed First, the baseflow fraction of the Huron River is higher (0.74) than all other 267 N.S Bosch et al / Journal of Great Lakes Research 37 (2011) 263–271 Table Calibration and confirmation results for monthly stream discharge (m3/s) for the six modeled watersheds Coefficient of determination (R2), Nash–Sutcliffe simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to observations standard deviation (RSR) are used as evaluators of model performance Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007) Observed mean (m3/s) Simulated mean (m3/s) R2 NSE PBIAS (%) RSR (a) Calibration Huron 14 Raisin 23 Maumee 151 Sandusky 25 Cuyahoga 21 Grand 19 14 23 162 26 21 18 0.73 0.87 0.93 0.88 0.89 0.89 0.70 0.87 0.92 0.87 0.88 0.85 1 −7 −2 0.54 0.36 0.28 0.35 0.34 0.38 (b) Confirmation Huron 11 Raisin 19 Maumee 174 Sandusky 39 Cuyahoga 34 Grand 32 10 21 176 38 30 30 0.75 0.88 0.95 0.90 0.90 0.83 0.71 0.87 0.95 0.90 0.87 0.82 − 11 −1 11 0.54 0.36 0.22 0.32 0.36 0.42 watersheds (0.39–0.60) included in this study (Arnold and Allen, 1999) Second, the Huron River has far more impoundments than the other five rivers combined, storing large volumes of water in its middle and lower sections Third, and related to the previous two characteristics, the Huron River has a much less flashy hydrograph than other rivers of this study and a slow return of discharge to baseflow conditions (Fig 2) While the relatively high urban land cover of the Huron (Table 1) might be expected to result in flashy stream discharges due to impervious surfaces, it seems that impoundments effectively dampen peak flows by temporarily retaining excess water volumes in the river system Sediment Sediment calibration was acceptable for the Raisin, Maumee, Sandusky, and Grand (Table 4) In fact, most statistic values for the confirmation time period were “very good” for these four rivers according to the ranges given by Moriasi et al (2007) For the Huron River, no observed sediment data suitable for calibration or confirmation was available, so sediment load prediction for the Huron could not be evaluated Sediment model output for the Cuyahoga River, despite a strong data set for observed sediments, was marginal to poor Three of the evaluation statistics had satisfactory values, while the remaining five were unsatisfactory This weak model performance for sediment loads for the Cuyahoga watershed may be related to its hydrology calibration process as well as the watershed's landcover This was the only model for which the observed-simulated sediment R2 decreased as a result of the stream Table Calibration and confirmation results for monthly sediment loads (Mg) for the six modeled watersheds Coefficient of determination (R2), Nash–Sutcliffe simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to observations standard deviation (RSR) are used as evaluators of model performance Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007) No sediment evaluation was completed for the Huron River due to the lack of observed sediment data Observed mean (g) Simulated mean (Mg) R2 NSE PBIAS (%) RSR – 5203 63475 9572 10087 5739 – 5008 63332 10490 10110 5718 – 0.65 0.68 0.74 0.46 0.85 – 0.63 0.67 0.73 0.25 0.83 – − 10 0 – 0.62 0.58 0.53 0.87 0.42 (b) Confirmation Huron – Raisin 4104 Maumee 77095 Sandusky 20082 Cuyahoga 29832 Grand 12789 – 4865 68423 17579 15745 9693 – 0.80 0.92 0.91 0.76 0.75 – 0.79 0.89 0.85 0.45 0.64 – − 19 11 12 47 24 – 0.47 0.34 0.39 0.75 0.61 (a) Calibration Huron Raisin Maumee Sandusky Cuyahoga Grand discharge calibration process In the other four models for which sediment calibration was possible, hydrology calibration improved sediment prediction prior to undertaking sediment calibration Parameter adjustments that improved calibration of hydrology in the Cuyahoga model apparently had a negative influence on subsequent sediment and nutrient calibration, suggesting trade-offs in calibrating for hydrologic vs water quality model performance In addition, the landcover of the Cuyahoga watershed is the least agricultural and most urban of the six study systems Despite the low extent of agriculture, the sediment load near the mouth of the Cuyahoga River is second only to the much larger and more agricultural Maumee River In fact, the Cuyahoga River had the highest single observed daily sediment load of all the watersheds, including the Maumee River This unique characteristic of the Cuyahoga was not adequately simulated within the sediment transport mechanisms included in SWAT Total phosphorus Model performance for TP ranged from unsatisfactory to satisfactory across the six study watersheds (Table 5) The Maumee and Sandusky models both showed satisfactory to strong performance across multiple parameters Fig depicts the daily TP load for the Maumee, which is the largest individual TP input to Lake Erie Simulated TP loads for the Raisin and Grand watersheds ranged from unsatisfactory to satisfactory, indicating marginal model performance Simulated TP for the Huron and Cuyahoga was mostly unsatisfactory for calibration and Fig Daily plot of observed and simulated mean stream discharge (m3/s) for the Huron River over the calibration (1998–2001) and confirmation (2002–2005) time periods 268 N.S Bosch et al / Journal of Great Lakes Research 37 (2011) 263–271 Table Calibration and confirmation results for monthly total phosphorus loads (Mg P) for the six modeled watersheds Coefficient of determination (R2), Nash–Sutcliffe simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to observations standard deviation (RSR) are used as evaluators of model performance Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007) Simulated mean (Mg P) R2 (a) Calibration Huron Raisin 11 Maumee 149 Sandusky 23 Cuyahoga 14 Grand 149 24 14 0.53 0.52 0.72 0.66 0.41 0.71 − 2.55 0.47 0.72 0.64 0.11 0.68 −2 29 −1 −7 1.93 0.74 0.53 0.60 0.95 0.57 (b) Confirmation Huron Raisin Maumee 182 Sandusky 43 Cuyahoga 29 Grand 13 137 30 16 0.16 0.50 0.86 0.87 0.68 0.30 − 46.12 0.49 0.74 0.77 0.39 0.28 − 220 11 25 29 44 25 56.51 0.72 0.51 0.49 0.79 0.86 Observed mean (Mg P) NSE PBIAS (%) RSR confirmation time periods, with particularly poor NSE values for the Huron model Poor performance of modeled TP for the Huron and Cuyahoga may be related to calibration methodology, land cover, and point source nutrient inputs Relatively sparse observed data are likely to blame for the particularly poor TP prediction for the Huron TP loads depend largely on sediment transport, and so the lack of sediment data and sediment calibration likely contributed to the poor prediction of TP loads In addition, only years of biweekly TP loads were available for Huron TP calibration and confirmation, compared to near daily TP loads for years for the other watersheds, making it difficult to fully parameterize or evaluate model performance for the Huron model A different explanation is necessary for the poor performance of the Cuyahoga model, since ample sediment data were available As noted previously with respect to sediment prediction, calibration to improve hydrologic performance of the Cuyahoga model also decreased R2 values between observed and simulated TP loads for both the Huron and Cuyahoga SWAT models Furthermore, these two watersheds have the most urban land use and least agriculture of the six study watersheds, indicating SWAT may better predict TP loads in agricultural than urban watersheds Both the Huron and Cuyahoga Rivers receive relatively high point source TP loads relative to total observed river loads For the Huron River, point source inputs of TP to the river are estimated (and thus modeled by SWAT) as 29 kg P/d while the observed average stream load near the Huron River mouth is 36 kg P/d Similarly, estimated point source inputs for the Cuyahoga River are 233 kg P/d, roughly one-third of the total stream load of 703 kg P/d SWAT estimates of TP loads for the Grand and Raisin models can be described as marginally satisfactory, likely due to characteristics of these two watersheds Compared to the Maumee and Sandusky watersheds, which produced strong models, the Grand watershed is much less agricultural and more forested (Table 1), which likely hampers SWAT's ability to predict TP loads Apparently, the high percentage of urban land in the Huron and Cuyahoga, and correspondingly high point source inputs, account for even poorer model performance for those two watersheds In the case of the Raisin, model TP estimation barely missed the satisfactory threshold for NSE and RSR, possibly due to the presence of impoundments in the upper Raisin watershed Prior work (Bosch, 2008; Bosch et al., 2009) has shown that impoundments alter the timing and magnitude of TP loads, and this may not have been adequately portrayed in the current Raisin SWAT model Despite these mixed performance evaluations, it is important to note that PBIAS values were largely categorized as “very good” across all watersheds, indicating that model predictions of TP loads were not generally higher or lower than observed data Thus, SWAT predicted TP loads accurately on average Soluble reactive phosphorus SRP estimation was the weakest output from SWAT models for all six watersheds (Table 6) This was apparent during both calibration and confirmation time periods, and evaluation statistics were unsatisfactory just over half of the time across all watersheds and for all statistical measures for SRP (Moriasi et al., 2007) Only the Raisin model consistently performed well in calibration and confirmation Model performance for the Maumee, Sandusky, and Grand was marginal, and more satisfactory during the calibration than the confirmation time period, as expected PBIAS was satisfactory for the Raisin, Maumee, Sandusky, and Grand, once again indicating strong model performance for these four watersheds As observed for TP, the Huron and Cuyahoga models were largely unsatisfactory in their estimation of SRP over time and as a monthly average Weak prediction of SRP loads for the Maumee and Sandusky is unexplained Inspection of observed and simulated means (Table 6) shows that observed SRP loads increased dramatically from the calibration time period (1998–2001) to the confirmation time period (2002–2005) This same increase is not mirrored in simulated SRP loads Although observed stream discharge increases as well (Table 3), this is insufficient to explain the SRP increase as SWAT should capture this effect Monitoring data show an increase in SRP as a fraction of TP over recent years in these two watersheds (Richards, 2006; Richards, 2007), with no confirmed explanation for this trend It is apparent Fig Daily plot of observed and simulated total phosphorus (TP) loads (kg P/d) for the Maumee River over the calibration (1998–2001) and confirmation (2002–2005) time periods 269 N.S Bosch et al / Journal of Great Lakes Research 37 (2011) 263–271 Table Calibration and confirmation results for monthly soluble reactive phosphorus loads (Mg P) for the six modeled watersheds Coefficient of determination (R2), Nash–Sutcliffe simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to observations standard deviation (RSR) are used as evaluators of model performance Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007) Observed mean Simulated mean R2 (Mg P) (Mg P) (a) Calibration Huron 0.2 Raisin 2.3 Maumee 31.5 Sandusky 5.2 Cuyahoga 2.8 Grand 0.6 (b) Confirmation Huron 0.1 Raisin 2.1 Maumee 56.9 Sandusky 11.2 Cuyahoga 3.9 Grand 1.4 0.2 2.2 32.2 5.1 8.0 0.6 0.9 2.1 29.8 7.1 8.6 0.8 0.37 0.81 0.65 0.43 0.05 0.53 NSE PBIAS (%) − 1.12 − 17 0.79 0.61 −2 0.44 − 38.26 − 182 0.50 10 Table Calibration and confirmation results for monthly total nitrogen loads (Mg N) for the six modeled watersheds Coefficient of determination (R2), Nash–Sutcliffe simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to observations standard deviation (RSR) are used as evaluators of model performance Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007) Simulated mean (Mg N) R2 NSE PBIAS (%) (a) Calibration Huron 43 Raisin 380 Maumee 3199 Sandusky 578 Cuyahoga 172 Grand 76 42 383 3189 604 190 77 0.71 0.77 0.86 0.70 0.40 0.66 0.68 0.77 0.81 0.66 0.24 0.66 −1 −5 − 11 −2 0.58 0.49 0.44 0.59 0.88 0.59 (b) Confirmation Huron 57 Raisin 332 Maumee 3806 Sandusky 867 Cuyahoga 243 Grand 125 59 338 3186 810 196 119 0.30 0.81 0.80 0.80 0.43 0.49 0.36 0.80 0.67 0.76 0.27 0.49 −3 −2 16 19 16.74 0.45 0.58 0.50 0.86 0.72 Observed mean (Mg N) RSR 1.49 0.47 0.63 0.75 6.33 0.72 0.75 − 182.45 − 709 41.87 0.70 0.70 0.55 0.69 0.29 48 0.85 0.45 0.35 37 0.82 0.11 − 34.31 − 119 6.00 0.63 0.40 42 0.78 that SWAT is not adequately representing the increase in SRP export regardless of the mechanism causing this shift Total nitrogen Model evaluation statistics indicate mixed success in estimating TN across the six study watersheds (Table 7) However, PBIAS for all models was less than 20% for the calibration and confirmation stages, indicating “very good” average estimation of monthly loads (Moriasi et al., 2007) Model performance for the three agricultural watersheds received all “good” and “very good” evaluation statistics for calibration and confirmation Fig illustrates daily TN loads for the Maumee, the largest single tributary TN load to Lake Erie TN estimation for the forested Grand watershed was satisfactory or nearly so for all evaluation statistics Models for the Huron and Cuyahoga were once again the weakest, with several unsatisfactory evaluation statistics for both calibration and confirmation time periods Similar to TP model results, TN prediction by SWAT was strongest for agricultural watersheds and weakest for highly urban watersheds The urbanized Huron and Cuyahoga once again had much higher point source inputs relative to observed stream loads, in comparison with other study watersheds For the Cuyahoga, point source inputs modeled by SWAT were 5851 kg N/d, while observed stream TN loads were only 6814 kg N/d In the Huron watershed, point source inputs were 1460 kg N/d and the river's observed load was 1638 kg N/d RSR Assuming additional nitrogen inputs from terrestrial lands from fertilizer, manure, soil, and atmospheric deposition, a great amount of N must be removed in the stream channels of these two watersheds SWAT only includes N settling as a removal mechanism in stream channels, despite strong evidence that denitrification in stream channels is an important N removal mechanism (Inwood et al., 2005; Smith et al., 2006) Due to this shortcoming, SWAT is unable to strongly predict the timing of TN loading, though it does predict average loads with reasonable accuracy as seen by the PBIAS statistic (Table 7) Nitrate Nitrate is the largest fraction of TN in river export from these watersheds and thus is modeled by SWAT with similar accuracy as was observed for TN (Table 8) Models for the Raisin, Maumee, and Sandusky performed acceptably across all four evaluation statistics during both the calibration and confirmation times periods The Huron, Cuyahoga, and Grand models performed inconsistently All six SWAT watershed models, however, had PBIAS statistic values considered to be “very good” It is evident that SWAT model performance is consistently better for the three agricultural watersheds than it is for the more urban and forested watersheds SWAT was developed and optimized for agricultural watersheds (Gassman et al., 2007), and thus incorporates mechanisms best suited for highly agricultural systems In the case of Fig Daily plot of observed and simulated total nitrogen (TN) loads (kg N/d) for the Maumee River over the calibration (1998–2001) and confirmation (2002–2005) time periods 270 N.S Bosch et al / Journal of Great Lakes Research 37 (2011) 263–271 Table Calibration and confirmation results for monthly nitrate loads (Mg N) for the six modeled watersheds Coefficient of determination (R2), Nash–Sutcliffe simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to observations standard deviation (RSR) are used as evaluators of model performance Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007) Simulated mean (Mg N) R2 NSE PBIAS (%) RSR (a) Calibration Huron 33 Raisin 306 Maumee 2613 Sandusky 515 Cuyahoga 115 Grand 36 34 303 2632 530 130 41 0.57 0.61 0.65 0.58 0.10 0.14 0.47 0.61 0.62 0.53 − 0.21 0.02 −2 −1 −3 − 13 − 15 0.75 0.63 0.62 0.69 1.11 1.00 (b) Confirmation Huron 51 Raisin 277 Maumee 2958 Sandusky 728 Cuyahoga 135 Grand 53 39 259 2659 717 130 66 0.71 0.72 0.69 0.59 0.16 0.23 0.49 0.69 0.61 0.59 0.11 0.19 23 10 − 24 6.00 0.57 0.63 0.65 0.95 0.91 Observed mean (Mg N) nitrate and denitrification in terrestrial soils, SWAT potentially predicts this removal mechanism more appropriately in agricultural soils compared to forest soils In the case of dominant nitrate sources in the watersheds, SWAT may more accurately simulate nitrate fertilizer application on agricultural lands than nitrate deposition on forested lands Related to nitrate dynamics and land cover is the urban influence seen in the Cuyahoga watershed where Cuyahoga N point source inputs are over twice as high as the stream loads Since the majority of the Cuyahoga River stream TN load is nitrate, a plot showing daily nitrate loads illustrates these effects (Fig 5) The most striking observation is the relatively high baseline of plotted nitrate daily loads (provided by the high point source inputs) with numerous peaks in both positive and negative directions These peaks stretching below the baseline are likely periods when the nitrate concentrations in the stream were diluted by moderately increased stream discharge SWAT is not able to adequately predict these downward peaks, which offers another explanation for weaker SWAT performance in predicting the Cuyahoga nitrate load Implications Model performance statistics and graphical plots reveal that SWAT was effective in capturing system dynamics and estimating nutrient and sediment export for the three agricultural watersheds SWAT also performed reasonably well for the highly forested Grand watershed Sediment and nutrient results for the more urban Cuyahoga and Huron watersheds, however, suggest that SWAT should be applied with caution when agriculture is not the dominant land use Although SWAT accurately simulated the hydrology of these urbanized watersheds, water quality simulation was disappointing, evidently because SWAT does not incorporate adequate mechanisms to remove high point source inputs from the channels This research also indicates that trade-offs may exist in calibrating SWAT parameters to best represent hydrologic response variables versus water quality variables This appears to be the case for the Cuyahoga sediment and TP calibration as well as Huron TP calibration; in each case, flow calibration resulted in a decreased fit as measured by the R2 between observed and simulated water quality data — 0.44 to 0.40, 0.52 to 0.30, and 0.44 to 0.27, respectively Usually calibration of hydrology results in better sediment calibration which, in turn, leads to better P and N calibration success In the two urban watersheds, however, it appears that hydrology calibration resulted in some parameters and processes important for sediment and TP simulation being altered negatively It is uncertain which parameters were responsible for this effect, but two hydrologic parameter values were notably different for the Huron and Cuyahoga compared to the other four watersheds Groundwater delay (GW_DELAY) was set to less than day for both the Huron and Cuyahoga watersheds, versus 30 to 31 days for the other four watersheds, and the snowpack temperature lag factor (TIMP) was higher for the Huron (0.74) and Cuyahoga (0.23) compared to other watersheds (0.06–0.13) This may be further cause for caution when applying SWAT to watersheds with substantial urban area It is apparent that extensive empirical data of high quality are critical for SWAT model applications In the case of the Huron watershed, the limited availability of water quality data for calibration and confirmation was a likely contributor to weak model performance Three years of biweekly water quality data for the Huron model was insufficient to separate the data set into meaningful calibration and confirmation time periods The years of water quality data that were available for the other five watersheds clearly enhanced model performance Even longer time series might be useful, but typically the land cover data comes from one time period (2001 in this study), so lack of correspondence between the time periods of hydrologic and water quality data with the land cover data may then limit the realism of scenarios Sampling frequency also is important, as biweekly sampling does not allow meaningful calibration with the model's daily time step, and even modeling at the monthly time step requires filling in many gaps using some estimation approach (such as the Beale Ratio Estimator or USGS Estimator) In such cases, the monthly calibration is done based on another largely simulated data set rather than observed data The final implication of this research is that despite weak performance results for some watersheds and certain water quality parameters, SWAT predicted loads, on average, that were not seriously biased This is evidenced by the relatively low PBIAS statistics calculated throughout the calibration and confirmation process While SWAT predictions of the timing or magnitude of Fig Daily plot of observed and simulated nitrate loads (kg N/d) for the Cuyahoga River over the calibration (1998–2001) and confirmation (2002–2005) time periods N.S Bosch et al / Journal of Great Lakes Research 37 (2011) 263–271 certain loading events or durations cannot be assumed to be accurate and should be evaluated using appropriate statistics, SWAT accurately predicted overall average loads for nearly all parameters for all six watersheds during both the calibration and confirmation time periods Thus, when accompanied by acknowledgment of model uncertainty, we have confidence that these six models will be readily useful for future studies in this region to predict average sediment or nutrient load changes in response to land use and climate alteration scenarios Conclusion The present study demonstrates the applicability of SWAT in the Great Lakes basin and also identifies certain considerations for future SWAT application in general SWAT effectively predicted hydrology and sediments at daily time steps across a range of watershed characteristics SWAT estimation of daily nutrient loads was weaker, although still satisfactory at least two-thirds of the time across all nutrient parameters and watersheds Furthermore, the PBIAS statistic consistently showed monthly average loads to be estimated without serious bias Agricultural and forested watersheds lend themselves particularly well to SWAT modeling of hydrology, sediments, and nutrients This study also emphasizes the importance of the availability of observed data with high sampling frequency and long duration for calibration and confirmation evaluation and effectiveness Despite these considerations, SWAT accurately predicted average stream discharge, sediment loads, and nutrient loads for the Raisin, Maumee, Sandusky, and Grand watersheds such that future use of these SWAT models for various scenario testing is reasonable and warranted Supplementary materials related to this article can be found online at doi:10.1016/j.jglr.2011.03.004 Acknowledgments We are grateful to the following people for their insight related to this work: Steve Davis, Tim Hunter, Les Ober, Jim Selegean, Michael Winchell, and Tom VanWagner Comments by reviewers on an earlier draft substantially improved the manuscript This is publication 10-003 of the EcoFore Lake Erie project, funded by the NOAA Center for Sponsored Coastal Ocean Research under award NA07OAR4320006 This work was performed under the authority of Section 516(e) of the Water Resources Development Act of 1996 through additional funding by the United States Army Corps of Engineers 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defined by a main outlet point for the drainage area of interest A variable number of subwatersheds... area The Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand watersheds drain into the western and central basins of Lake Erie (Fig 1) from southeastern Michigan, northeastern Indiana, and northern... Cuyahoga, and Grand watersheds draining into western and central Lake Erie as delineated in SWAT models for both row-crop and hay agricultural lands Most land classified as hay in these watersheds