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This study employed a novel combination of data (winter cover crop [WCC] cost-share enrollment records, satellite remote sensing of wintertime vegetation, and results of Soil and Water Assessment Tool [SWAT] water quality simulations) to estimate the environmental performance of WCC at the watershed scale, from 2008 through 2017, in the Tuckahoe Creek watershed, located within the Choptank River basin. The Choptank River is a tributary of the Chesapeake Bay, and, as a focus watershed for the USDA9s Conservation Effects Assessment Project, has been the subject of considerable study assessing linkages between land use and water quality. Farm enrollment data from the Maryland Agricultural Cost Share (MACS) program documented a large increase in the use of WCC within the Tuckahoe Creek watershed during the study period, rising from 27% of corn (Zea mays L.) fields and 9% of soybean (Glycine max L.) fields in 2008 to 89% of corn fields and 46% of soybean fields in 2016. Satellite remote sensing of wintertime ground cover detected increased wintertime vegetation following corn crops, in comparison to full season and double cropped soybean, consistent with patterns of cover crop implementation. Although interannual variation in climate strongly affected observed levels of vegetation, with warm winters resulting in increased vegetative cover, a 30-year analysis of wintertime greenness revealed significant increases in wintertime vegetation associated with increased adoption of WCC. The MACS WCC enrollment data were combined with output from the SWAT model, calibrated to streamflow and nutrient loading from the Upper Tuckahoe watershed, to estimate water quality impacts based on known distribution of cover crop species and planting dates (2008 to 2017). Results indicated a 25% overall 10-year reduction in nitrate (NO3−) leaching from cropland attributable to cover crop adoption, rising to an estimated 38% load reduction in 2016 when 64% of fields were planted to cover crops. Results suggest that increased environmental benefits would be achieved by shifting agronomic methods away from late-planted wheat (Triticum aestivum L.), which comprised 34.7% of all WCC planted between 2008 and 2017.

The USDA Natural Resources Conservation Service (NRCS) has proposed the Soil Vulnerability Index (SVI) as a standard tool to classify inherent soil vulnerability of cropland to loss of sediment and nutrients by runoff and leaching. The tool uses soil properties and topography, and does not consider crop management, except for the presence of artificial surface or subsurface drainage. For artificially drained cropland, SVI vulnerability to runoff remains unchanged but vulnerability to leaching is raised by two classes out of four to reflect the increased risk of nitrate (NO3) transport. The SVI was reviewed within different contexts, but there is a need for SVI evaluation when artificial drainage is present. Thus, the objectives of this evaluation were to (1) evaluate SVI vulnerability to runoff and leaching for artificially drained cropland, and (2) propose changes to the SVI ruleset based on the findings of Objective 1. The SVI was evaluated for eight sites with artificial drainage located in regions ranging from Idaho to Maryland. Seven sites were watersheds ranging in size from 600 to 113,600 ha, with 44% to 84% cropland consisting of row crops or small grains. The eighth site consisted of six fields ranging from 7 to 30 ha in size. Consistency between SVI vulnerability, hydrologic processes that take place on the landscape, and outcomes such as crops grown were examined, using the accumulated experience and knowledge of the coauthors of this paper. Overall, SVI vulnerability to runoff and leaching was consistent with earlier research for sites with artificial subsurface drainage unless rainfall intensities were greater than they are in the Upper Mississippi and Ohio-Tennessee River basins. SVI vulnerability to leaching was greater than expected in case of surface drainage. In addition, complex soil map units can cause incorrect vulnerability classification at field scale. At the watershed or regional scale, the leaching component should be considered both with and without artificial drainage so that the causes of the vulnerability (permeable soils or artificial drainage) can be distinguished.

In Part 1 of this two-part manuscript series, we presented an effective assessment method for mapping inundation of geographically isolated wetlands (GIWs) and quantifying their cumulative landscape-scale hydrological connectivity with downstream waters using time series remotely sensed data (Yeo et al., 2018). This study suggested strong hydrological coupling between GIWs and downstream waters at the seasonal timescale via groundwater. This follow-on paper investigates the hydrological connectivity of GIWs with downstream waters and cumulative watershed-scale hydrological impacts over multiple time scales. Modifications were made to the representation of wetland processes within the Soil and Water Assessment Tool (SWAT). A version of SWAT with improved wetland function, SWAT-WET, was applied to Greensboro Watershed, which is located in the Mid-Atlantic Region of USA, to simulate hydrological processes over 1985-2015 under two contrasting land use scenarios (i.e., presence and absence of GIWs). Comparative analysis of simulation outputs elucidated how GIWs could influence partitioning of precipitation between evapotranspiration (ET) and terrestrial water storage, and affect water transport mechanisms and routing processes that generate streamflow. Model results showed that GIWs influenced the watershed water budget and stream flow generation processes over the long-term (30 year), inter-annual, and monthly time scales. GIWs in the study watershed increased terrestrial water storage during the wet season, and buffered the dynamics of shallow groundwater during the dry season. The inter-annual modeling analysis illustrated that densely distributed GIWs can exert strong hydrological influence on downstream waters by regulating surface water runoff, while maintaining groundwater recharge and ET under changing (wetter) climate conditions. The study findings highlight the hydrological connectivity of GIWs with downstream waters and the cumulative hydrological influence of GIWs as hydrologic sources to downstream ecosystems through different runoff processes over multiple time scales.

In this study, we explore the use of different multi- and many-objective calibration approaches in hydrological modeling when considering both observed streamflow and remotely sensed actual...

Ecosystem services provided by depressional wetlands on the coastal plain of the Chesapeake Bay watershed (CBW) have been widely recognized and studied. However, wetland–groundwater interactions remain largely unknown in the CBW. The objective of this study was to examine the vertical interactions of depressional wetlands and groundwater with respect to different subsurface soil characteristics. This study examined two depressional wetlands with a low‐permeability and high‐permeability soil layer on the coastal plain of the CBW. The surface water level (SWL) and groundwater level (GWL) were monitored over 1 year from a well and piezometer at each site, respectively, and those data were used to examine the impacts of subsurface soil characteristics on wetland–groundwater interactions. A large difference between the SWL and GWL was observed at the wetland with a low‐permeability soil layer, although there was strong similarity between the SWL and GWL at the wetland with a high‐permeability soil layer. Our observations also identified a strong vertical hydraulic gradient between the SWL and GWL at the wetland with a high‐permeability soil layer relative to one with a low‐permeability soil layer. The hydroperiod (i.e., the total time of surface water inundation or saturation) of the wetland with a low‐permeability soil layer appeared to rely on groundwater less than the wetland with a high‐permeability soil layer. The findings showed that vertical wetland–groundwater interactions varied with subsurface soil characteristics on the coastal plain of the CBW. Therefore, subsurface soil characteristics should be carefully considered to anticipate the hydrologic behavior of wetlands in this region.

A Richards-equation-based soil moisture module was developed and integrated within the Soil and Water Assessment Tool (SWAT). Four years of daily soil moisture measurements from 10 monitoring stations at three depths (i.e., 5, 10, and 50 cm) in the Choptank River watershed, Maryland, were used to test the module performance. Results show that, as compared with the original SWAT soil moisture module, the Richards-equation-based soil moisture module improved R2 from 0.12 to 0.45 and reduced soil moisture simulation bias (mean[simulation] – mean[measurement]) from −0.10 to −0.02 (m3 m−3), averaged across the 10 stations at soil surface layer (i.e., 5 cm depth). Noticeable improvements were also observed for deeper soil layers, and for both dry and wet periods. Notably, the soil moisture coupling strength between different soil layers was substantially improved with the new module. The enhanced SWAT model is expected to better inform soil water and irrigation management.

Conservation practices are effective ways to mitigate non-point source pollutions, especially when implemented on critical source areas (CSAs) known as the areas contributing disproportionately high pollution loads. Although hydrologic models are promising tools to identify CSAs within agricultural landscapes, their application is limited to areas where data and modeling expertise are available. The Soil Vulnerability Index (SVI) developed by the USDA-Natural Resource Conservation Service (NRCS) Conservation Effects Assessment Project (CEAP) is regarded as a potentially powerful tool for supporting initial classification of inherent soil vulnerabilities at field scale and so could be useful for CSA identification. Its usefulness is being fully evaluated in this project and as part of a larger coordinated study. This particular study evaluated the suitability of the SVI classification scheme for identifying inherent vulnerability of cultivated soils to nitrate and organic N transport by surface runoff and nitrate leaching on two adjacent watersheds with contrasting soil drainage characteristics. We used simulated nitrate and organic N fluxes from the Soil and Water Assessment Tool (SWAT) as reference data. The results showed that the SVI runoff classification scheme was more suitable for organic N while the SVI leaching classification scheme was suited for nitrate due to pollutant transport characteristics. In addition, the SVI leaching classification scheme was more suitable for the poorly-drained croplands than the well-drained croplands. The SVI leaching classification scheme and SWAT output consistently classified nitrate leaching vulnerability based on soil drainage characteristics for the poorly-drained croplands, while the well-drained croplands were highly sensitive to a soil water content characteristic (i.e., gravitational water). Depending on the selection of reference data and test sites, however, the suitability of the SVI classification scheme could differ. Therefore, additional evaluation of the SVI using multiple validation data and sites is highly required to demonstrate its usefulness.

Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to be exacerbated by climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluated the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using the Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitivity scenarios to assess the individual impacts of variations in CO2 concentration (590 and 850 ppm), precipitation increase (11 and 21 %), and temperature increase (2.9 and 5.0 ∘C), based on regional general circulation model (GCM) projections. Further, we considered the ensemble of five GCM projections (2085–2098) under the Representative Concentration Pathway (RCP) 8.5 scenario to evaluate simultaneous changes in CO2, precipitation, and temperature. Using SWAT model simulations from 2001 to 2014 as a baseline scenario, predicted hydrologic outputs (water and nitrate budgets) and crop growth were analyzed. Compared to the baseline scenario, a precipitation increase of 21 % and elevated CO2 concentration of 850 ppm significantly increased streamflow and nitrate loads by 50 and 52 %, respectively, while a temperature increase of 5.0 ∘C reduced streamflow and nitrate loads by 12 and 13 %, respectively. Crop biomass increased with elevated CO2 concentrations due to enhanced radiation- and water-use efficiency, while it decreased with precipitation and temperature increases. Over the GCM ensemble mean, annual streamflow and nitrate loads showed an increase of ∼ 70 % relative to the baseline scenario, due to elevated CO2 concentrations and precipitation increase. Different hydrological responses to climate change were observed from the two watersheds, due to contrasting land use and soil characteristics. The watershed with a larger percent of croplands demonstrated a greater increased rate of 5.2 kg N ha−1 in nitrate yield relative to the watershed with a lower percent of croplands as a result of increased export of nitrate derived from fertilizer. The watershed dominated by poorly drained soils showed increased nitrate removal due do enhanced denitrification compared to the watershed dominated by well-drained soils. Our findings suggest that increased implementation of conservation practices would be necessary for this region to mitigate increased nitrate loads associated with predicted changes in future climate.

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73–0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error – observations standard deviation ratio (RSR) values

Winter cover crops (WCCs) have been widely implemented in the Coastal Plain of the Chesapeake Bay Watershed (CBW) due to their high effectiveness in reducing nitrate loads. However, future climate conditions (FCCs) are expected to exacerbate water quality degradation in the CBW by increasing nitrate loads from agriculture. Accordingly, the question remains whether WCCs are sufficient to mitigate increased nutrient loads caused by FCCs. In this study, we assessed the impacts of FCCs on WCC nitrate reduction efficiency in the Coastal Plain of the CBW using the Soil and Water Assessment Tool (SWAT). Three FCC scenarios (2085-2098) were prepared using general circulation models (GCMs), considering three Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) greenhouse gas emission scenarios. We also developed six representative WCC implementation scenarios based on the most commonly used planting dates and species of WCCs in this region. Simulation results showed that WCC biomass increased by ~58% under FCC scenarios due to climate conditions conducive to WCC growth. Prior to implementing WCCs, annual nitrate loads increased by ~43% under FCC scenarios compared to the baseline scenario (2001-2014). When WCCs were planted, annual nitrate loads were substantially reduced by ~48%, and WCC nitrate reduction efficiency was ~5% higher under FCC scenarios relative to the baseline scenario. The increase in WCC nitrate reduction efficiency varied with FCC scenario and WCC planting method. As CO2 concentrations were higher and winters were warmer under FCC scenarios, WCCs had greater biomass and thus demonstrated higher nitrate reduction efficiency. In response to FCC scenarios, the performance of less effective WCC practices (i.e., barley, wheat, and late planting) under the baseline scenario indicated a ~14% higher increase in nitrate reduction efficiency compared to WCC practices with greater effectiveness under the baseline scenario (i.e., rye and early planting) due to warmer temperatures. The SWAT simulation results indicated that WCCs were effective in mitigating nitrate loads accelerated by FCCs, suggesting the role of WCCs in mitigating nitrate loads will likely be even more important under FCCs. Keywords: Future climate conditions (FCCs), SWAT, Water quality, Winter cover crops (WCCs).

In this study, the spatiotemporal behavior of vegetation cover in the Kurdistan province of Iran was analyzed for the first time by TIMESAT and Breaks for Additive Season and Trend (BFAST) algorithms. They were applied on Normalized Vegetation Index (NDVI) time series from 2000 to 2016 derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The TIMESAT software package was used to estimate the seasonal parameters of NDVI and their relation to land covers. BFAST was applied for identifying abrupt changes (breakpoints) of NDVI and their magnitudes. The results from TIMESAT and BFAST were first reported separately, and then interpreted together. TMESAT outcomes showed that the lowest and highest amplitudes of NDVI during the whole time period happened in 2008 and 2010. The spatial distribution of the number of breakpoints showed different behaviors in the west and east of the study area, and the breakpoint frequency confirmed the extreme NDVI amplitudes in 2008 and 2010 found by TIMESAT. For the first time in Iran, a correlation analysis between accumulated precipitations and maximum NDVIs (from one to seven months before the NDVI maximum) was conducted. The results showed that precipitation one month before had a higher correlation with the maximum NDVIs in the region. Overall, the results describe the NDVI behavior in terms of greenness, lifetime, abrupt changes for the different land covers, and across the years, suggesting how the northwest and west of the study area can be more susceptible to drought conditions.

The potential impacts of climate change on critical source areas (CSAs) of surface runoff, sediments, nitrogen, and phosphorus were evaluated in an agricultural watershed of the Chesapeake Bay drainage basin, in the U.S. Northeast climate region. The SWAT model was calibrated for the study watershed and used to establish its baseline response and constituent CSAs under current climate (years 1990 to 2004). The calibrated model was then subjected to weather time series downscaled from the CMIP3 GFDL CM2.1 Atmosphere-Ocean Global Circulation Model (AOGCM) for IPCC SRES scenarios B1 (low emissions), A1B (medium emissions), and A2 (high emissions) to predict the watershed‘s response to climate change and identify how constituent CSAs may change under future climate (years 2046 to 2064 and 2081 to 2100). The utility of targeting best management practices (BMPs) to CSAs was assessed by computing advantage ratios that relate the fraction of watershed-generated constituents that emanate from CSAs to the fraction of watershed area occupied by these CSAs. Results indicated that, under current conditions, CSAs occupying 11% to 21% of the watershed area contribute 31% to 45% of constituents, corresponding to advantage ratios of 1.5:1 for runoff control and approximately 3:1 for other constituents. Under climate change scenario B1, constituent yields were predicted to increase by factors of 1.5 to 1.8 at the watershed outlet, from an increase in annual rainfall of 25% predicted by the AOGCM, over current conditions. Under scenarios A1B and A2, constituent yields were predicted to increase by factors of 1.8 to 2.3 over current conditions, from an increase of 30% in annual rainfall. The area of runoff CSAs was predicted to more than triple with climate change, leading to negligible advantage of targeting runoff control BMPs to CSAs under future climate. The areas of sediment, nitrogen, and phosphorus CSAs were predicted to increase by factors of 2 to 3 with climate change, causing BMP-targeting advantage ratios to decrease from approximately 3:1 (baseline) to 2:1 (future). While advantage ratios for suspended and dissolved constituents remain favorable, even under future climate, the much larger area predicted to be covered by CSAs (2 to 3 times current values) suggests that stakeholder involvement and community-oriented participatory approaches will be increasingly important for achieving Chesapeake Bay TMDLs with climate change.

Process based, distributed watershed models possess a large number of parameters that are not directly measured in field and need to be calibrated, in most cases through matching modeled in-stream fluxes with monitored data. Recently, concern has been raised regarding the reliability of this common calibration practice, because models that are deemed to be adequately calibrated based on commonly used metrics (e.g., Nash Sutcliffe efficiency) may not realistically represent intra-watershed responses or fluxes. Such shortcomings stem from the use of an evaluation criteria that only concerns the global in-stream responses of the model without investigating intra-watershed responses. In this study, we introduce a modification to the Soil and Water Assessment Tool (SWAT) model, and a new calibration technique that collectively reduce the chance of misrepresenting intra-watershed responses. The SWAT model was modified to better represent NO3 cycling in soils with various degrees of water holding capacity. The new calibration tool has the capacity to calibrate paired watersheds simultaneously within a single framework. It was found that when both proposed methodologies were applied jointly to two paired watersheds on the Delmarva Peninsula, the performance of the models as judged based on conventional metrics suffered, however, the intra-watershed responses (e.g., mass of NO3 lost to denitrification) in the two models automatically converged to realistic sums. This approach also demonstrates the capacity to spatially distinguish areas of high denitrification potential, an ability that has implications for improved management of prior converted wetlands under crop production and for identifying prominent areas for wetland restoration.

Quantifying crop residue cover is crucial for identifying tillage intensity and evaluating effectiveness of conservation management practices across large geographic areas. Current assessment protocols are labor intensive, time consuming, and costly. Our objective was to assess crop residue cover and soil tillage intensity in a watershed in central Iowa for three years (2009 to 2011) using multispectral satellite images. The watershed is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.), which are grown on glacial-till derived soils across 85% of the land area. For each year, crop residue cover was measured for a few fields using the line-point transect method or visually estimating surface cover through roadside surveys. Conservation tillage fields had ≥30% residue cover, while more intensively tilled fields had

Geographically isolated wetlands (GIWs) provide a portfolio of ecosystem services in low-gradient, Coastal Plain landscapes. Understanding how GIWs influence downstream waters is becoming increasingly important for conservation and management of these unique and important wetland ecosystems. Climatic conditions are known to be key drivers of water budgets at both individual GIW and landscape scales; however differences in hydrologic response across these scales may provide insights into how GIWs influence downstream waters. In this study, we use a combination of GIW water level, gaged streamflow, and climatic data to explore linkages between seasonal climatic drivers, GIW hydrology, and downstream discharges within the Coastal Plain of the Chesapeake Bay watershed. We first examine water balance components at the larger watershed scale, where climatic drivers result in an energy-limited wet season from December to May and a water-limited dry season from June to November. We compare long-term water levels of three GIWs with downstream discharges. GIW water level and downstream discharges are correlated at seasonal (R2: 0.52–0.60) and daily (R2: 0.52–0.76) time steps. However, during dry seasons, GIW water level receded at a faster rate than downstream discharges, highlighting the influence of evapotranspiration on surface and shallow subsurface water storage. Conversely during wet seasons, GIW water level receded slower than downstream discharges, highlighting a potential period for surface water connectivity between GIWs and downstream discharges. Cumulatively, these findings quantify the impact of seasonal climatic drivers on GIW hydrology and connectivity to downstream waters.

The coupled carbon (C) cycle across terrestrial and aquatic environments at the watershed scale has been identified as an important, but poorly constrained component of the global carbon budget. Here, we extended Soil and Water Assessment Tool (SWAT) with coupled riverine particulate organic carbon (POC) and dissolved organic carbon (DOC) modules (referred to as SWAT-C hereafter). Results show that SWAT-C reproduced daily POC and DOC fluxes well in two watersheds in the Northeastern United States. We found that SWAT-C tended to underestimate high flows and peak DOC and POC fluxes. Uncertainty analysis indicated flux uncertainties associated with POC and DOC simulations were larger than those for flow simulation. Sensitive parameters controlling POC and DOC biogeochemical processes were identified along with how these parameters influence mechanisms underlying C cycling. We anticipate that the tool developed and applied here will inform C related ecosystem services in watershed assessment and planning.

A WXGEN weather generator is commonly used to generate daily climate data for Soil and Water Assessment Tool (SWAT) model when input climate data are not fully available. Of all input data for WXGEN, precipitation is critical due to its sensitivity to the number of wet days. Since global climate model (GCM) data tend to have excessive wet days, use of GCM precipitation data for WXGEN may cause errors in the estimation of climate variables and therefore SWAT predictions. To examine such impacts of GCM data, we prepared two climate data for SWAT using WXGEN with both the original GCM data with the excessive number of wet days (EGCM) and the processed GCM data with the reasonable number of wet days (RGCM). We then compared SWAT simulations from EGCM and RGCM. Results show that because of the excessive wet days in EGCM, solar radiation generated by WXGEN was underestimated, subsequently leading to 143 mm lower ET and 0.6&ndash, 0.8 m3/s greater streamflow compared to the simulations from RGCM. Simulated crop biomass under EGCM was smaller than RGCM due to less solar radiation. Although use of WXGEN is increasing in projecting climate change impacts using SWAT, potential errors from the combination of WXGEN and GCM have not well investigated. Our findings clearly demonstrate that GCM bias (excessive wet days) leads WXGEN to generate inaccurate climate data, resulting in unreasonable SWAT predictions. Thus, GCM data should be carefully processed to use them for WXGEN.

In Part 1 of this two-part manuscript series, we presented an effective assessment method for mapping inundation of geographically isolated wetlands (GIWs) and quantifying their cumulative landscape-scale hydrological connectivity with downstream waters using time series remotely sensed data (Yeo et al., 2018). This study suggested strong hydrological coupling between GIWs and downstream waters at the seasonal timescale via groundwater. This follow-on paper investigates the hydrological connectivity of GIWs with downstream waters and cumulative watershed-scale hydrological impacts over multiple time scales. Modifications were made to the representation of wetland processes within the Soil and Water Assessment Tool (SWAT). A version of SWAT with improved wetland function, SWAT-WET, was applied to Greensboro Watershed, which is located in the Mid-Atlantic Region of USA, to simulate hydrological processes over 1985–2015 under two contrasting land use scenarios (i.e., presence and absence of GIWs). Comparative analysis of simulation outputs elucidated how GIWs could influence partitioning of precipitation between evapotranspiration (ET) and terrestrial water storage, and affect water transport mechanisms and routing processes that generate streamflow. Model results showed that GIWs influenced the watershed water budget and stream flow generation processes over the long-term (30 year), inter-annual, and monthly time scales. GIWs in the study watershed increased terrestrial water storage during the wet season, and buffered the dynamics of shallow groundwater during the dry season. The inter-annual modeling analysis illustrated that densely distributed GIWs can exert strong hydrological influence on downstream waters by regulating surface water runoff, while maintaining groundwater recharge and ET under changing (wetter) climate conditions. The study findings highlight the hydrological connectivity of GIWs with downstream waters and the cumulative hydrological influence of GIWs as hydrologic sources to downstream ecosystems through different runoff processes over multiple time scales.

Despite recognizing the importance of wetlands in the Coastal Plain of the Chesapeake Bay Watershed (CBW) in terms of ecosystem services, our understanding of wetland functions has mostly been limited to individual wetlands and overall catchment-scale wetland functions have rarely been investigated. This study is aimed at assessing the cumulative impacts of wetlands on watershed hydrology for an agricultural watershed within the Coastal Plain of the CBW using the Soil and Water Assessment Tool (SWAT). We employed two improved wetland modules for enhanced representation of physical processes and spatial distribution of riparian wetlands (RWs) and geographically isolated wetlands (GIWs). This study focused on GIWs as their hydrological impacts on watershed hydrology are poorly understood and GIWs are poorly protected. Multiple wetland scenarios were prepared by removing all or portions of the baseline GIW condition indicated by the U.S. Fish and Wildlife Service National Wetlands Inventory geospatial dataset. We further compared the impacts of GIWs and RWs on downstream flow (i.e., streamflow at the watershed outlet). Our simulation results showed that GIWs strongly influenced downstream flow by altering water transport mechanisms in upstream areas. Loss of all GIWs reduced both water routed to GIWs and water infiltrated into the soil through the bottom of GIWs, leading to an increase in surface runoff of 9% and a decrease in groundwater flow of 7% in upstream areas. These changes resulted in increased variability of downstream flow in response to extreme flow conditions. GIW loss also induced an increase in month to month variability of downstream flow and a decrease in the baseflow contribution to streamflow. Loss of all GIWs was shown to cause a greater fluctuation of downstream flow than loss of all RWs for this study site, due to a greater total water storage capacity of GIWs. Our findings indicate that GIWs play a significant role in controlling hydrological processes in upstream areas and downstream flow and, therefore, protecting GIWs is important for enhanced hydrological resilience to extreme flow conditions in this region.

Hydrological modeling of wetlands is important for reliable estimation of biogeochemical processes in soils subject to periodically inundating conditions. The present study has developed a wetland ...

Hydrologic and water quality models are increasingly used to determine the environmental impacts of climate variability and land management. Due to differing model objectives and differences in monitored data, there are currently no universally accepted procedures for model calibration and validation in the literature. In an effort to develop accepted model calibration and validation procedures or guidelines, a special collection of 22 research articles that present and discuss calibration strategies for 25 hydrologic and water quality models was previously assembled. The models vary in scale temporally as well as spatially from point source to the watershed level. One suggestion for future work was to synthesize relevant information from this special collection and to identify significant calibration and validation topics. The objective of this article is to discuss the importance of accurate representation of model processes and its impact on calibration and scenario analysis using the information from these 22 research articles and other relevant literature. Models are divided into three categories: (1) flow, heat, and solute transport, (2) field scale, and (3) watershed scale. Processes simulated by models in each category are reviewed and discussed. In this article, model case studies are used to illustrate situations in which a model can show excellent statistical agreement with measured stream gauge data, while misrepresented processes (water balance, nutrient balance, sediment source/sinks) within a field or watershed can cause errors when running management scenarios. These errors may be amplified at the watershed scale where additional sources and transport processes are simulated. To account for processes in calibration, a diagnostic approach is recommended using both hard and soft data. The diagnostic approach looks at signature patterns of behavior of model outputs to determine which processes, and thus parameters representing them, need further adjustment during calibration. This overcomes the weaknesses of traditional regression-based calibration by discriminating between multiple processes within a budget. Hard data are defined as long-term, measured time series, typically at a point within a watershed. Soft data are defined as information on individual processes within a budget that may not be directly measured within the study area, may be just an average annual estimate, and may entail considerable uncertainty. The advantage of developing soft data sets for calibration is that they require a basic understanding of processes (water, sediment, nutrient, and carbon budgets) within the spatial area being modeled and constrain the calibration.

This study aimed to statistically and hydrologically assess the performance of the four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT (CMORPH CRT), BLD (CMORPH BLD), CDR (PERSIANN CDR), 3B42 (TMPA 3B42 version 7) over the upper yellow river basins (UYRB) in china during 2001–2012 time period. The performances of the SGPEs were compared with the Chinese Meteorological Administration (CMA) datasets using the hydrologic model called Variable Infiltration Capacity (VIC) which is known as a land surface hydrologic model. Results indicated that irrespective of the slight underestimation in the western mountains and overestimation in the southeast, the four SGPEs could generally captured the spatial distribution of precipitation well. Although 3B42 exhibited a better performance in capturing the spatial distribution of daily average precipitation, BLD agreed best with CMA in the time series of watershed average precipitation, which resulted in BLD having a comparable performance to the CMA in the long-term hydrological simulations. Moreover, the potential for disastrous heavy rain mainly occurs in southeastern corner of the basin, and CRT and BLD comparisons showed to be closer to the CMA in the distribution of extreme precipitation events while 3B42 and CDR overestimated the extreme precipitation especially over the southeast of UYRB region. Therefore, CRT and BLD were able to match the high peak discharges very well for the wet seasons, while 3B42 and CDR overrated the high peak discharges. In addition, the four SGPEs performed well for the 2005 flood event but exhibited poorly when tested for the 2012 flood event. Results indicate that the application of the four SGPEs should be used with caution in simulating massive flood events over UYRB region.

High frequency in-situ measurements of nitrate can greatly reduce uncertainty in nitrate flux estimates. Water quality databases maintained by various federal and state agencies often consist of pollutant concentration data obtained from periodic grab samples collected from gauged reaches of a stream. Regression models, such as the LOAD ESTimator (LOADEST), are frequently used to model variations in concentrations associated with changes in water discharge to provide integrated solute flux measurements. However, uncertainty in the relationships between nutrient concentration and flow may lead to errors in the corresponding flux estimates. In this study, a high frequency, in-situ measurement of nitrate concentration was implemented to ascertain uncertainty in the concentration/discharge relationship caused by nitrate hysteresis. It was found that observed nitrate hysteresis, as influenced by complex storm/watershed interactions, was not readily predictable. Therefore, it can lead to substantial nitrate flux uncertainty, based on periodic grab sample monitoring approaches. Scientists and engineers should take advantage of the proposed findings in future studies to enhance the quality of the associated decision making processes.

Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to exacerbate under climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluates the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitive scenarios to assess the individual effects of variations in CO2 concentration (590 and 850 ppm), precipitation increase (11 and 21 %) and temperature increase (2.9 and 5.0 °C), and considered the predicted climate change scenario using five general circulation models (GCMs) under the Special Report on Emissions Scenarios (SRES) A2 scenario. Using SWAT model simulations from 2001 to 2014, as a baseline scenario, the predicted water and nitrate budgets under climate variability and change scenarios were analyzed at multiple temporal scales. Compared to the baseline scenario, precipitation increase of 21 % and elevated CO2 concentration of 850 ppm significantly increased stream flow and nitrate loads by 50 % and 52 %, respectively, while, temperature increase of 5.0 °C reduced stream flow and nitrate loads by 12 % and 13 %, respectively. Under the climate change scenario, annual stream flow and nitrate loads showed an average increase of nearly 40 %, relative to the baseline scenario. Differences in hydrological responses observed from the two watersheds were primarily attributed to contrasting land use and soil characteristics. The watershed with larger percent croplands indicated increased nitrate yield of 0.52 kg N ha−1 compared to the one with less percent croplands under the climate change scenario, due to increased export of nitrate derived from fertilizer. The watershed dominated by poorly-drained soils showed a lower increase in nitrate yield than one dominated by well-drained soils, due to a high potential of nitrate loss in surface runoff and enhanced denitrification. To mitigate increased nitrate loads potentially caused by climate change, the enhanced implementation of conservation practices would be necessary for this region in the future. These findings assist watershed managers and regulators as they seek to establish effective adaptation strategies to mitigate water quality degradation in this region.

Tuotuo River basin, known as the source region of the Yangtze River, is the key area where the impact of climate change has been observed on many of the hydrological processes of this central region of the Tibetan Plateau. In this study, we examined six Global Climate Models (GCMs) under three Representative Concentration Pathways (RCPs) scenarios. First, the already impacted climate change was analyzed, based on the historical data available and then, the simulation results of the GCMs and RCPs were used for future scenario assessments. Results indicated that the annual mean temperature will likely be increased, ranging from −0.66 °C to 6.68 °C during the three future prediction periods (2020s, 2050s and 2080s), while the change in the annual precipitation ranged from −1.18% to 66.14%. Then, a well-known distributed hydrological soil vegetation model (DHSVM) was utilized to evaluate the effects of future climate change on the streamflow dynamics. The seasonal mean streamflows, predicted by the six GCMs and the three RCPs scenarios, were also shown to likely increase, ranging from −0.52% to 22.58%. Watershed managers and regulators can use the findings from this study to better implement their conservation practices in the face of climate change.

Climate change is expected to exacerbate water quality degradation in the Chesapeake Bay Watershed (CBW). Winter cover crops (WCCs) have been widely implemented in this region due to their high effectiveness at reducing nitrate loads. However, little is known about climate change impacts on the effectiveness of WCCs for reducing nitrate loads. The objective of this study is to assess climate change impacts on WCC nitrate uptake efficiency on the Coastal Plain of the CBW using Soil and Water Assessment Tool (SWAT) model. We prepared climate change scenarios using General Circulation Models (GCMs) under three greenhouse emission scenarios (e.g., A1B, A2, and B1). Simulation results showed that WCC biomass increased by ~ 58 % under climate change scenarios, due to climate conditions conducive to WCC growth. Prior to WCC implementation, annual nitrate loads increased by ~ 43 % (5.3 kg N·ha-1) under climate change scenarios compared to the baseline scenario. When WCCs were planted, nitrate loads were substantially reduced and WCC nitrate reduction efficiency increased by ~ 5 % under climate change scenarios relative to the baseline, due to increased WCC biomass. Therefore, the role of WCCs in mitigating nitrate loads should increase in the future given predicted climate change.

Over 50% of streams in the Chesapeake Bay watershed have been rated as poor or very poor based on the index of biological integrity. The Choptank River estuary, a Bay tributary on the eastern shore, is one such waterway, where corn and soybean production in upland areas of the watershed contribute significant loads of nutrients and sediment to streams. We adopted a novel approach utilizing the relationship between the concentration of nitrate-N and the stable, water-soluble herbicide degradation product MESA {2-[2-ethyl-N-(1-methoxypropan-2-yl)-6-methylanilino]-2-oxoethanesulfonic acid} to distinguish between dilution and denitrification effects on the stream concentration of nitrate-N in agricultural subwatersheds. The ratio of mean nitrate-N concentration/(mean MESA concentration * 1000) for 15 subwatersheds was examined as a function of percent cropland on hydric soil. This inverse relationship (R2 = 0.65, p

This study presents an integrated wetland-watershed modeling approach that capitalizes on inundation maps and geospatial data to improve spatial prediction of wetland inundation and assess its prediction uncertainty. We outline problems commonly arising from data preparation and parameterization used to simulate wetlands within a (semi-) distributed watershed model. We demonstrate how wetland inundation can be better captured by the wetland parameters developed from remotely sensed data. We then emphasize assessing model prediction using inundation maps derived from remotely sensed data. This integrated modeling approach is tested using the Soil and Water Assessment Tool (SWAT) with an improved riparian wetlands (RWs) extension, for an agricultural watershed in the Mid-Atlantic Coastal Plain, US. This study illustrates how spatially distributed information is necessary to predict inundation of wetlands and hydrologic function at the local landscape scale, where monitoring and conservation decision making take place.

Wetlands play an important role in watershed hydrology and biogeochemistry. A geographically isolated wetland (GIW) module for Soil and Water Assessment Tool (SWAT) was developed to couple surface water storage and subsurface water dynamics to characterize hydroperiod. The new GIW module includes the following features: (1) a flexible geometric formula to characterize wetland surface water area, volume, and depth; (2) a revised algorithm to account for evaporation from both water and soil surfaces in wetlands; (3) application of the Richards equation to couple surface water storage and subsurface water dynamics; (4) use of Darcy's law with an effective hydraulic conductivity parameter to simulate groundwater discharge. We tested the GIW module using observed daily water level data from four wetlands at two sites (including restored and natural wetlands with and without a low-permeability soil layer) in the Delmarva Peninsula, USA. The results show that the wetland module reasonably reproduced observed water levels for both restored and natural wetlands with and without a low-permeability soil layer. The module was also able to reasonably simulate saturated and unsaturated portions of the soil corresponding to wet and dry periods. The ability of the GIW module to describe inundation conditions for wetlands holds promise to enhance the understanding and quantification of hydrological and biogeochemical roles of GIWs in a watershed context.

The Agricultural Policy/Environmental eXtender (APEX) model has been widely used to assess changes in agrochemical loadings in response to conservation and management led by US Department of Agriculture (USDA). However, the existing APEX model is limited in quantification of wetland water quality functions. This study improved the current model capacity to represent wetland water quality functions by addition of a new biogeochemical module into the APEX model. The performance of an enhanced APEX model was tested against five observed outgoing water quality variables (e.g., sediment, organic N, NO3, NH4 and PO4) from a wetland within the Eastern Shore of Maryland. Generalized Likelihood Uncertainty Estimation (GLUE) was implemented to assess model uncertainty. The enhanced APEX model demonstrated that it could effectively represent N and P cycling within the study wetland. Although improvement of model performance was limited, the additions of wetland biogeochemical routines to the APEX model improved our understanding of inner mass exchanges within N and P cycling for the study wetland. Overall, the updated APEX model can provide policymakers and managers with improved means for assessment of benefits delivered by wetland conservation.

With enhanced data availability, distributed watershed models for large areas with high spatial and temporal resolution are increasingly used to understand water budgets and examine effects of human activities and climate change/variability on water resources. Developing parallel computing software to improve calibration efficiency has received growing attention of the watershed modeling community as it is very time demanding to run iteratively complex models for calibration. In this research, we introduce a Python-based parallel computing package, PP-SWAT, for efficient calibration of the Soil and Water Assessment Tool (SWAT) model. This software employs Python, MPI for Python (mpi4py) and OpenMPI to parallelize A Multi-method Genetically Adaptive Multi-objective Optimization Algorithm (AMALGAM), allowing for simultaneously addressing multiple objectives in calibrating SWAT. Test results on a Linux computer cluster showed that PP-SWAT can achieve a speedup of 45-109 depending on model complexity. Increasing the processor count beyond a certain threshold does not necessarily improve efficiency, because intensified resource competition may result in an I/O bottleneck. The efficiency achieved by PP-SWAT also makes it practical to implement multiple parameter adjustment schemes operating at different scales in affordable time, which helps provide SWAT users with a wider range of options of parameter sets to choose from for model(s) selection. PP-SWAT was not designed to address errors associated with other sources (e.g. model structure) and cautious supervision of its power should be exercised in order to attain physically meaningful calibration results. Integrating Python, mpi4py, OpenMPI to efficiently perform parallel calibration of SWAT using AMALGAM.Enhancing calibration by applying multipliers and addends at higher resolutions than is typically done for SWAT.Discussion of implications of model structure setup for SWAT calibration.

Release of manure components is an important element of modeling applications in environmental water quality. The scarcity of experimental data and the multiplicity of the approaches for modeling release kinetics of the manure components introduce uncertainty and reduce reliability of overland flow and contaminant transport models. The goal of this study was to estimate release parameters for different manure components and provide input for KINEROS2/STWIR model developed for pathogen risk assessment associated with livestock operations. The objectives of this work were to evaluate reliability and robustness of the manure release parameters estimated based on individual and grouped release kinetics of soluble, particulate and combination of particulate and soluble materials from surface applied manure. The parameters of Bradford–Schijven model were evaluated from the experimental data on release of chloride, water-extractable phosphate-P, total bioactive P, organic carbon, enterococci and E. coli from surface applied manure measured in the runoff-box and runoff-plot experiments. The results showed that release of different manure components from surface applied manure can be reliably predicted with just a single set of parameters characterizing the kinetics of manure mass release. We demonstrated that the manure release parameters could be estimated more reliably when the model fit was performed using data for different manure components pooled together, while the model fit to a single release curve produced correlated parameters. The model parameters appeared to be robust and transferable from the calibration to validation datasets without any or with only minor losses of the model accuracy.

Wetlands are an integral part of many agricultural watersheds. They provide multiple ecosystem functions, such as improving water quality, mitigating flooding, and serving as natural habitats. Those functions are highly depended on wetland hydrological characteristics and their connectivity to the downstream waters. However, wetland hydrology has been poorly understood at the watershed scale. In this study, we simulated the Soil and Water Assessment Tool (or SWAT) model along with the inclusion of a Riparian Wetland Module (RWM) at Tuckahoe Creek, a sub-watershed within the Choptank River watershed on the Coastal Plain of the Chesapeake Bay watershed. The RWM, a SWAT extension module, was adopted to better simulate interactions between riparian wetlands and nearby streams. The SWAT-RWM was calibrated and validated against observed streamflow collected at the outlet of the watershed and then applied for assessing wetland hydrological benefits. The poster will demonstrate the hydrological benefits of wetlands to stabilize overall flow pattern and reduce peak flow at the storm event. The outcomes of this study will contribute to the enhanced understanding of the hydrological role of wetlands in the watershed.

Excess nutrients and agrochemicals from non-point sources contribute to water quality impairment in the Chesapeake Bay watershed and their loading rates are related to land use, agricultural practices, hydrology, and pollutant fate and transport processes. In this study, monthly baseflow stream samples from 15 agricultural subwatersheds of the Choptank River in Maryland USA (2005 to 2007) were characterized for nutrients, herbicides, and herbicide transformation products. High-resolution digital maps of land use and forested wetlands were derived from remote sensing imagery. Examination of landscape metrics and water quality data, partitioned according to hydrogeomorphic class, provided insight into the fate, delivery, and transport mechanisms associated with agricultural pollutants. Mean Nitrate-N concentrations (4.9 mg/L) were correlated positively with percent agriculture (R2 = 0.56) and negatively with percent forest (R2 = 0.60). Concentrations were greater (p = 0.0001) in the well-drained upland (WDU) hydrogeomorphic region than in poorly drained upland (PDU), reflecting increased denitrification and reduced agricultural land use intensity in the PDU landscape due to the prevalence of hydric soils. Atrazine and metolachlor concentrations (mean 0.29 μg/L and 0.19 μg/L) were also greater (p = 0.0001) in WDU subwatersheds than in PDU subwatersheds. Springtime herbicide concentrations exhibited a strong, positive correlation (R2 = 0.90) with percent forest in the WDU subwatersheds but not in the PDU subwatersheds. In addition, forested riparian stream buffers in the WDU were more prevalent than in the PDU where forested patches are typically not located near streams, suggesting an alternative delivery mechanism whereby volatilized herbicides are captured by the riparian forest canopy and subsequently washed off during rainfall. Orthophosphate, CIAT (6-chloro-N-(1-methylethyl)-1,3,5-triazine-2,4-diamine), CEAT (6-chloro-N-ethyl-1,3,5-triazine-2,4-diamine), and MESA (2-[(2-ethyl-6-methylphenyl) (2-methoxy-1-methylethyl)amino]-2-oxoethanesulfonic acid) were also analyzed. These findings will assist efforts in targeting implementation of conservation practices to the most environmentally-critical areas within watersheds to achieve water quality improvements in a cost-effective manner.

Concerns for microbial safety of surface water facilitate development of predictive models that estimate concentrations and total numbers of pathogen and indicator organisms leaving manure-fertilized fields in overland flow during runoff events. Spatial variability of bacterial concentrations in applied manure introduces high uncertainty in the model predictions. The objective of this work was to evaluate the uncertainty in model predictions of the manure-borne bacteria overland transport caused by limited information on the spatial distribution of bacteria in surface-applied manure. Experiments were carried out at the ARS Beltsville experimental watershed site (OPE3) in Maryland. Dairy bovine manure was applied at a 59·3 t/ha rate on the 3·55 hectare experimental field. Faecal coliform (FC) concentrations in manure measured in 2004, 2005, 2007, and 2009 varied by 4 orders of magnitude each year. Both runoff volume and FC concentrations in runoff water were monitored using a runoff flume equipped with a refrigerated pump sampler. Two runoff events occurred before the manure was incorporated into the soil. A bacteria transport add-on module simulator of transport with infiltration and runoff (STWIR) was linked with the event-based kinematic runoff and erosion model (KINEROS2) to simulate convective-dispersive overland transport, bacteria release from manure, reversible attachment–detachment to soil, and surface straining of infiltrating bacteria. The model was successfully calibrated with the field experiment data. Monte Carlo simulations were carried out to account for the spatial variation in FC in applied manure and uncertainty in the FC distribution in manure caused by the small number of samples. A tenfold and twofold variation in FC concentrations in the runoff were obtained within the 90% probability interval when initial FC spatial distributions in the manure were represented by 5 and 29 samples, respectively. Copyright © 2011 John Wiley & Sons, Ltd.

Watershed models are increasingly being utilized to evaluate alternate management scenarios for improving water quality. The concern for using these tools in extensive programs such as the national Total Maximum Daily Load (TMDL) program is that the certainty of model results and efficacy of management scenarios are not often measured and therefore are not well known. In this study, we used the mean value first-order reliability method (MFORM), a computationally efficient uncertainty analysis method, to determine the contribution of parameter uncertainty to total model uncertainty in streamflow, sediment, and nutrient outputs in a small Maryland watershed. Examination of sensitive and uncertain parameters revealed that parameters not considered highly sensitive contributed to model output uncertainty to a large extent. Therefore, highly sensitive parameters should not be the only parameters considered in uncertainty or calibration analyses. Measures of output uncertainty showed that sediment had the largest amount of variance from its mean value (CV = 28%), while nitrate, phosphate, and streamflow had considerably less variance, with annual average CVs of 19%, 17%, and 15%, respectively. The largest amounts of model uncertainty occurred during wet periods. This study concluded that with improved knowledge of the true value and associated uncertainty of input parameters, and improved algorithms to capture the variability of rainfall and associated flow, watershed water quality models will be of much greater use to TMDL studies and studies alike.

Hydrologic models are used to assess many water resource problems, from agricultural use and water quality to engineering issues. This study shows how the combination of rain gauges (accurate at a point) and NEXRAD precipitation data (covering a wide area) can provide accurate, timely rainfall data that can drive the Soil and Water Assessment Tool (SWAT) model, which provides a more reliable tool for producers and policy-makers alike. The treatment of rainfall data was tested in the South Fork of the Iowa River, which covers about 788 km2 (194,720 ac) and is one of 15 benchmark watersheds of the USDA Conservation Effects Assessment Project (CEAP). The results show significant improvement in model predictions in which rainfall data were correctly modified. Gauge records with values for trace events performed better as data sources than records in which trace events were given zero value, even though the mean annual rainfall differed by

The U.S. EPA's Total Maximum Daily Load (TMDL) program has encountered hindrances in its implementation partly because of its strong dependence on mathematical models to set limitations on the release of impairing substances. The uncertainty associated with predictions of such models is often not scientifically quantified and typically assigned as an arbitrary margin of safety (MOS) in the TMDL allocation. The Soil Water Assessment Tool (SWAT) model was evaluated to determine its applicability to identify the impairment status and tabulate a nutrient TMDL for a waterbody located in the Piedmont physiographic region of Maryland. The methodology for tabulating the nutrient TMDL is an enhancement over current methods used in Maryland. The mean-value first-order reliability method (MFORM) was paired with a stochastic approach to tabulate a science-based estimate of model uncertainty and MOS for the TMDL approach. Monthly streamflow estimates were quite good, with Nash-Sutcliffe efficiency (NSE) coefficients of 0.75 and 0.70 for the calibration and validation phases, respectively. Sediment and nutrients were not estimated as well as streamflow on a monthly basis; however, large improvements in model estimation were observed on an annual time scale. MOS was determined based on the desired level of confidence in meeting the water quality standard. The water quality standard was met at 20% nitrate reduction (9.9 kg N d-1) with a 37.5% level of confidence. The water quality goal was met by a 30% reduction in nitrate load (8.6 kg N d-1), in which case there was a 75% chance of meeting the water quality standard. Therefore, the MOS load (the difference between the standard and the goal) was 1.3 kg N d-1 or 10% of the baseline load. These results indicate that SWAT is a suitable model for use in TMDL assessments of impaired water bodies, especially assessments based on long-term simulations. In addition, the stochastic method used to quantify MOS for a nitrate TMDL is an improvement over current methods because it provides a formal, scientifically derived measure of model uncertainty.

Riparian forest (CP22) buffers are implemented in the Chesapeake Bay Watershed to trap pollutants in surface runoff thus minimizing the amount of pollutants entering the stream network. For these buffers to function effectively, overland flow must enter the riparian zones as dispersed sheet flow to facilitate slowing, filtering, and infiltrating of surface runoff. The occurrence of concentrated flowpaths, however, is prevalent across the watershed. Concentrated flowpaths limit buffer filtration capacity by channeling overland flow through or around buffers. In this study, two topographic metrics (topographic openness and flow accumulation) were used to evaluate the occurrence of concentrated flowpaths and to derive effective CP22 contributing areas in four Long-Term Agroecosystem Research (LTAR) watersheds within the Chesapeake Bay Watershed. The study watersheds include the Tuckahoe Creek watershed (TCW) located in Maryland, and the Spring Creek (SCW), Conewago Creek (CCW) and Mahantango Creek (MCW) watersheds located in Pennsylvania. Topographic openness identified detailed topographic variation and critical source areas in the lower relief areas while flow accumulation was better at identifying concentrated flowpaths in higher relief areas. Results also indicated that concentrated flowpaths are prevalent across all four watersheds, reducing CP22 effective contributing areas by 78% in the TCW, 54% in the SCW, 38% in the CCW and 22% in the MCW. Thus, to improve surface water quality within the Chesapeake Bay Watershed, the implementation of riparian forest buffers should be done in such a way as to mitigate the effects of concentrated flowpaths that continue to short-circuit these buffers.

A module to simulate bacteria fate and transport in watersheds was first tested in SWAT 2000 and fully integrated into the SWAT2005 code. Since then, few investigators have utilized SWAT to model bacteria fate and transport in spite of bacteria being a major cause of streams impairment in the U.S. In this article, bacteria equations are briefly presented. Modeling applications, which range from 16 to 3,870 km2, from Missouri, Kansas, and Georgia in the U.S. and from Brittany in France, are reviewed, highlighting the modeling successes and the challenges. In all cases, land use included agricultural and forested land with a mix of point and nonpoint sources. Nonpoint sources included indirect (manure deposited on land) and direct contributions from cattle or wildlife to the streams. In some cases, urban and residential contributions were included. Strategies to represent the different sources, calibration methods, and goodness of fit were compared. Changes to the model's code that were necessary to handle contributions from urban areas were reviewed. Overall, SWAT reasonably simulated the range and frequencies of bacteria concentrations. In all cases, direct bacteria inputs into streams appeared to have a major impact on the model results. This review also indicates that the model processes that simulate the release and transport of bacteria in surface runoff may need to be revisited. This improvement could enable SWAT to be more reliable for predicting bacteria concentrations and evaluating the impact of different management scenarios on bacteria contributions to surface water resources.

The value of watershed-scale, hydrologic and water quality models to ecosystem management is increasingly evident as more programs adopt these tools to evaluate the effectiveness of different management scenarios and their impact on the environment. Quality of precipitation data is critical for appropriate application of watershed models. In small watersheds, where no dense rain gauge network is available, modelers are faced with a dilemma to choose between different data sets. In this study, we used the German Branch (GB) watershed (~50 km2), which is included in the USDA Conservation Effects Assessment Project (CEAP), to examine the implications of using surface rain gauge and next-generation radar (NEXRAD) precipitation data sets on the performance of the Soil and Water Assessment Tool (SWAT). The GB watershed is located in the Coastal Plain of Maryland on the eastern shore of Chesapeake Bay. Stream flow estimation results using surface rain gauge data seem to indicate the importance of using rain gauges within the same direction as the storm pattern with respect to the watershed. In the absence of a spatially representative network of rain gauges within the watershed, NEXRAD data produced good estimates of stream flow at the outlet of the watershed. Three NEXRAD datasets, including (1) non-corrected (NC), (2) bias-corrected (BC), and (3) inverse distance weighted (IDW) corrected NEXRAD data, were produced. Nash-Sutcliffe efficiency coefficients for daily stream flow simulation using these three NEXRAD data ranged from 0.46 to 0.58 during calibration and from 0.68 to 0.76 during validation. Overall, correcting NEXRAD with rain gauge data is promising to produce better hydrologic modeling results. Given the multiple precipitation datasets and corresponding simulations, we explored the combination of the multiple simulations using Bayesian model averaging. The results show that this Bayesian scheme can produce better deterministic prediction than any single simulation and can provide reasonable uncertainty estimation. The optimal water balance obtained in this study is an essential precursor to acquiring realistic estimates of sediment and nutrient loads in future GB modeling efforts. The results presented in this study are expected to provide insights into selecting precipitation data for watershed modeling in small Coastal Plain catchments.