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Input data acquisition and preprocessing is time-consuming and difficult to handle and can have major implications on environmental modeling results. US EPA’s Hydrological Micro Services Precipitation Comparison and Analysis Tool (HMS-PCAT) provides a publicly available tool to accomplish this critical task. We present HMS-PCAT’s software design and its use in gathering, preprocessing, and evaluating precipitation data through web services. This tool simplifies catchment and point-based data retrieval by automating temporal and spatial aggregations. In a demonstration of the tool, four gridded precipitation datasets (NLDAS, GLDAS, DAYMET, PRISM) and one set of gauge data (NCEI) were retrieved for 17 regions in the United States and evaluated on 1) how well each dataset captured extreme events and 2) how datasets varied by region. HMS-PCAT facilitates data visualizations, comparisons, and statistics by showing the variability between datasets and allows users to explore the data when selecting precipitation datasets for an environmental modeling application.

Accurate modeling of crop growth within watershed hydrological models is essential, yet most studies pay little attention to parameterizing crop-growth sub-models or validating their performance. This study evaluated crop sub-model parameters of Soil and Water Assessment Tool (SWAT), a widely used, physically based, hydrological model. Baseline SWAT crop parameters were calibrated at the model hydrologic-response-unit-scale using 10 years of replicated field-scale data at one site and validated using 5 years at a second site for corn and grain sorghum, and new parameters were developed and tested for sweet sorghum (bioenergy crop) using 4 years of unreplicated field data. Calibration of crop yields focused on four parameters: lower harvest index (WYSF), harvest index for optimal growing condition (HVSTI), radiation use efficiency (BIO_E), and maximum leaf area index (BLAI). Calibration improved model performance and resulted in slight changes to SWAT default values for four parameters for corn and sorghum. These results provide important preliminary parameters for modeling sweet sorghum in SWAT; both BIO_E and BLAI were greater than default values for grain sorghum. Calibrated parameters improved model performance in validation of corn but not grain sorghum, which was heavily influenced by drought conditions and possibly other management differences at the validation site. Results of this study support use of sitespecific, rather than default or off-site, calibration of crop-model parameters to minimize effects on model performance of different soil, water, and nutrient management conditions. Watershed-specific, field-scale crop-yield calibration methods demonstrated in this study are recommended to reduce the

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.

6Many conservation programs have been established to motivate producers to adopt best management 7 practices (BMP) to minimize pasture runoff and nutrient loads, but a process is needed to assess BMP 8 effectiveness to help target implementation efforts. A study was conducted to develop and demonstrate a 9 method to evaluate water-quality impacts and the effectiveness of two widely used BMPs on a livestock 10 pasture: off-stream watering site and stream fencing. The Soil and Water Assessment Tool (SWAT) 11 model was built for the Pottawatomie Creek Watershed in eastern Kansas, independently calibrated at the 12 watershed outlet for streamflow and at a pasture site for nutrients and sediment runoff, and also employed 13 to simulate pollutant loads in a synthetic pasture. The pasture was divided into several subareas including 14 stream, riparian zone, and two grazing zones. Five scenarios applied to both a synthetic pasture and a 15 whole watershed were simulated to assess various combinations of widely used pasture BMPs: (1) proposed methodology provides an adaptable framework for pasture BMP assessment and was utilized to 26 represent a consistent, defensible process to quantify the effectiveness of BMP proposals in a BMP 27 auction in eastern Kansas. 28