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Hydrological models require complete and accurate weather data time series to represent watershed‐scale responses adequately. The Global Historical Climatology Network (GHCN) is the most comprehensive weather database used in hydrological modelling studies globally. Since higher‐density, lower‐reliability precipitation measurements from private citizens collected by the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network data were integrated into the GHCN, hydrological modellers in the United States have access to a much greater amount of weather data. However, the benefit of using CoCoRaHS data has not been assessed. The objectives of this work were to develop a method for generating a complete weather data time series based on the combination of data from multiple GHCN monitors and to assess several methods for the estimation of missing weather data. Weather data from GHCN monitors located within a specific radius of a watershed were obtained and interpolated using three estimation methods (Inverse Distance Weighting (IDW), Inverse Distance and Elevation Weighting (IDEW) and Closest Station), creating a seamless time series of weather observations. To evaluate the performance of the methodologies, weather data obtained from each estimation method was used to force the Soil and Water Assessment Tool (SWAT) and Thornthwaite‐Mather models for 21 US Department of Agriculture‐Conservation Effects Assessment Project watersheds in different climate regions to simulate daily streamflow for 2010–2021. Except for three watersheds, all of the SWAT models had Nash‐Sutcliffe Efficiency above 0.5, the ratio of the root mean square error to the standard deviation of observations below 0.7, and percent bias from −25% to 25% with a satisfactory performance rating. IDEW and IDW performed similarly, and the Closest Station method resulted in the poorest streamflow simulation. A comparison with published SWAT model results further corroborated improved model performance using novel combined GHCN data with all Closest Station, IDW and IDEW methods. [ABSTRACT FROM AUTHOR]

Many U.S. states require farmers to evaluate the risk of P loss from farm fields using P site assessment tools, such as the Phosphorus Index (P‐Index). The simplicity of the P‐Index allows for users with various backgrounds to utilize the tool; however, the P‐Index may be limited in its ability to reliably predict P losses across variable farm terrain, for instance where saturation‐excess runoff generation dominates. The objective of this study was to identify potential improvements that could be made to the Vermont P‐Index (VT P‐Index) by comparing VT P‐Index predictions of P loss with those of a model that considers topographic controls on P transport. Scenarios with varying P management strategies were developed and modeled for corn (Zea mays L.) silage production fields in a small agricultural watershed (360 ha) located in Vermont using both the VT P‐Index and a modified version of the Soil and Water Assessment Tool (SWAT). Modeled outputs from both the VT P‐Index and SWAT were aggregated based on topographic index (TI) class and compared for the directional agreement of P risk assessments and relative annual average dissolved and particulate P losses. The P‐Index nutrient management recommendations gave higher P loss risk ratings than SWAT for 94% of the 120 unique scenario combinations simulated. Results from this study suggest that the VT P‐Index could improve its ability to support farm nutrient management planning by incorporating topographic controls on runoff production into its estimation of P transport. Core Ideas: Consideration of topographic factors can improve estimation of dissolved P loss in the VT P‐Index.Best management practice effectiveness can be increased in the VT P‐Index by accounting for VSA hydrology.Generally, the VT P‐Index resulted in a higher risk of P loss rating than SWAT‐VSA. [ABSTRACT FROM AUTHOR]

Phosphorus flux from agricultural landscapes to surface waters may cause eutrophication. In the northeastern United States, P transport largely depends on P sorption of soils in variable source areas or in land treatment systems. Soil redox fluctuation commonly occurs in these areas. Nevertheless, the effect of soil redox on P sorption has been variable in the literature. This study investigated P sorption of an organic-rich northeastern glaciated silt loam (Langford) under air-dried, field-wet, and reduced conditions using batch P sorption experiments. Additionally, the influence of farm wastewater on soil P sorption was studied. Major results indicated that soil reduction increased the maximum amount of P that can be sorbed (Smax) and decreased the aqueous P concentration at which P sorption and desorption are equal (EPC0), both determined from a modified Langmuir isotherm model. The slightly reduced field-wet soils had no significant difference in Smax due to limited soil reduction. Using the diluted wastewater as the sorption solution matrices instead of 0.01 mol L-1 KCI solution, the soils generally exhibited greater Smax and lower EPC0 except for the EPC∅ of a reduced surface soil, implying more complex P sorption in the field. Identified P sorption mechanisms include phosphate precipitation, ligand exchange with organic matter, and adsorption onto Fe hydroxides. Transformation of Fe compounds during soil reduction is primarily responsible for the changes in soil P sorption. [ABSTRACT FROM AUTHOR]