Nutrient export from agricultural landscapes is a water quality concern and the cause of mitigation activities worldwide. Climate change impacts hydrology and nutrient cycling by changing soil moisture, stoichiometric nutrient ratios, and soil temperature, potentially complicating mitigation measures. This research quantifies the impact of climate change and climate anomalies on hydrology, nutrient cycling, and greenhouse gas emissions in an agricultural catchment of the Chesapeake Bay watershed. We force a calibrated model with seven downscaled and bias-corrected regional climate models and derived climate anomalies to assess their impact on hydrology and the export of nitrate (NO-), phosphorus (P), and sediment, and emissions of nitrous oxide (NO) and di-nitrogen (N). Model-average (±standard deviation) results indicate that climate change, through an increase in precipitation and temperature, will result in substantial increases in winter/spring flow (10.6 ± 12.3%), NO- (17.3 ± 6.4%), dissolved P (32.3 ± 18.4%), total P (24.8 ± 16.9%), and sediment (25.2 ± 16.6%) export, and a slight increases in NO (0.3 ± 4.8%) and N (0.2 ± 11.8%) emissions. Conversely, decreases in summer flow (-29.1 ± 24.6%) and the export of dissolved P (-15.5 ± 26.4%), total P (-16.3 ± 20.7%), sediment (-20.7 ± 18.3%), and NO- (-29.1 ± 27.8%) are driven by greater evapotranspiration from increasing summer temperatures. Decreases in NO (-26.9 ± 15.7%) and N (-36.6 ± 22.9%) are predicted in the summer and driven by drier soils. While the changes in flow are related directly to changes in precipitation and temperature, the changes in nutrient and sediment export are, to some extent, driven by changes in agricultural management that climate change induces, such as earlier spring tillage and altered nutrient application timing and by alterations to nutrient cycling in the soil.

10Greenhouse gas (GHG) emissions from agroecosystems, particularly nitrous oxide (N 2 O), are an 11 increasing concern. To quantify N 2 O emissions from agroecosystems, which occur as a result of nitrogen 12 (N) cycling, a new physically based routine was developed for the Soil and Water Assessment Tool 13 (SWAT) model to predict N 2 O flux during denitrification and an existing nitrification routine was 14 modified to capture N 2 O flux during this process. The new routines predict N 2 O emissions by coupling 15 the carbon (C) and N cycles with soil moisture/temperature and pH in SWAT. The model uses reduction 16 functions to predict total denitrification (N 2 + N 2 O) and partitions N 2 from N 2 O using a ratio method. The 17 modified SWAT nitrification routine likewise predicts N 2 O emissions using reduction functions. The new 18 denitrification routine and modified nitrification routine were tested using GRACEnet data at University19 Park, Pennsylvania, and West Lafayette, Indiana. Results showed strong correlations between plot 20 measurements of N 2 O flux and the model predictions for both test sites and suggest that N 2 O emissions 21 are particularly sensitive to soil pH and soil N, and moderately sensitive to soil temperature/moisture 22 and total soil C levels. 23 24 26 27 Software Availability 28 Model name: SWAT-GHG model. Developed by M.B. Wagena (bwmoges4@vt.edu) and Z.M. Easton 29 (zeaston@vt.edu), While these models are capable of predicting N 2 O emissions under relatively controlled and known 64conditions, these models can be challenging to apply outside of the range of conditions for which they 65 were developed and thus have limited utility to drive landscape management or predict the effects of 66 processes such as climate change. 67Most of the recent advances in N 2 O emission models have been made in process-based modeling, which 68 can generally be classified in to three model types (Parton et al., 1996): (1) microbial growth models, (2) 69 soil structure models, and (3) physically-based models. 70Microbial growth models simulate N 2 O emissions by representing the dynamics of the microbial 71 community (Heinen, 2006; Parton et al., 1996). Examples of such models include the DENLEFWAT model 72 (Leffelaar, 1988;Leffelaar et al., 1988), DNDC model (Frolking et al., 1992Li et al., 1997), NLOSS model 73 (Riley et al., 2000), ECOSYS model (Metivier et al., 2009), and the RZWQM model (Shaffer et al., 2001). 74Factors that affect the microbial growth rate in these models are the soil N and C content, soil 75 temperature, soil pH and soil moisture content. Microbial growth rates are assumed to be an estimate 76 of the N 2 O emission potential of a system that is higher microbial growth rates translate to higher N 2 O 77 emissions. The strength of these models is the representation of microbial growth and activity in the 78 model. This includes the number and type of microbes, the community structure and the death and 79 growth of microbes over time. 80Soil structural models are based on soil phy...

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The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure.

Few current modeling tools are designed to predict short-term, high-risk runoff from hydrologically sensitive areas (HSAs) in watersheds. This study couples the Soil and Water Assessment Tool-Variable Source Area model with the Climate Forecast System Reanalysis model and the Global Forecast System-Model Output Statistics model short term weather forecast, to develop a HSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for stream flow and soil moisture level were estimated in the South Fork of the Shenandoah river watershed. Daily volumetric flow forecasts were found to be satisfactory four days into the future, and distributed model predictions accurately captured sub-field scale HSAs. The model has the potential to provide valuable forecasts that can be used to improve the effectiveness of agricultural management practices and reduce the risk of non-point source pollution.