Suchergebnisse

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 (NO3-), phosphorus (P), and sediment, and emissions of nitrous oxide (N2O) and di-nitrogen (N-2). Modelaverage (+/- 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%), NO3-(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 N2O (0.3 +/- 4.8%) and N-2 (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 NO3-(-29.1 +/- 27.8%) are driven by greater evapotranspiration from increasing summer temperatures. Decreases in N2O (-26.9 +/- 15.7%) and N-2 (-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. (C) 2018 Elsevier B.V. All rights reserved. ; National Science FoundationNational Science Foundation (NSF) [1360415, 1343802]; USDAUnited States Department of Agriculture (USDA) [2012-67019-19434] ; We would like to acknowledge high-performance computing support from Yellowstone (http://n2t.net/ark:/85065/d7wd3xhc) provided by NCAR's Computational and Information Systems Laboratory, support from the National Science Foundation under award numbers 1360415 and 1343802, and funding support from the USDA under project number 2012-67019-19434. All data, methods, and code used in this manuscript are available upon request. ; Public domain authored by a U.S. government employee