Stochastic Downscaling of Projected Great Lakes Regional Climate and Implications for Maize Production and Nitrogen in Agricultural Landscapes

Authors: William Baule*, Michigan State University, Jeff Andresen, Michigan State University, Amor VM Ines, Michigan State University
Topics: Climatology and Meteorology, Agricultural Geography, Water Resources and Hydrology
Keywords: Climate Change, Precipitation, Maize, Nitrogen, Agriculture
Session Type: Poster
Day: 4/4/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded

Credible projections of future climate scenarios are essential for assessing potential impacts from a changing climate on a wide array of systems. This is particularly true for agriculture, where in addition to the distribution of climatic variables, the temporal sequencing of weather and climatic events is crucial for accurate simulations of physiological and biogeochemical processes. There is generally a spatial and temporal mismatch between the resolution of output from climate models and the resolution required for impact models. This mismatch necessitates downscaling of climate model output to finer spatial and temporal scales in most applications. This study presents the results of downscaling six regional climate model (RCM) simulations for the mid and late 21st century for the Great Lakes region of North America, with a focus on precipitation totals, intensity, and frequency. The six climate simulations were downscaled using a stochastic weather generator (DisAg) with multiple scenarios of projected climate, with a focus on the intensity and frequency of projected precipitation. Downscaled climate scenarios were then used as inputs in a process-based crop simulation model (DSSAT) for maize and impacts on yield and nitrogen cycling were evaluated for the various scenarios.

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