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United States Department of Agriculture

Agricultural Research Service

Research Project: INTEGRATION OF CLIMATE VARIABILITY AND FORECASTS INTO RISK-BASED MANAGEMENT TOOLS FOR AGRICULTURE PRODUCTION AND RESOURCE CONSERVATION

Location: Great Plains Agroclimate and Natural Resources Research Unit

Title: A Comparison of Explicit and Implicit Spatial Downscaling of Gcm Output for Soil Erosion and Crop Production Assessments

Author
item Zhang, Xunchang

Submitted to: Climatic Change
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 1, 2006
Publication Date: April 1, 2007
Citation: Zhang, X.J. 2007. A comparison of explicit and implicit spatial downscaling of gcm output for soil erosion and crop production assessments. Climatic Change. Available: http://www.climaticchange.com/DOI 10.1007/s10584-007-9256-1.

Interpretive Summary: Spatial treatment of climate change scenarios is extremely important for simulating impacts of climate change on soil erosion and crop production. The objective of this work is to compare responses of simulated soil erosion, and wheat and maize yields to two (implicit and explicit) spatial treatments that are used to downscale climate change scenarios projected at large spatial scales to a particular location or field for crop model simulation. Monthly precipitation and temperature of 1950-2039 projected by a global climate model (HadCM3) were used in the downscaling. In contrast to the explicit method, the implicit method does not consider spatial variability of climate change scenarios explicitly during downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat-wheat-maize rotation under conventional tillage at the 8.7 and 17.6% slopes at the Changwu station, Shaanxi, China, as an example. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010-2039 at Changewu. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more variable. As a result, simulated responses of soil loss and crop yields seamed more irregular. For a 1% increase in precipitation, percent increases in average annual soil loss were 4-10 times greater with the explicit method than those with the implicit method. Considerable differences in grain yield were also found. These contrasting results underscore the importance of proper spatial treatments of climate change scenarios prior to impact assessments. The results would help scientists and conservationists choose proper downscaling methods for evaluating climatic impacts on a particular farm or field.

Technical Abstract: Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impact on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). Monthly projections of precipitation and temperature during 1950-2039 were used in the downscaling. A stochastic weather generator (CLIGEN) was used to disaggregate monthly values to daily weather series following an implicit and explicit spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat-wheat-maize rotation under conventional tillage at the 8.7 and 17.6% slopes on southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010-2039 at the Changewu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more variable. Consequently, the responses simulated with the explicit method seamed more irregular. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3-6 (4-10) times greater with the explicit method than those with the implicit method. Considerable differences in grain yield were also found between the two downscaling methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscores the importance of proper spatial treatments of climate change scenarios including climate variability prior to impact simulation.

Last Modified: 8/1/2014
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