Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer reviewed journal
Publication Acceptance Date: 11/15/2005
Publication Date: 11/30/2005
Citation: Zhang, X.J. 2005. Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agricultural and Forest Meteorology. 135:215-229. Interpretive Summary: Climate change projected into the future by General Circulation Models (GCM, a computer program) needs to be spatially and temporally down-scaled for use in assessing the potential impacts of climate change on natural resources at particular locations. The objective of this work is to develop a mathematical method that can be used to transfer large-scale climate change of GCM projections to particular locations and to further downscale monthly GCM projections to daily weather data for use by agricultural systems models such as the Water Erosion Prediction Project (WEPP) model (a computer program). Simple equations were derived mathematically using statistical regressions between large–scale monthly GCM projections and station-scale monthly measurements. A procedure was further developed to disaggregate monthly GCM projections to daily weather data using a climate generator (CLIGEN, another computer program). The monthly GCM projections for the period of 2070-2099 were downscaled to the Kingfisher, Oklahoma location using the proposed method. Projected precipitation under the assumed climate change scenario would decrease by 5% during 2070-2099 at Kingfisher. The WEPP-simulated runoff would increase by 2-5%. Simulated soil evaporation and plant transpiration would decrease by 12 and 2%, respectively. However, simulated wheat yield would increase by some 19%, largely due to predicted increases in growing season precipitation and carbon dioxide concentration. The overall results indicate that the proposed method is sound and could be useful to scientists and conservationists for evaluating the potential impacts of climate change on ecosystems and agricultural production on particular farms or fields.
Technical Abstract: Spatial and temporal mismatches between coarse resolution output of General Circulation Models (GCMs) and fine resolution data requirements of ecosystems models are the major obstacles for assessing the site-specific climatic impacts of climate change on natural resources and ecosystems. The objectives of this study were to (i) develop a simple method for statistically downscaling GCM output of climate change at the native GCM grid scale to station-scale, and (ii) further demonstrate the site-specific impact assessment of climate change on natural resources at Kingfisher, Oklahoma, U.S. using the Water Erosion Prediction Project (WEPP) model. Monthly precipitation and temperature projected by the U.K. Hadley Centre’s GCM under the GGa1 emissions scenario were downloaded for the periods of 1900-1999 and 2070-2099 for the grid box containing the Kingfisher station. Univariate transfer functions were derived by matching probability distributions between station-measured and GCM-projected monthly precipitation and temperature for the 1950-1999 period. The derived functions were used to spatially downscale the GCM monthly projections of 2070-2099 to the Kingfisher station. The downscaled monthly data were further disaggregated to daily weather series using a stochastic weather generator (CLIGEN). Simulated annual runoff under the future climate, compared with the present climate, increased by 2-5% despite the projected 5% decrease in precipitation. Simulated plant transpiration, soil evaporation, and long-term soil water reserve decreased by 2, 12, and 6%, respectively. Simulated soil loss increased by 9% under conventional tillage while it remained unchanged under conservation and no-till. Simulated wheat yield increased by approximately 19% in all three tillage systems. The overall results show that the proposed downscaling technique is simple and sound, which provides an attractive alternative for assessing the site-specific impacts of climate change.