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Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion

Zhang, X.C.
Agricultural and forest meteorology 2005 v.135 no.1-4 pp. 215
climate models, spatial data, precision agriculture, simulation models, statistical analysis, temperature, precipitation, soil water content, soil erosion, crop production, climate change, soil-plant-atmosphere interactions, Oklahoma
Spatial and temporal mismatches between coarse resolution projections of global climate 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 monthly output at the native GCM grid scale to station-scale using transfer functions, and (ii) further demonstrate the site-specific impact assessment of climate change on water resources, soil erosion, and crop production at Kingfisher, OK, US using the water erosion prediction project (WEPP) model. Monthly precipitation and temperature projected by the UK Hadley Centre's Climate Model (HadCM3) under the GGa emissions scenario were downloaded for the periods of 1900-1999 and 2070-2099 for the grid box containing the target station. Univariate transfer functions were derived by calibrating probability distributions of GCM-projected monthly precipitation and temperature to match those of local climatology for the 1950-1999 period. Derived functions, which were tested for 1900-1949, were used to spatially downscale the HadCM3 monthly projections of 2070-2099 to the target station. Downscaled monthly data were further disaggregated to daily weather series using a stochastic weather generator (CLIGEN) for driving the WEPP model. Disaggregated daily series preserve the monthly means and variances of precipitation and temperature of the downscaled HadCM3 output. Simulated annual runoff under the changed climate, compared with the present climate, increased by 40-48% despite the projected 5% decrease in precipitation. Simulated plant transpiration, soil evaporation, and long-term soil water reserve decreased by 5, 16.5, and 5.5%, respectively. Simulated soil loss rates were increased by some 44% under conventional tillage and doubled under conservation tillage and no-till. Simulated wheat yield increased by approximately 14% in all three tillage systems. The overall results show that the proposed downscaling technique is simple and sound, which provides an effective alternative for assessing the site-specific impacts of climate change on soil erosion and crop production. Nevertheless, the technique suffers from the same shortcomings as all other statistical downscaling methods, as it largely relies upon the accuracy of GCM projections as well as the applicability of transfer functions to future climate.