1. Land Use, Land Cover, and the Impacts of Climate Change in Agriculture Hexbin plot of MLCT raw vs. NLCD (left) and of MLCT adjusted crop versus Agland2000 cropland (center) and versus NLCD crop plus hay/pasture (right) and a comparison of national statistics for several intermediate data sets in our algorithm. LAND COVER CLIMATE AND WEATHER Global remote sensing products are unable to adequately resolve small spatial scale features essential for agriculture, such as rural infrastructure, creeks and wetlands, and small farms. Our recent analysis of the MLCT product against the higher resolution US NLCD found that nearly 200 million acres of developed land, water features, and wetlands -- in the conterminous United States alone – are likely misclassified in MLCT as cropland or natural cover, due to the limitations of the 500m resolution on the MODIS instrument. This statistic is expected to be worse outside the US, where rural areas typically have much more varied land-cover profiles. AN ALGORITHMIC APPROACH Using a principled algorithmic approach, we show that its possible to systematically improve MLCT by taking into account the full set of data layers in the product and by incorporating and validating against more accurate regional datasets where available. CLIMATE IMPACTS Using Swift, a technology for parallel simulation developed at the Computation Institute, we are making unprecedented large simulations possible. Early tests running DSSAT over 30-120 years with 16 different management configurations at more than 120,000 different locations in the conterminous US, a campaign that would take a month to run in serial, can be completed in parallel in an afternoon. Difference between monthly averaged reanalysis daily min temp and monthly averaged model output daily min temp CCSM3 (top left), CRCM_ccsm (top right), CRCM_cgcm3 (bottom left), WRFG_ccsm (bottom right) Dynamic and statistical downscaling techniques and bias correction have seen substantial use in recent years to produce high resolution climate data products for improved impact analysis. How much, and at what scale, does the additional fidelity provided by these techniques effect impact measures in agriculture and how can we characterize the added value of these data products in terms of accuracy? Improved characterization of national statistics (shown above) and spatial distribution (show below) is produced by each step of the algorithm. Observable differences in daily maximum temperature output from three different GCM and RCM model combinations A sketch of the Bias-Correction and Spatial Disaggregation (BCSD) Method and an example of the resulting cumulative distribution functions. Using these ensembles, we are preparing validation and ensemble simulation experiments for detailed validation and sensitivity experiments on these techniques and data products. Example land cover upscaling masks for determining various important aggregate impact measure in the conterminous US. Simulated rain-fed yield on existing corn land in the cUSA at full resolution (left) and averaged by county (right). Using our climate, weather, and land-cover data products (along with substantial additional management and environmental data) we drive the DSSAT Cropping System Model (CSM) to measure climate impacts against a 1980-2009 baseline. Median (left) and standard deviation (right) of historical simulations of rain-fed corn yields for the cUSA To couple these impact measures into various economic models and for other analysis, we are developing tools for upscaling these measures to arbitrary spatial scales and political or environmental boundaries using various land-cover masks. UPSCALING CLIMATE AND ENSEMBLES AND VALIDATION DOWNSCALING AND BIAS CORRECTION We are using GCM data and RCM data from NARCCAP (i.e. dynamic downscaling), both at their native scales and with additional bias correction and downscaling using the BCSD method. Joshua Elliott, Neil Best, and Michael Glotter