Spatial Data and Scaling Methods for Assessment of Agricultural Impacts of Climate Managing Multiple Sources of Uncertainty Over Space

University Corporation For Atmospheric Res, Boulder CO

Investigators

Abstract

9909141 Mearns Support for this research is provided to develop a project focusing on agricultural assessment in the Southeastern United States that will formally quantify uncertainties in spatial assessments based on dataset sources and various methods of spatial scaling of the data sets and various means of calibrating and validating crop models over space. The objectives of this research are to 1. Evaluate and characterize uncertainties associated with alternative input data sources and methods for scaling up crop model estimations for regional climate impact assessments; 2. Identify appropriate spatial scale matches for observed crop data and the different types of crop model input data, and develop methods for aggregating or disaggregating data to a given spatial scale; 3. Characterize the errors and uncertainties that result when applying spatial data sets and crop simulation models to assess regional-scale impacts of climate on agriculture; 4. Provide a validated, gridded spatial data base of calibrated soil parameters, daily weather time series, representative management inputs, and historical crop yields in the Southeast US for climate impact assessment; and 5. Extend and apply these methods to another region of agricultural importance, the central Great Plains.