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

9909141 Mearns支持这项研究是为了开发一个项目，重点是在美国东南部的农业评估，将正式量化空间评估的不确定性的基础上，数据集的数据源和各种方法的空间缩放的数据集和各种手段的校准和验证作物模型在空间上。本研究的目的是1。评估和描述与替代输入数据源和方法相关的不确定性，以便为区域气候影响评估扩大作物模型估计;2.为观测到的作物数据和不同类型的作物模型输入数据确定适当的空间尺度匹配，并制定将数据汇总或分解到给定空间尺度的方法;3.描述在应用空间数据集和作物模拟模型评估区域尺度气候对农业影响时所产生的误差和不确定性;4.为气候影响评估提供美国东南部校准土壤参数、每日天气时间序列、代表性管理输入和历史作物产量的有效网格化空间数据库;将这些方法推广并应用到另一个重要的农业地区--中部大平原。

9909141Mearns 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 to1. 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; and5. Extend and apply these methods to another region of agricultural importance, the central Great Plains.