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United States Department of Agriculture

Agricultural Research Service

Title: Response of Model Prediction Errors and Uncertainties to Climate Scenarios

Authors
item Zhang, Xunchang
item Steiner, Jean

Submitted to: American Society of Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: September 1, 2004
Publication Date: September 15, 2004
Citation: Zhang, X.J., Steiner, J.L. 2004. Response of model prediction errors and uncertainties to climate scenarios [abstract]. American Society of Agronomy Abstracts. CD-ROM 2004.

Interpretive Summary: Abstract Only.

Technical Abstract: Model error and uncertainty need to be quantified before crop models can be used as decision support tools for predicting and optimizing crop growth and yield. The objectives of this work are to quantify model error and uncertainty of the CERES-wheat model in predicting wheat biomass growth and grain yield at several Oklahoma locations, and to further assess the impact of synthetic climate input vs. measured climate data on model output error and uncertainty. Measured long term weather, soil, and winter wheat biomass data from El Reno, Haskell, Lahoma, and Stillwater, Oklahoma are used to calibrate and validate the CERES-wheat model. Model error and uncertainty in terms of grain yield prediction are assessed by comparing probability distributions of model outputs and historical yields. In order to quantify prediction error and uncertainty induced by the use of synthetic climate input, a stochastic weather generator (WGEN), which is used to generate synthetic climate scenarios and future daily weather from seasonal climate forecasts, is used. Results will provide insights into the magnitudes of error and uncertainty associated with the model itself as well as induced by the use of synthetic weather input.

Last Modified: 11/21/2014
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