Enhancing the utility of daily GCM rainfall for crop yield prediction

Amor Valeriano M. Ines; James W. Hansen; Andrew W. Robertson

Enhancing the utility of daily GCM rainfall for crop yield prediction
Ines, Amor Valeriano M.
Hansen, James W.
Robertson, Andrew W.
International Research Institute for Climate and Society
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International Journal of Climatology
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Global climate models (GCMs) are promising for crop yield predictions because of their ability to simulate seasonal climate in advance of the growing season. However, their utility is limited by unrealistic time structure of daily rainfall and biases in rainfall frequency and intensity distributions. Crop growth is very sensitive to daily variations of rainfall; thus any mismatch in daily rainfall statistics could impact crop yield simulations. Here, we present an improved methodology to correct GCM rainfall biases and time structure mismatches for maize yield prediction in Katumani, Kenya. This includes GCM bias correction (BC), to correct over- or under-predictions of rainfall frequency and intensity, and nesting corrected GCM information with a stochastic weather generator, to generate daily rainfall realizations conditioned on a given monthly target. Bias-corrected daily GCM rainfall and generated rainfall realizations were used to evaluate crop response. Results showed that corrections of GCM rainfall frequency and intensity could improve crop yield prediction but yields remain under-predicted. This is strongly attributed to the time structure mismatch in daily GCM rainfall leading to excessively long dry spells. To address this, we tested several ways of improving daily structure of GCM rainfall. First, we tested calibrating thresholds in BC but were found not very effective for improving dry spell lengths. Second, we tested BC-stochastic disaggregation (BC-DisAg) and appeared to simulate more realistic dry spell lengths using bias-corrected GCM rainfall information (e.g., frequency, totals) as monthly targets. Using rainfall frequency alone to condition the weather generator removed biases in dry spell lengths, improved predicted yields, but under-predicted yield variability. Combining rainfall frequency and totals, however, not only produced more realistic yield variability but also corrected under-prediction of yields. We envisaged that the presented method would enhance the utility of daily GCM rainfall in crop yield prediction.
Rain and rainfall--Mathematical models
Crop yields--Forecasting
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Amor Valeriano M. Ines, James W. Hansen, Andrew W. Robertson, , Enhancing the utility of daily GCM rainfall for crop yield prediction, Columbia University Academic Commons, .

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