Abstract
We applied a simple statistical downscaling procedure for transforming daily global climate model (GCM) rainfall to the scale of an agricultural experimental station in Katumani, Kenya. The transformation made was two-fold. First, we corrected the rainfall frequency bias of the climate model by truncating its daily rainfall cumulative distribution into the station’s distribution based on a prescribed observed wet-day threshold. Then, we corrected the climate model rainfall intensity bias by mapping its truncated rainfall distribution into the station’s truncated distribution. Further improvements were made to the bias corrected GCM rainfall by linking it with a stochastic disaggregation scheme to correct the time structure problem inherent with daily GCM rainfall. Results of the simple and hybridized GCM downscaled precipitation variables (total, probability of occurrence, intensity and dry spell length) were linked with a crop model for a more objective evaluation of their performance using a non-linear measure based on mutual information based on entropy. This study is useful for the identification of both suitable downscaling technique as well as the effective precipitation variables for forecasting crop yields using GCM’s outputs which can be useful for addressing food security problems beforehand in critical basins around the world.
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Acknowledgments
The authors wish to thank the Associate Editor and the Reviewers for their useful comments and suggestions that helped to improve the quality of the manuscript. AKM and VPS acknowledge the support from USGS Grant 2009TX334G. AVMI and JWH acknowledge the support from NOAA Grant No. #NA05OAR4311004. The model outputs from IRI have been funded by a computing grant from the multi-agency Climate Simulation Laboratory (CSL) program.
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Mishra, A.K., Ines, A.V.M., Singh, V.P. et al. Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation. Stoch Environ Res Risk Assess 27, 449–457 (2013). https://doi.org/10.1007/s00477-012-0667-9
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DOI: https://doi.org/10.1007/s00477-012-0667-9