Page Banner

United States Department of Agriculture

Agricultural Research Service

Research Project: INTEGRATION OF CLIMATE VARIABILITY AND FORECASTS INTO RISK-BASED MANAGEMENT TOOLS FOR AGRICULTURE PRODUCTION AND RESOURCE CONSERVATION Title: Evaluation of a weather generator-based method for statistically downscaling non-stationary climate scenarios for impact assessment at a point scale

Authors
item Zhang, Xunchang
item Chen, Jie -
item Garbrecht, Jurgen
item Brissette, Francois -

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 28, 2012
Publication Date: October 30, 2012
Citation: Zhang, X.J., Chen, J., Garbrecht, J.D., Brissette, F.P. 2012. Evaluation of a weather generator-based method for statistically downscaling non-stationary climate scenarios for impact assessment at a point scale. Transactions of the ASABE. 55(5):1745-1756.

Interpretive Summary: Climate change is likely to have considerable impact on agricultural production and resource conservation. To estimate those potential impacts, climate change scenarios projected by global climate computer models have to be downscaled to a particular farm or field of interest statistically. Many statistical downscaling methods are invalid for downscaling dramatic (i.e., non-stationary) climate changes. This study is to evaluate whether the presented method is fully applicable to generate daily precipitation under non-stationary conditions in a wide range of climatic zones. Ten stations were selected from polar to tropical climate around the world. The measured data were split into calibration and validation periods in a way that the difference of the mean annual precipitation between the two periods was maximized. Precipitation occurrence probabilities generally increased linearly with an increase in mean monthly precipitation for all calendar months and locations in all climatic zones, and can be well predicted by mean monthly precipitation. Overall, characteristics of the downscaled daily and monthly precipitation amounts, annual maximum daily precipitation amounts, and durations of consecutive dry/wet days were similar to those of the measured data of the validation period for most stations. This work has demonstrated that the downscaling method is capable of generating suitable daily precipitation series for impact assessment for any climate change scenarios including non-stationary changes. This work should be useful to engineers and scientists who are interested in evaluating the potential impact of climate changes on agricultural production and resource conservation and in developing new measures and strategies for adapting and mitigating potential climate changes.

Technical Abstract: The non-stationarity is a major concern for statistically downscaling climate change scenarios for impact assessment. This study is to evaluate whether a statistical downscaling method is fully applicable to generate daily precipitation under non-stationary conditions in a wide range of climatic zones. Ten stations were selected from polar to tropical climate around the world. The measured data were split into a calibration and a validation period in a way that the difference of the mean annual precipitation between the two periods was maximized. Transition probabilities of wet-following-wet (Pw/w) and wet-following-dry (Pw/d) days generally increased linearly with an increase in mean monthly precipitation for all calendar months and locations in all climatic zones. The transition probabilities of the validation periods, interpolated with linear regressions, agreed well with those directly calculated from the observed data of the periods, with model efficiency ranging from 0.786 to 0.966. Due to good estimation of Pw/w and Pw/d, generated frequency distributions of dry and wet spell lengths agreed reasonably well with the measured distributions for the validation period. Overall, statistics of the downscaled daily and monthly precipitation amounts, annual maximum daily amounts, and dry and wet spells were similar to those of the measured data for stations whose skewness coefficients were not greater than 3.5, suggesting caution should be exercised when generating daily precipitation with the Pearson Type III distribution if skewness coefficient is greater than 3.5. This downscaling method can be easily used with the two parameter gamma distribution to circumvent the skewness issue if necessary. This work has demonstrated that the downscaling method is capable of generating daily precipitation series that possess desirable characteristics of daily precipitation amounts, frequency and wet/dry spells for any climate scenario including non-stationary changes.

Last Modified: 11/22/2014
Footer Content Back to Top of Page