气候变化领域集成服务门户

The uncertainty assessment of the changes in drought characteristics due to climate change has caught the attention of the scientific community. This study used gauge-based gridded precipitation data obtained from Global Precipitation Climatology Centre (GPCC) to reconstruct historical droughts and downscale future precipitation projected by seven general circulation models (GCMs) of Coupled Model Inter-comparison Project phase 5 (CMIP5) under four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Support vector machine (SVM) and quantile mapping were used for downscaling and GCM bias correction, respectively. The model performances were assessed based on statistical measures. The historical and future projected precipitation data were finally used to characterize the seasonal droughts using Standardized Precipitation Index (SPI) for different crop growing periods. The drought severity-area-frequency (SAF) curves for the historical (1961-2010) and three future periods (2010-2039, 2040-2069, and 2070-2099) were developed. The uncertainty band of future drought SAF curves was estimated using Bayesian bootstrap (BB) at a 95% confidence level. As a result, SVM was successful in downscaling the precipitation of all selected CMIP5 GCMs. The seasonal ensemble of GCMs projected an increase in precipitation ranging from 8% to 41% under all scenarios. The historical SAF curves revealed that for equal drought severity, larger areas are affected by droughts having higher return periods. Future projections of droughts revealed the increase in affected area for lower severity and return period droughts and the decrease for higher severity and return period droughts. The uncertainty bands of drought SAF curves with higher return periods were found much wider compared to those with lower return periods which indicates more uncertainty in the projection of higher severity and return period droughts.