Abstract
Investigating long-term drought trends is of great importance in coping with the adverse effects of global warming. However, little attention has been focused on studying the detailed spatial variability and attribution of drought variation in China. In this study, we first generated a 1 km resolution monthly climate dataset for the period 1901–2100 across China using the delta spatial downscaling method to assess the variability of the Standardized Precipitation Evaporation Index (SPEI). We then developed a simple approach to quantifying the contributions of water supply (precipitation) and demand (potential evapotranspiration, PET) on SPEI variability, according to the meaning of the differentiating SPEI equation. The results indicated that the delta framework could accurately downscale and correct low-spatial-resolution monthly temperatures and precipitation from the Climatic Research Unit and general circulation models (GCMs). Of the 27 GCMs analyzed, the BNU-ESM, CESM1-CAM5, and GFDL-ESM2M were found to be the most accurate in modeling future temperatures and precipitation. We also found that, compared with the past (1901–2017), the climate in the future (2018–2100) will tend toward significant droughts, although both periods showed a high spatial heterogeneity across China. Moreover, the proportion of areas with significantly decreasing SPEI trends was far greater than the proportion of those with increasing trends in most cases, especially for northwestern and northern China. Finally, the proposed approach to quantifying precipitation and PET contributions performed well according to logical evaluations. The percentage contributions of precipitation and PET on SPEI variability varied with study periods, representative concentration pathway scenarios, trend directions, and geographic spaces. In the past, PET contributions for significant downward trends and precipitation contributions for significantly upward trends accounted for 95% and 72%, while their future contributions were 57 ± 22%–149 ± 20% and 95 ± 27%–190 ± 58%, respectively. Overall, our results provide detailed insights for planning flexible adaptation and mitigation strategies to cope with the adverse effects of climate drought across China.
Highlights
Several recent studies have suggested that ongoing warming is accelerating evaporation [1] and creating more spatiotemporal heterogeneity in precipitation [2] than in the past
Many studies have investigated the standardized precipitation evapotranspiration index (SPEI) variation over China [12,13,14,15,16,20], little attention has been focused on the detailed spatial variability and attribution of SPEI
The results could aid the development of sustainable regional and local scale strategies to cope with the adverse effects of global warming and understanding of how global warming affects drought
Summary
Several recent studies have suggested that ongoing warming is accelerating evaporation [1] and creating more spatiotemporal heterogeneity in precipitation [2] than in the past. Because of the introduction of precipitation and evapotranspiration, SPEI can represent the surface moisture balance, and allows us to separate and quantify the influence of moisture supply and demand on drought variability [1,11] This separation is useful for unraveling the future contributions of precipitation versus those of potential evapotranspiration (PET) to future trends in SPEI, and enhancing our understanding of how global warming affects climatic drought. Several studies have investigated climatic drought trends using SPEI over all or a part of China Such studies were carried out based on either station observations [12,13,14] or climatic proxy datasets with low spatial resolutions [15,16,20]. This study investigated the detailed spatiotemporal trends and attributions of drought across China, using the annual SPEI driven by a high-spatial-resolution climate dataset. The contributions of moisture supply (i.e., precipitation) and demand (i.e., potential evapotranspiration) to annual SPEI variability were assessed across China
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