No abstract

Quantifying the contributions of air temperature and precipitation changes to drought events can inform decision-makers to mitigate the impact of droughts while existing studies focused mainly on long-term dryness trends. Based on the latest Coupled Model Intercomparison Project (CMIP6), we analyzed the changes in drought events and separated the contributions of air temperature and precipitation to the risk of future drought events. We found that drought frequency, duration, severity, and month will increase in the future (56.4%, 63.5%, 82.9%, and 58.2% of the global land area in SSP245, and 58.1%, 67.7%, 85.8%, and 60.5% of the global land area in SSP585, respectively). The intermediate scenario has a similar pattern to the most extreme scenario, but low emission was found to mitigate drought risk. Globally, we found that air temperature will have a greater impact than precipitation on intensifying drought. Increasing precipitation will mitigate drought risks in some middle and high northern latitudes, whilst the trend in increasing air temperature will counter the effects of precipitation and increase the impact of droughts. Our study improves the understanding of the dynamics of future devastating drought events and informs the decision-making of stakeholders.

Drought is an event of shortages in the water supply, whether atmospheric, surface water or ground water. Prolonged droughts have negative impacts on ecosystems, agriculture, society, and the economy. Although existing drought index products are widely utilized in drought monitoring, the coarse spatial resolution greatly limits their applications on regional or local scales. Machine learning driven by remote sensing observations offers an opportunity to monitor regional scale droughts. However, the limited time range of remote sensing observations such as vegetation index (VI) resulted in a substantial gap in generating high resolution drought index products before 2000. This study generated spatiotemporally continuous Standardized Precipitation Evapotranspiration Index (SPEI) data spanning from 1901–2018 in southwestern China by machine learning. It indicated that four Classification and Regression Tree (CART) approaches, decision trees (DT), random forest (RF), gradient boosted regression trees (GBRT) and extra trees (ET), can provide valid local drought information by downscaling the Estación Experimental de Aula Dei (EEAD) data. The in-situ SPEI dataset produced by the Penman–Monteith method was used as a benchmark to evaluate the temporal and spatial performance of the downscaled SPEI. In addition, the necessity of VI in SPEI downscaling was also assessed. The results showed that: (1) the ET-based product has the best performance (R2 = 0.889, MAE = 0.232, RMSE = 0.432); (2) the VI provides no significant improvement for SPEI re-construction; (3) topography exerts an obvious influence on the downscaling process, and (4) the downscaled SPEI shows more consistency with the in-situ SPEI compared with EEAD SPEI. The proposed method can be easily extended to other areas without in-situ data and enhance the ability of long-term drought monitoring.