A synthesis of rainfall interception data from past field campaigns is performed, including 166 forests and 17 agricultural plots distributed worldwide. These site data are used to constrain and validate an interception model that considers sub-grid heterogeneity and vegetation dynamics. A global, 40-year (1980–2019) interception dataset is generated at a daily temporal and 0.1° spatial resolution. This dataset will serve as a benchmark for future investigations of the global hydrological cycle.

In this study, three machine learning methods displayed a good detection capacity of flash droughts. The RF model was recommended to estimate the depletion rate of soil moisture and simulate flash drought by considering the multiple meteorological variable anomalies in the adjacent time to drought onset. The anomalies of precipitation and potential evapotranspiration exhibited a stronger synergistic but asymmetrical effect on flash droughts compared to slowly developing droughts.

7 CMIP5 climate models under RCP8.5 emissions scenarios were downscaled by means of bilinear interpolation and bias correction. The downscaled results are evaluated over China by comparing with the England Reanalysis CRU3.1. Time series of SPI has been used to identify drought from 20th century to 21st century over China. the SPI show a slightly increasing trend in the future period for the most parts of China, but drought in Southwest region of China will become the norm in the future.

In this paper, we employ two methods, i.e. hydrological sensitivity analysis and hydrological model simulation, to investigate the effect of climate variability and climatic change on actual evapotranspiration (Ea) within the Laohahe basin during 1964-2009.