Dynamic evolution of recent droughts in Central Asia based on microwave remote sensing satellite products.

• The drought events in Central Asia retrieved by ASWI are consistent with the records of actual situation. • The drought centroid of Central Asia hardly occurs in the southeast. • Central Asia has experienced drought in the past two decades, followed by gradual wetting. • The water vapor flux, precipitation, and temperature have obvious influence on the occurrence of drought in Central Asia. Under warm climates, the more complex and malleable relationship between water and heat and the changes in the water components at the land–atmosphere interface pose greater challenges to understanding hydro-meteorological processes. However, the emergence of the advanced microwave scanning radiometer (AMSR) has facilitated the precise monitoring of these changes. Therefore, this study investigated and analyzed the spatiotemporal transformation characteristics of drought–wetness and its driving mechanisms in Central Asia from July 2002 to December 2018 based on the AMSR surface wetness index (ASWI) coupled with multiple water components. Three major conclusions were drawn from this investigation. First, the spatiotemporal differences in multiple water components in Central Asia caused substantial spatiotemporal differences in the extent of changes in drought–wetness. For example, arid areas were mainly concentrated in the lower reaches of the Amu Darya and Syr Darya river basins, where the dominant drought periods were 2007 and 2018. Second, the centroids of the largest drought patches were mainly located in the northeast, northwest, and southwest regions of Central Asia. Central Asia experienced a continuous drought from 2002 to 2005, and then a gradual wetting from 2008 to 2017. Third, the drought–wetness variation can be attributed to the variation of water vapor flux in the typical barometric layer as well as the variation of precipitation and temperature. The ASWI may play an essential role in drought monitoring and tracking prediction, particularly for drought risk mitigation and aversion. [ABSTRACT FROM AUTHOR]