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Drought is a widespread natural hazard. In this study, the potential factors affecting spatiotemporal changes of drought in the Tarim River Basin (TRB), China, were investigated using the empirical orthogonal function (EOF) and multiple hydro-meteorological indicators such as the standardized precipitation index (SPI), standardized soil moisture index (SSI), and terrestrial water storage (TWS). The following major conclusions were drawn. (1) Inconsistent variations between SPIs/SSIs and TWS in the TRB indicate a groundwater deficit in 2002–2012. (2) The results of EOF indicate that soil moisture in the TRB was significantly affected by precipitation. However, the variations between the EOFs of SSIs and those of TWS were not identical, which indicates that soil water had less effect on TWS than groundwater. (3) Drought evaluations using SPI and SSI showed that a long drought duration occurred over a long accumulation period, whereas a high frequency of drought was related to a short accumulation period. (4) Hydrological features related to extreme soil moisture conditions in the TRB could also be influenced by the El Niño–Southern Oscillation. The findings of this study are significant for use in drought detection and for making water management decisions. doi: 10.2166/nh.2019.033 s://iwaponline.com/hr/article-pdf/50/2/761/549324/nh0500761.pdf Jun Xia Peng Yang (corresponding author) Chesheng Zhan Yunfeng Qiao Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China E-mail: yangpenghb@foxmail.com Peng Yang University of Chinese Academy of Sciences, Beijing 100049, China Jun Xia Peng Yang State Key Laboratory of Water Resources & Hydropower Engineering Sciences, Wuhan University, Wuhan 430000, China and School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

Analysis of changes in drought and terrestrial water storage in the Tarim River Basin based on principal component analysis