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Anthony M. DeAngelis, Hailan Wang, Randal D. Koster, Siegfried D. Schubert, Yehui Chang, and Jelena Marshak

directly from station data. Results using the CPC data are not explicitly shown but are discussed throughout the paper where appropriate. To evaluate soil moisture initialization accuracy in the SubX ensemble, we utilize data from phase 2 of the North American Land Data Assimilation System (NLDAS-2) ( Xia et al. 2012 ). NLDAS-2 is a collection of LSMs that were run offline and driven with common atmospheric forcing data to yield various surface fields over North America over the period from 1979 to

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Richard Seager, Jennifer Nakamura, and Mingfang Ting

-forcing-data . To examine the large-scale context of the DO&Ts, we use geopotential heights and SSTs from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Reanalysis ( Kistler et al. 2001 ; obtained from https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY/?Set-Language=en ) and precipitation over land and sea from the Global Precipitation Climatology Project (GPCP) version 2.3 ( Huffman et al. 1997 ; obtained from https

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Kingtse C. Mo and Dennis P Lettenmaier

soil moisture (SM), and/or runoff deficits, usually for 6 months or longer ( Svoboda et al. 2002 ). Flash droughts have much shorter durations—typically a few weeks. Furthermore, while conventional droughts develop slowly, a key feature of flash droughts is their rapid onset and intensification ( Pendergrass et al. 2020 ). Mo and Lettenmaier (2015 , 2016) studied flash droughts over the United States, and classified them into two categories based on their forcings: heat wave flash drought ( Mo

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Keyhan Gavahi, Peyman Abbaszadeh, Hamid Moradkhani, Xiwu Zhan, and Christopher Hain

atmospheric data such as precipitation and relative humidity, or land surface data acquisition such as SM and ET. The latter can be indirectly assimilated into the land surface models to achieve more accurate and reliable predictions of hydrologic fluxes as well as for monitoring purposes ( Kumar et al. 2014 ; Pan and Wood 2006 ; Pipunic et al. 2008 ; Reichle et al. 2014 ; Sawada et al. 2015 ; Xu et al. 2020 ). SM prediction using land surface models driven by meteorological forcing carries

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Shanshui Yuan, Steven M. Quiring, and Chen Zhao

varies over time and that it is modulated by remote sea surface temperature forcing. However, neither of these studies evaluated the suitability of using the SPI as a proxy for soil moisture. Therefore, it is not clear how sensitive their results are to using an indirect estimate of soil moisture. To date, there have only been a few studies that have examined the performance of drought indices using in situ soil moisture measurements because in situ soil moisture stations are too sparse or unevenly

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