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Wen-Ying Wu, Zong-Liang Yang, and Michael Barlage

( Jiang et al. 2009 ). The goal of this study is to understand the impact of the major processes represented in the state-of-the-art Noah-MP LSM on capturing the southern plains drought. Section 2 contains a description of the Noah-MP model, the experimental design, observational datasets, and the study region. In section 3 , the impact of groundwater and vegetation on water variability is assessed, and the simulation results are evaluated. Finally, results are summarized, and the study limitations

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

; Sun et al. 2018 ; Pegion et al. 2019 ). This is accomplished by removing model-dependent climatologies that are a function of both the day of year and length of time after initialization (or lead day) (see the appendix for details). In this paper, we focus on the prediction skill of the ensemble mean of the hindcasts from each model. We also evaluate the overall performance of the SubX ensemble by computing a multimodel mean (hereafter MMM), which is derived from the ensemble-mean hindcasts of

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

synergistic use ( Su et al. 2014 ). However, the literature shows that the majority of efforts have gone into individual assimilation of SM or ET into land surface models and few evaluated the merits of dual assimilation of these variables (see, e.g., Hain et al. 2012 ). Several studies have reported the SM assimilation for improving drought monitoring and forecasting purposes ( Bolten et al. 2010 ; Kumar et al., 2014 ; Yan et al. 2017 ; Yan et al. 2018 ). Kumar et al. (2014) showed that shorter

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David M. Mocko, Sujay V. Kumar, Christa D. Peters-Lidard, and Shugong Wang

. , 21 , 1723 – 1736 , https://doi.org/10.1111/ele.13139 . 10.1111/ele.13139 Fox , A. M. , and Coauthors , 2018 : Evaluation of a data assimilation system for land surface models using CLM4.5 . J. Adv. Model. Earth Syst. , 10 , 2471 – 2494 , https://doi.org/10.1029/2018MS001362 . 10.1029/2018MS001362 Gayler , S. , and Coauthors , 2014 : Incorporating dynamic root growth enhances the performance of Noah-MP at two contrasting winter wheat field sites . Water Resour. Res. , 50 , 1337

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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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Kingtse C. Mo and Dennis P Lettenmaier

evaluation depends on the definition of flash droughts, our strategy is readily adaptable to other definitions so long as the requisite quantities are reproduced by either the weather forecast model and/or the hydrology model. In particular, we use the Variable Infiltration Capacity (VIC) land hydrology model ( Liang et al. 1994 ), although any similar land surface model could be used. The forecast skill of medium range weather forecasts (typically defined as having lead times about 2 weeks or less) has

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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

remaining 30% of the SM observations are used to test the trained model’s performance. Out of the 70% training set, 10% of the data are used as validation set. The hyper parameters in the NN are tuned to improve its performance in predicting SM. Specifically, we conduct a grid search for key hyperparameters—batch size, learning rate, hidden layers, and number of units in each layer—for each soil layer (see Table 2 ) ( Gupta and Raza 2020 ). For the grid search, the optional values for each parameter

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Lu Su, Qian Cao, Mu Xiao, David M. Mocko, Michael Barlage, Dongyue Li, Christa D. Peters-Lidard, and Dennis P. Lettenmaier

such as SM, SWE, groundwater levels, and (in the case of dynamic vegetation runs) vegetation masses and the LAI and SAI (stem area index), as the initial values from runs for 1915–2018. 4. Results and discussion a. Evaluation of model outputs To test the robustness of Noah-MP, we compared streamflow, SWE, and SM outputs with observations under drought conditions. The observed streamflow data are from USGS gauges, mostly the MOPEX ( Schaake et al. 2006 ) dataset ( https

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Chul-Su Shin, Paul A. Dirmeyer, Bohua Huang, Subhadeep Halder, and Arun Kumar

.g., climatological) land initial conditions. This study sheds light on how uncertainties in land initialization may affect forecast skill. In our companion paper, we specifically examine sensitivity of U.S. drought prediction skill to land initial states ( Shin et al. 2020 ). Section 2 describes the coupled model used in this study and the identical-twin experiment design in details. Evaluation of 2-m air temperature prediction skill, soil moisture uncertainty between the two land surface analyses, and its

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Yizhou Zhuang, Amir Erfanian, and Rong Fu

–atmospheric feedbacks (e.g., Dirmeyer 1994 ; Hong and Kalnay 2000 ; Karl 1983 ; Myoung and Nielsen-Gammon 2010 ; Oglesby 1991 ; Oglesby and Erickson 1989 ; Rind 1982 ; Schubert et al. 2004 ; Zhuang et al. 2020 ), though most of these studies focus on local land–atmospheric feedbacks. Recently, numerical model experiments have shown remote land impacts on atmospheric circulation (e.g., Koster et al. 2016 ). Erfanian and Fu (2019) have evaluated the moisture budget over the Great Plains and found that

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