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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

Forecasting (WRF) regional atmospheric model in the NWM. Noah-MP extends the capabilities of the Noah LSM ( Chen et al. 1996 ; Chen and Dudhia 2001 ) and incorporates multiple options for key land–atmosphere interaction processes, such as surface water infiltration, runoff, groundwater transfer, and options for representing snow albedo and vegetation growth ( Niu et al. 2007 ; Niu et al. 2011 ). Here we used only Noah-MP, with its forcings provided by observations as described above, rather than in the

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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

connection between SM and crops. Simulating cropland carbon fluxes such as soil organic carbon (SOC) decomposition and soil respiration, which are closely affected by SM. Evaluating the effects of alternative cropping pattern on water and carbon fluxes, which may inform regional and local decision making pertaining to environmental and agricultural policies. c. Limitations and uncertainties The extrapolation of the NN model outside the range of training conditions may be the major uncertainty source of

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

and implications are discussed in section 4 . 2. Model, data, and study region a. Noah-MP Noah-MP is an LSM with multiple options for representing physical processes ( Niu et al. 2011 ; Yang et al. 2011 ). It supports a vegetation canopy layer, multiple snow and soil layers, and an optional unconfined aquifer layer for groundwater. Noah-MP is one of the LSMs in the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) used for weather and regional climate forecasts. Noah-MP is

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Christa D. Peters-Lidard, David M. Mocko, Lu Su, Dennis P. Lettenmaier, Pierre Gentine, and Michael Barlage

, state, regional, and national entities that have responsibility for providing, maintaining, and planning water resources and supplying relevant information. Monitoring the state of drought depends on integrating and discerning between myriad indicators of the water cycle ( Keyantash and Dracup 2002 ). Dozens of indicators are in common use (e.g., Heim 2002 ; Svoboda and Fuchs 2017 ), and each indicator captures particular aspects of hydrologic variability and various types and phases of drought

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

examining the impact of LAI assimilation on the LSM’s ability to estimate agricultural drought. The primary contribution of this study is the quantitative evaluation of the effects of LAI assimilation on model-estimated agricultural drought extent and severity against maps from a weekly operational drought monitor. The impact of LAI assimilation on drought characterization is examined by comparing drought categories from model simulations against drought monitor categories. Evaluations against in situ

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

. 2019 ). Regional land surface states (e.g., soil moisture, snow cover, vegetation properties, etc.) also contribute to drought severity and development (e.g., Higgins et al. 1998 ; Schubert et al. 2007 ; Koster et al. 2017 ). In particular, positive feedbacks between land and atmosphere can exacerbate or prolong dry anomalies, playing a role in maintaining droughts (e.g., Durre et al. 2000 ; Fischer et al. 2007 ; Koster et al. 2009 ; Kam et al. 2014 ; Dirmeyer et al. 2015 ; Fernando et al

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