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Jin-Ho Yoon, Kingtse Mo, and Eric F. Wood

). Meteorological drought is defined by precipitation ( P ) deficits. Agricultural drought is caused by soil moisture (SM) deficits and hydrological drought is reflective of runoff or streamflow deficits. In the recent decade, advances have been made in hydrologic prediction of streamflow and soil moisture by using climate forecasts at seasonal lead times ( Wood and Lettenmaier 2006 ; Luo et al. 2007 ). Based on the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal

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Shasha Han and Paulin Coulibaly

1. Introduction As the fifth Intergovernmental Panel on Climate Change (IPCC) climate assessment report concludes, acceleration of the hydrological cycle due to climate change has led to more frequent floods over the last few decades ( IPCC 2014 ). Large floods during recent years, such as the 2013 Southern Ontario flood (Canada), the 2017 Texas floods (United States), and the 2007 United Kingdom floods, increased demand for reliable flood forecasting systems and methods ( Reggiani et al. 2009

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Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Mohan Thota, Vineeth Valappil, Oliver Branch, Hans-Dieter Wizemann, Niranjan Kumar Kondapalli, Youssef Wehbe, Taha Al Hosary, Abdeltawab Shalaby, Noor Al Shamsi, and Hajer Al Naqbi

’s physical properties, and hence their representation in numerical models is very important for an accurate simulation of the surface and near-surface fields. An accurate modeling of land–atmosphere interactions strongly depends on how accurate the surface properties, in particular the predominant soil texture and LULC, are represented in the model. Göndöcs et al. (2015) investigated the sensitivity of the Weather Research and Forecasting (WRF; Skamarock et al. 2008 ) Model’s response to a more

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Kingtse C. Mo and Bradfield Lyon

1. Introduction The routine generation of global seasonal climate forecasts coupled with advances in near-real-time monitoring of the global climate has now allowed for testing the feasibility of generating global drought forecasts operationally. Indeed, the development of an experimental global drought information system (GDIS) that includes both real-time monitoring and forecasts has recently been recommended by a working group of the World Climate Research Programme ( WCRP 2012 ) and

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Dingchen Hou, Kenneth Mitchell, Zoltan Toth, Dag Lohmann, and Helin Wei

1. Introduction Flooding and drought are the most frequent natural hazards, and water resource management is one of the most challenging problems the world is facing. Therefore, hydrological forecast, especially streamflow forecast, is of great interest, and it is a major application of numerical weather prediction (NWP) output. NWP forecasts of precipitation and temperature can be incorporated into a flood warning system, and the forecast lead time can be significantly increased (e

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Andrew W. Wood and John C. Schaake

1. Introduction Operational streamflow prediction for water resources management in the western United States depends on winter and spring forecasts of runoff volumes for the relatively dry late-spring and summer period. Target forecast periods vary for different end use applications, and also regionally, with April–July common to the southern half of the domain and April–September more common to the northern half (where the peak runoff, a response to melting snowpack, occurs later), although

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Wade T. Crow, Concepcion Arroyo Gomez, Joaquín Muñoz Sabater, Thomas Holmes, Christopher R. Hain, Fangni Lei, Jianzhi Dong, Joseph G. Alfieri, and Martha C. Anderson

1. Introduction During the growing season, soil moisture (SM) typically controls the partitioning of available energy between sensible and latent heat flux at the soil–atmosphere interface and thereby influences the energetic relationship between the land surface and the lower atmosphere. Furthermore, SM time series contain significant temporal persistence that can be exploited to forecast this relationship out in time. Therefore, the realistic initialization of SM states in the land surface

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Jonathan J. Gourley and Humberto Vergara

1. Introduction The study of Herman and Schumacher (2018 , hereafter HS18 ) used radar-based quantitative precipitation estimates (QPEs) from different algorithms and compared exceedance thresholds to flash flood reports (FFRs) and flash flood warnings (FFWs). One objective of HS18 was to provide the best practices for using the tools in an operational flash flood monitoring and forecasting environment. The commentary presented herein is motivated by the following points: QPE algorithms in

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Zachary P. Brodeur and Scott Steinschneider

in their characterization ( Dacre et al. 2015 ; Guirguis et al. 2018 ; Hecht and Cordeira 2017 ), classification ( Dettinger et al. 2018 ; Ralph et al. 2019 ), and predictability ( Baggett et al. 2017 ; DeFlorio et al. 2018a , b ; Lavers et al. 2016 , 2017 ). Predictive skill at medium range (1–14 days) and subseasonal to seasonal (S2S; 15–90 days) time scales has become a topic of particular interest because of its relevance to water infrastructure management decisions, such as forecast

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Kristie J. Franz, Terri S. Hogue, and Soroosh Sorooshian

1. Introduction Hydrologic analyses seldom address a model’s forecasting ability, despite this being an often-stated motivation in many hydrologic modeling studies. Recently, Welles et al. (2007) identified forecast verification as an obvious gap in hydrologic research aimed at improving hydrologic forecasting. The hydrologic research community has generally focused on the validation of new techniques through simulation, while forecast verification has yet to be widely used. Forecast

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