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Simon R. Osborne and Graham P. Weedon

1. Introduction Representation of drought in land surface models (LSMs) is important for accurate weather forecasts, assessing water resources, and projecting the possible impacts of droughts under climate change. LSMs have been used to investigate both the changing frequency of drought under climate change and the potential for future droughts to have increased societal impacts ( Prudhomme et al. 2011 , 2014 ; Dai 2013 ). The trends of changing drought frequency and intensity are dependent

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Thomas M. Smith, Samuel S. P. Shen, and Ralph R. Ferraro

1. Introduction Accurate short- to medium-range climate forecasting, from weeks to seasons, is valuable for planning and preparing for situations that could have large economic or health impacts. Precipitation forecasting is particularly important because it is critical to agriculture, municipal water supplies and control, and disaster relief support. However, predicting precipitation tends to be more difficult than predicting temperature (see, e.g., Barnston and Smith 1996 ), and much effort

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James D. Brown and Dong-Jun Seo

1. Introduction Forecasts of hydrometeorological and hydrologic variables often contain large uncertainties ( Beven and Binley 1992 ; Anderson and Bates 2001 ; Gupta et al. 2005 ; NRC 2006 ; Ajami et al. 2007 ). Ensemble techniques are widely used in meteorology and, increasingly, in hydrology to quantify these uncertainties ( Stensrud et al. 1999 ; Jolliffe and Stephenson 2003 ; Brown and Heuvelink 2005 ; Olsson and Lindström 2008 ). For example, the National Weather Service (NWS) River

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Marc Berenguer, Carles Corral, Rafael Sánchez-Diezma, and Daniel Sempere-Torres

into the first group of the classification were recently developed ( Germann and Zawadzki 2002 ; Seed 2003 ). These techniques were tested using radar data, and, from the point of view of the forecasted precipitation fields, they turn out to improve the results obtained with Lagrangian persistence (which consists of simply advecting the most recently measured radar scan according to an estimation of the motion field). The two techniques developed by Germann and Zawadzki (2002) and Seed (2003

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Elisa Brussolo, Jost von Hardenberg, and Nicola Rebora

resolution, and sensitivity to initial conditions of atmospheric models. At large scales the different sources of uncertainty in meteorological forecasts can be captured by dynamical ensemble forecasting techniques ( Epstein 1969 ; Leith 1974 ; Toth and Kalnay 1993 ; Palmer 1993 ; Molteni et al. 1996 ; Toth and Kalnay 1997 ), in which the probability of occurrence of different meteorological scenarios is estimated from the relative frequency of different forecast ensemble members. Existing

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Elisa Brussolo, Jost von Hardenberg, Luca Ferraris, Nicola Rebora, and Antonello Provenzale

precipitation ( Hendrick and Comer 1970 ; Zawadski 1973 ). To cope with these “representativeness errors,” Tustison et al. (2001) suggested the use of stochastic models capable of representing the statistical properties of precipitation at multiple scales. In particular, rainfall downscaling techniques ( Droegemeier et al. 2000 ; Ferraris et al. 2003 ) allow us to derive, from a single precipitation forecast with limited spatial and temporal resolution, higher-resolution stochastic ensembles of

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Thomas C. Pagano, Andrew W. Wood, Maria-Helena Ramos, Hannah L. Cloke, Florian Pappenberger, Martyn P. Clark, Michael Cranston, Dmitri Kavetski, Thibault Mathevet, Soroosh Sorooshian, and Jan S. Verkade

verify forecast accuracy ( Stokstad 1999 ). The scientific community recently completed a decade-long initiative on prediction in ungauged basins, and although initiatives such as these contributed much new understanding and many innovative techniques ( Hrachowitz et al. 2013 ), real-time forecasting received less attention and remains a major challenge ( Randrianasolo et al. 2011 ). Remote sensing data sources such as satellites may be able to provide information about river width and water slope

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Kevin Werner and Kristen Yeager

1. Introduction This paper describes the 2011 peak streamflows in the Colorado basin and the Great Basin in an attempt to illuminate the forecasting efforts of the NOAA Colorado Basin River Forecast Center (CBRFC). A recent National Research Council (2012) report highlighted the difficulties in transferring research results into operational river forecasting as a major impediment to improving forecasts. The primary goal of this paper is to highlight three areas where research is most needed

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Hector Macian-Sorribes, Ilias Pechlivanidis, Louise Crochemore, and Manuel Pulido-Velazquez

forecasting approaches commonly relying on stochastic modeling techniques ( Foster et al. 2018 ; Pianosi and Soncini-Sessa 2009 ; Pina et al. 2017 ) have recently been complemented by forecasting services driven by seasonal dynamic prediction systems ( Arnal et al. 2018 ; Johnson et al. 2019 ; MacLachlan et al. 2015 ). A number of applications of seasonal forecast information for decision-making can be found in water-related sectors, i.e., urban water supply ( Guo et al. 2018 ), hydropower ( Giuliani

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Zhangkang Shu, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, Ji Liu, Yanli Liu, Ruimin He, Cuishan Liu, and Zhenxin Bao

only be improved through certain bias correction and data fusion techniques. Figure 6c shows the spatial distribution of the discrepancies in the forecast performance of the models. The areas with large discrepancies in forecast performance are centered on the Northwest Rivers Region, the upper reaches of the Yangtze River Region, the middle reaches of the Yellow River Region, the Liaohe River Region, and the Haihe River Region. The forecast performance of the models in no-rain events in these

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