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Amirhossein Mazrooei and A. Sankarasubramanian

1. Introduction Reliable monthly-to-seasonal streamflow forecasts can significantly improve the management of water resources systems and their subsequent plans ( Hamlet et al. 2002 ). Hydrologists provide deterministic-style forecasts that estimate the volume of streamflow for a month or a season ahead; alternatively, water managers are interested in categorical and probabilistic streamflow forecasts that represent the probability of occurrence of predefined events, such as below-normal or

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Yiming Hu, Maurice J. Schmeits, Schalk Jan van Andel, Jan S. Verkade, Min Xu, Dimitri P. Solomatine, and Zhongmin Liang

1. Introduction The advantages of ensemble forecasting have been widely recognized, as it takes into account uncertainty and provides a probabilistic forecast, for example, by running a forecast model several times with different initial conditions and often also with differences in model physics or configurations to produce a range of possible outcomes. However, because the perturbed initial conditions do not fully reflect the uncertainty of initial conditions and the forecasting models do not

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Di Tian and Christopher J. Martinez

–Monteith (PM) equation ( Allen et al. 1998 ) was adopted by the Food and Agricultural Organization (FAO) of the United Nations. While the physically based PM equation has been shown to accurately estimate ET 0 ( Chiew et al. 1995 ; Garcia et al. 2004 ; López-Urrea et al. 2006 ; Yoder et al. 2005 ), it requires a large amount of meteorological data that are often not available in many regions. Forecast output from numerical weather prediction models (NWPMs) and global climate models (GCMs) are

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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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Theodore J. Bohn, Mergia Y. Sonessa, and Dennis P. Lettenmaier

1. Introduction Multimodel methods have been explored in an attempt to reduce errors in hydrologic simulations and forecasts by a number of authors (e.g., Wang et al. 2009 ; Viney et al. 2009 ; Vrugt and Robinson 2007 ; Duan et al. 2007 ; Guo et al. 2007 ; Gao and Dirmeyer 2006 ; Ajami et al. 2006 ; Georgakakos et al. 2004 ). Furthermore, the spread among model results can provide an estimate of prediction uncertainty, while the mean of the model results is often regarded as a more

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Louise Arnal, Andrew W. Wood, Elisabeth Stephens, Hannah L. Cloke, and Florian Pappenberger

1. Introduction Unprecedented increases in computer capabilities have shaped the last several decades’ advances in numerical weather prediction (NWP), and with them, the development of environmental forecasting and modeling systems. This has led to a shift in the strategy of operational forecasting centers toward more integrated modeling and forecasting approaches, such as coupled systems and Earth system models (ESMs), with the final aim to extend the limits of predictability (i.e., from

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Chengzu Bai, Mei Hong, Dong Wang, Ren Zhang, and Longxia Qian

–runoff forecasting is a crucial precondition for hydrometeorological research and operational flood forecasting, especially in some undermonitored river basins. Tremendous efforts have been made over the last few decades to recover missing data and to improve hydrological predictions. Most of the missing data recovery methods, such as kriging interpolation, polynomial interpolation, optimal interpolation, Kalman filtering, the successive corrections method, fractal interpolation, and phase space reconstruction

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Thomas E. Adams III and Randel Dymond

1. Introduction The use of hydrologic ensembles to produce probabilistic flood and water resources forecasts, using ensemble prediction systems (EPSs), is rapidly gaining acceptance ( National Research Council 2006a , b ; Cloke and Pappenberger 2009 ; Demargne et al. 2014 ). However, full adoption of probabilistic forecasts by the public and decision-makers as a replacement to traditional, single-valued deterministic hydrologic forecasts is problematic, particularly with how risk

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Sarah A. Baker, Andrew W. Wood, and Balaji Rajagopalan

1. Introduction and background Subseasonal to seasonal (S2S) climate forecast skill has received greater attention in recent years due to the potential applications of climate forecasts. Many sectors including public health, disaster preparedness, energy, agriculture, and water management would benefit by applying S2S climate forecasts to their specific needs ( White et al. 2017 ). In the public health sector, S2S forecasts could help predict the probability of floods and droughts at longer

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James D. Brown, Dong-Jun Seo, and Jun Du

1. Introduction Reliable forecasts of precipitation and temperature are essential for operational streamflow forecasting. They are needed at space–time scales ranging from minutes and kilometers (e.g., for flash flood guidance) to multiple years and entire regions (e.g., for water supply outlooks). To produce reliable streamflow forecasts at multiple space–time scales, the River Forecast Centers (RFCs) of the U.S. National Weather Service (NWS) are evaluating temperature and precipitation

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