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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

countries is staggering, with disasters routinely displacing from tens to hundreds of thousands of people; for example, nearly 2000 people were dead or missing after the Philippines typhoon of 2012, with evacuations exceeding 780 000 people. Droughts can be just as damaging, with the U.S. drought of 2012 costing nearly $80 billion (U.S. dollars). Some of these consequences are avoidable through advance warning, emergency response, and other preparations; thus, operational river forecasters can help

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Francesco Silvestro, Nicola Rebora, and Luca Ferraris

atmospheric observations. The second is that, in order to cope with operational procedures and decision-making responsibility, hydrologists are allowed, in certain cases, to use “certified” predictions from (human) expert forecasters. These forecasters, on the basis of their knowledge, can analyze different meteorological models and estimate their reliability in different synoptic conditions and different local meteorological situations. This results in issuing a QPF that synthesizes a large quantity of

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Haksu Lee, Yu Zhang, Dong-Jun Seo, Robert J. Kuligowski, David Kitzmiller, and Robert Corby

) could augment operational river flow forecasting for some basins outside of radar coverage and in sparsely gauged areas. They will need to be used in a manner that considers their own limitations, of course, including complex terrain (orographic enhancements of convection are captured, but seeder–feeder enhancements are not) and snow cover [which precludes microwave (MW)-based retrievals but still allows infrared (IR)-based estimates]. Among real-time multisatellite QPE algorithms, this study uses

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A. Sankarasubramanian, Upmanu Lall, and Susan Espinueva

diagnostic analyses for each forecasting time step. Given that many research institutions and agencies are issuing climate forecasts on a monthly basis using general circulation models (GCMs), an alternate approach would be to utilize GCM-predicted fields to develop operational streamflow forecasts. However, GCM-predicted fields are typically available at larger spatial scales (2.5° × 2.5°), which need to be downscaled to obtain streamflow forecasts. Dynamical downscaling, a physical approach of nesting

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Ayumi Fujisaki-Manome, Greg E. Mann, Eric J. Anderson, Philip Y. Chu, Lindsay E. Fitzpatrick, Stanley G. Benjamin, Eric P. James, Tatiana G. Smirnova, Curtis R. Alexander, and David M. Wright

parameterizations of planetary and surface boundary layers ( Conrick et al. 2015 ; Minder et al. 2020 ). These previous studies collectively indicate that the turbulent heat fluxes (i.e., heat and moisture fluxes from the lakes) are critical factors that need to be represented well in the models to accurately simulate LES bands. Fujisaki-Manome et al. (2017) showed that operational forecast models present high uncertainty in turbulent sensible and latent heat fluxes over Lake Erie (i.e., heat and moisture

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C. Bryan Young, A. Allen Bradley, Witold F. Krajewski, Anton Kruger, and Mark L. Morrissey

1. Introduction In recent years, the National Weather Service (NWS) has installed the Next-Generation Weather Radar (NEXRAD) system at forecast offices across the country. The NEXRAD system consists of a network of Weather Surveillance Radar-1988 Doppler (WSR-88D) radars ( Crum et al. 1993 ). Reflectivity observations from each WSR-88D are used to generate many operational products, including estimates of precipitation developed with the NEXRAD precipitation processing system ( Klazura and Imy

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Jon Olav Skøien, Konrad Bogner, Peter Salamon, and Fredrik Wetterhall

Berrocal et al. (2007) for incorporating the correlation between lead times, which is higher for runoff than for meteorological variables. Their method was particularly useful when combining forecasts with different maximum lead times. Engeland and Steinsland (2014) , on the other hand, presented a method fitting different weights to each observation location and lead time, but with the same number of forecasts for each lead time. Our study of ensemble forecasts is related to the EFAS, an operational

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Thomas E. Adams III and Randel Dymond

is to determine the utility of using hydrologic ensemble mean or median forecasts of river stage from the NOAA/NWS Meteorological Model-Based Ensemble Forecast System (MMEFS), described in Adams and Ostrowski (2010) , as an alternative to current, operational, single-valued deterministic hydrologic stage forecasts at the OHRFC and, possibly, elsewhere. Section 2 of this paper describes the real-time hydrologic forecasting experiment used in this study. Model simulations are restricted to

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Felipe Quintero, Witold F. Krajewski, and Marcela Rojas

1. Introduction In principle, distributed hydrological models allow river discharge forecasts at every channel in a drainage network. The spatial and temporal distribution of peak flows across the channels is a reflection of the interactions among the spatial and temporal variability of the model input (e.g., rainfall, snowmelt), the soil properties controlling the generation of overland flow, and the structure of the drainage network (e.g., Lu et al. 2017 ; Mantilla et al. 2006 ; Ayalew et

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Feyera A. Hirpa, Peter Salamon, Lorenzo Alfieri, Jutta Thielen-del Pozo, Ervin Zsoter, and Florian Pappenberger

; Thielen et al. 2009 ; Bartholmes et al. 2009 ) and the African Flood Forecasting System (AFFS; Thiemig et al. 2014 ). At the global scale, however, there are only a few hydrological and flood-risk assessment modeling (e.g., Kim et al. 2009 ; Decharme et al. 2012 ; Pappenberger et al. 2012 ; Yamazaki et al. 2013 ; Winsemius et al. 2013 ) and operational flood forecasting systems. The Global Flood Awareness System (GloFAS; Alfieri et al. 2013 ) has been producing ensemble streamflow forecasts

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