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Witold F. Krajewski, Ganesh R. Ghimire, and Felipe Quintero

1. Introduction In this paper we demonstrate, in a systematic way, that persistence is a hard-to-beat streamflow forecasting method ( Palash et al. 2018 ), a fact well known to operational forecasters. We limit our considerations to real-time forecasting in space and time in a river network. River networks aggregate water flow that originates from the transformation of rainfall and/or snowmelt to runoff. Additional recognition of the importance of river networks stems from the fact that many

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C. Cattoën, D. E. Robertson, J. C. Bennett, Q. J. Wang, and T. K. Carey-Smith

1. Introduction Two trends have emerged in the development of new streamflow forecasting systems: (i) a shift from deterministic to ensemble streamflow predictions ( Alfieri et al. 2013 ; Cloke and Pappenberger 2009 ; Demargne et al. 2014 ; Thielen et al. 2009 ), and (ii) a move toward national/continental scale systems that attempt to describe hydrological fluxes for all reaches over a given domain ( Adams and Pagano 2016 ; Bell et al. 2017 ; Emerton et al. 2016 ; Maxey et al. 2012

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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

1. Introduction Recent water-related disasters have captured public attention and led to increased interest in hydrologic forecasting systems. Flooding was responsible for nearly half of all natural catastrophe-related losses in 2013, with floods in Europe, Asia, Canada, the United States, and Australia causing over $20 billion (U.S. dollars) in losses [see www.swissre.com/media/news_releases/nr_20130821_sigma_natcat_estimates_H1_2013.html and Coffman (2013) ]. The human toll in developing

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Tirthankar Roy, Xiaogang He, Peirong Lin, Hylke E. Beck, Christopher Castro, and Eric F. Wood

1. Introduction The North American Multi-Model Ensemble (NMME; Kirtman et al. 2014 ) system incorporates seasonal forecasts of different hydroclimatic variables from multiple U.S. and Canadian models. These forecasts are invaluable for a plethora of scientific and operational applications, including precipitation and temperature forecasting ( Setiawan et al. 2017 ; Wang 2014 ; Krakauer 2017 ; Cash et al. 2019 ; Wanders et al. 2017 ), prediction of extremes ( Yuan et al. 2015 ), atmospheric

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Simon Schick, Ole Rössler, and Rolf Weingartner

1. Introduction Subseasonal and seasonal forecasts of environmental conditions are increasingly based on numerically coupled models of the various Earth system components. These include general circulation models of the atmosphere and oceans and dynamical land surface or sea ice models ( National Academies 2016 ). Such forecast systems represent diverse physical, chemical, and biological processes and continuously progress toward Earth system models (ESMs). However, not all environmental

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Edwin Welles and Soroosh Sorooshian

1. Introduction Recently, Welles et al. (2007) evaluated National Weather Service (NWS) river stage forecasts. They found the forecast skill may not have improved as much as expected because, as they suggested, forecast system updates were not driven by objective measures of forecast skill. Many people have studied elements of the forecast process—calibration, state updating, and precipitation forecasts—but the forecast process itself with the various elements linked together has not been

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Allen B. White, Daniel J. Gottas, Arthur F. Henkel, Paul J. Neiman, F. Martin Ralph, and Seth I. Gutman

1. Introduction The “snow level” is a term used by National Oceanic and Atmospheric Administration’s (NOAA) forecasters at the National Weather Service (NWS) to ascribe the altitude in the atmosphere where falling snow melts to rain. The snow level should be distinguished from another term used by forecasters, the “free atmosphere freezing level,” which is the altitude corresponding to the 0°C isotherm. However, in cloud physics and in other fields, the term “melting level” is often used in

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Elisa Brussolo, Jost von Hardenberg, and Nicola Rebora

1. Introduction Precipitation intensity represents the crucial atmospheric variable for the operational assessment of hydrometeorological risks; unfortunately, it is also one of the most difficult to forecast reliably by current weather models. Uncertainty in quantitative precipitation forecasts (QPFs) arises as a result of measurement errors, incomplete observations, insufficiently resolved initial conditions, an incomplete representation of the physics of the problem, finite numerical

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Paul W. Miller and Craig A. Ramseyer

Centers for Environmental Prediction’s (NCEP) seasonal numerical weather prediction (NWP) model failed to detect the looming precipitation anomaly. The 2015 annual assessment of the Climate Forecast System, version 2 (CFSv2), found that it actually predicted slight positive precipitation anomalies for the March–May 2015 period when the historic drought initiated ( Wang 2016 ) as well as the entirety of the ERS in Puerto Rico ( Fig. 1 ). Fortunately, recent analyses of Puerto Rican hydroclimate have

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Tushar Sinha, A. Sankarasubramanian, and Amirhossein Mazrooei

1. Introduction Over the last decade, considerable progress has been made in the ability to forecast seasonal streamflow through better understanding of climatic teleconnections (e.g., ENSO) as well as through the development of subgrid-scale land surface models (LSMs) that capture land–atmosphere interactions ( Koster and Suarez 1995 ; Betts et al. 1997 ; Hamlet et al. 2002 ; Maurer et al. 2002 ; Mahanama et al. 2012 ). Nevertheless, the skill of climate forecasts varies significantly for

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