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Shahrbanou Madadgar and Hamid Moradkhani

1. Introduction Accurate forecasting of hydrologic extreme events plays a significant role in developing appropriate policies to plan for available water resources. Although several studies have proposed promising methods to improve hydrologic forecasts, the observed effects of climate change on floods and droughts across different regions of the globe in recent decades highlights the need for more sophisticated methods in predicting extreme events ( Mishra and Singh 2010 ; Moradkhani et al

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Xing Yuan, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan

parts of Ethiopia, Djibouti, Somalia, and northern Kenya. The related population displacement and price increase of food and fuel resulted in a serious humanitarian crisis ( Dutra et al. 2012 ; Peterson et al. 2012 ). Therefore, the provision of seasonal forecasts that can provide sufficient early warning has the potential to help the local governments and nongovernmental organizations (NGOs) to move from management of drought crises to management of drought risk, increasing the resilience to

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Eric F. Wood, Siegfried D. Schubert, Andrew W. Wood, Christa D. Peters-Lidard, Kingtse C. Mo, Annarita Mariotti, and Roger S. Pulwarty

, recreation economies, energy, and ecosystems. Recognizing the economic and social impacts from drought, the U.S. Congress in 2006 passed the National Integrated Drought Information System Act of 2006 (Public Law 109-430) with NOAA as the lead agency. The subsequent NIDIS Implementation Plan was developed to “[f]oster, and support, a research environment that focuses on risk assessment, forecasting, and management,” among other goals ( NIDIS 2007 , p. iii). Given its widespread support, NIDIS was

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Richard Seager, Lisa Goddard, Jennifer Nakamura, Naomi Henderson, and Dong Eun Lee

1. Introduction In the fall of 2010 the U.S. Drought Monitor showed no areas of the United States in drought, a situation essentially unique since the Drought Monitor was initiated in 1999. However, even as the Drought Monitor was showing unusually moist conditions across the country, seasonal-to-interannual forecasts were predicting a return to dry conditions across the southern United States and northern Mexico in the winter ahead. Those forecasts were based on predictions of a developing La

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Johnna M. Infanti and Ben P. Kirtman

damage, it is important to understand the current prediction capability within the region. It is now fairly well understood that a multimodel approach to prediction is an imperfect but still pragmatic method to estimating forecast uncertainty ( Krishnamurti et al. 1999 , 2000 ; Doblas-Reyes et al. 2000 ; Palmer et al. 2004 ; Hagedorn et al. 2005 ; Weigel et al. 2008 ; Kirtman and Min 2009 ). In this paper we utilize phase-1 data from the North American Multi-Model Ensemble (NMME) system, a

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Bart Nijssen, Shraddhanand Shukla, Chiyu Lin, Huilin Gao, Tian Zhou, Ishottama, Justin Sheffield, Eric F. Wood, and Dennis P. Lettenmaier

. From 2007 to 2013, Lloyd-Hughes and Saunders (2007) operated a global drought monitor, which was updated on a monthly basis and used station-based precipitation from the Global Precipitation Climatology Centre (GPCC; Schneider et al. 2014 ) and air temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). Their system used the standardized precipitation index (SPI) and the Palmer drought severity index (PDSI). Princeton University operates an African drought monitor

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Hailan Wang, Siegfried Schubert, Randal Koster, Yoo-Geun Ham, and Max Suarez

. (2011) , MERRA is an improvement over the previous generation of reanalyses and is in many aspects comparable to the other new reanalyses such as the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) ( Dee et al. 2011 ) and the National Oceanic and Atmospheric Administration Climate Forecast System Reanalysis (CFSR) ( Saha et al. 2010 ). There are, however, still substantial uncertainties and differences between these new reanalyses in poorly constrained quantities

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Jiarui Dong, Mike Ek, Dorothy Hall, Christa Peters-Lidard, Brian Cosgrove, Jeff Miller, George Riggs, and Youlong Xia

agriculture. Knowledge of various snowpack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project ( Mitchell et al. 2004 ). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions

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Sujay V. Kumar, Christa D. Peters-Lidard, David Mocko, Rolf Reichle, Yuqiong Liu, Kristi R. Arsenault, Youlong Xia, Michael Ek, George Riggs, Ben Livneh, and Michael Cosh

is spun up by running from 1979 to 2012 twice and then reinitializing the model in 1979. Noah LSM is used operationally at the National Centers for Environmental Prediction (NCEP) as the land component of regional and global weather forecasting models and at the Air Force Weather Agency (AFWA) in the offline land analysis system. More recent upgrades to the model have focused on improving the snow physics within Noah ( Barlage et al. 2010 ; Livneh et al. 2010 ; Wang et al. 2010 ) by providing

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Youlong Xia, Michael B. Ek, David Mocko, Christa D. Peters-Lidard, Justin Sheffield, Jiarui Dong, and Eric F. Wood

. (2011) evaluated soil moisture and water and energy fluxes from different systems, that is, the ensemble-mean of the NLDAS models ( Xia et al. 2012b ), the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR; Saha et al. 2010 ), and the North American Regional Reanalysis (NARR; Mesinger et al. 2006 ), using in situ observations. The results showed that the NLDAS ensemble-mean was the closest to the observations when compared with the other two systems

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