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

the end of (top) 2008 and (bottom) 2012. Fig . 6. Monthly mean image noise for (top) the total moisture percentiles and (bottom) the meteorological forcings. The vertical dashed lines correspond to 1 Jan 2009 and 1 Jul 2012. d. Multimodel drought estimates One of the distinguishing features of GDIS is the use of multiple land surface models in tracking the evolution in total moisture storage at the land surface. While an in-depth evaluation of the performance of the individual models is not the

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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

runoff estimation is the use of land surface models (LSMs) forced with observed meteorology, which generate spatially and temporally continuous estimates of land surface conditions ( Mitchell et al. 2004 ; Rodell et al. 2004 ; Kumar et al. 2006 ). The gridded runoff can be subsequently routed to generate estimates of streamflow, and several studies provide descriptions and evaluations of these approaches ( Schlosser et al. 1997 ; Nijssen et al. 1997 ; Boone et al. 2004 ; Lohmann et al. 2004

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

for improving the land initial conditions for numerical weather predictions. Since then, the system has expanded its scope to include model intercomparison studies ( Xia et al. 2012a , b ), evaluation of NLDAS products ( Peters-Lidard et al. 2011 ; Xia et al. 2012c ), and development of a near-real-time NLDAS drought monitoring system ( Ek et al. 2011 ; Sheffield et al. 2012 ; Xia et al. 2013 ). The drought monitoring system provides a range of drought indices, including daily, weekly, and

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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

systematic and comprehensive evaluation of the quality of existing drought monitoring and prediction services, their performance in a number of drought case studies, and their potential to support national and global DEWS. The Case Studies WG focused on identifying and analyzing several high-profile case studies that appear to have different drought mechanisms, feedbacks, and potential predictability. These cases consist of the southeastern U.S. drought during 2006/07, the Texas drought of 2011, the

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

covariances of departures of submonthly values from the monthly means. The NCEP–NCAR reanalysis provided the covariances and for ERA-I, they were evaluated with 6-hourly data. The vertical integrals extend to the monthly mean surface pressure using eight standard pressure levels for NCEP and 26 levels for ERA-I. Evaluating the moisture budget diagnostically from reanalysis data leads to errors compared to the actual moisture budget calculation in the models that produce the reanalyses due to differences

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Zengchao Hao and Amir AghaKouchak

through the Global Integrated Drought Monitoring and Prediction System (GIDMaPS; http://drought.eng.uci.edu/ ). The performances of these indices are evaluated against the USDM ( http://droughtmonitor.unl.edu/ ), which is a composite product including climate indices, numerical models, and inputs from regional and local experts from around the United States ( Svoboda et al. 2002 ). While the USDM data cannot be regarded as a metric of ground truth, it provides a baseline for evaluation of different

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Hongshuo Wang, Jeffrey C. Rogers, and Darla K. Munroe

values of these indices have an average value of zero and a unit standard deviation. One of the important characteristics of the SPI and SPEI lies in their multiple time scales that can be used to evaluate drought from the viewpoint of temporal accumulation of moisture anomalies. The PDSI is based on a water budget model from the water balance equation ( Palmer 1965 ) that incorporates precipitation, evapotranspiration, runoff, and recharge. The PDSI itself depends on a two-layer bucket model of the

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M. Hoerling, J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager

upcoming season. The full information of ensemble prediction systems, in particular the spread information contained in such tools, can thus not be readily incorporated into current practices for U.S. drought forecasting. Further research is also required on evaluating the spread information on drought statistics from such ensemble modeling systems. Much has yet to be learned about the robustness of spreads across multimodels and how those spreads differ when examined in simulation mode (using

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

Feng 2012 ; Dai 2013 ) and atmospheric general circulation model (AGCM) simulations (e.g., Hoerling and Kumar 2003 ; Schubert et al. 2004a , b ; Seager et al. 2005 ; Wang et al. 2010 ). An important finding from such studies is that ENSO and the PDO in their cold phases, and the AMO in its warm phase, produce a tendency for drought conditions over the United States, with the Pacific playing the dominant role (e.g., Mo et al. 2009 ; Schubert et al. 2009 ). In addition, the impact of SST

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