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Martha C. Anderson, Christopher Hain, Jason Otkin, Xiwu Zhan, Kingtse Mo, Mark Svoboda, Brian Wardlow, and Agustin Pimstein

future global drought monitoring efforts, providing a time-continuous suite of hydrologic variables generated from a unified modeling system or ensemble of systems. In preparation, intercomparisons between prognostic and diagnostic indicators provide insight regarding relative regional and seasonal performance. This study focuses on diagnostic remote sensing indicators that are responsive to short-term environmental changes, since early warning capabilities are limited in current drought monitoring

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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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Zengchao Hao and Amir AghaKouchak

through the Global Integrated Drought Monitoring and Prediction System (GIDMaPS; ). The performances of these indices are evaluated against the USDM ( ), 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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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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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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Xing Yuan, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan

percentiles are used as indices to assess probabilistic drought hindcasts for 1982–2007. The hindcast setup and evaluation methods are described in section 2 . The results are presented in section 3 , and a discussion and summary are given in sections 4 and 5 , respectively. 2. Hindcast setup and evaluation method Before calculating the SPI or running the VIC model over Africa, we downscale the CFSv2 monthly precipitation and temperature hindcasts for 1982–2007 from T126 resolution to 0.25°. The CFSv

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Shahrbanou Madadgar and Hamid Moradkhani

dry condition and return to the normal conditions over various time periods. The limitation, however, was using an unconditional gamma distribution—ignoring the dependency and correlation of precipitation in temporal scale—to obtain the probabilities. Lohani and Loganathan (1997) used a nonhomogeneous Markov chain model to generate the transition probability matrix of drought states. In another study, the Markov chain model was employed to evaluate drought transition probabilities, persistence

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