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Kingtse C. Mo and Dennis P. Lettenmaier

of hydrological drought. As a result of the sparseness of observations of SM and the complexities of the relationships between (spatially distributed) runoff and observed streamflow, the use of model-derived SM and runoff from efforts like the North American Land Data Assimilation System (NLDAS) ( Mitchell et al. 2004 ; Xia et al. 2012 ) and extensions thereof by Maurer et al. (2002) and Livneh et al. (2013) have become popular. The Environmental Modeling Center (EMC) of the National Centers

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Paul A. Dirmeyer, Jiangfeng Wei, Michael G. Bosilovich, and David M. Mocko

.g., Gimeno et al. 2012 ; Goessling and Reick 2011 ). Van der Ent (2010) developed a backtracking model based on the vertically integrated moisture transport and the constraints of atmospheric water balance, but still in the Eulerian framework. Keys et al. (2012) developed an interesting variant on that approach to estimate the “precipitation sheds” of regions to assess their potential vulnerability to changing evapotranspiration with land use changes. Another approach is to include water vapor

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

a suite of land surface models. The U.S. Drought Monitor ( Svoboda et al. 2002 ) has provided weekly drought updates since 1999 as part of a partnership between the National Drought Mitigation Center at the University of Nebraska–Lincoln, the U.S. Department of Agriculture, and the National Oceanic and Atmospheric Administration. Kogan and Sullivan (1993) made an early attempt to develop a global drought monitor using satellite information, primarily using satellite-based vegetation indices

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

( Camberlin et al. 2001 ). Recently, the Indian Ocean and Mediterranean Sea are receiving more attentions for their contributions to the rainfall variability over eastern and northern Africa, respectively ( Bowden and Semazzi 2007 ; Gimeno et al. 2012 ). These teleconnections have been used to form the basis for developing statistical approaches in forecasting drought at seasonal scales ( Barnston et al. 1996 ; Mason 1998 ). On the other hand, atmosphere–ocean–land coupled general circulation models

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

monthly anomalies and percentiles of hydrologic fields (soil moisture, snow water equivalent, total runoff, streamflow, evaporation, and precipitation) output from the four land surface models [Noah, Mosaic, Sacramento (SAC), and Variable Infiltration Capacity (VIC)] on a common ⅛° grid using common hourly meteorological forcing (see the drought tab on the NLDAS website, ). The climatology of each hydrologic field was calculated as the average of 28 yr (1980–2007) of

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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

central Great Plains drought of 2012, and the western U.S. drought from 1998 to 2002. The DEWS WG focus is on supporting the continued development and evaluation of drought monitoring and prediction tools, such as the North American Land Data Assimilation System (NLDAS) and the North American Multimodel Ensemble (NMME) system. Note that DEWS is used here to indicate a research focus on monitoring and prediction. Comprehensive early warning information systems, such as those intended as part of NIDIS

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

measurements are strongly tied to observations, but may have limits in spatial sampling and portability to other domains that lack dense in situ monitoring networks. Prognostic land surface models (LSMs) can provide quantitative estimates of a full suite of hydrologic variables, adding value to the precipitation data used as a primary input. However, model output may have significant biases because of inaccurate modeling assumptions, observational errors in the forcing data, and a reliance on surface

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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

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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Zengchao Hao and Amir AghaKouchak

used standardized drought indices (i.e., SPI and SSI), are derived using the National Aeronautics and Space Administration’s (NASA) land-only version of Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) data and validated with the U.S. Drought Monitor (USDM) data. 2. Method In a recent study, Hao and AghaKouchak (2013) proposed the MSDI for characterizing overall drought conditions, taking into account precipitation deficit and soil moisture deficit. This approach is an

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