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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

1. Introduction Drought is one of the costliest environmental disasters and has profound socioeconomic consequences, as it typically occurs at long time scales and in virtually all climatic zones. Droughts are generally classified into three physical types: meteorological drought resulting from precipitation deficits, agricultural drought due to total soil moisture deficits, and hydrological drought related to the shortage of streamflow or runoff ( Keyantash and Dracup 2002 ; Mo 2008 ; Shukla

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

1. Introduction Rain or snow falling over any particular location is composed of condensed water vapor that entered the atmosphere as surface evaporation from a range of upstream locations. Surface and atmospheric conditions along the paths of moisture advection determine the ultimate sources of evaporative moisture, which generally have a combination of oceanic and terrestrial origins. Knowledge of the sources of moisture supplying precipitation over a particular location could be used to

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

monitored from meteorological, agricultural, and hydrological considerations ( Dracup et al. 1980 ; Wilhite and Glantz 1985 ). Meteorological drought is usually quantified by the rainfall anomaly occurring over a time period ( Olukayode Oladipo 1985 ; Meze-Hausken 2004 ). Agricultural drought, probably the most important aspect of drought, is often characterized by the shortage of soil moisture within a period that influences crop production ( Palmer 1965 ). Hydrological drought occurs with the

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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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John M. Peters, Christopher J. Nowotarski, and Gretchen L. Mullendore

its original formulation, is unrealistically moist in the middle to upper troposphere (e.g., Potvin and Flora 2015 ). To add thermodynamic variability to our simulations, we used two different boundary layer moisture values of 14 and 16 g kg −1 , yielding two different convective available potential energy (CAPE) values and mixed-layer depths (0–1-km mean CAPE values of 1729 and 2744 J kg −1 , respectively). Runs with 14 and 16 g kg −1 are referred to as LOWCAPE and HICAPE, respectively. Fig . 1

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

, 1995 ). Second, the downscaled meteorological forcings are used to force well-calibrated land surface hydrologic models to resolve terrestrial water and energy variations, with the predicted land surface conditions, such as soil moisture and streamflow, used to derive agricultural and hydrological drought indices ( Luo and Wood 2007 ; Mo et al. 2012 ; Yuan et al. 2013 ). Most of the previous forecasting studies have focused on a single type of drought, such as meteorological drought or

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

the present (November 2013), so this event is not yet over. In this paper, we focus on the dynamical causes of the 2010/11 TexMex drought in terms of circulation anomalies and variations of surface evaporation and transports and convergence of moisture within the atmosphere and examine its evolution from fall of 2010 to its most extreme state in summer and fall of 2011. Our goal is to determine the ocean–atmosphere dynamics of this event and, by reference to prior work, assess how similar or

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

years, a number of near-real-time drought monitoring systems have been developed with regional or global extent. For example, the University of Washington has operated a surface water monitor for the United States since late 2005 ( Shukla et al. 2011 ; Wood 2008 ; Wood and Lettenmaier 2006 ), which uses station observations of temperature and precipitation to produce near-real-time estimates of drought conditions in terms of soil moisture (SM), snow water equivalent (SWE), and runoff, simulated by

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Zengchao Hao and Amir AghaKouchak

SPI can also be applied to other variables to derive drought indices such as the standardized soil moisture index (SSI; Hao and AghaKouchak 2013 ) and the standardized runoff index (SRI; Shukla and Wood 2008 ) for drought monitoring. The performance of different variables differs in detecting the drought onset, persistence, and termination. A meteorological drought (deficit in precipitation) may develop quickly and end abruptly, while the onset of an agricultural drought (deficit in soil

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Jason A. Otkin, Martha C. Anderson, Christopher Hain, Iliana E. Mladenova, Jeffrey B. Basara, and Mark Svoboda

, drought onset can be very rapid if extreme atmospheric anomalies persist for several weeks. Vegetation health can deteriorate very quickly during an extended period of dry weather if the lack of rainfall is also accompanied by anomalously warm surface temperatures, strong winds, and sunny skies because these conditions lead to increased evapotranspiration (ET) that can quickly deplete root zone moisture ( Mozny et al. 2012 ). This scenario is most likely to occur during the warm season when daily mean

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