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Manabendra Saharia, Pierre-Emmanuel Kirstetter, Humberto Vergara, Jonathan J. Gourley, Yang Hong, and Marine Giroud

the Mediterranean and decrease in intensity as one moves inland ( Gaume et al. 2009 ). Furthermore, they find that the seasonality of the inland, continental flash floods tends to be in the warm season months, while those closer to the Mediterranean Sea typically occur in autumn months. Until recently, the lack of a comprehensive database that catalogs information related to flash flood timing, location, and severity such as the causative rainfall and basin geomorphology have hindered broad

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Zhenwei Li, Xianli Xu, Chaohao Xu, Meixian Liu, Kelin Wang, and Bofu Yu

approaches . Rev. Geophys. , 52 , 218 – 242 , doi: 10.1002/2013RG000443 . 10.1002/2013RG000443 Hartmann , A. , T. Gleeson , R. Rosolem , F. Pianosi , Y. Wada , and T. Wagener , 2015 : A large-scale simulation model to assess karstic groundwater recharge over Europe and the Mediterranean . Geosci. Model Dev. , 8 , 1729 – 1746 , doi: 10.5194/gmd-8-1729-2015 . 10.5194/gmd-8-1729-2015 Huang , S. , J. Chang , Q. Huang , and Y. Chen , 2014 : Monthly streamflow prediction using modified EMD

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Felipe Quintero, Witold F. Krajewski, Ricardo Mantilla, Scott Small, and Bong-Chul Seo

-D-14-0105.1 . Lo Conti, F. , Hsu K.-L. , Noto L. V. , and Sorooshian S. , 2014 : Evaluation and comparison of satellite precipitation estimates with reference to a local area in the Mediterranean Sea . Atmos. Res. , 138 , 189 – 204 , doi: 10.1016/j.atmosres.2013.11.011 . Maggioni, V. , Reichle R. H. , and Anagnostou E. N. , 2011 : The effect of satellite rainfall error modeling on soil moisture prediction uncertainty . J. Hydrometeor. , 12 , 413 – 428 , doi: 10.1175/2011JHM

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Morteza Sadeghi, Ardeshir Ebtehaj, Wade T. Crow, Lun Gao, Adam J. Purdy, Joshua B. Fisher, Scott B. Jones, Ebrahim Babaeian, and Markus Tuller

. Remote Sens. , 39 , 1729 – 1735 , . 10.1109/36.942551 Konikow , L. F. , 2011 : Contribution of global groundwater depletion since 1900 to sea-level rise . Geophys. Res. Lett. , 38 , L17401 , . 10.1029/2011GL048604 Koster , R. D. , L. Brocca , W. T. Crow , M. S. Burgin , and G. J. De Lannoy , 2016 : Precipitation estimation using L-band and C-band soil moisture retrievals . Water Resour. Res. , 52 , 7213

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Han Zhou, Wen Zhou, Yuanbo Liu, Yanbin Yuan, Jiejun Huang, and Yongwei Liu

-Stations schemes. The comparison includes spatial coordinates, (a) longitude and (b) latitude, and (c) the mean drought severity, as well as (d) the number of stations and the number of drought modes. c. The possible role of spatial modes to understand the development of droughts Meteorological droughts (or precipitation deficit) are regional in nature and occur over almost all climatic zones ( Mishra and Singh 2010 ; Rajsekhar et al. 2013 ), which are generally caused by anomalous sea surface temperatures

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Kevin E. Trenberth, Lesley Smith, Taotao Qian, Aiguo Dai, and John Fasullo

. 2005 ). The strong relationships with sea surface temperatures (SSTs) allow estimates of column water vapor amounts since 1970 to be made and results indicate increases of about 4% over the global oceans, suggesting that water vapor feedback has led to a radiative effect of about 1.5 W m −2 ( Fasullo and Sun 2001 ), comparable to the radiative forcing of carbon dioxide increases ( Houghton et al. 2001 ). This provides direct evidence for strong water vapor feedback in climate change. The observed

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Jee-Hoon Jeong, Chang-Hoi Ho, Deliang Chen, and Tae-Won Park

over land areas. These differences demonstrate the problem of using CAM3 to drive an LSM, particularly for surface fluxes. The most notable features are warm and dry biases over northern Eurasia, Canada, East Asia, India, the Middle East, and the Sahara Desert, and relatively cold and wet biases over America, South America, and the coastal region around the Mediterranean Sea ( Figs. 1a and 1c ). Overall, there is a dry bias over the major precipitation zones and a wet bias over many arid or desert

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Martin Hirschi, Sonia I. Seneviratne, and Christoph Schär

the physical and dynamical formulations, land-use characteristics, and computational domains (see Table 4 ). All models cover the major part of Europe at a resolution of approximately 50 km. They simulate a control climate (1961–90), as well as an A2-scenario time slice (2071–2100), and are driven by the Hadley Centre HadAM3H simulations [except the Centre National de Recherches Meteorologiques (CNRM) control run driven by observed sea surface temperatures]. Here attention is restricted to the

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Ethan Gutmann, Idar Barstad, Martyn Clark, Jeffrey Arnold, and Roy Rasmussen

Thompson microphysics and with variations in the linear dynamics. A map of the domain is shown in Fig. 5 ; covering this domain with 4-km-wide grid cells results in a grid that is 318 and 264 grid points in the x and y dimensions, respectively. The WRF domain used 44 vertical layers ranging in thickness from 36 m at the surface to greater than 500 m at higher altitudes. The top of the WRF domain was specified at 100 hPa, approximately 16 km above sea level. The ICAR domain used 14 vertical layers

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Hugo G. Hidalgo, Daniel R. Cayan, and Michael D. Dettinger

impacts in water resources, ecosystems, and socioeconomic structures. Thus, study of changes and covariations of ETo and P are needed and may provide the basis for early warnings of such changes. Probably the most common practical use of ETo estimates is in scheduling irrigation in agricultural regions ( ITRC 2003 ). On farmed land, the amount of water that has to be applied to the soil (water demand) generally must be close to the potential evapotranspiration rate. Under California's Mediterranean

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