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Paul A. Dirmeyer

GCMs—on a global grid—with consistent realistic meteorological forcing. This was in contrast to the early multimodel experiments of the Project to Intercompare Land Surface Parameterization Schemes (PILPS; Henderson-Sellers et al. 1993 ), which until that time were conducted at a series of individual sites. The second recommendation was to produce a global soil wetness climatology using one or more LSMs driven by internally consistent gridded near-surface atmospheric data, like that beginning to

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Qing Liu, Rolf H. Reichle, Rajat Bindlish, Michael H. Cosh, Wade T. Crow, Richard de Jeu, Gabrielle J. M. De Lannoy, George J. Huffman, and Thomas J. Jackson

et al. 2001 ; Bindlish et al. 2003 ; Gao et al. 2006 ). Among the surface meteorological forcing inputs to the land model, precipitation has the most direct and important influence on the estimation of soil moisture. Improving precipitation forcing data, in particular through the use of satellite- and gauge-based measurements, can therefore substantially improve the soil moisture estimates from the land surface model ( Guo et al. 2006 ). Model soil moisture estimates can also be improved

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Yadu Pokhrel, Naota Hanasaki, Sujan Koirala, Jaeil Cho, Pat J.-F. Yeh, Hyungjun Kim, Shinjiro Kanae, and Taikan Oki

withdrawals from different sources is demonstrated. Finally, summary and conclusions are presented in section 6 . 2. Data a. Climate data The spatial resolution of forcing data and simulations is 1° × 1° (longitude and latitude) global grids with a land–sea mask defined by the Global Soil Wetness Project 2 (GSWP2; Dirmeyer et al. 2006 ). Atmospheric forcing data covering the 29-yr simulation period (1979–2007) are obtained from Kim et al. (2009) . The data include 6-hourly precipitation, temperature

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Dai Matsushima, Reiji Kimura, and Masato Shinoda

underlying surface temperature, ω = 2 π /86 400 is the insolation, L ↓ is the downward longwave radiation, T a is the air temperature, q a is the specific humidity, U is the wind speed, and t is time. The prime sign denotes difference from the daily average. Matrices and include parameters that are listed above. In the derivation of Eq. (1) , the force-restore formulation ( Stull 1988 ) is implemented for the ground heat flux G g as in which where P g is the subsurface thermal

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Akihiko Ito and Motoko Inatomi

, 2007 : Quantifying and mapping the human appropriation of net primary production in earth’s terrestrial ecosystems . Proc. Natl. Acad. Sci. USA , 104 , 12 942 – 12 947 . Hall, F. G. , and Coauthors , 2006 : ISLSCP Initiative II global data sets: Surface boundary conditions and atmospheric forcings for land-atmosphere studies . J. Geophys. Res. , 111 , D22S01 , doi:10.1029/2006JD007366 . Hanasaki, N. , and Coauthors , 2008 : An integrated model for the assessment of global water

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Sante Laviola, Agata Moscatello, Mario Marcello Miglietta, Elsa Cattani, and Vincenzo Levizzani

the Salento Peninsula is around 200 mm during the central day of the event, eventually decreasing to a minimum of 42 mm at the end of the last day. This trend agrees with the ECMWF pressure analysis of the cyclonic circulation on 12 and 13 November, which advects warm and moist air over southern Apulia, thus supporting the development of deep convection and the consequent production of heavy precipitation. Fig . 3. Cumulative 24-h precipitation from the Italian Air Force Weather Service surface

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Pablo Imbach, Luis Molina, Bruno Locatelli, Olivier Roupsard, Gil Mahé, Ronald Neilson, Lenin Corrales, Marko Scholze, and Philippe Ciais

performance in wet areas (with annual precipitation over 2000 mm) probably linked to hydrological processes that cannot be captured at monthly time steps over dry areas (i.e., rainstorms) and cloud water interception in cloud forests ( Bruijnzeel 2005 ) not being captured by current precipitation forcings. The bias in simulated annual runoff was tested across gradients of precipitation, altitude, forest cover, and catchment size and showed no trends except for small catchments (less than 10 pixels

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Minseok Kang, Hyojung Kwon, Jung Hwa Cheon, and Joon Kim

(originally not PAI but leaf area index) and n = ⅔, whereas S = 0.5 and n = 0.5 in the algorithm of Noah LSM. In both LSMs, the value of n determines the rate of E WC by adjusting the magnitude of W c / S . Unlike the Rutter model, which neglects n (i.e., n = 1), the two LSMs force the canopy to become dry faster by empirically setting the value of n < 1. The E P was calculated using the meteorological data (e.g., R N , T a , VPD, and ). The daily PAI was linearly interpolated

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