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David A MacLeod, Rutger Dankers, Richard Graham, Kiswendsida Guigma, Luke Jenkins, Martin C. Todd, Augustine Kiptum, Mary Kilavi, Andrew Njogu, and Emmah Mwangi

advisories with a few days’ lead time ( MacLeod et al. 2020 ) and a 3-day streamflow forecast is produced for the Nzoia River basin of western Kenya. In Uganda, the Uganda Red Cross in collaboration with other stakeholders are piloting the use of the Global Flood Awareness System (GloFAS) to initiate early actions up to a week in advance. These examples are representative of the lead time of currently available basin-scale flood forecasts in the region. Fig . 1. Topography of the study area, including

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Julie A. Vano, Tapash Das, and Dennis P. Lettenmaier

reductions in mean annual discharge of less than 10% to almost 50% by the mid-2000s ( Hoerling et al. 2009 ). Part of this range is attributable to differences in the forcing data used, both in resolution and in the variation of climate models included in the different studies. Differences also reflect biases in the runoff projections (as shown below, hydrologic sensitivities are highly nonlinear, and hence depend on the models’ estimates of current climate runoff) and reflect variations in hydrologic

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Tirthankar Roy, Xiaogang He, Peirong Lin, Hylke E. Beck, Christopher Castro, and Eric F. Wood

limitations persist. First, most of the above studies are regional assessments, which makes it difficult to generalize their results to other regions. Second, none of the studies considered all the models currently available within the NMME system (there are 16 in total that has both precipitation and temperature forecasts), raising concerns about the performance of the excluded models. Third, not all of them considered both precipitation and temperature in the skill assessment. Fourth, none of them

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Wen Li Zhao, Guo Yu Qiu, Yu Jiu Xiong, Kyaw Tha Paw U, Pierre Gentine, and Bao Yu Chen

spatiotemporal variations in the land surface and atmospheric conditions ( Irmak et al. 2008 ; Franks et al. 2017 ). Additionally, g s values for plant species under different water and climatic combinations are scarce. Moreover, current leaf-level models fail to capture the complex biological controls on water and carbon fluxes throughout the canopy and landscape ( Damour et al. 2010 ; Matheny et al. 2014 ; Mallick et al. 2015 , 2016 ; van Dijk et al. 2015 ; Buckley 2017 ; Bhattarai et al. 2018

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Zhangkang Shu, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, Ji Liu, Yanli Liu, Ruimin He, Cuishan Liu, and Zhenxin Bao

1. Introduction Numerical weather prediction (NWP) is a forecasting method used to solve atmospheric motion and weather phenomena in a particular period of future time for a defined set of initial values and boundary conditions. NWP is based on the hydrodynamic and thermodynamic equations of the weather evolution process. The effects of quantitative precipitation forecast (QPF) in NWP show clear temporal and spatial variations because of the differences in the initial fields, the generation of

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Xiang Gao, Alexander Avramov, Eri Saikawa, and C. Adam Schlosser

1. Introduction The state and amount of water in the soil is a critical determinant in many complex Earth system processes. Soil moisture serves as the reservoir for the land surface hydrologic cycle and a boundary condition for the atmosphere. It regulates the partitioning of land surface heat fluxes, affects the status of overlying vegetation, modulates the thermal properties of the soil, and controls the exchange of trace gases at Earth’s surface. Knowledge of the temporal and spatial

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Dazhi Xi, Ning Lin, and James Smith

profiles (RCLIPER) as a function of TC intensity. The model was further improved by Lonfat et al. (2007) to account for rainfall asymmetry caused by topographic and shear effects. In addition to the statistical approach, Langousis and Veneziano (2009a) developed a TC rainfall model based on basic thermodynamics and TC boundary layer theory. The model accounts for the azimuthal asymmetries in surface friction related to storm motion, but it does not account for the interaction of the storm with its

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Li Fang, Xiwu Zhan, Jifu Yin, Jicheng Liu, Mitchell Schull, Jeffrey P. Walker, Jun Wen, Michael H. Cosh, Tarendra Lakhankar, Chandra Holifield Collins, David D. Bosch, and Patrick J. Starks

finescale based on the regression relationship, a moving window approach was applied, in which the central prediction area moves across the whole experiment region with some overlap from the previous area ( Gao et al. 2012 ). Results showed that this method was effective to remove the boundary effects because of common samples from overlap regions. In our implementation, MODIS global LAI and Fraction of Photosynthetically Active Radiation (FPAR) product (MOD15A2, V005) and LST (MOD11A1.006) product are

Open access
Rhonalyn V. Macalalad, Roy A. Badilla, Olivia C. Cabrera, and Gerry Bagtasa

the east, Caraballo Mountains to the north, and parts of the Zambales Mountains where Mt. Pinatubo is located to the west. Of these boundaries, the Zambales Mountains to the west are characterized by ultramafic rocks with some volcanic components ( Schopka et al. 2011 ). The Sierra Madre range to the east comprise more of dissected and eroded terrain ( JICA 2011 ). The central part of the study area is called the Central Luzon Plain due to its relatively flat terrain. It is an alluvial plain with

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Andrea Manrique-Suñén, Annika Nordbo, Gianpaolo Balsamo, Anton Beljaars, and Ivan Mammarella

-weighted average. Koster and Suarez (1992) and Essery et al. (2003) compared both strategies and showed similar results, with the tiling method giving slightly lower turbulent fluxes, especially for contrasting surfaces. However, the former focused on heterogeneity due to different vegetation types, and in the latter the comparison is made against climatology. In the current study, we evaluate the tiling method in the more extreme case of a forest and a lake and concentrate on a local situation with

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