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Palabras clave – provistas por la editorial

precipitation datasets; evaluation; spatial scale; temporal scale; climate; Yellow River Basin; data assimilation; WRF; WRFDA; 3DVar; water levels; surface areas; volume variations; hypsometry; bathymetry; lakes; reservoirs; remote sensing; DAHITI; modified strahler approach; airborne LiDAR; DEM; flood inundation; flood map; flood model; LiDAR; terrestrial LiDAR; evapotranspiration; variability; uncertainty; unmanned aerial system; sUAS; multispectral; viticulture; water resources management; California; lake; Tibetan Plateau; hydrological changes; water balance; Chindwin basin; hydrological modelling; multi-variable calibration; satellite-based rainfall product; TRMM; temporal resolution; rainfall erosivity; combined approach; multi-objective optimization; modeling uncertainty; model constraint; SWAT; semiarid area; hydrological variations; normalized difference vegetation index; total water storage change; groundwater change; extreme precipitation; estimation; TMPA 3B42-V7; regional frequency analysis; China; satellite datasets; accuracy evaluation; hydrological applicability; Bosten Lake Basin; actual evapotranspiration; available water resources; climate change; vegetation greening; VIP-RS model; Lancang-Mekong river basin; MSWEP; AgMERRA; APHRODITE; CHIRPS; PERSIANN; error correction; agricultural water management; crop water consumption; remote sensing model; evapotranspiration allocation; inland water; IWCT; Tianjin; Landsat data; Tarim River Basin; desert-oasis ecotone; land-use change; CA-Markov model; remote sensing in hydrology; precipitation; performance evaluation; GPM; Poyang Lake; Yangtze River; assimilation; nonparametric modeling; multi-source; n/a; landscape pattern; spatiotemporal changes; influencing factors; watershed; China SE; satellite data; LUE-GPP; SPEI; copula function; conditional probability; soil moisture; neural network; downscaling; microwave data; MODIS data