Keywordsclimatic effects; agricultural production; farmer perceptions; adaptation constraints; Lawra district; SWAT model; precipitation; runoff; sediment yield; simulation; the Xichuan River; the Loess Plateau; HYDRUS-1D; ROSETTA; retention curve; hydraulic conductivity; sugarcane field; the island of Miyakojima; water bodies; Xinjiang Uygur Autonomous Region (XUAR); MODIS time series; streamflow; sediment load; precipitation; monsoon; soil and water conservation engineering; water resource variations; climate change; vegetation; semi-arid region; remote sensing; water managers; land-use planners; climate change; stressors; strategies; Portland; phoenix; hydrology; Chao Phraya; SWAT; Climate change; downscaling; temperature; precipitation; hydrologic modelling; water resources; Kunhar basin; Pakistan; Elbe river basin; water quality modeling; in-stream processes; nutrients; SWIM; climate change impact assessment; ENSEMBLES; management change impacts; climate change; model assessment; numerical modeling; SELFE; water quality; tidal estuarine system; extreme typhoon events; risk analysis; SOBEK; climate change; hydrological extreme; flood season division; flood limit level; climate change; precipitation; Fenhe River Basin; tree ring; hydrological drought; artificial neural network; copula method; multi-basin modelling; HYPE; climate change impacts; India; CORDEX; DBS; GRACE; Lake Chad basin; GLDAS; WGHM; groundwater; altimetry; hydrological changes; water resource variability; climate change; hydrological model; perception and adaptation of climate change
