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This Special Issue of Atmosphere focuses on hydrometeorological extremes and their local impacts on human–environment systems. Particularly, we accepted submissions on the topics of observational and model-based studies that could provide useful information for infrastructure design, decision making, and policy making to achieve our goals of enhancing the resilience of human–environment systems to climate change and increased variability.

Research & information: general --- Meteorology & climatology --- flood risk --- urban flood forecasting and warning --- inland-river combined flood system --- LSTM --- artificial neural network --- neurons --- layers --- temperature --- South Korea --- deep learning --- reference evapotranspiration --- climate change --- drought --- meteorological extremes --- climatic variables --- wind speed --- extreme El Niño event --- tropical cyclone --- tropical cyclone-induced precipitation --- China --- Bayesian approach --- nonstationarity --- reanalysis products --- quantile delta mapping --- ranges of flood sizes --- specific flood distributions --- ungauged watersheds --- influence of rainfall characteristics --- depth-averaged temperature --- decision tree --- lifetime maximum intensity --- climate variability --- seasonality --- dengue fever --- vector --- rainfall --- Bangladesh --- copula function --- drought duration --- drought severity --- land-ocean temperature contrast/meridional temperature gradient --- standardized precipitation evapotranspiration index

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This Special Issue of Atmosphere focuses on hydrometeorological extremes and their local impacts on human–environment systems. Particularly, we accepted submissions on the topics of observational and model-based studies that could provide useful information for infrastructure design, decision making, and policy making to achieve our goals of enhancing the resilience of human–environment systems to climate change and increased variability.

Research & information: general --- Meteorology & climatology --- flood risk --- urban flood forecasting and warning --- inland-river combined flood system --- LSTM --- artificial neural network --- neurons --- layers --- temperature --- South Korea --- deep learning --- reference evapotranspiration --- climate change --- drought --- meteorological extremes --- climatic variables --- wind speed --- extreme El Niño event --- tropical cyclone --- tropical cyclone-induced precipitation --- China --- Bayesian approach --- nonstationarity --- reanalysis products --- quantile delta mapping --- ranges of flood sizes --- specific flood distributions --- ungauged watersheds --- influence of rainfall characteristics --- depth-averaged temperature --- decision tree --- lifetime maximum intensity --- climate variability --- seasonality --- dengue fever --- vector --- rainfall --- Bangladesh --- copula function --- drought duration --- drought severity --- land-ocean temperature contrast/meridional temperature gradient --- standardized precipitation evapotranspiration index --- flood risk --- urban flood forecasting and warning --- inland-river combined flood system --- LSTM --- artificial neural network --- neurons --- layers --- temperature --- South Korea --- deep learning --- reference evapotranspiration --- climate change --- drought --- meteorological extremes --- climatic variables --- wind speed --- extreme El Niño event --- tropical cyclone --- tropical cyclone-induced precipitation --- China --- Bayesian approach --- nonstationarity --- reanalysis products --- quantile delta mapping --- ranges of flood sizes --- specific flood distributions --- ungauged watersheds --- influence of rainfall characteristics --- depth-averaged temperature --- decision tree --- lifetime maximum intensity --- climate variability --- seasonality --- dengue fever --- vector --- rainfall --- Bangladesh --- copula function --- drought duration --- drought severity --- land-ocean temperature contrast/meridional temperature gradient --- standardized precipitation evapotranspiration index