Hydrometeorological Extremes and Its Local Impacts on Human-Environmental Systems
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...
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Muut tekijät: | , , , |
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Aineistotyyppi: | Elektroninen Kirjan osa |
Kieli: | englanti |
Julkaistu: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2022
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Aiheet: | |
Linkit: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Kim, Jong-Suk |4 edt | |
700 | 1 | |a Dhakal, Nirajan |4 edt | |
700 | 1 | |a Jun, Changhyun |4 edt | |
700 | 1 | |a Lee, Taesam |4 edt | |
700 | 1 | |a Kim, Jong-Suk |4 oth | |
700 | 1 | |a Dhakal, Nirajan |4 oth | |
700 | 1 | |a Jun, Changhyun |4 oth | |
700 | 1 | |a Lee, Taesam |4 oth | |
245 | 1 | 0 | |a Hydrometeorological Extremes and Its Local Impacts on Human-Environmental Systems |
260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
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506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a 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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Research & information: general |2 bicssc | |
650 | 7 | |a Meteorology & climatology |2 bicssc | |
653 | |a flood risk | ||
653 | |a urban flood forecasting and warning | ||
653 | |a inland-river combined flood system | ||
653 | |a LSTM | ||
653 | |a artificial neural network | ||
653 | |a neurons | ||
653 | |a layers | ||
653 | |a temperature | ||
653 | |a South Korea | ||
653 | |a deep learning | ||
653 | |a reference evapotranspiration | ||
653 | |a climate change | ||
653 | |a drought | ||
653 | |a meteorological extremes | ||
653 | |a climatic variables | ||
653 | |a wind speed | ||
653 | |a extreme El Niño event | ||
653 | |a tropical cyclone | ||
653 | |a tropical cyclone-induced precipitation | ||
653 | |a China | ||
653 | |a Bayesian approach | ||
653 | |a nonstationarity | ||
653 | |a reanalysis products | ||
653 | |a quantile delta mapping | ||
653 | |a ranges of flood sizes | ||
653 | |a specific flood distributions | ||
653 | |a ungauged watersheds | ||
653 | |a influence of rainfall characteristics | ||
653 | |a depth-averaged temperature | ||
653 | |a decision tree | ||
653 | |a lifetime maximum intensity | ||
653 | |a climate variability | ||
653 | |a seasonality | ||
653 | |a dengue fever | ||
653 | |a vector | ||
653 | |a rainfall | ||
653 | |a Bangladesh | ||
653 | |a copula function | ||
653 | |a drought duration | ||
653 | |a drought severity | ||
653 | |a land-ocean temperature contrast/meridional temperature gradient | ||
653 | |a standardized precipitation evapotranspiration index | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/5254 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/81222 |7 0 |z DOAB: description of the publication |