Hydrological Modelling and Hydrometeorological Extreme Prediction
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 13259
Special Issue Editors
Interests: watershed flood forecating; deep learning; remotely sensed observations
Interests: water resources management; hydrological forecasting; drought evolution; remote sensing hydrology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hydrological modeling plays an extremely important role in the water resources management, agricultural irrigation, climate and ecological environment change research. Hydrological processes are influenced by complex weather and non-linear infiltration mechanisms, which are difficult to model and thus, reliable hydrological modeling remains a challenge. Research hotspots include large sacle flood forecasting, remotely sensed data use, hydrometeorological extreme analysis, and hydrological simulation in areas with no observation data. Recently new technologies and methods have also been used in hydrological simulation, such as satellite remote sensing technology, big data mining technology, artificial intelligence, etc. We sincerely invite the authors to contribute original review and research manuscripts focused on developing and improving hydrological modeling and investigating their application in water cycle as well as hydrometeorological extremes under changing climate. Potential topics include but are not limited to the following:
Dr. Lei Ye
Dr. Shuang Zhu
Dr. Weihong Liao
Guest Editors
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Keywords
- remote sensing observations and their usability in hydrological modeling
- integration of in-situ and remotely-sensed hydrological data
- calibration and validation studies
- modeling of basin drought and flood
- monitoring, modeling, predicting and understanding hydrological extremes
- calibration of hydrological model parameters
- multi-target parameter calibration
- uncertainty of hydrological model: model input, structure and parameter
- water resources assessment using hydrological modeling
- the use of machine learning for hydrological modeling and forecasting
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