Advances in Real-Time Flood Forecasting
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".
Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 3711
Special Issue Editors
Interests: hydrology and hydraulics; flood forecast modeling; surrogate modeling; machine learning; remote sensing; climate change
Interests: watershed modelling; sensitivity/uncertainty analysis; river corridor hydro-biogeochemistry modelling; disturbance and climate change; sampling design; remotely sensed data analysis
Interests: climate change; hindcast and forecast modeling; extreme value analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Extreme flooding is increasing worldwide and remains the deadliest weather-related hazard, especially in densely populated areas. Real-time forecasting with sufficient lead time is paramount in order to significantly mitigate damage from flooding. Such results must be delivered within a predetermined time horizon and with enough accuracy to promote community confidence in actions taken to prepare for an emergency.
Despite extensive efforts to improve forecasting accuracy, predictability, and efficiency, real-time flood forecasting is still being hindered due to the complexity of natural phenomena represented by equifinality, hysteresis, non-uniqueness, non-linearity, and internal variability. Application in urban environments can be more challenging, as much finer spatial resolution is needed in the models to resolve interactions among streets, buildings, and other infrastructures.
This Special Issue aims to collect papers on current efforts to simulate real-time flood forecasting in watersheds of varying scales and environments with urban characteristics. The following list provides an overview of the topics we are looking for, but is not exhaustive.
- Techniques to improve model accuracy and quantify model uncertainties, such as data assimilation, model calibration, and optimization.
- Data-driven methods to increase model efficiency while preserving model accuracy, such as deep learning and surrogate modeling.
- Reduced modeling techniques to reduce dimensionalities at larger spatial and finer temporal scales.
- Remote sensing techniques relevant to enriching the availability of model inputs and outputs.
- Application of real-time flood forecasting with a particular interest in developing countries and data-poor regions.
Prof. Dr. Jongho Kim
Dr. Kyongho Son
Dr. Seongho Ahn
Guest Editors
Manuscript Submission Information
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Keywords
- real-time flood forecasting
- urban flood
- uncertainty quantification
- deep learning
- surrogate modeling
- data assimilation
- remote sensing
- numerical models
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