Innovative Flood Risk Management under Changing Environments
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".
Deadline for manuscript submissions: closed (25 June 2024) | Viewed by 10213
Special Issue Editors
Interests: machine learning approach; water quality forecasting; urban defuse pollution; big data analysis; source apportionment
Special Issues, Collections and Topics in MDPI journals
Interests: transport and conversion processes in the sewer system; regional matter and water flux analysis; modelling of wastewater system; integrated water resources management
Interests: drinking water quality; intelligent modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Climate change and urbanization are altering regional hydro-environments, bringing new challenges to flood risk management. On the one hand, climate change-induced hydrological alterationin the severity of different types of floods (fluvial, pluvial, and coastal) and to the increasingly complex flood interaction across urban, basin, and national scales. On the other hand, urbanization has led to the creation of densely populated areas and intensified anthropogenic activities, not only increasing the risk of population exposure to floods but also potentially introducing flood-triggered pollution issues, posing a threat to urban resilience.
To address these issues, a novel framework for flood risk management under changing environments is necessary. Regarding flood complexity, multi-scale hydrological modeling approaches can combine different types and scales of floods, helping us unravel flood interactions during risk assessment. In terms of urban resilience, multi-dimensional flood risk assessment can analyze the direct hazards of floods whilst considering their associated impacts, such as pollution and exposure risks. In doing so, it can comprehensively optimize decision-making in urban flood adaptation in response to climate change and urbanization.
However, given the complexity of spatiotemporal trends in climate change and urbanization, traditional modeling-based approaches are facing increasing challenges. Therefore, integrating traditional methods with cutting-edge technologies could be a promising solution. For instance, machine learning methods have already shown great abilities in short-term flood forecasting, and data mining methods can offer a more integrated assessment of flood hazards by incorporating diverse data sources, such as CCTV, remote sensing, and social media big data.
Accordingly, the primary purpose of this Special Issue is to present recent studies on novel frameworks for flood risk management in terms of multi-scale hydrological modeling, multi-dimensional flood risk management, flood-triggered pollution, machine learning, and data mining-based flood analysis. The methods and findings of this Special Issue will provide additional insights into sustainable urban development under climate change and urbanization.
Prof. Dr. Jin Zhang
Prof. Dr. Peter Krebs
Prof. Dr. Pei Hua
Dr. Wenyu Yang
Guest Editors
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Keywords
- multi-scale hydrological modeling
- urban resilience under climate change and urbanization
- flood-triggered pollution
- multi-dimensional flood risk management
- machine learning flood forecasting
- data mining-based flood risk assessment
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