The impact of the spatiotemporal structure of rainfall on flood response over a piedmont urban basin: An approach coupling machine learning and hydrologic modeling

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133160 - 133160

Published: March 1, 2025

Language: Английский

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Flood Risk Assessments for Small Catchments Under Climate Change—How Can Scotland Improve Its Policy for Enhanced Flood Resilience and Preparedness

Journal of Flood Risk Management, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 1, 2025

ABSTRACT Flood risk assessments (FRAs) are essential tools in Scottish planning policy to identify and minimise flood new applications. Most FRAs Scotland performed (very) small (< 50km 2 ) < 100km catchments, particularly vulnerable increases rainfall intensity due climate change. This study provides a historical overview of the literature used as guidance then focuses on their application smaller addressing three areas: understanding different physical processes representation within guidelines how these applied practice. Our results highlight need move beyond simple mathematical hydrological methods for FRAs. We find that catchments' not adequately represented current methods, leading higher biases uncertainties modelling. When practice, techniques often unconventionally fulfilment established guidelines. Finally, change science implementation into also needs refinement, with regulations lacking sound scientific basis, catchments. underscore testing innovative solutions found academia utilisation additional data provide improved under

Language: Английский

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Exploring the impact of rainfall temporal distribution and critical durations on flood hazard modeling

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Language: Английский

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Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(4), P. 161 - 161

Published: April 8, 2025

Rapid urbanization and climate change are increasing the risks associated with natural hazards, especially in cities where socio-economic disparities significant. Current hazard risk assessment frameworks fail to consider factors, which limits their ability effectively address vulnerabilities at community level. This study introduces a machine learning framework designed assess flood susceptibility vulnerability, particularly urban areas limited data. Using Kigali, Rwanda, as case study, we quantified vulnerability through composite index that includes indicators of sensitivity adaptive capacity. We utilized variety data sources, such demographic, environmental, remotely sensing datasets, applying algorithms like Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), XGBoost. Among these, MLP achieved best predictive performance, an AUC score 0.902 F1-score 0.86. The findings indicate spatial differences central southern Kigali showing greater due mix challenges high risk. maps created were validated against historical records, research, expert insights, confirming accuracy relevance for assessment. Additionally, tested framework’s scalability adaptability Kampala, Uganda, Dar es Salaam, Tanzania, making context-specific adjustments model improves its transferability. offers solid, data-driven approach combining assessments filling important gaps resilience planning. results support advancement risk-informed decision-making, access detailed information.

Language: Английский

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Preserving the Past in a Changing Climate: An Approach to Assess the Impact of Urban Flooding in Cultural Heritage Cities

Journal of Flood Risk Management, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 25, 2025

ABSTRACT Urban floods are increasing due to the intensification of precipitation extremes in a changing climate and intensive expansion urbanscapes. Therefore, flood hazards can potentially increase losses historical buildings cultural heritage. In this context, study proposes methodology assess impact change on urban flooding at district building scale. The is applied Santa Croce District, where an extensive collection masterpieces city Florence (Italy) preserved exposed, especially National Central Library. hazard assessment obtained by using dual drainage hydraulic model quantify flooded area within overflow sewer systems. An ensemble 34 projections based output from Phase 6 Climate Model Intercomparison Project (CMIP6) two emission scenarios, or Shared Socioeconomic Pathways (SSP245 SSP585) time windows (2021–2050, Near Future, 2071–2100, Far Future) considered as input model. results show that will all SSP585 end century.

Language: Английский

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