Advanced Hydrological Modeling for Disaster Management in Data-Scarce Regions

Recent advances in hydrological science highlight the urgent need for robust and adaptive modeling approaches to support effective disaster management in data-scarce and climate-vulnerable regions. Conventional hydrological models often struggle in ungauged or poorly monitored basins, where limited observational data constrain reliable calibration, validation, and timely decision-making for hazard mitigation. As a result, communities remain highly exposed to flood, drought, and sediment-related risks. In response, advanced hydrological modeling approaches, including data-driven and hybrid frameworks, have emerged to improve predictive capability under limited data conditions. These approaches enhance the forecasting of extreme events and support early warning systems, risk-informed planning, and operational disaster management. This Research Topic aims to advance innovative modeling strategies that bridge data gaps and strengthen disaster management capacity across vulnerable hydrological systems.Hydrological prediction in data-scarce and ungauged regions remains a fundamental challenge, limiting effective disaster management, early warning, and climate adaptation efforts. This Research Topic seeks to address these limitations by advancing hydrological modeling approaches tailored for hazard-prone and data-constrained environments. It aims to bring together studies that integrate data-driven and physically based methods, leverage remote sensing and reanalysis datasets, and apply approaches such as regionalization and transferability to enhance predictive reliability under limited observations.Emphasis will be placed on scalable and operational solutions that directly support disaster management, including real-time flood forecasting, drought monitoring, and sediment-related hazard assessment. Contributions are expected to demonstrate practical applications in early warning systems, risk-informed decision-making, and disaster preparedness, particularly in regions with limited monitoring infrastructure. By bridging methodological innovation with real-world implementation, this Research Topic aims to deliver actionable insights and transferable frameworks that strengthen disaster management capacity, climate resilience, and community preparedness across diverse and vulnerable hydrological systems.This Research Topic focuses on advanced hydrological modeling approaches for disaster management in data-scarce and ungauged regions, with emphasis on practical and operational applications in hazard-prone environments. We invite contributions that demonstrate the application of modeling approaches, including data-driven and hybrid frameworks, to support flood forecasting, drought monitoring, and sediment-related hazard assessment under limited data conditions. Particular interest is given to studies that integrate remote sensing and reanalysis datasets, apply regionalization and transferability techniques, and develop scalable approaches for real-time forecasting, early warning systems, and risk-informed decision-making.Contributions should clearly demonstrate real-world implementation and direct relevance to disaster management, including improvements in preparedness, response, and community resilience in vulnerable regions. Topic editors: Jerome Gabutero Gacu, Sheikh Kamran Abid Source: https://www.frontiersin.org/research-topics/80444/advanced-hydrological-modeling-for-disaster-management-in-data-scarce-regions