Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses

Dear Colleagues,

Flooding is one of the most significant natural disasters, costing billions of dollars globally each year. Flood studies have seen significant advancements due to emerging technologies, improved computational methods, and enhanced data collection strategies. Hence, it is essential to bring together cutting-edge research that addresses key challenges in flood studies, with a focus on improving data assimilation, rating curve development, and hydrological loss estimation.

Key areas of interest include advancements in data assimilation techniques, remote sensing, IoT-based sensor networks, and citizen science to enhance real-time hydrological monitoring. This issue will also explore innovative applications of machine learning, AI, and hydraulic modelling in rating curve development, alongside approaches for handling uncertainty in flood modelling and hydrological predictions.

A significant focus will be hydrological losses, including improved parameterisation in rainfall–runoff models, probabilistic loss modelling, integration of parametric and non-parametric approaches, and the impact of land use changes on hydrological losses. Studies integrating GIS and RS with hydraulic and hydrological models, as well as field measurements with computational techniques, are of great interest to this issue. Additionally, research on probability-neutral flood estimations, storm pattern effects on flood risk assessments, and climate change impacts are highly relevant.

This Special Issue aims to serve as a platform for interdisciplinary collaboration, offering insights which enhance flood prediction, risk assessment, and mitigation strategies. We invite original research papers, review articles, and case studies that contribute novel methodologies, theoretical advancements, or practical applications in flood hydrology.

This Special Issue will welcome manuscripts that link the following themes:

• Data assimilation techniques for flood modelling;
• Remote sensing, sensor network, IoT for real-time hydrological monitoring;
• Citizen science and crowdsourced data for flood studies;
• Hydraulic modelling, machine learning, and AI applications in rating curve development;
• Uncertainty in rating curve development and extrapolations;
• Integrating field measurements with computational techniques;
• Hydrological modelling, rating improvements, and flood forecasting;
• Integration with geographic information systems (GISs) and hydrological models;
• Improving parameterisation and uncertainty analyses in rainfall–runoff models;
• Improved methodologies for estimating hydrological losses;
• Parametric and non-parametric loss modelling;
• Impact of land use changes on hydrological losses;
• Impact of land use changes on flood estimation, e.g., catchment fires;
• Probabilistic approaches for loss modelling;
• Effect of storm burst patterns and antecedent conditions in loss quantification;
• Probability-neutral flood estimations;
• Flood risk management for resiliency;
• Climate change and extreme flood events;
• Multi-criteria decision making in flood studies;
• Flood forecasting;
• Ungauged catchment predictions.

We look forward to receiving your original research articles and reviews.

Dr. Guna Hewa
Dr. Trevor Daniell
Dr. David Kemp
Dr. Ranjan Sarukkalige
Guest Editors

Manuscript Submission Information

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