Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses
A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 3210
Special Issue Editors
Interests: regional flood studies; ungauged catchment predictions; rainfall–runoff modelling; hydrological losses; urban hydrology; sustainable stormwater management; advanced techniques in hydrological data assimilation; rating curves; extreme-value analyses
Interests: urban hydrology including urban infrastructure development and wetlands; hydrological data analysis and modelling; water resource management for ecologically sustainable development; water resource assessment, systems analysis, and strategic planning; flood studies, including river, dam, catchment, and floodplain management; assessing sustainability in different developments; environmental hydrology; system thinking, promoting learning and personal development; use of artificial intelligence techniques in hydrology; extreme-event hydrology in arid zones; yield hydrology and drought management; hydrographic reviews and water data archiving systems
Interests: flood studies; runoff routing model structure; ungauged catchment predictions; hydrology model storage parameters; rating curves; hydrological losses; arid-zone hydrology; sustainable stormwater management
Interests: hydrology; stormwater management; impacts of global warming and climate change; groundwater modelling and development
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Special Issue Information
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- rating curves
- hydrological monitoring
- sensor networks
- hydrological losses
- hydraulic modelling
- rainfall–runoff modelling
- climate change impact
- land use change impacts
- flood forecasting
- flood risk management
- uncertainty analyses
- GIS/RS/IoT/machine learning/citizen science
- probability-neutral flood estimation
- flood forecasting
- ungauged catchment predictions
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