Integrated Approaches to Hydraulic and Sediment Transport Modeling for Flood Risk Management Using Advanced Models, Remote Sensing, GIS, and IoT
A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrology–Climate Interactions".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 30
Special Issue Editors
Interests: water resources management; flood risk management; protection and restoration of the environment; sustainable development; renewable energy sources; environmental impacts
Special Issues, Collections and Topics in MDPI journals
Interests: river hydraulic modeling; river sediment transport modeling; CFD modeling; flood risk assessment; flood extent mapping; flood prediction; GIS analysis; water resources management; environmental hydraulics; deep learning; CNN; random forest
Interests: integrated water resources management; AI and machine learning in water systems; optimization and metaheuristics in water management; pollution source identification; optimal monitoring network design; small dams and NBS; water monitoring IoT; gamification in water education
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Floods are among the most major and destructive natural disasters worldwide. In recent decades, flood frequency and extreme weather intensity have increased due to climate change, alterations in land-use patterns, urbanization, and human interventions in natural ecosystems. Hydrological and hydraulic simulation models serve as essential tools for understanding flow dynamics, predicting flood areas, and supporting decision-making processes aimed at reducing impacts on both natural and anthropogenic environments. In addition to intense rainfall events, which typically trigger flooding, sediment transport is a critical factor in flood risk assessment and management. Sediment dynamics can affect river morphology, reservoir capacity, and the overall efficiency of aquatic ecosystems. Sediment transport simulation integrated with hydraulic modeling has become essential for accurately estimating flood risk in fluvial, deltaic, and coastal systems. Meanwhile, technological advances in remote sensing, geographic information systems (GISs), the Internet of Things (IoT), and sensor networks have enhanced model development, calibration, validation, and the generation of digital twin models. Satellite imagery, unmanned aerial vehicle (UAV) observations and surveying, sensor networks, real-time data platforms, and citizen-science data provide detailed information that can improve hydraulic models, enhance the capability for flood monitoring and prediction, and support the development of effective early warning systems. Therefore, the importance of integrating advanced technologies into hydrological, hydraulic, and sediment transport simulations has become increasingly prominent.
The aim of this Special Issue is to highlight these interdisciplinary advances and to collect original research articles and reviews that enhance the simulation and management of flood processes for optimal flood risk management by integrating hydraulic or/and sediment transport modeling in combination with advanced computational tools, remote sensing data, GIS-based analysis, and IoT technologies.
This Special Issue welcomes manuscripts that deal with and perhaps link some of the following themes:
- Advanced hydraulic and hydrodynamic modeling for fluvial, urban, or coastal flooding;
- Sediment transport modeling;
- CFD modeling for flood risk management;
- Integration of remote sensing data (satellite, UAV, LiDAR, SAR) into flood and sediment transport modeling;
- Integrated modeling environments with hydraulic and sediment transport simulations;
- GIS-based approaches for flood hazard mapping, risk assessment, and decision-support tools;
- IoT and sensor networks for real-time hydrological monitoring and early warning systems;
- Coupled hydraulic–hydrological modeling for risk assessment;
- Impacts of climate change on flood regimes, sediment loads, and hydrological extremes;
- Model validation and uncertainty analysis using multi-source observational datasets;
- Applications in integrated water resources management, disaster risk reduction, and planning of infrastructure or nature-based solution projects.
We look forward to receiving your original research articles and reviews.
Prof. Dr. Nikolaos P. Theodossiou
Prof. Dr. Evangelia D. Farsirotou
Dr. Yiannis Kontos
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
- hydraulic–hydrodynamic modeling
- sediment transport modeling
- flood risk management
- flood forecasting
- remote sensing
- digital twins and IoT
- citizen science
- sensor networks
- early warning systems
- GIS analysis
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