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Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and...
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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 July 2026 | Viewed by 4860

Special Issue Editors

Dr. Guna Hewa

E-Mail Website
Guest Editor
Sustainable Infrastructure and Resource Management (SIRM), UniSA-STEM, University of South Australia, Mawson Lakes, Adelaide, SA 5095, Australia
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
Dr. Trevor Daniell

E-Mail Website
Guest Editor
Faculty of Sciences, Engineering and Technology, University of Adelaide, Adelaide SA 5005, Australia
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
Dr. David Kemp

E-Mail Website
Guest Editor
Mawson Lakes Campus, University of South Australia, Mawson Lakes, Aldgate, SA 5095, Australia
Interests: flood studies; runoff routing model structure; ungauged catchment predictions; hydrology model storage parameters; rating curves; hydrological losses; arid-zone hydrology; sustainable stormwater management
Dr. Ranjan Sarukkalige

E-Mail Website
Guest Editor
School of Civil and Mechanical Engineering, Curtin University, Perth, WA 6102, Australia
Interests: hydrology; stormwater management; impacts of global warming and climate change; groundwater modelling and development
Special Issues, Collections and Topics in MDPI journals

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

26 pages, 7374 KB  
Article
Anticipated Compound Flooding in Miami-Dade Under Extreme Hydrometeorological Events
by Alan E. Gumbs, Alemayehu Dula Shanko, Abiodun Tosin-Orimolade and Assefa M. Melesse
Hydrology 2026, 13(1), 34; https://doi.org/10.3390/hydrology13010034 - 16 Jan 2026
Viewed by 118
Abstract
Climate change and the resulting projected rise in sea level put densely populated urban communities at risk of river flooding, storm surges, and subsurface flooding. Miami finds itself in an increasingly vulnerable position, as compound inundation seems to be a constant and unavoidable [...] Read more.
Climate change and the resulting projected rise in sea level put densely populated urban communities at risk of river flooding, storm surges, and subsurface flooding. Miami finds itself in an increasingly vulnerable position, as compound inundation seems to be a constant and unavoidable occurrence due to its low elevation and limestone geomorphology. Several recent studies on compound overflows have been conducted in Miami-Dade County. However, in-depth research has yet to be conducted on its economic epicenter. Owing to the lack of resilience to tidal surges and extreme precipitation events, Miami’s infrastructure and the well-being of its population may be at risk of flooding. This study applied HEC-RAS 2D to develop one- and two-dimensional water flow models to understand and estimate Miami’s vulnerability to extreme flood events, such as 50- and 100-year return storms. It used Hurricane Irma as a validation and calibration event for extreme event reproduction. The study also explores novel machine learning metamodels to produce a robust sensitivity analysis for the hydrologic model. This research is expected to provide insights into vulnerability thresholds and inform flood mitigation strategies, particularly in today’s unprecedented and intensified weather events. The study revealed that Miami’s inner bay coastline, particularly the downtown coastline, is severely impacted by extreme hydrometeorological events. Under extreme event circumstances, the 35.4 km2 area of Miami is at risk of flooding, with 38% of the areas classified as having medium to extreme risk by FEMA, indicating severe infrastructural and community vulnerability. Full article
(This article belongs to the Special Issue Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses)
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18 pages, 3463 KB  
Article
Numerical Simulation of Typical River Closure Process and Sensitivity Analysis of Influencing Factors
by Lan Ma, Chao Li, Zhanquan Yao and Xuefei Ji
Hydrology 2026, 13(1), 29; https://doi.org/10.3390/hydrology13010029 - 12 Jan 2026
Viewed by 188
Abstract
River ice is a common natural phenomenon in cold regions during winter, and it is also one of the key factors that must be considered in the development and utilization of water resources in these areas. In this paper, based on a two-dimensional [...] Read more.
River ice is a common natural phenomenon in cold regions during winter, and it is also one of the key factors that must be considered in the development and utilization of water resources in these areas. In this paper, based on a two-dimensional hydrodynamic model and ice dynamics model coupled with a linear thermodynamic process, this study simulates and validates the formation, decay, transport, and accumulation of river ice at the Toudaoguai reach of the Yellow River in Inner Mongolia during the winters of 2019–2020 and 2020–2021. The influence of different parameters on backwater level variations caused by ice jams is further investigated using a modified Morris sensitivity analysis method. The results show that (1) the coupled thermal-dynamic model can accurately simulate the formation, transport, and accumulation process of river ice in natural river, as well as the freeze-up patterns and corresponding hydraulic characteristics. (2) Due to the influence of river topography, flow rate, and flow density, the freeze-up form is slightly different in different years, and the low discharge process favor a more stable freeze-up. (3) According to the modified Morris screening method, discharge (Q) and ice concentration (N) are the most sensitive to the change in the backwater water level after the ice jam, and the sensitivity is more than 50%. The next most sensitive factor is the ice-cover roughness (ni), whereas ice porosity (ef) exhibits a negative sensitivity to the water level after ice jam. Thus, this study provides effective tools to reproduce the process of river ice transport and accumulation in the reach of the Yellow River (Inner Mongolia section) and offers technical support and insights for ice-flood prevention and mitigation in this section. Full article
(This article belongs to the Special Issue Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses)
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27 pages, 20617 KB  
Article
Evaluation of a Computational Simulation Approach Combining GIS, 2D Hydraulic Software, and Deep Learning Technique for River Flood Extent Mapping
by Nikolaos Xafoulis, Evangelia Farsirotou, Spyridon Kotsopoulos and Aris Psilovikos
Hydrology 2026, 13(1), 26; https://doi.org/10.3390/hydrology13010026 - 9 Jan 2026
Viewed by 224
Abstract
Floods are among the most catastrophic natural disasters, causing severe impact on human lives and ecosystems. The proposed methodology integrates Geographic Information Systems, 2D hydraulic modeling, and deep learning techniques to develop a computational simulation approach for flood extent prediction and was implemented [...] Read more.
Floods are among the most catastrophic natural disasters, causing severe impact on human lives and ecosystems. The proposed methodology integrates Geographic Information Systems, 2D hydraulic modeling, and deep learning techniques to develop a computational simulation approach for flood extent prediction and was implemented in the Enipeas River basin, located within the Thessalia River Basin District, Greece. Hydrological analysis was performed using the HEC-HMS software (version 4.12), while hydraulic simulations were conducted with HEC-RAS 2D. The hydraulic modeling produced synthetic flood scenarios for a 1000-year return period, generating spatially distributed outputs of flood extents. The deep learning algorithm was based on a U-Net (CNN) architecture. The model was trained using multi-channel raster tiles, including open access geospatial data such as Digital Elevation Model, slope, flow direction, stream centerline, land use, and simulated flood extents. Model validation was carried out in two independent domains (TS1 and TS2) located within the same river basin. Model outputs are adequately compared with both 2D hydraulic simulations and official Flood Risk Management Plan maps, and the comparison indicates close spatial and quantitative agreement, with flood extent area differences below 8%. Based on the results, the proposed methodology presents a potential and efficient tool for rapid flood risk mapping. Full article
(This article belongs to the Special Issue Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses)
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17 pages, 3306 KB  
Article
SWOT Satellite Nodes as Virtual Stations During the 2024 Extreme Flood in Southern Brazil
by Luana Oliveira Sales, Thiago Lappicy, Daniel Beltrão, Alexandre de Amorim Teixeira, Rejane Cicerelli and Tati Almeida
Hydrology 2025, 12(10), 248; https://doi.org/10.3390/hydrology12100248 - 25 Sep 2025
Viewed by 1520
Abstract
In 2024, Rio Grande do Sul (RS), Brazil, faced the most severe flood event in its recorded history, which compromised several ground-based hydrological gauges. The SWOT (Surface Water and Ocean Topography) satellite, capable of measuring water surface elevation (WSE) in continental waters, is [...] Read more.
In 2024, Rio Grande do Sul (RS), Brazil, faced the most severe flood event in its recorded history, which compromised several ground-based hydrological gauges. The SWOT (Surface Water and Ocean Topography) satellite, capable of measuring water surface elevation (WSE) in continental waters, is a valuable tool for providing critical data. This study investigates whether node-level WSE data from the SWOT satellite can effectively function as virtual hydrological stations under such extreme conditions. The study was applied in all of RS state considering 100 in situ gauges and was subdivided into three sections: (i) an evaluation of the variation in SWOTʹs WSE data compared to the variation in in situ levels from telemetric gauges, considering subsequent cycles of passes between July 2023 and April 2025, yielding an MAE = 35 cm and an RMSE = 73 cm after outlier removal; (ii) an evaluation of the variation in SWOTʹs WSE data compared to the variation in telemetric level data, considering one window prior to and another during the extreme event, resulting an MAE = 26 cm and an RMSE = 34 cm; (iii) an analysis of SWOTʹs data availability during the extreme event, when in situ telemetric data were unavailable. The results demonstrate an agreement between the variation observed in SWOT data and that in telemetric gauges in RS, even during extreme events. Moreover, in the absence of in situ data, SWOT was still able to capture WSE data. Full article
(This article belongs to the Special Issue Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses)
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20 pages, 7673 KB  
Article
Impact of Elevation and Hydrography Data on Modeled Flood Map Accuracy Using ARC and Curve2Flood
by Taylor James Miskin, L. Ricardo Rosas, Riley C. Hales, E. James Nelson, Michael L. Follum, Joseph L. Gutenson, Gustavious P. Williams and Norman L. Jones
Hydrology 2025, 12(8), 202; https://doi.org/10.3390/hydrology12080202 - 1 Aug 2025
Cited by 1 | Viewed by 2146
Abstract
This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These [...] Read more.
This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application. Full article
(This article belongs to the Special Issue Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses)
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