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Advance in Flood Risk Management and Assessment Research

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 19131

Special Issue Editor


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Guest Editor
Department of Geoinformatics, VSB – Technical University of Ostrava, Ostrava-jih, Czech Republic
Interests: systems approach in geosciences; spatial reasoning; GIT applications in crisis management

Special Issue Information

Dear Colleagues,

As a result of ongoing climate change, among other things we can observe a gradual increase in both the frequency and (especially) the intensity of floods, which often threaten populated areas. Recent events in Germany, Belgium, France, the USA, and elsewhere, with significant damage and above all a large number of casualties, highlight the growing need to improve the effectiveness of flood-risk forecasting systems. They should be based on the latest research in this area. This research must be built on traditional numerical models and new approaches like signal processing, computational intelligence, and machine learning, all supported by increasingly available in situ and remote data acquisition tools. These forecasting systems will enable the development of services supporting effective flood risk management, starting with the design of preventive measures in the landscape, preparing emergency response plans, and early warnings of imminent threat, to the operational support of rescue services and returning to normal conditions.

The purpose of this Special Issue is to exchange knowledge in the field of flood and flood-risk modelling and assessment, the design of preventive measures to reduce flood risk in the threatened area, issuing warnings about the current threat of flooding, and the support of the liquidation of the consequences of the flood—all this supported by modern technologies collecting data on areas potentially at risk of floods, climate, and current weather conditions.

We encourage authors to share their knowledge, experience, and achievements regarding the impact of climate change and human activity on flood risk, the use of modern in situ and remote data acquisition tools, the accuracy of measurements, the interaction between floods and populated areas, the assessment of the impact of human activity on flood risk, the modelling of flood risk distribution in space and time using modern approaches like signal processing, computational intelligence and machine learning, and new methods for early warning.

Dr. Petr Rapant
Guest Editor

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Keywords

  • flood
  • flash flood
  • flood risk assessment
  • flood risk forecasting
  • flood risk management
  • early warning
  • remote sensing
  • machine learning
  • computational intelligence

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Published Papers (6 papers)

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Research

27 pages, 5379 KiB  
Article
Flood Simulations Using a Sensor Network and Support Vector Machine Model
by Jakub Langhammer
Water 2023, 15(11), 2004; https://doi.org/10.3390/w15112004 - 25 May 2023
Cited by 6 | Viewed by 2008
Abstract
This study aims to couple the support vector machine (SVM) model with a hydrometeorological wireless sensor network to simulate different types of flood events in a montane basin. The model was tested in the mid-latitude montane basin of Vydra in the Šumava Mountains, [...] Read more.
This study aims to couple the support vector machine (SVM) model with a hydrometeorological wireless sensor network to simulate different types of flood events in a montane basin. The model was tested in the mid-latitude montane basin of Vydra in the Šumava Mountains, Central Europe, featuring complex physiography, high dynamics of hydrometeorological processes, and the occurrence of different types of floods. The basin is equipped with a sensor network operating in headwaters along with the conventional long-term monitoring in the outlet. The model was trained and validated using hydrological observations from 2011 to 2021, and performance was assessed using metrics such as R2, NSE, KGE, and RMSE. The model was run using both hourly and daily timesteps to evaluate the effect of timestep aggregation. Model setup and deployment utilized the KNIME software platform, LibSVM library, and Python packages. Sensitivity analysis was performed to determine the optimal configuration of the SVR model parameters (C, N, and E). Among 125 simulation variants, an optimal parameter configuration was identified that resulted in improved model performance and better fit for peak flows. The sensitivity analysis demonstrated the robustness of the SVR model, as different parameter variations yielded reasonable performances, with NSE values ranging from 0.791 to 0.873 for a complex hydrological year. Simulation results for different flood scenarios showed the reliability of the model in reconstructing different types of floods. The model accurately captured trend fitting, event timing, peaks, and flood volumes without significant errors. Performance was generally higher using a daily timestep, with mean metric values R2 = 0.963 and NSE = 0.880, compared to mean R2 = 0.913 and NSE = 0.820 using an hourly timestep, for all 12 flood scenarios. The very good performance even for complex flood events such as rain-on-snow floods combined with the fast computation makes this a promising approach for applications. Full article
(This article belongs to the Special Issue Advance in Flood Risk Management and Assessment Research)
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16 pages, 3645 KiB  
Article
The Copula Application for Analysis of the Flood Threat at the River Confluences in the Danube River Basin in Slovakia
by Veronika Bačová Mitková, Dana Halmová, Pavla Pekárová and Pavol Miklánek
Water 2023, 15(5), 984; https://doi.org/10.3390/w15050984 - 4 Mar 2023
Cited by 6 | Viewed by 2338
Abstract
In hydrological practice, individual elements of the hydrological cycle are most often estimated and evaluated separately. Uncertainty in the size estimation of extrema discharges and their return period can affect the statistical assessment of the significance of floods. One example is the simultaneous [...] Read more.
In hydrological practice, individual elements of the hydrological cycle are most often estimated and evaluated separately. Uncertainty in the size estimation of extrema discharges and their return period can affect the statistical assessment of the significance of floods. One example is the simultaneous occurrence and joining of extremes at the confluence of rivers. The paper dealt with the statistical evaluation of the occurrence of two independent variables and their joint probabilities of occurrence. Bivariate joint analysis is a statistical approach for the assessment of flood threats at the confluence of rivers. In our study, the annual maximum discharges monitored on four selected Slovak rivers and their tributaries represent the analyzed variables. The Archimedean class of copula functions was used as a set of mathematical tools for the determination and evaluation of the joint probability of annual maximal discharges at river confluences. The results of such analysis can contribute to a more reliable assessment of flood threats, especially in cases where extreme discharges occur simultaneously, increasing the risk of devastating effects. Finally, the designed discharges of the different return periods calculated by using the univariate approach and the bivariate approach for the gauging station below the confluence of the rivers was evaluated and compared. Full article
(This article belongs to the Special Issue Advance in Flood Risk Management and Assessment Research)
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23 pages, 5380 KiB  
Article
Methodological Guide to Forensic Hydrology
by Alfonso Gutierrez-Lopez
Water 2022, 14(23), 3863; https://doi.org/10.3390/w14233863 - 27 Nov 2022
Cited by 5 | Viewed by 3128
Abstract
In Latin America and the Caribbean (LAC) region, geophysical, meteorological and hydrological disasters are increasing every year. With significantly limited resources, these countries are naturally forced to absorb lessons from these disasters. One of the fundamental activities during this learning task remains the [...] Read more.
In Latin America and the Caribbean (LAC) region, geophysical, meteorological and hydrological disasters are increasing every year. With significantly limited resources, these countries are naturally forced to absorb lessons from these disasters. One of the fundamental activities during this learning task remains the need to standardize the forensic reporting process. Like all academic disciplines, engineering is exceptional in its application to the forensic field. This feature makes it a unique input to the investigation of hydrological and environmental catastrophes. Based on the fundamental concepts of forensic investigation, ten principles for properly conducting forensic hydrology studies are proposed. The ten principles proposed are: (i) Principle of use, (ii) production, (iii) principle of exchange, (iv) recognition, (v) correspondence, (vi) reconstruction, (vii) principle of probability, (viii) uncertainty, (ix) principle of certainty, and (x) conclusion principle. A hypothetical case of urban infrastructure failure is used to explain, in detail, each of the proposed principles. This paper proposes a methodology to be considered as a reference point for a forensic hydrological analysis to be used at the LAC region. Full article
(This article belongs to the Special Issue Advance in Flood Risk Management and Assessment Research)
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21 pages, 13703 KiB  
Article
City Flood Disaster Scenario Simulation Based on 1D–2D Coupled Rain–Flood Model
by Guo Li, Huadong Zhao, Chengshuai Liu, Jinfeng Wang and Fan Yang
Water 2022, 14(21), 3548; https://doi.org/10.3390/w14213548 - 4 Nov 2022
Cited by 7 | Viewed by 5362
Abstract
In order to realize the reproduction and simulation of urban rainstorm and waterlogging scenarios with complex underlying surfaces, based on the 1D–2D coupled models, we constructed an urban storm–flood coupling model considering one-dimensional river channels, two-dimensional ground and underground pipe networks. Luoyang City, [...] Read more.
In order to realize the reproduction and simulation of urban rainstorm and waterlogging scenarios with complex underlying surfaces, based on the 1D–2D coupled models, we constructed an urban storm–flood coupling model considering one-dimensional river channels, two-dimensional ground and underground pipe networks. Luoyang City, located in the western part of Henan Province, China was used as a pilot to realize the construction of a one-dimensional and two-dimensional coupled urban flood model and flood simulation. The coupled model was calibrated and verified by the submerged water depths of 16 survey points in two historical storms flood events. The average relative error of the calibration simulated water depth was 22.65%, and the average absolute error was 13.93 cm; the average relative error of the verified simulated water depth was 15.27%, the average absolute error was 7.54 cm, and the simulation result was good. Finally, 28 rains with different return periods and different durations were designed to simulate and analyze the rainstorm inundation in the downtown area of Luoyang. The result shows that the R2 of rainfall and urban rainstorm inundation is 0.8776, and the R2 of rainfall duration and urban rainstorm inundation is 0.8141. The study results have important practical significance for urban flood prevention, disaster reduction and traffic emergency management. Full article
(This article belongs to the Special Issue Advance in Flood Risk Management and Assessment Research)
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17 pages, 5471 KiB  
Article
A Method for Assessing Flood Vulnerability Based on Vulnerability Curves and Online Data of Residential Buildings—A Case Study of Shanghai
by Zhuoxun Li, Liangxu Wang, Ju Shen, Qiang Ma and Shiqiang Du
Water 2022, 14(18), 2840; https://doi.org/10.3390/w14182840 - 12 Sep 2022
Cited by 4 | Viewed by 2840
Abstract
Flood vulnerability is the key to understanding and assessing flood risk. However, analyzing flood vulnerability requires sophisticated data, which is usually not available in reality. With the widespread use of big data in cities today, it is possible to quickly obtain building parameters [...] Read more.
Flood vulnerability is the key to understanding and assessing flood risk. However, analyzing flood vulnerability requires sophisticated data, which is usually not available in reality. With the widespread use of big data in cities today, it is possible to quickly obtain building parameters in cities on a large scale, thus offering the possibility to study the risk flooding poses to urban buildings. To fill this research gap, taking Shanghai as an example, this study developed a new research framework to assess urban vulnerability based on vulnerability curves and online data of residential buildings. First, detailed information about residential buildings was prepared via web crawlers. Second, the cleaned residential building information fed a support vector machine (SVM) algorithm to classify the buildings into four flood vulnerability levels that represented the vulnerability curves of the four building types. Third, the buildings of different levels were given vulnerability scores by accumulating the depth–damage ratios across the possible range of flood depth. Further, combined with the unit price of houses, flood risk was assessed for residential buildings. The results showed that the F1-score for the classification of buildings was about 80%. The flood vulnerability scores were higher in both the urban center and the surrounding areas and lower between them. Since 1990, the majority of residential buildings in Shanghai have switched from masonry–concrete structures to steel–concrete structures, greatly reducing the vulnerability to floods. The risk assessment showed decreasing risk trend from the center outward, with the highest risk at the junction of the Huangpu, Jing’an and Xuhui districts. Therefore, this framework can not only identify the flood vulnerability patterns but also provide a clue for revealing the flood risk of residential buildings. With real estate data becoming increasingly accessible, this method can be widely applied to other cities to facilitate flood vulnerability and risk assessment. Full article
(This article belongs to the Special Issue Advance in Flood Risk Management and Assessment Research)
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25 pages, 22461 KiB  
Article
Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery
by Ivana Hlaváčová, Michal Kačmařík, Milan Lazecký, Juraj Struhár and Petr Rapant
Water 2021, 13(23), 3392; https://doi.org/10.3390/w13233392 - 1 Dec 2021
Cited by 1 | Viewed by 2525
Abstract
Many technical infrastructure operators manage facilities distributed over large areas. They face the problem of finding out if a flood hit a specific facility located in the open countryside. Physical inspection after every heavy rain is time and personnel consuming, and equipping all [...] Read more.
Many technical infrastructure operators manage facilities distributed over large areas. They face the problem of finding out if a flood hit a specific facility located in the open countryside. Physical inspection after every heavy rain is time and personnel consuming, and equipping all facilities with flood detection is expensive. Therefore, methods are being sought to ensure that these facilities are monitored at a minimum cost. One of the possibilities is using remote sensing, especially radar data regularly scanned by satellites. A significant challenge in this area was the launch of Sentinel-1 providing free-of-charge data with adequate spatial resolution and relatively high revisit time. This paper presents a developed automatic processing chain for flood detection in the open landscape from Sentinel-1 data. Flood detection can be started on-demand; however, it mainly focuses on autonomous near real-time monitoring. It is based on a combination of algorithms for multi-temporal change detection and histogram thresholding open-water detection. The solution was validated on five flood events in four European countries by comparing its results with flood delineation derived from reference datasets. Long-term tests were also performed to evaluate the potential for a false positive occurrence. In the statistical classification assessments, the mean value of user accuracy (producer accuracy) for open-water class reached 83% (65%). The developed solution typically provided flooded polygons in the same areas as the reference dataset, but of a smaller size. This fact is mainly attributed to the use of universal sensitivity parameters, independent of the specific location, which ensure almost complete successful suppression of false alarms. Full article
(This article belongs to the Special Issue Advance in Flood Risk Management and Assessment Research)
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