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Hydrology
Special Issues
Flood Early Warning and Risk Modelling
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Flood Early Warning and Risk Modelling

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: closed (31 December 2021) | Viewed by 36653

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Marina Iosub

E-Mail Website
Guest Editor
1. Integrated Center of Environmental Science Studies in the North Eastern Region—CERNESIM, Sciences Department, Interdisciplinary Research Institute, ‘Alexandru Ioan Cuza’ University, of Iași, 700506 Iasi, Romania
2. Department of Geography, Faculty of Geography and Geology, "Alexandru Ioan Cuza" University, of Iași, 700505 Iasi, Romania
Interests: flash flood modeling; risk modeling; natural hazards; hydrological modeling and forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Andrei Enea

E-Mail Website
Guest Editor
Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, 700505 Iași, Romania
Interests: automation; flood risk; earth science; GIS; land use; hydrology; UAV; drone; remote sensing; structure from motion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Extreme hydrological phenomena are one of the most common causes of human life loss and material damage as a result of the manifestation of natural hazards around human communities. Climatic changes have directly impacted the temporal distribution of previously known flood events, inducing significantly increased frequency rates as well as manifestation intensities. Understanding the occurrence and manifestation behavior of flood risk as well as identifying the most common time intervals during which there is a greater probability of flood occurrence should be a subject of social priority, given the potential casualties and damage involved. However, considering the numerous flood analysis models that have been currently developed, this phenomenon has not yet been fully comprehended due to the numerous technical challenges that have arisen. These challenges can range from lack of measured field data to difficulties in integrating spatial layers of different scales as well as other potential digital restrictions.

The aim of the current Special Issue is to promote publications that address flood analysis and apply some of the most novel inundation prediction models, as well as various hydrological risk simulations related to floods, that will enhance the current state of knowledge in the field as well as lead toward a better understanding of flood risk modeling. Furthermore, the current Special Issue will address the temporal aspect of flood propagation, including alert times, warning systems, flood time distribution cartographic material, and the numerous parameters involved in flood risk modeling.

We welcome submissions of original research in the field of flood, flash flood risk modeling, and early flood warning, and these may include the following topics:

  • Flood early warning
  • Flood time analysis
  • Flood propagation time
  • Flood modeling
  • Flood risk modeling
  • Past flood research
  • Flood risk prediction
  • Flood risk mitigation

Dr. Marina Losub
Dr. Andrei Enea
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • flood analysis
  • flood modeling
  • flood risk
  • flood mitigation
  • geographic information system
  • remote sensing
  • UAV photogrammetry
  • land use

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Published Papers (7 papers)

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Editorial

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5 pages, 200 KiB  
Editorial
Flood Early Warning and Risk Modelling
by Marina Iosub and Andrei Enea
Hydrology 2022, 9(4), 57; https://doi.org/10.3390/hydrology9040057 - 31 Mar 2022
Viewed by 2192
Abstract
The evolution of mankind during the last 2 centuries has generated an ever growing thrive for increased production, for the need to create novel means to generate energy and for society to change into a more consumerism-oriented version [...] Full article
(This article belongs to the Special Issue Flood Early Warning and Risk Modelling)

Research

Jump to: Editorial

14 pages, 4194 KiB  
Article
Towards Coupling of 1D and 2D Models for Flood Simulation—A Case Study of Nilwala River Basin, Sri Lanka
by Lanthika Dhanapala, M. H. J. P. Gunarathna, M. K. N. Kumari, Manjula Ranagalage, Kazuhito Sakai and T. J. Meegastenna
Hydrology 2022, 9(2), 17; https://doi.org/10.3390/hydrology9020017 - 25 Jan 2022
Cited by 8 | Viewed by 4883
Abstract
The Nilwala river basin is prone to frequent flooding during the southwest monsoon and second intermonsoon periods. Several studies have recommended coupling 1D and 2D models for flood modelling as they provide sufficient descriptive information of floodplains with greater computational efficiency. This study [...] Read more.
The Nilwala river basin is prone to frequent flooding during the southwest monsoon and second intermonsoon periods. Several studies have recommended coupling 1D and 2D models for flood modelling as they provide sufficient descriptive information of floodplains with greater computational efficiency. This study aims to couple a 1D hydrological model (HEC-HMS) with a 2D hydraulic model (iRIC) to simulate flooding in the Nilwala river basin. Hourly rainfall and streamflow data of three flood events were used for calibration and validation of HEC-HMS. The model performed exceptionally well considering the Nash–Sutcliffe coefficient, percent bias, and root mean square error. The flood event of May 2017 was simulated on iRIC using the streamflow hydrographs modelled by HEC-HMS. An overall accuracy of 81.5% was attained when the simulated extent was compared with the surveyed flood extent. The accuracy of the simulated flood depth was assessed using the observed water level at Tudawa gauging station, which yielded an NSE of 0.94, PBIAS of −4.28, RMSE of 0.18 and R2 of 0.95. Thus, the coupled model provided an accurate estimate of the flood extent and depth and can be further developed for hydrological flood forecasting on a regional scale. Full article
(This article belongs to the Special Issue Flood Early Warning and Risk Modelling)
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20 pages, 2557 KiB  
Article
Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador
by Paul Muñoz, Johanna Orellana-Alvear, Jörg Bendix, Jan Feyen and Rolando Célleri
Hydrology 2021, 8(4), 183; https://doi.org/10.3390/hydrology8040183 - 16 Dec 2021
Cited by 15 | Viewed by 6460
Abstract
Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and [...] Read more.
Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods. Full article
(This article belongs to the Special Issue Flood Early Warning and Risk Modelling)
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15 pages, 13487 KiB  
Article
Flood Risk Communication Using ArcGIS StoryMaps
by Khalid Oubennaceur, Karem Chokmani, Anas El Alem and Yves Gauthier
Hydrology 2021, 8(4), 152; https://doi.org/10.3390/hydrology8040152 - 11 Oct 2021
Cited by 13 | Viewed by 6298
Abstract
In Canada, flooding is the most common and costly natural hazard. Flooding events significantly impact communities, damage infrastructures and threaten public security. Communication, as part of a flood risk management strategy, is an essential means of countering these threats. It is therefore important [...] Read more.
In Canada, flooding is the most common and costly natural hazard. Flooding events significantly impact communities, damage infrastructures and threaten public security. Communication, as part of a flood risk management strategy, is an essential means of countering these threats. It is therefore important to develop new and innovative tools to communicate the flood risk with citizens. From this perspective, the use of story maps can be very effectively implemented for a broad audience, particularly to stakeholders. This paper details how an interactive web-based story map was set up to communicate current and future flood risks in the Petite-Nation River watershed, Quebec (Canada). This web technology application combines informative texts and interactive maps on current and future flood risks in the Petite-Nation River watershed. Flood risk and climate maps were generated using the GARI tool, implemented using a geographic information system (GIS) supported by ArcGIS Online (Esri). Three climate change scenarios developed by the Hydroclimatic Atlas of Southern Quebec were used to visualize potential future impacts. This study concluded that our story map is an efficient flood hazard communication tool. The assets of this interactive web mapping tool are numerous, namely user-friendly mapping, use and interaction, and customizable displays. Full article
(This article belongs to the Special Issue Flood Early Warning and Risk Modelling)
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23 pages, 15507 KiB  
Article
Hydrological and Hydraulic Flood Hazard Modeling in Poorly Gauged Catchments: An Analysis in Northern Italy
by Francesca Aureli, Paolo Mignosa, Federico Prost and Susanna Dazzi
Hydrology 2021, 8(4), 149; https://doi.org/10.3390/hydrology8040149 - 5 Oct 2021
Cited by 8 | Viewed by 2749
Abstract
Flood hazard is assessed for a watershed with scarce hydrological data in the lower plain of Northern Italy, where the current defense system is inadequate to protect a highly populated urban area located at a river confluence and crossed by numerous bridges. An [...] Read more.
Flood hazard is assessed for a watershed with scarce hydrological data in the lower plain of Northern Italy, where the current defense system is inadequate to protect a highly populated urban area located at a river confluence and crossed by numerous bridges. An integrated approach is adopted. Firstly, to overcome the scarcity of data, a regional flood frequency analysis is performed to derive synthetic design hydrographs, with an original approach to obtain the flow reduction curve from recorded water stages. The hydrographs are then imposed as upstream boundary conditions for hydraulic modeling using the fully 2D shallow water model PARFLOOD with the recently proposed inclusion of bridges. High-resolution simulations of the potential flooding in the urban center and surrounding areas are, therefore, performed as a novel extensive application of a truly 2D framework for bridge modeling. Moreover, simulated flooded areas and water levels, with and without bridges, are compared to quantify the interference of the crossing structures and to assess the effectiveness of a structural measure for flood hazard reduction, i.e., bridge adaptation. This work shows how the use of an integrated hydrological–hydraulic approach can be useful for infrastructure design and civil protection purposes in a poorly gauged watershed. Full article
(This article belongs to the Special Issue Flood Early Warning and Risk Modelling)
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17 pages, 5260 KiB  
Article
Flood Mapping from Dam Break Due to Peak Inflow: A Coupled Rainfall–Runoff and Hydraulic Models Approach
by Mihretab G. Tedla, Younghyun Cho and Kyungsoo Jun
Hydrology 2021, 8(2), 89; https://doi.org/10.3390/hydrology8020089 - 6 Jun 2021
Cited by 24 | Viewed by 4769
Abstract
In this study, we conducted flood mapping of a hypothetical dam break by coupling the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS) models under different return periods of flood inflow. This study is presented as a case study [...] Read more.
In this study, we conducted flood mapping of a hypothetical dam break by coupling the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS) models under different return periods of flood inflow. This study is presented as a case study on the Kesem embankment dam in Ethiopia. Hourly hydrological and meteorological data and high-resolution land surface datasets were used to simulate the design floods for piping dam failure with empirical dam breach methods. Based on the extreme inflows and the dam physical characteristics, the dam failure was simulated by a two-dimensional, unsteady flow hydrodynamic model. As a result, the dam will remain safe for up to 50-year return-period inflows, but it breaks for 100- and 200-year return periods and floods the downstream area. For the 100-year peak inflow, a 208 km2 area will be inundated by a maximum depth of 20 m and for a maximum duration of 46 h. The 200-year inflow will inundate a 240 km2 area with a maximum depth of 31 m for a maximum duration of 93 h. The 2D flood map provides satisfactory spatial and temporal resolution of the inundated area for evaluation of the affected facilities. Full article
(This article belongs to the Special Issue Flood Early Warning and Risk Modelling)
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12 pages, 25050 KiB  
Article
Real-Time Flood Mapping on Client-Side Web Systems Using HAND Model
by Anson Hu and Ibrahim Demir
Hydrology 2021, 8(2), 65; https://doi.org/10.3390/hydrology8020065 - 11 Apr 2021
Cited by 35 | Viewed by 6770
Abstract
The height above nearest drainage (HAND) model is frequently used to calculate properties of the soil and predict flood inundation extents. HAND is extremely useful due to its lack of reliance on prior data, as only the digital elevation model (DEM) is needed. [...] Read more.
The height above nearest drainage (HAND) model is frequently used to calculate properties of the soil and predict flood inundation extents. HAND is extremely useful due to its lack of reliance on prior data, as only the digital elevation model (DEM) is needed. It is close to optimal, running in linear or linearithmic time in the number of cells depending on the values of the heights. It can predict watersheds and flood extent to a high degree of accuracy. We applied a client-side HAND model on the web to determine extent of flood inundation in several flood prone areas in Iowa, including the city of Cedar Rapids and Ames. We demonstrated that the HAND model was able to achieve inundation maps comparable to advanced hydrodynamic models (i.e., Federal Emergency Management Agency approved flood insurance rate maps) in Iowa, and would be helpful in the absence of detailed hydrological data. The HAND model is applicable in situations where a combination of accuracy and short runtime are needed, for example, in interactive flood mapping and supporting mitigation decisions, where users can add features to the landscape and see the predicted inundation. Full article
(This article belongs to the Special Issue Flood Early Warning and Risk Modelling)
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