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Advances in Modeling and Risk Analysis of Floods under Changing...
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Advances in Modeling and Risk Analysis of Floods under Changing Climate

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 9699

Special Issue Editor

Dr. Yonas K. Demissie

E-Mail Website
Guest Editor
Civil and Environmental Engineering, Washington State University, Richland, WA, USA
Interests: stochastic hydrology; hydroclimatology; flood modeling; hydroinformatics; hydrological and water quality modeling; system optimization

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed an alarming increase in the likelihood and intensity of heavy rainfall, causing severe flooding around the globe. For example, this year alone, the widespread record-breaking rainfall and flood events in the US, Europe, and Asia caused the worst fatalities on record. At least 219 people from Belgium and Germany, 192 from India, and 22 people from Tennessee in the US lost their lives from single flood events. The frequency and severity of such events are expected to continue to increase in the future as the climate warms and more areas are urbanized. In order to tackle this emerging global issue, it is essential to accurately predict the level, spatial extent, and potential impacts of severe floods on communities. This Special Issue aims to highlight the recent progress and help define the future directions of flood modeling. Potential topics include but are not limited to the following:

  1. Improved characterization of nonstationary storm and flood events considering the future climate.
  2. Novel modeling and uncertainty analysis for flood inundation and progression within stormwater systems, floodplains, coastal areas, and entire watersheds.
  3. Application of physically based and data-driven approaches for flood forecasting, mapping, and risk analysis by leveraging new methodologies and data sources.
  4. Integrated flood risk and hazard analyses to enhance decision making during flood events and aid long-term planning.

Dr. Yonas K. Demissie
Guest Editor

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. Water is an international peer-reviewed open access semimonthly 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 2600 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 inundation mapping
  • nonstationary storm and flood
  • flood risk and uncertainty analysis
  • stormwater flooding
  • watershed flooding
  • hydroclimatology of floods
  • big data for floods

Published Papers (4 papers)

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Research

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24 pages, 4753 KiB  
Article
Identifying Cost-Effective Low-Impact Development (LID) under Climate Change: A Multi-Objective Optimization Approach
by Yasir Abduljaleel and Yonas Demissie
Water 2022, 14(19), 3017; https://doi.org/10.3390/w14193017 - 25 Sep 2022
Cited by 10 | Viewed by 2345
Abstract
Low-impact development (LID) is increasingly used to reduce stormwater’s quality and quantity impacts associated with climate change and increased urbanization. However, due to the significant variations in their efficiencies and site-specific requirements, an optimal combination of different LIDs is required to benefit from [...] Read more.
Low-impact development (LID) is increasingly used to reduce stormwater’s quality and quantity impacts associated with climate change and increased urbanization. However, due to the significant variations in their efficiencies and site-specific requirements, an optimal combination of different LIDs is required to benefit from their full potential. In this article, the multi-objective genetic algorithm (MOGA) was coupled with the stormwater management model (SWMM) to identify both hydrological and cost-effective LIDs combinations within a large urban watershed. MOGA iteratively optimizes the types, sizes, and locations of different LIDs using a combined cost- and runoff-related objective function under both past and future stormwater conditions. The infiltration trench (IT), rain barrel (RB), rain gardens (RG), bioretention (BR), and permeable pavement were used as potential LIDs since they are common in our study area—the city of Renton, WA, USA. The city is currently adapting different LIDs to mitigate the recent increase in stormwater system failures and flooding. The results from our study showed that the optimum combination of LIDs in the city could reduce the peak flow and total runoff volume by up to 62.25% and 80% for past storms and by13% and 29% for future storms, respectively. The findings and methodologies presented in this study are expected to contribute to the ongoing efforts to improve the performance of large-scale implementations of LIDs. Full article
(This article belongs to the Special Issue Advances in Modeling and Risk Analysis of Floods under Changing Climate)
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13 pages, 2976 KiB  
Article
How Robust Is a Multi-Model Ensemble Mean of Conceptual Hydrological Models to Climate Change?
by Takayuki Kimizuka and Yohei Sawada
Water 2022, 14(18), 2852; https://doi.org/10.3390/w14182852 - 13 Sep 2022
Viewed by 1210
Abstract
It is a grand challenge to realize robust rainfall-runoff prediction for a changing climate through conceptual hydrological models. Although multi-model ensemble (MME) is considered useful in improving the robustness of hydrological prediction, it has yet to be thoroughly evaluated. We evaluated the robustness [...] Read more.
It is a grand challenge to realize robust rainfall-runoff prediction for a changing climate through conceptual hydrological models. Although multi-model ensemble (MME) is considered useful in improving the robustness of hydrological prediction, it has yet to be thoroughly evaluated. We evaluated the robustness of MME by 44 conceptual hydrological models in 582 river basins. We found that MME was more accurate and robust than each individual model alone. Although the performance of MME degrades in the validation period, the extent of degradation is smaller for MME than for individual models, especially when the climatology of river discharge in the validation period is greatly different from that in the calibration period. This implies the robustness of MME to climate change. It was found to be difficult to quantify the robustness of MME when the number of basins and models is small, which implies the importance of the large number of models and watersheds to evaluate the robustness and uncertainty in hydrological prediction. Full article
(This article belongs to the Special Issue Advances in Modeling and Risk Analysis of Floods under Changing Climate)
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29 pages, 2782 KiB  
Article
A Deliberative Rural Community Consultation to Assess Support for Flood Risk Management Policies to Strengthen Resilience in Malawi
by Ozius Dewa, Donald Makoka and Olalekan Ayo-Yusuf
Water 2022, 14(6), 874; https://doi.org/10.3390/w14060874 - 11 Mar 2022
Cited by 3 | Viewed by 2916
Abstract
As disasters increase in frequency and magnitude with adverse effects on population health, governments will be forced to implement disaster risk management policies that may include forced relocation. Ineffective public consultation has been cited as one reason for failure of these policies. Using [...] Read more.
As disasters increase in frequency and magnitude with adverse effects on population health, governments will be forced to implement disaster risk management policies that may include forced relocation. Ineffective public consultation has been cited as one reason for failure of these policies. Using the deliberative polling method, this study assessed the capacity of rural communities to participate in flood risk management policy priority setting and the impact of providing accurate and balanced information on policies by comparing pre-and post -deliberation data. The study also assessed the level of trust on whether government and community would use the results of this study. Results indicated strong community support for policy options to reduce vulnerability in communities and strong resistance to relocation. As all the top five ranked policy options were concerned with population pressure, gender, and social service issues, which are all conceptually considered social determinants of a healthy community, this study concludes that public health considerations are central to flood risk policy development and implementation. The study revealed high levels of trust in government and the community relating to flood risk management, which policymakers in low-to-middle income countries can capitalise on for meaningful community consultation for effective disaster risk management. Full article
(This article belongs to the Special Issue Advances in Modeling and Risk Analysis of Floods under Changing Climate)
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Review

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22 pages, 2591 KiB  
Review
Artificial Intelligence-Based Regional Flood Frequency Analysis Methods: A Scoping Review
by Amir Zalnezhad, Ataur Rahman, Nastaran Nasiri, Khaled Haddad, Muhammad Muhitur Rahman, Mehdi Vafakhah, Bijan Samali and Farhad Ahamed
Water 2022, 14(17), 2677; https://doi.org/10.3390/w14172677 - 29 Aug 2022
Cited by 8 | Viewed by 2547
Abstract
Flood is one of the most destructive natural disasters, causing significant economic damage and loss of lives. Numerous methods have been introduced to estimate design floods, which include linear and non-linear techniques. Since flood generation is a non-linear process, the use of linear [...] Read more.
Flood is one of the most destructive natural disasters, causing significant economic damage and loss of lives. Numerous methods have been introduced to estimate design floods, which include linear and non-linear techniques. Since flood generation is a non-linear process, the use of linear techniques has inherent weaknesses. To overcome these, artificial intelligence (AI)-based non-linear regional flood frequency analysis (RFFA) techniques have been introduced over the last two decades. There are limited articles available in the literature discussing the relative merits/demerits of these AI-based RFFA techniques. To fill this knowledge gap, a scoping review on the AI-based RFFA techniques is presented. Based on the Scopus database, more than 1000 articles were initially selected, which were then screened manually to select the most relevant articles. The accuracy and efficiency of the selected RFFA techniques based on a set of evaluation statistics were compared. Furthermore, the relationships among countries and researchers focusing on AI-based RFFA techniques are illustrated. In terms of performance, artificial neural networks (ANN) are found to be the best performing techniques among all the selected AI-based RFFA techniques. It is also found that Australia, Canada, and Iran have published the highest number of articles in this research field, followed by Turkey, the United Arab Emirates (UAE), India, and China. Future research should be directed towards identification of the impacts of data quantity and quality, model uncertainty and climate change on the AI-based RFFA techniques. Full article
(This article belongs to the Special Issue Advances in Modeling and Risk Analysis of Floods under Changing Climate)
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