E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Flood Risk and Resilience"

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

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editors

Guest Editor
Prof. Guangtao Fu

University of Exeter, Centre for Water Systems, Harrison Building, Exeter EX4 4QF, UK
Website | E-Mail
Interests: water resources management; flood risk analysis and management; system resilience analysis; smart water and wastewater; infrastructure systems; data and decision analytics
Guest Editor
Dr. Monica Rivas Casado

School of Water, Energy and Environment, Cranfield University, Senior Lecturer in Integrated Environmental Monitoring, College Road, Cranfield, Bedfordshire, MK430AL, UK
Website | E-Mail
Phone: 0044(0)1234753344
Interests: unmanned aerial vehicles; structure from motion; monitoring; ecological modeling; freshwater ecosystems; statistics; environmental engineering; autonomous systems
Guest Editor
Dr. Fanlin Meng

University of Exeter, Centre for Water Systems, Harrison Building, Exeter EX4 4QF, UK
Website | E-Mail
Interests: modelling and risk and resilience management of urban water systems; water policy; social network analysis
Guest Editor
Prof. Roy Kalawsky

Loughborough University, Wolfson School of Mechanical, Electric and Manufacturing Engineering
Website | E-Mail
Interests: systems engineering; systems of systems; modelling and simulation; next generation visual analytics; digitalization

Special Issue Information

Dear Colleagues,

Flooding has been increasingly recognized as a global threat due to the extent and magnitude of damage it poses around the world each year. Flooding, which can occur from fluvial, pluvial, coastal and groundwater sources, causes significant economic, social and environmental consequences, and the costs and disruption to communities, businesses and economies are expected to increase as a result of urbanization, economic growth and climate change. For example, flooding is the greatest threat posed by climate change in the UK, with 3.6 million people at risk by the 2050s according to the first UK climate change risk assessment report published in 2012.

To reduce flood losses, we need to understand both current and future risks, develop cost-effective intervention strategies, and increase the resilience of local communities and critical infrastructure to flood events. This requires innovative approaches, advanced computer models and tools to assess flood risk and resilience. In particular, there is a need to understand how resilience can be effectively built into flood defense systems and interlinked critical infrastructure systems in the flood management process. This Special Issue will provide a platform for researchers and engineers to share and discuss state-of-the-art scientific knowledge and best practices in flood management.

This Special Issue aims to address key challenges towards a more flood resilient future, encompassing not only both flood risk and resilience, but also their intersections. It will cover a full suite of flood issues, including, but not limited to, system monitoring and early warning, flood modelling and assessment, structural and non-structural measures, policies and financial instruments, social dynamics and communication by which flood management knowledge within communities is learned and shared.

Prof. Guangtao Fu
Dr. Monica Rivas Casado
Dr. Fanlin Meng
Prof. Roy Kalawsky
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 papers will be 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 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 1600 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

  • Adaptation strategy
  • cascade effect
  • community vulnerability
  • environmental sustainability
  • flood modelling
  • flood monitoring
  • flood insurance
  • mitigation strategy
  • resilience analysis
  • risk assessment

Published Papers (10 papers)

View options order results:
result details:
Displaying articles 1-10
Export citation of selected articles as:

Research

Open AccessArticle
Intelligent Storage Location Allocation with Multiple Objectives for Flood Control Materials
Water 2019, 11(8), 1537; https://doi.org/10.3390/w11081537
Received: 16 June 2019 / Revised: 15 July 2019 / Accepted: 18 July 2019 / Published: 25 July 2019
Cited by 1 | PDF Full-text (3427 KB) | HTML Full-text | XML Full-text
Abstract
Intelligent storage is an important element of intelligent logistics and a key development trend in modern warehousing and logistics. Based on the characteristics of flood control materials and their intelligent storage, this study established a flood control material storage location allocation model reflecting [...] Read more.
Intelligent storage is an important element of intelligent logistics and a key development trend in modern warehousing and logistics. Based on the characteristics of flood control materials and their intelligent storage, this study established a flood control material storage location allocation model reflecting the multiple objectives of retrieval efficiency and shelf stability and used a weighting method to transform a multi-objective optimization problem into a single-objective optimization problem. We then used the facilities and equipment planning and storage location allocation in the intelligent storage area for provincial flood control materials at the Zhenjiang warehouse of the Jiangsu water conservancy and flood control material reserve center as a case study. Empirical analysis was performed and used the genetic algorithm and Matrix Laboratory (MATLAB) software to optimize the storage location allocation of provincial flood prevention supplies at this warehouse, and it achieved effective results. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessCommunication
Achieving Urban Flood Resilience in an Uncertain Future
Water 2019, 11(5), 1082; https://doi.org/10.3390/w11051082
Received: 26 April 2019 / Revised: 21 May 2019 / Accepted: 22 May 2019 / Published: 24 May 2019
PDF Full-text (1281 KB) | HTML Full-text | XML Full-text
Abstract
Preliminary results of the UK Urban Flood Resilience research consortium are presented and discussed, with the work being conducted against a background of future uncertainties with respect to changing climate and increasing urbanization. Adopting a whole systems approach, key themes include developing adaptive [...] Read more.
Preliminary results of the UK Urban Flood Resilience research consortium are presented and discussed, with the work being conducted against a background of future uncertainties with respect to changing climate and increasing urbanization. Adopting a whole systems approach, key themes include developing adaptive approaches for flexible engineering design of coupled grey and blue-green flood management assets; exploiting the resource potential of urban stormwater through rainwater harvesting, urban metabolism modelling and interoperability; and investigating the interactions between planners, developers, engineers and communities at multiple scales in managing flood risk. The work is producing new modelling tools and an extensive evidence base to support the case for multifunctional infrastructure that delivers multiple, environmental, societal and economic benefits, while enhancing urban flood resilience by bringing stormwater management and green infrastructure together. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessArticle
Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events
Water 2019, 11(5), 973; https://doi.org/10.3390/w11050973
Received: 12 February 2019 / Revised: 30 April 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
PDF Full-text (6335 KB) | HTML Full-text | XML Full-text
Abstract
The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a [...] Read more.
The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessArticle
A Conceptual Time-Varying Flood Resilience Index for Urban Areas: Munich City
Water 2019, 11(4), 830; https://doi.org/10.3390/w11040830
Received: 9 March 2019 / Revised: 13 April 2019 / Accepted: 16 April 2019 / Published: 19 April 2019
Cited by 4 | PDF Full-text (9679 KB) | HTML Full-text | XML Full-text
Abstract
In response to the increased frequency and severity of urban flooding events, flood management strategies are moving away from flood proofing towards flood resilience. The term ‘flood resilience’ has been applied with different definitions. In this paper, it is referred to as the [...] Read more.
In response to the increased frequency and severity of urban flooding events, flood management strategies are moving away from flood proofing towards flood resilience. The term ‘flood resilience’ has been applied with different definitions. In this paper, it is referred to as the capacity to withstand adverse effects following flooding events and the ability to quickly recover to the original system performance before the event. This paper introduces a novel time-varying Flood Resilience Index (FRI) to quantify the resilience level of households. The introduced FRI includes: (a) Physical indicators from inundation modelling for considering the adverse effects during flooding events, and (b) social and economic indicators for estimating the recovery capacity of the district in returning to the original performance level. The district of Maxvorstadt in Munich city is used for demonstrating the FRI. The time-varying FRI provides a novel insight into indicator-based quantification methods of flood resilience for households in urban areas. It enables a timeline visualization of how a system responds during and after a flooding event. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessArticle
From Multi-Risk Evaluation to Resilience Planning: The Case of Central Chilean Coastal Cities
Water 2019, 11(3), 572; https://doi.org/10.3390/w11030572
Received: 1 February 2019 / Revised: 1 March 2019 / Accepted: 5 March 2019 / Published: 19 March 2019
Cited by 1 | PDF Full-text (4430 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Multi-hazard evaluations are fundamental inputs for disaster risk management plans and the implementation of resilient urban environments, adapted to extreme natural events. Risk assessments from natural hazards have been typically restricted to the analysis of single hazards or focused on the vulnerability of [...] Read more.
Multi-hazard evaluations are fundamental inputs for disaster risk management plans and the implementation of resilient urban environments, adapted to extreme natural events. Risk assessments from natural hazards have been typically restricted to the analysis of single hazards or focused on the vulnerability of specific targets, which might result in an underestimation of the risk level. This study presents a practical and effective methodology applied to two Chilean coastal cities to characterize risk in data-poor regions, which integrates multi-hazard and multi-vulnerability analyses through physically-based models and easily accessible data. A matrix approach was used to cross the degree of exposure to floods, landslides, tsunamis, and earthquakes hazards, and two dimensions of vulnerability (physical, socio-economical). This information is used to provide the guidelines to lead the development of resilience thinking and disaster risk management in Chile years after the major and destructive 2010 Mw8.8 earthquake. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessArticle
A Model-Based Engineering Methodology and Architecture for Resilience in Systems-of-Systems: A Case of Water Supply Resilience to Flooding
Water 2019, 11(3), 496; https://doi.org/10.3390/w11030496
Received: 5 February 2019 / Revised: 25 February 2019 / Accepted: 3 March 2019 / Published: 8 March 2019
PDF Full-text (6052 KB) | HTML Full-text | XML Full-text
Abstract
There is a clear and evident requirement for a conscious effort to be made towards a resilient water system-of-systems (SoS) within the UK, in terms of both supply and flooding. The impact of flooding goes beyond the immediately obvious socio-aspects of disruption, cascading [...] Read more.
There is a clear and evident requirement for a conscious effort to be made towards a resilient water system-of-systems (SoS) within the UK, in terms of both supply and flooding. The impact of flooding goes beyond the immediately obvious socio-aspects of disruption, cascading and affecting a wide range of connected systems. The issues caused by flooding need to be treated in a fashion which adopts an SoS approach to evaluate the risks associated with interconnected systems and to assess resilience against flooding from various perspectives. Changes in climate result in deviations in frequency and intensity of precipitation; variations in annual patterns make planning and management for resilience more challenging. This article presents a verified model-based system engineering methodology for decision-makers in the water sector to holistically, and systematically implement resilience within the water context, specifically focusing on effects of flooding on water supply. A novel resilience viewpoint has been created which is solely focused on the resilience aspects of architecture that is presented within this paper. Systems architecture modelling forms the basis of the methodology and includes an innovative resilience viewpoint to help evaluate current SoS resilience, and to design for future resilient states. Architecting for resilience, and subsequently simulating designs, is seen as the solution to successfully ensuring system performance does not suffer, and systems continue to function at the desired levels of operability. The case study presented within this paper demonstrates the application of the SoS resilience methodology on water supply networks in times of flooding, highlighting how such a methodology can be used for approaching resilience in the water sector from an SoS perspective. The methodology highlights where resilience improvements are necessary and also provides a process where architecture solutions can be proposed and tested. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessArticle
Impact of Urban Growth and Changes in Land Use on River Flood Hazard in Villahermosa, Tabasco (Mexico)
Water 2019, 11(2), 304; https://doi.org/10.3390/w11020304
Received: 29 December 2018 / Revised: 28 January 2019 / Accepted: 31 January 2019 / Published: 12 February 2019
Cited by 2 | PDF Full-text (39360 KB) | HTML Full-text | XML Full-text
Abstract
The city of Villahermosa, a logistical center in the State of Tabasco’s economy, is affected by recurrent river floods. In this study, we analyzed the impact of two factors that are the most probable causes of this increase in flood hazard: changes in [...] Read more.
The city of Villahermosa, a logistical center in the State of Tabasco’s economy, is affected by recurrent river floods. In this study, we analyzed the impact of two factors that are the most probable causes of this increase in flood hazard: changes in land use in the hydrological catchments upstream of the city, and the uncontrolled urbanization of the floodplains adjacent to the main river channels. Flood discharges for different return periods were evaluated, considering land uses of the catchments, both as they were in 1992 and as they are today. These flood discharges were then used in a 2D shallow water model to estimate the increase of water depths in the city from 1992 to the present day. To evaluate the influence of urban expansion on inundation levels, three future urbanization scenarios were proposed on the basis of the urban growth rate forecast for 2050. Results confirm that the change in land use in the hydrological catchments is the main factor that explains the increase in inundation events observed over recent years. This study also provides useful insights for future city planning that might help to minimize the flood impact on Villahermosa. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessArticle
Analysis of the Public Flood Risk Perception in a Flood-Prone City: The Case of Jingdezhen City in China
Water 2018, 10(11), 1577; https://doi.org/10.3390/w10111577
Received: 13 October 2018 / Revised: 31 October 2018 / Accepted: 1 November 2018 / Published: 4 November 2018
PDF Full-text (1086 KB) | HTML Full-text | XML Full-text
Abstract
Understanding and improving public flood risk perception is conducive to the implementation of effective flood risk management and disaster reduction policies. In the flood-prone city of Jingdezhen, flood disaster is one of the most destructive natural hazards to impact the society and economy. [...] Read more.
Understanding and improving public flood risk perception is conducive to the implementation of effective flood risk management and disaster reduction policies. In the flood-prone city of Jingdezhen, flood disaster is one of the most destructive natural hazards to impact the society and economy. However, few studies have been attempted to focus on public flood risk perception in the small and medium-size city in China, like Jingdezhen. Therefore, the purpose of this study was to investigate the public flood risk perception in four districts of Jingdezhen and examine the related influencing factors. A questionnaire survey of 719 randomly sampled respondents was conducted in 16 subdistricts of Jingdezhen. Analysis of variance was conducted to identify the correlations between the impact factors and public flood risk perception. Then, the flood risk perception differences between different groups under the same impact factor were compared. The results indicated that the socio-demographic characteristics of the respondents (except occupation), flood experience, flood knowledge education, flood protection responsibility, and trust in government were strongly correlated with flood risk perception. The findings will help decision makers to develop effective flood risk communication strategies and flood risk reduction policies. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessArticle
Automated Floodway Determination Using Particle Swarm Optimization
Water 2018, 10(10), 1420; https://doi.org/10.3390/w10101420
Received: 4 September 2018 / Revised: 5 October 2018 / Accepted: 6 October 2018 / Published: 10 October 2018
Cited by 1 | PDF Full-text (1185 KB) | HTML Full-text | XML Full-text
Abstract
The floodway plays an important role in flood modeling. In the United States, the Federal Emergency Management Agency requires the floodway to be determined using an approved computer program for developed communities. It is a local government’s interest to minimize the floodway area [...] Read more.
The floodway plays an important role in flood modeling. In the United States, the Federal Emergency Management Agency requires the floodway to be determined using an approved computer program for developed communities. It is a local government’s interest to minimize the floodway area because encroachment areas may be permitted for human activities. However, manual determination of the floodway can be time-consuming and subjective depending on the modeler’s knowledge and judgments, and may not necessarily produce a small floodway especially when there are many cross sections because of their correlation. Very little work has been done in terms of floodway optimization. In this study, we propose an optimization method for minimizing the floodway area using the Isolated-Speciation-based Particle Swarm Optimization algorithm and the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). This method optimizes the floodway by defining an objective function that considers the floodway area and hydraulic requirements, and automating operations of HEC-RAS. We used a floodway model provided by HEC-RAS and compared the proposed, manual, and default HEC-RAS methods. The proposed method consistently improved the objective function value by 1–40%. We believe that this method can provide an automated tool for optimizing the floodway model using HEC-RAS. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Open AccessArticle
Nonstationary Flood Frequency Analysis Using Univariate and Bivariate Time-Varying Models Based on GAMLSS
Water 2018, 10(7), 819; https://doi.org/10.3390/w10070819
Received: 16 May 2018 / Revised: 9 June 2018 / Accepted: 14 June 2018 / Published: 21 June 2018
Cited by 21 | PDF Full-text (2361 KB) | HTML Full-text | XML Full-text
Abstract
With the changing environment, a number of researches have revealed that the assumption of stationarity of flood sequences is questionable. In this paper, we established univariate and bivariate models to investigate nonstationary flood frequency with distribution parameters changing over time. Flood peak Q [...] Read more.
With the changing environment, a number of researches have revealed that the assumption of stationarity of flood sequences is questionable. In this paper, we established univariate and bivariate models to investigate nonstationary flood frequency with distribution parameters changing over time. Flood peak Q and one-day flood volume W1 of the Wangkuai Reservoir catchment were used as basic data. In the univariate model, the log-normal distribution performed best and tended to describe the nonstationarity in both flood peak and volume sequences reasonably well. In the bivariate model, the optimal log-normal distributions were taken as marginal distributions, and copula functions were addressed to construct the dependence structure of Q and W1. The results showed that the Gumbel-Hougaard copula offered the best joint distribution. The most likely events had an undulating behavior similar to the univariate models, and the combination values of flood peak and volume under the same OR-joint and AND-joint exceedance probability both displayed a decreasing trend. Before 1970, the most likely combination values considering the variation of distribution parameters over time were larger than fixed parameters (stationary), while it became the opposite after 1980. The results highlight the necessity of nonstationary flood frequency analysis. Full article
(This article belongs to the Special Issue Flood Risk and Resilience)
Figures

Figure 1

Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top