Special Issue "Environmental Risk Management"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: 30 September 2022.

Special Issue Editors

Dr. Monica Rivas Casado
E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: unmanned aerial vehicles; monitoring; ecological modelling; freshwater ecosystems; statistics; environmental engineering; robotics and autonomous systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guangtao Fu
E-Mail Website
Guest Editor
Centre for Water Systems, University of Exeter, North Park Road, Exeter, EX4 4QF, UK
Interests: artificial intelligence; water system analysis; flood management; system resilience analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paul Leinster
E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: environmental policy; environmental regulation; sustainability; governance; monitoring; natural capital; ecosystem services; risk assessment; emergency response; systems based approaches; operationalizing research findings

Special Issue Information

Dear Colleagues,

In recent years, there has been an increasing focus on ensuring urban settlements become more resilient to environmental change. The scale and frequency of extreme events affecting urban environments is expected to increase in the future, and their impacts are more difficult to assess due to an increasing number of systems and social–economic factors involved. Current management strategies are limited by lack of understanding of the spatiotemporal interactions between environmental and social processes, ineffective risk communication strategies, conflicts between socioeconomic, environmental and political priorities, risk perception and social behaviour, as well as the rate of technological uptake. Novel and multidisciplinary environmental risk assessment and management strategies need to be developed to effectively address the current and increasing challenges that will affect us in the coming years.

This Special Issue aims to address key gaps in knowledge in environmental risk assessment and management within all aspects of water, including water science, technology and governance. Of particular interest are papers focusing on new methodological approaches to risk management from data collection to communication and papers exploring new approaches to increase the uptake of resilient and resistance measures to mitigate the impacts of extreme events. It will cover a full suite of issues including, but not limited to, environmental risk management strategies that align with the United Nations’ Sustainable Development Goals, strategies to mitigate global and local environmental changes, advances in risk perception theory, multihazard assessment, uncertainty analysis, novel risk assessment methods, use of artificial intelligence and big data analytics.

Dr. Monica Rivas Casado
Prof. Dr. Guangtao Fu
Prof. Dr. Paul Leinster
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 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 2200 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

  • risk management
  • resilience
  • resistance
  • sustainable development goals
  • environmental challenges
  • impact mitigation
  • global change
  • risk perception theory

Published Papers (4 papers)

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Research

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Article
Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning
Water 2021, 13(24), 3520; https://doi.org/10.3390/w13243520 - 09 Dec 2021
Viewed by 423
Abstract
Urban flooding is a devastating natural hazard for cities around the world. Flood risk mapping is a key tool in flood management. However, it is computationally expensive to produce flood risk maps using hydrodynamic models. To this end, this paper investigates the use [...] Read more.
Urban flooding is a devastating natural hazard for cities around the world. Flood risk mapping is a key tool in flood management. However, it is computationally expensive to produce flood risk maps using hydrodynamic models. To this end, this paper investigates the use of machine learning for the assessment of surface water flood risks in urban areas. The factors that are considered in machine learning models include coordinates, elevation, slope gradient, imperviousness, land use, land cover, soil type, substrate, distance to river, distance to road, and normalized difference vegetation index. The machine learning models are tested using the case study of Exeter, UK. The performance of machine learning algorithms, including naïve Bayes, perceptron, artificial neural networks (ANNs), and convolutional neural networks (CNNs), is compared based on a spectrum of indicators, e.g., accuracy, F-beta score, and receiver operating characteristic curve. The results obtained from the case study show that the flood risk maps can be accurately generated by the machine learning models. The performance of models on the 30-year flood event is better than 100-year and 1000-year flood events. The CNNs and ANNs outperform the other machine learning algorithms tested. This study shows that machine learning can help provide rapid flood mapping, and contribute to urban flood risk assessment and management. Full article
(This article belongs to the Special Issue Environmental Risk Management)
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Article
Water Resource Risk Assessment Based on Non-Point Source Pollution
Water 2021, 13(14), 1907; https://doi.org/10.3390/w13141907 - 09 Jul 2021
Viewed by 851
Abstract
As one of the most important causes of water quality deterioration, NPS (non-point source) pollution has become an urgent environmental and livelihood issue. To date, there have been only a few studies focusing on NPS pollution conforming to the estimation, and the pollution [...] Read more.
As one of the most important causes of water quality deterioration, NPS (non-point source) pollution has become an urgent environmental and livelihood issue. To date, there have been only a few studies focusing on NPS pollution conforming to the estimation, and the pollution sources are mainly concentrated in nitrogen and phosphorus nutrients. Unlike studies that only consider the intensity of nitrogen and phosphorus loads, the NPS pollution risk for the China’s Fuxian Lake Basin was evaluated in this study by using IECM (Improve Export Coefficient Model) and RUSLE (Revised Universal Soil Loss Equation) models to estimate nitrogen and phosphorus loads and soil loss and by using a multi-factor NPS pollution risk assessment index established on the basis of the data mentioned above. First, the results showed that the load intensity of nitrogen and phosphorus pollution in the Fuxian Lake Basin is low, so agricultural production and life are important sources of pollution. Second, the soil loss degree of erosion in the Fuxian Lake is mild, so topography is one of the most important factors affecting soil erosion. Third, the risk of NPS pollution in the Fuxian Lake Basin is at a medium level and its spatial distribution characteristics are similar to the intensity characteristics of nitrogen and phosphorus loss. Nitrogen, phosphorus, sediment, and mean concentrations are important factors affecting NPS pollution. These factors involve both natural and man-made environments. Therefore, it is necessary to comprehensively consider the factors affecting NPS in order to assess the NPS risk more accurately, as well as to better solve the problem of ecological pollution of water resources and to allow environmental restoration. Full article
(This article belongs to the Special Issue Environmental Risk Management)
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Article
A Mixed-Methods Investigation into Barriers for Sharing Geospatial and Resilience Flood Data in the UK
Water 2021, 13(9), 1235; https://doi.org/10.3390/w13091235 - 29 Apr 2021
Viewed by 669
Abstract
With increases in average temperature and rainfall predicted, more households are expected to be at risk of flooding in the UK by 2050. Data and technologies are increasingly playing a critical role across public-, private- and third-sector organisations. However, barriers and constraints exist [...] Read more.
With increases in average temperature and rainfall predicted, more households are expected to be at risk of flooding in the UK by 2050. Data and technologies are increasingly playing a critical role across public-, private- and third-sector organisations. However, barriers and constraints exist across organisations and industries that limit the sharing of data. We examine the international context for data sharing and variations between data-rich and data-sparse countries. We find that local politics and organisational structures influence data sharing. We focus on the case study of the UK, and on geospatial and flood resilience data in particular. We use a series of semi-structured interviews to evaluate data sharing limitations, with particular reference to geospatial and flood resilience data. We identify barriers and constraints when sharing data between organisations. We find technological, security, privacy, cultural and commercial barriers across different use cases and data points. Finally, we provide three long-term recommendations to improve the overall accessibility to flood data and enhance outcomes for organisations and communities. Full article
(This article belongs to the Special Issue Environmental Risk Management)
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Review

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Review
Protection Motivation Theory: A Proposed Theoretical Extension and Moving beyond Rationality—The Case of Flooding
Water 2020, 12(7), 1848; https://doi.org/10.3390/w12071848 - 28 Jun 2020
Cited by 9 | Viewed by 2427
Abstract
Despite the significant financial and non-financial costs of household flooding, and the availability of products that can reduce the risk or impact of flooding, relatively few consumers choose to adopt these products. To help explain this, we combine the existing theoretical literature with [...] Read more.
Despite the significant financial and non-financial costs of household flooding, and the availability of products that can reduce the risk or impact of flooding, relatively few consumers choose to adopt these products. To help explain this, we combine the existing theoretical literature with evidence from 20 one-to-one discussions and three workshops with key stakeholders, as well as five round tables, to draw practical evidence of actual responses to flood risk. This analysis leads us to propose an extension to Protection Motivation Theory (PMT), which more accurately captures the decision-making process of consumers by highlighting the role of ‘ownership appraisal’. We then assess the extent to which behavioral biases impact on this revised framework. By highlighting the interaction with an augmented model of PMT and behavioral biases, the paper sheds light on potential reasons behind the fact that consumers are unlikely to adopt property-level flood resilience measures and identifies strategies to increase flood protection. The Augmented PMT suggests that policymakers might focus on increasing the Ownership Appraisal element, both directly and by targeting the creation of more supportive social norms. The work presented here opens up a wide range of areas for future research in the field. Full article
(This article belongs to the Special Issue Environmental Risk Management)
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