Assessment of vulnerabilities to climate change for urban water and wastewater infrastructure management: Case study in Dong Nai river basin, Vietnam

Noi, Lam Vu Thanh; Nitivattananon, Vilas

doi:10.1016/j.envdev.2015.06.014

Cited by 27 publications

(17 citation statements)

References 11 publications

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“…A high proportion of these studies have sought to understand climatic and/or non-climatic drivers and their impacts on water-related systems and to evaluate the performance of management options under these changing conditions. Effects of numerous climatic drivers have been considered, including sea-level rise on water supply management (e.g., [2]), and on water quality management (e.g., [49]); precipitation and temperature on groundwater management (e.g., [63,64,67]), on reservoir management (e.g., [135]), on water supply management (e.g., [3,132]), on water quality management (e.g., [27,43,45] and on nutrient management (e.g., [119,126]) and precipitation on water supply and demand management (e.g., [18,127,130,131]). Similarly, the non-climatic drivers that have been considered, have included effects of population growth on water supply and demand management (e.g., [128]), and on water quality management (e.g., [41]); crop production changes in irrigation system management (e.g., [96,102]); population growth and agricultural production on water supply and demand management (e.g., [128]); agricultural production on irrigation water management (e.g., [93][94][95]100]), on water supply management (e.g., [85]), and on groundwater management (e.g., [67]); changes in domestic use and in agricultural and industrial production on water supplies and demand management (e.g., [127]).…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…Institutional and social measures were found to be the most frequently assessed category of management measure. This may be because this category includes a wide range of measures, such as water pricing (e.g., [18,64,102,130]), compensation payments (e.g., [69]), discharge permits (e.g., [3,28]), educational programs (e.g., [2,67]) and improvements in management practices (e.g., [29,65,100,137]).…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

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Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Phan

Smart

Stewart‐Koster

et al. 2019

Water

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Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes.

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Section: Discussion and Recommendationsmentioning

confidence: 99%

Section: Discussion and Recommendationsmentioning

confidence: 99%

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Phan

Smart

Stewart‐Koster

et al. 2019

Water

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“…Kuikka and Varis (1997) show one of the first applications of this kind by using expert knowledge to assess climate change effects on watershed-level planning. Recently, BBNs have been used to support decision-making in urban infrastructure planning (Noi & Nitivattananon, 2015), irrigation system designs (Batchelor & Cain, 1999;Henriksen & Barlebo, 2008), environmental flow allocations (Chan et al, 2012;Pollino et al, 2007;Stewart-Koster et al, 2010) and sea level rise adaptation (Catenacci et al, 2013), and flood risk reduction (Noi & Nitivattananon, 2015). Some studies discuss the integration of BBNs with other decision-analysis tools and modeling techniques in water planning or similar fields.…”

Section: Bbnsmentioning

confidence: 99%

Incorporating Multidimensional Probabilistic Information Into Robustness‐Based Water Systems Planning

Taner

Ray

Brown

2019

Water Resources Research

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The widespread uncertainty regarding future changes in climate, socioeconomic conditions, and demographics have increased interest in vulnerability‐based frameworks for long‐term planning of water resources. These frameworks shift the focus from projections of future conditions to the weaknesses of the baseline plans and then to options for reductions in those weaknesses across a wide range of futures. A consistent challenge for vulnerability‐based planning is how to assess the relative likelihood of the occurrence of the multidimensional and codependent uncertainties to which the system or plan is vulnerable. This work proposes a methodological solution to the problem, demonstrated in this case as an extension to Decision Scaling framework. The proposed approach first generates a wide range of futures using stochastic simulators, and then stress tests the system across those futures to identify vulnerabilities relative to stakeholder‐defined performance thresholds. The relative likelihood of the vulnerabilities is then explored using a Bayesian belief network of the knowledge domain of the water resources system. The Bayesian network provides a formal representation of the joint probabilistic behavior of the system conditioned on the uncertain but potentially useful sources of information about the future, including historical trends, expert judgments, and model‐based projections. The proposed approach is applied to compare four design options for a dam project in the Coastal Province of Kenya with respect to the reliability and net present value metrics. Results show that incorporation of belief information helps better distinguishing of the available options, principally by magnifying the differences between the computed net present values.

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“…A corroded pipe can cause wastewater, sometimes untreated, to be diverted into nearby fields or bodies of water [25]. In addition, groundwater inundation leads to groundwater elevation and exerts uplift forces on buried water infrastructure [26]. Furthermore, land subsidence causes pressures on buried pipelines and utilities and also changes their gradient driving sewer flow.…”

Section: Slr Impacts On Water and Wastewater Infrastructurementioning

confidence: 99%

Resilience of Infrastructure Systems to Sea-Level Rise in Coastal Areas: Impacts, Adaptation Measures, and Implementation Challenges

Almeida

Mostafavi

2016

Sustainability

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Expansive areas of low elevation in many densely populated coastal areas are at elevated risk of storm surges and flooding due to torrential precipitation, as a result of sea level rise. These phenomena could have catastrophic impacts on coastal communities and result in the destruction of critical infrastructure, disruption of economic activities and salt water contamination of the water supply. The objective of the study presented in this paper was to identify various impacts of sea level rise on civil infrastructures in coastal areas and examine the adaptation measures suggested in the existing literature. To this end, a systemic review of the existing literature was conducted in order to identify a repository of studies addressing sea level rise impacts and adaptation measures in the context of infrastructure systems. The study focused on three infrastructure sectors: water and wastewater, energy, and road transportation. The collected information was then analyzed in order to identify different categories of sea level rise impacts and corresponding adaptation measures. The findings of the study are threefold: (1) the major categories of sea level rise impacts on different infrastructure systems; (2) measures for protection, accommodation, and retreat in response to sea level rise impacts; and (3) challenges related to implementing adaptation measures.

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Assessment of vulnerabilities to climate change for urban water and wastewater infrastructure management: Case study in Dong Nai river basin, Vietnam

Cited by 27 publications

References 11 publications

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Incorporating Multidimensional Probabilistic Information Into Robustness‐Based Water Systems Planning

Resilience of Infrastructure Systems to Sea-Level Rise in Coastal Areas: Impacts, Adaptation Measures, and Implementation Challenges

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