Quantifying resilience to flooding among households and local government units using system dynamics: a case study in Metro Manila

Gotangco, Charlotte Kendra Z; See, Justin; Dalupang, J P; Ortiz, Andrea Monica D.; Porio, Emma; Narisma, Gemma; Yulo-Loyzaga, Antonia; Dator-Bercilla, Jessica

doi:10.1111/jfr3.12222

Cited by 20 publications

(14 citation statements)

References 20 publications

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“…Conversely, according to the approach to the social-economic resilience, attention is focused on social systems and resilience is measured as the capability of communities to recover a good life quality level. These methods are mainly proposed in social sciences community [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41].…”

Section: Novel Understandings Around Resilience Quantificationmentioning

confidence: 99%

Urban Resilience: A Civil Engineering Perspective

2017

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Abstract:The concept of resilience is used in multiple scientific contexts, being understood according to several different perspectives. Essentially, resilience identifies the capability to recover, absorb shocks, and restore equilibrium after a perturbation. Recently, resilience is triggering increasing interest in engineering contexts, referring to communities and urban networked systems, as the capability to recover from natural disasters. The approach to the engineering resilience dates back to the early 1980s, when Timmerman defined resilience as "the ability of human communities to withstand external shocks or perturbations to their infrastructure and to recover from such perturbations". In this paper, a literature review of the existing methodologies to quantify urban resilience is presented according to a civil engineering perspective. Different approaches, for diverse applications, are examined and discussed. A particular focus is done on the studies from Cavallaro et al. and Bozza et al., approaching disaster resilience of urban environments to natural hazards according to the complex networks theory.

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Section: Novel Understandings Around Resilience Quantificationmentioning

confidence: 99%

Urban Resilience: A Civil Engineering Perspective

2017

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“…These methods allow for an explicit description of system processes, enabling the user to obtain concrete, replicable data related to the specific vulnerabilities and adaptive capacities of individual variables within a system. Several authors have explored the concept of quantifiable resilience characteristics through the application of system dynamics (SD) models (e.g., Simonovic and Peck 2013, Candy et al 2015, Gotangco et al 2016, Herrerra 2017, Herrera and Kopainsky 2020 and physically based models (e.g., Fowler et al 2003, Cox et al 2011, Miller-Hooks et al 2012). However, it can be argued that the dynamic nature of complex socioenvironmental systems is most reliably represented using a coupled physical-SD modeling approach because coupled models are able to incorporate the concrete nature of physical data modeling with the connectivity and feedback flow of SD models.…”

Section: Modeling Resiliencementioning

confidence: 99%

Quantifying the transient shock response of dynamic agroecosystem variables for improved socio-environmental resilience

Carper¹,

Alizadeh²,

Adamowski³

et al. 2021

E&S

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In classic resilience thinking, there is an implicit focus on controlling functional variation to maintain system stability. Modern approaches to resilience thinking deal with complex, adaptive system dynamics and true uncertainty; these contemporary frameworks involve the process of learning to live with change and make use of the consequences of transformation and development. In a socio-environmental context, the identification of metrics by which resilience can be effectively and reliably measured is fundamental to understanding the unique vulnerabilities that characterize coupled human and natural systems. We developed an innovative procedure for stakeholder-friendly quantification of socio-environmental resilience metrics. These metrics were calculated and analyzed through the application of discrete disturbance simulations, which were produced using a dynamically coupled, biophysical-socioeconomic modeling framework. Following the development of a unique shock-response assessment regime, five metrics (time to baseline-level recovery, rate of return to baseline, degree of return to baseline, overall post-disturbance perturbation, and corrective impact of disturbance) describing distinct aspects of systemic resilience were quantified for three agroecosystem variables (farm income, watertable depth, and crop revenue) over a period of 30 years in the Rechna Doab basin of northeastern Pakistan. Using this procedure, we determined that farm income is the least resilient variable of the three tested. Farm income was easily diverted from the "normal" functional paradigm for the Rechna Doab socio-environmental system, regardless of shock type, intensity, or duration combination. Crop revenue was the least stable variable (i.e., outputs fluctuated significantly between very high and very low values). Water-table depth was consistently the most robust and resistant to change, even under physical shock conditions. The procedure developed here should improve the ease with which stakeholders are able to conduct quantitative resilience analyses.

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“…According to Kelly et al [87], five modelling approaches are most commonly used for integrated environmental assessment and management. Each of these approaches has been used for modelling complex systems: system dynamics [88,89], Bayesian networks [90,91], coupled component models [92], agent-based models [93,94], and knowledgebased models [95]. In additional to the summary of these approaches (see Tables 1 and 3 in [87]), Kelly et al [87] provided a heuristic for selection of one of these approaches under standard application (see Figure 1 in [87]).…”

Section: Simulation Modellingmentioning

confidence: 99%

A Resilience Toolbox and Research Design for Black Sky Hazards to Power Grids

Borisoglebsky

Varga

2019

Complexity

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A structured collection of tools for engineering resilience and a research approach to improve the resilience of a power grid are described in this paper. The collection is organized by a two-dimensional array formed from typologies of power grid components and business processes. These two dimensions provide physical and operational outlooks, respectively, for a power grid. The approach for resilience research is based on building a simulation model of a power grid which utilizes a resilience assessment equation to assess baseline resilience to a hazards’ profile, then iteratively selects a subset of tools from the collection, and introduces these as interventions in the power grid simulation model. Calculating the difference in resilience associated with each subset supports multicriteria decision-making to find the most convenient subset of interventions for a power grid and hazards’ profile. Resilience is an emergent quality of a power grid system, and therefore resilience research and interventions must be system-driven. This paper outlines further research required prior to the practical application of this approach.

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Quantifying resilience to flooding among households and local government units using system dynamics: a case study in Metro Manila

Cited by 20 publications

References 20 publications

Urban Resilience: A Civil Engineering Perspective

Urban Resilience: A Civil Engineering Perspective

Quantifying the transient shock response of dynamic agroecosystem variables for improved socio-environmental resilience

A Resilience Toolbox and Research Design for Black Sky Hazards to Power Grids

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