Modeling dynamic resilience in coupled technological-social systems subjected to stochastic disturbance regimes

Klammler, Harald; Rao, P. Suresh C.; Hatfield, Kirk

doi:10.1007/s10669-017-9649-2

Cited by 16 publications

(33 citation statements)

References 49 publications

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“…We use the scaled parameters and normalized equations, so that the model is nondimensional (normalized to unit replenishment rate in adaptive capacity). The coupled temporal dynamics of these dimensionless state variables is written as (Klammler et al, ) follows:

\frac{dΔ}{normald t} = ((), 1 - normalΔ) b - a MΔ + normalξ

\frac{normald M}{normald t} = ((), 1 - c_{1} normalΔ) M ((), 1 - M) - normalr \frac{M^{n}}{β^{n} + M^{n}} - c_{2} ξ

…”

Section: Methodsmentioning

confidence: 99%

“…Dimensionless time ( t ) is scaled proportional to unit replenishment rate in service management ( t = t real r RF ; see (Klammler et al, ) for further details on normalization of equations and ). This means that t represents varying lengths of real time, depending on each type of city and the magnitude of shock impacts, and can be in the order of days or weeks (or less for resilient and water secure cities) or months to years (or even decades for nonresilient and transitional cities).…”

Section: Methodsmentioning

confidence: 99%

“…Adjustment by RP public indicates a city's ability to buffer shocks: According to Rodriguez‐Iturbe et al (), censoring (buffering) of shocks does not change shock magnitude but results in a lower frequency of shocks. We apply this logic as suggested by Klammler et al () by censoring shocks proportional to RP public . Acute shock frequencies are assumed to occur an order of magnitude less frequently:

λ_{acute} = \frac{acute shock score}{\sum acute shocks * 10} {(1 + {italicRP}_{public})}^{- 1}

…”

Section: Methodsmentioning

confidence: 99%

“…Services are aggregated across areas of high and low (or no) services, to reflect the reality of many urban areas worldwide (see Krueger et al, for details). The model framework of Klammler et al () is parameterized based on the CPA framework presented in Krueger et al (). Results by the former showed the existence of multiple stable states in the interaction of infrastructure services and adaptive management, as well as contingency of services on stochastic shock regimes.…”

Section: Introductionmentioning

confidence: 99%

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Resilience Dynamics of Urban Water Supply Security and Potential of Tipping Points

et al. 2019

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Cities are the drivers of socioeconomic innovation and are also forced to address the accelerating risk of failure in providing essential services such as water supply today and in the future. Here, we investigate the resilience of urban water supply security, which is defined in terms of the services that citizens receive. The resilience of services is determined by the availability and robustness of critical system elements or “capitals” (water resources, infrastructure, finances, management efficacy, and community adaptation). We translate quantitative information about this portfolio of capitals from seven contrasting cities on four continents into parameters of a coupled system dynamics model. Water services are disrupted by recurring stochastic shocks, and we simulate the dynamics of impact and recovery cycles. Resilience emerges under various constraints, expressed in terms of each city's capital portfolio. Systematic assessment of the parameter space produces the urban water resilience landscape, and we determine the position of each city along a continuous gradient from water insecure and nonresilient to secure and resilient systems. In several cities stochastic disturbance regimes challenge steady‐state conditions and drive system collapse. While water insecure and nonresilient cities risk being pushed into a poverty trap, cities which have developed excess capitals risk being trapped in rigidity and crossing a tipping point from high to low services and collapse. Where public services are insufficient, community adaptation improves water security and resilience to varying degrees. Our results highlight the need for resilience thinking in the governance of urban water systems under global change pressures.

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\frac{dΔ}{normald t} = ((), 1 - normalΔ) b - a MΔ + normalξ

\frac{normald M}{normald t} = ((), 1 - c_{1} normalΔ) M ((), 1 - M) - normalr \frac{M^{n}}{β^{n} + M^{n}} - c_{2} ξ

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

λ_{acute} = \frac{acute shock score}{\sum acute shocks * 10} {(1 + {italicRP}_{public})}^{- 1}

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resilience Dynamics of Urban Water Supply Security and Potential of Tipping Points

et al. 2019

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“…This paper highlights the important connection between resilience and finance. Second, Klammler et al (2018) describe a modeling approach for quantifying the resilience of coupled technological systems. The authors define and characterize two metrics under a regime of stochastic disruptions: critical service deficit and adaptive capacity.…”

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confidence: 99%

Resilience, sustainability, and complexity in social, environmental, and technical systems

2018

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Mobile phone data reveals the importance of pre-disaster inter-city social ties for recovery after Hurricane Maria

Yabe

Rao

2019

Appl Netw Sci

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Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the recovery process of cities, where inter-city dependencies and intra-city coupling of social and physical systems may affect the outcomes in unforeseen ways. Despite the large implications of understanding the recovery processes of cities after disasters for many domains including critical services, disaster management, and public health, little work have been performed to unravel this complexity. Rather, works are limited to analyzing and modeling cities as independent entities, and have largely neglected the effect that inter-city connectivity may have on the recovery of each city. Large scale mobility data (e.g. mobile phone data, social media data) have enabled us to observe human mobility patterns within and across cities with high spatial and temporal granularity. In this paper, we investigate how inter-city dependencies in both physical as well as social forms contribute to the recovery performances of cities after disasters, through a case study of the population recovery patterns of 78 Puerto Rican counties after Hurricane Maria using mobile phone location data. Various network metrics are used to quantify the types of inter-city dependencies that play an important role for effective post-disaster recovery. We find that inter-city social connectivity, which is measured by pre-disaster mobility patterns, is crucial for quicker recovery after Hurricane Maria. More specifically, counties that had more influx and outflux of people prior to the hurricane, were able to recover faster. Our findings highlight the importance of fostering the social connectivity between cities to prepare effectively for future disasters. This paper introduces a new perspective in the community resilience literature, where we take into account the inter-city dependencies in the recovery process rather than analyzing each community as independent entities.

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Modeling dynamic resilience in coupled technological-social systems subjected to stochastic disturbance regimes

Cited by 16 publications

References 49 publications

Resilience Dynamics of Urban Water Supply Security and Potential of Tipping Points

Resilience Dynamics of Urban Water Supply Security and Potential of Tipping Points

Resilience, sustainability, and complexity in social, environmental, and technical systems

Mobile phone data reveals the importance of pre-disaster inter-city social ties for recovery after Hurricane Maria

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