Estimating resilience for water resources systems

Abstract

[1] Resilience characterizes the recovery capacity of repairable systems from the failure state to the safe state. Resilience has been recognized as a meaningful probabilistic indicator for evaluating risk-cost trade-offs in water resources systems. Traditionally, the resilience in the discrete time domain is estimated by sampling methods, which have a high computational expense. No single approximation approach has been well developed for estimating resilience, even under stationary conditions. This paper proposes two practical approximation methods for estimating the lag-1 resilience in the discrete time domain. Both methods are theoretical developments, one based on a bivariate normal distribution, and the other based on a stochastic linear prediction of the performance function using the mean point of the failure domain. The foundations of both methods are the first-order reliability method and the periodic vector autoregressive moving-average time series model. The methods are robust for a wide range of problem characteristics and are applicable for systems facing stationary or nonstationary input conditions.

Urban Resilience: A Civil Engineering Perspective

“…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].…”

Identification of resilience factors, variables and indicators for sustainable management of urban drainage systems

“…This factor evaluates the capacity to recover and is associated with the time of failure, reliability and recovery speed of a system from the perspective of risk management. Flexibility has been quantified through semi-quantitative indices that measure the capacity to provide a service [20], mathematical functions that quantify the probability of system failure [23][24][25][26][27]29,32], system reliability [30,31] and indicators based on performance curves of a system that provide information on its behaviour before and after a disturbance [17,18,28,33,34].…”

“…The indicators proposed for this variable are the failure index, which quantifies the probability of system failure [23][24][25][26][27]29]; the gradualness, which measures the change in the response of a system with respect to the change of magnitude in a flood surge [17,18]; the recovery duration, which quantifies the time it takes for a system to recover from an unsatisfactory condition [17]; the recovery rate, which measures the recovery rate of the system after a flood [18]; the recovery loss, which quantifies the loss of quality in a system [28]; the environmental load capacity, which quantifies the amount of pollutant emissions that a system can endure [32]; and the recovery indicator, which measures the recovery time from a flood at each node of the system [33,34].…”

A Systematic Review of Quantitative Resilience Measures for Water Infrastructure Systems

“…Furthermore, previous measures typically have one or more of several drawbacks: (1) inconsistent and theoretically deficient approaches to defining resilience; (2) not adaptable for different types of water infrastructures; (3) dependency on parameter estimation assumptions; (4) substantial and intensive computational efforts when applying to complex real systems; and (5) insufficient information to guide decision making [17][18][19]28].…”

“…Over the past few years, attention is shifting more toward resilience-based strategies such as mitigation and recovery options, which make critical infrastructure systems (including water systems) more adaptively reliable [17][18][19][20]. The term resilience means to "bounce back" from a disruption and has been defined in various disciplines for their own targets [21].…”