the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying hazards resilience by modeling infrastructure recovery as a resource constrained project scheduling problem
Taylor Glen Johnson
Jorge Leandro
Divine Kwaku Ahadzie
Abstract. Reliance on infrastructure by individuals, businesses, and institutions creates additional vulnerabilities to the disruptions posed by natural hazards. In order to assess the impacts of natural hazards on the performance of infrastructure, a framework for quantifying resilience is presented. This framework expands upon prior work in the literature to improve the comparability of the resilience metric by proposing a standardized assessment period. With recovery a central component of assessing resilience, especially in cases of extreme hazards, we develop a recovery model based upon an application of the resource constrained project scheduling problem (RCPSP). This recovery model offers the opportunity to assess flood resilience across different events and also, theoretically, between different study areas. The resilience framework and recovery model have been applied in a case study to assess the resilience of buildings infrastructure to flooding hazards in Alajo, a neighborhood in Accra, Ghana. The results show that for the three flood events investigated (5, 50 and 500-year return periods), the 300-day resilience of the buildings infrastructure in Alajo was quantified as 0.94, 0.82 and 0.69, respectively. In practical terms, each value reflects the ability or inability of the system to maintain its function during the reference period for the given flood event, with zero corresponding to a complete loss of function and one when unaffected. This information is valuable for identifying the vulnerabilities of buildings infrastructure, assessing the impacts resulting in reduced performance, coordinating responses to flooding events, and preparing for the subsequent recovery.
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Taylor Glen Johnson et al.
Status: open (extended)
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RC1: 'Comment on egusphere-2023-1511', Anonymous Referee #1, 31 Jul 2023
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Summary
The paper presents a new approach to model and quantify infrastructure resilience to flooding. The paper uses an ecological conceptualisation of resilience defined as the ability of a system to maintain functionality during a shock event and to recover to a state of equilibrium in a timely manner. The study focus primarily focusses on measuring the time it takes a system to recover using a constrained project scheduling problem (RCPSP) model. This approach breaks the recovery process down into individual steps that are needed to repair or replace infrastructure after a shock event. Identifying the length of each step and whether the step is dependent on a previous one, the model estimates either the total recovery time or to what degree a system has recovered for pre-defined time steps (e.g. 300 days) for hazard events of different severities. This conceptual model is then applied to a neighbourhood in Accra, Ghana to estimate their resilience, i.e. the time to recover for statistical flood events with return periods of 5, 50 and 500 years. The results are presented as both the mean recovery time in days as well as the mean recovery state in percent 300 days after the event.
General comments
The paper addresses the very relevant question on how to measure and quantify the resilience of a system or community after a shock event. The study clearly defines what resilience means in this context and makes the argument that more complex conceptualisations of resilience might better reflect reality but are too complex to model and therefore often become a road block in operationalising resilience in disaster risk management planning. The paper is generally well written and structured. My two main points of criticism are:
- The authors describe in line 95ff that agent-based models have been used to model resilience, but have been criticized for their lack of transparency, difficulties to assess and little consensus about model complexity. While the authors argue that their model is an improvement to previous approaches, I struggle to see how the disadvantages of ABMs mentioned do not apply to RCPSP. From reading the manuscript my understanding of RCPSP is that it is very similar to an ABM in terms of specific assumptions of how different building classes are damaged and how specific tasks during the recovery process are carried out. I would argue that this creates the same challenges as using ABM as there is also little consensus about how these tasks are carried out, whether it would be affected by local supply chain issues etc. While I can see the author’s point that the model itself is more linear and therefore more transparent, my understanding is that it is still based on a large number of rules and values that are largely based on assumptions or best guesses. Would be great if the authors could discuss how their model and results compare to other approaches modelling resilience.
- It would be great if the authors could say a bit more about how decision makers and planners can use the outcomes of their model. Especially in regard to the 300-day resilience: what does a value of 0.69 mean for planning and decision making? In my view this information is only useful if the outcomes would be presented alongside specific thresholds above which the building is functional/inhabitable again (with restrictions). My impression is that this would be possible to include into the scenarios and could be helpful when planning for shelters, temporary accommodation etc. as it would mark the point when people and businesses can move back.
Specific comments
L9ff: I am not sure how useful the numbers are in the abstract as they are not very intuitive to interpret. I would recommend to replace this with a more qualitative statement about what the results mean.
L265: How do you come up with 300 days as a suitable assessment period. It seems a bit arbitrary and probably also dependent on the magnitude and size of the event, whether this is a sensible value to present. Was wondering how this metric is more useful than simply reporting the average number of days until full recovery. As mentioned in the general comments I am wondering how discretising the days it takes to recover is useful for decision making.
L284f: Would be good if the authors could mention what the metric for the benchmark performance is.
L302: A threshold of 100sqm seems large for footprints of informal buildings. Is there a reason for the threshold being this high?
L328ff: Would be could to briefly describe how the damage functions were developed and for which region instead of only referring to another paper.
Citation: https://doi.org/10.5194/egusphere-2023-1511-RC1
Taylor Glen Johnson et al.
Taylor Glen Johnson et al.
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