Assessing resilience against floods with a system dynamics approach: a comparative study of two models

Abstract

Purpose This paper aims to present the concepts of two different ways of generating a dynamic structure of the urban system to further allow in understanding specific urban behavior facing against flood and further evaluate the potential effect of specific resilience strategies aiming to decrease the exposure and vulnerability of the system. Design/methodology/approach Two system dynamics model structures are presented in form of Casual Loop Diagrams. Findings The main differences among the tow approaches are the time horizon and the approach that regulates the assessment of the resilience through a dynamic composite indicator: the first model refers to baseline at initial simulation time; the second model is focused on the ratio service supply to demand. Research limitations/implications Within the approach, the purpose is to properly and efficiently evaluate the effect of different Flood Risk Management strategies, i.e. prevention, defence, mitigation, preparation and recovery for consistent and resilient flood governance plans with different type of resilience scenarios. Originality/value The need for such tool is underlined by a lack on the assessment of urban resilience to flood as whole, considering the physical and social dimensions and the complex interaction among their main components. There are several assessment tools based on an indicator approach that have been proposed to meet this need. Nevertheless, indicator-based approach has the limitation to exclude the complexity of the system and its systemic interaction in terms of feedbacks’ effects among the identified components or variables selected for the system description. This peculiarity can be provided by System Dynamics modeling.

Urban Resilience Assessment: Mapping Knowledge Structure and Trends

“…In addition, for the first time, keywords with citation bursts were observed (Figure 6). Some of these are directly related to climate change (for example, "climate change adaptation" and "flood resilience"), confirming that urban resilience assessment has paid significant attention to this issue over the past five years [26,46,47]. Keywords related to flood resilience have the strongest burst, confirming the findings of the term co-occurrence analysis With more research being published during the growth period (2010-2014), the intellectual base also expanded and terms with high centrality values emerged.…”

“…Some of these are directly related to climate change (for example, "climate change adaptation" and "flood resilience"), confirming that urban resilience assessment has paid significant attention to this issue over the past five years [26,46,47]. Keywords related to flood resilience have the strongest burst, confirming the findings of the term co-occurrence analysis regarding the relatively high attention to resilience against water related risks [26,46,47]. Other keywords with high citation burst values are related to climate change adaptation and resilience of infrastructure systems [48,49].…”

Assessment of Urban Resilience to Natural Disasters with a System Dynamics Tool: Case Study of Latvian Municipality

“…Based on the adopted definition of urban resilience, indicators were selected for four distinguished dimensions of urban areas: social, economic, infrastructure and environmental. The given classification of urban area in four dimensions was found to be comprehensive to distinguish the main processes taking place in urban areas in r espect to socio-ecological and socio-technological contexts, as presented by concept of urban resilience SD model in a comparative study of two models [31].…”

Modeling the resilience of urban mobility when exposed to the COVID-19 pandemic: A qualitative system dynamics approach

“…One of the most used models to evaluate urban mobility resilience is equivalent to the indicator-based one. Nevertheless, it has the drawback of excluding the system's complexity and its systemic interaction regarding feedback effects among the system's components ( Feofilovs et al, 2020 ). Hence, assessing the resilience of urban mobility as a complex system using SD modeling can bring substantial advantages and contributions to the area.…”