Integrated Direct and Indirect Flood Risk Modeling: Development and Sensitivity Analysis

Abstract

In this article, we propose an integrated direct and indirect flood risk model for small- and large-scale flood events, allowing for dynamic modeling of total economic losses from a flood event to a full economic recovery. A novel approach is taken that translates direct losses of both capital and labor into production losses using the Cobb-Douglas production function, aiming at improved consistency in loss accounting. The recovery of the economy is modeled using a hybrid input-output model and applied to the port region of Rotterdam, using six different flood events (1/10 up to 1/10,000). This procedure allows gaining a better insight regarding the consequences of both high- and low-probability floods. The results show that in terms of expected annual damage, direct losses remain more substantial relative to the indirect losses (approximately 50% larger), but for low-probability events the indirect losses outweigh the direct losses. Furthermore, we explored parameter uncertainty using a global sensitivity analysis, and varied critical assumptions in the modeling framework related to, among others, flood duration and labor recovery, using a scenario approach. Our findings have two important implications for disaster modelers and practitioners. First, high-probability events are qualitatively different from low-probability events in terms of the scale of damages and full recovery period. Second, there are substantial differences in parameter influence between high-probability and low-probability flood modeling. These findings suggest that a detailed approach is required when assessing the flood risk for a specific region.

Regional disaster impact analysis: comparing input–output and computable general equilibrium models

“…In response, a large number as well as a variety of models have been applied to study the economic impacts of disasters. While the most common economic models for disaster impact analysis are input-output (IO) and computable general equilibrium (CGE) models, an increasing number of scholars employ hybrid models, combining the two or either of them with different (partly noneconomic) models (Baghersad and Zobel, 2015;Carrera et al, 2015;Koks et al, 2015). This wide variety of models, however, leads to an important question: how should (differences in) the outcomes of the models be interpreted?…”

“…Following this, the reconstruction demand is determined from the direct economic damage (and considered as additional final demand). This enables an assessment of the required production available to satisfy the final and reconstruction demand (Koks et al, 2015). Subsequently, the maximum possible production capacity and the required total production are compared to identify the production available for reconstruction, final demand, and export.…”

“…The model is adjusted in such a way that it cannot be directly described anymore as an IO or a CGE model as such. Examples are Hallegatte (2008), Carrera et al (2015), and Koks et al (2015). regions.…”

“…CGE modelling characteristics) or a combination of either of them with another type of model. Koks et al (2015) have coupled an IO model with a biophysical model to improve the accuracy of modelled economic disruption. Carrera et al (2015) and Ciscar et al (2014) have coupled a CGE model with a biophysical model.…”

“…To avoid a possible underestimation of the production losses, the assumption of constant returns to scale is essential (see Koks et al, 2015, for an extensive explanation). In standard input-output modelling, capital and labour belong to the value-added part of the model.…”