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

TLDR

The authors propose an integrated direct‑and‑indirect flood‑risk model that dynamically estimates total economic losses from a flood event through full recovery. The model translates direct capital and labor losses into production losses via a Cobb‑Douglas function, models economic recovery with a hybrid input‑output framework applied to Rotterdam’s port region across six flood scenarios, and conducts a global sensitivity analysis on key assumptions such as flood duration and labor recovery. The analysis shows that while direct losses are generally larger in expected annual damage, indirect losses dominate for low‑probability events, and that high‑ versus low‑probability floods differ markedly in damage scale, recovery time, and parameter sensitivity, underscoring the need for detailed, region‑specific risk assessments.

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.