Rescue requests during large-scale urban flood disasters can be difficult to validate and prioritise. High-resolution aerial imagery is often unavailable or lacks the necessary geographic extent, making it difficult to obtain real-time information about where flooding is occurring. In this paper, we present a novel approach to map the extent of urban flooding in Harris County, Texas during Hurricane Harvey (August 25–30, 2017) and identify where people were most likely to need immediate emergency assistance. Using Maximum Entropy, we predict the probability of flooding based on several spatially-distributed physical and socio-economic characteristics coupled with crowdsourced data. We compare the results against two alternative flood datasets available after Hurricane Harvey (i.e., Copernicus satellite imagery and riverine flood depths estimated by FEMA), and we validate the performance of the model using a 15% subset of the rescue requests, Houston 311 flood calls, and inundated roadways. We find that the model predicts a much larger area of flooding than was shown by either Copernicus or FEMA when compared against the locations of rescue requests, and that it performs well using both a subset of rescue requests (AUC 0.917) and 311 calls (AUC 0.929) but is less sensitive to inundated roads (AUC 0.721).

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Large, complex coastal regions often require a combination of interventions to lower the risk of flooding to an acceptable level. In practice, a limited number of strategies are considered and interdependencies between interventions are often simplified. This paper presents the Multiple Lines of Defence Optimization System (MODOS)‐model. This quick, probabilistic model simulates and evaluates the impact of many flood risk reduction strategies while accounting for interdependencies amongst measures. The simulation includes hydraulic calculations, damage calculations, and the effects of measures for various return periods. The application and potential of this model is shown with a conceptual and simplified case study, based on the Houston‐Galveston Bay area. The analyses demonstrate how the MODOS‐model identifies trade‐offs within the system and shows how flood risk, cost, and impact respond to flood management decisions. This improved understanding of the impact of design and planning choices can benefit the discussions in finding the optimal flood risk reduction strategy for coastal regions.@en

The Galveston Bay Area is a densely populated area which is highly prone to flooding. In 2008, Hurricane Ike hit with a combination of storm surge, wind and rain. This led to an estimated 25 billion dollars in damage and nearly a hundred deaths. As a response to Hurricane Ike, several institutions have explored strategies that would protect the region from coastal flooding. Because of the complex situation, discussions on the issue have not yet led to clear or definitive conclusions. Few flood risk reduction strategies have been comprehensively assessed with impact analyses and cost estimates. More measures have been proposed, but their effectiveness cannot be evaluated without a costly investigation. In these analyses, it is often concluded that no single flood risk reduction measure can solve the bay-wide problems on its own. This would mean that research is needed on how flood risk reduction measures act in combination with each other. Moreover, non-structural measures (e.g. Nature-based Solutions, policy measures like land use zones) are increasingly implemented. The design process and discussions would greatly benefit from an increased understanding of the effects of all the proposed flood risk reduction strategies in terms of aspects such as risk reduction, costs and environmental impacts. This report will show and explain the development of a risk-based optimization model, aimed especially to fill that knowledge gap. The goal is to provide more insight in the Galveston Bay Area and the effects of design choices on the flood risk management system.@en