Flow-tub model: A modified bathtub flood model with hydraulic connectivity and path-based attenuation

Global climate change and sea level rise are increasing the risks of flooding for coastal communities. Probabilistic coastal flood risk analysis at regional or global scales requires flood models with relatively low data requirements and low computational costs. Bathtub inundation models, which compute flood depth as the difference between water level and ground elevation, are well-suited for large-scale flood risk analysis. However, these models may overestimate floods because they do not capture some of the relevant underlying hydrodynamic processes that govern flood propagation on land. We present Flow-Tub, a modified bathtub inundation model that integrates two hydrodynamic processes to improve the accuracy of the bathtub inundation model while retaining computational efficiency: hydraulic connectivity and path-based attenuation.1.Hydraulic connectivity ensures that inundation is restricted to areas connected to the water source.2.Path-based attenuation ensures that the modeled flood water depths are reduced along the flow paths to represent the effects of surface friction and the temporary nature of storm surges. We validate the Flow-tub model against a hydrodynamic model. We also compare results of the bathtub model and the Flow-Tub model, highlighting the improved accuracy in the estimation of flood depths in the latter.

Order Matters: The Benefits of Ordinal Fragility Curves for Damage and Loss Estimation

Probabilistic loss assessments from natural hazards require the quantification of structural vulnerability. Building damage data can be used to estimate fragility curves to obtain realistic descriptions of the relationship between a hazard intensity measure and the probability of exceeding certain damage grades. Fragility curves based on the lognormal cumulative distribution function are popular because of their empirical performance as well as theoretical properties. When we are interested in estimating exceedance probabilities for multiple damage grades, these are usually derived per damage grade via separate probit regressions. However, they can also be obtained simultaneously through an ordinal model which treats the damage grades as ordered and related instead of nominal and distinct. When we use nominal models, a collapse fragility curve is constructed by treating data of "near-collapse" and "no damage" the same: as data of noncollapse. This leads to a loss of information. Using synthetic data as well as real-life data from the 2015 Nepal earthquake, we provide one of the first formal demonstrations of multiple advantages of the ordinal model over the nominal approach. We show that modeling the ordering of damage grades explicitly through an ordinal model leads to higher sensitivity to the data, parsimony and a lower risk of overfitting, noncrossing fragility curves, and lower associated uncertainty.

Filling the Disaster Data Gap: Lessons from Cataloging Singapore’s Past Disasters

International disaster databases and catalogs provide a baseline for researchers, governments, communities, and organizations to understand the risk of a particular place, analyze broader trends in disaster risk, and justify investments in mitigation. Perhaps because Singapore is routinely identified as one of the safest countries in the world, Singapore’s past disasters have not been studied extensively with few events captured in major global databases such as EM-DAT. In this article, we fill the disaster data gap for postwar Singapore (1950–2020) using specified metrics through an archival search, review of literature, and analysis of secondary sources. We present four key lessons from cataloging these events. First, we expand Singapore’s disaster catalog to 39 events in this time period and quantify the extent of this data gap. Second, we identify the mitigating actions that have followed past events that contribute to Singapore’s present-day safety. Third, we discuss how these past events uncover continuities among vulnerability bearers in Singapore. Last, we identify limitations of a disaster catalog when considering future risks. In expanding the disaster catalog, this case study of Singapore supports the need for comprehensive understanding of past disasters in order to examine current and future disaster resilience.