Comparative Urban Pluvial Flood Risk Assessment: A Globally Applicable Workflow for Data-Scarce Environments

Abstract

In this study we propose a new methodology for pluvial flood risk estimation, combining stochastic rainfall modelling, climate projection based adaptations of the rainfall frequency-intensity relations and DEM data sets, along with hydrodynamic modelling. New global precipitation datasets, such as CMORPH, GSMaP or MERRA2 offer an affordable and accessible solution for water resource and water-hazard risk management in data-scarce regions and enable comprehensive global comparative studies. However, these datasets, often derived from satellite observations and coarse-scale climate modelling, consistently underestimate short-duration, high-intensity rainfall events, particularly those lasting one hour or less, that belong to the tails of the distributions (i.e., return levels higher than 30-year). This underestimation goes beyond spatial scale considerations, commonly addressed by areal reduction factors. Consequently, utilizing these global datasets for pluvial flood risk analysis results in conservative flood risk estimates. The availability of global terrain models and mapped man-made structures like buildings, channels, and roads enables the generation of wide-coverage digital surface models. These can be used for flood inundation modelling in combination with corrected extremes of the global precipitation data sets, allowing near-global rough flood risk estimates. In this study, we introduce a methodology for estimating pluvial flood risk using openly available global datasets. To achieve this, we derive hourly-scale Intensity-Duration-Frequency (IDF) curves suitable for pluvial flood inundation modeling in ungauged areas using global precipitation datasets. The first step uses high temporal resolution satellite remote sensing rainfall data (GSMaP) to train a stochastic rainfall generator model - the point process Bartlet-Lewis model. Subsequently, the weather generator is used to disaggregate daily global precipitation data (GPCC) through stochastic ensemble simulation. The resulting disaggregated ensemble data is then utilized to generate more accurate IDF curves including uncertainty, forming the basis for pluvial flooding risk assessments. Our approach integrates the openly available FabDEM terrain model with OpenStreetMap to generate digital surface models for flood risk modeling analysis. Discrepancies in flood inundation risk estimates in urban environments, attributable to underestimated rainfall intensity, are demonstrated using CADDIES, a 2-dimensional hydrodynamic model. The workflow allows the IDF curves for the current climate to be adapted based on climate model projections of temperatures using the Clausius–Clapeyron relation, and to study their impact on future flood risk. A comparative risk analysis is presented for several tropical coastal cities, including future pluvial risk projections. All analytical steps adhere to FAIR principles, utilizing publicly available datasets. The proposed workflow provides globally applicable first order estimates of pluvial flood risk, especially in data-poor areas, with better quality than existing global IDF studies or IDF curves derived directly from global precipitation datasets.