Testing the validity of pluvial flood risk models

Pluvial floods present an increasing risk in urban environments all over the world. High-resolution, state-of-the-art pluvial flood risk assessments are urgently needed to inform disaster risk reduction measures and climate change adaptation. Still, pluvial flood risk models are generally not empirically tested because of the rarity of local high-intensity precipitation events and/or lack of monitoring and reporting capabilities.

With a combination of Volunteered Geographic Information (VGI) and insurance claims data, we test the validity of hazard, exposure, and multiple vulnerability components of a pluvial flood risk model. As background for our research, we use the city of Budapest, which suffered three heavy rainfall events associated with significant flood damages in just five years (2015, 2017 and 2020). For each pluvial flood event, we collected photographic evidence of flooding from different online media sources, as well as claims data from the Association of Hungarian Insurance Companies (MABISZ) for residential buildings.

Based on the context information shown in the photos, we identified their location in the city through comparison with Google Street View imagery and estimated the associated water depths. These were compared with the results of a generic-type pluvial flood model. The estimation of flood losses revealed spatial patterns of pluvial flood risk in Budapest. We tested the loss estimates against reported loss in the 23 districts of Budapest to better understand the reliability and accuracy of pluvial loss models.

In general, our findings highlight the untapped potential, but also reveal important challenges in using VGI for model evaluation. It is proposed that VGI are used more systematically to improve the confidence in model-based risk assessments for climate change adaptation and disaster risk reduction.