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Journal Article

The utility of using Volunteered Geographic Information (VGI) for evaluating pluvial flood models

Authors

Drews,  Martin
External Organizations;

Steinhausen,  Max
External Organizations;

Larsen,  Morten Andreas Dahl
External Organizations;

Dømgaard,  Mads Lykke
External Organizations;

Huszti,  Levente
External Organizations;

Rácz,  Tibor
External Organizations;

/persons/resource/michael.wortmann

Wortmann,  Michel
Potsdam Institute for Climate Impact Research;

/persons/resource/Fred.Hattermann

Hattermann,  Fred Fokko
Potsdam Institute for Climate Impact Research;

Schröter,  Kai
External Organizations;

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Fulltext (public)

1-s2.0-S0048969723035854-main.pdf
(Publisher version), 5MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Drews, M., Steinhausen, M., Larsen, M. A. D., Dømgaard, M. L., Huszti, L., Rácz, T., Wortmann, M., Hattermann, F. F., Schröter, K. (2023): The utility of using Volunteered Geographic Information (VGI) for evaluating pluvial flood models. - Science of the Total Environment, 894, 164962.
https://doi.org/10.1016/j.scitotenv.2023.164962


Cite as: https://publications.pik-potsdam.de/pubman/item/item_28880
Abstract
Pluvial floods are increasingly threatening urban environments worldwide due to human-induced climate change. High-resolution, state-of-the-art pluvial flood models are urgently needed to inform climate change adaptation and disaster risk reduction measures but are generally not empirically tested because of the rarity of local high-intensity precipitation events and the lack of monitoring capabilities. Volunteered Geographic Information (VGI) collected by professionals, non-professionals and citizens and made available on the internet can be used to monitor the dynamic extent of a pluvial flood during and after an extreme rain event but is sometimes considered to be unreliable. In this paper, we explore the general utility of VGI to evaluate the performance of pluvial flood models and gain new insights to improve these models. As background for our research, we use the capital city of Budapest, which recently suffered three heavy rainfall events in just five years (2015, 2017 and 2020). For each pluvial flood event, we collected photographic evidence from different online media sources and estimated the associated water depths at various locations in the city from the image context. These were compared with the results of a 2D pluvial flood model that has been shown to provide comparable results to other state-of-the-art inundation models and is easily transferred to other urban areas due to its reliance on open data sources. We introduce a general methodology for comparing VGI with model data by probing different spatial resolutions. Our findings highlight untapped potential and fundamental challenges in using VGI for model evaluation. It is proposed that VGI may become an essential tool and improve the confidence in model-based risk assessments for climate change adaptation and disaster risk reduction.