Articles | Volume 386
https://doi.org/10.5194/piahs-386-181-2024
https://doi.org/10.5194/piahs-386-181-2024
Post-conference publication
 | 
19 Apr 2024
Post-conference publication |  | 19 Apr 2024

Flood damage model bias caused by aggregation

Seth Bryant, Heidi Kreibich, and Bruno Merz

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Cited articles

Bryant, S.: cefect/2210_AggFSyn: 2024-03-12: PIAHS publication, Zenodo [code], https://doi.org/10.5281/zenodo.10810421, 2024. a
de Moel, H. and Aerts, J. C. J. H.: Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates, Nat. Hazards, 58, 407–425, https://doi.org/10.1007/s11069-010-9675-6, 2011. a, b
Denny, M.: The fallacy of the average: on the ubiquity, utility and continuing novelty of Jensen's inequality, J. Experiment. Biol., 220, 139–146, https://doi.org/10.1242/jeb.140368, 2017. a, b
Gerl, T., Kreibich, H., Franco, G., Marechal, D., and Schröter, K.: A Review of Flood Loss Models as Basis for Harmonization and Benchmarking, PloS one, 11, e0159791, https://doi.org/10.1371/journal.pone.0159791, 2016. a, b, c, d, e, f, g
Hall, J. W., Sayers, P. B., and Dawson, R. J.: National-scale Assessment of Current and Future Flood Risk in England and Wales, Nat. Hazards, 36, 147–164, https://doi.org/10.1007/s11069-004-4546-7, 2005. a
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Short summary
Our study found that simplifying data in flood risk models can introduce errors. We tested 344 damage functions and found errors up to 40 % of the total asset value. This means large-scale flood risk assessments may have significant errors due to the modelling approach. Our research highlights the need for more attention to data aggregation in flood risk models.