Global Flood Models

Publication date

2021-08-04

Authors

Trigg, Mark A.
Bernhofen, Mark
Marechal, David
Alfieri, Lorenzo
Dottori, Franceso
Hoch, J.M.ISNI 0000000492895365
Horritt, Matt
Sampson, Chris
Smith, Andy
Yamazaki, Dai

Editors

Wu, Huan
Lettenmeier, Dennis P.
Tang, Qiuhong
Ward, Philip J.

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

taverne

Abstract

Flooding is the most damaging natural hazard, both economically and by population affected. Flood models are important tools for evaluating the risks associated with flooding. Historically, the modeling domain has been limited in scale; however, advancements in computing power and global data sets have led to the development of global flood models (GFMs). This global modeling capability has benefited scientific studies of exposure and climate change impact, the insurance industry, and intergovernmental disaster risk reduction efforts. Global flood modeling has now progressed beyond its infancy to a point where coordinated and targeted model development can take place based on collective studies. This chapter provides a detailed summary of the current global flood modeling state-of-affairs. It begins with a summary of the history and challenges of GFM development. This is followed by a review of current GFMs and their structures, applications, and credibility. A section is also dedicated to describing global flood modeling in the context of the insurance catastrophe model, an important GFM category that is less visible due to their proprietary nature. The chapter concludes by looking to the future and highlighting how GFMs need to improve and the new data sets and methods that could contribute to their continued development.

Keywords

Taverne, SDG 13 - Climate Action

Citation

Trigg, M A, Bernhofen, M, Marechal, D, Alfieri, L, Dottori, F, Hoch, J, Horritt, M, Sampson, C, Smith, A, Yamazaki, D & Li, H 2021, Global Flood Models. in H Wu, D P Lettenmeier, Q Tang & P J Ward (eds), Global Drought and Flood: Observation, Modeling, and Prediction. Geophysical Monograph Series, vol. 265, AGU, pp. 181-200. https://doi.org/10.1002/9781119427339.ch10