Global Flood Models

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 con tribute to their continued development.

Unknown risk: assessing refugee camp flood risk in Ethiopia

“…While we recognize that flood flow velocity is an important indicator of vulnerability, particularly when assessing risk to human life [50], we note that global flood modeling products do not currently make extreme flow velocities available. Due to the coarse nature of the global flood modeling computations, depth averaged velocities are not considered indicative of local flood hazard velocities [11]. This limits risk assessments with global model outputs to flood extent and depth.…”

“…Global flood risk data, such as global flood model (GFM) hazard maps and global population (GP) maps, could represent a low-cost and easy-toimplement solution for understanding refugee camp flood risk [11]. These datasets are becoming increasingly relevant at the national scale, especially in regions lacking existing flood risk data [12].…”

Using global datasets to estimate flood exposure at the city scale: an evaluation in Addis Ababa

“…ERA5 has been shown to outperform other rainfall reanalysis products (McClean et al, 2021) and may represent the preferential source of precipitation data for estimating flood hazard in many data scare situations. Several GFMs use climate reanalysis products as their source of rainfall data (Trigg et al, 2021), and whilst the relatively coarse resolution of ERA5 limits its ability to characterise localised rainfall processes such as convective storms (Reder et al, 2022), it has been shown to provide useful flood risk information when applied at the catchment and city scales (see Cantoni et al, 2022;Mercogliano et al, 2022). Whilst the direct use of ERA5 to simulate historical flood events has been explored, the PXR IDF dataset has not, despite its potential utility in flood hazard and risk assessment.…”

“…Testing GFMs at the sub-national scale has been limited however. Here, we test flood extent maps produced by the Fathom 2.0 GFM (Sampson et al, 2015;Fathom, 2019), which at the time of writing is generally considered to be amongst the most locally relevant of the GFMs due to its relatively high 1 arc second spatial resolution and its relatively small minimum river size of 50 km 2 upstream drainage area (Trigg et al, 2021).…”

The Role of Global Data Sets for Riverine Flood Risk Management at National Scales

“…The two are often considered complimentary (Hawker et al., 2020), as RS data are used to validate the global models (Bernhofen et al., 2018; Mester et al., 2021). GFMs use global data sets, automated methods, and simplified hydraulic equations to simulate flood hazard globally (Trigg et al., 2020). These models, which began as research experiments, are now being used for disaster response (Emerton et al., 2020), to inform policy decisions (Ward et al., 2015), to assess business risks (Ward, Winsemius, et al., 2020), and recently their modeling frameworks have incorporated detailed national level data to assess national flood risk (Bates et al., 2021; Wing et al., 2017, 2018) Several GFMs have been developed in academia (Yamazaki et al., 2011; Ward et al., 2013), by research institutions (Dottori et al., 2016; Rudari et al., 2015), and by commercial companies (Sampson et al., 2015).…”