Toward an adequate level of detail in flood risk assessments

Abstract

Flood risk assessments require different disciplines to understand and model the underlying components hazard, exposure, and vulnerability. Many methods and data sets have been refined considerably to cover more details of spatial, temporal, or process information. We compile case studies indicating that refined methods and data have a considerable effect on the overall assessment of flood risk. But are these improvements worth the effort? The adequate level of detail is typically unknown and prioritization of improvements in a specific component is hampered by the lack of an overarching view on flood risk. Consequently, creating the dilemma of potentially being too greedy or too wasteful with the resources available for a risk assessment. A “sweet spot” between those two would use methods and data sets that cover all relevant known processes without using resources inefficiently. We provide three key questions as a qualitative guidance toward this “sweet spot.” For quantitative decision support, more overarching case studies in various contexts are needed to reveal the sensitivity of the overall flood risk to individual components. This could also support the anticipation of unforeseen events like the flood event in Germany and Belgium in 2021 and increase the reliability of flood risk assessments.

Funding rules that promote equity in climate adaptation outcomes

“…Finally, we highlight potential technical refinements. For example, our analysis could benefit from improved uncertainty characterization of several system components and a more sophisticated representation of compound flood hazards (78)(79)(80)(81). One approach to increase the robustness of the conclusions as well as the considered strategies is to additionally represent climate change impacts on flood hazards and important human system dynamics such as migration, social institutions, household willingness to accept funding offers, and the co-evolution of risks and hazards (68,(82)(83)(84)(85)(86)(87).…”

The value of ultra-detailed survey data for an improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0

“…However, the complexity of the systems involved, and the significant amount of data required, make flood damage estimation a challenging task, susceptible to uncertainties from input data, model structure and assumptions. Achieving a trade-off between outcome reliability (with a quantitative characterization of uncertainty) and estimation efforts (in terms of time and financial resources for both data retrieval and modelling) is essential for efficient and comprehensive risk assessments, enabling optimal decision-making (Apel et al, 2008;Merz et al, 2015;Sieg et al, 2023). To strike this balance, it is important to examine the possible added value of utilizing more detailed data and advanced methodologies, as well as identifying critical variables that reduce damage estimation uncertainty, justifying investments in data collection.…”

“…The divergent needs of balancing modelling costs and informative results (Di Bacco et al, 2023;Sieg et al, 2023) then pose two questions concerning the applicability of sophisticated and data-intensive models in flood damage assessments: (i) what is the added value, in terms of output quality and usefulness, attained by utilizing more detailed data and advanced methodologies? (ii) which are essential variables that play a key role in constraining the uncertainty bounds, making them worthy of investments in data collection?…”

UNSAFE: An UNcertain Structure And Fragility Ensemble framework for property-level flood risk estimation

“…One driver of this demand is that flood-risk estimates at coarser scales are susceptible to aggregation biases (Condon, 2023;Pollack et al, 2022). Sound flood-risk estimates hinge on careful representation of uncertainties surrounding key inputs driving hazards, exposures, and vulnerabilities at relevant scales (Bates, 2023;Hosseini-Shakib et al, 2024;Rözer et al, 2019;Saint-Geours et al, 2015;Sieg et al, 2023;Tate et al, 2015).…”