Risk-based flood protection planning under climate change and modeling uncertainty: a pre-alpine case study

Abstract

Abstract. Planning authorities are faced with a range of questions when planning flood protection measures: is the existing protection adequate for current and future demands or should it be extended? How will flood patterns change in the future? How should the uncertainty pertaining to this influence the planning decision, e.g., for delaying planning or including a safety margin? Is it sufficient to follow a protection criterion (e.g., to protect from the 100-year flood) or should the planning be conducted in a risk-based way? How important is it for flood protection planning to accurately estimate flood frequency (changes), costs and damage? These are questions that we address for a medium-sized pre-alpine catchment in southern Germany, using a sequential Bayesian decision making framework that quantitatively addresses the full spectrum of uncertainty. We evaluate different flood protection systems considered by local agencies in a test study catchment. Despite large uncertainties in damage, cost and climate, the recommendation is robust for the most conservative approach. This demonstrates the feasibility of making robust decisions under large uncertainty. Furthermore, by comparison to a previous study, it highlights the benefits of risk-based planning over the planning of flood protection to a prescribed return period.

Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates

“…The resulting loss estimates in these stage-damage functions are commonly expressed as point estimates for the repair and/or replacement costs in monetary values (i.e., U.S.$) or percentage of the depreciated value of a building. Instead of a direct quantification of uncertainty inherent to probabilistic predictions, uncertainty in stage-damage functions is often based on expert judgment and/or by calculating a range of possible outcomes using different loss functions (Dittes et al, 2018). Missing information, and/or a lack of consistency in quantifying how certain a loss estimate is, makes it challenging for decision makers to, for example, evaluate the potential of an adaptation measure to reduce future losses.…”