Climate uncertainty in flood protection planning

Abstract

Technical flood protection is a necessary part of integrated strategies to protect riverine settlements from extreme floods. Many technical flood protection measures, such as dikes and protection walls, are costly to adapt after their initial construction. This poses a challenge to decision makers as there is large uncertainty in how the required protection level will change during the measure life time, which is typically many decades long. Flood protection requirements should account for multiple future 10 uncertain factors: socio-economic, e.g. whether the population and with it the damage potential grows or falls; technological, e.g. possible advancements in flood protection; and climatic, e.g. whether extreme discharge will become more frequent or not. We focus here on the planning implications of the uncertainty in extreme discharge. We account for the sequential nature of the decision process, in which the adequacy of the protection is regularly revised in the future based on the discharges that have been observed by that point and that reduce uncertainty. For planning purposes, we categorize uncertainties as either 15 'visible', if they can be quantified from available catchment data, or 'hidden', if they cannot be quantified from catchment data and must be estimated, e.g. from literature. It is vital to consider the hidden uncertainty, since in practical applications only a limited amount of information (e.g. through projections, historic record) is available. We use a Bayesian approach to quantify the visible uncertainties and combine them with an estimate of the hidden uncertainties to learn a joint probability distribution of the parameters of extreme discharge. The methodology is integrated into an optimization framework and applied to a pre-20 alpine case study to give a quantitative, cost-optimal recommendation on the required amount of flood protection.

Robust Adaptation to Multiscale Climate Variability

“…As illustrated schematically in Figure 2, large uncertainties (e.g., as to future CO 2 concentrations and local climate impacts) lead to large intrinsic uncertainty in projections of future climate risk. As the physical mechanisms cascade from global (e.g., global mean surface temperature) to regional (e.g., storm track position; Barnes & Screen, 2015) and local (e.g., annual-maximum streamflows) scales, informational uncertainties also compound and increase (Dittes et al, 2017). With sufficient information (large N), this informational uncertainty may be reduced, but these data cannot address intrinsic uncertainty, and this zone is thus named the "intrinsic uncertainty zone."…”