Managing uncertainty in flood protection planning with climate projections

Abstract

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 lifetime, which is typically many decades long. Flood protection requirements should account for multiple future uncertain factors: socioeconomic, 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. This paper focuses on climatic uncertainty. Specifically, we devise methodology to account for uncertainty associated with the use of discharge projections, ultimately leading to planning implications. For planning purposes, we categorize uncertainties as either “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 the literature. It is vital to consider the “hidden uncertainty”, since in practical applications only a limited amount of information (e.g., a finite projection ensemble) 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-alpine case study to give a quantitative, cost-optimal recommendation on the required amount of flood protection. The results show that hidden uncertainty ought to be considered in planning, but the larger the uncertainty already present, the smaller the impact of adding more. The recommended planning is robust to moderate changes in uncertainty as well as in trend. In contrast, planning without consideration of bias and dependencies in and between uncertainty components leads to strongly suboptimal planning recommendations.

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

“…2.1, we outline how the climate-related uncertainty in discharge (f Q (q) in Eq. 1) is defined and quantified for use in decision making, following Dittes et al (2018b). In Sect.…”

“…where f is the prior PDF of the parameters, f Q| is the PDF of the applied extreme value distribution wherein the dependence on θ has been made explicit, and f Q| (q|θ ) = Y t=1 f Q| (q t |θ ) is the likelihood describing the data. The prior distribution is chosen to be non-informative in the return period of extreme discharge (for details on this, see Dittes et al, 2018b). In the equality, we assume independence among the annual discharge maxima q.…”

“…It is common to categorize uncertainty into aleatory uncertainty (natural variability), which cannot be reduced, and epistemic uncertainty, which can be reduced with more knowledge (Merz and Thieken, 2005). To account for the limited information available in typical flood protection planning problems, we here categorize the climatic uncertainties according to the following categories (Dittes et al, 2018b):…”

“…3.3). Note that this hidden uncertainty is a rough estimate for the situation in Rosenheim based on literature (Bosshard et al, 2013;Dittes et al, 2018b;Hawkins and Sutton, 2011;Maraun, 2013). It is given as a share of total uncertainty (in terms of the variance).…”

“…We multiply the PDFs of the members within one set and average in between sets to obtain a joint parameter PDF. Full details of the implementation can be found in Dittes et al (2018b).…”