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.
FLOPROS: an evolving global database of flood protection standards
