Managing Uncertainty in Flood Protection Planning with Climate
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Hydrol. Earth Syst. Sci., 22, 2511–2526, 2018 https://doi.org/10.5194/hess-22-2511-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Managing uncertainty in flood protection planning with climate projections Beatrice Dittes, Olga Špacková,ˇ Lukas Schoppa, and Daniel Straub Engineering Risk Analysis Group, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany Correspondence: Beatrice Dittes ([email protected]) Received: 24 September 2017 – Discussion started: 1 November 2017 Revised: 19 January 2018 – Accepted: 30 March 2018 – Published: 24 April 2018 Abstract. Technical flood protection is a necessary part The recommended planning is robust to moderate changes in of integrated strategies to protect riverine settlements from uncertainty as well as in trend. In contrast, planning without extreme floods. Many technical flood protection measures, consideration of bias and dependencies in and between un- such as dikes and protection walls, are costly to adapt af- certainty components leads to strongly suboptimal planning ter their initial construction. This poses a challenge to de- recommendations. cision makers as there is large uncertainty in how the re- quired protection level will change during the measure life- time, which is typically many decades long. Flood protec- tion requirements should account for multiple future uncer- 1 Introduction tain factors: socioeconomic, e.g., whether the population and with it the damage potential grows or falls; technological, The frequency of large fluvial flood events is expected to e.g., possible advancements in flood protection; and climatic, increase in Europe due to climate change (Alfieri et al., e.g., whether extreme discharge will become more frequent 2015). Therefore, planning authorities increasingly incorpo- or not. This paper focuses on climatic uncertainty. Specif- rate discharge projections into the assessment of future flood ically, we devise methodology to account for uncertainty protection needs, rather than considering past observations associated with the use of discharge projections, ultimately alone. However, projections differ widely in terms of the leading to planning implications. For planning purposes, we level and trend of extreme discharge that they forecast. Fu- categorize uncertainties as either “visible”, if they can be ture discharge extremes therefore should be modeled prob- quantified from available catchment data, or “hidden”, if they abilistically for flood protection planning (Aghakouchak et cannot be quantified from catchment data and must be es- al., 2013). This raises two main questions: (1) how does one timated, e.g., from the literature. It is vital to consider the quantify a relevant uncertainty spectrum and (2) how is this “hidden uncertainty”, since in practical applications only a then further used to identify a protection strategy? limited amount of information (e.g., a finite projection en- Recent studies have aimed at quantifying individual un- semble) is available. We use a Bayesian approach to quan- certainties in (extreme) discharge (Bosshard et al., 2013; tify the “visible uncertainties” and combine them with an Hawkins and Sutton, 2011; Sunyer, 2014). Sunyer (2014) has estimate of the hidden uncertainties to learn a joint prob- pointed out the usefulness of finding a methodology to com- ability distribution of the parameters of extreme discharge. bine uncertainties for flood protection planning. In the first The methodology is integrated into an optimization frame- part of this paper we present such a methodology for deriving work and applied to a pre-alpine case study to give a quanti- a probabilistic model of extreme discharge; it is a pragmatic tative, cost-optimal recommendation on the required amount approach to handling the limited available data in practical of flood protection. The results show that hidden uncertainty problems. We quantitatively incorporate climate uncertainty ought to be considered in planning, but the larger the uncer- from multiple information sources as well as an estimate of tainty already present, the smaller the impact of adding more. the “hidden uncertainty” into learning the probability distri- bution of parameters of extreme discharge. The term hidden Published by Copernicus Publications on behalf of the European Geosciences Union. 2512 B. Dittes et al.: Managing uncertainty in flood protection planning with climate projections j Flood k Hidden projections uncertainty m Account for n Bayesian decision l Account for uncertainty o Protection dependency among framework and bias within projections recommendation projections (incl. parameter uncertainty) Figure 1. Process of finding the recommended planning margin from projections and hidden uncertainty estimate. uncertainty refers to uncertainty components that cannot be (such as the 100-year flood). To protect for the 100-year flood quantified from the given projections and data. For example, is common European practice (Central European Flood Risk if the same hydrological model has been used for all projec- Assessment and Management in CENTROPE, 2013) and is tions, then the hydrological model uncertainty is “hidden”, also the requirement in the case study. since one effectively has only a single sample of hydrologi- In this paper, we show how to incorporate into the flood cal model output. It is vital to consider the hidden uncertainty planning process the “visible uncertainty” from an ensem- since in practical applications only a limited amount of infor- ble of climate projections as well as hidden uncertainties that mation and models is available and hidden uncertainty will cannot be quantified from the ensemble itself but may be es- always be present. timated from the literature. In the process of combining these Once established, the question is then how to deal with uncertainties, we account for uncertainty and bias in projec- the uncertainty in flood risk estimates when conducting flood tions as well as for dependencies among different projec- protection planning. Multiple approaches have been pro- tions. We provide reasoned estimates of climatic uncertain- posed (Hallegatte, 2009; Kwakkel et al., 2010), including the ties for a pre-alpine catchment, followed by an application addition of a planning margin to the initial design. The plan- of the previously proposed Bayesian decision framework, ning margin is the protection capacity implemented in excess sensitivity and robustness analysis. The process is shown in of the capacity that would be selected without taking into ac- Fig. 1: (1) projections of annual maximum discharges (see count the uncertainties. Such reserves are used in practice; Sect. 2.2) and (2) an estimate of the shares of various uncer- for example, in Bavaria, a planning margin of 15 % is ap- tainties that are not covered by the projection ensemble (see plied to the design of new protection measures to account Sect. 2.5) form the inputs to the analysis. (3) For each projec- for climate change (Pohl, 2013; Wiedemann and Slowacek, tion individually, a likelihood function of annual maximum 2013). Planning margins are typically implemented based on discharge is computed. This is done such that bias is inte- rule-of-thumb estimates rather than a rigorous quantitative grated out and projections later on the horizon are assigned analysis (KLIWA, 2005, 2006; De Kok et al., 2008). diminishing weights, making use of the hidden uncertainty We have previously proposed a fully quantitative Bayesian shares (see Sect. 3.2). (4) The likelihoods of individual pro- decision-making framework for flood protection (Dittes et jections are combined using the method of effective projec- al., 2018). Bayesian techniques are a natural way to model tions (Pennell and Reichler, 2011; Sunyer et al., 2013b) in or- discharge probabilistically (Coles et al., 2003; Tebaldi et al., der to account for dependencies among them (see Sect. 3.3). 2004). They also make it easy to combine several sources of (5) The Bayesian decision framework of Dittes et al. (2018) information (Viglione et al., 2013). Furthermore, Bayesian is used to obtain (6) a protection recommendation based on methods support updating the discharge distribution in the the likelihood of extreme discharge. The qualitative basis of future, when new information becomes available (Graf et al., the framework is outlined in Sect. 3.4. 2007). Our framework probabilistically updates the distribu- It is stressed that this paper focusses on the engineering as- tion of extreme discharge with hypothetical observations of pect of planning flood protection under climate change. We future discharge, which are modeled probabilistically. This aim to demonstrate how different sources of uncertainty can is an instance of a sequential (or “preposterior”) decision be combined probabilistically to make decisions, taking into analysis (Benjamin and Cornell, 1970; Davis et al., 1972; account future developments. This is to aid decision making Kochendorfer, 2015; Raiffa and Schlaifer, 1961). This en- under climate uncertainty, when there are limited data and ables a sequential planning process, where it is taken into models available. Some authors advocate not using a proba- consideration that the measure design may be revised in the bilistic approach when the uncertainty is very large. This is future. Furthermore, it naturally takes into account the uncer- because of the potential of surprises under large uncertainty tainty in the parameters of extreme discharge. The output of (Hall