Risk-Based Flood Protection Planning Under Climate
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Nat. Hazards Earth Syst. Sci., 18, 1327–1347, 2018 https://doi.org/10.5194/nhess-18-1327-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Risk-based flood protection planning under climate change and modeling uncertainty: a pre-alpine case study Beatrice Dittes1, Maria Kaiser2, Olga Špackovᡠ1, Wolfgang Rieger2, Markus Disse2, and Daniel Straub1 1Engineering Risk Analysis Group, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany 2Chair of Hydrology and River Basin Management, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany Correspondence: Beatrice Dittes ([email protected]) Received: 8 November 2017 – Discussion started: 21 November 2017 Revised: 2 April 2018 – Accepted: 17 April 2018 – Published: 15 May 2018 Abstract. Planning authorities are faced with a range of thorities to take decisions on flood protection planning that questions when planning flood protection measures: is the are economical while not leading to excessive losses or high existing protection adequate for current and future demands adjustment costs. It is important to consider costs – in con- or should it be extended? How will flood patterns change in struction, adjustment and flood damage – over the entire the future? How should the uncertainty pertaining to this in- measure life time. fluence the planning decision, e.g., for delaying planning or Ideally, the planning of flood protection infrastructure is including a safety margin? Is it sufficient to follow a pro- performed through a risk-based approach. Thereby, potential tection criterion (e.g., to protect from the 100-year flood) or damage is considered in the decision-making process. Con- should the planning be conducted in a risk-based way? How sidering that the annual maximum discharge Q is the main important is it for flood protection planning to accurately es- driver for flood damage, the annual flood risk in year t, rt , is timate flood frequency (changes), costs and damage? These defined as follows (e.g., Merz et al., 2010): are questions that we address for a medium-sized pre-alpine 1 catchment in southern Germany, using a sequential Bayesian Z decision making framework that quantitatively addresses the rt D fQ .q/dt .q/dq; (1) full spectrum of uncertainty. We evaluate different flood pro- 0 tection systems considered by local agencies in a test study catchment. Despite large uncertainties in damage, cost and where fQ .q/ is the probability density function (PDF) of the climate, the recommendation is robust for the most conser- annual maximum discharge and dt .q/ is the expected dam- vative approach. This demonstrates the feasibility of mak- age associated with the flood discharge q in year t. If deci- ing robust decisions under large uncertainty. Furthermore, by sions are based on a cost–benefit analysis (CBA), the optimal comparison to a previous study, it highlights the benefits of flood protection strategy, s, is the one that minimizes the sum risk-based planning over the planning of flood protection to of risks and costs over the lifetime of the protection system a prescribed return period. (Špackovᡠand Straub, 2015): sopt D argminctot .s/ C rtot .s/; (2) s 1 Introduction where ctot .s/ and rtot.s/ are the expected costs and risks as- sociated with strategy s. They are net present values, dis- Technical flood protection measures have long lifetimes counted to the time of planning (following Eq. 5). of, on average, 80 years (Bund/Länder-Arbeitsgemeinschaft In contrast, the current practice in most European coun- Wasser, 2005). The uncertainty over such a long plan- tries is to require protection from floods of a certain return ning horizon is large, both in terms of climatic and socio- period, often the 100-year flood (Central European Flood economic development. It is thus not trivial for planning au- Risk Assessment and Management in CENTROPE, 2013). Published by Copernicus Publications on behalf of the European Geosciences Union. 1328 B. Dittes et al.: Risk-based flood protection planning This simplifies planning, as it is not necessary to quantify assessment. Results are presented and discussed in Sect. 4, damage. While a well-chosen criterion may lead to near- followed by the conclusions in Sect. 5. optimal strategies, a suboptimal one may lead to strategies that are too risk-averse or too risky. To balance the optimal- ity of risk-based planning with the lower effort of criterion- 2 Theoretical background based planning, authorities can apply a “zoning” approach, In this section, we provide a short overview of the proposed with different regions being assigned a different protection methodology for flood protection planning under uncertainty. criterion. However, Kind (2014) performed CBA in Dutch In Sect. 2.1, we outline how the climate-related uncertainty catchments and found that despite the applied large-scale in discharge (f .q/ in Eq. 1) is defined and quantified for zoning, the fixed protection criterions were not economically Q use in decision making, following Dittes et al. (2018b). In efficient, because of both over and under protection. Sect. 2.2, we summarize the Bayesian sequential decision There is a growing consensus that costs and damage (and framework for optimizing the flood protection strategy un- thus the extreme discharge causing them) should be mod- der uncertainty. eled probabilistically (Aghakouchak et al., 2013). Probabilis- tic modeling allows for the consideration of the (large) un- 2.1 Uncertainty in determining future extreme certainty in future climate change in the CBA. The climatic discharges uncertainty is included in the PDF of the annual maximum discharge fQ .q/ of Eq. (1), which is expected to change In practical planning applications, there are only limited data depending on future climatic developments. Recent studies and models available for estimating future flood magnitude have aimed at quantifying the climatic uncertainty compo- and frequency, which leads to uncertainty in predictions. The nents in (extreme) discharge and precipitation, (Bosshard et problem is exacerbated when climate change effects (i.e., al., 2013; Hawkins and Sutton, 2011; Sunyer, 2014). non-stationarity, or trends) are present in this limited infor- Risk-based flood protection planning has a long history mation, since it means more parameters need to be learned (Clark, 1980; James and Hall, 1986; Lund, 2002) and ap- from a given, limited set of data. When planning under un- proaches to account for uncertainty in the risk estimate have certainty, it is important not to consider different uncertain- also been developed for some time (Kundzewicz et al., 2010; ties (from climate, lack of data, etc.) individually, but jointly USACE, 1996). Recent fields of interest in risk-based flood (Sunyer, 2014). protection planning are e.g., how to include the flexibility This section provides a short overview on how the uncer- of measures into the decision making (i.e., how costly it is tainty in future extreme discharges can be modeled from lim- to adjust measures later on) (Klijn et al., 2015; Kuklicke ited data, taking into account non-stationarity and consider- and Demeritt, 2016; Woodward et al., 2014) and how to ac- ing uncertainties jointly. The data consists of a historic record count for non-stationarity in discharges and risk estimates, of discharges and an (imperfect) ensemble of discharge pro- for example, due to climate change (Rehan and Hall, 2016; jections that are a result of climate modeling. The former Rosner et al., 2014; Sayers et al., 2013). We have recently are often of limited length, whereas the latter may exhibit presented a framework for flood protection planning that is bias and significant uncertainty that builds up over the cli- based on a quantitative, probabilistic joint estimate of cli- mate modeling chain (starting from global emission path- matic (and potentially other) uncertainties, naturally incor- ways down to downscaled values for a particular study catch- porating non-stationarity and the flexibility of the protection ment). system in a Bayesian framework (Dittes et al., 2018a, b). In The PDF of annual maximum discharges, fQ .q/, typi- this work, we apply the framework to a real decision mak- cally has the form of an extreme value distribution, e.g., ing problem of four potential protection strategies, as cur- the Gumbel or the generalized extreme value (GEV) dis- rently considered by the local water management authori- tribution. These distributions are parameterized by a set of ties. To our knowledge, this is the first risk-based applica- parameters θ. Non-stationarity in the discharge estimate is tion of a fully quantitative, continuous (not scenario-based) modeled by including additional parameters in θ describing decision-making framework for sequential flood protection the time-dependence. The distribution of the parameters θ planning that probabilistically includes future decisions and can be learned from Y years of annual maximum discharges discharges. We additionally compare the results from this q DTq1;:::; qY U, which is done here with Bayesian analysis. risk-based planning to previous results from a criterion-based Assuming independence between annual maxima of differ- planning for the same site. We find significant differences, ent years, the posterior PDF of the parameters f2jQ .θjq/ is which lead us to discuss the virtues of the two alternative as follows: planning paradigms. Y Y We provide an overview of the applied uncertainty quan- f2jQ .θjq/ / fQj2 .qjθ/ × f2 .θ/ D fQj2 .qt jθ/ tification and decision making methodology in Sect. 2. In tD1 Sect. 3, we give details of the case study, outlining the consid- × f2 .θ/; (3) ered strategies, the modeling of flood events and the damage Nat. Hazards Earth Syst. Sci., 18, 1327–1347, 2018 www.nat-hazards-earth-syst-sci.net/18/1327/2018/ B. Dittes et al.: Risk-based flood protection planning 1329 where f is the prior PDF of the parameters, f j is the 104 2 Q 2 3 PDF of the applied extreme value distribution wherein the dependence on θ has been made explicit, and fQj2 .qjθ/ D -1 2.5 Y 3 Q fQj2 .qt jθ/ is the likelihood describing the data.