Nat. Hazards Earth Syst. Sci., 19, 1737–1753, 2019 https://doi.org/10.5194/nhess-19-1737-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Uncertainty quantification of flood mitigation predictions and implications for interventions Koen D. Berends1,2, Menno W. Straatsma3, Jord J. Warmink1, and Suzanne J. M. H. Hulscher1 1Department of Marine and Fluvial Systems, Twente Water Centre, University of Twente, P. O. Box 217, 7500 AE Enschede, the Netherlands 2Department of River Dynamics and Inlands Shipping, Deltares, Boussinesqweg 1, 2629 HV Delft, the Netherlands 3Department of Physical Geography, Faculty of Geosciences, University of Utrecht, Princetonlaan 8, 3584 CS Utrecht, the Netherlands Correspondence: K. D. Berends ([email protected]) Received: 31 October 2018 – Discussion started: 14 November 2018 Revised: 18 June 2019 – Accepted: 19 June 2019 – Published: 13 August 2019 Abstract. Reduction of water levels during river floods is In general, we conclude that the uncertainty of model predic- key in preventing damage and loss of life. Computer mod- tions is not large enough to invalidate model-based interven- els are used to design ways to achieve this and assist in the tion design, nor small enough to neglect altogether. Instead, decision-making process. However, the predictions of com- uncertainty information is valuable in the selection of alter- puter models are inherently uncertain, and it is currently un- native interventions. known to what extent that uncertainty affects predictions of the effect of flood mitigation strategies. In this study, we quantify the uncertainty of flood mitigation interventions on the Dutch River Waal, based on 39 different sources of un- 1 Introduction certainty and 12 intervention designs. The aim of each inter- vention is to reduce flood water levels. Our objective is to in- The number of people living in areas exposed to river flood- vestigate the uncertainty of model predictions of intervention ing is projected to exceed 1 billion in 2050 (Jongman et al., effect and to explore relationships that may aid in decision- 2012). Therefore, it is increasingly important that the river making. We identified the relative uncertainty, defined as the system is designed in such a way that flood risk is minimised. ratio between the confidence interval and the expected effect, Human intervention in river systems has a long history, to as a useful metric to compare uncertainty between different a point that for some rivers human management has be- interventions. Using this metric, we show that intervention come the dominant factor driving hydrological change (Pin- effect uncertainty behaves like a traditional backwater curve ter et al., 2006; Bormann and Pinter, 2017). Today, the de- with an approximately constant relative uncertainty value. In cision to change an existing river system (e.g. by leveeing a general, we observe that uncertainty scales with effect: high channel to protect flood-prone areas) is increasingly based on flood level decreases have high uncertainty, and, conversely, predictions made by computer models. Various software sys- small effects are accompanied by small uncertainties. How- tems can be used to resolve flow conditions accurately, tak- ever, different interventions with the same expected effect do ing much of the complexity of rivers into account – such as not necessarily have the same uncertainty. For example, our terrain geometry, vegetation and hydraulic structures. How- results show that the large-scale but relatively ineffective in- ever, despite increasingly available data, not all model input tervention of floodplain smoothing by removing vegetation or model parameters can be reliably measured or represented. has much higher uncertainty compared to alternative options. For example, vegetation density and vegetation height, which Finally, we show how a level of acceptable uncertainty can be modify vegetation roughness (Baptist et al., 2007; Luhar and defined and how this can affect the design of interventions. Nepf, 2013), are variable both in time and space. This vari- ability is neither captured on the scale of river modelling, nor Published by Copernicus Publications on behalf of the European Geosciences Union. 1738 K. D. Berends et al.: Uncertainty quantification of flood mitigation predictions by the equations that resolve vegetation roughness. Examples complicates Monte Carlo simulation (MSC)-based quantifi- such as this introduce uncertainty in the modelling process cation methods, but also have a high number of model param- and in the model output (Oreskes et al., 1994; Walker et al., eters, which complicates the use of (data-based) surrogate 2003). model approaches (Razavi et al., 2012). The second issue is Proper understanding and communication of uncertainty that the analysis relies on at least one unmeasured environ- are important both for scientists and decision makers (Pap- mental system, namely, the river system altered by the pro- penberger and Beven, 2006; Uusitalo et al., 2015). Maier posed intervention. Therefore, there is no way to verify that et al.(2016) distinguished three complementary paradigms accuracy for the proposed future state of the river. Verifica- for modelling to support decision-making under (deep) un- tion of the accuracy of the current, unaltered state is likewise certainty: (1) using the best available knowledge, (2) quan- not necessarily available. For example, flood mitigation mea- tifying uncertainty and sensitivities of key parameters and sures tend to be designed for a low period of return, e.g. a 1 (3) exploring multiple plausible outcomes. One advantage in 100-year return or even 1 in 1250-year flood return (Klijn computational models can bring to decision-making for river et al., 2018), for which recent measurements are (by defini- engineering is an assessment of the impact of the planned in- tion) sparse. Under such conditions, the uncertainty of model tervention on hydraulics, such as water levels, flow velocities output cannot be expressed in terms of accuracy (e.g. stan- and the morphodynamic response of the river bed. While hy- dard deviation of model error) without additional untestable draulic effects are not the only impact of interventions (see, assumptions. for example, Straatsma et al., 2017, for effects on biodiver- In the present paper we address the lack of studies into the sity), they are considered important: for the 39 interventions model uncertainty of model predictions used in impact de- of the EUR 2.3 billion “Room for the River” programme in sign. Our objective is twofold. The first objective is to quan- the Netherlands, the hydraulic effect as predicted by mod- tify the effect of parameter uncertainty on the predicted effect els was a precondition for any design “to be taken seriously of flood mitigation measures, by implementing 12 different at all” (Klijn et al., 2013). Mosselman(2018) reported that interventions of varying type and intensity in a section of the quantified, large uncertainty in flood water levels is some- Dutch River Waal, using a detailed hydraulic model. To limit times played down when assessing effects, under the assump- the computational burden we use CORAL, which is an imple- tion that systematic errors cancel out when subtracting the mentation of the efficient uncertainty quantification method intervention case from the reference case. However, it was of Berends et al.(2018), that does not suffer from limitations recently demonstrated in an idealised study that uncertainty with regard to the number of model parameters. Our second in flood water levels does not cancel out but could be sig- objective is to explore the relationship between the expected nificant compared to the effect and sensitive to the specific reduction of flood levels and the uncertainty, to aid in the design of the intervention (Berends et al., 2018). Therefore, decision-making process. To this end we will introduce the uncertainty quantification of effect studies for a real-world “relative uncertainty” measure to facilitate inter-comparison case study is needed to assess the implications for the design between different intervention designs. of interventions. Our analysis proceeds in three steps. First, we set up the Uncertainty quantification of impact analysis for real- numerical model for the Dutch River Waal and select the un- world solutions suffers from two compounding issues spe- certainty sources in Sect.2. Here, we also introduce the rel- cific to intervention design studies. First, there is a practical ative uncertainty measure. We then present the results of the need for sufficiently detailed models given the increasingly uncertainty of the modelled water levels and reduction in wa- complicated design of interventions, which moves from tra- ter levels in Sect. 3.1 and 3.2. In Sect. 3.3 we show how un- ditional flood prevention (building dikes or levees) to more certainty may influence decision-making. In the Discussion holistic designs. This is in part motivated by the paradoxi- (Sect.4) we discuss reducing uncertainty in intervention de- cal “levee effect” that states that flood control measures do sign and the feasibility of probabilistic analysis for interven- not decrease but increase flood risk (White, 1945; Di Bal- tion design in practice. A general conclusion on the practi- dassarre et al., 2013; Munoz et al., 2018) and of which in- cal value of uncertainty quantification for decision-making sight has spurred policy change away from purely flood con- in flood mitigation