In Japan, estimating flood risk follows a deterministic approach. A probabilistic risk method, as adapted in the Netherlands, would be better suited for the quantitative evaluation of flood damage. This study applies such a method in Obihiro, Hokkaido, northern island of Japan. We modelled dike failure with the mechanism overtopping. The probability of dike failure is calculated with a Monte Carlo simulation, considering uncertainties in water levels, critical flow velocity, and dike heights. This results in more accurate failure probabilities compared to the deterministic approach. Additionally, we corrected the dike failure probability for upstream dike failures because these reduce downstream water levels. This conditional flood probability is about 1/10th of the independent situation, indicating a significant effect of considering dike failure in dependence on upstream failures.@en

Accurate estimation of extreme discharges in rivers, such as the Meuse, is crucial for effective flood risk assessment. However, hydrological models that estimate such discharges often lack transparency regarding the uncertainty in their predictions. This was evidenced by the devastating flood that occurred in July 2021, which was not captured by the existing model for estimating design discharges. This article proposes an approach to obtain uncertainty estimates for extremes with structured expert judgment using the classical model (CM). A simple statistical model was developed for the river basin, consisting of correlated generalized extreme value (GEV) distributions for discharges from upstream tributaries. The model was fitted to seven experts' estimates and historical measurements using Bayesian inference. Results were fitted only to the measurements were solely informative for more frequent events, while fitting only to the expert estimates reduced uncertainty solely for extremes. Combining both historical observations and estimates of extremes provided the most plausible results. The classical model reduced the uncertainty by appointing the most weight to the two most accurate experts, based on their estimates of less extreme discharges. The study demonstrates that with the presented Bayesian approach that combines historical data and expert-informed priors, a group of hydrological experts can provide plausible estimates for discharges and potentially also other (hydrological) extremes with relatively manageable effort.@en

Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model’s parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there is no consensus on a ‘best’ approach for eliciting these correlations, this paper proposes a framework that uses probabilities of concordance for assessing dependence, and the dependence calibration score to aggregate experts’ judgments. To demonstrate the relevance of our approach, the latter is implemented to populate a GCBN intended to estimate the condition of air handling units’ components—a key challenge in building asset management. While the elicitation of concordance probabilities was well received by the questionnaire respondents, the analysis of the results reveals notable disparities in the experts’ ability to quantify uncertainty. Moreover, the application of the dependence calibration aggregation method was hindered by the absence of relevant seed variables, thus failing to evaluate the participants’ field expertise. All in all, while the authors do not recommend to use the current model in practice, this study suggests that concordance probabilities should be further explored as an alternative approach for the elicitation of dependence.@en

This dissertation investigates the role of structured expert judgment in quantifying uncertainties central to flood risk assessments, particularly for engineered flood defense systems. Flood risk in the Netherlands involves rare events with high consequences that cannot adequately be quantified with empirical data alone. As an alternative to the traditional approach of physics-based modeling, this research explores the use of expert judgment to address these uncertainties. Through the Classical Model (Cooke's Method), expert estimates of uncertainty are evaluated and combined with the aim of improving the credibility of failure probability estimates. Four key research questions are explored: 1) the performance of statistical tests and distributions in the Classical Model, 2) the accuracy of expert estimates for different types of uncertainties related to flood defense safety, 3) the integration of expert judgment into a Bayesian framework to reduce uncertainty in hydrological extremes, and 4) the quantification of statistical dependence through expert judgment. The findings show that while structured expert judgment effectively addresses certain types of uncertainty and dependence, its accuracy depends on the nature of the variables and the methods used to process expert data. This research demonstrates several methods to incorporate expert judgment in flood risk modeling, offering insights, tools, and recommendations for future studies and practitioners.@en

Accurately modelling rainfall events is crucial for flood risk assessment and stormwater infrastructure design. However, transforming statistical characteristics of events into relevant rainfall patterns is challenging due to the natural variability of rainfall. Two commonly used methods to schematize rainfall events have limitations: the nested storm profile overestimates the resulting flow by assuming complete dependence between different durations, while determining the critical event duration by simulating each duration separately assumes independence and underestimates the flow. To overcome these limitations, this study presents a method that models the dependence between different rainfall durations using a Gaussian copula and combines this with marginal rain statistics to create a probabilistic model for the rain event. The SCS Curve Number approach is used to model the resulting flow, and a first-order reliability method (FORM) is applied to determine the critical combination of durations within an event. The findings of this study show that the rainfall events generated using the proposed method result in comparable flows to those produced by conventional design events. While this may not make the model a preferred choice for standard applications, it can still be valuable for flood risk assessments as it provides a probabilistic model that better captures critical rainfall patterns.@en

The 2021 summer flooding was an extremely rare event, driven by precipitation extremes that exceed Dutch design levels for flood protection of relatively small rivers and waterways. However, similar events in neighboring locations cannot be ruled out in the near future. The implications of such extreme rainfall amounts will vary by region, subject to local topography, water systems, and societal exposure. We explore the diversity of potential flood impacts induced by a similar event by constructing impact-oriented event storylines for characteristic water management regions in the Netherlands. The plausibility of the storylines is underlined by using physical evidence, proven impact-modelling concepts, and expert judgment successfully assessing the (sometimes unexpected) outcomes. The approach supports impact assessment and risk management of extraordinary rainfall and flood events. The outcomes show the relevance for crisis management and spatial policies, and confirms the need for in depth-analysis to assess concrete adaptation options.@en

The Wettelijk Beoordelingsinstrumentarium (WBI) is the legal set of instruments for flood risk analysis in the Netherlands. Often, engineers have the impression that some failure probabilities of flood defenses resulting from these instruments are overestimated. In an effort to better estimate the failure probabilities of dikes along the Dutch river Rhine, this study sets out to assess them with experts and compare them to model results. We used Cooke's method for combining experts’ estimates in a structured way and follow two approaches to estimate a system failure probability. In the first approach, experts estimate discharges that lead to at least one dike failure. This gives plausible results; failure probabilities between 1/30 and 1/17.000 in a year. The second approach is based on adjusting existing model-based assessment results, by estimating the model-bias and incorporating additional dependencies. This mostly leads to large, implausible, failure probabilities: Experts tend to give more conservative answers as they are asked for detailed estimates without clear reference values. This results in large uncertainty and consequently (too) high failure probabilities. Our research shows that when applied in a clear frame of reference, structured expert judgments can be successfully used for estimating the reliability of Dutch flood defenses.

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In juli 2021 zijn grote delen van Limburg getroffen door hevige regenval en overstromingen. Ook delen van België en Duitsland overstroomden met zeer veel schade en verlies aan mensenlevens tot gevolg. Dit betrof een extreme en ongeëvenaarde gebeurtenis met enorme impact. Daarom is naar aanleiding van de overstromingen deze verkenning uitgevoerd om een eerste stap te maken om beschikbare informatie over deze gebeurtenis te verzamelen en analyseren. Het onderzoek is uitgevoerd door een breed consortium (TU Delft, Deltares, HKV Lijn in Water, VU Amsterdam, Universiteit Utrecht, KNMI, WUR, Erasmus MC en Universiteit Twente) in opdracht van het Expertise Netwerk Waterveiligheid (ENW). Een overstroming heeft effect op de hele maatschappij. Daarom zijn niet alleen hydrologische en civieltechnische onderwerpen beschouwd, maar ook de maatschappelijke gevolgen van overstromingen, de crisisrespons en de gezondheidseffecten.

Contributors (in alphabetical order):
Nathalie Asselman (Deltares), Hermjan Barneveld (HKV / Wageningen UR), Jules Beersma (KNMI), Eline Boelee (Deltares), Wouter Botzen (VU Amsterdam), Eefke Copper (TU Delft), Dim Coumou (KNMI), Karin de Bruijn (Deltares), Anniek de Jong (Deltares), Jurjen de Jong (Deltares), Hans de Moel (VU Amsterdam), Ferdinand Diermanse (Deltares), Astrid Fischer (Evides) , Gert-Jan Geerling (Deltares), Marie-Louise Geurts (WML), Rob Groenland (KNMI), Mark Hegnauer (Deltares), Bas Jonkman (TU Delft), Nicole Jungermann (KNMI), Frans Klijn (Deltares), Andre Koelewijn (Deltares), Matthijs Kok (HKV / TU Delft), Elco Koks (VU Amsterdam), Bas Kolen (HKV / TU Delft), Marion Koopmans (Erasmus MC), Laurens Leunge (Deltares), Hans Middelkoop (Utrecht University), Roelof Moll (TU Delft), Jaap Mos (Dunea), Sjoukje Philip (KNMI), Gerbert Pleijter (HKV), Joost Pol (HKV / TU Delft), Stephan Rikkert (TU Delft), Guus Rongen (TU Delft), Rinus Scheele (KNMI), Julius Schlumberger (TU Delft), Peter Siegmund (KNMI), Kymo Slager (Deltares), Frederiek Sperna Weiland (Deltares), Bart Strijker (HKV / TU Delft), Henk v.d. Brink (KNMI), Janko van Beek (Erasmus MC), Marion van den Bulk (TU Delft), Bart van den Hurk (Deltares), Tim van Emmerik (Wageningen UR), Kees van Ginkel (VU Amsterdam / Deltares), Mick van Haren (TU Delft), Margreet van Marle (Deltares), Malou van Schaijk (TU Delft), Dennis Wagenaar (Nanyang TU), Davide Wüthrich (TU Delft)@en

This is an update to PII: S2352711018300608 and S2352711019302419 In this paper, we present three main improvements of ANDURIL and its python version ANDURYL. First the MATLAB version ANDURIL is brought to the Python version standard by implementing (i) user defined quantiles and (ii) the possibility to deal with missing values. Second, the computational engines of both ANDURIL and ANDURYL were significantly improved making calculation time lower and improving further accuracy. Finally a standalone Graphical User Interface is presented which we believe will make the software more accessible to practitioners of Cooke’s method.@en

With the intention to reduce the negative  effects of ongoing bed erosion, as well as to  improve several other river functions such as  protection against floods, provision of safe and  efficient navigation and ecology, a ‘pilot project  longitudinal training dams’ was initiated. The  training dams have recently been implemented  in the Waal between Tiel and Sind Andries. In  this project, river groynes have been  completely removed and replaced by dams  that lie parallel to the river bank. With help of  the longitudinal training dams, a two-channel  river system is created in which the river is  divided into a main and side channel. The  dams are placed in a continuous manner with  openings in between that are relatively small  compared to the dam length. At the beginning  and end of the dam an inlet and outlet region is  situated, as shown in Fig. 1.  The combination of inlet and openings  allows for water and sediment to be divided  between the main and the side channel. Both  inlet and openings are constructed with the  help of a porous rock-layer. The crest heights  can be altered by adding or removing stones.  This is expected to influence the amount of  water and sediment entering the side channel  and can therefore be used as a regulation tool.  @en

The one-dimensional model provides useful insight inm impacts of regulatory design parameters of longitudinal training dams. Preliminary conclusions include: 1. Adjusting the inlet weir height could influence the discharge distribution towards the side channel between 1% (fully closed but porous inlet) to 30% (fully open and porous inlet). 2. Smaller side channel width (e.g. sedimentation of the side channel) will result in significant reduction of discharge distribution toward the side channel. 3. The porous discharge through the inlet and openings is negligible compared to the weir discharge.@en