The Netherlands is a low-lying coastal area and therefore threatened by both extreme river discharges from the Meuse and Rhine rivers and storm surges along the North Sea coastline. To date, in most flood risk analyses these two hazardous phenomena are considered independent. However, if there were a dependence between high sea water levels and extreme discharges this might result in higher design water levels, which might consequently have implications for flood protection policy in the Netherlands. In this study we explore the relation between high sea water levels at Hoek van Holland and high river discharges at Lobith. Different from previous studies, we use physical models forced by the same atmospheric forcing leading to concomitant and consistent time series of storm surge conditions and river discharge. These time series were generated for present day conditions as well as future climate projections and analysed for dependence within the upper tails of their distribution. In this study, dependence between the discharge at Lobith and storm surge at Hoek van Holland was found, and the dependence was highest for a lag of six days between the two processes. As no significant dependence of the threats was found for cases without time lag, there is no need for considering dependence in flood protection and policy making. Although future climate change is expected to lead to more extreme conditions in river discharges, we cannot conclude from this study that it will change the magnitude of the dependence for extreme conditions.

Uncertainties in the rate and magnitude of sea-level rise (SLR) complicate decision making on coastal adaptation. Large uncertainty arises from potential ice mass-loss from Antarctica that could rapidly increase SLR in the second half of this century. The implications of SLR may be existential for a lowlying country like the Netherlands and warrant exploration of high-impact low-likelihood scenarios. To deal with uncertain SLR, the Netherlands has adopted an adaptive pathways plan. This paper analyzes the implications of storylines leading to extreme SLR for the current adaptive plan in the Netherlands, focusing on flood risk, fresh water resources, and coastline management. It further discusses implications for coastal adaptation in low-lying coastal zones considering timescales of adaptation including the decisions lifetime and lead-in time for preparation and implementation. We find that as sea levels rise faster and higher, sand nourishment volumes to maintain the Dutch coast may need to be up to 20 times larger than to date in 2100, storm surge barriers will need to close at increasing frequency until closed permanently, and intensified saltwater intrusion will reduce freshwater availability while the demand is rising. The expected lifetime of investments will reduce drastically. Consequently, step-wise adaptation needs to occur at an increasing frequency or with larger increments while there is still large SLR uncertainty with the risk of under-or overinvesting. Anticipating deeply uncertain, high SLR scenarios helps to enable timely adaptation and to appreciate the value of emission reduction and monitoring of the Antarctica contribution to SLR.

Abstract. This paper discusses a new method for flood risk assessment in river deltas. Flood risk analysis of river deltas is complex, because both storm surges and river discharges may cause flooding and the effect of upstream breaches on downstream water levels and flood risk must be taken into account. This paper presents a Monte Carlo-based flood risk analysis framework for policy making, which considers both storm surges and river flood waves and includes effects from hydrodynamic interaction on flood risk. It was applied to analyse societal flood fatality risk in the Rhine-Meuse delta.

Abstract. We present a new method to generate spatially coherent river discharge peaks over multiple river basins, which can be used for continental event-based probabilistic flood risk assessment. We first extract extreme events from river discharge time series data over a large set of locations by applying new peak identification and peak-matching methods. Then we describe these events using the discharge peak at each location while accounting for the fact that the events do not affect all locations. Lastly we fit the state-of-the-art multivariate extreme value distribution to the discharge peaks and generate from the fitted model a large catalogue of spatially coherent synthetic event descriptors. We demonstrate the capability of this approach in capturing the statistical dependence over all considered locations. We also discuss the limitations of this approach and investigate the sensitivity of the outcome to various model parameters.

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