Estimating future coastline changes along Holland coast, under different sea level rise scenarios, using a probabilistic approach

Due to climate change and sea level risethe coastal zones are getting exposed to increasing risks like coastalrecession, putting in risk human lives and coastal infrastructure being worthbillions of dollars. Low lying countries like the Netherlands are consideredmore vulnerable to the effects of sea level rise. Large parts of the Dutchcoast have been eroding for centuries and nourishments schemes of approximately12 million m3 have been implemented annually in order to maintainthe coastline as it was in 1990. However, the future dune erosion will further increasedue to the impacts of climate change and hence the adaptation strategies shouldbe in line with the accelerated sea level rise and the possible effects thatmay bring. The most commonly used method to assess sealevel rise impacts on shorelines is the Bruun rule. However,Bruun rule’s deterministic nature cannot align with the risk-based framework thatcoastal zone management requires nowadays. This necessity initiated thedevelopment of a process-based model, the Probabilistic Coastline Recession(PCR) model, estimating the future coastal recessions in a probabilisticapproach. In this research, the PCR framework wasapplied at eleven locations along the Holland coast, in the Netherlands, underthree different SLR scenarios, the RCP4.5, RCP8.5 and Deltascenario. The availabilityof coastal profile data (from 1965 until now) and coastline position data (from1843 till 1980) made the Holland coast an ideal location to explore and extendthe applicability of the PCR framework. The most relevant assumptions for thiscoast were identified and explored. The recovery rate of the dune was a weakpoint of the PCR model and Holland coast was an interesting area to be tested.Three approaches of calibrating the natural recovery rate of the dunes werefollowed. In addition, the alongshore sediment transport which was assumednegligible to the previous case studies, in this work it was integrated intothe PCR model and pointed out that its contribution is important to the PCR. For the eleven selected coastal profiles,20,000 simulations of 81 years (2020-2100) have been conducted and for everysimulation the most landward position of the coastline in every calendar yearhas been recorded. Hence, an empirical distribution of coastline recession forevery future year has been constructed. The ranges of the expected retreats in2100 (relative to 2020) for the different SLR scenarios are:0.5 m-155 m (for RCP4.5), 6 m-194 m (for RCP8.5) and18 m-172 m (for Deltascenario), corresponding to the 50 %exceedance probability values of the cumulative distribution function of thecoastline retreat. The average values of the coastal retreat for 2100 are 61 m,73 m and 97 m for RCP4.5, RCP 8.5, and Deltascenario respectively.The relevant average erosion volume by 2100 are 1664 m3/m,2005 m3/m and 2665 m3/m. According to thefindings, in 2100 the relative increase in volume loss along the entire theHolland coast is expected to be 95 %, 121 % and 173 %respectively for RCP4.5, 138 % for RCP8.5 and 195 % for Deltascenario.Finally, the results were compared to those raised from the Bruun rule method. Accordingto the findings, the majority of the profiles showing an erosive trend in thepast (before the ‘hold-the-line’ policy) raised slightly more conservativeresults when implementing the PCR model rather than when applying the Bruunrule method- especially under the Deltascenario. On the other hand, theBruun rule method is more conservative than PCR model for most of the accretiveprofiles. The PCR model can now be explored tolocations where the longshore sediment transport is not negligible. Theapproach followed in this study allows investigating the ability of the modelfor future coastal retreat estimates when a construction of a hard defence structureor a port may change abruptly the longshore sediment transport. Last, this studyadvances the PCR framework and can be a valuable assistance in the course offurther improving the model. Offshore and Dredging Engineerin