The Netherlands is world famous when it comes to coastal defence. The world is always changing, therefore the Netherlands, together with many countries, has to adapt constantly to the climate. This constant change means that in particular the coastline of the Netherlands requires extra attention because of the uncertainty of sea level rise. The Dutch coastline is protected by means of a static approach. However the intention for the future is to apply an adaptable design to better handle sea level rise. This report focuses on a method that uses a dynamic approach with the aim of keeping the Netherlands protected against rising sea levels that are uncertain. This dynamic approach consist of several pathways that each consist of different actions. With the help of evaluation-criteria, the pathways in the dynamic approach are evaluated. The outcome of this evaluation is described and recommendations for future research are given. The report focuses on keeping the coastline of the south-west of the Netherlands protected against the uncertainty of the rising sea. This will be done with the help of a dynamic approach. First, an area analysis was carried out to find out what aspects should be given the most attention. After that, the method of DAPP is used to function as a dynamic approach. This Dynamic Adaptive Pathway Policy is then used to implement the different pathways in a structured way. These pathways are made up of different actions. These actions are existing plans presented by Deltares and they form a big list. Not all plans do function properly in order to function as a flood protection and so a selection method is used to extract the right plans out of this list. The requirements that are used to select the right plans have its main focus on protecting the hinterland against sea level rise, storm surge and wave load. The extracted plans that function as an action are implemented in the different pathways of the DAPP. To evaluate these pathways, evaluation-criteria are used in a Multi Criteria Analysis. These criteria are extracted from sources like a stakeholder analysis, old and new watermasters and a brainstorm session with the group members who act through their own accumulated expertise. The extracted criteria in combination with determined weighting factors are placed in a Multi Criteria Analysis after which the pathways have been assessed individually. This evaluation process led to some pathways being iterated to a different shape for the final design of the DAPP. From the project can be concluded that the DAPP approach works well to combine different static plans into a comprehensive mitigation strategy. Secondly the evaluation criteria can be successfully derived from the old and new watermasters. The old watermasters already have one or more of their plans implemented. The new watermasters, are working on flood protection plans for the future in their daily life and have a lot of experience in the current engineering field. From the stakeholder analyses, the criteria can also be derived but than from the perspective of a variety of stakeholders. Thirdly, organizing criteria using a PESTLE (Political, Economic, Social, Technological, Environmental) and objectives tree has significant benefits for determining weighting factors. At last, it can be concluded that the main requirements used in this project are a good starting point, but they are only focused on reducing the flood risk (only technology). To select plans on a wider perspective(also Political, Economic, Social, Legal and Environmental), it is recommended to take a closer look to requirements from that perspectives. Advised is to consult experts in those fields to help with that.

Infrastructure is at risk to climate uncertainty due to a combination of long life spans, complexity of the systems it is embedded in, and the high investment costs often necessary. Current infrastructure planning approaches lose efficacy under deep uncertainty, necessitating new approaches that function better under conditions where the future cannot be predicted. The approaches that attempt to deal with this are also called Decision Making under Deep Uncertainty (DMDU). Bangladesh, the Netherlands, and New Zealand have all already adopted the use of these DMDU approaches in their delta protection guidance. One popular DMDU technique is Robust Decision Making (RDM). RDM can be seen as a computational extension of scenario planning, where proposed plans are tested against every potential combination of uncertainties. The Deep South Challenge (DSC), a New Zealand based research institute is looking into using RDM on a regional scale to discover vulnerabilities and identify robust strategies based on them. One of the test cases is in Helensville, where a Wastewater Treatment Plant (WTP) serving a small community is located in the middle of a floodplain. The WTP discharges its effluent into a strongly tidally influenced river, which drains the entire watershed and flows past large tidal flats into a dynamic estuarine environment. In order to identify potential vulnerabilities in the system, robust decision making uses a vulnerability analysis. This consists of a scenario discovery and global sensitivity analysis which sample through every combination of uncertainties to characterize the vulnerabilities of the system. In order to facilitate this, usually simple conceptual models are used due to the high number of runs necessary. However, these types of models can oversimplify complex physical processes and topography. These complicating factors are all present at the current case site selected by the DSC. This research investigates whether the added computational demand of a complex model is worth it compared to a simple conceptual model. To do this, two models are selected and forced for the same event. They are then compared on predicted system behavior, identified vulnerabilities, and potential policy advice. From a larger selection, the FLORES and SFINCS models were chosen. FLORES uses a simple hydrological balance to calculate the water level in the subbasins for each timestep. SFINCS is a reduced physics solver which only uses the Local Inertial Equations (LIE). Both models are forced by a compound rainfall and stormtide event for a storm with a 24 hour duration, for which they were calibrated and validated using the results of previous modeling efforts in the region. After the calibration and validation, a sensitivity analysis and scenario discovery were run for both models. The results of the sensitivity analysis show similar model behavior between FLORES and SFINCS. The upstream part of the model domain is only sensitive to rainfall, while the downstream part is mostly sensitive to storm surge and mean sea level, and to a lesser degree to tidal amplitude. This downstream part includes the wastewater treatment plant. Compared to SFINCS, the outcomes for FLORES on average overestimated water levels at the WTP. This is most likely due to the lack of flood attenuation taken into account by FLORES compared to SFINCS. The scenario discovery showed similar results for each model’s box describing 73% of the outcomes where failure occurs. Both models had the same three thresholds: storm surge, mean sea level, and tidal amplitude. The main difference between the boxes was the storm surge threshold being 21 centimeters lower for FLORES compared to SFINCS, indicating FLORES overestimates the water level at the WTP. The results of the scenario discovery also showed a linear relationship between these three factors. From this relationship, it is possible to see that, keeping all else similar, and with the same high tidal amplitude and storm surge, for SFINCS the plant only starts flooding when a mean sea level of at least 0.4 meters is reached, while for FLORES this is 0.25 meters. Using a RCP4.5 emissions scenario, a mean sea level of 0.25 meters will be reached in 20-30 years, and a mean sea level of 0.4 meters in 50 years. The proposed policy options for both SFINCS and FLORES would be to mitigate storm surge as long as possible, since the water level at the WTP is most sensitive to this factor. Once this is no longer possible, the WTP should be relocated. The results of SFINCS indicate this relocation is necessary later than for FLORES. These results show that while the behaviors exhibited by both models is relatively similar, the small differences in accuracy affect which are most likely due to the lack of flood attenuation taken into account for FLORES lead to a different timing of proposed adaptations. This leads to reason that while a conceptual model such as FLORES works well to identify important factors within the system, a more accurate model such as SFINCS can be more helpful once timing of adaptation becomes important. Further recommendations are to repeat this research for more models, further calibrate and validate the models, and to include scenario discovery methods that better deal with the found linear relation.

Poor and coastal regions are increasingly at risk from the effects of climate change. These risks are accompanied by a high level of uncertainty, which is also called deep uncertainty. Current planning approaches lose efficacy under deep uncertainty, necessitating new approaches that function better under these conditions. Decision making under deep uncertainty (DMDU) is the name for the family of approaches that attempt to deal with this level of uncertainty. Bangladesh, the Netherlands, and New Zealand have all already adopted the use of DMDU techniques. Research into local implementation of these techniques is also being done. Two DMDU techniques often deemed as complementary are robust decision making (RDM) and dynamic adaptive policy pathways (DAPP). RDM can be seen as a computational extension of scenario planning, where proposed plans are tested against every potential combination of uncertainties. DAPP is a flexible policy framework that allows decision-makers to keep long-term plans in mind while making short-term decisions. DAPP especially has seen increased adoption in national delta protection plans such as in the Netherlands, Bangladesh, and New Zealand. To design DAPP, currently a combination of many-objective robust optimization (MORO) and participatory processes are used. These methods both have their own issues. MORO requires the upfront specification of rules and policies and is computationally expensive, while the participatory approach is qualitative and can be insufficient when dealing with complex systems. RDM is seen as a potential improvement in supporting the DAPP policy structure in two main ways. First, RDM can be used to iteratively develop and/or stress-test potential actions and pathways. Second, the vulnerabilities identified through RDM can be used to lay the base for a monitoring system by identifying promising signposts and signals. While RDM is seen as a potentially helpful tool to support DAPP, there is a lack of studies that have established a systematic and analytical approach which uses the robust decision making process to support the development and monitoring of DAPP. This research proposes a novel approach based on literature to achieve this. The approach uses the vulnerabilities identified through RDM to iteratively inform and develop more robust actions and to lay the basis for the technical side of a monitoring system. This approach is then illustrated by way of the adaptation case of a wastewater treatment plant in Helensville, New Zealand. This wastewater treatment plant serves a small community and will have to retreat at some point in the future due to increasing risks from compound flooding, which are exacerbated by rising sea levels. The results of the case illustration show the benefits of using RDM to better understand vulnerabilities in the system in two main ways. First, the vulnerability analysis (which included a sensitivity analysis) helped to identify factors most important to the outcomes to inform potentially effective actions. Second, RDM helped in the development of the monitoring system. Those factors making up the identified vulnerabilities formed the basis of the technical signposts selected. Using the coverage-density tradeoff from the scenario discovery results, promising signals could be selected, although timing was not taken into account. This could potentially partially solve a common problem for monitoring DAPP: the selection of trustworthy signals. There were three main recommendations. The first is to further work through a case such as this, since due to time constraints only the first iteration of the process was followed in this research. This could help identify more potential benefits or issues. A main issue here is also how to identify when an action is fully developed, as the process could continue indefinitely. Second, it is recommended to do further research into determining adaptation tipping points using other scenario discovery methods, and to use the coverage-density tradeoff from the scenario discovery results to modify adaptation tipping points based on policy regret. Third, is to further the monitoring system by posing open questions to support the deliberation on signpost and signal selection, taking timing into account to identify triggers, and by adding a signpost map next to the signal map to visualize signpost interaction, hierarchy, and quality.