R. de Winter1*, M. Haasnoot1,2, W. van de Lageweg3, V. Bax3, T. van der Heide4,5, E. Lansu4, S. Hover4,5, A. Luijendijk1,6, T. Kettler6, A. van Oudenhoven7, H. Geukes7, M. de Schipper6

1 Deltares; 2 Utrecht University, 3 HZ University of Applied Sciences, 4 Royal Netherlands Institute for Sea Research (NIOZ), 5 University of Groningen, 6 TUDelft, 7 Leiden University

*corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Introduction

The sandy coastal zone of the Netherlands hosts a wide range of functions; it provides safety against flooding, is important for recreation and has high nature values. The sandy coast is regularly nourished to compensate for erosional losses and to allow for growing with a rising sea level. It is expected that nourishment volumes will increase in the future under accelerated sea-level rise (SLR). In this study, we explore how the solution space for nourishment strategies could be explored when multiple functions of the coastal zone are considered.

Methods

Since there is not yet a model that considers all relevant processes and indicators needed for a multi-objective assessment of nourishments, we combine the Dynamic Adaptive Policy Pathways (DAPP; Haasnoot et al. 2019) with the XLRM framework (Lempert et al. 2019). This combination enables us to evaluate how nourishment strategies affect a range of objectives under changing conditions. The resulting model is the first to consider all processes and indicators relevant for our multi-objective assessment of pathways. With the combined framework we can explicitly link the objectives to the different functions and the system characteristics these functions need. The result can then be evaluated for different futures and adaptation pathways using a set of outcome indicators (Figure 1). To test this novel approach, we applied it to a limited set of objectives (coastal safety, functional coastal ecosystem and recreation) and assessed the possibilities of the method with established outcome indicators that support the objectives. Total nourishment volumes are directly related to future SLR-rates; however, they can vary in frequency and type of nourishment, resulting in different adaptation strategies.

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Figure 1: Relation between policy objective, the system characteristic that supports this objective and the outcome indicator that describes the state of the system. Changing conditions and adaptation measures impact the system characteristic.

Results and Conclusion

We found that the solution space of sandy strategies under lower SLR-rates is relatively, with a large range of possible sandy strategies. However, as SLR-rates and nourishment volumes increase, the solution space becomes smaller as strategies with small scale and/or infrequent nourishments become less feasible. The strategies therefore converge in nourishment volume and frequency. For the chosen objectives a literature assessment shows that indicators are known that determine under which set of conditions it would be preferable (for that specific objective) to switch strategies. We argue that this integrated approach allows us to evaluate how an attractive multifunctional coast can be maintained when moving towards higher nourishment volumes under accelerating SLR-rates.

References

Lempert, R.J. (2019). Robust Decision Making (RDM). In: Marchau, V., Walker, W., Bloemen, P., Popper, S. (eds) Decision Making under Deep Uncertainty. Springer, Cham.
Haasnoot et al. 2019. Dynamic Adaptive Policy Pathways. In: Marchau, V., Walker, W., Bloemen, P., Popper, S. (eds) Decision Making under Deep Uncertainty. Springer, Cham.

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