Rising sea levels and anthropogenic activities are intensifying pressure on coastal zones. Process-based coastal morphodynamic models are increasingly used to forecast natural and anthropogenic beach morphology changes at various spatio-temporal scales. Such predictions are crucial for the sustainable management of coasts. However, process-based morphodynamic models contain numerous free model parameters, introducing uncertainty in predictions. Systematically exploring the parameter space has remained a challenge due to the high computational demands of these morphodynamic models. Here, for the first time we quantify parameter uncertainty of a state-of-the-art morphodynamic (2DH) coastal area model (Delft3D) by systematically varying key model parameters, utilizing the Dutch national supercomputer: SurfSara. We simulate the initial (14-month) response of the Sand Engine, an innovative mega-nourishment placed along the Holland coast with 1024 strategically chosen parameter sets. The resulting simulations are analysed using Generalised Likelihood Uncertainty Estimation (GLUE) to attain probability distributions of morphological evolution and its sensitivity to parameter settings. The model simulations all show an alongshore redistribution of sediment resembling what is observed. However, even simulations with similar skill reveal substantial differences in predicted morphologies (same order of magnitude as the predictions’ 90% confidence interval). Our findings suggest that identifying a single optimal parameter set for coastal numerical models might be unrealistic, even for well-defined cases like large-scale coastal interventions, and that an ensemble modeling approach that quantifies parameter uncertainty is likely better suited for studies relying on morphodynamic predictions. Furthermore, we find that the magnitude of the uncertainty induced by the free model parameters is comparable to that resulting from year-to-year variations in wave climate, underscoring the importance of including both sources in uncertainty assessments. ... Read more

Because of the large uncertainties associated with rising sea levels, present coastal management is inclined towards utilizing soft, nature-inclusive, and adaptive measures over traditional hard protection structures. This shift towards more sustainable and multi-functional solutions is often called Building with Nature. In the coastal zone, these nature-based solutions typically use sand as a building material, have a larger scale than traditional sand nourishments, and serve multiple purposes. The definition of effectiveness of these nature-based solutions can vary for the different project aims. This means that models that predict future states of these interventions must be able to forecast various coastal state indicators (CSIs), such as dune volume, beach width, or habitat area. Using loose-placed sand in coastal protection is intrinsically associated with increased uncertainties of the coastal state compared to more traditional hard protection measures. Therefore, this dissertation examines uncertainty in predicting largescale sandy interventions in the coastal zone and its effect on different CSIs. First, observations of a recent large-scale intervention, the Hondsbosse Dunes, were analyzed to illustrate the evolution of various CSIs in the first years after placement. This 35 million m3 nourishment included a shoreface, beach, and dune and was built in front of a sea dike to serve as flood protection while creating space for nature and recreation. The nourishment created a significant coastline curvature, leading to erosion in the central, most protruding part of the nourishment, bordered by zones with accretion. The artificial cross-shore profile rapidly mimics the surf zone slope and beach width of adjacent beaches. At the same time, the dune volume increased, and the dune foot migrated seaward along the entire nourished site, regardless of whether the subaqueous profile gained or lost sediment. These contrasting trends of different CSIs highlight the need to predict changes in CSIs individually to assess a project’s effectiveness Next, the balance between different sources of uncertainty in predicting the evolution of a large-scale sandy intervention was investigated for the Sand Engine mega-nourishment. The sources of uncertainty in such predictions can be either intrinsic or epistemic. Intrinsic sources are inherent in the system, whereas epistemic sources are related to limitations in knowledge (related to the model). The relative importance of intrinsic and epistemic uncertainty was investigated using a probabilistic framework in which sediment transport is considered a function of random wave forcing (intrinsic) and model (epistemic) uncertainty, calculating transport using a one-line model. The applied wave climate variability was obtained from long-term wave observations, whereas model uncertainty was quantified using Generalized Likelihood Uncertainty Estimation (GLUE) relying on monthly observations. .. ... Read more

Sandy nourishments can provide additional sediment to the coastal system to maintain its recreational or safety function under rising sea levels. These nourishments can be implemented at sandy beach systems, but can also be used to reinforce gray coastal infrastructure (e.g., dams, dikes, seawalls). The Hondsbossche Dunes project is a combined shoreface, beach, and dune nourishment of 35 million m3 sand. The nourishment was built to replace the flood protection function of an old sea-dike while creating additional space for nature and recreation. This paper presents the evolution of this newly created sandy beach system in the first 5 years after implementation based on bathymetric and topographic surveys, acquired every three to six months. A significant coastline curvature is created by the nourishment leading to erosion in the central 7 km bordered by zones with accretion. However, over the five-year period, net volume losses from the project area were less than 5% of the initial nourished sand volume. The man-made cross-shore beach profile rapidly mimics the characteristics of adjacent beaches. The slope of the surfzone is adjusted within two winters to a similar slope. The initially wide beaches (i.e., up to 225 m) are reduced to about 100 m-wide. Simultaneously, the dune volume has increased and the dune foot migrated seaward at the entire nourished site, regardless of whether the subaqueous profile gained or lost sediment. Our results show that the Hondsbossche Dunes nourishment, built with a natural slope and wide beach, created a positive sediment balance in the dune for a prolonged period after placement. As such, natural forces in the years after implementation provided a significant contribution to the growth in dune volume and related safety against flooding. ... Read more

Sand nourishments are increasingly applied as adaptive coastal protection measures. Predictions of the evolution of these nourishments and their impact on the surrounding coastline contain many uncertainties. The sources that add to this uncertainty can be delineated between intrinsic and epistemic uncertainty, i.e. inevitably in the system or related to knowledge limitations. Effects of intrinsic uncertainty (e.g. due to wave climate variability) on coastal evolution can be significant. In studying these effects, it has often been assumed that intrinsic uncertainty is dominant over epistemic uncertainty (e.g. introduced by the model), yet the magnitude of both contributions have not been explicitly quantified to assess the validity of this assumption. This paper examines the relative importance of intrinsic and epistemic uncertainty in coastline modeling of a large-scale nourishment. It uses a probabilistic framework in which sediment transport is considered to be a function of random wave forcing (intrinsic) and model (epistemic) uncertainty, calculating transport using a one-line model. The test case for this analysis is the mega-nourishment, the Sand Engine, located in the Netherlands. The applied wave climate variability is obtained from long term wave observations, whereas model uncertainty is quantified using the Generalized Likelihood Uncertainty Estimation (GLUE) method relying on monthly observations. We find that the confidence intervals on predicted volume losses increase substantially when including both intrinsic and epistemic sources of uncertainty. A global sensitivity analysis shows that ignoring model uncertainty would underestimate the variance by at least 50% after a 2.5-year simulation period for the Sand Engine, hence producing significant overconfidence in the results. These findings imply that for coastal modeling purposes a dual approach should be considered, evaluating both epistemic and intrinsic uncertainties. ... Read more

There are different sources of uncertainty in morphological modeling on time scales of years. The standard deterministic modelling approach does not provide any information on the amount of uncertainty contained in a forecast. This lack of information could provide a false sense of accuracy and skill. Quantitative insight in these prediction uncertainties is therefore of crucial importance for decision making in coastal engineering and management. ... Read more

In the present time of sea-level rise and climate change a global shift has occurred toward sandy coastal protection measures and Building with Nature. These type of protection measures impose extra uncertainty on the instantaneous state of the coastal system over time for which present deterministic forecasting techniques are not capable of providing necessary information on uncertainties and hence could display a false sense of accuracy and skill. At present in long term morphological modeling a full systemic approach for uncertainty assessment has not yet been applied. This paper investigates the use of a Bayesian Network as a tool for uncertainty assessment in decadal scale morphological modeling for the evolution of a mega nourishment at the Dutch North-Holland coast, the Hondsbossche Dunes (HBD). The Bayesian Network is trained with an existing set of model data and field data of one year bed development. The Bayesian Network successfully transfers the bandwidth in input variables, model uncertainty and calibration uncertainty to an uncertainty bandwidth around the output parameter of choice. ... Read more