A novel coastal landscape model for sandy systems: Community base for interdisciplinary research on coastal evolution

A common measure to mitigate erosion along sandy beaches is the implementation of sand nourishments. The design and societal acceptance of such a soft mitigation measure demands information on the expected evolution at various time scales ranging from a storm event to multiple decades. Process-based morphodynamic models are increasingly applied to obtain detailed information on temporal behaviour. This paper discusses the process-based morphodynamic model applied to the Sand Motor and how the morphodynamic forecasts have benefitted from the findings of an interdisciplinary research program called NatureCoast. The starting point is the morphodynamic prediction of the Sand Motor made for an Environmental Impact Assessment in 2008 before construction began. After the construction, the model computations were optimized using the first-year field measurements and insights by applying advanced model features. Next, an integrated model was developed that seamlessly predicts the morphodynamics in both the subaqueous and subaerial domains of the Sand Motor. Decadal predictions illustrate the need to be able to resolve the marine and aeolian processes simultaneously in one modelling framework in the case of dynamic coastal landscapes. Finally, a novel morphodynamic acceleration technique was developed that allows for predicting the morphodynamics for multiple decades while incorporating storm events in one simulation. Combining the above-mentioned developments has led to a unique, open-source, process-based landscape tool for (complex) coastal sandy systems, which can stimulate further collaboration between research communities. Moreover, this work demonstrates the evolution from mono- to interdisciplinary forecasts of coastal evolution

Regional-scale analysis of dune-beach systems using Google Earth Engine

Coastal sand dunes provide a large variety of ecosystem services, among which the inland protection from marine floods. Nowadays, this protection is fundamental, and its importance will further increase in the future due to the rise of the sea level and storm violence induced by climate change. Despite the crucial role of coastal dunes and their potential application in mitigation strategies, the phenomenon of the coastal squeeze, which is mainly caused by the urban sprawl, is progressively reducing the extents of the areas where dune can freely undergo their dynamics, thus dramatically impairing their capability of providing ecosystem services. Aiming to embed the use of satellite images in the study of coastal foredune and beach dynamics, we developed a classification algorithm that uses the satellite images and server-side functions of Google Earth Engine (GEE). The algorithm runs on the GEE Python API and allows the user to retrieve all the available images for the study site and the chosen time period from the selected sensor collection. The algorithm also filters the cloudy and saturated pixels and creates a percentile-composite image over which it applies a random forest classification algorithm. The classification is finally refined by defining a mask for land pixels only. According to the provided training data and sensor selection, the algorithm can give different outcomes, ranging from sand and vegetation maps, beach width measurements, and shoreline time evolution visualization. This very versatile tool that can be used in a great variety of applications within the monitoring and understanding of the dune-beach systems and associated coastal ecosystem services. For instance, we show how this algorithm, combined with machine learning techniques and the assimilation of real data, can support the calibration of a coastal model that gives the natural extent of the beach width and that can be, therefore, used to plan restoration activities

Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas

In recent years, satellite imagery has shown its potential to support the sustainable management of land, water, and natural resources. In particular, it can provide key information about the properties and behavior of sandy beaches and the surrounding vegetation, improving the ecomorphological understanding and modeling of coastal dynamics. Although satellite image processing usually demands high memory and computational resources, free online platforms such as Google Earth Engine (GEE) have recently enabled their users to leverage cloud-based tools and handle big satellite data. In this technical note, we describe an algorithm to classify the coastal land cover and retrieve relevant information from Sentinel-2 and Landsat image collections at specific times or in a multitemporal way: the extent of the beach and vegetation strips, the statistics of the grass cover, and the position of the shoreline and the vegetation–sand interface. Furthermore, we validate the algorithm through both quantitative and qualitative methods, demonstrating the goodness of the derived classification (accuracy of approximately 90%) and showing some examples about the use of the algorithm’s output to study coastal physical and ecological dynamics. Finally, we discuss the algorithm’s limitations and potentialities in light of its scaling for global analyses

Assessing climate change impacts on the stability of small tidal inlets:Part 1 - Data poor environments

Bar-built or barrier estuaries (here referred to as Small tidal inlets, or STIs), which are commonly found along wave-dominated, microtidal mainland coasts, are highly likely to be affected by climate change (CC). Due to their pre-dominance in tropical and sub-tropical regions of the world, many STIs are located in developing countries, where STI related activities contribute significantly to the national GDPs while community resilience to coastal changes is low, with the corollary that CC impacts on STIs may lead to very serious socio-economic consequences. While assessing CC impacts on tidal inlets is in general difficult due to inherent limitations of contemporary numerical models where long term morphodynamic simulations are concerned, these difficulties are further exacerbated due to the lack of sufficient model input/verification data in often data poor developing country STI environs. As a solution to this problem, Duong et al. (2016) proposed two different process based snap-shot modelling approaches for data poor and data rich environments. This article demonstrates the application of Duong et al.'s (2016) snap-shot modelling approach for data poor environments to 3 case study sites representing the 3 main STI types; Permanently open, locationally stable inlets (Type 1), Permanently open, alongshore migrating inlets (Type 2) and Seasonally/Intermittently open, locationally stable inlets (Type 3). Results show that Type 1 and Type 3 inlets will not change Type even under the most extreme CC driven variations in system forcing considered here. Type 2 inlets may change into Type 1 when CC results in a reduction in annual longshore sediment transport. Apart from Type changes, CC will affect the level of inlet stability and some key behavioural characteristics (e.g. inlet migration distances, inlet closure times). In general, CC driven variations in annual longshore sediment transport rates appear to be more relevant for future changes in inlet stability and behaviour, rather than sea level rise as commonly believed. Based on model results, an inlet classification scheme which, for the first time, links inlet Type with the Bruun inlet stability criteria is presented

Modelling of annual sand transports at the Dutch lower shoreface

Dutch coastal policy aims for a safe, economically strong and attractive coast. This is achieved by maintaining the part of the coast that support these functions; the coastal foundation. The coastal foundation is maintained by means of sand nourishments. Up to now, it has been assumed that net transports across the coastal foundation's offshore boundary at the 20 m depth contour are negligibly small. In the framework of the Coastal Genesis 2.0 program we investigate sand transports across this boundary and across other depth contours at the lower shoreface. The purpose of this paper is to provide knowledge for a well-founded choice of the seaward boundary of the coastal foundation. The lower shoreface is the zone where the mixed action of shoreface currents (tide-, wind- and density gradient driven) and shoaling and refracting waves is predominant. Transport rates are relatively small and hence the bed levels in the lower shoreface undergo relatively slow changes

Cross-shore stratified tidal flow seaward of a mega-nourishment

The Sand Engine is a 21.5 million m3 experimental mega-nourishment project that was built in 2011 along the Dutch coast. This intervention created a discontinuity in the previous straight sandy coastline, altering the local hydrodynamics in a region that is in influenced by the buoyant plume generated by the Rhine River. This work investigates the response of the cross-shore stratified tidal flow to the coastal protrusion created by the Sand Engine emplacement by using a 13 hour velocity and density survey. Observations document the development of strong baroclinic-induced cross-shore exchange currents dictated by the intrusion of the river plume fronts as well as the classic tidal straining which are found to extend further into the nearshore (from 12 to 6m depth), otherwise believed to be a mixed zone