Modelling wave attenuation by saltmarsh using satellite-derived vegetation properties

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Saltmarshes are increasingly recognised an important asset in coastal management as they dissipate wave energy and thus reduce the potential for coastal flooding. The frontal surface area (FSA) and the drag coefficient (Cd) are parameters commonly used in wave attenuation models to express the resistance of vegetation structure to incident waves. The FSA of vegetation represents the vertical surface area facing incoming waves which is calculated as the product of height, diameter and density whereas Cd is often used as tunable parameter that represents the vegetation-wave interactions that relies on both vegetation properties and wave conditions. Despite their importance in numerical modelling, substantial uncertainty remains in obtaining these parameters in the field due to the time-intensive and relatively expensive nature of data collection. An alternative structural vegetation parameter that can be included in wave attenuation models is the leaf area index (LAI). The primary advantage of the LAI is that it can be readily derived from satellite imagery, and thus provides a low-cost, fast alternative to field data collection. However, to date, its incorporation in widely-used coastal engineering models is lacking. The aim of this paper is to verify the use of remote-sensed LAI in numerical wave models as an alternative to FSA. Here, the widely used XBeach model for simulating storm impacts on a range of coastal systems is applied to two open coast sites with extensive saltmarsh; Chesapeake Bay, USA, and Brancaster, UK. To assess the performance of wave attenuation modelling using both methods, we compared the use of remote-sensed LAI from satellite imagery and field-based FSA as inputs into the model. The LAI-based model provides similar levels of accuracy as the FSA-based model. Likewise, higher uncertainties related to plant height, diameter, and density were found in the FSA-based model than in the LAI-based model. Therefore, the LAI-based model provides the advantage of a low-cost and fast method to accurately estimate and predict wave attenuation by vegetation using numerical models such as XBeach. Our practical application in the Brancaster site exemplifies an easy and fast approach to obtaining structural parameters of saltmarsh vegetation and estimating wave attenuation between natural and artificial saltmarshes as well as between seasons.