An integrated methodology to forecast the efficiency of nourishment strategies in eroding deltas.

Many deltas across the globe are retreating, and nearby beaches are undergoing strong erosion as a result. Among soft and prompt solutions, nourishments are the most heavily used. This paper presents an integrated methodology to forecast the efficiency of nourishment strategies by means of wave climate simulations, wave propagations with downscaling techniques, computation of longshore sediment transport rates and application of the one-line model. It was applied to an eroding deltaic beach (Guadalfeo, southern Spain), where different scenarios as a function of the nourished coastline morphology, input volume and grain size were tested. For that, the evolution of six scenarios of coastline geometry over a two-year period (lifetime of nourishment projects at the study site) was modelled and the uncertainty of the predictions was also quantified through Monte Carlo techniques. For the most efficient coastline shape in terms of gained dry beach area, eight sub-scenarios with different nourished volumes were defined and modelled. The results indicate that an input volume around 460,000m3 is the best strategy since nourished morphologies with higher volumes are more exposed to the prevailing storm directions, inducing less efficient responses. After setting the optimum coastline morphology and input sediment volume, eleven different nourished grain sizes were modelled; the most efficient coastline responses were obtained for sediment sizes greater than 0.01m. The availability of these sizes in the sediment accumulated upstream of a dam in the Guadalfeo River basin allows for the conclusion that this alternative would not only mitigate coastal erosion problems but also sedimentation issues in the reservoir. The methodology proposed in this work is extensible to other coastal areas across the world and can be helpful to support the decision-making process of artificial nourishment projects and other environmental management strategies.