A modeling approach for aeolian sediment input to coastal dunes

Coastal dune evolution results from a complex balance between beach and dune budgets, wind and wave activity, and a number of other factors which vary over different temporal and spatial scales. Traditional approaches based on instantaneous transport equations are insufficient to predict sediment input to the foredunes at medium scales, and inferring information at larger scales from short-term experiments results problematic without knowledge of the timing and magnitude of particular transport events. There is a need to explore different ways to model aeolian activity at a scale of months to years, where most management practices take place. Challenges consist in developing appropriate instrumentation, methodologies to analyze the output data, and theoretical frameworks where to place new modeling approaches. This paper summarizes the efforts taken at Greenwich Dunes (Canada) to develop strategies to quantify/model sediment input to the foredune at a medium scale. Fieldwork consisted on the deployment of a remote sensing station based on digital cameras and coupled with anemometers and safires. Data was processed using ArcGIS 9.2 and PCI Geomatica 9.1, and managed by an ArcCatalog Geodatabase. Time series covered factors such as shoreline position, fetch distances, or maps of surficial moisture content. Modelling followed two steps: an initial filtering technique that isolated potential transport events and determined when transport took place, and a second stage that calculated their magnitude while keeping the spatial and temporal variability of the factors involved. Filters included the presence of ice/snow, the range of wind angles that potentially deliver sediment to the dunes, a minimum threshold wind speed, and a maximum percentage of surficial moisture content, all of which could shut down aeolian sediment transport. Preliminary results show that this modeling approach can produce improved predictions of annual sediment supply to the foredune compared to models based on wind speed and direction only.