MODELS TO PREDICT NET AND GROSS SEDIMENTATION IN LAKES

This study models net and gross sedimentation in lakes. The fluxes of material play an important role in most lake contexts. They influence transport, bio-uptake and ecological effects of most toxins and nutrie nts. The aim is to present a new type of 'mixed' dynamic/statistical m odel and to discuss advantages and disadvantages with this approach. E mpirical data to validate the model emanate from sediment traps from 2 5 lakes, Rates of gross sedimentation in traps were compared to catchm ent and morphometric parameters determined from maps in an attempt to identify the processes that influence sedimentation. The most importan t variables are relative depth (linked to resuspension), forest and op en land cover (of the near area of the catchment), relief of catchment , coverage of mires and lakes, and the lake water retention time. This study shows that it is easy to lose predictive power in dynamic model s relative to empirical models, in which each parameter constant and x -parameter automatically accounts for the complexities in natural ecos ystems. In dynamic models empirical knowledge is replaced by logical c onstructions, which may not be accurate. The mixed model would need to be extended with new large submodels to predict, for example mean dis tribution coefficient of total-P in tributaries, tributary suspended l oad, rate of sedimentation and resuspension rate. This would result in a huge 'prescriptive' model with wide uncertainty limits for the pred icted mean values for net and gross sedimentation.