Event-based total suspended sediment particle size distribution model

Abstract

One of the most challenging modelling tasks in hydrology is prediction of the total suspended sediment particle size distribution (TSS–PSD) in stormwater runoff generated from exposed soil surfaces at active construction sites and surface mining operations. The main objective of this study is to employ gene expression programming (GEP) and artificial neural networks (ANN) to develop a new model with the ability to more accurately predict the TSS–PSD by taking advantage of both event-specific and site-specific factors in the model. To compile the data for this study, laboratory scale experiments using rainfall simulators were conducted on fourteen different soils to obtain TSS–PSD. This data is supplemented with field data from three construction sites in Ontario over a period of two years to capture the effect of transport and deposition within the site. The combined data sets provide a wide range of key overlooked site-specific and storm event-specific factors. Both parent soil and TSS–PSD in runoff are quantified by fitting each to a lognormal distribution. Compared to existing regression models, the developed model more accurately predicted the TSS–PSD using a more comprehensive list of key model input parameters. Employment of the new model will increase the efficiency of deployment of required best management practices, designed based on TSS–PSD, to minimize potential adverse effects of construction site runoff on aquatic life in the receiving watercourses.