Framework for assessment of relative pollutant loads in streams with limited data

A framework that integrates two data-driven techniques is proposed and developed to assess fecal coliform loadings in natural streams. A relationship between transport medium (streamflow) and non-conservative pollutant (fecal coliform) load is first developed using conventional regression technique. The spatial distribution of the fecal load over watersheds is then captured using artificial neural network through a disaggregation scheme. Streamflow, as a surrogate for non-conservative fecal load, has been used in the disaggregation process. The framework is applied to an area that encompasses four USGS 8-digit Hydrologic Unit Code (HUC) watersheds in he southeastern region of Kentucky, USA. The study attempts to address two major issues: (i) assessment of relative pollutant loads from watersheds and (ii) evaluation into possible reduction in the number of monitoring stations to meet the budgetary constraints. Preliminary results indicate the potential of this approach in assessing the relative fecal loading contribution from different watersheds with the help of conservative hydrological parameters, especially in data-poor conditions.