A framework for estimating pollutant export coefficients from long-term in-stream water quality monitoring data

Abstract

Modeling techniques for estimating pollutant loadings to water bodies range from simple export coefficient and regression models to more complex mechanistic models. All export coefficient models and many complex mechanistic models rely on pollutant export coefficients to estimate pollution sources and transport in large watersheds. Typically, pollutant export coefficients are determined by monitoring small catchments or field plots to isolate individual landuse contributions. However, pollutant export coefficients derived from small catchment and field plot scale studies cannot be confidently used in catchment-scale water quality modeling. The objective of this paper is to present a framework to estimate the export coefficients of pollutants from commonly available in-stream water quality monitoring data. A combination of readily and freely available statistical, spatial and hydrological tools and a multiple regression methodology is proposed to estimate pollutant export coefficients. A case study from the Fuji River catchment, Japan is presented where export coefficients of organic matters and nutrients are estimated. Most of the estimated pollutant export coefficients are significant at α equal to 0.05 and the landuse categories used in the multiple regression models explained more than 85% variability in loadings. These results are encouraging especially given the pressing need to identify appropriate management practices to improve the water quality within the catchment. It is recommended to investigate further the required number of water quality monitoring stations, sampling frequencies and sampling duration of water quality constituents to enhance the robustness and usefulness of the proposed methodology.