Improved calibration of a rainfall‐pollutant‐runoff model using turbidity and electrical conductivity as surrogate parameters for total nitrogen

Abstract

AbstractTo address data scarcity for calibration of rainfall‐pollutant‐runoff (RPR) models, we evaluated the suitability of nutrient levels estimated based on surrogate parameters as a novel source of data, using runoff of total nitrogen (TN) in the Tegiru basin as a case study. A linear regression equation was developed for estimating TN based on turbidity and electrical conductivity; this expression was then used to generate TN data (n = 113) for calibration of a catchment‐specific RPR model. Using solely the estimated TN concentrations for calibration, the model accurately predicted TN concentrations (21% error based on measured TN, n = 13) and revealed runoff trends during periods in which TN measurements were lacking. Finally, we utilised this model to show that TN runoff was highest during months with frequent and high intensity rainfall. In summary, this study demonstrates the applicability of surrogate parameters to extend data on difficult‐to‐monitor nutrient loads for model calibration.