Effect of watershed subdivision on water-phase phosphorus modelling: An artificial neural network modelling application

Abstract

This study is an effort to incorporate low-cost time-variant remote sensing (RS) information in watershed-scale total phosphorus (TP) modelling. Four watershed subdivisions were delineated to assess the impact of watershed subdivision on the prediction accuracy of TP concentration in stream water. Four TP artificial neural network (ANN) models were designed to incorporate RS data into a semi-distributed approach. The remotely derived enhanced vegetation index and the normalized difference water index were successful in representing vegetation dynamics in the devised models. The models were applied to a 15.6 km2 watershed in the Canadian Boreal Plain. Eight measures of goodness-of-fit statistics were used for model evaluation. Although statistical model evaluation did favour the finest resolution in this case study, the differences in performance indicators among the four models were insignificant for any practical application. The encouraging results from this exercise demonstrate the applicability of the ANN semi-distributed modelling approach and the usefulness of RS data in simulating TP dynamics. Such models can potentially serve as valuable tools for watershed-scale forest management.