Multi-model ensemble simulated non-point source pollution based on Bayesian model averaging method and model uncertainty analysis.

Abstract

Watershed models are cost-effective and powerful tools for evaluating and controlling non-point source pollution (NPSP), while the reliability of watershed models in a management context depends largely on inherent uncertainties in model predictions. The objective of this study is to present the use of multi-model ensemble applied to streamflow, total nitrogen (TN), and total phosphorus (TP) simulation and quantify the uncertainty resulting from model structure. In this study, three watershed models, which have different structures in simulating NPSP, were selected to conduct watershed monthly streamflow, TN load, and TP load ensemble simulation and 90% credible intervals based on Bayesian model averaging (BMA) method. The result using the observed data of the Yixunhe watershed revealed that the coefficient of determination and Nash-Sutcliffe coefficient of the BMA model simulate streamflow, TN load, and TP load were better than that of the single model. The higher the efficiency of a single model is, the greater the weight during the BMA ensemble simulation is. The 90% credible interval of BMA has a high coverage of measured values in this study. This indicates that the BMA method can not only provide simulation with better precision through ensemble simulation but also provide quantitative evaluation of the model structure through interval, which could offer rich information of the NPSP simulation and management.