SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Improvement of model evaluation by incorporating prediction and measurement uncertainty 
Authors:Chen, L., S. Li, Y. Zhong and Z. Shen 
Journal:Hydrology and Earth System Sciences 
Volume (Issue):22(8) 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:hydrologic and pollutant 
Primary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Secondary Application Category:pollutant cycling/loss and transport 
Watershed Description:Daning river located in the central part of the Three Gorges Reservoir drainage area in central China. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Numerous studies have been conducted to assess uncertainty in hydrological and non-point source pollution predictions, but few studies have considered both prediction and measurement uncertainty in the model evaluation process. In this study, the cumulative distribution function approach (CDFA) and the Monte Carlo approach (MCA) were developed as two new approaches for model evaluation within an uncertainty condition. For the CDFA, a new distance between the cumulative distribution functions of the predicted data and the measured data was established in the model evaluation process, whereas the MCA was proposed to address conditions with dispersed data points. These new approaches were then applied in combination with the Soil and Water Assessment Tool in the Three Gorges Region, China. Based on the results, these two new approaches provided more accurate goodness-of-fit indicators for model evaluation compared to traditional methods. The model performance worsened when the error range became larger, and the choice of probability density functions (PDFs) affected model performance, especially for non-point source (NPS) predictions. The case study showed that if the measured error is small and if the distribution can be specified, the CDFA and MCA could be extended to other model evaluations within an uncertainty framework and even be used to calibrate and validate hydrological and NPS pollution (H/NPS) models.