Title: | Improvement of model evaluation by incorporating prediction and measurement uncertainty |
Authors: | Chen, L., S. Li, Y. Zhong and Z. Shen |
Year: | 2018 |
Journal: | Hydrology and Earth System Sciences |
Volume (Issue): | 22(8) |
Pages: | 4145-4154 |
Article ID: | |
DOI: | 10.5194/hess-22-4145-2018 |
URL (non-DOI journals): | |
Model: | SWAT |
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. |
Language: | English |
Keywords: | |