Combining the SWAT model with sequential uncertainty fitting algorithm for streamflow prediction and uncertainty analysis for the Lake Dianchi Basin, China
Article first published online: 16 NOV 2012
Copyright © 2012 John Wiley & Sons, Ltd.
How to Cite
Zhou, J., Liu, Y., Guo, H. and He, D. (2012), Combining the SWAT model with sequential uncertainty fitting algorithm for streamflow prediction and uncertainty analysis for the Lake Dianchi Basin, China. Hydrol. Process.. doi: 10.1002/hyp.9605
- Article first published online: 16 NOV 2012
- Accepted manuscript online: 15 OCT 2012 11:35AM EST
- Manuscript Accepted: 10 OCT 2012
- Manuscript Received: 15 APR 2012
- National Natural Science Foundation of China. Grant Numbers: 41101180, 41222002
- China National Water Pollution Control Program. Grant Number: 2010ZX07102-006
- SUFI-2 algorithm;
- uncertainty analysis;
- Lake Dianchi Basin
Streams play an important role in linking the land with lakes. Nutrients released from agricultural or urban sources flow via streams to lakes, causing water quality deterioration and eutrophication. Therefore, accurate simulation of streamflow is helpful for water quality improvement in lake basins. Lake Dianchi has been listed in the ‘Three Important Lakes Restoration Act’ in China, and the degradation of its water quality has been of great concern since the 1980s. To assist environmental decision making, it is important to assess and predict hydrological processes at the basin scale. This study evaluated the performance of the soil and water assessment tool (SWAT) and the feasibility of using this model as a decision support tool for predicting streamflow in the Lake Dianchi Basin. The model was calibrated and validated using monthly observed streamflow values at three flow stations within the Lake Dianchi Basin through application of the sequential uncertainty fitting algorithm (SUFI-2). The results of the autocalibration method for calibrating and the prediction uncertainty from different sources were also examined. Together, the p-factor (the percentage of measured data bracketed by 95% prediction of uncertainty, or 95PPU) and the r-factor (the average thickness of the 95PPU band divided by the standard deviation of the measured data) indicated the strength of the calibration and uncertainty analysis. The results showed that the SUFI-2 algorithm performed better than the autocalibration method. Comparison of the SUFI-2 algorithm and autocalibration results showed that some snowmelt factors were sensitive to model output upstream at the Panlongjiang flow station. The 95PPU captured more than 70% of the observed streamflow at the three flow stations. The corresponding p-factors and r-factors suggested that some flow stations had relatively large uncertainty, especially in the prediction of some peak flows. Although uncertainty existed, statistical criteria including R2 and Nash–Sutcliffe efficiency were reasonably determined. The model produced a useful result and can be used for further applications. Copyright © 2012 John Wiley & Sons, Ltd.