由周璟博士、刘永研究员等合作的论文Combining the SWAT model with sequential uncertainty fitting algorithm for streamflow prediction and uncertainty analysis for the Lake Dianchi Basin, China近日在Hydrological Processes在线刊出（DOI: 10.1002/hyp.9605）。
摘要：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 to water-quality improvement in lake basins. In China, the degradation of water quality in Lake Dianchi has been of great concern since the 1980s. To assist environmental managers in 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) for predicting streamflow in the Lake Dianchi Basin. The model was calibrated and validated using monthly observed streamflow values for 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 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 of 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 larger uncertainty, especially in 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.