Combining the SWAT model with sequential uncertainty fitting algorithm for streamflow prediction and uncertainty analysis for the Lake Dianchi Basin, China
Combining the SWAT model with sequential uncertainty fitting algorithm for streamflow prediction and uncertainty analysis for the Lake Dianchi Basin, China
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SWAT模型与序贯不确定性拟合算法相结合用于滇池流域径流预测和不确定性分析
DOI：
10.1002/hyp.9605
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发表时间：
2014-01
影响因子：
3.2
通讯作者：
He, Dan
中科院分区：
文献类型：
--
作者：
Zhou, Jing;Liu, Yong;Guo, Huaicheng;He, Dan
小溪在连接陆地和湖泊方面起着重要作用。从农业或城市来源释放的营养物质通过溪流流入湖泊，造成水质恶化和富营养化。因此，准确的河流流量模拟有助于湖泊流域水质的改善。滇池被列入中国“三大重要湖泊修复法案”，自20世纪80年代以来，滇池水质的恶化一直备受关注。为了帮助环境决策，在流域尺度上对水文过程进行评估和预测是非常重要的。本研究对水土评价工具（SWAT）的性能进行了评价，并对该模型作为滇池流域径流预测决策支持工具的可行性进行了评价。采用序贯不确定性拟合算法（SUFI‐2），利用滇池流域3个流站的月观测流量值对模型进行了标定和验证。对自动标定方法的标定结果和不同来源的预测不确定度进行了检验。p因子（95%预测不确定度所占测量数据的百分比，即95PPU）和ther因子（95PPU波段的平均厚度除以测量数据的标准差）共同表明了校准和不确定度分析的强度。结果表明，SUFI‐2算法优于自动校准方法。SUFI‐2算法与自校正结果的比较表明，盘龙江流站上游部分融雪因子对模型输出较为敏感。95PPU在三个流量站捕获了超过70%的观测流量。相应的因子和r因子表明，某些流量站的不确定性较大，特别是在某些峰值流量的预测中。虽然存在不确定性，但包括gr2和Nash-Sutcliffe效率在内的统计标准是合理确定的。该模型产生了一个有用的结果，并可用于进一步的应用。版权所有©2012 John Wiley & Sons， Ltd。
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, thep‐factor (the percentage of measured data bracketed by 95% prediction of uncertainty, or 95PPU) and ther‐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 correspondingp‐factors andr‐factors suggested that some flow stations had relatively large uncertainty, especially in the prediction of some peak flows. Although uncertainty existed, statistical criteria includingR2and 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.
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DOI：
10.1111/j.1752-1688.1991.tb01471.x
发表时间：
1991-10
影响因子：
2.4
作者：
D. Changnon;T. Mckee;N. Doesken
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D. Changnon;T. Mckee;N. Doesken
DOI：
10.2166/wst.2006.007
发表时间：
2006
期刊：
Water science and technology : a journal of the International Association on Water Pollution Research
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A. Griensven;Thomas Meixner
DOI：
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发表时间：
2011
期刊：
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影响因子：
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作者：
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G. Huaicheng
DOI：
10.2464/jilm.21.771
发表时间：
1971
期刊：
Journal of Japan Institute of Light Metals
影响因子：
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通讯作者：
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DOI：
10.1111/j.1752-1688.2008.00276.x
发表时间：
2009-02
期刊：
JAWRA Journal of the American Water Resources Association
影响因子：
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作者：
K. Tobin;M. Bennett
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K. Tobin;M. Bennett