Methods to quantify and identify the sources of uncertainty for river basin water quality models.
Methods to quantify and identify the sources of uncertainty for river basin water quality models.
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DOI：
10.2166/wst.2006.007
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发表时间：
2006
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影响因子：
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通讯作者：
A. Griensven;Thomas Meixner
中科院分区：
文献类型：
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作者：
A. Griensven;Thomas Meixner
在世界范围内，流域水质模型的应用越来越多，往往是法律强制的。因此，了解与这些模型及其在特定流域的应用相关的不确定性程度是很重要的。当将模型输出与观测值进行比较时，这些不确定性会导致误差。这种不确定性通常通过计算残差来描述。然而，残差不应被视为总不确定性的估计，因为通过校准过程，残差可能会因对数据的过度调整而减少，这是过度参数化模型的典型情况。当模型应用于其他时期或环境条件时，在校准期间的过度调整也会导致高度偏差的结果。因此，模型的总不确定性由四个组成部分来评估：残差平方和（SSQ）、参数不确定性（当其误差远远小于SSQ时可以忽略）、输入数据不确定性，以及当模型应用于用于校准的数据以外的数据时出现偏倚时表示的额外预测不确定性。根据量化标准（量级）和识别标准对源进行排序，该标准取决于置信区域所覆盖的观测值的数量。这种方法用SWAT2003模拟了桑达斯基河流域（俄亥俄州）的一条支流蜜溪的水流和泥沙来说明。结果表明，模型不确定性占主导地位。输入数据的不确定性不太重要。
Worldwide, the application of river basin water quality models is increasing, often imposed by law. It is, thus, important to know the degree of uncertainty associated with these models and their application to a specific watershed. These uncertainties lead to errors that are revealed when model outputs are compared to observations. Such uncertainty is typically described by calculating the residuals. However, residuals should not be seen as an estimate of total uncertainty, since through the calibration process, the residuals may be reduced by over-adjustment to the data, which is typically the case for over-parameterised models. Over-adjustment during a calibration period can also lead to highly biased results when the model is applied to other periods or environmental conditions. The total model uncertainties are, therefore, assessed by four components: the sum of the squares of the residuals (SSQ), parameter uncertainties (that can be ignored when their error is much smaller than SSQ), input data uncertainties, and an additional predictive uncertainty that is expressed when the model appears to be biased when it is applied for data other than the data used for calibration. The sources are ranked according to a quantification criterion (magnitude) as well as an identification criterion that depends on the number of observations that are covered by the confidence region. This approach is illustrated with SWAT2003 simulations for flow and sediment of Honey Creek, a tributary of the Sandusky River basin (Ohio). The results show the dominance of the model uncertainty. The input data uncertainty is less important.