Effects of measurement uncertainties of meteorological data on estimates of site water balance components
Effects of measurement uncertainties of meteorological data on estimates of site water balance components
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DOI：
10.1016/j.jhydrol.2013.03.047
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发表时间：
2013-04
影响因子：
6.4
通讯作者：
U. Spank;K. Schwärzel;M. Renner;Uta Moderow;C. Bernhofer
中科院分区：
文献类型：
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作者：
U. Spank;K. Schwärzel;M. Renner;Uta Moderow;C. Bernhofer
水量平衡数值模型在生态和水文科学中有着广泛的应用。然而，它们的应用与具体问题和不确定性有关。模型预测的可靠性取决于（i）模型概念，（ii）参数，（iii）输入数据的不确定性，以及（iv）参考数据的不确定性。模型概念（i）和参数（ii）如何影响模型的性能是一个经常处理的问题。然而，（iii）和（iv）的影响通常被忽略或仅在区域化和概括的背景下几乎没有处理。在本研究中，输入和参考数据的实际测量不确定度是主要的焦点。此外，模型结果的评价进行了分析，参考数据的不确定性。一个特点是使用蒸散（通过涡度协方差测量），而不是径流模拟结果的评估。它表明，看似很小的测量不确定性，可以创建显着的不确定性，根据调查的时间尺度的模拟结果。作为一个例子，每天的全球辐射测量的不确定性之和为250兆焦耳（相当于100毫米）的年度尺度上，这将导致40毫米的不确定性模拟草地植被蒸散。总结和概括而言，所有输入数据的测量不确定性在模拟的年蒸散量中平均约为5%，在模拟的年渗漏量中平均约为10%。然而，在发生极端事件的年份，这种影响可能会显著增加，最高可达15%。结果表明，各变量的不确定性不是简单的叠加，而是以复杂的方式相互作用。因此，很明显，测量不确定性对模型结果的影响对于复杂模型和简单模型是相似的。
Numerical water balance models are widely used in ecological and hydro sciences. However, their application is related to specific problems and uncertainties. The reliability of model prediction depends on (i) model concept, (ii) parameters, (iii) uncertainty of input data, and (iv) uncertainty of reference data. How model concept (i) and parameters (ii) effect the model’s performance is an often treated problem. However, the effects of (iii) and (iv) are typically ignored or only barely treated in context of regionalisation and generalisation. In this study, the actual measurement uncertainties of input and reference data are the main focus. Furthermore, the evaluation of model results is analysed with regard to uncertainties of reference data. A special feature is the use of evapotranspiration (measured via the eddy covariance) instead of runoff for evaluation of simulation results. It is shown that seemingly small uncertainties of measurements can create significant uncertainties in simulation results depending on the temporal scale of investigation. As an example, the uncertainty of measurements of daily global radiation sum up to an uncertainty of 250MJ (equivalent to 100mm) on an annual scale, which causes an uncertainty of 40mm in simulated grass-reverence evapotranspiration. Summarised and generalised, the measurement uncertainties of all input data create an uncertainty on average of around 5% in the simulated annual evapotranspiration and of around 10% in the simulated annual seepage. However, the effects can be significantly higher in years with extreme events and can reach up to 15%. It is demonstrated that uncertainties of individual variables are not simply superposed but interact in a complex way. Thereby, it has become apparent that the effects of measurement uncertainties on model results are similar for complex and for simple models.