Uncertainties in Snowpack Simulations-Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance
Uncertainties in Snowpack Simulations-Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance
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DOI：
10.1029/2018wr023403
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发表时间：
2019-04-01
影响因子：
5.4
通讯作者：
Strasser, Ulrich
中科院分区：
文献类型：
--
作者：
Guenther, Daniel;Markel, Thomas;Strasser, Ulrich
在这项研究中，我们评估的影响，迫使数据错误，模型结构，和参数选择1-D雪模拟同时在全球方差为基础的敏感性分析框架。这种方法允许包括相互作用的影响，绘制一个更有代表性的图片所产生的敏感性。我们利用多物理场积雪模型的所有组合来反映模型结构的影响。模型参数和输入数据的不确定性范围从文献中提取。我们评估了一套230,000模型实现在雪监测站Kuhtai（蒂罗尔，奥地利，海拔1,920米）对雪水等效观测。结果表明，在整个过程中的25个冬季（1991-2015年）和不同的模式性能标准的强迫数据的不确定性及其相互作用对模式的性能有很大的影响。平均年际总敏感性指数的一般顺序为参数选择<模式结构<强迫误差，其中累积期的方差受降水、气温和辐射强迫的控制，消融期的方差受气温和长波辐照度的控制。模型技巧是高度敏感的积雪液态水运输计划在整个冬季期间，并在消融期间的Replado表示。我们发现，稳健的平均需要足够长的评估期（>10年）。揭示了相当大的相互作用效应，表明知识的提高（即，减少不确定性）可能不一定改善模型结果。
In this study, we assess the impact of forcing data errors, model structure, and parameter choices on 1-D snow simulations simultaneously within a global variance-based sensitivity analysis framework. This approach allows inclusion of interaction effects, drawing a more representative picture of the resulting sensitivities. We utilize all combinations of a multiphysics snowpack model to mirror the influence of model structure. Uncertainty ranges of model parameters and input data are extracted from the literature. We evaluate a suite of 230,000 model realizations at the snow monitoring station Kuhtai (Tyrol, Austria, 1,920 m above sea level) against snow water equivalent observations. The results show throughout the course of 25 winter seasons (1991-2015) and different model performance criteria a large influence of forcing data uncertainty and its interactions on the model performance. Mean interannual total sensitivity indices are in the general order of parameter choice < model structure < forcing error, with precipitation, air temperature, and the radiative forcings controlling the variance during the accumulation period and air temperature and longwave irradiance controlling the variance during the ablation period, respectively. Model skill is highly sensitive to the snowpack liquid water transport scheme throughout the whole winter period and to albedo representation during the ablation period. We found a sufficiently long evaluation period (>10 years) is required for robust averaging. A considerable interaction effect was revealed, indicating that an improvement in the knowledge (i.e., reduction of uncertainty) of one factor alone might not necessarily improve model results.