Skill of remote sensing snow products for distributed runoff prediction
Skill of remote sensing snow products for distributed runoff prediction
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DOI：
10.1016/j.jhydrol.2015.03.025
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发表时间：
2015-05
影响因子：
6.4
通讯作者：
T. Berezowski;J. Chormański;O. Batelaan
中科院分区：
文献类型：
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作者：
T. Berezowski;J. Chormański;O. Batelaan
随着越来越多的遥感积雪产品可用，我们的目标是评估这些数据集在水文流量模拟方面的技能。本文用全局优化算法对10个不同积雪数据(MOD10A1、IMS、AMSR-E SWE、GlobSnow SWE和现场观测积雪深度)的模型变量和两种不同的积雪和融雪转换模型结构(基于积雪数据时间序列或温度时间序列)进行了校正。模拟流量遵循五个标准进行验证，而GLUE方法用于对10个模型变体进行不确定度分析。数据集的技术在比布尔扎河流域进行了测试，该流域的水文状况以积雪融化为主导。流量模拟采用分布式降雨径流模型WetSpa。MOD10A1是参照标准模型改进验证Nash-Sutcliffe(NS)分数的唯一数据来源。然而，其他评价措施表明，以下数据来源的表现好于标准模式：MOD10A1，观测雪深和GlobSnow，用于Kling-Gupta效率和高流量；IMS和MOD10A1，用于偏差；GlobSnow和MOD10A1，用于确定系数。在所有被分析的数据源中，MOD10A1具有最高的空间分辨率，这可能有助于该数据的高技能。与基于温度的切换相比，基于数据的切换模型结构的使用在不确定性分析期间通常缩小了行为参数集。然而，预测可信区间与两种模型结构之间没有明显的关系。结果表明，该模型的雪盖遥感技术是积极的，但随数据源的不同而有很大的差异。
With increasing availability of remote sensing snow cover products we aim to evaluate the skill of these datasets with regard to hydrological discharge simulation. In this paper ten model variants using different snow cover data (MOD10A1, IMS, AMSR-E SWE, GLOBSNOW SWE and observed in situ snow depth) and two different model structures for snow accumulation and snowmelt switching (based on snow cover data time series or temperature time series) are calibrated with a global optimisation algorithm. The simulated discharge is subjected to five criteria for validation, while the GLUE methodology is used for uncertainty analysis of the ten model variants. The skill of the datasets is tested for the Biebrza River catchment, which has a hydrological regime dominated by snowmelt. The discharge simulations are conducted with the distributed rainfall–runoff model WetSpa. MOD10A1 was the only data source which improved the validation Nash–Sutcliffe (NS) scores in reference to a standard model. However, other evaluation measures indicate that the following data sources performed better than the standard model: MOD10A1, observed snow depth and GLOBSNOW for Kling–Gupta efficiency and for high flows; IMS and MOD10A1 for bias; GLOBSNOW and MOD10A1 for coefficient of determination. MOD10A1 has the highest spatial resolution of all analysed data sources which might contribute to the high skill of this data. The use of the data-based switching model structure generally narrowed the behavioural parameter sets during the uncertainty analysis when compared to the temperature-based switching. However, no clear relation was observed between the prediction confidence interval and the two model structures. It is concluded that the skill of the remote sensing snow cover data for the model is positive, although, strongly varying with the data source used.