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Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

机译:评估雪数据同化对高山流域径流建模的好处

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In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byrans Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a time-invariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation. This was especially true for snow-rich years. These findings suggest that with increasing elevation and the correspondingly increased contribution of snowmelt to runoff, the accurate estimation of snow water equivalent (SWE) and snowmelt rates has gained importance.
机译:在高山流域,融雪通常是径流的主要贡献。因此,在涉及洪水或干旱预报,水库运营和内陆水道管理时,对雪过程进行建模非常重要。在这项研究中,我们解决了以下问题:水文模型对积雪动力学表现的敏感性如何,以及是否可以通过整合专用外部积雪监测系统的数据来增强水文模型的性能。作为测试的框架,我们在HBV-light版本中使用了水文模型HBV(Hydrologiska Byrans Vattenbalansavdelning),该模型已在许多水文研究中得到应用,并且也已用于运营目的。虽然HBV最初遵循温度指数方法,并使用时不变的校准度-天数因子来表示融雪,但在本研究中,对HBV模型进行了修改,以使用外部和空间分布雪模型中的融雪时间序列作为模型输入。外部积雪模型将积雪监测数据的三维顺序同化与积雪融化模型集成在一起,该模型也基于温度指数方法,但使用时变程度-天数因子。应用了此外部降雪模型的以下三个变体:(a)具有来自密集监视网络的观测雪数据同化的完整模型;(b)具有相同的雪模型但数据同化已关闭;以及(c)降级的模型表示融雪的同一雪模型的时间不变度-日因子。在瑞士境内不同海拔的20个流域进行了模型运行15年。我们的结果表明,在中低海拔地区,径流模拟的性能不会因所选择的积雪模型版本而有很大差异。但是,在较高的海拔高度,使用来自数据模型的雪模型中的融雪时间序列时,在模拟径流方面获得了最佳性能。对于富雪年份尤其如此。这些发现表明,随着海拔的升高以及融雪对径流的贡献相应增加,准确估算雪水当量(SWE)和融雪速率变得越来越重要。

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