Abstract A combined snow modelling approach integrating remote sensing data, in-situ data, and an improved hydrological model is presented. Complementary information sources are evaluated in terms of its value for constraining the model parameters and to overcome limitations of individual data such as inadequate scale representation. The study site consists of the Upper Fagge river basin in the Austrian Alps featuring the Weisssee Snow Research Site. The available remote sensing datasets include Terra MODIS based medium resolution and Landsat-7/8 and Sentinel-2A based high resolution fractional snow covered area maps. Recently, Sentinel-1 based wet snow covered area maps have become increasingly available. To the knowledge of the authors the first evaluation of their value for snow-hydrological modelling is presented. Besides conventional small footprint station data, in-situ time-series of snow water equivalent (SWE) of a Cosmic-Ray Neutron Sensor (CRNS) having a footprint of several hectares is additionally used. For including these data the model now provides respective outputs such as fractional snow cover, wet/dry snow surface and SWE areal means equivalent to the CRNS sensor footprint. By means of 40,000 model runs the high complementary value of representative SWE data and remote sensing information was assessed with most promising results achieved by combining high resolution fractional snow covered area maps with CRNS-SWE data. Regarding mean SWE or mean snow covered area in the catchment the ensemble spreads are reduced by two thirds compared to the results of a benchmark simulation based only on runoff for model calibration. Wet snow covered area maps have a high potential for simulating SWE at Weisssee Snow Research Site but introduce additional uncertainties for runoff simulations likely caused by the uncertain detection of the snow covered area from Sentinel-1 backscatter. The approach has high potential for water resources management in gauged and ungauged mountain basin and gives guidance for efficient data assimilation schemes.

中文翻译：

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宇宙射线中子传感与积雪面积产品在积雪水文模拟中的互补价值

摘要 提出了一种将遥感数据、原位数据和改进的水文模型相结合的雪模拟方法。补充信息源根据其约束模型参数和克服个体数据的局限性（例如尺度表示不足）的价值进行评估。研究地点由奥地利阿尔卑斯山的上法格河流域组成，以魏斯雪研究地点为特色。可用的遥感数据集包括基于 Terra MODIS 的中分辨率和基于 Landsat-7/8 和 Sentinel-2A 的高分辨率部分积雪区域地图。最近，基于 Sentinel-1 的湿雪覆盖区域地图变得越来越可用。据作者所知，首次评估了他们对雪水文建模的价值。除了传统的小足迹站数据外，还使用了具有几公顷足迹的宇宙射线中子传感器 (CRNS) 的雪水当量 (SWE) 的原位时间序列。为了包含这些数据，该模型现在提供了相应的输出，例如部分积雪、湿/干雪面和相当于 CRNS 传感器足迹的 SWE 面积平均值。通过 40,000 次模型运行，评估了具有代表性的 SWE 数据和遥感信息的高互补价值，通过将高分辨率部分积雪面积图与 CRNS-SWE 数据相结合，取得了最有希望的结果。关于平均 SWE 或集水区平均积雪面积，与仅基于模型校准的径流的基准模拟结果相比，整体分布减少了三分之二。湿雪覆盖区域地图在 Weisssee 雪研究站点模拟 SWE 的潜力很大，但为径流模拟引入了额外的不确定性，这可能是由于 Sentinel-1 后向散射对雪覆盖区域的不确定检测造成的。该方法在已测量和未测量的山地盆地水资源管理方面具有很高的潜力，并为有效的数据同化计划提供指导。