Information about end-of-winter spatial distribution of snow depth is important for seasonal forecasts of spring/summer streamflow in high-mountain regions. Nevertheless, such information typically relies upon extrapolation from a sparse network of observations at low elevations. Here, we test the potential of high-resolution snow depth data derived from optical stereophotogrammetry of Pleiades satellites for improving the representation of snow depth initial conditions (SDICs) in a glacio-hydrological model and assess potential improvements in the skill of snowmelt and streamflow simulations in a high-elevation Andean catchment. We calibrate model parameters controlling glacier mass balance and snow cover evolution using ground-based and satellite observations, and consider the relative importance of accurate estimates of SDICs compared to model parameters and forcings. We find that Pleiades SDICs improve the simulation of snow-covered area, glacier mass balance, and monthly streamflow compared to alternative SDICs based upon extrapolation of meteorological variables or statistical methods to estimate SDICs based upon topography. Model simulations are found to be sensitive to SDICs in the early spring (up to 48% variability in modeled streamflow compared to the best estimate model), and to temperature gradients in all months that control albedo and melt rates over a large elevation range (>2,400 m). As such, appropriately characterizing the distribution of total snow volume with elevation is important for reproducing total streamflow and the proportions of snowmelt. Therefore, optical stereo-photogrammetry offers an advantage for obtaining SDICs that aid both the timing and magnitude of streamflow simulations, process representation (e.g., snow cover evolution) and has the potential for large spatial domains.

中文翻译：

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光学卫星冬季积雪深度在初始化高海拔、安第斯流域冰川水文模型中的应用

冬末积雪深度空间分布信息对于高山地区春/夏季水流的季节预报具有重要意义。然而，这些信息通常依赖于从低海拔的稀疏观测网络中推断出来的。在这里，我们测试了从昴宿星卫星光学立体摄影测量中获得的高分辨率雪深数据的潜力，以改善冰川水文模型中雪深初始条件 (SDIC) 的表示，并评估融雪和水流模拟技能的潜在改进在高海拔的安第斯流域。我们使用地面和卫星观测校准控制冰川质量平衡和积雪演变的模型参数，并考虑与模型参数和强迫相比，SDIC 的准确估计的相对重要性。我们发现，与基于气象变量外推或基于地形估计 SDIC 的统计方法的替代 SDIC 相比，昴宿星 SDIC 改进了对积雪覆盖面积、冰川质量平衡和月流量的模拟。模型模拟被发现对早春的 SDIC 敏感（与最佳估计模型相比，模拟的流量变化高达 48%），以及所有月份的温度梯度，这些温度梯度控制大海拔范围内的反照率和融化速率 (> 2,400 m）。因此，适当地表征总雪量随海拔的分布对于再现总流量和融雪比例很重要。所以，