The snow cover is a key component of land surface hydrology, especially in mountain areas where it governs the amount and timing of water availability in downstream areas. It is involved in relevant climate feedbacks and natural hazards such as avalanches and floods. Monitoring and forecasting snow cover characteristics is challenging. While snow cover extent is relatively easy to retrieve from satellite data, remote sensing retrievals of the snow water equivalent (SWE) is often inaccurate, particularly in complex mountainous terrain. Model-based snow cover estimates, driven by meteorological data, often bear significant uncertainties due to both input data and model errors. Data assimilation can combine both approaches to improve SWE estimates. In this paper, we review current state-of-the-art data assimilation methodologies used to optimally combine measurements with snow cover models in order to reduce uncertainties. The suitability of a given data assimilation method varies with the numerical complexity of snow models as well as the availability and the type of observations. This review describes the issues and challenges associated with data assimilation applied to the mountain snow cover, providing recommendations for existing and upcoming monitoring and prediction systems of snow hydrology in mountainous regions.

积雪是土地表面水文学的关键组成部分，尤其是在山区，积雪决定着下游地区可用水的数量和时间。它涉及相关的气候反馈和自然灾害，例如雪崩和洪水。监测和预测积雪特征具有挑战性。尽管从卫星数据中相对容易获得积雪的范围，但是对雪水当量（SWE）的遥感检索通常是不准确的，尤其是在复杂的山区。由于输入数据和模型误差，在气象数据的驱动下，基于模型的积雪估算通常具有很大的不确定性。数据同化可以结合两种方法来改善SWE估计。在本文中，我们回顾了当前的最新数据同化方法，这些方法用于将测量值与积雪模型最佳组合，以减少不确定性。给定数据同化方法的适用性随降雪模型的数值复杂性以及可用性和观测类型的不同而不同。这篇综述描述了与应用于高山积雪的数据同化相关的问题和挑战，为山区现有和即将到来的雪文水文监测和预报系统提供了建议。