High mountain glaciers (HMGs), called the water towers of the world, are vulnerable to the effects of climate change and thus are rapidly shrinking. Monitoring and evaluating large-scale glacier and snow (GS) mass changes are critical for humans and ecosystems. Although modern gravity satellites monitor GS on a global scale, the contemporary Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow−On (GRACE−FO) missions are only able to do so at coarse spatial resolutions and are restricted by their limited observation periods. Moreover, because of the complex mechanism behind a glacier's mass balance, existing reconstruction methods for GRACE-like mass anomalies, including machine learning approaches and statistical models, cannot be applied to HMGs. Here, we propose a precipitation and temperature data-driven statistical model combining hydrology and GS processes to reconstruct GRACE-like mass anomalies of HMGs, from which the hydrological and GS signals can be further separated. The prediction and reconstruction performance of this method was consistent with the GRACE/GRACE−FO observations (the median correlation coefficient/Nash-Sutcliffe efficiency: ~0.92/0.85). Additionally, the separated GS signals agreed with the independent external data used for comparison. Compared with machine learning methods, this method better reconstructed the long-term trend of GRACE-like mass anomalies. Our study provides an acceptable method for expanding mass anomaly time series on HMGs, thereby assisting in the sustainable management and protection of water resources in their downstream areas.

被称为世界水塔的高山冰川 (HMG) 很容易受到气候变化的影响，因此正在迅速缩小。监测和评估大规模冰川和雪 (GS) 质量变化对人类和生态系统至关重要。尽管现代重力卫星在全球范围内监测 GS，但当代重力恢复和气候实验 (GRACE) 和 GRACE 后续 (GRACE-FO) 任务只能在粗略的空间分辨率下进行，并且受到其有限的观测周期的限制. 此外，由于冰川质量平衡背后的复杂机制，现有的 GRACE 样质量异常重建方法，包括机器学习方法和统计模型，不能应用于 HMG。这里，我们提出了一个结合水文和GS过程的降水和温度数据驱动的统计模型来重建HMG的GRACE样质量异常，从中可以进一步分离水文和GS信号。该方法的预测和重建性能与 GRACE/GRACE-FO 观察结果一致（中值相关系数/Nash-Sutcliffe 效率：~0.92/0.85）。此外，分离的 GS 信号与用于比较的独立外部数据一致。与机器学习方法相比，该方法更好地重建了 GRACE 样质量异常的长期趋势。我们的研究为扩展 HMG 的质量异常时间序列提供了一种可接受的方法，从而有助于其下游地区水资源的可持续管理和保护。