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Abstract:
Although modern gravity satellites monitor glacier and snow (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 High mountain glaciers (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. We reconstruct and evaluate the HMG mass anomaly time series in 14 representative high mountain regions. This method's prediction and reconstruction performance were 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 satellite altimetry data used for comparison. Compared with machine learning methods, this method better reconstructed the long-term trend of GRACE-like mass anomalies, and the result from satellite laser range supports our conclusion. Our study provides an acceptable method for reconstructing and separating GRACE-like HMG mass anomaly time series, thereby assisting in the sustainable management and protection of water resources in their downstream areas.
