The Tibetan Plateau (TP), the highest and largest plateau in the world, with complex and competing cryospheric‐hydrologic‐geodynamic processes, is particularly sensitive to anthropogenic warming. The quantitative water mass budget in the TP is poorly known. Here we examine annual changes in lake area, level, and volume during 1970s–2015. We find that a complex pattern of lake volume changes during 1970s–2015: a slight decrease of −2.78 Gt yr⁻¹ during 1970s–1995, followed by a rapid increase of 12.53 Gt yr⁻¹ during 1996–2010, and then a recent deceleration (1.46 Gt yr⁻¹) during 2011–2015. We then estimated the recent water mass budget for the Inner TP, 2003–2009, including changes in terrestrial water storage, lake volume, glacier mass, snow water equivalent (SWE), soil moisture, and permafrost. The dominant components of water mass budget, namely, changes in lake volume (7.72 ± 0.63 Gt yr⁻¹) and groundwater storage (5.01 ± 1.59 Gt yr⁻¹), increased at similar rates. We find that increased net precipitation contributes the majority of water supply (74%) for the lake volume increase, followed by glacier mass loss (13%), and ground ice melt due to permafrost degradation (12%). Other term such as SWE (1%) makes a relatively small contribution. These results suggest that the hydrologic cycle in the TP has intensified remarkably during recent decades.

Dozens of missing epochs in the monthly gravity product of the satellite mission Gravity Recovery and Climate Experiment (GRACE) and its follow‐on (GRACE‐FO) mission greatly inhibit the complete analysis and full utilization of the data. Despite previous attempts to handle this problem, a general all‐purpose gap‐filling solution is still lacking. Here we propose a non‐parametric, data‐adaptive and easy‐to‐implement approach—composed of the Singular Spectrum Analysis gap‐filling technique, cross‐validation, and spectral testing for significant components—to produce reasonable gap‐filling results in the form of spherical harmonic coefficients (SHCs). We demonstrate that this approach is adept at inferring missing data from long‐term and oscillatory changes extracted from available observations. A comparison in the spectral domain reveals that the gap‐filling result in the same signal intensity as the product of GRACE missions for spherical harmonic degrees below 30. As the degree increases above 30, the degree variance of the gap‐filling result decreases more rapidly than that of GRACE/GRACE‐FO SHCs, demonstrating effective suppression of noise. As a result, our approach can reduce noise in the oceans without sacrificing resolutions on land. The quality of the gap‐filling product is evaluated through comparison with a surface mass balance based estimate in Greenland, Swarm gravity solutions and a climate‐driven water storage model, all of which confirm the good performance of our results. This study makes a ready‐to‐use gap‐filling product in the form of SHCs together with proper error estimates available. Nonetheless, our method is also applicable to smoothed gridded observations.

In this paper, 10 years of time‐variable gravity data from the Gravity Recovery and Climate Experiment Release 05 have been used to evaluate the glacier melting rate in high‐mountain Asia (HMA) using a new computing scheme, i.e., the Space Domain Inverse method. We find that in HMA area, there are three different kinds of signal sources that should be treated together. The two generally accepted sources, glacier melting and India underground water depletion, are estimated to change at the rate of −35.0 ± 5.8 Gt/yr (0.09 mm/yr sea level rising) and −30.6 ± 5.0 Gt/yr, respectively. The third source is the remarkable positive signal (+30 Gt/yr) in the inner Tibetan Plateau, which is challenging to explain. Further, we have found that there is a 5 year undulation in Pamir and Karakoram, which can explain the controversies of the previous studies on the glacier melting rate here. This 5 year signal can be explained by the influence of Arctic Oscillation and El Niño–Southern Oscillation.