Understanding groundwater storage (GWS) changes is vital to the utilization and control of water resources in the Tibetan Plateau. However, well level observations are rare in this big area, and reliable hydrology models including GWS are not available. We use hydro-geodesy to quantitate GWS changes in the Tibetan Plateau and surroundings from 2003 to 2009 using a combined analysis of satellite gravity and satellite altimetry data, hydrology models as well as a model of glacial isostatic adjustment (GIA). Release-5 GRACE gravity data are jointly used in a mascon fitting method to estimate the terrestrial water storage (TWS) changes during the period, from which the hydrology contributions and the GIA effects are effectively deducted to give the estimates of GWS changes for 12 selected regions of interest. The hydrology contributions are carefully calculated from glaciers and lakes by ICESat-1 satellite altimetry data, permafrost degradation by an Active-Layer Depth (ALD) model, soil moisture and snow water equivalent by multiple hydrology models, and the GIA effects are calculated with the new ICE-6G_C (VM5a) model. Taking into account the measurement errors and the variability of the models, the uncertainties are rigorously estimated for the TWS changes, the hydrology contributions (including GWS changes) and the GIA effect. For the first time, we show explicitly separated GWS changes in the Tibetan Plateau and adjacent areas except for those to the south of the Himalayas. We find increasing trend rates for eight basins: +2.46±2.24 Gt/yr for the Jinsha River basin, +1.77±2.09 Gt/yr for the Nujiang-Lancangjiang Rivers Source Region, +1.86±1.69 Gt/yr for the Yangtze River Source Region, +1.14±1.39 Gt/yr for the Yellow River Source Region, +1.52±0.95 Gt/yr for the Qaidam basin, +1.66±1.52 Gt/yr for the central Qiangtang Nature Reserve, +5.37±2.17 Gt/yr for the Upper Indus basin and +2.77±0.99 Gt/yr for the Aksu River basin. All these increasing trends are most likely caused by increased runoff recharges from melt water and/or precipitation in the surroundings. We also find that the administrative actions such as the Chinese Ecological Protection and Construction Project help to store more groundwater in the Three Rivers Source Region, and suggest that seepages from the Endorheic basin to the west of it are a possible source for GWS increase in this region. In addition, our estimates for GWS changes basically confirm previous results along Afghanistan, Pakistan, north India and Bangladesh, and clearly reflect the excessive use of groundwater. Our results will benefit the water resource management in the study area, and are of particular significance for the ecological restoration in the Tibetan Plateau

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy.