The Northwest India Aquifer (NWIA) has been shown to have the highest groundwater depletion (GWD) rate globally, threatening crop production and sustainability of groundwater resources. Gravity Recovery and Climate Experiment (GRACE) satellites have been emerging as a powerful tool to evaluate GWD with ancillary data. Accurate GWD estimation is, however, challenging because of uncertainties in GRACE data processing. We evaluated GWD rates over the NWIA using a variety of approaches, including newly developed constrained forward modeling resulting in a GWD rate of 3.1 ± 0.1 cm/a (or 14 ± 0.4 km3/a) for Jan 2005–Dec 2010, consistent with the GWD rate (2.8 cm/a or 12.3 km3/a) from groundwater-level monitoring data. Published studies (e.g., 4 ± 1 cm/a or 18 ± 4.4 km3/a) may overestimate GWD over this region. This study highlights uncertainties in GWD estimates and the importance of incorporating a priori information to refine spatial patterns of GRACE signals that could be more useful in groundwater resource management and need to be paid more attention in future studies.

Long-term datasets of number and size of lakes over the Tibetan Plateau (TP) are among the most critical components for better understanding the interactions among the cryosphere, hydrosphere, and atmosphere at regional and global scales. Due to the harsh environment and the scarcity of data over the TP, data accumulation and sharing become more valuable for scientists worldwide to make new discoveries in this region. This paper, for the first time, presents a comprehensive and freely available data set of lakes' status (name, location, shape, area, perimeter, etc.) over the TP region dating back to the 1960s, including three time series, i.e., the 1960s, 2005, and 2014, derived from ground survey (the 1960s) or high-spatialresolution satellite images from the China-Brazil Earth Resources Satellite (CBERS) (2005) and China's newly launched GaoFen-1 (GF-1, which means high-resolution images in Chinese) satellite (2014). The data set could provide scientists with useful information for revealing environmental changes and mechanisms over the TP region.

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Accurate estimation of precipitation from satellites at high spatiotemporal scales over the Tibetan Plateau (TP) remains a challenge. In this study, we proposed a general framework for blending multiple satellite precipitation data using the dynamic Bayesian model averaging (BMA) algorithm. The blended experiment was performed at a daily 0.25° grid scale for 2007–2012 among Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT and 3B42V7, Climate Prediction Center MORPHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR). First, the BMA weights were optimized using the expectation‐maximization (EM) method for each member on each day at 200 calibrated sites and then interpolated to the entire plateau using the ordinary kriging (OK) approach. Thus, the merging data were produced by weighted sums of the individuals over the plateau. The dynamic BMA approach showed better performance with a smaller root‐mean‐square error (RMSE) of 6.77 mm/day, higher correlation coefficient of 0.592, and closer Euclid value of 0.833, compared to the individuals at 15 validated sites. Moreover, BMA has proven to be more robust in terms of seasonality, topography, and other parameters than traditional ensemble methods including simple model averaging (SMA) and one‐outlier removed (OOR). Error analysis between BMA and the state‐of‐the‐art IMERG in the summer of 2014 further proved that the performance of BMA was superior with respect to multisatellite precipitation data merging. This study demonstrates that BMA provides a new solution for blending multiple satellite data in regions with limited gauges.

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Satellite remote sensing combined with water balance calculations provides a promising approach to estimating evapotranspiration (ET), a critical variable in water‐energy exchange. Here we compare ET estimates from terrestrial and atmospheric water balances, multisource remote sensing (AVHRR, GLEAM, and MOD16), and a land surface model (GLDAS NOAH) for headwaters on the Tibetan Plateau (TP), that is, headwaters of the Brahmaputra (HBR), Salween (HSR), Mekong (HMR), Yangtze (HYR), and Huang (Yellow; HHR) Rivers, for the 2003–2012 period. Results show that (1) ET estimated from terrestrial and atmospheric water balances agrees closely in three basins (HMR, HYR, and HHR) but has large discrepancies in the other two basins (HBR and HSR), mainly caused by uncertainties in the terrestrial water balance; (2) agreement between various ET products and water balance‐derived ET baselines is highest for GLEAM in two basins (HMR and HYR) and GLDAS NOAH in another two basins (HSR and HHR); and (3) large discrepancies between water balance‐derived ET and all ET products are found in the most glacierized HBR, which may reflect the importance of sublimation in the ET process. The decadal mean ET based on water balance‐derived ET baselines is highest in the HHR (447 mm/year) and HSR (430 mm/year) and lowest in the HBR (238 mm/year), ranging from 51% to 78% of mean precipitation in the five TP headwaters. These findings have important implications for ET estimation on the TP headwaters, which greatly influences downstream water availability.

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