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.

Lake surface water temperature (LSWT) is sensitive to long-term changes in thermal structure of lakes and regional air temperature. In the context of global climate change, recent studies showed a significant warming trend of LSWT based on investigating 291 lakes (71% are large lakes, ≥50 km2 each) globally. However, further efforts are needed to examine variation in LSWT at finer regional spatial and temporal scales. The Tibetan Plateau (TP), known as ‘the Roof of the World’ and ‘Asia’s water towers’, exerts large influences on and is sensitive to regional and even global climates. Aiming to examine detailed changing patterns and potential driven mechanisms for temperature variations of lakes across the TP region, this paper presents the first comprehensive data set of 15-year (2001–2015) nighttime and daytime LSWT for 374 lakes (≥10 km2 each), using MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products as well as four lake boundary shapefiles (i.e., 2002, 2005, 2009, and 2014) derived from Landsat/CBERS/GaoFen-1 satellite images. The data set itself reveals significant information on LSWT and its changes over the TP and is an indispensable variable for numerous applications related to climate change, water budget analysis (particularly lake evaporation), water storage changes, glacier melting and permafrost degradation, etc.

Satellite remote sensing is able to provide information on global rain and snow, but challenges remain in accurate estimation of precipitation rates, particularly in snow retrieval. In this work, the deep neural network (DNN) is applied to estimate rain and snow rates in high latitudes. The reference data for DNN training are provided by two spaceborne radars onboard the Global Precipitation Measurement (GPM) Core Observatory and CloudSat. Passive microwave data from the GPM Microwave Imager (GMI), infrared data from MODerate resolution Imaging Spectroradiometer and environmental data from European Centre for Medium‐Range Weather Forecasts are trained to the spaceborne radar‐based reference precipitation. The DNN estimates are compared to data from the Goddard Profiling Algorithm (GPROF), which is used to retrieve passive microwave precipitation for the GPM mission. First, the DNN‐based retrieval method performs well in both training and testing periods. Second, the DNN can reveal the advantages and disadvantages of different channels of GMI and MODerate resolution Imaging Spectroradiometer. Additionally, infrared and environmental data can improve precipitation estimation of the DNN, particularly for snowfall. Finally, based on the optimized DNN, rain and snow are estimated in 2017 from orbital GMI brightness temperatures and compared to ERA‐Interim and Modern‐Era Retrospective analysis for Research and Applications Version 2 reanalysis data. Evaluation results show that (1) the DNN can largely mitigate the underestimation of precipitation rates in high latitudes by GPROF; (2) the DNN‐based snowfall estimates largely outperform those of GPROF; and (3) the spatial distributions of DNN‐based precipitation are closer to reanalysis data. The method and assessment presented in this study could potentially contribute to the substantial improvement of satellite precipitation products in high latitudes.

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