Lakes are the most prevalent and predominant water repositories on land surface. A primary objective of the Surface Water and Ocean Topography (SWOT) satellite mission is to monitor the surface water elevation, area, and storage change in Earth’s lakes. To meet this objective, prior information of global lakes, such as locations and benchmark extents, is required to organize SWOT’s KaRIn observations over time for computing lake storage variation. Here, we present the SWOT mission Prior Lake Database (PLD) to fulfill this requirement. This paper emphasizes the development of the “operational PLD”, which consists of (1) a high-resolution mask of ~6 million lakes and reservoirs with a minimum area of 1 ha, and (2) multiple operational auxiliaries to assist the lake mask in generating SWOT’s standard vector lake products. We built the prior lake mask by harmonizing the UCLA Circa-2015 Global Lake Dataset and several state-of-the-art reservoir databases. Operational auxiliaries were produced from multi-theme geospatial data to provide information necessary to embody the PLD function, including lake catchments and influence areas, ice phenology, relationship with SWOT-visible rivers, and spatiotemporal coverage by SWOT overpasses. Globally, over three quarters of the prior lakes are smaller than 10 ha. Nearly 96% of the lakes, constituting over half of the global lake area, are fully observed at least once per orbit cycle. The PLD will be recursively improved during the mission period and serves as a critical framework for organizing, processing, and interpreting SWOT observations over lacustrine environments with fundamental significance to lake system science.

Water level and storage are the most critical components for understanding water cycle changes and mechanisms better. Given the lack of in-situ river level monitoring and consecutive satellite altimetry data in the Yarlung Zangbo River (YZR) in High Mountain Asia (HMA) caused by the harsh environment and complex terrain, obtaining accurate and long-term water characteristic changes is challenging. In this study, we reconstructed the dense time series of the YZR water characteristic changes from 2000 to 2020. The general idea of this method is to obtain the water area information derived from the Global Land Analysis and Discovery (GLAD) dataset, using the hypsometric curves to fill Hydroweb lacked river water level records from the available river area data, and to reconstruct the virtual stations (VSs) water levels in the YZR. Moreover, by combining area estimates with the changes in water level, the variations of the YZR water storage have been obtained. The obtained YZR water storage variation result was compared with the terrestrial water storage anomaly (TWSA) of Brahmaputra basin from Gravity Recovery and Climate Experiment (GRACE) data to discuss the response relationship. Results indicated that the reconstructed water area applied in the YZR with high quantity accuracy (approximately 82%) and the reconstructed water levels agreed reasonably well with Hydroweb water levels with an average R of 0.89. Furthermore, the densified reconstructed water levels provided critical and accurate information on the long-term monitoring in HMA. The YZR water area, level and storage have apparent significant seasonal fluctuations. The declining amplitude of water levels of VSs expanded from the upstream region to the downstream region. In addition, the change in river water storage only accounts for approximately one tenth of that in the basin. This study sheds new light on bridging the gap in monitoring the long-term water characteristic changes over poorly gauged basins by means of optical imaging in combination with partial altimetry satellite, and can be effectively applied in other large rivers in HMA.