Recent Abnormal Hydrologic Behavior of Tibetan Lakes Observed by Multi-Mission Altimeters

Comparison of Typical Alpine Lake Surface Elevation Variations and Different Driving Forces by Remote Sensing Altimetry Method

Alpine lakes play a significant role in improving watershed ecology, adjusting water storage, and managing regional water resources. They are also a valuable freshwater reservoir, flood storage, and species gene pool in Central Asia. This article validated the accuracy of the CryoSat-2 footprints altimetry dataset for the Lake Bosten and Lake Issyk-Kul ranges. The time series for the surface elevations of the Central Asian alpine lakes Karakul and Chatyrkul were established, based on footprints altimetry data. The lake hydrological drivers were analyzed using remote sensing meteorological reanalysis data of the lake basins. The following main conclusions were reached. The CryoSat-2 footprints altimetry dataset has high confidence in lake surface elevation monitoring. Compared with Hydroweb monitoring results, the agreement between the monitoring results in the range between Lake Bosten and Lake Issyk-Kul are 0.96 and 0.84. The surface elevation of Lake Karakul shows an overall increasing trend with a variation rate of +7.7 cm/yr from 2010 to 2020, which has a positive correlation with the temperature in the basin. This indicates that the increased temperature, which results in the increased snow and ice meltwater in the basin, is the main driving force of the increased lake evolution. The lake surface elevation of Lake Chatyrkul shows an overall decreasing trend, with a variation rate of -9.9 cm/yr from 2010 to 2020, which has a negative correlation with the temperature in the basin. This suggests that Lake Chatyrkul is poorly recharged by snow and ice meltwater. The main driving force of its evolution is the increased evaporative output of the lake due to the increase in temperature. These conclusions prove that temperature and alpine glacial variability within the lake basin play an important role in lake surface elevation variations in alpine regions of Central Asia.

Satellite Altimetry: Achievements and Future Trends by a Scientometrics Analysis

Scientometric reviews, facilitated by computational and visual analytical approaches, allow researchers to gain a thorough understanding of research trends and areas of concentration from a large number of publications. With the fast development of satellite altimetry, which has been effectively applied to a wide range of research topics, it is timely to summarize the scientific achievements of the previous 50 years and identify future trends in this field. A comprehensive overview of satellite altimetry was presented using a total of 8541 publications from the Web of Science Core Collection covering the years from 1970 to 2021. We begin by presenting the fundamental statistical results of the publications, such as the annual number of papers, study categories, countries/regions, afflictions, journals, authors, and keywords, in order to provide a comprehensive picture of satellite altimetry research. We discuss the co-occurrence of the authors in order to reveal the global collaboration network of satellite altimetry research. Finally, we utilised co-citation networks to detect the development trend and associated crucial publications for various specific topics. The findings show that satellite altimetry research has been changed immensely during the last half-century. The United States, France, China, England, and Germany made the most significant contributions in the field of satellite altimetry. The analysis reveals a clear link between technology advancements and the trend in satellite altimetry research. As a result, wide swath altimetry, GNSS-reflectometry, laser altimetry, terrestrial hydrology, and deep learning are among the most frontier study subjects. The findings of this work could guide a thorough understanding of satellite altimetry’s overall development and research front.

Long-Term Lake Area Change and Its Relationship with Climate in the Endorheic Basins of the Tibetan Plateau

Lakes are sensitive indicators of climate change in the Tibetan Plateau (TP), which have shown high temporal and spatial variability in recent decades. The driving forces for the change are still not entirely clear. This study examined the area change of the lakes greater than 1 km2 in the endorheic basins of the Tibetan Plateau (EBTP) using Landsat images from 1990 to 2019, and analysed the relationships between lake area and local and large-scale climate variables at different geographic scales. The results show that lake area in the EBTP has increased significantly from 1990 to 2019 at a rate of 432.52 km2·year−1. In the past 30 years, lake area changes in the EBTP have mainly been affected by local climate variables such as precipitation and temperature. At a large scale, Indian Summer Monsoon (ISM) has correlations with lake area in western sub-regions in the Inner Basin (IB). While Atlantic Multidecadal Oscillation (AMO) has a significant connection with lake area, the North Atlantic Oscillation (NAO) does not. We also found that abnormal drought (rainfall) brought by the El Niño/La Niña events are significantly correlated with the lake area change in most sub-regions in the IB.

Automated Water Level Monitoring at the Continental Scale from ICESat-2 Photons

Of the approximately 6700 lakes and reservoirs larger than 1 km2 in the Contiguous United States (CONUS), only ~430 (~6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA’s ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for ~6200 overlapping lakes and reservoirs (>1 km2) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine ~19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.