Spatial Difference of Terrestrial Water Storage Change and Lake Water Storage Change in the Inner Tibetan Plateau

Water resources are rich on the Tibetan Plateau, with large amounts of glaciers, lakes, and permafrost. Terrestrial water storage (TWS) on the Tibetan Plateau has experienced a significant change in recent decades. However, there is a lack of research about the spatial difference between TWSC and lake water storage change (LWSC), which is helpful to understand the response of water storage to climate change. In this study, we estimate the change in TWS, lake water storage (LWS), soil moisture, and permafrost, respectively, according to satellite and model data during 2005−2013 in the inner Tibetan Plateau and glacial meltwater from previous literature. The results indicate a sizeable spatial difference between TWSC and LWSC. LWSC was mainly concentrated in the northeastern part (18.71 ± 1.35 Gt, 37.7% of the total) and southeastern part (22.68 ± 1.63 Gt, 45.6% of the total), but the increased TWS was mainly in the northeastern region (region B, 18.96 ± 1.26 Gt, 57%). Based on mass balance, LWSC was the primary cause of TWSC for the entire inner Tibetan Plateau. However, the TWS of the southeastern part increased by 3.97 ± 2.5 Gt, but LWS had increased by 22.68 ± 1.63 Gt, and groundwater had lost 16.91 ± 7.26 Gt. The increased TWS in the northeastern region was equivalent to the increased LWS, and groundwater had increased by 4.47 ± 4.87 Gt. Still, LWS only increased by 2.89 ± 0.21 Gt in the central part, and the increase in groundwater was the primary cause of TWSC. These results suggest that the primary cause of increased TWS shows a sizeable spatial difference. According to the water balance, an increase in precipitation was the primary cause of lake expansion for the entire inner Tibetan Plateau, which contributed 73% (36.28 Gt) to lake expansion (49.69 ± 3.58 Gt), and both glacial meltwater and permafrost degradation was 13.5%

Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning

Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau. In this study, combined with satellite images and bathymetric data, we comprehensively evaluate the accuracy of a multi-factor combined linear regression model (MLR) and machine learning models, create a depth distribution map and compare it with the spatial interpolation, and estimate the change of water level and water storage based on the inverted depth. The results indicated that the precision of the random forest (RF) was the highest with a coefficient of determination (R2) value (0.9311) and mean absolute error (MAE) values (1.13 m) in the test dataset and had high reliability in the overall depth distribution. The water level increased by 9.36 m at a rate of 0.47 m/y, and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth. The water level change was consistent with that of the Shuttle Radar Topography Mission (SRTM) method. Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes

Continuous Intra-Annual Changes of Lake Water Level and Water Storage from 2000 to 2018 on the Tibetan Plateau

There is a large amount of lakes on the Tibetan Plateau (TP), which are very sensitive to climate change. Understanding the characteristics and driving mechanisms of lake change are crucial for understanding climate change and the effective use of water resources. Previous studies have mainly focused on inter-annual lake variation, but the continuous and long-term intra-annual variation of lakes on the TP remains unclear. To address this gap, we used the global surface water (GSW) dataset and the Shuttle Radar Topography Mission (SRTM) DEM to estimate the water level and storage changes on the TP. The results indicated that the average annual minimum lake water level (LWLmin) and the average annual maximum lake water level (LWLmax) increased by 3.09 ± 0.18 m (0.16 ± 0.01 m/yr) and 3.69 ± 0.12 m (0.19 ± 0.01 m/yr) from 2000 to 2018, respectively, and the largest change of LWLmin and LWLmax occurred in 2002–2003 (0.45 m) and 2001–2002 (0.39 m), respectively. Meanwhile, the annual minimum lake water storage change (LWSCmin) and annual maximum lake water storage change (LWSCmax) were 125.34 ± 6.79 Gt (6.60 ± 0.36 Gt/yr) and 158.07 ± 4.52 Gt (8.32 ± 0.24 Gt/yr) from 2000 to 2018, and the largest changes of LWSCmin and LWSCmax occurred in the periods of 2002–2003 (17.67 Gt) and 2015–2016 (17.51 Gt), respectively. The average intra-year changes of lake water level (LWLCintra-year) and the average intra-year changes of lake water storage (LWSCintra-year) were 0.98 ± 0.23 m and 40.19 ± 10.67 Gt, respectively, and the largest change in both LWLCintra-year (1.44 m) and LWSCintra-year (62.46 Gt) occurred in 2018. The overall trend of lakes on the TP was that of expansion, where the LWLC and LWSC in the central and northern parts of the TP was much faster than that in other regions, while the lakes in the southern part of the TP were shrinking, with decreasing LWLC and LWSC. Increased precipitation was found to be the primary meteorological factor affecting lake expansion, and while increasing glacial meltwater also had an important influence on the LWSC, the variation of evaporation only had a little influence on lake change