Linkage of Climatic Factors and Human Activities with Water Level Fluctuations in Qinghai Lake in the Northeastern Tibetan Plateau, China

Spatiotemporal patterns of organic carbon burial over the last century in Lake Qinghai, the largest lake on the Tibetan Plateau

Lakes are important carbon sinks in terrestrial environments. However, the estimation of the global lake carbon sink has large uncertainty. Data from plateau and remote lakes are rare, and most studies of carbon sequestration in large lakes have been based on single or a few sediment cores. Here, twenty-five sediment cores were collected by grid sampling covering Lake Qinghai, the largest lake on the Tibetan Plateau. Age models were established by combining radionuclide ²¹⁰Pb¹³⁷Cs dating with magnetic susceptibility chronostratigraphy of sediment cores. Furthermore, the spatiotemporal variations of the organic carbon burial rate (OCBR) over the past century were investigated. The OCBR of Lake Qinghai has increased significantly since the 1990s in association with warm-humid climates, increased nutrient supply and, enhanced land-use changes. The spatial distributions of OCBR were insignificant during the 1900s-1960s and 1960s-1990s and then shifted to a pattern of high values occurring in the southwestern lake areas during the post-1990s period. The spatial distribution of OCBR was mainly determined by sediment mass accumulation rate, primary production, and potential mineralization. The average OCBR in the all sediment cores showed no correlation with water depth (12-30 m) and was within one standard deviation of the whole-lake average value for most cores. These results suggest that the average OCBR of a sediment core in a relatively flat lakebed can generally represent the whole-basin level of Lake Qinghai. The average OCBR was 22.5 ± 5.5 g m^-2 yr^-1, which is close to those values reported previously for lakes of boreal forest and taiga but significantly higher than those reported for tundra lakes. Our findings highlight that the remote lakes on the Tibetan Plateau have great carbon sequestration potential in sediments and may act as a significant natural carbon sink.

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.

Ecological Niche and Interspecific Association of Plant Communities in Alpine Desertification Grasslands: A Case Study of Qinghai Lake Basin

The study of niche and interspecific relationships is one of the classical ecological theories. We set up four desertification gradients. The "Levins" and "Pianka" method were used to calculate the species' niche breadth and niche overlap. Interspecies associations were analyzed by the ratio of variance (VR), Chi-square test, association coefficient (AC) and Ochiai index (OI). The results showed that in grasslands with different degrees of desertification, Stellera chromosome (3.90), Thermopsis lanceolate (3.52) and Aster almanacs (3.99) had larger niche widths, which were wide-area species of plant communities in the desertification area. The ecological niches of the same species in different habitats or different species in the same habitat were multi-dimensional. Niche differentiation measured by niche overlap can occur at any community succession stage. Niche width and niche overlap were not always consistent with environmental changes. Moreover, there was no linear relationship between them. The interspecific connection coefficient fluctuated greatly with the environment. The results can provide a reference for the study of plant community competition mechanism and desertification control in desertification land of the study area. We still do not know the mechanism of how the plants were preserved and how the retained plants adapted to the new environment during the desertification process. We can further study these questions in the next step.

Natural and anthropogenic controls on lake water-level decline and evaporation-to-inflow ratio in the conterminous United States

Lake water levels are integral to lake function, but hydrologic changes from land and water management may alter lake fluctuations beyond natural ranges. We constructed a conceptual model of multifaceted drivers of lake water-levels and evaporation-to-inflow ratio (Evap:Inflow). Using a structural equation modeling framework, we tested our model on 1) a national subset of lakes in the conterminous United States with minimal water management to describe natural drivers of lake hydrology and 2) five ecoregional subsets of lakes to explore regional variation in water management effects. Our model fit the national and ecoregional datasets and explained up to 47% of variation in Evap:Inflow, 38% of vertical water-level decline, and 79% of horizontal water-level decline (littoral exposure). For lakes with minimal water management, Evap:Inflow was related to lake depth (β = -0.31) and surface inflow (β = -0.44); vertical decline was related to annual climate (e.g., precipitation β = -0.18) and water management (β = -0.21); and horizontal decline was largely related to vertical decline (β = 0.73) and lake morphometry (e.g., depth β = -0.18). Anthropogenic effects varied by ecoregion and likely reflect differences in regional water management and climate. In the West, water management indicators were related to greater vertical decline (β = 0.38), whereas in the Midwest, these indicators were related to more stable and full lake levels (β = -0.22) even during drought conditions. National analyses show how human water use interacts with regional climate resulting in contrasting impacts to lake hydrologic variation in the US.

Divergent change patterns observed in hydrological fluxes entering China's two largest lakes

Large lakes are key components of hydrological processes and have critical ecological and economic functions. Streamflow and riverine sediment loads exported from tributary rivers to lakes provide inputs of water and nutrients and greatly impact the health of lacustrine ecosystems. Understanding the spatiotemporal patterns of water and sediment transport dynamics within the basins of large lakes is therefore required for informed management of their resources. In particular, the hydrological processes of large lake basins in western China were not fully explored yet. This study investigated changes in runoff and sediment fluxes entering the two largest lakes in China (Qinghai Lake and Poyang Lake) over the past half century. Our results showed that the regional trends of the two lake basins have changed in divergent way. The patterns of change of streamflow and sediment transport-including the trends, periodicity, and transition points as well as the relationship between streamflow and sediment flux-were quite different. The results obtained by a Sediment Identity method revealed that increases of the precipitation and runoff coefficient controlled the observed increase of streamflow and sediment flux within the Qinghai Lake basin, whereas the decrease of the sediment flux entering Poyang Lake was caused mainly by a reduction in sediment concentration. Climate change was the dominant driver influencing the recent increases in surface water resources and sediment load in the Qinghai Lake basin as well as the long-term fluctuations of streamflow within the Poyang Lake basin. In contrast, the observed abrupt reduction in total sediment flux entering Poyang Lake was due mainly to declining sediment discharge from the Gan River; this decrease is attributed primarily to human activities, especially reservoir construction and soil conservation practices. Overall, this study discovers the divergent change patterns when investigating hydrological processes, and highlights the importance of developing policies for managing soil and water resources within large lake basins to the northwest and southeast of the Hu Line. Management policies should consider the tremendous differences in geography, climate, and population across China.

The Robust Study of Deep Learning Recursive Neural Network for Predicting of Turbidity of Water

Water treatment is an important process, as it improves water quality and makes it better for any end use, whether it be drinking, industrial use, irrigation, water recreation, or any other kind of use. Turbidity is one of the fundamental measurements of the clarity of water in water treatment. Specifically, this component is an optical feature of the amount of light on scatter particles when light is shined on a water sample. It is crucial in water reservoirs to provide clean water, which is difficult to manage and predict. Hence, this study focuses on the use of robust deep learning models to analyze time-series data in order to predict the water quality of turbidity in a reservoir area. Deep learning models may become an alternative solution in predicting water quality because of their accuracy. This study is divided into two parts: (a) the first part uses the optical bands of blue (B), green (G), red (R), and infrared (IR) to build a regression function to monitor turbidity in water, and (b) the second part uses a hybrid model to analyze time-series turbidity data with the recursive neural network (RNN2) model. The selected models’ accuracies are compared based on the accuracy using the input data, forecasting level, and training time. The analysis shows that these models have their strengths and limitations under different analyzed conditions. Generally, RNN2 shows the performance regarding the root-mean-square error (RMSE) evaluation metric. The most significant finding is that the RNN2 model is suitable for the accurate prediction of water quality. The RMSE is used to facilitate a comparison of the accuracy of the sampling data. In the training model, the training data have an RMSE of 20.89, and the testing data have an RMSE of 30.11. The predicted R-squared values in the RNN2 model are 0.993 (training data) and 0.941 (testing data).

Long-Term 10 m Resolution Water Dynamics of Qinghai Lake and the Driving Factors

As the largest inland saltwater lake in China, Qinghai Lake plays an important role in regional sustainable development and ecological environment protection. In this study, we adopted a spatial downscaling model for mapping lake water at 10 m resolution through integrating Sentinel-2 and Landsat data, which was applied to map the water extent of Qinghai Lake from 1991 to 2020. This was further combined with the Hydroweb water level dataset to establish an area-level relationship to acquire the 30-year water level and water volume. Then, the driving factors of its water dynamics were analyzed based on the grey system theory. It was found that the lake area, water level, and water volume decreased from 1991 to 2004, but then showed an increasing trend afterwards. The lake area ranges from 4199.23 to 4494.99 km2. The water level decreased with a speed of ~0.05 m/a before 2004 and then increased with a speed of 0.22 m/a thereafter. Correspondingly, the water volume declined by 5.29 km3 in the first 13 years, and rapidly increased by 15.57 km3 thereafter. The correlation between climatic factors and the water volume of Qinghai Lake is significant. Precipitation has the greatest positive impact on the water volume variation with the relational grade of 0.912, while evaporation has a negative impact.

Contrasting Evolution Patterns of Endorheic and Exorheic Lakes on the Central Tibetan Plateau and Climate Cause Analysis during 1988–2017

The alpine lakes on the Tibetan Plateau (TP) are indicators of climate change. The assessment of lake dynamics on the TP is an important component of global climate change research. With a focus on lakes in the 33° N zone of the central TP, this study investigates the temporal evolution patterns of the lake areas of different types of lakes, i.e., non-glacier-fed endorheic lakes and non-glacier-fed exorheic lakes, during 1988–2017, and examines their relationship with changes in climatic factors. From 1988 to 2017, two endorheic lakes (Lake Yagenco and Lake Zhamcomaqiong) in the study area expanded significantly, i.e., by more than 50%. Over the same period, two exorheic lakes within the study area also exhibited spatio-temporal variability: Lake Gaeencuonama increased by 5.48%, and the change in Lake Zhamuco was not significant. The 2000s was a period of rapid expansion of both the closed lakes (endorheic lakes) and open lakes (exorheic lakes) in the study area. However, the endorheic lakes maintained the increase in lake area after the period of rapid expansion, while the exorheic lakes decreased after significant expansion. During 1988–2017, the annual mean temperature significantly increased at a rate of 0.04 °C/a, while the annual precipitation slightly increased at a rate of 2.23 mm/a. Furthermore, the annual precipitation significantly increased at a rate of 14.28 mm/a during 1995–2008. The results of this study demonstrate that the change in precipitation was responsible for the observed changes in the lake areas of the two exorheic lakes within the study area, while the changes in the lake areas of the two endorheic lakes were more sensitive to the annual mean temperature between 1988 and 2017. Given the importance of lakes to the TP, these are not trivial issues, and we now need accelerated research based on long-term and continuous remote sensing data.

Assessing Changes of Water Yield in Qinghai Lake Watershed of China

Water yield is an important ecosystem service, which is directly related to human welfare and affects the sustainable development. Using the integrated valuation of environmental services and tradeoffs model (InVEST model), we simulated the dynamic change of water yield in Qinghai lake watershed, Qinghai, China, and verified the simulation results. This paper emphatically explored how precipitation change and land use/land cover change (LUCC) affected the change of water yield on the spatial and temporal scales. Before 2004, the areas of cultivated land and unused land showed a dramatic increasing tendency, while forestland and water area presented a decreasing trend. After 2004 cultivated land changed slowly, unused land decreased. Grassland revealed a general trend of decline during 1977–2018, while built-up land basically presented a linear increase. The results show that water yield fluctuated and increased during 1977–2018. From 1977 to 2000, the mean water yield in each sub-watershed showed an increasing trend and afterward a decreasing one. After 2000, the sub-watersheds basically showed an increasing tendency. There was a strong correlation, with a correlation coefficient of 0.954 ** (** correlation is significant at the 0.01 level), between precipitation and water yield. Land use/land cover change can change the hydrological state of infiltration, evapotranspiration, and water retention. Meanwhile, the correlation between built-up land and water yield was the highest, with a correlation coefficient of 0.932, followed by forestland, with a correlation coefficient of 0.897. Through the analysis of different scenarios, we found that compared with land use/land cover change, precipitation played a more dominant role in affecting water yield.