Abstract: Identifying water vapor sources in the natural vegetation of the Tianshan Mountains is of significant importance for obtaining greater knowledge about the water cycle, forecasting water resource changes, and dealing with the adverse effects of climate change. In this study, we identified water vapor sources of precipitation and evaluated their effects on precipitation stable isotopes in the north slope of the Tianshan Mountains, China. By utilizing the temporal and spatial distributions of precipitation stable isotopes in the forest and grassland regions, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and isotope mass balance model, we obtained the following results. (1) The Eurasia, Black Sea, and Caspian Sea are the major sources of water vapor. (2) The contribution of surface evaporation to precipitation in forests is lower than that in the grasslands (except in spring), while the contribution of plant transpiration to precipitation in forests (5.35%) is higher than that in grasslands (3.79%) in summer. (3) The underlying surface and temperature are the main factors that affect the contribution of recycled water vapor to precipitation; meanwhile, the effects of water vapor sources of precipitation on precipitation stable isotopes are counteracted by other environmental factors. Overall, this work will prove beneficial in quantifying the effect of climate change on local water cycles.
摘要：Intense human activities in arid areas have great impacts on groundwater hydrochemical cycling by causing groundwater salinization. The spatiotemporal distributions of groundwater hydrochemistry are crucial for studying groundwater salt migration, and also vital to understand hydrological and hydrogeochemical processes of groundwater in arid inland oasis areas. However, due to constraints posed by the paucity of observation data and intense human activities, these processes are not well known in the dried-up river oases of arid areas. Here, we examined spatiotemporal variations and evolution of groundwater hydrochemistry using data from 199 water samples collected in the Wei-Ku Oasis, a typical arid inland oasis in Tarim Basin of Central Asia. As findings, groundwater hydrochemistry showed a spatiotemporal dynamic, while its spatial distribution was complex. TDS and δ18O of river water in the upstream increased from west to east, whereas ion concentrations of shallow groundwater increased from northwest to southeast. Higher TDS was detected in spring for shallow groundwater and in summer for middle groundwater. Pronounced spatiotemporal heterogeneity demonstrated the impacts of geogenic, climatic, and anthropogenic conditions. For that, hydrochemical evolution of phreatic groundwater was primarily controlled by rock dominance and evaporation-crystallization process. Agricultural irrigation and drainage, land cover change, and groundwater extraction reshaped the spatiotemporal patterns of groundwater hydrochemistry. Groundwater overexploitation altered the leaking direction between the aquifers, causing the interaction between saltwater and freshwater and the deterioration of groundwater environment. These findings could provide an insight into groundwater salt migration under human activities, and hence be significant in groundwater quality management in arid inland oasis areas.
摘要：The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources. In this study, long short-term memory (LSTM), a state-of-the-art artificial neural network algorithm, is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia. Two other classic machine learning methods, namely extreme gradient boosting (XGBoost) and support vector regression (SVR), along with a distributed hydrological model (Soil and Water Assessment Tool (SWAT) and an extended SWAT model (SWAT_Glacier) are also employed for comparison. This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data. The two typical basins in this study are the main tributaries (the Kumaric and Toxkan rivers) of the Aksu River in the south Tianshan Mountains, which are dominated by snow and glacier meltwater and precipitation. Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations. The performance metrics Nash-Sutcliffe efficiency coefficient (NS) and correlation coefficient (R2) of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin, and NS and R2 are also higher than 0.70 in the Toxkan River Basin. Compared to classic machine learning algorithms, LSTM shows significant advantages over most evaluating indices. XGBoost also has high NS value in the training period, but is prone to overfitting the discharge. Compared with the widely used hydrological models, LSTM has advantages in predicting accuracy, despite having fewer data inputs. Moreover, LSTM only requires meteorological data rather than physical characteristics of underlying data. As an extension of SWAT, the SWAT_Glacier model shows good adaptability in discharge simulation, outperforming the original SWAT model, but at the cost of increasing the complexity of the model. Compared with the oftentimes complex semi-distributed physical hydrological models, the LSTM method not only eliminates the tedious calibration process of hydrological parameters, but also significantly reduces the calculation time and costs. Overall, LSTM shows immense promise in dealing with scarce meteorological data in glaciated catchments.
摘要： The increasing shortage in water resources is a key factor affecting sustainable socio-economic development in the arid region of Northwest China (ARNC). Water shortages also affect the stability of the region's oasis ecosystem. This paper summarizes the hydrological processes and water cycle of inland river basins in the ARNC, focusing on the following aspects: the spatial-temporal features of water resources (including air water vapor resources, runoff, and glacial meltwater) and their driving forces; the characteristics of streamflow composition in the inland river basins; the characteristics and main controlling factors of baseflow in the inland rivers; and anticipated future changes in hydrological processes and water resources. The results indicate that: (1) although the runoff in most inland rivers in the ARNC showed a significant increasing trend, both the glaciated area and glacial ice reserves have been reduced in the mountains; (2) snow melt and glacier melt are extremely important hydrological processes in the ARNC, especially in the Kunlun and Tianshan mountains; (3) baseflow in the inland rivers of the ARNC is the result of climate change and human activities, with the main driving factors being the reduction in forest area and the over-exploitation and utilization of groundwater in the river basins; and (4) the contradictions among water resources, ecology and economy will further increase in the future. The findings of this study might also help strengthen the ecological, economic and social sustainable development in the study region.