Qinghai Lake is the largest saline lake in China. The change in the lake volume is an indicator of the variation in water resources and their response to climate change on the Qinghai-Tibetan Plateau (QTP) in China. The present study quantitatively evaluated the effects of climate change and land use/cover change (LUCC) on the lake volume of the Qinghai Lake in China from 1958 to 2018, which is crucial for water resources management in the Qinghai Lake Basin. To explore the effects of climate change and LUCC on the Qinghai Lake volume, we analyzed the lake level observation data and multi-period land use/land cover (LULC) data by using an improved lake volume estimation method and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Our results showed that the lake level decreased at the rate of 0.08 m/a from 1958 to 2004 and increased at the rate of 0.16 m/a from 2004 to 2018. The lake volume decreased by 105.40×10⁸ m³ from 1958 to 2004, with the rate of 2.24×10⁸ m³/a, whereas it increased by 74.02×10⁸ m³ from 2004 to 2018, with the rate of 4.66×10⁸ m³/a. Further, the climate of the Qinghai Lake Basin changed from warm-dry to warm-humid. From 1958 to 2018, the increase in precipitation and the decrease in evaporation controlled the change of the lake volume, which were the main climatic factors affecting the lake volume change. From 1977 to 2018, the measured water yield showed an "increase-decrease-increase" fluctuation in the Qinghai Lake Basin. The effects of climate change and LUCC on the measured water yield were obviously different. From 1977 to 2018, the contribution rate of LUCC was -0.76% and that of climate change was 100.76%; the corresponding rates were 8.57% and 91.43% from 1977 to 2004, respectively, and -4.25% and 104.25% from 2004 to 2018, respectively. Quantitative analysis of the effects and contribution rates of climate change and LUCC on the Qinghai Lake volume revealed the scientific significance of climate change and LUCC, as well as their individual and combined effects in the Qinghai Lake Basin and on the QTP. This study can contribute to the water resources management and regional sustainable development of the Qinghai Lake Basin.

The rapid economic development that the Hotan Oasis in Xinjiang Uygur Autonomous Region, China has undergone in recent years may face some challenges in its ecological environment. Therefore, an analysis of the spatiotemporal changes in ecological environment of the Hotan Oasis is important for its sustainable development. First, we constructed an improved remote sensing-based ecological index (RSEI) in 1990, 1995, 2000, 2005, 2010, 2015 and 2020 on the Google Earth Engine (GEE) platform and implemented change detection for their spatial distribution. Second, we performed a spatial autocorrelation analysis on RSEI distribution map and used land-use and land-cover change (LUCC) data to analyze the reasons of RSEI changes. Finally, we investigated the applicability of improved RSEI to arid area. The results showed that mean of RSEI rose from 0.41 to 0.50, showing a slight upward trend. During the 30-a period, 2.66% of the regions improved significantly, 10.74% improved moderately and 32.21% improved slightly, respectively. The global Moran's I were 0.891, 0.889, 0.847 and 0.777 for 1990, 2000, 2010 and 2020, respectively, and the local indicators of spatial autocorrelation (LISA) distribution map showed that the high-high cluster was mainly distributed in the central part of the Hotan Oasis, and the low-low cluster was mainly distributed in the outer edge of the oasis. RSEI at the periphery of the oasis changes from low to high with time, with the fragmentation of RSEI distribution within the oasis increasing. Its distribution and changes are predominantly driven by anthropologic factors, including the expansion of artificial oasis into the desert, the replacement of desert ecosystems by farmland ecosystems, and the increase in the distribution of impervious surfaces. The improved RSEI can reflect the eco-environmental quality effectively of the oasis in arid area with relatively high applicability. The high efficiency exhibited with this approach makes it convenient for rapid, high frequency and macroscopic monitoring of eco-environmental quality in study area.

Studies on the ecosystem service value (ESV) of gardens are critical for informing evidence- based land management practices based on an understanding of the local ecosystem. By analyzing equivalent value factors (EVFs), this paper evaluated the values of 11 ecosystem services of gardens in the Yellow River Basin of China in 2019. High-precision land use survey data were used to improve the accuracy of the land use classification, garden areas, and spatial distribution of the ESVs of gardens. The results showed that garden ecosystem generally had high ESVs, especially in terms of the ESV of food production, which is worthy of further research and application to the practice of land use planning and management. Specifically, the value of one standard EVF of ecosystem services in 2019 was 3587.04 CNY/(hm²•a), and the ESV of food production of gardens was much higher than that of croplands. Garden ecosystem provided an ESV of 1348.66×10⁸ CNY/a in the Yellow River Basin. The areas with the most concentrated ESVs of gardens were located in four regions: downstream in the Shandong-Henan zone along the Yellow River, mid-stream in the Shanxi-Shaanxi zone along the Yellow River, the Weihe River Basin, and upstream in the Qinghai-Gansu-Ningxia-Inner Mongolia zone along the Yellow River. The spatial correlation of the ESVs in the basin was significant (global spatial autocorrelation index Moran's I=0.464), which implied that the characteristics of high ESVs adjacent to high ESVs and low ESVs adjacent to low ESVs are prominent. In the Yellow River Basin, the contribution of the ESVs of gardens to the local environment and economy varied across regions. We also put forward some suggestions for promoting the construction of ecological civilization in the Yellow River Basin. The findings of this study provide important contributions to the research of ecosystem service evaluation in the Yellow River Basin.

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.

Precipitation is one of the most important indicators of climate data, but there are many errors in precipitation measurements due to the influence of climatic conditions, especially those of solid precipitation in alpine mountains and at high latitude areas. The measured amount of precipitation in those areas is frequently less than the actual amount of precipitation. To understand the impact of climatic conditions on precipitation measurements in the mountainous areas of Northwest China and the applicability of different gauges in alpine mountains, we established a cryospheric hydrometeorology observation (CHOICE) system in 2008 in the Qilian Mountains, which consists of six automated observation stations located between 2960 and 4800 m a.s.l. Total Rain weighing Sensor (TRwS) gauges tested in the World Meteorological Organization-Solid Precipitation Intercomparison Experiment (WMO-SPICE) were used at observation stations with the CHOICE system. To study the influence of climatic conditions on different types of precipitation measured by the TRwS gauges, we conducted an intercomparison experiment of precipitation at Hulu-1 station that was one of the stations in the CHOICE system. Moreover, we tested the application of transfer functions recommended by the WMO-SPICE at this station using the measurement data from a TRwS gauge from August 2016 to December 2020 and computed new coefficients for the same transfer functions that were more appropriate for the dataset from Hulu-1 station. The new coefficients were used to correct the precipitation measurements of other stations in the CHOICE system. Results showed that the new parameters fitted to the local dataset had better correction results than the original parameters. The environmental conditions of Hulu-1 station were very different from those of observation stations that provided datasets to create the transfer functions. Thus, root-mean-square error (RMSE) of solid and mixed precipitation corrected by the original parameters increased significantly by the averages of 0.135 (353%) and 0.072 mm (111%), respectively. RMSE values of liquid, solid and mixed precipitation measurements corrected by the new parameters decreased by 6%, 20% and 13%, respectively. In addition, the new parameters were suitable for correcting precipitation at other five stations in the CHOICE system. The relative precipitation (RP) increment of different types of precipitation increased with rising altitude. The average RP increment value of snowfall at six stations was the highest, reaching 7%, while that of rainfall was the lowest, covering 3%. Our results confirmed that the new parameters could be used to correct precipitation measurements of the CHOICE system.

Vegetation near-soil-surface factors can protect topsoil from erosion, however, their contributions to the reduction of soil erosion, especially under natural rainfall events, have not been systematically recognized. This study was performed to quantify the effects of near-soil-surface factors on runoff and sediment under natural rainfall events on grasslands dominated by Bothriochloa ischaemum (Linn.) Keng (BI grassland) and Artemisia gmelinii Thunb. (AG grassland) in two typical watersheds on the Loess Plateau, China in 2018. By successive removal of the plant canopy, litter, biological soil crusts (BSCs) and plant roots, we established five treatments including plant roots, plant roots+BSCs, plant roots+BSCs+litter, intact grassland and bare land in each grassland type. In total, twenty runoff plots (5 m×3 m) with similar slopes and aspects were constructed in the two types of grasslands. Results showed that plant canopy, litter and roots reduced runoff, while BSCs, which swelled in the presence of water, increased runoff. In contrast, all of these factors reduced sediment yield. In addition, the reductions in runoff and sediment yield increased with I₃₀ (maximum 30-min rainfall intensity) for each vegetation near-soil-surface factor except for BSCs. Among these factors, plant canopy had the largest contribution to runoff reduction, accounting for 48.8% and 39.9% in the BI and AG grasslands, respectively. The contributions of these vegetation near-soil-surface factors to sediment yield reduction were similar (21.3%-29.9%) in the two types of grasslands except for BSCs in the AG grassland (10.3%). The total reduction in runoff in the BI grassland (70.8%) was greater than that in the AG grassland (53.1%), while the reduction in sediment yield was almost the same in both grasslands (97.4% and 96.7%). In conclusion, according to the effects of different vegetation near-soil-surface factors on runoff and sediment production, our results may provide more complete insight and scientific basis into the effects of various vegetation related factors in controlling soil erosion.

Soil salinization has adverse effects on the soil physical-chemical characteristics. However, little is known about the changes in soil salt ion concentrations and other soil physical-chemical characteristics within the Qarhan Salt Lake and at different soil depths in the surrounding areas. Here, we selected five sampling sites (S1, S2, S3, S4, and S5) alongside the Qarhan Salt Lake and in the Xidatan segment of the Kunlun Mountains to investigate the relationship among soil salt ion concentrations, soil physical-chemical characteristics, and environmental variables in April 2019. The results indicated that most sites had strongly saline and very strongly saline conditions. The main salt ions present in the soil were Na⁺, K⁺, and Cl^-. Soil nutrients and soil microbial biomass (SMB) were significantly affected by the salinity (P<0.05). Moreover, soil salt ions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^-, CO₃^2-, SO₄^2-, and HCO₃^-) were positively correlated with electrical conductivity (EC) and soil water content (SWC), but negatively related to altitude and soil depth. Unlike soil salt ions, soil nutrients and SMB were positively correlated with altitude, but negatively related to EC and SWC. Moreover, soil nutrients and SMB were negatively correlated with soil salt ions. In conclusion, soil nutrients and SMB were mainly influenced by salinity, and were related to altitude, soil depth, and SWC in the areas from the Qarhan Salt Lake to the Xidatan segment. These results imply that the soil quality (mainly evaluated by soil physical-chemical characteristics) is mainly influenced by soil salt ions in the areas surrounding the Qarhan Salt Lake. Our results provide an accurate prediction of how the soil salt ions, soil nutrients, and SMB respond to the changes along a salt gradient. The underlying mechanisms controlling the soil salt ion distribution, soil nutrients, and SMB in an extremely arid desert climate playa should be studied in greater detail in the future.