The lakes in the arid or semi-arid regions of western China are more sensitive to climate changes, and lake levels are considered as a direct indicator of regional climate variability. In this study, we combined satellite altimetry data from ICESat-1 with a smaller footprint and higher accuracy (compared to radar altimetry) and CryoSat-2 with a higher resolution in the along-track direction to monitor lake levels in western China and their trends over a long time period from 2003 to 2021. Our satellite altimetry derived lake levels were well-validated by comparing them against in situ measurements for a lake and independent altimetry-derived product from the DAHITI database for the common lakes. Furthermore, the commonly used linear model was applied to our derived lake level time-series to estimate the overall change trends in 67 typical lake levels over western China. Our results showed that 55 (82%) of these lakes displayed an increasing tendency in water levels, and the remaining 12 (18%) lakes showed a decreasing trend. Overall, the mean water level changing rate in western China was +0.15 m/yr (−1.40 to +0.58 m/yr) during the studied time period. The spatial patterns of the lake level variations can be grouped into three subregions: lake level changes between 2003 and 2021 showed general rising lake levels for the central–northern TP (Tibetan Plateau) endorheic region and Xinjiang, but declining levels for the southern TP exorheic region. The seasonal characteristic of lake level changes showed a significant increase during the summer monsoon season, followed by decreases during the non-monsoon season. The precipitation variations play a leading role in the lake level changes in the context of warm and humid climate states. There were good correspondences between the monthly variations in the lake level and monthly mean precipitation. Additionally, the lake levels also showed a relationship with the air temperature change, in particular, the lake level increase showed a small degree of hysteresis behavior compared with the rising temperatures. Geographically, the precipitation increase in the westerlies regions led to widespread lake expansion in the central–northern TP and Xinjiang. Conversely, precipitation decrease in the Indian monsoon regions caused lake shrinkage in the exorheic region of the southern TP. This study helps us achieve a better understanding of the spatial-temporal patterns of lake level changes in the arid or semi-arid region of western China.

Water-use efficiency (WUE) is a crucial physiological index in carbon–water interactions and is defined as the ratio of vegetation productivity to water loss. The variation in climatic variables and drought have the most significant effects on WUE and evapotranspiration (ET). Nevertheless, how WUE varies with climate factors and drought processes in the Tianshan Mountains (TMS) is still poorly understood. In the present work, we analyzed the spatiotemporal variations in WUE, and investigated the correlations between WUE, climate factors, and drought, in the study area. The results showed that, in the TMS during 2000–2020, annual net primary productivity (NPP) ranged from 147.9 to 189.4 gC·m⁻², annual ET was in the range of 212.5–285.8 mm, and annual WUE ranged from 0.66 to 0.78 gC·kg⁻¹·H₂O. Both NPP and ET exhibited an increasing trend with some fluctuation, whereas WUE showed the opposite tendency during the study period. The obtained results demonstrated that the decrease in WUE was primarily because of the increase in ET. There were obvious differences in WUE, under different land-use types, caused by NPP and ET. However, the interannual variation in WUE showed small fluctuations and the dynamic process of WUE in each land-use type showed good consistency. Temperature and wind speed had a positive influence on WUE in the middle and eastern regions of the TMS. Precipitation also played a mainly positive role in enhancing WUE, especially on the northern slope of the TMS. There was strong spatial heterogeneity of the correlation coefficient (0.68, p < 0.05) between WUE and the temperature vegetation drought index (TVDI). Moreover, the slopes of WUE and TVDI showed good consistency in terms of spatial distribution, suggesting that drought had a significant impact on ecosystem WUE. This work will enhance the understanding of WUE variation, and provide scientific evidence for water resource management and sustainable utilization in the study area.

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products.