Most studies attribute the glacier mass balance within High Mountain Asia (HMA) to climate change, ignoring the influence of its complex terrain. Knowledge of the influence of this complex terrain is crucial for understanding the spatial variability in its mass balance. However, there is a lack of any systematic assessment of this influence across HMA. Therefore, in this study, we used the glacier outlines and raster data (SRTM DEM, slope and aspect) to calculate the topographic shading of all 97,965 glaciers within HMA during the ablation period, which is regarded as a major index of the influence of complex terrain on the mass balance. The results showed that 27.19% of HMA glacier area was subjected to topographic shading, and regional differences were significant with respect to both their altitudinal and spatial distributions. The topographic shading contributed to the protection of the smallest glaciers from solar illumination. Furthermore, we found a significant correlation between the topographic shading and mass balance in these small north-facing glaciers. However, these small glaciers were most prevalent in the north-facing orientation, especially in West Kunlun, East Kunlun, Inner Tibet Plateau and Qilian Shan, where shading was found to increase with decreases in the glacier area. This indicates that complex terrain can affect the spatial distribution of the mass balance by altering the solar illumination pattern.

Carbon and oxygen isotope ratios (δ¹³C and δ¹⁸O) were measured in annual tree-ring cellulose samples dated from 1756 to 2015 CE. These samples were extracted from Chinese pine (Pinus tabulaeformis Carr.) trees located in a semi-arid region of north-central China. We found that tree-ring δ¹³C and δ¹⁸O values both recorded similar climatic signals (e.g., temperature and moisture changes), but found that tree-ring δ¹³C exhibited a stronger relationship with mean temperature, precipitation, average relative humidity, self-calibrating Palmer drought severity index (scPDSI), and standard precipitation evaporation index (SPEI) than δ¹⁸O during the period 1951–2015 CE. The strongest correlation observed was between tree-ring δ¹³C and scPDSI (previous June to current May), which explains ~43% of the variance. The resulting 130-year reconstruction reveals severe drought events in the 1920s and a sustained drying trend since the 1980s. This hydroclimate record based on tree-ring δ¹³C data also reveals similar dry and wet events to other proxy data (i.e., tree-ring width and historical documentation) that have allowed reconstructions to be made across the northern fringe of the Asian summer monsoon region. Our results suggest that both large-scale modes of climate variability (e.g., El Niño-Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation) and external forcing (e.g., solar variability) may have modulated moisture variability in this region. Our results imply that the relationship between tree-ring δ¹⁸O and local climate is less well-characterized when compared to δ¹³C and may be affected more strongly by the influences of these different atmospheric circulation patterns. In this semi-arid region, tree-ring δ¹³C appears to represent a better tool with which to investigate historical moisture changes (scPDSI).

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has demonstrated significant potential for soil moisture content (SMC) monitoring due to its high spatiotemporal resolution. However, GNSS-IR inversion experiments are notably influenced by vegetation and meteorological factors. To address these challenges, this study proposes a multi-factor SMC inversion method. Six GNSS stations from the Plate Boundary Observatory (PBO) were selected as study sites. A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. A multi-factor SMC inversion dataset was constructed, and three machine learning models were selected to develop the SMC prediction model: Support Vector Regression (SVR), suitable for small and medium-sized regression tasks; Convolutional Neural Networks (CNN), with robust feature extraction capabilities; and NRBO-XGBoost, which supports automatic optimization. The multi-factor SMC inversion method achieved remarkable results. For instance, at the P038 station, the model attained an R² of 0.98, with an RMSE of 0.0074 and an MAE of 0.0038. Experimental results indicate that the multi-factor inversion model significantly outperformed the traditional univariate model, whose R² (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R² outperforming SVR by 0.11 and CNN by 0.03. Additionally, the analysis of different surface types showed that the method achieved higher accuracy in grassland and open shrubland areas, with all models reaching R² values above 0.9. Therefore, the accuracy of the multi-factor SMC inversion model was validated, supporting the practical application of GNSS-IR technology in SMC inversion.

The value of ecosystem services (ESV) serves as a crucial metric for assessing the cost-effectiveness of ecosystems and evaluating their economic worth. Predicting the evolution of ESV across various land uses under different scenarios is essential for maintaining ecological stability and fostering sustainable developm0ent. Utilizing land use change data combined with the PLUS predictive model and ecosystem service value equivalence techniques, this study analyzes the spatiotemporal patterns and underlying drivers of ESV in Yuzhong County, China. The results indicate that the overall ESV distribution exhibits a “fragmented northeast, clustered southwest” pattern, dominated by high-high and low-low clustering. Among the driving factors, elevation exerts the greatest influence on ESV, followed by precipitation and population density, while slope contributes least. Under natural development scenarios, the ESV remains relatively stable compared to the base year of 2020. In contrast, the farmland protection scenario effectively preserves the ESV associated with cultivated land. However, the economic development scenario leads to a significant decline in the overall ESV, with a retraction of high-value areas and an expansion of low-value regions. These insights provide a fresh perspective for analyzing the factors influencing ESV and for conducting multi-scenario predictions, thereby aiding in the development of ecological resource conservation and landscape ecological risk prevention strategies in the study region.