Meiliang Zhao

This study, based on stable hydrogen and oxygen isotope observations of multiple water bodies (precipitation, river water, soil water, and groundwater) in the Ami Dongsou alpine arid watershed on the southern slope of the Qilian Mountains during 2023–2024, reveals significant seasonal fluctuations in water isotope characteristics and water source renewal mechanisms. The results show that precipitation and soil water exhibit notable enrichment during the dry season, primarily due to enhanced evaporation causing light isotopes to evaporate and heavy isotopes to accumulate. River water, influenced by both precipitation recharge and evaporation, shows smaller seasonal fluctuations. Groundwater isotopes remain stable, reflecting a slower water source renewal process with minimal seasonal influence. Through quantitative comparisons of the evaporation line’s slope and intercept, this study finds that precipitation is most significantly affected by evaporation, while groundwater is least influenced, showing more stable isotope characteristics. Climate and topography in high-altitude areas significantly regulate water isotope characteristics, especially during the dry season, where evaporation plays a dominant role in the enrichment of precipitation and river water isotopes. This study innovatively establishes an evidence framework for the linkage of multiple water body isotopes, revealing the “seasonal strong fluctuations + differential water body responses + high-altitude regulation” mechanism of water isotopes in alpine arid regions. It provides new data support for water resource management, particularly in aspects such as water source allocation during the dry season, groundwater protection, and evaporation enrichment effect prediction. Future research could expand the sample size and integrate multi-source data and hydrological models to further improve the accuracy of hydrological process predictions, offering more precise support for watershed water resource management and ecological protection.

Understanding the coupling mechanisms between vegetation phenology and carbon productivity is essential for assessing ecosystem responses to climate change and guiding sustainable grassland management. This study focuses on stable alpine grasslands on the southern slope of the Qilian Mountains from 2001 to 2020, a climatically sensitive but relatively under-investigated transition zone on the northeastern Tibetan Plateau. We utilized MODIS NDVI time-series (MOD13Q1) and the latest PML V2 gross primary productivity (GPP) product at 500 m resolution to quantify changes in the start (SOS), end (EOS), and length (LOS) of the growing season. A pixel-wise linear regression approach was applied to evaluate the sensitivity of GPP to phenological metrics, explicitly characterizing how much GPP changes in response to unit shifts in SOS, EOS and LOS. Compared with previous studies that mainly described large-scale correlations between phenology and GPP or relied on coarser GPP products, this study provides a pixel-level, sensitivity-based assessment of phenology–carbon coupling in alpine grasslands using a long-term, phenology–GPP dataset tailored to the Qilian alpine region. The results revealed trends of earlier SOS, delayed EOS, and extended LOS, accompanied by a gradual increase in GPP. However, phenology–GPP coupling exhibited notable spatial heterogeneity. In mid- and low-altitude areas, extended growing seasons enhanced GPP, whereas high-altitude zones showed limited or even negative responses, likely due to climatic constraints such as cold stress and thermal–moisture mismatches. To better understand these spatial differences, we constructed a three-dimensional phenology–GPP sensitivity space and applied k-means clustering to delineate three ecological functional zones: (1) high carbon sink potential, (2) ecologically fragile regions, and (3) neutral buffers. This sensitivity-based functional zonation moves beyond traditional correlation analyses and provides a process-oriented and spatially explicit framework for ecosystem service assessment, carbon sink enhancement and adaptive land-use strategies in sensitive mountain environments.

Understanding vegetation phenology dynamics is essential for evaluating ecosystem responses to environmental changes. While previous studies have primarily focused on the correlation between vegetation phenology and climate variables, the integrated effects of meteorological factors, topography, and greenhouse gas (GHG) have often been overlooked. This study aims to analyze the spatiotemporal variations in grassland phenology on the southern slopes of the Qilian Mountains from 2002 to 2022, investigating the combined effects of these environmental factors. Our findings reveal significant spatial heterogeneity in vegetation phenology during the study period. Specifically, the start of the growing season (SOS), length of growing season (LOS), and end of the growing season (EOS) advanced, lengthened, and delayed by 0.35, 0.55, and 0.20 days per year, respectively. Climate factors were the primary drivers of phenological changes, with annual precipitation being the main determinant of SOS and LOS, while annual minimum temperature significantly influenced EOS. Topography and GHG had indirect effects on phenology, influencing both annual precipitation and temperature. Additionally, topography affected phenology through its impact on N₂O and CO₂ emissions. This study highlights the complex interactions between climate, topography, and GHG in shaping vegetation phenology, providing new insights into the driving mechanisms behind phenological changes in semi-arid grassland ecosystems.

Soil organic carbon (SOC) and soil inorganic carbon (SIC) are key components of soil carbon pools in arid ecosystems, playing a crucial role in regional carbon cycling and climate change mitigation. However, the interactions between these two forms of carbon in arid alpine ecosystems remain underexplored. This study was conducted in the Heihe River Basin (HRB) in the northeastern Qinghai–Tibet Plateau, focusing on the distribution and dynamics of SOC and SIC in deep soil layers. Using data from 329 samples collected from 49 soil profiles extending to the bedrock, combined with path analysis, we explored the inter-relationships between SOC and SIC and quantified the influence of environmental factors. The results showed that (1) SOC exhibited a unimodal distribution with elevation, peaking at 3300–3600 m, while SIC continuously decreased with elevation, with reduction rates ranging from −0.39% to −31.18%; (2) SOC and SIC were significantly positively correlated (r = 0.55, p < 0.01), with SOC decreasing with depth and SIC showing an inflection point at 50 cm depth; (3) SOC was primarily driven by nutrient factors, such as total nitrogen (TN), with a path coefficient of 0.988, while SIC was influenced by abiotic factors, including potential evapotranspiration (PET), with a coefficient of −1.987; (4) SOC density accounted for 81.62% of the total soil carbon pool, playing a dominant role in carbon storage, whereas SIC density exhibited dynamic changes, particularly at depths of 110–150 cm. These findings advance our understanding of deep soil carbon dynamics in arid alpine ecosystems and provide critical data for improving carbon management strategies in similar regions.

In the context of climate change, the southern slope of the Qilian Mountains stands as a pivotal region for China’s ecological security, holding immense significance for sustaining ecological sustainable development. This study aims to precisely monitor and predict the dynamic changes in vegetation cover within this region, along with their time-lagged effects on climate change, thereby providing a scientific basis for ecological management. By calculating the kNDVI from 2001 to 2020 on the Google Earth Engine (GEE) platform, and integrating the Sen’s trend analysis, Hurst exponent, and partial correlation analysis, we have conducted an in-depth exploration of the long-term spatiotemporal variations in kNDVI on the southern slope of the Qilian Mountains and its delayed responses to climate factors. The primary research findings can be summarized as follows: The kNDVI on the southern slope of the Qilian Mountains exhibits an overall positive trend, with notable geographical variations in its spatial distribution. The proportion of areas showing vegetation improvement is as high as 84%, while degraded areas account for only 17%. Furthermore, there is an average lag response of 1.6 months for kNDVI to precipitation and 0.6 months to temperature in this region. The response speed positively correlates with the time-lagged partial correlation coefficient between kNDVI and climate factors. Notably, kNDVI is more sensitive to temperature variations in the southern slope area of the Qilian Mountains. This study not only fills the gap in long-term kNDVI monitoring for the southern slope of the Qilian Mountains but also offers scientific support for ecological governance and green development initiatives in the region. Additionally, it showcases the innovative application of advanced remote sensing technologies and statistical analysis methods in ecological research, providing fresh perspectives for future ecological management strategies. These findings hold profound implications for promoting sustainable development and ecological conservation in the Qilian Mountains area.