Important role of precipitation in controlling a more uniform spring phenology in the Qinba Mountains, China

Identification of degradation risk areas and delineation of key ecological function areas in Qinling region

As a critical component of the geographical divide between the northern and southern regions of China, the ecological stability of the Qinling region has profound implications for ecological balance within China and across East Asia. However, the degradation risk areas of the Qinling region remain unclear, and there are gaps in the delineation of key ecological protection areas. This study examines the improvement and decline in the Qinling region from 2000 to 2023 in terms of ecosystem patterns, quality, and functions. Moreover, key ecological function and degradation risk zones were identified, and future development paths were proposed for the Qinling region. The findings indicate that: (1) Urban area expansion was the most rapid, increasing by about 1800 km², with an average yearly growth rate of 2.43%. Ecosystem quality increased in 48.07% of the Qinling region. The degradation risk zones of ecosystem quality and function were primarily located in the Sanjiangyuan, the Minshan-Qinghai-Tibet Plateau, and the Loess Plateau in Shaanxi, Henan, and Gansu. The core areas for water and soil conservation only accounted for 17.92% and 10.47%, respectively, mainly distributed across the Qinling-Daba Mountains. Based on ecological patterns, quality, functions, and ecological protection and restoration projects, the Qinling region has been divided into two majority categories and 16 subcategories: 7 ecologically key functional areas and 9 degradation risk areas. This study offers recommendations for formulating ecological protection and restoration policies, thereby promoting the sustainable development of the region's ecology and economy.

Winter climate change mediates the sensitivity of vegetation leaf-out to spring warming in high latitudes in China

Global warming has significantly altered plant phenology by advancing the timing of leaf emergence, impacting vegetation productivity and adaptability. Winter and spring temperatures have commonly been used to explain spring phenology shifts, but we still lack a solid understanding of the effects of interactions between conditions in different seasons. This study utilizes normalized difference vegetation index (NDVI) and meteorological data to examine the effects of changes in winter and spring temperatures and precipitation on the start of the vegetation growing season (SOS) at high latitudes in China from 1982 to 2015. We found that SOS in Northeast China, as a whole, showed a weak advancing trend (moving earlier in the year), but with obvious regional differences. Even within the same vegetation type, changes in SOS were faster in the cold north (1.9 days/decade) and the cold and dry northwest (1.6 days/decade) than the regional averages for deciduous needleleaf forests (DNF; 1.2 days/decade) and grasslands (0.6 days/decade). Increases in spring temperatures dominate forest SOS advancement, while grassland SOS is mainly influenced by winter and spring precipitation. Decreases in winter minimum temperature (Tmin) enhance the spring temperature sensitivity of SOS. The way that winter precipitation regulates the spring temperature sensitivity of SOS differs among vegetation types: increasing sensitivity in grasslands but suppressing it in DNF. The moderating effects of winter conditions account for the greatest part of the regional differences in the magnitude of change in SOS. Our findings highlight that, although rising spring temperatures significantly affect SOS, winter Tmin and precipitation are crucial for understanding spatial SOS differences, particularly in cold, arid high-latitude regions. Winter conditions play an essential role in regulating the response of vegetation SOS to spring climate at high latitudes. These results suggest that considering the moderating effect of winter climate can facilitate more accurate predictions of temperature-driven phenological changes under future climate change.

Assessment of the elevation-dependent warming in the Qinling-Daba Mountains and its relationship with land surface albedo and aerosol optical depth from 2001 to 2021

In this paper, we examined the elevation-dependent warming (EDW) patterns of MODIS LST across different seasons in the Qinling-Daba Mountains, further investigate the connections between the EDW patterns of Land surface temperature (LST) and land surface albedo (ALB) as well as aerosol optical depth (AOD). The key findings include: (1) Our study reveals a robust correlation between LST and air temperature in the Qinling-Daba Mountains, suggesting the feasibility of using MODIS LST to predict the temperature trends (2) During the period from 2001 to 2010, MODIS LST shows a significant EDW trend, primarily in the spring season. In contrast, a negative EDW is observed in the period during 2011-2021, which is contrary to the earlier decade, particularly during the autumn and winter seasons. (3) EDW of MODIS LST is affected by the combination of ALB and AOD. The former has a negative influence on the change of LST, particularly above 2500 m in elevation. However, the latter is negatively correlated with the trend of MODIS LST, primarily at lower and middle altitudes (0-2500 m). This study gives a comprehensive explanation for the EDW of the temporal variations of LST in the Qinling-Daba Mountains to improve our understanding of the complex interactions and potential future climate scenarios in the region.

Grassland productivity in arid Central Asia depends on the greening rate rather than the growing season length

Climate change has induced substantial impact on the gross primary productivity (GPP) of terrestrial ecosystems by affecting vegetation phenology. Nevertheless, it remains unclear which among the mean rates of grass greening (RG), yellowing (RY), and the length of growing season (LOS) exhibit stronger explanatory power for GPP variations, and how RG and RY affect GPP variations under warming scenarios. Here, we explored the relationship between RG, RY, LOS, and GPP in arid Central Asia (ACA) from 1982 to 2019, elucidating the response mechanisms of RG, RY, and GPP to the mean temperature (TMP), vapor pressure deficit (VPD), precipitation (PRE), and soil moisture (SM). The results showed that the multi-year average length of greening (LG) in ACA was 22.7 days shorter than that of yellowing (LY) and the multi-year average GPP during LG (GPPlg) was 38.28 g C m-2 d -1 more than that of during LY (GPPly). RG and RY were positively correlated with GPPlg and GPPly, although the degree of correlation between RG and GPPlg was higher than that between RY and GPPly. Increases in RG and RY contributed to an increase in GPPlg (55.44 % of annual GPP) and GPPly (35.44 % of annual GPP). The correlation between RG and GPPlg was the strongest (0.49), followed by RY and GPPly (0.33), and LOS and GPP was the weakest (0.21). TMP, VPD, PRE, and SM primarily affected GPP by influencing RG and RY, rather than direct effects. The positive effects of TMP during LG (TMPlg), PRE during LG (PRElg), and SM during LG (SMlg) facilitated increases in RG and GPPlg, and higher VPD during LY (VPDly) and lower PRE during LY (PREly) accelerated increases in RY. Our study elucidated the impact of vegetation growth rate on GPP, thus providing an alternate method of quantifying the relationship between vegetation phenology and GPP.

Monitoring and influencing factors of grassland livestock overload in Xinjiang from 1982 to 2020

It is crucial to estimate the theoretical carrying capacity of grasslands in Xinjiang to attain a harmonious balance between grassland and livestock, thereby fostering sustainable development in the livestock industry. However, there has been a lack of quantitative assessments that consider long-term, multi-scale grass-livestock balance and its impacts in the region. This study utilized remote sensing and empirical models to assess the theoretical livestock carrying capacity of grasslands. The multi-scale spatiotemporal variations of the theoretical carrying capacity in Xinjiang from 1982 to 2020 were analyzed using the Sen and Mann-Kendall tests, as well as the Hurst index. The study also examined the county-level grass-livestock balance and inter-annual trends. Additionally, the study employed the geographic detector method to explore the influencing factors. The results showed that: (1) The overall theoretical livestock carrying capacity showed an upward trend from 1982 to 2020; The spatial distribution gradually decreased from north to south and from east to west. In seasonal scale from large to small is: growing season > summer > spring > autumn > winter; at the monthly scale, the strongest livestock carrying capacity is in July. The different grassland types from largest to smallest are: meadow > alpine subalpine meadow > plain steppe > desert steppe > alpine subalpine steppe. In the future, the theoretical livestock carrying capacity of grassland will decrease. (2) From 1988 to 2020, the average grass-livestock balance index in Xinjiang was 2.61%, showing an overall increase. At the county level, the number of overloaded counties showed an overall increasing trend, rising from 46 in 1988 to 58 in 2020. (3) Both single and interaction factors of geographic detectors showed that annual precipitation, altitude and soil organic matter were the main drivers of spatiotemporal dynamics of grassland load in Xinjiang. The results of this study can provide scientific guidance and decision-making basis for achieving coordinated and sustainable development of grassland resources and animal husbandry in the region.