Trend detection based on hydroclimatological time series is crucial for understanding climate change. In this study, the innovative trend analysis (ITA) method was applied to investigate trends in air temperature and precipitation over the Jinsha River Basin (JRB), China, from 1961 to 2016 based on 40 meteorological stations. Climatic factors series were divided into three categories according to percentile, and the hidden trends were evaluated separately. The ITA results show that annual and seasonal temperatures have significantly increased whereas the variation range of annual temperature tended to narrow. Spatial pattern analysis of the temperature indicates that high elevation areas show more increasing trends than flat areas. Furthermore, according to ITA, significant increase trends are observed in annual precipitation and “high” category of spring precipitation. The sub-basins results show a significant decreasing trend in elevation zones of ≤2000 m and an increasing trend where elevation is >2000 m. Moreover, linkage between temperature and precipitation was analyzed and the potential impact of the combined changes was demonstrated. The results of this study provide a reference for future water resources planning in the JRB and will help advance the understanding of climate change in similar areas.

Monitoring spatial – temporal variations of vegetation NPP (Net Primary Productivity) and understanding its driving factors is essential for achieving carbon neutrality and mitigating climate change. This study investigates the spatio – temporal variations and key influencing factors of vegetation NPP in the Qinling Mountains from 2001 to 2023, using the Geographic Detector Model, Theil – Sen Median, Mann – Kendall test, and Hurst index. The analysis based on MODIS NPP MOD17A3HGF v006 series products and incorporates environmental variables such as 10 m wind speed (vs), reference evapotranspiration (pet), maximum temperature (tmx), downward surface shortwave radiation (srad), Palmer Drought Severity Index (PDSI), soil moisture (soil), average temperature (tem), precipitation (pr) and Land use/cover change (LUCC). The results indicated that vegetation NPP in the Qinling Mountains showed an increasing trend with a slope of 5.74 × 10 ⁻³ gC/m ² yr from 2001 to 2023. Spatially, the vegetation NPP follows a spatial distribution pattern of “high in the east and low in the west”, with an average value of 629.89 gC/m ² yr. Over the past 23 years, vegetation NPP has demonstrated a significant increase, covering 77.71 % of the study area. The results of geographic detector factor detection show that among the nine influencing factors, LUCC has the greatest impact on vegetation NPP, followed by temperature, wind speed, evapotranspiration, maximum temperature, Palmer Drought Severity Index, radiation, and precipitation, while soil moisture has the smallest impact. In addition, the results also indicate a strong interaction between LUCC and temperature, as well as precipitation and temperature, on NPP in the Qinling Mountains. The Hurst index indicated a weak downward trend in future vegetation NPP, affecting 81.98 % of the study area.