Bosten Lake Basin not only is a major source of drinking water for the residents of the surrounding area, but also maintains the ecological balance of the region. However, with the influence of climate change and human activities, the water level of Bosten Lake fluctuates sharply and has a great impact on the surrounding ecological environment. Therefore, the study of its historical water flow changes as a reference has become a focus of research. In this study, the radial growth of Schrenk spruces (Picea schrenkiana Fisch. et Mey.) significantly correlated with the tributary streamflow coming from the mountainous region near Bosten Lake Basin. On the basis of this good coherence, the tree-ring chronologies were used to reconstruct the streamflow for Huangshuigou River from the previous August to the present July (r = 0.766, p < 0.0001, n = 50). The reconstructed streamflow series matched observations well, explaining 63.3% of the variation in the observed streamflow of 1956–2005. Then, the sum of the streamflow reconstruction of Huangshuigou River and another two tree-ring-based streamflow reconstructions (Kaidu River and Qingshui River) was used to represent the hydrological variation of the upper catchment of Bosten Lake Basin, and the reconstruction sequence was 306 years. The 10.7, 5.5, and 2.1 year cycles of the power spectrum and wavelet analysis revealed that the runoff series reconstructed from tree-ring hydrometeorology was related to solar activity. Some dry and wet years in the reconstructed streamflow series of the upper catchment of Bosten Lake Basin corresponded to the historical record. During the wet years, the Indian Ocean was probably the main source of precipitation.

Vegetation growth is influenced not only by current climatic conditions but also by growth-enhancing signals and preceding climate factors. Taking the dominant species, Juniperus seravschanica Kom, in Tajikistan as the research subject, this study combines tree-ring width data with early xylem growth season data (from the start of xylem growth to the first day of the NDVI peak month), simulated using the Vaganov–Shashkin (V-S) model, a process-based tree-ring growth model. This study aims to explore the effects of vegetation growth carryover (VGC) and lagged climatic effects (LCE) on tree rings and the early xylem growth season at two different scales by integrating tree-ring width data and xylem phenology simulations. A vector autoregression (VAR) model was employed to analyze the response intensity and duration of VGC and LCE. The results show that the VGC response intensity in the early xylem growth season is higher than that of tree-ring width. The LCE duration for both the early xylem growth season and tree-ring width ranges from 0 to 11 (years or seasons), with peak LCE response intensity observed at a lag of 2–3 (years or seasons). The persistence of the climate lag effect on vegetation growth has been underestimated, supporting the use of a lag of 0–3 (years or seasons) to study the long-term impacts of climate. The influence of VGC on vegetation growth is significantly stronger than that of LCEs; ultimately indicating that J. seravschanica adapts to harsh environments by modulating its growth strategy through VGC and LCE. Investigating the VGC and LCE of multi-scale xylem growth indicators enhances our understanding of forest ecosystem dynamics.

The maximum latewood density (MXD) of tree rings can reflect the temperature of the growing season, but the timing of the response differs among regions. We selected 152 maximum latewood density chronologies from the Northern Hemisphere that showed a significant response to temperature. Based on a cluster analysis and the sensitivity of MXD to the monthly mean temperature, the chronologies were classified into six clusters. The clusters showed distinct regional characteristics, and the period and peak month of significant response of the chronologies in each cluster to temperature were different. Spatial synchronization of the MXDs revealed that the two clusters distributed in Europe showed the most consistency and the strongest response to the April–September monthly mean temperature compared with the other clusters. Temperature accounted for more than 40% of the total MXD variance in all clusters, whereas the effect of precipitation was much smaller. In addition to climatic factors, the random effect of the latitude and longitude of sampling sites, elevation, and tree species was a major factor contributing to the variance in MXD in each cluster. Latitude and longitude had the strongest influence among the three random effects, and tree species had the weakest influence, except at high latitudes. The MXD of each cluster showed sensitivity to temperature within a certain interval, with a positive linear response, and the sensitivity interval was greatest at high latitudes. Certain clusters showed a negative linear sensitivity to precipitation. The results provide a reference for studying the climatic threshold of large-scale tree-ring density formation.

Vegetation growth is influenced not only by current climatic conditions but also by the growth-enhancing signals and preceding climate factors. In order to explore the effects of Vegetation growth carryover (VGC) and lagged climatic effect (LCE) on tree-rings and the early growth season of xylem at two different scales. Taking the dominant species Juniperus seravschanica in Tajikistan as the research subject, this study combines tree-ring width data with early of growth season of xylem data (from the start of xylem growth to the first day of the NDVI peak month) simulated using the Vaganov-Shashkin (V-S) model, a process-based tree-ring growth model. A vector autoregression (VAR) model was employed to analyze the response intensity and duration of vegetation growth carryover effect (VGC) and lagged climatic effect (LCE) to climate. The results show that the VGC response intensity in the early of growth season of xylem phase is higher than that of tree-ring width. The LCE duration for both early of growth season of xylem and tree-ring width ranges from 0 to 11 (year or season), with peak LCE response intensity observed at lag 2–3(year or season). The persistence of climate’s lag effect on vegetation growth has been underestimated, supporting the use of lag 0–3(year or season) to study the long-term impacts of climate. The influence of VGC on vegetation growth is significantly stronger than that of LCE；Ultimately, indicating that Juniperus seravschanica adapts to harsh environments by modulating its growth strategy through VGC and LCE. Investigating the VGC and LCE of multi-scale xylem growth indicators enhances our understanding of forest ecosystem dynamics.