# Climate Warming Dominates Vegetation Productivity in the Hanjiang River Basin, China
## 3 Results
### Response of Vegetation Productivity to Climate Factors  
To investigate the impact of climate factors on vegetation productivity in the HJRB over the study period, we initially conducted partial correlation analysis to examine the relationship between multi-year averages of GPP for various vegetation types and climate factors in the HJRB (Figure 7). The findings revealed that GPP in different vegetation types within the HJRB was primarily influenced by Tem and PET. Pre exhibited a strong correlation mainly in the upstream regions, while WS and SSD showed negative correlations in both upstream and downstream areas. Drought had a relatively minor impact on GPP in the HJRB. GPP demonstrated a negative correlation with SPEI1 and a positive correlation with SPEI3 and SPEI12, with SPEI3 exerting the most significant overall influence among the SPEIs.  
Figure 5: Interannual variation of GPP for different vegetation types in the HJRB from 2000 to 2020.  
Additionally, we performed spatial partial correlation analyses between GPP and climate factors (Figure 8). Our findings reveal spatial variations in the influence of climate variables on vegetation productivity in the HJRB. Within the HJRB, 99.40% of the area displays a positive correlation with Tem, with 90.68% of this region exhibiting statistically significant positive correlations. Similarly, PET, SPEI3, and Pre demonstrate positive correlations in over 80% of the HJRB (90.34%, 94.50%, and 85.68%, respectively), with 46.42%, 56.20%, and 33.46% of these areas showing statistically significant positive correlations. In contrast, only 70.99% of the region displays a positive correlation with SPEI12, and just 7.41% of this area exhibits statistically significant positive correlations. GPP exhibits negative correlations with SPEII, WS, and SSD in 77.44%, 69.44%, and 67.18% of the area, respectively, with statistically significant areas accounting for 9.67%, 17.18%, and 32.35%, respectively. In summary, the correlations between SSD, SPEI1, and SPEI12 with GPP are not statistically significant (significant areas <15%), while Tem, SPEI3, and PET display significant correlations (significant areas of 90.69%, 56.22%, and 44.92%, respectively). Pre and WS exhibit strong correlations only in specific regions.  
Figure 6: Seasonal variations in the multi-year average of GPP and climate factors in the HJRB: (**a**) GPP, (**b**) Tem, (**c**) Pre, (**d**) PET, (**e**) SSD, (**f**) WS, (**g**) SPEII, (**h**) SPEI3, (i**) SPEII2. Shaded areas represent standard deviations, with red and blue indicating the upstream and downstream of HJRB, respectively.  
Figure 8: Spatial distribution of partial correlations between GPP and climate factors in the HJRB from 2000 to 2020.  
Figure 7: The partial correlations between the mean GPP of various vegetation types and climate factors for the years 2000 to 2020 for (**a**) the upstream and (**b**) the downstream regions in the HJRB. One and two asterisks (*) represent significance at the 95% and 99% levels, respectively.  
To investigate how vegetation productivity responds to climate factors, we analyzed the temporal patterns of mean vegetation productivity in the HJRB from 2000 to 2020 in relation to changes in climate variables (Figure 9). Our study reveals distinct response patterns of GPP to various climate factors. Specifically, as Pre, Tem, PET, and WS increase, GPP gradually rises, indicating a positive influence. However, once it reaches its peak, GPP begins to exhibit a gradual decline. In contrast, SSD, initially showing a negative impact, stabilizes over time. In terms of the SPEI, we observed similar increasing effects on GPP for both SPEI3 and SPEI12. It is worth noting that GPP remains at lower levels when both SPEI3 and SPEI12 are low.