# Dual Roles of Water Availability in Forest Vigor:
## Discussion  
### Uncertainties and Prospects  
The CRU-TS grid dataset (e.g., \(P\), \(PET\), and \(T\)) and their integrated index (i.e., CRU \(SPEI\)) were used in our research. To assess the suitability of these CRU data, we also compared them with site-based values (Figure 11). Specifically, we calculated \(PET\) and \(SPEI\) based on 593 stations of observed meteorological variables in China and compared them with those in CRU datasets. We found that all of the CRU data were consistent with those derived from observation stations, demonstrating a high consistency (Figure 11a-d and Figure S2a-d). Therefore, the CRU data were suitable for this study. Because the statistical significance level of 0.05 (i.e., \(p=0.05\)) is commonly used in related research, our research focused on the area with \(|\,r\,|<0.514\) (i.e., \(p<0.05\)). However, the correlation is an estimate with associated uncertainty and statistical significance itself is a rather artificial criterion. Therefore, we compared the results of the statistical significance level of 0.05 with that of 0.1 (i.e., \(|\,r\,|<0.441\)). We found that there was no difference in the thresholds between two significant levels (Figure 12), because the \(p\) values of the \(t\)-test (with a bootstrapped sampling method, 1000 samples) for temperature and water balance thresholds were 0.899 and 0.191, respectively. Figure 10: The (**a**) temperature thresholds and (**b**) water balance thresholds for different time-scales’ Standardized Precipitation Evaportranspiration Index (\(SPEI\)). The PC, PM, NC, and NM represent planted coniferous, planted mixed, natural coniferous, and natural mixed forests, respectively. The thresholds of environmental temperature (\(T_{\text{threshold}}\)) and water balance (\(WB_{\text{threshold}}\)) for 1–12, 1–24, and 1–36 month time scales were acquired by the same method as that used for 1–48 time scales, which were described in Section 2.3. The whiskers of the point are the actual value of \(T\) or \(WB\) around each threshold (detailed information can be found in Table S3), and their value depends on the distribution of actual data of samples. Despite these efforts, some uncertainties still remain, such as the occurrence of possible errors during the matching process between the forest distribution map and remote sensing  
Figure 11: Comparisons of CRU data and the counterparts derived from meteorological stations. (**a**) The spatial distribution of meteorological stations in China (N = 593), (**b**) the potential evapotranspiration, (**c**) the temperature, and (**d**) the SPEI at a time scale of 48 months. The potential evapotranspiration (_PET_) was estimated using the FAO-56 Penman—Monteith method based on six primary variables (i.e., vapor pressure, relative humidity, sun hours, wind speed, and maximum and minimum temperatures) during 1960–2015. The _SPEI_ calculated using the “spei” package in R software (Version 3.6.1). The colors of figures (i.e., b, c, and d) represent the number of data samples, and the red and black line represent the fitting line and 1:1 line, respectively. Figure 12: Comparison of (**a**) the temperature and (**b**) water balance thresholds between the statistically significant level of 0.05 and 0.1. The whiskers of the point are the actual value of \(T\) or _WB_ around each threshold (detailed information can be found in Table S4); their value depended on the distribution of actual data of samples. The results considered 1–48 month time scales of water availability. The \(T_{\text{threshold}}\) and \(W\text{B}_{\text{threshold}}\) for a 0.1 significant level were acquired by the same method as that used for a 0.05 significant level, which was described in Section 2.3.  
images, as well as in the spatial resolution resampling process. Therefore, more matchable data on time and space are needed in the future to continue to decrease the uncertainties.