To control soil and water erosion, large-scale vegetation restoration projects (e.g., the Grain-for-Green Program and the Three-North Shelter Forest Program) were implemented by China's Central Government in this region in the late 1990s (Jia et al., 2017). Native PT, a pioneer species for vegetation restoration, was widely planted due to its drought resistance and is now mainly distributed across Gansu, Shaanxi, and Shanxi provinces on the Loess Plateau (Wang, 2016). Therefore, the green area in Fig. 1 was selected as the simulation area to analyze the growth and drought-induced mortality risk of PT plantations. The study area (33.72–40.74° N, 102.68–114.55° E) is located in the Loess Plateau, covering approximately 380 000 km2 (Fig. 1), and encompasses diverse topographic features with hills, gullies, ridges, and basins. The predominant soil types include silty clay, silty clay loam, silty loam, loam, and sandy loam (Jia et al., 2019). From southeast to northwest, the climate transitions from semi-humid to semi-arid, the mean annual precipitation (MAP) decreases from 700 to 200 mm with 55 %–78 % occurring from June to September, and the mean annual temperature (MAT) ranges from 14.3 to 3.6 °C (Zhang et al., 2024b). The region is characterized by loess deposits ranging from 30 to 200 m thick. Precipitation in the study area is the primary source of recharge and the depth to groundwater is typically between 30 and 100 m (Zhu et al., 2018).

BBGC-Sperry is a coupled model integrating the biome biogeochemical cycle model (Biome-BGC model) with the plant supply-demand hydraulic model (Sperry model). Conventional climate data, soil parameters, and physiological-ecological parameters were incorporated into this model to simulate the dynamic changes in plant hydraulic properties at the daily scale, along with water, carbon, and nitrogen cycling processes. The BBGC-Sperry model is described in detail in previous articles (Zhang et al., 2020; Yan et al., 2022). Here, we briefly outline several key processes.

Under drought stress, plants regulate stomatal function to influence carbon metabolism processes, which is considered a core mechanism in drought-induced mortality. A prolonged high daily percentage loss of whole-plant hydraulic conductance (PLK) serves as a key indicator of drought mortality risk, calculated as follows: 6PLK=1-kplantkmax×100% where kplant is the whole plant hydraulic conductance and kmax is the maximum soil–plant hydraulic conductance. We assumed the xylem embolism of PT could not be refilled during the growing season, and that full hydraulic recovery occurs at the start of the next growing season due to the generation of root and/or stem pressure and the new growth of plant tissues in the spring (Choat et al., 2018). Under this assumption, daily PLK did not decrease following soil moisture recovery after drought events. Annual average PLK (APLK, %) was then determined to represent the inter-annual variation in plant hydraulic safety. It was calculated as the arithmetic mean of daily PLK values during the modeled growing season of each year. The modeled growing season was defined as days with leaf carbon pool greater than zero (LeafC > 0). Previous studies have suggested that sustained losses of whole-plant hydraulic conductance exceeding 60 % are associated with a high risk of drought-induced mortality (Sperry and Love, 2015). Tai et al. (2018) further applied a growing-season mean whole-plant PLK threshold of 60 % for evaluating drought-induced mortality risk. So, we adopted this threshold for assessing drought-induced mortality risk in this study. To evaluate the sensitivity of drought-induced mortality risk probability (DMRP) estimates to the hydraulic mortality threshold, we further calculated DMRP using three thresholds (50 %, 60 %, and 70 %) and compared their spatial patterns and temporal variations (Fig. S3). After preliminary tests, the beta distribution was selected to fit the probability distribution of APLK at each site. The probability value of APLK ≥ 60 % was defined as the threshold of DMRP (Zhang et al., 2024b). The primary long-term objectives for the PT plantations in this study area are soil and water conservation and ecosystem restoration. The functional cycle for stand stability of PT plantations ranges from 20 to 50 years (Zhang et al., 2024b). The study area is divided into high-risk (DMRP > 5 %), medium-risk (2 % ≤ DMRP ≤ 5 %), and low-risk (DMRP < 2 %) zones with drought mortality event recurrence intervals of < 20, 20–50, and > 50 years, respectively.

To comprehensively assess the impact of future climate change on the sustainability of PT plantations, this study developed a carbon-water-hydraulic risk index (CWHRI) by integrating carbon assimilation, water consumption, and plant hydraulic risk into a unified metric. These three components were selected because they represent key dimensions of plantation functioning that are commonly evaluated separately. Specifically, NPP reflects carbon sequestration and serves as the positive contribution to sustainability, ET characterizes water consumption, and DMRP represents the probability of tree mortality associated with drought-induced hydraulic stress. The CWHRI is formulated as follows: 7CWHRI=NPPET×(1-DMRP) where (1-DMRP) represents the probability of avoiding drought-induced mortality. The unit of CWHRI is gCkg-1 H2O, representing the expected carbon gain per unit water consumed after accounting for drought-induced mortality risk. The multiplicative form ensures that as DMRP approaches 1 (certain mortality), CWHRI approaches zero regardless of NPP or ET, reflecting the assumption that trees experiencing drought-induced mortality no longer contribute to ecosystem functioning. A higher value of CWHRI indicates safer water transport in plantations and greater efficiency in using water to support growth.

Annual mean NPP, ET, APLK, DMRP, and CWHRI were calculated for the baseline period and NFP, MFP, and FFP under three future climate scenarios (SSP1–2.6, SSP2–4.5, and SSP5–8.5). Spatial distribution maps were generated using Kriging interpolation in ArcGIS 10.8 software. Annual values for the study area were obtained by weighting annual data from 130 stations using the Thiessen polygons method. Temporal variations of indicators were analyzed using one-way analysis of variance (ANOVA), with multiple comparisons performed using Duncan's test at a significance level of p < 0.05. A multiple linear regression method was used to calculate the contribution of NPP, ET, and DMRP to changes in CWHRI. The relative importance of six meteorological variables (mean annual precipitation, MAP; mean annual temperature, MAT; mean annual maximum temperature, MATmax; mean annual minimum temperature, MATmin; mean annual vapor pressure deficit, VPD, mean annual soil water storage, SWS) for predicting CWHRI was assessed using XGBoost. Hyperparameters were tuned through five-fold cross-validation combined with grid search, while early stopping was applied to track validation error (Gong et al., 2026). Variables with high relative importance identified by the XGBoost model were interpreted as factors statistically associated with the dynamic changes in CWHRI within the modelling framework, rather than as evidence of causal relationships.

Model accuracy was evaluated using the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (rRMSE), and Nash–Sutcliffe efficiency coefficient (NSE) (Zhang et al., 2024b). The NSE is used to measure the goodness of fit of a model, with values closer to 1.0 indicating higher simulation accuracy. Lower values of MAE, RMSE, and rRMSE indicate higher accuracy. For leaf water potential (Ψmd), rRMSE was calculated using the absolute mean observed value as the denominator.

Measurements of Tday, SWC (0–500 cm), and Ψmd at the Ansai site during the 2024 growing season (1 May to 30 September) were used to calibrate the BBGC-Sperry model. As shown in Fig. 2a, c, and e, the R2 between simulated and observed Tday was 0.91, with NSE, MAE, RMSE, and rRMSE values of 0.90, 0.16 mmd-1, 0.23 mmd-1, and 13.61 %, respectively. For SWC (0–500 cm), the R2 value between simulated and observed values was 0.97, with NSE, MAE, RMSE, and rRMSE values of 0.97, 0.00 cm3cm-3, 0.00 cm3cm-3, and 1.75 %, respectively. The R2 value for Ψmd between simulated and observed values was 0.97, with NSE, MAE, RMSE, and rRMSE values of 0.98, 0.09 MPa, 0.09 MPa, and 7.42 %, respectively.

Validation of the BBGC-Sperry model included hydrological and growth processes. Hydrological validation was conducted based on Tday, SWC in the 0–500 cm profile, and Ψmd at the Ansai site during the 2025 growing season (1 May to 30 September), as well as multi-point transpiration data during the growing season. As shown in Fig. 2b, d, and f, the R2 between the simulated and observed Tday was 0.77, with NSE, MAE, RMSE, and rRMSE values of 0.67, 0.24 mmd-1, 0.33 mmd-1, and 27.98 %, respectively. For SWC (0–500 cm), the R2 value between the simulated and observed values was 0.94, with NSE, MAE, RMSE, and rRMSE values of 0.89, 0.00 cm3cm-3, 0.00 cm3cm-3, and 3.16 %, respectively. The R2 value for Ψmd between simulated and observed values was 0.99, with NSE, MAE, RMSE, and rRMSE values of 0.98, 0.11 MPa, 0.11 MPa, and 7.24 %, respectively. Figure 2g shows that the R2 between the simulated and observed T exceeded 0.7 for both sap flow measurements and water balance estimates. The NSE, MAE, RMSE, and rRMSE values for the sap flow measurements were 0.76, 23.69 mm, 34.26 mm, and 17.65 %, respectively. For water balance estimates, these values were 0.93, 20.52 mm, 27.18 mm, and 10.40 %, respectively. Growth process validation data were based on AGB observations from 27 sites in the study area. The R2 value between simulated and observed AGB was 0.81, with NSE, MAE, RMSE, and rRMSE values of 0.80, 0.42 kgCm-2, 0.93 kgCm-2, and 44.61 %, respectively (Fig. 2h). These results indicate the BBGC-Sperry model can accurately simulate hydrological and growth processes for PT plantations at both spatial and temporal scales. The high correlation between T, ET, AGB, and NPP indicates the BBGC-Sperry model can also effectively simulate long-term dynamic changes in NPP and ET in this region.

We validated the simulation performance of the regional climate models by comparing observed climate data for the historical period (1979–2014) with the simulation results from a single climate model and a multi-model average (Fig. 3). The multi-model average showed the highest correlation coefficients (R) and the lowest centered root-mean-square error (CRMSE) values among all simulations. Specifically, the corresponding R and CRMSE values were 0.66 and 0.76 for precipitation, 0.99 and 0.22 for maximum temperature, and 0.97 and 0.34 for minimum temperature, respectively. The normalized standard deviations of the multi-model average were generally comparable to those of individual models. These results indicate that these downscaled data effectively represent the actual climate conditions of the study area. Therefore, multi-model average climate data were employed to simulate the response of PT plantations to future climate change.

Based on the linear trends (slope values) of MAP, MAT, MATmax, and MATmin from 2000 to 2099, we analyzed the spatiotemporal changes in climate during the study period (Fig. 4). The slope values of climate variables under different SSPs showed significant spatial heterogeneity (Fig. 4a–l). The highest slope values were mainly concentrated in the southeastern part of the study area, with a general decrease from southeast to northwest. Under the SSP1–2.6 scenario, 52.9 % of the region showed an increasing trend in MAP during the study period, primarily in the southwest and southeast, with 32.8 % of the area experiencing significant increases (p < 0.05). Conversely, decreases in MAP were mainly located in the central and northern regions. MAT exhibited an increasing trend over a larger portion of the study area, covering 71.5 %, with 58.9 % of the area experiencing significant increases (p < 0.05). Decreases in MAT were limited to the southwest and northeast regions. MATmax primarily increased in the central region, while decreasing regions were limited to the southwest and northeast. MATmin primarily increased in the western and central regions, while decreasing regions were limited to the northeast and southeast. Compared to the SSP1–2.6 scenario, over 95 % of the study area showed increases in MAP and MAT under the SSP2–4.5 and SSP5–8.5 scenarios. The areas with the largest increases were concentrated in the southern part of the study area.

Figure 4m–o shows future climate in the study area is projected to become warmer under all SSPs, while precipitation changes vary among different scenarios compared to the baseline period. Under the SSP1–2.6 scenario, MAP ranged from 482 to 633 mm, with an average decrease of 0.105 mmyr-1. In contrast, MAT ranged from 12.99 to 14.64 °C, with an average increase of 0.020 °Cyr-1. MATmax ranged from 15.79 to 17.97 °C, with an average increase of 0.025 °Cyr-1. MATmin values ranged from 5.21 to 6.46 °C, with an average increase of 0.007 °Cyr-1. Under the SSP2–4.5 scenario, MAP values ranged from 524 to 769 mm, with an average increase of 0.77 mmyr-1. Notably, the MAP growth rate fluctuated. In contrast, MAT increased more steadily, with an average increase of 0.014 °Cyr-1 for MAT, 0.010 °Cyr-1 for MATmax, and 0.025 °Cyr-1 for MATmin. Under the SSP5–8.5 scenario, the average increases of MAP, MAT, MATmax, and MATmin were 1.806 mmyr-1, 0.070, 0.069, and 0.060 °Cyr-1, respectively. Overall, the FFP climate is expected to experience the most significant fluctuations under the SSP5–8.5 scenario (p < 0.05).

Figure 5 shows spatiotemporal changes in the mean annual NPP of PT plantations during the baseline and future periods. The NPP of the study area was 533.5 gCm-2yr-1 in the baseline period but will be significantly lower in the future periods under the SSP1–2.6 and SSP2–4.5 scenarios (Fig. 5a and b). However, the NPP in the FFP will be 577.9 gCm-2yr-1 under the SSP5–8.5 scenario, which will be higher than in the baseline period (Fig. 5c). The highest NPP values of the baseline period were in the south and central regions, and the lowest were in the west, with a gradual decrease noted from southeast to northwest. This distribution pattern continues in the future periods.

Figure 6 shows the spatiotemporal changes in the mean annual ET values during the baseline and future periods. The ET was 572.9 mm during the baseline period and will show no significant changes under the SSP1–2.6 scenario in the future periods. However, it will significantly increase under the SSP2–4.5 scenario and particularly under the SSP5–8.5 scenario (Fig. 6c). The ET value decreased gradually from southeast to northwest in the baseline period. This pattern is maintained in all SSPs.

Figure 7 shows spatial and temporal changes in the annual APLK of PT plantations in the study area during the baseline and future periods. APLK for the baseline period was 24.9 %. Under the SSP1–2.6 scenario, APLK values in the NFP, MFP, and FFP are projected to be 28.2, 28.7, and 29.5 %, respectively, indicating an increasing trend with a relatively slow rate of change. Under the SSP2–4.5 scenario, APLK values for future periods are projected to be 29.1 % (NFP), 33.3 % (MFP), and 32.8 % (FFP), indicating a larger increase compared to the baseline period. Under the SSP5–8.5 scenario, APLK values increased the most, with NFP, MFP, and FFP values of 31.7 %, 41.1 %, and 46.4 %, respectively, indicating APLK values will significantly increase under a more severe climate change context. APLK values during the baseline period showed an increasing trend from southeast to northwest. In the future, high APLK values will gradually expand from the northwest and northeast to the central and southern regions. Under the SSP5–8.5 scenario, only very central and southern regions will have APLK values below 35 %, while most areas in the central and northern regions will have values above 50 % in the FFP (Fig. 7o).

Based on a threshold of APLK ≥ 60 %, we calculated the spatiotemporal variations of DMRP of PT plantations during the baseline and future periods (Fig. 8). Low-risk zones for DMRP were mainly located in the southern part of the study area, medium-risk zones in the central and eastern regions, and high-risk zones in the northwest, north, and east, exhibiting a clear gradient from southeast to northwest in the baseline period. Under the SSP1–2.6 scenario, the distribution of high-risk zones will be similar to the baseline period, but the distribution of low- and medium-risk zones will change. In the NFP, low-risk zones will be primarily concentrated in the west. In the MFP, low-risk zones will be primarily concentrated in the west, central, and eastern regions. In the FFP, low-risk zones will be primarily concentrated in the central and eastern regions. Under the SSP2–4.5 and SSP5–8.5 scenarios, the distribution patterns of low-, medium- and high-risk zones will be similar to the baseline period but the high-risk zones will generally expand from northwest to southeast, covering a larger area. DMRP will show an increasing trend under the SSP2–4.5 and SSP5–8.5 scenarios, but a slight decreasing trend under the SSP1–2.6 scenario.

To comprehensively assess the growth of PT plantations, NPP, ET, and DMRP were used to calculate the CWHRI (Fig. 9). The CWHRI during the baseline period was 0.89 gCkg-1 H2O and will decrease under the SSP1–2.6, SSP2–4.5, and SSP5–8.5 scenarios to 0.63–0.67, 0.66–0.67, and 0.50–0.51 gCkg-1 H2O, respectively. This indicates that PT plantations may experience greater constraints on growth and increased risks of hydraulic failure and drought-induced mortality under future climate scenarios. CWHRI showed considerable spatial variability, with low-value zones mainly located in the western and northern parts of the study area, showing a distinct decreasing gradient from southeast to northwest in the baseline period. This pattern was maintained in all SSPs. Generally, areas with low CWHRI values will expand in the future periods. Under the SSP5–8.5 scenario, the CWHRI in the western parts of the study area will even fall below 0.25 gCkg-1 H2O. This suggests PT plantations in the western and northern parts of the study area may face greater constraints on carbon gain and increased hydraulic risks under future climate change.

To quantify the extent to which NPP, ET, and DMRP influence changes in the CWHRI, we used multiple linear regression to calculate the relative contribution of each variable (Fig. 10a). During the baseline period, NPP and ET explained 98 % of the variation in CWHRI. NPP and ET remained the most important contributors to CWHRI variation in the future; however, the contribution of DMRP increased in all SSPs over the three future periods, reaching 17 % in the FFP under the SSP5–8.5 scenario.

To deepen understanding of the factors associated with CWHRI variation and how the relative importance of these factors changes, Fig. 10b presents an XGBoost-based assessment of the relative importance of six environmental factors for regions with different risk levels during the baseline and future periods. In the baseline period, VPD and SWS showed the highest relative importance among the six environmental factors. Specifically, the relative importance of VPD was 86 % in the low-risk zones, and of SWS was 42 % and 61 % in the medium- and high-risk zones, respectively. Under the SSP1–2.6 scenario, VPD continued to show the highest relative importance but its relative importance decreased by 49 % and 59 % in the low-risk zones during the NFP and MFP, respectively. MAP showed the highest relative importance value in the low-risk zones during the FFP, with a value of 56 %. In the medium-risk zones, MAP showed the highest relative importance during the NFP and MFP, whereas SWS showed the highest relative importance during the FFP, similar to the baseline period. In high-risk zones, MAP exhibited the highest relative importance in all three periods, ranging from 62 % to 81 %. Under the SSP2–4.5 scenario, MATmax showed the highest relative importance values in the NFP and FFP in low-risk zones, with values of 51 % and 60 %, respectively. MAP, VPD and MATmax collectively accounted for 91 % of the relative importance in the MFP. In the medium-risk zones, MAP showed the highest relative importance in the NFP and MFP, while MATmax showed the highest relative importance in the FFP. In high-risk zones, MAP showed the highest relative importance in all three periods, ranging from 67 % to 76 %. Under the SSP5–8.5 scenario, VPD showed the highest relative importance in low-risk zones during all three future periods, with values exceeding 50 %. In medium-risk zones, MATmax showed the highest relative importance during the NFP and MFP, with values exceeding 70 %. VPD showed a relative importance of 99 % during the FFP. In high-risk zones, MAP showed the highest relative importance in all three periods, ranging from 49 % to 68 %. Generally, the relative importance of MAP and MATmax increased from the baseline period into the future, suggesting that moisture- and temperature-related variables may become increasingly associated with CWHRI variation under future climate change.

The BBGC-Sperry model is a coupled model. Therefore, a thorough validation is essential when using this model for simulation and prediction (Yan et al., 2022). Transpiration, SWC, Ψmd, and AGB are critical indicators for assessing the complex effects of climate change on vegetation growth and physiological characteristics. Accordingly, we validated the reliability of the BBGC-Sperry model in simulating the growth dynamics of PT plantations by using data from 36 meteorological stations on the Loess Plateau (Fig. 2). The results indicate R2 and NSE values for all simulated and measured indicators exceed 0.6, demonstrating the model effectively captures the dynamic changes in growth and ecohydrological processes in the PT plantations. This ability is closely tied to the model structure and input parameters.

The Sperry model replaces the SPAC water transport module of the original Biome-BGC model with an improved soil moisture transport module developed by Huang et al. (2013). This allows for greater control over dynamic changes in plant hydraulic traits and enables the simulation of interactions among the water, carbon, and nitrogen cycles. The BBGC-Sperry model provides a more refined simulation of vegetation growth processes and physiological mechanisms than the original model, and has been successfully applied to the Loess Plateau (Zhang et al., 2020, 2024b; Yan et al., 2022). Meteorological parameters were sourced from continuous observational data, and soil and vegetation physiological parameters were derived from field measurements or published literature, thereby ensuring the accuracy of the validation process.

Against the backdrop of global warming, the study area is projected to become generally warmer and wetter in the future under the SSP2–4.5 and SSP5–8.5 scenarios. These findings are consistent with previous studies (Xu et al., 2024). An assessment of the growth status of planted forests must not only consider their carbon sequestration capacity but also involve a comprehensive evaluation of related water fluxes and transport processes. Especially in water-scarce regions, water availability is the primary factor limiting the growth of planted forests. Previous studies have shown that relying solely on indicators such as leaf area index, carbon sequestration, and ET to assess vegetation growth is insufficient as these macroscopic indicators may mask the physiological stress experienced by vegetation (Tang et al., 2026).

Thus, we used the BBGC-Sperry model not only to simulate carbon and water fluxes of the PT plantations in the study area (Figs. 5 and 6) but also to simulate indicators that reflect vegetation physiology (Figs. 7 and 8). The results show that NPP in the study area will remain below the baseline level under future climate scenarios, except for the FFP under the SSP5–8.5 scenario. Previous studies have shown that precipitation, temperature, and carbon dioxide concentrations all significantly impact NPP. Specifically, increasing precipitation generally improves the water-use efficiency of vegetation, while moderate temperature increases and rising carbon dioxide concentrations promote photosynthesis. However, rising temperatures may decrease precipitation-use efficiency, thereby negatively impacting NPP (Gong et al., 2026). At the same time, high temperatures can inhibit the opening of plant stomata, thereby reducing plant productivity (He et al., 2024). Although NPP under most future climate scenarios will remain below the baseline level, it will increase progressively from the NFP to the FFP under all SSPs. This upward trend may be associated with increasing precipitation and atmospheric CO2 concentrations, both of which may promote vegetation growth (Walker et al., 2021; Yan et al., 2022; Ouyang et al., 2014).

Compared with the baseline period, ET will show an increasing trend under future climate scenarios. It will also increase from the NFP to the FFP. These changes may be associated with increasing precipitation and temperature. Precipitation serves as a source of water for ET. Rising temperatures may prolong the growing season and increase atmospheric evaporative demand, thereby promoting the transfer of more water from the soil and vegetation to the atmosphere. Chai et al. (2025) and Lu et al. (2025) also reported this phenomenon.

These macroscopic observations reveal profound changes in plant hydraulic mechanisms. As climate change intensifies from the baseline period to the SSP1–2.6, SSP2–4.5, and SSP5–8.5 scenarios, rising temperatures combined with increased potential ET will trigger mechanisms of plant hydraulic failure, as evidenced by significant increases in APLK and DMRP. The intensification of hydraulic failure leads to a decline in stomatal conductance, which in turn impairs cell turgor, membrane permeability, and xylem conduction, significantly increasing plant vulnerability to heat stress and outbreaks of pests and diseases (Torres-Ruiz et al., 2024). This ultimately results in limited photosynthetic rates and a significant decline in productivity. The findings from this study are consistent with previous research. Bonal et al. (2016) pointed out that tree mortality caused by drought and high temperatures not only reduces regional NPP but also weakens the cooling effect of ET, thereby creating a positive feedback loop that exacerbates global warming. Choat et al. (2018) also found that a slight increase in temperature may result in hydraulic imbalance or even death for most tree species.

Notably, NPP, ET, and DMRP all show a synchronous increase in the FFP under the SSP5–8.5 scenario. This phenomenon may be related to projected changes in precipitation, temperature, and atmospheric CO2 concentrations, which may jointly influence vegetation productivity and water fluxes (Walker et al., 2021; Yan et al., 2022; Ouyang et al., 2014). Although we observe an increase in carbon accumulation, the internal physiological stress the plants endure also significantly increases. The above results indicate that relying solely on macroscopic indicators is insufficient to capture the true growth state of vegetation. Therefore, a comprehensive assessment method that incorporates water transport processes is of great significance for accurately predicting vegetation growth dynamics and stability under future climate change.

In this study, we calculated the CWHRI based on the three fundamental indicators (NPP, ET, and DMRP) (Fig. 9). Compared to the baseline period, the CWHRI will decrease significantly in the future, indicating PT plantation sustainability may face increasing constraints under future climate change. The greater fluctuations in NPP during the study period were the dominant contributor to CWHRI variation, but the relative importance of DMRP increased steadily under all SSPs, rising from 2 % during the baseline period to 17 % in the FFP under SSP5–8.5. This increasing contribution suggests that incorporating the increasing influence of hydraulic failure risk under future climate change may provide a more comprehensive assessment of long-term plantation sustainability. These findings also corroborate the concerns raised by Wang et al. (2025) regarding future vegetation growth.

The environmental variables associated with CWHRI are related to the availability of water resources. During the baseline period, MAP, VPD, and SWS showed the highest relative importance. In all SSPs, the relative importance of MAP and MATmax will increase. SWS directly determines the amount of water available to vegetation, while MATmax and VPD are related not only to the degree of environmental aridity but also the stomatal conductance of vegetation and upward water transport driven by transpiration (Wang et al., 2009; Wang et al., 2024; Tang et al., 2026). Accordingly, these factors showed the highest relative importance for CWHRI variability. These findings indicate water-related ecological factors and meteorological variables remain key elements for future model improvements and ecological management strategies. Furthermore, the CWHRI declines steadily from the baseline period to the future periods, underscoring the urgency to formulate new management strategies and implement proactive intervention measures.

This study used the eco-hydrological BBGC-Sperry model to predict the growth, water consumption, and drought-induced mortality risk of PT plantations under future climate scenarios. The vegetation parameters in this model are fixed, which typically implies the spatiotemporal variability of vegetation growth is ignored, thereby introducing uncertainty into the simulation results. Recent studies based on the theory of plant hydraulic adaptation have predicted plant hydraulic, stomatal, and photosynthetic traits across various spatiotemporal scales (Joshi et al., 2022; Sperry et al., 2019). Future research should focus on integrating vegetation growth dynamics to further enhance model calibration and accuracy. We used meteorological data with a resolution of 0.25°, which effectively captures the dynamics of future climate change. However, limitations in accuracy may persist, particularly in precipitation prediction, which has notably lower accuracy. Spatially, we extended the simulated data from 130 meteorological stations across the region using Kriging interpolation. Although this method is computationally efficient, it may have limitations in resolution and accuracy. Additionally, although the BBGC-Sperry model has improved accuracy for simulating hydraulic transport processes compared to the original Biome-BGC model, the recently introduced BBGCMuSo model has more comprehensively simulated management practices. These improvements need to be integrated into the BBGC-Sperry model to better analyze the ability of different management strategies to improve the sustainability of forest plantations.

Despite certain methodological limitations, this study provides valuable scientific insights for the sustainable development of PT plantations. Compared to previous simulations, our study offers a comprehensive explanation of the drought response mechanisms of PT plantations under future climate change. Notably, under the backdrop of global climate change, the CWHRI of plantations is projected to decline and the contribution of DMRP is expected to increase under all SSPs, which indicated that current forest management practices may face increasing challenges in maintaining plantation sustainability. Future studies should accurately assess the optimal vegetation cover using soil moisture carrying capacity thresholds or early-warning models, and further evaluate the potential of adaptive strategies, including thinning management for existing PT plantations, mixed-species planting in low-risk southeastern regions, slope engineering practices in moderate-risk central and western regions (Yang et al., 2023), and gradual conversion of plantations toward drought-tolerant shrubs or sparse grasslands in high-risk northwestern regions.