Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long−term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time−varying parameter vector autoregressive model (TVP−VAR) was used to analyze the temporal and spatial differences of the time−lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time−lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP−VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP−VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP−VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time−lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management.