## 4 Discussion
### Uncertainties and Future Works  
On the one hand, we combined the bookkeeping model with the CASA model to achieve spatially detailed carbon budget estimates due to forest cover change [9; 25]. Experimental intervals of 10 and 12 years were used to simplify forest cover change in the model. However, due to uncertainties in predicting when transitions occur, there is a possibility of overestimating the duration of forest cover change, leading to increased carbon budgets [31]. At the same time, the input data of the CASA model include the remote sensing vegetation index, air image data, land cover data, and so on [94]. These data's quality, resolution, and spatiotemporal coverage affect the model's accuracy [94]. The bookkeeping model parameters do not fully represent vegetation across an entire climatic zone [9]. This involves the accuracy of the model and the uncertainty of the experimental results to some extent.  
On the other hand, we quantified the response of forest carbon dynamics to CDHEs using GD and the photosynthesis equation [13; 29]. This provided new insights into the influence of CDHEs on forest carbon dynamics. However, the spatial scale of meteorological data was relatively coarse, and small-scale spatial resolution meteorological data were mostly obtained through interpolation methods, which led to a certain degree of error in the accuracy of the meteorological data. Hence, incorporating higher-resolution meteorological datasets into future research can significantly enhance data accuracy. In addition, this research used monthly time scale results, mainly focusing on the short-term effects of CDHEs on forests. At the same time, this study also considers the delayed response of forests to drought (heat) events and vegetation [30]. The cumulative effects of drought and heat events on forest carbon fixation were ignored. Furthermore, we lacked specific experimental studies to explore the biophysical and biochemical mechanisms underlying regional differences in forest carbon dynamics, especially those closely related to key environmental variables such as soil properties, water status, and water availability.  
Future studies should reduce the experimental interval to ensure that forest cover changes can be captured in the short term to more accurately assess the process and influencing factors of the forest carbon cycle. Given the parameter accuracy of the CASA and bookkeeping models, we should focus on optimizing model parameter settings. By introducing higher-quality datasets to improve the prediction accuracy and reliability of the models, we can ensure that the meteorological data can more genuinely reflect the actual state of the forest ecosystem. Finally, because of the lack of discussion on the dynamic influencing mechanism of forest carbon in different regions in current studies, we should focus on soil characteristics, water status, climate change, human activities, and other factors and thoroughly analyze how these factors affect the storage, absorption, and release of forest carbon through field observation, experimental operation, model simulation, and other means. And the mechanism of their interaction should also be studied. At the same time, we will supplement the cumulative effect of extreme weather events on forest carbon dynamics [13; 95].