## 4 Discussion
### Scientometric Analysis Insight: Role of Co-Citation Reference and Keyword Co-Occurrence Analysis
#### 4.2.2 Modeling the Impacts of Climate Change on Forest WUE  
Water-use efficiency (WUE) in forests is an important factor in understanding how forests react to ongoing climate change. Forest ecosystems are essential carbon sinks, and their response to climate change in terms of WUE may significantly influence global carbon and water cycles [12]. Several methodologies can be found in the literature to model the impacts of climate change on forest WUE, including empirical approaches, complex process-based models, dynamic global vegetation models, and remote sensing-based data-assimilation and -modeling approaches. Empirical approaches to evaluating the impact of climate change on forest WUE include using observational data and statistical methods to apprehend and predict potential changes. Empirical models often rely on long-term observational data to develop relationships between climatic variables (such as temperature, precipitation rates, and atmospheric CO\({}_{2}\)) and WUE [6]. These approaches primarily rely on long-term datasets from eddy covariance flux towers, which record water and carbon fluxes in forests [92]. Researchers are able to identify trends by correlating water and carbon fluxes with various environmental variables to predict future changes in WUE. For instance, one study analyzed flux tower data of various forest ecosystems to investigate the WUE sensitivity to rising temperatures and atmospheric CO\({}_{2}\)[93]. These empirical models are appreciated for their reliance and simplicity with direct observations; however, these approaches are constrained by the resolution and accessibility of observational data. For modeling climate change impact on forest WUE under scenarios, process-based models are often used, such as the Soil-Vegetation-Atmosphere Transfer (SVAT) models [94; 95]. SVAT models are essential tools for understanding forest WUE. These models utilize interactions between soil, vegetation, and the atmosphere, offering comprehensive understandings of how forests manage water resources under various conditions [96]. For example, the MAESTRA model [97] simulates the canopy microclimate and gas-exchange processes to evaluate and predict WUE at the canopy level [97]. These models can be used in combination with various climate models to evaluate future climate change impacts on forest WUE [98]. These approaches offer a more systematic understanding compared to empirical approaches; however, these approaches require detailed data input and intensive computations. Dynamic Global Vegetation Model (DGVM) tools are used to simulate and evaluate the interactions between forest vegetation and the environmental variables on a global scale [99; 100]. For modeling forest WUE, DGVMs integrate different processes and variables to provide comprehensive insights into how forests manage water resources. GVMs use important physiological processes (such as photosynthesis and transpiration), which are crucial for understanding WUE [101]. These models consider the responses of forests to various environmental factors, including humidity and temperature [102; 103]. DGVM models, such as the Lund-Potsdam-Jena (LPJ) model, are used to simulate the interactions between vegetation, climate dynamics, and biogeochemical cycles on a global scale by integrating the effects of temperature variations, CO\({}_{2}\) fertilization, and precipitation trends on forest WUE. For instance, One study used the LPJ model to predict future WUE changes in forest under different climate scenarios [104]. The capacity of DGVM models to simulate global scale trends and feedbacks in the Earth system makes them valuable, even though these models often rely on simplifications that might not capture local variability. Recent developments in remote sensing have improved the capacity to model impacts of climate change on forest WUE.