What is the current state and potential for hydrologic models to simulate vegetation dynamically?

• Provides a comprehensive overview of vegetation parameterization in SWAT, WRF-Hydro, and VIC models. • Emphasizes the limitations of using LAI and NDVI for modeling the seasonal photosynthetic processes of evergreen forests. • Explores the potential for enhancing model performance through the integration of remote sensing data. • Demonstrates that representing vegetation as a dynamic component improves models' performance under ongoing climate and land-use changes. Vegetation, a key component of terrestrial ecosystems, regulates the mass, momentum, and energy fluxes between the land surface and the atmosphere. The 6th Intergovernmental Panel on Climate Change (IPCC) reports evident climate change will continue to accelerate impacts on the biophysical and structural characteristics of vegetation and water resources into the next century. Given the undeniable role of vegetation in land–atmosphere interactions, its ever-changing composition and functionality can significantly impact the water and energy balance on earth. This underscores the necessity for considering vegetation dynamics in simulating terrestrial water cycles and watershed responses under current and future climates. This paper reviews the physical processes and parameters used within three widely used hydrological models, SWAT (Soil and Water Assessment Tool), WRF-Hydro (Weather Research and Forecasting Model Hydrological Modeling system), and the VIC (Variable Infiltration Capacity), to parameterize vegetation dynamics. We reviewed the relative strengths and limitations of each model’s physical processes to represent vegetation as a dynamic component, including recent advancements in modeling vegetation dynamics and their potential to decrease model uncertainty, particularly in the context of climate variability and land use/land cover changes. The paper highlights the shortcomings of LAI for correctly capturing the timing and magnitude of plant physiological activity in evergreen coniferous forests where light and temperature are not the only growth constraints. Recommendations for future model improvements and research directions are discussed, emphasizing the opportunity for incorporating remote sensing data to address some of the current shortcomings. These enhancements are essential for improving water resource management and ensuring sustainable watershed management in the face of ongoing environmental changes.