Quantifying vegetation canopy interception drivers and runoff effects using a coupled RS-Gash-WEP model

Area of Science:

• Hydrology
• Environmental Science
• Remote Sensing

Background:

• Watershed-scale runoff influence and canopy interception mechanisms are critical research areas.
• Existing hydrological models require enhancement for accurate simulation.

Purpose of the Study:

• To improve the WEP (Water and Energy transfer Processes) model by integrating canopy interception.
• To identify the primary drivers of canopy interception and their effects on watershed runoff.
• To assess the impact of enhanced interception on hydrological processes.

Main Methods:

• Coupling the WEP model with the RS-Gash canopy interception model.
• Incorporating remote-sensing data for vegetation cover and leaf area index.
• Applying structural equation modeling to analyze interception drivers and runoff effects.

Main Results:

• The enhanced WEP model demonstrated improved simulation accuracy (NSE = 0.79, R² = 0.80).
• Canopy saturated rainfall, influenced by rainfall intensity, leaf area index, and vegetation cover, was the dominant interception driver.
• Increased canopy interception significantly reduced surface runoff (impact coefficient = 0.47), mitigating hydrological cycles.

Conclusions:

• The study provides a robust method for understanding canopy interception's role in watershed hydrology.
• Enhanced canopy interception modules improve hydrological model performance.
• Findings offer a scientific basis for watershed management and hydrological modeling.