Understanding the Effect of Forest Recovery Parameterization on Modeled Streamflow Using Airborne Laser Scanning and Optical Leaf Area Index Time Series

Characterizing the hydrological effects of forest disturbance and recovery is complex. Process-based hydrological models can address this challenge, but rely on accurate parameterization, which is often limited by data availability. Leaf area index (LAI) is a key parameter in many process-based models however, few datasets provide sufficient spatiotemporal resolution to represent forest recovery. This study addresses this limitation using two remote sensing datasets: a monthly 30 m satellite-derived LAI time series and an airborne laser scanning (ALS)-derived LAI dataset. These data were combined to characterize forest recovery across the catchment and within ecozones, enabling the development of new LAI model parameterizations. Daily streamflow simulations using these parameterizations were compared with a baseline. ALS-derived LAI differed by up to 40% from the satellite product, which underestimated LAI in dense forests. In five years of post-disturbance, the LAI reached one third of pre-disturbance values. After 10 years, LAI recovery ranged from 45% to 73% across ecozones. The data-driven LAI parameterizations resulted in notable differences in daily and mean annual flow, as well as 2-year and 20-year peak flows. This work shows that remotely sensed datasets can constrain LAI parameters for improved hydrological modeling of forest recovery.