An investigation into remote sensing techniques and field observations to model hydraulic roughness from riparian vegetation. (16th September 2022) - Chaulagain, Smriti; Stone, Mark C.; Dombroski, Daniel; Gillihan, Tyler; Chen, Li; Zhang, Su
Abstract: Riparian vegetation provides many noteworthy functions in river and floodplain systems, including its influence on hydrodynamic processes. Traditional methods for predicting hydrodynamic characteristics in the presence of vegetation involve the application of static Manning's roughness, which does not directly account for vegetation characteristics and neglects changes in roughness due to local water depth and velocity. The objectives of this study were to (1) implement numerical routines for simulating vegetation‐induced hydraulic roughness in a two‐dimensional (2D) hydrodynamic model; (2) evaluate the performance of two vegetation roughness approaches; and (3) compare vegetation parameters and hydrodynamic model results based on field‐based and remote sensing acquisition methods. Two roughness algorithms were coupled to an existing 2D hydraulic solver, which requires vegetation parameters to calculate spatially distributed roughness coefficients. Vegetation parameters were determined by field survey and using airborne light detection and ranging (LiDAR) data for San Joaquin River, California, USA. Water surface elevations modeled using vegetation‐based roughness approaches produced an acceptable overall performance, but the results were sensitive to the vegetation parameterization method (field based vs. LiDAR). Spatial variations in roughness and hydraulic conditions (water depth and velocity) were observed based on vegetation species and discharges for vegetation‐based approaches. The proposed approach accounts for the complexities of the physical environment instead of relying on traditional roughness as model inputs. Thus, the method proposed here is beneficial for describing the hydraulic conditions for the area having spatial variation of vegetation (e.g., species and density). However, additional research is needed to quantify model performance with respect to spatially distributed water depth and velocity and parameterization of vegetation characteristics.