2024 CSDMS meeting-099


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Enhancing Numerical Modeling of River Dynamics: Remote Sensing Solutions for Data-Scarce Environments

Abhinav Sharma, (he/him/his),North Carolina State University Raleigh North Carolina, United States. asharm35@ncsu.edu
Celso Castro-Bolinaga, North Carolina State University Raleigh North Carolina, United States. cfcastro@ncsu.edu

Numerical models play a vital role in understanding river channel and floodplain evolution, yet their setup often requires extensive measured data. Maintaining continuity in monitoring fluvial geomorphology and sediment transport globally poses a significant challenge. This study introduced a remote sensing-based methodology for constructing and calibrating a reach scale 1D hydrodynamic numerical model, particularly suited for data-scarce regions. The effectiveness of this approach was assessed on the Elwha River in Washington. The methodology employed a supervised image classification technique to extract a river mask, especially useful in areas with significant shadow pixels. Subsequently, channel attributes such as width, sinuosity, and slope were derived, and river segments with similar cross-sectional properties were identified using a multivariate change point approach, resulting in delineation of four distinct segments for the Elwha River. Next, hydraulic calibration of the numerical model accurately simulated water surface elevation (NSE: 0.93, PBIAS: -7%, RSR: 0.27). The sediment transport sub-model provided precise estimates of Suspended Sediment Concentration for mid-discharge values of 70 – 100 m3/s, associated with exceedance probabilities ranging from 0.4 to 0.04. Furthermore, the numerical model accurately reproduced channel deposition-erosion patterns estimated using publicly available aerial imagery from 2015 to 2017 (56 m vs. 48 m). These findings demonstrate the successful utilization of remote sensing datasets to supplement data requirements for numerical model setup and calibration, as well as to generate validation datasets. The methodology holds promise for accurately simulating hydromorphodynamic processes in both data-rich and data-scarce regions.