Nearshore waters are governed by complex hydrodynamic interactions within landscapes that vary globally. Many of these often-energetic flows are intricate, diverse, and fine-scale, making holistic understanding difficult. Advancements in computational processing, coupled with ever growing environmental datasets and refining remote sensing technologies, offer new opportunities to constrain the controls behind these processes. This dissertation investigates complex, important nearshore hydrodynamics through applications of remote sensing technologies and data analysis. Focusing on the nearshore ocean off the geomorphologically diverse coastline of Northern California, we develop and implement novel methodologies to observe, quantify, and analyze fine-scale processes in each chapter of this dissertation, thereby illuminating coastal hydrodynamics that have been difficult to monitor.
In chapter 1, we analyze the dispersion of turbid freshwater plumes from the Russian River, California, a prototypical small mountainous river system. River plumes of this size, although common and vital in Mediterranean climate regions, have been understudied, leading to significant gaps in understanding. Using 15 years of daily MODIS satellite imagery and environmental data, we reveal the interplay of river discharge, waves, winds, and tides in shaping plume behavior. This analysis serves as a ground truth for previous studies and uncovers previously undiscussed patterns of small to moderate sized river plume dynamics.
Chapter 2 presents a methodology that enhances nearshore temperature monitoring capabilities by utilizing calibration data between high-resolution (100m) Landsat thermal infrared data and coincident moderate resolution (1km) MODIS sea surface temperature (SST) data. Data calibrated by this methodology is tested against in-situ measurements at various distances from the Northern California coast and presents use cases for this high-resolution dataset, demonstrating significant advancement over traditional SST products and offering initial insights into fine-scale temperature mixing processes.
In Chapter 3, we investigate wave-driven cross-shore sediment transport using high resolution (10m) Sentinel-2 remote sensing data, enhanced by machine learning post-processing utilized to resolve nearshore heterogeneity. By isolating turbid water signals and analyzing them alongside wave model data, tidal data, and high resolution (2m) bathymetric data, we characterize sediment transport dynamics across diverse coastal facies. This work constrains how the interplay between wave climate, bathymetric complexity, and sediment availability influences the extent and patterns of offshore turbidity transport in both sandy and rocky environments.
Collectively, the studies in this dissertation advance our understanding of nearshore hydrodynamics by leveraging remote sensing and data analysis constrain to their controls. The methodologies and findings presented here contribute to improved coastal monitoring, management, and research, with potential applications in similar coastal regions globally.
