This research incorporates streambank erosion and failure processes into a distributed watershed model and evaluates the impacts of climate change on the processes driving streambank sediment mobilization at a watershed scale. Excess sediment and nutrient loading are major water quality concerns for streams and receiving waters. Previous work has established that in addition to surface and road erosion, streambank erosion and failure are primary mechanisms that mobilize sediment and nutrients from the landscape. This mechanism and other hydrological processes driving sediment and nutrient transport are likely to be highly influenced by anticipated changes in climate, particularly extreme precipitation and flow events. This research has two primary goals: to develop a physics-based watershed model with more inclusive representation of sediment by including simulation of streambank erosion and geotechnical failure; and to investigate the impacts of climate change on unstable streams and suspended sediment mobilization by overland erosion, erosion of roads, and the erosion as well as failure of streambanks. This advances mechanistic simulation of suspended sediment mobilization and transport from watersheds, which is particularly valuable for investigating the impacts of climate and land use changes, as well as extreme events. Model development involved coupling two existing physics-based models: the Bank Stability and Toe Erosion Model (BSTEM) and the Distributed Hydrology Soil Vegetation Model (DHSVM). This approach simulates streambank erosion and failure in a spatially explicit environment. The coupled model is applied to the Mad River watershed in central Vermont as a test case. I then use the calibrated Mad River model to predict the response in watershed sediment loading to future climate scenarios that specifically represent local temperature and precipitation trends for the northeastern US, particularly changing trends in the frequency and magnitude of extreme precipitation. Overall the streambank erosion and failure processes are captured in the coupled model approach. Although the presented calibration of the model underestimates suspended sediment concentrations resulting from relatively small storm/flow events, it still improves prediction of cumulative loads and in some cases suspended sediment concentrations during elevated flow events in comparison to model results without including BSTEM. Increases in temperature affect the timing and magnitude of snow melt and spring flows, as well as associated sediment mobilization, in the watershed. Increases in annual precipitation and in extreme precipitation events produce increases in annual as well as peak discharge and sediment loads in the watershed. This research adds to the body of evidence indicating that streambank erosion and failure can be a major source of suspended sediment, and thereby a major source of phosphorus as well. It also shows that local climate trends in the Northeast are likely to result in higher peak discharges and sediment yields from meso-scale, high-gradient watersheds that encompass headwater forested streams and agricultural floodplains. One limitation was that we could not drive the model with meteorological data that represented changes in both temperature and precipitation, highlighting the need for improved climate predictions. This coupled model approach could be parameterized for alternative watersheds and be re-applied to answer various questions related to erosion processes and sediment transport in a watershed. These findings have important implications for resource allocation and targeted watershed management strategies.

Excessive erosion and fine sediment delivery to river corridors and receiving waters degrade aquatic habitat, add to nutrient loading, and impact infrastructure. Understanding the sources and movement of sediment within watersheds is critical for assessing ecosystem health and developing management plans to protect natural and human systems. As our changing climate continues to cause shifts in hydrological regimes (e.g., increased precipitation and streamflow in the northeast U.S.), the development of tools to better understand sediment dynamics takes on even greater importance. In this research, advanced geomatics and machine learning are applied to improve the (1) monitoring of streambank erosion, (2) understanding of event sediment dynamics, and (3) prediction of sediment loading using meteorological data as inputs. Streambank movement is an integral part of geomorphic changes along river corridors and also a significant source of fine sediment to receiving waters. Advances in unmanned aircraft systems (UAS) and photogrammetry provide opportunities for rapid and economical quantification of streambank erosion and deposition at variable scales. We assess the performance of UAS-based photogrammetry to capture streambank topography and quantify bank movement. UAS data were compared to terrestrial laser scanner (TLS) and GPS surveying from Vermont streambank sites that featured a variety of bank conditions and vegetation. Cross-sectional analysis of UAS and TLS data revealed that the UAS reliably captured the bank surface and was able to quantify the net change in bank area where movement occurred. Although it was necessary to consider overhanging bank profiles and vegetation, UAS-based photogrammetry showed significant promise for capturing bank topography and movement at fine resolutions in a flexible and efficient manner. This study also used a new machine-learning tool to improve the analysis of sediment dynamics using three years of high-resolution suspended sediment data collected in the Mad River watershed. A restricted Boltzmann machine (RBM), a type of artificial neural network (ANN), was used to classify individual storm events based on the visual hysteresis patterns present in the suspended sediment-discharge data. The work expanded the classification scheme typically used for hysteresis analysis. The results provided insights into the connectivity and sources of sediment within the Mad River watershed and its tributaries. A recurrent counterpropagation network (rCPN) was also developed to predict suspended sediment discharge at ungauged locations using only local meteorological data as inputs. The rCPN captured the nonlinear relationships between meteorological data and suspended sediment discharge, and outperformed the traditional sediment rating curve approach. The combination of machine-learning tools for analyzing storm-event dynamics and estimating loading at ungauged locations in a river network provides a robust method for estimating sediment production from catchments that informs watershed management.