Predicting bed grain size in Maine rivers using lidar topographic data
River channel morphology in northern New England depends on channel position relative to glacial geomorphology and history. This thesis considers three paraglacial Maine rivers: the West Branch of the Pleasant River (WBPR), a steep inland imposed-form tributary of the Piscataquis River, and the Narraguagus and Sheepscot rivers, two coastal low-gradient rivers. I use a simple model based on the Shields and Manning equations to predict median bed grain size in these recently deglaciated watersheds. The main objectives of this study are to: (1) understand how bedrock controls on the longitudinal profile and sediment inputs impact substrate grain size and channel morphology in the WBPR; (2) apply a model predicting substrate grain size based on digital elevation model (DEM)-derived geometric channel parameters; (3) compare the results from the high gradient WBPR to previously studied low-gradient coastal Maine rivers; and (4) explore the implications of my findings on channel and habitat restoration in paraglacial rivers. I use standard and lidar (light detection and ranging) digital elevation models (DEMs) and spatial analyses to measure channel parameters necessary to predict bed grain size and compare them to field measurements. Predicted bed grain size falls within a factor of two of the field-measured median in ~70% of the study sites. The model performs best in supply-limited alluvial single-thread channel segments with gravel-cobble lag deposit beds, and is less successful in transport-limited depositional segments with relatively fine beds and greater channel variability. Channel segments that are transitional between these two cases (intermediate channel complexity and grain size) are associated with intermediate grain size prediction accuracy. Model failures occur in segments that deviate from the single-thread gravel-bed channel type, and may indicate areas to focus restoration efforts. This study builds on previous research on low-gradient coastal rivers in Maine, and has wide application to future research or restoration projects concerned with sediment mobilization and fluvial ecology.