Material supply

Why study material supply?

River landscapes are formed to transport the material — sediment, water, wood, etc. — supplied to them. Supply of this material is a function of several interacting variables, including climate, vegetation, topography, geology, and human activity. If we can better understand the web of processes that produce and supply material to the river landscape, we can better anticipate how future changes (in climate, in urbanization, in management, etc.) might alter that supply and, in turn, drive the evolution of the river landscape. With this enhanced knowledge, we can be better equipped to manage river systems for our changing world. 


Sediment supply & tributary erosion in a large river basin

Take-home point: Erosion of several key tributaries substantially increased the sediment loads of the Yampa River from 1880-1940, resulting in enhanced main-stem channel change


Kemper, Thaxton, Rathburn, Friedman, Mueller, Scott, 2022, ESPL (link)

Geomorphic impact of increased sediment supply

Take-home point: The scale of channel response to an increase in sediment supply (i.e., a change in grain-size vs. a change in planform) is dependent on whether thresholds of influx grain size and volume relative to antecedent channel conditions are crossed 


Kemper, Rathburn, Friedman, Mueller, Wohl, & Scamardo, 2023, Earth-Science Reviews (link)

Nutrient supply in urban watersheds

Take-home point: Interactions between runoff, groundwater, and the built environment result in groundwater contaminant zones that are a steady source of various contaminants to urban streams


Welty, Moore, Bain, Talebpour, Kemper, Groffman, Duncan, 2023, Water. Resour. Res. (link)

Ducan, Welty, Kemper, Groffman, Band, 2017, Water Resour. Res. (link)

Snow avalanches & large wood supply in mountain watersheds

Take-home point: Avalanches deliver orders-of-magnitude greater wood to stream channels than background recruitment processes or similar mass movement events, suggesting they are a major (and perhaps overlooked) player in the natural wood regime of mountain streams


Kemper & Scamardo, 2023. Geophys. Res. Letters (link)

Forecasting sediment & nutrient loading with large-scale physical models & machine learning

this work is ongoing —

Using ML models built on water quality monitoring data (high-frequency & discrete) in conjunction with hydrological forecasts from the National Water Model, we are looking to predict nutrient & turbidity loading with 1-7 days lead time


Kemper, Underwood, Hamshaw, Davis, Siemion, Shanley, & Scroth, in review


Cooperative Institute for Research to Operations in Hydrology (CIROH);

Vermont Department of Environmental Conservation;

NYC Departmental of Environmental Protection;


Multi-scalar patterns & drivers of phosphorus export 

this work is ongoing —

Using high-frequency sensor data and machine learning techniques, we are seeking to understand what drives patterns of phosphorus export at the annual, seasonal, and event-scales and the differences therein 


Andrew Schroth, University of Vermont;

Kristin Underwood, University of Vermont;

Scott Hamshaw, US Geological Survey;

James Shanley, US Geological Survey