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Mike Kennedy

Publications and source records attributed to Mike Kennedy.

3 recordsLinked to original sources

Near-channel erosion as a driver of watershed-scale sediment and phosphorus loading in a forested sub-watershed of Lake of the Woods

The Little Fork River, a forested watershed in northern Minnesota, United States, is a disproportionate contributor of sediment and phosphorus to Rainy River and Lake of the Woods. Sources of sediment and sediment-bound phosphorus to the Little Fork were investigated using complementary lines of evidence in the form of geochemical sediment fingerprinting and a stream-corridor sediment budget that included harvested forest and ravines, in addition to upland forest and agriculture, roads, and streambanks. Near-channel ravine and streambank sources comprised 95–100 % of streambed sediment and 79–100 % of suspended sediment at the river mouth, while tributaries had up to 50% of suspended sediment from upland sources including harvested forest, agriculture, and roads. Repeat suspended-sediment sampling at eight sites showed more source variability among sites than across events. Ravine erosion was the watershed’s largest contributor to downstream sediment and sediment-bound phosphorus loading, while streambed sediment had a larger streambank source. Near-channel erosion also contributed phosphorus, though with less labile and redox-sensitive forms than forest and agricultural soils. In contrast to observations from agricultural settings, suspended-sediment phosphorus concentrations were elevated above some, but not all sources. The effects of watershed size, glacial history, land cover, and variable temperature and precipitation on runoff generation and peak streamflow are important considerations for mitigating near-channel loss of sediment and phosphorus in the Little Fork. These results suggest that management of sediment and phosphorus export from the Little Fork could incorporate context for location in the watershed, geomorphic setting, and runoff characteristics.

Minnesota↗

Using machine learning in Minnesota’s StreamStats to predict fluvial sediment

A thorough understanding of fluvial sediment transport is essential for addressing key environmental issues such as aquatic habitat degradation, flooding, excess nutrients, and challenges with river restoration. Fluvial sediment samples are valuable for addressing these concerns, but their collection is often impractical across all rivers and timeframes of interest. In addition, previously used analytical and numerical methods have not allowed for the transfer of knowledge from sites that have data to sites that do not have data. To overcome this limitation, the U.S. Geological Survey developed machine learning models to predict suspended-sediment concentrations and bedload transport in Minnesota rivers that lack physical sediment data and integrated them into the U.S. Geological Survey StreamStats web application.

Minnesota↗