Browsing by Author "Kovalenko, Katya"
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Item Assessing forest land conversion risk to maintain water quality in North Central Minnesota(University of Minnesota Duluth, 2018-07) Host, George E; Kovalenko, Katya; Meysembourg, PaulItem Minnesota Restorable Wetland Index(2024-01-11) Johnson, Lucinda; Bartsch, Will; Kovalenko, Katya; Kloiber, Steve; Nixon, Kristi; wbartsch@d.umn.edu; Bartsch, Will; Natural Resources Research InstituteThe Minnesota Restorable Wetland Index (RWI) was developed by the Natural Resources Research Institute (NRRI) in collaboration with the Minnesota Department of Natural Resources (MN DNR). The RWI was developed statewide on a 3m grid by applying machine learning models to predict the location of existing and restorable wetlands based on hydrological, geomorphological, and geological variables. Post-processing was done to remove existing wetlands and smooth the results. This data layer replaces the original NRRI Restorable Wetland Inventory that was developed statewide on a 30m grid using a different methodology.Item Predicting Potential Beaver Dam Sites on Lake Superior's North Shore(University of Minnesota Duluth, 2020-04) Johnson-Bice, Sean; Gorzo, Jessica; Kovalenko, Katya; Brown, Terry; Host, George EBeavers (Castor canadensis) play a substantial role in coastal Lake Superior ecosystems, as the creation of beaver dams and ponds results in riparian habitat that can be vastly different from habitats before beaver activity (Naiman et al. 1988, Rosell et al. 2005). Beaver dams can influence water temperatures, flow regimes, channel morphology, and sediment dynamics (Gurnell 1998, Pollock et al. 2003, Westbrook et al. 2006, Burchsted et al. 2010, Bouwes et al. 2016). Understanding where beavers are likely to build dams and ponds has many practical implications for resource managers and citizens. Current climate models have predicted an increase in the prevalence of extreme precipitation events that may cause an increase in erosion and flooding events in the North Shore (Herb et al. 2016). Beaver dams have been shown to mitigate the downstream effects of high-precipitation events by reducing stream energy and increasing water retention time (Law et al. 2016, Puttock et al. 2017, Karran et al. 2017); their presence in the North Shore may be an important natural mechanism for minimizing impacts from natural hazards. But they also may have an adverse effect on fisheries, particularly steelhead (Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis) (Johnson-Bice et al. 2018), leading to controversy within coastal communities and complex management decisions. The goal of this study was to create a model that predicts potential beaver dam locations based on existing habitat characteristics, stream gradient, and stream power and flow estimates. Building from an existing data set that includes spatial information of beaver-created wetlands within five North Shore watersheds, we also conducted a rapid assessment of water and sediment storage contained within beaver ponds across the North Shore. The key deliverables of this project are: ● An interactive online map showing historic and potential beaver dam locations ● Estimates of water and sediment storage in North Shore beaver wetlands ● A downloadable spatial database of existing and predicted beaver dam locations, with FGDC-compliant metadata ● This report describing methods, results, and interpretations These products will be important for resource managers that make land-use decisions based on current and future hydrologic and sediment pathways in Lake Superior tributaries.Item Watershed-based Stressors for the Great Lakes Basin(2024-01-11) Host, George; Kovalenko, Katya; Brown, Terry; Johnson, Lucinda; Ciborowski, Jan; ljohnson@d.umn.edu; Johnson, Lucinda; Natural Resources Research InstituteThe Watershed-based Stressors for the Great Lakes Basin dataset includes component and aggregated measures of environmental stress to coastal ecosystems from watersheds of the Great Lakes Basin. Stressors include the amount of agricultural and developed land use, as well as road and population density. These summaries are based on a set of 5971 watersheds that cover the US and Canadian Great Lakes basin, derived using methods from Hollenhorst et al. (2007). Indices presented in this dataset include SumRel (Host et al. 2011) and the more recent combined Agriculture and Development - AgDev index (Host et al. 2019). These were developed as part of the Great Lakes Environmental Indicators II (GLEI-II) project, funded through the Great Lakes Restoration Initiative and used to quantify the response of biota (birds, fish, macroinvertebrates, diatoms and wetland vegetation) to varying degrees of watershed stress (Kovalenko et al. 2014). As of 2015, a more recent version of watersheds has been created by the Great Lakes Aquatic Habitat Framework and stressors recalculated based on those watersheds.