Data
UMESC Data
Long Term Resource Monitoring
Long Term Resource Monitoring
The LTRM element is one of two components of the federally mandated Upper Mississippi River Restoration Program.
ScienceBase
ScienceBase
ScienceBase is data and information management infrastructure that enables data upload, documentation, sharing, and dynamic data services using standards-compliant methods and technological components. ScienceBase furnishes a foundation for data stewardship, government open data, and scientific discovery.
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Concentrations and laboratory quality-assurance data for six unregulated contaminants measured in source and finished drinking-water samples collected from public water systems throughout Minnesota by using ELISA and MS-based analytical methods Concentrations and laboratory quality-assurance data for six unregulated contaminants measured in source and finished drinking-water samples collected from public water systems throughout Minnesota by using ELISA and MS-based analytical methods
The U.S. Geological Survey, in cooperation with the Minnesota Department of Health, conducted a study to determine the occurrence of six unregulated contaminants in source and finished drinking-water samples collected from 67 public water supply systems throughout Minnesota. Minnesota relies on groundwater and surface water sources for drinking water. Land use, such as wastewater...
Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019 Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019
A series of machine learning (ML) models were developed for Minnesota. The ML models were trained and tested using suspended sediment, bedload, streamflow, and geospatial data to predicted suspended sediment and bedload. Suspended sediment, bedload, and streamflow data were collected during water years 2007 through 2019. The ML models were used to improve understanding of sediment...
Water temperature data in the Milwaukee Estuary of Lake Michigan, Milwaukee County, Wisconsin Water temperature data in the Milwaukee Estuary of Lake Michigan, Milwaukee County, Wisconsin
This dataset contains water temperature data collected by boat tow on August 30, 2019 between approximately 8:00 AM and 12:15 PM Central Standard Time (CST) in the Milwaukee Estuary of Lake Michigan. The data includes measured water temperatures, depth of collection, water column depth, time of collection, and geospatial coordinates. The objective of this data collection was to produce...
Fluvial Erosion Hazard Rapid Geomorphic Assessment Data from the Marengo Watershed, Ashland County, Wisconsin Fluvial Erosion Hazard Rapid Geomorphic Assessment Data from the Marengo Watershed, Ashland County, Wisconsin
An extreme flood in 2016 caused widespread culvert blockages and road failures across northern Wisconsin, including extensive damage along steep tributaries and ravines in the Marengo River watershed. Along with the flooding, there were fluvial erosion hazards (FEH) associated with a large amount of erosion in headwater areas. Of special concern were FEHs associated with gullying, loss...
Field observation of wind waves (2019) along the Chincoteague Living Shoreline, Virginia Field observation of wind waves (2019) along the Chincoteague Living Shoreline, Virginia
This dataset contains measured (interval = 0.5 hour) wave height, peak wave period, water level, and water depth during March 1 to May 1, 2019, at five wave gage locations along the Chincoteague Living Shoreline, Virginia. These wave gages were sampled continuously at 10 Hz to take 20-min bursts every 30 min. These data were used for the analysis of wave attenuation along the oyster-reef...
Field observation of current velocities (2019) along the Chincoteague Living Shoreline, Virginia Field observation of current velocities (2019) along the Chincoteague Living Shoreline, Virginia
This dataset contains measured current velocity during March 1 to May 2, 2019, at eleven locations along the Chincoteague Living Shoreline, Virginia.
Input data, trained model data, and model outputs for predicting streamflow and base flow for the Mississippi Embayment Regional Study Area using a random forest model Input data, trained model data, and model outputs for predicting streamflow and base flow for the Mississippi Embayment Regional Study Area using a random forest model
This data release contains datasets developed for the purpose of training and applying random forest models to the Mississippi Embayment Regional Study Area. The random forest models are designed to predict total stream flow and baseflow as a function of a combination of watershed characteristics and monthly weather data. These datasets are associated with a report (SIR 2022-xxxx) and...
Above- and belowground biomass production, decomposition, and wetland elevation change in transitional coastal wetland communities exposed to elevated CO2 and sediment deposition: a mesocosm study from 2012 to 2014 Above- and belowground biomass production, decomposition, and wetland elevation change in transitional coastal wetland communities exposed to elevated CO2 and sediment deposition: a mesocosm study from 2012 to 2014
This data release includes belowground primary productivity, decomposition, and surface elevation change data from a two-year mesocosm experiment from 2012 to 2014. We conducted experimental greenhouse manipulations of atmospheric CO2 (double ambient CO2) and sediment deposition to simulate a land-falling hurricane under future climate conditions. Experimental greenhouse conditions...
Hydrologic metrics, biological metrics, R scripts, and model archives associated with regression analyses used to quantify relations between altered hydrological and biological responses in rivers of Minnesota, 1945-2015 Hydrologic metrics, biological metrics, R scripts, and model archives associated with regression analyses used to quantify relations between altered hydrological and biological responses in rivers of Minnesota, 1945-2015
The U.S. Geological Survey (USGS) and the Minnesota Pollution Control Agency (MPCA) conducted a cooperative study to develop linear regression models that quantify relations among 173 hydrologic explanatory metrics in five categories (duration, frequency, magnitude, rate-of-change, and timing) computed from streamgage records and 132 biological response metrics in six categories...
Groundwater data and age information from samples collected in Minnesota (ver. 3.0, December 2025) Groundwater data and age information from samples collected in Minnesota (ver. 3.0, December 2025)
Groundwater age distributions and susceptibility to natural and anthropogenic contaminants were assessed for selected wells, streambed piezometers, and springs in southeastern Minnesota. The data provide information to understand how long it will take to observe groundwater quality improvements from best management practices implemented at land surface to reduce losses of nitrate (and...
Source Identification of Mercury and Methylmercury using Stable Isotope Analysis in the Fox River, WI Source Identification of Mercury and Methylmercury using Stable Isotope Analysis in the Fox River, WI
The lower Fox River in Wisconsin is a heavily industrialized system and the major tributary to Green Bay within Lake Michigan. The region has been a listed as Area of Concern by the United States Environmental Protection Agency (USEPA), indicating severe impairment of the ecological health of the system. Remedial action has taken place along the river to remove extensive polychlorinated...
Submersed Macrophyte Biomass Estimates in Pools 4, 8 and 13 of the Upper Mississippi River, 1998-2018 Submersed Macrophyte Biomass Estimates in Pools 4, 8 and 13 of the Upper Mississippi River, 1998-2018
System-scale restoration efforts within the Upper Mississippi River Restoration Program have included annual monitoring of submersed aquatic vegetation (SAV) since 1998 in four representative reaches spanning approximately 440 river km. We developed predictive models relating monitoring data (site-scale SAV abundance indices) to diver-harvested SAV biomass, used the models to back...