Historical water quality data of the Ohio River. This dataset is associated with the following publication: Levine, A., J. Yang , and J. Goodrich. Enhancing climate Adaptation Capacity for Drinking Water Treatment Facilities. Journal of Water and Climate Change. IWA Publishing, London, UK, 7(3): 1-13, (2016).

Monthly raw water quality data on alkalinity, hardness, TDS, Turbidity, TOC, pH, and temperature. This dataset is associated with the following publication: Levine, A., J. Yang , and J. Goodrich. Enhancing climate Adaptation Capacity for Drinking Water Treatment Facilities. Journal of Water and Climate Change. IWA Publishing, London, UK, 7(3): 1-13, (2016).

Monthly raw water quality data on alkalinity, hardness, TDS, Turbidity, TOC, pH, and temperature. This dataset is associated with the following publication: Levine, A., J. Yang , and J. Goodrich. Enhancing climate Adaptation Capacity for Drinking Water Treatment Facilities. Journal of Water and Climate Change. IWA Publishing, London, UK, 7(3): 1-13, (2016).

We developed four 2011-2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long-term planning for the city of Minneapolis, MN. Baseline land cover was from the NLCD2011 database. IPCC SRES scenarios (https://www.ipcc.ch/site/assets/uploads/2018/03/sres-en.pdf) interpreted by Sohl et al. (2014) (https://doi.org/10.1890/13-1245.1) for the conterminous United States were downscaled from 250 meters to 30 meters. The baseline and scenario land cover data were used as input into the Soil Water and Assessment Tool (SWAT) model to assess the effect of outyear projected land cover change on the source of raw water for the city of Minneapolis, MN. SWAT was used to assess the effect of land cover change on raw water concentrations of sediment, total nitrogen, and total phosphorus. The IPCC SRES evaluated were A1B, A2, B1, and B2. The four scenarios were implemented with (A1Bf) and without (A1B) forest recovery (e.g., conversion of cropland (2011) to forest (2050). Data on treatment of raw (source) water quality, provided by the city of Minneapolis, MN, were used in autoregressive models to determine if there was a temporal trend in mass of treatment chemicals applied. Models were run separately for each treatment chemical. Data are monthly application rates from 2008 through 2017. The day of the month for the date variable was nominally set to one (1). Data for alum were incomplete from 2008 through 2011, which were set to zero (0) and treated as missing in the autoregressive model. Water volume treated is in megagallons (Mg); 1 Mg = 1000 gallons. A dummy variable for change in management philosphy was included in the model. The dummy variable was set to zero (0) for the period 2008 - 2014 and one (1) afterward. The dummy variable is not included in the file. It had a significant effect only for the CO2 treatment chemical.

File (spreadsheet) contains a summary of data presented in the journal article titled "Effects of future hydroclimatic conditions on microbial water quality and management practices in two agricultural watersheds". This dataset is associated with the following publication: Coffey, R.P., J. Butcher, B. Benham, and T. Johnson. Modeling the Effects of Future Hydroclimatic Conditions on Microbial Water Quality and Management Practices in Two Agricultural Watersheds. Transactions of the ASABE. AMERICAN SOCIETY OF AGRICULTURAL AND BIOLOGICAL ENGINEERS, ST. JOSEPH, MI, USA, 63(3): 753-770, (2020).

This data release contains a data compilation and analysis of the hydrogeology in the U.S. portion of the Lake Superior watershed, for the purpose of providing background data for future study and modeling of groundwater and contaminant movement in the watershed. The data support an analysis of groundwater contributions to the water budget of Lake Superior and provide hydrogeologic context for future modeling and sampling of groundwater in the region. The data release contains three child items: Baseflow analysis for tributaries to Lake Superior from 1946 to 2020; Geohydrology data for groundwater analysis in the Lake Superior Watershed; and Groundwater wells from Minnesota, Wisconsin, and Michigan state databases and the U.S. Geological (USGS) National Water Information System (NWIS) database with static water level data within 10km of the Lake Superior watershed.

This data release contains a data compilation and analysis of the hydrogeology in the U.S. portion of the Lake Superior watershed, for the purpose of providing background data for future study and modeling of groundwater and contaminant movement in the watershed. The data support an analysis of groundwater contributions to the water budget of Lake Superior and provide hydrogeologic context for future modeling and sampling of groundwater in the region. The data release contains three child items: Baseflow analysis for tributaries to Lake Superior from 1946 to 2020; Geohydrology data for groundwater analysis in the Lake Superior Watershed; and Groundwater wells from Minnesota, Wisconsin, and Michigan state databases and the U.S. Geological (USGS) National Water Information System (NWIS) database with static water level data within 10km of the Lake Superior watershed.