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/loads 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)).

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 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) for a total of eight scenarios. The LULC net change file reports total change in hectares between 2011 and 2015 for forest, developed, cropland, pastures, and wetland for the four IPCC SRES scenarios with and without forest recovery.

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 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) for a total of eight scenarios. The LULC net change file reports total change in hectares between 2011 and 2015 for forest, developed, cropland, pastures, and wetland for the four IPCC SRES scenarios with and without forest recovery.

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.

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.

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). Units for the MeanDifference variable are different for TN/TP and sediment due to SWAT reporting conventions; TN and TP are in kilograms (kg) and sediment is in megagrams (Mg). 1 Mg = 1,000 kg. Differences are scenario minus baseline; negative values indicate constituent loads were less for the scenario relative to the baseline. NLCD2001 was included as a scenario to assess the effect of land cover change. Negative values for the NLCD2001 scenario indicate that a constituent load was less in 2001 relative to 2011.

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). Units for the MeanDifference variable are different for TN/TP and sediment due to SWAT reporting conventions; TN and TP are in kilograms (kg) and sediment is in megagrams (Mg). 1 Mg = 1,000 kg. Differences are scenario minus baseline; negative values indicate constituent loads were less for the scenario relative to the baseline. NLCD2001 was included as a scenario to assess the effect of land cover change. Negative values for the NLCD2001 scenario indicate that a constituent load was less in 2001 relative to 2011.

Data supporting results presented in "Modeling future land cover change scenarios in Minneapolis, MN, to support drinking water source protection decisions.". This dataset is associated with the following publication: Woznicki, S., G. Kraynick, J. Wickham, M. Nash, and T. Sohl. Modeling future land cover and water quality change in Minneapolis, MN, USA to support drinking water source protection decisions. Global Environmental Change. Elsevier B.V., Amsterdam, NETHERLANDS, 59(4): 726-742, (2023).

This USGS data release contains 2013 streamflow, baseflow, and precipitation data from three hydrologically-diverse streams in the United States used to develop a conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities. The framework combined generalized concepts on the movement of water, the environmental behavior of chemicals and eroded soil, and the designed functions of various agricultural activities. The framework addresses the impacts on water quality of a broad range of agricultural chemicals and sediment across a variety of hydrologic settings. • Chesterville Branch near Crumpton, Maryland, (USGS site ID - 01493112) had substantial baseflow throughout the year with increased streamflow within a day of rainfall. • Indian Creek at State Line RD, Leawood, Kansas (USGS site ID - 06893390) was a fastflow-dominated urban steam that was not well connected to shallow groundwater. • The watershed of Leary-Weber Ditch at Mohawk, Indiana (USGS site ID - 03361638) has an extensive subsurface drainage network within its watershed. These data support the following publication: Capel, P.D., Wolock, D.M., Coupe, R.H., and Roth, J.L., 2017, A conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities: U.S. Geological Survey Scientific Investigations Report 2017-5095, 35 p., https://doi.org/10.3133/sir20175095.