Data

This dataset details mercury (Hg) stable isotope values in dragonflies and fish tissues collected across U.S. National Parks. Dragonfly samples were collected as part of the Dragonfly Mercury Project (https://geonarrative.usgs.gov/dmp/), a citizen science project focused on using dragonfly larvae as biosentinels for Hg bioaccumulation. Fish were collected as part of separate park-wide monitoring i

This U.S. Geological Survey (USGS) Data Release describes field methods, lab methods, and data for groundwater and soil gas samples collected in the vicinity of the Red Hill Bulk Fuel Storage Facility, O‘ahu, Hawai‘i, from September 2022 through April 2023. The Red Hill facility consists of 20 underground fuel storage tanks that can each hold approximately 12.5 million gallons of fuel and are conn

This data release contains nutrient and sediment concentrations, loads, and yields from a USGS subsurface-tile edge-of-field (EOF) agricultural monitoring site. Sampling and flow monitoring were performed at the outlet of a subsurface-tile that drains 14.7 acres of cultivated cropland. The site is located in South Central Michigan and discharges into a headwater stream of the Maumee watershed. Thr

This archive contains the code and other files to demonstrate the creation and application of an impulse-response emulator for process-based groundwater flow and transport models in MODFLOW/MT3D. This includes creating a synthetic modeling environment in MODFLOW/MT3D, using PEST++ to derive an impulse-response (A) matrix (as described in White et al., 2020), using this A matrix to create a groundw

Surface water samples (n = 33) were collected in fall of 2023 at stream sites in Scott County Iowa, USA and were analyzed for microbial source tracking markers by quantitative polymerase chain reaction at the Laboratory for Infectious Disease and the Environment (LIDE). Microbial source tracking markers identify fecal sources of contamination by detecting microbes that are specific to certain anim

A histrogram-based boosted regression tree (HBRT) method was used to predict the depth to the surficial aquifer water table (in feet) throughout the State of Wisconsin. This method used a combination of discrete groundwater levels from the U.S. Geological Survey National Water Information System, continuous groundwater levels from the National Groundwater Monitoring Network, the State of Wisconsin

Green and others (2021) developed a gradient boosted regression tree model to predict the mean groundwater age, or travel time, for shallow wells across a portion of the Great Lakes basin in the United States. Their study applied machine learning methods to predict ages in wells using well construction, well chemistry, and landscape characteristics. For a dataset of age tracers in 961 water sample

A multivariate regression model was developed to predict zero-order oxygen reduction rates (mg/L/yr) in aquifers across the State of Wisconsin. The model used a combination of dissolved oxygen concentrations and mean groundwater ages estimated with sampled age tracers from wells in the U.S. Geological Survey National Water Information System and previously published project reports from state agen

This data set was compiled to support the development of a model of oxygen reduction rates in Wisconsin groundwater wells; a model which is part of a Groundwater Nitrate Decision Support Tool for Wisconsin. Data were compiled from previously published studies with data collection from 1987 to 2009. Only data describing redox condition, groundwater age, depth to water, and well construction were co

This data release contains a netCDF file containing decadal estimates of nitrate leached from septic systems (kilograms per hectare per year, or kg/ha) in the state of Wisconsin from 1850 to 2010, as well as the python code and supporting files used to create the netCDF file. The netCDF file is used as an input to a Nitrate Decision Support Tool for the State of Wisconsin (GW-NDST; Juckem and othe

A Groundwater Nitrate Decision Support Tool (GW-NDST) for wells in Wisconsin was developed to assist resource managers with assessing how legacy and possible future nitrate leaching rates, combined with groundwater lag times and potential denitrification, influence nitrate concentrations in wells (Juckem et al. 2024). The GW-NDST relies on several support models, including machine-learning models

A groundwater Nitrate Decision Support Tool (GW-NDST) for wells in Wisconsin was developed to assist resource managers with assessing how legacy and possible future nitrate leaching rates, combined with groundwater lag times and potential denitrification, influence nitrate concentrations in wells (Juckem et al. 2024). The GW-NDST relies on an ensemble of calibrated parameters to make nitrate predi