Data

The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, which was burned during the Waldo Canyon fire in the summer of 2012. The ensemble consists of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network. This e

These datasets provide early estimates of 2024 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from April to late June. Typically, the EAG estimates are publicly released within 7-13 days of the latest satellite observation used for that version. Each weekly release contains five fractional cover maps along with their corresponding co

This data release contains site information and potential explanatory factor data for 1,899 groundwater sites. These sites were used to assess groundwater quality in aquifers used for domestic and public drinking water supply in the southeastern San Joaquin Valley. The southeastern San Joaquin Valley (SESJV) study unit consists of five study areas whose boundaries are defined by the eponymous Cali

This dataset includes otolith and water chemistry used for determining natal origins of individuals from six species. The dataset contains Sr:Ca and Ba:Ca of water samples for the Mississippi River and tributaries as well as otolith Sr, Ba, Mg values from fishes collected in navigation pools 4, 8, 13, and 26 of the Upper Mississippi River, as well as the Open River Reach of the Middle Mississippi

The risk of lampricide applications (such as 4-Nitro-3-(trifluoromethyl)phenol, TFM) to non-target fauna continues to be a concern within the Great Lakes Fishery Commission sea lamprey control program, especially among imperiled aquatic species—such as native freshwater mussels. The Grand River (Ohio) is routinely treated for larval sea lampreys (Petromyzon marinus) and this river contains populat

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

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

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

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

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). Running and using the GW-NDST software involves downloading the software code (v

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