Data

This data release contains input data used in model development and TIF raster files used to predict the probability of low dissolved oxygen (DO) and high dissolved iron (Fe) in groundwater within the glacial aquifer system in the northern continental United States. Input data include measured DO and Fe concentrations at groundwater wells, and associated predictor variable data. The...

This dataset provides the expected and determined concentrations of selected inorganic and organic analytes for spiked reagent-water samples (calibration standards and limit of quantitation standards) that were used to calculate detection limits by using the United States Environmental Protection Agency's (USEPA) Method Detection Limit (MDL) version 1.11 or 2.0 procedures, ASTM...

Valley Water (formerly Santa Clara Valley Water District) provides stream stewardship, wholesale water supply, and flood protection for Santa Clara County, California, in the southern San Francisco Bay Area. A number of the reservoirs owned by the agency sit in the eastern foothills of the Santa Cruz mountains above the city of San Jose and in the watershed below the former New Almaden...

Efficiency of seepage meters, long considered a fixed property associated with the meter design, is not constant in highly permeable sediments. Instead, results from this study indicate that efficiency varies substantially with seepage-bag fullness, duration of bag attachment, depth of meter insertion into the sediments, and seepage velocity. Efficiency also varies substantially in...

This data release consists of orthophotographs of the Snow River in Alaska acquired on September 1, 2018. The orthophotographs were produced from images obtained using a Hasselblad A6D-100C 100 megapixel digital mapping camera deployed within a pod mounted on the landing gear of a Robinson R44 helicopter. Images were acquired as the helicopter transited a series of flight lines designed...

This dataset includes discrete water quality and discharge data for three streams that flow through wetlands. There are two measurement and sampling locations on each stream: one immediately upstream from the wetland and one immediately downstream from the wetland. Measurements and sample collection occurred in 2010 and 2011 at West Twin Creek, AK; in 2019 and 2020 at Allequash Creek, WI...

The U.S. Geological Survey monitored algal biomass accrual and concentrations of metals and other trace elements in stream water and periphytic algae at 3-4 day intervals over a 2-week period at 3 locations within the upper and middle portions of the mining-impacted Clark Fork River, Montana, and at one location on the relatively unimpacted Blackfoot River tributary. This data release...

This data release contains five groundwater particle-tracking models (MODPATH6) of northeastern Wisconsin, USA, that were developed to work with three MODFLOW models that have differing levels of complexity. The previously developed MODFLOW models (https://doi.org/10.3133/sir20175010 and https://doi.org/10.5066/F73J3B3P) were modified slightly to ensure proper functioning with MODPATH6

Sediments and sediment transport influence the physical habitat and the ecology of the San Francisco Bay estuary. Bed sediments provide habitat for benthic organisms and the transport of sediment transports nutrients and contaminants throughout the San Francisco Bay. As part of the SF Bay-Delta Priority Ecosystems Science Program project: Biophysical Controls on Erosion and Near-Bed...

This data release includes field measurements of flow depth and optical image sequences acquired from the Salcha River in Alaska on July 25, 2019. These data were used to develop and test a spectrally based remote sensing technique for estimating water depth from passive optical image data. The purpose of this study was to assess the feasibility of inferring water depths from optical...

This data release provides source code and an R workspace with functions comprising a non-linear baseflow separation model, calibrated values of parameters and estimates of the baseflow component of daily streamflow at selected streamflow gages. Parameter values were determined by calibration of the model. Estimates of the baseflow component include daily values and the total baseflow as...

Ensemble-tree machine learning (ML) regression models can be prone to systematic bias: small values are overestimated and large values are underestimated. Additional bias can be introduced if the dependent variable is a transform of the original data. Six methods were evaluated for their ability to correct systematic and introduced bias: (1) empirical distribution matching (EDM); (2)...