Skip to main content
U.S. flag

An official website of the United States government

Data

The USGS Water Resources Mission Area provides water information that is fundamental to our economic well-being, protection of life and property, and effective management of our water resources. Listed below are discrete data releases and datasets produced during our science and research activities. To explore and interact with our data using online tools and products, view our web tools.

Filter Total Items: 563

Wetland Stream Water Quality Data for West Twin Creek, AK, Allequash Creek, WI, and Big Thompson River, CO, 2010-2020

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; and in 2

Biomass accrual and trace-element concentrations in water and periphytic algae at select locations in the Clark Fork and Blackfoot Rivers, Montana, 2015

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 makes availa

MODPATH6 models used to evaluate effects of complexity on groundwater age metrics in the Fox-Wolf-Peshtigo watersheds, Wisconsin

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. Modifi

Priority Ecosystem Science Program: benthic community and bivalve metrics data in Grizzly Bay and San Pablo Bay (2019-20)

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 Turbulence: St

Field measurements of flow depth and optical image sequences acquired from the Salcha River, Alaska, on July 25, 2019

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 image seque

Non-linear baseflow separation model with parameters and results (ver. 2.0, October 2022)

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 a fractio

Data Release for Evaluation of Six Methods for Correcting Bias in Estimates from Ensemble Tree Machine Learning Regression Models

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) regression of

MODFLOW, MT3D-USGS and VS2DH simulations used to estimate groundwater and nutrient inflow to Upper Klamath Lake, Oregon

One-dimensional vertical models of GW flow (MODFLOW-2005) and solute transport (MT3D-USGS) were calibrated (UCODE) to 2014 observed dissolved silica (Si, 0.2-micron filtered) porewater concentrations in the upper 0.1 m of lakebed sediment to estimate GW flow and Si exchange across the lakebed interface. The Si-based calibrated GW flow rates were then used in conjunction with observed dissolved pho

Water quality of samples collected in the Russian River Watershed (2017-2019)

The Russian River watershed is an important resource for drinking water and recreation. Sonoma Water relies on the Russian River to provide drinking water to over 620,000 Sonoma County and Marin, CA residents. Nearly 1 million visitors enjoy numerous recreational opportunities on the Russian River annually. Wildfires have increased in frequency and intensity in Northern California and research is

Modeled Stream Metabolism in Boulder Creek near Boulder, CO (2016 - 2018)

The data presented here was collected adjacent to USGS stream gage 06730200 and includes both observed and modeled values. Dissolved oxygen, dissolved carbon dioxide, stream depth, water temperature, and light intensity were collected via passive water quality sensors. Modeled values include gross primary production, ecosystem respiration, net ecosystem production, reaeration, and light (when obse

Groundwater chemistry in the Lower East Rift Zone and summit of Kilauea Volcano, Hawai'i

Chemical and isotopic analyses are reported for water samples collected from water supply wells, a geothermal well in the Puna Geothermal Venture (PGV) power plant, a hot spring in the Puna District, a research well on the summit of Kilauea Volcano (informally called "NSF Well", or "Keller Well "), and a water catchment in the headquarters area of Hawai'i Volcanoes National Park, Hawai'i. These wa

Remotely sensed bathymetry and field measurements from the Colorado River near Lees Ferry, Arizona, September 23, 2019

To support an investigation of the feasibility of measuring river bathymetry using a polarizing lidar sensor, lidar and field measurements were collected on the Colorado River near Lees Ferry, Arizona on September 23, 2019. This parent data release includes links to child pages for the following data sets: 1) Lidar data used for mapping channel bathymetry (depth), acquired with a novel instrument
Was this page helpful?