Lower Mississippi-Gulf Water Science Center

Data and Tools

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Real-time data:

Streamflow:   AL || AR || LA || MS || TN

Water quality:   AL || AR || LA || MS || TN

Groundwater levels:    AL || AR || LA || MS || TN

Precipitation:    AL || AR || LA || MS || TN

Water use:    AR || LA || MS || TN

Additional Information: USACE River Gages, NOAA Lower Mississippi River Forecast Center, NOAA South Atlantic Gulf River Forecast Center, NOAA Southeast River Forecast Center.

USGS StreamStats

USGS StreamStats

StreamStats users can select USGS data-collection station locations shown on a map and obtain previously published information for the stations.

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USGS WaterWatch

USGS WaterWatch

WaterWatch is a U.S. Geological Survey (USGS) World Wide Web site that displays maps, graphs, and tables describing real-time, recent, and past streamflow conditions for the United States.

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USGS NWISWeb

USGS NWISWeb

These pages provide access to water-resources data collected at approximately 1.5 million sites in all 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, American Samoa and the Commonwealth of the Northern Mariana Islands.

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Filter Total Items: 256
Date published: September 9, 2020

Sparta-Memphis aquifer well point dataset, in Arkansas, January-May 2013 (ver. 1.1, September 2020)

This dataset contains the groundwater well locations and water-level measurements for 306 wells measured during the water-level survey of the Sparta-Memphis aquifer, in Arkansas, January through May 2013. Well-location and water-level data is publicly available from the U.S. Geological Survey's National Water Information System.

Date published: September 4, 2020

Estimated Use of Water by Subbasin (HUC8) in the Red River Basin, 2010 and 2015

The Red River basin is one of several national "focus area studies" in the U.S. Geological Survey National Water Census.The objective of the National Water Census is to provide nationally-consistent base layers of well-documented data that account for water availability and use nationally. A focus area study (FAS) is a stakeholder-driven assessment of water availability in river basins wi

Date published: August 24, 2020

Stream habitat characteristics and relative abundances of the Yellowcheek Darter (Nothonotus moorei) at select riffle sites among the four headwater forks of the Little Red River in Arkansas

The Yellowcheek darter (YCD), Nothonotus moorei (Robison and Buchanan 2020), is endemic to the headwater tributaries (a.k.a. ‘forks’) of Little Red River system (South Fork, SF; Middle Fork, MF; Archey Fork, AF; and Beech Fork, BF) in north central Arkansas. Large portions of critical stream habitat in each stream fork were inundated in 1964 with

Date published: August 4, 2020

Simulated groundwater residence times in two principal aquifers of the Mississippi embayment physiographic region

Groundwater residence times and flow path lengths were simulated for two major aquifers of the Mississippi embayment region using particle tracking (Pollock, 2012; Starn and Belitz, 2018) in a regional groundwater-flow model (Haugh and others, 2020). The Mississippi embayment physiographic region includes two principal aquifer systems: the surficial aquifer system, which is do

Date published: July 29, 2020

Machine-learning model predictions and groundwater-quality rasters of total dissolved solids in aquifers of the Mississippi Embayment

Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML appr

Date published: July 29, 2020

Machine-learning model predictions and groundwater-quality rasters of specific conductance in aquifers of the Mississippi Embayment

Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML appr

Date published: July 29, 2020

Depth rasters in aquifers of the Mississippi embayment

Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML appr

Date published: July 29, 2020

Machine-learning model predictions and groundwater-quality rasters of specific conductance, total dissolved solids, and chloride in aquifers of the Mississippi Embayment

Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML appr

Date published: July 29, 2020

Machine-learning model predictions and groundwater-quality rasters of chloride in aquifers of the Mississippi Embayment

Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML appr

Date published: July 10, 2020

Elevation contours of beach topography and nearshore bathymetry of Lake Superior at Minnesota Point, Duluth, MN, from hydrographic survey August 2019

The elevation contours in this dataset have a 2-foot (ft) interval and were derived from a digital elevation model (DEM) of beach topography and nearshore bathymetry of Lake Superior at Minnesota Point, Duluth, Minnesota. The DEM has a 10-meter (32.8084 ft) cell size and was created from LiDAR data representing beach topography and sonar data representing bathymetry to a distance

Date published: July 10, 2020

LAS dataset of LiDAR data collected at Lake Superior at Minnesota Point, Duluth, MN, August 2019

This dataset is a LAS (industry-standard binary format for storing large point clouds) dataset containing light detection and ranging (LiDAR) data representing beach topography of Lake Superior at Minnesota Point, Duluth, Minnesota. Average point spacing of the LiDAR points in the dataset is 0.137 meters (m; 0.45 feet [ft]). The LAS dataset was used to create a 1-m (3.28

Date published: July 10, 2020

Digital elevation model (DEM) of beach topography of Lake Superior at Minnesota Point, Duluth, MN, August 2019

This dataset is a digital elevation model (DEM) of the beach topography of Lake Superior at Minnesota Point, Duluth, Minnesota. The DEM has a 1-meter (m; 3.28084 foot [ft]) cell size and was created from a LAS dataset of terrestrial light detection and ranging (LiDAR) data with an average point spacing of 0.137 m (0.45 ft). LiDAR data were collected August 10, 2019 u