AndreaBozzo/ceres-open-data-index huggingface dataset:
Single-beam Echosounder bathymetry of the Nehalem Bay near Wheeler, Oregon 2019

Bathymetric data were collected by the U.S. Geological Survey (USGS) in 2019 for approximately 2.2 square kilometers of the Nehalem Bay between the Highway 101 bridge and Nehalem Bay State Park (about 6.5 kilometers) near Wheeler, Oregon. The data were collected using a Trimble R8 Global Navigation Satellite System (GNSS) receiver combined with a Seafloor Systems Hydrolite TM single-beam 200 kilohertz echosounder mounted to a motorized boat. GPS positions received corrections in real time from the Oregon Real-Time GNSS Network. Sound velocity profiles were recorded at 19 different locations evenly spaced throughout the survey area using an AML Oceanographic Base X2 100 meter sound velocity profiler in order to quantify the spatial and temporal variation in sound velocity. Data was collected using the UTM Zone 10 North (meters) (NAD83[2011]) coordinate system, with elevations relative to NAVD88 using Geoid 12B. Data was post-processed to remove spurious data points. Raw depths were corrected using the mean value of the recorded sound velocity profiles, then converted to bed elevations. The mean vertical and horizontal uncertainties are 0.04145 and 0.00945 meters, respectively.
This product is a comma-delimited text file of the corrected survey data. Each corrected bed elevation point includes the date and time of collection, as well as horizontal and vertical precision information.

Tags: bathymetry, bathymetry-measurement, elevation, estuary, gps, gps-measurement, inlandwaters, nehalem, nehalem-river, oregon, rtk, single-beam-echo-sounder, sonar, stream-bed-elevation, topography, usgs-5dfab2cbe4b0413cb421d469, wheeler
Organization: Department of the Interior
Concentrations of pesticides in sediment in 82 streams and in biofilms in 54 streams in California in 2017

Hydrophobic (sediment-associated) pesticides were measured in sediment samples collected from 82 wadeable streams and in biofilm in 54 of those streams in the Central California Foothills and Coastal Mountains ecoregion.115 current-use and 3 legacy pesticides were measured in stream sediment; 93 of the current-use pesticides and the same 3 legacy pesticides were measured in biofilm. On average 4 times as many current-use pesticides were detected in biofilm at a site (median of 2) as in sediment (median of 0.5). This data release provides data for the pesticide concentrations, information on the pesticide compounds, and input data and R scripts for statistical models used in the analysis presented in the journal article "Biofilms provide new insight into pesticide occurrence in streams and links to aquatic ecological communities," by BJ Mahler, TS Schmidt, LH Nowell, SL Qi, PC Van Metre, ML Hladik, DM Carlisle, MD Munn, and J May, http://dx.doi.org/xxxxxxxx.

Tags: biofilm, biota, california, central-california-coastal, pesticide, sediment, stream, usgs-5dfa73fee4b0cdded73d1b4f
Organization: Department of the Interior
State transition model of cumulative burned area to annual grass in the Great Basin region of the Western U.S.

A raster identifying previously burned areas as being 1) recovered (to sagebrush-dominant ecosystem), 2) recovering, or 3) transitioned to annual grass-dominated.

Tags: annual-grass, biota, california, fires, great-basin, idaho, montana, nevada, oregon, recovery, resilience, resistance, sagebrush, usgs-5dfa6050e4b0cdded73c6b62, utah, western-united-states, wildfire
Organization: Department of the Interior
Additional mapping tools for Great Basin wildfire and conifer management to increase operational resilience: integrating sagebrush ecosystem and sage-grouse response

Conservation planning efforts for sagebrush ecosystems of western North America increasingly focus on enhancing operational resilience though decision-support tools that link spatially explicit variation in soil and plant processes to outcomes of biotic and abiotic disturbances spanning large spatial extents. However, failure to consider higher trophic-level fauna (e.g. wildlife) in these tools can hinder efforts to operationalize resilience owing to spatiotemporal lags between slower reorganization of plant and soil processes following disturbance, and faster behavioral and demographic responses of fauna to disturbance. These spatial products provide additional examples for managers of sagebrush ecosystems and greater sage-grouse (Centrocercus urophasianus) populations in the Great Basin to aid with decisions regarding: 1) wildfire prevention, suppression, and management; and 2) removal of encroaching conifers. These products integrate models of ecological resilience mapped to variation in soil moisture and temperature regimes, wildlife risk and recovery processes, and potential ecological traps with measures of sage-grouse habitat selection and abundance. Please refer to Ricca and Coates (2019) and examples within for further details on methodology.

Tags: biota, california, conservation-planning, decision-support, fires, great-basin, idaho, montana, nevada, oregon, sage-grouse, suitability, umbrella-species, usgs-5dfa5a6be4b0cdded73c2cdc, utah, western-united-states, wildfire
Organization: Department of the Interior
Digital Shoreline Analysis System (DSAS) version 4.4 transects with short-term end-point rate-of-change calculations for the exposed north coast of Alaska, from Icy Cape to Cape Prince of Wales

This dataset consists of short-term (less than 37 years) shoreline change rates for the exposed coast of the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using an end point rate-of-change (epr) method based on available shoreline data between 1980 and 2016. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate shoreline change rates.

Tags: arctic, bering-land-bridge-national-preserve, cape-espenberg, cape-krusenstern-national-monument, cape-lisburne, cape-prince-of-wales, cape-thompson, chukchi-sea, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-processes, deering, digital-shoreline-analysis-system, distributions, dsas, environment, geoscientificinformation, icy-cape, kivalina, kotzebue-sound, north-slope, oceans, pacific-coastal-and-marine-science-center, pcmsc, physical-chemical-features, point-hope, point-lay, red-dog-dock, shishhmaref, shoreline, shoreline-accretion, shoreline-change, shoreline-erosion, u-s-geological-survey, usgs, usgs-5dfb19ffe4b0b5e61c053995, wales
Organization: Department of the Interior
Data and Regression Models for Suspended Sediment, Total Nitrogen, and Total Phosphorus for School Branch at CR750N at Brownsburg, Indiana, June 23, 2015 to February 6, 2019

The primary data sets consist of continuous water-quality data (temperature, specific conductance, pH, dissolved oxygen, turbidity, nitrate plus nitrite, and streamflow) from in-situ equipment, and discrete water-quality samples (total nitrogen, total phosphorus, suspended sediment concentration, and suspended sediment sieve diameter) collected during site visits at the USGS streamgage School Branch at CR750N at Brownsburg, Indiana, November 17, 2015 to February 6, 2019. The continuous and discrete measurements were used to develop regression models which may be used to compute concentrations and loads of suspended sediment, total nitrogen, and total phosphorus. The secondary data sets consist of daily streamflow, daily turbidity, daily specific conductance, and daily nitrate plus nitrite values collected continuously by in-situ monitors at School Branch at CR750N at Brownsburg, Indiana June 23, 2015 to February 6, 2019 which serve as input explanatory variables for the developed regression models to compute suspended sediment, total nitrogen, and total phosphorus at School Branch at CR750N at Brownsburg. The tertiary data sets for June 23, 2015 to February 6, 2019 are the output data sets that were developed by application of each regression model and include the computed daily mean constituent concentrations (with upper 95-percent prediction interval, and lower 95-percent prediction interval) and daily mean constituent loads (with upper 95-percent prediction interval, and lower 95-percent prediction interval).

Tags: environment, indiana, midwest, nutrient-content, nutrient-pollution, sediment-transport, statistical-modeling, surface-water-quality, usgs-5dfa4cfde4b0cdded73bad40, water-quality
Organization: Department of the Interior
Boundaries and Grid Cells of the Redding-Red Bluff Shallow Aquifer Study Unit, 2018-2019

The U.S. Geological Survey collected groundwater samples from 50 wells used for domestic and small-system drinking water supplies in Shasta and Tehama Counties, California in 2018-2019. The wells were sampled for the Redding-Red Bluff Shallow Aquifer study unit of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project’s assessment of the quality of groundwater resources used for domestic and small-system drinking water supplies. Domestic and small-system wells are commonly screened at shallower depths than public-supply wells. The Redding-Red Bluff Shallow Aquifer study unit consisted of 2 study areas corresponding to 11 groundwater subbasins (subbasins listed clockwise beginning in the north): Enterprise, Millville, South Battle Creek, Bend, Antelope, Dye Creek, Los Molinos, Red Bluff, Bowman, Rosewood, and Anderson (California Department of Water Resources, 2003). Wells in the study unit were located in alluvial aquifers composed of sediments derived from the Coast Ranges to the west and the Cascades to the east of the study unit. Each of the two study areas were divided into equal area grid cells and the goal was to sample one well in each cell. This dataset identifies the boundaries and grid cells for the two study areas of the Redding-Red Bluff Shallow Aquifer Study Unit.
This data release supports the following publication: Groundwater quality in the Redding-Red Bluff Shallow Aquifer Study Unit of the Northern Sacramento Valley, California: U.S. Geological Survey Fact Sheet 2020–3025, 4 p., https://doi.org/10.3133/fs20203025

Tags: california, domestic-wells, gama, groundwater, priority-basin-project, shallow-aquifer, shasta-county, tehama-county, united-states, usgs-5df95d59e4b0cdded73b425c, water-quality, western-united-states
Organization: Department of the Interior
Debris-flow monitoring data, Chalk Cliffs, Colorado, USA, 2014

This data release includes 2014 time-series data from three debris-flow monitoring stations at Chalk Cliffs in Chaffee County, Colorado, USA. The data were collected to help identify the triggering conditions, magnitude, and mobility of debris flows at the site.  The three stations are located sequentially along a channel draining the 0.3 km^2 study area. The Upper, Middle, and Lower stations have respective drainage areas of 0.06, 0.16, and 0.24 km^2. The location (UTM zone 13) of each station is: 396826E/4287851N (Upper), 396893E/ 4287815N (Middle), and 396929E/4287712N (Lower). See also “ChalkStationLocations.jpg” in the README.zip file. 
The 2014 data includes three types of time series: (1) 1-minute time series of rainfall recorded by tipping bucket rain gages at each station, (2) 10-Hz time series of flow stage recorded by laser distance meters at each station, and (3) 333-Hz time series of ground vibrations and basal normal force at the Upper station only. Ground vibrations were recorded by two 4.5 Hz triaxial geophones separated by 18 m along the channel. Basal normal force was recorded by a 232 cm^2 force plate installed in the bedrock channel bed directly beneath the laser distance meter. The 10-Hz stage data is collected only when it is raining due to data storage limitations. Similarly, the 333-Hz ground motion and force data are provided only during significant flow events. These events occurred on 4 July 2014, 31 July 2014, 1 August 2014, 4 August 2014, and 6 August 2014.  The first three events are primarily debris flows and the last two events are debris floods. Note that the rain gage at the Lower station, which is partially shielded by a near-vertical cliff, is used primarily as a trigger for sampling 10-Hz stage data rather than providing an accurate representation of rainfall at the station. 
Details of the sensors and photos of each station are contained in the “README.zip” file.  The file also contains formulas for (1) converting the distance between the laser and the flow surface (or stationary bed surface) to stage above the datum for each station, and (2) converting the raw voltage readings from the geophones and force plate transducer into engineering units of ground velocity and normal force, respectively.  
Additional details of the study are provided in the journal articles:
McCoy, S. W., J. W. Kean, J. A. Coe, G. E. Tucker, D. M. Staley, and T. A. Wasklewicz (2012), Sediment entrainment by debris flows: In situ measurements from the headwaters of a steep catchment, J. Geophys. Res., 117, F03016, doi:10.1029/2011JF002278.
Kean, J. W., J. A. Coe, V. Coviello, J. B. Smith, S. W. McCoy, and M. Arattano (2015), Estimating rates of debris flow entrainment from ground vibrations, Geophys. Res. Lett., 42, doi:10.1002/2015GL064811.

Tags: debris-flow, earth-science, flood, geologic-hazards-science-center, ghsc, landslide-hazards-program, monitoring, usgs, usgs-5e30a148e4b0a79317d62738
Organization: Department of the Interior
Cumulative antecedent precipitation data associated with well water samples collected in eastern Pennsylvania June-November 2017

The dataset includes aggregated precipitation data from a USGS rain gauge for specified 7- or 8-day time periods between May and November 2017. Daily precipitation measurements were summed to produce 7- or 8-day totals over the specified time period.

Tags: precipitation, usgs-5df9545ce4b0cdded73b424d
Organization: Department of the Interior
Demographic data from two chorus frog populations in Colorado

We used 29 years of data on boreal chorus frogs at two sites to view life-history, estimate demographic parameters, assess weather-related covariates, and determine the magnitude of process variation in target parameters. Average estimates of survival probabilities were 0.51 (Standard error [SE]=0.04) and 0.43 (SE=0.04), and average estimates of recruitment probabilities were 0.64 (SE=0.07) and 0.44 (SE=0.04). Process variation accounted for greater than 76% of the total temporal variation in both parameters at one pond and in survival probability alone at the other.

Tags: amphibian-decline, amphibians, biota, chorus-frog, usgs-5df94d4fe4b0cdded73b423c
Organization: Department of the Interior
Groundwater-Quality Data in the Redding-Red Bluff Shallow Aquifer Study Unit, 2018-2019: Tables

The U.S. Geological Survey collected groundwater samples from 50 wells used for domestic and small system drinking water supplies in Shasta and Tehama Counties, California in 2018-2019. The wells were sampled for the Redding-Red Bluff Shallow Aquifer Study Unit of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project’s assessment of the quality of groundwater resources used for domestic and small system drinking water supplies. Domestic and small-system wells are commonly screened at shallower depths than public-supply wells. Wells in the study unit generally tap unconsolidated to moderately consolidated coarse and fine-grained sediments derived from the northern Coast Ranges to the west and the Cascade Range to the east. The Redding-Red Bluff Shallow Aquifer study unit included 11 groundwater subbasins of the Sacramento Valley groundwater basin (California Department of Water Resources, 2003) and was divided into two study areas: Redding (Enterprise, Millville, South Battle Creek, Bowman, Rosewood, and Anderson) and Red Bluff (Bend, Antelope, Dye Creek, Los Molinos, and Red Bluff). In each study area, the collective area of all one-mile square sections estimated to contain domestic wells (Johnson and Belitz, 2015) was divided into equal-area grid cells, and a well was sampled in each cell: Redding, twenty-five 48-square kilometer cells, and Red Bluff, twenty-five 43 square kilometer grid cells. Groundwater samples from all 50 wells were analyzed for field water-quality parameters and microbial indicators, volatile organic compounds, pesticides and pesticide degradates, major ions and trace elements, chromium (VI), perchlorate, nutrients, gross alpha and gross beta particle activities, carbon-14 in dissolved inorganic carbon, stable isotopic ratios of water, tritium, sulfur hexafluoride, and noble gases. Groundwater levels were measured in 41 of the 50 wells. All results are presented in this data release, except results for sulfur hexafluoride and noble gases.
This data release supports the following publication: Groundwater quality in the Redding-Red Bluff Shallow Aquifer Study Unit of the Northern Sacramento Valley, California: U.S. Geological Survey Fact Sheet 2020–3025, 4 p., https://doi.org/10.3133/fs20203025.

Tags: california, environment, gama, geoscientificinformation, groundwater, groundwater-ambient-monitoring-and-assessment-program, shasta-county, tehama-county, united-states, usgs-5df94d47e4b0cdded73b4239, water-quality
Organization: Department of the Interior
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 approach was used because it accommodates a large and diverse set of explanatory variables, does not assume monotonic relations between predictors and response data, and results can be extrapolated to areas of the aquifer not sampled. These aspects of ML allowed potential drivers and sources of high salinity water that have been hypothesized in other studies to be included as explanatory variables. The ML approach integrated output from a groundwater-flow model and water-quality data to predict salinity, and the approach can be applied to other aquifers to provide context for the long-term availability of groundwater resources.
The Mississippi embayment includes two principal regional aquifer systems; the surficial aquifer system, dominated by the Quaternary Mississippi River Valley Alluvial aquifer (MRVA), and the Mississippi embayment aquifer system, which includes deeper Tertiary aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling focused on the MRVA, middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ). Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were developed to predict SC and Cl to 1-kilometer (km) raster grid cells of the National Hydrologic Grid (Clark and others, 2018) for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework of Hart and others (2008). TDS maps were created using the correlation between SC and TDS. Explanatory variables for the BRT models included attributes associated with well location and construction, surficial variables (such as soils and land use), and variables extracted from a MODFLOW groundwater flow model for the Mississippi embayment (Haugh and others, 2020a; Haugh and others, 2020b). Prediction intervals were calculated for SC and Cl by bootstrapping raster-cell predictions following methods from Ransom and others (2017). For a full description of modeling workflow and final model selection see Knierim and others (2020).


Tags: alabama, aquifer, arkansas, boosted-regression-tree, groundwater, groundwater-quality, illinois, kentucky, louisiana, machine-learning, mathematical-modeling, mississippi, tennessee, usgs-5df7fd87e4b0cdded7393fcf
Organization: Department of the Interior
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 approach was used because it accommodates a large and diverse set of explanatory variables, does not assume monotonic relations between predictors and response data, and results can be extrapolated to areas of the aquifer not sampled. These aspects of ML allowed potential drivers and sources of high salinity water that have been hypothesized in other studies to be included as explanatory variables. The ML approach integrated output from a groundwater-flow model and water-quality data to predict salinity, and the approach can be applied to other aquifers to provide context for the long-term availability of groundwater resources.
The Mississippi embayment includes two principal regional aquifer systems; the surficial aquifer system, dominated by the Quaternary Mississippi River Valley Alluvial aquifer (MRVA), and the Mississippi embayment aquifer system, which includes deeper Tertiary aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling focused on the MRVA, middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ). Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were developed to predict SC and Cl to 1-kilometer (km) raster grid cells of the National Hydrologic Grid (Clark and others, 2018) for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework of Hart and others (2008). TDS maps were created using the correlation between SC and TDS. Explanatory variables for the BRT models included attributes associated with well location and construction, surficial variables (such as soils and land use), and variables extracted from a MODFLOW groundwater flow model for the Mississippi embayment (Haugh and others, 2020a; Haugh and others, 2020b). Prediction intervals were calculated for SC and Cl by bootstrapping raster-cell predictions following methods from Ransom and others (2017). For a full description of modeling workflow and final model selection see Knierim and others (2020).


Tags: alabama, aquifer, arkansas, boosted-regression-tree, groundwater, groundwater-quality, illinois, kentucky, louisiana, machine-learning, mathematical-modeling, mississippi, tennessee, usgs-5df7f6c1e4b0cdded7393fb0
Organization: Department of the Interior