Welcome to the Department of Ecology Well Logs. A Well Log means a Well Report and describes the location, ownership, construction details and lithology of a completed well. This web site enables you to search for wells which have well reports and to view the well report using a variety of search tools.

Point geodatabase with a record for each well report in Ecology's. Points are located by quarter quarter section centroid. Points contain all well report types including water wells, resource protection wells, and decommissioned wells.

[This dataset will be retired after October 18, 2025. Contact opendata@wa.gov with questions.] This dataset is part of the Washington Geological Survey's (WGS) delivery to the National Groundwater Monitoring Network (NGWMN). It contains water level data for wells submitted the NGWMN.

These data describe the wells compiled for the Columbia Plateau Regional Aquifer Study (CPRAS). The data included are well ids used in the study, the X and Y coordinates of each well, in feet, in Washington State Plane South NAD 1983 coordinate system (zone 4602), land-surface elevation, in feet, of each well in North American Vertical Datum of 1988 (NAVD 88), the date each well was drilled, well depth, in feet, and quality flags for well location and land-surface elevation.

Well Record at Illinois State Geological Survey Type: Water Well Depth:

The Washington State Department of Ecology (Ecology) Water Quality Program undertook the Nitrate Prioritization Project in 2014 (Morgan, 2014) because of growing concerns about groundwater contamination by nitrates, and the inability to display and evaluate nitrate data on a statewide basis. This report originated from the agriculture and water quality talks that took place in 2012. Participating agencies agreed that if data exists, everyone should be able to see it in one central location. Agencies that contributed included the Washington Dept. of Ecology, Washington Dept. of Health, Washington Dept. of Agriculture, U.S. Environmental Protection Agency, U.S. Natural Resource Conservation Service, U.S. Geological Survey, and the Washington Conservation Commission. The Safe Drinking Water Act nitrate limit for delivery of water from public water systems is 10 mg/L. This limit has been exceeded in public water supplies and private wells in various areas of the state going back decades. Not only is contaminated groundwater a public health issue, treatment is also very costly to the public water supply systems and individual households who must deal with contamination on their own. The goals of this project were to: Collect and organize statewide information about nitrate monitoring results, the physical factors that tend toward nitrate contamination, and United States Geological Survey (USGS) risk studies that evaluate the physical factors against monitoring results. Delineate areas where high nitrates in groundwater occur. Prioritize those areas by potential impacts to people and resources. Make the information available to everyone. The inputs for developing candidate Nitrate Priority Areas include: A single database of nitrate sampling results for groundwater compiled from state and federal databases. USGS nitrate risk studies. Surficial geology, soil properties, topography, well locations and depths, agricultural land use, irrigated areas, annual average precipitation, nitrate concentrations, and population. Monitoring data from the USGS and the Washington State Departments of Health and Ecology were collected and summarized. The well locations were mapped using a Geographic Information System (GIS). Clusters of wells where a sample has exceeded 10 mg/L are a strong indicator that groundwater at that location is at high risk of, or currently is contaminated by nitrate. Other indicators include USGS nitrate risk analyses, Natural Resources Conservation Service (NRCS) soil drainage classes and travel time through the soil profile (Ksat), surficial geology, recharge and well depths.Boundaries for candidate Nitrate Priority Areas were developed based on section lines that approximate natural boundaries. These areas will be subject to review and change where appropriate. Once the proposed Nitrate Priority Areas have been reviewed, section line-based boundaries may be replaced by natural boundaries where appropriate. Time series plots were produced for wells with four or more sample results with at least one result over 5 mg/L. This resulted in a distribution of over 1200 graphs across the state. These are accessible through the GIS as a popup from the well location point for those who have a GIS system with this capability, and who request and receive the necessary files. A web-based application would make these graphs widely and easily available. Challenges with databases always include checking for errors, such as the occasional locational or data entry error. Care must be used to understand the limitations of the data and the peculiarities of each data source. These issues are described more in this report. Recommendations include developing a web application to make this information easily accessible by anyone with internet access, and automating the data downloads so they are easily updated. Management of nitrate sources to prevent groundwater contamination should be adjusted for sensitive conditions like excessively draining soils and very hydrologically conductive geologic materials. Nitrate source loading needs to be reduced in impacted areas to prevent groundwater contamination. Results of this study can be used to protect public drinking water supplies by focusing actions on areas within the state that have the highest potential for impacts due to nitrate contamination of groundwater.

PWS SRC's whose well depth is less than 50ft.

PWS SRCs whose source capacity is less than 20 gal/min and well depth is less than 50ft.

The Environmental Information Management System (EIM) is the Department of Ecology's main database for environmental monitoring data. EIM contains records on physical, chemical, and biological analyses and measurements. Supplementary information about the data (metadata) is also stored, including information about environmental studies, monitoring locations, and data quality. Data in EIM is collected by Ecology or on behalf of Ecology by environmental contractors - and by Ecology grant recipients, local governments, and volunteers. EIM Locations is a point feature service representing the monitoring locations from EIM. The locations consist of both surface locations for monitoring air, water, and habitat and wells for monitoring ground water. This feature service queries directly the EIM publication database which is updated nightly from the production transactional database.

This map shows the USGS (United States Geologic Survey), NWIS (National Water Inventory System) Hydrologic Data Sites for Washington County, Utah.

The scope and purpose of NWIS is defined on the web site:

http://water.usgs.gov/public/pubs/FS/FS-027-98/

The U.S. Geological Survey (USGS), in cooperation with the Washington State Department of Ecology (Ecology), conducted a study to describe the current understanding of the regional groundwater system of the lower Duwamish River valley and groundwater and surface-water interactions in the lower Duwamish Waterway. A nearshore site along the western shoreline of the Duwamish River, about 1.5 mi upstream from the river mouth, was selected for focused groundwater data collection by USGS. Data loggers were deployed in seven groundwater wells and one stilling well in the Duwamish River to measure specific conductance, temperature, and depth at 15-minute intervals for a period of about 2 years.This data release contains data supporting the larger work: (1) groundwater-level data used to generate a regional potentiometric-surface map, and (2) bathymetry data collected for a 3-acre embayment adjacent to the primary site of groundwater data collection for the study.

Well Record at Illinois State Geological Survey Type: Dry Hole (water well) Depth: 326

The Northwest Environmental Moorings program maintains a number of buoys thoughout the Puget Sound region, taking regular profiling casts with a winched CTD attached to a fixed mooring. These profiles measure water pressure, water temperature, and conductivity. Depending on the mooring configuration, additional instruments are added to take further measurements, which can include dissolved oxygen, nitrate concentrations, fluoroescence (a proxy measurement for chlorophyll concentrations), and turbidity.

This data has converted raw profiles into a gridded data product. Data from the downcast (i.e., from surface to maximum depth) is extracted, and used to grid each variable$

This dataset has historical data, and adds new data shortly after any additional casts have been performed. Data quality delay is usually less than 6 hrs. _NCProperties=version=2,netcdf=4.9.2,hdf5=1.14.6 author=Seth Travis buoy_designation=NPBY1 buoy_latitude=47.76116666 degrees N buoy_longitude=-122.39716666 degrees E buoy_name=Pt. Wells buoy_reference_number=5 cdm_data_type=TimeSeriesProfile cdm_profile_variables=cast_num cdm_timeseries_variables=buoy_name, longitude, latitude citation=University of Washington NWEM Mooring Data, 2005-2025; https://nwem.apl.washington.edu/, enabled by the Northwest Association of Networked Ocean Observing Systems (NANOOS, https://www.nanoos.org, https://ror.org/01a258x16) and the Washington State Ocean Acidification Center (WOAC, https://oceanacidification.uw.edu/) contact=setht1@uw.edu contributor_email=setht1@uw.edu, mickett@uw.edu contributor_name=Seth Travis, John Mickett contributor_role=principalInvestigator, principalInvestigator contributor_role_vocabulary=https://vocab.nerc.ac.uk/collection/G04/current/ contributor_url=https://nwem.apl.washington.edu/, https//www.nanoos.org/, https://nwem.apl.washington.edu/ conventions=CF-1.6, ACDD-1.3, IOOS-1.2 create_state=WA defaultDataQuery=sea_water_temperature[last-30:last][0:last],sea_water_practical_salinity[last-30:last][0:last],sea_water_sigma_theta[last-30:last][0:last],mass_concentration_of_oxygen_in_sea_water[last-30:last][0:last] defaultGraphQuery=sea_water_temperature[last-30:last][0:last]&.draw=surface&.vars=cast_start_time|sea_water_pressure|sea_water_temperature&.yRange=0|100|false description=ORCA Buoy Cast Profile at Pt. Wells featureType=TimeSeriesProfile geospation_vertical_positive=down gts_ingest=false history=2025-Nov-23 07:15:06: Generate gridded data 2025-Nov-23 06:15:07: Generate gridded data 2025-Nov-23 05:15:06: Generate gridded data 2025-Nov-23 04:15:07: Generate gridded data 2025-Nov-23 03:15:07: Generate gridded data 2025-Nov-23 02:15:07: Generate gridded data 2025-Nov-23 01:15:07: Generate gridded data 2025-Nov-23 00:15:07: Generate gridded data 2025-Nov-22 23:15:06: Generate gridded data 2025-Nov-22 22:15:06: Generate gridded data 2025-Nov-22 21:15:07: Generate gridded data 2025-Nov-22 20:15:07: Generate gridded data 2025-Nov-22 19:15:06: Generate gridded data 2025-Nov-22 18:15:07: Generate gridded data 2025-Nov-22 17:15:07: Generate gridded data 2025-Nov-22 16:15:07: Generate gridded data 2025-Nov-22 15:15:06: Generate gridded data 2025-Nov-22 14:15:07: Generate gridded data 2025-Nov-22 13:15:07: Generate gridded data 2025-Nov-22 12:15:06: Generate gridded data 2025-Nov-22 11:15:07: Generate gridded data 2025-Nov-22 10:15:07: Generate gridded data 2025-Nov-22 09:15:07: Generate gridded data 2025-Nov-22 08:15:06: Generate gridded data 2025-Nov-22 07:15:07: Generate gridded data 2025-Nov-22 06:15:07: Generate gridded data 2025-Nov-22 05:15:07: Generate gridded data 2025-Nov-22 04:15:07: Generate gridded data 2025-Nov-22 03:15:06: Generate gridded data 2025-Nov-22 02:15:07: Generate gridded data 2025-Nov-22 01:15:07: Generate gridded data 2025-Nov-22 00:15:07: Generate gridded data 2025-Nov-21 23:15:06: Generate gridded data 2025-Nov-21 22:15:07: Generate gridded data 2025-Nov-21 21:15:08: Generate gridded data 2025-Nov-21 20:15:07: Generate gridded data 2025-Nov-21 19:15:07: Generate gridded data 2025-Nov-21 18:15:07: Generate gridded data 2025-Nov-21 17:15:06: Generate gridded data 2025-Nov-21 16:15:06: Generate gridded data 2025-Nov-21 15:15:07: Generate gridded data 2025-Nov-21 14:15:06: Generate gridded data 2025-Nov-21 13:15:07: Generate gridded data 2025-Nov-21 12:15:06: Generate gridded data 2025-Nov-21 11:15:07: Generate gridded data 2025-Nov-21 10:15:06: Generate gridded data 2025-Nov-21 09:15:09: Generate gridded data 2025-Nov-21 08:15:06: Generate gridded data 2025-Nov-21 07:15:06: Generate gridded data 2025-Nov-21 06:15:09: Generate gridded data 2025-Nov-21 05:15:07: Generate gridded data 2025-Nov-21 04:15:07: Generate gridded data 2025-Nov-21 03:15:07: Generate gridded data 2025-Nov-21 02:15:06: Generate gridded data 2025-Nov-21 01:15:09: Generate gridded data 2025-Nov-21 00:15:07: Generate gridded data 2025-Nov-20 23:15:06: Generate gridded data 2025-Nov-20 22:15:06: Generate gridded data 2025-Nov-20 21:15:08: Generate gridded data 2025-Nov-20 20:15:06: Generate gridded data 2025-Nov-20 19:15:06: Generate gridded data 2025-Nov-20 18:15:07: Generate gridded data 2025-Nov-20 17:15:08: Generate gridded data 2025-Nov-20 16:15:06: Generate gridded data 2025-Nov-20 15:15:06: Generate gridded data 2025-Nov-20 14:15:07: Generate gridded data 2025-Nov-20 13:15:06: Generate gridded data 2025-Nov-20 12:15:06: Generate gridded data 2025-Nov-20 11:15:06: Generate gridded data 2025-Nov-20 10:15:06: Generate gridded data 2025-Nov-20 09:15:08: Generate gridded data 2025-Nov-20 08:15:06: Generate gridded data 2025-Nov-20 07:15:06: Generate gridded data 2025-Nov-20 06:15:07: Generate gridded data 2025-Nov-20 05:15:09: Generate gridded data 2025-Nov-20 04:15:06: Generate gridded data 2025-Nov-20 03:15:06: Generate gridded data 2025-Nov-20 02:15:06: Generate gridded data 2025-Nov-20 01:15:07: Generate gridded data 2025-Nov-20 00:15:06: Generate gridded data 2025-Nov-19 23:15:07: Generate gridded data 2025-Nov-19 22:15:06: Generate gridded data 2025-Nov-19 21:15:08: Generate gridded data 2025-Nov-19 20:15:06: Generate gridded data 2025-Nov-19 19:15:05: Generate gridded data 2025-Nov-19 18:15:06: Generate gridded data 2025-Nov-19 17:15:07: Generate gridded data 2025-Nov-19 16:15:06: Generate gridded data 2025-Nov-19 15:15:07: Generate gridded data 2025-Nov-19 14:15:06: Generate gridded data 2025-Nov-19 13:37:09: Generate gridded data 2025-Nov-19 13:35:26: Generate gridded data 2025-Nov-19 13:33:56: Generate gridded data 2025-Nov-19 13:31:02: Generate gridded data 2025-Nov-19 13:26:48: Generate gridded data infoUrl=??? institution=Northwest Environmental Moorings Group at University of Washington - Applied Physical Laboratory keywords_vocabulary=GCMD Science Keywords modification_date=2025-Nov-23 07:15:06 reference=UNESCO 1983 - Algorithms for computation of fundamental properties of seawater; https://unesdoc.unesco.org/ark:/48223/pf0000059832_eng ror=https://ror.org/01a258x16 sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v70 testOutOfDate=now-1day water_depth=100 meters wmo_platform_code=46120

Pending Water Right Applications in Washington State. Includes both applications for new water rights and to change existing water rights. Updated weekly. Live data available at: https://fortress.wa.gov/ecy/waterresources/map/QuantityReport.aspx and https://fortress.wa.gov/ecy/waterresources/map/WaterResourcesExplorer.aspx.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two-digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use(s) from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to include within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state. For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. This object holds the the data that is exported to created the xxGroundwaterHarmonizedRights.csv files. rightsByState_surface: A list of data frames with the cleaned surface water rights collected from each state. This object holds the the data that is exported to created the xxSurfaceWaterHarmonizedRights.csv files. fullRightsRecs: A list of the combined groundwater and surface water records for each state. This object holds the the data that is exported to created the xxFullHarmonizedRights.csv files. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page. The code related to the creation of this dataset can be viewed within HarDWR GitHub Repository/dataHarmonization.

This resource is a compilation of borehole lithology observation data from boreholes in Washington state, provided by the Washington Division of Geology and Earth Resources. The data are available in the following formats: web feature service, web map service, ESRI service endpoint, and an Excel workbook for download. The workbook contains 7 worksheets, including information about the template, notes related to revisions of the template and resource provider information. Each feature includes a well name, well type, status, and lithology at some intercept. This resource was provided by the Washington Division of Geology and Earth Resources and made available for distribution through the National Geothermal Data System.

Long-term datasets can reveal otherwise undetectable ecological trends, illuminating the historical context of contemporary ecosystem states. We used two decades (1997–2019) of scientific trawling data from a subtidal, benthic site in Puget Sound, Washington, USA to test for gradual trends and sudden shifts in total sea star abundance across 11 species. We specifically assessed whether this community responded to the sea star wasting disease (SSWD) epizootic, which began in 2013. We sampled at depths of 10, 25, 50 and 70 m near Port Madison, WA, and obtained long-term water temperature data. To account for species-level differences in SSWD susceptibility, we divided our sea star abundance data into two categories, depending on the extent to which the species is susceptible to SSWD, then conducted parallel analyses for high-susceptibility and moderate-susceptibility species. The abundance of high-susceptibility sea stars declined in 2014 across depths. In contrast, the abundance of moderate-susceptibility species trended downward throughout the years at the deepest depths – 50 and 70 m – and suddenly declined in 2006 across depths. Water temperature was positively correlated with the abundance of moderate-susceptibility species, and uncorrelated with high-susceptibility sea star abundance. The reported emergence of SSWD in Washington State in the summer of 2014 provides a plausible explanation for the subsequent decline in abundance of high-susceptibility species. However, no long-term stressors or mortality events affecting sea stars were reported in Washington State prior to these years, leaving the declines we observed in moderate-susceptibility species preceding the 2013–2015 SSWD epizootic unexplained. These results suggest that the subtidal sea star community in Port Madison is dynamic, and emphasizes the value of long-term datasets for evaluating patterns of change. Methods In each year from 1997 to 2019 (except for 1998), we chartered a vessel equipped with a Southern California Coastal Waters Research Program (SCCWRP) otter trawl net for an annual research cruise near Port Madison, a bay on the west side of Puget Sound, WA. The vessel, which ran each year for two days in mid-May, towed the SCCWRP net along the bottom in set trawling locations corresponding to four depths: 10, 25, 50 and 70 m. Each year, five trawls took place at each depth, corresponding to discrete time periods: early morning (~6:00–8:00), morning (~10:00–12:00), afternoon (~15:00–17:00), evening (~20:00–22:00), and night (~1:00–3:00). Each trawl sampled benthic habitat for ~5 min over 370 m. Following each trawl, all sea stars caught were identified to species, counted, recorded, and released. This data was ultimately used to test for gradual trends and sudden shifts in total sea star abundance during the study period (1997–2019). We also obtained data on water temperature collected from 1999–2017 by the Washington State Department of Ecology at a site ~9 km southeast of our trawling sites (47.66001°, –122.4417°) to assess the effect of temperature on sea star catch over time. See below for a description of each data file pertaining to the analyses conducted by Casendino et al. (2023). List of data files included:

invertebrates_dataset.csv invertebrates_dataset_1997.csv trawling_dataset.csv trawling_dataset_1999.csv WADeptEcology_CTD.csv

invertebrates_dataset.csv – documents invertebrates caught in sampling bottom trawls in Puget Sound, WA, from 1999 to 2019. Includes trawl information such as year, date, time, and depth. Categorizes invertebrates by informal group (e.g., crab, sea star, or shrimp), common name (e.g., vermillion star, sand star, or spiny red star), genus species (e.g., Pycnopodia helianthoides) and the number caught during a particular trawl. invertebrates_dataset_1997.csv – documents invertebrates caught in sampling bottom trawls in Puget Sound, WA, in 1997. The row labelled “TRAWL” includes labels to indicate trawl time, where numbers 1, 2, 3, 4, and 5 correspond to afternoon, evening, night, early morning, and morning trawls. Depth is also listed. Sea stars collected during trawls are listed under the “echinoderms” heading by scientific name, and corresponding columns show how many individuals were caught during particular trawls. trawling_dataset.csv – includes trawling information from 2000 to 2019. The column labelled “Station Name” includes basic information such as the type of trawl employed and depth sampled, and “Date” includes the day, month and year of each trawl. The exact time and depth of the start and end of each trawl tow are also listed, as well as the Mean Lower Low Water depth. Coordinates of the start and end of trawl tows are listed in degrees decimal minutes form, as well as the distance, speed and direction of each trawl. trawling_dataset_1999.csv – reports trawling data from 1989 to 2000, including the same information presented in Dataset_trawlmaster.csv. From 1995 onwards, spatial coordinates are listed in degrees decimal minutes form. WADeptEcology_CTD.csv – reports environmental data (including temperature data) collected by the Washington State Department of Ecology from 1999 to 2017 at a site in Puget Sound ~9 km southeast of our trawling sites.

The Washington State Department of Ecology has defined a facility/site as an operation at a fixed location that is of interest to the agency because it has an active or potential impact upon the environment. Ecology recognizes that this definition is broad and generic; but the agency has found that such a definition is required in order to encompass all the facilities and sites in Washington that are within the purview of its programs. These programs cover a wide variety of environmental aspects and conditions including air quality, water quality, shorelands, water resources, toxics cleanup, hazardous waste, toxics reduction, and nuclear waste. The definitions of a facility and/or a site vary significantly across these programs, both in practice and law. Examples of facilities/sites include: operation that pollutes the air or water, spill cleanup site, hazardous waste management facility, hazardous waste generator, licensed laboratory, SUPERFUND site, farm which draws water from a well, solid waste recycling center, etc.

The semi-arid Walla Walla River Basin (WWRB) spans 1,777 square miles in Washington and Oregon and supports a diverse agricultural region as well as cities and rural communities that are partially reliant on groundwater. Historically, surface-water and groundwater data have been collected in the WWRB by federal, state, local, and tribal governments, irrigation districts, universities, and non-profit entities. This data release presents the surface-water and groundwater data collection by the U.S. Geological Survey (USGS) from February 2018 to April 2022. Data were collected and compiled for 237 sites — 191 wells and 46 surface-water discharge sites.