This subset of the USGS Water Boundary Dataset contains the polygons of the 50 8-digit Hydrologic Units that comprise the greater Central Valley study site. The Watershed Boundary Dataset is a comprehensive set of digital spatial data that represents the surface drainages areas of the United States. The information included with the features includes a feature date, a unique common identifier, name, the feature length or area, and other characteristics. Names and their identifiers are assigned from the Geographic Names Information System. The data also contains relations that encode metadata. The names and definitions of all these feature attributes are in the Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD). The document is available online at https://pubs.usgs.gov/tm/11/a3/

The Watershed Boundary Dataset (WBD) from USGS' The National Map (TNM) defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area, such as the Sacramento River, will be composed of multiple smaller drainage areas, such as the Feather River. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. The WBD is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds. For additional information on the WBD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset. DWR was the steward for NHD and WBD in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's WBD improvements are sent to USGS for certification and incorporation to the national geodatabase. The certified WBD is included within the National Hydrography Dataset downloadable file geodatabase, and is also available in shapefile format and as web map services accessible from the USGS website. (https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products).

The final static version of the WBD was published in January 2025. No edits to this dataset are being accepted by USGS. Future mapping of drainage areas will be done by USGS as the 3D Hydrography Program (3DHP) is built out. DWR and its stewardship partners are actively producing LiDAR-derived hydrography data for inclusion in the California portion of 3DHP data and eventually its companion set of drainage areas. For now, the WBD is considered the authoritative source for watershed delineations in California.

The Watersheds Hydrologic Unit 12 (HUC12) dataset used in the CA Nature Biodiversity Explorer was taken from the National Watershed Boundary (WBD) Dataset published as part of the National Hydrography Dataset (NHD), developed and maintained by the US Geological Survey. The watersheds are clipped to the California boundary.This version of the WBD for California was downloaded from the NHD prior to 2018 by the California Department of Fish and Wildlife for use in the Areas of Conservation Emphasis (ACE) project, which is a basis of much of the data in the Biodiversity Explorer. Therefore, this HUC12 dataset may not be up to date with the most recent official release from the NHD/WBD.For additional information on the WBD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset.

This layer shows a multi-component analysis for California stream gage priorities at the Hydrologic Unit Code 12 (HUC12) watershed resolution. The Stream Gage Prioritization Analysis is part of the Senate Bill 19 Stream Gaging Plan, which seeks to prioritize areas for additional stream gaging efforts. The dataset includes the gaged proportion of each HUC12 based on a Gage Gap Analysis and information on prioritization results based on five management criteria. The management criteria include scores for ecosystem, water quality, water supply, flood, and reference gages. The priorities are the top scoring 200 watersheds for each management criteria area, for a total of 921 watersheds with overlap between the priorities. The 921 priorities are assigned a primary (highest scoring) benefit. Included is the presence of gages that could be reactivated or upgraded in the watershed. The presence of underrepresented communities, as defined in the Department of Water Resources DAC Mapping Tool, is also noted for each watershed.Data Dictionary: Field Name Range Description

huc12 4469 Hydrologic unit code (HUC) size 12, subbasin, 10-40 thousand acres

huc12_name

Name of the HUC12

huc10 1039 Hydrologic unit code (HUC) size 12, watershed, 40-250 thousand acres

huc10_name

Name of the HUC10

tier 3-Jan Prioritization category number, with 1 being the highest

category

Prioritization category description

best_in_huc10 0 or 1 1 indicates that HUC12 watershed is the top score within its HUC10, which comprises several HUC12s; 0 indicates that it is not the top scoring HUC12.

primary benefit

Primary benefit based on top score for management criteria. If "multi-benefit", it scores in top 100 in multiple categories; if "high combined score", it scores in the top 200 in no categories but the sum of the scores is in the top 200. If the category has a "+" this indicates a multibenefit that has a clear primary benefit in top 100.

total_individual_priority_score 0 - 5 Summary of XX priority scores, with a maximum possible value of 5.

ecosystem priority 1, 0.5, 0 Score = 1 is rank 1-100; Score = 0.5 is rank 101-200 for ecosystem

waterquality priority 1, 0.5, 0 Score = 1 is rank 1-100; Score = 0.5 is rank 101-200 for waterquality

watersupply priority 1, 0.5, 0 Score = 1 is rank 1-100; Score = 0.5 is rank 101-200 for watersupply

flood priority 1, 0.5, 0 Score = 1 is rank 1-100; Score = 0.5 is rank 101-200 for flood

reference priority 1, 0.5, 0 Score = 1 is rank 1-100; Score = 0.5 is rank 101-200 for reference

score_summary priority 1, 0.5, 0 Score = 1 is rank 1-100; Score = 0.5 is rank 101-200 for raw score summary

huc12_proportion_need_gage 0 - 1 Proportion of huc12 watershed that is ungaged, by stream segment length

community_present_in_huc12

Presence and type of underrepresented community in HUC12 watershed

huc12_gg_combined 0 - 4 Sum ecosystem_gg, watersupply_gg, waterquality_gg, and flood_gg

huc12_gg_combined_plus_reference 0 - 5 Same as huc12_gg_combined plus reference_score

huc12_raw_combined

Sum Ecosystem Raw, Water Supply Raw, Water Quality Raw, and Flood Raw

ecosystem_gg 0 – 1 Final ecosystem score = ecosystem_raw x huc12_proportion_need_gage

ecosystem_rank Jan-96 Final ecosystem ranking based on ecosystem_gg, with 1 the highest. Equal scores receive equal highest rank.

ecosystem_raw 0 - 1 Raw management priority score, without gage gap

waterquality_gg 0 – 1 Final waterquality score = waterquality_raw x huc12_proportion_need_gage

waterquality_rank 1 - 1299 Final waterquality ranking based on waterquality_gg, with 1 the highest. Equal scores receive equal highest rank.

waterquality_raw 0 - 1 Raw management priority score, without gage gap

watersupply_gg 0 – 1 Final watersupply score = watersupply_raw x huc12_proportion_need_gage

watersupply_rank Jan-66 Final watersupply ranking based on watersupply_gg, with 1 the highest. Equal scores receive equal highest rank.

watersupply_raw 0 - 1 Raw management priority score, without gage gap

flood_gg 0 – 1 Final flood score = flood_raw x huc12_proportion_need_gage

flood_rank Jan-25 Final flood ranking based on flood_gg , with 1 the highest. Equal scores receive equal highest rank.

flood_raw 0 - 1 Raw management priority score, without gage gap

reference_score 0 – 1 Final reference gage score = (1 – reference_gagepair_score) * reference_impairment_score

reference_rank 1 – 2231 (4469) Final reference ranking based on reference_score, with 1 the highest. Equal scores receive equal highest rank.

reference_gagepair_score 0- 1 Final gage pairing score, which indicates how well each watershed matches to the best fit reference gage. 1 indicates a perfect match.

reference impairment score 0 or 1 Assesses whether or not the watershed is impaired (0) or potentially not impaired (1). Impairment is evaluated based on upstream water storage only.

site_id_upgrade

Recommended Reactivation Gage ID

site_name_upgrade

Recommended Reactivation Gage Name

site_id_reactivate

Recommended Upgrade Gage ID

site_name_reactivate

Recommended Upgrade Gage Name

This data set is a complete digital hydrologic unit boundary layer of the Subbasin (8-digit) 4th level for the entire United States. This data set consists of geo-referenced digital data and associated attributes created in accordance with the "Federal Guidelines, Requirements, and Procedures for the National Watershed Boundary Dataset; Chapter 3 of Section A, Federal Standards, Book 11, Collection and Delineation of Spatial Data; Techniques and Methods 11-A3" (04/01/2009). http://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/water/watershed... . Polygons are attributed with hydrologic unit codes for 4th level sub-basins, 5th level watersheds, 6th level subwatersheds, name, size, downstream hydrologic unit, type of watershed, non-contributing areas and flow modification. The data is currently updated through the USGS National Hydrography Dataset (NHD) Program and replicated to NRCS twice per year.

This data set is a complete digital hydrologic unit boundary layer to the watershed (10-digit) 5th level, clipped for the Lake Winnipeg Watershed. This data set consists of geo-referenced digital data and associated attributes created in accordance with the "FGDC Proposal, Version 1.0 - Federal Standards For Delineation of Hydrologic Unit Boundaries 3/01/02"(http://www.ftw.nrcs.usda.gov/huc_data.html). Polygons are attributed with hydrologic unit codes for 4th level sub-basins, 5th level watersheds, 6th level subwatersheds, name, size, downstream hydrologic unit, type of watershed, non-contributing areas and flow modification. Arcs are attributed with the highest hydrologic unit code for each watershed, linesource and a metadata reference file.

This cartographic quality series of 1:1 000 000 scale colour maps cover the provincial extent of Alberta. The primary provincial base map displays the Alberta Township System (ATS), major hydrographic features, municipalities, major roads, railways and select geoadministrative features (parks, reserves, etc.). In addition to the primary provincial base map, this series includes various themes that overlay the primary base map. The update of this map series is based on the provincial Base Features Access Update Program that has an approximate 5 year update cycle. Each individual map sheet is provided in Adobe .pdf format. The Hydrologic Unit Code (HUC) Watersheds of Alberta represents a collection of five nested hierarchically structured drainage basin feature classes that have been created using the Hydrologic Unit Code system of classification developed by the United States Geological Survey (USGS) with accommodation to reflect the pre-existing Canadian classification system. The HUC Watersheds of Alberta consist of successively smaller hydrologic units that nest within larger hydrologic units, resulting in a hierarchal grouping of alphanumerically-coded watersheds feature classes. There are currently individual feature classes for HUC 2 (coarsest level), HUC 4, HUC 6, HUC 8 and HUC 10 (finest level). This Georeferenced PDF map features HUC 6 and HUC 8.

These datasets accompany a publication in Geophysical Research Letters by Martens et al. (2024), entitled: "GNSS Geodesy Quantifies Water-Storage Gains and Drought Improvements in California Spurred by Atmospheric Rivers." Please refer to the manuscript and supporting information for additional details.Dataset 1: Seasonal Changes in TWS based on the Mean and Median of the Solution SetWe estimate net gains in water storage during the fall and winter of each year (October to March) using the mean TWS solutions from all nine inversion products, subtracting the average storage for October from the average storage for March in the following year. One-sigma standard deviations are computed as the square root of the sum of the variances for October and for March. The variance in each month is computed based on the nine independent estimates of mean monthly storage (see “GNSS Analysis and Inversion” in the Supporting Information).The dataset includes net gains in water storage for both the Sierra Nevada and the SST watersheds (see header lines). For each watershed, results are provided in units of volume (km3) and in units of equivalent water height (mm). Furthermore, for each watershed, we also provide the total storage gains based on non-detrended and linearly detrended time series. In columns four and five, respectively, we provide estimates of snow water equivalent (SWE) from SNODAS (National Operational Hydrologic Remote Sensing Center, 2023) and water-storage changes in surface reservoirs from CDEC (California Data Exchange Center, 2023). In the final column, we provide estimates of net gains in subsurface storage (soil moisture plus groundwater), which are computed by subtracting SWE and reservoir storage from total storage.For each data block, the columns are: (1) time period (October of the starting year to March of the following year); (2) average gain in total water storage constrained by nine inversions of GNSS data; (3) one-sigma standard deviation in the average gain in total water storage; (4) gain in snow water equivalent, computed by subtracting the average snow storage in October from the average snow storage in March of the following year; (5) gain in reservoir storage (CDEC database; within the boundaries of each watershed), computed by subtracting the average reservoir storage in October from the average reservoir storage in March of the following year; and (6) average gain in subsurface water storage, estimated as the average gain in total water storage minus the average gain in snow storage minus the gain in reservoir storage.For the period from October 2022 to March 2023, we also compute mean gains in total water storage using daily estimates of TWS. Here, we subtract the average storage for the first week in October 2022 (1-7 October) from the average storage for the last week in March 2023 (26 March – 1 April). The one-sigma standard deviation is computed as the square root of the sum of the variances for the first week in October and the last week in March. The variance in each week is computed based on the nine independent estimates of daily storage over seven days (63 values per week). The storage gains for 2022-2023 computed using these methods are distinguished in the datafile by an asterisk (2022-2023*; final row in each data section).Dataset 1a provides estimates of storage changes based on the mean and standard deviation of the solution set. Dataset 1b provides estimates of storage changes based on the median and inter-quartile range of the solution set.Dataset 2: Estimated Changes in TWS in the Sierra NevadaChanges in TWS (units of volume: km3) in the Sierra Nevada watersheds. The first column represents the date (YYYY-MM-DD). For monthly solutions, the TWS solutions apply to the month leading up to that date. The remaining nine columns represent each of the nine solutions described in the text. “UM” represents the University of Montana, “SIO” represents the Scripps Institution of Oceanography, and “JPL” represents the Jet Propulsion Laboratory. “NGL” refers to the use of GNSS analysis products from the Nevada Geodetic Laboratory, “CWU” refers to Central Washington University, and “MEaSUREs” refers to the Making Earth System Data Records for Use in Research Environments program. The time series have not been detrended.We highlight that we have added changes in reservoir storage (see Dataset 8) back into the JPL solutions, since reservoir storage had been modeled and removed from the GNSS time series prior to inversion in the JPL workflow (see “Detailed Description of Methods” in the Supporting Information). Thus, the storage values presented here for JPL differ slightly from storage values pulled directly from Dataset 6 and integrated over the area of the Sierra Nevada watersheds.Dataset 3: Estimated Changes in TWS in the Sacramento-San Joaquin-Tulare BasinSame as Dataset 2, except that data apply to the Sacramento-San Joaquin-Tulare (SST) Basin.Dataset 4: Inversion Products (SIO)Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the Scripps Institution of Oceanography (SIO) using the methods described in the Supporting Information.Dataset 5: Inversion Products (UM)Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the University of Montana (UM) using the methods described in the Supporting Information.Dataset 6: Inversion Products (JPL)Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the Jet Propulsion Laboratory (JPL) using the methods described in the Supporting Information.Dataset 7: Lists of Excluded StationsStations are excluded from an inversion for TWS change based on a variety of criteria (detailed in the Supporting Information), including poroelastic behavior, high noise levels, and susceptibility to volcanic deformation. This dataset provides lists of excluded stations from each institution generating inversion products (SIO, UM, JPL).Dataset 8: Lists of Reservoirs and LakesLists of reservoirs and lakes from the California Data Exchange Center (CDEC) (California Data Exchange Center, 2023), which are shown in Figures 1 and 2 of the main manuscript. In the interest of figure clarity, Figure 1 depicts only those reservoirs that exhibited volume changes of at least 0.15 km3 during the first half of WY23.Dataset 8a includes all reservoirs and lakes in California that exhibited volume changes of at least 0.15 km3 between October 2022 and March 2023. The threshold of 0.15 km3 represents a natural break in the distribution of volume changes at all reservoirs and lakes in California over that period (169 reservoirs and lakes in total). Most of the 169 reservoirs and lakes exhibited volume changes near zero km3. Datasets 8b and 8c include subsets of reservoirs and lakes (from Dataset 8a) that fall within the boundaries of the Sierra Nevada and SST watersheds.Furthermore, in the JPL data-processing and inversion workflow (see “Detailed Description of Methods” in the Supporting Information), surface displacements induced by volume changes in select lakes and reservoirs are modeled and removed from GNSS time series prior to inversion. The water-storage changes in the lakes and reservoirs are then added back into the solutions for water storage, derived from the inversion of GNSS data. Dataset 8d includes the list of reservoirs used in the JPL workflow.Dataset 9: Interseismic Strain Accumulation along the Cascadia Subduction ZoneJPL and UM remove interseismic strain accumulation associated with locking of the Cascadia subduction zone using an updated version of the Li et al. model (Li et al., 2018); see Supporting Information Section 2d. The dataset lists the east, north, and up velocity corrections (in the 4th, 5th, and 6th columns of the dataset, respectively) at each station; units are mm/year. The station ID, latitude, and longitude are listed in columns one, two, and three, respectively, of the dataset.Dataset 10: Days Impacted by Atmospheric RiversA list of days impacted by atmospheric rivers within (a) the HUC-2 boundary for California from 1 January 2008 until 1 April 2023 [Dataset 10a] and (b) the Sierra Nevada and SST watersheds from 1 October 2022 until 1 April 2023 [Dataset 10b]. File formats: [decimal year; integrated water-vapor transport (IVT) in kg m-1 s-1; AR category; and calendar date as a two-digit year followed by a three-character month followed by a two-digit day]. The AR category reflects the peak intensity anywhere within the watershed. We use the detection and classification methods of (Ralph et al., 2019; Rutz et al., 2014, 2019). See also Supporting Information Section 2i.Dataset 10c provides a list of days and times when ARs made landfall along the California coast between October 1980 and September 2023, based on the MERRA-2 reanalysis using the methods of (Rutz et al., 2014, 2019). Only coastal grid cells are included. File format: [year, month, day, hour, latitude, longitude, and IVT in kg m-1 s-1]. Values are sorted by time (year, month, day, hour) and then by latitude. See also Supporting Information Section 2g.

This cartographic quality 1:750 000 scale colour map covers the provincial extent of Alberta and also displays the Alberta Township System (ATS), major hydrographic features, municipalities, major roads, railways and select geoadministrative features (parks, reserves, etc.). In addition to the primary provincial base map, this series includes various themes that overlay the primary base map. Each individual map sheet is provided in Adobe .pdf format. This series is generally updated on an annual basis. The Hydrologic Unit Code (HUC) Watersheds of Alberta represents a collection of five nested hierarchically structured drainage basin feature classes that have been created using the Hydrologic Unit Code system of classification developed by the United States Geological Survey (USGS) with accommodation to reflect the pre-existing Canadian classification system. The HUC Watersheds of Alberta consist of successively smaller hydrologic units that nest within larger hydrologic units, resulting in a hierarchal grouping of alphanumerically-coded watersheds feature classes. There are currently individual feature classes for HUC 2 (coarsest level), HUC 4, HUC 6, HUC 8 and HUC 10 (finest level). This Georeferenced PDF map features HUC 8 and HUC 10.

Analysis of ‘SWAP Aquatic Targets - 2015 [ds2733]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/eb4e84af-e527-4f3f-ba01-21921fb728c4 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

General InformationThe SWAP defines "targets" throughout the state and describes "Strategies" for each target. The grouping for these targets is based on "Hydrologic Units" which are defined by "Hydrologic Unit Codes" (HUCs). Each code defines a watershed. 4-digit HUCs describe large watersheds which group the watersheds of many rivers. As the watersheds become more specific, numbers are added to the HUC to define them in more detail, so that an 8-digit hydrologic unit (HUC8) is much smaller than a 4 -digit hydrologic unit (HUC4). The HUCs in this data set are defined in the National Hydrography Dataset (NHD), which describes watersheds and waterbodies throughout the United States.Aquatic Targets in the SWAP are grouped by 4 to 12-digit HUCs into "Conservation Units". These units are described in the first table of each section of Chapter 5 of the SWAP report, which is available at https://www.wildlife.ca.gov/SWAP/Final. Each 4-digit HUC has one or more "Conservation Targets", which are composed of groups of specific targeted fish and/or other aquatic species or, in the Deserts, specific aquatic features. The foundation of the smaller groupings of this data set is the "Zoogeographic Provinces of California", a map in "Inland Fishes of California" by Peter Moyle. This document describes the native fish assemblage areas. The data defining these units was acquired from University of California at Davis Center for Watershed Science. This data set describes the Conservation Units, Targets, and conservation strategies (including Climate Change strategies) and provides the specific SWAP document pages on which to find details about strategies and other information about the targets.

--- Original source retains full ownership of the source dataset ---

An Advanced Protection Management Program (APMP) is a management program that establishes standards for Onsite Wastewater Treatment Systems (OWTS) near impaired waterbodies.

The Action Plan for the Russian River Pathogen TMDL establishes minimum requirements for all OWTS within the designated APMP area for the Russian River Watershed. Owners of existing, new and replacement OWTS whose OTWS are located entirely outside the boundaries of the APMP are not subject to the APMP requirements, but must still comply with relevant requirements of the OWTS Policy and any approved Local Agency Management Program (LAMP), and if applicable, individual/general waste discharge requirements or waiver of waste discharge requirements.

The Action Plan establishes a 600-foot zone of influence for OWTS adjacent to perennial streams, which for the purposes of the Action Plan are blueline streams that are depicted on the USGS 1:100,00 scale topographic map, and a 200-foot zone of influence for lower class streams that are derived using a LIDAR dataset. The 600-foot distance is based on a microbial contamination zone that was recommended by the California Department of Public Heath (CDPH, 1999) to protect water supply from viral, microbial and direct chemical contamination.

The Great Basin is characterized by strong patterns of precipitation along approximate north-south gradients (Miller and others, 2013). Hence, we used a hydrographic boundary layer developed by Mason (1999), to divide the region-wide extent of sage-grouse habitat mapping analysis into North and South regions that align coarsely with respective mesic (wet) and xeric (dry) regions of the state. Flood regions are based largely on patterns of snowmelt, summer thunderstorms or cyclonic rainfall, and the 8-digit Watershed Boundary Dataset (WBD, 2015) was used to select appropriate watersheds within our mapping extent that corresponded to the Mason (1999) boundary. Slight adjustments, made in ArcMap 10.3, included joining region 2 and 3 to comprise the majority of the North region (where a relatively low number of sampled sites precluded keeping regions 2 and 3 separate), and pooling the more xeric Owyhee Desert (located in the center of the northern part of Nevada) within the drier South region. Use of the hydrographic boundary allowed for an accounting of broad-scale variation in habitat availability and selection patterns for sage-grouse (for example, habitat classified as highly suitable in wet areas could be classified as less suitable in drier areas because these habitats are less available). Interim statewide habitat suitability maps were clipped by the hydrographic boundary and relativized according to their respective maximum values for map classification purposes (see Coates and others 2014), the independent set of sage-grouse telemetry points was also split by the hydrographic boundary. For the spring map, 837 points informed the North region while 794 informed the South region. For the summer map, 604 points informed the North and 794 the South. For winter, 326 informed the North and 411 the South. For our composite annual map made from the multiplicative product of the seasonal maps, 1767 points were used for the North and 1999 for the South. References: Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Miller RF, Chambers JC, Pyke DA, Pierson FB, Williams CJ. 2013. A review of fire effects on vegetation and soils in the Great Basin Region: response and ecological site characteristics. Gen. Tech. Rep. RMRS-GTR-308. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. http://www.fs.fed.us/rm/pubs/rmrs_gtr308.html. WBD, 2015. Coordinated effort between the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS), the United States Geological Survey (USGS), and the Environmental Protection Agency (EPA). The Watershed Boundary Dataset (WBD) was created from a variety of sources from each state and aggregated into a standard national layer for use in strategic planning and accountability. Watershed Boundary Dataset for {HUC#8}, Nevada_ST.zip [ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydro/FileGDB101/]. Available URL: http://datagateway.nrcs.usda.gov [Accessed 01/10/2015].

This dataset depicts the general boundaries of the California Coastal Chinook Salmon evolutionarily significant unit (ESU) (i.e., a distinct population segment (DPS) under the U.S. Endangered Species Act) as well as the historical population structure of the species. These boundaries represent the freshwater spawning areas occupied currently and historically. Only the areas coded as Accessible (in attribute "Class") constitute the defined ESU/DPS boundary.This dataset is intended to provide GIS users with data for creating maps that depict the ESU/DPS boundary as well as the historical structure of the population. The data are based on the most recent species description found in agency regulations and published in the Federal Register. The polygons illustrate the location and extent of freshwater spawning areas occupied currently (the ESU/DPS boundary) or historically. However, these data DO NOT constitute a legal boundary or definition for this DPS. These data are based on the most recent species description found in agency regulations at 50 CFR 17.11, 50 CFR 223.102, and 50 CFR 224.101.The original spatial dataset representing this DPS' boundary was created in 1994. This updated dataset incorporates a significant review, revision, and refinement of the boundary using more recent watershed and species' distribution data layers. Specifically, NOAA Fisheries used the 6th field hydrologic unit polygon spatial dataset from the U.S. Department of Agriculture, Natural Resources Conservation Service, National Cartography and Geospatial Center (12-Digit Watershed Boundary Dataset (WBD), 2009, 1:24,000, http://www.ncgc.nrcs.usda.gov/products/datasets/watershed/) to build the freshwater boundaries for this and other salmon and steelhead DPSs listed under the U.S. Endangered Species Act in California, Oregon, Washington, and Idaho. NOAA Fisheries also reviewed barrier information for each DPS including (for California) the Passage Assessment Database (Pacific States Marine Fisheries Commission, September 2010, http://www.calfish.org) NOTE: The boundaries contained in this dataset focus specifically on watersheds known to currently or historically support spawning habitat for each ESU/DPS. Other downstream areas used for rearing and migration to/from the ocean are NOT necessarily included within these boundaries. Also, while the ESU/DPS is known to occur within the general boundary depicted in this dataset, it does not necessarily inhabit all river reaches within the boundary. The dataset was developed based on each species' legal description (found at 50 CFR 17.11, 50 CFR 223.102, and 50 CFR 224.101) as well as the historical population structure defined by NOAA Fisheries Science Center Biological Review Teams (BRTs). Using the 6th field HUCs, watersheds within the spawning range of each DPS were identified and combined into larger polygons to form the overall boundary. HUC polygons were not altered in any manner except as noted below under "HUC CHANGES." All HUCs within the legal boundary descriptions and below identified impassible anthropogenic barriers were deemed accessible and considered part of the DPS boundary. If any part of a HUC was accessible then the entire HUC was coded as accessible. All HUCs upstream of impassible anthropogenic barriers and within the DPS historical structure recognized by the BRT have been identified as anthropogenically blocked. These areas are not contained in the legal definition but their identification contributes to an overall understanding of the species/DPS range.