This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Polygonal mapping of an apparent lot of record to the North and East of the City of Salinas.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

Aerial digital ortho-photography was the foundation imagery for map development. For Abó, the photography was acquired on April May 15, 2002 at a scale of approximately 1:3,000; for Quarai and Gran Quivira it was flown on April 2, 2003 at scales of 1:3,600 and 1:3000, respectively. The 2002-03 digital imagery has a base pixel resolution of 1.0 m. We also made use of statewide 1-meter resolution, true-color imagery from 2005 that became available in 2006 through the New Mexico Resource Geographic Information System. A 10 m spatial resolution USGS Digital Elevation Model (DEM) was used, in conjunction with ground data, to help discriminate between vegetation types based on elevation gradients and terrain. All imagery and other spatial data layers were compiled into a geodatabase and GIS using ArcGIS 9.3 (ESRI 2008).

The Digital Geologic-GIS Map of Salinas Pueblo Missions National Monument and Vicinity, New Mexico is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (sapu_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (sapu_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (sapu_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sapu_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sapu_geology_metadata_faq.pdf). Please read the sapu_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: New Mexico Bureau of Geology and Mineral Resources. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sapu_geology_metadata.txt or sapu_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This dataset is directly derived from Monterey-Salinas Transit GIS data, available at https://mst.org. The geometry, locations, and attribute information of the features in this dataset are all created by and provided by MST, and no direct edits or modifications are made by City of Salinas GIS Staff. The City of Salinas does not maintain any part of this dataset and only provides automated, regular updates of the dataset on a periodic basis. Any questions on any part of this dataset should be directed to Monterey-Salinas Transit (https://mst.org).

This map depicts the locations of survey monuments within and surrounding the City of Salinas, Monterey County, California. Included are National Geodetic Survey (NGS) and city maintained monuments datasets. See NGS website for additional details on NGS monuments. This is not a comprehensive dataset for all survey monuments within the city, additional sources of monuments may be present within the city. City maintained monuments may not be included in the NGS dataset. This service was created by a member of the GIS team in 2017.

This digital dataset contains the land use data used for the Salinas Valley Hydrologic Models (SVHM): the Salinas Valley Watershed Model (SVWM), the Salinas Valley Integrated Hydrologic Model (SVIHM), and Salinas Valley Operational Model (SVOM). Land use data were compiled from available state, local, and federal datasets. Available multi-year composite land use data were integrated with national scale land use and land cover data and supplemented and refined with information from the California Pesticide Use Reporting (CalPUR) database (California Department of Pesticide Regulation, 2024) to provide a comprehensive edge-to-edge land use map for each year. Native vegetation was defined using the National Land Cover Database (NLCD) (U.S. Geological Survey, 2000; U.S. Geological Survey, 2003; U.S. Geological Survey, 2011; U.S. Geological Survey, 2014; Dewitz, 2021) and intersected in a GIS with other available land use data. If available land use data for an irrigated crop was pres ...

Created on 20231018 by overlaying the Althouse and Meade Salinas River Action Area over the RA Branch polylines. The Althouse and Meade polygon did not extend south beyond Charolais Rd so a best guess was implemented as to the boundary in this location based on the 2021 aerial photography and the 2018 lidar as to the riverbed location in this area.

CDFW BIOS GIS Dataset, Contact: Jason Casanova, Description: Invasive Plant Survey Within California Coastal Watersheds from Salinas to Tijuana (currently in DRAFT form). Funded through Prop 50, this data is intended to represent a comprehensive collection of digitally available invasive plant surveys for giant reed, pampas grass, jubata grass, Mexican fan palm, and Canary Island date palm within California coastal watersheds from Salinas to Tijuana. For any given region, there can be additional high-priority species inventoried.

description: This Sea-Level Affecting Marshes Model (SLAMM) report presents a model for projecting the effects of sea-level rise on coastal marshes and related habitats on Salinas River NWR. The model is spatially explicit, using GIS technology to produce maps and tables that summarize the projected effects. The SLAMM simulations include five primary processes that affect wetland fate under different scenarios of sea-level rise including: inundation, erosion, overwash, saturation, and accretion.; abstract: This Sea-Level Affecting Marshes Model (SLAMM) report presents a model for projecting the effects of sea-level rise on coastal marshes and related habitats on Salinas River NWR. The model is spatially explicit, using GIS technology to produce maps and tables that summarize the projected effects. The SLAMM simulations include five primary processes that affect wetland fate under different scenarios of sea-level rise including: inundation, erosion, overwash, saturation, and accretion.

These data provide an accurate high-resolution shoreline compiled from imagery of PUNTA SALINAS, PUERTO RICO . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Att...

These data were automated to provide an accurate high-resolution historical shoreline of Tunitas Creek to Vicinity South of Salinas River, CA suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery an...

This map depicts the location of all fire stations within the City of Salinas, Monterey County, California. This service was created by the GIS team in 2017.

Product: This orthoimagery data set includes 3-inch 8-bit 4-band (RGBN) digital orthoimage mosaics in MrSID format (20:1 compression). Geographic Extent: The AOI covers approximately 28 total square miles of the City of Salinas in the State of California. Dataset Description: City of Salinas, CA 2018 Orthoimagery project called for the planning, acquisition, processing, and derivative products of imagery data to be collected at a ground sample distance (GSD) of 0.25 foot. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Ortho Specification, Version 1.0. The data were developed based on a horizontal projection/datum of NAD83 (2011) California State Plane Coordinate System, Zone 4, US Survey Feet. Orthoimagery data were delivered as stereo imagery pairs in GeoTIFF format, 131 individual 0.25-foot 2,640 feet x 2,640 feet 8-bit, 4-band (RGBN) GeoTIFF tiles, and a 0.25-foot 8-bit, 4-band (RGBN) 20:1 compressed MrSID mosaic. Ground Conditions: Aerial photography was captured during the summer of 2018, while no snow was on the ground and rivers were at or below normal levels. In order to post process the imagery data to meet task order specifications and meet horizontal accuracy guidelines, Quantum Spatial, Inc. utilized a total of 16 surveyed control points throughout the project area to assess the horizontal accuracy of the data. These checkpoints were not used to calibrate or post process the data.

This map depicts the treatments from the Neighborhood Traffic Management Program (NTMP) within the City of Salinas, Monterey County, California, This includes traffic calming techniques to modify traffic flow and speed throughout the city. See the City of Salinas Neighborhood Traffic Management Program Report and Traffic and Engineering website for additional details on these projects. This service was created by a member of the GIS team in 2017.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Imagery was collected for and provided to the City of Salinas.

This service consumes Monterey-Salinas Transit General Transit Feed Specification at: https://www.mst.org/google/google_transit.zip. The service is queried daily.

Salinas River Overlay District as described in the City of El Paso de Robles General Plan

The 1997 Monterey County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). This survey is unique in that three site visits were completed in the area. Each of the three site visits was digitized as a separate survey, the Salinas Valley in the spring, the entire county in the summer, and the Salinas Valley in the fall. The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s San Joaquin District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and San Joaquin District. The finalized data includes one countywide shapefile, two Salinas Valley shapefiles (land use vector data) and JPEG files (raster data from aerial imagery). Important Points about Using this Data Set: 1. The land use boundaries were either drawn on-screen using developed photoquads, or hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. For the Salinas Valley portion of Monterey County, the survey was not a "snapshot" in time, but incorporated three field visits for agricultural areas. The land use attributes of each delineated area (polygon) were based upon the surveyor’s observations in the field at those times. For the DWG and shapefiles, the attributes in the files are the observations, not the interpreted results. 3. For the area of Monterey County outside of the Salinas Valley, the survey was a "snapshot" in time (summer). The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Water source and irrigation method information were not collected for this survey. 5. Not all land use codes will be represented in the survey.