Download .zipMaps and data associated with oil-and-gas wells represent one of the largest datasets at the Ohio Department of Natural Resources. This GIS data layer contains all the locatable oil-and-gas wells in Ohio. The feature is derived from coordinates obtained from the Division of Oil and Gas Resources Management (DOGRM) oil and gas well database – Risk Based Data Management System (RBDMS). The RBDMS database has a long history and is a comprehensive collection of well data from historic pre-1980 paper well records (digitized by the Division of Geological Survey (DGS)) to post-1980 DOGRM database solutions.Since 1860, it is estimated that more than 267,000 oil-and-gas wells have been drilled in Ohio. The compressed file also includes a feature used to connect the surface location to the bottom location of a well that has been drilled directionally or horizontally. This feature is NOT the actual wellbore path, it is simply a graphical representation indicating the relationship between the two well points.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Oil & Gas ResourcesOil and Gas Resources Management2045 Morse Road Bldg F-2Columbus, OH, 43229-6693Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Napa County has used a 2004 edition vegetation map produced using the Manual of California Vegetation classification system (Thorne et al. 2004) as one of the input layers for land use decision and policy. The county decided to update the map because of its utility. A University of California, Davis (UCD) group was engaged to produce the map. The earlier map used black and white digital orthophoto quadrangles from 1993, with a pixel resolution of 3 meters. This image was delineated using a heads up digitization technique produced by ASI (Aerial Services Incorporated). The resulting polygons were the provided vegetation and landcover attributes following the classification system used by California State Department of Fish and Wildlife mappers in the Manual of California Vegetation. That effort included a brief field campaign in which surveyors drove accessible roads and verified or corrected the dominant vegetation of polygons adjacent to roadways or visible using binoculars. There were no field relevé or rapid assessment plots conducted. This update version uses a 2016 edition of 1 meter color aerial imagery taken by the National Agriculture Imagery Program (NAIP) as the base imagery. In consultation with the county we decided to use similar methods to the previous mapping effort, in order to preserve the capacity to assess change in the county over time. This meant forgoing recent data and innovations in remote sensing such as the use LiDAR and Ecognition’s segmentation of imagery to delineate stands, which have been recently used in a concurrent project mapping of Sonoma County. The use of such technologies would have made it more difficult to track changes in landcover, because differences between publication dates would not be definitively attributable to either actual land cover change or to change in methodology. The overall cost of updating the map in the way was approximately 20% of the cost of the Sonoma vegetation mapping program.Therefore, we started with the original map, and on-screen inspections of the 2004 polygons to determine if change had occurred. If so, the boundaries and attributes were modified in this new edition of the map. We also used the time series of imagery available on Google Earth, to further inspect many edited polygons. While funding was not available to do field assessments, we incorporated field expertise and other map data from four projects that overlap with parts of Napa Count: the Angwin Experimental Forest; a 2014 vegetation map of the Knoxville area; agricultural rock piles were identified by Amber Manfree; and parts of a Sonoma Vegetation Map that used 2013 imagery.The Angwin Experimental Forest was mapped by Peter Lecourt from Pacific Union College. He identified several polygons of redwoods in what are potentially the eastern-most extent of that species. We reviewed those polygons with him and incorporated some of the data from his area into this map.The 2014 Knoxville Vegetation map was developed by California Department of Fish and Wildlife. It was made public in February of 2019, close to the end of this project. We reviewed the map, which covers part of the northeast portion of Napa County. We incorporated polygons and vegetation types for 18 vegetation types including the rare ones, we reviewed and incorporated some data for another 6 types, and we noted in comments the presence of another 5 types. There is a separate report specifically addressing the incorporation of this map to our map.Dr Amber Manfree has been conducting research on fire return intervals for parts of Napa County. In her research she identified that large piles of rocks are created when vineyards are put in. These are mapable features. She shared the locations of rock piles she identified, which we incorporated into the map. The Sonoma Vegetation Map mapped some distance into the western side of Napa County. We reviewed that map’s polygons for coast redwood. We then examined our imagery and the Google imagery to see if we could discern the whorled pattern of tree branches. Where we could, we amended or expanded redwood polygons in our map.The Vegetation classification systems used here follows California’s Manual of California Vegetation and the National Vegetation Classification System (MCV and NVCS). We started with the vegetation types listed in the 2004 map. We predominantly use the same set of species names, with modifications/additions particularly from the Knoxville map. The NVCS uses Alliance and Association as the two most taxonomically detailed levels. This map uses those levels. It also refers to vegetation types that have not been sampled in the field and that has 3-6 species and a site descriptor as Groups, which is the next more general level in the NVCS classification. We conducted 3 rounds of quality assessment/quality control exercises.

The Ord Valley Airborne Electromagnetics (AEM) Interpretation Project was undertaken to provide information in relation to groundwater salinity management in the Ord River Irrigation Area (ORIA), and to assess the salinity hazard in areas of potential irrigation expansion. Salinity hazard maps were produced using an informed GIS-based approach. The salinity hazard maps combined AEM-derived maps of the shallow alluvial sediments, salt stored in the unsaturated zone and maps of groundwater salinity, with drilling data and maps of depth to the watertable.

Hydrographic analysis showed that under current climate conditions, water tables were rising, and it was therefore assumed for GIS modeling purposes that water levels would continue to rise after land clearing and the onset of irrigation. It was also assumed that if shallow watertables developed at some time in the future, that salt accumulation through capillary rise (if within 2m of the surface) may lead to salinisation. This assumption was informed by prior geochemical modeling that inferred that if relatively modest groundwater salinity levels (>750 mg/l TDS) were evapo-concentrated that it may cause a significant salinity hazard to irrigated agriculture. Salinity hazard was assessed as high where there were significant quantities of salt stored in the alluvium in areas of shallow groundwater, and lowest where there is little or no salt stored in alluvium and groundwater tables are deep. The salinity hazard was forecast to be high to very high in the Mantinea Plain, Carlton Hill, Parry's Lagoon and lower Ord Floodplain areas. In the Knox Creek and Keep River Plains, the hazard was low in the north of the area, but moderate to high in the southern-central and areas of the southern Knox Creek Plain. In the priority development area (Weaber Plain), the salinity hazard was estimated to be highly variable.

The GIS database for the Broken Hill Managed Aquifer Recharge (BHMAR) project contains the spatial data associated with the project including interpreted products derived to identify groundwater resource and managed aquifer recharge targets in the Darling River floodplain study area. Themes include airborne electromagnetics (AEM), boreholes, surface topography (including LiDAR), geology and geomorphology, hydrology, hydrostratigraphy, hydrogeology and vegetation. The data is stored in an ArcGIS geodatabase in both vector and raster formats and metadata can be accessed using ArcCatalog.