AndreaBozzo/ceres-open-data-index huggingface dataset:
Wind Resource Areas (2023)

<div style='text-align:Left;'><p style='margin:7 0 7 0;'><span style='background-color:rgb(255, 255, 255);'><font size='4'>The method to create the Wind Resource Area datasets is to:</font></span></p><p style='margin:7 0 7 0;'><span style='background-color:rgb(255, 255, 255);'><font size='4'>Query Power Plant point locations from the California Energy Commission, <a href='https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcaenergy.maps.arcgis.com%2Fhome%2Fitem.html%3Fid%3D4a702cd67be24ae7ab8173423a768e1b&amp;data=05%7C01%7C%7Caa3642d83c6249bdd4ab08db2f143937%7Cac3a124413f44ef68d1bbaa27148194e%7C0%7C0%7C638155536144205242%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=be9B3%2BXmAp670440N9shHj1t71ysxPrjlPAyZa3pkyc%3D&amp;reserved=0' style='text-decoration:underline;' rel='nofollow ugc'><span style='font-size:16pttext-decoration:underline;'>California Power Plants</span></a> data set by operational status and capacity greater than or equal to 2 MW at each facility from the <a href='https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.energy.ca.gov%2Fdata-reports%2Fenergy-almanac%2Fcalifornia-electricity-data%2Fquarterly-fuel-and-energy-report-qfer-data&amp;data=05%7C01%7C%7Caa3642d83c6249bdd4ab08db2f143937%7Cac3a124413f44ef68d1bbaa27148194e%7C0%7C0%7C638155536144205242%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=8aOrH0H7%2FT2u%2BA2%2FwiYc35mSHvNeDWPk08YpqCWfUjM%3D&amp;reserved=0' style='text-decoration:underline;' rel='nofollow ugc'><span style='font-size:16pttext-decoration:underline;'>Quarterly Fuel and Energy Report, CEC-1304A</span></a>. Plants tracked include those of at least 1 MW, which are considered of commercial size.  A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons.  The buffer does not represent information regarding environmental analysis.  It is used only to depict plant concentration regions.   </font></span></p><p></p><div><div style='font-size:12pt;'><p><span></span></p></div></div></div>
Single Climate Model, 30-year Rolling Average Precipitation

<p>This dataset contains a 30-year rolling average of annual
average precipitation from the four models and two greenhouse gas (RCP) scenarios
included in the four model ensemble for the years 1950-2099.<span>  </span>The year identified is the mid-point of the
30-year average. eg. The year 2050 includes the values from 2036 to 2065. </p>

<p>The downscaling and selection of models for inclusion in ten
and four model ensembles is described in <a href='https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Pierce
et al. 2018</a>, but summarized here. Thirty two global climate models (GCMs)
were identified to meet the modeling requirements. From those, ten that closely
simulate California’s climate were selected for additional analysis (<a href='https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Table
1, Pierce et al. 2018</a>) and to form a ten model ensemble. From the ten model
ensemble, four models, forming a four model ensemble, were identified to
provide coverage of the range of potential climate outcomes in California.<span>  </span>The models in the four model ensemble and
their general climate projection for California are: </p><p></p><ul><li><span style='text-indent:-0.25in;'>HadGEM2-ES (warm/dry), </span></li><li><span style='text-indent:-0.25in;'>CanESM2 (average), </span></li><li><span style='text-indent:-0.25in;'>CNRM-CM5 (cooler/wetter), </span></li><li><span style='text-indent:-0.25in;'>and MIROC5 the model least like the others to
improve coverage of the range of outcomes.</span></li></ul><p></p>

<p>These data were downloaded from Cal-Adapt and prepared for
use within CA Nature by California Natural Resource Agency and ESRI staff.</p>

<p>Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived
from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by
University of California at Berkeley’s Geospatial Innovation Facility under
contract with the California Energy Commission. Retrieved  from <a href='https://cal-adapt.org/' target='_blank' rel='nofollow ugc noopener noreferrer'>https://cal-adapt.org/</a></p>

<p>Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps
Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise
Scenarios for the Fourth California Climate Assessment. California’s Fourth
Climate Change Assessment, California Energy Commission. Publication Number:
CNRA-CEC-2018-006.</p>
Single Climate Model, 30-year Rolling Average Minimum and Maximum Average Temperatures

<p>This dataset contains a 30-year rolling average of annual
average minimum and maximum temperatures from the four models and two greenhouse
gas (RCP) scenarios included in the four model ensemble for the years
1950-2099.The year identified is the
mid-point of the 30-year average. eg. The year 2050 includes the values from
2036 to 2065. </p>

<p>The downscaling and selection of models for inclusion in ten
and four model ensembles is described in <a href='https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Pierce
et al. 2018</a>, but summarized here. Thirty two global climate models (GCMs)
were identified to meet the modeling requirements. From those, ten that closely
simulate California’s climate were selected for additional analysis (<a href='https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Table
1, Pierce et al. 2018</a>) and to form a ten model ensemble. From the ten model
ensemble, four models, forming a four model ensemble, were identified to
provide coverage of the range of potential climate outcomes in California.<span>  </span>The models in the four model ensemble and
their general climate projection for California are:</p>

<p></p><ul><li>HadGEM2-ES (warm/dry),</li><li>CanESM2 (average), </li><li>CNRM-CM5 (cooler/wetter), </li><li>and MIROC5 the model least like the others to
improve coverage of the range of outcomes.</li></ul><p></p>







<p>These data were downloaded from Cal-Adapt and prepared for
use within CA Nature by California Natural Resource Agency and ESRI staff. </p>


<p>Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived
from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by
University of California at Berkeley’s Geospatial Innovation Facility under
contract with the California Energy Commission. Retrieved  from <a href='https://cal-adapt.org/' target='_blank' rel='nofollow ugc noopener noreferrer'>https://cal-adapt.org/</a></p>

<p>Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps
Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise
Scenarios for the Fourth California Climate Assessment. California’s Fourth
Climate Change Assessment, California Energy Commission. Publication Number:
CNRA-CEC-2018-006.</p>
Single climate model annual, temperature

<p>This dataset contains annual average minimum and maximum
temperatures from the four models and two greenhouse gas (RCP) scenarios
included in the four model ensemble for the years 1950-2099. </p>

<p>The downscaling and selection of models for inclusion in ten
and four model ensembles is described in <a href='https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Pierce
et al. 2018</a>, but summarized here. Thirty two global climate models (GCMs)
were identified to meet the modeling requirements. From those, ten that closely
simulate California’s climate were selected for additional analysis (<a href='https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Table
1, Pierce et al. 2018</a>) and to form a ten model ensemble. From the ten model
ensemble, four models, forming a four model ensemble, were identified to
provide coverage of the range of potential climate outcomes in California.<span>  </span>The models in the four model ensemble and
their general climate projection for California are: </p><p></p><ul><li><span style='text-indent:-0.25in;'>HadGEM2-ES (warm/dry), </span></li><li><span style='text-indent:-0.25in;'>C</span><span style='text-indent:-0.25in;'>anESM2 (average), </span></li><li><span style='text-indent:-0.25in;'>CNRM-CM5 (cooler/wetter), </span></li><li><span style='text-indent:-0.25in;'>and MIROC5 the model least like the others to
improve coverage of the range of outcomes.</span></li></ul><p></p>

<p>These data were downloaded from Cal-Adapt and prepared for
use within CA Nature by California Natural Resource Agency and ESRI staff. </p>

<p>Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived
from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by
University of California at Berkeley’s Geospatial Innovation Facility under
contract with the California Energy Commission. Retrieved from <a href='https://cal-adapt.org/' target='_blank' rel='nofollow ugc noopener noreferrer'>https://cal-adapt.org/</a><br /></p>

<p>Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps
Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise
Scenarios for the Fourth California Climate Assessment. California’s Fourth
Climate Change Assessment, California Energy Commission. Publication Number:
CNRA-CEC-2018-006.</p>
Water Rights Demand Analysis Methodology Datasets

The following datasets are used for the Water Rights Demand Analysis project and are formatted to be used in the calculations. The State Water Resources Control Board Division of Water Rights (Division) has developed a methodology to standardize and improve the accuracy of water diversion and use data that is used to determine water availability and inform water management and regulatory decisions. The Water Rights Demand Data Analysis Methodology (Methodology  https://www.waterboards.ca.gov/drought/drought_tools_methods/demandanalysis.html ) is a series of data pre-processing steps, R Scripts, and data processing modules that identify and help address data quality issues related to both the self-reported water diversion and use data from water right holders or their agents and the Division of Water Rights electronic water rights data.
Vegetation Map - Johnson Valley - CDFW [ds1019]

<div style='text-align:Left;'><div><div><p><span>The 17,158 acre Johnson Valley project area is located in San Bernardino County, 32 miles east of Victorville, CA bisected by highway 247. The fine-scale vegetation map was created as part of a collaborative project between the United States Geological Survey (USGS) and the Vegetation Classification and Mapping Program of the California Department of Fish and Wildlife (CDFW) to show the correlation between vegetation and geomorphology. The vegetation mapping types are based on the vegetation classification that was developed for the larger Desert Renewable Energy Conservation Plan (DRECP) mapping project (CDFW 2013). The vegetation classification was developed using a compilation of data collected for several projects including Anza-Borrego Desert State Park and Environs (Keeler-Wolf et al. 1998), the Mojave Desert Ecosystem Program’s Vegetation Database (Thomas et al. 2004), Vegetation of Joshua Tree National Park (La Doux et al. 2013), and Vegetation Classification and Mapping at Lake Mead National Recreation Area, Mojave National Preserve and Death Valley National Park (in progress, draft as of 2014).</span></p></div></div></div>
Vegetation - Carrizo Plain National Monument, Ecological Reserve and Adjacent Elk Range [ds1094]

<div style='text-align:Left;'><div><div><p><span>The California Department of Fish and Wildlife (CDFW) Vegetation Classification and Mapping Program (VegCAMP) has created a fine-scale vegetation map of part of the range of the American Ranch and Chimineas Ranch tule elk herds. This section of the elk range is adjacent to the Carrizo Plain National Monument and the Chimineas Ranch Unit of the Carrizo Plain Ecological Reserve, both of which have been previously mapped. This map has been seamed to the vegetation map of the Chimineas Ranch completed by VegCAMP (VegCAMP 2010) and the map of the Carrizo Plain National Monument produced by the California Native Plant Society (Stout et al. 2013), and completes the range of these two elk herds in San Luis Obispo County, California. Like those maps, this mapping follows Survey of California Vegetation, Federal Geographic Data Committee (FGDC), and National Vegetation Classification (NVC) standards (FGDC 2008, Jennings et al. 2009). </span></p><p><span>The map legend is based on the classification in Stout et al. (2013), with slight modifications as discussed in Appendix C of the report, which is available here: </span><a href='https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=92951' rel='nofollow ugc'><span>https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=92951</span></a><span>. Reconnaissance-level sampling of vegetation stands in the project area was conducted in the spring of 2013. Polygons were drawn using heads-up digitizing with true color 1-foot aerial imagery from August 1, 2007 as the map base. Supplemental imagery included National Agricultural Imagery Program (NAIP) true color and color infrared (CIR) 1-meter resolution data from 2010–2012, Bing imagery, and current and historical imagery from Google Earth. The minimum mapping unit (MMU) is 1 acre, with the exception of wetland types, which have an MMU of ½ acre. Mapping is to the NVC hierarchy Association, Alliance, or Group level based on the ability of the photointerpreters to distinguish types based on all imagery available and on the field data. Two sub-Group level mapping units were used in instances where the vegetation types could not differentiated on the imagery. The first mapping unit is composed of the Salvia leucophylla, Eriogonum fasciculatum, Artemisia californica, and Artemisia californica-Eriogonum fasciculatum Alliances; the second is composed of the Atriplex polycarpa and Atriplex canescens Alliances. Accuracy assessment (AA) data was collected in spring of 2014. Map accuracy was calculated to be 89 percent; corrections were made to the map based on the AA data to increase the final accuracy.</span></p></div></div></div>
Vegetation - North Carrizo Plain Mitigation Lands [ds1095]

<div style='text-align:Left;'><div><div><p><span>The California Department of Fish and Wildlife (CDFW) Vegetation Classification and Mapping Program (VegCAMP) has created a fine-scale vegetation map of the Department’s mitigation lands in the northern portion of the Carrizo Plain, San Luis Obispo County, California. The mapping follows Survey of California Vegetation, Federal Geographic Data Committee (FGDC), and National Vegetation Classification (NVC) standards (FGDC 2008, Jennings et al. 2009). </span><span>The map legend is based on the classification in Stout et al. (2013), with slight modifications as discussed in Appendix C of the project report, which is available here: </span><a href='https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=92969' rel='nofollow ugc'><span>https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=92969</span></a><span>. Reconnaissance-level sampling of vegetation stands in the project area was conducted in the spring of 2013. Polygons were drawn using heads-up digitizing using the July 2011 1-foot imagery acquired for San Luis Obispo County and downloaded from US Geological Survey (USGS) EarthExplorer as the map base. Supplemental imagery included National Agricultural Imagery Program (NAIP) true color and color infrared (CIR) 1-meter resolution data from 2010–2012, Bing imagery, and current and historical imagery from Google Earth. The minimum mapping unit (MMU) is ½ acre in order to capture small, remnant native stands of trees, shrubs, and bunch grasses. Mapping is to the NVC hierarchy Association, Alliance, or Group level based on the ability of the photointerpreters to distinguish types based on all imagery available and on the field data. Field verification data was collected in the spring of 2014 and corrections were made to the map based on that data to increase the final accuracy.</span></p></div></div></div>
Vegetation - Napa County and Blue Ridge Berryessa [ds201]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>In 1995, the Manual of California Vegetation (MCV) introduced a quantitatively based method for classifying and mapping vegetation in California. In 2002 Department of Fish and Wildlife, Information Center for the Environment, and Aerial Information Systems used this method to develop a classification of vegetation types for Napa County, and to attribute the polygons of a new vegetation map. The complete report for this study can be viewed at: </SPAN><A href="http://www.nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=14660"><SPAN>http://www.nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=14660</SPAN></A><SPAN>.</SPAN></P></DIV></DIV></DIV>
Peninsular Bighorn Sheep Habitat Vegetation Map [ds2660]

<div style='text-align:Left;'><div><div><p><span>Aerial Information Systems, Inc. (AIS) was contracted by the Coachella Valley Conservation Commission (CVCC) through a Local Assistance Grant originating from the California Department of Fish and Wildlife (CDFW) to map and describe the essential habitats for bighorn sheep monitoring within the San Jacinto-Santa Rosa Mountains Conservation Area. This effort was completed in support of the Coachella Valley Multiple Species Habitat Conservation Plan (CVMSHCP). The completed vegetation map is consistent with the California Department of Fish and Wildlife classification methodology and mapping standards. </span></p><p><span>The mapping area covers 187,465 acres of existing and potential habitat on the northern slopes of the San Jacinto and Santa Rosa Mountains ranging from near sea level to over 6000 feet in elevation. The map was prepared over a baseline digital image created in 2014 by the US Department of Agriculture – Farm Service Agency’s National Agricultural Imagery Program (NAIP). Vegetation units were mapped using the National Vegetation Classification System (NVCS) to the Alliance (and in several incidences to the Association) level (See Appendix A for more detail) as described in the second edition of the Manual of California Vegetation Second Edition (Sawyer et al, 2009). </span></p><p><span>The mapping effort was supported by extensive ground-based field gathering methods using CNPS rapid assessment protocol in the adjacent areas as part of the Desert Renewable Energy Conservation Plan (DRECP) to the north and east; and by the 2012 Riverside County Multiple Species Habitat Conservation Plan vegetation map in the western portion of Riverside County adjacent to the west. These ground-based data have been classified and described for the abovementioned adjacent regions and resultant keys and descriptions for those efforts have been used in part for this project. </span></p><p><span>For detailed information please refer to the following report: </span></p><p><a href='http://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=138663' style='text-decoration:underline;' rel='nofollow ugc'><span>Menke, J. and D. Johnson. 2015. Vegetation Mapping – Peninsular Bighorn Sheep Habitat. Final Vegetation Mapping Report. Prepared for the Coachella Valley Conservation Commission. Aerial Information Systems, Inc., Redlands, CA.</span></a></p><p><span /></p></div></div></div>
Vegetation - Clear Creek Monocline Ridge [ds995]

<div style='text-align:Left;'><div><div><p><span>California Native Plant Society (CNPS) and California Department of Fish and Wildlife (CDFW) cooperated to created a detailed vegetation inventory to assist the Bureau of Land Management in the development of long-range management decisions in the area, which has a range of uses from a wilderness study area to grazing, mining, and off-road vehicles. In the spring and summer of 2004 and 2005, a field crew of vegetation ecologists collected 570 vegetation rapid assessments. In the fallwinter of 2004-2005, these data were entered into a database and analyzed using multivariate statistical programs to develop a floristic classification, keys, and descriptions of the vegetation types identified in the area. AIS then did a fine-scale mapping based on the classification which was compared to mapping based on CalVeg methodologies. This provided a test case for the development of vegetation mapping methodologies with recommendations to the California State Vegetation Memorandum of Understanding Committee.</span></p></div></div></div>
Vernal Pool Complexes - Central Valley - 1989-1998 [ds36]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This Arc/Info coverage is a polygon layer of vernal pool complexes greater than 40 acres in size for 29 counties throughout the greater Central Valley, and some north bay counties. These data were collected and vernal pool complexes were identified and mapped by Dr. Robert F. Holland in 1995 and refined in a 1998 update. This layer does not contain delineations of individual vernal pools and is not intended for use for wetland delineations or regulatory use. For additional information on the vernal pools mapping projects see the report, Changes in Great Valley Vernal Pool Distribution from 1989 to 1997, June 1998, by Robert F. Holland, Ph.D. It is available online at: http://www.dfg.ca.gov/bdb/wetlands/vp_holland/report_index.htm .</SPAN></P></DIV></DIV></DIV>
Vegetation - Suisun Marsh - 2000 [ds161]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This vegetation mapping project of Suisun Marsh blends ground-based classification, aerial photo interpretation, and GIS editing and processing. The method is based on the development of a quantitative vegetation classification, which is used to describe the vegetation map units of the marsh. The classification is defined to meet the specifications of the National and State standards for vegetation classification, but is related through a cross-walking table to other standard classifications in use locally or statewide ( http://www.nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=24570 ).</SPAN></P></DIV></DIV></DIV>
Vegetation - Suisun Marsh - 2003 [ds162]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This vegetation mapping project of Suisun Marsh blends ground-based classification, aerial photo interpretation, and GIS editing and processing. The method is based on the development of a quantitative vegetation classification, which is used to describe the vegetation map units of the marsh. The classification is defined to meet the specifications of the National and State standards for vegetation classification, but is related through a cross-walking table to other standard classifications in use locally or statewide.</SPAN></P></DIV></DIV></DIV>
Vegetation - Suisun Marsh - Change 1999 to 2000 [ds163]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This vegetation mapping project of Suisun Marsh blends ground-based classification, aerial photo interpretation, and GIS editing and processing. The method is based on the development of a quantitative vegetation classification, which is used to describe the vegetation map units of the marsh. The classification is defined to meet the specifications of the National and State standards for vegetation classification, but is related through a cross-walking table to other standard classifications in use locally or statewide ( https://nrmsecure.dfg.ca.gov/FileHandler.ashx?DocumentID=42580 ).</SPAN></P></DIV></DIV></DIV>