AndreaBozzo/ceres-open-data-index huggingface dataset:
Base Exclusions (Solar)

The base exclusions consist of two main categories of exclusion – the [protected area layer] and the [techno-economic exclusion layer]. The base exclusions form the fundamental exclusion set that the additional criteria of the Core and SB 100 Terrestrial Climate Resilience Study screens are added to. The merged footprints of these primary two layers are modified by the Bureau of Land Management Land Use Plan Amendment (LUPA) development focus areas (DFAs) and variance process lands, and the general public lands in the DRECP. These areas allow for renewable energy applications and are therefore exempt (erased) from the base exclusions layer, even if the protected area layer or techno-economic exclusion layer identified the area as an exclusion. The DFAs are partitioned by technology type so that only the DFAs that allow solar energy are applied in this modification. The area of California remaining after removing the base exclusions is called the resource potential basemap. It forms the starting point (or base) used in renewable resource estimation and defines where environmental and land-use datasets can be applied in exploring implications. More information about this layer and its use in electric system planning is available in the [Land Use Screens Staff Report in the CEC Energy Planning Library]. Change Log: Version 1.1 (January 22, 2024 10:49 AM) * Layer revised with latest protected area layer, which allows for gaps to remain when combining all components.
California Building Climate Zones

Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone. The numbers used in the climate zone map don't have a title or legend. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors. This is explained in the Title 24 energy efficiency standards glossary section: "The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year." "The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone. "Using this information, they created representative temperature data for each zone. The remainder of the weather data for each zone is still that of the representative city." The representative city for each climate zone (CZ) is: * CZ 1: Arcata * CZ 2: Santa Rosa * CZ 3: Oakland * CZ 4: San Jose-Reid * CZ 5: Santa Maria * CZ 6: Torrance * CZ 7: San Diego-Lindbergh * CZ 8: Fullerton * CZ 9: Burbank-Glendale * CZ10: Riverside * CZ11: Red Bluff * CZ12: Sacramento * CZ13: Fresno * CZ14: Palmdale * CZ15: Palm Spring-Intl * CZ16: Blue Canyon
EVSE Deployment and Grid Evaluation (EDGE)

The EVSE Deployment and Grid Evaluation (EDGE) Tool is a mapping and visualization tool that provides the ability for users to assess the feasibility of deploying charging infrastructure in the context of local grid constraints. EDGE inputs information from two sources: * California Investor-Owned Utility (IOU) Grid Needs Assessment (GNA) datasets - The GNA data provide information regarding the amount of "headroom" that primary circuits and substations have to accommodate new load before requiring attention, such as equipment upgrades. Notably, there is no information present regarding the secondary distribution system. The GNA dataset EDGE currently uses is forecasted through 2025. * Results from the [CEC's AB 2127 Assessment] Infrastructure Models, EVI-Pro and HEVI-LOAD - Hourly load profiles from both models are combined and their overall peak values are assessed. The main result presented by EDGE is the capacity indicator metric (CIM), which is obtained by subtracting the peak EV charging load from the aggregated circuit capacity value for each TAZ throughout the state. Data last updated on 8/10/2023.
Low-Income or Disadvantaged Communities Designated by California

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes. Data downloaded in May 2022 from [https://webmaps.arb.ca.gov/PriorityPopulations/].
California Streams

Notes: As of June 2020 this dataset has been static for several years. Recent versions of NHD High Res may be more detailed than this dataset for some areas, while this dataset may still be more detailed than NHD High Res in other areas. This dataset is considered authoritative as used by CDFW for particular tracking purposes but may not be current or comprehensive for all streams in the state. National Hydrography Dataset (NHD) high resolution NHDFlowline features for California were originally dissolved on common GNIS_ID or StreamLevel* attributes and routed from mouth to headwater in meters. The results are measured polyline features representing entire streams. Routes on these streams are measured upstream, i.e., the measure at the mouth of a stream is zero and at the upstream end the measure matches the total length of the stream feature. Using GIS tools, a user of this dataset can retrieve the distance in meters upstream from the mouth at any point along a stream feature.** CA_Streams_v3 Update Notes: This version includes over 200 stream modifications and additions resulting from requests for updating from CDFW staff and others***. New locator fields from the USGS Watershed Boundary Dataset (WBD) have been added for v3 to enhance user's ability to search for or extract subsets of California Streams by hydrologic area. *See the Source Citation section of this metadata for further information on NHD, WBD, NHDFlowline, GNIS_ID and StreamLevel. **See the Data Quality section of this metadata for further explanation of stream feature development. ***Some current NHD data has not yet been included in CA_Streams. The effort to synchronize CA_Streams with NHD is ongoing.
BLM LUPA General Public Lands

Downloaded on 10/25/2023 from: https://databasin.org/datasets/1cb6eabad6bf48b5b8e24f45b33ff028/ BLM LUPA General Public Lands General Public Lands ("Unallocated Lands" in the Proposed LUPA). Designation on BLM-administered lands that do not have a specific land allocation or designation. These areas are available to renewable energy applications but are not subject to permit review streamlining or other incentives, and thus would be subject to site-specific plan amendment for such development. The Approved LUPA includes CMAs that apply to activities in General Public Lands. Data Provided By: BLM, ASPEN, DUDEK, CEC Content date: September 2016 Citation: September 2016. BLM LUPA Record of Decision for the Desert Renewable Energy Conservation Plan and CDCA Amendment. Contact Organization: US Bureau of Land Management, California Office. Contact Person(s): not specified Use Constraints: [[Creative Commons License]]This work is licensed under a [Creative Commons Attribution 3.0 License].
BLM LUPA Renewable Energy Designations

From Databasin: https://drecp.databasin.org/datasets/c61b0e256e494fc5b6958d6c3999a19a/ Development Focus Areas (DFAs). Designation on BLM-administered lands within which solar, wind, and geothermal renewable energy development and associated activities are allowable uses and that have been determined to be of low or lower resource conflict. The intent is to incentivize and streamline such development in these areas. Variance Process Lands (VPLs). Designation on BLM-administered lands that are available for solar, wind, and geothermal renewable energy development. Renewable energy projects on VPLs have minimal streamlining and must comply with a specific set of CMAs. Renewable energy applications in VPLs will follow the variance process described in the Western Solar Plan ROD.
Terrestrial Intactness (Classified)

This category of planning priorities in the CEC 2023 Land-Use Screens provides an estimate of terrestrial landscape condition based on the extent to which human impacts such as agriculture, urban development, natural resource extraction, and invasive species have disrupted the landscape across the State of California. It is based on the open-source logic modeling framework Environmental Evaluation Modeling System (EEMS) developed by Conservation Biology Institute (CBI). This multicriteria evaluation model result, last updated in 2016 and resolved at 1-kilometer square, spans values ranging from -1 to 1. The higher end of the spectrum indicates areas that are relatively intact based on the more than 30 input variables, and values in the lower end of the spectrum indicate where these human impacts to disturb the landscape and ecological function are relatively high.¹ In the adapted version of the CBI Terrestrial Landscape Intactness given here, the dataset is partitioned into high and low categories based on the mean. Values of the dataset that lie above 0.3 are considered highly intact and are used as an exclusion. Values of the dataset that are less than or equal to 0.3 are allowed to remain in consideration for resource potential. Applying the partition at the mean allows for lands that are relatively more intact than disturbed to be considered for resource potential. The high category of landscape intactness given by this dataset is used as an exclusion in both the Core and SB 100 Terrestrial Climate Resilience Study screens. This layer is featured in the [CEC 2023 Land-Use Screens for Electric System Planning] data viewer. More information about this layer and its use in electric system planning is available in the [Land Use Screens Staff Report in the CEC Energy Planning Library]. [1] Degagne, R., J. Brice, M. Gough, T. Sheehan, and J. Strittholt. 2016. “[Terrestrial Landscape Intactness 1 kilometer, California].” Conservation Biology Institute.https://databasin.org/datasets/e3ee00e8d94a4de58082fdbc91248a65/
ACE Biodiversity (Rank 5)

[Terrestrial Biodiversity Summary] is the combined result of three measures of biodiversity in CA Department of Fish and Wildlife’s [Areas of Conservation Emphasis] (ACE) project. Those pillars that make up this metric include terrestrial native species richness (diversity of all currently tracked species in the state), terrestrial rare species richness (diversity of special status species), and terrestrial irreplaceability (highlights unique endemic species). As such, this dataset describes the relative biodiversity levels for birds, amphibians, plants, mammals and reptiles across each USDA ecoregion. Rank 5, the highest rank, is used as an exclusion in the biological planning priorities component of the Core and SB 100 Terrestrial Climate Resilience Study Screens. This ensures that areas of technical resource potential identified through screening avoid lands with higher conservation value for biodiversity. This layer is featured in the [CEC 2023 Land-Use Screens for Electric System Planning] data viewer. For more information about this layer and its use in electric system planning, please refer to the [Land Use Screens Staff Report in the CEC Energy Planning Library].
ACE Climate Resilience (Ranks 4 & 5)

The full dataset comes from CA Department of Fish and Wildlife’s [Areas of Conservation Emphasis] (ACE) project. [ACE Terrestrial Climate Change Resilience] incorporates statewide information about lands that have a higher probability of serving as refugia for species adapting to climate change. Based on projections from climate models, this dataset indicates the relative likelihood that an area will experience shifts in temperature, precipitation, or other important climate variables that would negatively impact the current array of plants (and by extension animals) that can thrive under those future conditions. Ranks 4 and 5 are used as an exclusion in the SB 100 Terrestrial Climate Resilience Study Screen. This allows areas of lower climate resilience rank to be considered for exploration of renewable resource technical potential, while keeping areas of higher resilience rank for conservation planning. This layer is featured in the [CEC 2023 Land-Use Screens for Electric System Planning] data viewer. For more information about this layer and its use in electric system planning, please refer to the [Land Use Screens Staff Report in the CEC Energy Planning Library].
ACE Irreplaceability (Rank 4 & 5)

The full Terrestrial Irreplaceability dataset is part of the [Areas of Conservation Emphasis (ACE)] project from CA Department of Fish and Wildlife (CDFW). [ACE Terrestrial Irreplaceability] provides a measure of the relative importance of an area based on the number of California endemic, special-status species found there, and the breadth of their distribution, so that species which are more geographically constrained produce a higher score. This geographic weighting system makes a site more ‘irreplaceable’ because it supports species which are found in fewer locations. Rare endemic species are identified by the California Species of Special Concern reports, the California Rare Plant Rank 1B plants, State or Federally-listed species (threatened, endangered, or candidate species), and fully-protected species. Ranks 4 and 5 are used as an exclusion in the biological planning priorities component of the Core and SB 100 Terrestrial Climate Resilience Study Screens. This ensures that areas of technical resource potential identified through screening avoid lands with higher conservation value for irreplaceability. This layer is featured in the [CEC 2023 Land-Use Screens for Electric System Planning] data viewer. For more information about this layer and its use in electric system planning, please refer to the [Land Use Screens Staff Report in the CEC Energy Planning Library.]
ACE Connectivity (Ranks 4 & 5)

[Terrestrial Connectivity] is one of the main outputs of the CA Department of Fish and Wildlife’s [Areas of Conservation Emphasis] (ACE) project. This dataset evaluates how an area contributes to animal movement and general ecological flow. It includes information on corridors that allow for species migration, including narrow channels through highly disturbed areas which are critical for retaining the last threads of connectivity in these areas, as well as high usage areas between large, contiguous and natural landscapes which are described as intact. The ACE Ranks are used to indicate level of connectivity conservation urgency, with essential corridors and linkages emphasized with highest level scores of 4 or 5. Areas that have high connectivity, but have not been identified as having channelized areas for species corridors or habitat linkages, are given a rank 3. Large, intact regions which also contribute to connectivity but possess greater redundancy on account of their size, are given a lower rank of 2. Areas that show no opportunity for connectivity are given the lowest rank of 1.<o:p></o:p> Ranks 4 and 5 are used as an exclusion in the biological planning priorities component of the Core and SB 100 Terrestrial Climate Resilience Study Screens. This ensures that areas of technical resource potential identified through screening avoid lands with higher conservation value for connectivity.<o:p></o:p> For more information about this layer and its use in electric system planning, please read the Land Use Screens Staff Report in the CEC Energy Planning Library. <o:p></o:p>
Critical Habitat

This layer consists of the merged footprints of the [USFWS critical habitat] and the [USFWS proposed Bi-State Sage-Grouse critical habitat],¹ clipped to California. Critical habitat constitutes areas considered essential for the conservation of a listed species. These areas provide notice to the public and land managers of the importance of the areas to the conservation of this species. Special protections and/or restrictions are possible in areas where Federal funding, permits, licenses, authorizations, or actions occur or are required. The critical habitat footprint shown here is used as part of the biological planning priorities in the CEC 2023 Land-Use Screens and removes technical resource potential from the state. More information about this layer and its use in electric system planning is available in the Land Use Screens Staff Report in the CEC Energy Planning Library. [1] This dataset is obtained from the "Web Links" section (USFWS Proposed Critical Habitat Map) of the Bi-State Sage-Grouse Maps & GIS webpage, available at [Maps & GIS | Bi-State Sage-Grouse (bistatesagegrouse.com)].
Elk Home Range - Bella Vista - 2020-2023 [ds3166]

The project lead for the collection of this data was Erin Zulliger. Elk (6 adult females) were captured and equipped with GPS collars (Litetrack/Pinpoint Iridium collars, Lotek Wireless Inc., Newmarket, Ontario, Canada or Vectronic Aerospace) transmitting data from 2020-2023. The Bella Vista herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 6 hour intervals in the dataset. To improve the quality of the data set, the GPS data locations fixed in 2D space and visually assessed as a bad fix by the analyst were removed. The methodology used for this migration analysis allowed for the mapping of the herd’s annual range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 6 elk, including 16 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Annual home range designations for this herd may expand with a larger sample.
Elk Home Range - Dixie Valley - 2019-2023 [ds3167]

The project lead for the collection of this data was Erin Zulliger. Elk (5 adult females) were captured and equipped with GPS collars (Litetrack/Pinpoint Iridium collars, Lotek Wireless Inc., Newmarket, Ontario, Canada or Vectronic Aerospace) transmitting data from 2019-2023. The Dixie Valley herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 1-6 hour intervals in the dataset. To improve the quality of the data set, the GPS data locations fixed in 2D space and visually assessed as a bad fix by the analyst were removed. The methodology used for this migration analysis allowed for the mapping of the herd’s annual range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 5 elk, including 15 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Annual home range designations for this herd may expand with a larger sample.
Elk Home Range - Egg Lake - 2017-2020 [ds3168]

The project lead for the collection of this data was Erin Zulliger. Elk (6 adult females) were captured and equipped with GPS collars (Litetrack/Pinpoint Iridium collars, Lotek Wireless Inc., Newmarket, Ontario, Canada or Vectronic Aerospace) transmitting data from 2017-2020. The Egg Lake herd migrates between traditional summer and winter seasonal ranges, and migration corridors, migration stopovers, and winter ranges were modeled separately for this herd, but are not a part of this analysis. Annual home ranges were modeled using year-round data to demarcate high use areas. GPS locations were fixed at 1-4 hour intervals in the dataset. To improve the quality of the data set, the GPS data locations fixed in 2D space and visually assessed as a bad fix by the analyst were removed. The methodology used for this migration analysis allowed for the mapping of the herd’s annual range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 5 elk, including 9 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Annual home range designations for this herd may expand with a larger sample.
Elk Home Range - Long Prairie - 2016-2023 [ds3169]

The project lead for the collection of this data was Erin Zulliger. Elk (11 adult females) were captured and equipped with GPS collars (Litetrack/Pinpoint Iridium collars, Lotek Wireless Inc., Newmarket, Ontario, Canada or Vectronic Aerospace) transmitting data from 2016-2023. The Long Prairie herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 1-13 hour intervals in the dataset. To improve the quality of the data set, the GPS data locations fixed in 2D space and visually assessed as a bad fix by the analyst were removed. The methodology used for this migration analysis allowed for the mapping of the herd's annual range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 11 elk, including 34 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Annual home range designations for this herd may expand with a larger sample.