SoilWeb applications can be used to access and explore USDA-NCSS detailed soil survey maps and data (SSURGO) for most of the United States, as well as maps and data outside of Web Soil Survey. Developed by the University of California. Available interface apps:

SoilWeb SoilWeb Earth SEE: Soil Series Extent Explorer Soil Properties Soil Agricultural Groundwater Banking Index (SAGBI) Resources in this dataset:Resource Title: Website Pointer for SoilWeb Apps. File Name: Web Page, url: https://casoilresource.lawr.ucdavis.edu/soilweb-apps/ SoilWeb products that can be used to access USDA-NCSS detailed soil survey data (SSURGO) for most of the United States.

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Cropland Index The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better CroplandsCalifornia Important Farmland data – statistical data used for analyzing impacts on California’s agricultural resources from the Farmland Mapping and Monitoring Program. Agricultural land is rated according to soil quality and irrigation status. The maps are updated every two years (on even numbered years) with the use of a computer mapping system, aerial imagery, public review, and field reconnaissance. Cropland Index Mask - This is a constructed data set used to define the model domain. Its footprint is defined by combining the extent of the California Important Farmland data (2018) classifications listed above and the area defined by California Statewide Crop Mapping for the state of California.Prime Farmland – farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.Farmland of Statewide Importance – farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date. Unique Farmland – farmland of lesser quality soils used for the production of the state’s leading agricultural crops. This land is usually irrigated but may include Non irrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date. Gridded Soil Survey Geographic Database (gSSURGO) – a database containing information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. California Revised Storie Index - is a soil rating based on soil properties that govern a soil’s potential for cultivated agriculture in California. The Revised Storie Index assesses the productivity of a soil from the following four characteristics: Factor A, degree of soil profile development; factor B, texture of the surface layer; factor C, slope; and factor X, manageable features, including drainage, microrelief, fertility, acidity, erosion, and salt content. A score ranging from 0 to 100 percent is determined for each factor, and the scores are then multiplied together to derive an index rating.Electrical Conductivity - is the electrolytic conductivity of an extract from saturated soil paste, expressed as Deci siemens per meter at 25 degrees C. Electrical conductivity is a measure of the concentration of water-soluble salts in soils. It is used to indicate saline soils. High concentrations of neutral salts, such as sodium chloride and sodium sulfate, may interfere with the adsorption of water by plants because the osmotic pressure in the soil solution is nearly as high as or higher than that in the plant cells. Sodium Adsorption Ratio - is a measure of the amount of sodium (Na) relative to calcium (Ca) and magnesium (Mg) in the water extract from saturated soil paste. It is the ratio of the Na concentration divided by the square root of one-half of the Ca + Mg concentration. Soils that have SAR values of 13 or more may be characterized by an increased dispersion of organic matter and clay particles, reduced saturated hydraulic conductivity (Ksat) and aeration, and a general degradation of soil structure.

This digital dataset defines the model grid, water-balance subregions (WBSs), soil types, and virtual crops for the five land-use time-frames in the transient hydrologic model of the Central Valley flow system. The Central Valley encompasses an approximate 50,000 square-kilometer region of California. The complex hydrologic system of the Central Valley is simulated using the USGS numerical modeling code MODFLOW-FMP (Schmid and others, 2006a, b). This simulation is referred to here as the Central Valley Hydrologic Model (CVHM) (Faunt, 2009). Utilizing MODFLOW-FMP, the CVHM simulates groundwater- and surface-water flow, irrigated agriculture, land subsidence, and other key processes in the Central Valley on a monthly basis from 1961-2003. The total active modeled area is 20,334 square-miles on a finite-difference grid comprising 441 rows and 98 columns. Slightly less than 50 percent of the cells are active. The CVHM grid has a uniform horizontal discretization of 1x1 square mile and is oriented parallel to the valley axis, 34 degrees west of north (Faunt, 2009). The 21 WBSs initially were identified by the California Department of Water Resources (CA-DWR) and Bureau of Reclamation (BOR) as numbered "Depletion Study Areas" (California Department of Water Resources, 1977). The WBSs are used as accounting units for surface-water delivery and for estimation of groundwater pumpage. The boundaries generally represent hydrographic rather than political subdivisions, particularly in the San Joaquin and Tulare Basins. The soils were simplified into sandy loam, silty clay, and silt from the State Soil Geographic Database STATSGO (U.S. Department of Agriculture Natural Resources Conservation Service, 2005b). The soil type covering the maximum area of each cell was assigned to each cell. The land-use attributes are defined in the model on a cell-by-cell basis and include urban and agricultural areas, water bodies, and natural vegetation. The land use, referred to as "virtual crops," that covered the largest fraction of each 1 square mile model cell was the representative land use specified for that cell. Land-use maps were developed for five different time frames during the 42.5-year simulation period. The CVHM is the most recent regional-scale model of the Central Valley developed by the U.S. Geological Survey (USGS). The CVHM was developed as part of the USGS Groundwater Resources Program (see "Foreword", Chapter A, page iii, for details).

This digital dataset contains the soil data for the updated Central Valley Hydrologic Model (CVHM2). The soil data is based on California Department of Water Resource’s C2VSim’s fine grid model soil curve number data set (C2VSimFG Version 1.0 - Datasets; CNRA, 2011). These values were originally obtained from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic Database (SSURGO) soil map of the State of California (USDA, 2004), and then an area-weighted average value for each hydrologic soil group within each subregion was calculated to the C2VSim element. Curve number values obtained from SSURGO were converted for use in C2VSimFG. To translate the curve number from the C2VSim grid to the CVHM2 grid, the curve number from the C2VSim that covered the largest area of a CVHM2 cell was used as zone number. There were 8 unique curve numbers in C2VSimFG. Then each of these numbers were given a different zone number for each of t ...

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Geodatabase of ultramafic substrates in South Coast Ranges, California, USA Ryan O’Dell Natural Resource Specialist Bureau of Land Management Central Coast Field Office Marina, California, USA rodell@blm.gov August 2025 Background and Goals Rock type and soil type have a strong influence on plant species distribution. There are about 250 plant taxa endemic to ultramafic substrate in California (Miller and Safford 2020). Many have small ranges (local endemics) and are rare or endangered. Plant ecologists and conservationists wish to develop Species Distribution Models (SDMs) for ultramafic endemic plant species in GIS. However, there is no existing, continuous, high accuracy geodatabase of ultramafic substrates for most of California. Most ultramafic Species Distribution Models (SDMs) produced in the past 15 years have used the Geologic Map of California (USGS 2010; 1:750,000 scale) and/or Gridded Soil Survey Geographic Database (gSSURGO; NRCS; 1:6,000 scale). Neither of these data sources delineate ultramafic substrates with high enough accuracy for satisfactory SDMs. Polygon lines often do not closely match ultramafic geologic boundaries clearly visible in the high-resolution satellite imagery. Additionally, smaller ultramafic masses, ultramafic landslides, and ultramafic alluvial deposits are either not mapped, or not identified by rock type (e.g. Qls or Qa, only). The goal is to produce a continuous, fine scale (4,000 m2 MMU), high accuracy (± 20 m) geodatabase of ultramafic substrates for most of California. The first stage (2020 – 2025) will be to delineate polygons of ultramafic masses, ultramafic landslides, and ultramafic alluvium as accurately as possible. The second stage (2025 - 2030) will be to divide the polygons further and assign attributes based on field observations including - rock_type · serpentinite, 75-100% · peridotite, serpentinized 25 - 75% · peridotite, serpentinized 0 - 25% shear_strength · block, pulverized matrix · block, sheared matrix · block, no matrix soil_series soil_depth · < 10 cm · 10 - 30 cm · 30 cm+ soil_texture · clay · clay-loam · loam · sandy-loam · sand Data sources 1) High resolution satellite imagery from Google Earth – portable basemap server. Airbus; Landsat/Copernicus. https://mt1.google.com/vt/lyrs=s&x={col}&y={row}&z={level} 2) National Geologic Map Database (NGMD). https://ngmdb.usgs.gov/ngmdb/ngmdb_home.html Dibblee Maps (1:24,000 scale) are generally the most detailed and highest accuracy for South Coast Ranges. https://store.aapg.org/ATSResources/product-splash/dibblee.aspx 3) Gridded Soil Survey Geographic Database (gSSURGO). gSSURGO_CA.gdb https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database Extracting and drawing the polygons 1) Trace ultramafic rock polygons from geologic maps. The National Geologic Map Database (NGMD). I examined the digitized maps to identify those with highest accuracy. For the South Coast Ranges, these tended to be the Dibblee Maps, which are not available for download on NGMD - viewing only. For the Dibblee Maps, I collected screen capture images (JPEG) from the NGMD map viewer, then manually georeferenced them in ArcGIS Pro (GIS). All other maps in NGMD (GeoTiff) were downloaded and opened in GIS. I then manually traced all polygons mapped as ultramafic rock types. 2) Extract ultramafic soil polygons from gSSURGO. I carefully identified all of the map unit key (MUKEY) codes corresponding to soils derived from ultramafic rock and extracted the polygons from gSSURGO. A spreadsheet of these can be found in Figshare – “Ultramafic soils NRCS – CA, OR, WA.” SQLs to extract the polygons from gSSURGO is in “Extract all ultramafic soil polygons from gSSURGO for California, Oregon, and Washington, USA.” 3) Compare ultramafic rock polygon lines to ultramafic soil polygons and high-resolution satellite imagery, and adjust the lines. Ultramafic soil polygons and high-resolution satellite imagery were used to adjust the ultramafic rock polygon lines through consensus of data and visual indicators. Ultramafic rock and soil has a distinctive color, compared to adjacent non-ultramafic rock types. Serpentinite rock has a blue hue. Weathered ultramafic rock (especially peridotite) and soil typically has a substantially redder hue (oxidized iron), than adjacent non-ultramafic rock types. Vegetative cover on ultramafic rock is typically much lower than the adjacent non-ultramafic rock, so both the color of the rock and soil is visible in satellite imagery. The vegetation type (color and patterns) on ultramafic rock also typically contrasts sharply with the surrounding non-ultramafic rock. In South Coast Ranges, the strict ultramafic endemic large woody shrub Quercus durata var. durata appears as a distinctive gray-green color. I used my 20+ years of field observations and knowledge of ultramafic areas from throughout California to manually adjust the ultramafic polygons based on rock, soil, and vegetation color patterns in the high resolution satellite imagery. I also carefully examined the satellite imagery and delineated small ultramafic masses, ultramafic landslides, and ultramafic alluvial deposits not mapped (represented) in the geologic maps or soil surveys. 4) Follow-up field observations and reexamination of polygons in GIS. Most of the ultramafic polygons for South Coast Ranges were drawn in 2020 and 2021. I conducted additional field work 2021 – 2025 (as checks), reexamined polygons in GIS, and continued to improve polygon line accuracy.

This polygon shapefile contains data derived from the Soil Survey Geographic Database (SSURGO) to show the area of Tehama County, California. SSURGO depicts information about the kinds and distribution ofsoils on the landscape. The soil map and data used in the SSURGOproduct were prepared by soil scientists as part of the NationalCooperative Soil Survey. This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Analysis of ‘Vegetation - Mendocino Cypress and Related Vegetation [ds2805]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2261c310-9408-4b75-9cc3-90c7cf4d4114 on 26 January 2022.

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

The Mendocino Pygmy Forest is one of the best-known examples of a rare natural community in California. The unique soil and climatic attributes and the resulting vegetation of the Mendocino coastal terraces described by Jenny et al (1969), Westman (1975), Westman and Whittaker (1975), Sholars (1979), Sholars (1982), Sholars (1984) and others are well- known in the scientific and conservation literature.The mapping and classification process assumed that the unique and biologically significant elements of the pygmy forest ecosystem were definable without a complete inventory of the surrounding regional vegetation and land-use patterns. The boundary of the mapped areas was created using existing geographic information on soils, topography, land use, along with fieldwork from previous efforts. Within that area, an array of vegetation samples were collected and classified representing the full array of vegetation patterns within it. The boundary was refined as part of the mapping process. It was later expanded to include property owned by the Mendocino Coast Park and Recreation District after receiving permission to conduct surveys as part of this project. (Polygons that would not have been mapped for the original project but are within the MCPRD property are marked “MCPRD Additional” in the Notes field.)The map was produced using a classification based on an analysis of surveys taken throughout the range of the oligotrophic areas supporting Pygmy Forest vegetation. This classification has been incorporated into the Manual of California Vegetation Online Database. The map classification is mostly at the Association Level of the NVCS hierarchy (12 types), with some at the Alliance Level (5 types) and Group Level (3 types), and 4 land use and water classes. It was hand-digitized using photointerpretation based on the 2014 NAIP Imagery, with other ancillary data used to help with the identification of vegetation types. The minimum mapping unit was 1 acre for vegetation types, and 0.25 acres for water, developed and agricultural type. The total area mapped was 9782 acres.An accuracy assessment performed on the map. The overall accuracy of each of the 5 most reliably sampled types was between 82 and 92 % accuracy, meeting minimum accuracy standards.For more information, see the supplemental information below and the report for the map cited in the references.ReferencesCalifornia Department of Fish and Wildlife, Vegetation Classification and Mapping Program. Classification and Mapping of Pygmy Forest and Related Mendocino Cypress (Hesperocyparis pygmaea) Vegetation, Mendocino and Sonoma Counties, California. CDFW; 11/2018. https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=161736A Manual of California Vegetation, Online Edition. http://www.cnps.org/cnps/vegetation/. California Native Plant Society, Sacramento, CA.USNVC [United States National Vegetation Classification]. http://usnvc.org/. 2017. United States National Vegetation Classification Database, V2.01. Federal Geographic Data Committee, Vegetation Subcommittee, Washington DCJenny, H. R.J. Arkley, and A.M. Schultz. 1969. The pygmy forest-podsol ecosystem and its dune associates of the Mendocino coast. Madroño20:60-74.Westman, W.E. 1975. Edaphic climax pattern of the pygmy forest region of California. Ecological Monographs30:279-338.Westman, W.E. and R.H. Whittaker. 1975. The pygmy forest region of northern California: studies on biomass and primary productivity. Journal of Ecology63:493-520.Sholars, R.E. 1979. Water relations in the pygmy forest of Mendocino County. Ph.D. diss. University of California, Davis.Sholars, R.E. 1982. The pygmy forest and associated plant communities of coastal Mendocino County, California; genesis, soils, vegetation. Black Bear Press, Mendocino, CA.Sholars, R.E. 1984. The pygmy forest of Mendocino. Fremontia12(3): 3-8.Bowles, C.J. and E. Cowgill. 2012. Discovering marine terraces using airborne LiDAR along the Mendocino-Sonoma coast, northern California. Geosphere8(2):386''402.Soil Survey Staff, Natural Resources Conservation Service (NRCS), United States Department of Agriculture. Web Soil Survey. Available online at https://websoilsurvey.nrcs.usda.gov/. Accessed [October 13, 2014].National Agriculture Imagery Program (NAIP), United States Department of Agriculture. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index

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

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The Santa Clara County Planning Office is part of the Department of Planning and Development. Their primary function is to plan and regulate land use and development within the unincorporated portions of Santa Clara County. Other responsibilities include policy analysis, GIS services, research and technical assistance relating to land use, housing, environmental protection, historic preservation and demographics. The Geographic Information Services Department has taken on all those activities related to GIS data and GIS process and procedures that cross organizational boundaries. Santa Clara County encompasses 15 cities and approximately 1.7 million people. This coverage can be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analyses of geospatial data.

The California Institute for Biodiversity (CIB) has received one time funding from the State of California to support the fungi and soil biodiversity ATBI (All Taxa Biodiversity Inventory) as part of the 30X30 Pathways Strategy. CIB’s goal is to develop a specimen-based DNA barcode reference library for all soil types in California. With a reference library of barcodes, we can identify thousands of organisms found in even small soil samples. Combined with other advanced methods, we can map relationships between soil biodiversity and carbon storage, water retention, agricultural productivity, and even disease and pest suppression. This dataset was collected with this over-arching purpose in mind and includes details pertaining to soil, insect, eDNA, and leaf litter field sample collections between May 2023 and April 2025.

This data package include GIS layers stored in Geodatabase. The layers describe the characteristics of the catchments along the southern coast of Santa Barbara County used in the article Aguilera, R., & Melack, J. M. (2018). Relationships among nutrient and sediment fluxes, hydrological variability, fire, and land cover in coastal California catchments. Journal of Geophysical Research: Biogeosciences, 123, 2568– 2589. https://doi.org/10.1029/2017JG004119. Catchment characteristics include: Land cover and land use based on hyperspectral imagery obtained by the Airborne Visible/Infrared Imaging Spectrometer; number of inhabitants based on population counts by block from the 2010 census spatial database; relief and slopes estimated from a 30 m digital elevation model; geological substrata obtained from geologic maps of California; soil textural types based on the Soil Survey Geographic data, and fire perimeters for the Gaviota, Gap, Tea and Jesusita fires.

This polygon shapefile depicts the farmland classification for soil map units in the County of Santa Clara, California as Prime Farmland, Farmland of Statewide Importance, Farmland of Local Importance or Unique Farmland. It identifies the location and extent of the soils that are best suited to food, feed, fiber, forage and oilseed crops. The United States Department of Agriculture Natural Resources Conservation Service policy and procedures on prime and unique farmlands are published in the "Federal Register," Vol. 43, No. 21, January 31, 1978. This layer is part of a collection of GIS data for Santa Clara County, California.

Introduction

This group of maps shows relative susceptibility of hill slopes to the initiation sites of rainfall-triggered soil slip-debris flows in southwestern California. As such, the maps offer a partial answer to one part of the three parts necessary to predict the soil-slip/debris-flow process. A complete prediction of the process would include assessments of "where", "when", and "how big". These maps empirically show part of the "where" of prediction (i.e., relative susceptibility to sites of initiation of the soil slips) but do not attempt to show the extent of run out of the resultant debris flows. Some information pertinent to "when" the process might begin is developed. "When" is determined mostly by dynamic factors such as rainfall rate and duration, for which local variations are not amenable to long-term prediction. "When" information is not provided on the maps but is described later in this narrative. The prediction of "how big" is addressed indirectly by restricting the maps to a single type of landslide process soil slip-debris flows.

The susceptibility maps were created through an iterative process from two kinds of information. First, locations of sites of past soil slips were obtained from inventory maps of past events. Aerial photographs, taken during six rainy seasons that produced abundant soil slips, were used as the basis for soil slip-debris flow inventory. Second, digital elevation models (DEM) of the areas that were inventoried were used to analyze the spatial characteristics of soil slip locations. These data were supplemented by observations made on the ground. Certain physical attributes of the locations of the soil-slip debris flows were found to be important and others were not. The most important attribute was the mapped bedrock formation at the site of initiation of the soil slip. However, because the soil slips occur in surficial materials overlying the bedrocks units, the bedrock formation can only serve as a surrogate for the susceptibility of the overlying surficial materials.

The maps of susceptibility were created from those physical attributes learned to be important from the inventories. The multiple inventories allow a model to be created from one set of inventory data and evaluated with others. The resultant maps of relative susceptibility represent the best estimate generated from available inventory and DEM data.

Slope and aspect values used in the susceptibility analysis were 10-meter DEM cells at a scale of 1:24,000. For most of the area 10-meter DEMs were available; for those quadrangles that have only 30-meter DEMs, the 30-meter DEMS were resampled to 10-meters to maintain resolution of 10-meter cells. Geologic unit values used in the susceptibility analysis were five-meter cells. For convenience, the soil slip susceptibility values are assembled on 1:100,000-scale bases. Any area of the 1:100,000-scale maps can be transferred to 1:24,000-scale base without any loss of accuracy. Figure 32 is an example of part of a 1:100,000-scale susceptibility map transferred back to a 1:24,000-scale quadrangle.

For lands used to produce crops, CEC developed a suitability model to simultaneously evaluate several factors that impact an area’s relative implication for croplands. In the CEC land use screens, implication is defined as a possible significance or a likely consequence of an action. For example, planning for energy infrastructure development in areas with more factors that support high-value croplands has implications for opportunities to preserve agricultural land. The variables used in the CEC Cropland Index Model contain information on soil quality (CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio), farmland designations (Prime Farmland, Unique Farmland and Farmland of Statewide Importance), and current existence of crops (as indicated by the California Statewide Crop Mapping). The CEC Cropland Index Model does not include statewide information for grazing lands or rangelands, and it is only applied to solar technology. Each input data layer is transformed onto a common scale and weighted according to each dataset’s relative importance. The result is a summation of the input data layers into a single-gridded map. This final model output provides a numerically weighted index of importance for croplands at a given location. The classified version of the model output, given in this dataset, partitions the CEC Cropland Index Model at the mean into areas of high and low implication. The high implication area is used as an exclusion in the CEC Land Use Screens for solar technology. These regions have a relatively higher implication for cropland than the lower implication region. The table below provides data sources that the CEC Cropland Index Model relies on. For a complete description of the model and its use in the 2023 CEC Land-Use Screens, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library. Dataset Name Source Usage Gridded Soil Survey Geographic (gSSURGO) Database Soil Survey Staff. 2020. "The Gridded Soil Survey Geographic (gSSURGO) Database for California." United States Department of Agriculture, Natural Resources Conservation Service. https://gdg.sc.egov.usda.gov/ Provides CA Revised Storie Index, Electrical Conductivity, and Sodium Adsorption Ratio for the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Important Farmland "2018 California Important Farmland.” Farmland Mapping and Monitoring Program." California Department of Conservation. https://www.conservation.ca.gov/dlrp/fmmp Prime Farmland, Unique Farmland, and Farmland of Statewide Importance is used in the CEC Cropland Index Model for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential California Statewide Crop Mapping (2019) "2019 California Statewide Crop Mapping." California Department of Water Resources. https://data.cnra.ca.gov/dataset/statewide-crop-mapping The footprint is used as part of the mask for the CEC Cropland Index Model’s domain of analysis for the Core and SB 100 Terrestrial Climate Resilience Screens for solar resource potential

Biomass estimates for shrubland-dominated ecosystems in southern California have, to date, been limited to national or statewide efforts which can underestimate the amount of biomass; are limited to one-time snapshots; or estimate aboveground live biomass only. We developed a consistent, repeatable method to assess four vegetative biomass pools from 2001-2023 for our southern California study area (totaling 6,441,208 ha), defined by the Level IV Ecoregions (Bailey 2016) that intersect with USDA Forest Service lands (Figure 1). We first generated aboveground live biomass estimates (Schrader-Patton and Underwood 2021), and then calculated belowground, standing dead, and litter biomass pools using field data in the peer-reviewed literature (Schrader-Patton et al. 2022) (Figure 2). Over half (52.3%) of the study area is shrubland, and our method accounts for three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding shrubs. We also generate biomass estimates for trees and herbs, giving a total of five life form/life history types. These data provide an important contribution to the management of shrubland-dominated ecosystems to assess the impacts of wildfire and management activities, such as fuel management and restoration, and for monitoring carbon storage over the long term. The biomass data are a key input into the online web mapping tool SoCal EcoServe, developed for US Department of Agriculture Forest Service resource managers to help evaluate and assess the impacts of wildfire on a suite of ecosystem services including carbon storage. The tool is available at https://manzanita.forestry.oregonstate.edu/ecoservices/ and described in Underwood et al. (2022). REFERENCES Bailey, R.G. 2016. Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Forest Service Research Data Archive. (Fort Collins, Colorado). https://doi.org/10.2737/RDS-2016-0003 Schrader-Patton, C.C. and E.C. Underwood. 2021. New biomass estimates for chaparral-dominated southern California landscapes. Remote Sensing, 13, 1581. https://doi.org/10.3390/rs13081581 Schrader-Patton et al. 2022. “Estimating Wildfire Impacts on the Biomass of Southern California’s Chaparral Shrublands.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Underwood et al. 2022. “Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Methods METHODS We generated spatial estimates of above ground live biomass (AGLBM, in kg/m2) for 2000-2021 for our southern California study area. The study area, totaling 6,441,208 ha, is defined by the 42 Level IV Ecoregions (Bailey 2016) that intersect the four southern US Department of Agriculture (USDA) National Forests in southern California (Figure 1). We created biomass raster layers (30m spatial resolution) by modeling a set of covariates (Normalized Difference Vegetation Index [NDVI], precipitation, solar radiation, actual evapotranspiration, aspect, slope, climatic water deficit, elevation, flow accumulation, landscape facets, hydrological recharge and runoff, and soil type) to predict AGLBM using 766 field plots of biomass from the USDA Forest Service Forest Inventory and Analysis (FIA); the Landfire Reference Database (LFRDB) plot data; and other research plots. The dates of field data spanned 2001-2012. The NDVI raster data were derived from Landsat TM/ETM+/OLI multispectral satellite data (onboard Landsat 5, 7, and 8, respectively). NDVI data were composited from all available Landsat images for the months of July and August for each year 2001-2023. We also downloaded annual precipitation data for each water year (October 1 - September 30) 2001-2021 from PRISM (http://www.prism.oregonstate.edu/). For each field plot, we extracted the raster values for all covariates; NDVI and precipitation data were matched to the year of plot visit. We predicted AGLBM using the set of 17 covariates (Random Forest [RF] algorithm in R statistical computing software). To create an AGLBM raster surface for each year 2001-2023, we used NDVI and precipitation raster data specific to each year in the RF (using predict function in the R raster module) (see Schrader-Patton and Underwood 2021 for details). To estimate other shrubland biomass pools (standing dead, litter, and below ground) we employed a multi-step process: 1) First, we segregated the study area by community type using the California Wildlife Habitat Relationships (CWHR) data (Mayer and Laudenslayer 1988). The wildland vegetation of the study area (excluding agricultural, urban, water, and barren classes) contains 45 CWHR classes, 14 of which are >=0.75% of the wildland vegetation in the study area. We divided these 14 classes into shrubland dominated versus non-shrubland dominated types (annual grass, oak, conifer, mixed hardwood) (Table 1). Table 1. The Community types (WHR class) that are >= 0.75% of all wildland vegetation in the study area and their % area of the southern California ecoregion

Community type (WHR class)

Vegetation type

Percent of wildland vegetation in study area

Mixed Chaparral

Shrub

29.2

Annual Grassland

Annual grass

15.9

Desert Scrub

Shrub

12.7

Coastal Scrub

Shrub

12.5

Coastal Oak Woodland

Oak

6.4

Chamise-Redshank Chaparral

Shrub

6.0

Pinyon-Juniper

Conifer

2.5

Montane Hardwood

Mixed hardwood

2.3

Blue Oak Woodland

Oak

2.0

Sierran Mixed Conifer

Conifer

1.2

Juniper

Conifer

1.1

Montane Hardwood-Conifer

Mixed hardwood-conifer

1.1

Montane Chaparral

Shrub

1.0

Sagebrush

Shrub

0.9

2) Second, for the shrubland types we determined the per pixel proportion of biomass by three plant life forms: tree, shrub, and herb. We further subdivided the shrub life form into three life history classes based on shrub post-fire regeneration strategies: Obligate Resprouters (OR), obligate seeders (OS), and facultative seeders (FS), providing five plant types in total. We created rasters depicting the proportion of biomass in each of the five plant types by first calculating the proportion of biomass in each type for the plots used in Schrader-Patton and Underwood (2021). The plot data contained individual plant species, crown width and height measurements. Using these measurements, we estimated the biomass for each individual plant within the plot by applying published allometric equations (see Schrader-Patton and Underwood 2021 for details). The individual plants in the plots were classified into the five plant types and the proportion of biomass in each type were calculated for each plot. A multinomial model was used to relate these proportions to a suite of geophysical and remote sensing variables which, in turn, was applied to raster surfaces of these predictors. The final outputs were raster maps of the proportion of biomass by life form (tree, shrub, herb) and, for shrubs, the proportion of biomass by life history type (OR, OS, and FS) (Underwood et al. in review). 3) Third, we estimated the standing dead, litter, and below ground biomass pools by either applying fractions of AGLBM gleaned the available published literature or by using biomass estimates in existing spatial datasets. The specific method used was dependent on the percentage of the WHR class in the study area and the vegetation type (shrub or non-shrub) (Figure 2).
a) For shrubland types >= 0.75% of all wildland vegetation in the study area (Mixed Chaparral, Desert Scrub, Coastal Scrub, Chamise Redshank Chaparral, Montane Chaparral, and Sagebrush), we used the proportion of the five plant types as a basis for applying the AGLBM factors from the literature. For litter estimates, we applied AGLBM factor of 0.78 (derived from Bohlman et al. 2018) to Mixed chaparral, Chamise-Redshank Chaparral, and Coastal scrub WHR classes. These shrubland types also contained tree and herb biomass. We estimated the litter and standing dead biomass for these plant types by multiplying the plant type proportion by AGLBM (Tree and herb AGLBM), or by the North American Wildland Fuels Database (NAWFD, Pritchard et al. 2018) litter biomass (Tree and herb litter and standing dead biomass), or by literature-derived factors (Tree and herb belowground biomass). Sagebrush, Montane chaparral, and Desert scrub were assigned litter biomass from the NAWFD data as these WHR types had no litter estimates in the literature.
b) For non-shrubland types >= 0.75% all wildland vegetation in the study area (Coastal Oak Woodland, Pinyon-Juniper, Montane Hardwood, Blue Oak Woodland, Sierran Mixed Conifer, Juniper, and Montane Hardwood-Conifer), the snag and litter NAWFD biomass estimates were used for standing dead and litter estimates, respectively. For belowground biomass, we used AGLBM factors from the literature based on the gross vegetation type (Oak, Conifer, or Mixed) and amount of total per pixel AGLBM. For example, for Oak WHR types (Coastal Oak Woodland, Blue Oak Woodland) <= 7 kg/m2 we used an AGLBM factor of 0.46 (see Mokany et al. 2006 for breakdown by class breaks). c) For all the remaining WHR classes (each < 0.75% of all wildland vegetation in the study area) and Annual Grasslands, we used the NAWFD snag and litter estimates (standing dead and litter biomass), and the California Air Resources Board (CARB, Battles et al. 2014) for our belowground estimates. The above ground, litter, standing dead, and below ground biomass raster layers for each

Two maps of vegetation transects relative to soil types and elevation at Carmel River State Beach. List of transect descriptions including soil type, habitat, slope, aspect, altitude, direction, and latitude and longitude of ends of transects.

This data is from the paper calcium is associated with specific soil organic carbon decomposition products, published in SOIL. DOI: https://doi.org/10.5194/soil-11-381-2025, 2025. This file contains CSVs with spectral data and bulk soil data and there is no specific program required to open this data. The data includes Scanning transmission X-ray microscopy carbon near-edge X-ray absorption fine structure spectroscopy. data from the measurement of samples from the Whole-soil Warming project, run by the Belowground Biogeochemistry team at Blodgett Forest Research Center, Georgetown, California run by the University of California, Berkeley. It also includes bulk soil chemical properties. The University of California's Blodgett Forest Research Station (Forest) is situated in the Sierra Nevada foothills (1370 m a.s.l.) near Georgetown, California. The samples were collected from here: 38.912013, -120.661469, https://maps.app.goo.gl/291bCJ1zVqUhgktz6. The Forest soils were characterised as Alfisols, which are equivalent to Dystric Cambisols (IUSS Working Group WRB, 2015), and formed in granitic parent materials, in a temperate climate, under thinned, mixed-coniferous forest (Fig. S3; Gaudinski et al., 2009). With these analyses we aimed to answer the question, is calcium associated with a specific type of organic matter enriched in aromatic and phenolic carbon at the microscale in samples from Blodgett Forest Research Center? and how does this specific type of carbon respond to experiments targetted at removing and adding calcium to the soils, specifically cation exchange and incubation after calcium addition? Abstract from the paper can be found below: Calcium (Ca) may contribute to the preservation of soil organic carbon (SOC) in more ecosystems than previously thought. Here we provide evidence that Ca is co-located with SOC compounds that are enriched in aromatic and phenolic groups, across different acidic soil-types and locations with different ecosystem properties, differing in terms of climate, parent material, soil type, and vegetation. In turn, this co-localised fraction of Ca-SOC is removed through cation-exchange, and the association is then only re-established during decomposition in the presence of Ca (Ca addition incubation). Thus, highlighting a causative link between decomposition and the co-location of Ca with a characteristic fraction of SOC. Decomposition increases the relative proportion of negatively charged functional groups, which can increase the propensity for the association between SOC and Ca, and in turn, this association inhibits dissolved organic carbon export or further decomposition. We propose that this mechanism could be driven by Ca hotspots on the microscale shifting local decomposition processes and thereby explaining the colocation of Ca with SOC of a specific composition across different acidic soil environments. Incorporating this biogeochemical process into Earth System Models could improve our understanding, predictions, and management of carbon dynamics in soils, and account for their response to Ca-rich amendments.