At 282 feet below sea level, Death Valley in the Mojave Desert, California is the lowest point of elevation in the United States (and North America). Coincidentally, Death Valley is less than 85 miles from Mount Whitney, the highest point of elevation in the mainland United States. Death Valley is one of the hottest places on earth, and in 1913 it was the location of the highest naturally occurring temperature ever recorded on Earth (although some meteorologists doubt its legitimacy). New Orleans Louisiana is the only other state where the lowest point of elevation was below sea level. This is in the city of New Orleans, on the Mississippi River Delta. Over half of the city (up to two-thirds) is located below sea level, and recent studies suggest that the city is sinking further - man-made efforts to prevent water damage or flooding are cited as one reason for the city's continued subsidence, as they prevent new sediment from naturally reinforcing the ground upon which the city is built. These factors were one reason why New Orleans was so severely impacted by Hurricane Katrina in 2005 - the hurricane itself was one of the deadliest in history, and it destroyed many of the levee systems in place to prevent flooding, and the elevation exacerbated the damage caused. Highest low points The lowest point in five states is over 1,000 feet above sea level. Colorado's lowest point, at 3,315 feet, is still higher than the highest point in 22 states or territories. For all states whose lowest points are found above sea level, these points are located in rivers, streams, or bodies of water.

The United States has an average elevation of roughly 2,500 feet (763m) above sea level, however there is a stark contrast in elevations across the country. Highest states Colorado is the highest state in the United States, with an average elevation of 6,800 feet (2,074m) above sea level. The 10 states with the highest average elevation are all in the western region of the country, as this is, by far, the most mountainous region in the country. The largest mountain ranges in the contiguous western states are the Rocky Mountains, Sierra Nevada, and Cascade Range, while the Appalachian Mountains is the longest range in the east - however, the highest point in the U.S. is Denali (Mount McKinley), found in Alaska. Lowest states At just 60 feet above sea level, Delaware is the state with the lowest elevation. Delaware is the second smallest state, behind Rhode Island, and is located on the east coast. Larger states with relatively low elevations are found in the southern region of the country - both Florida and Louisiana have an average elevation of just 100 feet (31m) above sea level, and large sections of these states are extremely vulnerable to flooding and rising sea levels, as well as intermittent tropical storms.

At 20,310 feet (6.2km) above sea level, the highest point in the United States is Denali, Alaska (formerly known as Mount McKinley). The highest point in the contiguous United States is Mount Whitney, in the Sierra Nevada mountain range in California; followed by Mount Elbert, Colorado - the highest point in the Rocky Mountains. When looking at the highest point in each state, the 13 tallest peaks are all found in the western region of the country, while there is much more diversity across the other regions and territories.

Despite being approximately 6,500 feet lower than Denali, Hawaii's Mauna Kea is sometimes considered the tallest mountain (and volcano) on earth. This is because its base is well below sea level - the mountain has a total height of 33,474 feet, which is almost 4,500 feet higher than Mount Everest.

The East Tehama herd is the largest migratory population of mule deer in California (Hill and Figura, 2020). Population numbers peaked in the 1960s, but have declined in recent decades (Ramsey and others, 1981; California Department of Fish and Wildlife unpublished data). These mule deer migrate from a lower elevation winter range in the foothills east of the Sacramento Valley to upper elevation summer ranges in the southern Cascades and northern Sierra Nevada. Although portions of the herd winter on the California Department of Fish and Wildlife’s Tehama Wildlife Area and other public lands, the winter range also comprises many private ranchlands. The herd’s summer range includes significant portions of Lassen National Forest as well as Lassen Volcanic National Park and private timberlands. Primarily oak woodlands and annual grasslands characterize the winter range, while the summer range consists of conifer forests, montane meadows, and montane chaparral. Potential threats to the herd include habitat changes resulting from fire management (including fire suppression and catastrophic wildfires), forest succession, vegetation management, and climate change. A small percentage of the herd are residents, inhabiting areas along the Sacramento River and areas of irrigated agriculture. These mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the East Tehama population in California. They were developed from 63 migration sequences collected from a sample size of 33 animals comprising GPS locations collected every 1-23 hours.

The Sierra Nevada is a tectonic uplift mountain range with a gradual gain in elevation on the west side and a steep escarpment on the east. Most of the mapped locations are east of the Sierran crest. The climate in the region is Mediterranean, with most of the precipitation in the winter months coming as snow at high elevations, but with some monsoonal precipitation in the summer, particularly in the southern end of the range. There is a very steep gradient in decreasing precipitation eastward from the Sierran crest as a result of rain-shadow orographic effects from the predominately eastward-moving Pacific frontal storms. PRISM-estimated (Daly et al. 1994) annual precipitation for the RIF locations ranged from 580 to 1880 mm; July precipitation from 5 to 33 mm. Mean annual maximum temperatures ranged from 1.8 to 15.2 oC, January from -5.7 to 6.2 oC, and July from 11 to 26.3 oC. Mean annual minima ranged from -8.1 to -0.4 oC, January from -15 to -7 oC, and July from 1.2 to 8.9 oC. The highest temperatures and lowest precipitation were largely at relict Pleistocene rock glaciers, which tended to be farthest east of the Sierran crest and lower in elevation. We mapped over 430 RIFs, based on field surveys and grouped them into six classes based on morphology and location. These categories constitute a greater range of frozen-ground features than are commonly described in rock-glacier surveys. Although granitic substrates dominate the Sierra Nevada, they do not in the eastern escarpment, so substrates in the RIF database are about equally divided between granitic and metamorphic. Ages of rock glaciers ranged from current (active) to relict (late Pleistocene). We interpreted the presence of outlet springs, basal lakes, suspended silt in outlet streams, and fringing phreatophytic vegetation, in addition to morphologic indications of current rock movement, as evidence for interstitial ice, either persistent or seasonal.One comma-delimited ASCII file containing information for 430 rock-ice features is available.

The Sierra Nevada is a tectonic uplift mountain range with a gradual gain in elevation on the west side and a steep escarpment on the east. Most of the mapped locations are east of the Sierran crest. The climate in the region is Mediterranean, with most of the precipitation in the winter months coming as snow at high elevations, but with some monsoonal precipitation in the summer, particularly in the southern end of the range. There is a very steep gradient in decreasing precipitation eastward from the Sierran crest as a result of rain-shadow orographic effects from the predominately eastward-moving Pacific frontal storms. PRISM-estimated (Daly et al. 1994) annual precipitation for the RIF locations ranged from 580 to 1880 mm; July precipitation from 5 to 33 mm. Mean annual maximum temperatures ranged from 1.8 to 15.2 oC, January from -5.7 to 6.2 oC, and July from 11 to 26.3 oC. Mean annual minima ranged from -8.1 to -0.4 oC, January from -15 to -7 oC, and July from 1.2 to 8.9 oC. The highest temperatures and lowest precipitation were largely at relict Pleistocene rock glaciers, which tended to be farthest east of the Sierran crest and lower in elevation. We mapped over 430 RIFs, based on field surveys and grouped them into six classes based on morphology and location. These categories constitute a greater range of frozen-ground features than are commonly described in rock-glacier surveys. Although granitic substrates dominate the Sierra Nevada, they do not in the eastern escarpment, so substrates in the RIF database are about equally divided between granitic and metamorphic. Ages of rock glaciers ranged from current (active) to relict (late Pleistocene). We interpreted the presence of outlet springs, basal lakes, suspended silt in outlet streams, and fringing phreatophytic vegetation, in addition to morphologic indications of current rock movement, as evidence for interstitial ice, either persistent or seasonal.One comma-delimited ASCII file containing information for 430 rock-ice features is available.

We assessed the response of densely forested watersheds with little apparent annual water limitation to forest disturbance and climate variability, by studying how past wildfires changed forest evapotranspiration and what past evapotranspiration patterns imply for the availability of subsurface water storage for drought resistance. We determined annual spatial patterns of evapotranspiration using a top–down statistical model, correlating measured annual evapotranspiration from eddy-covariance towers across California with normalized difference vegetation index (NDVI) measured by satellite and with annual precipitation. The study area was the Yuba and American River watersheds, two densely forested watersheds in the northern Sierra Nevada. Wildfires in the 1985–2015 period resulted in significant post-fire reductions in evapotranspiration for at least 5 years and in some cases for more than 20 years. The levels of biomass removed in medium-intensity fires (25–75% basal area loss), similar to magnitudes expected from forest treatments for fuel reduction and forest health, reduced evapotranspiration by as much 150–200 mm year−1 for the first 5 years. Rates of recovery in post-wildfire evapotranspiration confirm the need for follow-up forest treatments at intervals of 5–20 years to sustain lower evapotranspiration, depending on local landscape attributes and interannual climate. Using the metric of cumulative precipitation minus evapotranspiration (P-ET) during multiyear dry periods, we found that forests in the study area showed little evidence of moisture stress during the 1985–2018 period of our analysis, owing to relatively small reliance on interannual subsurface water storage to meet dry-year evapotranspiration needs of vegetation. However, more severe or sustained drought periods will push some lower-elevation forests in the area studied toward the cumulative P-ET thresholds previously associated with widespread forest mortality in the southern Sierra Nevada.

Canada's relief is shown by a colour ramp to show elevation ranges. The highest values appear in Western Canada where the Rocky Mountains are located and also in Canada's North (on parts of Ellesmere and Baffin Islands). The lowest elevation values exist along all the coastlines and the areas of least topography exists in the prairies of Central Canada. The map also depicts four photographic images of Canadian terrain as well as tabular data on the highest elevations in Canada by Province and Territory.

Total habitable area projections (C. maritimus) for the year 2050 and 2100.

Representative concentration pathway scenarios and associated SLR projections.

This publication includes forest composition and structure data from two observational field studies conducted across the Abies concolor-Abies magnifica ecotone in the central Sierra Nevada range of California, and data from a growth chamber experiment designed to evaluate the sensitivity of Abies concolor and Abies magnifica seedling growth and survival to current and projected future climate conditions. The 1981 field study includes tree size and abundance measurements collected in 30 plots located on sites with equal abundances of Abies concolor and Abies magnifica overstory trees along the Tioga Pass Road and Glacier Point Roads in Yosemite National Park, California. The 2016 field study includes tree size and abundance measurements collected in 60 plots randomly located on north-facing aspects across an elevation gradient that spans the lower and upper elevation bounds of the Abies concolor and Abies magnifica ecotone in the central Sierra Nevada Range of California. Plots in 2016 were distributed across the Tioga Pass Road and Glacier Point areas in Yosemite National Park, and the Crabtree and Herring Creek areas in the Stanislaus National Forest. Also included are historic and projected future climate data (1981-2010, RCP 8.5 2040-2069, RCP 8.5 2070-2099) extracted at 2016 plot locations using ClimateNA software. Finally, seedling survival and biomass growth measurements were collected in a fully factorial growth chamber experiment that assessed the effects of present (2001-2010), projected mid-21st century (2040-2069), and projected late-21st century (2070-2099) temperature, soil drying, and growing season length conditions. Each of 27 treatment combinations was replicated with 14 seedlings per species. Climate conditions were based on predictions made by the Canadian Centre for Climate Modelling and Analysis (CanESM2) under the RCP 8.5 emissions forcing scenario.2016 Field Observational Study: Seedling, sapling and tree demography was assessed across an elevation gradient that encompassed the lower and upper elevation bounds of the Abies concolor and Abies magnifica ecotone in the central Sierra Nevada Range of California. The purposes of these data were to a) determine the lower and upper elevational bounds of the ecotone, b) evaluate macro-climate drivers of species-specific tree abundances, and c) determine whether species demography is shifting under recent climate conditions.

1981 Field Observational Study: Seedling, sapling, and tree composition was assessed in an Abies concolor and Abies magnifica ecotone in the central Sierra Nevada Range of California to determine the environmental conditions encountered in these ecotone communities, and to determine site differentiation between Abies concolor and Abies magnifica tree species.

2016 Growth Chamber Experiment: The sensitivity of Abies concolor and Abies magnifica seedlings growth and survival to current and projected climate conditions was tested in a growth chamber experiment to better understand how these two species might respond to future climate conditions.These data were published on 07/15/2021. Minor metadata updates were made on 11/22/2024.

The National Ecological Framework for Canada's "Elevation by Ecodistrict” dataset provides elevation information for ecodistrict framework polygons, in meters. It includes codes and descriptions for minimum elevation, maximum elevation, mean elevation and the difference in elevation.

Total habitable area projections (L. ramosissimum) for the year 2050 and 2100.

Lidar data was collected within the Klamath Forest region between February 16 2105 and March 08 2015 for the USDA Forest Service between. The critical challenge for this project was to acquire the data before the leaf-on conditions but without snow on the highest peaks. For this particular reason, the Happy Camp Complex project area was split into Northern and Southern subdivisions. This dataset covers the Northern section of the Happy Camp Complex which represents the lower elevation area of the two subdivisions.

Canada's relief is shown by a colour ramp to show elevation ranges. The highest values appear in Western Canada where the Rocky Mountains are located and also in Canada's North (on parts of Ellesmere and Baffin Islands). The lowest elevation values exist along all the coastlines and the areas of least topography exists in the prairies of Central Canada. The map also depicts four photographic images of Canadian terrain as well as tabular data on the highest elevations in Canada by Province and Territory.

All available bathymetry and related information for Lower Mann Lake were collected and hard copy maps digitized where necessary. The data were validated against more recent data (Shuttle Radar Topography Mission 'SRTM' imagery and Indian Remote Sensing 'IRS' imagery) and corrected where necessary. The published data set contains the lake bathymetry formatted as an Arc ascii grid. Bathymetric contours and the boundary polygon are available as shapefiles.

description: A high-resolution (10-meter per pixel) digital elevation model (DEM) was created for the Sacramento-San Joaquin Delta using both bathymetry and topography data. This DEM is the result of collaborative efforts of the U.S. Geological Survey (USGS) and the California Department of Water Resources (DWR). The base of the DEM is from a 10-m DEM released in 2004 and updated in 2005 (Foxgrover and others, 2005) that used Environmental Systems Research Institute(ESRI), ArcGIS Topo to Raster module to interpolate grids from single beam bathymetric surveys collected by DWR, the Army Corp of Engineers (COE), the National Oceanic and Atmospheric Administration (NOAA), and the USGS, into a continuous surface. The Topo to Raster interpolation method was specifically designed to create hydrologically correct DEMs from point, line, and polygon data (Environmental Systems Research Institute, Inc., 2015). Elevation contour lines were digitized based on the single beam point data for control of channel morphology during the interpolation process. Checks were performed to ensure that the interpolated surfaces honored the source bathymetry, and additional contours and(or) point data were added as needed to help constrain the data. The original data were collected in the tidal datum Mean Lower or Low Water (MLLW), or the National Geodetic Vertical Datum of 1929 (NGVD29). All data were converted to NGVD29. The 2005 USGS DEM was updated by DWR, first by converting the DEM to the current modern datum of National Geodetic Vertical Datum of 1988 (NGVD88) and then by following the methodology of the USGS DEM, established for the 2005 DEM (Foxgrover and others, 2005) for adding newly collected single and multibeam bathymetric data. They then included topographic data from lidar surveys, providing the first DEM that included the land/water interface (Wang and Ateljevich, 2012). The USGS further updated and expanded the DWR DEM with the inclusion of USGS interpolated sections of single beam bathymetry data collected by the COE and USGS scientists, expanding the DEM to include the northernmost areas of the Sacramento-San Joaquin Delta, and by making use of a two-meter seamless bathymetric/topographic DEM from the USGS EROS Data Center (2013) of the San Francisco Bay region. The resulting 10-meter USGS DEM encompasses the entirety of Suisun Bay, beginning with the Carquinez Strait in the west, east to California Interstate 5, north following the path of the Yolo Bypass and the Sacramento River up to Knights Landing, and the American River northeast to the Nimbus Dam, and south to areas around Tracy. The DEM incorporates the newest available bathymetry data at the time of release, as well as including, at minimum, a 100-meter band of available topography data adjacent to most shorelines. No data areas within the DEM are areas where no elevation data exists, either due to a gap in the land/water interface, or because lidar was collected over standing water that was then cut out of the DEM.; abstract: A high-resolution (10-meter per pixel) digital elevation model (DEM) was created for the Sacramento-San Joaquin Delta using both bathymetry and topography data. This DEM is the result of collaborative efforts of the U.S. Geological Survey (USGS) and the California Department of Water Resources (DWR). The base of the DEM is from a 10-m DEM released in 2004 and updated in 2005 (Foxgrover and others, 2005) that used Environmental Systems Research Institute(ESRI), ArcGIS Topo to Raster module to interpolate grids from single beam bathymetric surveys collected by DWR, the Army Corp of Engineers (COE), the National Oceanic and Atmospheric Administration (NOAA), and the USGS, into a continuous surface. The Topo to Raster interpolation method was specifically designed to create hydrologically correct DEMs from point, line, and polygon data (Environmental Systems Research Institute, Inc., 2015). Elevation contour lines were digitized based on the single beam point data for control of channel morphology during the interpolation process. Checks were performed to ensure that the interpolated surfaces honored the source bathymetry, and additional contours and(or) point data were added as needed to help constrain the data. The original data were collected in the tidal datum Mean Lower or Low Water (MLLW), or the National Geodetic Vertical Datum of 1929 (NGVD29). All data were converted to NGVD29. The 2005 USGS DEM was updated by DWR, first by converting the DEM to the current modern datum of National Geodetic Vertical Datum of 1988 (NGVD88) and then by following the methodology of the USGS DEM, established for the 2005 DEM (Foxgrover and others, 2005) for adding newly collected single and multibeam bathymetric data. They then included topographic data from lidar surveys, providing the first DEM that included the land/water interface (Wang and Ateljevich, 2012). The USGS further updated and expanded the DWR DEM with the inclusion of USGS interpolated sections of single beam bathymetry data collected by the COE and USGS scientists, expanding the DEM to include the northernmost areas of the Sacramento-San Joaquin Delta, and by making use of a two-meter seamless bathymetric/topographic DEM from the USGS EROS Data Center (2013) of the San Francisco Bay region. The resulting 10-meter USGS DEM encompasses the entirety of Suisun Bay, beginning with the Carquinez Strait in the west, east to California Interstate 5, north following the path of the Yolo Bypass and the Sacramento River up to Knights Landing, and the American River northeast to the Nimbus Dam, and south to areas around Tracy. The DEM incorporates the newest available bathymetry data at the time of release, as well as including, at minimum, a 100-meter band of available topography data adjacent to most shorelines. No data areas within the DEM are areas where no elevation data exists, either due to a gap in the land/water interface, or because lidar was collected over standing water that was then cut out of the DEM.

This repository contains supporting material for a journal article being submitted to one of the journals published by the American Geophysical Union, titled Earth’s Future. The repository contains the following items: 1. README file of what is in the repository including methods associated with the geodatabase 2. File Geodatabase 1. README file The files collected here relate to a study being submitted to the American Geophysical Union’s journal, Earth’s Future. The title of the paper being submitted is, “The contribution of Microrefugia to landscape thermal inertia for climate-adaptive conservation and adaptation strategies.” The study was conducted across 40,250 km2 of complex mountainous terrain in Northern California. The objective of the study was to consider whether it was possible to identify the relative strength of microrefugia systematically in order to provide conservation and climate-adaptation strategies with information that could help with prioritizing actions. We selected an operational scale of 10 ha (25 acres) as a scale that is suitable for various types of landscape planning exercises, and created a hexagon grid for the region. We calculated the mean value for multiple variables and appended them into the hexagons. For thermal inertia, we calculated the mean elevation per hexagon and then its coolest (highest) point using an environmental lapse rate. We also calculated solar energy loading, calculated the mean solar load per hexagon, and calculated its effect on air temperature. We combined these two temperature metrics to identify how much thermal buffering capacity each hexagon contains, as measured by how much warming it could experience before the mean temperature, as determined from a baseline time period, is no longer found anywhere within the hexagon. We tied the mean annual temperature from 1981–2010 to the mean elevation in each hexagon, as well as a temperature from an earlier period, and from several future periods, based on global circulation models. The study shows how long current (baseline) climate conditions found in each hexagon may persist and shows how the resulting map of landscape thermal inertia can be used when considering natural vegetation types for conservation, identifying which parts of high-priority wildlife corridors have the greatest capacity to retain their current climate conditions, and what the potential for retaining baseline climate conditions is for areas with late-seral forest conditions as represented by forest canopy height. The methods section below describes the data used in the study to create the data in the geodatabase that is posted here. The Geodatabase itself provides all the data needed to replicate the various results presented in the paper. Further information can be found in Thorne et al. 2020. That report is more extensive than the results in our associated paper, but it contains more information on the calculation of various metrics associated with and was the foundation from which we developed this study. The report is provided here in order to keep all the relevant materials compiled for potential use by others. 2. File Geodatabase The geodatabase is provided as a separate file. Name: ThermalInertiaIndex.gdb Contents:

AllHexagons

A feature class containing all 408,948 hexagon grids used in this study Fields within the feature class:

Id

A unique ID for each hexagon

Watershed

Watershed the hexagon falls within

DomWHR

Habitat type (WHR) that had the majority coverage within the hexagon

WHR_Name

Descriptive name of the habitat type

WHR_GroupName

Major vegetation type

CanopyHt_Score

Canopy Height Score ranging from 1 (under 1m) to 5 (over 25m)

CanopyHt_m

Average canopy height within the hexagon (m)

Conn_Score

Connectivity Score ranging from 1 (low) to 5 (high)

dem10m

Average elevation within the hexagon (m)

dem10m_min

Minimum elevation within the hexagon (m)

dem10m_max

Maximum elevation within the hexagon (m)

SRtemp_min

The lowest Solar Radiation load within the hexagon (degree C)

ElevLR_NegEff2

Effect of elevation on air temperature (degree C)

Thermal_Inertia

Hexagon buffering capacity (degree C)

tave_5180

Average temperature 1951-1980

tave_8110

Average temperature 1981-2010

tave_1039mi8

Average temperature 2010-2039 (MIROC-ESM RCP 8.5)

tave_4069mi8

Average temperature 2040-2069 (MIROC-ESM RCP 8.5)

tave_7099mi8

Average temperature 2070-2099 (MIROC-ESM RCP 8.5)

tave_1039cn8

Average temperature 2010-2039 (CNRM-CM5 RCP 8.5)

tave_4069cn8

Average temperature 2040-2069 (CNRM-CM5 RCP 8.5)

tave_7099cn8

Average temperature 2070-2099 (CNRM-CM5 RCP 8.5)

Connectivity_Scores

90m raster containing all 3 connectivity scores Fields within the raster:

TNC_Conn_Score

Connectivity Score from reclassed TNC/Omniscape

CEHC_Score

Connectivity Score from reclassed California Essential Habitat Connectivity

Combined_Score

Overall Connectivity Score

Methods These methods describe the steps taken to calculate the attribute columns in the associated database. Compilations were done on publicly available data such as digital elevation models, climate data and others. For references to the public base data used, please see references in Table 1. There are two sections a. How we processed material into the hexagon framework b. The sequence of steps for each of the analyses presented in the results section of the main report a. How we processed material into the hexagon framework We created a geodatabase of 10 ha hexagons for the region in order to summarize the spatial data in this study into spatial units that are comparable across the region but that also represent an area size that is relevant for site-level plans such as landscape connectivity or forest conservation. The hexagon geodatabase covers 28,269 km2 in within the 5 watersheds in northern California, and 40,895 km2 in the 5 watersheds plus a 10 km buffer area. Integrating data into the hexes Data from a variety of grid scales, including 10, 30, 90, and 270m was added using the ArcGIS sample tool with the Hexagon centroids to sample the 270m resolution data, and the zonal statistics tool within Hexagon boundaries for raster data with smaller grid cell sizes.

This study used four types of data (Table 1):

Air temperature & topographic – Topographic data was used to calculate microrefugia buffering capacity for each hexagon. Temperature data was used to evaluate the effect of historical and projected future warming on the ability of local sites to retain baseline temperature conditions. Habitats / Dominant Vegetation Types – Habitat data was used to profile the presence and extent of microrefugia by habitat type for the region Landscape Connectivity Models – were used to find microrefugia in areas that are highly ranked for landscape connectivity Forest Structure data – was used to identify where large, late seral trees occupy microrefugia sites.

Microrefugia – Air temperature & topographic

National Elevation Dataset

www.usgs.gov/core-science-systems/ngp/tnm-delivery

Raster - 10m

Solar Radiation Model

Developed at UC Davis for this study from 25m DEM

Raster - 25m

Environmental Lapse Rate Model

Developed at UC Davis for this study from 10m DEM

Raster - 10m

Linking Temperature to Hexagons

Downscaled PRISM Tmax & Tmin – BCM – current & historical

http://climate.calcommons.org/dataset/2014-CA-BCM

Raster – 270 m

Downscaled future climate projections MIROC & CNRM RCP8.5

http://climate.calcommons.org/dataset/2014-CA-BCM

Raster – 270 m

Habitats / Dominant Vegetation Types

FVEG - CalFire (FRAP)

https://frap.fire.ca.gov/mapping/gis-data/

Raster - 30m

Vegetation and Climate Refugia

Vegetative Climate Exposure (UCD Modeling)

Raster - 270m

Landscape Connectivity Models

California Essential Connectivity

https://wildlife.ca.gov/Conservation/Planning/Connectivity/CEHC

Polygon

Omniscape Climate Connectivity

https://omniscape.codefornature.org/

90 m

Forest Structure

Canopy Height - SALO Sciences

https://forestobservatory.com/

Raster - 10m

Table 1: Data sources b. The sequence of steps for each of the analyses presented in the results section of the main report Microrefugia – thermal buffering capacity Thermal buffering capacity combined two metrics that represent potential modifications to the air temperature in each 10-ha hexagon. First, a 10m digital elevation model was used to calculate the variation in air temperature within each hexagon due to variations in elevation, using a standard environmental lapse rate. Second, the influence of solar radiation on air temperature was calculated. These two metrics were combined. Elevational Effect on Air Temperature Column: ElevLR_NegEff2 Zonal Statistics was performed on a 10m DEM for each hex. The range of elevation was used with environmental lapse rate to calculate “buffering capacity” within each Hexagon. We used an environmental lapse rate of 0.00649606 C⁰/ meter (International Civil Aviation Organization, 1993) to calculate the range of temperatures within the hexagon. To calculate the effect of elevation on air temperature within each hexagon we used the following equation: (Average Elevation – Maximum Elevation) x 0.00649606

Solar Radiation Effect on Air Temperature: – Column: SRtemp_min We ran the analysis on a 25 m-resolution DEM. We calculated annualized solar radiation via the r.sun model available in GRASS 7.8 (https://grass.osgeo.org/grass70/manuals/r.sun.html) which calculates direct, diffuse, and reflected solar irradiation for a given day, location, topography, and atmospheric conditions. We assumed clear-sky conditions to run this model, and ran the model for 2 days in each month, from which we calculated solar