DOI: 10.5061/dryad.kwh70rz74
title: Spatial data for creating a thermal inertia index and incorporating it for conservation applications

description: 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  
description: 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 <strong>a.       </strong><strong>How we processed material into the hexagon framework</strong> <strong>b.      </strong><strong>The sequence of steps for each of the analyses presented in the results section of the main report</strong> <strong>a.  How we processed material into the hexagon framework</strong> 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 km<sup>2</sup> in within the 5 watersheds in northern California, and 40,895 km<sup>2</sup> 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 <strong>b.           The sequence of steps for each of the analyses presented in the results section of the main report</strong> <strong>Microrefugia – thermal buffering capacity</strong> 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 radiation as a yearly total in watt-hours/m<sup>2</sup>. We converted the output to megajoules as follows. Convert yearly watt-hours to daily megajoules We used a regionally calibrated conversion factor to determine air temperature from Watts/M<sup>2</sup>/time from a study in the Sierra Nevada Mountains (Curtis et al.; Flint et al. 2021), who determined the relationship of solar radiation with air temperature by using snowmelt and air temperature measurements in the field, which were then used to calibrate a solar radiation model for topographically diverse locations (Flint and Childs, 1987). The best fit resulted in an increase of 0.25 C for every 2.5 MJ/day of solar radiation above 7.5 MJ/day (Table below). Zonal Statistics was performed for each hexagon and the minimum value of solar radiation-driven air temperature was retained. Daily MJ/sec Degree C &lt; 7.5 0 7.5 - 10.5 0.25 10.5 - 12.5 0.5 12.5 - 15 0.75 15 - 17.5 1 17.5 - 20 1.25 20 - 22.5 1.5 22.5 - 25 1.75 25 - 27.5 2 27.5 - 30 2.25 &gt; 30 2.5   Calculate thermal buffer by hexagon: – Column: Thermal_Inertia Hexagon buffering capacity was calculated by adding the temperature equivalent of the lowest Solar Radiation load with the coldest point within each hexagon. The baseline temperature at the mean elevation in each hexagon was defined as its mean temperature for the time period 1981–2010. This permits a view of how long every hexagon can retain local temperatures by examining future climate change, and identification of which hexagons have already warmed more than their buffering capacity by comparing the baseline period to other time periods.   Average temperature through time: – Columns: tave_5180 through tave_7099mi8 Minimum and Maximum annual average temperature (tave; 270m rasters) were used from the Basin Characterization Model (BCM) outputs that generate monthly and yearly values. Eight 30-year time periods were calculated. Historic was 1951–1980, baseline is 1981–2010, and the futures are 2010–2039, 2040–2069, and 2070–2099 under 2 GCMs using the RCP8.5 emission scenario and climate change projection data generated for California’s 4<sup>th</sup> climate vulnerability assessment, the MIROC ESM model which is hotter and drier, and the CNRM CM5 model, which projects warmer and wetter conditions on average for California (Data from Thorne et al., 2017). Average monthly temperatures were calculated by averaging the minimum and maximum temperatures and then compiling the 30-year mean values. Zonal Statistics were then performed for each hex, and average temperature for the 8 30-year time periods mentioned above was retained.   Three applications of the microsite thermal buffering are presented in the study: vegetation/habitat types; landscape connectivity; and forest conservation. Each of the applications has data in this geodatabase. Vegetation/habitat types: – Columns: DomWHR, WHR_Name, WHR_GroupName Landscape Connectivity: – Column: Conn_Score Forest Conservation: – Columns: CanopyHt_Score, CanopyHt_m   Vegetation/habitat types: – Columns: DomWHR, WHR_Name, WHR_GroupName <strong><em>Dominant Habitat Types</em></strong> FVEG (https://frap.fire.ca.gov/mapping/gis-data/), a 30m raster of vegetation types uses the California Wildlife Habitat Relationships System (WHR; https://wildlife.ca.gov/Data/CWHR/Wildlife-Habitats) to classify the vegetation. The FVEG raster was converted to a polygon grid and unionized to our Hexagon grid to determine the dominant WHR type. We used FVEG to calculate: 1.       The major vegetation types. A crosswalk table between WHR types and major vegetation types was used to assign each hexagon a major vegetation type using the following rules: <strong>WHR Code</strong> <strong>WHR Name</strong> <strong>Major Vegetation Type</strong> ADS Alpine-Dwarf Shrub High Elevation Forests and Meadows AGS Annual Grassland Grasslands ASC Alkali Desert Scrub Arid Shrublands ASP Aspen High Elevation Forests and Meadows BAR Barren Barren BBR Bitterbrush Arid Shrublands BOP Blue Oak-Foothill Pine Oak Woodlands BOW Blue Oak Woodland Oak Woodlands COW Coastal Oak Woodland Oak Woodlands CPC Closed-Cone Pine-Cypress Chaparral CRC Chamise-Redshank Chaparral Chaparral CRP Cropland Agriculture CSC Coastal Scrub Chaparral DFR Douglas Fir Conifer Forest DGR Dryland Grain Crops Agriculture DOR Deciduous Orchard Agriculture EOR Evergreen Orchard Agriculture EPN Eastside Pine Conifer Forest EUC Eucalyptus Agriculture FEW Fresh Emergent Wetland Mesic IGR Irrigated Grain Crops Agriculture IRF Irrigated Row and Field Crops Agriculture IRH Irrigated Hayfield Agriculture JPN Jeffrey Pine Conifer Forest JUN Juniper Conifer Forest KMC Klamath Mixed Conifer Conifer Forest LAC Lacustrine Other LPN Lodgepole Pine Conifer Forest LSG Low Sage High Elevation Forests and Meadows MCH Mixed Chaparral Chaparral MCP Montane Chaparral Chaparral MHC Montane Hardwood-Conifer Hardwood-Conifer MHW Montane Hardwood Oak Woodlands MRI Montane Riparian Conifer Forest PAS Pasture Agriculture PGS Perennial Grassland Grasslands PPN Ponderosa Pine Conifer Forest RFR Red Fir Conifer Forest RIC Rice Agriculture RIV Riverine Other SCN Subalpine Conifer High Elevation Forests and Meadows SGB Sagebrush Arid Shrublands SMC Sierran Mixed Conifer Conifer Forest URB Urban Urban VIN Vineyard Agriculture VOW Valley Oak Woodland Oak Woodlands VRI Valley Foothill Riparian Oak Woodlands WFR White Fir Conifer Forest WTM Wet Meadow Mesic   2.       The thermal buffering capacity of all hexagons of each vegetation type was then rank-ordered, to identify the extents with different levels of thermal buffer potential. Landscape Connectivity: – Column: Conn_Score <strong><em>Landscape & Climate Connectivity</em></strong> There were two datasets that identify landscape connectivity across the region we were modeling: 1.       The Nature Conservancy (TNC): Omniscape 2.       California Essential Habitat Connectivity (CEHC) The first examines habitat continuity and tracks analog climates through time (TNC) and the second identifies cores and corridors and then ranks corridors according to how easily an animal could move across the terrain (CEHC). How was each input reclassed 1.       Omniscape/TNC a.       Received Connectivity raster data from TNC (https://omniscape.codefornature.org/) It was a 90m raster with 13 categories so we crosswalked the 13 categories to 4 and ranked them 0-3 (3 is high, 1 is low, 0 is limited connectivity). <strong>Connectivity Score</strong> <strong>Description</strong> 0 Limited regional connectivity potential 1 Intact landscape 2 Climate linkage (HADGEM2-ES) through an intact landscape 2 Climate linkage (CNRM_CM5) through an intact landscape 3 Climate linkage (both climate models) through an intact landscape 1 Multiple present-day linkage options 2 Climate linkage (HADGEM2-ES) among multiple present-day linkage options 2 Climate linkage (CNRM_CM5) among multiple present-day linkage options 3 Climate linkage (both climate models) among multiple present-day linkage options 1 Present-day linkage 1 Climate linkage (HADGEM2-ES) within a present-day linkage 1 Climate linkage (CNRM_CM5) within a present-day linkage 3 Climate linkage (both climate models) within a present-day linkage 2.       CEHC a.       Obtained the data from the California Department of Fish and Wildlife (CDFW; https://wildlife.ca.gov/Conservation/Planning/Connectivity/CEHC) We defined General Natural Landscape Blocks as Cores and Essential Connectivity Areas as Corridors. Corridors were split into 2 groups based on permeability (CDFW; ds620_EssentialConnectivityAreas_CaliforniaEssentialHabitatConnectivity; https://wildlife.ca.gov/Data/BIOS). We then combined the TNC and CEHC datasets to create an overall connectivity ranking between 0-5 (5 is high, 1 is low, 0 is limited connectivity)     CEHC     Other areas Corridor - Less Permeable Corridor - More Permeable core TNC 0 <strong>0</strong> <strong>1</strong> <strong>2</strong> <strong>2</strong> 1 (low) <strong>1</strong> <strong>2</strong> <strong>3</strong> <strong>3</strong> 2 <strong>2</strong> <strong>3</strong> <strong>4</strong> <strong>4</strong> 3 (high) <strong>3</strong> <strong>4</strong> <strong>5</strong> <strong>5</strong>   Forest Conservation: – Columns: CanopyHt_Score, CanopyHt_m <strong><em>Forest Structure/Canopy Height Score</em></strong> Received Fall 2019 Canopy Height Raster from Salo Sciences (https://forestobservatory.com/). Performed zonal statistics to get the average forest canopy height per hexagon. Classified the heights into 5 classes.   <strong>Canopy Height Score</strong> <strong>Height (m)</strong> 1 0 to 1 2 1 to 4 3 4 to 15 4 16 to 25 5 &gt; 25 <strong>References</strong>: Curtis, J. A., Flint, L.E., Flint, A. L., Lundquist, J. D., Hudgens, B., Boydston, E. E., & Young, J. K. (2014) Incorporating Cold-Air Pooling into Downscaled Climate Models Increases Potential Refugia for Snow-Dependent Species within the Sierra Nevada Ecoregion, CA. PLOS ONE, 9: e106984. Flint, L.E., Flint, A.L., & Stern, M.A. (2021) The Basin Characterization Model version 8 – A Regional Water Balance Software Package. U.S. Geological Survey Techniques and Methods, 6–H1, 85 p., https://doi.org/ 10.3133/ tm6H1. Flint, A. & Childs, S. W. (1987) Calculation of solar radiation in mountainous terrain. Agricultural and Forest Meteorology, 40, 233-249. International Civil Aviation Organization. 1993. Manual of the ICAO Standard Atmosphere (extended to 80 kilometers (3rd edition)). Montréal (Quebec) Canada. Thorne, J. H., Boynton, R. M., Flint, L. E., & Flint, A. L. (2015). The magnitude and spatial patterns of historical and future hydrologic change in California's watersheds. Ecosphere, 6(2), ES14-00300. Online https://esajournals.onlinelibrary.wiley.com/doi/10.1890/ES14-00300.1 Thorne, J. H., Boynton, R. M., Wayburn, L., & Urban, D. L. (2020). Planning for Species Adaptation and Climate Resilience in California’s Primary Source Headwaters. Pacific Forest Trust. San Francisco, CA. https://escholarship.org/uc/item/6r2801jn#main
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