# Spatial data for creating a thermal inertia index and incorporating it for conservation applications --- This repository contains supporting material for a journal article being submitted to one the journals published by the American Geophysical Union, titled Earth’s Future. The repository contains the following items: 1. Read me file of what is in the repository including methods associated with the geodatabase 2. File Geodatabase ## Description of the Data and file structure 1. Read me 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 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 (https://escholarship.org/uc/item/6r2801jn#main). 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 o A feature class containing all 408,948 hexagon grids used in this study o 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 o A 90m raster containing all 3 connectivity scores o 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 ## Sharing/access Information Links to other publicly accessible locations of the input data: Downscaled PRISM Tmax & Tmin – BCM – current & historical http://climate.calcommons.org/dataset/2014-CA-BCM Downscaled future climate projections MIROC & CNRM RCP8.5 http://climate.calcommons.org/dataset/2014-CA-BCM FVEG - CalFire (FRAP) https://frap.fire.ca.gov/mapping/gis-data/ California Essential Connectivity https://wildlife.ca.gov/Conservation/Planning/Connectivity/CEHC Omniscape Climate Connectivity https://omniscape.codefornature.org/ Canopy Height - SALO Sciences https://forestobservatory.com/