This README.txt file was generated on 2021-07-29 by Isaac W. Park GENERAL INFORMATION 1. Title of Dataset: 2. Author Information A. Principal Investigator Contact Information Name: Isaac W Park Institution: University of California - Santa Barbara Address: 4117 Life Sciences Building Email: isaac_park@ucsb.edu 3. Date of data collection: 2020-09-01 4. Geographic location of data collection: California 5. Information about funding sources that supported the collection of the data: These data were developed with support by the University of California's National Laboratories (UCNL) Laboratory Fees grant program under grant number LFR-18-542511 (as a part of the California Ecosystems Futures project), as well as by by the California Department of Forestry and Fire Protection, Fire and Resource Assessment Program (CAL FIRE – FRAP, https://frap.fire.ca. gov/) and California Climate Investments, under CAL FIRE contract numbers 8CA03698 and 8GG20803. SHARING/ACCESS INFORMATION 1. Licenses/restrictions: This work is licensed under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. 2. Links to publications that cite or use the data: 5. These data were derived from the following sources: a) FRAP fire perimeter data (https://frap.fire.ca.gov/frap-projects/fire-perimeters/) b) 2018 Basin Characterization data (https://frap.fire.ca.gov/frap-projects/fire-perimeters/) c) CALFIRE FRAP FVEG vegetation data [ds1327] (https://map.dfg.ca.gov/metadata/ds1327.html) d) 2016 Landfire Existing Vegetation Type data (https://landfire.gov/evt.php) 6. Recommended citation for this dataset: Park I. W.,Fauss, K., Moritz M. Forecasting Live Fuel Moisture of Adenostema fasciculatum and Its Relationship to Regional Wildfire Dynamics across Southern California Shrublands. FIRE. In press DATA & FILE OVERVIEW 1. File List: Sites: Historical records of observed LFM at each site used in this study SiteData_Full: Geospatial records of locations, years, months, and species of all live fuel moisture observations used in this study. FRAP_Fire_Scars: annual 1km rasters identifying those pixels in which fires occurred each year (derived from FRAP fire perimeter Data) Assembled_Data: data recording all climate conditions across California at 1-km resolution FINAL_TMAX_PPT: Monthly predictions of live fuel moisture across California Burn_Scars: shapefile of all fire perimeters used in this study State_Mask: raster masking areal outside of California or 1-km pixels consisting of >=50% cover by water LFM_Normalization_Files: Predicted and observed LFM corresponding to each fire event used in this study, as well as the corresponding percentile of predicted LFM relative to predicted LFM across the entire study area and time period 2. Relationship between files, if important: all files in the following folders were used to create files in FINAL_TMAX_PPT: -Sites -SiteData_Full -Assembled_Data -State_Mask files in the folder FINAL_TMAX_PPT were developed from data in the folders: -LFM_Normalization_Files METHODOLOGICAL INFORMATION 1. Methods for processing the data: In seasonally dry environments, the amount of water held in living plant tissue—live fuel moisture (LFM)—is central to vegetation flammability. LFM-driven changes in wildfire size and frequency are particularly important throughout southern California shrublands, which typically produce intense, rapidly spreading wildfires. However, the relationship between spatiotemporal variation in LFM and resulting long-term regional patterns in wildfire size and frequency within these shrublands is less understood. In this study, we demonstrated a novel method for forecasting the LFM of a critical fuel component throughout southern California chaparral, Adenostema fasciculatum (chamise) using gridded climate data. We then leveraged these forecasts to evaluate the historical relationships of LFM to wildfire size and frequency across chamise-dominant California shrublands. We determined that chamise LFM is strongly associated with fire extent, size, and frequency throughout southern California shrublands, and that LFM–wildfire relationships exhibit different thresholds across three distinct LFM domains. Additionally, the cumulative burned area and number of fires increased dramatically when LFM fell below 62%. These results demonstrate that LFM mediates multiple aspects of regional wildfire dynamics, and can be predicted with sufficient accuracy to capture these dynamics. Furthermore, we identified three distinct LFM ‘domains’ that were characterized by different frequencies of ignition and spread. These domains are broadly consistent with the management thresholds currently used in identifying periods of fire danger. 3. This code requires Python v3.7 DATA-SPECIFIC INFORMATION FOR: Sites: folder of txt files Number of Cases: 155 Number of Rows: variable Variable List: (all data corresponds to values within a given pixel over a three-year period) GACC- group collection cade State- state of collection site Group- collection group Site- site name Date-date of observation Fuel- type of fuel Percent - observed LFM DATA-SPECIFIC INFORMATION FOR SiteData_Full: locational data for all sites at which LFM was observed Number of Rows: 170 Variable List: Name- Site Name web_lat- Latitude os recorded in original data web_long- Longitude os recorded in original data Lat_deg_ra- Degrees Latitude Lon_deg_ra- Degrees Longitude Lat_min_ra- Minutes Latitude Lon_min_ra- Minutes Longitude Lat_sec_ra- Seconds Latitude Lon_sec_ra- Seconds Longitude Lat_Decima- Decimal Latitude Lon_Decima- Decimal Longitude Elevation- Elevation Slope- Slope Aspect- Aspect Lat_Proj- projected Latitude Lon_Proj- projected Longitude pixel_id- unique numerical ID for each pixel DATA-SPECIFIC INFORMATION FOR Burn_Scars: shapefile of all fire perimeters used in this study Variable List: Year- Year of fire ALARM_DATE- date of fire alarm (year) ALARM_DA_1- date of fire alarm (month) ALARM_DA_2- date of fire alarm (day of month) STATE- state of fire AGENCY- recording agency UNIT_ID- unit in which fire occurs FIRE_NAME- Name of Fire INC_NUM- incident number ALARM_DA_3- date of first alarm ALARM_DA_3- date of last alarm(if present) CAUSE- code for cause of fire DATA-SPECIFIC INFORMATION FOR FRAP_Assembled_Data: folder of 1-km resolution csv files identifying conditions in each month and year. Variables: pixel_id- unique numerical ID for each pixel Elev- Elevation Region- Ecoregion of pixel X_Pos- X Position of pixel Y_Pos- Y Position of pixel tmx_mean_1len_2lag- mean Maximum Temperature two months prior ppt_mean_1len_2lag- Precipitation two months prior tmx_mean_6len_0lag- mean Maximum Temperature over prior six months ppt_mean_6len_0lag- mean Maximum Temperature over prior six months values of -9999 represent null values in all cases DATA-SPECIFIC INFORMATION FOR State_Mask: folder of 1-km resolution raster StatePoly_buf_water_Flint.tif: Raster mask eliminating all pixels lacking climate data, beyond California State Boundaries, or in which water covered >=50% of a pixel. Values of 1 indicate selected pixels. DATA-SPECIFIC INFORMATION FOR Combined_Data: Combined climate and LFM data at each site of LFM observation Number of Rows: 32000 Variables: Name- Site Name web_lat- Latitude os recorded in original data web_long- Longitude os recorded in original data Lat_deg_ra- Degrees Latitude Lon_deg_ra- Degrees Longitude Lat_min_ra- Minutes Latitude Lon_min_ra- Minutes Longitude Lat_sec_ra- Seconds Latitude Lon_sec_ra- Seconds Longitude Lat_Decima- Decimal Latitude Lon_Decima- Decimal Longitude Elevation- Elevation Slope- Slope Aspect- Aspect Lat_Proj- projected Latitude Lon_Proj- projected Longitude pixel_id- unique numerical ID for each pixel GACC- group collection cade State- state of collection site Group- collection group Site- site name Date-date of observation Fuel- type of fuel Percent - observed LFM Year- Year of observation Month- Month of observation Day_Of_Month- day of month of observation climate data variables were recorded in the following format: data type_duration(in months)_lag(in months) including the following types of climate data: tmx- mean maximum monthly temperature (C) ppt- precipitation (mm) aet- actual evapotranspiration cwd- climatic water deficit str- soil moisture storage pck- snowpack