DOI: 10.5061/dryad.sj3tx964v
title: 2020 Critical update to Caltrans wildfire vulnerability analysis

description: The data and materials presented here support a research project conducted
 by UC Davis in support of Caltrans, a transportation agency that
 is conducting a wildfire vulnerability risk assessment for fuels
 reduction in the right of Way (ROW) to protect Caltrans’ infrastructure
 and travelers. We reviewed the 21 layers used in a 2019
 fire risk model and updated input spatial data as available. We
 then reran the risk assessment for 2020 and developed an
 updated risk map. The risk map was used to rank the highway
 network into 4 classes: Top 10%, top 17% (cross comparable with the 2019
 priority routes), top 20%, & top 30%. Here is the abstract from
 the associated report. Catastrophic wildfires over the past five years
 (2015-2020) have caused damage to the Caltrans road network in 81 separate
 wildfire events, leading to expenditures of over $590,000,000 to repair
 highway assets.  To reduce the risk of further wildfire damage
 and to improve public safety, particularly for disadvantaged communities,
 Caltrans has engaged in assessing the priority locations for vegetation
 treatment within the lands it owns called the Right of Way (ROW). A 2019
 analysis provided a map showing the top 17% of vulnerabilities in the road
 network, representing both the risk of wildfire and to disadvantaged
 communities that might need to use the transportation network as means of
 evacuation. This UC Davis research project was designed to support efforts
 within Caltrans in conducting a wildfire vulnerability risk assessment for
 fuels reduction in the right of Way (ROW) to protect Caltrans’
 infrastructure and travelers. The project involved four components: 1)
 conducting a rigorous peer review of the 2019 GIS-based study commissioned
 by Caltrans; 2) collecting and assessing the outputs of several climate
 change, fire, and other models currently developed or under development
 for California, as well as future climate projections; 3) developing a
 framework for the use of the prioritized segment model with other data
 further identify priority areas for fuels and risk reduction; and 4)
 interviews with Caltrans staff on opportunities and obstacles to
 increasing the pace and scale of vegetation treatments. The results
 contribute to infrastructure risk assessments, can be used to prioritize
 areas for treatment, to create a tracking system of areas treated and risk
 lowered over multiple years, and to engage local governments and wildfire
 fighting units to coordinate landscape fire risk reductions.
description: A
 wildfire risk reduction model is begun Using the CAL
 FIRE 45-day Report as a guide, Caltrans and Davey Resource Group, Inc.
 created a priority map for vegetation treatments in the Caltrans-owned
 rights-of-way. Data sources used in this assessment mirror those in the
 CAL FIRE 45-day report with the addition of Caltrans internal data for
 traffic volumes, highway classes, and emergency routes.  This project
 incorporated the principles of socio-economic analysis and vulnerable
 communities to complete a thorough assessment of the Caltrans
 rights-of-way going through these communities while also factoring in
 environmental risks of wildfire to natural resources (i.e. loss of trees,
 carbon storage, negatively impacting water supplies, etc.). The goal of
 this analysis was to prioritize Caltrans highways that serve those
 communities in order to address vegetative fuel loading concerns. By
 prioritizing route segments, Caltrans can work with other stakeholder
 groups to reduce fuels in and around these critical communities. By
 keeping evacuation routes clear, Caltrans can create defensible space and
 possibly save lives in the event of wildfire outbreaks. To address
 route prioritization, a geospatial risk-based model was constructed by
 Davey Resource Group, Inc. during Summer/Fall 2019. Datasets were
 collected and assessed through a variety of sources (listed below). Davey
 Resource Group, Inc., in conjunction with Caltrans, deliberated each
 potential data source to determine which pieces of information would be
 most beneficial to the model. Construction of the risk-based model was
 intended to identify priority routes or segments along Caltrans operated
 highways where reducing roadside vegetation would most reduce risk and
 provide benefits to Caltrans and transportation users because of
 newly-created defensible space along the rights-of-way. 2019
 Priority Map: Table 1: The 21 spatial
 data inputs 
 Source Map Type
 Dataset name 
 Caltrans Average Daily Traffic
 Count AADT.tif 
   Highway Class
 HighwayClass.tif 
   Emergency Evacuation Routes
 EmergencyLifeRoute.tif 
 Calfire Fire Threat
 Threat.tif 
   SRA / FHSZ
 SRA_FHSZ.tif 
   Fire History
 FireHistory2020.tif 
   Large Trees
 LargeTrees.tif US
 Census Bureau Families in Poverty
 acs2018_fampovrank.tif 
   People with Disabilities
 acs2018_disablerank.tif 
   People that have Difficulty Speaking
 English acs2018_poorengrank.tif
   People over
 65 acs2018_elderlyrank.tif 
   People Under 5
 acs2018_underfiverank.tif 
   Households Without a Car
 acs2018_nocarrank.tif 
   Housing Density
 acs2018_housingrank.tif 
 USA Forest Service Wildlife-Urban Interface
 (WUI) WUI.tif 
   Fire Return Interval Departure
 (FRID) FRID2019.tif 
   Carbon Storage
 CarbonStorage.tif 
   Wildfire Threat to Water
 F2F2_WildfireThreatToWater.tif 
   Surface Waters
 F2F2_SurfaceWaters.tif 
   Site Quality
 SiteQuality.tif 
   Standing Timber
 StandingTimber.tif 
 The 2019 map
 used 21 spatial data layers (Table 1). All data was converted to 30m
 raster datasets to complete the analysis. An intra-dataset ranking was
 constructed for each variable in the model using values ranging from 0-7.
 A value of zero (0) was indicative of No Data. Lower values were reasoned
 by carrying less risk. A weighted overlay analysis was implemented to
 determine risk throughout the state. Data were summarized using a
 1/10-mile buffer and calculating the average risk value within the buffer
 around all Caltrans operated state highways. Each road
 segment was summarized for its average risk value within the buffered
 zone. The scores were then normalized by dividing the score by the highest
 score and multiplying by 100. This puts the scale in an easy 0-100 scale,
 where 100 is the highest priority. The normalized values were
 statistically binned into seven (7) classes within ArcGIS using Natural
 Breaks classification. The top 3 classes were deemed as the priority range
 for this analysis. With the priority segments identified, additional GIS
 analysis was performed to assign each segment a Caltrans District, Cal
 Fire Unit, California State Park, National Forest, BLM Unit, Tribal Land,
 and County, if applicable. Data was also
 processed to include post mile markers for the closest ranges to give a
 better understanding of the location for each potential fuel reduction
 project. Weighted
 Overlay Equation (output created a statewide risk layer used in
 prioritization): (0.07*"AADT.tif")+(0.07*"HighwayClass.tif")+(0.14*"EmergencyLifeRoute.tif")+(0.06*"WUI.tif")+(0.06*"FRID.tif")+(0.03*"FamiliesInPoverty.tif")+(0.03*"PeoplewithDisabilties.tif")+(0.03*"DifficultySpeakingEnglish.tif")+(0.03*"PeopleOver65.tif")+(0.03*"PeopleUnder5.tif")+(0.03*"NoTransportation.tif")+(0.04*"HousingDensity.tif")+(0.08*"Threat.tif")+(0.05*"SRA_FHSZ.tif")+(0.05*"FireHistory.tif")+(0.05*"LargeTrees.tif")+(0.03*"CarbonStorage.tif")+(0.03*"FireThreatToWater.tif")+(0.03*"SurfaceDrinkingWater.tif")+(0.03*"SiteQuality.tif")+(0.03*"StandingTimber.tif") 2020 Update: We reviewed the data inputs and the spatial methods used to create the 2019 risk map. Because the model is using a state-wide framework also in use by other agencies, and because the methods were transparent and replicable, we accepted the methodology used in the 2019 report. We note that in some districts, additional priorities could be added. We recommend retaining the original model, and incorporating either district or more localized considerations afterwards. If those are available in map form, they can be overlaid in a GIS. If they are in the form of verbal suggestions, named areas can be inspected using the GIS and additional contextual data such as high-resolution aerial imagery. We replicated the model outputs from the 2019 effort, despite somewhat limited methods being available. We then updated the input data, such as the census elements used, and updated the risk model for 2020. Note that because the Davey Resource Group Executive Report (2019) contains additional methods as to the weightings assigned to the 21 data layers, we include that report here for reference as Appendix 1. The intent of including it is to retain all the information associated with the creation of the spatial model in a recoverable format. 2020 GIS data used to update the model: There were 21 data layers used in the Davey group’s model (Table 1). We obtained the data and reconstructed the model outputs. We used the following steps to review the previous model and to build the updated 2020 spatial model and maps: Evaluate 2019 assessment data layers and bring them up-to-date with most current data Updated layers included fire history data, fire return interval, wildfire threat to water, surface waters drinking water, and all socioeconomic data from the US Census American Community Survey Create priority index raster layer by combining stack of 21 updated layers using weightings from 2019 analysis Buffer state highway vector layer by 1/10th mile Use state highway buffer as mask on raster priority index layer Buffer 1-mile interval postmile points to create half-mile radius circles covering state highway network Overlay circular point buffers on priority index layer to extract index values for 1-mile segments along highway network Import data table of values along segments into R for analysis Calculate breakpoints for priority index using percentile ranges of values 0-17% range to give corresponding mileage to 2019 analysis Breakpoints of 0-10%, 10-20%, 20-30%,30-60%, 60-100% for alternative presentation Focus on 0-10%, 10-20%, 20-30% classes Assign circular point buffers to these range classes Map the highway network to these index classes using postmile ranges corresponding to these circular point buffers To join the circular point buffers to the post mile and road segments used by Caltrans for mapping its road network we: 1. Segmented state highway network by 1/10 mile postmiles 2. Spatially joined the ½ mile circles (above) with the 2020 priority index value to the highway segments (using the road segment’s centroid). 3. Assigned each segment in the highway network to the range classes (based on the breakpoints: the top 10%, 17%, 20%, & 30%). These steps permitted Selection of ranking criteria, in order to identify the segments of roads that have the highest level of risk, according to the model. In discussions with Caltrans, we identified two levels of ranking. We used the top 17% of the ranked state highway ROWs as the first cutoff, because that was also used in the 2019 report (Appendix 1). We also used the top 10%, the next 10-20%, and the 20-30% rankings, to show what a program of 3 years might look like with vegetation treatments for fire risk reduction within the ROW. The data review examined the suitability of a variety of spatial data for use in creating a baseline inventory of vegetation type, density and size within Caltrans Right of Ways. We considered the date of publication, spatial extent, the mapping grain size, level of processing, difficulty with which the data could be used to accomplish the inventory, and other aspects of the data. We identified several modes of analysis. For rapid regional screening, the use of our 2020 version of the risk abatement priority model provides the lowest cost method to identify candidate roads for fuels reduction. However, these results need to be provided to Caltrans region personnel, and to local firefighting groups, in order to ensure that actions they have already prioritized are supported, or extended through the use of the 2020 model. 2020 Risk Model of Caltrans Road Network and Communities to Wildfire: The Risk model is a spatial product. The input data layers and the final GIS maps are posted here.
description: The 3 input .tif files from Caltrans (AADT.tif,
 EmergencyLifeRoutes.tif, HighwayClass.tif) are not posted here because
 they are maintained/updated by Caltrans. We recommend getting the most
 current data through their website or requesting it directly from
 them. AADT data is available for both trucks and
 vehicles: Truck AADT - https://gis.data.ca.gov/datasets/dfe7fd95282946db98145e9bcaf710fb_0 Vehicle AADT - https://gis.data.ca.gov/datasets/f71f49fb87b3426e9688fe66039170bc_0 Highway Functional Class:  https://dot.ca.gov/programs/research-innovation-system-information/highway-performance-monitoring-system/functional-classification Emergency Lifeline Routes is an inexact term and has been recently updated to Emergency Evacuation Routes. Due to this change, it is currently being updated so please request this layer directly from Caltrans. Contacts at Caltrans: GIS Coordinator at Caltrans HQ-Maintenace: Affi N'Guessan - N'Guessan.Affi@dot.ca.gov Project Manager at Caltrans HQ-Maintenace: Lisa Worthington - lisa.worthington@dot.ca.gov