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2020 Critical update to Caltrans wildfire vulnerability analysis

Citation

Boynton, Ryan; Thorne, James; Hollander, Allan (2021), 2020 Critical update to Caltrans wildfire vulnerability analysis, Dryad, Dataset, https://doi.org/10.5061/dryad.sj3tx964v

Abstract

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.

Methods

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:

  1. Evaluate 2019 assessment data layers and bring them up-to-date with most current data
    1. 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
  2. Create priority index raster layer by combining stack of 21 updated layers using weightings from 2019 analysis
  3. Buffer state highway vector layer by 1/10th mile
  4. Use state highway buffer as mask on raster priority index layer
  5. Buffer 1-mile interval postmile points to create half-mile radius circles covering state highway network
  6. Overlay circular point buffers on priority index layer to extract index values for 1-mile segments along highway network
  7. Import data table of values along segments into R for analysis
  8. Calculate breakpoints for priority index using percentile ranges of values
    1. 0-17% range to give corresponding mileage to 2019 analysis
    2. Breakpoints of 0-10%, 10-20%, 20-30%,30-60%, 60-100% for alternative presentation
    3. Focus on 0-10%, 10-20%, 20-30% classes
  9. Assign circular point buffers to these range classes
  10. 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.

Usage Notes

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:

  1. Truck AADT - https://gis.data.ca.gov/datasets/dfe7fd95282946db98145e9bcaf710fb_0
  2. 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

Funding

National Center for Sustainable Transportation Technology, Award: 65A0686

California Department of Transportation, Award: TO 038

U.S. Department of Transportation, Award: 69A3551747114