Key Terms, Metrics, and Data Layer Definitions

Key terms, metrics, and data layer definitions used in Technosylva’s Wildfire Planning (FireSight) include:

Wildfire Response

Active Fire Incidents

Point-based locations representing reported wildfire ignitions currently under active management, sourced from the National Interagency Fire Center (NIFC) or integrated federal/state fire reporting systems.

Active Fire Perimeters

The mapped outer boundaries of wildfires currently burning or recently active, typically sourced from federal and state agencies via the National Interagency Fire Center (NIFC) and similar feeds. Perimeters are derived from aerial infrared mapping, satellite detection, and ground-based GPS tracking. In FireSight, this layer provides situational awareness of ongoing fire events within or adjacent to a utility's service territory.

CA High Fire Threat District (HFTD)

A regulatory classification established by the California Public Utilities Commission (CPUC) and California Public Resources Code that designates areas of elevated wildfire ignition and spread risk from utility infrastructure. The HFTD is divided into two tiers (Tier 2: Elevated and Tier 3: Extreme) based on fire threat modeling conducted by CAL FIRE. Utilities operating within HFTD boundaries are subject to enhanced wildfire mitigation requirements, including Wildfire Mitigation Plan (WMP) filings and stricter equipment and vegetation management standards.

NWS Fire Weather Zones

Geographic areas designated by the National Weather Service (NWS) used to issue fire weather forecasts, watches, and warnings (including Red Flag Warnings). Zone boundaries are drawn to reflect regional topography, vegetation, and prevailing weather patterns.

Utility Infrastructure

• Distribution Line (DL)

• Transformers (TF)

• Transmission Line (TL)

• Zone of Protection (ZOP)

Risk and Probability

Probability of Failure (POF)

Probability of Failure (POF) is the likelihood that a utility asset will experience a fault resulting in a spark or burning material under a given set of weather conditions. It represents the first link in the risk chain before Probability of Ignition (POI). The POF model looks at events that could potentially cause ignition (such as equipment failure, object contact, or vegetation contact) and integrates them with historical weather data to create dynamic circuit fragility curves. These curves consist of a static POF (representing the POF in windless conditions) and the dynamic exponential increase due to wind.

Probability of Ignition (POI)

Probability of Ignition (POI) is the likelihood that a failure resulting in spark or burning material at a utility asset results in an ignition. Environmental variables such as the National Fire Danger Rating System’s Ignition Component (IC) are used to generate a POI estimate. This analysis considers fuel variables along with wind speed to determine the likelihood of a wildfire starting from burning material on the ground. POI represents the second link in the risk chain after Probability of Failure (POF): given that an asset has failed, POI determines how likely that failure is to ignite a fire.

Combined Probability of Failure (POF) x Probability of Ignition (POI)

Combined Probability of Failure (POF) x Probability of Ignition (POI) is the combined probability that an asset fails under current conditions and that failure produces a sustained ignition. The "combined" framing acknowledges that neither alone is sufficient: a high POF with low POI (failure in a wet, sparse area) produces low risk, and a high POI environment with no realistic failure pathway also produces low risk. The product of the two is what drives the ignition probability side of the risk equation.

Conditional Risk

Conditional Risk quantifies the wildfire consequences associated with a utility asset, assuming an ignition occurs at that location. Conditional Risk answers the question: "if a fire starts here, what could happen?"

For each asset, FireSight evaluates seven risk metrics at 100-meter intervals along the line, taking the maximum value for each risk type to represent the worst-case consequence. These values are then aggregated across all simulated weather days to build a statistical distribution, from which FireSight derives percentiles, average, and standard deviation. For example, a cold, wet, low wind speed day may represent the lowest percentile while a hot, dry, windy day may represent the upper percentile.

The seven risk metrics considered for Conditional Risk are:

• Acres Burned (AC)

• Population Impacted (POP)

• Buildings Threatened (BU)

• Buildings Destroyed (BU_DES)

• Fire Behavior Index (FBI) or Fireline Intensity

• Rate of Spread (ROS)

• Flame Length (FL)

Expected Risk

Expected Risk quantifies the wildfire risk associated with a utility asset by integrating three factors: the Probability of Failure (POF), the Probability of Ignition (POI) given that failure, and the Conditional Risk of a resulting fire. Expected Risk reflects the full chain of events from equipment fault to fire consequence, making it the right metric when the question is "which assets pose the greatest overall risk of causing a damaging wildfire?"

For each simulated weather day, FireSight calculates the likelihood that an asset experiences an outage, the likelihood that the outage produces an ignition, and the potential fire consequences if ignition occurs. These daily values are combined and aggregated across all weather days to build a statistical distribution including percentiles, average, and standard deviation.

Risk Associated with an Ignition Location (RAIL)

Risk Associated with an Ignition Location (RAIL) assigns risk scores to specific utility assets based on their potential to ignite wildfires and the resulting consequences. A RAIL analysis incorporates:

• Historic weather data

• Fire spread potential from wildfires starting at the asset's ignition location

• Outage analytics (Probability of Failure (POF) and Probability of Ignition (POI))

The final output is a Conditional Risk and Expected Risk for each asset. By combining these components, RAIL generates asset-specific risk scores that enable utilities to prioritize mitigation efforts, operational decisions, and resource allocation based on the highest-risk equipment in their network.

Risk Associated with Value Exposure (RAVE)

Risk Associated with Value Exposure (RAVE) determines which locations have the highest risk from all surrounding assets, considering both fire spread exposure and demographic characteristics such as population, buildings, and infrastructure. By combining these components, RAVE generates risk scores across the landscape, linking them directly to electric utility assets as potential ignition sources.

RAVE differs from Risk Associated with an Ignition Location (RAIL), though the two are complementary. RAIL focuses on location-based consequences of a potential ignition, while RAVE incorporates environmental and community consequences. Together, RAVE and RAIL support composite asset risk metrics for both short-term operations and long-term mitigation planning.

Hexagonal Hierarchical Spatial Index (H3) System

To accurately assess fire spread risk across a landscape, a consistent analysis unit is essential. While traditional Geographic Information Systems (GIS) often use geo-administrative polygons for socio-economic and demographic data, and raster grids for terrain features, these methods have limitations for fire spread simulations.

Geo-administrative boundaries, for example, do not offer the detailed resolution needed to effectively characterize risk factors for fire spread. A smaller, more granular unit is necessary.

Technosylva addresses this by using the Hexagonal Hierarchical Spatial Index (H3) system as its analysis unit for RAVE analysis. Hexagons offer significant advantages over the traditional raster grid approach, particularly when analyzing connectivity and movement paths, which are crucial for predicting wildfire spread. Hexagons are also better at reflecting distortion over large areas, a vital aspect for this type of analysis.

Plexels

For this specific analysis, Level 8 hexagons were used. These hexagons are approximately 1,100 meters from centroid to centroid and cover about 200 acres.

In the RAVE analysis, these hexagons are called plexels. The term "plexel" combines "hexagons," "pixels," and "population," because population is the primary landscape attribute in this analysis.

RAVE (Locational Risk Factors)

RAVE Locational Risk Factors represent the static and semi-static components of the RAVE framework including locational characteristics (terrain difficulty, suppression access) and vulnerability (population demographics). These are independent of fire behavior or ignition probability. Locational Risk Factors are also sometimes referred to as Static Risk and include data primarily related to population, buildings, and manmade infrastructure.  Current metrics calculated and summarized on a per plexel basis are:

RAVE (Asset Susceptibility)

Asset Fire Susceptibility Factors quantify how exposed individual utility assets are to wildfire risk based on the RAVE framework, combining the asset's ignition probability with the risk profile of the surrounding landscape it could impact. Asset Susceptibility translates the spatial RAVE risk surface into asset-level scores, enabling direct comparison and ranking of assets across the portfolio. Asset Susceptibility is also sometimes referred to as Dynamic Risk.

Asset Fire Susceptibility Factors are calculated based on fire simulations from asset ignition points and aggregated on a per plexel basis.  The metrics represent vulnerability based only on asset ignited fires. By default, historical weather scenarios identified for the climatology have been used, although daily weather forecasts could be used and RAVE metrics calculated on a daily forecast basis if desired.

These metrics include:

• Acres Burned (8-hour simulations)

• Population Impacted

• Buildings Impacted

• Buildings Destroyed

• Building Loss Factor: estimated building loss factor within the plexel.

• Burn Frequency: the number of times the plexel was burned from fire simulations from asset ignitions.

Composite Risk Metrics

See the Using Composite Risk Metrics Tutorial for more information.

• Fire Behavior Composite: A combined measure of Rate of Spread (ROS), Flame Length (FL), and Acres Burned that characterizes the intensity, speed, and overall magnitude of a potential fire.

• Immediate Impacts Composite: A combined measure of Buildings Threatened, Population Impacted, and Acres Burned that quantifies the direct consequences a fire would have on nearby communities.

• Suppression Difficulty Composite: A combined measure of Terrain Difficulty and Fire Behavior Composite that reflects how challenging a fire would be to control.

• Utility Risk Composite: A combined measure of Suppression Difficulty and Immediate Impacts that identifies where infrastructure-caused wildfires pose the greatest direct risk to communities, supporting initial prioritization of system hardening and asset replacement.

• Utility Risk Vegetation Composite: A composite measure of Utility Risk Composite and Canopy Risk focused on high-risk locations where vegetation management such as tree trimming and canopy thinning can most effectively reduce wildfire risk to both utility infrastructure and surrounding communities.

• Utility Risk Fuel Loading Composite: A combined measure of Utility Risk Composite and Total Fuel Loading focused on high-risk locations where fuel reduction treatments can most effectively reduce wildfire risk, supporting strategic planning for fuel treatment programs and coordination with land management agencies.

Sub-Composite Risk Metrics

• Acres Sub-Composite: A normalized score representing the total area that simulated fires originating at a given asset are projected to burn across weather scenarios (Acres Burned). Reflects the potential geographic scale of a fire event.

• Buildings Threatened Sub-Composite: A normalized score representing the number of structures within the projected fire footprint across simulated weather scenarios (Buildings Threatened). Derived from conditional fire simulations, it reflects the structural exposure a fire originating at a given asset could produce.

• Flame Length Sub-Composite: A normalized score representing the simulated Flame Length associated with fires originating at a given asset. Flame Length is a primary indicator of fire intensity and a key driver of both suppression difficulty and structural damage potential.

• Population Impacted Sub-Composite: A normalized score representing the number of people within the projected fire footprint across simulated weather scenarios (Population Impacted). Captures the human exposure dimension of conditional fire risk.

• Rate of Spread Sub-Composite: A normalized score representing the simulated rate at which fires originating at a given asset are projected to spread across the landscape. Rate of Spread reflects how quickly a fire could grow and how difficult it would be to contain in the early stages.

• Terrain Difficulty Sub-Composite: A normalized score reflecting the difficulty of the terrain surrounding a given asset as it relates to fire suppression access and operations (Terrain Difficulty Index). Steeper slopes, rugged topography, and limited access routes increase both fire spread rates and the challenge of deploying firefighting resources.

• Canopy Risk Score: Quantified vegetation canopy characteristics that promote crown fire behavior and vertical fire spread.

• Total Fuel Loading: Sum of combustible material loads across fuel size classes:

  • 1-hour fuels

  • 10-hour fuels

  • 100-hour fuels

  • Herbaceous fuels

  • Woody fuels

Risk Domain

Risk Domain (which may also be known as Customer Domain or Project Domain) is the geographic boundary of the area included in a Planning analysis. It appears as an outline layer on the map and represents the analyzed area within which customer assets are evaluated.

Fuels

Terms like 2022B or 2030Fuels indicate the fuel scenario used for the risk metric. These scenarios indicate baseline or projected future vegetation and fuel conditions derived from the specific reference year, used as the current or future state fuel input for risk metric calculations. Risk metrics calculated against a future fuels scenario allow utilities to evaluate how wildfire risk may evolve over time and to assess whether planned mitigation activities are sufficient to offset increasing fuel loads or changing conditions. For more information on fuels, visit the Technosylva Fuels Glossary.

Other Terms and Abbreviations

Statistics

• E or underscore e: Expected

• C or underscore c: Conditional

• Min: Minimum

• Max: Maximum

• Stddev: Standard Deviation

• Percentiles:

  • 20th: 20th percentile 

  • 40th: 40th percentile

  • 60th: 60th percentile

  • 80th: 80th percentile

  • 90th: 90th percentile

  • 95th: 95th percentile

  • 98th: 98th percentile

Other Terms

• Line Miles: Line miles per circuit.