Data to Decisions | Wildfire Mitigation
Wildfire operations in Central Texas increasingly hinge on localized, time-sensitive weather information. While data availability is relatively high from National Weather Service forecasts, Remote Automated Weather Stations (RAWS), local weather stations, Texas Mesonet, and Climate Prediction Center seasonal outlooks, the usefulness of this information at the city scale remains limited, as the resolution of data products for decision-making is not always in alignment. The most significant operational constraints come from coarse spatial data, variable data quality, and limited wind characterization. Additionally, this information has generally not been applied at different temporal scales.
There are two seasons for wildfire in Central Texas
Decision flow of winter wildfire season for AFD.
Decisions were dependent on freezing time, cold front patterns, relative humidity (RH) recovery, wind gusts, and weather station sampling. However, the resolution and availability of surface level weather data remained an issue.
Decision flow of summer wildfire season for AFD.
Decision flow of summer wildfire season for AFD, which is the most intense season of the year. Decisions were dependent on drought risk, wind gusts, fuel moisture loads, and observations from local weather stations. Similarly, the resolution of the data and availability relative to wildfire risk areas were lacking for operations.
Prescribe Burn Operations
Analyzing the Current Mitigation Efforts
An important part of the project is analyzing the mitigation efforts of AFD and the decision tipping points for prescribed burns. Decisions are dependent on communication and input from the National Weather Service, along with on-site or nearby RAWS stations, and a handheld weather Kestrel. Case studies analyzed emphasized the risk of prescribed burns close to “go, no-go" thresholds, and poor availability of accurate weather information led to wildfire risk and outbreak.
Operational Gaps and Data Needs
Wind is the primary wildfire risk driver, but localized changes often exceed the detail captured by regional forecasts. Neighborhood-scale weather, fuel, and topographic data are essential for WUI operations and prescribed-burn decisions. While AFD’s wildfire operations utlizes local weather data there is a need for, an integrated system with reliable instruments, automated cross-checks, AI tools, and digital twins would help close critical data gaps
Tool in development. Smoke mapper tool, which provides the live fire incidnet map (provided by Austin Fire), and combines a smoke plume model to estimate, smoke risk and direction relative to currently available wind data.
Contact and Further Information
Allysa Dallman
TExUS Lab Planning and Collaborator Lead
Braniff Davis
Senior Geospatial Analyst at the Wildfire Division
Dev Niyogi
Professor, CoLab Leadership Team