Project Title: Integrated weather/fire/economics modeling framework for fire risk assessment system
Principal Investigator: Armando González-Cabán, USDA Forest Service, Pacific Southwest Research Station, Riverside, CA
Collaborators: Francis Fujioka, John Benoit, José Sánchez: USFS - PSW Research Station, Riverside; Ken Baerenklau, University of California Riverside; Ken Wright , Los Padres National Forest
Key Issues/Problem Addressed:
Insofar as wildland fire behavior depends on weather conditions local to the fire, a comprehensive treatment of the spatial and temporal variability of weather patterns on a landscape scale is essential to development of a modeling framework for fire risk assessment. Operational weather and climate models generally are too coarse to resolve the spatial variations of fire weather variables at the watershed to forest level. A high resolution weather/climate simulation capability is needed to describe the atmospheric conditions that affect fire behavior. We propose to develop a risk assessment system to predict fire probability fields and their resulting economic impacts with a lead time varying from a day to a season .This multi-disciplinary research will provide a tool that demonstrates fire risk assessment that supports decisionmaking for incident and forest management strategies.
Setting and Approach:
The meteorological global to regional forecasting system developed at the Experimental Climate Prediction Center (ECPC) will provide the atmospheric modeling platform in this study. The output of the global model (GSM) will used to drive a regional forecast using the regional spectral model (RSM). The spatial and temporal resolution of the mesoscale output will be at the kilometer scale for hourly intervals. To improve the economic analysis capabilities to evaluate the potential impact of wildland fires we will develop an economic value geographic information system (GIS) data layer for the San Jacinto Ranger District, San Bernardino National Forest. A Viewshed analysis technique will be used to estimate the economic impact of fire on wilderness recreation in the San Jacinto Ranger District as well as on potential visitation patterns in the area. . For each pixel in the San Jacinto Ranger District the market and nonmarket economic values information for the most relevant resources present will be developed. Summing over all these pixels will give us an indication of the total economic value of the market and nonmarket resources present in the District. Given this information, a loss function will be developed. Finally, the loss function will be combined with the forecasted probabilities of fire size classes to produce maps of fire risk. This information would allow fire managers to make decisions to pre-position or re-position wildfire protection resources based on the resulting combination of the probability of fire occurrence and estimated loss from wildland fires. Results from the integrated models could be compared with historic losses from wildland fires in the District to calibrate and evaluate the robustness of the methodology.
Progress to Date:
Data collection has been completed. There are a total of 763 completed online surveys. Preliminary analysis has been conducted on the first 100 observations to test the travel cost model. The next steps are:
1. Travel cost model analysis on entire dataset
a. Estimate the willingness to pay (access value) to visit the San Jacinto Ranger District
2. Travel cost model and Contingent valuation method
a. Estimate the willingness to pay to visit the San Jacinto Ranger District for:
i. Change in cost -an increased cost to visit San Jacinto Ranger District3. Viewshed analysis technique to estimate the economic impact of fire on wilderness recreation
ii. Change in scenic quality-decrease scenic quality due to a wildfire
a. This will provide nonmarket economic value information for each pixel in the San Jacinto Ranger District
4. Develop a loss function and combined with the forecasted probabilities of fire size classes to produce maps of fire risk Using the information obtained from the models may result in a reduction in fire suppression expenditures and area burned by prepositioning or repositioning firefighting resources to locations that allow a more efficient respond to fires.
Impacts/Applications: Using this tool may result in a reduction in fire suppression expenditures and area burned by prepositioning or repositioning firefighting resources to locations that allow a more efficient respond to fires.