# Forecasting the Impacts of Prescribed Fires
## 3 Results
### Evaluation of Burn Area Forecasts  
By regressing forecast versus permitted daily total burn area statewide, we estimated the temporal accuracy of the models in forecasting the PB area. Figure 4 shows the regression lines for both the statewide and county-specific models. The coefficients of regression (slope and intercept) with margins of error (using 95% confidence interval), the coefficient of determination (R\({}^{2}\)) and the standard error of estimate (\(\sigma\)(y)) are also shown. Each data point in these scatter plots represents a day from the January-April 2015 period. For both models, there are more points below the 1:1 line indicating an overall underestimation of daily total burn areas. In general, small burn area totals were overestimated while large totals were underestimated. This was more pronounced with the county-specific models which underestimated all burn totals above 7000 ha. The standard error of estimate given by the county-specific models (2170 ha) was smaller than that of the statewide model (5070 ha); also, the R\({}^{2}\) of regression was larger in the case of county-specific models (0.34) than that of the statewide model (0.11). Figure 3: **(a) NOAA’s Hazard Mapping System Fire and Smoke Analysis; (b) forecast of burn impacts on PM\({}_{2.5}\) levels; and (c) cloud cover for 3 March 2016. The largest impacts on PM\({}_{2.5}\) (shown in purple) can be used as a proxy for burn locations. **  
The regression of forecast versus permitted county total PB area over the season gave us an idea about the spatial accuracy of the models in forecasting PB area. Figure 5 depicts the regression lines for both the statewide and county-specific models. Each data point in these scatter plots represents a county. Once again, the burn areas were generally underestimated, the standard error of estimate given by the county-specific models (2150 ha) was smaller than that by the statewide model (2820 ha) and the R\({}^{2}\) of regression was larger in the case of county-specific models (0.47) than that in the statewide model (0.29). The slope and R\({}^{2}\) values for the regressions of the county totals in Figure 5 were greater than those for the regressions of the daily totals in Figure 4. Further, both the intercepts of the lines and the standard errors of the estimates were smaller for the county totals than those for the daily totals. Therefore, the overall spatial accuracy in forecasting the location of the burns at the county level resolution was higher than the temporal accuracy in forecasting the day of the burns. Figure 4: Forecast versus permitted daily total burn areas (ha) in Georgia for the January–April 2015 period: (**a**) statewide model; and (**b**) county-specific models. Figure 5: Forecast versus permitted county total burn areas (in ha) in Georgia for the January–April 2015 period: (**a**) statewide model; and (**b**) county-specific models. We expected similar performance for the burn area forecast in 2016. However, when we compared our forecast burn areas to the permitted burn areas, we found that the underestimation in forecast burn areas was greater both in terms of daily statewide totals and county totals for the 2016 burn season (Figure 6). From January to April 2016, a total of 480,000 ha was permitted to be treated by PB while only 200,000 ha was forecast. The correlation between forecast and permitted burn areas has also degraded compared to 2015. In contrast to 2015, the spatial accuracy in forecasting the burns in each county was worse than the temporal accuracy in forecasting the day of the burns. These changes in the performance of the burn area forecast were most likely because of the El Nino-Southern Oscillation (ENSO) and the La Nina conditions of 2015-2016 that created very different precipitation patterns in Southeastern USA during the 2016 burning season compared to the 2010-2014 period. The temperatures during the month of March, at the peak of PB activity, were much above average and precipitation was below average (www.ncdc.noaa.gov/temp-and-precip).