## 2 Methods
### Fuel Dynamics Estimated from MODIS Products  
Intra- and interannual variability in live fuel conditions was analyzed using weekly eMODIS NDVI data in the Owyhee/Twin Falls region. eMODIS data [18] are weekly composited MODIS data for the underutil States generated at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center with the primary purpose of facilitating MODIS data use. The pixel with the highest NDVI value in each week is used to generate the composite value for the weekly composite data. NDVI data are available from 2000 to the present at 250 m resolution and provide historical observations that enable time series analyses and comparison among selected time intervals. While eMODIS data are relatively spatially coarse, the temporal resolution of the data provides excellent information about weekly to monthly intra-annual vegetation status and conditions. eMODIS NDVI data do not directly measure biomass (or fuel loads) and additional methods are required to translate spectral information into fuel loads, which are required for simulating fire behavior and spread. We developed an empirical model to convert NDVI values to aboveground live biomass using biomass information from the literature [23; 24; 25] and by assuming a log relationship between the NDVI and the live biomass [26]. We assumed that the higher NDVI values were associated with pixels that had live biomass levels at the upper end of the values reported in the literature, and that the lower NDVI values were close to zero biomass, and interpolated the values in between. We then generated the 2000 through 2011 weekly time series spatial products depicting live biomass for areas depicted as shrublands and grasslands in the LANDFIRE data. Dead biomass production was derived by subtracting the weekly live biomass datasets from the maximum live biomass that occurred in spring (highest level occurring in weeks 19 to 26). Near zero values indicated little change in live biomass levels from the maximum, implying low levels of dead biomass, whereas relatively high values indicated large changes from the live to dead biomass. We also estimated the annual live and dead fuel loads from the MODIS NPP data in the Murphy fire region. NPP represents the amount of biomass that vegetation assimilates through photosynthesis [27; 28]. NPP is also the difference between Gross Primary Production (GPP) and the Autotrophic Respiration (AR) [27; 29]. GPP can be estimated from satellite-derived NDVI data and the Photosynthetically Active Radiation (PAR) with a conversion efficiency factor \(\varepsilon\):  
\[GPP=\varepsilon\times FPAR\times PAR\approx\varepsilon\times NDVI\times PAR \tag{1}\]  
_FPAR_ is the fraction of _PAR_ that is absorbed by vegetation. The conversion factor \(\varepsilon\) is the light use efficiency (LUE) factor and its value is affected by biological and environmental factors [29; 30]. In the MODIS NPP algorithm, \(AR\) is estimated from the MODIS LAI/FPAR product and NPP is calculated annually as the difference between _GPP_ and _AR_[31; 32]. We used the global MODIS NPP (MOD17A3) product provided by the Numerical Terradynamic Simulation Group (NTSG) located at the University of Montana [33]. The MODIS NPP product was generated at 1 km spatial resolution from 2000 to 2010 using the most recent version of the algorithm [32; 34]. We resampled the LANDFIRE surface fuel map to 1 km resolution and used the resampled map as the mask for estimating the NPP for grass fuel type and the grass-shrub fuel type. We assumed a 100% NPP allocation to grass (live herbaceous fuel) whereas for the grass-shrub fuel type, NPP allocations were 50% to grass and 50% to shrub (live woody fuel). The amount of aboveground biomass is allocated by partitioning the overall NPP. We used the partition parameters from the Biome-BGC model to estimate the live fuel load changes from MODIS NPP. Biome-BGC, a process-based model developed for studying terrestrial ecosystem carbon cycling, has been widely used for ecosystem level studies [35; 36; 37].