# cfis: Canopy Fire Initiation & Spread model In firebehavioR: Prediction of Wildland Fire Behavior and Hazard

## Description

Prediction of crown fire probability, crown fire rate of spread and separation distance (Alexander and Cruz 2006). Separation distance is distance ahead of main fire front required for a spot fire to form, separate of a main fire.

## Usage

 `1` ```cfis(fsg, u10, effm, sfc, cbd, id) ```

## Arguments

 `fsg` a numeric vector of fuel stratum gaps (m) `u10` a numeric vector of 10-m open wind speeds (km/hr) `effm` a numeric vector of effective fine fuel moistures (%) `sfc` a numeric vector of surface fuel consumed (Mg/ha) `cbd` a numeric vector of canopy bulk densities (kg/m3) `id` a numeric vector of spot ignition delays, the time during which a given firebrand generates, is transported aloft, and ignites a receptive fuelbed (min)

## Value

a data frame with type of fire, probability of crown fire occurrences (%), crown fire rate of spread (m/min), and critical spotting distance (m)

## Author(s)

Justin P Ziegler, justin.ziegler@colostate.edu

## References

Alexander M.E., Cruz M.G. 2006. Evaluating a model for predicting active crown fire rate of spread using wildfire observations. Canadian Journal of Forest Research. 36:2015-3028.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```data("coForest") # show the data format: head(coForest) # Predict crown fire, using coForest # measurements and assumed weather # parameters df.cfis = cfis(fsg = coForest\$cbh_m, u10 = 20, effm = 6, sfc = coForest\$sfl_kgm2*10, cbd = coForest\$cbd_kgm3, id = 1) print(df.cfis) # Examine differences between treatment # statuses aggregate(x = df.cfis\$cROS, by = list(treatmentStatus = coForest\$status), FUN = mean) # Now, examine the sensitivity of fire # type designations to wind speed by # treatment status coForest = coForest[rep(seq_len(nrow(coForest)), 11), ] coForest\$u10 = sort(rep(10:20, 14)) coForest\$type = cfis(coForest\$cbh_m, coForest\$u10, 6, coForest\$sfl_kgm2*10, coForest\$cbd_kgm3, 1)\$type table(u10 = coForest\$u10, coForest\$type, coForest\$status) ```

### Example output

```  site status trees_perha basalArea_m2ha qmd_cm height_m sfl_kgm2 cbd_kgm3
1 Heil    pre         418           19.1   24.1      9.8     1.04     0.14
2 Heil   post         295           14.5   25.0     10.1     0.76     0.10
3  Unc    pre         504           30.0   27.5     22.1     1.20     0.15
4  Unc   post         314           17.6   16.7     25.5     1.06     0.08
5 Pike    pre         934           24.8   18.4     15.8     1.33     0.15
6 Pike   post         352            9.5   18.5     16.6     1.33     0.06
cbh_m cfl_kgm2
1  3.63    0.837
2  3.69    0.628
3  4.35    2.653
4  4.14    1.674
5  2.90    2.096
6  3.60    0.830
type pCrown  cROS sepDist
1   active  99.30 40.54  381.88
2   active  89.63 38.03  358.23
3   active  98.84 41.08  386.92
4  passive  99.00 13.79  129.91
5   active  99.58 41.08  386.92
6  passive  99.32 17.31  163.03
7  passive  99.66 13.79  129.91
8  passive  91.40 19.13  180.17
9   active  78.55 39.97  376.54
10  active  50.50 38.03  358.23
11  active  90.82 40.54  381.88
12 passive  90.21 17.31  163.03
13  active  88.67 37.28  351.13
14 passive  81.92 22.36  210.59
treatmentStatus        x
1            post 23.70857
2             pre 36.32571
, ,  = post

u10  active passive surface
10      0       2       5
11      0       2       5
12      0       2       5
13      0       2       5
14      0       5       2
15      0       5       2
16      1       5       1
17      1       5       1
18      1       5       1
19      1       5       1
20      2       5       0

, ,  = pre

u10  active passive surface
10      3       1       3
11      3       1       3
12      3       1       3
13      3       1       3
14      4       1       2
15      4       2       1
16      4       2       1
17      5       2       0
18      6       1       0
19      6       1       0
20      6       1       0
```

firebehavioR documentation built on May 2, 2019, 11:48 a.m.