-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathindex.html
518 lines (507 loc) · 35 KB
/
index.html
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
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
<!DOCTYPE html>
<html>
<head>
<link rel="shortcut icon" href="https://dl.dropboxusercontent.com/u/345322813/logos/Div6Logo_PNG_16x16.png" type="image/x-icon">
<link rel="icon" href="/favicon.ico" type="image/x-icon">
<meta charset="utf-8">
<title>FDOP Guide</title>
<link href="css/doctor.css" type="text/css" rel="stylesheet">
<!-- Google Analytics -->
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-36803360-1']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
</head>
<body>
<a id="github-ribbon" href="https://github.com/SLUGIS/FDOP"><img alt="Fork me on GitHub" src="https://s3.amazonaws.com/github/ribbons/forkme_right_darkblue_121621.png"></a>
<div id="nav">
<div class="separator"></div>
<a title="FDOP Guide" class="brand" href="#" style="background-image:url(https://dl.dropboxusercontent.com/u/345322813/logos/slo_co_fire_fdop_t.png)"></a>
<div class="separator"></div>
<ul id="sections"><li class="l2"><a href="#what-is-a-fdop">What is a FDOP?</a></li><li class="l2"><a href="#creating-an-fdop">Creating an FDOP</a><ul><li class="l3"><a href="#creating-an-fdop/getting-started">Getting Started</a></li><li class="l3"><a href="#creating-an-fdop/station-catalog">Station Catalog</a></li><li class="l3"><a href="#creating-an-fdop/weather">Weather</a></li><li class="l3"><a href="#creating-an-fdop/fire">Fire</a></li><li class="l3"><a href="#creating-an-fdop/stats-graphs">Stats Graphs</a></li><li class="l3"><a href="#creating-an-fdop/staffing-level">Staffing Level</a></li><li class="l3"><a href="#creating-an-fdop/preparedness-level">Preparedness Level</a></li><li class="l3"><a href="#creating-an-fdop/pocket-cards">Pocket Cards</a></li><li class="l3"><a href="#creating-an-fdop/updating-the-fdop-annually">Updating the FDOP Annually</a></li><li class="l3"><a href="#creating-an-fdop/cause-code-conversion-chart">Cause Code Conversion Chart</a></li></ul></li><li class="l2"><a href="#fuels-info">Fuels Info</a></li></ul>
<div class="separator"></div>
<div class="extra" id="example">
<a href="http://www.slocountyfire.org/FDOP_Annual">Example FDOP</a>
</div>
<div class="extra" id="github">
<a href="https://github.com/SLUGIS/FDOP">Source on Github</a>
</div>
<div class="extra" id="github-issues">
<a href="https://github.com/SLUGIS/FDOP/issues">Issues</a>
</div>
<div class="extra generated">
Generated by <a href="https://github.com/DimitarChristoff/doctor" target="_blank" title="generate documentation from markdown">Doctor, MD</a>
</div>
</div>
<div id="content" class="container">
<h1 id="fdop-guide">FDOP guide</h1>
<h4 id="-fire-danger-operating-plan-information-material-"><em>Fire Danger Operating Plan information & material</em></h4>
<h2 id="what-is-a-fdop">What is a FDOP?</h2>
<p>Fire Danger Operating Plan </p>
<p>A fire danger operating plan is a fire danger rating applications guide for agency users at the
local level. A fire danger operating plan documents the establishment and management of the
local unit fire weather system and incorporates fire danger modeling into local unit fire
management decisions. Fire danger operating plans include but are not limited to responsible
parties (e.g. station maintenance, data entry); fire danger rating areas (e.g. location, development
criteria); NFDRS thresholds and breakpoints (e.g. staffing levels, adjective ratings, preparedness
levels, and indexes used for each); operational procedures; and Fire Danger PocketCards. </p>
<p>source: <a href="http://fam.nwcg.gov/fam-web/pocketcards/master_gaining.pdf">http://fam.nwcg.gov/fam-web/pocketcards/master_gaining.pdf</a></p>
<h2><a href="http://www.slocountyfire.org/FDOP_Annual">Example FDOP</h2>
<h2 id="creating-an-fdop">Creating an FDOP</h2>
<h3 id="creating-an-fdop/getting-started">Getting Started</h3>
<hr>
<p><strong>1. Download FireFamilyPlus from:</strong></p>
<p><a href="http://www.firemodels.org/index.php/firefamilyplus-software/firefamilyplus-downloads">http://www.firemodels.org/index.php/firefamilyplus-software/firefamilyplus-downloads</a></p>
<p><strong>2. Create a Database:</strong>
File> New> Name your Database and save it to an appropriate location> Give it a description.</p>
<h3 id="creating-an-fdop/station-catalog">Station Catalog</h3>
<hr>
<p><strong>1. Download station catalog information from:</strong></p>
<p><a href="https://fam.nwcg.gov/fam-web/kcfast/mnmenu.htm">https://fam.nwcg.gov/fam-web/kcfast/mnmenu.htm</a></p>
<p>Select Weather> Station Catalog> Station Information> Select ‘Single Station’> Enter in Station ID> Select Output Destination to “Send file to FTP site”> Save .txt file to appropriate folder</p>
<p><strong>2. Importing the Station Catalog:</strong></p>
<ul>
<li>On the Data menu> Select Import> Select WIMS Station Catalogs> Select the appropriate .txt file> </li>
<li>From the Data menu> Select Stations> This will show you which stations are available for you to work with in the data base and you should see the station catalog you have just imported. From this screen you can highlight your station and select ‘Edit’. This will show you all the attributes of your station.<ul>
<li><strong>(You should make note of the USFS region – you will need to know that value for when you download your fire data).</strong> </li>
</ul>
</li>
<li>In your working set dialog box under ‘SIG/Station’> Select your station from the drop down menu. </li>
</ul>
<h3 id="creating-an-fdop/weather">Weather</h3>
<hr>
<p><strong>1. Download weather data from:</strong></p>
<p><a href="https://fam.nwcg.gov/fam-web/kcfast/mnmenu.htm">https://fam.nwcg.gov/fam-web/kcfast/mnmenu.htm</a></p>
<ul>
<li>Select Weather> Data Extract> Historical> Enter in Station ID and date range> Select ‘Raw Datafile – 1998 Data Format> Save .fw9 file to appropriate folder. The file can be downloaded from either <a href="http://fam.nwcg.gov/fam-web/kcfast/batchout/?M=D">http://fam.nwcg.gov/fam-web/kcfast/batchout/?M=D</a> or <a href="ftp://ftp2.fs.fed.us/incoming/wo_fam">ftp://ftp2.fs.fed.us/incoming/wo_fam</a></li>
</ul>
<p><strong>2. Importing the Weather Data:</strong></p>
<ul>
<li>From the Data Menu> Select Import> Select Old Wx Import> The Selected Fields should include: Station ID, Obs Date, Obs Time, Obs Type, OMC10, RH, and Windspeed> Select the StationID Box> Select Overwrite Duplicates.<br>UPDATED: Select Data> Import> Generic Wx> upload your .fw9 files.</li>
</ul>
<p><strong>3. Missing Records:</strong></p>
<ul>
<li><p>Once you import your weather data> Select from the Weather Menu> ‘View Observations’> ‘Daily’> go through to look for missing SOW’s or missing records. </p>
</li>
<li><p>If you have missing information you need to go to: <a href="http://www.raws.dri.edu/index.html">http://www.raws.dri.edu/index.html</a> to down load the missing data. Select CA> select your RAWS site from the list to the left or select the station on the map> Select ‘Data Lister’ from the list on the left hand side of the screen> Specify the dates in which you are missing data > If you are downloading more than 30 days worth of data you need to enter in a password (for Southern CA, try contacting the San Luis Unit)> Save this as a .txt file to the appropriate folder. </p>
</li>
</ul>
<p>In my case, there were so many non-consecutive missing dates that I downloaded data for the entire 10 years rather than many multiple single date files. The directions that follow will explain how to edit and filter out only the records you need. </p>
<p><strong>4. Editing Weather data</strong></p>
<ul>
<li><p>Open the .fw9 file you downloaded in Notepad++. </p>
</li>
<li><p>Remove all html code from the bottom of the file and the top of the file. </p>
</li>
<li><p>Remove colons from the beginning of the first two strings of line. (This is to help format and line up the data correctly for import back into FFP).</p>
</li>
<li><p>If there are empty lines between string of data: Find and Replace> Find: \n\r\ Replace: [nothing] >Extended Search Mode</p>
</li>
<li><p>To isolate ‘O’ : Find and Replace> ‘Mark’ tab> Find What: 00R> Check the bookmark option> Mark All> Search> Bookmark> Remove Bookmarked lines</p>
</li>
<li><p>To replace first four letters with station ID: Find and Replace> Find What: _ _XXXX> Replace with: ######</p>
</li>
<li><p>Remove the top two strings of text that were used to format the data.</p>
</li>
<li><p>Save as a .txt file for import. </p>
</li>
</ul>
<p><strong>5. Importing Edited Weather data</strong></p>
<ul>
<li>From the Data Menu> Select ‘Import’> Select ‘New FW9 Files’> Select your .txt file> Import. </li>
</ul>
<p><strong>6. Searching for Anomalies in the data</strong></p>
<ul>
<li><p>Once you have a complete weather database it is important to examine the data for anything that may not be accurate. Poor quality data is due to either human error in entering in daily weather observations or mechanical errors at the RAWS stations itself. Things to look out for:</p>
<ul>
<li><p>Do the values make sense? For example, if the temperature was recorded being 100 degrees in December, there may be a problem. </p>
</li>
<li><p>Repetitive values for any variable. In my case there was an occurrence of a Relative Humidity of 4 for about three months straight. While this may be possible, it is highly unlikely that this is accurate.</p>
</li>
<li><p>Are there any missing SOW (State of the Weather)? If so, consider the time of the year and the SOW of days before and after the date you are determining to give you insight as to what the SOW for that day is likely to be. </p>
</li>
<li><p>If you find data that is not accurate or that which yield values that seem to be outrageous you can use some of the resources below to fill in the gaps and/or verify or edit the values. If there is no data to fix the issues you find, you will have to omit the records. If this happens to be the case state in the FDOP that there was an omissions of records due to poor quality data. </p>
</li>
</ul>
</li>
</ul>
<h3 id="creating-an-fdop/fire">Fire</h3>
<hr>
<p><strong>1. Download fire data from:</strong> </p>
<p><a href="https://fam.nwcg.gov/fam-web/kcfast/mnmenu.htm">https://fam.nwcg.gov/fam-web/kcfast/mnmenu.htm</a></p>
<p>Select Fire> Standard Extract> Enter in Region/Forest and date range</p>
<p><strong>2. Importing Fire Data</strong></p>
<ul>
<li>From the Data Menu> import>Select from drop down menu ‘USFS’> Select ‘Raw Files’> Select your .raw file<ul>
<li>Region; Unit; Discovery Date; Fire Number; Acerage; Fire Name; Cause; Latitude; Longitude</li>
</ul>
</li>
</ul>
<p><strong>3. Obtain LE-66 Data</strong></p>
<ul>
<li>Create an excel spread sheet for your 10 years worth of data.</li>
<li>The LE-66 data is stored in on the (F:) drive so you need to be connected to the VPN in order to get access.
<em>(F:)>data>prevent>Prevention>STATS3421</em></li>
</ul>
<p><strong>4. Preparing LE-66 Data</strong></p>
<ul>
<li>Create columns for date, incident number, cause, comments, latitude, longitude and copy and populate the fields. </li>
<li>Save this file as a .txt for import. </li>
<li>Make sure the date has been formatted correctly YYYYMMDD (right click>format cells)</li>
<li><p>Change all cause codes (refer to FDOP for conversion chart)</p>
<ul>
<li><em>Make sure all the years are correct. In my experience, I found incidents for 2005 in the 2004 LE-66 stat document, so correct as necessary.</em> </li>
<li>Cause will be represented by a letter, number, or a combination of both. For the years 2009-2012 there is a document on the (f:) that will have a description for each letter/number value.
<em>(F:)>data>prevent>Prevention>STATS3421>2009</em> and after log cover causes.doc
I created another spread sheet in order to edit just the data from 2009-2012.</li>
</ul>
</li>
<li><p>Once I had this data isolated I performed a filter function on the ‘cause’ column. Using this function I isolated each variable and replaced it with the cause description. For the data 2002-2008 refer to
<em>(F:)>data>prevent>Prevention>STATS3421>log cover causes.doc</em>
when performing your filter to find and replace the cause variables. </p>
</li>
</ul>
<p><strong>5. Additional Editing for LE-66 Data</strong></p>
<p>If you do not need your lat and long data to be in DD, then disregard the following instructions..</p>
<ul>
<li><p>The lat and long data I gathered from the LE-66 STATS were in different formats i.e. DMS, DM, and were also in different variations of each format. I created ‘working data’ columns of lat and long in order to apply excel formulas to convert these formats all into DD (the format in which my mapping platform of choice required for import). </p>
</li>
<li><p>For DMS ## ## ##: </p>
<pre class="prettyprint linenums"><code> =MID(B23,FIND(" ",B23)+2,2)/3600+MID(B23,FIND(" ",B23)+1,2)/60+LEFT(B23,FIND(" ",B23)-1)
</code></pre></li>
<li><p>For DMS ## ##’##”:</p>
<pre class="prettyprint linenums"><code> =MID(A1,FIND("'",A1)+2,2)/3600+MID(A1,FIND(" ",A1)+1,2)/60+LEFT(A1,FIND(" ",A1)-1)
</code></pre></li>
<li><p>For MD ## ##.##:</p>
<pre class="prettyprint linenums"><code> =MID(A1,FIND(" ",A1)+1,2)/60+Left(A1,FIND(" ",A1)-1)
</code></pre></li>
</ul>
<p>Once all the data has been converted to DD I made sure that all the Longitude values were (-). I then created final columns for both lat and long and copied and pasted only the values from my lat and long ‘working data’ columns into my lat and long ‘final data’.</p>
<p><strong>6. Using Fire Reports</strong></p>
<ul>
<li><p>Using Internet Explorer go to:
<a href="http://webrpt.fire.ca.gov/InfoViewApp/logon.aspx">WebRPTSite</a></p>
</li>
<li><p>Navigate to: MyInbox> Public Folders> CAD Shared Reports> Click on the FC34 Reports> Select: FC34 Detail-All-Seg (Incident Number)</p>
</li>
<li>Enter in YYYY###### to look up each individual fire report. Verify date, author, lat, and long.</li>
<li>Save as a .csv for import.</li>
</ul>
<p><strong>7. Populating the ‘SubUnit’ Field</strong></p>
<ul>
<li><p>Export your fire data from FF+ as a shapefile. Be sure to include ‘SubUnit’ as a field during export. Import your ignition and FDRA .shps into arc. > Open the Ignition attributes table and create a text field called ‘SubUnit’ > In the ‘Select’ menu chose ‘Select by Location’.</p>
<ul>
<li>I want to: ‘select features from’</li>
<li>The following layer(s): [your ignition data]</li>
<li>That: ‘are completely within’</li>
<li>The features in this layer: [your FDRA]</li>
</ul>
</li>
<li><p>In your attribute table select ‘Show: selected’ > start an editing session> right click on the SubUnit field and select ‘Field Calculator’> select ‘string’> in double quotes enter in the name of this subunit field. Complete this same process for each FDRA. If there are ignitions that fall outside of your FDRAs clear all selections> select all the data> from your Selection by Location dialog select ‘remove from current selection’ and remove all FDRAs that you just assigned. The remaining ignitions can be assigned ‘Other’ via the process described above.
Save all edits and close editing session. </p>
</li>
<li><p>Open the ignition shapefile in Quantum. Select the ‘Table Manager’ tab. Arrange the fields in the appropriate order (i.e. region, unit, discovery date, fire number, acreage, fire name, cause, lat, long, subunit) and delete any fields that have been created. Save this as a .csv. </p>
</li>
<li><p>Open your CSV. If your date appear to be ######## right click on the DiscoveryDate column and select ‘Format Cells’. Select ‘date’ from the menu on the left. Select the default date format (##/##/####). Save the csv. </p>
</li>
<li><p>Import your CSV back into FF+ and be sure to include ‘SubUnit’ at the end of your field items to import and also verify that the date format matches the format you adjusted your ‘DiscoveryDate’ values in the CSV.</p>
</li>
</ul>
<h3 id="creating-an-fdop/stats-graphs">Stats Graphs</h3>
<hr>
<p><strong>1. Fire Occurrence graphs</strong></p>
<ul>
<li>One including All FDRAs: Coastal, Inland, and Ob. Coasc.<ul>
<li>‘Fires’>’Summary’>’Working Set’>Select the ‘CAL FIRE’ Tab>Region ‘3RSS’> Unit ‘SLU San Luis Obispo’>Sub Unit ‘COASTAL Coastal’> ‘Ok’</li>
</ul>
</li>
<li>Annual Filter should be set for the entire calendar year for these graphs. </li>
<li>Screen Shot the graph and insert into FDOP</li>
<li>Complete the Steps above for each Sub Unit.
All fire occurrence graphs need to be based on the entire calendar year.</li>
</ul>
<p><strong>2. Determining Thresholds</strong><br>For each FDRA the thresholds need to be determined. The program will do this for you however; you need to confirm that the default percentage values for the thresholds are indeed accurate after all climatology statistics graphs have been run. The given percentages should coincide with the data output of the fire analysis. </p>
<p><strong>3. Three indice graphs for each FDRA</strong></p>
<ul>
<li>ERC displaying: min, max, avg <ul>
<li>‘Weather’>’Climatology’>Select the ‘Stats Graph’ box for the Variable ‘Energy Release Component’>’Run’</li>
<li>Maximize the left portion of the screen </li>
<li>Select ‘Options’> ‘Graph Options’> Select the ‘Lines at Average’ tab> Deselect the ‘Mins’ and ‘Maxs’>’Apply’
<br>iv. Screen Shot the Graph and insert into the FDOP</li>
</ul>
</li>
<li>BI displaying: min, max, avg<ul>
<li>‘Weather’>’Climatology’>Select the ‘Stats Graph’ box for the Variable ‘Burning Index’>’Run’</li>
<li>Maximize the left portion of the screen </li>
<li>Select ‘Options’> ‘Graph Options’> Select the ‘Lines at Average’ tab> Deselect the ‘Mins’ and ‘Maxs’>’Apply’</li>
<li>Screen Shot the Graph and insert into the FDOP</li>
</ul>
</li>
<li>ERC displaying: a year in which the ERC threshold before the onset of fire season (May 15th) and a year in which the ERC threshold had not been obtained until after the start of fire season. <ul>
<li>‘Weather’>’Climatology’>Select the ‘Stats Graph’ box for the Variable ‘Energy Release Component’>Change the ‘CP #2’ value from 97 to 38 (the point at which an ERC of 200 occurs)>’Run’</li>
<li>Maximize the left portion of the screen</li>
<li>Select ‘Options’> ‘Graph Options’> Select the ‘Lines at Average’ tab> Deselect the ‘Mins’ and ‘Maxs’>’Apply’</li>
<li>The onset of fire season is illustrated here when the ‘Average’ line crosses the 38th percentile threshold. The purpose of this graph is to show that fire season can start well before the traditional start (May 15th) of fire season and conversely, that fire season sometimes starts after the common start date. </li>
<li>Select ‘Options’>’Overlays’>’New’> Toggle between the years and find the year that has the earliest start date (When it crosses the 38th percentile threshold) and which has the latest start date. Give each a unique color, a line width of 3, and make it a solid line. Select ‘Ok’. </li>
<li>Screen Shot the Graph and insert into the FDOP</li>
</ul>
</li>
</ul>
<p><strong>4. Decision Points for Dispatch Level</strong></p>
<ul>
<li>Decision points should be set using BI. <ul>
<li>Select the ‘A’ on the ribbon at the top of the screen> run the operation with default values selected> Click on the ‘Fires Probability Analysis’ Window> Select ‘View’> ‘Decision Points’> Delete 2 records so you are left with 3 classes</li>
<li>Set the first class to 0, set the second class to the value at which fires begin to take off. Visually, this is when there appears to be a large jump from low to high. The last class should be set at the BI value where the crest of the graph is no longer in a drastic incline and starts to plateau. The second decision class created should hold the greatest number of fires whereas the first and last should contain the least amount of fire occurrence. </li>
<li>When these levels have been adjusted to accommodate the fire data select ‘Apply’> Adjust the screen so that all graphs fit appropriately>Screen shot and insert into the FDOP. </li>
<li>Adjust the ‘Dispatch Level: Fire Family Plus Analysis Factors and Determinations’ Table (Specifically, the ‘Index Break Points’ section).<br>NOTE: All Decision Point graphs need to be based on Fire season (May1-Oct 31), not the calendar year.</li>
</ul>
</li>
</ul>
<p><strong>5. When updating numbers and percent values in FDOP</strong></p>
<ul>
<li>Follow instructions to look at fire occurrence chart> Select ‘Options’> ‘View Graph Data’</li>
<li>This ‘Fire Occurrence Summary’ page will provide the exact values behind all the graphics illustrated for fire occurrence. </li>
</ul>
<h3 id="creating-an-fdop/staffing-level">Staffing Level</h3>
<hr>
<p>Staffing Level is an important component of the Adjective Fire Danger Rating and is calculated in WIMS. It is a way for us to break up the BI continuum based on percentile to make it more useful. Staffing level, along with Ignition Component, is a way for describe relative fire risk. </p>
<p>Staffing Level will be determined using fuel model G for the San Luis Obispo Unit. This is determined by running different fuel models against fire and weather history data. When running the different fuel models with your data you should choose the fuel model that yields the ideal statistical output. The ideal statistical output includes a BI of at least 100, all graphs should be a bell curve, the data should have a linear distribution across the spectrum, and no graphs should be inverted. If you find a fuel model that yields these results, it means that fuel model is the best for fit for your data set and should be the fuel model you should used in the analysis of staffing level. In this analysis, fuel model G (which also happens to be the standard fuel model) was determined to be the best fit for the data set. </p>
<ul>
<li>Select the appropriate ‘SIG/Station’ , ‘Data years’, ‘Annual Filter’: May 1 – Oct 31 > Select ‘Fires’ > ‘Fire Analysis’> Select: Large Fire (Acres):8, Multi Dire Dat (Fires): 3, Select ‘BI’ from the drop down, check the box for ‘Conditional Probability Analysis-FireDays Only> ‘OK’> Select the ‘Fires Probability Analysis’ Window> Select ‘View’ > ‘Decision Points’> close the ‘Class Lower Limits” Window </li>
<li>The default ‘Class BI Ranges’ will be the values entered into the “Staffing Level: Break Points chart”. Round the Values for the chart.</li>
</ul>
<h3 id="creating-an-fdop/preparedness-level">Preparedness Level</h3>
<hr>
<p>The point at which large fires start to take off based on ERC. </p>
<ul>
<li>Select appropriate ‘SIG/Station’> Data Years> Annual Filter set for fire season> Select ‘Fires’> ‘Fire Analysis’> Large Fires (Acres):8> Multi Fire Day (Fire):3> Select ERC from the drop down> Check the ‘Conditional Probability Analysis Fire Days Only’ box> </li>
<li>Close the ‘Cumulative Fires Analysis’ Window. Looking at the ‘Fires Probability Analysis’ you can see when large fires start to take off as illustrated by the magenta line with the open square (Large Fire Day). When the data indicates an increase in occurrence of large fires, this is the point at with the threshold should be placed. Click directly on the pink square to view the value. The X-value in the bottom right hand corner of the screen in the ERC threshold value that will be recorded in FDOP. In order to verify this threshold value:</li>
<li>Select ‘View’> ‘Decision Points’> In the ‘Class Lower Limits’ window delete all but two of the threshold classes> Make Class 1 and value of ‘0’ and Class 2 the threshold X-value> Select ‘Apply’</li>
<li>In the ERC Percentile Graph the threshold line should be drawn a the point in which the large fire data starts to rapidly increase. </li>
<li>In the ERC Probability Graph directly below, the threshold line should be drawn through the data point that you clicked before, which is the point at which large fires start to increase. </li>
<li>If the threshold line is not drawn at the necessary position on the graphs adjust the threshold value in the ‘Class Lower Limits’ window to determine the appropriate preparedness threshold value.</li>
<li>Complete this process for both FDRAs and record the appropriate threshold value in the Preparedness Level chart. </li>
</ul>
<h3 id="creating-an-fdop/pocket-cards">Pocket Cards</h3>
<hr>
<p>A pocket card must be created for each FDRA, which will display the three largest fires. </p>
<p>For the Inland FDRA, instead of using the largest fires by acreage we used the top three most well known fires in San Luis County. This is because these fires are the events that resonate with the SLU. </p>
<ul>
<li>Select ‘Weather’> ‘Pocket Card’> </li>
<li>Fire Danger Area: ‘Inland FDRA’ </li>
<li>Area Locator Bitmap is located: X://projectdata>master_data>Inland.bmp </li>
<li>Years to Remember: 1950 – Present</li>
<li>Area Locator Bullets: <ul>
<li>Line 1 – Interior Valley</li>
<li>Line 2 – Las Tablas RAWS </li>
<li>Line 3 – La Panza RAWS</li>
</ul>
</li>
</ul>
<p><strong>Fires</strong> </p>
<table>
<thead>
<tr>
<th>Fire Name</th>
<th>Date</th>
</tr>
</thead>
<tbody>
<tr>
<td>.</td>
<td>7/1/1985</td>
</tr>
<tr>
<td>.</td>
<td>8/18/1994</td>
</tr>
<tr>
<td>.</td>
<td>8/15/1996</td>
</tr>
</tbody>
</table>
<p>Fire Name is left Blank or replaced with '.' because the symbology is cluttered and not legible on the graph. Once the graph is complete, Photoshop/edit the names in. (I did so using paint)</p>
<p>Past Experience Text:</p>
<ul>
<li>Pilitas 1 fire 7/1/1985 burned 84,271 acres</li>
<li>Highway 41 fire 8/18/1995 burned 50,729 acres</li>
<li>Highway 58 fire 8/15/1996 burned 33,094 acres</li>
</ul>
<p>This bit of text can be typed into the Pocket card window. All other descriptive text and the logo was edited using paint. Unless a fire occurs that exceeds one of these three major fires, these are the fires that are to be displayed on the Inland Pocket Card. The Coastal FDRA pocket card displayed the top three largest fires in that FDRA. </p>
<p>The bit map for each FDRA is already created:
<em>X://projectdata>master_data>Coastal.bmp</em></p>
<p>Under the ‘Fire Danger Area’ enter in the region (i.e. coastal or inland), the RAWS located in the region, </p>
<h3 id="creating-an-fdop/updating-the-fdop-annually">Updating the FDOP Annually</h3>
<hr>
<ul>
<li>Enter in all fire and weather records for the past year</li>
<li>Run the above statistical graphics to produce new charts. These charts shouldn’t change too drastically unless there were exceptionally large or small fires, or drastically hot or cold days. <ul>
<li>Fire Occurrence Graphs will need to be updated. All descriptive text and percentages need to be adjusted accordingly. </li>
</ul>
</li>
<li><p>Climatology Graphs of Temperature and Rainfall. These can be obtained from:</p>
<ul>
<li>Use <a href="http://www.ncdc.noaa.gov/cdo-web/datatools/normals">http://www.ncdc.noaa.gov/cdo-web/datatools/normals</a>, Paso Robles for inland and San Luis Obispo for coastal.</li>
</ul>
</li>
<li><p>Tables in the FDOP that may need to be updated depending on new data include: </p>
<ul>
<li>10 Largest Fires Recorded in the Inland FDR</li>
<li>Historic Fires for Reference Depicted on the Inland FDRA Pocket Card</li>
<li>10 Largest Fires Recorded in the Coastal FDRA</li>
<li>Historic Fires for Reference Depicted on the Coastal FDRA Pocket Card </li>
</ul>
</li>
<li>If the information in the station catalog in WIMS changes or is updated a new screenshot should be included in place of the current. If the conditions of the RAWS change, it should be noted in the ‘RAWS Site Description and Photos’ section. All pocket cards in the appendix need to be updated. </li>
</ul>
<h3 id="creating-an-fdop/cause-code-conversion-chart">Cause Code Conversion Chart</h3>
<table>
<thead>
<tr>
<th>CDF Cause Code</th>
<th>Description</th>
<th>Federal Cause Code</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>Unknown</td>
<td>9</td>
</tr>
<tr>
<td>1</td>
<td>Undetermined</td>
<td>9</td>
</tr>
<tr>
<td>2</td>
<td>Lightning</td>
<td>1</td>
</tr>
<tr>
<td>3</td>
<td>Campfire</td>
<td>4</td>
</tr>
<tr>
<td>4</td>
<td>Smoking</td>
<td>3</td>
</tr>
<tr>
<td>5</td>
<td>Debris Burning</td>
<td>5</td>
</tr>
<tr>
<td>6</td>
<td>Arson</td>
<td>7</td>
</tr>
<tr>
<td>7</td>
<td>Equiptment Use</td>
<td>2</td>
</tr>
<tr>
<td>8</td>
<td>Playing w/ Fire</td>
<td>8</td>
</tr>
<tr>
<td>9</td>
<td>Miscellaneous</td>
<td>9</td>
</tr>
<tr>
<td>10</td>
<td>Vehicle</td>
<td>2</td>
</tr>
<tr>
<td>11</td>
<td>Railroad</td>
<td>6</td>
</tr>
<tr>
<td>12</td>
<td>Powerline</td>
<td>2</td>
</tr>
</tbody>
</table>
<p>(Obtained from CAL FIRE intranet)</p>
<p>In preparation for statistical analysis, the CAL FIRE cause code must be translated to the federal cause code for use in Fire Family Plus software. The chart above outlines the cause code conversion from CDF to Federal cause codes.</p>
<h2 id="fuels-info">Fuels Info</h2>
<p><strong>Q: How are live and woody Fuel Moistures Calculated?</strong> </p>
<p>A: "Measured Woody Fuel Moisture: The modeled fuel moisture of live woody material does
not always track with the measured woody fuel moistures from sampling sites. This is
because live fuel moisture values in the NFDRS are modeled values designed for the
broad scale of fire danger, rather than site-specific measured values. In these instances,
fire managers have a couple of options. Physical measurements of the moisture content
of the small branch wood and foliage of live woody plants can be collected monthly.
Preferably the user will already be monitoring these measured live fuel moistures in
parallel to the outputs of the NFDRS and using experience to include the measured fuel
moisture values as another tool in their decision-making toolbox. This allows the
NFDRS model to work for the user as it was intended.</p>
<p>Alternatively, the user can regularly enter measured live woody fuel moisture into the
NFDRS processor to calibrate the woody fuel moisture model. The calibration based on
measured woody fuel moisture is valid for 30 days. If no new measured value is entered
within 30 days, the model returns to using only weather data. The woody moisture
computed solely from weather data may be quite different from that computed from both
weather data and the measured woody moisture. This may result in sudden or
unacceptable changes to NFDRS outputs. This approach requires consistent care and
feeding of the processor (i.e., a regular live fuel monitoring program) and has been
discouraged by some agencies." </p>
<p>"X-1000 Hr Fuel Moisture Value – The X-1000 value is not truly a dead fuel moisture
value. It is the live fuel moisture recovery value. It is discussed here since it is derived
from the 1000-hr fuel moisture value. It is an independent variable used in the
calculation of the herbaceous fuel moisture. The X-1000 is a function of the daily change
in the 1000-hour timelag fuel moisture, and the average temperature. Its purpose is to
better relate the response of the live herbaceous fuel moisture model to the 1000-hour
timelag fuel moisture value. The X-1000 value is designed to decrease at the same rate
as the 1000-hour timelag fuel moisture, but to have a slower rate of increase than the
1000-hour timelag fuel moisture during periods of precipitation, hence limiting excessive
herbaceous fuel moisture recovery. "</p>
<p>"Live woody and herbaceous moistures fluctuate in response to drying and
wetting cycles. Greenness factor values fluctuate up and down within the 20 and 1 range during
this period. Annual herbaceous vegetation most likely will cure sometime during this period. In
the NFDRS model, the live fuel moisture is initially calculated using the same formulas as are
used after the completion of greening in the 1978 models, but is adjusted by a factor equal to the
greenness factor divided by 20. The woody fuel moisture is calculated using the same formulas
as are used in the 1978 models, and it too is adjusted by the greenness factor." <a href="http://fam.nwcg.gov/fam-web/pocketcards/master_gaining.pdf">http://fam.nwcg.gov/fam-web/pocketcards/master_gaining.pdf</a></p>
<p><strong>Q: What is the difference between Annuals and Perennials?</strong> </p>
<p>A: "Grass Type (live fuel type): The National Fire Danger Rating System recognizes that there are
seasonal differences in fire danger related to the type of grass vegetation present. Annual
vegetation produces a different dynamic situation within the fuel complex than does perennial
vegetation. Annuals sprout from a seed each year, grow, reach maturity and die usually all in
one season. This process is not affected significantly by seasonal weather factors such as
temperature or precipitation. Perennial grasses on the other hand, generally start in a dormant
condition, grow, reach maturity, then go back into dormancy. Their cycle is greatly affected by
temperature and precipitation. Because of these differences, the mathematical formulas or
algorithms associated with the drying of herbaceous vegetation are different for the two types of
grasses. The loading of fine fuels associated with annual grasses shifts from live to dead and
stays there for the duration of the season. For perennial grasses the shift from live to dead is
much slower and may even stop or reverse if the right combinations of temperature and
precipitation occur during the season. Where both annual and perennial grasses occur together
select the type that predominates the site." <a href="http://fam.nwcg.gov/fam-web/pocketcards/master_gaining.pdf">http://fam.nwcg.gov/fam-web/pocketcards/master_gaining.pdf</a></p>
<p><strong>Q: When should we start seeing changes in our fuel moistures after Green Up?</strong></p>
<p>A: "The equations that predict 1000-hr and live fuel moisture contents require at least four weeks to stabilize and predict
accurate fuel moisture content values." <a href="http://fam.nwcg.gov/fam-web/pocketcards/master_gaining.pdf">http://fam.nwcg.gov/fam-web/pocketcards/master_gaining.pdf</a></p>
</div>
<script src="js/mootools-yui-compressed.js"></script>
<script src="js/moostrap-scrollspy.js"></script>
<script src="js/prettify.js"></script>
<script src="js/lang-css.js"></script>
<script src="js/ace/ace.js" type="text/javascript" charset="utf-8"></script>
<script src="js/doctor.js"></script>
</body>
</html>