View source for Simple empirical fire model

==Summary==


==Screenshot==

[[Image:TimeSinceFire.gif]]

==Download Model==

Download the .scn, .sel and .lse files by clicking on the following link: [[Image:SimpleFireModel.zip]]

==Model Code Exploration==

In the following sections we will examine all of the model files for this model. Note that instead of downloading the zip file above, you could just copy the text in the boxes below into a text editor and save it with the appropriate name (Section title). Opening the resulting .scn file in the SELES simulator would run this model.

===Fire.scn===
 initialTimeSinceFire = ..\gisData\cell\initialTSF1
 Model Dimensions: initialTimeSinceFire
 FireModel.sel

 Minimize Initial State
 Tile

===Model.sel===
 Seles Model Information
 Time Units: Day Year 365.25
 Landscape Events:
  Succession.lse
  Fire.lse
  ReportResults.lse
 Variable-Input View Maps:
  Species = initialSpecies
  TimeSinceFire = initialTimeSinceFire
 Variable-Output View Maps:
  Species
  TimeSinceFire
 Global Constants:
  MaxTSF = 200

  rAspen = 1
  rPine = 2
  rSpruce = 3
  rBalsam = 4
  Aspen = 1
  Pine = 2
  Spruce = 3
  Balsam = 4

 Output Model Bounds:
  TimeSinceFire: MaxTSF
  Species: 4
 Global Variables:
  ForestSize = 250000
  MeanFireSize = 100
  MeanFiresPerYear = 10
  FireCycle = ForestSize /(MeanFireSize * MeanFiresPerYear)
  oldFireCycle = FireCycle OFF

  PatchBased = TRUE
  SProbAspen2Spruce = 0.1
  SProbPine = 0.01
  SProbPine2Spruce = 0.5
  
  RecordResults = TRUE
 Output Frequency: 365.250000


===consumer.lse===
  // Process based definition for  consumber behaviour in producer/consumer  model
 LSEVENT: Consumder
 DEFINITIONS
   LAYER: LSState, PrevLSState
   GLOBAL CONSTANT: Uncolonised, Producer,  Consumer
   GLOBAL VARIABLE: pInitialConsumer,   pSpreadConsumer, pDeathConsumer //,  SpreadRateConsumer
 ENDDEF
 // Just start once
 RETURNTIME = 0
 // Initialize in an empty cell
 PROBINIT = IF LSState EQ Uncolonised THEN  pInitialConsumer ELSE 0
 TRANSITIONS
   // Decide if this cell survives
   Dies = UNIFORM(0,1) < pDeathConsumer
   // If the consumer is dying
   IF Dies
       LSState = Uncolonised
   ENDFN
   TRANSITIONS = !Dies
   LSState = Consumer
 ENDTR
 SPREADTIMESTEP = 1 //SpreadRateConsumer
 SPREADLOCATION
   REGION CENTRED(0,1, WRAPPED)
 ENDSL
 SPREADPROB
   // Always spread to the same cell  (self), and spread probabilistical to  neighbouring producer cells
  SPREADPROB = IF Location EQ (SOURCE  Location) THEN 1
               ELSE IF PrevLSState EQ   Producer  THEN pSpreadConsumer
               ELSE 0
 ENDSP

===producer.lse===
 // Process based definiton of producer  behaviour in producer/consumer model
 LSEVENT: Producer
 DEFINITIONS
   LAYER: LSState, PrevLSState
   GLOBAL CONSTANT: Uncolonised, Producer,  Consumer
   GLOBAL VARIABLE: pInitialProducer,  pSpreadProducer, pDeathProducer //,  SpreadRateProducer
 ENDDEF
 // Just start once
 RETURNTIME = 0
 // Initialize in an empty cell
 PROBINIT = IF LSState EQ Uncolonised THEN  pInitialProducer ELSE 0
 TRANSITIONS
   // Decide if this cell survives
   Dies = UNIFORM(0,1) < pDeathProducer
   // If the producer is dying
   IF Dies
      LSState = Uncolonised
   ENDFN
   TRANSITIONS = (!Dies)
   LSState = Producer
 ENDTR
 SPREADTIMESTEP = 1 //SpreadRateProducer 
 SPREADLOCATION
   notEaten = PrevLSState EQ Producer //  check that the producer wasn't eaten  during the last step
   REGION CENTRED(0, 1, WRAPPED)
      DECISION notEaten // don't spread if  eaten (i.e. stop process)
 ENDSL
 SPREADPROB
   // Always spread to the same cell  (self), and spread probabilistically to  empty neighbouring cells
  SPREADPROB = IF Location EQ (SOURCE  Location) THEN 1 // spread to same cell
               ELSE IF PrevLSState EQ  Uncolonised THEN pSpreadProducer
               ELSE 0
 ENDSP

===update.lse===
 // Event to update time step
 LSEVENT: Update
 DEFINITIONS
   LAYER: LSState, PrevLSState
 ENDDEF
 RETURNTIME
   RETURNTIME = 1
   PrevLSState = LSState
 ENDRT
 NUMCLUSTERS = 0

==Suggested Experiments==

To explore this cellular automata model further, try the following: