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Computational Sustainability: Computational Methods for a Sustainable Environment, Economy, and Society Optimal Forest Fire Fuel Management and Timber Harvest In The Face Of Endogenous Spatial Risk The Next Step Cl i Claire Montgomery M t Forest Economics Oregon State University C ll College of Forestry f F t

Wha What’s the the Pr PrOBLEM and how did w OBLEM and how did we g get her t here? FIRE SUPPRESSION POLICY FIRE SUPPRESSION POLICY William Greeley USFS chief 1920-9 “the conviction was burned into me that that fire prevention is the number 1 job of American foresters” (Greeley, WB. 1951. “Forests and men” NY: Doubleday.) “10:00 am policy” Goal – to contain every wildfire Goal to contain every wildfire by 10:00 am the day after it is reported – regardless of cost. www.mtmultipleuse.org/images/smokey.jpg

Fi Fire i in t the we western U U.S. NATU TURAL r RAL regime – gime – frequent (15-20 quent (15-20 year ears) low-intensity f s) low-intensity fires favor ors PONDEROSA PINE s PONDEROSA PINE thic thick bar bark to to sur surviv ive low-intensity f e low-intensity fire tak take out w out weak eaker tr er trees ees -- -- "na "natur tural al thinning“ thinning“ strong str onger tr er trees esta ees establish dominance ish dominance RESUL RESULT -- -- open stands of open stands of big big tr trees ees.

Lodgepole Pine Lodg pole Pine Mountain Pine Beetle Mountain Pine Beetle • • Lar Large ar e areas of eas of dead tr dead trees ees pioneer species pioneer species • Enormous f Enormous fuel b build ild-ups -ups • seroti tinous c nous cones nes • “k-str “k -strategy” s y” seed in eed in at g great d t density nsity Wh Wh Wh When en wildf ildf ildfi ildfire res DO DO DO occur DO occur ch choking out other out other s species ecies • don’ n’t e t esta tablish d ish dominance minance • overstoc stocked, s stagnant s nant stands • Can be Can be ca cata tastrophic • vulner lnerable t le to in insect a sect and d d disease ase • Har Hard to con to contai ain picasaweb.google.com helenair.com

What is a ca Wha is a catastr tastroph phic f ic fire? • Kills all • Kills all Kills all (or Kills all (or (or most) of (or most) of most) of the most) of the the vegeta the vegeta tation tation tion tion • Destr Destroys or organic ma nic matter in tter in the soil the soil • “R “Red soil” ed soil” – burned urned so hot tha so hot that o t oxida xidation ion occur occurs

Potential tential OBJECTIVES BJECTIVES of f of f fire f fire f fuel m fuel m management management Existing analy Existing analyses es: • maximiz maximize minim e minimum tr travel time acr el time across a oss a landsca landscape • minimiz minimize e expected loss fr pected loss from a om a fire • maximiz maximize e e expected net pected net pr present v esent value lue of of timber har of of timber har timber harvest timber harvest est est less tr less trea eatment cost on a tment cost on a landsca landscape My desir My desired objectiv d objective: • maximiz maximize e e expected net pected net pr present v esent value lue of timber har of timber harvest est less tr less trea less tr less trea eatment and eatment and tment and suppr tment and suppr suppression cost suppression cost ession cost ession cost • subject to subject to • wildlif wildlife ha e habita bitat g t goal al • en endi di di ding ng f for f f ores est t con t conditi dition diti diti on i in whi i i hi hi h hich h h natural f fire re re regime i is re restored .

Potential tential Activities f ctivities for eac r each unit: h unit: Existing Analy Existing Analyses es • Do Nothing Do Nothing • Trea eat fuels (mec t fuels (mechanical r hanical remo moval, pr l, prescribed b escribed burning) rning) • Timber har mber harvest est I’ I’d d lik like to add e to add: • Modi Modified f ed fire suppr suppress ssion ( ( (e. e.g. l l l t fi let f t fire re b burn b urn i in mo i i moder d erate we t weath ther th er) )

Assessing Assessing CONSEQUENCES: ONSEQUENCES: Inte Integration of ion of sim simula lation models into optimiza tion models into optimization: tion: 1) Vegeta 1) tation and tion and fuels fuels FOREST VEGET FOREST VEGETATION TION SIMUL SIMULATOR (FVS) R (FVS) with FOREST FUEL with FOREST FUELS EXTEN S EXTENTION ION (FFE) (FFE) 2) 2) Fi 2) 2) Fi Fire b Fire b behavior – FL behavior – FL FLAMMAP (F FLAMMAP (F AMMAP (Finne AMMAP (Finne inney 2006) inney 2006) y 2006) pr y 2006) pr predicts predicts edicts edicts FIRE SPREAD – FIRE SPREAD – as s a a function of: function of: vegeta tativ tive co cover and fuels r and fuels topog topography phy -- -- slope slope, aspect aspect weather – ther – wind, fuel moistur ind, fuel moisture using minim using minimum tr m travel time el time alg algorithm rithm FIRE INTENSITY FIRE INTEN ITY-- flame length and lame length and other other attrib ttributes utes as a as a function function of: of: vegeta tativ tive co cover and fuels r and fuels t t topog opography h h weather ther

Trade-of de-offs and Optimiza s and Optimization ion Eleme Elements nts of of the pr the prob oblem lem • ST STOCHASTIC OCHASTIC – fir ire occur e occurrence and e ence and extent is unpr tent is unpredicta dictable le • DYNAMIC • DYNAMIC MIC MIC – optimal decisions in ptimal decisions in period period t de depend pend on f on fire occur occurrence and fuel tr ence and fuel trea eatments tments in previo in pr ious periods periods. • SP SPATIAL IAL -- -- fuel tr fuel trea eatment tment af affects ects fire re s spre read rat rates and, hence, fi and, hence fire r risk in adjacent units in adjacent units -- -- dama damage b by f fire in one in one unit ma unit may af y affect v ect values lues in in other units e other units e.g. Grizzly cor Grizzly corridor idors

Emphasize DECISION MODEL Emphasiz e DECISION MODEL Konos K K onoshi h hima, hi ma, M M , et al. 2008. Spatial endogenous fire risk and M t l 2008 S M ti l d fi i k d efficient fuel management and timber harvest. Land Economics . Specif Specifies d ies decision model a cision model as s stoc ochastic d hastic dynamic p namic prog ogram Si Si Si Simplif plifies lif lifi es spec ecif ifica ifi if cati tion ti on of t f th f the p th pro robl blem bl em t t t to ma make it t k it t it t it tra ractabl t bl ble Emphasiz Emphasize PROBLEM SPECIFICA e PROBLEM SPECIFICATION ION Finne nney, M. M. 2007. A computational method for optimizing fuel treatment locations. International Journal of Wildland Fire. Wei Y We Wei, Y Y et Y., ., et et al et al al 2008 An optimization model for locating fuel al. 2008. An optimization model for locating fuel treatments across a landscape to reduce expected fire losses. Canadian Journal of Forest Research . Chung Chung W Chung Chung, W W et W., ., et et al et al al 2009 OptFuels: a decision support system to al. 2009. OptFuels: a decision support system to optimize spatial and temporal fuel treatments. presented at Symp.on Systems Analysis in Forest Resources. Sim Simplifies d ies decisi sion m on model del Sim Simula lates f fire on on lands landscape as r as reali ealistica call lly as poss y as possible

Konoshima, M onoshima, M , et al. 2008 et al. 2008. Spa Spatial ial endo endogenous fi fire r risk and and ef effici cien ent fuel man fuel management and timbe and timber har harvest. La Land Ec nd Economics onomics . Method Method – stoc tochastic dynamic pr hastic dynamic prog ogram -- -- “cur “curse of of d dimensionality” mensionality” SO SO k kept it SIMPLE it SIMPLE 2 periods 2 p eriods Sty Styliz lized land ed landscape e • 7 7 identically sha identically shaped units ed units • 2 initial 2 initial vegetat g ation s stat ates • 4 4 decisions – decisions – treat, , cut, tr cut, trea eat&cut, lea t&cut, leave Stoc Stochastic w hastic weather (2) ther (2) and and ignition points (7) ignition points (7) Sim Simula lated f ted fire spread p ead initially – initially – no no wind, no wind, no slope slope added slope and wind indivi added slope and wind individu dual ally Solv Solve b e by com y y complete en p p lete enumer umeration ion

Look a Look at the r the results sults to dr to draw out g out gener neralities: lities: