Decision support for strategic forest-fuels management in the Pacific Northwest Keith Reynolds, Paul Hessburg, James Dickinson, Brion Salter USDA-Forest.

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Keith Reynolds, Paul Hessburg, James Dickinson, Brion Salter
Decision support for watershed assessment, protection and restoration
Presentation transcript:

Decision support for strategic forest-fuels management in the Pacific Northwest Keith Reynolds, Paul Hessburg, James Dickinson, Brion Salter USDA-Forest Service, PNW Research Station, and Robert Keane USDA-Forest Service, Rocky Mtn Research Station

 We present a DSS for the evaluation of severe wildfire danger  Built with EMDS system  We demonstrate the system in the PNW Region  but expansion to the CONUS is underway

LANDFIRE Map Zones: Biophysical land units (66) defined by similar landforms, land cover & natural resources CA OR ID NV WA Subwatersheds: eval. unit 12 digit HUCs, USGS_NHD 5,052 subwatersheds in PNW Average size: 8,637 ha Total area: ~ million ha 7 map zones EROS Data Center

1) DSS consists of a logic model (NetWeaver) and a decision model (CriteriumDecisionPlus, CDP) a) Outline of logic 2) Summary of data sources 3) Logic model evaluates the existing state of each watershed with respect to fire danger a) Structural and behavioral variants b) Ensembles of behavioral variants 4) Decision model considers fire danger conditions in the context of other values/conditions to determine fuel treatment priorities for watersheds. Outline

Fire danger Fire hazard Surface fuels FBFM Canopy fuels CBD CBH Fire behavior (FIREHARM) Crown fire potential Flame length Ignition risk Lightning strikes Climate influence Drought KBDI PDSI Temperature Above90 Degree Days Curing PCP Index Con. No PCP VaporDays Logic model – basic outline Fire density

Data sources attributed to watersheds *Metrics are an index, combining percent land area and an aggregation index from Fragstats an aggregation index from Fragstats TopicMetricsSource Surface fuelsFire behavior fuel models * LANDFIRE Canopy fuelsCrown bulk density*, crown base ht * LANDFIRE Fire behaviorCrown fire potential, flame lengthFIREHARM Burn probability, flame lengthFSIM Fire behavior statisticParisien MaxEnt full model Ignition riskLightning strikes National Lightning Detection Network Fire density Federal Wildland Fire Occurrence Database DroughtPalmer Drought Severity IndexNational Climate Data Center Keetch-Byram Drought IndexFIREHARM TemperatureDays > 90F, Mean DD heating > 64.4FClimate Source via ORNL CuringInverted precip for growing season, Ave max consecutive days w/o rain, Ave max days w/ vapor pressure deficit <1000 Pa Climate Source via ORNL

Six structural variants Fire danger 6 Fire danger 2 Fire danger 5 Fire danger 3 Fire danger 4Fire danger 1 (Fuel treatment) (Preparedness)

1) Behavioral variants a) FIREHARM, event mode (Keane) b) FIREHARM, probabilistic mode (Keane) c) Large fire simulator (Finney) d) Statistical model (Parisien MaxEnt MLA, full) 2) Ensembles of behavioral variants a) AND (limiting factors) b) UNION (compensating factors) c) OR (least limiting) d) These are a logical analog to confidence limits Four behavioral variants and three ensembles

Level 1 Evaluation – Propositions (all take the null form) Fire danger Danger of a severe wildfire is low. fire hazard vegetation and fuel conditions within the watershed do not support a severe wildfire. fire behavior expected fire behavior within the watershed is not severe. ignition risk likelihood of a wildfire ignition within the watershed is low. climate influence Weather and climate data do not support severe wildfire. U Fire danger Climate influence

Level 2 Evaluation – Propositions (null form) Fire hazard surface fuels Condition of surface fuels not conducive to severe wildfire in the watershed fire behavior fuel model (FBFM)*; H is FM>9 (AIPL of High), using the Scott and Burgan FBFM40 canopy fuels Condition of crown fuels not conducive to severe wildfire in the watershed canopy bulk density (CBD)*; H >0.15 kg/m 3 canopy base height (CBH)*; H < 2.0 m (AIPL of High) AIPL evaluates the %area and degree of aggregation of that area w/ values of “High” * Data layers (30-m pixel resolution) from LANDFIRE project at fire hazard

Values above/below either MIN and MAX are interpolated from a ramp function of the associated index. MAX (no support) MIN (full support) Median 80% range of AIPL of “High” CBD AIPL value 10%90% Support Full None With increasing AIPL of High CBD, we see decreasing support for the premise that crown fuels do not support severe wildfire in the w’shed

FIREHARM eventFIREHARM probabilistic FSimMaxEnt Ensemble ANDEnsemble UNIONEnsemble OR Fire Behavior

FIREHARM eventFIREHARM probabilistic FSimMaxEnt Ensemble ANDEnsemble UNIONEnsemble OR Fire Danger 3 Fire danger 3

FIREHARM eventFIREHARM probabilistic FSimMaxEnt Ensemble ANDEnsemble UNIONEnsemble OR Fire Danger 4 Fire danger 4

Fire Behavior Ensemble UNION Ignition RiskClimate Influence Fire Hazard Fire Danger 4 with ensemble union Fire danger 4

Decision model for fuel treatment Decision criteria are the wildfire danger topics, threat to WUI, and biomass opportunity in a watershed. Ensemble union, structural variant 4

Additional data sources for decision model 1) Threat to WUI a) LANDSCAN 2006 i. Percent subwatershed area in WUI Class 1 (Intermix, >= 1 house per 40 acres) b) FIREHARM event mode, threat class 3 i. Fireline intensity, flame length, and rate of spread, exceeding high thresholds of 400 kW·m -1, 2m, and 5.0 km·hr -1 2) Biomass a) FIA 2008, biomass within 500m of local and secondary roads b) Roads from 2010 Tiger Census and FS geodata road layers

Priority scores from PA engine (CDP) Highlighted region in histogram corresponds to selected watersheds in the map. 117 top-rated watersheds for fuel treatment priority (2.3%).

EMDS support for fuel treatment 1.The DSS provides a rational, transparent, repeatable process to prioritize watersheds for fuels treatments 2.The system is highly adaptable as available data, experience, and knowledge change 3.Contributions of variables & decision criteria to outcomes are transparent and known. 4.Mid-scale basis enables multi-scale decision analysis by map zone, Dept, agency, region, forest, district… a.Analyses can include all ownerships to support integrated cross- ownership decisions. 5.Current model addresses 7 map zones. 6.We are expanding to the CONUS. CA OR ID NV WA

Financial and in-kind support: Pacific Northwest Research Station Rocky Mountain Research Station USFS Region 6 National Fire Plan LANDFIRE project

Map zoneNFHprobParisienFSIM FHevent FHprob Parisien Correlations between behavior models All Pearson correlation coefficients are significant at alpha = 0.01 except those in red.