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Evapotranspiration Landscape Disturbance Succession Modeling Linking comprehensive ecosystem simulations for

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Evapotranspiration Landscape Disturbance Succession Modeling Linking comprehensive ecosystem simulations for Powered By Docstoc
					    Landscape Disturbance
    Succession Modeling:
     Linking comprehensive ecosystem
        simulations for management
                applications


Bob Keane,
USDA Forest Service,
Rocky Mountain Research Station,
Missoula Fire Sciences Laboratory,
Missoula, MT
     Guiding philosophy
“All models are
     WRONG,
 but some are
     useful”
         George Box


     Relative Comparison
              vs
      Absolute Answers
    Landscape Disturbance
     Succession Modeling


Simulation of the interaction of
 disturbances with vegetation
development in a spatial domain

   Spatially Explicit vs Spatial
     Why build landscape
disturbance succession models
          (LDSMs)?
             Research
                Understand and explore
                 ecosystem dynamics
             Management
                  Compare alternatives
                  Compute effects
                  Plan activities
                  Allocate resources
History of landscape disturbance
       succession models
Started in 1980’s
Needed other
 technological
 advances
   GIS
   Computers
   Remote sensing
           Reality of
landscape disturbance succession
             models
Not prognostic or
 predictive
Not well suited for
 individual events
Simulate regimes
Data intensive
Computer intensive
      Model Approaches

Stochastic
Empirical
Mechanistic
    LDSM simulation approaches
                             stand   watershed   region


                 year
• Mechanistic
• Empirical      decades

• Stochastic     centuries




Approaches often overlap and the
best models probably contain
elements of all these approaches.
Uses of landscape disturbance succession models….

               stand       watershed     region


   year                Evaluate risk


  decades        Schedule treatments


 centuries             Provide targets
Uses of landscape disturbance succession models….

               stand    watershed     region


year


                                    Prioritize/
            Prescribe   Locate
decades                              Allocate
                                    resources

centuries
               Stand      Watershed    Region

             FOFEM       FARSITE
             Consume                  NWS Models
 Year                    Flammap
              Burnup


                                      GCM’s
            FVS          SIMPPLLE
Decades
                             FETM



                         RMLANDS      CRBSUM
            Gap Models
Centuries                 Fire-BGC     MAPPS
            FOREST-BGC
                           LANDSUM     DGVM’s
               LDSM Conundrum
 Delicate balance between realism and reality


   Detailed, Complex           Simple, Efficient
 Hard to interpret          Inadequate ecological
 Computationally             representation
  demanding                  Limited applications
 Difficult to               Not robust
  parameterize               Inaccurate
 Difficult to initialize    Insensitive
   Landscape Processes

             Climate           Seed abundance
             Weather            and dispersal
 Fire
                                            Land use
                 Landscape
Patch              Level
Dynamics
                                           Insect/
 Hydrology                                 Disease

             Pollution/
             Deposition                                         Fire effects

                                                                          Decomposition
                              Soil and           Stand and
                            fuel moisture        Plant Level
                                                                           Carbon/nutrient
                          Photosynthesis                                     cycling
                           respiration
                                                                       Stand
                               Evapotranspiration      Understory    development
                                                       dynamics
          Landscape-Level Model Components:
            Climate and Weather
Basic driving variables
 common to all other model
 components
 Long term weather stream
  • Daily measurements
 Mountain weather generator
 Major weather variables:
  • Temp (max, min), humidity,
    radiation, precipitation, wind
      Landscape-Level Model Components:
     Disturbance – Wildland Fire
Disturbance simulation
 broken into three
 processes
 Initiation
  • Fire starts on landscape
 Spread
  • Fire spread, rate, intensity,
    termination
 Effects
  • Results of fire on ecosystem
                                    Wildfire in Selway-Bitterroot
   Landscape-Level Disturbance Components:
              Fire Ignition
Difficult process to
  mechanistically model
  because detailed
  information needed at
  multiple scales
 Fire start dynamics
   Lightning, human
 Fuel bed receptivity
   Size, moisture content
 Ambient weather              Lightning Storm

   Temperature, wind
      Landscape-Level Disturbance Components:
           Fire Behavior (spread)
Most studied and modeled
 fire component
 Landscape simulation
  • Independent of polygons
 Link to weather stream
  • Wind, moisture content
 Provide appropriate fuels
  • Describe fuel types and condition
 Some models available
  • FARSITE, cell automata, etc.


                                        Lethal fire in Douglas-fir
      Landscape-Level Disturbance Components:
           Fire Behavior (spread)
Several methods for
  simulating disturbance
  spread
 Cell automata/percolation
   • Spread to surrounding
     pixels

 Mechanistic pixel
   • Spread across pixels based
     on physical processes

 Mechanistic vector
   • Vector based spread
     simulations (Hygen’s )
                    Stand Model Components:
                  Disturbance Effects
                                  Important fire effects to
                                    include in simulation
                                   Fuel consumption
                                    • Mechanistic/Empirical approach
                                   Plant mortality
                                    • Empirical/stochastic approach
                                   Soil heating
                                    • Heat transfer approach
                                   Smoke
                                    • Linked to consumption
                                    • Emission characteristics
Fire-scarring on lodgepole pine
        Landscape-Level Model Components:
 Seed Dispersal / Species Migration
                       Provide mechanism for
                         species to move across
                         landscapes
                        Seed dispersal
                         • Include topography, wind,
                           seed characteristics,
                           animal, water effects
                        Propagule survival
                         • Include vital attributes
                        Seed crop frequency and
                         amount
Whitebark pine cones
          Landscape-Level Model Components:
                      Hydrology
Provide for water routing
  within landscapes
 Many models available
  • e.g. WEPP, TOPMODEL
 Important for specialized
  habitats
  • Seeps, riparian areas, valley
    bottoms
 Important for aquatic
  processes
  • Stream flow, water quality


                                    McDonald Lake
       Landscape-Level Model Components:
            Insects and Diseases
                       Provide for multiple
                         disturbance applications
                        Link to fire simulations
                          • Disturbance effects
                        Important for long-term
                         simulations
                          • Exotic diseases, insect epidemics
                        Difficult to implement
                          • Limited process models available


Mountain pine beetle
  Landscape-Level Model Components:
Other Important Components
             Inclusion based on
              simulation objective
             Land use
                • Harvesting, settlement
             Pollution
                • Deposition, ozone, etc.
             Exotic invasions
                • Blister rust, weeds
             Climate            Seed abundance
             Weather             and dispersal
 Fire
                                              Land use
Patch
                   Landscape
Dynamics
                     Level

                                             Insect/
 Hydrology                                   Disease


             Pollution/
             Deposition                                                  Fire effects


                                                                                        Decomposition
                               Soil and
                                                       Stand and
                             fuel moisture
                                                       Plant Level
                                                                                        Carbon/nutrient
                          Photosynthesis                                                   cycling
                           respiration
                                                                                 Stand
                                Evapotranspiration          Understory         development
                                                            dynamics




                                             Stand Processes
             Stand-Level Model Components:
       Succession Driver Examples
 Simple, single variable models
   • FIRESCAPE, SEM-LAND
 State and transition models
   • SIMMPPLE, LANDSUM, RMLANDS
 Empirical stand models
   • FVS, DFSIM
 Cohort models
   • LANDIS, QLAND
 Traditional Gap-phase models
   • JABOWA, SILVA, FIRESUM, FORET,
     LINKAGES, ZELIG
 Mechanistic Gap-phase
   • FORSKA, FORSUM, FORCLIM,
     FORCYTE
 Mechanistic ecosystem models
   • HYBRID, Fire-BGC, ECOPHYS,       Glacier NP 1967 fire
     TREEDYN, TREE-BGC
                   Succession Driver
  Simple, single variable models
Use a single variable to
  represent all of
  vegetation development
  processes

 Most used variables
   • Stand age
   • Fuel accumulation
   • Canopy cover

 Example models
   • FIRESCAPE
   • SEM-LAND
                   Succession Driver
    State and transition models
Use linked vegetation classes
  (communities) along pathways
  of successional change driven by
  simulation time steps

 Other names
   • Succession pathway diagrams
   • Box models

 Example models
   • SIMMPPLE
   • LANDSUM
   • RMLANDS
                     Succession Driver
 Empirical stand growth models
Use empirical data to drive growth
  in tree and stand attributes

 Other names
   •   Growth and Yield
   •   Yield tables
   •   Statistical growth models
   •   Individual tree growth models


 Example models
   • FVS
   • DF-SIM
                   Succession Driver
                  Cohort models
Represent stand using fixed
  number of categories and
  simulate transition between
  these categories

 Common cohorts
   •   Diameter
   •   Species
   •   Height
   •   Cover

 Example models
   • LANDIS
   • QLANDS
                     Succession Driver
  Traditional gap-phase models
Simulate individual tree diameter
  and height growth using general
  climate and soils drivers

 Other names
   • Gap models
 Other simulations
   • Mortality
   • Regeneration
 Example models
   • JABOWA, SILVA, FIRESUM,
     FORET, LINKAGES, ZELIG
   • ZELIG, LAMOS,
                       Succession Driver
  Mechanistic gap-phase models
Gap models that mechanistically
  simulate ecological processes using
  physical relationships

 Other simulations
   •   Hydrology (water balance)
   •   Photosynthesis
   •   Respiration
   •   Decomposition

 Example models
   • FORSKA, FORSUM, FORCLIM,
     FORCYTE
   • HYBRID, FireBGCv2, ECOPHYS,
     TREEDYN, TREE-BGC
                         Succession Driver
   Mechanistic ecosystem simulation models
Stand level models that mechanistically
   simulate ecological processes using
   physical relationships

These models DO NOT simulate at tree level

 Other names
    • Big leaf models
    • Ecosystem dynamics models
• Other simulations
    •   Hydrology (water balance)
    •   Photosynthesis
    •   Respiration
    •   Decomposition
 Example models
    • BIOME-BGC, FOREST-BGC,
      MAPPS, LPG
   Landscape Disturbance Succession Models
            Three Examples

LANDSUM (Keane et al. 2001)
LANDIS (Mladenoff 1998)
FireBGCv2 (Keane et al. 2008)
              LANDSUM
       Thumbnail description
 Stand-level model –
  no plants
 State and transition
  succession driver
 Stochastic
  disturbance initiation
  simulation
 Independent fire
  growth simulation
 Stochastic
  simulation of effects
 “Parsimonious”
               LANDSUM
             Fire Simulation
 No fuels or weather inputs
 Rothermel (1991) spread           Fires
  • Uses only slope and wind
 Fuels specified by fire
  probabilities
 Pixel to pixel spread
   Simulates fire independent of
    polygon boundaries
LANDSUM
Applications
     Describe HRV
      landscape patch
      dynamics
     Compare
      management
      alternatives
     Explore advantages
      and disadvantages of
      simulation approach
                     LANDIS
         Thumbnail description
 Cross between a gap and
  vital attributes model
 Tracks presence and
  absence of species age
  cohorts at decade time
  steps
 Contains many disturbances
 Contains seed dispersal
 No dynamic fuel simulation
 Stochastic ignition
  simulation
 Independent fire growth
  simulation using percolation
  model
 Generalized simulation of
  effects
 Management oriented
                   LANDIS
               Fire Simulation
 Ignition is stochastic
  from a fire cycle
  probability distribution
 Fine fuel keyed to
  vegetation type
 Coarse fuels from tree
  mortality
 Spread to surrounding
  pixels based on fuel
  rating, slope, wind
  vectors
 Pixel to pixel spread
 Classification of fire
  effects
               LANDIS
              Applications
 Describe HRV
  landscape patch
  dynamics
 Compare
  management
  alternatives
 Prioritize large
  regions
 Explore advantages
  and disadvantages of
  simulation approach
                 FireBGCv2
        Thumbnail description
                            Highly complex,
                             mechanistic process model
                            Tree-level simulation
                            Multiscale implementation
                            Integration of many
                             ecosystem submodels
                            Ecosystem simulation
                            Merger of BGC and
                             FIRESUM models

Glacier NP landscape
Net Primary Productivity
        FireBGCv2
Stand-Level Modeling Diagram
                     FireBGCv2
                    Applications
                              Explore effects of climate
                               change on fire, landscape,
                               and vegetation dynamics

                              Understand ecosystem
                               response to climate and
                               disturbance regimes

Fire-maintained shrubfield
     LANDSUM vs FireBGCv2
              Comparison
    LANDSUM                     Fire-BGCv2
 Fast                    Slow
 Low memory use          High memory use
 Less disk space         More disk space
 Easy to run             Difficult to run

 Limited output          Greater output detail
 Limited applications    Robust application
 Little cause-effect     Greater understanding
 Highly stochastic       Highly deterministic
                   LDSM
            Simulation Problems
   Changing map resolution
   Fire, climate, vegetation linkage
   Fire regime simulation
   Stochastic effects
   Initialization influences
   Landscape shape effects
   Parameterization problems
   Landscape size dilemma
   Output interval quandary
   Validation concerns
                   Summary
  Landscape Disturbance Succession Modeling
 Many landscape disturbance succession models
  available and many more will be developed

 Development and selection of best model will
  always depend on simulation objective

 The “perfect” landscape disturbance succession
  model has not and probably will never be built

 Model selection and development is nearly always
  based on compromise between development cost,
  detail, expertise, input requirements, and time