Model Name: FORCLIM (Forests in a Changing Climate) FOREL (Spatially-Explicit Landscape Model with Climate Response Functions Similar to FORCLIM)

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Model Name: FORCLIM (FORests in a changing CLIMate) FOREL (spatially-explicit landscape model with climate response functions similar to FORCLIM)

Authors/Developers: H. Bugmann (ETHZ Zurich), A.M. Solomon (USEPA Corvallis, now USFS Washington Office), R.T. Busing (USGS Corvallis)

Model Category (related to what types of ecosystem services): Forest community and ecosystem dynamics model

Ecosystem types (e.g., forest, aquatic): Forests

Time Step: Annual

Maximum run length: Multiple Centuries

Spatial Scale/representation: Sites, watersheds, landscapes/grid-based, spatially interactive model (FOREL, Busing 2007) is also available for landscape dynamics

Language: MODULA 2, C, C# (FORCLIM), and FORTRAN (FOREL) versions are available

Computing Environment (UNIX, PC, LINUX): UNIX or PC

Equations mainly statistical or process-based? Process-based

Processes represented (please list in general terms; e.g., evaporation, transpiration, allocation, etc) FORCLIM simulates the dynamics of multiple tree species populations and ages on patches of land. It tracks the establishment, growth and mortality of each tree in response to temperature extremes, growing degree days, drought, light availability and nutrient availability. Mortality can also be affected by disturbances of varying severity. For example, fire effects can be simulated with the PNW version developed ca. 2005.

Forcing/Driving variables: Site temperature, precipitation, soil water holding capacity, soil depth, soil fertility, population responses of tree species

Output variables: Forest composition, forest structure, density, basal area, biomass, detritus mass, LAI

Availability/Source (e.g., freeware, website, need to contact authors) FORCLIM: contact Harald Bugmann FOREL (spatially-explicit model): contact Rick Busing ([email protected])

Key Publications Bugmann (1996) ECOLOGY 77:2055-2074 Bugmann & Solomon (2000) ECOLOGICAL APPLICATIONS 10:95-114 Busing et al. (2007) ECOLOGICAL APPLICATIONS (in press) Busing (2007) USGS SIR 2007-5040

URL Links pubs.er.usgs.gov (search author: busing)

LTER6 user contact: Tom Spies, Rick Busing ([email protected]) Bob McKane ([email protected])

1 Model Name: GTHM-MEL (Georgia Tech Hydrology Model coupled with the Multiple Element Limitation model)

Authors/Developers: GTHM: Marc Stieglitz & Feifei Pan, Georgia Institute of Technology, Atlanta, GA; MEL: Ed Rastetter, Marine Biological Laboratory, Woods Hole, MA

Model Category (related to what types of ecosystem services) Eco-hydrology model, linking hydrologic and biogeochemical processes in a spatially explicit framework

Ecosystem types (e.g., forest, aquatic): Forests, grasslands, agricultural, tundra…

Time Step: Daily

Maximum run length: Centuries

Spatial Scale/representation: Hillslopes, watersheds, landscapes/grid-based

Language: Mathematica/Delphi

Computing Environment (UNIX, PC, LINX): PC

Equations mainly statistical or process-based? Process-based

Processes represented (please list in general terms; e.g., evaporation, transpiration, allocation, etc) The coupled GTHM-MEL eco-hydrology model simulates the cycling and transport of water and nutrients (C, N, P) within hillslopes and watersheds. GTHM is a spatially-distributed representation of land-surface hydrology, including ET, infiltration, and surface and subsurface runoff within multiple soil layers. The approach is based on individual soil column models, each of which simulates ET and the vertical movement of water within the soil. Soil columns may be variable in depth and surface area (delineated by user based on soils and LULC maps). Downslope lateral flow from one column to another, or from a column to the stream, is based on flow routing information. MEL simulates the interaction of carbon, nitrogen and water cycles in terrestrial vegetation and soils. MEL is based on a novel resource-optimization algorithm that simulates how plants and microbes allocate their internal assets (biomass, proteins, carbohydrate...) to acquire multiple resources from the environment (CO2, NH4, NO3, water, light...). For example, as the availabilities of different resources change during succession, vegetation in MEL acclimates by reallocating biomass and other internal assets to maintain a balanced uptake for all resources. This ensures that all resources in the environment equally limit production, thereby preventing too many assets from being expended for acquiring a non-limiting resource. The intent is to provide a more realistic approach for simulating biogeochemical responses to environmental disturbances.

Forcing/Driving variables: Topography (DEM); atmospheric CO2; daily Tmin, Tmax, precipitation, irradiance and N deposition.

Output variables: Eco-hydrology: vertical and lateral transport of water, NH4, NO3, DON, DOC within multiple soil layers, and discharge to surface waters. Biogeochemistry: gross photosynthesis, autotrophic and heterotrophic respiration, nutrient uptake by plants and microbes, vegetation growth and detritus production, formation of soil organic matter, N fixation, denitrification, production & leaching of NH4, NO3, DON, DOC.

Availability/Source (e.g., freeware, website, need to contact authors) GTHM: contact Marc Stieglitz ([email protected]) and Feifei Pan ([email protected])

2 MEL: contact Ed Rastetter ([email protected])

Key Publications MEL: see URL links http://ecosystems.mbl.edu/Research/Models/mel/welcome.html GTHM: in prep

URL Links MEL: http://ecosystems.mbl.edu/Research/Models/mel/welcome.html

HJA LTER6 user contact: Bob McKane ([email protected])

3 Model Name: LANDCARB

Authors/Developers: Harmon, Domingo, Smithwick

Model Category (related to what types of ecosystem services) Carbon, Timber Harvest

Ecosystem types (e.g., forest, aquatic):forest

Time Step : annual for most processes, but monthly for climatic indices

Maximum run length: decades to hundreds of years

Spatial Scale/representation: the grain is 0.2 to 1 ha. The extent is thousands to millions of ha/grid-based

Language: C++

Computing Environment (UNIX, PC, LINX): PC

Equations mainly statistical or process-based? Process based

Processes represented (please list in general terms; e.g., evaporation, transpiration, allocation, etc)

Community/population processes- colonization, establishment, mortality, competition, succession

Hydrological processes-interception, evaporation, transpiration, throughfall, run-off

Physiological processes- light absorption, transpiration, allocation, autotrophic respiration, heart- rot

Ecosystem processes-growth/primary production, mortality, decomposition, formation of “stable” organic matter

Disturbance processes- regular mortality (gap formation), fire, harvest, (all of these are spatially explict)

Forcing/Driving variables: solar radiation, minimum, maximum, and mean air temperature, precipitation, soil characteristics (depth, coarse fraction, texture), topography (slope steepness and aspect), disturbance regime, management system

Output variables: the major pools predicted are: live, dead, stable, and total carbon pools, the volume and density of trees, and the amount of harvest. For the major pools there are subpools (i.e., live contains foliage, branches, fine roots, coarse roots, sapwood, heartwood and heart-rot). Other output variables can be requested for information about climatic indices, and processes rates.

Availability/Source (e.g., freeware, website, need to contact authors): Need to contact authors as the model is being significantly revamped in the next year.

4 Key Publications

Cohen, W. B., M. E. Harmon, D. O. Wallin, and M. Fiorella. 1996. Two decades of carbon flux from forests of the Pacific Northwest. Bioscience 46:836-844.

Smithwick, E. A. H., M. E. Harmon, and J. B. Domingo. 2007. Changing temporal patterns of forest carbon stores and net ecosystem carbon balance: The stand to landscape transformation. Landscape Ecology 22:77-94.

URL Links: not available

HJA LTER6 user contact: Mark Harmon

5 Model Name: LPJ-GUESS (Generalized Ecosystem Simulator)

Authors/Developers: Ben Smith (Lund Univ.), I. Colin Prentice (Univ. Bristol), Martin Sykes (Lund Univ.), Stephen Sitch (UK MetOffice, Wallingford, UK)

Model Category (related to what types of ecosystem services) Plant biodiversity, production, carbon sequestration

Ecosystem types (e.g., forest, aquatic): Terrestrial plant species, plant functional types (PFTs, e.g., grass, needleleaf evergreen trees), biomes (e.g., grassland, boreal forest, desert)

Time Step: Daily

Maximum run length: Centuries or longer

Spatial Scale: Landscapes (approx. 30-second grid cell resolution) to global

Language: C++

Computing Environment (UNIX, PC, LINX): PC.

Equations mainly statistical or process-based? Process-based

Processes represented (please list in general terms; e.g., evaporation, transpiration, allocation, etc): Carbon and nutrient dynamics: photosynthesis, respiration, and carbon allocation for individual plants; soil and litter decomposition

Biodiversity: taxa and PFT distributions, mortality, establishment, and resource competition for light and water among individual plants; fire disturbance

Hydrology: interception, evaporation, percolation, surface and subsurface runoff, snowmelt, transpiration.

Forcing/Driving variables: Daily or monthly temperature, precipitation, and sunshine, annual atmospheric CO2 concentration, soil variables (e.g., water-holding capacity)

Output variables: Taxa or PFT-specific variables describing vegetation types, plant productivity (e.g., NPP, leaf area index, respiration), soil-hydrology (e.g., evapotranspiration, available soil-water), soil organic matter, litter, fire regime dynamics, etc.

Availability/Source (e.g., freeware, website, need to contact authors) Model code may be requested from Ben Smith (Lund Univ.)

Key Publications Sitch, S., B. Smith, I. C. Prentice, A. Arneth, A. Bondeau, W. Cramer, J. O. Kaplan, S. Levis, W. Lucht, M. T. Sykes, K. Thonicke, and S. Venevsky. 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9:161-185. Smith, B., I. C. Prentice, M. T. Sykes. 2001. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecology & Biogeography 10:621-637.

URL Links http://www.nateko.lu.se/embers/ http://www.pik-potsdam.de/members/erbrecht/lpjweb/

HJA LTER6 user contact: Sarah Shafer ([email protected])

7 Model Name: LPJ (Lund-Potsdam-Jena)

Authors/Developers :I. Colin Prentice (Univ. Bristol), Wolfgang Cramer (Potsdam Institute for Climate Impacts Research), Martin Sykes (Lund Univ.), Stephen Sitch (UK MetOffice, Wallingford, UK), Ben Smith (Lund Univ.) and the LPJ consortium members

Model Category (related to what types of ecosystem services): Dynamic global vegetation model (DGVM)

Ecosystem types (e.g., forest, aquatic): Terrestrial plant functional types (PFTs, e.g., grass, needleleaf evergreen trees), biomes (e.g., grassland, boreal forest, desert)

Time Step: Daily

Maximum run length: Centuries or longer

Spatial Scale: Landscapes (approx. 30-second grid cell resolution) to global

Language: FORTAN77, FORTRAN90, C, C++ (Different versions of the model are written in different languages)

Computing Environment (UNIX, PC, LINX): PC windows or PC-LINX.

Equations mainly statistical or process-based? Process-based

Processes represented (please list in general terms; e.g., evaporation, transpiration, allocation, etc): Carbon and nutrient dynamics: photosynthesis, respiration, carbon allocation, soil and litter decomposition

Biodiversity: PFT and biome distributions, mortality, establishment, and resource competition for light and water, fire disturbance

Hydrology: interception, evaporation, percolation, surface and subsurface runoff, snowmelt, transpiration

Forcing/Driving variables: Daily or monthly temperature, precipitation, and sunshine, mean annual atmospheric CO2 concentration, soil variables (e.g., water-holding capacity)

Output variables: PFT-specific variables describing vegetation types, plant productivity (e.g., NPP, leaf area index, respiration), hydrology (e.g., evapotranspiration, available soil-water), soil organic matter, litter, fire regime dynamics, etc.

Availability/Source (e.g., freeware, website, need to contact authors): Older versions of the model are available via the LPJ project website (http://www.pik- potsdam.de/members/erbrecht/lpjweb/), newer versions of the model may be requested from the authors.

Key Publications Gerten, D., S. Schaphoff, U. Haberlandt, W. Lucht, S. Sitch. 2004. Terrestrial vegetation and water balance—hydrological evaluation of a dynamic global vegetation model. Journal of Hydrology 286:249-279.

8 Sitch, S., B. Smith, I. C. Prentice, A. Arneth, A. Bondeau, W. Cramer, J. O. Kaplan, S. Levis, W. Lucht, M. T. Sykes, K. Thonicke, and S. Venevsky. 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9:161-185.

URL Links:http://www.pik-potsdam.de/members/erbrecht/lpjweb/

HJA LTER6 user contact: Sarah Shafer ([email protected])

9 Model Name: Stream Ecosystem Model

Authors/Developers: McIntire and Colby

Model Category (related to what types of ecosystem services): aquatic

Ecosystem types (e.g., forest, aquatic): Aquatic and riparian

Time Step: mostly daily but riparian and primary production hourly

Maximum run length: 1year

Spatial Scale/representation: reach or point

Language: fortran

Computing Environment (UNIX, PC, LINX): PC

Equations mainly statistical or process-based? Process

Processes represented (please list in general terms; e.g., evaporation, transpiration, allocation, etc) - The M & C Stream Model has a hierarchical structure that represents biological processes that are usually active in most lotic ecosystems. From this perspective, stream ecosystems are conceptualized as two coupled subsystems, the processes of primary consumption and predation. Primary Consumption represents all processes associated with the direct consumption and decomposition of both autotrophic organisms and detritus, including that of autochthonous production dynamics of the autotrophic organisms collectively. Predation includes processes related to the transfer of energy among primary, secondary, and tertiary macroconsumers. The subsystems of Predation are the processes of invertebrate and vertebrate predation, whereas Primary Consumption is represented by the processes of herbivory and detritivory. Herbivory consists of all processes associeated with the production and consumption of autotrophic organisms within the system, whereas Detritivory includes the consumption and decomposition of detrital inputs. The corresponding subsystems of Herbivory are Primary Production and Grazing, and those of Detritivory include Shredding, Collecting, and Microbial Decomposition.

Forcing/Driving variables: physical chemical

Output variables: standing stocks of functional feeding groups,

Availability/Source (e.g., freeware, website, need to contact authors) Freeware on hja web page.

Key Publications McIntire, C. David; Colby, Jonathon A. 1978. A hierarchical model of lotic ecosystems. Ecological Monographs. 48(1): 167-190

McIntire, C. David; Gregory, Stanley V.; Steinman, Alan D.; Lamberti, Gary A. 1996. Modeling benthic algal communities: an example from stream ecology. In: Stevenson, R. J.; Bothwell, M.; Lowe, R. L., eds. Benthic algal ecology in freshwater ecosystems (Algal Ecology). Academic Press, Inc.: 669-704.

McIntire, C. David; Colby, Jonathon A.; Hall, James D. 1975. The dynamics of small lotic ecosystems: a modeling approach. Verhandlungen International Verein Limnologie. 19: 1599-1609. URL Links

10 http://www.fsl.orst.edu/lter/data/tools/models/strmeco.cfm?topnav=148

HJA LTER6 user contact: Sherri Johnson