EFIMOD

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General System description

System name: EFIMOD-Discrete Lattice Ecosystem Simulator

Acronym: EFIMOD-DLES

Brief overview

EFIMOD is a spatially-explicit individual-based model that simulates carbon and nitrogen flows in forest ecosystems with strong feedback mechanism between soil and stand. It coupled with biodiversity calculator BioCalc and special toolkit for spatial analysis.

Scope of the system

  • tool encourages decision maker to discover new problems or opportunities by exposing to new information or results
  • tool helps decision makers in recognizing upcoming problems for which solutions have been developed previously
  • tool allows decision maker to actively create new knowledge when faced with a new problem and to develop novel solutions
  • tool allows decision maker to capture knowledge, making it available to decision makers who are seeking solutions from previously solved problems

System origin

  • Who and when was it developed
  • how was it developed
  • is it a commercial product
  • does it have real-life application cases

Support for specific issues

Is the system designed to take into account specific uses? E.g. guidance on ways to characterize biodiversity, economic-biodiversity tradeoff analysis methods, risk assessment methods, landscape analysis methods, timber harvest effects, climate change effects, biological effects (pests, pathogens, invasives), fire,...

Support for specific thematic areas of a problem type

  • Silvicultural
  • Certification
  • Conservation
  • Restoration
  • Transportation
  • Development choices / land use zoning
  • Policy/intervention alternatives
  • Sustainability impact assessment (SIA)

Capability to support decision making phases

(NOTE I do not quite know what to do with this, as I do not understand it myself, although it seems related to system use)

(Click here to see a more detailed explanation)

  • Intelligence (+ explicit description of the support given by the DSS)
  • Design (+ explicit description of the support given by the DSS)
  • Choice (+ explicit description of the support given by the DSS)
  • Monitor (+ explicit description of the support given by the DSS)

Related systems

Describe (and/or link to) other systems related

Data and data models

Typical spatial extent of application

The application can be used on regional or forest-enterprise scale.

Forest data input

EFIMOD uses soil and stand-level inputs from forest inventory database. Each record such databases contains the description of one forest compartment. There is the data characterizing the forest compartment as a whole as well as the data on individual forest elements. Forest inventory data are: stand composition, dominant tree species in overstorey, relative stand density, stand growing stock, yield class (designating site productivity), and forest site class (denoting environmental conditions). Characteristics of separate forest element are: age, average height and average stem diameter at breast height (DBH), number of trees per hectare. Also the characteristics of soil organic matter pool are needed: the stocks of soil organic matter and nitrogen in different soil layers.

Type of information input from user (via GUI)

User may select the scenario of forest ecosystem development via specifying various management options, such as different types of cuttings, plantings etc.

Models

Forest models

The model of forest ecosystem EFIMOD[1][2][3] is an individual-based spatially explicit simulator of tree-soil system that calculates parameters of carbon balance and standard forest inventory characteristics: NPP, Rh, soil available nitrogen, tree and stand biomass by tree compartments, soil organic matter (SOM) and N pools, stand density, height, DBH, growing stock and some other parameters. It includes soil model ROMUL as an important component[4] that is driven by soil water, temperature and SOM parameters. The statistical generator of soil climate SCLISS was compiled to run ROMUL. The EFIMOD allows for a calculation the effect of silvicultural operations and forest fires. Now it is linked with a system of plant biodiversity assessment BioCalc. The ecosystem model EFIMOD was comprehensively calibrated and validated for European boreal and temperate forests in a frame of the European Forest Institute (EFI) projects, EU Project RECOGNITION[5] and later for Canadian boreal forests[6][7][8][9]. There is a positive promising experience for the implementation of the EFIMOD model at wide range from East Europe till North America in combination with regional forest databases for the estimation of the components of carbon balance and decision making[10][11][12]. They were also implemented for West Europe in a frame of the EU Project RECOGNITION[13][14]. The special version of the EFIMOD model (IMPACT of the EFI[15]) was implemented in Finland for ecological certification of forest products to calculate C, N and energy losses from forest ecosystems due to forest exploitation. The EFIMOD was also implemented for evaluation of the different forestry regimes from point of view of carbon budget, forest productivity, climate change and decision making[16][17][18] and for modelling carbon balance in a frame of the National Program of Russian Academy of Sciences “Change of Environment and Climate”[19].

Soil models

The ROMUL model[20][21] of soil organic matter (SOM) and nitrogen mineralisation and humification is based on a classic concept of ‘humus type’ (Humusform) as a succession stage of SOM decomposition marked by different groups of soil micro-organisms and fauna inherent to forest soils. It has the following processes: (i) full mineralisation of every compartment, (ii) processes of organic debris transformation to humified matter by three different complexes of reducer organisms that are responsible for the formation of the three main humus forms. It calculates the transformation (humification and mineralisation) of litter and SOM compartments, the gross carbon dioxide flow from the soil due to SOM mineralisation and the nitrogen available for plant growth. The rate of litter and SOM mineralisation and humification is dependent on the litter quality, soil temperature and moisture, and on some soil parameters. The main specific feature of this model is that it calculates the processes of SOM transformation separately for organic layer (forest floor, peat) and mineral topsoil. The other important peculiarity of the model is the use of the results of laboratory experiments on the decomposition of different organic debris in controlled conditions as the experimental data for the model compilation obtained from published and authors’ own data[22][23][24][25]. However, the model does not take into account a biomass of reducer-organisms and litter biochemical composition. Neither does it calculate separately nitrate and ammonium nitrogen. The specific features mentioned above expand the applicability of the model both for forest and wetland peat soils without limitations for its use in grassland and agricultural soils as well. The model validation and sensitivity analyses had been performed using a set of published laboratory and field experiments[26][27][28]. The first version of the model showed good results for forest datasets (and satisfactory results for agricultural soils) in the comparative test of different models against the long-term experimental data from various other sites around the world[29][30]. To run the ROMUL model the following input data are necessary: the amount of litter input with an unlimited number of cohorts (which are characterised by their specific nitrogen and ash content), initial SOM pools, and climate data, air temperature, soil temperature at the 20 cm depth, forest floor moisture and soil moisture. The output parameters of the model include data on all residual litter cohorts, all SOM and nitrogen pools, and some flow parameters: gross carbon dioxide and available mineral nitrogen production from all litter and SOM pools. A special climate generator SCLISS was compiled to produce time series of soil temperature and moisture from standard monthly meteorological data on air temperature and precipitation. The ROMUL model has been used for the theoretical analysis of SOM decomposition and nitrogen supply in forest soils, for the regional evaluation of SOM dynamics in boreal forests, for evaluation of succession dynamics of forest soils, SOM stability under impact of pollution, historical SOM dynamics under rotation of forest and agricultural lands and for global evaluation of SOM dynamics under climate change[31][32][33][34][35][36][37][38][39]. Various versions of the model have been incorporated in four forest ecosystem models (SPECOM[40]; FinFor[41]; IMPACT[42]; EFIMOD[43].

Climate models

A soil climate generator SCLISS[44] is used in the model for two purposes: (1) as a method of evaluation of soil temperature and moisture using measured standard meteorological long-term data; (2) statistical simulation (generation) of realisations of long-term series of necessary input climate data with known statistical properties. The model uses monthly average data on air, litter and soil temperature, precipitation, litter and mineral soil moisture. Air temperature and precipitation are usually measured at numerous meteorological stations, soil and litter data are seldom measured and, moreover, these data are mostly a result of scientific forest studies. Therefore, the procedure of simulating the necessary monthly meteorological input data is an important sub-model of the whole ecosystem model and should be linked with the soil organic matter model. One tried to develop a simple statistical model for the simulations of these data.

Models of biodiversity

Decision Support

Definition of management interventions

Define what is available for the manager to intervene in the forest: time of harvest, plantations, thinnings, reconversions... Existence of prescription writer, simple enumeration of all possibilities, scenario simulation , etc.

Typical temporal scale of application

Define the temporal scale of the application: E.g., operational and immediate level, Tactical planning (short term) and strategic level.

Types of decisions supported

  • Management level
    • strategic decisions
    • administrative decisions
    • operating control decisions
  • Management function
  • planning decisions
    • organizing decisions
    • command decisions
    • control decisions
    • coordination decisions
  • decision making situation
    • unilateral
    • collegial
    • Bargaining / participative decision making

Decision-making processes and models

  • Logic modeling
  • Operations research modeling
    • Direct approaches
    • Heuristic manipulation of simulation models
  • Business modeling
  • Simulation (with and without stochasticity)
  • Multiple criteria/ranking
  • Other

Output

Types of outputs

Types of outputs produced (tables, maps, 3-D visualizations, pre-programmed summaries, etc)

Spatial analysis capabilities

  • integrated capabilities
  • facilitates links to GIS (wizards, etc.)
  • provides standard data import/export formats
  • allows spatial analysis (e.g. topology overlays (e.g. multi layering of different maps, selection of objects based on selection criteria, aggregation by attributes (e.g. areas of similar characteristics), Linking by logical means, Statistics by area, analysis with digital terrain model)

Abilities to address interdisciplinary, multi-scaled, and political issues

Evaluate interactions between different basic information types (biophysical, economic, social). Produce coordinated results for decision makers operating at different spatial scales facilitate social negotiation and learning

System

System requirements

  • Operating Systems: (Windows, Macintosh, Linux/UNIX, Web-based, Others)
  • Other software needed (GIS, MIP packages, etc...)
  • Development status

Architecture and major DSS components

Describe the basic architecture of the system in software and hardware. Desktop client-server, web based, as well as the integration with available systems. Basic data flow, focusing on retrieval of required input and propagation and implementations of decisions. Mention its modular and scalability capabilities.

Usage

Describe the level of use: Research level use, Industry use, Government use

Computational limitations

Describe the system limitations: e.g. number of management units, number of vehicles, time horizon

User interface

Describe the quality of user interface and the Prerequisite knowledge for using the system

Documentation and support

Describe the connection to Help-system and possibilities for assistance, as well as the required training and user support levels

Installation

  • Prerequisite knowledge: Level of effort to become functional
  • Cost: (purchase price, development costs, demonstrated return on investment, cost of use, training costs, licence and maintenance costs)
  • Demo: allows the download/utilization of a trial version. If yes, where is it available and what are the trial conditions.

References

Cited references

  1. ↑ Chertov, O.G. Komarov, A.S., Tsiplianovsky, A.V. 1999. A combined simulation model of Scots pine, Norway spruce and Silver birch ecosystems in European boreal zone. Forest Ecology and Management 116: 189-206.
  2. ↑ Komarov, A., Chertov, O., Zudin, S., Nadporozhskaya, M., Mikhailov, A., Bykhovets, S., Zudina, E., Zoubkova. 2003. EFIMOD 2 - - A model of growth and elements cycling in boreal forest ecosystems. Ecological Modelling 170 (2-3): 373-392.
  3. ↑ Komarov, A.S., Chertov, O.G., Mikhailov, and Autors’ Collective (14 names). 2007. Modelling Dynamics of Organic Matter in Forest Ecosystems [Responsible editor V.N. Kudeyarov]. Nauka, Moscow. 380 p. In Russian with English contents. ISBN 5-02-034053-7.
  4. ↑ Chertov, O.G. Komarov, A.S., Nadporozhskaya, M.A., Bykhovets, S.A., Zudin, S.L. 2001. ROMUL – a model of forest soil organic matter dynamics as a substantial tool for forest ecosystem modelling. Ecological Modelling 138 (1-3): 289-308.
  5. ↑ Kahle, H.-P., Karjalainen, T., Schuck, A. , Ågren, G., Kellomäki, S., Mellert, K., Prietzel, J., Rehfuess, K.E., Spiecker, H. Eds. Eds. 2008. Causes and Consequences of Forest Growth Trends in Europe - Results of the RECOGNITION Project. EFI Res. Rep. 21. Brill, Leiden, Boston. 262 pp.
  6. ↑ Shaw C., Chertov O., Komarov A., Bhatti J., Nadporozskaya M., Apps M., Bykhovets S., Mikhailov A. 2006. Application of the forest ecosystem model EFIMOD 2 to jack pine along the Boreal Forest Transect Case Study. Canadian J. Soil Sci. 86: 171-185.
  7. ↑ Larocque, G.R., Bhatti, J. S., Boutin, R., Chertov, O. 2008. Uncertainty analysis in carbon cycle models of forest ecosystems: Research needs and development of a theoretical framework to estimate error propagation. Ecological Modelling 219: 400–412.
  8. ↑ Chertov, O., Bhatti, J., Komarov, A., Mikhailov, A., Bykhovets, S. 2009. Influence of climate change, fire and harvest on the carbon dynamics of black spruce in Central Canada. Forest Ecology Management 257: 941-950. DOI:10.1016/j.foreco.2008.10.038.
  9. ↑ Bhatti, J. Chertov, O. Komarov, A. 2009. Influence of climate change, fire, insect and harvest on C dynamics for jack pine in central Canada: simulation approach with the EFIMOD model. Int. J. Climate Change: Impacts and Responses 1(3): 43-61.
  10. ↑ Chertov, O., Komarov, A., Mikhailov, A., Andrienko, G., Andrienko, N., Gatalsky. P. 2005. Geovisualisation of forest simulation modelling results: a case study of carbon sequestration and biodiversity. Computers and Electronics in Agriculture 49: 175–191.
  11. ↑ Shaw C., Chertov O., Komarov A., Bhatti J., Nadporozskaya M., Apps M., Bykhovets S., Mikhailov A. 2006. Application of the forest ecosystem model EFIMOD 2 to jack pine along the Boreal Forest Transect Case Study. Canadian J. Soil Sci. 86: 171-185.
  12. ↑ Komarov, A.S., Chertov, O.G., Mikhailov, and Autors’ Collective (14 names). 2007. Modelling Dynamics of Organic Matter in Forest Ecosystems [Responsible editor V.N. Kudeyarov]. Nauka, Moscow. 380 p. In Russian with English contents. ISBN 5-02-034053-7.
  13. ↑ Komarov, A.S., Chertov, O.G., Mikhailov, and Autors’ Collective (14 names). 2007. Modelling Dynamics of Organic Matter in Forest Ecosystems [Responsible editor V.N. Kudeyarov]. Nauka, Moscow. 380 p. In Russian with English contents. ISBN 5-02-034053-7.
  14. ↑ Kahle, H.-P., Karjalainen, T., Schuck, A. , Ågren, G., Kellomäki, S., Mellert, K., Prietzel, J., Rehfuess, K.E., Spiecker, H. Eds. Eds. 2008. Causes and Consequences of Forest Growth Trends in Europe - Results of the RECOGNITION Project. EFI Res. Rep. 21. Brill, Leiden, Boston. 262 pp.
  15. ↑ Chertov, O., Komarov, A., Kolström, M., Pitkänen, S., Strandman, H., Zudin, S., Kellomäki, S. 2003. Modelling the long-term dynamics of populations and communities of trees in boreal forests based on competition for light and nitrogen. Forest Ecol. Management. 176: 355-369.
  16. ↑ Mikhailov, A.V., Komarov, A.S., Chertov, O.G. 2004. Simulation of the carbon budget for different scenarios of forest management. Eurasian Soil Sci. 37: 93–96.
  17. ↑ Chertov, O., Komarov, A., Mikhailov, A., Andrienko, G., Andrienko, N., Gatalsky. P. 2005. Geovisualisation of forest simulation modelling results: a case study of carbon sequestration and biodiversity. Computers and Electronics in Agriculture 49: 175–191.
  18. ↑ Komarov, A.S., Chertov, O.G., Mikhailov, and Autors’ Collective (14 names). 2007. Modelling Dynamics of Organic Matter in Forest Ecosystems [Responsible editor V.N. Kudeyarov]. Nauka, Moscow. 380 p. In Russian with English contents. ISBN 5-02-034053-7.
  19. ↑ Kudeyarov, V.N., Zavarzin, G.A., Blagodatsky, S.A., Borisov, A.V., Voronin, P.Yu., Demkin, V.A., Demkina, T.S., Evdokimov, I.V., Zamolodchikov, D.G., Karelin, D.V., Komarov, A.S., Kurganova, I.N., Larionova, A.A., Lopes de Gerenu, V.O., Tchertov, O.G. Utkin, A.I., 2007. Carbon Pools and Flows in Russian Terrestrial Ecosystems. Nauka, Moscow. 315 p. In Russian. ISBN 978-5-02-034064-0.
  20. ↑ Chertov O.G., Komarov A.S. 1997. SOMM -- a model of soil organic matter dynamics. Ecological Modelling 94(2-3): 177-189.
  21. ↑ Chertov, O.G. Komarov, A.S., Nadporozhskaya, M.A., Bykhovets, S.A., Zudin, S.L. 2001. ROMUL – a model of forest soil organic matter dynamics as a substantial tool for forest ecosystem modelling. Ecological Modelling 138 (1-3): 289-308.
  22. ↑ Chertov O.G. 1985. Simulation model of forest litter and floor mineralization and humification. J. Obshchei Biol., Moscow (J. General Biol.). 46: 794-804. In Russian with English summary.
  23. ↑ Nadporozhskaya, M.A., 2000. Modelling of plant debris organic matter transformation in a soil. Ph.D. Dissertation. St. Petersburg Agr. University, St. Petersburg, 193 pp. (in Russian).
  24. ↑ Nadporozhskaya, M.A., Chertov, O.G., Kovsh, N.V. 2000. Comparison dynamic of nitrogen and carbon loss during organic matter transformation in the model experiments. In:Efimov,V.N., Tsarenko, V.P., Buren, V.M., et al. (Eds.), Humus and Soil Formation. St. Petersburg, State Agr. University, St. Petersburg, pp.15–30 (in Russian).
  25. ↑ Komarov, A.S., Chertov, O.G., Mikhailov, and Autors’ Collective (14 names). 2007. Modelling Dynamics of Organic Matter in Forest Ecosystems [Responsible editor V.N. Kudeyarov]. Nauka, Moscow. 380 p. In Russian with English contents. ISBN 5-02-034053-7.
  26. ↑ Chertov O.G., Komarov A.S. 1997. SOMM -- a model of soil organic matter dynamics. Ecological Modelling 94(2-3): 177-189.
  27. ↑ Chertov, O.G. Komarov, A.S., Nadporozhskaya, M.A., Bykhovets, S.A., Zudin, S.L. 2001. ROMUL – a model of forest soil organic matter dynamics as a substantial tool for forest ecosystem modelling. Ecological Modelling 138 (1-3): 289-308.
  28. ↑ Komarov, A.S., Chertov, O.G., Mikhailov, and Autors’ Collective (14 names). 2007. Modelling Dynamics of Organic Matter in Forest Ecosystems [Responsible editor V.N. Kudeyarov]. Nauka, Moscow. 380 p. In Russian with English contents. ISBN 5-02-034053-7.
  29. ↑ Chertov O.G., Komarov A.S. 1997. SOMM -- a model of soil organic matter dynamics. Ecological Modelling 94(2-3): 177-189.
  30. ↑ Smith, P., Smith, J.U., Powlson, D.S., McGill, W.B., Arah J.R.M., Chertov, O.G., Coleman, K., Franko, U., Frolking, S., Jenkinson, D.S., Jensen, L.S., Kelly, R.H., Klein-Gunnewiek, H., Komarov, A.S., Li, C., Molina J.A.E., Mueller, T., Parton, W.J., Thornley, J.H.M., Whitmore, A.P. 1997. A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments. Geoderma 81 (1/2): 153-225.
  31. ↑ Chertov, O.G. Komarov, A.S., Nadporozhskaya, M.A., Bykhovets, S.A., Zudin, S.L. 2001. ROMUL – a model of forest soil organic matter dynamics as a substantial tool for forest ecosystem modelling. Ecological Modelling 138 (1-3): 289-308.
  32. ↑ Chertov, O.G., Komarov, A.S., Bykhovets, S.S. and Kobak, K.I. 2002. Simulated soil organic matter dynamics in forests of the Leningrad administrative area, northwestern Russia. Forest Ecology and Management 169 (1-2): 29-44.
  33. ↑ Chertov, O. G., Komarov, A. S., Nadporozhskaya, M. A. 2007. Analysis of the dynamics of plant residue mineralization and humification in soil. Eurasian Soil Science 40(2): 140-149.
  34. ↑ Chertov, O.G., Komarov, A.S., Nadporozhskaya, M.A., Mikhailov, A.V., Bykhovets, S.S., Zudin, S.L., Zubkova, E.V. 2007. Dynamic Modelling of Soil Organic Matter Transformation. Simulation Model ROMUL. St. Petersburg State University, St. Petersburg. 97 p. In Russian.
  35. ↑ Nadporozhskaya, M.A. Mohren, G.M.J., Chertov, O.G., Komarov, A.S., Mikhailov, A.V. 2006. Soil organic matter dynamics at primary and secondary forest succession on sandy soils in The Netherlands: an application of soil organic matter model ROMUL. Ecological Modelling Vol. 190(3/4): 399-418.
  36. ↑ Nadporozhskaya, M. A., Cudlin, P., Novak, F., Bykhovets, S. S., Chertov, O. G., Komarov, A. S., Mikhailov, A. V. 2009. Analysis of the soil organic matter stability in spruce forests of Krkonose in Czechia on the basis of ROMUL mathematical model. Eurasian Soil Sci. 42: 657-667. DOI:10.1134/S1064229309060118.
  37. ↑ Peltoniemi, M., Thürig, E., Ogle, S., Palosuo, T., Schrumpf, M., Wutzler, T., Butterbach-Bahl, K., Chertov, O., Komarov, A., Mikhailov, A., Gärdenäs, A., Perry, C., Liski, J., Smith, P., Mäkipää, R. 2007. Models in country scale carbon accounting of forest soils. Silva Fennica 41(3): 575–602.
  38. ↑ Bobrovsky, M., Komarov, A., Mikhailov, A., Khanina, L. 2009. Modelling dynamics of soil organic matter under historical land-use management in European Russia. Ecological Modelling (accepted after revision).
  39. ↑ Yurova, A.Yu., Volodin, E.M., Ågren, G.I., Chertov, O.G., Komarov, A.S. 2009. Effects of variations in simulated changes in soil carbon contents and dynamics on future climate projections. Global Change Biology, In press, DOI: 10.1111/j.1365-2486.2009.01992.x.
  40. ↑ Chertov O.G. 1990. SPECOM - a single tree model of pine stand - raw humus soil ecosystem. Ecological Modelling 50: 107-132.
  41. ↑ Matala, J., Hynynen, J., Miina, J., Ojansuu, R., Peltola, H., SIevanen, R., Väisänen, H., Kellomäki, S. 2003. Comparison of a physiological model and statistical model for prediction of growth and yield in boreal forests. Ecologicla Modelling 161, 95-116.
  42. ↑ Chertov, O., Komarov, A., Kolström, M., Pitkänen, S., Strandman, H., Zudin, S., Kellomäki, S. 2003. Modelling the long-term dynamics of populations and communities of trees in boreal forests based on competition for light and nitrogen. Forest Ecol. Management. 176: 355-369.
  43. ↑ Komarov, A., Chertov, O., Zudin, S., Nadporozhskaya, M., Mikhailov, A., Bykhovets, S., Zudina, E., Zoubkova. 2003. EFIMOD 2 - - A model of growth and elements cycling in boreal forest ecosystems. Ecological Modelling 170 (2-3): 373-392.
  44. ↑ Chertov, O.G. Komarov, A.S., Nadporozhskaya, M.A., Bykhovets, S.A., Zudin, S.L. 2001. ROMUL – a model of forest soil organic matter dynamics as a substantial tool for forest ecosystem modelling. Ecological Modelling 138 (1-3): 289-308.

External resources

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