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Projection Systems in Europe and North America: Concepts and Approaches

  • Susana Barreiro
  • Margarida Tomé
Chapter
Part of the Managing Forest Ecosystems book series (MAFE, volume 29)

Abstract

Many scenario analyses have been conducted to assess woody biomass availability, although the variety of projection systems used in different countries makes the comparison of results difficult. Understanding the projection systems used for scenario analysis and their limitations is crucial for better interpretations of results. This chapter presents an analysis of the structure of the projection systems from Europe and North America that are described in the second part of the book. The chapter describes these projection systems in terms of the applicable ranges of the tools, the modelling philosophies and model types, the temporal scales, and the external variables that drive the systems. A detailed description of each projection system is found in Part II.

References

  1. Alvarez-Marty S (1989) La méthode des générations dans l’étude de la ressource d’une forêt équienne. Afocel-Armef, Informations Forêt 3:135–146Google Scholar
  2. Barreiro S, Tomé M (2011) SIMPLOT: simulating the impacts of fire severity on sustainability of eucalyptus forests in Portugal. Ecol Indic 11:36–45CrossRefGoogle Scholar
  3. Barreiro S, Tomé M (2012) Analysis of the impact of the use of eucalyptus biomass for energy on wood availability for eucalyptus forest in Portugal: a simulation study. Ecol Soc 17(2):14CrossRefGoogle Scholar
  4. Bento JMRS (1994) Oferta sustentada de material lenhoso de pinheiro bravo – uma aplicação a nível nacional. PhD dissertation Universidade de Trás-os-Montes e Alto Douro, 274 pGoogle Scholar
  5. Burkhart HE, Tomé M (2012) Modeling forest trees and stands. Springer, Dordrecht/New York, 446 pGoogle Scholar
  6. Dixon GE (2002) Essential FVS: a user’s guide to the Forest Vegetation Simulator. Internal Report. US Department of Agriculture, Forest Service, Forest Management Service Center, Fort Collins, 226 p (Revised: November 2, 2015)Google Scholar
  7. Eid T, Hobbelstad K (2000) AVVIRK-2000: a large-scale forestry scenario model for long-term investment, income and harvest analyses. Scand J For Res 15:472. doi: 10.1080/028275800750172736 CrossRefGoogle Scholar
  8. Franklin JF, Spiesb TA, van Pelta R, Careyc AB, Thornburghd DA, Berge DR, Lindenmayerf DB, Harmong ME, Keetona WS, Shawh DC, Biblea K, Cheni J (2002) Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. For Ecol Manag 155:399–423CrossRefGoogle Scholar
  9. Garegnani G, Geri F, Zambelli P, et al. (2015) A new open source DSS for assessment and planning of renewable energy: r.green. In: Proceedings of FOSS4G Europe, Como 2015, 14–17 June 2015. Geomatics Workbooks, pp 39–50. ISSN: 1591-092XGoogle Scholar
  10. Hofer P, Altwegg J, Schoop A et al (2011) Holznutzungspotenziale im Schweizer Wald. Auswertung von Nutzungsszenarien und Waldwachstumsentwicklung. Umwelt-Wissen Nr. 1116:80 S. Bundesamt für Umwelt, Bern.Google Scholar
  11. Jonsson B (1974) The thinning response of Scots pine (Pinus sylvestris) in northern Sweden. Skogshögskolan, Inst för skogsproduktion, Rapp o upps no 28, 41ppGoogle Scholar
  12. Kaufmann E (2001) Prognosis and management scenarios. In: Brassel P, Lischke H (eds) Swiss national forest inventory: methods and models of the second assessment. Swiss Federal Research Institute (WSL), Birmensdorf, pp 197–206Google Scholar
  13. Kaufmann E (2011) Nachhaltiges Holzproduktionspotenzial im Schweizer Wald. Schweiz Z Forstwes 162(9):300–311. doi: 10.3188/szf.2011.0300 CrossRefGoogle Scholar
  14. Kimmins JP, Blanco JA, Seely B, Welham C, Scoullar K (2010) Forecasting forest futures: a hybrid modelling approach to the assessment of sustainability of forest ecosystems and their values. Earthscan, London. doi: 10.1080/00207233.2011.552232 Google Scholar
  15. Kindermann G (2010a) Weiterentwicklung eines Kreisflächenzuwachsmodells – Refining a basal area increment model. In: Deutscher Verband Forstlicher Forschungsanstalten Sektion Ertragskunde. Jahrestagung vom 17–19 Mai 2010, Möhnesee, pp 82–95Google Scholar
  16. Kindermann G (2010b) Eine klimasensitive Weiterentwicklung des Kreisflächenzuwachsmodells aus PrognAus – A climate sensitive refining of the basal area increment model in PrognAus. Centralblatt für das gesamte Forstwesen. Austrian J For Sci 127(3–4):147–178Google Scholar
  17. Kostov G (1993) The Bulgarian Forest Resources Assessment Model – some base data and results. SUAS. Department of Operational Efficiency, College of Forestry, GarpenbergGoogle Scholar
  18. Kramer K, van der Werf DC (2010) Equilibrium and non-equilibrium concepts in forest genetic modelling: population- and individually-based approaches. For Syst 19:100–112. doi: 10.5424/fs/201019S-9312 Google Scholar
  19. Kramer K, Degen B, Buschbom J, Hickler J, Thuiller W, Sykes MT, Winter WD (2010) Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change--range, abundance, genetic diversity and adaptive response. For Ecol Manag 259:2213–2222. doi: 10.1016/j.foreco.2009.12.023 CrossRefGoogle Scholar
  20. Kull SJ, Rampley GJ, Morken S et al (2011) Operational-scale carbon budget model of the Canadian forest sector (CBM-CFS3) version 1.2: user’s guide. Canadian Forest Service, EdmontonGoogle Scholar
  21. Kurz WA, Dymond CC, White TM et al (2009) CBM-CFS3: a model of carbon-dynamics in forestry and land-use change implementing IPCC standards. Ecol Model 220:480–504CrossRefGoogle Scholar
  22. Landsberg J (2003) Modelling forest ecosystems: state of the art, challenges, and the future directions. Can J For Res 26:1174–1186Google Scholar
  23. Ledermann T (2006) Description of PrognAus for Windows 2.2. In: Hasenauer H (ed) Sustainable forest management – growth models for Europe. Springer, Berlin/Heidelberg, pp 71–78Google Scholar
  24. Liski J, Palosuo T, Peltoniemi M, Sievänen R (2005) Carbon and decomposition model Yasso for forest soils. Ecol Model 189:168–182. doi: 10.1016/j.ecolmodel.2005.03.005 CrossRefGoogle Scholar
  25. Lundström A, Söderberg U (1996) Outline of the Hugin system for long-term forecasts of timber yields and possible cut. In: Päivinen R, Roihuvuo L, Siitonen M (eds) Proceedings no. 5: in large-scale forestry scenario models: experiences and requirements. European Forest Institute, Joensuu, pp 63–77Google Scholar
  26. Mäkelä A (2009) Hybrid models of forest stand growth and production. In: Dykstra DP, Monserud RA (eds) Forest growth and timber quality: crown models and simulation methods for sustainable forest management. Proceedings of an international conference. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, pp 43–47Google Scholar
  27. McMahan AJ, Milner KS, Smith EL (2002) FVS-BGC: a process-model extension to the Forest Vegetation Simulator. U.S. Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, Fort Collins, 51pGoogle Scholar
  28. Medema EL, Hatch CR (1982) Computerized help for economic analysis of Prognosis Model outputs: a user’s manual. Contribution no. 227. University of Idaho, Forest, Wildlife, and Range Experiment Station, Moscow, 72 pGoogle Scholar
  29. Milner KS, Coble DW (1995) The ground surface vegetation model: a process-based approach to modeling vegetative interactions. Unpublished manuscriptGoogle Scholar
  30. Monserud RA (2003) Evaluating forest models in a sustainable forest management context. FBMIS 1:35–47Google Scholar
  31. Munro D (1974) Forest growth models – a prognosis. In: Fries J (ed) Growth models for tree and stand simulation. Royal College of Forestry Res Notes 30, Stockholm, pp 7–21Google Scholar
  32. Nord-Larsen T, Suadicani MK (2010) Træbrændselsressourcer fra danske skove over ½ ha: opgørelse og prognose 2010. Skov & Landskab, Københavns Universitet. Arbejdsrapport / Skov & Landskab, no. 113Google Scholar
  33. Peng C (2000) Growth and yield models for uneven-aged stands: past, present and future. For Ecol Manag 132:259–279CrossRefGoogle Scholar
  34. Perera AH, Sturtevant BR, Buse LJ (2015) Simulation modeling of forest landscape disturbances: an overview. In: Perera AH, Sturtevant BR, Buse LJ (eds) Simulation modeling of forest landscape disturbances. Springer, Geneva, pp 1–15CrossRefGoogle Scholar
  35. Petrauskas E, Kuliešis A (2004) Scenario-based analysis of possible management alternatives for Lithuanian forests in the 21st century. Balt For 10:72–82Google Scholar
  36. Phillips H (2011) All Ireland roundwood production forecast 2011–2028. COFORD, DublinGoogle Scholar
  37. Pilli R, Grassi G, Kurz WA et al (2013) Application of the CBM-CFS3 model to estimate Italy’s forest carbon budget, 1995–2020. Ecol Model 266:144–171CrossRefGoogle Scholar
  38. Redsven V, Hirvelä H, Härkönen K, Salminen O, Siitonen M (2013) MELA2012 reference manual, 2nd edn. The Finnish Forest Research Institute. ISBN: 978-951-40-2451-1Google Scholar
  39. Reed D (1999) Ecophysiological models of forest growth: uses and limitations. In: Amaro A, Tomé M (eds) Empirical and process based models for forest tree and stand growth simulation. Edições Salamandra, Novas Tecnologias, Lisboa, pp 305–311Google Scholar
  40. Reed DD, Amaro A, Amateis R, Huang S, Tomé M (2003) Emerging trends and future directions: a workshop synthesis. In: Amaro A, Reed D, Soares P (eds) Modelling forest systems. CAB International, Oxford, pp 389–394Google Scholar
  41. Rennolls K, Tomé M, McRoberts RE, Vanclay JK, LeMay V, Guan BT, Gertner GZ (2007) Potencial contributions of statistics and modelling to sustainable forest management: review and synthesis. In: Reynolds KM, Thomson AJ, Kohl M, Shannon MA, Ray D, Rennolls K (eds) Sustainable forestry: from monitoring and modeling to knowledge management & policy science. CABI, Wallingford/Cambridge, pp 314–341CrossRefGoogle Scholar
  42. Rock J, Bösch B, Kändler G (2013). WEHAM 2012 – Waldentwicklungs- und Holzaufko-mmensmodellierung für die dritte Bundeswaldinventur. In: Klädtke J, Kohnle U (eds) Deutscher Verband Forstlicher Forschungsanstalten. Sektion Ertragskunde. Jahrestagung, Rychnov nad Kneznou/Tschechien, pp 127–133. ISSN: 1432-2609. http://sektionertragskunde.fvabw.de/2013/Beitrag_13_17.pdf. Accessed 14 May 2016
  43. Sallnäs O (1990) A matrix growth model of the Swedish forest. Studia Forestalia Suecica. Sveriges lantbruksuniversitet, Uppsala. ISBN: 91-576-4174-9Google Scholar
  44. Schelhaas MJ, Eggers J, Lindner M, Nabuurs GJ, Pussinen A, Päivinen R, Schuck A, Verkerk PJ, van der Werf DC, Zudin S, (2007) Model documentation for the European Forest Information Scenario model (EFISCEN 3.1.1). Wageningen, Alterra, Alterra report 1559, EFI technical report 26, Joensuu, 118p. http://www.efi.int/files/attachments/publications/alterrarapport1559.pdf. Accessed 5 July 2015
  45. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O, Teuri M (1996) MELA Handbook—1996 edn. Metsäntutkimuslaitoksen tiedonantoja 622, p 452Google Scholar
  46. Snorrason A (2006) Langtímaspá um kolefnisbindingu nýskógræktar. Skógræktarritið 58–64 (in Icelandic)Google Scholar
  47. Stage AR (1973) Prognosis Model for stand development. Res. Paper INT-137. U. S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, 32pGoogle Scholar
  48. Sterba H, Moser M, Monserud RA (1995) PROGNAUS – Ein Waldwachstumssimulator für Rein- und Mischbestände. Österreichische Forstzeitung 106(5):19–20Google Scholar
  49. Thürig E, Palosuo T, Bucher J, Kaufmann E (2005) The impact of windthrow on carbon sequestration in Switzerland: a model-based assessment. For Ecol Manag 210:337–350. doi: 10.1016/j.foreco.2005.02.030 CrossRefGoogle Scholar
  50. Tomé M, Faias S (2011) Report describing the regional simulators and the European simulator. EFI Technical Report 69. European Forest Institute, Finland, 65ppGoogle Scholar
  51. Vanclay JK (1994) Modelling forest growth and yield – applications to mixed tropical forests. CAB International, OxonGoogle Scholar
  52. Wernsdörfer H, Colin A, Bontemps J-D, Chevalier H, Pignard G, Caurla S, Leban J-M, Herve J-C, Fournier M (2012) Large scale dynamics of a heterogeneous forest resource are driven jointly by geographically varying growth conditions, tree species composition and stand structure. Ann For Sci 69:829–844CrossRefGoogle Scholar
  53. Wilson FG (1946) Numerical expression of stocking in term of height. J For 44(10):758–761Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Forest Research Centre (CEF), School of AgricultureUniversity of LisbonLisbonPortugal

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