Incorporating management history into forest growth modelling

doi:10.3832/ifor0597-004

Incorporating management history into forest growth modelling

iForest - Biogeosciences and Forestry, Volume 4, Issue 5, Pages 212-217 (2011)
doi: https://doi.org/10.3832/ifor0597-004
Published: Nov 03, 2011 - Copyright © 2011 SISEF

Research Articles

Collection/Special Issue: COST Action FP0903 (2010) - Rome (Italy)
Research, monitoring and modelling in the study of climate change and air pollution impacts on forest ecosystems
Guest Editors: E Paoletti, J-P Tuovinen, N Clarke, G Matteucci, R Matyssek, G Wieser, R Fischer, P Cudlin, N Potocic

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Mechanistic modelling is an important tool for understanding the impacts of climate change and pollutants on forest growth. One of the common practical limitations of these models is a lack of specific information regarding management activities such as thinning or harvesting, which can have a very strong influence on the accuracy of results. The use of inventory data for model parameterization and calibration is also problematic, as inventories are designed to have large volumes of data amalgamated to give accurate mean results across large areas. The precision of single point estimates is often quite low.This study uses BIOME-BGC to model forest growth on 1133 sites of the Austrian National Forest Inventory, and develops a method to estimate timber removal patterns prior to the commencement of record keeping on the sites. Recognizing the poor precision of individual point estimates in the data, we do not seek to precisely calibrate the model to the data on each point. Rather, we assume that the point-wise inventory estimates will be normally distributed around the true values. We then model each site assuming no management interventions, and compare this with inventory results. Plotting the “error” between model results and NFI data shows a strong right-skew, reflecting the modelled lack of timber removals. A Box-Cox transformation of the error plot, centred on zero, would represent an unbiased model estimate of the data, thus we can determine the historic timber removals as the difference between the original error curve and its Box-Cox transformation. Calibrating the model with this information allow us to represent forest volume with greater accuracy than would otherwise be possible.

Keywords

BIOME-BGC, Inventory, Uncertainty, Thinning, Model initialisation

Authors’ address

(1)

Institute of Silviculture, University of Natural Resources and Life Sciences (BOKU), Vienna (Austria).

Corresponding author

CS Eastaugh
chris.eastaugh@boku.ac.at

Citation

Eastaugh CS, Hasenauer H (2011). Incorporating management history into forest growth modelling. iForest 4: 212-217. - doi: 10.3832/ifor0597-004

Paper history

Received: Nov 30, 2010
Accepted: Aug 22, 2011

First online: Nov 03, 2011
Publication Date: Nov 03, 2011
Publication Time: 2.43 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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List of the papers citing this article based on CrossRef Cited-by.

(1)

Bland JM, Altman DG (1986)
Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327 (8476): 307-310.
CrossRef | Gscholar

(2)

Box GEP, Cox DR (1964)
An analysis of transformations. Journal of the Royal Statistical Society Series B 26 (2): 211-252.
Gscholar

(3)

Cienciala E, Cerný M, Tatarinov F, Apltauer J, Exnerová Z (2006)
Biomass functions applicable to Scots pine. Trees 20: 483-495.
CrossRef | Gscholar

(4)

Cienciala E, Tatarinov F (2006)
Application of BIOME-BGC model to managed forests 2. Comparison with long-term observations of stand production for major tree species. Forest Ecology and Management 237: 252-256.
CrossRef | Gscholar

(5)

Cienciala E, Apltauer J, Exnerová Z, Tatarinov F (2008)
Biomass functions applicable to oak trees grown in Central-European forestry. Journal of Forest Science 54: 109-120.
Gscholar

(6)

Eastaugh CS, Hasenauer H (2010)
The usefulness of time series angle-count forest inventory data in assessing forest growth model accuracy. Forestry Ideas 16 (2) 171-180.
Gscholar

(7)

Eastaugh CS, Petritsch R, Hasenauer H (2010)
Climate characteristics across the Austrian forest estate from 1960 to 2008. Austrian Journal of Forest Science 127 (3): 133-146.
Gscholar

(8)

Eastaugh CS, Pötzelsberger E, Hasenauer H (2011)
Assessing the impacts of climate change and nitrogen deposition on Norway spruce (Picea abies L. Karst) growth in Austria with BIOME-BGC. Tree Physiology 31 (3): 262-274.
CrossRef | Gscholar

(9)

Gabler K, Schadauer K (2006)
Methoden der Österreichischen Waldinventur 2000/02. BFW-Berichte 135, Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Vienna, Austria, pp. 135.
Gscholar

(10)

Grosenbaugh LR (1958)
Point-sampling and line-sampling: probability theory, geometric implications, synthesis. USDA Forest Service, South Forest Experimental Station, Occasional Paper 160, pp. 34.
Gscholar

(11)

Jandl R, Lindner M, Vesterdal L, Bauwens B, Baritz R, Hagedorn F, Johnson DW, Minnkkinen K, Byrne K (2007)
How strongly can forest management influence soil carbon sequestration? Geoderma 17: 253-268.
CrossRef | Gscholar

(12)

Jochheim H, Puhlmann M, Beese F, Berthold D, Einert P, Kallweit R, Konopatzky A, Meesenburg H, Meiwes KJ, Raspe S, Schulte-Bisping H, Schulz C (2009)
Modelling the carbon budget of intensive forest monitoring sites in Germany using the simulation model BIOME-BGC. iForest 2: 7-10.
CrossRef | Gscholar

(13)

Joosten R, Schumacher J, Wirth C, Schulte A (2004)
Evaluating tree carbon predictions for beech (Fagus sylvatica L.) in western Germany. Forest Ecology and Management 189: 87-96.
CrossRef | Gscholar

(14)

Landsberg JJ, Kaufmann MR, Binkley D, Isebrands J, Jarvis PG (1991)
Evaluating progress towards closed forest models based on fluxes of carbon, water and nutrients. Tree Physiology 9: 1-15.
Gscholar

(15)

Lange H (2007)
Modelling carbon dynamics in forest ecosystems using Biome-BGC. In: “Greenhouse gas budget of soils under changing climate and land use” (Jandl R, Olsson M eds). BFV, Wien, Austria, pp. 63-70.
Gscholar

(16)

Lehtonen A, Cienciala E, Tatarinov F, Mäkäpää R (2007)
Uncertainty estimation of biomass expansion factors for Norway spruce in the Czech Republic. Annals of Forest Science 64: 133-140.
CrossRef | Gscholar

(17)

Lindner M, Green T, Woodall CW, Perry CH, Nabuurs GJ, Sanz MJ (2008)
Impacts of forest ecosystem management on greenhouse gas budgets. Forest Ecology and Management 256: 191-193.
CrossRef | Gscholar

(18)

Nabuurs GJ, Hengeveld GM, van der Werf DC, Heidema AH (2010)
European forest carbon balance assessed with inventory based methods - an introduction to a special section. Forest Ecology and Management 260: 239-240.
CrossRef | Gscholar

(19)

Petritsch R (2002)
Anvendung und Validierung des Klimainterpolationsmodells DAYMET in Österreich. MSc thesis, University of Natural Resources and Applied Life Sciences, Institute of Silviculture, Vienna, Austria, pp. 95.
Gscholar

(20)

Petritsch R (2008)
Large scale mechanistic ecosystem modeling in Austria. Ph.D. thesis, University of Natural Resources and Applied Life Sciences, Institute of Silviculture, Vienna, Austria, pp. 135.
Gscholar

(21)

Petritsch R, Hasenauer H (2007)
Interpolating input parameters for large scale ecosystem models. Austrian Journal of Forest Science 124 (2): 135-151.
Gscholar

(22)

Petritsch R, Hasenauer H, Pietsch SA (2007)
Incorporating forest growth response to thinning within biome-BGC. Forest Ecology and Management 242 (1-2): 324-336.
CrossRef | Gscholar

(23)

Pietsch SA, Hasenauer H, Thornton PE (2005)
BGC-model parameters for tree species growing in central European forests. Forest Ecology and Management 211: 264-295.
CrossRef | Gscholar

(24)

Pietsch SA, Hasenauer H (2006)
Evaluating the self-initialization procedure for large-scale ecosystem models. Global Change Biology 12: 1658-1669.
CrossRef | Gscholar

(25)

Pollanschütz J (1974)
Formzahlfunktionen der Hauptbaumarten Österreichs. Allgemeine Forstzeitung 85: 341-343.
Gscholar

(26)

Pretzsch H, Grote R, Reinking B, Rötzer TH, Seifert ST (2008)
Models for forest ecosystem management: A European perspective. Annals of Botany 101: 1065-1087.
CrossRef | Gscholar

(27)

Purves D, Pacala S (2008)
Predictive models of forest dynamics. Science 320: 1452-1453.
CrossRef | Gscholar

(28)

Pötzelsberger E (2008)
Assessing the productivity and water regime in the Schmittenbach catchment area. Diploma thesis, University of Natural Resources and Applied Life Sciences, Institute of Silviculture,Vienna, Austria, pp. 75.
Gscholar

(29)

Rennolls K, Tomé M, McRoberts RE, Vanclay J, LeMay V, Guan BT, Gertner G (2007)
Potential contributions of statistics and modelling to sustainable forest management: review and synthesis. In: “Sustainable forestry: from monitoring and modelling to knowledge management and policy science” (Reynolds KM, Thomson AJ, Kohl M, Shannon MA, Ray D, Rennolls K eds). CABI, pp. 525.
Gscholar

(30)

Rypdal K, Baritz R (2002)
Estimating and managing uncertainties in order to detect terrestrial greenhouse gas removals. CICERO Working Paper 2002:07, pp. 10.
Online | Gscholar

(31)

Schieler K (1997)
Methode der Zuwachsberechnung der Österreichischen Waldinventur. Ph.D. Thesis, University of Natural Resources and Applied Life Sciences Vienna, Institute for Forest Growth, Vienna, Austria, pp. 99.
Gscholar

(32)

Schimel D, Melillo J, Tian H, McGuire AD, Kicklighter D, Kittel T, Rosenbloom N, Running S, Thornton P, Ojima D, Parton W, Kelly R, Sykes M, Neilson R, Rizzo B (2000)
Contribution of increasing CO2 and climate to carbon storage by ecosystems in the United States. Science 287 (5460): 2004-2006.
CrossRef | Gscholar

(33)

Thornton PE (1998)
Description of a numerical simulation model for predicting the dynamics of energy, water carbon and nitrogen in a terrestrial ecosystem. PhD thesis, University of Montana, Missoula, USA, pp. 280.
Gscholar

(34)

Tupek B, Zanchi G, Verkerk PJ, Churkina G, Viovy N, Hughes JK, Lindner M (2010)
A comparison of alternative modelling approaches to evaluate the European forest carbon fluxes. Forest Ecology and Management 260: 241-251.
CrossRef | Gscholar

(35)

WAMOD (2010)
Auswirkungen des Klimawandels auf Österreichs Wälder - Entwicklung und vergleichende Evaluierung unterschiedlicher Prognosemodelle (WAMOD). Projektbericht 2010 (Antragsnummer A760631). BOKU, Institut für Waldbau; Institut für Waldwachstumsforschung, BFW, Institut für Waldinventur, Wien, Österreich (in German).
Gscholar

(36)

Wirth C, Schumacher J, Schultze ED (2004)
Generic biomass functions for Norway spruce in Central Europe - a meta-analysis approach towards prediction and uncertainty estimation. Tree Physiology 24: 121-139.
Online | Gscholar

(37)

Wutzler T, Wirth C, Schumacher J (2008)
Generic biomass functions for Common Beech (Fagus sylvatica L.) in Central Europe - predictions and components of uncertainty. Canadian Journal of Forest Research 38: 1661-1675.
CrossRef | Gscholar

(38)

Zaehle, S, Sitch, S, Prentice IC, Liski J, Cramer W, Erhard M, Hickler T, Smith B (2006)
The importance of age-related decline in forest NPP for modelling regional carbon balances. Ecological Applications 16: 1555-1574.
CrossRef | Gscholar

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