Modelling the carbon budget of intensive forest monitoring sites in Germany using the simulation model BIOME-BGC

Jochheim, Hubert; Puhlmann, Martina; Beese, F.; Berthold, D.; Einert, Peter; Kallweit, R.; Konopatzky, A.; Meesenburg, Henning; Meiwes, K. J.; Raspe, Stephan; Schulte‐Bisping, Hubert; Schulz, Christoph

doi:10.3832/ifor0475-002

Cited by 13 publications

(14 citation statements)

References 6 publications

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“…This offers many options for integrated evaluations of soil data with other temporal assessments on the same plots such as crown condition, foliar chemistry, or tree growth, allowing in-depth analyses with mechanistic models (e.g. van der Salm et al 2007;Reinds et al 2008;Jochheim et al 2009;Mol Dijkstra et al 2009) in order to better understand cause-effect relationships in forest processes and responses to environmental impacts (Lorenz and Fischer 2013).…”

Section: Potentiality Of Databasementioning

confidence: 99%

The Level II aggregated forest soil condition database links soil physicochemical and hydraulic properties with long-term observations of forest condition in Europe

Fleck

Cools

Vos

et al. 2016

Annals of Forest Science

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Key message Aggregated, consolidated, and derived soil physicochemical data of 286 ICP Forests Level II plots were completed with soil hydraulic properties for integrated use with forest monitoring data. Database access should be requested at http://icp-forests.net . Metadata associated available at https://metadata-afs.nancy.inra.fr/geonetwork/apps/georchestra/?uuid=153e599e-6624-4e2b-b862-8124386ea9cd&hl=eng Context The ICP Forests database is one of the most comprehensive forest ecosystem datasets in Europe and contains the accumulated results of more than two decades of harmonised forest monitoring all over Europe. Aims The aim of this paper is to share knowledge on the ICP Forests Level II soil data for broader use among forest scientists. Methods After standard analysis, quality checks, aggregation, and calculation of derived variables (e.g. nutrient stocks, base saturation, C:N ratio, and water retention parameters), data have been gathered into a static database (AFSCDB.LII.2.2), which will be updated to new versions as soon as new measurements become available. Results The database provides a basis for the combined evaluation of up to 130 unique soil variables of 286 plots with dynamic data on tree growth, ground vegetation, foliar chemistry, crown condition, tree phenology, leaf area index, ozone injury, litterfall, soil solution chemistry, deposition, ambient air quality, and meteorological data assessed on the same plots. Conclusion The unprecedented comprehensiveness and level of detail in this newly aggregated database may overcome existing restrictions so far impeding the realisation of large-scale forest ecosystem studies in Europe.

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Section: Potentiality Of Databasementioning

confidence: 99%

The Level II aggregated forest soil condition database links soil physicochemical and hydraulic properties with long-term observations of forest condition in Europe

Fleck

Cools

Vos

et al. 2016

Annals of Forest Science

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“…Jochheim et al (2009) simulated NPP values between 5 and 7 t C ha -1 year -1 for Scots pine, beech and Norway spruce forests on nine level II plots in Germany. Similarly to our results, they found that the differences in NPP between sites are larger than between the different tree species.…”

Section: Model Evaluationmentioning

confidence: 99%

Evaluating the productivity of four main tree species in Germany under climate change with static reduced models

Gutsch

Lasch-Born

Suckow

et al. 2015

Annals of Forest Science

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“…The model has been widely applied across a range of forested ecosystems around the world, has been extensively compared with flux measurements (Schimel et al 2000), and was specifically parameterized and validated for major central European species by Pietsch et al (2005). Norway spruce in particular has been widely studied with BIOME-BGC , Lange 2007, Jochheim et al 2009), giving confidence that the model can accurately represent spruce growth across a range of different circumstances. Forest management operations were included in BIOME-BGC by and , based on prescribed management regimes.…”

Section: Methodsmentioning

confidence: 99%

Incorporating management history into forest growth modelling

Eastaugh¹,

Hasenauer²

2011

iForest

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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

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Modelling the carbon budget of intensive forest monitoring sites in Germany using the simulation model BIOME-BGC

Cited by 13 publications

References 6 publications

The Level II aggregated forest soil condition database links soil physicochemical and hydraulic properties with long-term observations of forest condition in Europe

The Level II aggregated forest soil condition database links soil physicochemical and hydraulic properties with long-term observations of forest condition in Europe

Evaluating the productivity of four main tree species in Germany under climate change with static reduced models

Incorporating management history into forest growth modelling

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