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Articles by Thomas Wutzler

Category: Research article

article id 449, category Research article
Thomas Wutzler, Ingolf Profft, Martina Mund. (2011). Quantifying tree biomass carbon stocks, their changes and uncertainties using routine stand taxation inventory data. Silva Fennica vol. 45 no. 3 article id 449. https://doi.org/10.14214/sf.449
For carbon (C) trading or any other verifiable C reports, it would be reasonable to identify and quantify continuous changes in carbon stocks at regional scales without high investments into additional C-specific, time- and labor-intensive inventories. Our study demonstrates the potential of using routine stand taxation data from large scale forestry inventories for verifiable quantification of tree biomass C stocks, C stock change rates, and associated uncertainties. Empirical models, parameters, and equations of uncertainty propagation have been assembled and applied to data from a forest management unit in Central Germany (550 000 ha), using stand taxation inventories collected between 1993 and 2006. The study showed: 1) The use of stand taxation data resulted in a verifiable and sufficiently precise (cv = 7%) quantification of tree biomass carbon stocks and their changes at the level of growth-regions (1700 to 140 000 ha). 2) The forest of the test region accumulated carbon in tree biomass at a mean annual rate of 1.8 (–0.9 to 4.5) tC/ha/yr over the studied period. 3) The taxation inventory data can reveal spatial patterns of rates of C stock changes, specifically low rates of 0.4 tC/ha/yr in the northwest and high rates of 3.0 tC/ha/yr in the south of the study region.
  • Wutzler, Max Planck Institute for Biogeochemistry, Hans-Knöll-Strasse 10, 07745 Jena, Germany ORCID ID:E-mail: twutz@bgc-jena.mpg.de (email)
  • Profft, Max Planck Institute for Biogeochemistry, Hans-Knöll-Stra§e 10, 07745 Jena, Germany ORCID ID:E-mail:
  • Mund, Max Planck Institute for Biogeochemistry, Hans-Knöll-Stra§e 10, 07745 Jena, Germany ORCID ID:E-mail:
article id 232, category Research article
Thomas Wutzler. (2008). Effect of the aggregation of multi-cohort mixed stands on modeling forest ecosystem carbon stocks. Silva Fennica vol. 42 no. 4 article id 232. https://doi.org/10.14214/sf.232
Studies of the carbon sink of forest ecosystems often stratify the studied stands by the dominating species and thereby abstract from differences in the mixed-species, multi-cohort structure of many forests. This case study infers whether the aggregation of forestry data introduces a bias in the estimates of carbon stocks and their changes at the scale of individual stands and the scale of a forest district. The empirical TreeGrOSS-C model was applied to 1616 plots of a forest district in Central Germany to simulate carbon dynamics in biomass, woody debris, and soil. In a first approach each stand was explicitly simulated with all cohorts. In three other approaches the forest inventory data were aggregated in several ways, including a stratification of the stands to 110 classes according to the dominating species, age class, and site conditions. A small but significant bias was confirmed. At stand scale the initial ecosystem carbon stocks by the aggregated approach differed from that of the detailed approach by 2.3%, but at the district scale only by 0.05%. The differences in age between interspersed and dominant cohorts as well as differences in litter production were important for the differences in initial carbon stocks. The amounts of wood extracted by thinning operations were important for the differences in the projection of the carbon stocks over 100 years. Because of the smallness of bias, this case study collects evidence that the approaches, that represent stands or stratums by a single cohort, are valid at the scale of a forest district or larger.
  • Wutzler, Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, DE-07745, Jena, Germany ORCID ID:E-mail: thomas.wutzler@bgc-jena.mpg.de (email)

Category: Special section

article id 290, category Special section
Mikko Peltoniemi, Esther Thürig, Stephen Ogle, Taru Palosuo, Marion Schrumpf, Thomas Wutzler, Klaus Butterbach-Bahl, Oleg Chertov, Alexander Komarov, Aleksey Mikhailov, Annemieke Gärdenäs, Charles Perry, Jari Liski, Pete Smith, Raisa Mäkipää. (2007). Models in country scale carbon accounting of forest soils. Silva Fennica vol. 41 no. 3 article id 290. https://doi.org/10.14214/sf.290
Countries need to assess changes in the carbon stocks of forest soils as a part of national greenhouse gas (GHG) inventories under the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol (KP). Since measuring these changes is expensive, it is likely that many countries will use alternative methods to prepare these estimates. We reviewed seven well-known soil carbon models from the point of view of preparing country-scale soil C change estimates. We first introduced the models and explained how they incorporated the most important input variables. Second, we evaluated their applicability at regional scale considering commonly available data sources. Third, we compiled references to data that exist for evaluation of model performance in forest soils. A range of process-based soil carbon models differing in input data requirements exist, allowing some flexibility to forest soil C accounting. Simple models may be the only reasonable option to estimate soil C changes if available resources are limited. More complex models may be used as integral parts of sophisticated inventories assimilating several data sources. Currently, measurement data for model evaluation are common for agricultural soils, but less data have been collected in forest soils. Definitions of model and measured soil pools often differ, ancillary model inputs require scaling of data, and soil C measurements are uncertain. These issues complicate the preparation of model estimates and their evaluation with empirical data, at large scale. Assessment of uncertainties that accounts for the effect of model choice is important part of inventories estimating large-scale soil C changes. Joint development of models and large-scale soil measurement campaigns could reduce the inconsistencies between models and empirical data, and eventually also the uncertainties of model predictions.
  • Peltoniemi, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: mikko.peltoniemi@metla.fi (email)
  • Thürig, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland; European Forest Institute, Joensuu, Finland ORCID ID:E-mail:
  • Ogle, Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, USA ORCID ID:E-mail:
  • Palosuo, European Forest Institute, Joensuu, Finland ORCID ID:E-mail:
  • Schrumpf, Max-Planck-Institute for Biogeochemistry, Jena, Germany ORCID ID:E-mail:
  • Wutzler, Max-Planck-Institute for Biogeochemistry, Jena, Germany ORCID ID:E-mail:
  • Butterbach-Bahl, Institute for Meteorology and Climate Research, Forschungszentrum Karlsruhe GmbH, Garmisch-Partenkirchen, Germany ORCID ID:E-mail:
  • Chertov, St. Petersburg State University, St. Petersburg-Peterhof, Russia ORCID ID:E-mail:
  • Komarov, Institute of Physicochemical and Biological Problems in Soil Science of Russian Academy of Sciences, Pushchino, Russia ORCID ID:E-mail:
  • Mikhailov, Institute of Physicochemical and Biological Problems in Soil Science of Russian Academy of Sciences, Pushchino, Russia ORCID ID:E-mail:
  • Gärdenäs, Dept. of Soil Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden ORCID ID:E-mail:
  • Perry, USDA Forest Service, Northern Research Station, St. Paul, MN USA ORCID ID:E-mail:
  • Liski, Finnish Environment Institute, Helsinki, Finland ORCID ID:E-mail:
  • Smith, School of Biological Sciences, University of Aberdeen, Aberdeen, UK ORCID ID:E-mail:
  • Mäkipää, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: raisa.makipaa@metla.fi
article id 289, category Special section
Thomas Wutzler, Martina Mund. (2007). Modelling mean above and below ground litter production based on yield tables. Silva Fennica vol. 41 no. 3 article id 289. https://doi.org/10.14214/sf.289
Estimates of litter production are a prerequisite for modeling soil carbon stocks and its changes at regional to national scale. However, the required data on biomass removal is often available only for the recent past. In this study we used yield tables as a source of probable past forest management to drive a single tree based stand growth model. Next, simulated growth and timber volume was converted to tree compartment carbon stocks and biomass turnover. The study explicitly accounted for differences in site quality between stands. In addition we performed a Monte Carlo uncertainty and sensitivity analysis. We exemplify the approach by calculating long-term means of past litter production for 10 species by using yield tables that have been applied in Central Germany during the last century. We found that litter production resulting from harvest residues was almost as large as the one from biomass turnover. Differences in site quality caused large differences in litter production. At a given site quality, the uncertainty in soil carbon inputs were 14%, 17%, and 25% for beech, spruce, and pine stands, respectively. The sensitivity analysis showed that the most influential parameters were associated with foliage biomass and turnover. We conclude that rates of mean past litter production and their uncertainties can reliably be modeled on the basis of yield tables if the model accounts for 1) full rotation length including thinning and final harvest, 2) differences in site quality, and 3) environmental dependency of foliage biomass and foliage turnover.
  • Wutzler, Max Planck Institute for Biogeochemistry, Jena, Germany ORCID ID:E-mail:
  • Mund, Max Planck Institute for Biogeochemistry, Jena, Germany ORCID ID:E-mail:

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