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| Titel |
Productivity of Northern Eurasian forests: Analysis of uncertainties |
| VerfasserIn |
Anatoly Shvidenko, Dmitry Schepaschenko, Ian McCallum |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250041389
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| Zusammenfassung |
| Indicators of biological productivity of forests (live and dead biomass, net primary
production, net and gross growth) are crucial for both assessment of the impacts of terrestrial
ecosystems on major biogeochemical cycles and practice of sustainable forest management.
However, different information and the diversity of methods used in the assessments of
forests productivity cause substantial variation in reported estimates. The paper contains a
systems analysis of the existing methods, their uncertainties, and a description of
available information. With respect to Northern Eurasian forests, the major reasons
for uncertainties could be categorized as following: (1) significant biases that are
inherent in a number of important sources of available information (e.g., forest
inventory data, results of measurements of some indicators in situ); (2) inadequacy and
oversimplification of models of different types (empirical aggregations, process-based
models); (3) lack of data for some regions; and (4) upscaling procedure of “point”
observations.
Based on as comprehensive as possible adherence to the principles of systems analysis,
we made an attempt to provide a reanalysis of indicators of forests productivity of Russia
aiming at obtaining the results for which uncertainties could be estimated in a reliable
and transparent way. Within a landscape-ecosystem approach it has required (1)
development of an expert system for refinement of initial data including elimination
of recognized biases; (2) delineation of ecological regions based on gradients of
major indicators of productivity; (3) transition to multidimensional models (e.g., for
calculation of spatially distributed biomass expansion factors); (4) use of process-based
elements in empirical models; and (5) development of some approaches which
presumably do not have recognized biases. However, taking into account the fuzzy
character of the problem, the above approach (as well as any other individually used
method) is not able to recognize structural uncertainties. In order to assess those, a
special statistical procedure for harmonizing the multiple constraints of the estimates
obtained by independent methods (landscape-ecosystem approach; flux measurements;
process-based vegetation models; inverse modeling) was used to estimate uncertainty
of the final results. Application of the above methodology resulted in a reliable
assessment of major indicators of productivity. For instance, live biomass at the
country’s level is estimated with uncertainty of 4-6%, net primary production –
7-10% (confidential interval 0.9). It was recognized the tendency of increasing
productivity of Russian forests during the last four decades at level of 0.5±0.2% year-1.
However, all the above procedures includes expert estimates and judgements, and
some problems still require additional considerations (e.g., elimination of biases of
remote sensing data; assessment of uncertainties of process-based models; formal
procedures of multiple constraints of the results were obtained by different methods). |
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