Understanding Carbon Cycling of Terrestrial Ecosystems as a Fuzzy System

Shvidenko, A., Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Kraxner, F., & Maksyutov, S. (2015). Understanding Carbon Cycling of Terrestrial Ecosystems as a Fuzzy System. In: Systems Analysis 2015 - A Conference in Celebration of Howard Raiffa, 11 -13 November, 2015, Laxenburg, Austria.

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Abstract

We outline a methodology of full and verified carbon account of terrestrial ecosystems (FCA) that supposes unbiased assessment of relevant proxy values (here: Net Ecosystem Carbon Budget) and reliable estimation of uncertainties. The FCA is a fuzzy (underspecified) system, of which membership function is inherently stochastic. Thus, any individually used method of FCA is not able to estimate structural uncertainties, that is why usually reported “within method” uncertainties are inevitably partial. Attempting at estimation of “full uncertainties” of the studied system we combine the major methods of terrestrial ecosystems carbon account (landscape-ecosystem method, LEA; process-based models; eddy covariance; and inverse modeling). Assessment of the uncertainties of FCA is provided within each method. Landscape-ecosystem approach (LEA) presents the empirical basis of the FCA in form of an Integrated Land Information System; serves for strict systems designing the account; contains all relevant empirical and semi-empirical data and models. By-pixel parametrization of land cover is provided by utilizing multi-sensor remote sensing data within Geo-Wiki platform and other relevant information based on special optimization algorithms. Major carbon fluxes within the LEA (NPP, HR, disturbances, etc.) are estimated based on fusion of empirical data with process-based elements by sets of regionally distributed models. “Within method” results and uncertainties of the methods examined are harmonized and mutually constrained based on the Bayesian approach. The above methodology have been applied to carbon account of Russian forests for 2000-2010; uncertainties of the FCA for individual years were estimated in limits of ±25%, CI 0.9. We discussed strengths and weaknesses of the approach; system requirements to different methods of the FCA, information and research needs; unresolved problems of cognition of fuzzy system; and obtained and potential levels of uncertainties.

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