Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales—review and recommendations
| Autor: | O. J. Kjønaas, Elena Vanguelova, Pavel Pavlenda, Eleonora Bonifacio, Kęstutis Armolaitis, Lars Vesterdal, Miglena Zhiyanski, Primož Simončič, Brian Reidy, Torsten W. Berger, Brian Tobin, B. De Vos, Luisella Celi, Ülle Püttsepp, M. R. Hoosbeek, Lucian Dinca, Jukka Pumpanen |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Bodemscheikunde en Chemische Bodemkwaliteit
010504 meteorology & atmospheric sciences Climate Management Monitoring Policy and Law Carbon sequestration Forests European 01 natural sciences Soil Environmental impact assessment Landscape Forest soils Sampling Stock (geology) 0105 earth and related environmental sciences General Environmental Science Hydrology WIMEK Soil organic matter Uncertainty Soil classification 04 agricultural and veterinary sciences General Medicine Soil carbon Soil type National Pollution Carbon Carbon stocks Plot Soil profile 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Soil horizon Environmental science Physical geography Soil Chemistry and Chemical Soil Quality |
| Zdroj: | Environmental Monitoring and Assessment 188 (2016) 11 Europe PubMed Central Environmental Monitoring and Assessment, 188(11) |
| ISSN: | 0167-6369 |
| Popis: | Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales—sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale. |
| Databáze: | OpenAIRE |
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