Legacy data for 3D modelling of peat properties with uncertainty estimation in Dava bog - Scotland
| Autor: | Rupert Hough, Laura Poggio, Inge Aalders, Alessandro Gimona, Jane Morrice |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Peat
Exploit Soil Science Climate change Geostatistics 010501 environmental sciences Structural basin 01 natural sciences Digital soil mapping Histosols Land use land-use change and forestry Bog 0105 earth and related environmental sciences geography geography.geographical_feature_category business.industry Environmental resource management Generalized additive model Uncertainty 04 agricultural and veterinary sciences Soil carbon 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science business Legacy data ISRIC - World Soil Information |
| Zdroj: | Geoderma Regional, 22 Geoderma Regional 22 (2020) |
| ISSN: | 2352-0094 |
| Popis: | Peatlands are an important potential sink or source of carbon and play a significant role in climate change regulation. Understanding peatland as 3D-landforms is as important as mapping their spatial extent. The main aim of this work was to estimate a 3D representation of peat properties and assess the associated spatial uncertainty, to provide baseline information for climate and land use change analyses. In this study a combination of 3D Generalized Additive Models and 3D geostatistics was applied to a raised basin bog using legacy data to map carbon content. The study presents a novel approach based on methods providing quantification of the spatial uncertainty and the possibility to model complex relationships. The approach fully exploits the 3D spatial relationships between the survey points while supported by environmental variables. The methods proved to be general and highly flexible. The results of this study showed that it is possible to model peat properties to obtain a detailed volumetric assessment of the peat, including carbon stocks from a limited set of legacy data. The estimates of spatial uncertainty are important when including the results in further environmental and climate-change models or for decision making to provide alternatives and prioritisation. |
| Databáze: | OpenAIRE |
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