Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/32636
Title: Modelling soil bulk density at the landscape scale and its contributions to C stock uncertainty
Authors: Taalab, K. P.
Corstanje, R.
Creamer, R.
Whelan, M. J.
First Published: 12-Jul-2013
Publisher: Copernicus Publications on behalf of the European Geosciences Union
Citation: Biogeosciences, 2013, 10 (7), pp. 4691-4704
Abstract: Soil bulk density (D[subscript: b]) is a major contributor to uncertainties in landscape-scale carbon and nutrient stock estimation. However, it is time consuming to measure and is, therefore, frequently predicted using surrogate variables, such as soil texture. Using this approach is of limited value for estimating landscape-scale inventories, as its accuracy beyond the sampling point at which texture is measured becomes highly uncertain. In this paper, we explore the ability of soil landscape models to predict soil D[subscript: b] using a suite of landscape attributes and derivatives for both topsoil and subsoil. The models were constructed using random forests and artificial neural networks. Using these statistical methods, we have produced a spatially distributed prediction of D[subscript: b] on a 100 m × 100 m grid, which was shown to significantly improve topsoil carbon stock estimation. In comparison to using mean values from point measurements, stratified by soil class, we found that the gridded method predicted D[subscript: b] more accurately, especially for higher and lower values within the range. Within our study area of the Midlands, UK, we found that the gridded prediction of D[subscript: b] produced a stock inventory of over 1 million tonnes of carbon greater than the stratified mean method. Furthermore, the 95% confidence interval associated with total C stock prediction was almost halved by using the gridded method. The gridded approach was particularly useful in improving organic carbon (OC) stock estimation for fine-scale landscape units at which many landscape–atmosphere interaction models operate.
DOI Link: 10.5194/bg-10-4691-2013
ISSN: 1726-4170
eISSN: 1726-4189
Links: http://www.biogeosciences.net/10/4691/2013/bg-10-4691-2013.html
http://hdl.handle.net/2381/32636
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2014. This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Geography

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