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| Titel |
Probability maps as a way to communicate uncertainty in soil texture classes at landscape scale |
| VerfasserIn |
Barry Rawlins, Murray Lark |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250098073
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| Publikation (Nr.) |
 EGU/EGU2014-13714.pdf |
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| Zusammenfassung |
| Soil texture is critical for a range of functions and degradation threats including soil carbon
cycling, hydrology and erosion. The texture of a soil at a point in the landscape is often
expressed as a class in a soil texture triangle. The boundaries between these classes are
based on the proportions of sand, silt and clay-sized particles. Soils are typically
attributed to a single class, without considering the uncertainty associated with class
membership.
We demonstrate an approach for communicating uncertainty in spatial prediction of soil
texture classes using a database of 2600 measurements of particle size distribution across part
of England. A subset of these measurements included repeated analyses of separate aliquots
from the same sample from which we could compute uncertainties associated with analytical
and subsampling variance to include in our uncertainty analysis. After appropriate
transformation for compositional variables, the spatial variation of the soil particle size
classes was modelled geostatistically using robust variogram estimators to produce a
validated linear model of coregionalization. This was then used to predict the composition of
topsoil at the nodes of a fine grid. The predictions were backtransformed to the original scales
of measurement by a Monte Carlo integration over the prediction distribution on the
transformed scale. This approach allowed the probability to be computed for each class in
the soil texture classification, at each node on the grid. The probability of each
class, and derived information such as the class of maximum probability could
therefore be mapped. We validated the predictions at a set of randomly sampled
locations. We consider this technique has the potential to improve the communication of
uncertainty associated with the application of soil texture classifications in soil science. |
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