Guio Blanco, C.M.; Brito Gómez, V.M.; Crespo, P. & Ließ, M. (2018): <b>Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest</b>. <i>Geoderma</i> <b>316</b>, 100-114.
Resource Description
Title:
Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest
FOR816dw ID:
1701
Publication Date:
2018-04-15
License and Usage Rights:
Please site as: Guio Blanco CM, Brito Gomez VM, Crespo P, Ließ M (2018) Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest. Geoderma 316, 100-114.
Resource Owner(s):
Individual:
Carlos Manuel Guio Blanco
Contact:
email:
carlos.guio-blanco <at> uni-bayreuth.de
University of Bayreuth
Dept. of Geosciences/ Soil Physics Division
D-95447 Bayreuth
Germany
Individual:
Victor Manuel Brito Gómez
Contact:
email:
britogomezvictor <at> yahoo.es
University of Bayreuth
Dept. of Geosciences/ Soil Physics Division
D-95447 Bayreuth
Dept. of Geosciences/ Soil Physics Devision
Germany
Individual:
Patricio Crespo
Contact:
email:
pcrespos <at> yahoo.com
168 Cuenca
Ecuador
Individual:
Mareike Ließ
Contact:
email:
mareike.liess <at> ufz.de
Helmholtz Centre for Environmental Research - UFZ
Department of Soil Physics
D-06120 Halle (Saale), Theodor-Lieser-Str. 4
Germany
Abstract:
Soils of Páramo ecosystems regulate the water supply to many Andean populations. In spite of being a necessary input to distributed hydrological models, regionalized soil water retention data from these areas are currently not available. The investigated catchment of the Quinuas River has a size of about 90 km2 and comprises parts of the Cajas National Park in southern Ecuador. It is dominated by soils with high organic carbon contents, which display characteristics of volcanic influence. Besides providing spatial predictions of soil water retention at the catchment scale, the study presents a detailed methodological insight to model setup and validation of the underlying machine learning approach with random forest. The developed models performed well predicting volumetric water contents between 0.55 and 0.9 cm3 cm? 3. Among the predictors derived from a digital elevation model and a Landsat image, altitude and several vegetation indices provided the most information content. The regionalized maps show particularly low water retention values in the lower Quinuas valley, which go along with high prediction uncertainties. Due to the small size of the dataset, mineral soils could not be separated from organic soils, leading to a high prediction uncertainty in the lower part of the valley, where the soils are influenced by anthropogenic land use.
Keywords:
| Páramo | random forest | water retention | validation | parameter tuning |
Literature type specific fields:
ARTICLE
Journal:
Geoderma
Volume:
316
Page Range:
100-114
Metadata Provider:
Individual:
Mareike Ließ
Contact:
email:
mareike.liess <at> ufz.de
Helmholtz Centre for Environmental Research - UFZ
Department of Soil Physics
D-06120 Halle (Saale), Theodor-Lieser-Str. 4
Germany
