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Title (Primary) Spatial prediction of soil water retention in a Páramo landscape: methodological insight into machine learning using random forest
Author Guio Blanco, C.M.; Brito Gomez, V.M.; Crespo, P.; Ließ, M.;
Journal Geoderma
Year 2018
Department BOSYS;
Volume 316
Language englisch;
POF III (all) T53; T31; T12;
Keywords Water retention; Páramo; Random Forest; Validation; Parameter tuning
UFZ wide themes RU1;
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.
ID 19724
Persistent UFZ Identifier http://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=19724
Guio Blanco, C.M., Brito Gomez, V.M., 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