Journal
GEODERMA
Volume 316, Issue -, Pages 100-114Publisher
ELSEVIER
DOI: 10.1016/j.geoderma.2017.12.002
Keywords
Water retention; Paramo; Random Forest; Validation; Parameter tuning
Categories
Funding
- German Research Foundation (DFG) as part of the Platform for Biodiversity and Ecosystem Monitoring and Research in South Ecuador [PAK 823, PAK 824, PAK 825, LI 2360/1-1]
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Soils of Paramo 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 km(2) 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 cm(3) 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.
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