Dataset underlying the publication: Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability

DOI:10.4121/6e994451-5c8e-41c6-a9e3-4f7343bec22a.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/6e994451-5c8e-41c6-a9e3-4f7343bec22a

Datacite citation style

Mohammed Ali, Awad; Ruben Imhoff; Albrecht Weerts (2025): Dataset underlying the publication: Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/6e994451-5c8e-41c6-a9e3-4f7343bec22a.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Two globally distributed maps of horizontal-to-vertical saturated hydraulic conductivity (fKh0) were generated using machine learning algorithms using random forest and boosted regression trees. Linking the calibrated benchmark of fKh0 achieved by Weerts et al. (2024) over 551 subbasins over the Great Britain to the structural soil properties from SoilGrids v1.0, we estimate pedo-transfer functions to predict fKh0 values globally at 250m spatial resolution.


Reference:

Weerts, A. H. (2024). Dataset underlying the publication: Revealing spatial patterns of lateral hydraulic conductivity through sensitivity analysis. 4TU.ResearchData. Retrieved from https://doi.org/10.4121/6026ee8f-1e37-4760-abb6-b0a6251b3089.v2

History

  • 2025-08-27 first online, published, posted

Publisher

4TU.ResearchData

Format

.txt and .tif

Funding

  • European Space Agency (ESA) (grant code 4000141141/23/I-EF)
  • European High-Performance Computing Joint Undertaking (grant code 955648)

Organizations

Operational Water Management & Early Warning, Department of Inland Water Systems, Deltares, The Netherlands;
Hydrology and Environmental Hydraulics Group, Department of Environmental Sciences, Wageningen University & Research

DATA

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