cff-version: 1.2.0
abstract: "<p>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.</p><p><br></p><p>Reference:</p><p>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</p>"
authors:
  - family-names: Mohammed Ali
    given-names: Awad
    orcid: "https://orcid.org/0000-0002-4618-6037"
  - family-names: Imhoff
    given-names: Ruben
    orcid: "https://orcid.org/0000-0002-4096-3528"
  - family-names: Weerts
    given-names: Albrecht
    orcid: "https://orcid.org/0000-0002-3249-8363"
title: "Dataset underlying the publication: Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/6e994451-5c8e-41c6-a9e3-4f7343bec22a.v1
license: CC BY 4.0
date-released: 2025-08-27