TY - DATA
T1 - Dataset underlying the publication: Machine learning for predicting spatially variable lateral hydraulic conductivity: a step towards efficient hydrological model calibration and global applicability
PY - 2025/08/27
AU - Awad Mohammed Ali
AU - Ruben Imhoff
AU - Albrecht Weerts
UR - 
DO - 10.4121/6e994451-5c8e-41c6-a9e3-4f7343bec22a.v1
KW - Pedotransfer functions
KW - Lateral saturated hydraulic conductivity
KW - Distributed hydrological modelling
KW - wflow_sbm
KW - Machine learning
N2 - <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>
ER -