Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-151-2021
https://doi.org/10.5194/gmd-14-151-2021
Development and technical paper
 | 
12 Jan 2021
Development and technical paper |  | 12 Jan 2021

Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2)

Brigitta Szabó, Melanie Weynants, and Tobias K. D. Weber

Related authors

Addressing soil data needs and data gaps in catchment-scale environmental modelling: the European perspective
Brigitta Szabó, Piroska Kassai, Svajunas Plunge, Attila Nemes, Péter Braun, Michael Strauch, Felix Witing, János Mészáros, and Natalja Čerkasova
SOIL, 10, 587–617, https://doi.org/10.5194/soil-10-587-2024,https://doi.org/10.5194/soil-10-587-2024, 2024
Short summary
Hydro-pedotransfer functions: a roadmap for future development
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024,https://doi.org/10.5194/hess-28-3391-2024, 2024
Short summary
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023,https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary

Cited articles

Araya, S. N. and Ghezzehei, T. A.: Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations, Water Resour. Res., 55, 5715–5737, https://doi.org/10.1029/2018WR024357, 2019. 
Assouline, S. and Or, D.: Conceptual and Parametric Representation of Soil Hydraulic Properties: A Review, Vadose Zone J., 12, 1–20, https://doi.org/10.2136/vzj2013.07.0121, 2013. 
Børgesen, C. D. and Schaap, M. G.: Point and parameter pedotransfer functions for water retention predictions for Danish soils, Geoderma, 127, 154–167, https://doi.org/10.1016/j.geoderma.2004.11.025, 2005. 
Botula, Y.-D., Nemes, A., Mafuka, P., Van Ranst, E., and Cornelis, W. M.: Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric -Nearest Neighbor Approach, Vadose Zone J., 12, 1–17, https://doi.org/10.2136/vzj2012.0123, 2013. 
Boulesteix, A. L., Janitza, S., Kruppa, J., and König, I. R.: Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics, Wires. Data Min. Knowl., 2, 493–507, https://doi.org/10.1002/widm.1072, 2012. 
Download
Short summary
This paper presents updated European prediction algorithms (euptf2) to compute soil hydraulic parameters from easily available soil properties. The new algorithms lead to significantly better predictions and provide a built-in prediction uncertainty computation. The influence of predictor variables on predicted soil hydraulic properties is explored and practical guidance on how to use the derived PTFs is provided. A website and an R package facilitate easy application of the updated predictions.
Share