Articles | Volume 29, issue 22
https://doi.org/10.5194/hess-29-6735-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-6735-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: An illustrative introduction to the domain dependence of spatial principal component patterns
Christian Lehr
CORRESPONDING AUTHOR
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
University of Potsdam, Institute for Environmental Sciences and Geography, Potsdam, Germany
Tobias L. Hohenbrink
German Weather Service (DWD), Agrometeorological Research Centre, Braunschweig, Germany
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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
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Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Tobias L. Hohenbrink, Conrad Jackisch, Wolfgang Durner, Kai Germer, Sascha C. Iden, Janis Kreiselmeier, Frederic Leuther, Johanna C. Metzger, Mahyar Naseri, and Andre Peters
Earth Syst. Sci. Data, 15, 4417–4432, https://doi.org/10.5194/essd-15-4417-2023, https://doi.org/10.5194/essd-15-4417-2023, 2023
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The article describes a collection of 572 data sets of soil water retention and unsaturated hydraulic conductivity data measured with state-of-the-art laboratory methods. Furthermore, the data collection contains basic soil properties such as soil texture and organic carbon content. We expect that the data will be useful for various important purposes, for example, the development of soil hydraulic property models and related pedotransfer functions.
Andre Peters, Tobias L. Hohenbrink, Sascha C. Iden, Martinus Th. van Genuchten, and Wolfgang Durner
Hydrol. Earth Syst. Sci., 27, 1565–1582, https://doi.org/10.5194/hess-27-1565-2023, https://doi.org/10.5194/hess-27-1565-2023, 2023
Short summary
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The soil hydraulic conductivity function is usually predicted from the water retention curve (WRC) with the requirement of at least one measured conductivity data point for scaling the function. We propose a new scheme of absolute hydraulic conductivity prediction from the WRC without the need of measured conductivity data. Testing the new prediction with independent data shows good results. This scheme can be used when insufficient or no conductivity data are available.
Conrad Jackisch, Sibylle K. Hassler, Tobias L. Hohenbrink, Theresa Blume, Hjalmar Laudon, Hilary McMillan, Patricia Saco, and Loes van Schaik
Hydrol. Earth Syst. Sci., 25, 5277–5285, https://doi.org/10.5194/hess-25-5277-2021, https://doi.org/10.5194/hess-25-5277-2021, 2021
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Short summary
In hydrology, domain dependence (DD) of spatial Principal Component patterns is a rather unknown feature of the widely applied Principal Component Analysis. It easily leads to wrong hydrological interpretations. DD reference patterns enable to differentiate from the effect. Here, we (1) explore the DD effect, (2) present two methods to calculate DD reference patterns and (3) discuss considering DD. Scripts with an introduction to the DD effect and an implementation of both methods are provided.
In hydrology, domain dependence (DD) of spatial Principal Component patterns is a rather unknown...