Journal cover Journal topic
Earth System Science Data The Data Publishing Journal
doi:10.5194/essd-2017-13
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Review article
23 Feb 2017
Review status
This discussion paper is under review for the journal Earth System Science Data (ESSD).
A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves
Carsten Montzka1, Michael Herbst1, Lutz Weihermüller1, Anne Verhoef2, and Harry Vereecken1 1Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich, Germany
2University of Reading, Department of Geography and Environmental Science, Reading, UK
Abstract. Agroecosytem models, regional and global climate models, as well as numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid Earth and Atmosphere, and regulate evapotranspiration, infiltration, and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller-Miller scaling that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Miller-Miller scaling parameter lambda, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014). The example data set is provided at a global resolution of 0.25° at DOI:10.1594/PANGAEA.870605 (DOI registration in progress, so far the data can be accessed under https://doi.pangaea.de/10.1594/PANGAEA.870605).

Citation: Montzka, C., Herbst, M., Weihermüller, L., Verhoef, A., and Vereecken, H.: A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves, Earth Syst. Sci. Data Discuss., doi:10.5194/essd-2017-13, in review, 2017.
Carsten Montzka et al.
Carsten Montzka et al.

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A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves
C. Montzka, M. Herbst, L. Weihermüller, A. Verhoef, and H. Vereecken
doi:10.1594/PANGAEA.870605
Carsten Montzka et al.

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Short summary
Global climate models require adequate parameterization of soil hydraulic properties, but typical resampling to the model grid introduce uncertainties. Here we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the problems. It preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters that enables modellers to perturb hydraulic parameters for model ensemble generation.
Global climate models require adequate parameterization of soil hydraulic properties, but...
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