Hydrosphere > Soil Moisture

Soil Moisture

A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves

Climate and numerical weather prediction models, re-analyses, as well as agroecosystem models, require adequate parameter values for soil hydraulic properties (describing e.g. the shape of the soil water retention and hydraulic conductivity curves) at the global scale. Resampling of soil hydraulic properties to a model grid 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 imprecision and parameter value discrepancies throughout spatial scales due to nonlinear shape of the hydraulic conductivity and water retention curves. Therefore, we developed a method to scale van Genuchten hydraulic parameters (theta_s, theta_r, alpha, n, Ks) to individual model grids and provide a global data set that overcomes the mentioned problems.

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The data set is based on the ROSETTA pedotransfer function of Schaap et al. (2001, doi:10.1016/S0022-1694(01)00466-8) applied to the SoilGrids1km data set of Hengl et al. (2014, doi:10.1371/journal.pone.0105992). The approach is based on Miller-Miller scaling that fits the shape parameters of the water retention curve to all sub-grid water retention curves to provide the best-fit parameter values for the grid cell at model resolution, here 0.25°; at the same it maintains 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 scaling parameters are also valid for this function. In addition, information on global sub-grid scaling variance is given that enables modelers to improve dynamical downscaling of (regional) climate models or to perturb soil hydraulic parameters for model ensemble generation. These improvements should allow for more informed studies of the effects of variability in soil physical properties on land surface-atmosphere exchange.

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Data Publication

Montzka, Carsten; Herbst, Michael; Weihermüller, Lutz; Verhoef, Anne; Vereecken, Harry (2017): A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves, link to model result files in NetCDF format. PANGAEA, doi:10.1594/PANGAEA.870605.

Related Jounal Article

C. Montzka, M. Herbst, L. Weihermüller, A. Verhoef, H. Vereecken: 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. 2017, 1-25, 2017, doi:10.5194/essd-2017-13.

Soil moisture time series from GNSS interferometric reflectometry (WP H3)

Soil moisture is a geophysical key observable for predicting floods and droughts, modelling weather and climate and optimizing agricultural management. Currently available in-situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m2. In this case study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate near-surface soil moisture variations between January 1, 2008 and September 1, 2014. The results capture the wetting and drying cycles in response to rainfall. The data are daily averages based on signals of 6 GPS satellites. The GNSS derived soil moisture was validated by Time Domain Reflectometry (TDR) in-situ observations.

Data Publication

Vey, Sibylle; Güntner, Andreas; Wickert, Jens; Blume, Theresa; Ramatschi, Markus (2015): Supplement to: Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: A case study for Sutherland, South Africa. GFZ German Research Centre for Geosciences. doi:10.5880/GFZ.1.1.2015.001.

Related Jounal Article

Vey, S., Güntner, A., Wickert, J., Blume, T., & Ramatschi, M. (2015). Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa. GPS Solutions. doi:10.1007/s10291-015-0474-0.

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