EGU General Assembly 2020
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the Creative Commons Attribution 4.0 License.

Process-based hydrological modeling: accounting for subsurface heterogeneity by integrating pedology, geophysics and soil hydrology

Edoardo Martini1,2, Ute Wollschläger3, Marco Bittelli4, Fausto Tomei5, Ulrike Werban2, Steffen Zacharias2, and Kurt Roth1
Edoardo Martini et al.
  • 1Institute of Environmental Physics, University of Heidelberg, Germany
  • 2Dept. Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
  • 3Dept. Soil System Science, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
  • 4Dept. Agricultural Sciences, University of Bologna, Italy
  • 5Hydro-Meteo-Climate Service, ARPAE Emilia-Romagna, Bologna, Italy

As most hydrological processes are highly nonlinear and controlled by time-varying boundary conditions, numerical models are required for their comprehensive representation. However, one of the major difficulties in vadose zone processes modeling is due to the fact that soils are heterogeneous at all spatial scales. The identification and accurate representation of such heterogeneity can be crucial for quantifying the subsurface hydrological states and water fluxes but it is still a challenge in soil hydrology.

We present an integrated approach for process-based modeling of the vadose zone for a typical hillslope. The approach builds on the integration of classical soil mapping, on accurate monitoring of soil water content as well as on geophysical measurements for characterizing the subsurface heterogeneity. It finally integrates the gathered information into a physical model for simulating the vadose-zone processes with high spatial and temporal resolution.

Starting with a simple soil representation, we present the modeling results for different scenarios of increasing complexity with focus on the discretization and corresponding hydrological parameterization of the soil structures in three dimensions. We highlight and discuss the key challenges that need to be addressed when continuous information about the subsurface heterogeneity is to be mapped in the field and represented in a numerical model.

We argue that linking state-of-the-art experimental methods to advanced numerical tools, and bridging the gap between different disciplines such as pedology, hydrology and geophysics can be the key for improving our ability to measure, predict and better understand the vadose-zone processes. This will provide important knowledge needed for transferring this approach to larger scales where the accurate quantification of the soil water fluxes is required for a more efficient water management in the context of sustainable food production and climate change.

How to cite: Martini, E., Wollschläger, U., Bittelli, M., Tomei, F., Werban, U., Zacharias, S., and Roth, K.: Process-based hydrological modeling: accounting for subsurface heterogeneity by integrating pedology, geophysics and soil hydrology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9894,, 2020

Comments on the presentation

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Presentation version 1 – uploaded on 29 Apr 2020
  • AC1: Comment on EGU2020-9894, Edoardo Martini, 29 Apr 2020

    Complete presentation material here:

  • CC1: Comment on EGU2020-9894, Sarah Garré, 05 May 2020

    Hi Eduardo, nice trailer :) I checked you material on the heidelberg website and will definitely follow up on your project and check out CRITERIA-3D

    • AC2: Reply to CC1, Edoardo Martini, 05 May 2020

      Hi Sarah, many thanks for your feedback!

  • CC2: Comment on EGU2020-9894, Arjun Chakrawal, 06 May 2020

    Hey Edoardo,

    Thank you for this cool presentation. The text was a bit difficult to read from the blackboard format of your ppt (you know how we can be used to the plane PowerPoint formats :) ). Anyhow, excellent content. 

    If I understand correctly, in your model, it the hydraulic parameter which defines the heterogeneity,  right? E.g., hydraulic conductivity or VGM model parameters? Further, the inversion is done on the soil profile data so how do you obtain these parameters where the data from inversion is not available? If, some kind of spatial interpolation, which method?

    My last question, your modeling results seem to do poorly with increasing depth, any particular reason for this.  



    • AC3: Reply to CC2, Edoardo Martini, 07 May 2020

      Hello Arjun,

      thanks for your comment.

      In the numerical model we can specify both the subsurface geometry and the MvG parameters, so this is what makes the heterogeneity. And, yes, the major challenge here will be to spatialise the hydrological properties of the single soil horizons in the 3D domain. But we are not there yet.

      You are right, the current version of the model does not catch well the dynamics of the deepest sensors. This is not surprising because the parameterisation there is not the best, for the examples that you see in the slides (large uncertainty in the volumetric sampling and BD estimation particularly for the soil horizons rich in rock fragments, inverse modelling based on SWC data from a monitoring network that we had installed at the site until a couple of years ago, max depth 50 cm, only, etc.). Furthermore, at this stage of the modeling, we have not included the groundwater data, yet. Indeed, we are going to test further levels of complexity.

      I hope my answer is exhaustive, feel free to contact me for discussing further!