From Deterministic to Probabilistic: Quantifying Layer Boundary Uncertainty in Hydrostratigraphic Models

Hydrostratigraphic models are commonly used as structural frameworks for groundwater and subsurface studies. Traditionally, these models are treated as deterministic representations, providing a single “best estimate” of subsurface structure. While practical, this approach conceals the inherent uncertainty in geological interpretation, particularly in the spatial placement of layer boundaries, and limits the transparency and robustness of subsequent modelling workflows. Recognising and quantifying this uncertainty is a necessary step towards more probabilistic approaches to hydrostratigraphic modelling.

This contribution presents GDM (geology-driven modelling), a method for explicitly quantifying interpretation uncertainty in the placement of hydrostratigraphic layer boundaries through ensembles of 3D subsurface realisations. GDM operates on existing hydrostratigraphic models, assuming a fixed framework in terms of layer definition and conceptual interpretation, while focusing on the spatial variability of layer interfaces. The method is computationally efficient, enabling application at regional or national scales. Its national-scale implementation, allows interpretation uncertainties to be assessed across entire hydrostratigraphic frameworks, providing a consistent basis for revisiting legacy models.

As an illustration, we demonstrate how GDM was used to quantify interpretation uncertainties in the national-scale hydrostratigraphic model of Denmark and how the resulting ensemble of subsurface realisations was incorporated into the hydrological modelling workflow. The ensemble describes the range of equally plausible geometries supported by the available data and assumptions, providing a structured way to explore how interpretation uncertainty propagates through geological models.

This example serves as a starting point for reflecting on broader implications. In particular, it illustrates how approaches that explicitly quantify interpretation uncertainty can help bridge the gap between established deterministic models and future strategies that increasingly embrace probabilistic representations. At the same time, these approaches introduce new considerations for both modellers and users/end-users of geological models.