Stochastic Modeling in Hydrogeology

Predicting the fate and transport of solutes in aquifers is challenging, and sometimes hardly possible. Everybody seems to agree that uncertainty about the underground is the main culprit. Let it be uncertainty about the heterogeneous distributions of parameters controlling the underlying physico-chemical processes, or uncertainty about the processes themselves, or uncertainty about the state of the aquifer system, or uncertainty about how the aquifer system interacts with the surroundings. We have been told that stochastic models are the tool to cope with uncertainties. Correspondingly, many theoretical developments have been carried out in the last decades. The scientific literature is abundant with papers on stochastic models for groundwater flow and solute transport, which present and develop new algorithms that are proven to work on synthetic exercises, which try to mimic real cases. Yet, stochastic models are seldom used in practice.

We have not been able to develop easy-to-use general algorithms and computer codes for the routine use of stochastic models in “real” hydrogeology, in a manner similar to how MODFLOW, FEFLOW, MT3D and other codes are routinely used. This Research Topic calls for papers that can prove that stochastic modeling has a place in daily practice.

Topics of interest include, but are not limited to:
• Geostatistical inverse modeling,
• Data assimilation,
• Parameter estimation,
• Machine learning, and
• Case studies.

Dr Andres Alcolea is employed by Geo-Energie Suisse AG and is the funder and CEO of HydroGeoModels. All other Topic Editors declare no competing interests with regards to the Research Topic subject