Dominant factors determining the hydraulic conductivity of sedimentary aquitards

Dominant factors determining the hydraulic conductivity of sedimentary aquitards: A random forest approach

van Leer, Martijn D. (Universiteit Utrecht)
Zaadnoordijk, Willem (TU Delft Water Resources; TNO)
Zech, Alraune (Universiteit Utrecht)
Buma, Jelle (TNO)
Harting, Ronald (TNO)
Bierkens, Marc F.P. (Universiteit Utrecht; Deltares)
Griffioen, Jasper (Universiteit Utrecht; TNO)

2023

Aquitards are common hydrogeological features and their hydraulic conductivity is an important property for various groundwater management issues. Predicting their hydraulic conductivity proves challenging, given its dependence on numerous variables. In this study, the dominant factors for predicting aquitard hydraulic conductivity are identified. To this end, a random forest model is trained on a dataset consisting of more than 1000 hydraulic conductivity measurements of core-scale sediment samples from a wide range of stratigraphic units and depths in the Netherlands. The dataset contains textural properties, such as the grain size distribution and porosity, as well as structural data, such as location, sampling depth, stratigraphical unit, lithofacies, organic carbon content, carbonate content and groundwater chloride concentration. Results show that clay fraction, stratigraphic unit, depth, lithofacies and x-coordinate are the most important features for predicting the hydraulic conductivity. Here, x-coordinate is presumably a proxy for distance from marine influence. Using a more detailed grain size distribution or using derived parameters such as the grain size percentiles does not improve the model any further. Our findings indicate that structural properties play a significant role in predicting aquitard conductivity, as they serve as indicators of processes such as compaction and soft-sediment deformation. The model is furthermore an effective method to estimate hydraulic conductivity for sediment samples without conducting costly and time-consuming hydraulic conductivity measurements.

Aquitards
Groundwater
Hydraulic conductivity
Machine learning
Parameterisation
the Netherlands

http://resolver.tudelft.nl/uuid:bfceaa04-f03c-4811-ae97-88b17723c48d

https://doi.org/10.1016/j.jhydrol.2023.130468

0022-1694

Journal of Hydrology, 627

Institutional Repository

journal article

© 2023 Martijn D. van Leer, Willem Zaadnoordijk, Alraune Zech, Jelle Buma, Ronald Harting, Marc F.P. Bierkens, Jasper Griffioen