Geophysical instruments are often used in hydrological surveys. A geophysical model that couples electrical conductivity in the subsurface layers with measurements from an electromagnetic induction instrument was combined with a machine learning algorithm. The study reveals that this combination can estimate the identifiability of electrical conductivity in a layered soil and provide insight into the best way to configure the instrument for a specific field site.

We present and compare different geostatistical methods for underglacial bedrock interpolation. Variogram-based interpolations are compared with a multipoint statistics approach on both test cases and real glaciers. Using the modeled bedrock, the ice volume for the Scex Rouge and Tsanfleuron glaciers (Swiss Alps) was estimated to be 113.9 ± 1.6 million cubic meters. Complex karstic geomorphological features are reproduced and can be used to improve the precision of underglacial flow estimation.

This study applies multiple metrics including the fraction of young water and its discharge sensitivity and mean transit time using young as well as old groundwater age tracers to improve understanding of the dynamic nature of hydrologic flow paths at a sub-humid mountainous site. The results show that the aforementioned metrics yield unique information and they are helpful in understanding the nature of transient flow paths and observable storage volumes that contribute to streamflow.

Soil moisture levels have decreased significantly over the past 2 decades. This decrease is not uniformly distributed over the observation period. The largest changes occur at tipping points during years of extreme drought, after which soil moisture levels reach significantly different alternate stable states. Not only the overall trend in soil moisture is affected, but also the seasonal dynamics.

The shallow underground is constituted of sediments that present high spatial variability. This upper layer is the most extensively used for resource exploitation (groundwater, geothermal heat, construction materials, etc.). Understanding and modeling the spatial variability of these deposits is crucial. We present a high-resolution electrical resistivity dataset that covers the upper Aare Valley in Switzerland. These data can help develop methods to characterize these geological formations.

Due to climate and population evolution, increased pressure is put on the groundwater resource, which calls for better understanding and models. In this paper, we describe a novel workflow to model the geological heterogeneity of coastal aquifers and apply it to the Roussillon plain (southern France). The main strength of the workflow is its capability to model aquifer heterogeneity when only sparse data are available while honoring the local geological trends and quantifying uncertainty.