Sustainable management schemes in agriculture require knowledge of site-specific soil hydrological processes, especially the interplay between soil heterogeneities and crops. We disentangled such effects on soil moisture in a diversified arable field with different crops and management schemes by applying a principal component analysis. The main effects on soil moisture variability were quantified. Meteorological drivers, followed by different seasonal behaviour of crops, had the largest impact.

Scientists use a physics-based equation to simulate water dynamics that influence hydrological and ecological phenomena. We present hybrid physics-informed neural networks (PINNs) to leverage the growing availability of soil moisture data and advances in machine learning. We showed that PINNs perform comparably to traditional methods and enable the estimation of rainfall rates from soil moisture. However, PINNs are challenging to train and significantly slower than traditional methods.

Water flows through soils with more difficulty when the soil is dried out. Scant rainfall in deserts may therefore result in a seemingly wet soil, but the water will often not penetrate deeply enough to replenish the groundwater. We compared the mathematical functions that describe how well different soils hold their water and found that only a few of them are realistic. The function one chooses to model the soil can have a large impact on the estimate of groundwater recharge.

The estimation of flow and solute transport in unsaturated soil is essential for quantifying groundwater resources or pollution. Usual column laboratory experiments and a new method are analyzed using a global sensitivity analysis. The data sets are composed of water pressure and water content measured inside the column and water flow rate and solute BTC measured at the outflow. Non-invasive methods (using flow rate and BTC only) provide comparable results than usual invasive methods.

A complicating factor in describing the flow of two immiscible fluids in a porous medium is ensuring that experiments, theory, and simulation are all formulated at the same length scale. We have quantitatively analyzed the internal structure of a two-fluid system including the distribution of phases and the location of interfaces between phases. The data we have obtained allow for a clearer definition of capillary pressure at the averaged scale as a state function that describes the system.