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Soil hydraulic conductivity (K) varies greatly with matric potential (h) and exhibits a high variability at the field scale. However, this key property for estimating water flux in soils is difficult to measure. In contrast, soil electrical conductivity (σ) is easier to measure and is influenced by the same parameters affecting K. We derive a simple relationship between σ and K(h) and test it against laboratory and literature data. Importantly, we show that parameters of this σ‐K(h) relationship can be completely determined with accessible measurements of saturated hydraulic conductivity, electrical conductivity of the soil solution, and clay content. This results in K(h) estimation with a RMSE ranging between 0.4 and 0.5 for log K, i.e., of the order of most experimental determinations of K. A further test of the σ‐K(h) relationship on the large UNSODA hydraulic database shows good agreement and the robustness of the relationship. Such a relation could be useful in the spatial monitoring of water fluxes at the field scale using electrical resistivity tomography if the σ(h) relationship can be obtained.

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means, electronic or mechanical, including photocopying, recording, or any informa on storage and retrieval system, without permission in wri ng from the publisher. S S : V Z M Water fl uxes in the subsurface of forested and agricultural ecosystems vary spa ally across wide ranges because of several interconnected phenomena. On the one hand, subsurface environments are o en highly heterogeneous. On the other hand, infi ltra on water is o en distributed unevenly due to aboveground intercep on and redistribu on of rainfall by the plant canopy. These phenomena have important hydro-ecological consequences because they signifi cantly aff ect groundwater recharge and nutrient leaching. Field experiments involving subsurface lysimeters and tensiometers were performed to quan fy the spa al distribu on of fl uxes in an Andisol under a banana plant (Musa acuminata Colla).Wick lysimeters were installed at a depth of 70 cm at several loca ons with respect to the banana stem to measure the spa al distribu on of subsurface water fl uxes. Collected experimental data were simulated using the HYDRUS so ware package, which numerically solves the Richards equa on describing three-dimensional variably saturated water fl ow in the subsurface. Spa ally distributed drainage fl uxes were well reproduced with the numerical model. Due to the impact of stemfl ow, drainage volumes under the banana stem were up to six mes higher than in the row downstream from the stem, as well as between rows, as these areas were sheltered from direct rainfall by the banana leaves and received only throughfall.

International audienceUnderstanding the processes and mechanisms that control preferential flow in soils in relation to the properties of their structures is still challenging since fast flow and transport occur in a small fraction of the porosity, that is, the functional macropore network, making it difficult to image and characterize these processes at decimeter scales. The aim of the paper was therefore to propose a new image acquisition and analysis methodology to characterize preferential flow at the core scale and identify the resulting active macropore network. Water infiltration was monitored by a sequence of three-dimensional images (taken at 5-, 10-, or 15-min intervals) with an X-ray scanner that allows very fast acquisitions (10 s for a 135-mm diameter). A simultaneous dye tracer experiment was also conducted. Water infiltration was then imaged at each acquisition time by the voxels impacted by water during infiltration, named the water voxels. The number of times a voxel was impacted by water during the experiment was converted into data reflecting the water detection frequency at the given position in the soil column, named the local detection frequency. Compared with dye staining, the active macropore network was defined by macropores in which water voxels were the most frequently detected during the experiment (local detection frequency above 65%). The geometric properties of this active network, such as the connectivity, were significantly different from those of the total structure. This image processing methodology coupled to dynamic acquisitions can be used to improve the analysis of preferential flow processes related to soil structures at the core scale

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