Characterization of Water Infiltration and Redistribution for Two‐Dimensional Soil Profiles by Moment Analyses

Abstract

Soil water moments are a set of statistical characteristics calculated from the water‐content values of a soil profile. The moments indicate the water's center of mass location on the x and z axes and the water distribution around those axes, thereby producing a clear and unique temporal and spatial description of water location using only four parameters. Our objective in this research was to investigate soil water behavior during and after an infiltration event using the soil water moments parameters. Despite their advantages, soil water moments have been mainly presented for numerical solutions rather than physical infiltration experiments because their calculation requires a vast collection of water‐content values in the soil profile. Because it is difficult to obtain such data by known methods of soil sampling or by sensors installed in situ, we developed a method based on image analysis. Continuous images were analyzed for their color intensities. These were converted to water‐content values and the soil water moments were then calculated. We performed laboratory infiltration experiments with two soils, sand and sandy loam, for analysis and comparison using the water moments. Infiltration occurred for 35 min at a discharge rate of 0.285 cm3 min−1, followed by 130 min of redistribution. The water moments presented a clear picture of water infiltration and redistribution, as they varied in time and differed between soils. While in the sand the water's center of mass reached a depth of 5 cm, and the water was distributed almost uniformly around it, in the sandy loam, the center of mass only reached a depth of 2.5 cm, and water distribution resembled a flat horizontal ellipse. Any fraction of water added can be related to a “probability” curve relating the size of the ellipse that contains that amount of water. Remarkably, the probability curves are identical for all times and soils. The consistency of the probability relationships can be exploited to pinpoint the extent of subsurface water for any fraction of the volume added. Furthermore, a high correspondence was found between the results and previous numerical solution outputs.