Analysis of soil water spatio-temporal variability at high resolution in a maize field

Soil water content (SWC) is a key factor in numerous water flow and transport processes taking place in the earth surface layer. As these processes are nonlinearly related to SWC, characterizing and predicting SWC variability is essential to properly address hydrological issues. This study focuses on the understanding of within-field SWC variability and its control by maize plant and atmospheric conditions. An experimental plot within a maize field was set up and equipped with different SWC probes. Soil moisture was monitored during 3 years with Time Domain Reflectometry with high temporal resolution and Electrical Resistivity Tomography with 3D high spatial resolution. The standard deviation of the SWC (σ), estimated from the SWC data, was maximum (~0.05 cm3cm-3) during drying periods in the first soil horizon. This level of σ is quite close to observations made by other studies in much larger areas, illustrating the importance of local scale heterogeneity. The maize sowing pattern in rows (R) and inter-rows (IR) and its interactions with weather conditions had a major contribution to the SWC spatial variability. Increase of SWC during rainy periods can be promoted up to six times more in R than in IR. During dry periods, root water uptake created drying patterns with less water in R than in IR. The intra-plant variability, mainly induced by root distribution, is thus not negligible and should be inserted into hydrological models. Using a soil-plant detailed water flow model R-SWMS and simulation of three different maize plants, we properly represented 70% of the SWC variability during a drying period.