Multiscaling analysis of Soil Water Content during irrigation events. Comparison between surface and subsurface drip irrigation

Abstract A detailed knowledge of the spatial variability and temporal evolution of soil water content (SWC) is a key element for maximizing the irrigation efficiency. This paper presents a novel approach that uses Multifractal analysis (MFA) to characterize the scaling properties of SWC evolution during irrigation events through the Generalized Structure Function (GSF). A set of experimental runs were performed in two irrigated plots with either surface or subsurface drip irrigation. SWC was estimated through the cumulative temperature (Tcum) from a Distributed Temperature Sensor (DTS) recordings aided by the Active Heated Fiber Optic (AHFO) technique. The fiber optic cable was deployed at 5 and 25 cm underneath the soil in both plots. Results showed that SWC evolution during the experiments in subsurface irrigation was purely random and more homogeneous than in surface irrigation. The spatial and temporal variability of data from experiments in the surface irrigated plot is higher than that of the subsurface irrigated one, and clearly distant of a pure Brownian motion. The data revealed a persistent character at the upper layer of the surface irrigated plot caused by the unequal distribution of ponded water around certain emitters and a subsequent anti-persistent character at the bottom as a result of the heterogeneous infiltration. From an agronomic point of view, the paper provides an insight into the required adaptation of both irrigation water depths and frequency to avoid water loss in either surface or subsurface irrigation systems.