Towards quantifying plot-scale overland flow connectivity using acoustic proximal remote sensing

Abstract

Soil surface depressions affect overland flow generation and related hydrological processes. Overland flow connectivity (C) of a field increases as more water ponds in and flows through local depressions, leading to flow across field boundaries. Quantifying the development of C during an irrigation or rainfall event is key to predicting the initiation of overland flow. A novel method to continuously monitor the development of C during an irrigation event is proposed. The method comprises two elements: (i) a new proximal sensing technique, which exploits acoustic technology to continuously monitor the proportion of the soil surface covered in water (ASW), and (ii) an overland flow model which simulates the flow of water over a rough soil surface and assists by converting ASW into C. A series of experiments were conducted to examine the proposed method. Directional acoustic transmitter and receiver arrays were used to estimate ASW in real-time from changes in reflectance. A structured light 3D camera was used to validate ASW estimated using acoustic reflectance. The results showed a significant correlation between observed and estimated ASW (R2 = 0.94, p < 0.001). We further demonstrated that the ASW, as measured using the acoustic proximal sensing, can be related to C using an overland flow model which allows to identify critical value of ASW needed to initiate overland flow. Our results show that this real-time method of monitoring C has a considerable potential in irrigated fields where prediction of overland flow is desirable.