Wavelet analysis of soil water state variables for identification of lateral subsurface flow: Lysimeter vs. field data. Issue 3 (10th May 2021) - Ehrhardt, Annelie; Groh, Jannis; Gerke, Horst H.
Abstract: Preferential and lateral subsurface flow (LSF) may be responsible for the accelerated transport of water and solutes in sloping agricultural landscapes; however, the process is difficult to observe. One idea is to compare time series of soil moisture observations in the field with those in lysimeters, where flow is vertically oriented. This study aims at identifying periods of deviations in soil water contents and pressure heads measured in the field and in a weighing lysimeter with the same soil profile. Wavelet coherency analysis (WCA) was applied to time series of hourly soil water content and pressure head data (15‐, 32‐, 60‐, 80‐, and 140‐cm depths) from Colluvic Regosol soil profiles. The phase shifts and periodicities indicated by the WCA plots reflected the response times to rain events in the same depth of lysimeter and field soil. For many rain events and depths, pressure and moisture sensors installed in the field soil responded earlier than those in the lysimeter. This could be explained by either vertical preferential flow or LSF from upper hillslope positions. Vice versa, a faster response in the lysimeter soil could be indicative for vertical preferential flow effects. Dry weather conditions and data gaps limited the number of periods with elevated soil moisture in 2016–2018, in which LSF was likely to occur. The WCA plots comprise all temporal patterns of time shifts and correlations between larger data time series in a condensed form to identify potentially relevant periods for more detailed analyses of subsurface flow dynamics. Core Ideas: Soil water content time series in weighing lysimeters are compared with those of field soil. Identification of lateral subsurface flow in sloping hummocky soil landscape. Deviations of lysimeter from field data depending on infiltration events. Wavelet coherency analysis to quantitatively describe temporal patterns of time shifts.