Disentangling the Effects of Land Management and Soil Heterogeneity on Soil Moisture Dynamics

Abstract

Core Ideas We performed time series analysis of existing large soil moisture data sets. Observed behavior was decomposed into respective contributions of single processes. Considering input signal transformation instead of mass flux considerations. Our approach opens many new possibilities to analyze complex soil system behavior. Soil moisture is the essential control of water and energy dynamics at arable sites. Time series of soil moisture reflect the interplay of various processes, each of which influences the overall soil moisture dynamics. In this study we tested an approach to break down observed soil moisture behavior into the respective contributions of individual processes. We applied a principal component analysis to soil moisture time series from a field experiment comprising two crop rotation systems and two different soil tillage practices. We concentrated on 57 soil moisture time series measured over nearly 4 yr at 12 plots and five soil depths, down to 1.5 m. About 77.9% of the variance was reflected by the first component being almost identical to a time series of averaged soil moisture. It described the effect of the meteorological boundary conditions. The second component described the effect of the input signal damping increasing with soil depth and accounted for 7.8% of total variance. The signal transformation over depth proved to be more or less uniform throughout the test site, despite considerable soil heterogeneity. Another 3.6% of the total variance (third component) was unambiguously explained by the different cropping systems. On the contrary, different soil tillage practices had no significant effect. The suggested approach opens up many possibilities to analyze and better understand complex soil system behavior. The data‐based approach of time series analysis provides model‐independent, quantitative information about the key factors and processes controlling soil water dynamics. Hence, it is especially valuable for model building, calibration, and evaluation.