Memory character and predictive period of soil moisture in the root-zone and along hillslope

Soil moisture plays a crucial role in hydrology, influencing interactions between land and surface, hydrological processes, flood forecasting, and soil degradation. Its behavior varies over different timescales, but most research to date has concentrated on large-scale or long-term data. There is a notable gap in the understanding of soil moisture memory characteristics and its predictive periods, especially at smaller scales like the root zone and hillslopes. This research aims to address this gap by using power spectrum analysis to investigate long-term memory (LTM) characteristics and second-order detrended fluctuation analysis (DFA-2) to assess predictive periods. Data were gathered from greenhouse experiments monitoring soil moisture during the full growth cycle of tomato plants, as well as from field measurements on hillslopes. Findings indicate that the soil moisture predictive period increased from the first (T_s) to the third (T_f) growth stages. Additionally, vegetated slopes showed stronger memory of soil moisture from May to October compared to bare slopes. This study offers essential insights for improving irrigation planning, drought management, and water resource strategies.