Abstract
Here, we review in depth how soils can remember moisture anomalies
across spatial and temporal scales, embedded in the concept of soil
moisture memory (SMM), and we explain the mechanisms and factors that
initiate and control SMM. Specifically, we explore external and internal
drivers that affect SMM, including extremes, atmospheric variables,
anthropogenic activities, soil and vegetation properties, soil
hydrologic processes, and groundwater dynamics. We analyze how SMM
considerations should affect sampling frequency and data source
collection. We discuss the impact of SMM on weather variability, land
surface energy balance, extreme events (drought, wildfire, and flood),
water use efficiency, and biogeochemical cycles. We also discuss the
effects of SMM on various land surface processes, focusing on the
coupling between soil moisture, water and energy balance, vegetation
dynamics, and feedback on the atmosphere. We address the spatiotemporal
variability of SMM and how it is affected by seasonal variation,
location, and soil depth. Regarding the representation and integration
of SMM in land surface models, we provide insights on how to improve
predictions and parameterizations in LSMs and address model complexity
issues. The possible use of satellite observations for identifying and
quantifying SMM is also explored, emphasizing the need for greater
temporal frequency, spatial resolution, and coverage of measurements. We
provide guidance for further research and practical applications by
providing a comprehensive definition of SMM, considering its
multifaceted perspective.