What Are the Key Soil Hydrological Processes to Control Soil Moisture Memory?

Abstract

Abstract. Soil moisture memory (SMM), which refers to how long a perturbation in Soil Moisture (SM) can last, is critical for understanding climatic, hydrologic, and ecosystem interactions. Most land surface models (LSMs) tend to overestimate surface soil moisture and its persistency, sustaining unexpectedly large soil surface evaporation. In general, LSMs show an overestimation of long-term SMM and an underestimation of short-term SMM. This study aims to 1) identify key soil hydrological/hydraulic processes that contribute to the amount and persistence of SM and 2) improve the physical representations of soil hydrology in the widely-used Noah-MP LSM with optional schemes of soil hydrology/hydraulics. We test the effects of different processes on SMM, including soil water retention characteristics (or soil hydraulics), soil permeability, and surface ponding. We compare SMMs computed from various Noah-MP configurations against that derived from the Soil Moisture Active Passive (SMAP) Level 3 soil moisture and in-situ measurements from the International Soil Moisture Network (ISMN) from year 2015 to 2019 over the contiguous United States (CONUS). The results suggest that 1) soil hydraulics plays a dominant role, and the Van-Genuchten hydraulic scheme reduces the overestimation of the long-term surface SMM produced by the Brooks-Corey scheme, which is commonly used in LSMs; 2) explicitly representing surface ponding improves SMM accuracy for both the surface layer and the root zone; and 3) enhanced permeability through macropores improves the overall representation of soil hydraulic dynamics. The combination of schemes introduced in this study can significantly improve the long-term memory overestimation and short-term memory underestimation issues in LSMs.