Spatio-temporal variations in global surface soil moisture based on multiple datasets: Intercomparison and climate drivers

Yansong Guan, Xihui Gu*, Louise J. Slater, Jianfeng Li*, Dongdong Kong, Xiang Zhang

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    4 Citations (Scopus)


    Accurate soil moisture datasets are essential to understand the impacts of climate change. However, few studies have evaluated the consistency and drivers of long-term trends in soil moisture among different dataset types (satellite, assimilation, reanalysis, and climate model) at the global scale. Here we analyze the spatio-temporal variations of global surface soil moisture and associated climate dynamics over 1980–2020 using multiple soil moisture datasets, i.e., multi-satellite assimilated remote sensing datasets (ESA CCI), simulated soil moisture based on LSMs (GLDAS, GLEAM, CMIP6), and reanalysis (ECMWF ERA5, MERRA2, CRA-Land). Most of these datasets indicate pervasive drying of global surface soil moisture over the last four decades. Prominent soil moisture drying is detected in North America, Europe, northeastern Asia, North Africa, and the Arabian Peninsula. The cross-correlations among the five synthetic soil moisture datasets are the highest between GLEAM and the reanalysis datasets. Using the Aridity Index (AI, the ratio between annual total precipitation and potential evapotranspiration), we find that soil moisture drying is the most intensive in the humid-arid transitional regions with AI ranging 0.8–1.2. Surface soil moisture drying is primarily driven by increases in temperature, followed by ENSO, as indicated by Maximum Covariance Analysis (MCA). However, the significance of the impact of ENSO on soil moisture variability is sensitive to the choice of soil moisture dataset used in the MCA.

    Original languageEnglish
    Article number130095
    JournalJournal of Hydrology
    Early online date25 Aug 2023
    Publication statusPublished - Oct 2023

    Scopus Subject Areas

    • Water Science and Technology

    User-Defined Keywords

    • Climate change
    • Dynamical processes
    • ENSO
    • Maximum Covariance Analysis
    • Soil moisture


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