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Soil Moisture Memory: State-of-the-art and the way forward
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  • Mehdi Rahmati,
  • Wulf Amelung,
  • Cosimo Brogi,
  • Jacopo Dari,
  • Alessia Flammini,
  • Heye Bogena,
  • Luca Brocca,
  • Hao Chen,
  • Jannis Groh,
  • Randal D. Koster,
  • Kaighin A McColl,
  • Montzka Carsten,
  • Shirin Moradi,
  • Arash Rahi,
  • Farnaz Sharghi S.,
  • Harry Vereecken
Mehdi Rahmati
Forschungszentrum Jülich GmbH

Corresponding Author:[email protected]

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Wulf Amelung
University of Bonn
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Cosimo Brogi
Forschungszentrum Jülich GmbH
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Jacopo Dari
University of Perugia
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Alessia Flammini
University of Perugia - Italy
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Heye Bogena
Forschungszentrum Juelich, GmbH
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Luca Brocca
National Research Council
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Hao Chen
Tianjin University
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Jannis Groh
University of Bonn
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Randal D. Koster
NASA Goddard SFC
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Kaighin A McColl
Harvard University
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Montzka Carsten
Forschungszentrum Jülich IBG-3 Wilhelm-Johnen-Straße 52428 Jülich Germany
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Shirin Moradi
Forschungszentrum Jülich Gmbh
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Arash Rahi
Perugia university
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Farnaz Sharghi S.
University of Bonn
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Harry Vereecken
Forschungszentrum Jülich GmbH
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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.
24 Apr 2024Submitted to ESS Open Archive
25 Apr 2024Published in ESS Open Archive
Jun 2024Published in Reviews of Geophysics volume 62 issue 2. 10.1029/2023RG000828