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We present a global framework to infer sub-seasonal subsurface water storage dynamics from daily satellite surface soil moisture by optimizing the time constant of an exponential filter used for the depth extrapolation against GRACE and GRACE-FO terrestrial water storage (TWS) anomalies. The approach uses ESA CCI v9.1 surface and root-zone soil moisture products together with daily ITSG-Grace2018 gravity fields on a 1° global grid. For each grid cell, the time constant T of the filter is optimized to maximize the correlation between exponentially filtered soil moisture and GRACE-based TWS from which snow, surface-water, and seasonal components have been removed before. On average, the global area-weighted correlation increases from 0.19 for unfiltered surface soil moisture to 0.39 after optimization. The optimal T values decrease systematically with the depth of the soil moisture layer used as input and show physically consistent patterns related to climatic and hydrogeological controls such as aridity, soil characteristics, and depth to the groundwater table. In contrast to existing approaches that use in-situ soil moisture data to compute globally uniform T parameters, our approach allows to capture spatially varying infiltration dynamics into deeper soil layers. The resulting global T field thus provides an observation-driven proxy for subsurface storage dynamics at weekly-to-monthly time scales, offering a simple and transferable approach for linking satellite surface soil moisture to terrestrial water storage variations.

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Assessing Sub-seasonal Subsurface Water Storage Dynamics Using Exponential Filtering of ESA CCI Soil Moisture and Daily GRACE Terrestrial Water Storage Data

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  • 出版模式：

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  • 发表日期：

    2026年

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  • Daniel Blank,
  • Daniel Rasche,
  • Andreas Güntner,
  • Annette Eicker

摘要

We present a global framework to infer sub-seasonal subsurface water storage dynamics from daily satellite surface soil moisture by optimizing the time constant of an exponential filter used for the depth extrapolation against GRACE and GRACE-FO terrestrial water storage (TWS) anomalies. The approach uses ESA CCI v9.1 surface and root-zone soil moisture products together with daily ITSG-Grace2018 gravity fields on a 1° global grid. For each grid cell, the time constant T of the filter is optimized to maximize the correlation between exponentially filtered soil moisture and GRACE-based TWS from which snow, surface-water, and seasonal components have been removed before. On average, the global area-weighted correlation increases from 0.19 for unfiltered surface soil moisture to 0.39 after optimization. The optimal T values decrease systematically with the depth of the soil moisture layer used as input and show physically consistent patterns related to climatic and hydrogeological controls such as aridity, soil characteristics, and depth to the groundwater table. In contrast to existing approaches that use in-situ soil moisture data to compute globally uniform T parameters, our approach allows to capture spatially varying infiltration dynamics into deeper soil layers. The resulting global T field thus provides an observation-driven proxy for subsurface storage dynamics at weekly-to-monthly time scales, offering a simple and transferable approach for linking satellite surface soil moisture to terrestrial water storage variations.