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Joint analysis of remotely sensed soil moisture and daily satellite gravimetry to describe water storage dynamics around hydrological extremes

Authors

Blank,  Daniel
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Eicker,  Annette
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

/persons/resource/guentner

Güntner,  A.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;
4.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Blank, D., Eicker, A., Güntner, A. (2023): Joint analysis of remotely sensed soil moisture and daily satellite gravimetry to describe water storage dynamics around hydrological extremes, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-2026


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018842
Abstract
Information on water storage changes in the soil can globally be obtained from different types of satellite observations. While microwave remote sensing is limited to the upper few centimeters of the soil, satellite gravimetry can detect changes in the full column of terrestrial water storage (TWS), but cannot distinguish between storage variations in different soil depths. Jointly analyzing both data types promises insights into the underlying hydrological dynamics and may enable a better process understanding of water storage change in the subsurface. In this study, we investigate the global relationship of (1) several satellite soil moisture (SM) products and (2) non-standard daily TWS data from the GRACE/-FO satellite gravimetry missions. The analysis of these data on a daily basis could be beneficial for identifying hydro-climatic extreme events such as heavy precipitation or flood events. Therefore, we evaluate the correspondence of particularly high and low percentiles in both TWS and SM time series to identify extreme events. We discuss the different temporal dynamics in near-surface SM vs. TWS before and after flood and drought events to identify antecedent conditions leading to floods and recovery times after droughts in different storage compartments. Furthermore, temporally high-frequency variations are discussed using sub-monthly SM and TWS variations derived by high-pass filtering. We find increasingly large correlations between the TWS and SM for deeper SM integration depths (root zone vs. surface layer). Even for high-pass-filtered (sub-monthly) variations, significant correlations of up to 0.6 can be found in regions with large high-frequency variability.