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Watershed Planning within a Quantitative Scenario Analysis Framework
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Bridging the gap between GRACE and GRACE-FO using a hydrological model.

Xu Zhang1, Jinbao Li2, Qianjin Dong3

  • 1Department of Geography, University of Hong Kong, Hong Kong SAR, China.

The Science of the Total Environment
|February 5, 2022
PubMed
Summary
This summary is machine-generated.

A new hydrological model bridges the data gap between the GRACE and GRACE-FO satellite missions, accurately reconstructing terrestrial water storage anomalies (TWSA) without de-trending for improved physical understanding.

Keywords:
GRACEGRACE-FOGap fillingTerrestrial water storage

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Area of Science:

  • Earth Science
  • Hydrology
  • Climate Science

Background:

  • The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions provide crucial global terrestrial water storage anomaly (TWSA) data.
  • A temporal gap between GRACE and GRACE-FO (July 2017-May 2018) introduces uncertainties in TWSA calculations and modeling.
  • Existing gap-filling methods often lack physical foundations, relying on statistical or AI models that de-trend TWSA.

Purpose of the Study:

  • To develop a physically-based empirical hydrological model for reconstructing TWSA during the GRACE-GRACE-FO data gap.
  • To ensure robustness and accuracy in TWSA reconstruction, facilitating the seamless integration of GRACE and GRACE-FO datasets.
  • To enhance the physical understanding of global TWSA dynamics by avoiding de-trending transformations.

Main Methods:

  • Developed an empirical hydrological model incorporating the "abcd" model for runoff generation.
  • Included a T-based snow component to represent snow dynamics and a linear correction for long-term trends.
  • Validated the model's performance against sea-level budget data and Global Land Data Assimilation System (GLDAS)-derived TWSA.

Main Results:

  • The hydrological model successfully reconstructed TWSA across tropical, temperate, and continental climates.
  • High accuracy and robustness were achieved in filling the temporal data gap.
  • Performance was comparable to AI and statistical methods but maintained physical basis and avoided de-trending.

Conclusions:

  • The developed physically-based hydrological model effectively bridges the GRACE-GRACE-FO data gap for TWSA reconstruction.
  • The method enhances data continuity and improves physical understanding of terrestrial water storage changes.
  • This approach offers a more robust alternative to statistical or AI gap-filling techniques for climate and hydrological studies.