Tang et al. (2025) Mass Change Index for Characterizing Hydrological Extremes Every Few Days From Satellite Gravity Measurements

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water Resources Research
• Year: 2025
• Date: 2025-12-01
• Authors: Miao Tang, Shin‐Chan Han, Linguo Yuan, Xinghai Yang, In‐Young Yeo, Matthew Rodell, B. Li, Eunjee Lee, Zhongshan Jiang
• DOI: 10.1029/2025wr040534

Research Groups

Not specified in the abstract.

Short Summary

This paper introduces the Mass Change Index (MCI), a new hydrological index derived from GRACE Follow-On (GRACE-FO) satellite data, enabling the assessment of instantaneous extreme wet and dry events every few days. It demonstrates MCI's superior ability to detect the severity and timing of the 2020 Yangtze River flood and the 2022 MLYRB drought compared to traditional monthly GRACE-FO and streamflow indices.

Objective

• To introduce and develop the Mass Change Index (MCI) for assessing instantaneous extreme hydrological events (both wet and dry) using GRACE Follow-On satellite data with high temporal resolution.
• To demonstrate the application and effectiveness of MCI in investigating hydrological extremes, specifically in the middle-lower Yangtze River Basin (MLYRB), and compare its performance against existing monthly indices.

Study Configuration

• Spatial Scale: Middle-lower Yangtze River Basin (MLYRB) for demonstration; applicable to most low and mid-latitude regions globally.
• Temporal Scale: MCI retrievals every 5–6 days; assessment of instantaneous mass changes; investigation of events in 2020 and 2022.

Methodology and Data

• Models used: Mass Change Index (MCI), developed by standardizing instantaneous satellite gravity anomalies computed directly from orbit perturbations. It is based on hydrology-related gravity change (total water storage change).
• Data sources: GRACE Follow-On (GRACE-FO) satellite mission (orbit perturbations, intersatellite range measurements); daily streamflow observations (for validation); typical GRACE-FO based monthly drought indices (for comparison); streamflow index (for comparison).

Main Results

• The Mass Change Index (MCI) successfully detects extreme wet conditions (standardized index of 2.0–3.0) along the Yangtze River mainstream during the catastrophic 2020 flood, consistent with daily streamflow observations.
• MCI also detects extreme dry conditions (−2.0 to −2.5) within the MLYRB during the unprecedented 2022 heatwave and drought event.
• In contrast, typical GRACE-FO based monthly drought indices and a streamflow index significantly underestimate the severity of these extreme events and misidentify their onset timing.
• MCI retains more information from intersatellite range measurements that may be lost in monthly gravity solutions and can be processed more rapidly, enhancing its potential for operational hydrological monitoring.

Contributions

• Introduction of a novel hydrological index, the Mass Change Index (MCI), capable of assessing instantaneous extreme wet and dry events from GRACE-FO data with a high temporal resolution (every 5–6 days).
• Demonstration of MCI's superior performance in accurately capturing the severity and timing of hydrological extremes (e.g., 2020 flood, 2022 drought) compared to traditional monthly GRACE-FO-based indices and streamflow indices.
• Highlighting the operational value of MCI due to its ability to retain more information from intersatellite range measurements and its faster processing capability, making it suitable for hydrological monitoring systems.

Funding

Not specified in the abstract.

Citation

@article{Tang2025Mass,
  author = {Tang, Miao and Han, Shin‐Chan and Yuan, Linguo and Yang, Xinghai and Yeo, In‐Young and Rodell, Matthew and Li, B. and Lee, Eunjee and Jiang, Zhongshan},
  title = {Mass Change Index for Characterizing Hydrological Extremes Every Few Days From Satellite Gravity Measurements},
  journal = {Water Resources Research},
  year = {2025},
  doi = {10.1029/2025wr040534},
  url = {https://doi.org/10.1029/2025wr040534}
}