Achison et al. (2026) Downscaling of GRACE Data for Hydrological Applications—A Review

Identification

• Journal: Lecture notes in civil engineering
• Year: 2026
• Date: 2026-01-01
• Authors: Rinu J. Achison, N. R. Chithra
• DOI: 10.1007/978-3-032-04178-4_57

Research Groups

• Department of Civil Engineering, National Institute of Technology, Calicut, India
• Department of Civil Engineering, Federal Institute of Science and Technology, Angamaly, India

Short Summary

This review synthesizes state-of-the-art downscaling techniques for GRACE and GRACE-FO terrestrial water storage data, demonstrating their effectiveness in enhancing spatial resolution for local-scale hydrological applications. It categorizes methods, highlights their capabilities and limitations, and identifies future research directions.

Objective

• To synthesize and review state-of-the-art downscaling techniques developed to enhance the spatial resolution of GRACE and GRACE-FO derived terrestrial water storage data for hydrological applications.

Study Configuration

• Spatial Scale: GRACE data with coarse resolution (~300–500 km) downscaled to local-scale and basin-level applications (e.g., groundwater monitoring, drought assessment). Case studies cover regional scales across the United States, India, China, Iran, and Sub-Saharan Africa.
• Temporal Scale: Review of techniques applied to GRACE and GRACE-FO data, which provide insights into terrestrial water storage changes over time, supporting monitoring of hydroclimatic extremes.

Methodology and Data

• Models used: Statistical methods, machine learning (ML) models (e.g., random forest), hybrid approaches, and physics-informed deep learning models (future direction). Land surface models are used for evaluation.
• Data sources: Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) satellite data for terrestrial water storage (TWS) changes. Evaluation data include in situ well observations, land surface models, and geodetic measurements (e.g., GPS, InSAR).

Main Results

• Downscaling techniques are categorized into statistical, machine learning, and hybrid methods, each with distinct methodological foundations and predictive capabilities.
• Downscaled GRACE data effectively identifies groundwater depletion hotspots, monitors hydroclimatic extremes, and supports transboundary water governance in various regions.
• Key challenges include data uncertainty, model transferability, lack of standardization, and high computational requirements.
• Future research directions emphasize physics-informed deep learning, real-time downscaling frameworks, and integration with upcoming satellite missions.

Contributions

• Provides a comprehensive synthesis of state-of-the-art downscaling techniques for GRACE and GRACE-FO data.
• Highlights the effectiveness of these techniques in addressing the coarse spatial resolution limitation of GRACE for local-scale hydrological applications.
• Identifies critical challenges and proposes key research directions to advance the field, particularly for water-scarce and data-limited regions.

Funding

• Not specified in the provided text.

Citation

@article{Achison2026Downscaling,
  author = {Achison, Rinu J. and Chithra, N. R.},
  title = {Downscaling of GRACE Data for Hydrological Applications—A Review},
  journal = {Lecture notes in civil engineering},
  year = {2026},
  doi = {10.1007/978-3-032-04178-4_57},
  url = {https://doi.org/10.1007/978-3-032-04178-4_57}
}