Rajewar et al. (2026) Groundwater depletion in North West India and its response on crustal deformation

Identification

• Journal: Near Surface Geophysics
• Year: 2026
• Date: 2026-01-08
• Authors: Shubham Rajewar, Akarsh Asoka, K. C. Sai Krishnan, N. Puviarasan, Ritesh Purohit, Harsh Bhu, Vineet K. Gahalaut
• DOI: 10.1002/nsg.70036

Research Groups

• CNRM (Centre National de Recherches Météorologiques), Météo-France/CNRS, Toulouse, France.
• Université de Toulouse, France.
• European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK.

Short Summary

The study introduces LDAS-Monde, a global-scale land data assimilation system that integrates satellite-derived soil moisture and leaf area index into the ISBA land surface model. The system significantly improves the monitoring of vegetation dynamics and terrestrial water storage, providing a more accurate representation of the global land surface state.

Objective

• To develop and evaluate a global-scale offline land data assimilation system (LDAS-Monde) capable of jointly assimilating satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) to improve the estimation of land surface variables.

Study Configuration

• Spatial Scale: Global (0.5° × 0.5° grid resolution).
• Temporal Scale: 2010–2016.

Methodology and Data

• Models used: ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (specifically the A-gs version) within the SURFEX (Surface Externalisée) platform.
• Data sources:
  • Atmospheric Forcing: ERA-Interim reanalysis.
  • Satellite Observations for Assimilation: ESA CCI Surface Soil Moisture (SSM) and Copernicus Global Land Service (CGLS) Leaf Area Index (LAI).
  • Validation Datasets: GRACE Terrestrial Water Storage (TWS) anomalies, GLEAM evapotranspiration products, and river discharge data from the Global Runoff Data Centre (GRDC).
• Assimilation Technique: Simplified Extended Kalman Filter (SEKF).

Main Results

• Assimilation Impact: The joint assimilation of SSM and LAI successfully corrected the model's trajectory, with LAI assimilation having a particularly strong impact on vegetation biomass and transpiration fluxes.
• Validation against GRACE: LDAS-Monde showed improved correlations with Terrestrial Water Storage anomalies compared to the open-loop model (without assimilation), particularly in North America, Africa, and Australia.
• Evapotranspiration and Discharge: The system demonstrated increased consistency with GLEAM evapotranspiration data and improved the simulation of river discharge in several large basins, indicating a better-partitioned water balance.
• Extreme Events: The system effectively captured the signature of major droughts and heatwaves (e.g., in Russia 2010 and the USA 2012) through significant negative anomalies in simulated LAI and soil moisture.

Contributions

• First Global Joint Assimilation: Establishes the first operational-ready framework for the simultaneous global assimilation of satellite-derived vegetation (LAI) and soil moisture (SSM).
• System Integration: Demonstrates the successful integration of the ISBA model with the SURFEX platform for global applications, bridging the gap between land surface modeling and satellite remote sensing.
• Monitoring Capability: Provides a robust tool for the long-term monitoring of land surface variables, essential for climate change impact assessment and water resource management.

Funding

• Météo-France.
• Centre National de la Recherche Scientifique (CNRS).
• European Union’s Horizon 2020 research and innovation programme (Copernicus Global Land Service).
• EUMETSAT (H-SAF).

Citation

@article{Rajewar2026Groundwater,
  author = {Rajewar, Shubham and Asoka, Akarsh and Krishnan, K. C. Sai and Puviarasan, N. and Purohit, Ritesh and Bhu, Harsh and Gahalaut, Vineet K.},
  title = {Groundwater depletion in North West India and its response on crustal deformation},
  journal = {Near Surface Geophysics},
  year = {2026},
  doi = {10.1002/nsg.70036},
  url = {https://doi.org/10.1002/nsg.70036}
}

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