Chakraborty et al. (2026) Trends and drivers of ecosystem water use efficiency and carbon uptake modeled across South Asia

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-04-02
• Authors: Arijit Chakraborty, Manabendra Saharia, Sumedha Chakma, Sujay V. Kumar
• DOI: 10.1016/j.agwat.2026.110321

Research Groups

• Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
• Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
• Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States

Short Summary

This study quantifies the spatiotemporal variability, long-term trends, and climatic drivers of gross primary productivity (GPP), evapotranspiration (ET), and ecosystem water-use efficiency (WUE) across South Asia from 1985–2023 using the Indian Land Data Assimilation System (ILDAS) with a dynamic vegetation scheme, revealing significant increases in GPP and WUE attributed to vegetation greening.

Objective

• To observe the spatiotemporal variation of model estimated GPP, ET, and resulting WUE across different seasons and land covers within South Asia.
• To analyze long-term trends in GPP and WUE over the study period (1985–2023).
• To assess the impact of different forcing variables on model estimated WUE in terms of sensitivity analysis.

Study Configuration

• Spatial Scale: South Asian domain, covering Indian national and transboundary river basins (64.5°E – 100.0°E and 5.0°N – 38.0°N).
• Temporal Scale: 40-year period from 1985 to 2023.

Methodology and Data

• Models used:
  • Indian Land Data Assimilation System (ILDAS)
  • Noah-Multiparameterization (Noah-MP) Land Surface Model, Version 4.0.1
  • Dynamic vegetation scheme (Dickinson et al., 1998a, 1998b)
  • Ball-Berry photosynthesis-based stomatal resistance scheme (Ball et al., 1987)
  • Farquhar Model (Farquhar et al., 1980) for photosynthesis rates
• Data sources:
  • Meteorological Forcings:
    • India Meteorological Department (IMD) daily precipitation data (0.25° spatial resolution, 1981–2022).
    • Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) (hourly, 0.625° x 0.5° spatial resolution, from 1980) for near-surface air temperature, specific humidity, shortwave radiation, longwave radiation, windspeed, and surface pressure.
  • Geophysical Parameters (LSM inputs): MODIS IGBP landcover, FAO soil texture, MERIT Digital Elevation Model (elevation, slope, aspect), NCEP albedo and greenness, Barlage maximum snow albedo, ISLSCP-1 bottom temperature.
  • Evaluation Datasets:
    • MODIS Gross Primary Productivity (MOD17A2H, Collection 6.1; 8-day cumulative, 1 km spatial resolution).
    • FluxSat Global Gross Primary Production (machine learning-derived from MODIS reflectance and eddy covariance).
    • MODIS Evapotranspiration (MOD16A2 Version 6.1; 8-day composite, 500 m spatial resolution).
    • Global Land Evaporation Amsterdam Model (GLEAM version 3.0a; 0.25° daily).
    • MODIS-derived WUE (ratio of MODIS GPP and MODIS ET) for cross-validation.

Main Results

• Spatial Variability: High GPP (>2500 g C/m²/year) and WUE (>2 g C/kg H₂O/year) are consistently observed over the lower Himalayan and northeastern regions, characterized by dense forest cover. Low values are found over the arid northwestern region.
• Seasonal Variability: GPP (spatial standard deviation, σ = 95.44 g C/m²) and ET (σ = 39.60 mm) exhibit peak spatial variability during the monsoon season. WUE shows the highest variability in the pre-monsoon season (σ = 12.48 g C/m²/mm), indicating vegetation adaptation under water-limited conditions. Forest lands demonstrate the highest GPP and ET, while shrublands show high WUE despite lower GPP and ET.
• Sensitivity Analysis: Non-parametric elasticity analysis identifies temperature (ε = –21.78) and pressure (ε = 76.30) as the dominant climatic drivers of WUE. Soil moisture and leaf area index (LAI) are the primary internal drivers. WUE shows positive sensitivity to soil moisture and ET at lower WUE ranges (0–1 g C/m²/mm), suggesting a moisture-threshold behavior. Conversely, at higher WUE ranges (2.5–3 g C/m²/mm), WUE is negatively sensitive to soil moisture, ET, and LAI.
• Long-term Trends (1985–2023): Significant increasing trends (p < 0.05) are observed for both GPP and WUE across large parts of South Asia, particularly in northern and central agro-ecological zones, consistent with regional vegetation greening. Decreasing trends are noted over southern and northwestern India.
• Model Evaluation: Modeled GPP shows stronger agreement with FluxSat (median R = 0.70; KGE = 0.43) than with MODIS (median R = 0.68; KGE = 0.32). Modeled ET exhibits robust performance against MODIS (median R = 0.81; KGE = 0.44) and good consistency with GLEAM (median R = 0.78; KGE = 0.35). Cross-validation of modeled WUE with MODIS-derived WUE indicates moderate agreement (median R = 0.36) but low KGE (0.21), reflecting inherent uncertainties in ratio-based diagnostics and model limitations.

Contributions

• First integration of a dynamic vegetation scheme into the Indian Land Data Assimilation System (ILDAS) to simulate carbon cycle and evaluate ecosystem water-use efficiency (WUE) across South Asia, moving beyond static vegetation parameterizations.
• Provides a comprehensive spatiotemporal analysis of GPP, ET, and WUE, including long-term trends and sensitivity to climatic and internal drivers, for South Asia over a 40-year period (1985-2023).
• Highlights the regime-dependent sensitivity of WUE to soil moisture and LAI, demonstrating a moisture-threshold behavior not extensively explored in the region.
• Utilizes a hybrid meteorological forcing (local IMD precipitation with transboundary consistent MERRA2) for improved regional accuracy.
• Offers a scalable framework for regional land-atmosphere interaction studies and provides insights for water and carbon management strategies in climate-vulnerable regions of South Asia.

Funding

• Ministry of Earth Sciences/IITM Pune Monsoon Mission III (RP04574)
• Ministry of Earth Sciences (RP04741)
• DST IC-IMPACTS (RP04558)

Citation

@article{Chakraborty2026Trends,
  author = {Chakraborty, Arijit and Saharia, Manabendra and Chakma, Sumedha and Kumar, Sujay V.},
  title = {Trends and drivers of ecosystem water use efficiency and carbon uptake modeled across South Asia},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110321},
  url = {https://doi.org/10.1016/j.agwat.2026.110321}
}