Mendoza et al. (2026) Assessing Hydrological Model Representation of Irrigation and Subsurface Flow in a Heavily Anthropogenic‐Influenced Catchment

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Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-10-01
• Authors: Raul Mendoza, Willem J. van Verseveld, Frederiek Sperna Weiland, Anjali Bhagwat, Chris Seijger, Albrecht Weerts
• DOI: 10.1029/2026wr043528

Research Groups

[Information not available in the provided abstract.]

Short Summary

This study assesses the impact of different model representations of irrigation and subsurface flow dynamics on water balance partitioning in the anthropogenically influenced Hindon River Basin. It reveals significant spatio-temporal variability in simulated river discharge and evapotranspiration, highlighting the importance of comprehensive model assessment and uncertainty reduction.

Objective

• To assess the impact of different model representations of irrigation and subsurface flow dynamics on water availability and water balance partitioning in a distributed hydrological model applied to a catchment with widespread anthropogenic influences.

Study Configuration

• Spatial Scale: Hindon River Basin, part of the larger Ganges River Basin in India.
• Temporal Scale: Across monsoon and non-monsoon seasons (implying multi-seasonal or annual analysis).

Methodology and Data

• Models used: Distributed hydrological model wflow_sbm, three irrigation calendars, two subsurface flow model concepts.
• Data sources: Remote sensing data sets (for irrigation calendars), observed river discharge, observed groundwater level changes (for model evaluation).

Main Results

• Irrigation calendars dynamically influence water balance across seasons, causing differences of 9–43 mm/year in river discharge and 32–147 mm/year in evapotranspiration.
• The subsurface flow model concept consistently impacts the magnitude of simulated fluxes across seasons, with differences of 81–90 mm/year in river discharge and 30–37 mm/year in evapotranspiration. This impact varies spatially, being strongest in areas with limited groundwater storage and near rivers.
• Model evaluation showed varying performances: Kling-Gupta Efficiency (KGE) of 0.62–0.7 for observed river discharge and Mean Absolute Error (MAE) of 1.6–2.0 m for groundwater level changes.

Contributions

• Provides a comprehensive inter-comparison of water balance components under different irrigation timing and subsurface flow process representations using remote sensing data.
• Highlights the necessity of reducing uncertainties in model estimates when utilizing remote sensing data to constrain irrigation timing and increasing the complexity of groundwater process model representation.

Funding

[Information not available in the provided abstract.]

Citation

@article{Mendoza2026Assessing,
  author = {Mendoza, Raul and Verseveld, Willem J. van and Weiland, Frederiek Sperna and Bhagwat, Anjali and Seijger, Chris and Weerts, Albrecht},
  title = {Assessing Hydrological Model Representation of Irrigation and Subsurface Flow in a Heavily Anthropogenic‐Influenced Catchment},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2026wr043528},
  url = {https://doi.org/10.1029/2026wr043528}
}