Sharma et al. (2026) Small and Medium‐Sized Inland Waterbodies: Water Volume Predictions and Flood Implications

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

Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-01-01
• Authors: Anjali Sharma, Idhayachandhiran Ilampooranan
• DOI: 10.1029/2024wr038283

Research Groups

Not explicitly mentioned in the abstract. The study focuses on the Adyar-Chennai basin, India.

Short Summary

This study developed a machine learning model to estimate monthly water volume changes in 914 small and medium waterbodies in the Adyar-Chennai basin, India, from 1988 to 2023, revealing a significant volume reduction and proposing data-driven flood mitigation strategies.

Objective

• To develop a machine learning model for accurate, temporally continuous estimation of water volume changes in small and medium inland waterbodies and assess their flood mitigation potential.

Study Configuration

• Spatial Scale: 914 small (<10 ha) and medium (10–100 ha) inland waterbodies in the Adyar-Chennai basin, India.
• Temporal Scale: Monthly changes from January 1988 to December 2023, with future volume projections.

Methodology and Data

• Models used: Machine learning model for volume estimation, and a hydrology and land-use-based novel tankshed overflow index for identifying suitable sites for new waterbodies.
• Data sources: 86 in situ bathymetry measurements and spatio-temporal water spread area data.

Main Results

• The developed machine learning model demonstrated superior performance for volume estimation (R² = 0.94), significantly outperforming global (R² = 0.57) and regional (R² = 0.24) models.
• The total water volume of small and medium waterbodies in the Adyar-Chennai basin nearly halved from approximately 102.28 million cubic meters (95% CI: 93.38–114.28) in January 1988 to approximately 40.13 million cubic meters (32.25–61.42) in December 2023.
• Future volumes for these waterbodies were projected with an R² of 0.62.
• Analysis of flood mitigation potential showed that the peak flood rate in the basin would increase by 50% in the absence of these waterbodies.
• To completely mitigate floods in the basin, the study proposes creating 90 new waterbodies and deepening existing waterbodies by 1 meter.
• Suitable sites for creating new waterbodies were identified using hydrology and a land-use-based novel tankshed overflow index.

Contributions

• Development of a highly accurate machine learning model for estimating non-linear water volume changes in small and medium inland waterbodies, addressing a critical data gap.
• First-ever future volume projections for small and medium inland waterbodies, providing foresight for water resource management.
• Quantification of the crucial role of small and medium waterbodies in flood control, demonstrating a 50% increase in peak flood rate in their absence.
• Proposal of specific, actionable flood mitigation strategies, including the creation of new waterbodies and deepening existing ones, supported by site identification using a novel index.

Funding

Not mentioned in the abstract.

Citation

@article{Sharma2026Small,
  author = {Sharma, Anjali and Ilampooranan, Idhayachandhiran},
  title = {Small and Medium‐Sized Inland Waterbodies: Water Volume Predictions and Flood Implications},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2024wr038283},
  url = {https://doi.org/10.1029/2024wr038283}
}