Prakash et al. (2026) Locally relevant streamflow reanalysis using ensemble data assimilation and a gauge-based adaptive localization scheme

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-08-19
• Authors: Ved Prakash, Manabendra Saharia
• DOI: 10.1016/j.jhydrol.2026.136277

Research Groups

Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India

Short Summary

This study presents a novel streamflow Data Assimilation (DA) framework that combines Ensemble Kalman Filter (EnKF) with gauge-based covariance localization and targeted covariance inflation to improve high-resolution streamflow estimates in the Indian subcontinent.

Objective

• Investigate the efficacy of ensemble-based DA techniques like EnKF for reducing forecast errors during extreme events such as floods.
• Develop a novel streamflow DA framework that incorporates gauge-based covariance localization and targeted covariance inflation.

Study Configuration

• Spatial Scale: The study covers 553,312 river reaches across the Indian subcontinent.
• Temporal Scale: Daily time scale.

Methodology and Data

• Models used: Noah-MP land surface model and MizuRoute vector-based streamflow routing model.
• Data sources: Gauge stations and satellite observations.

Main Results

• The assimilation framework significantly improves streamflow simulations, increasing the median Kling-Gupta Efficiency (KGE) by 0.22 and the median KGE skill score by 0.15 across both gauged and ungauged stations.
• The DA also enhances key hydrological signatures and exhibits strong performance during peak flow conditions.

Contributions

• This study offers a robust foundation for enhancing flood forecasting systems and informing sustainable water resource management under changing climate scenarios in the Indian subcontinent.
• The novel streamflow DA framework presented in this study can be applied to other regions with similar hydrological characteristics.

Funding

• This research was funded by [project/program name] (reference code: [insert reference code]).

Citation

@article{Prakash2026Locally,
  author = {Prakash, Ved and Saharia, Manabendra},
  title = {Locally relevant streamflow reanalysis using ensemble data assimilation and a gauge-based adaptive localization scheme},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136277},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136277}
}