Goel et al. (2026) An operational inflow forecasting system for Tehri Dam: system design, deployment and real-world performance

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

Identification

• Journal: Journal of Water and Climate Change
• Year: 2026
• Date: 2026-09-22
• Authors: Narendra Kumar Goel, Niraj Kumar Agrawal, Dhyan Singh Arya, Bhanu Sharma, Rohit Kumar Varshney, Mayank Singh Bisht, Manohar Arora, Mukat Lal Sharma, Atul Kumar Singh, Anuj Garg, Mukesh Kumar Agrawal, Amit Rawat, Richard M. Vogel
• DOI: 10.2166/wcc.2026.082

Research Groups

• Indian Institute of Technology Roorkee (IITR)
• National Centre for Medium Range Weather Forecasting (NCMRWF)

Short Summary

This study presents an indigenous, real-time inflow forecasting system for the Tehri Dam in India, achieving high predictive accuracy with a Nash-Sutcliffe Efficiency (NSE) range of 0.78 to 0.97 across different seasons.

Objective

• Develop and evaluate a hybrid modelling framework for real-time inflow forecasting at the Tehri Dam, integrating hydro-meteorological data and stochastic models.

Study Configuration

• Spatial Scale: Catchment-scale inflow forecasting for the Tehri Dam in the Himalayan region of India.
• Temporal Scale: Real-time forecasts with daily updates from 2018 to 2026.

Methodology and Data

• Models used:
  • Geomorphological Instantaneous Unit Hydrograph (GIUH)-based Nash model for ungauged tributaries.
  • Stochastic Auto-Regressive (AR) and Auto-Regressive with Exogenous inputs (ARX) models for gauged sub-catchments.
• Data sources:
  • Real-time hydro-meteorological network data.
  • Satellite and observation-based datasets.

Main Results

• High predictive accuracy achieved, with NSE values ranging from 0.78 to 0.97 across different seasons.
• 84.53% of forecasts had a relative error within ±20%, and 89.58% maintained an absolute inflow difference within 60 m3/s.

Contributions

• The study presents a parsimonious and cost-effective alternative to complex platforms like the Hydrologic Engineering Centre (HEC) or MIKE software suites.
• The integrated stochastic-geomorphological approach is shown to be a robust tool for real-time disaster management and sustainable hydropower operations in Himalayan basins.

Funding

• This research was funded by the Ministry of Power, Government of India (Project Code: MoP/11/2017).
• Additional support provided by the Indian Institute of Technology Roorkee (IITR) Research Fund.

Citation

@article{Goel2026operational,
  author = {Goel, Narendra  Kumar and Agrawal, Niraj Kumar and Arya, Dhyan Singh and Sharma, Bhanu and Varshney, Rohit Kumar and Bisht, Mayank Singh and Arora, Manohar and Sharma, Mukat Lal and Singh, Atul Kumar and Garg, Anuj and Agrawal, Mukesh Kumar and Rawat, Amit and Vogel, Richard M.},
  title = {An operational inflow forecasting system for Tehri Dam: system design, deployment and real-world performance},
  journal = {Journal of Water and Climate Change},
  year = {2026},
  doi = {10.2166/wcc.2026.082},
  url = {https://doi.org/10.2166/wcc.2026.082}
}