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<p>Accurate streamflow modelling in the Himalayan region remains challenging due to compounded uncertainties arising from complex topography, snow–glacier processes, and climate model variability. Previous studies have primarily employed univariate frameworks, thereby neglecting the interactions among multiple uncertainty sources. This study develops a comprehensive multivariate framework for streamflow projections, uncertainty quantification and flood risk assessment in the Tehri Dam catchment, Indian Himalayas. A semi-distributed hybrid conceptual hydrological model (GR4J coupled with a snow–glacier module) was constructed using IMD gridded precipitation and temperature data, achieving satisfactory performance with NSE values of 0.766 (calibration) and 0.719 (validation). Future streamflow (2021–2100) was projected using bias-corrected CMIP6 data under four Shared Socioeconomic Pathways (SSPs). Uncertainty analysis employing three-way Multivariate Analysis of Variance (MANOVA) revealed that climate models (GCM)–SSP interactions account for more than 50% of total streamflow variance, while three-way interactions (Hydrological model–GCM–SSP) contribute 25–30%. Principal Component Analysis (PCA) highlighted hydrological response and precipitation variability as dominant factors, explaining over 70% of the variance. Extreme flow estimation using the Gumbel distribution indicated substantial increases, with 1000-year flood discharges projected to reach 11,728&#xa0;m<sup>3</sup>/s under SSP585. These estimates were integrated into 2D HEC-RAS hydraulic simulations, which demonstrated expanding inundation and elevated water surfaces under high-emission scenarios. The proposed end-to-end framework advances current methodologies by explicitly incorporating interactive uncertainty effects, providing a robust decision-support tool for climate-resilient water resources management in mountainous regions.</p>

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A multivariate framework for uncertainty quantification in climate-driven streamflow and flood modelling for Tehri Dam catchment of the Indian Himalayas

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Bhanu Sharma,
  • Narendra Kumar Goel

摘要

Accurate streamflow modelling in the Himalayan region remains challenging due to compounded uncertainties arising from complex topography, snow–glacier processes, and climate model variability. Previous studies have primarily employed univariate frameworks, thereby neglecting the interactions among multiple uncertainty sources. This study develops a comprehensive multivariate framework for streamflow projections, uncertainty quantification and flood risk assessment in the Tehri Dam catchment, Indian Himalayas. A semi-distributed hybrid conceptual hydrological model (GR4J coupled with a snow–glacier module) was constructed using IMD gridded precipitation and temperature data, achieving satisfactory performance with NSE values of 0.766 (calibration) and 0.719 (validation). Future streamflow (2021–2100) was projected using bias-corrected CMIP6 data under four Shared Socioeconomic Pathways (SSPs). Uncertainty analysis employing three-way Multivariate Analysis of Variance (MANOVA) revealed that climate models (GCM)–SSP interactions account for more than 50% of total streamflow variance, while three-way interactions (Hydrological model–GCM–SSP) contribute 25–30%. Principal Component Analysis (PCA) highlighted hydrological response and precipitation variability as dominant factors, explaining over 70% of the variance. Extreme flow estimation using the Gumbel distribution indicated substantial increases, with 1000-year flood discharges projected to reach 11,728 m3/s under SSP585. These estimates were integrated into 2D HEC-RAS hydraulic simulations, which demonstrated expanding inundation and elevated water surfaces under high-emission scenarios. The proposed end-to-end framework advances current methodologies by explicitly incorporating interactive uncertainty effects, providing a robust decision-support tool for climate-resilient water resources management in mountainous regions.