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<p>The escalating impacts of climate change demand accurate, data-driven hydrological modelling to ensure sustainable water resource management. Remote sensing plays a crucial role in this domain by providing high-resolution climate and hydrological datasets, enabling precise assessment of changing precipitation and temperature patterns. This study explores the selection and application of Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) for hydrological modelling in the Seonath River Basin, Chhattisgarh, India. Given the inherent uncertainties in climate projections, the research emphasizes the integration of remote sensing datasets and climate model improvisation to enhance regional accuracy and improve prediction reliability. By evaluating multiple CMIP6 models and coupling them with hydrological simulations, this study addresses the basin’s unique climatic challenges. The methodology incorporates Taylor’s Diagram and Compromise Programming using four performance metrics based on seasonal precipitation and temperature, applied across 13 CMIP6 GCMs. The MPI-ESM1-2-HR (CP = 0.113) and EC-Earth3-Veg (CP = 0.225) models demonstrate strong performance due to their balanced accuracy across multiple metrics. In contrast, CanESM5 (CP = 3.016) exhibits persistent biases, underscoring its limitations in capturing regional climate dynamics. By integrating satellite-derived precipitation products, digital elevation models (DEMs), and land-use data, this research enhances the hydrological modelling framework, ensuring a more robust representation of streamflow dynamics. The model projects an increasing trend in annual streamflow of 39.84 m<sup>3</sup>/s/year, indicating a steady rise in water availability. Furthermore, validation against observed streamflow data confirms alignment with low- and high-flow years during the wet season, with biases ranging from 0.5 to 5.7%. These findings highlight the critical role of remote sensing, advanced climate model evaluation, and dynamic model improvisation in enhancing the accuracy of hydrological projections. By refining climate forecasts and reducing uncertainties, this study contributes to strengthening the resilience of hydrological systems in climate-sensitive river basins.</p>

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Improvisation in Climate Model Selection and Effective Hydrological Modelling in Face of Climate Change: A Case Study of Seonath River Basin, India

  • 期刊论文

  • 发表日期：

    2025年10月22日

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  • P. Kishore Kumar Reddy,
  • M. K. Harikeerthan,
  • Shivaraj G. Nayak,
  • Saurabh S. Joshi,
  • Nruthya Kishore,
  • Ramgopal T. Sahu

摘要

The escalating impacts of climate change demand accurate, data-driven hydrological modelling to ensure sustainable water resource management. Remote sensing plays a crucial role in this domain by providing high-resolution climate and hydrological datasets, enabling precise assessment of changing precipitation and temperature patterns. This study explores the selection and application of Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) for hydrological modelling in the Seonath River Basin, Chhattisgarh, India. Given the inherent uncertainties in climate projections, the research emphasizes the integration of remote sensing datasets and climate model improvisation to enhance regional accuracy and improve prediction reliability. By evaluating multiple CMIP6 models and coupling them with hydrological simulations, this study addresses the basin’s unique climatic challenges. The methodology incorporates Taylor’s Diagram and Compromise Programming using four performance metrics based on seasonal precipitation and temperature, applied across 13 CMIP6 GCMs. The MPI-ESM1-2-HR (CP = 0.113) and EC-Earth3-Veg (CP = 0.225) models demonstrate strong performance due to their balanced accuracy across multiple metrics. In contrast, CanESM5 (CP = 3.016) exhibits persistent biases, underscoring its limitations in capturing regional climate dynamics. By integrating satellite-derived precipitation products, digital elevation models (DEMs), and land-use data, this research enhances the hydrological modelling framework, ensuring a more robust representation of streamflow dynamics. The model projects an increasing trend in annual streamflow of 39.84 m3/s/year, indicating a steady rise in water availability. Furthermore, validation against observed streamflow data confirms alignment with low- and high-flow years during the wet season, with biases ranging from 0.5 to 5.7%. These findings highlight the critical role of remote sensing, advanced climate model evaluation, and dynamic model improvisation in enhancing the accuracy of hydrological projections. By refining climate forecasts and reducing uncertainties, this study contributes to strengthening the resilience of hydrological systems in climate-sensitive river basins.