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<p>Projecting the hydrological response of river basins to climate change is a fundamental challenge for sustainable water resource management. This study presents a comprehensive framework to assess future streamflow dynamics in the Babolrood River Basin, Iran. First, a rigorous evaluation of eleven CMIP6 global climate models identified the top performers for the region: MRI-ESM2-0 for precipitation, CMCC-ESM2 for minimum temperature, and MPI-ESM1-2-LR for maximum temperature. Their outputs were then downscaled using LARS-WG for 2031–2090 under three climate scenarios. The climate projections revealed a significant warming trend, with monthly minimum and maximum temperatures projected to rise by up to 5.16 and 4.12&#xa0;°C, respectively, alongside extreme fluctuations in precipitation. To translate these projections into hydrological impacts, five machine learning models including K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were developed. A key innovation was the systematic optimization of their hyperparameters using both the Flow Direction Algorithm (FDA) and Particle Swarm Optimization (PSO). Our comparative analysis identified RF-FDA as the most robust model, achieving outstanding performance in the test phase (<i>R</i> = 0.86, RMSE = 14.38, NSE = 0.79, KGE = 0.82). Projections from this optimal model revealed a fundamental shift in the watershed’s hydrology. Projected monthly discharge based on optimal model alterations ranges from a decrease of 24.2&#xa0;m³/s to an increase of 22.4&#xa0;m³/s relative to the baseline period. This integrated methodology demonstrates high reliability and serves as a powerful tool for developing adaptive strategies in river engineering and water management.</p>

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Enhancing Streamflow Projections Under Climate Change Using Optimized Machine Learning: A Comparative Study in the Babolrood River Basin, Iran

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Ali Akbar Eatesam,
  • Khosrow Hoseini,
  • Hojat Karami

摘要

Projecting the hydrological response of river basins to climate change is a fundamental challenge for sustainable water resource management. This study presents a comprehensive framework to assess future streamflow dynamics in the Babolrood River Basin, Iran. First, a rigorous evaluation of eleven CMIP6 global climate models identified the top performers for the region: MRI-ESM2-0 for precipitation, CMCC-ESM2 for minimum temperature, and MPI-ESM1-2-LR for maximum temperature. Their outputs were then downscaled using LARS-WG for 2031–2090 under three climate scenarios. The climate projections revealed a significant warming trend, with monthly minimum and maximum temperatures projected to rise by up to 5.16 and 4.12 °C, respectively, alongside extreme fluctuations in precipitation. To translate these projections into hydrological impacts, five machine learning models including K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were developed. A key innovation was the systematic optimization of their hyperparameters using both the Flow Direction Algorithm (FDA) and Particle Swarm Optimization (PSO). Our comparative analysis identified RF-FDA as the most robust model, achieving outstanding performance in the test phase (R = 0.86, RMSE = 14.38, NSE = 0.79, KGE = 0.82). Projections from this optimal model revealed a fundamental shift in the watershed’s hydrology. Projected monthly discharge based on optimal model alterations ranges from a decrease of 24.2 m³/s to an increase of 22.4 m³/s relative to the baseline period. This integrated methodology demonstrates high reliability and serves as a powerful tool for developing adaptive strategies in river engineering and water management.