<p>This study evaluates the effectiveness of open-source hydrological modeling for assessing climate change impacts on water resources in the Middle Euphrates Watershed, Türkiye. Sentinel-2 satellite imagery and the European Center for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) data were integrated into the Soil and Water Assessment Tool (SWAT) to enhance model accuracy, especially in data-scarce regions. Bayesian Optimization was employed for SWAT calibration and validation. Model performance metrics showed robust reliability, with Nash-Sutcliffe Efficiency values of 0.74 (calibration) and 0.61 (validation), coefficients of determination (R<sup>2</sup>) of 0.78 (calibration) and 0.70 (validation), Percent Bias of 1.42 (calibration) and 9.40 (validation), and Kling-Gupta Efficiency of 0.65 (calibration) and 0.77 (validation). Future streamflow projections under Representative Concentration Pathways 4.5 and 8.5 scenarios indicated significant flow reductions, ranging from 41% to 73% across the years 2030, 2060, and 2090. These findings highlight the importance of advanced open-source datasets in hydrological modeling to support sustainable water resources management and climate adaptation.</p> GRAPHICAL ABSTRACT <p></p>

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Open-source hydrological modeling: Assessing climate change impacts using SWAT with Sentinel-2 and ERA5 data integration

  • M. Matin Saddiqi,
  • M. Ekrem Karpuzcu,
  • Recep Kaan Dereli

摘要

This study evaluates the effectiveness of open-source hydrological modeling for assessing climate change impacts on water resources in the Middle Euphrates Watershed, Türkiye. Sentinel-2 satellite imagery and the European Center for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) data were integrated into the Soil and Water Assessment Tool (SWAT) to enhance model accuracy, especially in data-scarce regions. Bayesian Optimization was employed for SWAT calibration and validation. Model performance metrics showed robust reliability, with Nash-Sutcliffe Efficiency values of 0.74 (calibration) and 0.61 (validation), coefficients of determination (R2) of 0.78 (calibration) and 0.70 (validation), Percent Bias of 1.42 (calibration) and 9.40 (validation), and Kling-Gupta Efficiency of 0.65 (calibration) and 0.77 (validation). Future streamflow projections under Representative Concentration Pathways 4.5 and 8.5 scenarios indicated significant flow reductions, ranging from 41% to 73% across the years 2030, 2060, and 2090. These findings highlight the importance of advanced open-source datasets in hydrological modeling to support sustainable water resources management and climate adaptation.

GRAPHICAL ABSTRACT