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<p>Climate change (CC) significantly affects streamflow decline, especially in semi-arid climates. The Mediterranean region is expected to face severe effects, threatening future water resources. This research aims to create a framework for assessing future streamflow sensitivity in data-sparse regions due to CC. It analyzes historical trends and predicts future conditions based on temperature increases of 1.5° and 3.0&#xa0;°C. The historical trends of decreasing streamflow were confirmed by the Mann-Kendall statistic and Theil-Sen median trend estimator. The predictive future conditions framework employs the Climate Generator (CLIGEN) to generate 30 years of daily climate data for the TOPography-based hydrological model (TOPMODEL), which was assessed for uncertainty using generalized likelihood uncertainty estimation (GLUE) method. With a Nash-Sutcliffe efficiency (NSE) of 0.73 and uncertainty bounds ranging from − 52.7% to 35.4%, the best-fit for the calibration underestimated the observed streamflow by 16%. With an NSE of 0.61 and bounds ranging from − 46.5% to 18.4%, it overestimated by 12% during validation. The inferred future streamflow indicated a decline of 32% and 50% with temperature increases of 1.5° and 3.0&#xa0;°C, respectively. The summer was the most notable, which projected streamflow shifts with increasing trends that could be triggered by changes in the timing of spatiotemporal precipitation patterns and increased evapotranspiration. The findings also indicated that precipitation and temperature have different effects, with temperature having a greater effect on streamflow decrease.</p>

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Predicted Streamflow Sensitivity to Climate Change Using TOPMODEL with CLIGEN Weather Generator in a Data-Sparse Medium-Sized Mediterranean Watershed

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Pece V. Gorsevski

摘要

Climate change (CC) significantly affects streamflow decline, especially in semi-arid climates. The Mediterranean region is expected to face severe effects, threatening future water resources. This research aims to create a framework for assessing future streamflow sensitivity in data-sparse regions due to CC. It analyzes historical trends and predicts future conditions based on temperature increases of 1.5° and 3.0 °C. The historical trends of decreasing streamflow were confirmed by the Mann-Kendall statistic and Theil-Sen median trend estimator. The predictive future conditions framework employs the Climate Generator (CLIGEN) to generate 30 years of daily climate data for the TOPography-based hydrological model (TOPMODEL), which was assessed for uncertainty using generalized likelihood uncertainty estimation (GLUE) method. With a Nash-Sutcliffe efficiency (NSE) of 0.73 and uncertainty bounds ranging from − 52.7% to 35.4%, the best-fit for the calibration underestimated the observed streamflow by 16%. With an NSE of 0.61 and bounds ranging from − 46.5% to 18.4%, it overestimated by 12% during validation. The inferred future streamflow indicated a decline of 32% and 50% with temperature increases of 1.5° and 3.0 °C, respectively. The summer was the most notable, which projected streamflow shifts with increasing trends that could be triggered by changes in the timing of spatiotemporal precipitation patterns and increased evapotranspiration. The findings also indicated that precipitation and temperature have different effects, with temperature having a greater effect on streamflow decrease.