Harnessing artificial ıntelligence for streamflow predictions under climate change scenarios in arid region

Understanding the streamflow fluctuation of the Minab River basin in the south of Iran is of great importance due to the presence of the Esteghlal Reservoir Dam at its outlet and agricultural activities that produce food for the residents and support neighboring cities. Water resources of this basin have been affected by climate change and it is expected to observe more effects in the future. Therefore, this study was conducted to evaluate the impact of climate change on river flow levels in the Minab River basin, comparing the future period (2020–2050) with the baseline period (1985–2014). Using the CanESM5 model, future climate scenarios SSP1-2.6, SSP3-7.0, and SSP5-8.5 were simulated to project changes in precipitation and maximum, and minimum temperatures. For streamflow simulation, five machine learning models including Generalized Linear Regression (GLR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Random Forest (RF) were employed, with KNN showing superior performance. The findings indicate max and min temperatures could rise by 4.9℃ and 5.2℃ respectively under the SSP3-7.0 scenario until 2050. Mean annual streamflow will experience an increase of 33% and the highest values will probably be observed in fall, which can be the trigger for more floods and decreasing streamflow during warmer seasons can cause many challenges in the availability of water resources. These insights are crucial for water resource management by local authorities and organizations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.