Assessment of Hydroclimatic Change Impacts on Water Resources Through Hydrological Indicators and Machine Learning

This study investigates the hydroclimatic impacts of climate change on the Göynük Stream Basin, a snow-fed tributary within the Euphrates River Basin, utilizing flow, precipitation, and temperature data from 1975 to 2022. The Göynük Stream Basin is characterized by high-altitude, harsh continental conditions, with its flow regime heavily influenced by snowmelt, rendering it particularly sensitive to climate change. Employing a suite of trend analysis methods, including Mann–Kendall, Spearman Rho, Theil–Sen, Şen-Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA), the research evaluated annual and seasonal data from one stream and four meteorological stations across multiple significance levels (90%, 95%, 99%). Unlike conventional hydroclimatic studies based solely on monotonic trend detection, this study integrates classical trend tests, innovative trend approaches, temporal regime-based analysis (RAPS), and machine learning techniques within a unified assessment framework to evaluate both hydroclimatic variability and runoff predictability under climate change conditions. Key findings indicate a significant decline in annual flow rates by approximately 9.37%, with a notable decrease in maximum flow rates evidenced by a negative trend slope of −0.2726 m3/s/year. While precipitation trends were generally decreasing, temperature data exhibited significant increases, especially during winter and spring. Seasonal analysis revealed substantial flow reductions in summer and autumn, coupled with an earlier timing of the annual maximum flow, shifting from mid-May to late March/early April, suggesting earlier snowmelt. The study concludes that the Göynük Stream Basin is experiencing increasing hydroclimatic pressures attributable to climate change. These insights are crucial for water resource management and serve as a guideline for similar snow-fed sub-basins within the broader Euphrates River Basin. Furthermore, the integration of a machine learning approach, utilizing meteorological and seasonal data, demonstrated strong monthly runoff prediction capabilities with NRMSE of 4.11% and R2 equal to 0.951. Feature importance analysis highlighted seasonality and temperature as primary predictive factors. However, a marked decline in model accuracy after 2011 was observed, indicating a non-stationarity in the hydroclimatic system, likely driven by climate change impacts and underscoring the need for adaptive management strategies.