Assessing future hydrological and sediment transport response of an urban watershed using a machine learning-based...

Assessing the impacts of land cover change (LCC) on hydrology and sediment load is essential for the sustainable management of urban watersheds. Modeling LCC using machine learning techniques enhances the ability to generate realistic future scenarios, providing a robust basis for informed watershed management decisions. This study projects future LCC and evaluates its effects on hydrological processes and sediment load in the Alibeyköy Watershed, one of the key water sources for Istanbul, Türkiye. Future LCC scenarios were generated using the Multilayer Perceptron-Markov Chain (MLP-MC) approach. The Soil and Water Assessment Tool (SWAT) was subsequently used to simulate the hydrological process and sediment transport in the watershed for the baseline year and projected future scenarios. The SWAT model was calibrated and validated for streamflow and sediment loads using continuous data. In addition, field-measured sediment load data collected were used for further validation. The main findings of this study are as follows: (1) built-up areas are projected to expand substantially over the coming decades, while natural land covers, including forests and rangelands, are expected to decline markedly; (2) this urban expansion is associated with increased surface runoff; and (3) a notable rise in dead storage volume. While the increase is notable, it represents only a small fraction of reservoir storage; however, its cumulative effect justifies continued monitoring and management attention. This study combines machine learning-based land cover change, hydrological modeling, and the use of field-based monitoring data to develop an integrated approach for watershed analysis. The resulting framework enables reliable prediction of hydrological components and sediment loads, supporting the design of effective and evidence-based watershed management strategies.