Assessing future hydrological and sediment transport response of an urban watershed using a machine learning–based land cover change model

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...

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Published in:	Environmental monitoring and assessment Vol. 197; no. 11; p. 1200
Main Authors:	Peker, İsmail Bilal, Cuceloglu, Gokhan, Sökmen, Eren Dağra, Gülbaz, Sezar
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 13.10.2025 Springer Nature B.V
Subjects:	Airport expansion Atmospheric Protection/Air Quality Control/Air Pollution Cities Dead storage Drinking water Earth and Environmental Science Ecology Ecotoxicology Environment Environmental Management Environmental Monitoring - methods Geographic information systems Geologic Sediments - analysis Hydrologic models Hydrologic processes Hydrology Land cover Land use Learning algorithms Machine Learning Markov analysis Markov chains Modelling Monitoring Monitoring/Environmental Analysis Multilayer perceptrons Population growth Precipitation Project management Rangelands Regression analysis Reservoir storage Sediment Sediment load Sediment transport Stream discharge Stream flow Surface runoff Sustainability management Turkey Urban sprawl Urban watersheds Urbanization Water Movements Water quality Water resources Water sources Watershed management Watersheds Turkey Istanbul Turkey Multilayer Perceptron–Markov chain Türkiye Field monitoring Urban watershed Land cover change Sediment transport
ISSN:	1573-2959, 0167-6369, 1573-2959
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Abstract	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.
AbstractList	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. 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.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.
ArticleNumber	1200
Author	Cuceloglu, Gokhan Sökmen, Eren Dağra Gülbaz, Sezar Peker, İsmail Bilal
Author_xml	– sequence: 1 givenname: İsmail Bilal orcidid: 0000-0001-9133-6797 surname: Peker fullname: Peker, İsmail Bilal email: pekerbilal@iuc.edu.tr organization: Civil Engineering Department, Istanbul University-Cerrahpaşa – sequence: 2 givenname: Gokhan orcidid: 0000-0002-9534-250X surname: Cuceloglu fullname: Cuceloglu, Gokhan organization: Institute of Earth and Marine Sciences, Gebze Technical University – sequence: 3 givenname: Eren Dağra orcidid: 0000-0002-1091-4207 surname: Sökmen fullname: Sökmen, Eren Dağra organization: Landscape Architecture Department, Istanbul University-Cerrahpaşa – sequence: 4 givenname: Sezar orcidid: 0000-0002-2274-6896 surname: Gülbaz fullname: Gülbaz, Sezar organization: Civil Engineering Department, Istanbul University-Cerrahpaşa
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Keywords	Multilayer Perceptron–Markov chain Türkiye Field monitoring Urban watershed Land cover change Sediment transport
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Snippet	Assessing the impacts of land cover change (LCC) on hydrology and sediment load is essential for the sustainable management of urban watersheds. Modeling LCC...
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SubjectTerms	Airport expansion Atmospheric Protection/Air Quality Control/Air Pollution Cities Dead storage Drinking water Earth and Environmental Science Ecology Ecotoxicology Environment Environmental Management Environmental Monitoring - methods Geographic information systems Geologic Sediments - analysis Hydrologic models Hydrologic processes Hydrology Land cover Land use Learning algorithms Machine Learning Markov analysis Markov chains Modelling Monitoring Monitoring/Environmental Analysis Multilayer perceptrons Population growth Precipitation Project management Rangelands Regression analysis Reservoir storage Sediment Sediment load Sediment transport Stream discharge Stream flow Surface runoff Sustainability management Turkey Urban sprawl Urban watersheds Urbanization Water Movements Water quality Water resources Water sources Watershed management Watersheds
Title	Assessing future hydrological and sediment transport response of an urban watershed using a machine learning–based land cover change model
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