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Hajian, Farhad, Yusefi, Elham, Monshizadeh Naeen, Hossein. (1403). Evaluating Hybrid Models and Google Earth Engine for Predicting Climate Change Impacts on Runoff in the Kasilian Catchment, Northern Iran. , (), -. doi: 10.22077/jdcr.2025.8683.1102
Farhad Hajian; Elham Yusefi; Hossein Monshizadeh Naeen. "Evaluating Hybrid Models and Google Earth Engine for Predicting Climate Change Impacts on Runoff in the Kasilian Catchment, Northern Iran". , , , 1403, -. doi: 10.22077/jdcr.2025.8683.1102
Hajian, Farhad, Yusefi, Elham, Monshizadeh Naeen, Hossein. (1403). 'Evaluating Hybrid Models and Google Earth Engine for Predicting Climate Change Impacts on Runoff in the Kasilian Catchment, Northern Iran', , (), pp. -. doi: 10.22077/jdcr.2025.8683.1102
Hajian, Farhad, Yusefi, Elham, Monshizadeh Naeen, Hossein. Evaluating Hybrid Models and Google Earth Engine for Predicting Climate Change Impacts on Runoff in the Kasilian Catchment, Northern Iran. , 1403; (): -. doi: 10.22077/jdcr.2025.8683.1102

Evaluating Hybrid Models and Google Earth Engine for Predicting Climate Change Impacts on Runoff in the Kasilian Catchment, Northern Iran

مجله پژوهش های خشکسالی و تغییراقلیم
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 بهمن 1403
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22077/jdcr.2025.8683.1102
نویسندگان
Farhad Hajian* 1؛ Elham Yusefi2؛ Hossein Monshizadeh Naeen3
1Department of Civil Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.
2Department of Environment, Faculty of Natural Resources and Environment, University of Birjand. Iran.
3Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.
چکیده
This study investigates the impact of climate change on annual rainfall and runoff of Kasilian catchment through two distinct approaches. Firstly, it utilizes hybrid models by integrating the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) with Artificial Neural Networks (ANN), Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP) separately, as well as employing the Long Ashton Research Station Weather Generator (LARS-WG). Secondly, it employs Google Earth Engine (GEE) to analyze changes in annual rainfall and runoff for the observed period, compensating for incomplete data from hydrometric and climatological stations. The results demonstrate that under the SSP585 scenario, from various climate models in LARS WG and when employing hybrid models, the median annual rainfall is projected to increase in the future compared to the base period, while the median annual runoff is expected to decrease due to rising temperatures and increased evapotranspiration. Consistent with these projections, GEE data from 1981 to 2023 also indicates an increase in annual rainfall and a decrease in annual runoff. Additionally, there is a reduction in annual erosion and sedimentation rates, attributed to the reduced capacity of runoff to transport sediment. These findings highlight the potential for more extreme rainfall events, increased annual precipitation, and a subsequent decrease in annual runoff and sediment yield in the Kasilian catchment, providing valuable insights for future water resource management and climate adaptation strategies in the region.
کلیدواژه‌ها
Remote sensing؛ hybrid models؛ Google Earth Engine (GEE)؛ Erosion؛ sediment yield
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تعداد مشاهده مقاله: 78


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