آمار

تعداد نشریات21
تعداد شماره‌ها353
تعداد مقالات3,936
تعداد مشاهده مقاله5,389,313
تعداد دریافت فایل اصل مقاله3,608,899
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. , 2(4), 141-160. 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". , 2, 4, 1403, 141-160. 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', , 2(4), pp. 141-160. 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; 2(4): 141-160. 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
دوره 2، شماره 4 - شماره پیاپی 8، اسفند 1403، صفحه 141-160    اصل مقاله (1.02 M)
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
مراجع

Abbaspour K.C., Faramarzi M., Ghasemi S.S., & Yang H. (2009). Assessing the impact of climate change on water resources in Iran, Water Resources, 45, W10434. Doi:10.1029/2008WR007615.
Asadi, H., & Santos, C.A.G. (2022). A hybrid artificial intelligence and semi-distributed model for runoff prediction, Water Supply, 22(7), 6181-6195. doi: https://doi.org/10.2166/ws.2022.123

Babaeian I., Najafinik Z., Zabolabasi F., Habibinokhandan M., Adab H., & Malbosi S. (2007). Climate change investigation of Iran for 2010-2039 using downscaled output of ECHO-G climate model, Geography and Development Iranian Journal, 16, 135-152. [in Persian]. https://doi.org/10.22111/gdij.2009.1179.
Bae D.H., Jung W., & Chang H. (2008). Potential changes in Korean water resources estimated by high-resolution climate resolution, Climate Research, 35, 213-226. https://doi. org/10.3354/cr00704.
Bae D.H., Jung W., & Lettenmaier D.P. (2011). Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju Catchment, Korea, Journal of Hydrology, 401, 90–105. https://doi. org/10.1016/j.jhydrol.2011.02.012

Benavidez, R., Jackson, B., Maxwell, D., & Norton, K. (2018). A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates. Hydrology and Earth System Sciences, 22(11), 6059-6086. doi:10.5194/hess-22-6059-2018.
Cho, J., Komatsu, H., Pokhrel, Y. et al.(2011). The effects of annual precipitation and mean air temperature on annual runoff in global forest regions. Climatic Change, 108, 401–410. https://doi.org/10.1007/s10584-011-0197-3.

Fahimi, F., Yaseen, Z. M., & El-shafie, A. (2017). Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theoretical and applied climatology, 128, 875-903. doi: https://doi.org/10.1007/s00704-016-1735-8
Farfan, J.F., Palacios, K., Ulloa, J., & Avilés, A. (2020). A hybrid neural network-based technique to improve the flow forecasting of physical and data-driven models: Methodology and case studies in Andean watersheds. Journal of Hydrology: Regional Studies, 27, 100652. https://doi.org/10.1016/j.ejrh.2020.100652.
Gebremichael, M., & Hailu, A. (2024). Comparative analysis of HEC-HMS and machine learning models for rainfall-runoff prediction, Hydrology Research, 55(3), pp. 456-470. doi: https://doi.org/10.2166/nh.2024.032

Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feed forward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings.
Hajian, F. (2013). Effects of land cover and climate changes on runoff and sediment yield from a forested catchment in northern Iran. PhD thesis. Kingston University.

Hitokoto, M., & Sakuraba, M. (2020). Hybrid deep neural network and distributed rainfall-runoff model for real-time river-stage prediction, Journal of Japan Society of Civil Engineers (JSCE), 8(1), 46–58. https://doi.org/10.2208/journalofjsce.8.1_46
Horanyi, A. (2023). CMIP6: Global climate projections. European Centre for MediumRange Weather Forecasts (ECMWF). https:// confluence.ecmwf.int/display/COPSRV/CMIP6%3A+Global+climate+projections.
IPCC. (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. doi:10.59327/IPCC/AR6-9789291691647

Islam, Z. (2022). Soil loss assessment by RUSLE in the cloud-based platform (GEE) in Nigeria. Modeling Earth Systems and Environment, 8, 4579-4591. doi:10.1007/s40808-022-01467-7.
Kavoosi, M., & Khozeymehnezhad, H. (2021). Review and compare performance of 4 modeling methods LS-SVM, NN, GEP, and ANFIS-PSO in simulation of rainfall-runoff (Study Area: Halil River - Jiroft Dam). Iranian Journal of Ecohydrology, 8(2), 123-135. doi:10.22125/iwe.2021.128115.

Kisi, O., Shiri, J., & Tombul, M. (2013). Modeling rainfall-runoff process using soft computing techniques, Computers & Geosciences, 51, 108–117. doi: https://doi. org/10.1016/j.cageo.2012.07.001
Kumar, A., Kumar, P., & Singh, V. K. (2019). Evaluating different machine learning models for runoff and suspended sediment simulation. Water Resources Management, 33(4), 1217-1231. doi:10.1007/s11269-018-2178-z.
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS journal of photogrammetry and remote sensing, 66(3), 247-259. doi: https:// doi.org/10.1016/j.isprsjprs.2010.11.001

Pashazadeh, A., & Javan, M. (2019). Comparison of the gene expression programming, artificial neural network (ANN), and equivalent Muskingum inflow models in the flood routing of multiple branched rivers, Theoretical and Applied Climatology, doi: https://doi.org/10.1007/s00704-019-03032-2.
Ripple, W. J., Wolf, C., Gregg, J. W., Rockström, J., Mann, M. E., Oreskes, N., Lenton, T. M., Rahmstorf, S., Newsome, T. M., Xu, C., Svenning, J.-C., Pereira, C. C., Law, B. E., & Crowther, T. W. (2024). The 2024 state of the climate report: Perilous times on planet Earth. BioScience, 74(10), 1-13. doi:10.1093/biosci/biae087.

Sakhraoui, F., & Hasbaia, M. (2023). Evaluation of the sensitivity of the RUSLE erosion model to rainfall erosivity: A case study of the Ksob watershed in central Algeria. Water Supply, 23(8), 3262-3284. https://doi. org/10.2166/ws.2023.207.
Semenov M.A., Brooks R.J., Barrow E.M., & Richardson C.W. (1998). Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates, Climate Research, 10, 95–107.
Semenov, M., & Shewry, P.R. (2011). Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe, Scientific Reports, 1, 66. doi: https://doi. org/10.1038/srep00066

Shifteh Some’e B., Ezani A., & Tabari H. (2012). Spatiotemporal trends and change point of precipitation in Iran, Atmospheric Research, 113, 1-12. https://doi.org/10.1016/j. atmosres.2012.04.016.
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14, 199-222. doi: https://doi. org/10.1023/B:STCO.0000035301.49549.88

Tabatabaei, S. M., Nazeri Tahroudi, M., & Hamraz, B. S. (2021). Comparison of the performances of GEP, ANFIS, and SVM artificial intelligence models in rainfall simulation. IDŐJÁRÁS / Quarterly Journal of the Hungarian Meteorological Service, 125(2),  195-209. doi:10.28974/idojaras.2021.2.21.
Tiwari V, Kumar V, Matin MA, Thapa A, Ellenburg WL, Gupta N, et al. (2020). Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS ONE, 15(8): e0237324. https://doi. org/10.1371/journal.pone.0237324

Yousefi, E., Kaffash, M., & shariati, M. (2024). Spatial analysis of flood risk in Tabas watershed using satellite images and geographic information system (GIS). Journal of Drought and Climate change Research, 2(2), 105-118. doi:10.22077/jdcr.2024.7489.1066
Yousefi, E., Sayadi, M. H., & Chamenhpour, E. (2022). Google Earth Engine platform to calculate the hydrometeorology and hydrological water balance of wetlands in arid areas and predict future changes. Journal of Applied Research in Water and Wastewater, 9(1), 52-68. doi: 10.22126/arww.2022.7033.1228
Zhang, Y., Li, X., & Wang, J. (2022). A Hybrid Rainfall-Runoff Model: Integrating Initial Loss and LSTM for Improved Forecasting. Journal of Hydrology, 610, 127-139. doi:10.1016/j. jhydrol.2022.127139.

آمار
تعداد مشاهده مقاله: 802
تعداد دریافت فایل اصل مقاله: 537


نماد الکترونیک 

 

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب