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شبیه‌سازی پارامترهای مؤثر بر روند دبی جریان رودخانه با استفاده از مدل بارش-رواناب HACRES در دوره‌های آتی (مطالعۀ موردی: رودخانۀ زولاچای) | ||
| اکوهیدرولوژی | ||
| مقاله 13، دوره 8، شماره 1، فروردین 1400، صفحه 177-193 اصل مقاله (1.17 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2021.311773.1394 | ||
| نویسندگان | ||
| مجید قربانی دایلاری1؛ صابره دربندی2؛ اسماعیل اسدی* 3؛ مرتضی صمدیان4 | ||
| 1دانش‌آموختۀ کارشناسی ارشد، علوم و مهندسی آب، دانشگاه تبریز | ||
| 2دانشیار گروه علوم و مهندسی آب، دانشگاه تبریز | ||
| 3استادیار گروه علوم و مهندسی آب، دانشگاه تبریز | ||
| 4دانشجوی دکتری علوم و مهندسی منابع آب، دانشگاه تبریز و مدرس گروه عمران، مؤسسۀ آموزش عالی علم و فن ارومیه | ||
| چکیده | ||
| نظر به اهمیت آثار تغییر اقلیم بر منابع آبی، بررسی رفتار رودخانه به‌ویژه میزان آبدهی آن در دوره‌های آتی برای مدیریت منابع آب و ارائۀ راهکارهای سازگاری با پدیدۀ تغییر اقلیم امری مفید و ضروری است. هدف از تحقیق حاضر، بررسی اثر پدیدۀ تغییر اقلیم بر دبی رودخانۀ زولاچای واقع در استان آذربایجان غربی است. به این منظور، ابتدا با استفاده از مدل LARS-WG مقادیر بارش و دمای ایستگاه هواشناسی چهریق علیا تحت سناریوهای مختلف از سال 2021 تا سال 2080 پیش‌بینی شد. سپس، بر اساس داده‌های ریزمقیاس‌شدۀ بارش و دمای آینده، به کمک مدل بارش-رواناب IHACRES واسنجی و صحت‌سنجی‌شدۀ حجم رواناب خروجی حوضۀ آبخیز در دوره‌های آتی شبیه‌سازی شد. نتایج پیش‌بینی رواناب طی دوره‌های اقلیمی آتی نشان داد متوسط تغییرات رواناب سالانه درازمدت طی دورۀ 2021ـ 2080 به میزان 12/1 مترمکعب در ثانیه (34/33 درصد) تحت سناریوی RCP2.6، 17/1 مترمکعب در ثانیه (67/33 درصد) تحت سناریوی RCP4.5 و 37/1 مترمکعب در ثانیه (42/39 درصد) تحت سناریوی RCP8.5 نسبت به دورۀ پایه کاهش خواهد یافت. | ||
| کلیدواژه‌ها | ||
| تغییر اقلیم؛ رودخانه زولاچای؛ مدل بارش-رواناب؛ LARS-WG؛ IHACRES | ||
| عنوان مقاله [English] | ||
| Simulation of Parameters Affecting the River Flow Trend using the IHACRES Rainfall-runoff Model in Future Periods (Case Study: Zolachai River) | ||
| نویسندگان [English] | ||
| Majid Ghorbani Dailari1؛ Sabereh Darbandi2؛ Esmail Asadi3؛ Morteza Samadian4 | ||
| 1Master of Science in Water Engineering, University of Tabriz, Iran | ||
| 2Associate Professor, Department of Water Engineering, University of Tabriz, Iran | ||
| 3Assistant Professor, Department of Water Engineering, University of Tabriz, Iran | ||
| 4Ph.D Candidate in Water Resources Engineering, University of Tabriz &Lecturer Department of Civil Engineering, Institute of Science and Technology, Iran | ||
| چکیده [English] | ||
| Considering the importance of the effects of climate change on water resources, it is useful and necessary to study the behavior of the river, especially its discharge in future periods, to manage water resources and provide solutions to adapt to the phenomenon of climate change. The purpose of this study is to investigate the effect of climate change on the discharge of the Zolachai River in West Azerbaijan Province. For this purpose, using the LARS-WG model, the precipitation and temperature values of the Upper Chehriq meteorological station under different scenarios from 2021 to 2080 were predicted. Then, based on the microscale data of future precipitation and temperature, the volume of run-off output of the basin in future periods was simulated using the IHACRES rainfall-runoff model. The results of run-off forecast during future climatic periods showed that the average long-term annual run-off changes during the period 2080-2021 at the rate of 1.12 cubic meters per second (33.34%) under the RCP2.6 scenarios, 1.17 cubic meters per second (0.67 33%) under the RCP4.5 scenario and 1.37 cubic meters per second (39.42%) under the RCP8.5 scenario compared to the base period. | ||
| کلیدواژه‌ها [English] | ||
| Rainfall- Run-off Model, Climate Change, LARS-WG, IHACRES, Zolachai River | ||
| مراجع | ||
|
]1[. Karamoz M. Iraqi Nejad Sh. Advanced hydrology. Amirkabir University Press. Tehran; 2005. pp. 464. ]Persian[ ]2[. Ghorbani Kh. 1394. Evaluation of data mining models in exponential precipitation microscale based on NCEP public circulation model data, Case study: Kermanshah synoptic station. Iranian Journal of Water Research.2015; pp. 186-177. ]Persian[ ]3[. Rahimi R. Rahimi M. Spatial and temporal analysis of climate change in the coming years and comparison of microscale methods of SDSM, LARS-WG and artificial neural network (Case study: Khuzestan province). Journal of Echo Hydrology.2018; 4, pp. 1174-1161. ]Persian[ ]4[. Mesbah B. Murid S. Effects of Climate Change on the Zayandehrood River in Isfahan. Agricultural Science and Technology and Natural Resources Journal. 2005; 4, pp. 27-17. ]Persian[ ]5[. Sood A. Smakhtin V. Global hydrological models: a review. Hydrological Sciences Journal. 2015; 60(4): 549-565. ]6[. Koukidis E.N. Berg A. Ensitivity of statistical downscaling model (SDSM) to reanalysis products. Atmosphere–ocean. 2009; 1: pp. 1-18.
]7[. Karimi M. Kaki S. Rafati S. The future climatic conditions and hazards of Iran in climate research. Journal of Spatial Analysis of Environmental Hazards.2018; 3, pp. 22-1. ]Persian[
]8[. Osman Y. Al-Ansari N. Abdellatif M. Aljawad S.B. Knutsson. S. Expected Future Precipitation in Central Iraq Using LARS-WG Stochastic Weather Generator. Engineering. 2014; 06 (13), pp. 948-959.
]9[. Dagnenet M and Disse M. Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques. Hydrology and Earth System Sciences journal. 2018; 22(4): pp. 2391-2408.
]10[.Sha J and Li X. Estimation of future climate change in cold weather areas with the LARS-WG model under CMIP5 scenarios. Theoretical and Applied Climatology journal. 2019; 137(3-4): pp.1-13.
]14[. Javan Kh and Erfanian M. Assessing the impact of climate change on drought status in Tabriz station during future periods using LARS-WG. Iranian Water Researches Journal. 2020; 14(3). ]Persian[
]15[. Dye P. Croke B. Evaluation of stream flow predictions by the IHACRES rainfall-runoff model in two South African catchments. Environmental Modeling and Software. 2003; 18:pp. 705-712.
]16[. Fauzi M. Malik A. Putra D. Putra A. Application of Hybrid-IHACRES models for water availability in Siak River. MATEC Web of Conferences. 2018; pp.1-6.
]17[. Borzi I. Bonaccorso B. Fiori A. A Modified IHACRES Rainfall–Runoff Model for Predicting Hydrologic Response of a River Basin System with a Relevant Groundwater Component. Presented at the 3rd International Electronic Conference on Water Sciences. 2018; pp.1-8.
]18[. Hope A. Decker J. Jankowski P. Daily river flow predictions in southern California Catchments using the IHACRES model and gridded rainfall input data. Ame Rican Geophysical Union fall meeting, San Francisco, USA. 2006; pp.11.
]19[. Carla Carcano E. Bartolini P. Muselli M. Piroddi L. Jordan recurrent neural network versus IHACRES in modelling daily stream flows. Journal of Hydrology. 2008; 362(3), pp. 291-307.
]20[. McIntyre N. Al-Qurashi A. Performance of ten rainfall-runoff models applied to an arid catchment in Oman. Environmental Modeling and Software J. 2009; 24: 726-738.
]21[. Vaze J. Post D. Chiew F. Perraud N.R. Viney and J. Teng. Climate non-stationarity-validity of calibrated rainfall–runoff models for use in climate change studies. Journal of Hydrology. 2010; 394: pp. 447-457.
]22[. Kim H.S. Croke A.J. Jakeman F. An assessment of modeling capacity to identify the impacts of climate variability catchment hydrology. Mathematics and Computer in Simulation. 2011; 81, pp. 1419-1429.
]23[. Abushandi E.H. Merkel B. Aplication of IHACRES rainfall-runoff model to the Wadi Dhuliel arid catchment, Jordan. Journal of Water and Climate Change. 2011; 2, pp. 56-71/
]24[. Ahmadi M. Moeini A. Motamedvaziri B. Zehtabiyan Gh. Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran). Physics and Chemistry of the Earth. 2019; 111, pp. 65-77.
]25[. Esamari E. Golshan M. Witness K. Jahanshahi A. Evaluation of efficiency of SWAT and IHACRES models in runoff simulation of Khorramabad watershed. Water and Soil Knowledge. 2015; 26(1), pp. 42-29. ]Persian[
]26[. Gorbani Kh. Sohrabian A. Evaluation of hydrological methods and data mining in simulation and forecasting of monthly flow rate (Case study: Arakkoseh hydrometric station). Journal of Soil and Water Conservation Research.2016; 1, pp. 217-203. ]Persian[
]27[. Goodarzi M. Motameduziri b. Mir Hosseini M.R. Evaluation of application of IHACRES model for simulation of surface runoff in climate change conditions (Case study: Watershed). Iranian Watershed Management Science and Engineering j. 2017; 11(38), pp. 95-83. ]Persian[
]28[. Yazdani M. Najafian S. Azeri A. Rahimi M. The effect of climate change on the maximum daily discharge under uncertainty conditions (Dinur Basin, Kermanshah province). Journal of Soil and Water Conservation Research. 2017; 24(1), pp. 156-139. ]Persian[
]29[.Rezaei MoghaddamM. Hejazi M. Behbuodi A. A Calibration and Dvaluation of IHACRES Model in Runoff Simulation the Lanbaran Sub-basin, Ahar Chay. Journal of Hydrogeomorphology. 2019; 5(20), pp. 187-204. ]Persian[
]30[.Motiee H. Shirkhodaei F. Motiee AR. Predicting the effects of climate change on the inflow of Karkheh dam reservoir using CMIP5-RCP scenarios. Journal of Dam and Hydroelectric Powerplant. 2020; 7(25), pp. 51-38. ]Persian[
]31[. Racsko P. Szeidl L. Semenov M. A serial approach to local stochastic weather models. Ecological Modelling. 1991; 57, pp.27-41.
]32[. Semenov M.A. Barrow E.M. Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change. 1997; 35, pp. 397-414.
]33[. Semenov M.A. Brooks R.J. Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research. 1999; 11, pp.137 – 148.
]34[. Zakerinia M. Nirumandfard F. Investigation of the effect of climate change on river flow using IHACRES rainfall-runoff model (Case study: Mohammadabad basin in Golestan province). Journal of Irrigation Science and Engineering. 2018; 41(3), pp. 117-103. ]Persian[
[35[. Vardian F. Shahedi K. Habibnejad Roshan M. and Zarei M. Evaluating the efficiency of IHACRES runoff model in simulating the daily and monthly flow of Navarood watershed in Guilan province. Iranian Water Researches Journal, 2014; pp. 229-233. ]Persian[ [36[. Lotfi M. Adib A. and Haghighi A. Estimation of daily runoff using semi-conceptual IHACRES model in Navroud catchment of Guilan. EcoHydrology, 2018: 5(2): 449-460.
[37[. Jakeman AJ, Hornberger GM. How much complexity is warranted in a rainfall-runoff model? Water Resour Research. 1993; 29(8): 2637– 2649.
]38[. Khosravanian J. The effect of climate change on surface runoff. Master Thesis in Watershed Management. Faculty of Natural Resources, Gorgan University. 2013.]Persian[
]39[. Zarei M. Ghanbarpour M. Habibnejad Roshan M. Shahedi K. Simulation of river flow using IHACRES rainfall-runoff model (Case study: Kasilian watershed). Iranian Journal of Watershed Management Science and Engineering. 2009; pp. 20-11. ]Persian[
]40[. Abushandi, E.H., and Broder, M. Application of IHACRES rainfall-runoff model to the Wadi Dhuliel arid catchment Jordan. Journal of Water and Climate Change, 2011; 2 (1), 56-71.
]41[. Ashofteh, P., Bozorg Hadad, O., 2012, An Approach to Assessing Climate Change Impacts on Runoff. Water Resources Engineering Journal. 2012; 6, pp 51-66 (in Persian). | ||
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