بسط منحنی‌های IDF تحت سناریوهای مختلف تغییر اقلیم و تخمین دبی بیشینه سیلاب در حوضه درکه تهران (مطالعه موردی: ایستگاه‌های سینوپتیک شمیران، ژئوفیزیک و مهرآباد)

نویسندگان

1 دانشیار گروه آموزشی عمران - آب، دانشکده مهندسی عمران ، دانشگاه سمنان

2 دانشجوی کارشناسی ارشد مهندسی عمران گرایش مهندسی آب و سازه‌های هیدرولیکی دانشگاه سمنان

3 دانشیار گروه آموزشی عمران- آب، دانشکده مهندسی عمران، دانشگاه سمنان

چکیده

با مقایسه منحنی‌های IDF‌ به دست آمده معلوم شد که برای دوره بازگشت 50 سال شدت بارش در ایستگاه‌های همدید شمیران، ژئوفیزیک و مهرآباد تحت سناریوی RCP2.6 نسبت به دوره پایه به ترتیب به اندازه 66/45، 49/54 و 74/31 درصد افزایش یافته است. این رقم برای سناریوی RCP4.5 به ترتیب معادل 89/40، 5/61 و 9/43 درصد و برای سناریوی RCP8.5 معادل 77/65، 12/66 و 6/48 درصد می‌باشد. با استفاده از منحنی‌های IDF استخراج شده برای ایستگاه‌های منتخب مقدار حداکثر دبی سیلاب با سناریوهای مختلف در خروجی حوضه آبریز درکه با روش استدلالی محاسبه شد. دبی بیشینه با به کارگیری رابطه منطقی، تحت تأثیر تغییر اقلیم با دبی دوره پایه مورد مقایسه قرار گرفت. نتایج نشان داد که برای دوره بازگشت 50 سال دبی بیشینه بر اساس شدت بارش به دست آمده در ایستگاه شمیران با افزایش حداقل 89/40 درصد و حداکثر 77/65 درصد تحت تأثیر سناریوهای RCP4.5 و RCP8.5 نسبت به دبی بیشینه دوره پایه مواجه است. با توجه به شدت بارش محاسبه شده در ایستگاه‌ ژئوفیزیک مقدار حداکثر دبی سیل با سناریوی RCP2.6 حدود 54 درصد و با سناریوی RCP8.5 حدود 66 درصد نسبت به دوره پایه افزایش نشان داد. در نهایت با مقایسه دبی بیشینه تحت تأثیر سناریوهای اقلیمی و دبی بیشینه دوره پایه (محاسبه شده بر اساس شدت بارش) در ایستگاه مهرآباد نیز مشخص شد، حداقل افزایش دبی بیشینه مربوط به سناریوی RCP2.6 به میزان 74/31 درصد و حداکثر میزان افزایش تحت تأثیر سناریوی RCP8.5 به میزان 61/48 درصد می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Extension of IDF curves under different scenarios of climate change and estimation of maximum flood discharge in Darkeh basin in Tehran (Case study: Shemiran, Geophysic and Mehrabad synoptic stations

نویسندگان [English]

  • Hojat Karami 1
  • Marzieh Khaleghi Meybodi 2
  • Khosrow Hosseini 3
1 Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
2 Master student of Civil Engineering, Dept. of Water Engineering and Hydraulic Structures, Semnan University, Semnan, Iran
3 Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
چکیده [English]

Background and Objectives
Intensity-duration-frequency (IDF) curve is one of the most common tools used in water resources management (Bernard 1932). In this regard, obtaining IDF curves plays an important role in designing of hydraulic structures such as dams and water transfer canals. Intensity, duration and frequency of precipitation are changed according to the change in the hydrological cycle and the increase of greenhouse gases. The optimal designs of surface runoff systems extremely rely on IDF rainfall curves (Liew, et al. 2014). Since rainfall characteristics are often used for designing hydraulic structures, it is necessary to review and update rainfall characteristics such as the IDF curve for the future climate scenarios (De Paola, et al. 2014). Climate change affects the intensity and frequency of rainfall as well as runoff in future periods. According to the studies conducted in the western reversible flood basin, the amount of runoff and maximum flows and the probability of flooding will increase significantly in the future time horizon, (Binesh, et al. 2018). As well, it is necessary to define the flood peak thresholds in order to determine the discharge values for different return periods, for which it is necessary to determine the maximum precipitation values with a certain intensity and continuity for future periods (Van, et al. 2020). According to the researches, it seems that the effect of climate change on marginal currents in urban basins has been less considered. Therefore, in the present study, using the SDSM model outputs according to the fifth report, intensity- duration- frequency (IDF) curves under the influence of climate change scenarios were extracted for Shemiran, Geophysic and Mehrabad synoptic stations located in Tehran province with different return periods in order to better evaluate the role of climate change on the intensity of rainfall and floods in the basin and to consider the necessary measures in accordance with the conditions ahead.
Methodology
The data of historical storms of meteorological stations is used to obtain the intensity-duration-frequency curves. In the present study, IDF curves of Shemiran, Geophysic and Mehrabad synoptic stations located in Tehran province influenced by climate change of the historical period (1991-2015) with 2, 5, 10, 25, 50 and 100 year return periods were extracted. SDSM software version 5.3 has been used for downscaling. Based on the following downscaling, precipitation forecasting was performed under three scenarios: RCP2.6, RCP4.5 and RCP8.5 for 5 periods of 15 years and a period of 10 years from 2016 to 2100. The SCS method has been used to calculate the concentration time in this study. Three criteria of correlation (r), Nash Sutcliffe coefficient (NSE) and Bias were used to evaluate the efficiency of SDSM model. The proposed method of the US Soil Conservation Organization is based on the proposal of the World Meteorological Organization (WMO) about the temporal distribution pattern of storms. The amount and intensity of precipitation in each return period is calculated by means of this temporal distribution pattern. 6-hour precipitation was obtained under RCP scenarios for different return periods using the hero relationship.
Findings
In this study, forecasts for the next 50 years indicate that the rainfall will decrease by 16.55% and 14.36% at Shemiran station according to the RCP2.6 and RCP8.5 scenarios. Compared to the observational state; however, precipitation will increase by 45.24% based on the RCP4.5 scenarios. At the Geophysic station, the annual rainfall will decrease by 25.12 and 16.8 percent based on the scenarios of RCP2.6 and RCP8.5, but it will increase by 20.61 percent based on the RCP45. The precipitation at Mehrabad station will decrease in all scenarios. The precipitation at Mehrabad station will decrease by 11.038, 10.6 and 5.75, respectively under the scenarios of RCP2.6, RCP4.5 and RCP8.5.

Conclusion
In the present study, it is found that the rainfall intensity by comparing the obtained IDF curves in Shemiran, Geophysic and Mehrabad synoptic stations under RCP2.6 scenario has dramatically increased by 45.66, 54.49 and 31.74 percent in comparison to the base period for the return period of 50 years. RCP4.5 scenario contains 40.89, 61.5 and 43.9% and RCP8.5 scenario contains 65.77, 66.12 and 48.6 percent. Finally, using the extracted IDF curves for Shemiran, Geophysic and Mehrabad synoptic stations, the maximum flow influenced by climate change was compared with the base flow. The results show that the maximum discharge is increasing. According to the results, the maximum flow rate based on the rainfall intensity obtained at Shemiran station increased by at least 40.89% and at most 65.77% under the influence of RCP4.5 and RCP8.5 scenarios for the return period of 50 years compared to the maximum of base flow. According to the intensity of precipitation calculated at the geophysic station, the maximum flood discharge increased by about 54% with the RCP2.6 scenario and about 66% with the RCP8.5 scenario compared to the base period. Finally, by comparing the maximum discharge under the influence of climatic scenarios and the maximum of base flow (based on calculated by rainfall intensity) at Mehrabad station, it was determined that the minimum increase of the maximum of base flow is related to RCP2.6 scenario was 31.74% and the maximum increase was related to RCP8.5 scenario which is 48.61%

کلیدواژه‌ها [English]

  • Climate change
  • Flood
  • IDF curves
  • Maximum flood discharge
  • Rational relationship
Abdullah J, Muhammad NS, Julien PY, Ariffin J and Shafie A, 2018. Flood flow simulations and return period calculation for the Kota Tinggi watershed, Malaysia. Journal of Flood Risk Management 11:766-782.
Adib A and Ghafari Rad S, 2019. Development of a new integrated method for generation IDF curves based on three climatic changes scenarios. Scientia Iranica 26:742-751.
Ahmadabadi A and Sedighifar Z, 2019. Prediction of climate change induced hydrogeomorphology by using SDSM in can watershed. Journal of Geographical Sciences 18:103-114. (In Persian with English abstract)
Alizadeh A, 2006. Principles of Applied Hydrology. Pp. 1-811. Astan Quds Razavi Publications, 29th edition. (In Persian)
Anaraki MV, Farzin S, Mousavi SF and Karami H, 2021. Application of  hybrid least square support vector machine-whale optimization algorithm (LSSVM-WOA) for downscaling and prediction of precipitation under climate change (Case Study: Karun3 Basin). Iranian Journal of Irrigation and Water Engineering 11:253-271. (In Persian with English abstract)
Anaraki MV, Mousavi SF, Farzin S and Karami H, 2020. Introducing a nonlinear model based on hybrid machine learning for modeling and prediction of precipitation and comparison with SDSM method (Case study: Shahrekord, Barez, and Yasuj). Iranian Journal of Soil and Water Research 51:325-339. (In Persian with English abstract)
Bernard MM, 1932. Formulas for rainfall intensities of long duration. Transactions of the American Society of Civil Engineers 96:592-606.
Binesh N, Niksokhan MH and Sarang A, 2018. A study of rainfall and urban runoff flow regime under future climate condition (Case study: West Flood-Diversion Catchment in Tehran). Amirkabir Journal of Civil Engineering 50:815-826. (In Persian with English abstract)
De Paola F, Giugni M, Elena Topa M and Bucchignani E, 2014. Intensity-duration-frequency (IDF) rainfall curves, for data series and climate projection in African cities. SpringerPlus 3:1-18.
Ghahraman B and Abkhezr H, 2004. Improvement in intensity-duration-frequency relationships of rainfall in Iran. Journal of Water and Soil Science 8:1-14. (In Persian with English abstract)
Habibnejad R and Shokoohi A, 2020. Evaluating intensity, duration and frequency of short duration rainfalls using a regional climate change model (Case study: Tehran). Iran-Water Resources Research 15:412-424. (In Persian with English abstract)
Khan MS, Coulibaly P and Dibike Y, 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology 319:357-382.
Khazaei MR, Zahabiyoun B and Hasirchian M, 2020. Comparison of IWG and SDSM weather generators for climate change impact assessment. Theoretical and Applied Climatology 140:859-870.
Liew SC, Raghavan  SV and Liong SY, 2014. How to construct future IDF curves, under changing climate, for sites with scarce rainfall records? Hydrological Processes 28:3276-3287.
Mirzayi S and Raoof M, 2015. Comparing experimental methods and analyzing flood hydrograph in estimating time of concentration, (Case study: Atashgah Watershed, Ardabil Province). Journal of Watershed Engineering and Management 6:407-414. (In Persian with English abstract)
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD and Veith TL, 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50:885-900.
Mozayyan M, Akhoond Ali AM, Massah Bavani AR, Radmanesh F and Zohrabi N, 2015. The impact of climate change in low flows (Case study: Sepid Dasht Sezar). Irrigation Sciences and Engineering  38:1-19. (In Persian with English abstract)
Safavi HR, Dadjou SH and Naeimi G, 2019. Extraction of intensity-duration-frequency (IDF) curves under climate change. (Case study: Isfahan Synoptic Station). Iran-Water Resources Research 15:217-227. (In Persian with English abstract)
Sarhadi A and Soulis ED, 2017. Time‐varying extreme rainfall intensity‐duration‐frequency curves in a changing climate. Geophysical Research Letters 44:2454-2463.
Simonovic SP, Schardong A, Sandink D and Srivastav R, 2016. A web-based tool for the development of intensity duration frequency curves under changing climate. Environmental Modelling & Software 81:136-153.
Souvignet M and Heinrich J, 2011. Statistical downscaling in the arid central Andes: uncertainty analysis of multi-model simulated temperature and precipitation. Theoretical and Applied Climatology 106:229-244.
Sy B, Frischknecht C, Dao H, Consuegra D and Giuliani G, 2019. Flood hazard assessment and the role of citizen science. Journal of Flood Risk Management 12:12505-12519.
Van Campenhout J, Houbrechts G, Peeters A and Petit F, 2020. Return period of characteristic discharges from the comparison between partial duration and annual series, application to the Walloon Rivers (Belgium). Journal of Water 12:792-825.
Vu MT, Raghavan SV, Liu J and Liong S, 2018. Constructing short‐duration IDF curves using coupled dynamical–statistical approach to assess climate change impacts. International Journal of Climatology 38:2662-2671.
Wilby RL, Dawson CW and Barrow EM, 2002. SDSM — a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software 17:145-157.
Wilby RL and  Harris I, 2006. A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK. Water Resources Research 42:1-10.
Xing W, Wang W, Shao Q and  Ding Y, 2018. Estimating net irrigation requirements of winter wheat across central-eastern China under present and future climate scenarios. Journal of Irrigation and Drainage Engineering 144:05018005.
Yang L, Li J, Sun H, Guo Y and Engel BA, 2019. Calculation of nonstationary flood return period considering historical extraordinary flood events. Journal of Flood Risk Management 12:12453-12463.