Evaluation of the impact of climate change on extreme flows in Kan watershed
Subject Areas : Farm water management with the aim of improving irrigation management indicatorsBaharak Motamedvaziri 1 , mehdi ahmadi 2 , Hasan Ahmadi 3 , Abolfazl Moeini 4 , Gholam Reza Zehtabian 5
1 - Department of Forest, Range and Watershed Management, Faculty Natural Resources and Environmental, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Forest, Range and Watershed Management, Faculty Natural Resources and Environmental, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj, Iran
4 - Department of Forest, Range and Watershed Management, Faculty Natural Resources and Environmental, Science and Research Branch, Islamic Azad University, Tehran, Iran
5 - Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj, Iran
Keywords: Rainfall- Runoff, Climate modeling, HadCM3, CanESM2,
Abstract :
Climate change is one of the major challenges affecting the natural ecosystems and various aspects of human life. The effects of global warming on the hydrology and water cycle in nature are very serious, and the quantitative recognition of these effects creates more readiness to deal with its consequences. In the present study, the 2010-2100 periods is predicted based on SDSM and ASD. Finally, the effect of climate change on the hydrological conditions in the Kan watershed is simulated using the ANN and IHACRES. The results of the study, while confirming the efficiency of both SDSM and ASD models in climate simulations and ANN and IHACRES in hydrological simulation, showed that the increase in precipitation (2-27%) and temperature (0.3- 4/4 C) is probable in future climate conditions for the 2010-2100 periods. Runoff changes in the upcoming period (2010-2039) show an increase (5- 36 %) in the scenario of RCPs and a decrease (32- 41%) in scenario A2. The high flow value in the upcoming period is increased, and the low flow decrease. Most changes were observed in spring. The results of research, while highlighting the importance of effects of climate change, make it essential to apply them for proper management in order to adapt to climate change in the future policies of the Kan watershed management.
احمدی، م. 1393 .ارزیابی اثر تغییر اقلیم بر روی دبی سالیانه حوزه آبخیز قرآن طاالر. پایان نامه کارشناسی ارشد آبخیزداری. دانشگاه منابع طبیعی و علوم زمین. دانشگاه کاشان.
حاجی محمدی، م.، عزیزیان، ا. و قرمزچشمه، ب. 1397 .ارزیابی اثر تغییر اقلیم بر رواناب حوزه کن. انتشارات پژوهشکده حفاظت خاک و آبخیزداری، 10(2): 21-34.
قرمزچشمه، ب. 1392 .ارزیابی عدم قطعیت ناشی از ریز مقیاس گردانی AOGCM با تحلیل دما و بارش )مطالعه موردی: حوضه دریاچه ارومیه(. پایان نامه دکتری سنجش از دور. دانکده جغرافیا. دانشگاه تبریز.
گودرزی، محمد رضا. و فاتحیفر، آتیه. 1398 .پهنه بندی خطر سیالب در اثر تغییرات اقلیمی تحت سناریو RCP 8.5 با استفاده از مدل هیدرولوژیکی SWAT در محیط GIS (حوضه آذرشهر چای). نشریه تحقیقات کاربردی علوم جغرافیایی، 19(53): 99-117.
میر اکبری، م.، مصباحزاده، ط.، محسنی ساروی، م.، خسروی، ح و مرتضایی فریزهندی، ق. 1397 .ارزیابی کارایی مدل سری CMIP5 در شبیهسازی و پیشبینی پارامترهای اقلیمی بارندگی، دما و سرعت باد (مطالعه موردی: استان یزد). پژوهشهای جغرافیای طبیعی، 50(3): 593-609.
میر دشتوان، م.، ملکیان، آ. و محسنی ساروی، م. 1397 .شبیه سازی جریان سطحی از طریق کوچک مقیاس سازی آماری داده های اقلیمی: حوزه دریاچه ارومیه. تهران: انتشارات دانشگاه تهران، 25(2): 419-431.
Ashraf Vaghefi, S., Mousavi, S.J., Abbaspour, K.C., Srinivasan, R. and Yang, H. 2014. Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. Hydrol. Process, 28: 2018–2032.
Carcano, E.C., Bartolini, P., Muselli, M. and Piroddi, L. 2008. Jordan recurrent neural network versus IHACRES in modelling daily streamflows. J. Hydrol, 362: 291–307. https://doi.org/10.1016/j.jhydrol.2008.08.026
Croke, B.F.W. and Jakeman, A.J. 2004. A catchment moisture deficit module for the IHACRES rainfall-runoff model. Environ. Model. Softw, 19: 1–5.
da Silva, R.M., Dantas, J.C., de Araújo Beltrão, J. and Santos, C.A.G. 2018. Hydrological simulation in a tropical humid basin in the Cerrado biome using the SWAT model. Hydrol. Res, nh2018222.
Fenta Mekonnen, D. and Disse, M. 2018. Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques. Hydrol. Earth Syst. Sci, 22: 2391–2408.
Gosling, S., Taylor, R.G., Arnell, N. and Todd, M.C. 2011. A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol. Earth Syst, Sci. 15: 279–294.
Hassan, Z., Shamsudin, S., Harun, S., Malek, M.A. and Hamidon, N. 2015. Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia. Environ. Earth Sci, 74: 463–477.
Holcomb, M.K., Alexander, K.A., Krometis, L.H. and Holcomb, M.K. 2013. The Challenges and Opportunities in Monitoring and Modeling Waterborne Pathogens in Water- and Resource-Restricted Africa: Highlighting the critical need for multidisciplinary research and tool advancement.
Jahanbani, H., Shui, L.T., Bavani, A.M. and Ghazali, A.H. 2011. Uncertainty of climate change and its impact on reference evapotranspiration in Rasht City, Iran. J. Water Clim. Chang, 2: 72–83.
Jin, L., Whitehead, P.G., Addo, K.A., Amisigo, B., Macadam, I., Janes, T., Crossman, J., Nicholls, R.J., McCartney, M. and Rodda, H.J.E. 2018. Modeling future flows of the Volta River system: Impacts of climate change and socio-economic changes. Sci. Total Environ, 637: 1069–1080.
Liu, G., He, Z., Luan, Z. and Qi, S. 2018. Intercomparison of a lumped model and a distributed model for streamflow simulation in the Naoli River Watershed, Northeast China. Water (Switzerland) 10. https://doi.org/10.3390/w10081004
Mahmood, R. and Babel, M.S. 2013. Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India. Theor. Appl. Climatol, 113: 27–44.
Meenu, R., Rehana, S. and Mujumdar, P.P. 2013. Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin, India with HEC‐HMS and SDSM. Hydrol. Process, 27: 1572–1589.
Mirdashtvan, M., Najafinejad, A., Malekian, A. and Sa’doddin, A. 2018. Downscaling the contribution to uncertainty in climate‐change assessments: representative concentration pathway (RCP) scenarios for the S outh A lborz R ange, I ran. Meteorol. Appl, 25: 414–422.
Narsimlu, B., Gosain, A. K., & Chahar, B. R. 2013. Assessment of future climate change impacts on water resources of Upper Sind River Basin, India using SWAT model. Water Resources Management, 27: 3647– 3662.
Nasseri, M. and Zahraie, B. 2013. Improved statistical downscaling of daily precipitation using SDSM platform and data-mining methods 2578, 2561–2578. https://doi.org/10.1002/joc.3611
Noori, N. and Kalin, L. 2016. Coupling SWAT and ANN models for enhanced daily streamflow prediction. J. Hydrol, 533: 141–151.
Ramak, Z., Porhemmat, J., Sedghi, H., Fattahi, E. and Lashni-Zand, M. 2018. The Climate Change Effect on the Water Regime. The Case Study: the Karun Catchment, Iran. Russ. Meteorol. Hydrol, 43: 544–550.
Raziei, T., Arasteh, P.D. and Saghfian, B. 2005. Annual Rainfall Trend in Arid and Semi-arid Regions of Iran. ICID 21st Eur. Reg. Conf. 1–8.
Resende, N. C., Miranda, J. H., Cooke, R., Chu, M. L., & Chou, S. C. 2019. Impacts of regional climate change on the runoff and root water uptake in corn crops in Parana, Brazil. Agricultural Water Management, 221: 556–565.
Saatloo, S.M.E., Siosemarde, M., Hosseini, S.A. and Rezaei, H. 2019. The effects of climate change on groundwater recharge for different soil types of the west shore of Lake Urmia—Iran. Arab. J. Geosci, 12: 263-277.
Taie Semiromi, M. and Koch, M., 2017. Downscaling of daily precipitation using a hybrid model of Artificial Neural Network, Wavelet, and Quantile Mapping in Gharehsoo River Basin, Iran, in: AGU Fall Meeting Abstracts.
Tahir, T., Hashim, A. M., & Yusof, K. W. 2018. Statistical downscaling of rainfall under transitional climate in Limbang River Basin by using SDSM. In IOP Conference Series: Earth and Environmental Science (Vol. 140, p. 12037). IOP Publishing.
Wilby, R.L., Charles, S.P., Zorita, E., Timbal, B., Whetton, P. and Mearns, L.O. 2004. Guidelines for use of climate scenarios developed from statistical downscaling methods. Support. Mater. Intergov. Panel Clim. Chang. available from DDC IPCC TGCIA 27.
Wilby, R.L. and Dawson, C.W., 2013. Statistical downscaling model–decision centric (SDSM-DC) version 5.1 supplementary note. Loughbrgh. Univ. Loughbrgh.
Wilby, R.L. and Dawson, C.W., 2007. SDSM 4.2-A decision support tool for the assessment of regional climate change impacts. United Kingdom.
Wilby, R.L., Dawson, C.W. and Barrow, E.M., 2002. SDSM—a decision support tool for the assessment of regional climate change impacts. Environ. Model. Softw, 17: 145–157.
Zang, C.F., Liu, J., Van Der Velde, M. and Kraxner, F., 2012. Assessment of spatial and temporal patterns of green and blue water flows under natural conditions in inland river basins in Northwest China. Hydrol. Earth Syst. Sci, 16: 2859–2870. https://doi.org/10.5194/hess-16-2859-2012.
Zarghami, M., Abdi, A., Babaeian, I., Hassanzadeh, Y. and Kanani, R. 2011. Impacts of climate change on runoffs in East Azerbaijan, Iran. Glob. Planet, Change 78: 137–146.
Zehtabian, G.R., Salajegheh, A., Malekian, A., Boroomand, N. and Azareh, A. 2016. Evaluation and comparison of performance of SDSM and CLIMGEN models in simulation of climatic variables in Qazvin plain. Desert, 21; 155–164.
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