تغییرات برخی شاخص های کم آبی تحت تاثیر تغییر اقلیم در حوضه آبریز تنگ پنج سزار

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه صنعتی خاتم الانبیاء بهبهان

2 چمران اهواز

3 پردیس ابوریحان دانشگاه تهران

4 دانشگاه شهید چمران اهواز

چکیده

با توجه به اثرات تغییر اقلیم بر منابع آب و هیدرولوژی، تغییرات جریان کم آبی به عنوان بخش مهمی از چرخه آب، مورد توجه محققین، مدیران و استفاده کنندگاه از آب در زمینه‌های مختلف می باشد. رشد جمعیت و کاهش سرانه آب، محدودیت منابع آبی تجدیدپذیر، همچنین وقوع خشکسالی‌های پر تکرار در دهه های اخیر در نقاط مختلف جهان، اهمیت پیش بینی وضعیت جریان رودخانه را خصوصاً در فصول خشک سال به منظور مدیریت منابع آبی منطقه ضروری می سازد. شاخص‌های مختلفی به منظور سنجش جریان کم آبی یک منطقه وجود دارند. در این تحقیق اثرات تغییر اقلیم بر سه شاخص Q70، Q90 و Q95 مستخرج از منحنی تداوم جریان در حوزه آبریز رودخانه سزار در دوره آتی 2040-2011 مورد بررسی قرار گرفت. به منظور لحاظ کردن عدم قطعیت مدل‌های AOGCM در تولید سناریوهای اقلیمی از 10 مدل گردش عمومی جو استفاده و تاثیر سناریوهای مذکور در وضعیت جریان رودخانه، پس از ریزمقیاس نمایی توسط مدل LARS-WG، با استفاده از مدل مفهومی بارش-رواناب IHACRES مورد ارزیابی قرار گرفت. با توجه به نتایج، تغییرات Q70 از 26- درصد تا 190 درصد، Q90 از 54- درصد تا 221 درصد و Q95 از 64- درصد تا 332 درصد در زیرحوضه‌های مختلف میباشد. نتایج نشان از افزایش نسبی مقادیر شاخص‌های کم آبی در زیرحوضه‌های مورد مطالعه در دوره آتی مورد مطالعه دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Changes of Some Indices of Low Flow affected by Climate Change in the Tang Panj Sezar Basin

نویسندگان [English]

  • M. Mozayyan 1
  • ali mohammad akhondali 2
  • A.R. Massah Bavani 3
  • F. Radmanesh 4
1 Behbahan Khatam Alanbia University of Technology
3 Tehran University, Abourraihan Pardis
4 Shahid Chamran University of Ahwaz
چکیده [English]

Introduction: Due to the effects of climate change on water resources and hydrology, Changes in low flow as an important part of the water cycle, is of interest to researchers, water managers and users in various fields. Changes in characteristics of low flows affected by climate change may have important effects on various aspects of socioeconomic , environmental, water resources and governmental planning. There are several indices to assess the low flows. The used low flow indices in this research for assessing climate change impacts, is include the extracted indices from flow duration curve (Q70, Q90 and Q95), due to the importance of these indices in understanding and assessing the status of river flow in dry seasons that was investigated in Tang Panj Sezar basin in the west of Iran.
Materials and methods: In this paper, the Tang Panj Sezar basin with an area of 9410 km2 was divided into 6 smaller sub catchments and the changes of low flow indices were studied in each of the sub catchments. In order to consider the effects of climate change on low flow, scenarios of temperature and precipitation using 10 atmospheric general circulation models (to investigate the uncertainty of GCMs) for both the baseline (1971-2000) and future (2011-2040) under A2 emission scenario was prepared. These scenarios, due to large spatial scale need to downscaling. Therefore, LARS-WG stochastic weather generator model was used. In order to consider the effects of climate change on low flows in the future, a hydrologic model is required to simulate daily flow for 2011-2040. The IHACRES rainfall-runoff model was used for this purpose . After simulation of daily flow using IHACRES, with two time series of daily flow for the observation and future period in each of the sub catchment, the low flow indices were compared.
Results Discussion: According to results, across the whole year, the monthly temperature in the future period has increased while rainfall scenarios show different variations for different months, also within a month for different GCMs. Based on the results of low flow indices, in most cases, the three indices of Q70, Q90, and Q95 will show incremental changes in the future compared to the past. Also, the domain simulation by 10 GCMs for all three indices is maximum in Tang Panj Sezar and less for other sub catchments, which is related to better performance of IHACRES model in smaller sub catchments. In order to investigate the uncertainty of type changes in different indices in every sub catchment, changes in any of the indices were considered based on the median of GCMs. To achieve the correct type of changes in low flow indices, the amount of error in a simulation of the indices of IHACRES rainfall-runoff model should also be taken into consideration. Therefore, considering the error, the three indices Q70, Q90 and Q95 in all sub catchments (except for Tang Panj Sezar) will have the relative increase in the future period. The improvement of low flow state in the future period is related to the changes occurred in the state of climate scenarios. As the results indicated, most often, there is an increase in rainfall in dry seasons. Also, in different months of the wet season wet season, if the result of changes in quantity of rainfall is incremental, it can lead to an increase in river flow through groundwater recharge. On the other hand due to the limestone and karst forms in most of the basin area, water storage ability and increase the amount of river flow during low water season in this area is expected. The study on rainfall quantity in Tang Panj Sezar sub catchment also indicated that, there will be no significant increase or decrease in the quantity of rainfall in the dry season. Thus, it is expected that there will not be significant changes in low flow indices. In this sub catchment, changes in various low flow indices do not match perfectly, so more difficult to obtain reliable results. With regard to incremental changes of Q95, low flow index with less uncertainty, as well as improving indices of low flow in other sub-basins, it is possible to predict a relatively better state for low flow indices of Tang Panj Sezar in the future period.
Conclusion: Using temperature and rainfall scenarios to simulate river flow in the future, a relative increase of all three low flow indices Q70, Q90 and Q95 was predicted compared with the past period. Although all three of mentioned indices show the amount of low flow in the dry season, it is recommended that only two indices of Q90 and Q95 to assess the effects of climate change be considered. Q90 and Q95 indices are more suitable indices than Q70 for studying the effects of climate change on low flow state. These two indices indicate less quantity of flow in dry seasons; therefore, the changes of the two indices are more important in identifying the low flow state. However, there is less uncertainty in the estimation of the two Q90 and Q95 indices than Q70.

کلیدواژه‌ها [English]

  • Climate change
  • Flow duration curve
  • LARES-WG model
  • Low flow
  • Tang Panj Sezar
Azari M., Moradi H.R., Saghafian B., and Faramarzi M. 2013. Evaluation of hydrological effects of climate changes on the Gorganrood basin, Journal of Water and Soil (Agricultural Sciences and Technology), 27(3): 537-547.
2- Babaeian I., Kwon W.T., and Im E.S. 2004. Application of weather generator technique for climate change assessment over Korea, Korea Meteorological Research Institute, Climate Research Lab.
3- Babaeian I., and Najafi Nic Z. 2007. Introduction and evaluation LARS-WG model for modeling of meteorological parameters of Khorasan state, priod 1961-2039, Technical workshop on the effects of climate change on water resources management, the National Committee on Irrigation and Drainage, Tehran.
4- Baguis P., Roulin E., Willems P., and Ntegeka V. 2010. Climate change and hydrological extremes in Belgian catchments, Hydrology and Earth System Sciences, 7: 5033–5078.
5- Bazrafshan J., Khalili A., Hoorfar A., Torabi S., and Hejam S. 2009. Investigate and compare the performance of two models LARS-WG and ClimGen in simulation of meteorological variables in different climatic conditions of Iran, Journal of Iran Water Resources Researches, 13: 44-57.
6- Brilly M., Kobold M., and Vidmar A., 1997. Water information management system and low flow analysis in Slovenia, FRIEND ‘97 – Regional Hydrology: concepts and models for sustainable water resource management, Proceedings from the International Conference, 246: 117-124.
7- Croke B.F.W., Andrews F., Spat J., and Cuddy S., 2005. IHACRES User Guide, Technical Report 2005/19. Second Edition, iCAM, School of Resources, Environment and Society, The Australian National University, Canberra. http://www.toolkit.net.au/ihacres
8- Croke B.F.W., and Jakeman A.J. 2005. Use of the IHACRES rainfall-runoff model in arid and semi arid regions, in: Hydrological Modelling in Arid and Semi-Arid Areas, edited by: Wheater, H., Sorooshian, S. and Sharma, K. D., Cambridge University Press, Cambridge, 41–48, 2007.
9- De Wit M.J.M., Van den Hurk B., Warmerdam P.M.M., Torfs P.J.J.F., Roulin E. and Van Deursen W.P.A. 2007. Impact of climate change on low-flows in the river Meuse, Climatic Change, 82:351–372.
ِ10- Filiz D.C., and Heinz G.S. 2009. Stream Flow Response to Climate in Minnesota, Project Report No. 510, University of Minnesota.
11- Fowler H.J., Blenkinsopa S., and Tebaldi C. 2007. Review: Linking climate change modeling to impacts studies: recent advances in downscaling techniques for hydrological modeling, International Journal of Climatology, 27(12): 1547–1578.
12- Gain A.K., Immerzeel W.W., Sperna Weiland F.C., and Bierkens M.F.P., 2011. Impact of climate change on the stream flow of lower Brahmaputra: trends in high and low flows based on dischargeweighted ensemble modeling, Hydrology and Earth System Sciences Discussions, 8: 365–390.
13- Gosain A.K., Rao S., and Basuray D., 2006. Climate change impact assessment on hydrology of Indian river basins, Current Science, 90(3): 346-353.
14- Huang Sh., Krysanova V., and Hattermann F., 2013. Projection of low flow conditions in Germany under climate change by combining three RCMs and a regional hydrological model, Acta Geophysica, 61(1): 151-193.
15- Intergovernmental Panel on Climate Change (IPCC), Climate Change and Water. Bates Bryson C., Kundzewicz Zbigniew W., Wu Sh. and Palutikof J., Technical Paper VI of the Intergovernmental Panel on Climate Change, 200, Geneva, Switzerland.
16- Jakeman A.J., and Hornberger G.M., 1993. How much complexity is warranted in a rainfall-runoff model? Water Resources Research, 29(8): 2637-2649
17- Kavvas M.L., Chen Z.Q., Ohara N., Bin Shaaban A.J., and Amin M.Z.M., 2006. Impact of climate change on the hydrology and water resources of Peninsular Malaysia, International Congress on River Management 2a.
18- Krause P., Boyle D.P., and Base F., 2005. Comparison of different efficiency criteria for hydrological model assessment, Advances in Geosciences, 5: 89–97.
Sref-ID: 1680-7359/adgeo/2005-5-89
19- Massah Bavani A.R., 2006. Assessing the risk of climate change and its impact on water resources (Case Study: Isfahan Zayanderood basin), The PhD thesis, the field of Water Resources, Department of Water Structures Engineering, Faculty of Agriculture, Tarbiat Modarres University.
20- Mauser W., Marke T., and Stoeber S., 2008. Climate Change and Water Resources: Scenarios of Low-flow Conditions in the Upper Danube River Basin, XXIVth Conference of the Danubian Countries, IOP Conf. Series: Earth and Environmental Science 4 (2008) 012027.
21- Pyrce R.S., 2004. Hydrological Low Flow Indices and their Uses, Watershed Science Centre Report No.04-2004, Peterborough, Ontario, 33 p.
22- Qian B., Gameda S., Hayhoe H., De Jong R., and Bootsma A., 2004. Comparison of LARS-WG and AAFC-WG stochastic weather generators for diverse Canadian climates, Climate Research, 26: 175–191.
23- Qian B., Gameda S. and Hayhoe H. 2008. Performance of stochastic weather generators LARSWG and AAFC-WG for reproducing daily extremes of diverse Canadian climates, Climate Research, 37: 17–33.
24- Reaney S.M., and Fowler H. 2008. Uncertainty estimation of climate change impacts on river flow incorporating stochastic downscaling and hydrological model parametrisation error sources, BHS 10th National Hydrology Symposium, Exeter.
25- Riggs H.C., Caffey J.E., Orsborn J.F., Schaake J.C., Singh K.P., and Wallace J.R. (Task Committee of Low-Flow Evaluation, Methods, and Needs of the Committee on Surface-Water Hydrology of the Hydraulics Division), 1980. Characteristics of low flows, Journal of the Hydraulics Division, Proceedings of the American Society of Civil Engineers, 106: 717-731.
26- Semenov M.A., Brooks R.J., Barrow E.M., and Richardson C.W. 1998. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates, Climate Research 10: 95–107.
27- Semenov M.A. 2008. Simulation of weather extreme events by stochastic weather generator, Climate Research, 35: 203–212.
28- Smakhtin V.U. 2001. Low flow hydrology: a review, Journal of Hydrology, 240:147–186.
29- Stahl K., Hisdal H., Hannaford J., Tallaksen L.M., van Lanen H.A.J., Sauquet E., Demuth S., Fendekova M. and Jodar J. 2010. Streamflow trends in Europe: evidence from a dataset of near-natural catchments, Hydrology and Earth System Sciences Discussions, 14: 2367-2382.
30- Tharme R.E. 2003. A global prespective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers, River Research and Applications, 19: 397-441.
31- Vogel R.M., and Fennessey N.M. 1995. Flow duration curves. II. A review of applications in water resource planning, Water Resources Bulletin, 31(6): 1029–1039.
32- Wallace T.B., and Cox W.E. 2002. Locating information on surface water availability in Virginia.
< http://www.rappriverbasin.va.us/studies/locatingsurfacewaterinfo.doc>
33- Wilby R., Greenfield B., and Glenny C. 1994. A coupled synoptichydrological model for climate change impact assessment, Journal of Hydrology, 153: 265–290.
34- WMO., 2008. Manual on low flow estimation and prediction, Operational hydrology report No.50 (WMO-No.1029), Geneva.
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