بررسی اثرات تغییر اقلیم بر سطح آب زیرزمینی با استفاده از مدل مفهومی بیلان (مطالعه موردی: دشت بیرجند)

فصلنامه علمی مهندسی منابع آب

مقاله 1، دوره 12، شماره 41 - شماره پیاپی 2، شهریور 1398، صفحه 1-16   

نوع مقاله: مقاله پژوهشی

نویسندگان

احمد جعفرزاده^{* 1}؛ عباس خاشعی²؛ علی شهیدی²

¹دانشجوی دوره دکتری مهندسی منابع آب، دانشکده کشاورزی، دانشگاه بیرجند

²دانشیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند

تاریخ دریافت: 15 بهمن 1394،  تاریخ بازنگری: 21 آذر 1396،  تاریخ پذیرش: 04 شهریور 1398 

چکیده

مدیریت منابع آب در مناطق خشک از اهمیت بالایی برخوردار است. از آنجا که در این مناطق بیشتر سهم مصارف متوجه منابع آب زیزمینی است، اطلاع از وضعیت کمی و کیفی این منابع ضروری است. خروجی‌های مدل‌های گردش عمومی ابزاری سودمند در جهت پیش‌بینی تغییرات متغیرهای اقلیمی به شمار می‌رود. در این پژوهش در ابتدا جهت بررسی پیامدهای کمی گرم شدن جهانی، با استفاده از بهترین خروجی مدل‌های گردش عمومی اثر تغییر اقلیم بر بارش براورد گردید، و در ادامه به منظور تحلیل وضعیت کمی آبخوان دشت بیرجند، از مدل مفهومی بیلان استفاده شد. واسنجی و صحت‌سنجی مؤلفه‌ها و اجزاء مدل مفهومی بیلان با استفاده از الگوریتم‌ بهینه‌سازی چندهدفه MOPSO، و میانگین سطح آب زیرزمینی حدفاصل سال‌های 1390 تا 1393 (به عنوان ارتفاع مشاهداتی سطح آبخوان) انجام شد. از شاخص‌های R² و RMSE به عنوان توابع هدف در این بهینه‌سازی استفاده شد. بازه‌ی تغییرات توابع هدف در مرحله‌ی واسنجی اعضای جمعیت نیز بین 96/0 تا 2/0 برای تابع RMSE، و بین 77/0 تا 97/0 برای تابع R² رسید. همچنین میزان دقت توابع هدف در مرحله‌ی صحت‌سنجی برای تابع RMSE و R²به ترتیب برابر است با 84/0 و 91/0. در مرحله‌ی بعد، براورد سطح آب زیرزمینی آبخوان حدفاصل سال‌های 2015 تا 2030 میلادی انجام شد. نتایج مطالعه‌ی حاضر نشان داد که سطح آب زیرزمینی آبخوان دشت بیرجند در بازه مطالعه شده با روندی نزولی مواجه خواهد گردید. طبق شبیه‌سازی‌های مدل مفهومی در خصوص سطح آب زیرزمینی، انتظار می‌رود که حدوداً افت تجمعی و ارتفاع سطح آب زیرزمینی در پایان دوره‌ی آتی به ترتیب به ارقام 7  و 9/1315 متر برسد.

کلیدواژه‌ها

بهینه‌سازی؛ دشت بیرجند؛ مدل مفهومی بیلان؛ MOPSO

عنوان مقاله [English]

Impact assessment of climate change on groundwater table using of balance conceptual model (Case study: Birjand Plain)

نویسندگان [English]

ahmad jafarzadeh¹؛ abbas khashei²؛

¹department of science and water engineering, university of birjand

²department of science and water engineering, university of Birjand

چکیده [English]

Water resource management in arid and regions has high importance. Since in these regions most of demands are programmed based on groundwater so inform of quantity and quality situation of groundwater is very necessary. General Circulation Models (GCMs) outputs are useful instrument for forecast of variations of hydrological parameters. In this study to assessment of global warming impacts variations of rainfall patterns which has been influenced by climate change effects were first evaluated by using of best GCMs outputs and in continue balance conceptual model was examined to analysis of quantity situation of Birjand Aquifer. Calibration and validation of balance conceptual model was performed by using of Multi-objective Particle Swarm Optimization and observation water table (from 1390 to 1393). Fitness functions in this optimization were RMSE and R2. Results of optimization showed that ranges of fitness function in calibration period reached from 0.96 to 0.2 and from 0.77 to 0.97 for RMSE and R2 respectively. Also value of these indices reached 0.84 and 0.91 for RMSE and R2 respectively in validation period. In after step estimation of aquifer water table performed by input of rainfall predicted into balance conceptual model. Relying of conceptual simulations it is expected that cumulative head loss and groundwater table will reach to 7 and 1315.9 m in end of future period respectively.

کلیدواژه‌ها [English]

Optimization, Birjand Plain, Balance Conceptual Model, MOPSO

مراجع

1)       Akbarpour, A., Ghoochanian, E., Etebari, E. 2012. Integrating groundwater management with WEAP and MODFLOW models. 3th national conference on national conference on integrated water resources management, Sari, University of Agricultural Sciences and Natural Resources. (In Persian).

2)       Abbaspour, M., S. Mirbagheri, M. Monavvari, A. Javid & H. Zarei 2009. Conceptual hydrosalinity model for prediction of salt load from wastewater flows into soil and ground water. International Journal of Environmental Science & Technology, 6, 359-368.

3)       Akabrzadeh, Y., Eslahi, M., Sadegh. Sh, F, Babai, and M. 2013. Investigating the Effects of Climate Change on Groundwater Resources (Case Study: Soofi Chay Basin). Second Regional Conference on Climate Change and Earth’ warming. (In Persian).

4)       Banihashmi, A. 2011. Optimization and Calibration of Groundwater Dynamic Components Using GA Algorithm. Master's thesis. Department of Water Science and Engineering. Faculty of Agriculture, University of Birjand (In Persian).

5)       Barron, O. V., Crosbie, R. S., Dawes, W. R., Charles, S. P., Pickett, T., & Donn, M. J. 2012. Climatic controls on diffuse groundwater recharge across Australia. Hydrology and Earth System Sciences, 16(12), 4557-4570.

6)       Castle, S. L., Thomas, B. F., Reager, J. T., Rodell, M., Swenson, S. C., & Famiglietti, J. S. 2014. Groundwater depletion during drought threatens future water security of the Colorado River Basin. Geophysical Research Letters, 41(16), 5904-5911.

7)       Coello, C. C., & Lechuga, M. S. 2002. MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1051-1056). IEEE.

8)       Crosbie, R. S., McCallum, J. L., Walker, G. R., & Chiew, F. H. 2010. Modelling climate-change impacts on groundwater recharge in the Murray-Darling Basin, Australia. Hydrogeology Journal, 18(7), 1639-1656.

9)       Eberhart, R., and J., Kennedy. 1995. A new optimizer using particle swarm theory. The Sixth International Symposium on Micromachining and Human Science, Nagoya, Japan, 1995, pp. 39-43.

10)   Eckhardt, K., & Ulbrich, U. 2003. Potential impacts of climate change on groundwater recharge and streamflow in a central European low mountain range. Journal of Hydrology, 284(1), 244-252.

11)   Gates, J. B., Edmunds, W. M., Darling, W. G., Ma, J., Pang, Z., & Young, A. A. (2008). Conceptual model of recharge to southeastern Badain Jaran Desert groundwater and lakes from environmental tracers. Applied Geochemistry, 23(12), 3519-3534.

12)   Ghoochanian, E., Etebari, B., & Akbarpour, A. 2013. Integrating groundwater management with WEAP and MODFLOW models (Case study: Birjand Plain, east of Iran). MODFLOW and More, 2-5.

13)   Hashemi, H., Bertacchi Uvo, C., & Berndtsson, R. 2015. Coupled modeling approach to assess climate change impacts on groundwater recharge and adaptation in arid areas. Hydrology and Earth System Sciences, 19(10), 4165-4181.

14)   Jackson C R, Meister R, Prudhomme c, 2011. Modelling the effects of climate change and its uncertainty on UK Chalk groundwater resources from an ensemble of global climate model projections. Journal of Hydrology 399 (2011) 12–28.

15)   Jafarzadeh, A., Khaseii, A., Shahidi, A. 2016. Designing a multiobjective decision-making model to determine optimal crop pattern influenced by climate change phenomenon (case study: Birjand plain). Iranian Journal of Soil and Water Research, 47(4), 849-859 (In Persian).

16)   Jafarzadeh, A., Pourreza-Bilondi, M., Afshar, A. A., Khashei-Siuki, A., & Yaghoobzadeh, M. 2018. Estimating the reliability of a rainwater catchment system using the output data of general circulation models for the future period (case study: Birjand City, Iran). Theoretical and Applied Climatology, 1-12.

17)   Jukić, D., & Denić-Jukić, V. 2009. Groundwater balance estimation in karst by using a conceptual rainfall–runoff model. Journal of hydrology, 373(3), 302-315.

18)   Jyrkama, M. I., & Sykes, J. F. 2007. The impact of climate change on spatially varying groundwater recharge in the Grand River watershed (Ontario). Journal of Hydrology, 338(3), 237-250.

19)   Kløve, B., Ala-Aho, P., Bertrand, G., Gurdak, J. J., Kupfersberger, H., Kværner, J., & Uvo, C. B. 2014. Climate change impacts on groundwater and dependent ecosystems. Journal of Hydrology, 518, 250-266.

20)   Kundzewicz, Z. W., Mata, L. J., Arnell, N. W., Döll, P., Jimenez, B., Miller, K., ... & Shiklomanov, I. 2008. The implications of projected climate change for freshwater resources and their management.

21)   Kurdwani P. 1983. Resources and Water Issues in Iran: Agah Publishing (in Persian).

22)   Luoma, S., & Okkonen, J. 2014. Impacts of future climate change and Baltic sea level rise on groundwater recharge, groundwater levels, and surface leakage in the Hanko aquifer in southern Finland. Water, 6(12), 3671-3700.

23)   Mogheir Y, Ajjur S 2013. Effects of Climate Change on Groundwater Resources (Gaza Strip Case Study). International Journal of Sustainable Energy and Environment Vol. 1, No. 8, PP: 136- 149.

24)   Mohtasham, M., Dehghani, A., Akbarpour, A., Meftah Halaghi, M. 2011. Prediction of water level in aquifer using GMS software. Case study: Birjand aquifer. 4th Conference on Water Resources Management, Tehran, Amirkabir University of Technology (in Persian).

25)   Mohtashami, A., Akbarpour, A., Mollazadeh, M. 2017. Development of two dimensional groundwater flow simulation model using meshless method based on MLS approximation function in unconfined aquifer in transient state. Journal of Hydroinformatics, 19(5), 640-652.

26)   Mohtashami, A., Akbarpour, A., Mollazadeh, M. 2017. Modeling of groundwater flow in unconfined aquifer in steady state with meshless local Petrov-Galerkin. Modares Mechanical Engineering, Vol. 17, No. 2, pp. 393-403, 2017 (in Persian).

27)   Momeni, M. 2003. The role of climate change and its impact on ecological instability in Iran ", Third Regional and First National Conference on Climatology, Isfahan University (in Persian).

28)   Parhizkari, M. 2019. Multi-objective operation optimization of hydropower reservoirs by MOPSO Case study: Karun Dam 5. Iran Water Resources Research, 15(1), 250-255.

29)   Portmann, F. T., Döll, P., Eisner, S., & Flörke, M. (2013). Impact of climate change on renewable groundwater resources: assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections. Environmental Research Letters, 8(2), 024023.

30)   Rojas, R., Feyen, L., & Dassargues, A. 2008. Conceptual model uncertainty in groundwater modeling: Combining generalized likelihood uncertainty estimation and Bayesian model averaging. Water Resources Research, 44(12).

31)   Salami, H., Nassery, H., Massah Bavani, A. 2015. Probabilistic forecast of climate change effects on Hamadan-Bahar aquifer. Water and Irrigation Management, 5(1), 27-41 (In Persian).

32)   Salehnia, N., Alizadeh, A., Sayari, N. 2014. Comparison of Two Downscaling Methods (LARS-WG and ASD) In Predicting Precipitation and Temperature under Climate Change in Different Climate. Iranian Journal of Irrigation & Drainage, 8(2), 233-245 (In Persian).

33)   Sanchez f, Gunnink E, van Baaren S, Oude Essink G. H. P,  Siemon B, Auken E,  Elderhorst W,  and de Louw P. G. B 2012. Modelling climate change effects on a Dutch coastal groundwater system using airborne electromagnetic measurements. Hydrology and Earth System Sciences, 16(12), 4499-4516.

34)   Semenov, M.Barrow E. 1997. Use of a Stochastic Weather Generator in the Development of Climate Change Scenarios Climatic Change. Kluwer Academic Publishers. Printed in the Netherlands. 35: 397–414.

35)   Son, K., & Sivapalan, M. 2007. Improving model structure and reducing parameter uncertainty in conceptual water balance models through the use of auxiliary data. Water resources research, 43(1).

36)   South Khorasan Regional Water Authority. 2016. Birjand’s groundwater Balance report (in Persian).

37)   Thomas, B. F., Behrangi, A., & Famiglietti, J. S. 2016. Precipitation intensity effects on groundwater recharge in the southwestern United States. Water, 8(3), 90.

38)   Thompson J. R, Gavin H, Refsgaard A, Refstrup Sorenson H,  Gowing D. J, 2009. Modelling the hydrological impacts of climate change on UK lowland wet grassland. Wetlands Ecology and Management, 17(5), 503-523.

39)   Toews MW, Allen DM 2009. Simulated response of groundwater to predicted recharge in a semi-arid region using a scenario of modelled climate change. Environmental Research Letters, 4(3), 035003.

40)   Touhami, I., E. Chirino, J. Andreu, J. Sánchez, H. Moutahir & J. Bellot 2015. Assessment of climate change impacts on soil water balance and aquifer recharge in a semiarid region in south east Spain. Journal of Hydrology, 527, 619-629.

41)   Wang, H., Gao, J. E., Zhang, M. J., Li, X. H., Zhang, S. L., & Jia, L. Z. 2015. Effects of rainfall intensity on groundwater recharge based on simulated rainfall experiments and a groundwater flow model. Catena, 127, 80-91.

42)   Wilks, D. 1992. Adapting stochastic weather generation algorithms for climate change studies. Climate Change, 22, 67-84