Evaluating the effects of climate change on Lar Basin Water Resources Using SWAT Model and comparing its results with Bayesian Networks and Hybrid Intelligent Models

Subject Areas : Climatology

Mahsa Solimani Puor ¹ , Amirpouya Sarraf ²

1 - Master student of Civil Engineering, Roodehen Branch, Islamic Azad University, Roodehen, Iran
2 - Assistant Professor, Department of Civil Engineering, Roodehen Branch, Islamic Azad University, Roodehen, Iran

Received: 2021-02-04 Accepted : 2021-02-04 Published : 2020-12-21

Keywords: LARS-WG, Climate Change, SWAT, Bayesian networks, Wavelet-Neural Network Model, Sensetivity analysis,

Abstract :

Iran's location on the arid and semi-arid belt of the world, as well as the mismanagement of water resources, has created a warning situation of water shortage in many parts of the country. The present research evaluates the effects of climate change on temperature, rainfall and runoff in future periods with the help of LARS-WG statistical model and SWAT hydrological conceptual model for Lar Basin. To estimate the flow rate of the river, the performance of Bayesian network and the combined wavelet-neural network model are also examined. After entering the rainfall and temperature information of the region, runoff was simulated for two hydrometric stations of Gozeldareh and Plour and the outflow runoff of Plour station between 1979 to 2018 was calibrated and validated as a control point. In order to evaluate the efficiency of the models, the criteria of Nash-Sutcliffe and explanation coefficient are used. According to climate models, the highest temperature increase in the final period and under the RCP8.5 climate scenario shows about 10% increase in temperature in spring and winter. Finally, among these models, the physical model with an average annual prediction of 6.04 cubic meters per second according to the observation period, showed a decrease in runoff.

References:

1- افخمی‌فر س. صراف ا پ. (1399): پیش‌بینی تراز سطح آب زیرزمینی آبخوان دشت ارومیه با استفاده از مدل هیبرید تبدیل موجک - ماشین یادگیری بیشینه و بهینه‌سازی با ازدحام ذرات کوانتومی، نشریه علمی - پژوهشی مهندسی و مدیریت آبخیز، دوره 12، شماره 2، تابستان 1399، صص 351-364.

2- جیحونی ا. محمدی م‌ع. اسلامیان س‌س. زارعیان م‌ج (1397): ارزیابی اثر تغییر اقلیم بر تراز آب زیرزمینی با استفاده از روش‌های هوش مصنوعی (مطالعه موردی: دشت شبستر)، اولین کنفرانس ملی مهندسی زیرساخت‌ها (دانشگاه ارومیه).

3- رجایی ط. شهابی. (1393): کاربرد مدل ترکیبی موجک - شبکه عصبی در پیش‌بینی تغییرات کوتاه مدت تراز سطح دریا (مطالعه موردی: بندر چابهار)، نشریه علمی - پژوهشی دریا فنون. دوره 1. شماره 2. صص 42-53.

4- سلیمانی‌پور م. صراف ا پ. (1398): ارزیابی اثرات تغییرقلیم بر منابع آب حوضه آبریز لار با استفاده از مدل SWAT و مقایسه نتایج آن با شبکه‌های بیزین و مدل‌های هوشمند هیبریدی، پایان‌نامه کارشناسی ارشد، رشته مهندسی عمران، دانشگاه آزاد اسلامی واحد رودهن.

5- مرادی ح ر. آذریم. ثقفیان ب. فرامرزیم. (1392): ارزیابی اثرات هیدرولوژیکی تغییر اقلیم در حوضه آبخیز گرگانرود، نشریه آب و خاک، شماره 3، صص 537-547.

6- نوریم. میرحسینی سم. زینال‌زاده م ب. (1386): الگوی جدید بارش - رواناب حوضه آبریز هلیل‌رود با استفاده از مدل هیبرید موجک - شبکه عصبی مصنوعی. نشریه زمین‌شناسی مهندسی، جلد ۲، شماره ۲: صص ۴۷۲-۴۵۱.

 

7- Banihabib, M. E. Arabi, A. And Salha, A. A. (2015): A Dynamic Artificial Neural Network For Assessment Of Land-Use Change Impact On Warning Lead-Time Of Flood. International Journal Of Hydrology Science And Technology. 5(2): Pp. 163-178.

8- Baran, E. And Jantunen. (2004): Stakholders Consultationfor Bayesian Decision Systems In Environmental Management, Proceeding Of The Regional Conference On Ecological And Environmental Modeling (ECOMOD 2004), Universiti Stans Malaysia.

9- Kazemi, D. H. Rasul, G. Li, J. And Cheema. S. B. (2014): Comparative Study For ECHAM5 And SDSM In Downscaling Temperature For A Geo-Climatically Diversified Region. Pakistan. Applied Mathematics. 5: Pp. 137-143.

10- Lotfimanesh, B. And Barani, G.A. (2019): Optimal Utilization Of Surface Waters Due To Climate Change Effect By Genetic Algorithm (Case Study Of Zayandeh-Rood Basin), 5th Annual National Conference On Civil Engineering. Architecture And Design Of Iran. Mashhad Scientific Institute And Research, Mashhad, Iran (In Persian).

11- Maheswaran, R And Khosa, R. (2013): Long Term Forecasting Of Groundwater Levels With Evidence Of Nonstationary And Nonlinear Characteristics. Computers & Geosciences. 52: Pp. 422-436.

12- Moazami Goudarzi F. Sarraf A. P. And Ahmadi H. (2020): Prediction Of Runoff Within Maharlu Basin For Future 60 Years Using RCP Scenarios. Arabian Journal Of Geosciences, (2020) 13: Pp. 605, 1-17.

13- Moazami Goudarzi F. Sarraf A. P. And Ahmadi H. (2020): Assessment Of SM2RAIN-ASCAT And CMORPH Satellite Precipitation Products Over Maharlu Lake Basin In Iran. Water Supply (2020) 20 (5): Pp. 1799-1806.

14- Moosavi, V. Vafakhah, M. Shirmohammadi, B. And Ranjbar, M. (2014): Optimization Of Wavelet-ANFIS And Wavelet-ANN Hybrid Models By Taguchi Method For Groundwater Level Forecasting. Arabian Journal For Science And Engineering, 39(3): Pp. 1785-1796.

15- Souvignet, M. Gaese, H. Ribbe, L. Kretschmer, N. And Oyarzun, R. (2010): Statistical Downscaling Of Precipitation And Temperature In North- Central Chile: An Assessment Of Possible Climate Change Impacts In An Arid Andean Watershed. Hydrological Sciences Jornal. 55(1): Pp.  41-57.

16- Zamani, R. Akhund Ali, A. And Roozbahani, M. A. (2019): Investigating The Impact Of Climate Change On Runoff Under Different Probabilistic Levels Using The Uncertainty Approach (Case Study: Yellow River Basin). Irrigation Science And Engineering, In Press (In Persian).