Helmand River Flow Modeling Using Copula and Seasonal Auto Regressive Moving Average

Document Type : Research Article

Authors

• A.A. Keikha ¹
• M. Mosannan Mozafari ²
• M. Sabouhi ²
• Gh. Soltani ³

¹ University of Zabol

² Ferdowsi University of Mashhad

³ University of Shiraz

10.22067/jsw.v0i0.29031

Abstract

River flow modeling has special importance in water resources management. Since the actual river flow data are often low and they correlate and depend yearly and monthly, making the data similar to historical data is so difficult and complex. In this study, 50 year data and Seasonal Auto Regressive Moving Average (SARMA) and Clayton and Frank Copulas which are the prediction and simulation methods of the river flow molding, were used to generate random flow data of Helmand River. Results show, SARMA model forecasts minimum river flow data very good, but the generated data hasn’t correlation of historical data and usually the maximum river flow is greater than real data. Otherwise, Copula preserved concordance of real data and make the data that are similar to real river flow. Therefore it is proposed that Copula is used for Helmand river flow modeling. Also this method use for simulating other river flows and also using other Copulas for river flow modeling could have the subject of future researches.

Keywords

• simulation
• prediction
• water resources management
• Correlation

20.1001.1.20084757.1393.28.3.8.8

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References

1- ابریشمی ح. 1383. مبانی اقتصادسنجی (ترجمه). چاپ سوم، انتشارات دانشگاه تهران، صفحات 907 تا 925.

2- خلقی اشکلک م. 1386. اندازه‌گیری میزان رادون و رادیوم آب منطقه سیستان. پایان‌نامه کارشناسی ارشد. گروه فیزیک. دانشکده علوم دانشگاه پیام نور، واحد مشهد.

3- دلیری ف.، و خلقی م. 1388. اصلاح روش IUDRN به منظور شبیه‌سازی استوکاستیکی دبی سالانه رودخانه ها (مطالعه موردی: رودخانه اریه استان خراسان رضوی). علوم و مهندسی آبخیزداری ایران. 3 (6)، 1-8.

4- اندرس و. 1386. اقتصادسنجی سری های زمانی با رویکرد کاربردی (صادقی م.، و شوال پور س.). چاپ اول، انتشارات دانشگاه امام صادق، 526 ص.

5- طرازکار م.، و صدق آمیز ع. 1387. مقایسه پیش بینی دبی جریان ماهانه رودخانه کرخه با استفاده از روش های سری زمانی و هوش مصنوعی. پژوهش و سازندگی در منابع طبیعی. 80، 51-58.

6- نوفرستی م. 1378. ریشه واحد و همجمعی در اقتصادسنجی. چاپ اول، موسسه خدمات فرهنگی رسا، 183 ص.

7- وزارت نیرو. 1390. گزارش برنامه‌ریزی منابع آب رودخانه و مخازن چاه نیمه‌های سیستان. جلد دوم. شرکت سهامی آب منطقه ای استان سیستان و بلوچستان، زابل.

8- Dogan I., Toluk T., and Sandalci M. 2007. Daily stream flow forecasting using artificial neural networks, International Congress River Flood Management, 448-459.

9- Favre A.C., El Adlouni S., Perreault L., Thiemonge N., and Bobee B. 2004. Multivariate hydrological ferequency analysis using copulas, Water Resource Research, 40:1-12.

10- Genest C., and Mackay L. 1986. The joy of copulas: Bivariate distributions with uniform marginals, The American Statistician, 40 (4), 280–283.

11- Hardaker J.B., Hurine R.B.M., Anderson J. R., and Lein G. 2004. Coping with Risk in Agriculture. 2nd den, CABI, Wallingford, Oxford, U.K.

12- Hope A., and Bart R. 2012. Evaluation of a regionalization approach for daily flow duration curves in central and southern California watersheds, Journal of the American Water Resources Association (JAWRA), 48(1): 123-133.

13- http://www.vosesoftware.com/index.php

14- Kao S.C., and Govindaraju R.S. 2010. A copula-based joint deficit index for droughts, Journal of Hydrology, 380: 121–134.

15- Kehkha A,A. 2005. Modeling water resources management in the Sistan region of Iran, Thesis for the degree of Doctor of Philosophy, University of New England.

16- Remillard B., Papageorgiou N., and Soustra F. 2012. Copula-based semi parametric models for multivariate time series, Journal of Multivariate Analysis, 110:30-42.

17- Renard B., and Lang M. 2007. Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology, Advances in Water Resources, 30: 897–912.

18- Van der Lee J.J. 2002. A decision support framework for participative and integrated river basin management: an application of the triple bottom line, Ph.D. thesis, University of New England, Australia.

19- Wang W. 2005. Testing and modeling autoregressive conditional heteroskedasticity of stream flow process, Nonlinear processes in Geophysics, 12: 55-66.

20- Zhang L., and Singh V.P. 2007. Bivariate rainfall frequency distributions using Archimedean copulas, Journal of Hydrology, 332, 93–109