Forecasting of Groundwater Fluctuations Using Time Series and GMS Models (Case Study: Rafsanjan Plain)

Document Type : Research Article

Authors

1 Ph.D Student, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran

2 Associate Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran

3 Assistant Professor, Department of Geography, University of Jiroft, Jiroft, Iran

4 Assistant Professor, Department of Natraul Resources, University of Jiroft, Jiroft, Iran

Abstract

Awareness of precipitation changes as an important hydrological component in water resources is essential to provide appropriate management and management approaches for proper utilization of groundwater in arid and semi-arid regions, caused by the lack of rainfall in these areas. Regarding the importance of the subject, in this study, the prediction of fluctuations in groundwater level was influenced by stochastic models in the Rafsanjan plain. Future precipitation was projected using the ARIMA model in EViews9 software for 2017-2023, then groundwater drainage was simulated using the groundwater model system (GMS) during the base period (2003-2016) and results from the ARIMA model for the upcoming period. The results of groundwater drainage simulation showed that in the whole region, groundwater abatement occurred in the upcoming period relative to the base period, and the most groundwater losses occurred in the southwest of the plain, and an annual increase of approximately 130 million cubic meters Groundwater resources are made. In general, groundwater has the highest level (upper level) at the beginning of the period and the lowest level (lowest level) at the end of the statistical period. After modeling the groundwater level for the base period, rainfall prediction from the ARIMA model was applied to the groundwater model with the assumption that the aquifer was operationally constant. The results showed that the aquifer volume deficit was 1021.09 million cubic meters in the final model year (2023). Also, the changes in the level of the aquifer in the Rafsanjan Plain from 2003 to 2023 indicate that, given the estimated rainfall from the ARIMA model, it can be admitted that an average of 1 meter annual waterfall will occur in this plain.

Keywords

References

[1]. Gleeson T, Befus K.M, Jasechko S, Luijendijk E, Cardenas M.B. The global volume and distribution of modern groundwater. Nat. Geosci. 2016; 9 (2):161.
[2]. Liu F, Song X.F, Yang L.H, Han D.M, Zhang Y.H, Ma Y, Bu H.M. The role of anthropogenic and natural factors in shaping the geochemical evolution of groundwater in the Subei Lake basin, Ordos energy base. Northwest. China. Sci. Total. Environ. 2015; 538, 327e340.
[3]. Foster S, Shah T. Groundwater Resources and Irrigated Agriculture-making a Beneficial Relation More Sustainable. Global Water Partnership Perspectives Paper (Stockholm). 2012.
[4]. Jasrotia A. S, Taloor A. K, Andotra U, BhagatB. D. Geoinformatics based groundwater quality assessment for domestic and irrigation uses of the Western Doon valley, Uttarakhand, India. Groundwater for Sustainable Development, 2018; 6, 200-212.
[5]. Salih A. Contribution of UNESCO-international hydrological programme to water resources management in the arabian gulf countries. In: Alsharhan, A.S., Wood, W.W. (Eds.), Water Resources Perspectives: Evaluation. Management and Policy Published in by Elsevier Science, Amsterdam, The Netherlands, 2003; pp. 129–139.
[6]. Basahi J. M, Masoud M. H, & Rajmohan N. Effect of flash flood on trace metal pollution in the groundwater-Wadi Baysh Basin, western Saudi Arabia. Journal of African Earth Sciences, 2018; 147: 338 351.
[7]. Nsubuga F.W.N, Botai O.J, Olwoch J.M, Dew Rauten bach C.J., Yvette B, & Adebayo O.A. The nature of rainfall in the maindrainage sub-basins of Uganda. Hydrological Sciences Journal, 2014; 59 (2): 278-299.
[8]. Haiyun S, Tiejian L, Jiahua W, Wang F, Guangqian W. Spatial and temporal characteristics of precipitation over the Three-River Headwaters region during 1961–2014. Journal of Hydrology: Regional Studies 6 (2016) 52–65.
[9]. Guobin F, Stephen P. C, Francis H.S.C, Jin T, Hongxing Z, Andrew J. F, Wenbin L, Sergey K. 2013.Modelling runoff with statistically downscaled daily site, gridded and catchment rainfall series. Journal of Hydrology, 2013; 492 : 254-265.
[10].            Cao Don N., Thi minh hang N., Araki H., Yamanishi H., Koga K. Groundwater resources management under enviromental in shiroishi of saga plain, japan. Enviromental geology. 2006; 49: 601-609.
[11].            Bear J., Cheng A. H. D. 2010: Modeling Groundwater Flow and Contaminant Transpor. Technion-Israel Institute of Technology, Haifa, and School of Engineering, Kinneret College on the Sea of Galilee, Israel., 2010; 23: 850pp.
[12].            Cao Don N, Araki H, and Yamanishi H, Koga K. Simulation of groundwater flow and enviromental effects resulting from pumping. Enviromental geology.2005; 47: 361-374.
[13].            Asghari Moghadam A, Mahmoudi T, Impacts of Maragheh Industrial Town Wastewater on Groundwater Pollution of Maragheh-Bonab Plain. Environmental Studies. 2008; 34 (45): 15-22. [In Persian].
[14].            Mohamadi M, Moaradi H, Vafakhah M. Characteristics of drought and its impact on groundwater level fluctuations in Arak plain with GIS approach. M.Sc., Faculty of Natural Resources, Tarbiat Modarres University. 2016; 108 p. [In Persian].
 
[15].            PANDA D.K. and KUMAR A. (2011) Evaluation of an over-used costal aquifer (Orissa, India) using statistical approaches. Hydrol. Sci. Jour., 2011; 56(3): 486-497.
[16].            Wang Q. Y, Zhang P. C, Cao B. B, & Hao Y. H. Comparison between grey system and ARIMA model in groundwater simulation—A case study of Liulin Springs discharge simulation. In Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services, 2011; (pp. 400-405). IEEE.
[17].            Rahaman M. M, Thakur B, Kalra A, & Ahmad S. Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin. Hydrology, 2019; 6(1): 19.
[18].            de Moraes Takafuji E. H, da Rocha M. M, & Manzione R. L. Groundwater level prediction/forecasting and assessment of uncertainty using SGS and ARIMA Models: A case study in the Bauru Aquifer System (Brazil). Natural Resources Research, 2019; 28(2):487-503
[19].            Chubin B, Malekian A, Sajedi Hosseini F, Rahmati O. Water level prediction using time series and adaptive neural fuzzy inference system. Iranian Soil and Water Research, 2014; 45 (1): 19-28. [In Persian].
[20].            Nozarpour L, Chitsazan M, Nodri A. Frhadmanesh. M. Evaluation of the Hydraulic Relationship of the Andimeshk Plain and Dez River Aquifer Using the Mudflow Model. Journal of Advanced Applied Geology, 2015; 17: 36-23. [In Persian].
[21].            Shafie M, Musae Sanjari M, Almodaresi A. Investigating the Impact of Climate Change on Groundwater Level Using ARIMA and GCM Models and GIS Modeling in Abarkoh Plain, Yazd, Second National Conference on Application of Advanced Spatial Models (Remote Sensing and GIS) to Land Preparation, 2019; p. 11 -1.
[22].            de Vries J.J, Simmers I. Groundwater recharge: an overview of processes and challenges. Hydrogeol. J. 2002; 10 (1): 5–17.
[23].            Scanlon B.R, Keese K.E, Flint, A.L, Flint L.E, Gaye C.B, Edmunds W.M, Simmers I. Global synthesis of groundwater recharge in
semiarid and arid regions. Hydrol. Process. 2006; 20 (15): 3335–3370.
[24].            Jafari Gadaneh M, Tahedini M, Bakhtiarpour A. Investigation of Spatial and Temporal Changes in Groundwater Level in Rafsanjan Plain, First International Silk Road Scientists Conference, 2019. [In Persian].
[25].            Box G. E. P. and Jenkins G. M. Time series analysis forecasting and control, Holden-Day, San Francisco.1976.
[26].            Karamuz M. Araghonejad SH. Advanced Hydrology, Amir Kabir University Press (Tehran Polytechnic). 2009. [In Persian].
[27].            Modarres, R., & Ouarda, T. B. (2013). Testing and modelling the volatility change in ENSO. Atmosphere-Ocean, 51(5), 561-570.
[28].            Jafari Gadaneh M, Salajeghe A, Malekian A. The Impact of Climate Change on the Quantity and Quality of Groundwater (Case Study: Kerman Plain), MSc Thesis, Faculty of Natural Resources, University of Tehran. 2016. [In Persian].
[29].            Afrouzi A, Zareabianeh H. Groundwater Level Modeling and Forecasting Using Time Series Models (Case Study: Hamadan Plains Plain), Watershed Management Journal. 2017; 8(15): 111-102. [In Persian].
[30].            Jabarbarezi B, Khosravi H. Tavili, A. Investigation of the effects of hail on the aquifer of Jafari plain of Qom, M.Sc., Faculty of Natural Resources, University of Tehran, 2017. [In Persian].
[31].            Mazadeh Y. Groundwater quantitative modeling using GMS software in Quchan plain. Faculty of Agriculture, Ferdowsi University of Mashhad. 2003. [In Persian].
[32].            Gibrilla A, Anornu G, & Adomako D. Trend analysis and ARIMA modelling of recent groundwater levels in the White Volta River basin of Ghana. Groundwater for Sustainable Development, 2018; 6: 150-163.
[33].            Patle G. T, Singh D. K, Sarangi A, Rai A, Khanna M, & Sahoo R. N. Time series analysis of groundwater levels and projection of future trend. Journal of the Geological Society of India, 2015; 85(2): 232-242.
Iranian journal of Ecohydrology
Volume 7, Issue 1
April 2020
Pages 97-109

Files

History

  • Receive Date: 07 December 2019
  • Revise Date: 09 February 2020
  • Accept Date: 09 February 2020
  • First Publish Date: 20 March 2020
  • Publish Date: 20 March 2020

Share

How to cite

Statistics