Numerical modeling of the Ardabil plain aquifer and its management using optimization of Groundwater extraction

Document Type : Research paper

Authors

1 Department of Earth Sciences, Faculty of Sciences, University of Kurdistan, Sanandaj, Iran

2 Department of Earth Sciences, University of Tabriz, Tabriz, Iran

3 Faculty of Earth Sciences, Kharazmi University, Tehran, Iran

Abstract

The rapid increase in the water demand has led to large-scale groundwater developments in Ardabil plain, and intense extraction of groundwater has caused water table to decline as much as 12 m during the past 25 years. Optimizing the groundwater extraction in order to prevent declination of the water table and the possible compensation of the reduction in the aquifer storage are the main purposes of this dissertation. In this study, according to the modeling protocol, at first the conceptual model of Ardabil plain aquifer has been designed. For this purpose, a geodatabase consist of qualitative and quantitative data has been created. Then Kriging and fuzzy logic methods have been applied to create different kinds of zoning maps. After determining of the aquifer geometry, input and output parameters, groundwater flow system and hydraulic parameters, Modflow 2005 code was used by the PMWIN8 software for modeling the groundwater flow in Ardabil plain aquifer. After the modeling, based on the declination of the water table, the Ardabil plain aquifer has been divided to 15 zones. Then, the optimized values of the permissible extraction have been estimated using the PEST software. Based on the obtained results to balance the water table of Ardabil aquifer, the highest and the least exploitation should be at the east and center of the plain respectively.

Keywords


نیکبخت، ج.، ذوالفقاری، م.، نجیب، م.، 1395. پیش‌بینی سطح آب زیرزمینی دشت تسوج-آذربایجان‌شرقی با کمک شبکه‌های عصبی مصنوعی. هیدروژئولوژی، دوره 1، شماره 2، 99-115.
میرعباسی نجف‌آبادی، ر.، ستاری، م. ت.، برقی ولینجق، و.، 1395. شبیه‌سازی و مدیریت بهره‌برداری از آب زیرزمینی دشت عجب‌شیر. هیدروژئولوژی، دوره 1، شماره 1، 57-75.
 Anderson, M.P., Woessner, W.W., 1992. Applied groundwater modeling flow and advective transport. Academic press, Inc. 381 p.
Chiang, W. H., 2001. 3D-groundwater modeling with PMWIN: A simulation system for modeling groundwater flow and transport processes, Springer, New York.
Chiang, W. H., Kinzelbach, W., 2001. 3D-groundwater modeling with PMWIN, Springer, New York, 346 p.
Ghosh, N. C., Sharma, K. D., 2006. Groundwater modeling management. Capital Publishing Company. New Delhi, 594 p.
Hill, M. C., 1998. Methods and guidelines for effective model calibration. U. S. Geol. Survey water- Res. Invest. Rep. 98-4005: 90pp.
Nishikawa, T., 1998. Water resources optimization model for Santa Barbara, California. Journal of Water Resources Planning and Management. 124 (5): 1213 – 1235.
Sulaiman Kharmah, R. A., 2007. Optimal management of groundwater pumping, the case of the Eocene Aquifer, Palestine. MSc thesis. Faculty of Graduate Studies, at An-Najah National University, Nablus, Palestine, 136 p.
Switzerland, Z., 1999. Calibration and reliability in groundwater modeling coping with uncertainty. IAHR Model Care. 99: 739-744.
Yeh, J., Mock, P. A., 1995. A structured approach for calibrating steady- state groundwater flow models. Groundwater. 18(2): 444-450.
Yan, Q., Ma, C., 2016. Application of integrated ARIMAand RBF network for groundwater level forecasting. Environmental Earth Sciences. 75(5): 1-13.
Karayiannis, N.B., Venetsanopoulos, A.N., 1993. Artificial Neural Network: Learning Algorithms, Performance Evaluation, and Application, Kluwer Academic Publisher. Boston. 523p. Mason, J.C., Price, R.K., Tem, m.e., 1996. A neural network model of rainfall-runoff using radial basis functions. Hydraulic Research. 34: 537-548.
Mishra, A.K., Desai, V.R., 2006. Drought forecasting using feed- forward recursive neural network, Ecological modeling. 98: 127-138.
Rajaee, T., Mirbagheri, S. A., Nourani, V., Alikhani, A., 2010. Prediction of daily suspendedsediment load using wavelet and neuro-fuzzycombined model. Environmental Science and Technology. 7(1): 93-110.