Development of a System Dynamics Model for Prediction of Karaj Reservoir Share in Tehran Water Supply

Document Type : Research

Authors

1 PhD Candidate of Environmental engineering, Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran

2 Associate Professor of Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran

3 Associate professor, School of Civil Engineering, College of Engineering, University of Tehran

Abstract

Tehran's water consumption (TWC) is rising as a result of rapid population growth, climate change, and precipitation decline. As water resources of Tehran are also affected by a variety of factors, the water supply scheme becomes so complicated and it is necessary to consider the complexity and dynamics interactions in water supply system before any decision making. In this study, Karaj reservoir as an important surface water resource of Tehran’s water supply system was modeled through system dynamics (SD) approach for prediction of Karaj Dam share in Tehran water supply. The SD model was implemented in AnyLogic software using the historical data from April 2006 to March 2022, and the stock and flows and dynamics variables were predicted for April 2023 to March 2023. The novelty of this research is the development of SD model of Karaj Dam to simulate its relationships and interactions for prediction of Karaj Dam share in Tehran water supply in 2023. In this regard, the TWC and Karaj Dam inflow were predicted by using SARIMA(1,0,0)(0,1,1)12 model for April 2023 to March 2023. Finally, to assess the precision of the results obtained from the SARIMA and SD models, the criteria of coefficient of determination (R2), Error percentage (E%), and Nash–Sutcliffe model efficiency coefficient (NS%) was calculated. The results showed that the Karaj Dam inflow will be decreased during April 2023 to March 2023 due to the precipitation decline, consequently the Karaj Dam reservoir volume will be reduced and for this reason less water can be harvested from Karaj Dam reservoir for different applications. Therefore, it is clear that in the future we will have faced the challenge of water supply in Tehran.

Keywords

Main Subjects


[1] B.A. Bryan, Future global urban water scarcity and potential solutions, Nat. Commun. (2021) 1–11. https://doi.org/10.1038/s41467-021-25026-3.
[2] O. Bozorg-Haddad, P. Dehghan, B. Zolghadr-Asli, V.P. Singh, X. Chu, H.A. Loáiciga, System dynamics modeling of lake water management under climate change, Sci. Rep. 12 (2022) 5828.
[3] H. Sarmadi, E. Salehi, N. Kusari, The mega city of Tehran water quantity assessment based on DPSIR model, J. Phys. Conf. Ser. 1834 (2021) 0–6. https://doi.org/10.1088/1742-6596/1834/1/012007.
[4] R. Modarres, V. de Paulo Rodrigues da Silva, Rainfall trends in arid and semi-arid regions of Iran, J. Arid Environ. 70 (2007) 344–355. https://doi.org/10.1016/j.jaridenv.2006.12.024.
[5] worldpopulationreview, No Title, (n.d.) https://worldpopulationreview.com/search?query=teh.
[6] M. Zomorodian, S.H. Lai, M. Homayounfar, S. Ibrahim, S.E. Fatemi, A. El-Shafie, The state-of-the-art system dynamics application in integrated water resources modeling, J. Environ. Manage. 227 (2018) 294–304. https://doi.org/10.1016/j.jenvman.2018.08.097.
[7] H. Nozari, P. Moradi, E. Godarzi, Simulation and optimization of control system operation and surface water allocation based on system dynamics modeling, J. Hydroinformatics. 23 (2021) 211–230. https://doi.org/10.2166/HYDRO.2020.294.
[8] J.M. García, Theory and practical exercises of system dynamics: modeling and simulation with Vensim PLE. Preface John Sterman, Juan Martin Garcia, 2023.
[9] T.D. Phan, E. Bertone, R.A. Stewart, Critical review of system dynamics modelling applications for water resources planning and management, Clean. Environ. Syst. 2 (2021) 100031. https://doi.org/10.1016/j.cesys.2021.100031.
[10] A. Bafkar, J. Mozafari, H. Alizadeh, Determination of Optimal Irrigation Water Supply Scenario for Karkheh Dam to Prevent Drainage Problems of Dashte Abbas Plain Using System Dynamics Approach, J. Agric. Sci. Technol. 24 (2022) 707–722.
[11] A.F. Abdolvandi, S.S. Eslamian, M. Heidarpour, H. Babazadeh, Armaghanparsamehr, Simultaneous simulation of both surface and groundwater resources using system dynamics approach (Case study: Taleghan dam), Adv. Environ. Biol. 7 (2013) 562–570.
[12] Z. Li, C. Li, X. Wang, C. Peng, Y. Cai, W. Huang, A hybrid system dynamics and optimization approach for supporting sustainable water resources planning in Zhengzhou City, China, J. Hydrol. 556 (2018) 50–60. https://doi.org/10.1016/j.jhydrol.2017.11.007.
[13] E. Bakhshianlamouki, S. Masia, P. Karimi, P. van der Zaag, J. Sušnik, A system dynamics model to quantify the impacts of restoration measures on the water-energy-food nexus in the Urmia lake Basin, Iran, Sci. Total Environ. 708 (2020) 134874. https://doi.org/10.1016/j.scitotenv.2019.134874.
[14] A. Babolhakami, M.A. Gholami Sefidkouhi, A. Emadi, Application of system dynamics model for reservoir performance under future climatic scenarios in Gelevard Dam, Iran, AQUA — Water Infrastructure, Ecosyst. Soc. 00 (2023) 1–15. https://doi.org/10.2166/aqua.2023.193.
[15] K.B. Tadesse, M.O. Dinka, Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa, J. Water L. Dev. 35 (2017) 229–236. https://doi.org/10.1515/jwld-2017-0088.
[16] M. Imran, M.D. Majeed, M. Zaman, M.A. Shahid, D. Zhang, S.M. Zahra, R.M. Sabir, M. Safdar, Z. Maqbool, Artificial Neural Networks and Regression Modeling for Water Resources Management in the Upper Indus Basin, (2023) 53. https://doi.org/10.3390/ecws-7-14199.
[17] H. Allah, M. Sadegh, Estimation of Water Demand in Iran Based on SARIMA Models, (2013) 559–565. https://doi.org/10.1007/s10666-013-9364-4.
[18] A.S. Azad, R. Sokkalingam, H. Daud, S.K. Adhikary, H. Khurshid, S.N.A. Mazlan, M.B.A. Rabbani, Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study, Sustain. 14 (2022). https://doi.org/10.3390/su14031843.
[19] M. Valipour, M.E. Banihabib, S.M.R. Behbahani, Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir, J. Hydrol. 476 (2013) 433–441. https://doi.org/10.1016/j.jhydrol.2012.11.017.
[20] W. chuan Wang, K. wing Chau, D. mei Xu, X.Y. Chen, Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition, Water Resour. Manag. 29 (2015) 2655–2675. https://doi.org/10.1007/s11269-015-0962-6.
[21] R.M. Adnan, X. Yuan, O. Kisi, V. Curtef, Application of time series models for streamflow forecasting, Civ. Environ. Res. 9 (2017) 56–63.
[22] I.M. Almanjahie, Z. Chikr-Elmezouar, A. Bachir, Modeling and forecasting the household water consumption in Saudi Arabia, Appl. Ecol. Environ. Res. 17 (2019) 1299–1309. https://doi.org/10.15666/aeer/1701_12991309.
[23] P. Aghelpour, V. Varshavian, Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series, Stoch. Environ. Res. Risk Assess. 34 (2020) 33–50. https://doi.org/10.1007/s00477-019-01761-4.
[24] X. Wang, W. Tian, Z. Liao, Statistical comparison between SARIMA and ANN’s performance for surface water quality time series prediction, Environ. Sci. Pollut. Res. 28 (2021) 33531–33544. https://doi.org/10.1007/s11356-021-13086-3.
[25] M.M.R. Tabari, R. Safari, Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: a case study of Karaj AmirKabir dam, Soft Comput. 27 (2023) 6521–6547. https://doi.org/10.1007/s00500-023-07885-8.
[26] X. jun Wang, J. yun Zhang, J. fu Liu, G. qing Wang, R. min He, A. Elmahdi, S. Elsawah, Water resources planning and management based on system dynamics: A case study of Yulin city, Environ. Dev. Sustain.13(2011),331–351. https://doi.org/10.1007/s10668-010-9264-6.
[27] F. Nasiri, T. Savage, R. Wang, N. Barawid, J.B. Zimmerman, A system dynamics approach for urban water reuse planning: A case study from the Great Lakes region, Stoch. Environ. Res. Risk Assess. 27 (2013) 675–691. https://doi.org/10.1007/s00477-012-0631-8.
[28] L. Willuweit, J.J. O’Sullivan, A decision support tool for sustainable planning of urban water systems: Presenting the dynamic urban water simulation model, Water Res. 47 (2013) 7206–7220. https://doi.org/10.1016/j.watres.2013.09.060.
[29] W. Zeng, B. Wu, Y. Chai, Dynamic simulation of urban water metabolism under water environmental carrying capacity restrictions, Front. Environ. Sci. Eng.10,(2016),114–128. https://doi.org/10.1007/s11783-014-0669-6.
[30] A. Kumar Dubey, A. Kumar, V. García-Díaz, A. Kumar Sharma, K. Kanhaiya, Study and analysis of SARIMA and LSTM in forecasting time series data, Sustain. Energy Technol. Assessments. 47 (2021) 101474. https://doi.org/10.1016/j.seta.2021.101474.
[31] W.M. Thupeng, K. Samakokore, Modelling Monthly water consumption for Gaborone using log-logistic and related distributions, (n.d.).
[32] P.P. Dabral, M.Z. Murry, Modelling and Forecasting of Rainfall Time Series Using SARIMA, Environ. Process. 4 (2017) 399–419. https://doi.org/10.1007/s40710-017-0226-y.
[33] A. Chakrabarti, J.K. Ghosh, AIC, BIC and Recent Advances in Model Selection, Elsevier B.V., 2011. https://doi.org/10.1016/B978-0-444-51862-0.50018-6.
[34] S.O. Adams, M. Bamanga, M. Ardo Bamanga, Modelling and Forecasting Seasonal Behavior of Rainfall in Abuja, Nigeria; A SARIMA Approach, Am. J. Math. Stat. 2020 (2020) 10–19. https://doi.org/10.5923/j.ajms.20201001.02.
[35] D.K.D. et. al. . D. K. Dwivedi et.al., Forecasting Mean Temperature using Sarima Model for Junagadh City of Gujarat, Int. J. Agric. Sci. Res. 7 (2017)183–194. https://doi.org/10.24247/ijasraug201723.