Multi-objective planning for optimal utilization of surface and groundwater resources and artificial recharge system

Document Type : Research Article

Authors

1 Department of Water Science and Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Professor, Department of Water Science and Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Agronomy, Karaj Branch, Islamic Azad University, Karaj, Iran

Abstract

Population growth and water needs in developed areas have created many challenges in supplying water needs. This causes numerous problems in the quantity and quality of surface and groundwater resources. Given this issue, optimal water resources management is essential. In this research, the mathematical model (HEC-HMS) was used for flood routing in Karaj river and artificial recharge system reservoirs located in the north of Shahriar plain. Then, the volume of flood infiltration in Karaj river and also the volume of flood storage in the reservoirs of artificial recharge plan entered the multi-objective genetic algorithm (NSGA-II) from which to plan the integrated exploitation of water resources of Shahriar plain and optimal utilization of the artificial recharge system and It was used with the aim of minimizing the lack of need supply and maximizing the volume of penetration in the artificial recharge system. Considering to the results of the optimization model, the optimal allocation volume of surface water, groundwater, Wastewater and Robat Karim canal and the amount of optimal recharge volume in the artificial recharge system were determined. The results of this study show the high performance of optimal planning models to increase the stability of groundwater system, optimal utilization of surface water resources and wastewater, more efficient use of artificial recharge system and irrigation and drainage network of Robat Karim.

Keywords

References

 [1]. Peralta RC, Forghani A, Fayad H. Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow. J Hydrol. 2014;511:776-85.
[2]. Das B, Singh A, Panda SN, Yasuda H. Optimal land and water allocation policies for sustainable irrigated agriculture. Land Use Policy. 2015;42:527-37.
[3]. Zekri S, Triki C, Al-Maktoumi A, Bazargan-Lari MR. An optimization-simulation approach for groundwater abstraction under recharge uncertainty. Water Resour Manag. 2015;29(10):3681-95.
[4]. Dalcin AP, Fernandes Marques G. Integrating water management instruments to reconcile a hydro‐economic water allocation strategy with other water preferences. Water Resour Manag. 2020;56(5):e2019WR025558.
[5]. Danapour M, Fienen MN, Højberg AL, Jensen KH, Stisen S. Multi‐constrained catchment scale optimization of groundwater abstraction using linear programming. Groundwater. 2021;59(4):503-16.
[6]. Matsukava J, Finney B, Willis R. Conjunctive-Use planning in Mad river basin, California. J Water Resour Plan Manag. 1992;118(2).
[7]. Rao SVN, Murty Bhallamudi S, Thandaveswara BS, Mishra GC. Conjunctive Use of Surface and Groundwater for Coastal and Deltaic Systems. J Water Resour Plan Manag. 2004;130(3):255–67.
[8]. Vedula S, Mujumdar PP, Chandra Sekhar G. Conjunctive Use Modeling for Multicrop Irrigation. Agric Water Manag. 2005;73(3):193–221.
[9]. Barlow PM, Ahlfeld DP, Dickerman DC. Conjunctive-Management Models for Sustained Yield of Stream-Aquifer Systems. J Water Resour Plan Manag. 2003;129(1):35–48.
[10]. Yan Z, Sha J, Liu B, Tian W, Lu J. An ameliorative whale optimization algorithm for multi-objective optimal allocation of water resources in Handan, China. Water. 2018 Jan;10(1):87.
[11]. Karamouz M, Rezapour Tabari MM, Kerachian R. Application of Genetic Algorithms and Artificial Neural Networks in Conjunctive Use of Surface and Groundwater Resources. Water Int. 2007;32(1):163–76.
[12]. Safavi HR, Darzi F, Mariño MA. Simulation-Optimization Modeling of Conjunctive Use of Surface Water and Groundwater. Water Resour Manag. 2010;24(10):1965–88.
[13]. Pulido-Velazquez D, Ahlfeld D, Andreu J, Sahuquillo A. Reducing the Computational Cost of Unconfined Groundwater Flow in Conjunctive-Use Models at Basin Scale Assuming Linear Behaviour: The Case of Adra-Campo de Dalías. J Hydrol. 2008;353(1–2):159–74.
[14]. Pereira-Cardenal SJ, Mo B, Gjelsvik A, Riegels ND, Arnbjerg-Nielsen K, Bauer-Gottwein P. Joint optimization of regional water-power systems. Adv Water Resourc. 2016;92:200-7.
[15]. Mooselu MG, Nikoo MR, Latifi M, Sadegh M, Al-Wardy M, Al-Rawas GA. A multi-objective optimal allocation of treated wastewater in urban areas using leader-follower game. J Clean Product. 2020;267:122189.
[16] Dai C, Qin XS, Chen Y, Guo HC. Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach. J Hydrol. 2018;561:322-34.
[17]. Guan H, Chen L, Huang S, Yan C, Wang Y. Multi-objective optimal allocation of water resources based on ‘three red lines’ in Qinzhou, China. Water Policy. 2020 Aug 1;22(4):541-60.
 
[18]. Fatkhutdinov A, Stefan C. Multi‐Objective Optimization of Managed Aquifer Recharge. Groundwater. 2019;57(2):238-44.
[19]. Ghayoumian J, Mohseni Saravi M, Feiznia S, Nouri B, Malekian A. Application of GIS techniques to determine areas most suitable for artificial groundwater recharge in acoastal aquifer in southern Iran. J Asian Earth Sci. 2007;30:364–74.
[20]. Senanayake IP, Dissanayake DM, Mayadunna BB, Weerasekera WL. An approach to delineate groundwater recharge potential sites in Ambalantota, Sri Lanka using GIS techniques. Geoscience Frontiers. 2016;7:115e124.
[21]. Nasiri H, Darvishi boloorani A, Faraji sabokbar AH, Jafari HR, Hamzeh M, Rafii Y. Determining the most suitable areas for artificial groundwater recharge via an integrated PROMETHEE II-AHP method in GIS environment (case study: Garabaygan basin, Iran). Environ Monit Assess 2013;185(1):707-18.
[22]. Singh A, Panda SN, Kumar KS, Shekhar Sharma C. Artificial groundwater recharge zones mapping using remote sensing and GIS: a case study in Indian Punjab. Environ Manag 2013;52(1):61-71.
[23]. Chenini I, Abdallah BM. Groundwater recharge study in arid region: An approach using GIS techniques and numerical modeling. Comput Geosci. 2010;36(6):801-17.
[24]. Chowdhury A, Jha MK, Chowdary VM. Delineation of groundwater recharge zones and
identification of artificial recharge sites in west Medinipur district, west Bengal, using RS, GIS and MCDM techniques. Environ Earth Sci. 2010;59(6):1209–22.
[25]. Ahmadi MM, Mahdavirad H, Bakhtiari B. Multi-criteria analysis of site selection for groundwater recharge with treated municipal wastewater. Water Sci Technol. 2017;76(4):909-19.
[26]. Humberto HA, Raúl CC, Lorenzo VV, Jorge RH. Aquifer recharge with treated municipal wastewater: long-term experience at San Luis Rio Colorado, Sonora. Sustain Water Resour Manag. 2018;4(2):251-60.
[27]. Voudouris K, Diamantopoulou P, Giannatos G, Zannis P. Groundwater recharge via deep boreholes in the Patras Industrial Area aquifer system (NW Peloponnesus, Greece). Bull Eng Geol Environ. 2005;65(3):297-308.
[28]. Agarwal R, Garg PK, Garg RD. Remote Sensing and GIS Based Approach for Identification of Artificial Recharge Sites. Water Resour Manag. 2013;27(7):2671-89.
[29]. Ye Q, Li Y, Zhuo L, Zhang W, Xiong W, Wang C, Wang P. Optimal allocation of physical water resources integrated with virtual water trade in water scarce regions: A case study for Beijing, China. Water Res. 2018;129:264-76.
[30]. Sayit AP, Yazicigil H. Assessment of artificial aquifer recharge potential in the Kucuk Menderes River Basin, Turkey. Hydrogeol J. 2012;20(4):755-66.
Iranian journal of Ecohydrology
Volume 9, Issue 1
April 2022
Pages 77-95

Files

History

  • Receive Date: 23 September 2021
  • Revise Date: 21 November 2021
  • Accept Date: 31 January 2022
  • First Publish Date: 21 March 2022
  • Publish Date: 21 March 2022

Share

How to cite

Statistics