Modeling groundwater level fluctuations in Tehran aquifer: Results from a 3D unconfined aquifer model
Graphical abstract
Introduction
Most parts of Iran (~ 85%) are considered to be arid and semi-arid. The average precipitation in this country is about 250 mm (mm) per year, less than one-third of the average annual precipitation at the global level (Emamgholizadeh et al., 2014). For many developed basins in Iran, groundwater is the main source of drinking, agriculture, and industrial consumptions (Emamgholizadeh et al., 2014). Over-extraction of this valuable resource has many negative consequences such as: lowering of groundwater table and pollution of aquifer system (Lashkaripour and Ghafoori, 2011), rupture of well casings, cracking of building and roads (Mahmoudpour et al., 2013), higher risk of land subsidence, and thus aquifer compaction (Motagh et al., 2008). The compacted aquifer system permanently loses its capacity to store water. This damage is irreversible and the sediments remain compacted, even if water level rises (Galloway et al., 1999). In order to mitigate the negative effects of groundwater level decline, proper modeling and management of the groundwater resources are essential (Konikow and Kendy, 2005). As the rate of subsidence depends on the loss of hydraulic pressure (Terzaghi, 1925), having an accurate groundwater level model also helps in detecting susceptible areas to deformation (Gurwin and Lubczynski, 2005, Burbey et al., 2006, Yan and Burbey, 2008, Galloway and Burbey, 2011, Ye et al., 2015).
Since late 1960s several studies have been done on modeling of groundwater flow and groundwater transport (Appel and Reilly, 1994). Groundwater modeling is based on solving two equations: i) groundwater flow equations, developed with combination of Darcy's law and continuity of mass equation (Anderson and Woessner, 1992), and ii) the transport models which are simulated by applying advection and dispersion equations (Freeze and Cherry, 1979), in the form of partial differential equations, which can be solved either analytically or numerically.
In order to find an analytical solution of groundwater flow equations, different approaches can be applied including Laplace transform, Hankel transform, Fourier transform, Mellin transform, Green's function, dual integral series equation method, and Boltzmann transform (Yeh and Chang, 2013). Deriving analytical solutions are challenging or even not feasible for many practical problems of groundwater flow due to complexity of aquifer system, such as irregular boundaries, multi-layer heterogeneous aquifer, and non-analytic forms of the source/sink terms. To deal with realistic scenarios, numerical techniques provide more flexible and powerful tools for solving groundwater flow problems in complex field situations (Hemker and Bakker, 2006). Four widely used numerical methods in well-hydraulics are finite difference, finite element, boundary element, and analytic element methods (Yeh and Chang, 2013). Trescott et al. (1976) and Narasimhan and Witherspoon (1976) used finite difference theory for aquifer behavior simulation. Pinder and Frind (1972) applied finite element method and show that numerical results converge to analytical solutions when the number of elements becomes large. Liggett and Liu (1983) presented a detailed application of the boundary integral equation method in groundwater flow modeling. Strack (1989) provided a review of the analytical elements and their mathematical descriptions. In addition to several research studies, a variety of software have also been developed for numerical groundwater modeling (Diersch, 2005). A widely used groundwater flow simulation program is MODFLOW (McDonald and Harbaugh, 1988), in which finite difference method is used to solve flow equations. Other popular software such as Groundwater Modeling System (GMS) and Visual Modular three Dimensional Flow (Visual MODFLOW) (Visual MODFLOW, 2000) and Processing MODFLOW for Window (PMWIN) (Chiang, 2005) have been developed based on MODFLOW architecture.
Many studies have addressed spatio-temporal pattern of deformation in developed groundwater basins in Iran using modern geodetic and remote sensing observations 20–30 cm/yr (Akbari and Motagh, 2012, Anderssohn et al., 2008, Dehghani et al., 2009, Dehghani et al., 2013, Motagh et al., 2007, Motagh et al., 2017, Motagh et al., 2008; Haghshenas Haghighi and Motagh, 2019). Recently, several research efforts have been done to simulate water level drawdown and assess land subsidence in different aquifers. Fotovat-Eskandari and Karami (2009) used PMWIN to model Shahryar aquifer with the maximum rates of ground subsidence being equal to 30 cm/yr. Karimipour and Rakhshandehroo (2011), Mahdavi et al. (2013), and Mohammadi et al. (2014) simulated hydraulic behavior of Shiraz plain, Hamedan–Bahar aquifer, and Shirvan aquifer using PMWIN, respectively. Emamgholizadeh et al (2014) used Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the groundwater level of Bastam Plain. The effects of water table decline on the groundwater quality in aquifer of Torbat Jam plain was investigated by Lashkaripour and Ghafoori (2011). Parhizkar et al. (2015) and Mahmoudpour et al. (2016) simulate landsubsidence in Damghan aquifer and the southwest plain of Tehran, respectively.
In this paper, groundwater flow model in Tehran aquifer is investigated and hydraulic heads is simulated using GMS software. To the best of our knowledge, none of the previous researches have studied Tehran aquifer system completely.
We study a wide area of 2250 located between the latitudes 35° 10' to 36° 00' N and longitudes 50° 40' to 51° 40' E which belongs to Tehran and Karaj provinces (Fig. 1). Tehran, the capital city of Iran with the population of more than 12 million, started to use its subsurface resources since 1963 (Tehran Municipality). With the increase in the population and rapid developments in agriculture and industry, exploitation of groundwater has also significantly increased. Nowadays, a considerable part of city depends on groundwater with a major source of water for irrigating green spaces, orchards, and farms is supplied from the wells made both inside and outside the city area (Tehran Municipality).
The main goal of this study is to develop a highly detailed numerical model for simulating and predicting groundwater level in Tehran aquifer. By investigating geological and hydrological data such as alluvium depths, hydraulic conductivity, piezometric measurements, and pumping well information, we propose an accurate model to simulate the groundwater level in Tehran aquifer. The results are compared against the measured ones by calculating variety of different performance metrics.
Section snippets
Groundwater equation
The water flow through an aquifer is usually described using differential equations. The three-dimensional groundwater flow in a porous media for transient state condition is described using the following equation (Biot, 1941, Biot, 1955):where are the main components of the hydraulic conductivity tensor to x, y, z orientation, respectively. Ss represents the specific storage, R is the recharge term and t denotes the time. In the steady
Geological characteristics
Tehran is located on the alluvial fans of the southern Alborz Mountains. The fans are settled on the volcanic-sedimentary base stones of the Mesozoic era (Khaksar and Tavassoli, 2002). The alluvial deposit thickness of Tehran is about 1100 m and is divided into four parts (Khaksar and Tavassoli, 2002), namely as Hezardareh Formation, Kahrizak Formation, Tehran alluvium and Holocene stage. The oldest alluvial formations of Tehran region is Hezardareh Formation which belongs to Eocene age. It is
Tehran aquifer's groundwater modeling using GMS
Groundwater Modeling System (GMS) is a software package, which provides strong models such as MODFLOW for simulating different groundwater problems. One of the models that GMS supports is MODFLOW. The first and the main step of the modeling a process is to develop the conceptual model. Conceptual model contains the shape, boundary conditions, discharge and recharge sources, that helps for a better understanding of the behavior of the model. Another important step is calibration. During the
Results and discussion
Fig. 12 shows the generated 3D model of Tehran aquifer which is consistent with the characteristics of Tehran aquifer from the measured data. As it can be seen in Fig. 12, the thickness of the model generally increases from the south to the north due to overall increase in the surface topography and the decrease in the bedrock topography.
Conclusion
Knowledge of groundwater condition is necessary for effective management and protection of water resources. It is also important in predicting surface subsidence due to excessive groundwater withdrawal. In this work, we studied the dynamics of groundwater variations in Tehran aquifer and modeled this aquifer as a one-layer unconfined aquifer. The model is constructed based on the information inferred from the geophysical and hydrological measurements: (1) the thickness of alluvium in Tehran
Acknowledgments
The authors wish to thank Tehran Regional Water Authority for supplying the hydrogeology data and Geological Survey of Iran for supplying Geological data.
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