Evaluation of Groundwater Resources in alluvial aquifer Based on MODFLOW Program, Case Study: Evan plain (Iran)

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1 International Journal of Agriculture and Crop Sciences. Available online at IJACS/2013/5-11/ ISSN X 2013 IJACS Journal Evaluation of Groundwater Resources in alluvial aquifer Based on MODFLOW Program, Case Study: Evan plain (Iran) Nassim sohrabi 1, manochehr chitsazan 2, vahab amiri 3 1. M.Sc of Hydrogeology, Shahid Chamran University of Ahwaz 2. Assistant Professor of Hydrogeology, Shahid Chamran University of Ahvaz 3. Ph. D Student of Hydrogeology, Kharazmi University of Tehran Corresponding author n.sohrabi1986@gmail.com ABSTRACT: This paper presents the results of a mathematical groundwater model (Groundwater Modeling Software (GMS) & MODFLOW-2000 code) developed for Evan sub basin - the semi-arid region at northwestern Khuzestan province (Iran) - employing conceptual groundwater modeling approach. The source/ sink coverage, recharge coverage, extraction coverage, return flow coverage was considered for this work. The model was calibrated and verified using historical and observed water level data for periods 2005 to 2006 and 2006 to 2007, respectively. The model was run to generate groundwater scenario for a 10 year period from 2005 to 2015 considering the existing rate of groundwater draft and recharge. The water budget predictions indicate a decrease from 8.34 to 4.43 MCM in groundwater storage system. The predicted water table contour maps for the years 2015 have been generated. Results show that in this plain, over exploitation of groundwater will leads to extreme reduction of water resources in period High accuracy of verified model in prediction of aquifer behavior can ensure the researchers to planning and decision in management strategies. Keywords: MODFLOW, GMS, Groundwater Modeling, Evan plain INTRODUCTION A direct approach to designing MODFLOW finite difference model is tedious and less intuitive, specifically for complex boundary and initial conditions. Therefore, a MODFLOW model can be developed either using a grid or conceptual model approach. The conceptual model is created using Geographic Information System (GIS) objects including points, arcs and polygons so that it can more accurately represent real world condition. It is a simplified representation of the site to be modelled including the model domain, boundary conditions, sources, sinks and material zones. Advantage of conceptual model is that most of the input can be in terms of physical objects, such as wells, lakes, recharge zones etc which can then be converted to a grid based mathematical model with the help of preprocessor software. Many researchers in the field of groundwater modeling such as, Lautz, LK, DI Siegel(2006), Axel Herzog(2007) Klqve & Ronkanen(2008), Cho et al (2009) Ibrahim, et al(2010) have done studies the results of this study provide vary managerial methods. Khuzestan province occupies a unique geographic position in SW Iran on account of variable and adverse climatic conditions (arid to semi-arid), low to scanty and erratic rainfall. Further, the increasing demand for water for irrigation, domestic and industrial use is contributing to the water deficiency in the province. Therefore, there is tremendous pressure on the already critical groundwater regime, even total surface and groundwater resources put together are not sufficient to meet the requirement. The balance between returnable water and its withdrawal is disturbed and there is general groundwater deterioration, both in quantitative and qualitative terms. Therefore, conservation and management of groundwater resources are critical issues in the context of Khuzestan. We have undertaken to study the process and behavior of groundwater regime in Evan Basin in NW Khuzestan (Fig.1) to develop a mathematical groundwater flow model through a conceptual modeling approach. The main objective was to identify vulnerable areas susceptible to face groundwater crisis in future.

MATERIAL AND METHODS In general, this study carried out the alluvial aquifer by various steps. The modeling was done by GMS (Groundwater Modeling System) software and MODFLOW code.

In this study, the aquifer system for the modeling purpose has been assumed as homogenous, isotropic, and single layer and unconfined aquifer resting on a horizontal impermeable base.

The data were organized using Geographic Information System (GIS) wherein separate GIS layers for water table, elevation, soil layers, depth to bed rock, aquifer basement, land use/land cover etc.

2 MATERIAL AND METHODS In general, this study carried out the alluvial aquifer by various steps. The modeling was done by GMS (Groundwater Modeling System) software and MODFLOW code. The MODFLOW is a 3D, cell-centered, finite difference, saturated flow model developed by the United States Geological Survey (McDonald and Harbaugh, 1988). In this study, the aquifer system for the modeling purpose has been assumed as homogenous, isotropic, and single layer and unconfined aquifer resting on a horizontal impermeable base. Primary hydrological and aquifer characteristic data were collected in the field. The data were organized using Geographic Information System (GIS) wherein separate GIS layers for water table, elevation, soil layers, depth to bed rock, aquifer basement, land use/land cover etc. was created from the raw data as well as secondary data, such as survey of topographic maps, Geological Survey of Iran (GSI) maps, etc. These layers were imported into the GMS software to develop the conceptual model by various coverage. The conceptual model was converted into finite difference MODFLOW based numerical model by placing a 3D grid over the conceptual model. The conceptual model of the study area has been developed using software GMS. This model allows a much better understanding of site conditions to define the groundwater problem for development of a numerical model and to aid in selecting a suitable numerical model (Spitez, 1996). The conceptual model includes the potentiometer surface, hydraulic properties, and recharge and discharge components. An important part of any groundwater modeling exercise is the model calibration process. In order to use of a groundwater model in any type of predictive role, it must be proved that the model can successfully simulate observed aquifer behavior. Calibration is a process wherein certain parameters of the model such as recharge and hydraulic conductivity are altered in a systematic fashion and the model is repeatedly run until the computed solution matches field-observed values within an acceptable level of accuracy. The Model Calibration illustrates how head levels from observation wells and observed flows from streams can be entered into GMS and how these data can be compared to model computed values. It also describes how a trial and error method can be used to iteratively adjust model parameters until the model computed values match the field observed values to an acceptable level of agreement. Verification of model is a very important and essential part of modeling process. A model is verified if its accuracy and predictive capability have been proven to lie within acceptable limits of errors, independent of the calibration data (Anderson and Woessner, 1992). Site Description General info and Geological setting Evan plain with an area of about 195km 2 and geographical coordinates to 48 9'east longitudes and, 32 14' 30" to 32 24' 30" north latitude is located in northwest of Khuzestan province, Iran. The study area falls in the semi-arid tract of NW Khuzestan where recurrent drought conditions and increases in exploration of groundwater have resulted in considerable drop in groundwater levels. So, this area has been categorized as overexploited. In view of Geology, the construct this plain consisted of sandstone Formation of Aghajari along the section of Lahbary, formation of conglomerate Bakhtiari and Quaternary alluvial deposits (Fig.1). The Evan plain has a semi-arid climate with hot and dry summers, and mild and wet winters. The average annual rainfall for this period was about 282 mm. Iran Figure1. Location of study area 1165

Hydro geological setting Quaternary sediments, covering about 90% the total area, Groundwater in alluvium Occurs under water table condition and depth to water in this formation varies from 8 to 40 m.

RESULTS AND DISCUSSION Model Construction Conceptual Model Developing the conceptual model is the most important part of the modeling process.

3 Hydro geological setting Quaternary sediments, covering about 90% the total area, Groundwater in alluvium Occurs under water table condition and depth to water in this formation varies from 8 to 40 m. The groundwater contour map of the area reveals that hydraulic gradient of the water table is in the NW-SE direction and fairly uniform in nature indicating homogenous structure of the aquifer. RESULTS AND DISCUSSION Model Construction Conceptual Model Developing the conceptual model is the most important part of the modeling process. It simplifies the field situation and organizes associated field data for easy analysis of the system. It is critical that the conceptual model be a valid representation of the vital hydrogeological conditions and involves definition of the hydro-stratigraphic units, water balances and flow system (Nabidi, 2002). In this study, the conceptual model was constructed using parameters of three coverages, as discussed below. Source/Sink Coverage In order to determine the boundary conditions, the water level in piezometers and observation wells was mapped in Arc GIS software for years, and then streamlines were drawn. Based on these maps, the No-flow boundary conditions were used for southern and south-western parts which had not any role in recharging the plain. Portions of the northwestern margin of the western boundary of the plain adjacent to a small part of the Bakhtiari Formation according to stream channel, it is through this aquifer is recharge. The General Hydraulic Boundary (GHB) was selected for positions where the water table is not fixed and may change by some stresses in basin. Recharge Coverage The Evan aquifers has recharged by precipitation and return water of irrigation. The precipitation was found based on meteorological data for each month that there has rainfall. The amount of agricultural return water was classified based on the agricultural well and irrigation systems of agricultural land that covered almost all the lowland zone. The total value of the aquifer recharges find in the various areas of recharge. Figure 2, 3 show various areas of recharge. The values which used for each of regions in the software are presented in table 1. Figure 2. Distribution of irrigation network Figure 3. Zonation of recharge areas in model Table 1. Recharge values of different regions in the ninth period Stress Hydraulic parameter Parameter number Initial value Recharge (m/day) Extraction Coverage Well Exploitation in Evan is mostly for agricultural applications. This coverage is based on the recharge package, which simulates pumping well using rate (Q) specified by the user for each stress period of simulation. 1166

4 Intl J Agri Crop Sci. Vol., 5 (11), , 2013 Model Run Model Calibration The model was calibrated using observed water level data collected, so that model was capable to producing field measured hydraulic heads and flow. Calibration of Evan model in steady state is discussed as follow. Calibration of model in steady state was used in reduction of unknown terms of governing equations of groundwater system and uses the results in making the unsteady model. The results of the calibration model in steady state for estimating aquifer hydraulic conductivity and recharge in the transient-statee model has been used. Figure 4 showss the observed versus computed values of hydraulic head in the latest run of steady state. Figure 4. Observed and computed hydraulic head in steady-state calibration Comparison of calculated and observed hydraulic head contour maps shows the high similarity between visual patterns and spatial distribution of error in the calibration process. Figure 5 is presented the observed and simulated water table in steady-state. Figure 5. observed and computed water level in steady state Calibration of model in unsteady state carried out for 12 stress periods during 356 days. Unsteady state model was run for october 2005 to septamber Initial hydraulic head in the unsteady condition was similar to calibrated model in the steady state. According to crop pattern in agricultural lands, discharge rate of pumping wells as well as rainfall and returns water from irrigation network was defined in 12 different courses. Finally, the model was calibrated and run. The hydraulic conductivity, specific discharge, and recharge values was corrected manually and automatically. Figure 6 shows the water table in unsteady state in first step of calibration process. Model Validation After calibration process, the observed values of piezometeric heads in period 2006 to 2007 were used for model validation. The predicted values of piezometeric heads had very good accordance to observed and this depict that model was verified (Anderson and Woessner, 1992). Then, the validated model was considered as a useful tool for predicting aquifer response in various management strategies. One of the important applications of verified model is to calculate the water balance in domain. In Table 2, changes of water budget components in period are presented. 1167

Figure 6. Water table map for first Stress period (October 2005) after calibration Figure 7 shows the mean error of model calibration in unsteady conditions.

Mean error on unsteady state In Figure 8, observed and simulated water table for first steps is showed.

5 Figure 6. Water table map for first Stress period (October 2005) after calibration Figure 7 shows the mean error of model calibration in unsteady conditions. As mentioned, if error has the normally distribution, RMSE is the best measure of error in modeling. Figure 7. Mean error on unsteady state In Figure 8, observed and simulated water table for first steps is showed. Relative good match of these values reveals that this designed model is appropriate for simulation of groundwater situation on Evan plain. The hydrodynamic coefficients K and S y were obtained from the calibrated model and used in validation part. Figure 8.Observed and calculated water level for first period of stress Table 2. Computed water budget components in period Input (m 3 ) Output (m 3 ) Wells recharge Underground flows Total input- output

6 The water balance for 4 separated part of this plain (Fig. 3) are presented in Table 3. Table 3. Water budget of each zone in period Water budget of 4 zones ID ID=1 ID=2 ID=3 ID=4 Value (m 3 /day) In next and main part of this study, the calibrated and verified model was used to prediction of water resources situation for next ten years (2005 to 2015). Table 4 and figure 9 are presented the predicted water budget and water level in Evan plain during year , respectively. Results show that over exploitation of groundwater will leads to extreme reduction in water resources in this plain. Table4. Predicted values of water budget components in period Input (m 3 ) Output (m 3 ) Wells recharge Underground flows Total input- output Figure 9.Predicted Water table in period CONCLUSION In the steady condition, the mean error of model in computing of hydraulic head was approximately 0.573, and this value is located in defined acceptable range of model efficiency. According to model run for period and consequently next years, regions ID=2 and ID=4 are face to high negative water budget and this critical problem can be focused by responsible peoples to manage the water resources in these zones. Predicted water budget and water level in Evan plain during year show that over exploitation of groundwater will leads to extreme reduction in water resources in this plain. Results show that the designed model can appropriately apply for groundwater modeling in Evan plain. Also, high accuracy of verified model in prediction of aquifer behavior can ensure the researchers to planning and decision in management strategies. REFERRENCES Anderson MP, Woessner WW Applied Groundwater Modeling- Simulation of Flow and Advective Transport, Academic Press, San Diego, 381p. Axel H Transient Groundwater Modelling In Peri-Urban Kampala,Uganda. Cho J, Barone VA, Mostaghimi S Simulation of land use impacts on groundwater levels and streamflow in a Virginia watershed, Agricultural Water Management 96 (1), pp Gupta CP, Thangarajan M Management of groundwater resources in India using simulation models. Water Resources Jour., March 1990, pp Ibrahim S, Al-Salamah Yousry M, Ghazam AR Gh Environ Monit Assess. 173: Kresic N Quantitative solutions in hydrogeology and ground water modeling. CRC press LLc, USA, 443p. 1169

7 Latuz Lk, DI Siegel modeling surface and groundwater mixing in the hyprotheic zone using mudflow and MT3D. Advances in water Resources, 29; Maidment DR Handbook of Hydrology. McGraw Hill, New York, pp Mcdonald MG, Harbaugh AW A modular three dimensional finite difference groundwater flow model. Washington, USGS. Nabidi IK Development of 3-D conceptual hydrogeological model for Lake Navaisha area. WREM. Enschede, ITC, The Netherlands. Ronkanen A, KlΦve B Hydraulics and flow modelling of water treatment wetlands constructed on peatlands in Northern Finland. Water research ;42(14): Spitez K A practical guide to groundwater and solute transport modeling. John Wiley and Sons Inc., 238p. 1170

CHAPTER 7 GROUNDWATER FLOW MODELING

148 CHAPTER 7 GROUNDWATER FLOW MODELING 7.1 GENERAL In reality, it is not possible to see into the sub-surface and observe the geological structure and the groundwater flow processes. It is for this reason