MODELLING THE GROUNDWATER FLOW FOR ESTIMATING THE PUMPING COST OF IRRIGATION IN THE AQUIFER OF N. MOUDANIA, GREECE

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1 Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 MODELLING THE GROUNDWATER FLOW FOR ESTIMATING THE PUMPING COST OF IRRIGATION IN THE AQUIFER OF N. MOUDANIA, GREECE I. SIARKOS 1, K. KAKOUDAKIS 1 and D. LATINOPOULOS 2 1 Department of Civil Engineering, School of Engineering Aristotle University of Thessaloniki, GR , Thessaloniki, Greece. 2 Department of Spatial Planning and Development, School of Engineering Aristotle University of Thessaloniki, GR , Thessaloniki, Greece. isiarkos@civil.auth.gr EXTENDED ABSTRACT The continuous over-exploitation of groundwater resources due to the increased demand for irrigation water has lowered water tables in many riparian areas, threatening thus many water-stressed regions. This over-exploitation, especially when combined with quality degradation, has significant ecological and economic implications. Therefore, future water management requires both environmental and economic information. The simulation of a real situation, by means of a mathematical model, contributes to this direction. Namely, a two-dimensional transient flow simulation in the aquifer of N. Moudania was performed, in order to assess: (a) the impact of hydraulic head distribution on the pumping depth of groundwater, as well as, (b) its relative (long-term) effect on pumping costs. The simulation was carried out using the modular three-dimensional finite-difference groundwater flow model MODFLOW, while the economic cost was estimated based on the energy (electricity) cost for pumping irrigation water. Spatial analysis results showed an association of pumping costs to (a) the depth of water abstraction, as well as (b) to the current water consumption levels. Moreover, the range of future marginal pumping costs seems to vary throughout the study area, as it is directly linked to the drawdown levels. Furthermore, according to current and future estimations, pumping costs constitute and will continue to constitute only a small part of farmers production costs, which is well below the full cost of irrigation water supply. Within this framework, future farming (irrigation) decisions will inevitably lead to wasteful use and depletion of groundwater resources. Keywords: groundwater resources, groundwater modelling, pumping depth, pumping cost, spatial analysis 1. INTRODUCTION Groundwater is the major freshwater resource of our planet. However meeting all human water needs has often resulted in an excessive and wasteful use of this resource, leading many aquifers across the earth in a state of exhaustion (Siarkos et al, 2011). The problem appears to be more intense in rural areas where the over-exploitation of groundwater for the satisfaction of irrigation needs has become more intensive. The basin of N. Moudania, in the regional unit of Halkidiki, is a typical intensive irrigation area where groundwater is the main source of irrigation. Water for agriculture is up to now provided free of charge and irrigation plans are almost non-existent. The effect of those practices is to overexploit regional groundwater resources. In this paper, a MODFLOW-based model was employed within the framework of the Groundwater Modelling System (GMS 8.1) in order to study: (a) the groundwater processes of the N. Moudania semi-confined aquifer system and more specifically the distribution of hydraulic head, as well as, its impact on the pumping depth of groundwater in the location of irrigation wells, (b) the long-term effect of the later on the pumping costs

2 of irrigation wells. The modular three-dimensional finite-difference groundwater flow model MODFLOW (McDonald and Harbaugh, 1988) was used for the simulation of the hydraulic head, while the economic cost was estimated based on the energy (electricity) cost of irrigation water. 2. DESCRIPTION OF THE STUDY AREA The basin of the study area extends to the South-west of the regional unit of Halkidiki, occupies an area of Km 2 and is divided into two sub-regions: (a) a hilly area in the North and (b) a flat area in the South (Figure 1) (Latinopoulos et al, 2003). It is a rural region whose irrigation needs are satisfied by a confined aquifer system, which is considered to be semi-confined based on the storage coefficient values resulted from various pumping tests and the fact that the soil layers in the study area are relatively permeable (Siarkos & Latinopoulos, 2013). A more comprehensive description of the study area regarding its geomorphology, geology, hydrogeology, hydrologic and climate conditions can be found in Siarkos and Latinopoulos (2013). Agriculture dominates both local economy and land use in the study area. Intensive farming practices (including irrigation) are observed throughout the region (Siarkos et al, 2011). As a consequence, a large number of irrigation wells (482) are densely located in the (municipal districts of the) study area as follows: St. Panteleimon (33), Zografou (12), Dionisiou (122), Portaria (140), Flogita (30), Simantra (94) and N. Moudania (51). This distribution corresponds to the year 2001, on which both steady and transient state MODFLOW simulations were based. 3. GROUNDWATER FLOW MODELLING 3.1. Modelling strategies Simulation was conducted in two steps. A steady-state simulation was first carried out in order to adjust specific model parameters, such as hydraulic conductivity and conductance, as well as to get the initial head values for the transient simulation. A transient-state simulation was then performed to adjust the rest of the parameters (e.g. groundwater recharge, storage coefficient), as well as to observe the aquifer response (head distribution) at different time periods under different stresses (recharge and discharge). To this task, pumping/irrigation (1 st May 30 th September, 153 days) and non-pumping (1 st October 30 th April, 212 days) periods were considered, since both recharge and discahrge receive different values in each period.

The conceptualization includes synthesis and framing up of data pertaining to geology, hydrogeology, hydrology, meteorology and groundwater stresses (Ahmed and Umar, 2009).

3 Figure 1: Location of the study area and distribution of irrigation wells 3.2. Conceptual model of the study area The purpose of building a conceptual model is to simplify the field problem and organize the associated field data, so that the system can be analyzed more readily. The conceptualization includes synthesis and framing up of data pertaining to geology, hydrogeology, hydrology, meteorology and groundwater stresses (Ahmed and Umar, 2009). The conceptual model and input data of the study area were primarily developed based upon compiled information derived from Latinopoulos et al (2003). The model domain was discretized into a single layer finite difference grid of 120 rows and 80 columns, making a total of 6,555 active cells of dimensions 150x150 m. The layer in the Z-axis represents the semi-confined aquifer, which constitutes the main exploitable aquifer of the study area. Regarding the geometry of the aquifer, the upper limit was set at the depth at which the clay coating reaches below the ground level, while the lower limit resulted from the upper by subtracting the theoretical thickness of the aquifer (an average value of 250m was obtained based on well logs and geological sections). The boundary conditions (Figure 2) were specified based on previous piezometric information, geological data and wells in the study area. The eastern and western boundaries are no-flow boundaries, as there is no hydraulic connection between the aquifer body and the eastern and western regions. The south and north boundaries were both simulated as general head boundaries (GHB), illustrating the lateral groundwater

isopiozometric contour line of 150m, varying in time as far as the transient simulation is concerned.

4 inflows or outflows occurring from them. The south boundary coincides with the sea level (0m), while the north boundary does not coincide with the physical boundary of the aquifer, but, based on observed data, it was set at the isopiozometric contour line of 150m, varying in time as far as the transient simulation is concerned. This was based on the fact that above this contour there is no available and reliable data concerning the various parameters of the local aquifers. The various aquifer parameters, such as hydraulic conductivity and storage coefficient were obtained from pumping tests. The values of these parameters were assigned to six distinct zones using the Thiessen Polygon method (Figure 3). This method is preferred over contouring, where data points are sparse (Ahmed and Umar, 2009). Figure 2: Model boundary conditions Figure 3: The six distinct zones of the study area Rainfall, irrigation return water and wastewater return flows are considered to be the exclusive factors that contribute to the recharge of the aquifer. It should be mentioned that irrigation return water values were estimated per municipal district considering an average 15% return of total irrigation water. Regarding transient simulation, recharge receives different values in each period, since both pumping and non-pumping periods were taken into consideration. The discharge from the aquifer occurs from withdrawals for domestic, irrigation and livestock purposes. However, it should be mentioned that as regards the transient simulation, irrigation wells contrary to domestic supply wells operate only during the pumping/irrigation period, so that their pumping rate equals to zero during the rest of the year. More details about the determination of recharge and discharge can be found in Siarkos and Latinopoulos (2013).

5 3.3. Model calibration The purpose of model calibration is to establish that the model can reproduce adequately field measured heads and flows (Ahmed and Umar, 2009). In this model as already mentioned, a two-stage simulation calibration procedure was followed. First, a quasisteady state calibration was performed by using a trial-and-error process. This process aims to adjust the initial estimates of specific model parameters, such as hydraulic conductivity and conductance (within acceptable ranges), in order to obtain the best match between simulated and measured water levels. The calibration was made using 18 observation wells monitored during September The hydraulic conductivity values ranged between 0.09 and 0.60 m/d (maximum values occurred in coastal zones), while conductance received a value of 1.1 m 2 /d in the north boundary and 1.0 m 2 /d in the south boundary. The computed water level accuracy was tested by comparing the calculated mean error (ME), mean absolute (MAE) and root mean square error (RMSE). The ME is found equal to indicating that, on average, the simulated groundwater levels were slightly higher than the corresponding observed groundwater levels. The MAE and RMSE estimates were equal to and respectively. Then, a transient state calibration was carried out during the one-year period: 1/10/ /9/2002. The one-year period was then divided into two stress periods, each representing the pumping and non-pumping period. As before, the transient calibration was performed by using a trial-and-error process of adjusting the initial estimates of recharge and specific storage (within acceptable ranges) until a satisfactory convergence between model results and observed field data was obtained. The calibration was made using 13 observation wells, which were monitored during September The recharge values ranged between and mm/d in the pumping period and between and mm/d in the non-pumping period, while storage coefficient values ranged between 2.5x10-2 and 3.75x10-2. Finally, the ME, MAE and RMSE estimates were found equal to 0.615, and 2.345, respectively. The largest residuals at certain wells of the study area (especially in the case of transient state simulation) may be due to anisotropy and heterogeneity of the aquifer, that is to properties (i.e. hydraulic conductivity, storage coefficient) which were not represented properly when dividing the study area into six distinct zones. Moreover, in our analysis the effect of seawater intrusion to density variations was not taken into consideration. This is likely to be the main reason for the increased discrepancies between modelled and observed water levels in the coastal part of the aquifer. However, a perfect fit was not the goal of this study, since it primarily focuses on the generic process of the aquifer system Model results After the calibration process, the model was run for a 20-year simulation period ( ) in order to observe the distribution of hydraulic head at certain time points (pumping periods of years 2012 and 2022) and then to estimate the pumping depth in the location of irrigation wells. Figure 4 illustrates the isopiezometric contours at the end of the pumping period, in year 2022 (which coincides with the end of the simulation period). This figure presents a gradual decline of groundwater level, especially in areas with a high density of wells (central part of the flat area). Figure 5 shows the pumping depth contours at the end of pumping period, in year 2022, which were estimated by subtracting the hydraulic head (Figure 4) from the surface elevations. According to this figure, pumping depths are closely related to drawdown (e.g. central part of the flat area) and to surface elevations (e.g. north part of the hilly area). This can be also concluded from Table 1, which presents the forecasted pumping depths per municipal district in year Comparing the average (or median) pumping depth values, it is clear that Portaria and Zografou districts, which are both centrally located in the study area, are characterized by increased pumping depths due to the low hydraulic head. On the other

6 hand, high values were also found in the district of Simantra, mainly due to its higher average surface elevation. Figure 4: Simulated groundwater levels at the end of the pumping period (2022) Figure 5: Pumping depth at the end of the pumping period (2022) Table 1: Pumping depth per municipal district (2022) Municipal Pumping depth (m) District Min Max Average Median Simantra 33,10 124,95 72,92 71,24 Ag. Panteleimon 27,71 102,20 58,02 57,73 Portaria 34,51 155,92 96,24 100,50 Zografou 73,64 118,67 103,49 104,97 Flogita 21,99 108,76 61,50 57,84 N. Moudania 28,35 130,01 62,57 53,79 Dionisiou 25,71 82,85 52,54 55,24 Finally, it is worth-mentioning the remarkable temporal variation of hydraulic heads during each year. This outcome is indicative of the rate of aquifer s natural drainage and recharge, as well as of the rate of groundwater exploitation in order to meet the regional water needs. Figure 6 depicts the hydraulic head variation of a typical irrigation well located in the most affected area throughout the whole simulation period. Apart from the seasonal variation (between pumping and non-pumping periods), the annual decline of hydraulic head is also depicted, reflecting the continuous depletion of groundwater reserves.

7 Figure 6: Hydraulic head variation in a typical irrigation well 4. PUMPING COST OF IRRIGATION As already mentioned, the economic cost of irrigation water is estimated according to the energy (electricity) cost for pumping irrigation water. Namely, the following simple formula was used in order to calculate annual pumping costs (C) in each well: (1) where p e=energy price ( /J), P=pumping power (Watt) and Δt=irrigation period. Pumping power per well is then estimated as follows: P d g( z 0 h) Q (2) where d=water density(kg/m 3 ), g=gravitational acceleration (m/s 2 ), z 0=surface elevation (m), h=hydraulic head (m) and Q=pumping rate, which is constant in time (m 3 /s). Current pumping costs per unit of water ( /m 3 ) were then estimated at each well (Figure 7). The reasoning for this is to examine the spatial variability of pumping costs throughout the region and to investigate its relation with surface elevation and hydraulic head. Furthermore, inter-temporal comparisons were made for the time period The aim of this analysis was to identify the effect of local/regional drawdown to future irrigation costs. According to Figure 7, average pumping costs are currently (2012) higher in the municipal districts of Zografou ( /m 3 ) and Portaria ( /m 3 ) due to the combined effect of lower pumping levels and higher irrigation water needs (i.e. higher pumping rates). According to our simulation model, future costs (in the year 2022) will be higher in all regions of the study area. More specifically, higher rates of change are estimated in the municipal districts of Ag.Panteleimon and Simantra (+8.9% and +6.59% respectively), due to the percentage changes in drawdown, which are mainly determined by the initial distribution of hydraulic heads (2012). However, it is worth-mentioning that pumping costs constitute and will continue to constitute only a small part of farmers production costs, which is well below the full cost of water supply (as defined in Water Framework Directive). Within this framework, future farming (irrigation) decisions will inevitably lead to wasteful use and depletion of groundwater resources. C pep t 5. CONCLUSIONS In this study both steady and transient models were developed, calibrated, and used to simulate groundwater flow in N. Moudania aquifer. The analysis aims to estimate the hydraulic head values required for the determination of pumping depth and subsequently of pumping cost per well. Regarding the groundwater simulation, increased drawdown is observed especially in the central part of the region. If current irrigation usage and practices continue, a considerable water-level decline will take place in the area, causing the depletion of groundwater reserves. Spatial economic analysis results showed - as expected an association of pumping costs to: (a) the depth of water abstraction, as well as (b) to the current water consumption levels. Moreover, the future variation in pumping

costs seems to reflect the regional drawdown contours. For future research, more thorough study of some aquifer (e.g. thickness, boundary conditions, hydraulic conductivity, storage coefficient, and recharge) and agricultural parameters (e.

REFERENCES Figure 7: Spatial variability of irrigation pumping costs (2022) 1. Ahmed I. and Umar R.

8 costs seems to reflect the regional drawdown contours. For future research, more thorough study of some aquifer (e.g. thickness, boundary conditions, hydraulic conductivity, storage coefficient, and recharge) and agricultural parameters (e.g. crop pattern and irrigation method at the farm level) should be done in order to achieve more adequate calibration regarding the transient model. REFERENCES Figure 7: Spatial variability of irrigation pumping costs (2022) 1. Ahmed I. and Umar R. (2009) Groundwater flow modelling of Yamuna Krishni interstream, a part of central Ganga Plain Uttar Pradesh, Journal of Earth System Science, 118, No.5, pp McDonald M. and Harbaugh A. (1988) A modular three-dimensional finite-difference groundwater flow model: Techniques of Water- Resources Investigations, U.S. Geological Survey, Book 6, Chapter A1. 3. Latinopoulos P., Theodosiou N., Xefteris A., Mallios Z., Papageoriou A. and Fotopoulou E. (2003) Developing water resources management plan for water supply-irrigation, Final Research Project Report prepared for: Municipality of Moudania. 4. Siarkos I., Katirtzidou M. and Latinopoulos D. (2011) Establishing cost-effective wellhead protection zones to control nitrate pollution from agriculture activities, Proceedings of the 12 th International Conference on Environmental Science and Technology, 8-10 September, Rhodes, pp Siarkos I. and Latinopoulos P. (2013) Delineation of wellhead protection zones for the control of point pollution sources in the aquifer of N. Moudania, European Water Journal (accepted for publication, 29/12/2012).

Delineation of wellhead protection zones for the control of point pollution sources in the aquifer of N. Moudania, Greece

European Water 40: 3-17, 2012. 2012 E.W. Publications Delineation of wellhead protection zones for the control of point pollution sources in the aquifer of N. Moudania, Greece I. Siarkos and P. Latinopoulos