# Assessment of future groundwater level using modflow code under different managerial scenarios in the Serres river basin, Greece

Christos Mattas, Stylianos Voudouris, Sofia Foti, Konstantinos Voudouris

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Christos Mattas, Stylianos Voudouris, Sofia Foti, Konstantinos Voudouris. Assessment of future groundwater level using modflow code under different managerial scenarios in the Serres river basin, Greece. Earth Science Informatics, 2025, 18 (3), pp.465. 10.1007/s12145-025-01969-9. hal-05214821

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# Assessment of future groundwater level using modflow code under different managerial scenarios in the Serres river basin, Greece

Mattas Christos<fnref n="1" /> · Voudouris Stylianos<fnref n="2" />,<fnref n="3" /> · Foti Sofia<fnref n="4" /> · Voudouris Konstantinos<fnref n="1" />

Received: 3 December 2024 / Accepted: 29 June 2025

© The Author(s) 2025

## Abstract

Given the lack of rainfall during the dry season in Greece and other countries, groundwater is important in meeting water needs for all uses. Reliable simulation of groundwater flow and predictions of groundwater levels under different management scenarios, in light of water crisis, are essential tools for the sustainable management of aquifers. In this paper, the MODFLOW code for simulating groundwater flow was applied to the Greek section of the transboundary Serres river basin in a complex aquifer system developed in alluvial deposits. The groundwater of the basin is critical for the fulfillment of both the irrigation requirements of an extensive local agricultural activity and the domestic needs of a significant population. Data necessary for model construction and simulation was provided by the relevant authorities and literature. The model calibration is considered successful as the simulated values show a statistically acceptable fit to the measured data. Sensitivity analysis shows that the aquifer's storage coefficient change has the greatest impact on the groundwater level. In a management scenario where groundwater extraction is reduced by 5% over a ten-year period, the groundwater level is projected to rise by approximately 0.17 m. In contrast, in the worst-case scenario, where groundwater extraction increases by 6.5% reaching at a critical point regarding the sustainability of the groundwater resources in the area, the average groundwater level is expected to drop by 0.72 m, resulting in the reduction of aquifer reserves. The model serves as a valuable tool for mitigating and adapting to the impacts of the climate crisis, as well as for formulating policies that promote the sustainable management and rational use of the aquifer.

**Keywords** Groundwater flow · MODFLOW · Water management · Greece

## Introduction

Groundwater is critical to supporting agricultural activities and food production and ensuring human resilience to climate change. This is particularly important as surface water systems become increasingly unsustainable due to rapid population growth and changes in climate and land use (Ngo et al. 2024). Population growth and the intensification and expansion of economic activities have led to increased demand for freshwater, exacerbating global water scarcity problems. Countries in the Mediterranean region, including Greece, are currently struggling to achieve sustainable water management, particularly in agriculture. They also face growing nutrition and health insecurity due to the intensifying effects of climate change (Differenbaugh and Giorgi 2012; Jacob et al. 2018; Guiot and Cramer 2016; Tzanakakis et al. 2020).

Precipitation in Greece is unevenly distributed in space and time as a result of the country's varied geology and

Communicated by Hassan Babaie

<figure><img src="image_2.png" /></figure>

Mattas Christos
cmattas@geo.auth.gr

<footnote n="1">¹ Laboratory of Engineering Geology & Hydrogeology, Department of Geology, Aristotle University of Thessaloniki (AUTh), Thessaloniki 54124, GR, Greece</footnote>

<footnote n="2">² IFP Energies Nouvelles – Earth Sciences and Environmental Technologies, Paris, France</footnote>

<footnote n="3">³ Département d' Hydrogéologie, Avignon Université, UMR 1114 EMMAH, Campus Jean- Henri Fabre, 301 Rue Baruch de Spinoza, Avignon, France</footnote>

<footnote n="4">⁴ Geosynolo I.K.E. Consultants Engineers & Geologists, Athens, Greece</footnote>
topography. Greece’s climate is characterized by dry, hot summers and wet winters, requiring irrigation for many crops to achieve satisfactory production and income for farmers. Groundwater is the main source of freshwater worldwide (Siebert et al. 2010; Famiglietti 2014), as well as in Greece and it is the main source of irrigation and domestic water in many provinces. Approximately 3,500,000 hectares are cultivated in Greece, of which 1,400,000 hectares are irrigated using collective irrigation networks and private boreholes. The agriculture sector is responsible for 80–85% of annual water consumption (Dercas 2020). Climate change models predict prolonged drought periods and further reductions in water supply in many parts of Greece (Kourgialas 2021). It should be noted that aquifer systems are complex and the effects of climate change are subject to a high degree of uncertainty.

Human activities, such as overexploitation of water resources and overpumping of groundwater due to the continuous intensification of agriculture, tourism development, and industry, have exacerbated the challenge, causing significant environmental damage, such as seawater intrusion, desertification, degradation, and land subsidence. The degradation of natural resources, particularly water, poses a threat to the socioeconomic growth of water-dependent sectors and can even threaten human survival (McCaffrey 1992). Current water management paradigms in many areas are ineffective in terms of sustainability.

Water sustainability involves conserving significant amounts of freshwater for future generations to meet their drinking water needs, maintain food security, and support industrial needs. Achieving water sustainability is complex and requires effective management and a balance between human demands and environmental needs (Jarzebski et al. 2024). Groundwater resources remain poorly understood and managed in many parts of the world (Smith et al. 2016). To gain insight into system behavior and the complex feedback between groundwater, landscape, and surface water, numerical models that approximate physical flow processes are typically used.

Groundwater flow simulation and numerical modeling using the public domain computational code MODFLOW have proven to be an effective tool for groundwater resource management (Aghlmand and Abbasi 2019). MODFLOW provides crucial information to support decision-makers and policy planning. The models can assess current conditions and predict future responses of aquifers under different scenarios regarding climate conditions or water use and demands. Predicting groundwater levels is challenging, especially in complex aquifer systems and heterogeneous formations (Anderson et al. 2015). Advances in technology and increased computing power now allow for complex computations and data processing tasks to be executed in a

short time, overcoming many challenges of the past. However, the modeling process requires a significant amount of data, including the geometry of the aquifer, the interaction between the aquifer and the overlying hydrographic network, borehole locations and pumping rates, water level measurements, aquifer recharge from precipitation and adjacent aquifers, and hydraulic properties (e.g., hydraulic conductivity, storage coefficient). This information is often unavailable and difficult to find, especially in terms of spatial zonation, but it is essential for conceptualizing the modeled aquifer systems. Conceptualization involves understanding the mechanisms that regulate groundwater flow and formulating a simplified view that represents the groundwater system. The conceptual view should be as simple as possible while adequately representing the system’s behavior (Kpegli 2020; Hill 2006).

To achieve a good status of water quantity and quality, Greece has established and implemented Management Plans (MP) at the river basin scale, providing necessary information and operational instructions for effective integrated water management. These plans describe the main pressures on water resources and their impacts on human activities, evaluate the ecological, chemical, and quantitative status based on recent measurements and sampling, and set environmental objectives. They also include appropriate measures combined with economic analysis of water uses to achieve good water quality and economic viability for stakeholders. The approved River Basin Management Plans (RBMP) for the 13 water districts in Greece have been prepared in compliance with Directive 2000/60/EC (Article 13; Annex VII), according to Law 3199/2003 and Presidential Decree 51/2007, and are revised every five years. An important component of the RBMP is the groundwater flow simulation and the projection of the results under different management scenarios for the future.

MODFLOW (Anderson et al. 2015) has been used to simulate groundwater flow and estimate groundwater head in many aquifers worldwide and in Greece. Numerous free or commercial software are based on the MODFLOW hydrology model (McDonald and Harbaugh 1984; Winston 2009; De Filippis et al. 2020). Indicative recent case studies include the prediction of groundwater level variation using deep learning and MODFLOW in the basin of Qara Chai river in Iran (Amiri et al. 2023), the prediction of groundwater level under land use and climate change scenarios in Chitravathi river basin in India (Chandra and Sahoo 2023), and the investigation of seawater intrusion at the coastal aquifer of Gaza Strip in Palestine (Musallam et al. 2023). Case studies from the literature in Greece include the Almyros aquifer (Lyra et al. 2021), Lake Karla aquifer (Sidiropoulos et al. 2018, 2019; Tziatzios et al. 2015), Eidomeni-Evzones region in Axios basin (Psilovikos 1999), Moudania aquifer
(Siarkos and Latinopoulos 2016), Thira aquifer in Santorini island (Kopsiaftis et al. 2009; Kourakos and Mantoglou 2009), Glafkos aquifer in Patras Gulf (Kaleris and Ziogas 2020), Cyclades (Kopsiaftis et al. 2017), Viotikos-Kifisos river basin (Panagopoulos et al. 2004), Eastern delta of Nestos river (Gkiougkis et al. 2021), Ismarida plain (Pisinaras et al. 2007), and Messara aquifer system in Crete (Kritsotakis and Tsanis 2009). MODFLOW models have been effective management tools in these hydrogeological systems, although few investigations focus on the links between climate change and groundwater reserves in Greek case studies.

In this study, the MODFLOW code was applied to the Greek section of the transboundary Serres river basin, as part of the second update of the River Basin Management Plan (RBMP) consultation for the Water District of Eastern Macedonia (EL11). The main objectives were to: (a) simulate groundwater flow and replicate historical water levels; (b) identify existing challenges and limitations in the model development; (c) predict future aquifer levels under various recharge and pumping scenarios; and (d) propose guidelines for actions to improve the models.

The literature review shows that MODFLOW software (Anderson et al. 2015), in the GMS environment (Waterloo Hydrogeologic 1999; Mustafa and Mawlood 2024), used in this study, has been enabled for groundwater flow simulations in various applications, including agriculture, climate change, civil protection, and environmental studies. The advantage of GMS 10.7 is its ability to work with other modeling software such as GIS, SWAT, and HEC-RAS (Brunner 1997; Arnold and Fohrer 2005; Yang et al. 2006; Khattak et al. 2016). The integration of different modeling tools adds novelty to research (Hariharan and Shankar 2017).

# Materials and methods

## Study area

The Strymonas River basin is shared among four countries: Bulgaria (50.6%, 8,473 km²), Greece (35.8%, 5,990 km²), North Macedonia (9.8%, 1,641 km²), and Serbia (3.8%, 643 km²). The Strymonas River (approximately 390 km long) originates in Bulgaria, after traversing about 25 km in Greek territory, flows into artificial Lake Kerkini, then continues downstream, draining the Serres plain and ultimately discharging into the Strymonikos Gulf (Fig. 1). Lake Kerkini, constructed between 1933 and 1936, was created by building a dam at Lithotopos, which drained the natural lake near Achinos village that existed until 1932. The primary purposes of Lake Kerkini were flood protection of the broader plain area and serving as a reservoir for irrigation

water. Following the reservoir's completion, a significant wetland developed in the broader Kerkini Lake area. The Ramsar Convention and EU directives protect this unique ecosystem as a Wetland of International Importance, a Site of Community Importance, and a Special Protection Area for birds. Along its course, the Strymonas River receives water from another major river, the Aggitis, which drains the Drama basin, and then flows into the Strymonikos Gulf (Doulgeris et al. 2012).

The Strymonas River provides irrigation water to one of the most productive agricultural areas in Greece (Hatzigianakis et al. 2016). Agriculture is the largest consumer of water in the region, with irrigation accounting for 79% of total water demands in the Greek and Bulgarian parts of the basin (Halkidis and Papadimos 2007; Doulgeris et al. 2008). The Greek part of the catchment is mainly covered by agricultural areas, which are irrigated and drained through a dense network of canals and ditches (Chalkidis et al. 2016). The Strymonas channel downstream of Kerkini was constructed to handle a peak flow of 700 m³/s. After joining the Aggitis River, the maximum discharge capacity increases to 1,200 m³/s (Capolongo et al. 2019).

## Geology and hydrogeology

The Strymonas basin is a typical post-orogenic graben. Geologically, the Greek part of the catchment belongs to the Serbomacedonian massif (SRB) to the west and the Rho-dope massif to the east (Karydakis et al. 2005). The granular aquifer system in the area is formed in the Quaternary and Neogene deposits (Fig. 2), with thickness varying depending on the region's tectonics. The maximum depth occurs at the center of the basin, reaching 4,000 m. These deposits overlay the metamorphic formations from the Paleozoic Era, magmatic infiltrations, alpine formations from the Mesozoic Era, and meta-alpine formations from the Cenozoic Era (Karydakis et al. 2005; Doulgeris et al. 2012).

A complex aquifer system has developed in the area, comprising an upper phreatic aquifer found at shallow depths of 0.5 to 7 m below the ground surface, along with successive semi-confined and confined aquifers. The succession of different aquifers is due to the alternation of high-permeability sediments (sand, gravel, loose conglomerates) and low-permeability sediments (silty clay, silty sand, etc.). Fine-grained sediments form a transition zone rather than an impermeable layer between successive aquifers, making it difficult to delineate their boundaries. Artesian wells have been recorded in the area.

The simulated part of the aquifer (Fig. 2) covers 583 km², formed in the plain central part of the basin, where the depth of the Quaternary deposits is sufficient to provide significant
<figure><img src="image_3.png" /><figcaption>Fig. 1 Strymonas river basin (Greek part) land cover map</figcaption></figure>

water storage capacity in relatively uniform geological formations.

EAGME (2010) studied in detail the basin and conducted
several groundwater level measurements. The main out-
comes of this research are as follows:

- There is an interaction of the hydrographic network with the phreatic aquifer, with the network both recharging and being recharged by the aquifer along its route.

- The aquifer system in the northern and central parts of the basin has a greater water-bearing capacity.
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Geologic units in the Strymonas River basin (based on the geological map of Greece, scale 1: 500,000, Bornovas and Rondoyanni 1983)</figcaption></figure>
- In the southern part of the basin, the groundwater head is below sea level due to over-pumping for irrigation purposes, and artesian phenomena are recorded.

- Lateral recharge from karstic formations at the basin boundaries occurs at great depths.

Wells and boreholes drilled in the area provide flow rates ranging from 20 to 200 m³/h. The transmissivity (T) of the aquifer ranges from 1 to 850 m²/day, while hydraulic conductivity ranges from 0.086 to 40 m/day (data provided by Municipal Water Supply Company of Serres). The groundwater level fluctuation of a representative borehole is shown in Fig. 3. The location of the borehole is depicted in the map of Fig. 4.

The predominant hydrochemical type of groundwater of the aquifer is Ca-HCO₃. High concentrations of nitrate (NO₃⁻) are locally recorded, exceeding the upper acceptable limit of 50 mg/L set by the European Union for drinking water. These concentrations are associated with agricultural activities and the use of fertilizers.

## Model description and setup in steady-state conditions

Developing a conceptual model to simulate an aquifer requires a thorough understanding of its hydrogeologic regime and hydraulic parameters. Model construction involves the selection of appropriate equations and mathematical solutions to describe groundwater flow. In addition, it is critical to use the most appropriate tools within the software to represent the conceptual model and achieve the mathematical solution. The GMS 10.7 is a user-friendly software that includes many groundwater models such as

FEMWATER (Lin et al. 1997), SEEP2D (SEEP2D Primer 1998; El-Molla and El-Molla 2021), SEAM3D (Waddill and Widdowson 2000), MT3DMS (Zheng and Wang 1999; Langevin and Guo 2006; Yashooa and Mawlood 2024), and MODFLOW (Harbaugh 2005). In this study, the MODFLOW model has been applied. The MODFLOW code is constructed based on the Darcy equation and the principle of conservation of mass. The governing equation that describes groundwater flow in a porous medium was described by McDonald and Harbaugh (1988):

$$ \frac{\partial}{\partial x} \left( K_{xx} \frac{\partial h}{\partial x} \right) + \frac{\partial}{\partial y} \left( K_{yy} \frac{\partial h}{\partial y} \right) + \frac{\partial}{\partial z} \left( K_{zz} \frac{\partial h}{\partial z} \right) + W = \cdot S_s \frac{\partial h}{\partial t} $$

where: Kxx, Kyy and Kzz are the hydraulic conductivities in the x, y, and z directions respectively (L/T), h is the pressure head (L), W is the recharge/discharge rate per unit volume(T⁻¹), Ss is the specific storage of the porous medium (L⁻¹) and t is time (T). Since the analytical solution of the equation is almost impossible, the finite-difference method is applied in MODFLOW code to simulate groundwater flow (Harbaugh 2005).

At the end of the modelling process, the results must be evaluated based on field data measurements for a selected set of parameters (Ngenzebuhoro et al. 2021; Awais et al. 2023; Sanaullah et al. 2023).

The poor spatial distribution of boreholes and the fact that they often drill through different successive aquifers make identifying their boundaries practically impossible. Therefore, the aquifer system in the area was simulated as a single layer. The confined/unconfined layer type was selected in the Block Centered Flow (BCF) package, meaning that depending on the groundwater head, the aquifer

<figure><img src="image_5.png" /><figcaption>Fig. 3 Groundwater level fluctuation (EAGME 2010)</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Boundary conditions and sink/source terms of the simulated aquifer area</figcaption></figure>

can be either confined or unconfined. The BCF package is considered most appropriate for simple, single-layer, simple stratigraphy models where many parameters have a constant value across the model domain. It calculates the conductance between each of the grid cells and sets up the finite difference equations for cell-to-cell flow. The discretization of the grid cells is 250 m × 250 m, resulting in a total of 9,384 cells.

The model geometry was developed in the Greek Grid coordinate system using the Copernicus DEM, EEA-10 (https://doi.org/10.5270/ESA-c5d3d65). This Digital Surface Model (DSM) represents the Earth's surface at a resolution of 10 m. The upper topographic surface derived from the DEM was incorporated into the model as the upper aquifer boundary. Many boreholes in the area reach a depth of 350 m beneath the surface, which was also selected as the depth of the aquifer in the model geometry.

Information regarding groundwater recharge and discharge, which is required as a model input, was provided by EAGME (2010). The main recharge sources are infiltration from precipitation and irrigation water, seepage from the hydrographic network, and lateral recharge from neighboring aquifers. Discharge mainly occurs through pumping for irrigation purposes, interaction with the hydrographic

network, and discharge to downstream areas. Hydraulic conductivity values play a major role in these processes. Due to the anisotropy of the sedimentary deposits, the horizontal hydraulic conductivity values were divided into two zones. In the north zone, the value is set to 5.6 m/d, while in the south it is 4.8 m/d.

The interpolation of groundwater level values from 2018, measured in seven boreholes using the Simple Kriging method, was employed to distribute the initial head values used as input in the model.

The average precipitation over forty years (1980–2020) was calculated to be 510 mm, which correlates with the average elevation of the watershed. About 18% of the annual precipitation is considered as the effective infiltration. Total groundwater abstraction for irrigation, industrial, and drinking purposes is estimated to be up to $95.4 \times 10^6$ m³ (EAGME 2010). This abstraction in the model domain takes place through 127 wells and boreholes, the locations of which were available and incorporated using the wells package. Approximately 5% of the irrigation water is considered as irrigation returns to the aquifer. A value of 0.0003 m/day was incorporated into the model as the total recharge rate from precipitation and irrigation returns. The
interaction between the hydrographic network and the aquifer was simulated using the River package.

The general head boundary and specified head boundary conditions were selected for the aquifer’s lateral inflows and outflows. The structure of the created model is depicted in Fig. 4. The methodology used to construct the model and the successive steps are illustrated in Fig. 5.

Transient state model

For the construction of the transient state model, the simulation time is divided into eighty (80) stress periods, each with a computational interval of six months, starting from October 1st, 1980 to September 30th, 2020. These stress periods distinguish between the “wet” and “dry” seasons

<figure><img src="image_7.png" /><figcaption>Fig. 5 Flowchart of groundwater flow model generation using the MODFLOW code</figcaption></figure>
of each year. During the wet period (October-April), most of the rainfall occurs, resulting in the groundwater level peaking at the end of this period. In the dry period (May-September), irrigation is prevalent while rainfall is low and temperatures are extremely high. Throughout each stress period, all inputs to the model, such as boundary conditions and pumping rates, remain constant. The precipitation data used to estimate the recharge rate for each stress period are shown in Supplementary Material, Table SM1.

Most boreholes and wells in the area are used for irrigation, but a significant number also serve drinking, domestic, or other purposes, given the presence of many urban areas within or near the simulated aquifer. Consequently, the pumping rate for the dry season was estimated to be 75% of the total annual groundwater abstraction, with the remaining 25% being distributed during the wet season. The storage coefficient value is set to 0.04 for the entire model area.

## Sensitivity analysis

Sensitivity analysis highlights crucial parameters for the reliability of outputs and provides insight into the range of possible results a model can produce. This is particularly important when accurate or reliable input measurements are lacking. Aquifer recharge is always difficult to estimate because of the complex interactions between surface and groundwater. Aquifer recharge estimations were made based on precipitation data. Pumping rate calculations rely on crop type and area. Therefore, in this study, total recharge and groundwater pumping rates were excluded from the sensitivity analysis as they are based on actual measurements. For hydrogeological parameters, the uncertainty is due to spatial heterogeneity. So, hydraulic conductivity and storativity (storage coefficient) were included, because they are typically unmeasured and are often estimated or derived from limited pumping test information and literature.

As previously mentioned, the model simulates a complex system of confined and unconfined aquifers. Therefore, the storativity value used in the model falls within the upper range for confined aquifers and the lower limit for unconfined aquifers to represent the average prevailing conditions. The hydraulic heads at selected observation points serve as reference points for comparing results. Due to the limited data, the mean deviation between observed and calculated heads at each observation point, after parameter changes, was used as an index to measure changes in results.

## Managerial scenarios

Two work case scenarios were approved by the Directorate of Waters in Central Macedonia of the Decentralized Administration Unit of Macedonia & Thrace and

implemented in the transient model to project groundwater hydraulic heads in the near future. In both scenarios, a 6.5% reduction in aquifer recharge due to precipitation is assumed based on climate projections for the broader area. The Best Case Scenario (BCS) assumes a percentage reduction of 5% from current groundwater withdrawals as a result of the full and successful implementation of the Good Agricultural Practices and Sustainable Water Management measures proposed in the approved Second Update of the River Management Plans (Government Gazette Issue A, No. 82, June 12, 2024).

Conversely, the Worst-Case Scenario (WCS) hypothesizes that these measures will not be properly applied, leading to a 6.5% increase in current water abstractions. The recharge and groundwater pumping regimes proposed by these scenarios cover 10 years starting from the end point of the calibrated model, projecting results for October 2030.

## Results and discussion

### Model calibration in steady-state conditions

In steady-state and transient models, there is a balance between the inflows and outflows of the system. The software calculates a water budget based on the input data, showing the amounts of water entering or exiting the aquifer through the activated packages and boundary conditions. Calibration is considered successful when the measured water level closely matches the calculated level at the observation points in the shallow phreatic aquifer. In addition to the absolute deviation in meters between observed and calculated heads, statistical indices are used to further confirm and validate the calibration process. The Root Mean Square Error (RMSE) was applied in this study: $RMSE=\{[(H_{obs}-H_{sim})^2]/N\}^{0.5}$.

where $H_{obs}$ and $H_{sim}$ are observed and simulated hydraulic head values, respectively, while N represents the total number of monitoring wells.

Small values of the Root Mean Square Error (RMSE) indicate that the model calibration meets the established criteria. After numerous model runs and parameter adjustments in the simulation of the Serres aquifer, the results show a significant statistical correlation between the observed and calculated heads. The RMSE is equal to 1.06.

The model demonstrates a reliable representation of the aquifer, supported by robust statistical performance. The deviation between observed and calculated values at the selected observation points ranges from 0.4 to 1.96 m (Table 1). At five out of six observation points, the deviation reaches a maximum of 1 m. The maximum deviation is
<table><caption>**Table 1** Measured and simulated head values (meters above sea level) in the observation points for the steady state model</caption><thead><tr><th>Code number</th><th>X</th><th>Y</th><th>Measured head (m)</th><th>Simulated head (m)</th><th>Deviation (m)</th></tr></thead><tbody><tr><td>EL11011107</td><td>436,774</td><td>4,564,690</td><td>34.34</td><td>33.44</td><td>0.90</td></tr><tr><td>EL11011135</td><td>436,630</td><td>4,560,245</td><td>26.13</td><td>27.13</td><td>-1.00</td></tr><tr><td>EL11011110</td><td>456,933</td><td>4,548,128</td><td>21.04</td><td>20.04</td><td>1.00</td></tr><tr><td>EL11011126</td><td>469,662</td><td>4,536,288</td><td>20.76</td><td>18.80</td><td>1.96</td></tr><tr><td>EL11011116</td><td>440,993</td><td>4,550,800</td><td>21.91</td><td>21.51</td><td>0.40</td></tr><tr><td>EL11011127</td><td>449,065</td><td>4,550,344</td><td>24.18</td><td>25.02</td><td>-0.84</td></tr><tr><td>EL11011101</td><td>458,619</td><td>4,541,228</td><td>17.43</td><td>17.99</td><td>-0.56</td></tr><tr><td colspan="5">Absolute residual mean</td><td>0.95</td></tr><tr><td colspan="5">Standard Deviation</td><td>0.49</td></tr></tbody></table>

recorded at borehole EL11011126, the location of which is shown in Fig. 4.

According to previous measurements (EAGME 2010), this is an artesian borehole with the groundwater level above the ground surface, making it difficult for the model to achieve accurate simulation at this specific location. At the end of the trial and error process which was implemented for the calibration, the Root Mean Square Error (RMSE) is calculated by the software equal to 1.06, which

<table><caption>**Table 2** Volumetric mean annual groundwater budget of the simulated aquifer</caption><thead><tr><th>Inflows (x10<sup>6</sup> m<sup>3</sup>)</th><th></th><th>Outflows (x10<sup>6</sup> m<sup>3</sup>)</th></tr></thead><tbody><tr><td>Infiltration and irrigation returns</td><td>63.9</td><td>Abstractions</td><td>95.4</td></tr><tr><td>Lateral inflows</td><td>27.0</td><td>Hydrographic network</td><td>18.7</td></tr><tr><td>Streambed infiltration from hydrographic network</td><td>39.5</td><td>Subsurface outflows</td><td>16.3</td></tr><tr><td>Total</td><td>130.4</td><td>Total</td><td>130.4</td></tr></tbody></table>

is considered to be statistically satisfactory. The results are consistent with those of previous studies. The piezometric data of the aquifer (Fig. 6) indicate that groundwater flow does not align with the main branches of the hydrographic network, following an irregular pattern instead. The groundwater head distribution forms a hydraulic swell in the southern region, where artesian phenomena have been observed. This is due to the coexistence of phreatic and confined aquifers, necessitating the selection of the confined/unconfined layer condition option in the Block-Centered Flow package available in the software.

The cumulative volumetric groundwater budget of the aquifer is shown in Table 2. A total of 130.4 million cubic meters flows into and out of the aquifer. Recharge is mainly

<figure><img src="image_8.png" /><figcaption>**Fig. 6** Piezometric map of the simulated aquifer for the steady state conditions</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 7** Piezometric map of the transient state model for October 2020</figcaption></figure>

through precipitation and irrigation returns (49%). Percolation of river water accounts for 30.3% of total inflows, and the remaining 20.7% comes from lateral recharge from the northeast boundaries. There is a two-way interaction between rivers and boundary conditions with the aquifer; therefore, significant amounts of water also outflow from the aquifer. However, the majority of the outflows (73.2%) is due to abstraction via boreholes and wells for irrigation purposes.

## Model calibration in transient state

The mean deviation between calculated and observed heads for the period 2018–2020 is 1.34 m. The maximum deviation recorded is 2.2 m, while the minimum is 0.1 m. Generally, the majority of the boreholes show a mean deviation of less than 2 m. The results are considered satisfactory. Some of the recorded deviations could be attributed to the fact that some measurements were conducted at different times than those for which the model provides a head value (Table SM2). They could also be attributed to the DEM resolution. Since most of the calculated values are smaller than the observed values, it is logical to assume that rainfall

<table><caption>**Table 3** Deviation between the observed and the calculated values of heads for different storativity values</caption><thead><tr><td>Storativity</td><td>Mean value of deviation (m)</td></tr></thead><tbody><tr><td>0.02</td><td>1.56</td></tr><tr><td>0.03</td><td>1.43</td></tr><tr><td>0.04</td><td>1.34</td></tr></tbody></table>

events, which were not accounted for in the model, could have influenced the water levels in the boreholes. October and November are months when rainfall events commonly occur in the study area. Indicative piezometric maps for the period of October 2018–2020 are shown in Fig. 7. It can be concluded that the general direction of groundwater flow is from NE to SW.

## Sensitivity analysis results

It was discovered that the model-calculated heads are quite sensitive to the value of storativity, while hydraulic conductivity does not have a significant effect. The sensitivity analysis was performed for values smaller than 0.04, which was used as input in the model, as depicted in Table 3.
<figure><img src="image_10.png" /><figcaption>Fig. 8 Piezometric map for October 2030 (a) for the Best Case Scenario (b) for the Worst Case Scenario</figcaption></figure>

Higher values seem to yield better results for simulating groundwater heads, however, they are not considered realistic because they represent unconfined aquifers, which would not be appropriate for this specific case study that simulates a complex system of both confined and unconfined aquifers. Halving the storativity value increases the deviation between observed and calculated heads by 14% compared to the initial value. Values less than 0.02 are invalid because the model cannot run successfully.

## Projected results based on different managerial scenarios

The scenario simulation period spans from October 2020 to October 2030. The transient model calibration relied on a relatively short time series of data from 2018 to 2020; therefore, the selected 10-year period in this study is considered large enough, increasing the degree of uncertainty in the reliability of the results. The calculated results for October 2020 from the calibrated model were compared with those for October 2030 after the scenario simulations.

In the Best Case Scenario (BCS), an increase in groundwater levels is estimated in 6 out of 7 boreholes and wells. The rise ranges from 0.03 to 0.41 m, with a mean value of 0.17 m. In the Worst Case Scenario (WCS), a groundwater head drawdown is calculated at all observation points, ranging from 0.09 to 1.79 m. The mean projected decrease is estimated to be 0.72 m. Piezometric maps produced from both scenarios are depicted in Fig. 8.

## Proposals for model improvement

To optimize the model's results, detailed data is required, starting with the Digital Elevation Model (DEM) of the area. The DEM files must be updated and be of high resolution, ensuring that the deviation between the actual water table head elevations measured in the field and those used as model inputs is negligible. Remote sensing technologies can significantly contribute by providing real-time landscape data in a cost-effective and time-efficient manner, reducing the need for extensive fieldwork and financial resources. The spatial distribution of water level observation points must cover the entire study area. Where the spatial distribution is inadequate, new boreholes must be dug to increase the monitoring network's density. Pumping tests shall be conducted to provide more detailed information on hydraulic parameters such as hydraulic conductivity, transmissivity, and storage coefficient. While the hydraulic conductivity and groundwater head values used in this model are considered valid to represent field conditions, improvements are always possible as new data become available. Besides field measurements, the application of different geostatistical methods could improve accuracy by refinishing interpolated values based on measured data. The PEST (Parameter ESTimation) tool, available within the software, allows for automated parameter estimation (Doherty and Hunt 2010). However, in this study, its application did not lead to significant improvements in model performance. Although the deviation between computed and observed groundwater heads is minimal, the discrepancies in the
water balance suggest that they are very large. Incorporating additional data collected over time could increase the effectiveness of automated optimization methods and improve model calibration in the near future. Additional data would also allow for a reduction in stress-period length, enabling a more dynamic simulation of groundwater fluctuations immediately following precipitation or water abstraction events, rather than only at the end of wet or dry periods. The geometry of the aquifer system boundaries can be derived from lithological profiles of existing wells and boreholes, or from new exploratory wells drilled for this purpose. Water level and geometry data must extend beyond the boundaries of the study area to define the boundary conditions that determine water exchange between the aquifer and adjacent areas.

Detailed records of groundwater withdrawals at each pumping point are essential. Although existing laws require the installation of water quantity meters for irrigation use, many farmers do not comply. Consumption must be measured by the amount of water used, rather than calculated based on the extent and type of crops. Installation of monitoring systems for water depth and quantity at selected locations in the hydrographic network, along with tracking water flow within the aquifer boundaries, provides vital information for runoff estimation and the area's water budget. Since precipitation is the most crucial source of aquifer recharge, a sufficient network of rain gauges must be established. All types of measurements always show a degree of uncertainty, often called error analysis as a result of a variety of sources. Ignoring data uncertainty can lead to errors in hydrologic predictions (McMillan et al. 2018). Uncertainty analysis, such as the Monte Carlo method, can help quantify the reliability of models when a sufficient amount of sample data is available. Technological advancements have made the economic and user-friendly application of autographic monitoring systems feasible for most of the aforementioned parameters, creating large time series of data. In recent decades, Greece's competent authorities have made significant progress in this field, but further effort and funding are needed in the coming years.

Open-source modeling codes, which are freely available, could help minimize licensing costs, allowing authorities to allocate more resources to field measurement equipment. Additionally, they could foster collaboration between public authorities, institutions, and universities, leading to improved models and more accurate results, ultimately optimizing water management practices. Free codes also support the implementation of EU water policies (Rossetto et al. 2018). In this study, all available data were incorporated into the model's design. Data gaps were filled by the research team using literature, personal knowledge of the study area, and scientific expertise.

## Conclusions

The presented groundwater numerical flow model reliably represents the complex aquifer system in the study area. According to the water balance analysis, the main sources of aquifer recharge are precipitation, irrigation return flows, and river water percolation. The generated piezometric map suggests that groundwater flow does not follow a specific pattern or coincide with the main branches of the hydrographic network, but it indicates a general NE to SW route. The strong statistical correlation between observed and calculated values at the available observation points, in both steady and transient states, demonstrates the good responsiveness of the model to spatial and temporal water table fluctuations. This model has proven to be suitable for areas with limited data availability or complex hydrogeological and hydrological conditions.

The objectives of this study were successfully met, as the model can be used to project results based on predictive scenarios formulated by climate change models, future land use changes, or drinking water demands based on future population estimates. Sustainable groundwater management can be achieved by selecting a set of measures and testing their effectiveness using the model. The scenarios applied in the Serres basin, proposed by the competent authority (Directorate of Waters in Central Macedonia of the Decentralized Administration Unit of Macedonia & Thrace), are based on such assumptions. In the scenario where groundwater abstraction is reduced, the groundwater head will rise. Conversely, if measures are not undertaken promptly, the mean water level will decline by 0.72 m, leading to the depletion of millions of cubic meters of aquifer reserves, which poses a direct threat to food security in this highly productive land.

The methodological approach of this study can be reproduced in other areas with similar characteristics. The value of this study lies in highlighting not only the application of the models to address problems, but also the importance of basic hydrogeological research that must be conducted before model setup and the crucial importance of a sufficient monitoring network for meteorological, runoff, and groundwater level data. The current situation in the case study area must be improved. Existing observation points must be maintained over time, and new points should be added. Measurements must be taken at regular intervals. This work reveals the importance of good GIS and remote sensing data regarding elevation and land uses. Finally, it is critical to incorporate results from different software on components of the water budget, such as SWAT for calculating surface runoff in the study area. The importance of free open-source codes that could support this effort is also highlighted.

The research presented in this paper builds on previous efforts in the area, enhancing existing knowledge and
optimizing results using updated data and new available tools. The results prompt policymakers and stakeholders to undertake actions. In conclusion, the presented model is a useful tool for evaluating the impact of possible future climate change on water demand, as well as the effectiveness of designed policies and precautionary measures.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s12145-025-01969-9.

**Acknowledgements** The authors greatly appreciate the comments of Dr. Patrikaki O., Dr. Papatolios K., and Dr. Kampas G., officers of the Directorates of Waters in Central Macedonia and Eastern Macedonia & Thrace of the Decentralized Administration Unit of Macedonia & Thrace.

**Author contributions** C.M.: Methodology, Draft preparation, Writing, Software, Data curation. S.V.: Software, Data Curation. S.F.: Data Resources, Funding Acquisition, Project Administration. K.V.: Conceptualization, Methodology, Review, Project Administration. All authors have read and agreed to the published version of the manuscript.

**Funding** Open access funding provided by HEAL-Link Greece. The present work was funded in the frame of the project entitled "Simulation of groundwater flow in five alluvial aquifers of Eastern Macedonia and simulation of seawater intrusion in two coastal aquifers using the MODFLOW code", signed between the Laboratory of Engineering Geology and Hydrogeology of AUTh and Geosynolo I.K.E. Consultants Engineers & Geologists.

**Data availability** No datasets were generated or analysed during the current study.

## Declarations

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Aghlmand R, Abbasi A (2019) Application of MODFLOW with boundary conditions analyses based on limited available observations: A case study of Birjand plain in East Iran. Water 11:9. https://doi.org/10.3390/w11091904

Amiri S, Rajabi A, Shabanlou S, Yosefvand F, Izadbakhsh MA (2023) Prediction of groundwater level variations using deep learning methods and GMS numerical model. Earth Sci Inform 16, 3227–3241. https://doi.org/10.1007/s12145-023-01052-1

Anderson MP, Woessner WW, Hunt RJ (2015) Applied Groundwater Modeling: Simulation of Flow and Advective Transport. 2nd Edition, Academic Press, Cambridge, 535–564

Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modeling. Hydrol Process 19(3):563–572

Awais M, Arshad M, Ahmad SR, Nazeer A, Waqas MM, Aziz R, Sha-koor A, Rizwan M et al (2023) Simulation of groundwater flow dynamics under different stresses using MODFLOW in Rechna doab. Pakistan Sustain 15:661. https://doi.org/10.3390/su15010661

Bornovas I, Rondoyanni T (1983) Geological map of Greece scale 1: 500,000. Institute of Geology and Mineral Exploration, Athens

Brunner GW (1997) HEC-RAS river analysis system. Hydraulic reference manual. Version 1.0

Capolongo D, Refice A, Bocchiola D, D'Addabbo A, Vouvalidis K, Soncini A, Zingaro M, Bovenga F, Stamatopoulos L (2019) Coupling multitemporal remote sensing with geomorphology and hydrological modeling for post-flood recovery in the Strymonas dammed river basin (Greece). Sci Total Environ 15(651):1958–1968. https://doi.org/10.1016/j.scitotenv.2018.10.114

Chalkidis I, Seferlis M, Sakellariou-Makrantonaki M (2016) Evaluation of the environmental impact of an irrigation network in a Ramsar area of the Greek part of the Strymonas river basin using a coupled MIKE SHE/MIKE 11 modelling system. Glob Nest J 18:56–66

Chandra NA, Sahoo SN (2023) Groundwater levels and resiliency mapping under land cover and climate change scenarios: a case study of Chitravathi basin in Southern India. Environ Monit Assess 195:1394. https://doi.org/10.1007/s10661-023-11995-z

De Filippis G, Pouliaris C, Kahuda D, Vasile TA, Manea VA, Zaun F, Panteleit B, Dadaser-Celik F, Positano P, Nannucci MS et al (2020) Spatial data management and numerical modelling: demonstrating the application of the QGIS-Integrated FREEWAT platform at 13 case studies for tackling groundwater resource management. Water 12:41. https://doi.org/10.3390/w12010041

Dercas N (2020) Agricultural Water Management in Greece. Water Resources Management in Balkan Countries, 421–453

Diffenbaugh NS, Giorgi F (2012) Climate change hotspots in the CMIP5 global climate model ensemble. Clim Chang 114:813–822

Doherty JE, Hunt RJ (2010) Approaches to highly parameterized inversion—A guide to using PEST for groundwater-model calibration: U.S. Geological Survey Scientific Investigations Report 2010–5169, 59 p

Doulgeris C, Halkidis I, Papadimos D (2008) Use of Modern Technology for the Protection and Management of Water Resources in Strymonas/Struma River Basin. Technical Report. Greek Biotope/Wetland Centre (EKBY), Thermi, Greece, 82 p

Doulgeris C, Georgiou P, Papadimos D, Papamichail D (2012) Ecosystem approach to water resources management using the MIKE 11 modeling system in the Strymonas river and lake kerkini. J Environ Manage 94(1):132–143

EAGME, Hellenic Authority of Geological and Mineral Surveys (2010) Subproject: water balances of basins. Monitoring water quality and protection measures for the waters of central Macedonia. Y D Act 09:10 11 West. Unpublished Technical Report

El-Molla DA, El-Molla MA (2021) Seepage losses from trapezoidal Earth canals with an impervious layer under the bed. Water Pract Technol 16(2):530–540

Famiglietti JS (2014) The global groundwater crisis. Nat Clim Change 4:945–948

Gkiougkis I, Pouliaris C, Pliakas FK, Diamantis I, Kallioras A (2021) Conceptual and mathematical modeling of a coastal aquifer in Eastern Delta of river Nestos (N. Greece). Hydrology 8:23
Guiot J, Cramer W (2016) Climate change: the 2015 Paris agree-
ment thresholds and mediterranean basin ecosystems. Science
354:465–468

Halkidis I, Papadimos D (2007) Technical Report of LIFE Environ-
ment Project: Ecosystem-based water resources management to
minimize environmental impacts from agriculture using state-
of-the-art modeling tools in Strymonas Basin. Greek Biotope/
Wetland Centre (EKBY), Thermi, Greece (in Greek, summary in
English)

Harbaugh AW (2005) MODFLOW-2005: The U.S. Geological Survey modular groundwater model—the groundwater flow process. In: USGS Numbered Series (Ed.), Techniques and Methods, USGS Numbered Series. https://doi.org/10.3133/tm6A16

Hariharan V, Shankar MU (2017) A review of visual MODFLOW applications in groundwater modelling. In IOP Conference Series: Materials Science and Engineering (Vol. 263, No. 3, p. 032025). IOP Publishing

Hatzigiannakis E, Pantelakis D, Hatzispiroglou I, Arampatzis G, Ilias A, Panagopoulos A (2016) Discharge measurements and roughness coefficient Estimation in a river: the case of Strymonas river in Northern Greece. Environ Processes 3(1):263–275

Hill MC (2006) The practical use of simplicity in developing ground-
water models. Ground Water 44:775–781

Jacob D, Kotova L, Teichmann C, Sobolowski SP, Vautard R, Don-
nelly K, Koutroulis AG, Grillakis MG, Tsanis IK, Damm A et al
(2018) Climate impacts in Europe under +1.5 °C global warming.
Earth's Future 6:264–285

Jarzebski MP, Karthe D, Chapagain SK, Setiawati MD, Wadumestrige DCG, Pu J, Fukushi K (2024) Comparative analysis of water sustainability indices: A systematic review. Water 16:961. https://doi.org/10.3390/w16070961

Kaleris VK, Ziogas AI (2020) Using electrical resistivity logs and short duration pumping tests to estimate hydraulic conductivity profiles. J Hydrol 590:125–277

Karydakis G, Arvanitis A, Andritsos N, Fytikas M (2005) Low enthalpy geothermal fields in the Strymon basin (Northern Greece). In Proceedings World Geothermal Congress, Turkey

Khattak MS, Anwar F, Saeed TU, Sharif M, Sheraz K, Ahmed A (2016) Floodplain mapping using HEC-RAS and arcgis: a case study of Kabul river. Arab J Sci Eng 41:1375–1390

Kopsiaftis G, Mantoglou A, Giannoulopoulos P (2009) Variable den-
sity coastal aquifer models with application to an aquifer on Thira
Island. Desalination 237:65–80

Kopsiaftis G, Tigkas D, Christelis V, Vangelis H (2017) Assessment of drought impacts on semi-arid coastal aquifers of the mediterranean. J Arid Environ 137:7–15

Kourakos G, Mantoglou A (2009) Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models. Adv Water Resour 32:507–521

Kourgialas NN (2021) A critical review of water resources in greece: the key role of agricultural adaptation to climate-water effects. Sci Total Environ 775:145857

Kpegli KAR (2020) Flow characterization and modelling of a South Benin artesian aquifer system: effects of hydraulic and storage properties and recharge on simulated heads across the artesian zone. Dissertation, Wageningen University and Research

Kritsotakis M, Tsanis IK (2009) An integrated approach for sustain-
able water resources management of Messara basin. Crete Greece
Eur Water 27:15–30

Langevin CD, Guo W (2006) MODFLOW/MT3DMS-based simulation of variable density groundwater flow and transport. Ground Water 44:339–351

Lin HCJ, Richards DR, Yeh G, Cheng JRC, Cheng HP, Jones NL (1997) FEMWATER: a three-dimensional finite element computer model for simulating density-dependent flow and transport in variably saturated media. US Army Corps of Engineers,

Waterways Experiment Station, Coastal and Hydraulics Labora-
tory, p 143

Lyra A, Loukas A, Sidiropoulos P, Tziatzios G, Mylopoulos N (2021) An integrated modeling system for the evaluation of water resources in coastal agricultural watersheds: application in Almyros basin. Thessaly Greece Water 13:268. https://doi.org/10.3390/w13030268

McCaffrey SC (1992) A human right to water: domestic and interna-
tional implications. Georget Environ Law Rev 5:1

McDonald MG, Harbaugh AW (1984) A modular three-dimensional finite-difference ground-water flow model. U.S. Geological Survey Open-File Report 83:875

McDonald M, Harbaugh AW (1988) A Modular Three-Dimensional Finite Difference Ground-Water Flow Model. In: Techniques of Water-Resources Investigations, Book 6, U.S.G.S., p.588

McMillan HK, Westerberg IK, Krueger T (2018) Hydrological data uncertainty and its implications. Wiley Interdisciplinary Reviews, Water, 5(6), e1319

Musallam I, Zhou Y, Jewitt G (2023) Simulation of groundwater flow and seawater intrusion in response to climate change and human activities on the coastal aquifer of Gaza strip, Palestine. Hydrogeol J 31:1953–1969. https://doi.org/10.1007/s10040-023-0269-4-7

Mustafa JS, Mawlood DK (2024) Mathematical modelling for ground-
water management for multilayers aquifers (Erbil basin). Ain
Shams Eng J 15(7):102781

Ngenzebuhoro PC, Dassargues A, Bahaj T, Orban P, Kacimi I, Nahi-
mana L (2021) Groundwater flow modeling: A case study of
the lower Rusizi alluvial plain aquifer, North-Western Burundi.
Water 13:3376. https://doi.org/10.3390/w13233376

Ngo TML, Wang SJ, Chen PY (2024) Assessment of future climate change impacts on groundwater recharge using hydrological modeling in the Choushui river alluvial fan. Taiwan Water 16:419. https://doi.org/10.3390/w16030419

Panagopoulos A, Drakopoulou E, Perleros V (2004) Application of the mathematical model MODFLOW on a karstic aquifer: the case of Viotikos-Kifisos basin. Bull Geol Soc Greece 36:2021–2030

Pisinaras V, Petalas C, Tsihrintzis VA, Zagana E (2007) A groundwa-
ter flow model for water resources management in the ismarida
plain, North Greece. Environ Model Assess 12:75–89

Psilovikos AA (1999) Optimization models in groundwater man-
agement, based on linear and mixed integer programming. An
application to a Greek hydrogeological basin. Phys Chem Earth
24:139–144

Rossetto R, De Filippis G, Borsi I, Foglia L, Cannata M, Criollo R, Vázquez-Suñé E (2018) Integrating free and open source tools and distributed modelling codes in GIS environment for data-based groundwater management. Environ Model Softw 107:210–230

Sanaullah M, Wang X, Ahmad SR, Mirza K, Mahmood MQ, Kamran M (2023) Optimized irrigated water management using numerical flow modeling coupled with finite element model: A case study of Rechna doab, Pakistan. Water 15:4193. https://doi.org/10.3390/w15234193

SEEP2D Primer (1998) Brigham Young University, Engineering Computer Graphics Laboratory

Siarkos I, Latinopoulos P (2016) Modeling seawater intrusion in over-
exploited aquifers in the absence of sufficient data: application
to the aquifer of Nea moudania, Northern Greece. Hydrogeol J
24:2123–2141

Sidiropoulos P, Tziatzios G, Vasiliades L, Papaioannou G, Mylopoulos N, Loukas A (2018) Modelling flow and nitrate transport in an over-exploited aquifer of rural basin using an integrated system: The case of Lake Karla watershed. Proceedings 2:667

Sidiropoulos P, Tziatzios G, Vasiliades L, Mylopoulos N, Loukas A (2019) Groundwater nitrate contamination integrated modeling
for climate and water resources scenarios: the case of lake Karla
Over-Exploited aquifer. Water 11:1201

Siebert S, Burke J, Faures JM, Frenken K, Hoogeveen J, Doll P, Port-
mann FT (2010) Groundwater use for irrigation: a global inven-
tory. Hydrol Earth SystSci 14:1863–1880

Smith M, Cross K, Paden M, Laban P (2016) Spring – Managing groundwater sustainably. IUCN, Gland, Switzerland

Tzanakakis VA, Angelakis AN, Paranchianakis NV, Dialynas YG, Tchobanoglous G (2020) Challenges and opportunities for sustainable management of water resources in the Island of crete, Greece. Water 12:1538. https://doi.org/10.3390/w12061538

Tziatzios G, Sidiropoulos P, Vasiliades L, Mylopoulos N, Loukas A (2015) Simulation of Nitrate Contamination in Lake Karla Aquifer. In Proceedings of the 14th International Conference on Environmental Science and Technology (CEST2015), Rhodes, Greece. https://cest2015.gnest.org/papers/cest2015_00121_oral_paper.pdf. Accessed 21 January 2021

Waddill DW, Widdowson MA (2000) SEAM3D: A numerical model for three-dimensional solute transport and sequential electron acceptor-based biodegradation. Ground water. ERDC/EL TR-00-X. U.S. Army Engineer Research and Development Center, Vicksburg, MS

Waterloo H (1999) Visual MODFLOW, User's Manual 32

Winston RB (2009) ModelMuse-A graphical user interface for MOD-
FLOW-2005 and PHAST. Chapter 29 in: U.S. Geological Survey,
Techniques of Water-Resources Investigations (book 6) – Ground
Water Techniques and Methods (section A29), 59 p

Yang J, Townsend RD, Daneshfar B (2006) Applying the HEC-RAS model and GIS techniques in river network floodplain delineation. Can J Civ Eng 33(1):19–28

Yashooa NK, Mawlood D (2024) A review of the software resources in modeling groundwater contamination transport: case studies in Iraq. Intech Open. https://doi.org/10.5772/intechopen.114933

Zheng C, Wang PP (1999) MT3DMS: A modular three-dimensional multispecies transport model for simulation of advection, dispersion and chemical reactions of contaminants in ground water systems: documentation and user's guide. Contract Report SERDP-99-1, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi (available at http://hydro.geo.ua.edu/mt3d)

**Publisher's note** Springer Nature remains neutral with regard to juris-
dictional claims in published maps and institutional affiliations.