# The Significance of Groundwater Table Inclination for Nature-Based Replenishment of Groundwater-Dependent Ecosystems by Managed Aquifer Recharge

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## Abstract

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## 1. Introduction

- i.
- to evaluate the effects of groundwater table inclination and further influencing parameters (topography, model length, groundwater depth, material properties, heterogeneity, and infiltration basin parameters) on downgradient water level increase and to estimate infiltration-based MAR efficiency from the perspective of water level and GDE restoration for a simple half-basin; and
- ii.
- to demonstrate the applicability of this method through a close-to-real situation, answering the hypothetical question: “Can this be a possible measure to rehabilitate the former Lake Kondor, Danube-Tisza Interfluve, Hungary?”

## 2. Theoretical Models

#### 2.1. Methods

_{xx}is the hydraulic conductivity in the x-direction [m/s], K

_{zz}is the hydraulic conductivity in the z-direction [m/s], Q is the applied boundary flux [m

^{3}/s], θ is the volumetric water content [-], m

_{w}is the slope of the saturated water content function, γ

_{w}is the water specific weight [kN/m

^{3}], and t is the time [s] [65]. The hydraulic conductivity in the saturated zone is constant; however, it is dependent on θ and estimated by the van Genuchten method [66] in the unsaturated zone. Steady-state calculations were performed using a simplified version of Equation (1) (t = 0) to determine the initial conditions for the time-dependent calculations.

**n**is the normal vector, ρ

_{w}is the water density, and

**q**denotes the Darcy flux. These boundary conditions were used for both steady-state and time-dependent calculations (Figure 1b,c).

_{l}and right—h

_{r}), material properties (i.e., horizontal hydraulic conductivity (K

_{xx}), anisotropy (ε = K

_{xx}/K

_{zz}), saturated water content (θ

_{s})), heterogeneities of the porous media, and parameters of the infiltration basin, such as width (w) and water depth (d).

_{xx}): 10

^{−5}m/s, saturated water content (θ

_{s}): 0.35, residual water content (θ

_{r}): 0.035 [67,68]. The inbuilt silty sand sample curve was used to estimate the volumetric water content function. For SG-4, the anisotropy coefficient of hydraulic conductivity (ε = K

_{xx}/K

_{zz}) was changed between 1 and 100. For SG-5, five types of heterogeneous geological settings were used: a 10-m-thick layer or different lenses of 400 m length intersected the model domain between Z = 25 and 35 m (Figure 1d) with different hydraulic conductivity values (K’

_{xx}changing between 1∙10

^{−7}and 1∙10

^{−5}m/s; Table 1). In these scenarios, the mesh was further refined between Z = 25 and 35 m, using a general mesh size of 2.5 m to achieve more reliable results.

_{l}) was 38 m, so the water level depth at this point was 2 m (except for SG-3, see Table 1). On the right side, the hydraulic head (h

_{r}) was the function of Δh, which is the hydraulic head difference between the two sides of the model (i.e., water table inclination). This parameter was tested in SG-1–4 and SG-6. The hydraulic head difference (Δh) as a parameter was chosen instead of hydraulic gradient due to the fact that the hydraulic gradient is dependent on hydraulic head difference and model length, as well.

_{l}and h

_{r}were not specified. The infiltration basin was defined by a fixed hydraulic head at the bottom of the basin maintaining a 1 m water column (h = Z + 1) throughout the modelling time (except for SG-6/B; Table 1). A total simulation time of five years was used in every scenario. The simulations had 100 exponentially increasing time steps from which every fourth was saved.

_{l}and the bottom of the infiltration basin) with two parameters in order to compare the results of different scenarios. Water level increase (ΔΨ) noticed at h

_{l}(at the local discharge area) was analysed, as it is one of the main interests of this study. Furthermore, the cumulative volume of water entering the model domain from the infiltration basin (V

_{tot}) was also evaluated.

_{l}), V

_{tot}[m

^{3}] is the cumulative water volume entering the model from the infiltration basin during the examined time period, L [m] is the length of the model, and y [m] is the element thickness of the model in the y-direction, which was defined as 1 m. Thus, the most efficient system is the one which can achieve a higher water level increase with a smaller infiltration volume in the model domain for a selected time interval. (Note that, if the main aim of water infiltration is water storage without the risk of flooding the downgradient areas, this equation can be inverted).

#### 2.2. Results

#### 2.2.1. Topography and Hydraulic Head Difference (SG-1)

_{tot}) in time. It shows that the water level starts to increase visibly after approx. 1.5 years and reaches an increase of the order of 0.1 m. By maintaining a 1 m water column in the basin, 100 m

^{3}of water infiltrates from the basin within five years (Figure 2). The infiltration is more rapid at the beginning, and then the process starts to slow down.

_{tot}ranges between 1089–1903 m

^{3}, 3149–3928 m

^{3}, 5251–5975 m

^{3}, and 7435–8045 m

^{3}for Δz = 10, 20, 30, and 40 m, respectively. In every case, the highest V

_{tot}values are related to the lowest Δh values (Figure 4b). Overall, the scenarios described by Δh = 0 m show the lowest efficiency values (EI = 0.15–0.19), while the highest ones are connected to those scenarios with Δh = 6 m (EI = 0.76–4.86). Higher Δz resulted in lower efficiencies, and these differences are more significant for higher Δh values (Figure 4c).

#### 2.2.2. Model Length (SG-2)

_{tot}is 3149 m

^{3}, 2976 m

^{3}, and 2926 m

^{3}for the scenarios with L = 2000, 5000, and 10,000 m, respectively (Figure 4e). The highest efficiencies (EI = 0.19–1.62) are related to the smallest model (L = 2000 m); however, the other two scenario types (L = 5000 m and L = 10,000 m) can be similarly efficient (EI = 0–1.48 and EI = 0–1.58; Figure 4f). Regarding different Δh scenarios, similar patterns can be noticed with SG-1.

#### 2.2.3. Elevation of Water Table (SG-3)

_{l}) was changed between 36–39 m (meaning, 4–1 m water depth on the left side), in this case, h

_{r}changed based on Δh; (ii) SG-3/B, where the hydraulic head on the right side was changed (h

_{r}) between 36–39 m (meaning, 22–19 m water depth on the right side), in this case, h

_{l}changed based on Δh (Table 1). These parameters were specified for the steady-state models determining the initial conditions.

_{l}values of 36, 37, 38, and 39 m, respectively (Figure 4g). The results related to Δh = 0 m showed the lowest ΔΨ values, while the highest ones were noticed in connection with Δh = 6 m. V

_{tot}ranges between 3521–4241 m

^{3}, 3329–4051 m

^{3}, 3152–3895 m

^{3}, and 2942–3724 m

^{3}for h

_{l}= 36, 37, 38, 39 m, respectively. In every case, the highest V

_{tot}values are related to the lowest Δh values (Figure 4h). In general, EI was lower for scenarios with a deeper water table (h

_{l}= 36 m, EI = 0.11–1.17), and it was higher for scenarios with a higher water table (h

_{l}= 39 m, EI = 0.17–2.17) (Figure 4i).

_{r}values of 36, 37, 38, and 39 m) and change only with respect to Δh (Figure 4j). The results related to Δh = 0 m showed the lowest values (ΔΨ = 0.25–0.33 m), while the highest ones were noticed in connection with Δh = 6 m (ΔΨ = 1.69–1.85 m). V

_{tot}ranges between 4311–4532 m

^{3}, 4121–4402 m

^{3}, 3899–4219 m

^{3}, and 3781–4051 m

^{3}for h

_{r}= 36, 37, 38, and 39 m, respectively. In this case, the highest V

_{tot}values are related to the highest Δh values (Figure 4k), contrary to the results obtained for SG-3/A. In general, EI was lower for scenarios with a deeper water table (h

_{r}= 36 m, EI = 0.11–0.75) and higher for scenarios with a higher water table (h

_{r}= 39 m, EI = 0.17–0.91). Regarding different Δh scenarios, similar patterns can be noticed with SG-1 (Figure 4l), both in the case of SG-3/A and SG-3/B.

#### 2.2.4. Material Properties (SG-4)

_{xx}), anisotropy coefficient (ε), and saturated water content (θ

_{s}) were varied separately, creating SG-4/A, SG-4/B, and SG-4/C, respectively (Table 1). For these scenarios, the topography, the model length, and the water level at the left side were fixed (Δz = 20 m, L = 2000 m, h

_{l}= 38 m), and Δh changed between 0 and 6 m.

^{−7}m/s and 1∙10

^{−5}m/s (SG-4/A). Water level increase after five years ranged from 0 m to 0.37 m for the scenarios with Δh = 0 m and ranged from 0.44 m to 2.54 m for the scenarios with Δh = 6 m (Figure 5a). The results related to K

_{xx}= 1∙10

^{−7}m/s showed the lowest values, while the highest ones were noticed in connection with K

_{xx}= 1∙10

^{−5}m/s (Figure 5a). Concerning the infiltrating water amount in five years, V

_{tot}ranged between 3168–3904 m

^{3}and 508–539 m

^{3}for the scenarios with horizontal hydraulic conductivities of 1∙10

^{−5}m/s and 1∙10

^{−7}m/s, respectively. The highest V

_{tot}values are related to the lowest Δh values, in general (Figure 5b). In most cases, the highest EI values were noticed in connection with K

_{xx}= 1∙10

^{−5}m/s (EI = 0.19–1.6); however, with higher Δh values, lower K

_{xx}values could also result in high efficiency indices (e.g., Δh = 6 m K

_{xx}= 1∙10

^{−7}m/s, EI = 1.73; Figure 5c).

_{tot}ranged between 3153–3919 m

^{3}, 2772–3278 m

^{3}, and 706–1305 m

^{3}for ε = 1, 10, 100, respectively (Figure 5e). Compared to the other two scenario types, for those with ε = 100, they showed increasing V

_{tot}with increasing hydraulic head difference. Apart from Δh = 0 m, the highest efficiencies are related to ε = 100 (EI = 0.84–3.29), and the lowest ones are related to ε = 1 (EI = 0.19–1.61; Figure 5f).

_{s}= 0.45 showed the lowest values, while the highest ones were noticed in connection with θ

_{s}= 0.25 (Figure 5g). Concerning the infiltrating water amount in five years, V

_{tot}ranged between 2642–3279 m

^{3}and 3546–4421 m

^{3}for the scenarios with a saturated water content of 0.25 and 0.45, respectively. The highest V

_{tot}values are related to the highest θ

_{s}and lowest Δh values (Figure 5h). The highest EI values were noticed in connection with θ

_{s}= 0.25 (EI = 0.68–3.25), and the lowest were noticed in connection with θ

_{s}= 0.45 (EI = 0.06–1.08; Figure 5i). Regarding different Δh scenarios, similar patterns can be noticed for SG-1 (Figure 4l) in all three scenario groups (SG-4/A, SG-4/B, SG-4/C).

#### 2.2.5. Heterogeneity (SG-5)

- with a continuous layer (“Layer”);
- with a lens below the recharge area (“Lens RA”);
- with a lens below the throughflow area (“Lens TA”);
- with a lens below the discharge area (“Lens DA”);
- with all three of these lenses (“Lenses”).

_{xx}= 1∙10

^{−5}m/s, while K’

_{xx}was changed between 1∙10

^{−5}and 1∙10

^{−7}m/s for the intersecting layer and lenses. For these scenarios, the topography, the model length, the water level at the left side, as well as the hydraulic head difference, were fixed (Δz = 20 m, L = 2000 m, h

_{l}= 38 m, Δh = 3 m).

_{xx}/K

_{xx}ratios (Figure 6a). For “Lens DA” and “Lens TA”, V

_{tot}ranged between 3472–3505 m

^{3}. In the case of the other three scenario types, it varied between 2958 m

^{3}and 3505 m

^{3}, showing an increasing trend towards higher K’

_{xx}/K

_{xx}ratios (Figure 6b). The Efficiency Indices were similar (EI = 0.63–0.91), “Lense RA” showed a decreasing trend, and “Lense DA” showed an increasing trend towards lower K’

_{xx}/K

_{xx}ratios (Figure 6c).

#### 2.2.6. Parameters of the Infiltration Basin (SG-6)

_{tot}ranges between 2929–3633 m

^{3}, 3168–3904 m

^{3}and 3375–4162 for w = 50, 100, and 150 m, respectively (Figure 7b). In every case, the highest V

_{tot}values are related to the lowest Δh values. Below Δh = 2 m, the scenarios described by w = 50 m showed the lowest efficiency values (EI = 0.17–0.32), and the ones with w = 150 m showed the highest ones (EI = 0.22–0.35). However, above Δh = 2 m, this relationship is reversed: the scenarios with w = 50 m resulted in the highest EI values (EI = 0.73–1.66), and the ones with w = 150 m showed the lowest efficiencies (EI = 0.7–1.57; Figure 7c). Regarding different Δh scenarios, similar patterns can be noticed to SG-1.

_{tot}ranges between 3063–3821 m

^{3}, 3168–3904 m

^{3}, 3225–3983 m

^{3}, and 3293–4069 m

^{3}for d = 0.5, 1, 1.5, and 2 m, respectively. In every case, the highest V

_{tot}values are related to the lowest Δh values (Figure 7e). Below Δh = 2 m, the scenarios showed similar Efficiency Indices (EI = 0.18–0.2 for Δh = 0 m and EI = 0.33 for Δh = 1 m); however, above Δh = 2 m, a decrease in EI was noticed towards higher d values (EI = 0.51–1.64 for d = 0.5 m and EI = 0.49–1.58 for d = 2 m; Figure 7f). Regarding different Δh scenarios, similar patterns can be noticed for SG-1.

#### 2.3. Interpretation

_{tot}, e.g., Figure 4b,e,h) infiltrating from the infiltration basin. On the other hand, by comparing scenarios with different topography, more significant changes can be noticed (Figure 4b). In the case of Δh = 6 m, there is a sevenfold difference between the cumulative water volume (V

_{tot}= 1089 m

^{3}and 7435 m

^{3}) related to the scenarios with Δz = 10 m and Δz = 40 m, respectively (Figure 4b). These differences can be explained by the storage capacity of the model domain. Higher Δz means a thicker unsaturated zone, thus more water can be stored, and it takes more time for the infiltrated water to reach the initial water table. As h

_{l}is initially fixed in these cases, h

_{r}changes based on Δh. In the case of Δh = 6 m, h

_{r}is closer to the surface, causing a thinner unsaturated zone. This way, water reaches the saturated zone sooner than in the case of Δh = 0 m.

_{l}), the water level increase is higher than in the case of deeper water tables (Figure 4g). The difference is especially significant in the case of h

_{l}= 39 m, where the water level is only in 1 m depth. The model set-up and the boundary conditions possibly induce this phenomenon. On the other hand, no significant difference can be observed in the water level increase achieved after five years if the initial hydraulic head on the right side of the model (h

_{r}) is changed (Figure 4j). The different Δh values had an effect on the water level increase in each case (SG-3/A and SG-3/B): higher Δh caused a higher water level increase. The cumulative water volume is slightly higher when the water table is deeper (Figure 4h,k), which can be explained by higher storage capacity. While, in the case of SG-3/A, higher Δh induced a lower amount of water infiltration (Figure 4h), and, for SG-3/B, a slight increase can be noticed by increasing Δh (Figure 4k). This difference is connected to storage capacity, as well. When h

_{r}was fixed (SG-3/B, e.g., h

_{r}= 38 m), Δh = 6 m meant a deeper water table at the left side (h

_{l}= 32 m), thus representing higher storage capacity than in the case of Δh = 0 m (h

_{l}= 38 m). For SG-3A, this is the other way around. Both in the case of SG-3/A and SG-3/B, efficiency indices were higher when the water table was closer to the surface (Figure 4i,l).

_{tot}. Model scenarios with higher K

_{xx}values induced higher water level increases (Figure 5a). Almost one order of magnitude difference was noticed between the scenarios with K

_{xx}= 1∙10

^{−7}m/s and K

_{xx}= 1∙10

^{−5}m/s. The infiltrated water volume after five years also increased with higher K

_{xx}values (Figure 5b). Efficiency indices of SG-4/A (Figure 5c) show that even smaller hydraulic conductivity values can be enough to reach sufficient water level increase downgradient with a lower amount of water infiltration.

_{s}resulted in a lower water level increase after five years (Figure 5d) while inducing a higher amount of infiltration (Figure 5e). These processes can be explained by higher porosity. Thus, the higher storage capacity of the modelling framework is represented. Consequently, the efficiency was the highest in the case of the lowest θ

_{s}value (Figure 5f) from the perspective of GDE rehabilitation.

## 3. Case Study

#### 3.1. Study Area

#### 3.2. Numerical Settings

- “K-1”: A homogeneous model with horizontal hydraulic conductivity (K
_{xx}) of 5∙10^{−6}m/s. - “K-2”: A model with three layers, where the upper and lower layers were described by K
_{xx}= 5∙10^{−6}m/s and the middle layer by K_{xx}= 5∙10^{−7}m/s. The upper layer was 5 m thick on the left side and 10 m thick on the right side; the bottom of the middle layer was at 85 m a.s.l. - “K-3”: A model with lenses, where the model domain and the lenses were characterised by K
_{xx}= 5∙10^{−6}m/s and K_{xx}= 5∙10^{−7}m/s, respectively.

_{s}) was 0.35, the residual water content (θ

_{r}) was defined as 0.035, and the in-built silty sand sample curve was used for the estimation of volumetric water content function.

_{l}= 101 m (2 m below the surface) and h

_{r}= 111 m. Thus, the hydraulic gradient is approx. 0.0015, similar to the scenarios where the length of the model was 2000 m and Δh was 3 m (SG-1, SG-3–6). A transient model of 50 years (18,250 days) was built with 200 exponentially increasing steps. For the transient model, h

_{l}and h

_{r}were not specified, and it used the steady-state model to acquire initial hydraulic head conditions. The infiltration basin was identical to the ones presented in SG-1–5 and was defined in the same way.

#### 3.3. Results

^{3}for K-1, K-2, and K-3 scenarios (Figure 9b).

#### 3.4. Interpretation

## 4. Discussion

#### 4.1. Relevance and Limitations of the Theoretical Models

_{s}). Heterogeneity plays a role in this regard, especially in those cases when there is a layer or lens with lower hydraulic conductivity below the recharge area. The results of this research are in accordance with the ones acquired by Wu et al. [88], who investigated the effects of geological heterogeneity on MAR efficiency.

_{xx}= 1∙10

^{−7}m/s and Δh = 6 m can seem to be highly efficient (EI = 1.73; SG-4/A, Figure 5c), because only 508 m

^{3}water was enough to increase the water level by 0.44 m in five years. However, one must ask if a 0.44 m water level increase is enough to reach the aims of implementing the MAR system. Thus, specifying the aims of the project during the planning phase is essential both in terms of water level increase and time. The EI can be used in the context of realistic scenarios considering the available source water and the spatial extent of the project.

#### 4.2. The Relevance and Limitations of the Case Study

^{3}of ecological water demand per year and to raise the groundwater level by 0.5–1.5 m near the reservoirs. The most significant problem is that the ridge region is topographically higher than the rivers and larger channels in the valleys, so the water has to be pumped up, which is quite expensive. Furthermore, the water can easily infiltrate from the channels, and it would not reach the higher regions in sufficient amounts.

#### 4.3. Nature-Based Solutions and GDE Replenishment

## 5. Conclusions

- The theoretical models for a simple basin revealed the significance of groundwater table inclination for infiltration-based MAR planning and operation.
- The achieved water level increase (ΔΨ) was approx. one order of magnitude higher in the case of higher initial hydraulic head difference (Δh = 6 m) than in the case of Δh = 0 m. In addition, the distance between the recharge and discharge areas and the hydraulic conductivity has the most significant effect on the water level increase at the discharge area.
- The results showed that the amount of water infiltrated from the infiltration basin (V
_{tot}) is principally governed by topographic difference and the depth of water table, thus by the thickness of the unsaturated zone. There was a sevenfold difference between the cumulative water volumes related to the scenarios with Δz = 10 m and Δz = 40 m, in the case of Δh = 6 m. Furthermore, the material properties of the aquifer, such as hydraulic conductivity, anisotropy, saturated water content, and heterogeneity, have an effect on the infiltrated volumes. - From the perspective of groundwater-dependent ecosystem preservation and restoration, the most efficient scenarios are when the hydraulic gradient and the horizontal hydraulic conductivity are high, and the aquifer has a lower storage capacity. This means that exactly the opposite conditions are required, as in the case of long-term water storage in an aquifer.
- The established efficiency index, involving the achieved water level increase and the infiltrated water volumes, can be used to differentiate between realistic scenarios and to optimise the MAR design in the future.
- The investigated case study proved the applicability and efficiency of the initial concept and offered a possible water management measure for increasing the water reserves and restoring the GDE of the area. The applied approach offers a smart, diverse, and nature-based solution, and it can be advantageous compared to the previously proposed water replenishment plans for the area.
- Based on the results of the theoretical and simplified case study models, a conceptual model was built: if water is infiltrated at the local recharge area (elevated area), the water table will increase at the local discharge area (local topographical depression), as well, due to hydraulic continuity, which can have a positive effect on GDEs, using natural settings and processes. Furthermore, the water level beneficially increases around the recharge and discharge area, as well.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- NRMMC; EPHC; NHMRC. Australian Guidelines for Water Recycling, Managing Health and Environmental Risks, Vol 2C: Managed Aquifer Recharge; Biotext: Canberra, Australia, 2009; 237p. [Google Scholar]
- Bouwer, H. Artificial Recharge of Groundwater: Hydrogeology and Engineering. Hydrogeol. J.
**2002**, 10, 121–142. [Google Scholar] [CrossRef] [Green Version] - Gale, I. Strategies for Managed Aquifer Recharge in Semi-Arid Areas; UNESCO: Paris, France, 2005; 30p. [Google Scholar]
- Dillon, P.; Toze, S.; Page, D.; Vanderzalm, J.; Bekele, E.; Sidhu, J.; Rinck-Pfeiffer, S. Managed Aquifer Recharge: Rediscovering Nature as a Leading Edge Technology. Water Sci. Technol.
**2010**, 62, 2338–2345. [Google Scholar] [CrossRef] [PubMed] - Casanova, J.; Devau, N.; Pettenati, M. Managed Aquifer Recharge: An Overview of Issues and Options. In Integrated Groundwater Management; Springer International Publishing: Cham, Switzerland, 2016; pp. 413–434. [Google Scholar]
- Dillon, P. Future Management of Aquifer Recharge. Hydrogeol. J.
**2005**, 13, 313–316. [Google Scholar] [CrossRef] - Gale, I.N.; Macdonald, D.M.J.; Calow, R.C.; Neumann, I.; Moench, M.; Kulkarni, H.; Mudrakartha, S.; Palanisami, K. Managed Aquifer Recharge: An Assessment of Its Role and Effectiveness in Watershed Management; British Geological Survey: Nottingham, UK, 2006. [Google Scholar]
- Scherberg, J.; Baker, T.; Selker, J.S.; Henry, R. Design of Managed Aquifer Recharge for Agricultural and Ecological Water Supply Assessed Through Numerical Modeling. Water Resour. Manag.
**2014**, 28, 4971–4984. [Google Scholar] [CrossRef] - Van Houtte, E.; Verbauwhede, J. Environmental Benefits from Water Reuse Combined with Managed Aquifer Recharge in the Flemish Dunes (Belgium). Int. J. Water Resour. Dev.
**2021**, 37, 1027–1034. [Google Scholar] [CrossRef] - O’Hogain, S.; McCarton, L. A Technology Portfolio of Nature Based Solutions; Springer International Publishing: Cham, Switzerland, 2018; ISBN 978-3-319-73280-0. [Google Scholar]
- UN WATER. The United Nations World Water Development Report 2018: Nature-Based Solutions for Water; UN WATER: Paris, France, 2018. [Google Scholar]
- Sprenger, C.; Hartog, N.; Hernández, M.; Vilanova, E.; Grützmacher, G.; Scheibler, F.; Hannappel, S. Inventory of Managed Aquifer Recharge Sites in Europe: Historical Development, Current Situation and Perspectives. Hydrogeol. J.
**2017**, 25, 1909–1922. [Google Scholar] [CrossRef] [Green Version] - Dillon, P.; Stuyfzand, P.; Grischek, T.; Lluria, M.; Pyne, R.D.G.; Jain, R.C.; Bear, J.; Schwarz, J.; Wang, W.; Fernandez, E.; et al. Sixty Years of Global Progress in Managed Aquifer Recharge. Hydrogeol. J.
**2019**, 27, 1–30. [Google Scholar] [CrossRef] [Green Version] - Stefan, C.; Ansems, N. Web-Based Global Inventory of Managed Aquifer Recharge Applications. Sustain. Water Resour. Manag.
**2018**, 4, 153–162. [Google Scholar] [CrossRef] [Green Version] - Fernández Escalante, E.; Henao Casas, J.D.; San Sebastián Sauto, J.; Calero Gil, R. Monitored and Intentional Recharge (MIR): A Model for Managed Aquifer Recharge (MAR) Guideline and Regulation Formulation. Water
**2022**, 14, 3405. [Google Scholar] [CrossRef] - Fernandez Escalante, E.; Henao Casas, J.D.; Vidal Medeiros, A.M.; San Sebastián Sauto, J.S.S.S. Regulations and Guidelines on Water Quality Requirements for Managed Aquifer Recharge. International Comparison. Acque Sotter.-Ital. J. Groundw.
**2020**, 9, 7–22. [Google Scholar] [CrossRef] - Imig, A.; Szabó, Z.; Halytsia, O.; Vrachioli, M.; Kleinert, V.; Rein, A. A Review on Risk Assessment in Managed Aquifer Recharge. Integr. Environ. Assess. Manag.
**2022**, 18, 1513–1529. [Google Scholar] [CrossRef] - Tóth, J. Groundwater as a Geologic Agent: An Overview of the Causes, Processes, and Manifestations. Hydrogeol. J.
**1999**, 7, 1–14. [Google Scholar] [CrossRef] - Tóth, J. A Conceptual Model of the Groundwater Regime and the Hydrogeologic Environment. J. Hydrol.
**1970**, 10, 164–176. [Google Scholar] [CrossRef] - IGRAC. Artificial Recharge of Groundwater in the World; IGRAC: Delft, The Netherlands, 2007. [Google Scholar]
- Pyne, R.D.G. Aquifer Storage Recovery: A Guide to Groundwater Recharge through Wells, 2nd ed.; ASR Press: Gainesville, FL, USA, 2005. [Google Scholar]
- Ward, J.D.; Simmons, C.T.; Dillon, P.J.; Pavelic, P. Integrated Assessment of Lateral Flow, Density Effects and Dispersion in Aquifer Storage and Recovery. J. Hydrol.
**2009**, 370, 83–99. [Google Scholar] [CrossRef] - Gale, I.; Neumann, I.; Calow, R.; Moench, D.M. The Effectiveness of Artificial Recharge of Groundwater: A Review; British Geological Survey: Nottingham, UK, 2002. [Google Scholar]
- Dillon, P.; Pavelic, P.; Page, D.; Beringen, H.; Ward, J. Managed Aquifer Recharge: An Introduction. Waterlines Report Series No. 13; National Water Commission: Canberra, Australia, 2009. [Google Scholar]
- Dillon, P.J. General Design Considerations. In Water Reclamation Technologies for Safe Managed Aquifer Recharge; Kazner, C., Wintgens, T., Dillon, P., Eds.; IWA Publishing: London, UK, 2012; pp. 299–310. [Google Scholar]
- Ward, J.; Dillon, P. Principles to Coordinate Managed Aquifer Recharge with Natural Resource Management Policies in Australia. Hydrogeol. J.
**2012**, 20, 943–956. [Google Scholar] [CrossRef] - Missimer, T.; Guo, W.; Woolschlager, J.; Maliva, R. Long-Term Managed Aquifer Recharge in a Saline-Water Aquifer as a Critical Component of an Integrated Water Scheme in Southwestern Florida, USA. Water
**2017**, 9, 774. [Google Scholar] [CrossRef] [Green Version] - Hantush, M.S. Growth and Decay of Groundwater-Mounds in Response to Uniform Percolation. Water Resour. Res.
**1967**, 3, 227–234. [Google Scholar] [CrossRef] [Green Version] - Marino, M.A. Growth and Decay of Groundwater Mounds Induced by Percolation. J. Hydrol.
**1974**, 22, 295–301. [Google Scholar] [CrossRef] - Marino, M.A. Rise and Decline of the Water Table Induced by Vertical Recharge. J. Hydrol.
**1974**, 23, 289–298. [Google Scholar] [CrossRef] - Marino, M.A. Hele-Shaw Model Study of the Growth and Decay of Groundwater Ridges. J. Geophys. Res.
**1967**, 72, 1195–1205. [Google Scholar] [CrossRef] - Singh, R. Prediction of Mound Geometry under Recharge Basins. Water Resour. Res.
**1976**, 12, 775–780. [Google Scholar] [CrossRef] - Ganot, Y.; Holtzman, R.; Weisbrod, N.; Nitzan, I.; Katz, Y.; Kurtzman, D. Monitoring and Modeling Infiltration–Recharge Dynamics of Managed Aquifer Recharge with Desalinated Seawater. Hydrol. Earth Syst. Sci.
**2017**, 21, 4479–4493. [Google Scholar] [CrossRef] [Green Version] - Alkhatib, J.; Engelhardt, I.; Sauter, M. Identification of Suitable Sites for Managed Aquifer Recharge under Semi-Arid Conditions Employing a Combination of Numerical and Analytical Techniques. Environ. Earth Sci.
**2021**, 80, 554. [Google Scholar] [CrossRef] - Masetti, M.; Pedretti, D.; Sorichetta, A.; Stevenazzi, S.; Bacci, F. Impact of a Storm-Water Infiltration Basin on the Recharge Dynamics in a Highly Permeable Aquifer. Water Resour. Manag.
**2016**, 30, 149–165. [Google Scholar] [CrossRef] - Rahman, M.A.; Rusteberg, B.; Uddin, M.S.; Lutz, A.; Saada, M.A.; Sauter, M. An Integrated Study of Spatial Multicriteria Analysis and Mathematical Modelling for Managed Aquifer Recharge Site Suitability Mapping and Site Ranking at Northern Gaza Coastal Aquifer. J. Environ. Manag.
**2013**, 124, 25–39. [Google Scholar] [CrossRef] - Massuel, S.; Perrin, J.; Mascre, C.; Mohamed, W.; Boisson, A.; Ahmed, S. Managed Aquifer Recharge in South India: What to Expect from Small Percolation Tanks in Hard Rock? J. Hydrol.
**2014**, 512, 157–167. [Google Scholar] [CrossRef] - Bahar, T.; Oxarango, L.; Castebrunet, H.; Rossier, Y.; Mermillod-Blondin, F. 3D Modelling of Solute Transport and Mixing during Managed Aquifer Recharge with an Infiltration Basin. J. Contam. Hydrol.
**2021**, 237, 103758. [Google Scholar] [CrossRef] - Caligaris, E.; Agostini, M.; Rossetto, R. Using Heat as a Tracer to Detect the Development of the Recharge Bulb in Managed Aquifer Recharge Schemes. Hydrology
**2022**, 9, 14. [Google Scholar] [CrossRef] - Smith, A.J.; Pollock, D.W. Assessment of Managed Aquifer Recharge Potential Using Ensembles of Local Models. Ground Water
**2012**, 50, 133–143. [Google Scholar] [CrossRef] - Zlotnik, V.A.; Kacimov, A.; Al-Maktoumi, A. Estimating Groundwater Mounding in Sloping Aquifers for Managed Aquifer Recharge. Groundwater
**2017**, 55, 797–810. [Google Scholar] [CrossRef] - Pavelic, P.; Hoanh, C.T.; Viossanges, M.; Vinh, B.N.; Chung, D.T.; D’haeze, D.; Dat, L.Q.; Ross, A. Managed Aquifer Recharge for Sustaining Groundwater Supplies for Smallholder Coffee Production in the Central Highlands of Vietnam: Report on Pilot Trial Design and Results from Two Hydrological Years (May 2017 to April 2019); International Water Management Institute (IWMI): Colombo, Sri Lanka, 2020. [Google Scholar]
- Da Costa, L.R.D.; Monteiro, J.P.P.G.; Hugman, R.T. Assessing the Use of Harvested Greenhouse Runoff for Managed Aquifer Recharge to Improve Groundwater Status in South Portugal. Environ. Earth Sci.
**2020**, 79, 253. [Google Scholar] [CrossRef] - Amanambu, A.C.; Obarein, O.A.; Mossa, J.; Li, L.; Ayeni, S.S.; Balogun, O.; Oyebamiji, A.; Ochege, F.U. Groundwater System and Climate Change: Present Status and Future Considerations. J. Hydrol.
**2020**, 589, 125163. [Google Scholar] [CrossRef] - Atawneh, D.; Cartwright, N.; Bertone, E. Climate Change and Its Impact on the Projected Values of Groundwater Recharge: A Review. J. Hydrol.
**2021**, 601, 126602. [Google Scholar] [CrossRef] - Harrison, P.A.; Dunford, R.; Savin, C.; Rounsevell, M.D.A.; Holman, I.P.; Kebede, A.S.; Stuch, B. Cross-Sectoral Impacts of Climate Change and Socio-Economic Change for Multiple, European Land- and Water-Based Sectors. Clim. Chang.
**2015**, 128, 279–292. [Google Scholar] [CrossRef] - Arnell, N.W.; Brown, S.; Gosling, S.N.; Gottschalk, P.; Hinkel, J.; Huntingford, C.; Lloyd-Hughes, B.; Lowe, J.A.; Nicholls, R.J.; Osborn, T.J.; et al. The Impacts of Climate Change across the Globe: A Multi-Sectoral Assessment. Clim. Chang.
**2016**, 134, 457–474. [Google Scholar] [CrossRef] [Green Version] - Scanlon, B.R.; Reedy, R.C.; Faunt, C.C.; Pool, D.; Uhlman, K. Enhancing Drought Resilience with Conjunctive Use and Managed Aquifer Recharge in California and Arizona. Environ. Res. Lett.
**2016**, 11, 035013. [Google Scholar] [CrossRef] [Green Version] - Dahlke, H.E.; LaHue, G.T.; Mautner, M.R.L.; Murphy, N.P.; Patterson, N.K.; Waterhouse, H.; Yang, F.; Foglia, L. Managed Aquifer Recharge as a Tool to Enhance Sustainable Groundwater Management in California. In Advances in Chemical Pollution, Environmental Management and Protection Vol. 3; Elsevier: Amsterdam, Netherlands, 2018; pp. 215–275. [Google Scholar] [CrossRef]
- Alam, S.; Borthakur, A.; Ravi, S.; Gebremichael, M.; Mohanty, S.K. Managed Aquifer Recharge Implementation Criteria to Achieve Water Sustainability. Sci. Total Environ.
**2021**, 768, 144992. [Google Scholar] [CrossRef] - Van Engelenburg, J.; Hueting, R.; Rijpkema, S.; Teuling, A.J.; Uijlenhoet, R.; Ludwig, F. Impact of Changes in Groundwater Extractions and Climate Change on Groundwater-Dependent Ecosystems in a Complex Hydrogeological Setting. Water Resour. Manag.
**2018**, 32, 259–272. [Google Scholar] [CrossRef] [Green Version] - Havril, T.; Tóth, Á.; Molson, J.W.; Galsa, A.; Mádl-Szőnyi, J. Impacts of Predicted Climate Change on Groundwater Flow Systems: Can Wetlands Disappear Due to Recharge Reduction? J. Hydrol.
**2018**, 563, 1169–1180. [Google Scholar] [CrossRef] [Green Version] - Trásy-Havril, T.; Szkolnikovics-Simon, S.; Mádl-Szőnyi, J. How Complex Groundwater Flow Systems Respond to Climate Change Induced Recharge Reduction? Water
**2022**, 14, 3026. [Google Scholar] [CrossRef] - Aldous, A.R.; Gannett, M.W. Groundwater, Biodiversity, and the Role of Flow System Scale. Ecohydrology
**2021**, 14, 1–14. [Google Scholar] [CrossRef] - Engelen, G.B.; Kloosterman, F.H. Hydrological Systems Analysis: Methods and Applications. Water Science and Technology Library; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1996; Volume 20. [Google Scholar]
- Fernández Escalante, E.; San Sebastián Sauto, J.; Calero Gil, R. Sites and Indicators of MAR as a Successful Tool to Mitigate Climate Change Effects in Spain. Water
**2019**, 11, 1943. [Google Scholar] [CrossRef] [Green Version] - Henao Casas, J.D.; Fernández Escalante, E.; Calero Gil, R.; Ayuga, F. Managed Aquifer Recharge as a Low-Regret Measure for Climate Change Adaptation: Insights from Los Arenales, Spain. Water
**2022**, 14, 3703. [Google Scholar] [CrossRef] - Ghasemi, A.; Saghafian, B.; Golian, S. Optimal Location of Artificial Recharge of Treated Wastewater Using Fuzzy Logic Approach. J. Water Supply Res. Technol.-Aqua
**2017**, 66, 141–156. [Google Scholar] [CrossRef] - Tóth, J. A Theory of Groundwater Motion in Small Drainage Basins in Central Alberta, Canada. J. Geophys. Res.
**1962**, 67, 4375–4388. [Google Scholar] [CrossRef] - Jiang, X.-W.; Wan, L.; Wang, X.-S.; Ge, S.; Liu, J. Effect of Exponential Decay in Hydraulic Conductivity with Depth on Regional Groundwater Flow. Geophys. Res. Lett.
**2009**, 36, L24402. [Google Scholar] [CrossRef] - Freeze, R.A.; Witherspoon, P.A. Theoretical Analysis of Regional Groundwater Flow: 1. Analytical and Numerical Solutions to the Mathematical Model. Water Resour. Res.
**1966**, 2, 641–656. [Google Scholar] [CrossRef] [Green Version] - Domenico, P.A.; Palciauskas, V.V. Theoretical Analysis of Forced Convective Heat Transfer in Regional Ground-Water Flow. Geol. Soc. Am. Bull.
**1973**, 84, 3803. [Google Scholar] [CrossRef] - An, R.; Jiang, X.-W.; Wang, J.-Z.; Wan, L.; Wang, X.-S.; Li, H. A Theoretical Analysis of Basin-Scale Groundwater Temperature Distribution. Hydrogeol. J.
**2015**, 23, 397–404. [Google Scholar] [CrossRef] - Szijártó, M.; Galsa, A.; Tóth, Á.; Mádl-Szőnyi, J. Numerical Investigation of the Combined Effect of Forced and Free Thermal Convection in Synthetic Groundwater Basins. J. Hydrol.
**2019**, 572, 364–379. [Google Scholar] [CrossRef] - GEO-SLOPE. Seepage Modeling with SEEP/W—An Engineering Methodology. Users Guide; GEO-SLOPE International Ltd.: Calgary, AB, Canada, 2015. [Google Scholar]
- Van Genuchten, M.T. A Closed-Form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils. Soil Sci. Soc. Am. J.
**1980**, 44, 892–898. [Google Scholar] [CrossRef] [Green Version] - Freeze, R.A.; Cherry, J.A. Groundwater; Prentice-Hall Inc.: Englewood Cliffs, NJ, USA, 1979. [Google Scholar]
- Woessner, W.W.; Poeter, E.P. Hydrogeologic Properties of Earth Materials and Principles of Groundwater Flow; The Groundwater Project: Guelph, ON, Canada, 2020. [Google Scholar]
- Kiss, T.; Hernesz, P.; Sümeghy, B.; Györgyövics, K.; Sipos, G. The Evolution of the Great Hungarian Plain Fluvial System—Fluvial Processes in a Subsiding Area from the Beginning of the Weichselian. Quat. Int.
**2015**, 388, 142–155. [Google Scholar] [CrossRef] [Green Version] - Gábris, G. Pleistocene Evolution of the Danube in the Carpathian Basin. Terra Nova
**1994**, 6, 495–501. [Google Scholar] [CrossRef] - Gábris, G.Y.; Horváth, E.; Novothny, Á.; Ruszkiczay-Rüdiger, Z.S. Fluvial and Aeolian Landscape Evolution in Hungary—Results of the Last 20 Years Research. Neth. J. Geosci.-Geol. Mijnb.
**2012**, 91, 111–128. [Google Scholar] [CrossRef] [Green Version] - Major, P.; Neppel, F. A Duna-Tisza Közi Talajvízszint-Süllyedések (Water Level Decline in the Danube-Tisza Interfluve). Vízügyi Közlemények
**1988**, 70, 605–623. [Google Scholar] - Kovács, A.D.; Hoyk, E.; Farkas, J.Z. Homokhátság—A Special Rural Area Affected by Aridification in the Carpathian Basin, Hungary. Eur. Countrys.
**2017**, 9, 29–50. [Google Scholar] [CrossRef] [Green Version] - Pálfai, I. A Duna-Tisza Közi Hátság Vízháztartási Sajátosságai (Water Management in the Region between Danube and Tisza). Hidrológiai Közlöny
**2010**, 90, 40–44. [Google Scholar] - Pálfai, I. Talajvízszint-Süllyedés a Duna-Tisza Közén (Water Level Decline in the Danube-Tisza Interfluve). Vízügyi Közlemények
**1993**, 75, 431–434. [Google Scholar] - Szilágyi, J.; Vorosmarty, C.J. Modelling Unconfined Aquifer Level Reductions in the Area between the Danube and Tisza Rivers in Hungary. J. Hydrol. Hydromech.
**1997**, 45, 328–347. [Google Scholar] - Orlóci, I. A Tiszát a Dunával Összekötő Csatorna: A Duna-Tisza Csatorna (A Conception over the Canal between Danube and Tisza in Our Days). Hidrol. Közlöny
**2003**, 83, 243–250. [Google Scholar] - Alföldi, L.; Kapolyi, L. Szükséges-e a Tisza Térség Vízhiányának a Pótlására És/Vagy a Hajózó Út Vonal Lerövidítésére Duna-Tisza Csatornát Építeni? Ha Igen, Miért Nem, És Ha Nem, Miért Igen? (Conception over the Canal between the Danube and Tisza in Hungary). Hidrol. Közlöny
**2011**, 91, 1–28. [Google Scholar] - Nagy, I.; Tombácz, E.; László, T.; Magyar, E.; Mészáros, S.; Puskás, E.; Scheer, M. Vízvisszatartási Mintaprojektek a Homokhátságon: „Nyugati És Keleti” Mintaterületek (Surface Water Detention Pilot Projects in the Danube-Tisza Sand Plateau Region of Hungary: „Western and Eastern” Sample Areas). Hidrol. Közlöny
**2016**, 96, 42–60. [Google Scholar] - Nemere, P. Javaslat a Duna-Tisza Közi Hátság Mélységi Vízkészletének Pótlására (Supplementing the Deep Groundwater Resource of the Area between the Rivers Danube and Tisza). Vízügyi Közlemények
**1994**, 76, 339–342. [Google Scholar] - Gyirán, I. A Duna-Tisza Közi Homokhátság Vízgazdálkodásának Fenntartható Fejlesztése (Sustainable Development of the Water Management of the Danube-Tisza Interfluve Area). In Proceedings of the A Magyar Hidrológiai Társaság XXVII. Országos Vándorgyűlése, 2. Szekció, Baja, Hungary, 1–3 July 2009. [Google Scholar]
- Ujházy, N.; Biró, M. A Vizes Élőhelyek Változásai Szabadszállás Határában (Changes of Wetland Habitats in the Territory of Szabadszállás, Hungary). Tájökológiai Lapok
**2013**, 11, 291–310. [Google Scholar] [CrossRef] - Mádl-Szőnyi, J.; Tóth, J. A Hydrogeological Type Section for the Duna-Tisza Interfluve, Hungary. Hydrogeol. J.
**2009**, 17, 961–980. [Google Scholar] [CrossRef] - Kuti, L.; Kőrössy, L. Az Alföld Földtani Atlasza—Magyarázó. Dunaújváros–Izsák (The Geological Atlas of the Great Hungarian Plain—Map Explainer. Dunaújváros–Izsák); Magyar Állami Földtani Intézet: Budapest, Hungary, 1989. [Google Scholar]
- Oláh, S. Felszínközeli Víztartók Vízgazdálkodási Célú Térképezés Geofizikai Módszerekkel Kerekegyháza Területén (Mapping of near-Surface Aquifers for Water Management Purposes with Geophysical Methods in the Kerekegyháza Area). Bachelor’s Thesis, Eötvös Loránd University, Budapest, Hungary, 2022. [Google Scholar]
- Yousif, N. Potential of Rooftop-Rainwater Harvesting through Shallow Wells for Kerekegyháza -Hungary. Master’s Thesis, Eötvös Loránd University, Budapest, Hungary, 2022. [Google Scholar]
- Szabó, Z.; Pedretti, D.; Masetti, M.; Ridavits, T.; Csiszár, E.; Falus, G.; Palcsu, L.; Mádl-Szőnyi, J. Rooftop Rainwater Harvesting by a Shallow Well—Impacts and Potential from a Field Experiment in the Danube-Tisza Interfluve, Hungary. Groundw. Sustain. Dev.
**2023**, 20, 100884. [Google Scholar] [CrossRef] - Wu, P.; Shu, L.; Comte, J.-C.; Zuo, Q.; Wang, M.; Li, F.; Chen, H. The Effect of Typical Geological Heterogeneities on the Performance of Managed Aquifer Recharge: Physical Experiments and Numerical Simulations. Hydrogeol. J.
**2021**, 29, 2107–2125. [Google Scholar] [CrossRef] - Clark, R.; Gonzalez, D.; Dillon, P.; Charles, S.; Cresswell, D.; Naumann, B. Reliability of Water Supply from Stormwater Harvesting and Managed Aquifer Recharge with a Brackish Aquifer in an Urbanising Catchment and Changing Climate. Environ. Model. Softw.
**2015**, 72, 117–125. [Google Scholar] [CrossRef] - Racz, A.J.; Fisher, A.T.; Schmidt, C.M.; Lockwood, B.S.; Huertos, M.L. Spatial and Temporal Infiltration Dynamics During Managed Aquifer Recharge. Groundwater
**2012**, 50, 562–570. [Google Scholar] [CrossRef] - Qi, T.; Shu, L.; Li, H.; Wang, X.; Men, Y.; Opoku, P.A. Water Distribution from Artificial Recharge via Infiltration Basin under Constant Head Conditions. Water
**2021**, 13, 1052. [Google Scholar] [CrossRef] - Zou, Z.; Shu, L.; Min, X.; Chifuniro Mabedi, E. Clogging of Infiltration Basin and Its Impact on Suspended Particles Transport in Unconfined Sand Aquifer: Insights from a Laboratory Study. Water
**2019**, 11, 1083. [Google Scholar] [CrossRef] [Green Version] - Cannavo, P.; Coulon, A.; Charpentier, S.; Béchet, B.; Vidal-Beaudet, L. Water Balance Prediction in Stormwater Infiltration Basins Using 2-D Modeling: An Application to Evaluate the Clogging Process. Int. J. Sediment Res.
**2018**, 33, 371–384. [Google Scholar] [CrossRef] - Alam, M.F.; Pavelic, P. Underground Transfer of Floods for Irrigation (UTFI): Exploring Potential at the Global Scale; International Water Management Institute (IWMI): Colombo, Sri Lanka, 2020. [Google Scholar]
- Harbaugh, A.W. MODFLOW-2005, the US Geological Survey Modular Ground-Water Model: The Ground-Water Flow Process; US Department of the Interior, US Geological Survey: Reston, VA, USA, 2005; Volume 6. [Google Scholar]
- Diersch, H.J.G. FEFLOW: Finite Element Modeling of Flow, Mass and Heat Transport in Porous and Fractured Media; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
- Zimmerman, W.B. Multiphysics Modeling with Finite Element Methods; World Scientific Publishing Company: Singapore, 2006; Volume 18. [Google Scholar]
- Tzoraki, O.; Dokou, Z.; Christodoulou, G.; Gaganis, P.; Karatzas, G. Assessing the Efficiency of a Coastal Managed Aquifer Recharge (MAR) System in Cyprus. Sci. Total Environ.
**2018**, 626, 875–886. [Google Scholar] [CrossRef] - Abbo, H.; Gev, I. Numerical Model as a Predictive Analysis Tool for Rehabilitation and Conservation of the Israeli Coastal Aquifer: Example of the SHAFDAN Sewage Reclamation Project. Desalination
**2008**, 226, 47–55. [Google Scholar] [CrossRef] - Ringleb, J.; Sallwey, J.; Stefan, C. Assessment of Managed Aquifer Recharge through Modeling—A Review. Water
**2016**, 8, 579. [Google Scholar] [CrossRef] [Green Version] - Simon, S.; Mádl-Szőnyi, J.; Müller, I.; Pogácsás, G. Conceptual Model for Surface Salinization in an Overpressured and a Superimposed Gravity-Flow Field, Lake Kelemenszék Area, Hungary. Hydrogeol. J.
**2011**, 19, 701–717. [Google Scholar] [CrossRef] - Kacimov, A.R.; Zlotnik, V.; Al-Maktoumi, A.; Al-Abri, R. Modeling of Transient Water Table Response to Managed Aquifer Recharge: A Lagoon in Muscat, Oman. Environ. Earth Sci.
**2016**, 75, 318. [Google Scholar] [CrossRef] - Yaraghi, N.; Ronkanen, A.; Darabi, H.; Kløve, B.; Torabi Haghighi, A. Impact of Managed Aquifer Recharge Structure on River Flow Regimes in Arid and Semi-Arid Climates. Sci. Total Environ.
**2019**, 675, 429–438. [Google Scholar] [CrossRef] - Kourakos, G.; Dahlke, H.E.; Harter, T. Increasing Groundwater Availability and Seasonal Base Flow Through Agricultural Managed Aquifer Recharge in an Irrigated Basin. Water Resour. Res.
**2019**, 55, 7464–7492. [Google Scholar] [CrossRef] [Green Version] - Tóth, J. Hydraulic Continuity in Large Sedimentary Basins. Hydrogeol. J.
**1995**, 3, 4–16. [Google Scholar] [CrossRef]

**Figure 1.**(

**a**): The applied theoretical approach for a simple half-basin adapted from Tóth [59]. (

**b**): Boundary conditions in the steady-state flow simulations. (

**c**): Boundary conditions in the transient flow simulations. (

**d**): The geometry, the geology, and the model parameters. The effect of heterogeneities was studied in the SG-5 scenarios (abbreviations: RA—below the recharge area, TA—below the throughflow area, DA—below the discharge area).

**Figure 2.**Water level increase (ΔΨ) and cumulative water volume (V

_{tot}) in time (SG-1, Δz = 0 m, Δh = 0 m).

**Figure 3.**Water level increase (ΔΨ) and cumulative water volume infiltrating from the infiltration basin (V

_{tot}) in time ((

**a**,

**b**): Δz = 10 m, (

**c**,

**d**): Δz = 20 m, (

**e**,

**f**): Δz = 30 m, (

**g**,

**h**): Δz = 40 m) for SG-1.

**Figure 4.**Effect of the topography (

**a**–

**c**), the model length (

**d**–

**f**) and the elevation of the water table (

**g**–

**l**) on the water level increase ΔΨ—(

**a**,

**d**,

**g**,

**j**), the cumulative volume of infiltrating water V

_{tot}—(

**b**,

**e**,

**h**,

**k**), and the Efficiency Index EI—(

**c**,

**f**,

**i**,

**l**) after five years ((

**a**–

**c**): SG-1; (

**d**–

**f**): SG-2; (

**g**–

**i**): SG-3/A; (

**j**–

**l**): SG-3/B) plotted against the hydraulic head difference (Δh).

**Figure 5.**The effect of material properties ((

**a**–

**c**): K

_{xx}, (

**d**–

**f**): ε, (

**g**–

**i**): θ

_{s}) on the water level increase ΔΨ—(

**a**,

**d**,

**g**), the cumulative water volume of infiltrating water V

_{tot}—(

**b**,

**e**,

**h**) and the Efficiency Index EI—(

**c**,

**f**,

**i**) after five years ((

**a**–

**c**): SG-4/A, (

**d**–

**f**): SG-4/B, (

**g**–

**i**): SG-4/C) plotted against the hydraulic head difference (Δh).

**Figure 6.**The effect of heterogeneity (K’

_{xx}/K

_{xx}) on the water level increase ΔΨ—(

**a**), the cumulative water volume of infiltrating water V

_{tot}—(

**b**), and the Efficiency Index EI—(

**c**) after 5 years (SG-5).

**Figure 7.**The effect of infiltration basin width (w) and water depth in the basin (d) on the water level increase ΔΨ—(

**a**,

**d**), the cumulative water volume of infiltrating water V

_{tot}—(

**b**,

**e**), and the Efficiency Index EI—(

**c**,

**f**) after five years ((

**a**–

**c**): SG-6/A, (

**d**–

**f**): SG-6/B) plotted against the hydraulic head difference (Δh).

**Figure 8.**(

**a**): Location of the study area in Hungary. (

**b**): Location of Kerekegyháza, Lake Kondor and the line of the simulated cross-section. (

**c**): Model geometry indicating the location of the middle layer and lenses marked by “L”. The vertical exaggeration is 1:30. Note: The map of Figure 8b uses the Hungarian EOV grid (Uniform National Projection) in which EOV X represents northing and EOV Y represents easting in meters. The topographical data were acquired from the Lechner Knowledge Centre with a resolution of 5 m. The topographical elevation is referenced to the Baltic Sea level. The water level contour map was constructed by the Kriging method based on field measurements in dug wells indicated on the map (time of measurement: 16 September 2020).

**Figure 9.**(

**a**): Water level increase (ΔΨ), (

**b**): cumulative water volume infiltrating from the infiltration basin (V

_{tot}) and (

**c**): the Efficiency Index (EI) in time. (

**d**): Initial water level and water level increase for the case study cross-section after 25 and 50 years (K-3). The hydraulic head (h) contours represent the last step of the model (after 50 years). The vertical exaggeration is 1:30.

**Figure 10.**Conceptual approach of nature-based rehabilitation of GDEs by taking advantage of groundwater table inclination in a simple basin while using managed aquifer recharge.

**Table 1.**Model scenarios with the studied parameters for the theoretical simulations (1–6) and the case study (7).

Parameters | Units | 1. Topography (SG-1) | 2. Model Length (SG-2) | 3. Elevation of Water Table (SG-3) | 4. Material Properties (SG-4) | 5. Heterogeneity (SG-5) | 6. Basin Parameters (SG-6) | 7. Case Study (K1–3) | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

A. Discharge Area | B. Recharge Area | A. K_{xx} | B. ε | C. θ_{s} | A. w | B. d | ||||||

Length (L) | m | 2000 | 2000–10,000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 2000 | 6500 |

Topography (Δz) | m | 0–40 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 14 |

Hydraulic head difference (Δh) | m | 0–6 | 0–6 | 0–6 | 0–6 | 0–6 | 0–6 | 0–6 | 3 | 0–6 | 0–6 | 10 |

Water level at the left side (h_{l}) | m | 38 | 38 | 36–39 | changing based on Δh and h_{r} | 38 | 38 | 38 | 38 | 38 | 38 | 101 |

Water level at the right side (h_{r}) | m | changing based on Δh | changing based on Δh | changing based on Δh and h_{l} | 36–39 | changing based on Δh | changing based on Δh | changing based on Δh | 41 | changing based on Δh | changing based on Δh | 111 |

Horizontal hydraulic conductivity (K_{xx}) | m/s | 1∙10^{−5} | 1∙10^{−5} | 1∙10^{−5} | 1∙10^{−5} | 1∙10^{−7}–1∙10^{−5} | 1∙10^{−5} | 1∙10^{−5} | 1∙10^{−5}, layer/lenses with different K’_{xx} changing between 1∙10^{−7} and 1∙10^{−5} | 1∙10^{−5} | 1∙10^{−5} | 5∙10^{−6}, layer/lenses with K’_{xx} = 5∙10^{−7} |

Anisotropy coefficient (ε) | - | 1 | 1 | 1 | 1 | 1 | 1, 10, 100 | 1 | 1 | 1 | 1 | 1 |

Saturated water content (θ_{s}) | - | 0.35 | 0.35 | 0.35 | 0.35 | 0.35 | 0.35 | 0.25–0.45 | 0.35 | 0.35 | 0.35 | 0.35 |

Infiltration basin width (w) | m | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 50–150 | 100 | 100 |

Water depth in the infiltration basin (d) | m | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.5–2 | 1 |

Number of scenarios | 29 | 21 | 28 | 28 | 49 | 21 | 35 | 35 | 21 | 28 | 3 |

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Szabó, Z.; Szijártó, M.; Tóth, Á.; Mádl-Szőnyi, J.
The Significance of Groundwater Table Inclination for Nature-Based Replenishment of Groundwater-Dependent Ecosystems by Managed Aquifer Recharge. *Water* **2023**, *15*, 1077.
https://doi.org/10.3390/w15061077

**AMA Style**

Szabó Z, Szijártó M, Tóth Á, Mádl-Szőnyi J.
The Significance of Groundwater Table Inclination for Nature-Based Replenishment of Groundwater-Dependent Ecosystems by Managed Aquifer Recharge. *Water*. 2023; 15(6):1077.
https://doi.org/10.3390/w15061077

**Chicago/Turabian Style**

Szabó, Zsóka, Márk Szijártó, Ádám Tóth, and Judit Mádl-Szőnyi.
2023. "The Significance of Groundwater Table Inclination for Nature-Based Replenishment of Groundwater-Dependent Ecosystems by Managed Aquifer Recharge" *Water* 15, no. 6: 1077.
https://doi.org/10.3390/w15061077