# 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

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**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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**MDPI and ACS 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*, 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