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Article

Investigating Climate Change Effects on Evapotranspiration and Groundwater Recharge of the Nile Delta Aquifer, Egypt

by
Mohamed Galal Eltarabily
1,2,
Ismail Abd-Elaty
3,
Ahmed Elbeltagi
4,
Martina Zeleňáková
5,* and
Ismail Fathy
3
1
Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA
2
Civil Engineering Department, Faculty of Engineering, Port Said University, Port Said 42523, Egypt
3
Department of Water and Water Structures Engineering, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
4
Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
5
Department of Environmental Engineering, Faculty of Civil Engineering, Technical University of Košice, 04200 Košice, Slovakia
*
Author to whom correspondence should be addressed.
Water 2023, 15(3), 572; https://doi.org/10.3390/w15030572
Submission received: 28 December 2022 / Revised: 20 January 2023 / Accepted: 29 January 2023 / Published: 1 February 2023
(This article belongs to the Special Issue Assessment and Management of Hydrological Risks Due to Climate Change)
Browse Figures
Review Reports Versions Notes

Abstract

:
Climate change (CC) directly affects crops’ growth stages or level of maturity, solar radiation, humidity, temperature, and wind speed, and thus crop evapotranspiration (ETc). Increased crop ETc shifts the fraction of discharge from groundwater aquifers, while long-term shifts in discharge can change the groundwater level and, subsequently, aquifer storage. The long-term effect of CC on the groundwater flow under different values of ETc was assessed for the Nile Delta aquifer (NDA) in Egypt. To quantify such impacts, numerical modeling using MODFLOW was set up to simulate the groundwater flow and differences in groundwater levels in the long term in the years 2030, 2050, and 2070. The model was initially calibrated against the hydraulic conductivity of the aquifer layers of the groundwater levels in the year 2008 from 60 observation wells throughout the study area. Then, it was validated with the current groundwater levels using an independent set of data (23 points), obtaining a very good agreement between the calculated and observed heads. The results showed that the combination of solar radiation, vapor pressure deficit, and humidity (H) are the best variables for predicting ETc in Nile Delta zones (north, middle, and south). ETc among the whole Nile Delta will increase by 11.2, 15.0, and 19.0% for the years 2030, 2050, and 2070, respectively. Zone budget analysis revealed that the increase of ETc will decrease the inflow and the groundwater head difference (GWHD). Recharge of the aquifer will be decreased by 19.74, 27.16, and 36.84% in 2030, 2050, and 2070, respectively. The GWHD will record 0.95 m, 1.05 m, and 1.40 m in 2030, 2050, and 2070, respectively when considering the increase of ETc. This reduction will lead to a slight decline in the storage of the Nile Delta groundwater aquifer. Our findings support the decision of the designers and the policymakers to guarantee a long-term sustainable management plan of the groundwater for the NDA and deltas with similar climate conditions.

1. Introduction

Water resources are one of the most important natural resources worldwide [1,2]; sustainable management plans are essential for ensuring the long-term availability of these sources, especially in arid and semi-arid regions [3,4,5,6]. The agriculture sector is the highest water consumer. Therefore, successful irrigation management practices should consider the rational use of water alongside the maximum water use efficiency, high yield of crops, and return profit [7,8,9]. The source of irrigation water can be surface water or groundwater. The quality of irrigation water should be considered when looking for sustainable agricultural practices [10]. Knowing exactly how much water is needed to fulfill crop requirements helps growers to utilize the irrigation water more efficiently with minimal deterioration to water sources [11]. Thus, the accurate estimation of crop evapotranspiration (ETc) is crucial for the operation and designing of irrigation systems, irrigation scheduling, and entire management plans of irrigation water [12].
Evapotranspiration is a process governed by the energy and heat exchanges at the land surface, which is controlled by the amount of available energy on the ground [13]. Additionally, it reflects the crop water requirements during the growing season and how much water is required for plants [14]. The estimation of spatially distributed crop water consumption is challenging but important to determine water balance at different scales to promote the efficient management of water resources [15]. Additionally, ETc from irrigated agriculture (e.g., the case of the Nile Delta of Egypt) is an important concern in arid and semi-arid regions, where it has a substantial impact on groundwater resource depletion and water management plans [16,17].
Moreover, ETc employs a key role in the watershed budget analysis [18], and a wide range of techniques have been developed to precisely estimate its spatial and temporal variability at site-specific locations. Moreover, it is generally considered in the hydrological modeling of surface water [19] by integrating empirical formulas, such as the Penman–Monteith equation [20], based on climate variables including temperature, solar radiation, wind speed, and humidity. The estimation of ETc is most important in the hydrological modeling of surface water, where it is not directly included in groundwater modeling [21]. ETc is neglected in groundwater modeling when aquifers are formed by coarse-grained sediments where the capillary rise is very low [22]. For leaky aquifers such as the NDA, ETc must be considered by subtracting from precipitation and irrigation water to calculate the input recharge to the groundwater flow models [23], especially when the water table is relatively shallow [24].
There are several ways to calculate the actual ETc, but surface energy balance methods [25] and surface water balance methods [25,26] are mainly used. Rana and Katerji [27] provided a summary of 10 methods for measuring and calculating actual ETc. The approaches were classified as hydrological, micrometeorological, and plant-physiological, depending on their use. Every approach has advantages and disadvantages. Xu and Chen [28] showed that a weighted lysimeter, for example, can offer precise information on water balance; nevertheless, it is difficult to estimate the ETc across large regions for an extended length of time. As a result, actual ETc is typically calculated using less sophisticated physically based or empirical methods. According to Linacre [29], actual ETc may be estimated using a link between accessible soil water content and evaporation rate. Actual ETc was estimated using two methods: the first method is the ETc model developed by Han and Tian [30], and the second method is the soil water balance model, in which the actual ETc is estimated by the fraction of potential ETc; this fraction increases as soil water content increases.
Egypt faces several serious risks from CC, and the Nile Delta (ND) is now seriously suffering from these risks, such as rising sea level, saltwater intrusion, salinization, higher temperatures, and land subsidence [31]. The impacts of these effects on surface water and groundwater should be carefully addressed and the adaptation scenarios for mitigation must be prepared [32,33]. The agriculture sector has been negatively influenced by higher temperatures for lower crop productivity and the loss of some agricultural lands in the ND [34], where the productivity of crops was estimated to decrease by 1 to 17%. Moreover, climatic changes have economic and environmental impacts which should be considered to ensure food security [35].
The groundwater recharge in the NDA is mainly sourced from irrigation water, in-flow from the two branches of the Nile River, Damietta and Rosetta, and rainfall along the northern coast [36]. Groundwater recharge depends on the rainfall duration and intensity, evapotranspiration rates, infiltration rates, soil moisture, aquifer permeability, and groundwater table depths [37]. Moreover, ETc and soil moisture depletion depend on climatic parameters such as temperature, relative humidity, wind speed, and sunshine hours. The climatic parameters should be considered for estimating the ETc and soil moisture depletion pattern to investigate the influence of CC on groundwater recharge [38]. While flood irrigation is traditionally practiced in the ND of Egypt and the return flow from irrigation water is the main component of groundwater recharge for the NDA, accurate predicting of ETc is essential to quantify how much water will recharge the aquifer in the long term when the same irrigation method is still applied.
To date, there has been no accurate estimation of actual ETc where assumptions and uncertainty of CC patterns exist. Based on the hydrological cycle, Milly and Dunne [39] determined that the actual ETc over the Mississippi River basin increased between 1949 and 1997 due to higher precipitation and increasing water usage. Golubev et al. [40] discovered that the actual ETc increased in certain relatively dry sections of southern Russia and Ohio over the warm seasons of 1950–1990 using large weighted lysimeters, whereas actual evaporation decreased in two wetter taiga areas. Similarly, Linacre [29] observed that decreased pan evaporation did not always imply declining actual ETc. Trends for potential ETc during the last 50 years in the ND have been established recently [41,42,43,44], but a predictive trend of actual ETc has not been included in the ND aquifer modeling. Thus, our novel approach is to incorporate “near to accurate” predictive values of ETc into the numerical simulation model of the groundwater in the ND of Egypt. The objective of this study is therefore to investigate the effect of climate change on annual actual ETc throughout three zones of the ND (north, middle, and south) based on the historical data from 1958 to 2020 for predicting groundwater flow and differences in groundwater heads, and aquifer storage for the years 2030, 2050, and 2070. Our findings support the sustainable management plans of groundwater in the ND of Egypt for guaranteeing the sustainable use of water resources.

2. Materials and Methods

2.1. The Nile Delta Study Area

The Nile Delta (ND) of Egypt is located between the latitudes 30°00′ and 31°45′ N and longitudes 29°30′ and 32°30′ E (Figure 1a). This area is bounded by the Mediterranean Sea in the north and Ismailia and Nubaria canals in the east and west, respectively. The direction of the ground surface slopes is from the south to the north (the Mediterranean Sea) [45]. The average range of the daily temperature is between 17 °C to 20 °C [46], while the average rainfall is between 250 mm year−1 in the north to 200 mm year−1 in the south and middle parts of the ND [47]. Additionally, the evaporation rates range between 7 mm day−1 in Upper Egypt to about 4 mm day−1 on the Northern Mediterranean coast [48]. Figure 1b shows the hydrological cross-section from south to north along the ND aquifer where the hydraulic conductivity is in m day−1. Figure 1c shows the land use and land cover for the ND in 2015 which was used as a base map for assigning ETc in the model simulation. Only the temporal variation of ETc was considered in the simulation, while the spatial variation of ETc was not included where the land use/land (LU/LC) cover in the year 2015 (LU/LC) was assumed to be almost the same during the simulation period. This is because a precise prediction of LU/LC maps requires detecting and analyzing the changes in LU/LC satellite high-resolution images over an adequate period, which was not our main objective.
The Nile Delta aquifer (NDA) is a semi-confined aquifer that is covered by a clay cap. The thickness of clay ranges from 5 m in the south to 20 m in the middle, reaching 50 m in the North of the ND [51,52,53]. Said [54] illustrated that the stratigraphic of the quaternary sediments is divided into two units: the first unit is the Holocene deposits, formed by silty and sandy clay, and the second is the Pleistocene deposits, which represent the main aquifer. The average thickness of the NDA is about 200 m on the south to 900 m on the north. The hydraulic conductivity of the NDA ranges between 50 m day−1 to 120 m day−1 [55]. The aquifer transmissivity ranges between 2000 m2 day−1 to 3000 m2 day−1, and the effective and total porosity ranges between 12% to 40% [56]. According to Morsy [57] and Abd-Elhamid et al. [58], the average depth of the groundwater levels ranges from 3 m to 5 m, while the groundwater flow direction is from the southeast to the northwest (Figure 2).
The NDA recharge rates, groundwater heads, flow directions, thickness of clay cap, and hydraulic conductivities are controlled and managed by the distribution and the flow of the surface water system [59,60]. This aquifer is recharged by infiltration from rainfall, surface water, and excess irrigation water. The total groundwater abstractions from the NDA are 85% of the groundwater resources in Egypt [61,62,63].
Water 15 00572 g002 550
Figure 2. Average depth of groundwater in m [57,64].
Figure 2. Average depth of groundwater in m [57,64].
Water 15 00572 g002

2.2. Crop Consumptions

Crop water requirements are the major component of the water balance in the ND. Thus, updating the crop requirements is essential and important when considering all agroclimatic zones, climatic-dependent ETc, and crop coefficients [65]. Figure 3 shows the yearly averaged values of the actual ETc (in cm) for the three zones of the ND, according to the finding of “The National Commission on Water Requirements” using the Penman–Monteith equation based on the monthly meteorological data. Actual ETc was calculated based on the crop water requirements for maize and wheat, which were selected as two major crops for this study area. This is because wheat is the most important food security crop and is the major winter cereal grain crop, while maize is the second most important grain crop according to the Food and Agricultural Organization [66]. Additionally, Egypt is currently cultivating the largest area of wheat in its history at 3.6 million acres, which is equivalent to a third of Egypt’s agricultural area. Maize is grown and harvested principally in summer from May to October, while wheat is grown in the winter season. ETc increased from 1958 to 1964 and decreased from 1964 to 1981. Additionally, it increased in the year 1982 and returned to decreasing from 1982 to 1999, while it increased in the year 2000. From 2000 to 2007, it decreased, returning to an increase from 2007 to 2020. This cycling behavior of the ETc can be observed over the years studied (Figure 3), which is attributed to the variation of these climate variables and climate change.

2.3. Data Collection

Environmental Systems Research Institute (ESRI) Arc GIS software 10.2.2 was used to extract the monthly minimum temperature (Tmin), mean temperature (Tmean), maximum temperature (Tmax), wind speed (WS), solar radiation (SR), humidity (H), and precipitation (rainfall, P) from the open access data, with a geographical resolution of around 4 km2. Temperature data were collected from the Climatic Research Unit (CRU) Time-Series (Ts) 4.0 in Network Common Data Format (NetCDF) format at 0.5° grid for the global land surface [67]. The Japanese Reanalysis (JRA-55) was used to collect the solar radiation, wind speed, and vapor pressure deficit data in NetCDF format as well [68]. The JRA-55 provided complete spatio-temporal data covering the period from 1958 to the present [69]. Additionally, World Weather Online (https://www.worldweatheronline.com/eg.aspx (accessed on 1 August 2022) was used to collect the humidity data. These datasets have been used in previous research of modeling the long-term dynamics of crop ETc and the impact of CC on the water footprint in the ND using an artificial neural network [69], using deep learning [70], using a deep neural network [71], and using Gaussian process regression [72].

2.4. Model Description

The groundwater modeling system (GMS) was applied and used to develop the required 3D simulation model in the current study area. The MODFLOW is a modular, three-dimensional finite-difference groundwater flow model. The governing flow equation based on water balance [73] is written as follows:
∂ ∂ x K x ∂ h ∂ x + ∂ ∂ y K y ∂ h ∂ y + ∂ ∂ z K Z ∂ h ∂ z = S s   ∂ h ∂ t + q s   Eqn .
where Kx, Ky, and Kz are the hydraulic conductivities along the x, y, and z directions (LT−1), respectively, h is the piezometric head (L), qs is the volumetric flux per unit volume at sources or sinks in the porous medium (T−1), Ss is the specific storage of the porous medium (L−1), and t is time (T). Figure 4a shows the model grid and boundary conditions used in the simulation, while Figure 4b,c show a vertical and horizontal cross-section along the conceptual model.

Model Calibration

The groundwater flow model was calibrated using 60 field observation wells during the year 2008 [57]. The model was calibrated for steady-state conditions using the initial hydraulic parameters presented in Table 1 by trial and error until simulated groundwater levels matched those measured in the 60 observation wells. After model calibration, the groundwater flow model was validated with an independent set of data (23 points). The residuals between the simulated and the observed groundwater heads ranged from −0.834 to 0.01 m. The absolute mean residual was 0.277 m, while the root mean squared error (RMSE) was 0.354 m, with a normalization RMSE of 2.72% (Figure 5a). This relative agreement of parameters suggests a good correlation between simulated groundwater levels (Figure 5b). The locations of the observation wells used for model calibration and validation are shown in Figure 5c. The results of the groundwater levels in the NDA ranged from 14 m to 0 m above mean sea level (MSL) (Figure 5d); additionally, the groundwater flowed from the South to the North.

2.5. Climate Trend Analysis

Fundamentally, trend analysis is a strategy for understanding how and why things have changed—or will change—over time. One thing to keep in mind while seeking to comprehend trend analysis is the vast range of disciplinary contexts in which it is addressed. This makes it more difficult to describe universally, but for the sake of clarity, it may be defined here as a technique for analysis that collects data and then seeks to uncover patterns, or trends, within that data to explain or anticipate behaviors. A statistical study of trends inside time-series datasets would allow for direct conclusions on existing trends or patterns within the datasets, as well as future projections (e.g., population growth or decline). These fundamental ideas have been used in a variety of statistical trend studies in the past and continue to be relevant in scenario-building exercises and future research. The statistical methods employed in such studies vary, and there are no “automatic” trend analysis procedures that can supply all of the answers, such as reference evapotranspiration [74,75], evaporation [39], and actual ETc [76]. Therefore, trend analysis is imperfect in certain ways, but it can help analysts to recognize patterns and trends that would not be feasible otherwise. Many statistical approaches, such as the Bayesian process, Spearman’s Rho test, Mann–Kendall test, and Sen’s slope estimator, have been developed to find trends within time series. In this study, to detect actual ETc trends, one parametric approach (linear regression) was used.

2.6. Case Scenarios

The MODFLOW simulated the groundwater flow and predicted the water heads for three projected periods, including the years 2030, 2050, and 2070, considering the impact of changing ETc as a discharge component of the out-flow budget of the groundwater aquifer. These simulation procedures allow a long-term assessment of the NDA behavior and the storage potential for the increasing pattern of temperature.

3. Results and Discussion

3.1. Trend Analysis of Actual Evapotranspiration in the Nile Delta

Results of the trend analysis for the annual actual ETc series over 1958–2020 are shown in Figure 6a–c for the three zones in the study area. The mean, median, and mode of the historical data from 1958 to 2020 for the north zone of the ND were 72.13, 69.6, and 67.6, respectively. They recorded 37.28, 36, and 36 for the middle zone, and 27.06, 26.7, and 35 for the southern zone of the ND. The analysis showed increases in annual values of actual ETc in the three zones. Increased actual ETc was due to increasing values of climate parameters. The combination of solar radiation, vapor pressure deficit, and humidity (H) were found to be the best variables for predicting ETc. The increasing percentages were 16.26, 8.76, and 12.31% for the northern zone, middle zone, and southern zone of the ND, respectively.
Furthermore, Figure 7 shows the future linear projections for actual ETc in comparison with historical time series. In the North zone of the ND, the actual ETc will reach 78.80, 82.05, and 85.30 cm year−1 in the years 2030, 2050, and 2070, respectively, compared with 72.13 cm year−1. This represents an increase of 9.25, 12.59, and 16.05% in crop ETc in 2030, 2050, and 2070, respectively. ETc at the middle zone of the ND will reach 40.87, 42.62, and 44.37 cm year−1 compared with 37.28 cm year−1, with an increase of 9.63, 13.07, and 16.65%, respectively, for the years 2030, 2050, and 2070, respectively. In addition, the actual ETc for the southern zone will increase to 32.10, 34.56, and 37.03 cm year−1 compared with 27.06 cm year−1 which equals 18.65, 23.38, and 28.84% increase that the ETc values between 1958 and 2020. The average value of actual ETc among the whole ND will reach 50.60, 53.08, and 55.57 cm year−1 compared with 45.49 cm year−1, with an increase of 11.2, 15.0, and 19.0% for the years 2030, 2050, and 2070, respectively. These values of ETc were used as the mean ETc rate and applied to the whole NDA when running MODFLOW to simulate the groundwater modeling, and in order to determine the decline in groundwater levels by the years 2030, 2050, and 2070.

3.2. Hydrological Water Balance of Nile Delta Aquifer

The calibrated model was applied to simulate the future changes of ETc and its impact on the groundwater levels in the ND for the years 2030, 2050, and 2070. Table 2 presents the zone budget analysis for the three predefined predictive periods in which the constant heads inflow was increased to 1,073,700, 1,102,500, and 1,208,900 m3 day−1 compared with 840,030 m3 day−1 at base case (2008); additionally, the aquifer recharge was decreased to 4,815,900, 4,692,300, and 4,271,700 m3 day−1 compared with 840,030 m3 day−1 at base case, while the canal leakage increased to 985,600, 1,014,400 and 1,116,100 m3 day−1 compared with 732,910 m3 day−1 for the base case.
Thus, the outflow for the constant heads was decreased to 1,394,600, 1,370,400, and 1,292,200 m3 day−1 compared with 1,656,200 m3 day−1 at base case, the well abstraction was 4,378,700 m3 day−1, the drain leakage decreased to 1,068,100, 1,027,700, and 897,500 m3 day−1 compared with 1,480,900 m3 day−1. The canal leakage decreased to 6,167.40, 5,572.80, and 3,888.50 m3 day−1 compared with 23,176 m3 day−1. The general heads decreased to 27,614, 26,913, and 24,522 compared with 34,346 m3 day−1 at the base case.
On the other hand, the constant heads in flow were increased by 27.8, 31.2, and 43.9 for 2030, 2050, and 2070, respectively, compared to the base case. The flow to the aquifer was decreased by 19.7, 21.8, and 28.3 for 2030, 2050, and 2070, respectively, compared to the base case, while canal leakage was increased by 34.5, 38.4, and 52.3 for 2030, 2050, and 2070, respectively. The outflow to drains decreased by 27.9, 30.6, and 39.4 for 2030, 2050, and 2070, respectively, while outflow to wells remained constant where the same discharges were assigned for the model to strictly limit the discharge from the aquifer. The total inflow/outflow decreased by 9.2%, 10.10%, and 12.90% for the years 2030, 2050, and 2070, respectively. Table 3 summarizes these percentages of increase (+)/decrease (−) of each boundary parameter for the years 2030, 2050, and 2070 compared to the current situation of the aquifer (base case).
Figure 8a–c present the distribution of groundwater heads difference in the ND for the three predictive periods until the years 2030, 2050, and 2070. The results showed that increasing the ET will decrease the aquifer recharge and increase the groundwater head difference (GWHD).

3.3. Quantification of Evapotranspiration–Aquifer Interaction

The relation between ETc and the flow to the NDA is presented in Figure 9. The results indicated that the future increase in ETc to 50.60 mm, 51.20 mm, and 53.70 mm year−1, compared with 44.15 mm year−1 by an increase of 19.70%, 21.80%, and 28.80%, led to a decrease in the aquifer recharge to 4,815,900, 4,692,300, and 4,271,700 m3 day−1 compared with 6,000,400 m3 day−1 in the base case. The GWHD reached 0.95 m, 1.05 m, and 1.40 m while considering the increase of ETc in years 2030, 2050, and 2070, respectively. This indicated that future ETc should be considered in water resources management planning to mitigate the reduction of groundwater levels and any potential reduction in aquifer storage while monitoring abstraction by wells for limiting discharges from the NDA. Thus, a sustainable operation of the ND aquifer can be achieved.

4. Conclusions

Groundwater management of the Nile Delta aquifer (NDA) in Egypt was assessed by considering climate change’s effect on actual evapotranspiration, ETc (water requirements of a typical pattern). Firstly, historical data over 62 years from 1958 to 2020 were acquired, and the study area was categorized into three zones: the North (along the Mediterranean coast), the Middle, and the South. The conceptual model of MODFLOW was utilized to identify the current case of the aquifer and calculate the budget analysis after model calibration and validation in steady and transient states. Then, the visual MODFLOW was run to predict the aquifer behavior and calculate the different boundary parameters at three predictive dates: until 2030, until 2050, and until 2070, including the change of the annual actual ETc in the North, Middle, and South zones of the Nile Delta (ND). In addition, zone budget analysis was conducted for the total inflow and outflow at the end of the three predictive periods.
Actual evapotranspiration (ETc) in ND zones correlated with the combination of solar radiation, vapor pressure deficit, and humidity (H), which were found to be the best variables for predicting ETc. The increasing solar radiation and vapor pressure deficit convert a considerable amount of liquid water into water vapor and water demands, while increasing humidity tends to reduce transpiration. Zone budget analysis revealed that the increase of ETc will decrease the inflow and the outflow by 9.2, 10.1, and 12.9% in 2030, 2050, and 2070, respectively. Based on the model simulation results, the groundwater head difference (GWHD) reached 0.95, 1.05, and 1.40m while considering the increase of ETc in 2030, 2050, and 2070, respectively. Our results are helpful for addressing the behavior of the ND groundwater aquifer for ensuring a sustainable management plan considering the climate effect on crop water requirements. However, the combination of the spatial and temporal changes of ETc is an interesting topic to be studied in further research.

Author Contributions

All authors whose names appear on the submission made substantial contributions. Site investigation and soil specimens were acquired and prepared for testing by M.G.E., I.A.-E., A.E., M.Z. and I.F.; numerical simulation and analysis were performed by M.G.E., I.A.-E. and I.F.; the first draft of the manuscript was written by M.G.E., I.A.-E., A.E. and I.F.; revisions and suggestions/comments on the previous versions of the manuscript were added by M.G.E., I.A.-E., A.E., M.Z. and I.F. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Slovak Research and Development Agency under the Contract no. APVV-20-0281 and SK-SRB-21-0052. This work was supported by the project of the Ministry of Education of the Slovak Republic VEGA 1/0308/20 Mitigation of hydrological hazards, floods, and droughts by exploring extreme hydroclimatic phenomena in river basins. The authors are grateful to the projects 011TUKE-2-1/2021.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data for this work can be found within the article; for further data, please contact the first and corresponding authors.

Conflicts of Interest

The authors confirm that there are no conflicts concerning the publication of this manuscript.

References

  1. Loucks, D.P.; van Beek, E. Water Resources Planning and Management: An Overview. In Water Resource Systems Planning and Management; Springer: Cham, Switzerland, 2017. [Google Scholar] [CrossRef]
  2. Abd-Elaty, I.; Saleh, O.K.; Ghanayem, H.M.; Zeleňáková, M.; Kuriqi, A. Numerical assessment of riverbank filtration using gravel back filter to improve water quality in arid regions. Front. Earth Sci. 2022, 10, 1006930. [Google Scholar] [CrossRef]
  3. Genanu, M.; Alamirew, T.; Senay, G.; Gebremichael, M. Remote Sensing Based Estimation of Evapo-Transpiration Using Selected Algorithms: The Case of Wonji Shoa Sugar Cane Estate, Ethiopia. Int. J. Sensors Sens. Netw. 2017, 5, 1–13. [Google Scholar] [CrossRef]
  4. Morote, A.-F.; Olcina, J.; Hernández, M. The Use of Non-Conventional Water Resources as a Means of Adaptation to Drought and Climate Change in Semi-Arid Regions: South-Eastern Spain. Water 2019, 11, 93. [Google Scholar] [CrossRef]
  5. Abd-Elaty, I.; Straface, S. Mathematical Models Ensuring Freshwater of Coastal Zones in Arid and Semiarid Regions. In Earth Systems Protection and Sustainability; Furze, J.N., Eslamian, S., Raafat, S.M., Swing, K., Eds.; Springer: Cham, Switzerland, 2022. [Google Scholar] [CrossRef]
  6. El Shinawi, A.; Kuriqi, A.; Zelenakova, M.; Vranayova, Z.; Abd-Elaty, I. Land subsidence and environmental threats in coastal aquifers under sea level rise and over-pumping stress. J. Hydrol. 2022, 608, 127607. [Google Scholar] [CrossRef]
  7. Ward, F.A.; Pulido-Velazquez, M. Water conservation in irrigation can increase water use. Proc. Natl. Acad. Sci. USA 2008, 105, 18215–18220. [Google Scholar] [CrossRef]
  8. Levidow, L.; Zaccaria, D.; Maia, R.; Vivas, E.; Todorovic, M.; Scardigno, A. Improving water-efficient irrigation: Prospects and difficulties of innovative practices. Agric. Water Manag. 2014, 146, 84–94. [Google Scholar] [CrossRef]
  9. Abd-Elaty, I.; Kushwaha, N.; Grismer, M.E.; Elbeltagi, A.; Kuriqi, A. Cost-effective management measures for coastal aquifers affected by saltwater intrusion and climate change. Sci. Total. Environ. 2022, 836, 155656. [Google Scholar] [CrossRef]
  10. Li, P.; Qian, H.; Wu, J. Conjunctive use of groundwater and surface water to reduce soil salinization in the Yinchuan Plain, North-West China. Int. J. Water Resour. Dev. 2018, 34, 337–353. [Google Scholar] [CrossRef]
  11. Obaideen, K.; Yousef, B.A.; AlMallahi, M.N.; Tan, Y.C.; Mahmoud, M.; Jaber, H.; Ramadan, M. An overview of smart irrigation systems using IoT. Energy Nexus 2022, 7, 100124. [Google Scholar] [CrossRef]
  12. Saggi, M.K.; Jain, S. A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches. Arch. Comput. Methods Eng. 2022, 29, 4455–4478. [Google Scholar] [CrossRef]
  13. Taheri, M.; Mohammadian, A.; Ganji, F.; Bigdeli, M.; Nasseri, M. Energy-Based Approaches in Estimating Actual Evapotranspiration Focusing on Land Surface Temperature: A Review of Methods, Concepts, and Challenges. Energies 2022, 15, 1264. [Google Scholar] [CrossRef]
  14. Aghelpour, P.; Bahrami-Pichaghchi, H.; Karimpour, F. Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH. Complexity 2022, 2022, 4534822. [Google Scholar] [CrossRef]
  15. Wanniarachchi, S.; Sarukkalige, R. A Review on Evapotranspiration Estimation in Agricultural Water Management: Past, Present, and Future. Hydrology 2022, 9, 123. [Google Scholar] [CrossRef]
  16. Tasumi, M.; Allen, R.G. Satellite-based ET mapping to assess variation in ET with timing of crop development. Agric. Water Manag. 2007, 88, 54–62. [Google Scholar] [CrossRef]
  17. Abd-Elaty, I.; Zelenakova, M. Saltwater intrusion management in shallow and deep coastal aquifers for high aridity regions. J. Hydrol. Reg. Stud. 2022, 40, 101026. [Google Scholar] [CrossRef]
  18. Wilson, K.B.; Hanson, P.J.; Mulholland, P.J.; Baldocchi, D.D.; Wullschleger, S.D. A comparison of methods for determining forest evapotranspiration and its components: Sap-flow, soil water budget, eddy covariance and catchment water balance. Agric. For. Meteorol. 2001, 106, 153–168. [Google Scholar] [CrossRef]
  19. Zhao, L.; Xia, J.; Xu, C.-Y.; Wang, Z.; Sobkowiak, L.; Long, C. Evapotranspiration estimation methods in hydrological models. J. Geogr. Sci. 2013, 23, 359–369. [Google Scholar] [CrossRef]
  20. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration, Guidelines for Computing Crop Water Requirements; FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998; ISBN 92-5-104219-5. [Google Scholar]
  21. De Caro, M.; Perico, R.; Crosta, G.B.; Frattini, P.; Volpi, G.A. Regional-scale conceptual and numerical groundwater flow model in fluvio-glacial sediments for the Milan Metropolitan area (Northern Italy). J. Hydrol. Reg. Stud. 2020, 29, 100683. [Google Scholar] [CrossRef]
  22. Shah, N.; Nachabe, M.; Ross, M. Extinction Depth and Evapotranspiration from Ground Water under Selected Land Covers. Groundwater 2007, 45, 329–338. [Google Scholar] [CrossRef]
  23. Doble, R.C.; Crosbie, R. Review: Current and emerging methods for catchment-scale modelling of recharge and evapotranspiration from shallow groundwater. Hydrogeol. J. 2017, 25, 3–23. [Google Scholar] [CrossRef]
  24. Chen, M.; Izady, A.; Abdalla, O.A. An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model. J. Hydrol. 2017, 544, 591–603. [Google Scholar] [CrossRef]
  25. Liou, Y.-A.; Kar, S.K. Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review. Energies 2014, 7, 2821–2849. [Google Scholar] [CrossRef]
  26. Elkamhawy, E.; Zelenakova, M.; Abd-Elaty, I. Numerical Canal Seepage Loss Evaluation for Different Lining and Crack Techniques in Arid and Semi-Arid Regions: A Case Study of the River Nile, Egypt. Water 2021, 13, 3135. [Google Scholar] [CrossRef]
  27. Rana, G.; Katerji, N. Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: A review. Eur. J. Agron. 2000, 13, 125–153. [Google Scholar] [CrossRef]
  28. Xu, C.-Y.; Chen, D. Comparison of seven models for estimation of evapotranspiration and groundwater recharge using lysimeter measurement data in Germany. Hydrol. Process. 2005, 19, 3717–3734. [Google Scholar] [CrossRef]
  29. Linacre, E.T. Evaporation trends. Theor. Appl. Climatol. 2004, 79, 11–21. [Google Scholar] [CrossRef]
  30. Han, S.; Tian, F. A review of the complementary principle of evaporation: From the original linear relationship to generalized nonlinear functions. Hydrol. Earth Syst. Sci. 2020, 24, 2269–2285. [Google Scholar] [CrossRef]
  31. Abd-Elaty, I.; Sallam, G.A.; Straface, S.; Scozzari, A. Effects of climate change on the design of subsurface drainage systems in coastal aquifers in arid/semi-arid regions: Case study of the Nile delta. Sci. Total. Environ. 2019, 672, 283–295. [Google Scholar] [CrossRef]
  32. El Shinawi, A.; Zeleňáková, M.; Nosair, A.M.; Abd-Elaty, I. Geo-spatial mapping and simulation of the sea level rise influence on groundwater head and upward land subsidence at the Rosetta coastal zone, Nile Delta, Egypt. J. King Saud Univ. Sci. 2022, 34, 102145. [Google Scholar] [CrossRef]
  33. Abd-Elaty, I.; Pugliese, L.; Zelenakova, M.; Mesaros, P.; El Shinawi, A. Simulation-Based Solutions Reducing Soil and Groundwater Contamination from Fertilizers in Arid and Semi-Arid Regions: Case Study the Eastern Nile Delta, Egypt. Int. J. Environ. Res. Public Health 2020, 17, 9373. [Google Scholar] [CrossRef]
  34. Eltarabily, M.G. Reuse of Agriculture Drainage Water—Case Studies: Central Valley of California and the Nile Delta in Egypt. In The Handbook of Environmental Chemistry; Springer: Berlin/Heidelberg, Germany, 2022. [Google Scholar] [CrossRef]
  35. Ashour, M.A.; Aly, T.E.; Eldegwee, Y.A. An Investigation Concerning the Impact of Climate Changes on the Water Equilibrium in the Egyptian Nile Delta. Ann. Valahia Univ. Targoviste Geogr. Ser. 2017, 17, 58–69. [Google Scholar] [CrossRef]
  36. Eltarabily, M.G.; Moghazy, H.E.M. GIS-based evaluation and statistical determination of groundwater geochemistry for potential irrigation use in El Moghra, Egypt. Environ. Monit. Assess. 2021, 193, 306. [Google Scholar] [CrossRef]
  37. Abd-Elaty, I.; Zelenakova, M.; Straface, S.; Vranayová, Z.; Abu-Hashim, M. Integrated Modelling for Groundwater Contamination from Polluted Streams Using New Protection Process Techniques. Water 2019, 11, 2321. [Google Scholar] [CrossRef]
  38. Kambale, J.B. Impact of climate change on groundwater recharge in a semi-arid region of northern India. Appl. Ecol. Environ. Res. 2017, 15, 335–362. [Google Scholar] [CrossRef]
  39. Milly, P.C.D.; Dunne, K.A. Trends in evaporation and surface cooling in the Mississippi River Basin. Geophys. Res. Lett. 2001, 28, 1219–1222. [Google Scholar] [CrossRef]
  40. Golubev, V.S.; Lawrimore, J.H.; Groisman, P.Y.; Speranskaya, N.A.; Zhuravin, S.A.; Menne, M.J.; Peterson, T.C.; Malone, R.W. Evaporation changes over the contiguous United States and the former USSR: A reassessment. Geophys. Res. Lett. 2001, 28, 2665–2668. [Google Scholar] [CrossRef]
  41. Mostafa, A.N.; Wheida, A.; El Nazer, M.; Adel, M.; El Leithy, L.; Siour, G.; Coman, A.; Borbon, A.; Magdy, A.W.; Omar, M.; et al. Past (1950–2017) and future (−2100) temperature and precipitation trends in Egypt. Weather. Clim. Extrem. 2019, 26, 100225. [Google Scholar] [CrossRef]
  42. Yassen, A.N.; Nam, W.-H.; Hong, E.-M. Impact of climate change on reference evapotranspiration in Egypt. Catena 2020, 194, 104711. [Google Scholar] [CrossRef]
  43. Ajjur, S.B.; Al-Ghamdi, S.G. Evapotranspiration and water availability response to climate change in the Middle East and North Africa. Clim. Chang. 2021, 166, 28. [Google Scholar] [CrossRef]
  44. Nikiel, C.A.; Eltahir, E.A.B. Past and future trends of Egypt’s water consumption and its sources. Nat. Commun. 2021, 12, 4508. [Google Scholar] [CrossRef]
  45. Eltarabily, M.G.A.; Negm, A.M. Groundwater Management for Sustainable Development Plans for the Western Nile Delta. In Groundwater in the Nile Delta; The Handbook of Environmental Chemistry; Negm, A., Ed.; Springer: Cham, Switzerland, 2018; Volume 73. [Google Scholar] [CrossRef]
  46. SNC. Egypt’s Second National Communication, Egyptian Environmental Affairs Agency (EEAA-May 2010), under the United Nations Framework Convention on Climate Change on Climate Change (UNFCCC). 2010. Available online: https://unfccc.int/resource/docs/natc/egync2.pdf (accessed on 1 August 2022).
  47. RIGW. Hydrogeological Map of Nile Delta. Scale 1: 500,000, 1st ed.; Nile Delta; Research Institute for Groundwater (RIGW), National Water Research Center (NWRC): El-Qanatir, Egypt, 1992; Internal report. [Google Scholar]
  48. Abdelaty, I.M. Numerical and Experimental Study for Simulating Climatic Changes Effects on Nile Delta Aquifer. Ph.D. Thesis, Faculty of Engineering, Zagazig University, Zagazig, Egypt, 2014. [Google Scholar]
  49. Elewa, H.H. Potentialities of Water Resources Pollution of the Nile River Delta. Open Hydrol. J. 2010, 4, 1–13. [Google Scholar] [CrossRef]
  50. Radwan, T.M.; Blackburn, G.A.; Whyatt, J.D.; Atkinson, P.M. Dramatic Loss of Agricultural Land Due to Urban Expansion Threatens Food Security in the Nile Delta, Egypt. Remote Sens. 2019, 11, 332. [Google Scholar] [CrossRef]
  51. Al-Agha, D.E.; Closas, A.; Molle, F. Survey of groundwater use in the central part of the Nile Delta. Water and Salt Management in the Nile Delta: Activity Report Number 6. 2015. Available online: https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers16-02/010066349.pdf (accessed on 1 August 2022).
  52. Stanley, J.-D.; Clemente, P.L. Increased Land Subsidence and Sea-Level Rise Are Submerging Egypt’s Nile Delta Coastal Margin. GSA Today 2017, 27, 4–11. [Google Scholar] [CrossRef]
  53. Abd-Elaty, I.; Pugliese, L.; Bali, K.M.; Grismer, M.E.; Eltarabily, M.G. Modelling the impact of lining and covering irrigation canals on underlying groundwater stores in the Nile Delta, Egypt. Hydrol. Process. 2021, 36, e14466. [Google Scholar] [CrossRef]
  54. Said, R. The Geologic Evolution of the River Nile; Springer: Berlin/Heidelberg, Germany, 1981. [Google Scholar]
  55. Abd-Elaty, I.; Javadi, A.A.; Abd-Elhamid, H. Management of saltwater intrusion in coastal aquifers using different wells systems: A case study of the Nile Delta aquifer in Egypt. Hydrogeol. J. 2021, 29, 1767–1783. [Google Scholar] [CrossRef]
  56. Ebraheem, A.-A.M.; Senosy, M.M.; Dahab, K.A. Geoelectrical and Hydrogeochemical Studies for Delineating Ground-Water Contamination Due to Salt-Water Intrusion in the Northern Part of the Nile Delta, Egypt. Groundwater 1997, 35, 216–222. [Google Scholar] [CrossRef]
  57. Morsy, W.S. Environmental Management of Groundwater Resources for Nile Delta Region. Ph.D. Thesis, Faculty of Engineering, Cairo University, Cairo, Egypt, 2009. [Google Scholar]
  58. Abd-Elhamid, H.F.; Abd-Elaty, I.; Negm, A.M. Control of Saltwater Intrusion in Coastal Aquifers. In Groundwater in the Nile Delta; Negm, A., Ed.; The Handbook of Environmental Chemistry; Springer: Cham, Switzerland, 2018; Volume 73. [Google Scholar] [CrossRef]
  59. Eltarabily, M.G.; Moghazy, H.E.; Abdel-Fattah, S.; Negm, A.M. The use of numerical modeling to optimize the construction of lined sections for a regionally-significant irrigation canal in Egypt. Environ. Earth Sci. 2020, 79, 80. [Google Scholar] [CrossRef]
  60. Abd-Elaty, I.; Saleh, O.K.; Ghanayem, H.M.; Grischek, T.; Zelenakova, M. Assessment of hydrological, geohydraulic and operational conditions at a riverbank filtration site at Embaba, Cairo using flow and transport modeling. J. Hydrol. Reg. Stud. 2021, 37, 100900. [Google Scholar] [CrossRef]
  61. Abd-Elaty, I.; Zeleňáková, M.; Krajníková, K.; Abd-Elhamid, H. Analytical Solution of Saltwater Intrusion in Costal Aquifers Considering Climate Changes and Different Boundary Conditions. Water 2021, 13, 995. [Google Scholar] [CrossRef]
  62. Mabrouk, M.; Jonoski, A.; Essink, G.O.; Uhlenbrook, S. Assessing the Fresh–Saline Groundwater Distribution in the Nile Delta Aquifer Using a 3D Variable-Density Groundwater Flow Model. Water 2019, 11, 1946. [Google Scholar] [CrossRef]
  63. Abd-Elaty, I.; Shahawy, A.E.; Santoro, S.; Curcio, E.; Straface, S. Effects of groundwater abstraction and desalination brine deep injection on a coastal aquifer. Sci. Total. Environ. 2021, 795, 148928. [Google Scholar] [CrossRef]
  64. Eltarabily, M.G.; Negm, A.M.; Yoshimura, C.; Saavedra, O.C. Modeling the impact of nitrate fertilizers on groundwater quality in the southern part of the Nile Delta, Egypt. Water Supply 2017, 17, 561–570. [Google Scholar] [CrossRef]
  65. Smith, M. Cropwat: A Computer Program for Irrigation Planning and Management (No. 46); Food & Agriculture Org.: Roma, Italy, 1992. [Google Scholar]
  66. Available online: https://www.fao.org/3/V9978E/v9978e0e.htm (accessed on 1 August 2022).
  67. Abatzoglou, J.T.; Dobrowski, S.; Parks, S.A.; Hegewisch, K.C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 2018, 5, 170191. [Google Scholar] [CrossRef]
  68. Kobayashi, S.; Ota, Y.; Harada, Y.; Ebita, A.; Moriya, M.; Onoda, H.; Onogi, K.; Kamahori, H.; Kobayashi, C.; Endo, H.; et al. The JRA-55 Reanalysis: General Specifications and Basic Characteristics. J. Meteorol. Soc. Jpn. Ser. II 2015, 93, 5–48. [Google Scholar] [CrossRef]
  69. Elbeltagi, A.; Aslam, M.R.; Mokhtar, A.; Deb, P.; Abubakar, G.A.; Kushwaha, N.; Venancio, L.P.; Malik, A.; Kumar, N.; Deng, J. Spatial and temporal variability analysis of green and blue evapotranspiration of wheat in the Egyptian Nile Delta from 1997 to 2017. J. Hydrol. 2021, 594, 125662. [Google Scholar] [CrossRef]
  70. Elbeltagi, A.; Deng, J.; Wang, K.; Hong, Y. Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt. Agric. Water Manag. 2020, 235, 106080. [Google Scholar] [CrossRef]
  71. Elbeltagi, A.; Deng, J.; Wang, K.; Malik, A.; Maroufpoor, S. Modeling long-term dynamics of crop evapotran-spiration using deep learning in a semi-arid environment. Agric. Water Manag. 2020, 241, 106334. [Google Scholar] [CrossRef]
  72. Elbeltagi, A.; Azad, N.; Arshad, A.; Mohammed, S.; Mokhtar, A.; Pande, C.; Etedali, H.R.; Bhat, S.A.; Islam, A.R.M.T.; Deng, J. Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt. Agric. Water Manag. 2021, 255, 107052. [Google Scholar] [CrossRef]
  73. Wang, H.F.; Anderson, M.P. Introduction to Groundwater Modeling: Finite Difference and Finite Element Methods; Academic Press: Cambridge, MA, USA, 1995. [Google Scholar]
  74. Ghafouri-Azar, M.; Bae, D.-H.; Kang, S.-U. Trend Analysis of Long-Term Reference Evapotranspiration and Its Components over the Korean Peninsula. Water 2018, 10, 1373. [Google Scholar] [CrossRef]
  75. Gocic, M.; Trajkovic, S. Analysis of trends in reference evapotranspiration data in a humid climate. Hydrol. Sci. J. 2014, 59, 165–180. [Google Scholar] [CrossRef]
  76. Adeyeri, O.E.; Ishola, K.A. Variability and Trends of Actual Evapotranspiration over West Africa: The Role of Environmental Drivers. Agric. For. Meteorol. 2021, 308, 108574. [Google Scholar] [CrossRef]
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Figure 1. (a) Key map of the Nile Delta. (b) Hydrogeological cross-section from south to north along the Nile Delta aquifer [49]. (c) The major five land use/land cover classes in the Nile Delta in 2015 [50].
Figure 1. (a) Key map of the Nile Delta. (b) Hydrogeological cross-section from south to north along the Nile Delta aquifer [49]. (c) The major five land use/land cover classes in the Nile Delta in 2015 [50].
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Figure 3. Historical data between 1958 and 2020 for the average ETc (cm) for the three zones of the Nile delta in the previous 62 years.
Figure 3. Historical data between 1958 and 2020 for the average ETc (cm) for the three zones of the Nile delta in the previous 62 years.
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Figure 4. (a) Model grid and boundary conditions; (b) hydrogeological vertical cross-section; (c) hydrogeological horizontal cross-section of the underlying aquifer assigned in the simulation.
Figure 4. (a) Model grid and boundary conditions; (b) hydrogeological vertical cross-section; (c) hydrogeological horizontal cross-section of the underlying aquifer assigned in the simulation.
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Water 15 00572 g005a 550Water 15 00572 g005b 550
Figure 5. Observed vs. calculated groundwater heads (m) in steady-state condition for (a) model calibration; (b) model validation (solid line represents the 45o line while the red and blue dotted lines represent the error bounds of ± 5% and ± 10%, respectively; (c) location of the observation wells used for model calibration and validation; (d) groundwater head distributions for the Nile Delta Quaternary aquifer (contour lines are in black while arrows represent velocity vectors).
Figure 5. Observed vs. calculated groundwater heads (m) in steady-state condition for (a) model calibration; (b) model validation (solid line represents the 45o line while the red and blue dotted lines represent the error bounds of ± 5% and ± 10%, respectively; (c) location of the observation wells used for model calibration and validation; (d) groundwater head distributions for the Nile Delta Quaternary aquifer (contour lines are in black while arrows represent velocity vectors).
Water 15 00572 g005aWater 15 00572 g005b
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Figure 6. Trend analysis of actual evapotranspiration for historical data from 1958 to 2020, and the linear regression for the predictive values until 2070 for the three zones of the Nile Delta: (a) North, (b) Middle, and (c) South.
Figure 6. Trend analysis of actual evapotranspiration for historical data from 1958 to 2020, and the linear regression for the predictive values until 2070 for the three zones of the Nile Delta: (a) North, (b) Middle, and (c) South.
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Figure 7. Future projection of actual ET compared with the historical period.
Figure 7. Future projection of actual ET compared with the historical period.
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Figure 8. Distribution of groundwater head differences (in meters) for the Nile Delta aquifer for the three predictive periods: (a) until 2030, (b) until 2050, and (c) until 2070.
Figure 8. Distribution of groundwater head differences (in meters) for the Nile Delta aquifer for the three predictive periods: (a) until 2030, (b) until 2050, and (c) until 2070.
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Figure 9. Comparison between the predicted average ETc in 2030, 2050, and 2070, and the corresponding reduction in aquifer recharge (in Mm3 day−1).
Figure 9. Comparison between the predicted average ETc in 2030, 2050, and 2070, and the corresponding reduction in aquifer recharge (in Mm3 day−1).
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Table
Table 1. Initial and calibrated hydraulic parameters of the two geologic units of the Nile Delta aquifer.
Table 1. Initial and calibrated hydraulic parameters of the two geologic units of the Nile Delta aquifer.
CaseUnitLayer No.Horizontal and
Vertical Hydraulic Conductivities 		Specific Storage
Ss (m−1)		Specific Yield
Sy (-)		Effective Porosity
(%)		Recharge
(mm day−1)
Kh (m day−1)Kv (m day−1)
InitialClay cap10.10–0.250.01–0.02510−30.1050–600.25–0.80
Quaternary aquifer2–115–1000.50–105 × 10−3–5 × 10−40.15–0.2030–20
CalibratedClay cap10.350.03510−30.1050–600.01–1.05
Quaternary aquifer2–1125–1502.5–155 × 10−3–5 × 10−40.15–0.2030–20
Table
Table 2. Zone budget analysis for the base case (at 2020), 2030, 2050, and 2070 (values in m3 day−1).
Table 2. Zone budget analysis for the base case (at 2020), 2030, 2050, and 2070 (values in m3 day−1).
Boundary ParameterBase CaseSimulation Period
Until 2030Until 2050 Until 2070
Constant heads 840,0301,073,7001,102,5001,208,900
Flow into the aquifer6,000,4004,815,9004,692,3004,271,700
Canals leakage732,910985,6001,014,4001,116,100
Total inflow7,573,3406,875,2006,809,2006,596,700
Constant heads1,656,2001,394,6001,370,4001,292,200
Wells4,378,7004,378,7004,378,7004,378,700
Drains1,480,9001,068,1001,027,700897,500
Canal leakage23,1766,167.405,572.803,888.50
General heads34,34627,61426,91324,522
Total outflow7,573,3226,875,1816,809,2866,596,811
Table
Table 3. Percentage of difference (increase (+), or decrease (−)) for each simulation scenario (until the years 2030, 2050, and 2070) compared to the base case (current situation of the aquifer).
Table 3. Percentage of difference (increase (+), or decrease (−)) for each simulation scenario (until the years 2030, 2050, and 2070) compared to the base case (current situation of the aquifer).
Boundary Parameter% Increase (+) or Decrease (−) Compared to the Base Case
Until 2030Until 2050 Until 2070
Constant heads 27.831.243.9
Flow into the aquifer−19.7−21.8−28.8
Canals leakage34.538.452.3
Total inflow−9.2−10.1−12.9
Constant head−15.8−17.3−22.0
Wells0.00.00.0
Drains−27.9−30.6−39.4
Canals leakage−73.4−76.0−83.2
General heads−19.6−21.6−28.6
Total outflow−9.2−10.1−12.9
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Eltarabily, M.G.; Abd-Elaty, I.; Elbeltagi, A.; Zeleňáková, M.; Fathy, I. Investigating Climate Change Effects on Evapotranspiration and Groundwater Recharge of the Nile Delta Aquifer, Egypt. Water 2023, 15, 572. https://doi.org/10.3390/w15030572

AMA Style

Eltarabily MG, Abd-Elaty I, Elbeltagi A, Zeleňáková M, Fathy I. Investigating Climate Change Effects on Evapotranspiration and Groundwater Recharge of the Nile Delta Aquifer, Egypt. Water. 2023; 15(3):572. https://doi.org/10.3390/w15030572

Chicago/Turabian Style

Eltarabily, Mohamed Galal, Ismail Abd-Elaty, Ahmed Elbeltagi, Martina Zeleňáková, and Ismail Fathy. 2023. "Investigating Climate Change Effects on Evapotranspiration and Groundwater Recharge of the Nile Delta Aquifer, Egypt" Water 15, no. 3: 572. https://doi.org/10.3390/w15030572

APA Style

Eltarabily, M. G., Abd-Elaty, I., Elbeltagi, A., Zeleňáková, M., & Fathy, I. (2023). Investigating Climate Change Effects on Evapotranspiration and Groundwater Recharge of the Nile Delta Aquifer, Egypt. Water, 15(3), 572. https://doi.org/10.3390/w15030572

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