Assessment of Groundwater Resources based on IT Platform of

GIS-Mathematical Model to Account for Climate Change in the Sahel

Aquifer (Morocco)

Mohamed Jalal El Hamidi

, Abdelkader Larabi

, Mohamed Faouzi

and Ismail Er-Rakady

Regional Water Centre of Maghreb, LAMERN, EMI, Mohammed V University in Rabat, Rabat, Morocco

Keywords: Sahel, climate change, intensive pumping, GIS, thematic maps, groundwater models, IT techniques.

Abstract: The Sahel is part of the coastal Sahel basin of Doukkala that borders the Atlantic Ocean on the northwest and

covers an area of approximately 4146 km

. It includes several urban centres and a large industrial complex.

In this area, the Sahel aquifer is vulnerable to Climate Change (CC) and intensive pumping mainly in the

coastal fringe, where the agricultural activities are significant. The main objectives of this study are to develop

IT (Information Technology) platform based on a Geographic Information System (GIS) and mathematical

modelling environment to produce thematic maps for analysing and treatment of spatial and temporal

characteristics with respect to groundwater flow and water resources management and CC of the Sahel aquifer.

Based on this GIS database, a conceptual model followed by groundwater models were designed and

simulated piezometric levels, drawdowns and water balance under steady state conditions. The obtained

results show the pertinence of different IT techniques used and evaluate their contribution and also their

possible constraints in a coastal environment, including the improvement of the Sahel aquifer knowledge on

water resources that assist managers in monitoring, planning and operating measures to satisfy water supply

and irrigation demand under CC constraints in the Sahel area.

1 INTRODUCTION

Climate change (CC) refers to significant changes and

measures of climate like temperature and

precipitation over long time periods and large

geographic area. It’s human activities from pollution

to overpopulation are driving up the temperature and

fundamentally changing the world around us. CC has

consequences for our oceans, our weather, our food

sources and our health, others forecast rising sea

levels which could flood coastal areas around the

world. Weather patterns could change making

weather more extreme (Stott et al., 2016). This means

not only more intense major storms floods and heavy

snowfall, but also longer and more frequent droughts,

less stream flow and less groundwater recharge and

indirectly through changes in groundwater

abstraction and use patterns.

Morocco currently faces major water challenges

related to the sustainable management of water

resources and the delivery of water services for

https://orcid.org/0000-0002-6401-6357

domestic, agricultural and industrial use. CC and

climate variability can increase the risks and the costs

of water resources management, impact the quantity

and quality of water resources, and generate

secondary effects that influence socio-economic

vulnerability and environmental sustainability (El-

Fadel and Bou-Zeid, 2005). This is the case of the

coastal zones which are heavily urbanized, a fact that

makes the need for freshwater even more acute.

Inappropriate management of coastal aquifers may

lead to the intrusion of saltwater into freshwater

wells, destroying them as sources of freshwater

supply. The degradation or the unavailability of

groundwater present a great risk for the future of

Drinking Water Supply/Industry (DWSI) and

irrigation agriculture, since some farms and pumping

wells for DWSI in the coast would be abandoned. So,

they would result in serious socio-economic

consequence to people living there. Hence, a clear

understanding of these risks and impacts is necessary

to inform policy formulation and decision-making in

support of efforts to achieve sustainable development

in Morocco.

The Regional Initiative for the Assessment of

Climate Change Impacts on Water Resources and

Socio-Economic Vulnerability in the Arab Region

(RICCAR) (ACSAD and ESCWA, 2017) has shown

that the Arab region will experience an increase of

temperature and a decrease of precipitation. More

specifically, the temperature increases one to two

degrees on average by mid-century and by end we

will get two to three degrees increasing in

temperature under Representative Concentration

Pathway (RCP) 4.5. At the higher emission scenario,

the RCP 8.5, by mid-century we will reach

temperatures of two to three degrees increase and by

the end we could even reach four to five degrees

Celsius increase temperatures on average (Graham

and Sjökvist, 2017 ; ACSAD and ESCWA, 2017).

Hence, it is projected that these groundwater

resources will be affected by CC due to a reduction in

natural recharge from reduced precipitation, the rise

in temperature and the decrease in evapotranspiration

caused in part by lower precipitations.

2 BACKGROUND

The Sahel aquifer is located in the Oum Er Ribia basin

and belongs to the Western Moroccan Meseta,

between latitudes, 32°15’and 33°15’ and between

longitudes, West 7°55’and 9°15’. It covers the coastal

front of the hydrogeological basin between Safi and

El Jadida (Fig. 1). This region borders the: (1)

Atlantic Ocean on the northwest; (2) Sahel of Safi on

the south; (3) Oum

-Er-Rbia and the El Jadida plateau

on the north; (4) Doukkala plain on the east.

In general, the Sahel is located between the

Doukkala region and the coast with an area of

approximately 4146 km

, it appears as a band 25 to

44 km wide and 140 km long covering the coastal

front of the hydrogeological basin between Safi and

El Jadida.

The Sahel is defined by the tabular regime of the

secondary and tertiary deposits on primary grounds

strongly pleated by the hercynian mountain chain and

described four main hydrogeological units closely

dependent, with age ranging from cretaceous to Plio-

Quaternary (Ferré and Ruhard, 1975). It is the main

supplier of water resources for drinking water of

several urban centers of the area (the cities of El

Jadida and Safi) and the industrial water supply of the

OCP installations and the processes of phosphate

washing (Jorf Lasfar). Furthermore, the Sahel aquifer

is suffering from intensive pumping mainly in the

coastal fringe, where the agricultural activities are

carried out significantly to produce vegetable crops

by irrigation from several pumping wells (ABHOER,

2012).

Figure 1: Location map of the study area.

For this purpose, all collected information

(relevant technical reports and RICCAR data relevant

to datasets and outputs of the study area) were

processed and led to: (i) Study the hydrogeology

characteristics and hydrodynamic functioning of the

SCA ; (ii) Analyse the CC impact on groundwater

resources in the SCA based on emission scenarios

(RCP 4.5 and 8.5) ; (iii) Elaborate a GIS database to

produce decisional thematic maps for the area ; (iv)

Design a conceptual three-dimensional groundwater

model of the Sahel Aquifer; and (v) Develop a Three-

dimensional model in steady state to assess the water

balance.

2.1 Hydrogeological Setting

The hydrogeologic formations are dominated by

limestone and formed mainly by four

hydrogeological units (Ferré and Ruhard, 1975):

 Sandstone and limestone of the Plio-

quaternary;

 Limestones of the Middle Cenomanian;

 Dridrate limestones of the upper Hauterivian;

 Marl-limestones of the Lower Cretaceous.

These aquifer units constitute water resources of

variable importance according to sectors.

In the Sahel, the piezometric evolution depends

on the natural conditions of aquifer system

recharge/discharge which were modified by the

projects of hydro-agricultural development, the

Under-Service irrigated perimeter in Doukkala and

pumping for irrigation purposes in coastal Sahel. The

extensive extraction of groundwater for the irrigation

caused in many places deteriorated water quality

linked to the salinity increase of groundwater due to

the saltwater intrusion (ABHOER, 2012).

2.2 Impacts of Climate Change

Datasets used in this assessment comprises a

combination of regional climate modelling

projections data generated from RICCAR, and a set

of local observation datasets for precipitation,

temperature for our study area. This section is based

on extracting time series of Pr (Precipitation) and Ta

(Temperature) variables for the entire time period

from 1951 to 2100. We read NetCDF files (.nc) using

MATLAB to extract time series of Pr (Precipitation)

and Ta (Temperature) variables for the entire time

period from 1951 to 2100. We extract data for a

specific latitude and longitude coordinates. The

location of our study area is at latitude = 33° North

and longitude = 8.36° West.

Based on the RICCAR data, we could present

some plots of time series that summarize and show

the updated knowledge on the climatology of our

study area. We have displayed and edited some in Fig.

2 and 3, which show the evolution of projected P, T

for various climate models and scenarios. The main

trends of the parameter variation are also provided to

analyse and measure the CC trends. The procedure to

extract Pr data is provided in Fig. 5.

These figures show clearly that temperatures are

mainly increasing, while precipitations are mainly

decreasing for both scenarios. Hence, the CC impacts

in the area cause recurrent droughts and decrease in

aquifer recharge (Fig. 4) that directly affects the

groundwater level (e.g. increase of water depth as

demonstrated in the well no. 26/122).

Figure 2: Precipitations over time (1951-2100) in the study

area for: a. RCP 4.5 and b. RCP 8.5.

Figure 3: Mean temperature (°C) over time (1951-2100) in

the study area for climate models : a. CNRM- CM5, b. EC-

EARTH and c. GFDL- ESM2M (RCP 4.5 and RCP 8.5).

Figure 4: Annual variation of natural recharge from 1978

to 2100 (e.g. CNRM-CM5, RCP 8.5) and observed

piezometric records for observation well 26/122 from 1964

to 2020 located in the coast near sidi moussa city.

Ye a

ly total o

ecipitation (mm)

No available data

Import daily NetCDF file (e.g. *.nc in 1951)

Read the information (for each dimension: latitude, longitude, time)

Display contents of NetCDF data source in Command Window

Extract longitude, latitude and time values of all the grid points

Find and Extract our specific location (Longitude and Latitude)

Print output locations found in Command Window

For CNRM-CM5, RCP4.5 For EC-EARTH, RCP4.5 For GFDL-ESM2M, RCP4.5

Read multiple NetCDF files (daily

time series from 1951 to 2100)

Read multiple NetCDF files (daily

time series from 1951 to 2100)

Read multiple NetCDF files (daily

time series from 1951 to 2100)

Extract time series of precipitation

of our study area

Extract time series of precipitation

of our study area

Extract time series of precipitation

of our study area

Calculate annual rainfall from daily

data

Calculate annual rainfall from daily

data

Calculate annual rainfall from daily

data

Calculate trend of precipitation Calculate trend of precipitation Calculate trend of precipitation

Plot Precipitation versus Time of CNRM-CM5, EC-EARTH and GFDL-ESM2M Models

Plot fit linear trend of precipitation of CNRM-CM5, EC-EARTH and GFDL-ESM2M Models

Add legend with title and axis labels

Export rainfall

data to Excels

Export legend to

vector

Figure 5: Flow chart of the steps followed for extraction of precipitations versus time (1950-2100) for CNRM-CM5,

EC-EARTH and GFDL-ESM2M climate Models, RCP4.5 scenarios.

2.3 Hydrogeological

Database

This section is the results from the development of the

hydrogeological geodatabase (Fig. 6) to produce

decisional thematic maps and diagrams, providing

more information layers to managers in water

resources. The obtained thematic layers were

organized according to the needs of managers and

decision makers. This action facilitates consultation,

customization and duplication of information in

relation to the various aspects of water resources.

Figure 6: Geodatabase structure of the SCA with layers.

The lateral and vertical measurements carried out

in well O45 are integrated in our GIS. The results

show an increase of water Electrical Conductivity

(EC) horizontally and within the depth, due to

seawater intrusion as illustrated by figure 7 (Fadili et

al., 2018). This led to groundwater quality

degradation (by salinisation), especially in the

Oualidia sector.

Figure 7: Map and vertical profile of water EC in well O45

and Oualidia sector (in 2011), (Fadili et al., 2018).

2.4 Groundwater Management Model

Based on the GIS database, the conceptual model of

the SCA was established on the basis of the

hydrogeological characteristics, the hydrodynamic

parameters and the spatial variations of the aquifer

piezometry in 1976, which allowed understanding the

hydrogeological functioning of the SCA, its structure

and its geometrical extension based on the

hydrogeological database developed in this work.

Then, the formulation of this conceptual model

led to develop a three-dimensional numerical

groundwater flow model. It simulates this flow,

under steady state conditions, by solving Equation (1)

(McDonald and Harbaugh, 1988) by means of the

Visual Modflow (USGS, 2005).

Equation (1):

𝜕

𝜕𝑥

𝐾



∗

𝜕ℎ

𝜕𝑥



𝜕

𝜕𝑦

𝐾



∗

𝜕ℎ

𝜕𝑦



𝜕

𝜕𝑧

𝐾



∗

𝜕ℎ

𝜕𝑧

𝑊0

Where Kxx, Kyy, and Kzz are the hydraulic

conductivities along the x, y, and z coordinate axes

(L/T), h is hydraulic head (L), W is the volumetric flux

per unit volume and represents sources and (or) sinks

of water (T

-1

2.4.1 Discretization and Calibration

The 3D discretization method used is the finite

difference. The domain was cut into grid square cells

of 1000 m side oriented along the main Cartesian

axes. Thus, the idealized domain has 125 columns

along the X axis and 136 rows along the Y axis. The

number of active cells is 4114 (Fig. 8). Vertically, we

have considered one layer only. The blue cells

represent the inactive cells.

Figure 8: Discretization of the simulated domain by the

finite difference technique.

The calibration of the model is performed by

comparing the simulated piezometric heads obtained

by the model to the measured piezometric heads, and

the water balance dealt in the hydrogeological

characterization section. As for the measurement

network, 12 observation wells are available from

1976 and cover the whole area of SCA to carry out

the periodic piezometric measurements. The Kriging

method has been used to interpolate a data set to the

model grid.

2.4.2 Simulation and Results

The initial distribution of hydraulic conductivity

obtained mainly by the distribution of four

hydrogeological units allowed to shorten the

calibration. However, the initial calibration was

undertaken in ways, that not only minimizing the

difference between the measured and simulated

piezometric heads at 12 observation wells, but also,

to restore at best the general structure of the reference

piezometric head map of 1976 (Fig. 9).

Figure 9: Simulated piezometry (m) and correlation

between measured and simulated heads for 1976.

At the end of this initial calibration, the model

provides the simulated water balance of the aquifer

system, where different terms of the water balance are

assessed. Calculated fluxes in each cell of the model

can locate the hydraulic exchange zones, especially at

the boundaries. The results in Table 1 shows that the

main inflow consists of the boundary inputs from

Doukkala aquifer and the main outflow is composed

of the natural drainage to the sea.

Table 1: Water balance of the aquifer system calculated

after calibration in steady state.

Inputs (in Mm

/year) Outputs (in Mm

/year)

Rain-infiltration 101.6 Agricultural pumping 36

Irrigation 4 DWSI pumping 10

Boundary inputs 574 Boundary outputs/sea 633.6

Total 679.6 Total 679.6

3 CONCLUSION

The IT platform composed of the established

hydrogeological database and the groundwater model

for the SCA has contributed to better understand the

hydrogeological characteristics and hydrodynamic

functioning of the aquifer, especially under CC.

Indeed, these results (1st step) are of great importance

to identify the impacts of CC (Pr and Ta for various

climate models extracted from 1951 to 2100) on

groundwater resources by coupling them to a

transient groundwater flow model (1976-2100, last

step going on). The final results will analyse the

implication these pose for socio-economic

vulnerability and sustainable development and

identify vulnerability hotspots that the managers have

to take into account for water resources management.

ACKNOWLEDGEMENTS

Data were collected from RICCAR, ABHOER,

ORMVA, and ONEE institutions. This research work

is also funded by the OCP Foundation (FOCP).

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