Estimation of irrigation water requirements in the Gharb-irrigated perimeter (north-western Morocco)

Climate change has significant implications in semi-arid regions, including the Gharb Plain in North-Western Morocco. The short- or long-term consequences can have serious impacts on the population and the ecosystem, and more particularly on agricultural activities. Addressing the challenges of sustainable food production within the existing agricultural land while minimizing ecological disruption poses a pressing concern. A key aspect in achieving this balance lies in accurately estimating water requirements and maintaining water balance in irrigated agricultural areas, thereby ensuring efficient allocation of water resources while minimizing the ecological footprint in arid and semi-arid regions. This study employs NASA POWER meteorological data, GIS technology, and the Penman–Monteith equation to estimate irrigation water requirements (IWRs) for rice, sugarcane, and citrus crops in the Gharb Plain. Our study shows a significant decrease in IWRs in the Gharb Plain region, due to improved agricultural practices and efficient irrigation techniques. The peak period for crop water needs is between May and September. Sugarcane has the highest water consumption compared to rice and citrus. Climate variables, irrigation efficiency, and changes in cultivation impact water requirements. Our findings aid in estimating irrigation needs for different crops in the Gharb Plain, promoting sustainable water management.

HIGHLIGHTS

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Addressing sustainable food production while minimizing ecological disruption is a pressing concern in the irrigated area of the Gharb Plain.
Accurate estimation of water requirements and maintaining water balance in irrigated areas is crucial for efficient water allocation and reducing ecological footprint.

climate change, Gharb Plain, irrigation, water requirement

INTRODUCTION

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The concerning levels of water resource scarcity and degradation caused by climate change in Morocco have reached alarming levels. The IPCC 2021 report predicts that by the end of the 21st century, significant changes in precipitation and temperature will have a substantial impact on hydrological regimes. The challenges and issues surrounding water resources in Morocco have made the study of their effects on hydrological regimes a top priority for scientists and decision-makers (Saouabe et al. 2022).

Several studies have been carried out to study this change (Planton et al. 2005; Schilling et al. 2012; Tremblay et al. 2016; Woillez 2019; Driouech et al. 2021; Mahdad et al. 2021; Alitane et al. 2022; Saouabe et al. 2022).

The agriculture sector, which accounts for approximately 13% of the Gross Domestic Product (GDP) (El Ghmari et al. 2022), stands out as one of the largest consumers of water. This often leads to degradation in irrigated perimeters, affecting ecosystems, water accessibility, and food security (Youssfi et al. 2020).

Over the past years, annual water withdrawal has averaged between 11 billion and 15 billion cubic meters per year in Morocco, where about 75–87% was used for irrigation (FAO 2018). Making the agriculture sector one of the biggest consumers of water often leads to its degradation in irrigated perimeters, affecting ecosystems, water accessibility, and food security.

Climate change in Morocco generates disruptive impacts on the water cycle including temperature, precipitation, and water scarcity (Taheripour et al. 2020), which adversely affect growth and sustainable development. From one perspective, the continuously growing population, giving rise to a heightened need for food, water, and energy resources, as identified by the Food and Agriculture Organization (FAO) in 2017. In another perspective, climate change influences the precipitation patterns that affect crop growth, yield instability, and agriculture production (Fao 2015).

In response to these challenges, Morocco has made substantial investments in water resource development, including the Dam's policy and the Green Morocco plan (Ahmed & Belabbes 2002)

In the Gharb-irrigated perimeter, managed by the ORMVAG (The Regional Office for Agricultural Development of Gharb), the Sebou River serves as the primary source of irrigation water for agricultural activities. This area participates in the national agricultural production by 100% of the production of rice, sugar 33%, and citrus fruits 25% (Saguem & Moutaouakil 2020).

Moreover, in semi-arid regions like the Gharb, irrigation efficiency plays an important role in agricultural productivity and in meeting the national food supply (Smith et al. 2020). This study aims to fill a critical gap in the current knowledge by estimating the annual IWRs for the main crops – rice, sugarcane, and citrus – across their entire growth stages from 1990 to 2021, providing valuable insights into sustainable water management in the face of changing climatic conditions. The workflow of the present paper is presented in Figure 1.

Figure 1

Integrated workflow for estimating the annual IWRs in the Gharb Plain.

MATERIALS AND METHODS

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Study area

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The Gharb Plain (34°15′N 6°35′W) is located in the North West of Morocco (Figure 2). It covers an area of about 6,160 km², it extends about 80 km along the Atlantic coast and reaches some 110 km inland. This area profits from a Mediterranean climate, characterized by hot and dry summers, and the temperature is mild in the winter. The climate presents an important local variation; a semi-arid climate in the interior and subhumid climate on the coast. This variation results in a cool and wet season lasts 7 months from October to April. The average annual rainfall exceeds 400 mm in most of the plain and the average temperatures oscillate around 18.6 °C. The Gharb perimeter is bordered by the hills of Lalla Zohra to the North, the hills of the Prerifans to the East, the Maamora plateau to the south (part of the Moroccan Meseta), and the Atlantic Ocean to the West.

Figure 2

Location map of the study area (study area localization map).

The study area is an integral part of the Gharb Plain Sedimentary Basin. It is located in the contact zone between Morocco's two major structural areas: in the North the pre-Rifain and south Rifain wrinkles, which belong to the Alpine domain of the western Mediterranean, and in the South the Maamoura plain and the Hercynian meseta, which gradually plunges south to north.

The hydrographic network is represented by one of the principal rivers of the kingdom: Sebou River and its tributaries, Ouergha, Beht, Rdom, and Tiflet.

According to the Sebou Hydraulic Basin Agency ABHS, Sebou River mobilizes 30% of the potential surface water resources and 20% of groundwater in the country. These reserves are reinforced by the following dams: El Ouahda Dam, Idriss First Dam, Allal El Fassi Dam, Oued El Makhazine Dam, and El Kansera Dam.

Data

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In 1998 and 2010, the ORMVAG supplied land use/cover data and socioeconomic data, which included information on crop types and areas. We integrated these datasets with meteorological data to analyze the spatial and temporal patterns of crop water consumption in the Gharb-irrigated perimeter from 1990 to 2021.

This study involves three of the main crops in the Gharb area (rice, sugarcane, and citrus), where rice is practiced in the Northwest part of the irrigated perimeter along six prefectures (Sidi Allal Tazi, Sidi Mohamed Lahmer, Souk Tlet El Gharb, Ermilate, Sefsaf, and Sidi Al Kamel) on both banks of Oued Sebou, sugarcane is located in the central part of the study area and planted in 16 prefectures (Figure 3), citrus is widespread along both banks of Oued Sebou in 22 prefectures of the study area. The selection of rice, sugarcane, and citrus for analysis was based on their significant contribution to agricultural activities in the Gharb area, reflecting their economic and ecological importance.

Figure 3

Distribution of the croplands in the Gharb Plain.

We calculated the IWRs for crops such as rice, citrus, and sugarcane by considering factors such as evapotranspiration, crop coefficients, effective rainfall, crop water requirement, and irrigation coefficients, using pertinent meteorological data from studies conducted by Allen et al. (1998), Allen (2003), and Cammarano et al. (2016).

The selection of solar and meteorological data obtained through Power Data Access Viewer (DAV) (power.Iarc.nasa.gov) from NASA along with data from seven meteorological stations provided by the ORMVAG, was based on their established reliability and relevance to the study area. We had access to monthly and annual data observed maximum and minimum air temperature, rainfall, wind speed measured at 2 m height, relative humidity, and daily sunshine duration. We used this data to derive the FAO Penman–Monteith method to estimate ET₀ (Table 1).

Table 1

Variables, source, and units of NASA POWER

Variable	Source	Description	Units
T2M	MERRA-2	Temperature at 2 m	°C
PRECTOTCORR	MERRA-2	Precipitation corrected	mm/day
T2M_MAX	MERRA-2	Temperature at 2 m maximum	°C
T2M_MIN	MERRA-2	Temperature at 2 m minimum	°C
PS	MERRA-2	Surface pressure	kPa
R2HM	MERRA-2	Relative humidity at 2 m	%
WS2M	MERRA-2	Wind speed at 2 m	m/s
TOA_SW_DWN	CERES SYN1deg	Top of atmosphere shortwave downward irradiance	kW-h/m²/day
ALLSKY_SFC_SW_DWN	CERES SYN1deg	All sky surface shortwave downward irradiance	kW-h/m²/day
ALLSKY_SFC_LW_DWN	CERES SYN1deg	All sky surface longwave downward irradiance	W/m²

Variable	Source	Description	Units
T2M	MERRA-2	Temperature at 2 m	°C
PRECTOTCORR	MERRA-2	Precipitation corrected	mm/day
T2M_MAX	MERRA-2	Temperature at 2 m maximum	°C
T2M_MIN	MERRA-2	Temperature at 2 m minimum	°C
PS	MERRA-2	Surface pressure	kPa
R2HM	MERRA-2	Relative humidity at 2 m	%
WS2M	MERRA-2	Wind speed at 2 m	m/s
TOA_SW_DWN	CERES SYN1deg	Top of atmosphere shortwave downward irradiance	kW-h/m²/day
ALLSKY_SFC_SW_DWN	CERES SYN1deg	All sky surface shortwave downward irradiance	kW-h/m²/day
ALLSKY_SFC_LW_DWN	CERES SYN1deg	All sky surface longwave downward irradiance	W/m²

References evapotranspiration (ET₀)

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We calculated ETo using the FAO-56 Penman–Monteith equation (Allen et al. 1998), which is a modified version of the original Penman–Monteith equation. The equation is expressed as follows:

where ET₀ is the reference evapotranspiration (mm/d), T is the air temperature (°C), R_n (net surface radiation), G (soil heat flux density), u₂ (wind speed at 2 m height), e_s (saturated vapor pressure), e_a (actual vapor pressure), Δ (slope of vapor pressure curve), and γ (psychrometric constant).

In this study, we derived the FAO Penman–Monteith from the NASA POWER meteorological data and GIS technology to estimate the monthly and yearly references evapotranspiration ET₀ and IWRs for rice, sugarcane, and citrus crops, between 1990 and 2021 following the below-mentioned steps (Zotarelli et al. 2015):

Step 1 – Mean temperature

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The (average) maximum and minimum air temperatures in degrees Celsius (°C) are required. Average temperature is calculated by:

where T_mean indicates mean daily air temperature, °C; T_max indicates maximum daily air temperature, °C; T_min indicates minimum daily air temperature, °C.

Step 2 – Mean solar radiation (R_s)

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The average net radiation expressed in megajoules per square meter per day (MJ m^–² day^–¹) is obtained from the NASA POWER meteorological data. The conversion of units may be required when solar radiation is expressed in watts per square meter per day (W m^–² day^–¹).

The mean solar radiation R_s is the variable ALLSKY_SFC_SW_DWN represented in Table 1.

Step 3 – Wind speed (u₂)

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The average daily wind speed in meters per second (m/s) measured at 2 m above the ground level is obtained through the NASA POWER meteorological data.

The wind speed u₂ is represented as the variable WS2M in Table 1.

Step 4 – Slope of saturation vapor pressure curve (î)

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The slope of the relationship between saturation vapor pressure and temperature, δ:

here Δ is the slope of the saturation vapor pressure curve; T_mean indicates mean daily air temperature, °C.

Step 5 – Atmospheric pressure (P)

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Evaporation at high altitudes is promoted due to low atmospheric pressure. This effect is, however, small and in the calculation procedures, the average value for a location is sufficient. A simplification of the ideal gas law, assuming 20 °C for a standard atmosphere, can be employed to calculate P in kPa at a particular elevation:

where z indicates elevation above sea level, m.

The variable z was obtained using GIS technology, depending on each location in the irrigated perimeter of the Gharb Plain.

Step 6 – Psychrometric constant (³)

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The psychrometric constant is kept constant for each location depending of the altitude, γ:

where γ indicates psychrometric constant, kPa °C^–¹; P indicates atmospheric pressure, kPa;

indicates latent heat of vaporization, 2.45, MJ kg^–¹; Cp indicates specific heat at constant pressure, 1.013 10-3, MJ kg^–¹ °C^–¹; μ indicates ratio molecular weight of water vapour/dry air = 0.622.

Step 7 – mean saturation vapor pressure derived from air temperature (e_s)

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As saturation vapor pressure is related to air temperature, it can be calculated from the air temperature. The relationship is expressed by:

where e_(T) indicates saturation vapor pressure at the air temperature T, kPa; T indicates air temperature, °C.

Therefore, the mean saturation vapor pressure is calculated as the mean between the saturation vapor pressure at both the maximum and minimum air temperatures.

where T_max indicates maximum air temperature, °C; T_min indicates minimum air temperature, °C.

The mean saturation vapor pressure for a day, week, decade, or month should be computed as the mean between the saturation vapor pressure at the mean maximum and minimum air temperatures for that period:

Step 8 – Actual vapor pressure (e_a) derived from relative humidity

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The actual vapor pressure can also be calculated from the relative humidity. Depending on the availability of the humidity data, different equations should be used.

where, e_a indicates actual vapour pressure, kPa;

indicates saturation vapour pressure at daily minimum temperature, kPa;

indicates saturation vapour pressure at daily maximum temperature, kPa; RH_max indicates maximum relative humidity, %; RH_min indicates minimum relative humidity, %.

In the absence of RH_max and RH_min, we can use this equation:

Step 9 – Extraterrestrial radiation (R_a)

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Extraterrestrial radiation is obtained from the NASA POWER meteorological data, represented by TOA_SW_DWN in Table 1.

Step 10 – Clear sky solar radiation (R_so)

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The calculation of the clear sky radiation is given by:

where z indicates elevation above sea level, m; R_a indicates extraterrestrial radiation, MJ m^–² day^–¹.

Step 11 – net solar or net shortwave radiation (R_ns)

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The net shortwave radiation resulting from the balance between incoming and reflected solar radiation is given by:

where R_ns indicates net solar or shortwave radiation, MJ m^–² day^–¹; ± indicates albedo or canopy reflection coefficient, which is 0.23 for the hypothetical grass reference crop, dimensionless; R_s indicates the incoming solar radiation, MJ m^–² day^–¹.

Step 12 – Net outgoing long wave solar radiation (R_nl)

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The rate of longwave energy emission is proportional to the absolute temperature of the surface raised to the fourth power. This relation is expressed quantitatively by the Stefan–Boltzmann law. The net energy flux leaving the earth's surface is, however, less than that emitted and given by the Stefan-Boltzmann law due to the absorption and downward radiation from the sky. Water vapor, clouds, carbon dioxide, and dust are absorbers and emitters of longwave radiation. It is thereby assumed that the concentrations of the other absorbers are constant (Zotarelli et al. 2015):

where R_nl indicates net outgoing longwave radiation, MJ m^–² day^–¹; σ indicates Stefan–Boltzmann constant [4.903 10^–9 MJ K^–4 m^–² day^–¹]; T_max indicates K maximum absolute temperature during the 24-hour period [K indicates °C + 273.16], T_min indicates K minimum absolute temperature during the 24-hour period [K indicates °C + 273.16], e_a indicates actual vapor pressure, kPa; R_s indicates the incoming solar radiation, MJ m^–² day^–¹; R_so indicates clear sky solar radiation, MJ m^–² day^–¹.

Step 13 – Net radiation (Rn)

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The net radiation (R_n) is the difference between the incoming net shortwave radiation (R_ns) and the outgoing net longwave radiation (R_nl):

where R_ns indicates net solar or shortwave radiation, MJ m^–² day^–¹; R_nl indicates net outgoing longwave radiation, MJ m^–² day^–¹.

Final step

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- Calculation of ET0

Crop water requirement (CWR)

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The rice, sugarcane, and citrus CWR during the growth period in the study area were estimated using the crop coefficient method by the FAO:

where ET_c is the crop evapotranspiration, ET₀ is the reference evapotranspiration, and Kc is the crop coefficient (Allen et al. 1998).

The water requirement for the three crops is calculated as follows:

where CWR is the crop water requirement and P_e is the effective precipitation.

Crop coefficient (Kc)

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We calculated Kc at different stages (Kc_ini, Kc_mid, and Kc_end) of rice, citrus, and sugarcane using the FAO single crop coefficient method. These values are based on meteorological and soil conditions and are provided in FAO Irrigation and Drainage Paper No. 24 for various climates and locations (Table 2).

Table 2

Lengths of the main crop development stages for various planting periods (days) (FAO)

Crop	Init. (L_ini)	Dev. (L_dev)	Mid (L_mid)	Late (L_late)	Total	Plant date	Region
Rice	30	30	60	30	150	May/October	Mediterranean
Sugarcane	35	60	190	120	405	Mars	Low Latitudes
Citrus	60	90	120	95	365	Jan	Mediterranean

Crop	Init. (L_ini)	Dev. (L_dev)	Mid (L_mid)	Late (L_late)	Total	Plant date	Region
Rice	30	30	60	30	150	May/October	Mediterranean
Sugarcane	35	60	190	120	405	Mars	Low Latitudes
Citrus	60	90	120	95	365	Jan	Mediterranean

During the early crop growth stage, Kc_ini values are mainly based on FAO's standard values at the initial stage multiplied by the fraction of surface wetted by irrigation or rain.

Kc_mid values in the middle stage of crop growth, the following formula is applied:

where Kc_mid (s) is the standard value from FAO, u₂ is the average daily wind speed at 2 m height (ms^–¹), RH_min is the average daily minimum relative humidity (%), and h is the average plant height (m) (Pereira & Alves 2013).

In the late growth stage and for specific adjustment in climates where RH_min differs from 45% or where u₂ is larger or smaller than 2.0 ms^–¹, the following equation is applicable:

where Kc_end (s) is from the FAO-recommended standard values, and RH_min is calculated as follows (FAO-24 and FAO-33):

where T_min and T_max are the minimum and maximum air temperature, respectively, and e^o is the saturated vapor pressure at the corresponding temperature.

Effective precipitation

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We calculated the Effective rainfall using the Department of Soil and Water Conservation of the United States Department of Agriculture method, according to the following equation:

where P_month is the actual monthly precipitation.

Irrigation efficiency (I_e)

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Water use efficiency in irrigated agriculture is the ratio of estimated IWRs to the actual water withdrawal from river channels or reservoirs (FAO 2022).

The Ie values utilized in this study to calculate the irrigation water requirement (IWR) for different provinces in the Gharb region are obtained from the ORMVAG. These values, outlined in Table 3, take into account factors such as irrigation system management, water distribution characteristics, crop water use rates, as well as weather and soil conditions.

Table 3

Irrigation efficiency for the equipped area of the Gharb Plain in 2021

Irrigation station	Souk Larbaa	Sidi Slimane	Belksiri	Sidi Allal Tazi
*I_e*	84%	74%	74%	75%

Irrigation water requirement

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To calculate the net IWR, we used the formula provided by (Li et al. 2020):

where A is the planting area of the crop and CWR is the crop water requirement.

Soil–water balance

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The soil–water balance approach relies on the conservation of mass, and can be used to estimate indirectly the crop evapotranspiration:

where ΔW is the change in soil–water storage (Zhang et al. 2007), P is the precipitation, I is the irrigation, Q_g is the contribution from the water table, R is the runoff, D is the deep drainage and ET is the evapotranspiration.

The contribution from the water table is considered negligible because the water table is often deep in the Gharb Plain (El Mahmouhi et al. 2018):

R and D are often neglected as the topography of the agriculture fields is flat (Amrani et al. 2012) and the precipitation is not intensive in the irrigated area.

The soil–water balance can be simplified:

And based on the approach (Li et al. 2020):

where I is the irrigation in the crop sectors, CWR is the crop irrigation water requirement in the irrigated perimeter.

The soil–water balance equation can be further expressed as (Li et al. 2020):

where I_a is the irrigation amounts consumed in the area, IWR is the irrigation water requirement of the crops in the area.

Linear regression model

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Applying a linear regression model using the Xlstat in Excel (Lange & Sippel 2020), we explored the relationship between IWRs, temperatures and the irrigation efficiency, to model changes in IWR, the main crops (rice, sugarcane and citrus) across three provinces over 31 years.

RESULTS AND DISCUSSION

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Spatial scale analysis of evapotranspiration and precipitation references

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Utilizing the Penman–Monteith equation and ArcGIS, we conducted calculations for the multiyear annual references evapotranspiration (ET₀) and Precipitation (P) of the Gharb-irrigated perimeter from 1990 to 2021 and the spatial distribution results were shown in Figure 4.

Figure 4

Spatial distribution of multiyear annual ET0 and precipitation over 1990–2021 in the Gharb-irrigated perimeter.

Spatial distribution of multiyear annual ET₀ and precipitation over 1990–2021 in the Gharb-irrigated perimeter.

The provinces of Sidi Kacem and Sidi Slimane including Bir Taleb, Boumaiz, Chbanate, Msaada, Khnichet, Oulad Hcine, Zirara, and Sidi Azzouz prefectures recorded the highest average of ET₀ at 6.19 mm/day. Conversely the lowest ET0 values were recorded in Sidi Kacem and Sidi Slimane provinces, including Mechraa Belksiri and Azghar, with an average of 5.39 mm/day. Along the coastal region has, an average ET₀ value of 5.99 mm/day was registered.

It is noteworthy that the regional ET0 shows a clear association with precipitation variations (Figure 4). In the Gharb-irrigated perimeter, Sidi Slimane experienced the lowest precipitation levels, with averaging 1.2 mm/day, while Sidi Kacem recorded the highest precipitation, at of 1.6 mm/day.

CWRs and evapotranspiration in temporal scale

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Monthly CWRs for the major crops in the Gharb-irrigated perimeter were calculated between 1990 and 2021 as illustrated in Figure 5. Within the summer season, the variations in CWRs ranged from 8.9 to 9.9 mm/day for rice, 9.7 to 11.1 mm/day for sugarcane, and 6.03 to 8.67 mm/day for citrus.

Figure 5

Monthly crop water requirements and evapotranspiration of the main crops.

The results indicate that both the average monthly reference evapotranspiration (ET0) and CWRs attain their highest levels during the dry season. Rice and sugarcane exhibit water demand from February to November, while citrus requires irrigation from March to October.

IWRs

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The line graph (Figure 6) illustrates the IWRs for the main crops in the provinces of the Gharb-irrigated perimeter.

Figure 6

Total annual IWRs of the main crops in the Gharb Plain.

The graph reveals a consistent trend in the IWRs for Sidi Kacem and Kenitra provinces from 1990 to 1994, stabilizing at approximately 450 million m³ and 400 million m³, respectively. Subsequently, there was significant fluctuation between 1994 and 2002, followed by a sharp decline after 2002, reaching a low point at 152 million m³ for both Sidi Kacem and Kenitra. Sidi Slimane's IWR, showed a variability over the years, but the overall trend was downward, concluding at approximately 178 million m³ in 2021.

In the early years, irrigation efficiency hovered around 40%, but it experienced a substantial increase after 2002, reaching its highest point at 91% in 2006.

The amounts of the IWRs are proportionally related to the irrigation efficiency.

IWRs for crops

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The bar chart (Figure 7) illustrates the total IWRs for three crops in the Gharb-irrigated perimeter from 1990 to 2021.

Figure 7

Total annual IWRs for the crops.

In general, sugarcane and rice exhibited a declining trend, while citrus demonstrated an upward trajectory over the entire period. The IWR of the three crops displayed fluctuations. Although, citrus initially had a lower rate, it surpassed sugarcane and rice at the end of the period.

Sugarcane's IWR was approximately 426 million m³ in 1990, surpassing rice by 102 million m³ and citrus by 179 million m³. It then rose to 524 million m³ in 1998 but experienced a sharp decline after 2002 reaching a low point of about 189 million m³ between 2010 and 2018. Subsequently, it increased to 297 million m³ in 2021.

The IWR of rice was around 324 million m³ in 1990, it declined and exhibited fluctuations throughout the years, with a general downward trend, reaching approximately 152 million m³ in 2021.

Regarding citrus, the figure exhibited considerable fluctuations over the years, reaching a minimum of around 77 million m³ in 2010. However, the overall trend was upward, surpassing sugarcane and rice in the last 5 years and reaching around 396 million m³ in 2021.

The analysis of IWRs across different provinces reveals distinct patterns for major crops (Figure 8).

Figure 8

IWR of the main crops in the provinces of the Gharb Plain during 1990–2021.

In Kenitra and Sidi Kacem, sugarcane consistently exhibits the highest IWR since the beginning of the observed period. Meanwhile, rice displays notable fluctuations over the years, and citrus consumes comparatively less water in these two provinces.

In Sidi Slimane, where citrus and sugarcane are the predominant crops, the IWR of citrus surpasses that of sugarcane since the commencement of the period.

For rice in Kenitra, the IWR initiated at 177 million m³ and experienced fluctuations, but the overall trend was, reaching 72 million m³ in 2021. In Sidi Kacem, the IWR of rice declined from 146 million m³ in 1990 to 79 million m³ in 2021.

Additionally, Citrus demonstrated an ascending trend in all three provinces, peaking at 211 million m³ by 2021 in Sidi Kacem province.

Regarding the temperature and precipitation changes, the temperature fluctuated in all three provinces over the years. In Kenitra, temperatures ranged from a low of 18.28 °C in 2018 to a high of 20.15 °C in 2020. Sidi Kacem experienced a low temperature of 17.5 °C in 2018 and a high of 20.06 °C in 2017. Sidi Slimane recorded a low of 18.25 °C and a high of 20.58 °C between 2018 and 2020.

Significant precipitation changes occurred across provinces from 1990 and 2021. In Kenitra, precipitation ranged from a yearly minimum of 0.69 mm/day to a maximum of 2.58 mm/day. In Sidi Kacem, the range was between 0.83 and 3.05 mm/day. Sidi Slimane, registred a minimum precipitation of 0.85 mm/day and a maximum of 3.22 mm/day in 2005 and 2010, respectively.

Our research revealed that temperature and irrigation efficiency are key factors influencing changes in IWRs (Figure 9). To further understand and quantify the contributions of these factors to IWR, we intend to employ linear regression modeling. This analytical approach assumes a role in obtaining a water allocation policy, contributing to the pursuit of sustainable water management in agriculture.

Figure 9

Model performance using Xlstat of predicted IWR and calculated IWR for rice, sugarcane, and citrus.

The results in Table 4 indicate that the IWR validations, specifically R² and RMSE, were 0.88 and 0.11 for rice, 0.90 and 0.10 for sugarcane, and 0.76 and 0.16 for citrus, respectively. To evaluate the model performance, scatterplots of predicted and calculated IWR were generated (Figure 9), with the figure displaying a linear fit to the scatter. The formula and the R² value for this fitting line are provided in Figure 9.

Table 4

Assessing modeling, verification, and prediction accuracy using linear regression

	R²	RMSE	MAE	MAPE
Rice	0.88	0.111	0.08	9.7
Sugarcane	0.90	0.101	0.06	7.4
Citrus	0.76	0.169	0.12	21.5

In assessing the agreement between the actual and the predicted IWR, the mean absolute percent error is 9.7% for rice, 7.4% for sugarcane, and 21.4% for citrus (Table 4).

Water balance in different provinces

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Figure 10 depicts the disparity between the water demand and water supply in the three provinces in 2016, reflecting the precision of the obtained data. The total water consumption for the three crops in the Gharb-irrigated perimeter was approximately 585 million m³, with an average irrigation efficiency of 75%.

Figure 10

Towards a Decision-Making Approach of Sustainable Water Resources Management Based on Hydrological Modeling: A Case Study in Central Morocco

Analysis of changes in IWRs and actual water consumption (a) and assessment of water scarcity conditions (b).

In 2016, Sidi Kacem province recorded the highest irrigation water demand at 260 million m³, followed by Kenitra with 206 million m³ and Sidi Slimane with117 million m³, respectively.

Upon comparing the IWRs of the crops in the three provinces of the Gharb, a noticeable disparity between the water demand and water supply becomes evident, underscoring the region's challenge with water scarcity (Figure 10).

DISCUSSIONS

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In Morocco, where water scarcity is prevalent, irrigation plays a pivotal role in enhancing agricultural productivity and income for the rural population in Morocco.

Accurately estimating IWR is essential for judicious resource allocation, particularly in semi-arid regions such as the Gharb Plain. The estimation of the water requirements for various crops during their development stages is primarily influenced by key parameters, encompassing planting surfaces, fluctuating irrigation efficiency, and changes in temperature, and precipitation.

The variations in IWR for major crops in the Gharb-irrigated perimeter are mainly affected by climatic variability and change, irrigation management, and crop adaptability. The major factors impacting the variations in the climatic properties are the spatial and temporal distribution of the key meteorological parameters determining evapotranspiration, the impact of the temperature and precipitation changes on the main crops, the degree of continentality, and the juxtaposition of two environments. Given that the provinces are situated in the Gharb Plain, where Kenitra is in the coastal zone, Sidi Kacem is in the central zone, and Sidi Slimane is in the intern zone.

It is crucial to note that crop evapotranspiration is considered under optimal conditions: a disease-free crop, growing in unrestricted soil conditions, well-fertilized crops, and a full production capacity under the growing environment (Allen et al. 1998). The crop water requirement is the sum of ETc for the entire crop growth period.

However, for the irrigated crops, Crop Water Requirement (CWR) must be supplemented by the IWR, especially in the Gharb-irrigated perimeter, where irrigation dominates due to the low productivity of rainfed cropping.

Two distinct datasets, from 1990 to 2002 and from 2002 to 2021, may explain variations in estimating water requirements in the Gharb-irrigated perimeter between the two periods. The ‘National Program for Water Savings in Irrigation’ (PNEEI) in Morocco aims to conserve irrigation water by incorporating water-saving techniques. Changes in irrigation efficiency, as presented in Table 3, stand out as one of the primary factors influencing the irrigation water amounts.

In 2021, irrigated superficies from dams for major crops were distributed as follows: rice fields occupied a surface of 5,827.33 ha, citrus fields 11,707.43 ha, and sugarcane fields 5,829.37 ha. Citrus, with the largest water consumption, underscores the impact of land expansion on IWR. The Irrigation Extension Program (PEI) suggests an impending increase in water demand, emphasizing the pivotal role of irrigation strategies and cultivation technology in the efficient use and preservation of water resources in agriculture.

Comparing estimated IWR and the actual water consumption in 2021 for the Gharb-irrigated perimeter, reveals an imbalance between water demand and water supply. This imbalance can be attributed to factors such as private water pumping, rain dependency, and uneven spatial distribution of actual IWR.

Precision agriculture, particularly smart irrigation, proves effective in managing irrigation to conserve water resources without compromising plant moisture levels (Pierce 2010; Singh et al. 2019). This involves precise water application at the right time and location, optimizing water usage. By integrating smart agriculture strategies, including appropriate irrigation technologies, optimal results can be achieved in terms of crop yield and agricultural production efficiency.

Considering climate change and the impacts of irrigation on crop yields, the imbalance between the water demand and water supply in the Gharb-irrigated perimeter raises questions about its repercussions on crop yield and agriculture productivity.

CONCLUSION

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The Penman–Monteith approach is a FAO-recommended method, used to estimate the IWR and water balance from 1990 to 2021 in the Gharb-irrigated perimeter. The highest total IWR among provinces was observed in in Sidi Kacem, reaching an estimated 403 million m³ in 2021, followed by Kenitra with 264 million m³ in the same year, and Sidi Slimane with a total amount of 164 million m³.

In 2021, the cumulative IWR for main crops reached approximately 846 million m³ a decrease of 151 million m³ compared to 1990. Notably, the distribution of water consumption among crops, identifying sugarcane leading at the beginning of the period, surpassed by rice, and then citrus at the end of the period. The changes in IWR for the main crops were influenced by factors such as the land expansion and crop adaptability.

The estimation of the water requirements for the crops during the development stage in the Gharb-irrigated perimeter is mainly influenced by the irrigation efficiency, temperature, precipitation changes, and land expansion. In the context of climatic change and variability, effective irrigation management, and crop adaptability emerge are crucial elements for conserving water resources without compromising crop yield potential. The crop production track combined with the water balance track serves as a pivotal component of an efficient water use strategy.

ACKNOWLEDGEMENTS

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The authors would like to express appreciation to the editors and reviewers for their valuable support and assistance.

FUNDING

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

DATA AVAILABILITY STATEMENT

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All relevant data are included in the paper or its Supplementary Information.

CONFLICT OF INTEREST

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The authors declare there is no conflict.

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