Hamed et al.
Progress in Earth and Planetary Science(2022) 9:35
https://doi.org/10.1186/s40645-022-00494-3

Progress in Earth andPlanetary Science

Open Access

RESEARCH ARTICLE

Climatic zonation of Egypt basedon high‑resolution dataset using image
clustering technique
Mohammed Magdy Hamed1* , Mohamed Salem Nashwan2 and Shamsuddin Shahid3

Abstract
Egypt, a predominantly arid and hyper-arid country, is one of the environmentally most fragile regions of the world.
The country became a hot spot for climatic extremes and aridity change in the global warming context. The unavailability of a detailed and reliable climate zonation map is a major hindrance to climatic studies in Egypt. This study
attempted to generate a high-resolution climate zone map of Egypt based on a novel image analysis technique. For
this purpose, a colored image representing Egypt’s composite climatology was developed using three high-resolution(1-km) climate variables: rainfall, maximum temperature and minimum temperature during 1979–2013. A spherical evolution algorithm was used to classify the image into different climate zones. Subsequently, the climate zones
representing similar climate distribution were merged to generate the climate map of Egypt. The study revealed that
Egypt’s distinguishable climate zones could be recognized when the land area was classified into nine zones using
the image analysis technique. The statistical analysis of climate variables of each zone revealed similar climatology
only in two pairs of zones. The merging of similar climate zones yielded seven climate zones having distinct climatecharacteristics. The validation of climate zonation using various statistical tests revealed the robustness of the proposed method in classifying climate. The climate zone map generated in the study can be used as a reference forclimate change analysis in Egypt.
Keywords: Egypt, Climate zone, Spherical evolution, Data clustering optimization, Image clustering
1 Introduction
Climate defines ecology, species distribution, water availability, land suitability for cropping, irrigation needs,
harvesting scheduling and prevalence of diseases in anarea (Belda et al. 2014; Trewartha and Horn 1980). Therefore, climate classification has paramount importance forregional development planning. The climate of an area
is generally classified based on vegetation distribution
as different species have a climatic niche (Bailey 2009;
Feddema 2005). However, vegetation distribution data
*Correspondence: eng.mohammedhamed@aast.edu
1Construction and Building Engineering Department, College of Engineeringand Technology, Arab Academy for Science, Technology and Maritime
Transport (AASTMT), B 2401 Smart Village, Giza 12577, Egypt
Full list of author information is available at the end of the article

are unavailable in most parts of the world. Therefore,
different climate variables like rainfall, temperature andhumidity are mostly used for climate classification (Baker
et al. 2010; Sa’adi et al. 2021). The selection of the climate
variables depends on the target application of the climateclassification and the spatial scale of the study area (Sihag
et al. 2020; Tai et al. 2010).
Many studies have been conducted for global andregional climate classification (Belda et al. 2014; Hubalek
and Horakova 1988; Netzel and Stepinski 2017; Sa’adi
et al. 2021; Sánchez-García et al. 2020; Xiong et al. 2019).
The studies used different classification schemes, such as
Köppen (Köppen 1936), Köppen–Geiger (Geiger 1954),
Thornthwaite (1948) and Köppen–Trewartha (Trewartha and Horn 1980). The existing classification scheme
provides a broad climate classification of a region. For© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

example, most parts of the Middle East and North
Africa (MENA) belong to arid climate (BW) according
to Köppen and Köppen–Trewartha climate classification
scheme (Belda et al. 2014). However, rainfall and temperature in the BW zone in MENA significantly vary fromplace to place. Therefore, a more detailed land classification according to rainfall and temperature variability isvital for regional planning and economic activities.
In recent years, different methods have been proposed
to classify climate at a regional scale (Bienvenido-Huertas
et al. 2021; Netzel and Stepinski 2017; Sa’adi et al. 2021;
Yanling et al. 2008). Those methods mostly classify thelands according to homogeneity in different climaticcharacteristics and, therefore, are considered more reliable for representing ecological distribution (Fávero andBelfiore 2019). Hubalek and Horakova (1988) used cluster
analysis with Euclidean distance to evaluate the climate
similarity in Europe. Sa’adi et al. (2021) used hierarchical(e.g., Ward’s method) and non-hierarchical (e.g., k-mean)
methods to define the climate zone of Borneo using
a 0.25° spatial resolution climate dataset. Netzel andStepinski (2017) classified the world’s climate using the
dynamic time warping similarity function. Among themethods, the cluster analysis, such as the newly developed spherical evolution algorithm (SEA) (Tang 2019),
has been found more reliable as it can efficiently categorize individual locations based on their homogeneity andheterogeneity (Bienvenido-Huertas et al. 2021; Kaufman
and Rousseeuw 2009; Sa’adi et al. 2021; Sánchez-García
et al. 2020; Xiong et al. 2019). Tang (2019) compared ten
clustering methods with SEA using six benchmark datasets and revealed that SEA has superior local and global
search capabilities for solving unimodal and multidisciplinary optimization problems.
Generally, in situ observed climate data are used forclimate classification. The quality of climate observations
largely affects climate classification. However, reliableclimate observation is not available or limited in many
regions (Hamed et al. 2022c; Hamed et al. 2021; Salehieet al. 2021; Salman et al. 2019). Climate classification
based on less quality or sparsely distributed observed
data is prone to high uncertainty. Gridded climate data
have been extensively used in recent years to overcome
the challenge of data scarcity. The gridded climate data
have also been used for climate classification. Kottek
et al. (2006) first used gridded climate data of the climate
research unit (CRU) for classifying the globe into different climate regions. In recent years, many other studies
used gridded climate data to classify global and regional
climates (Belda et al. 2014; Rubel and Kottek 2010; Sa’adi
et al. 2021). The major challenge of climate classification
using gridded data is that the spatial details of climate
zones are limited to the resolution of gridded climate

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data. Most previous studies used gridded climate datawith spatial resolutions ranging from 0.25° to 1.00° forclimate classification (Belda et al. 2014; Sa’adi et al. 2021).
Thus, it was not possible to provide climatic details below
the resolution of the data used. However, a high-resolution climate classification is very important, particularly in arid regions, considering the limited availabilityof suitable land for agricultural, human settlement andother development activities.
Egypt, located in North Africa, is an arid country
with inadequate available land for development activities. High aridity has made about 96% of Egypt a desert.
Therefore, only a limited amount of land in the country
is suitable for agricultural activities (AFED 2017; Collins
et al. 2017). Egypt’s population is over 100 million andgrowing by 2 million per year (Sweed 2016). Expansionof agricultural land and human settlement is essential
to ensure food and housing for the growing population.
A detailed evaluation of land according to climate canaid in identifying suitable land for agricultural development and establishing new settlements. Besides, Egypt’s
climate showed a rapid change in recent years (Gadoand El-Agha 2021; Hamed et al. 2022a, b; Nashwan andShahid 2022). The maximum temperature has increased
from 0.07 to 0.24 °C/decade and the minimum temperature by 0.08–0.29 °C/decade in the last five decades(Nashwan et al. 2019b). At the same time, the rainfall
decreased up to − 5.00 mm/decade in the Nile Delta(Gado and El-Agha 2020; El Kenawy and Mccabe 2016;
Nashwan et al. 2019c). The climate changes worsened thecountry’s status of high-risk desertification (Abuzaid and
Abdelatif 2022; Badreldin et al. 2014; Gad 2020). Therefore, the country must identify vulnerable climate zones
to combat desertification.
In this study, a new methodology is presented to classify the recent (1979–2013) climate of Egypt based on theimage clustering technique and SEA using the fine-resolution gridded climate data, Climatologies at high resolution for the earth’s land surface areas (CHELSA). The
SEA adopts a novel spherical search mechanism insteadof the conventional hypercube search mechanism. This
study is the first to adopt the SEA for climate classification. The climate zone map generated in this study could
aid in deciding region-specific mitigation measures tocombat Egypt’s growing pressure on land resources. The
method employed in this study can be replicated in any
other region or on a global scale for a detailed and reliable climate classification.

2 Study area and data
2.1 Study area

This study covers Egypt, which is around (1,010,408­km2) area. It is located on the edge of Africa and Asia,

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

within the geographic boundaries of latitude 22°–32° N
and longitude 25°–35° E, as shown in Fig. 1. Egypt has
the largest population within the MENA region. Generally, the climate of Egypt is relatively wet and cool in
winter (October to March) and dry and hot in summer(April–September) (Nashwan and Shahid 2019a). The
four seasons show geographical and temporal variety
due to the country’s significant regional and temporalvariability in climate. Egypt’s location and topography play a role in the distribution of rainfall and temperature. The rainfall in Egypt is relatively more in the
coastal area along the Mediterranean Sea in the north(> 200 mm/year) and the Red Sea in the east than in theinland areas. Most areas in the south and west of Egypt,a part of the Sahara, receive an average rainfall of below
5 mm. Rainfall in the north is influenced by the North
Atlantic Oscillation (NAO) (Elmallah and Elsharkawy
2011; Shaltout et al. 2013), Mediterranean Oscillation(MO) (Elmallah and Elsharkawy 2011; Hamed et al.
2021; Redolat et al. 2019) and the East Atlantic–West
Russia index (Hasanean 2004). In contrast, the Red
Sea through influences the east and south (Tsvieli and
Zangvil 2007). Furthermore, temperature varies in
Egypt by location and altitude. The temperature generally gets cooler in the north than in the south. The
mean temperature in the north is around 20 °C, while
25.9 °C in the south. However, the temperature in

Fig. 1 Study area location and topography

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higher elevation lands, such as Saint Catherine Mountain, may reach subzero in winter.
2.2 Previous climate classification in Egypt

The review of existing literature suggests four climate
zones in Egypt. A description of those classifications isprovided in this section. The Köppen–Geiger climateclassification method (Geiger 1954; Köppen 1936) categorized Egypt into three climate zones (Peel et al. 2007):dry semiarid with low latitude and altitude (BSh), dry arid
with low latitude and altitude (BWh) and dry arid with
mid-latitude and high altitude (BWk) (Fig. 2a). The Köppen–Geiger classification considered the climate of Egypt
as mainly BWh and missed the distinctive details of theregional climate, which different researchers attemptedto address later.
Secondly, few other studies also tried to classify the climate of Egypt. Ibrahim et al. (1994) presented a climatological classification of Egypt broadly into six different
regions as shown in Fig. 2b: Mediterranean (MD), Nile
Delta (ND), Middle Egypt (ME), Upper Egypt (UE), Red
Sea (RS) and Sinai Mountains (SM). Thin strips along
the Mediterranean and the Red Sea with high rainfall aredefined as MD and RS, respectively, while SM is defined
based on altitude. The remaining zones are defined basedon temperature variation by latitude. A detailed description of each zone is provided in Nashwan et al. (2019a).
Another study classified Egypt’s climate into eightzones (Fig. 2c): North Coast, East Coast, Delta and Cairo,
Northern Upper Egypt, Southern Upper Egypt, Southern
Egypt, Altiplano and Desert (HBRC 2006; Sayed et al.
2013). This classification is an updated version of theearlier climate zonation. It was proposed for engineering
applications in the Egyptian Code of Practice for Enhancing Energy Use in Buildings. The classification is basedon the mean temperature, rainfall, humidity, wind speed,solar radiation, altitude and physical terrain. However,it focused only on Egypt’s occupied land, the Nile Delta
and Valley, the coastal regions while classifying the restof the land as ‘Desert.’ This classification neglected thatthe north and south of eastern and western deserts havedistinct climatological characteristics. Besides, the Nile
valley was climatologically divided between the east andthe west without proper justification.
Lastly, Fig. 2d, e presents the agroclimatic classification proposed by Ouda and Norledin (2017) based on
10 (2005–2014) and 20 (1995–2014) years of potential
evapotranspiration data, respectively. Both classifications
were based on reference evapotranspiration (­ ETo) for different study periods in different governorates. The calculation of E
­ To was based on daily solar radiation, Tmax,
Tmin, Tmean, wind speed and dew point temperaturefrom meteorological stations. The main drawbacks ofHamed et al. Progress in Earth and Planetary Science(2022) 9:35

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Fig. 2 Maps of previous studies on climate zoning of Egypt. a Köppen–Geiger climate classification. b Six climate zones for the whole land:
Mediterranean (MD), Nile Delta (ND), Middle Egypt (ME), Upper Egypt (UE), Red Sea (RS) and Sinai Mountains (SM). c Eight climate zones for thewhole land. d, e Agro-climate classification zones according to potential evapotranspiration for 10 and 20 years

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

these two classifications are: (1) the use of few meteorological records to represent a vast area; (2) the usage ofthe administrative boundaries as frontiers of each climate zone; and (3) the limited land coverage of theseclassifications as they focused only on the fertile land in
the Nile Delta and Valley. This largely hindered their use,
especially for future desert land reclamation and development. Several studies stated the importance of using a
representable number of gauge records for a given area
or catchment to minimize error in estimating the spatial
characteristic of climate variability (Barbalho et al. 2014;
Hwang and Ham 2013; Lee et al. 2013; Morrissey et al.
1995; Sharp et al. 1961; Zawadzki 1973).
Following observations can be made from the reviewof the previous classifications. Previous classifications
mostly concentrate on the broad classification of Egyptian climate using sparsely distributed climate observation data. The frontiers of each zone were outlined basedon administrative border or naive ingenuous vectors.
Furthermore, they ignored the desert areas (nearly 96%
of Egypt’s land) and thus limited their usage only to occupied regions in Egypt, especially for the last three classifications. Furthermore, they also ignored the distinct
climatological characteristics of the unoccupied regions
as the Qattara depressions, which cover nearly 20 thousand ­km2 area (El-Ramly 1965) and the Al-Jelf Alkabir
Plateau, which receives the least rainfall in the Sahara
Desert (Kelley 2014). Lastly, Egypt suffers from the lackof adequately distributed gauges leading to sizable areas
being ungauged. The unavailability of in situ observations
has limited the knowledge of the climatic characteristicsof the country. This emphasizes using high-resolution
gridded climate data to understand the regions’ climatology and climate classifications.
2.3 Gridded rainfall and temperature dataset

CHELSA is a high-resolution (1-km) reanalysis ofmonthly gridded rainfall (R), maximum temperature(Tmax) and minimum temperature (Tmin) time series
datasets for 1979–2013 generated by downscaling of
ERA-Interim dataset (Karger et al. 2017). The mean
monthly temperature was downscaled from ERA-Interim
six-hourly monthly means of daily mean temperature
data (Karger et al. 2017). The temperature was interpolated to sea level, then to grid cells, and finally projectedback on the elevation obtained from the digital elevation model (DEM) (Karger et al. 2017). The minimum
and maximum temperature at 2 m was calculated by climatological aided interpolation of three-hourly data ofminimum or maximum temperature in ERA-Interim andB-spline interpolation of mean monthly temperatures(Karger et al. 2017).

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For the rainfall, the influence of orographic predictors,
such as boundary layer height, wind fields and valley
exposition, was incorporated into the Global Precipitation Climatology Centre (GPCC) and Global Historical
Climate Network (GHCN) datasets using a bias correction process (Karger et al. 2017). Some station data from
GHCN, Federal Office of Meteorology and Climatology
MeteoSwiss and Deutscher Wetterdienst (DWD) wereused to correct the bias in the resulting data using a multilevel B-spline interpolation (Karger et al. 2017). A windindex was used to get the windward impact on rainfall
intensity based on prevailing wind direction (Karger et al.
2017). For this purpose, the u-wind and v-wind components of ERA-Interim were interpolated by the B-spline
method (Karger et al. 2017).
Previous studies found CHELSA v.1.2 as the mostpowerful high-resolution reanalysis gridded dataset
in reproducing gauge observations for Egypt (Hamed
et al. 2021; Nashwan 2020; Nashwan and Shahid 2019b).
CHELSA showed significant correlations with NAO and
MO in Egypt. It has a far higher spatial resolution than
any gauge-based gridded climate data. It also provides
both the rainfall and temperature time series. Therefore,
CHELSA was selected and used in the present study. Figure 3 presents the spatial distribution of rainfall, Tmax and
Tmin in Egypt as estimated by CHELSA.

3 Methodology
This study proposes a novel clustering method for climate zoning using an image classification method called
spherical evolution algorithm (SEA). The flowchart
presented in Fig. 4 describes the methodology of the
proposed climate zoning method. In the proposed technique, Tmax, Tmin and rainfall were considered the threefundamental color bands of red, green and blue (RGB)
to form a complete image, representing the climatology
as discussed in Hamed et al. (2022b). These variables
are the most influential factors for climate characterization (El-Geziry et al. 2021; Haque et al. 2015). Other climatic parameters directly or indirectly depend on eitherof them. The image classification algorithm initially considered a different number of climate zones. The climate
zones were validated using multivariate analysis of variance (MANOVA) and probability density function (PDF)
for selecting the suitable number of classes to present theclimate in Egypt. The methodology is divided into fivemain steps, as described below.
3.1 Producing images

A colored image is the rendering of three bands, red,green and blue (RGB) conjointly. Each band contributes
an image in a grayscale format. In the grayscale image,each pixel represents the brightness level. The byte image

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

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Fig. 3 Spatial variation in CHELSA maximum and minimum temperatures (°C) and annual mean rainfall (mm) over Egypt for 1979–2013

Fig. 4 Flowchart describing the methodology proposed in this study for climate zoning of Egyptis the most common pixel format, where the pixel brightness is encoded as an 8-bit integer or as a value between
0 and 255. Pixels with a zero value mean zero visibility or
black and pixels with a value of 255 indicate maximum

brightness or white. Hamed et al. (2022a) proposed that
this concept can be utilized to represent and visualize theclimate of a given zone by scaling the values of rainfall,
Tmax and Tmin between 0 and 255. For instance, a pixel

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

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indicates the mean annual rainfall at the grid locationin the greyscale image. As a result, the grids with higher
rainfall are lighter than those with lower rainfall. The
same process is applied for temperature greyscale images.
CHELSA grayscale images of Tmax, Tmin and rainfall
were used as the three essential colors of red, green andblue (RGB), respectively, to form an image in the proposed procedure. Thus, the rendered RGB image can represent the composite climatology of Egypt. A few steps
transform the values of each climate variable into a grayscale value. (1) Rainfall, Tmax and Tmin at each grid wereconverted into the annual average. (2) Then, the annual
averages were rescaled to 8-bit integer using Eq. 1.

Yi =[xi − min(x)] × 255
max(x) − min(x)(1)

where Yi is the rescaled value of i grid, xi is the grid value,
and max(x) and min(x) are the max and min values ofgiven climate variables found among the reanalysis gridded data. The scaling procedure makes the distributionof the original climate value the same as the output values. Finally, the three scaled bands were merged to form
a complete colored image to visualize Egypt’s climate as
estimated by CHELSA for 1979‒2013.
3.2 Image clustering

Most of the search styles clustering methods as K-means
may fall into local optimum due to the initial selection of clusters centroid (Abidi et al. 2021). SEA, anature-inspired meta-heuristic algorithm consisting ofa spherical search style and search pattern (Tang 2019),
is proposed to overcome this problem. The sphericalsearch style can perform a more solid assessment thanthe hypercube search style, and it can be presented either
as one-, two- or high-dimensional space (dim ≥ 3). The
parameters of the spherical search style may require
some tuning, and these parameters are the scale anddimension selection factors. By using Randomly Selection Method (RSM) and Designated Individual Selection
Method (DISM) along with the search pattern equationand spherical search style equations, the ‘SE/current/1’
operator was chosen to image clustering as it showed
the best performance in solving data clustering problems(Tang 2019). This operator has superior local and global
search capabilities for solving unimodal and multidisciplinary optimization problems. Tang (2019) performed areal-world data clustering optimization problem.
As the output of the previous step was an image thatvisualizes Egypt’s climate, the same concept of image
classification could be used to identify the climate zones
in Egypt. As a result, different zones defined by the SEA
should have the highest variance against each other. Also,
each zone should have data with the highest similarityand lowest distance to the centroid. The objective function of image clustering can be presented as:

MinJ =n kdij (Xj − Cj )2

i=1 j=1(2)

where Xj is the image data (3 bands), and Cj is the centroid of each zone, while dij (Xj − Cj )2 is the distance
between both parameters using the Euclidean distancemethod. The number of gridded data is n and k is thenumber of zones. The main objective function is to minimize the J by finding the best centroid for each zone.
As the SEA needs an initial value of zone numbers (k),this study preliminary assumed that there could be six,seven, eight or nine climate zones based on previous
climate classification in Egypt as presented earlier in
Sect. 2.2. The classification of Egypt’s climate into threezones based on the Köppen–Geiger climate is primitive.
Thus, it was not considered the initial value of K. The climate zones prepared using each of the selected numbers
were validated through visual inspection and statisticalmethods. The procedure is presented in the following
subsection.
3.3 Validation and enhancement

The MANOVA is a statistical approach to discoveringsignificant differences among samples when all dependent variables of interest are examined (Stahle and Wold
1990). It is used to examine differences between two or
more samples with the assessment of all dependent variables concurrently. It generates a single F-statistic basedon Wilks’ lambda (λ), which evaluates all descriptors’
effects concurrently. A significant MANOVA F-statistic
shows that the climate of a zone differs considerably fromother zones. In this study, ‘rankMANOVA’ package in R
was used for the MANOVA test, where its result depends
on wild bootstrap resampling with test statistics and pvalue. A p value less than 0.001 indicates significant dissimilarity in climate among the zones.

4 Results
4.1 Formulation of CHELSA combined image

To create RGB composite image, the recent (1979–2013)
annual averages of rainfall, Tmax and Tmin of CHELSA
reanalysis datasets were calculated and rescaled individuality from 0 to 255. Then, the grayscale images wererendered together to form a colored image, as shown in
Fig. 5, which visualizes the recent climate of Egypt in a
unitless value. The figure shows that the effect of theblue band is greater in the north of Egypt, where rainfall is higher, and vice versa in the south, where rainfall is
extremely low.

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4.2 Image classification results

Fig. 5 The rendered RGB image of Egypt’s climate

The SEA was used on the rendered RGB image of Egypt’s
climate to classify the area into different zones. Climateclassification maps for the different number of zones(6, 7, 8 and 9) are presented in Fig. 6. Although using
the same number of climate zones, the resulting climate zones differed from previous studies as they do notdepend on administrative boundaries. Furthermore, thezoning seems to follow the different climatic variables
and elevation distribution. The results showed that thenine climate zones map better represents the climate of
Egypt. It could classify the climate of the Saint Catherinearea in the Sinai and Suez mountains as a separate zone.
The climate of this mountainous region is truly different
from other parts of Egypt. This zone was only identified

Fig. 6 Resulting climate zonation maps when the number of climate zones was considered a 6, b 7, c 8 and d 9 zones

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

when the number of classes was considered nine. Therefore, the nine climate zones were considered the base forfurther analysis and merging of climate zones to prepare
the final map of Egypt’s climate zone.
The PDFs of Tmax, Tmin and rainfall for the ninezones were constructed (Fig. 7) to show each climatezone’s characteristics. The highest Tmax and Tmin wasnoticed in zone 8, followed by zone 7, while the lowest
values were noticed in zone 9 for Tmin and zone 1 and 9
for Tmax. It is also observed that two pairs of zones, (5Page 9 of 16

and 6) and (7 and 8), have close Tmax and Tmin. The highest annual rainfall was noticed in zone 1, followed byzone 2. Zones 5, 6, 7 and 8 receive low amount of rainfall, as shown in Fig. 7. Table 1 presents each climatezone’s average values of the selected nine zones. The
average values were similar to the results obtained fromthe PDFs. The values of zones 5 and 6 were very similarin all three variables. Similar observations can also bemade for zones 7 and 8. The mean Tmax was 30.21 °C forzones 5 and 6 and 33.60 °C for zones 7 and 8. At theFig. 7 Normal probability density function (PDF) curve for maximum and minimum temperature (°C) along with annual rainfall (mm) for theselected nine climate zones

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

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Table 1 Average Tmax, Tmin and rainfall for the selected nineclimate zones
Zone

Average Tmax (°C)

Average Tmin (°C)

Annualrainfall
(mm)

1

25.00

16.30

118.30

2

26.10

15.20

58.10

3

27.30

14.30

20.30

4

29.13

15.54

7.55

5

29.52

14.12

2.19

6

30.90

15.88

1.76

7

32.50

17.80

2.60

8

34.75

19.27

5.72

9

24.40

11.00

18.20
Fig. 8 The seven climate zones of Egypt

Fig. 9 Normal probability density function (PDF) curve for maximum and minimum temperature (°C) along with annual rainfall (mm) for theselected seven zones

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

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4.3 Climate zone of Egypt

Fig. 10 Box and whisker plots show the maximum (purple) andminimum (orange) temperature (°C) along with annual rainfall (pink)(mm) for the seven climate zones

same time, the mean Tmin was 15.00 °C for zones 5 and
6 and 18.50 °C for zones 7 and 8. The rainfall for all fourzones was less than 6.00 mm/year. Therefore, zones 5
and 6 were merged into a single zone. Similarly, zones 7
and 8 were merged into a single zone.

After merging the two pairs of zones, the climate zonesof Egypt are shown in Fig. 8. The rainfall varies between
climatic zones due to the terrain and geographical position, which determine the local climate, as shown in
Fig. 9. Zone 1 is located on Egypt’s north coast, where therainfall is the highest (118.30 mm) and Tmax is relatively
low (25.00 °C), followed by zone 2, a strip that parallels
the Mediterranean Sea, which receives the second-highest rainfall (58.10 mm) and Tmax of 26.10 °C and Tminof 15.20 °C. The rainfalls of zones 3 and 7 were similar(19.00 mm), but Tmax and Tmin were less in zone 7 (24.40
and 11.00 °C, respectively) than in zone 3 due to theirhigher elevations. Zones 5 and 6 experience the lowest
rainfall due to their inland location in the south of Egypt.
Zone 6 has the highest Tmax and Tmin due to its locationin the south and southeast of the study area.
Figure 10 shows the annual average of Tmax, Tmin andthe total rainfall for the seven climate zones using box
and whisker plots. The upper chart contains Tmax as purple color and Tmin as orange, while the lower chart presents the annual rainfall as pink color. It is observed that
Tmax gradually increased from zone 1 (median 25.00 °C)
to zone 6 (median 34.00 °C), while Tmin remains closely
similar (median 14.00–18.00 °C). Both Tmax and Tmin
dramatically decreased in zone 7, 24.40 °C and 11.00 °C,
respectively. The rainfall mostly occurs in zones 1, 2, 3
and 7, while nearly < 5 mm in other zones. The highest
rainfall occurs in zone 1 (median 120.00 mm), followedby zone 2 (median 60.00 mm), zone 3 (median 20.00 mm)
and the least in zone 7 (median 19.00 mm).
The seven climatic zones identified in this study are
unique compared to previous climate classifications. This
classification tried to cover what was missed in earlier
classifications, as presented in Sect. 2.2. The rainy stripsof zone 1 were much wider and more outlined than theclassification presented in Ibrahim et al., (1994). Zone
4 defined Qattara depressions, which all previous zonation methods ignored. Zone 3 covers a part of the Al-Jelf

Table 2 MANOVA test statistic results for different zones with each other
Zone nos.

1

2

28,528.50

2

3

4

5

3

60,794.20

43,519.30

477,554.10

77,553.10

97,694.10

5146,415.20

129,210.40

145,796.80

115,796.40

6136,163.60

152,597.90

164,993.80

162,276.30

189,559.90

716,188.80

31,911.92

37,515.90

81,622.90

171,622.60

6

127,626.40

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

Fig. 11 The probability density function curve of seasonal Tmax (°C) at seven zones

Page 12 of 16

Hamed et al. Progress in Earth and Planetary ScienceFig. 12 Same as Fig. 11, but for Tmin

(2022) 9:35

Page 13 of 16

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

Alkabir Plateau. The zonation process also displayed thechain of Red Sea mountains, the Sinai mountains andthe Zaafarana mountains. Although elevation was notconsidered a predicate in the classification, its effect was
clearly visible in the final climate zonation (Fig. 8). This isdue to the use of CHELSA data for classification, which
has been developed considering elevation as a covariate.
Overall, all zones demonstrated reasonable borders of
Egypt’s climate.
4.4 MANOVA results

The MANOVA test results for different pairs of zones arepresented in Table 2. The p values for the statistics wereless than 0.001. The p value and test statistics revealed
significant dissimilarities between each climate zone.
This indicates that each climate zone has its own distinguishable characteristics.
4.5 Seasonal variability

The PDF of Tmax and Tmin for four seasons in eachclimatic zone is shown in Figs. 11 and 12, respectively.
The four seasons include winter (December–February), spring (March–May), summer (June–August) and
autumn (September–November). Overall, summer had
the highest Tmax and Tmin for all zones, while winter had
the lowest values. Spring and autumn Tmax was comparable (medium temperature) for all zones, while autumn
Tmin showed a higher value than spring. The higher
autumn Tmin for zones 1 and 2 was due to their locations
near the Mediterranean Sea, while it was due to elevation
for zone 7. Zone 6 experiences the highest Tmax and Tminin all seasons, while zone 7 experiences the lowest Tmin in
all seasons.
The quantitative statistics for each climate zone forseasonal and annual Tmax, Tmin and rainfall are presented
in Supplementary material (Additional file 1: Table S1).
There was a distinguishable difference in the coefficientof variance, skewness and kurtosis of the three variables
in each climate zone in the seasonal and annual timeframes. Sometimes there were some similar results for asingle variable, for example, the kurtosis of Tmax and Tminin zones 1 and 2. However, the differences were distinct
when the statistics of all variables were considered.

5 Conclusion
A novel image analysis technique is proposed in thisstudy for climate classification based on both temperature and rainfall. The use of high-resolution gridded
data enabled a more detailed characterization of Egypt’s
climate than that was previously possible. The spherical evolution algorithm and statistical analysis classified

Page 14 of 16

Egypt’s climate into seven zones. Climatic zones aredefined by the effect of large-scale climate events andland–sea interactions in and around the study area. The
PDF illustrated the different behavior of climatic parameters for each climate zone. The visual inspection also
ascertains the ability of the method to distinguish different climate zones, including the small cold zones in themountainous northeast. Therefore, the climate zonation
map generated in this study can be considered reliable.
For the first time, the present study established a precise
categorization of Egypt’s climate, which can aid in theunderstanding of the physics governing the local climate.
Additionally, the reported results can be used by different
stakeholders, particularly in climate change research andadaptation, water resource conservation, infrastructuredevelopment, agricultural planning and biological conservation. Other climatic factors, such as humidity, evapotranspiration, solar radiation and wind speed, may beincorporated in the future for climate zonation. Besides,
other climate datasets, such as WorldClim and TERRA,
can be used in the future to quantify uncertainty in climate zonation caused by gridded datasets. The climate
zoning presented in this study only represents Egypt’s
recent climate (1979–2013). It would be fascinating tolearn how Egypt’s climatic zone has evolved throughtime and how it may change in the future due to climatechange.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​022-​00494-3.
Additional file 1. Statistics of seasonal and annual time series.
Acknowledgements
The authors thank the three anonymous reviewers for their insightful comments. The authors are thankful to the Swiss Federal Research Institute forproviding CHELSA meteorological data.
Author contributions
MMH contributed to formal analysis, investigation, methodology and writingof the original draft. MMH and SS were involved in data curation, investigation
and resources. MSN and SS contributed to review and editing. MSN and SS
were involved in conceptualization, review and editing. All authors read andapproved the final manuscript.
Funding
Open access funding provided by The Science, Technology & Innovation
Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank
(EKB). This research did not receive any specific grant from funding agencies in
the public, commercial or not-for-profit sectors.
Data availability
A shapefile of the proposed climate zones is available for free at Figshare(https://​doi.​org/​10.​6084/​m9.​figsh​are.​17278​256.​v1).
Code availability
The code was written using R software, R.3.4, to produce the data. The code isavailable when requested.

Hamed et al. Progress in Earth and Planetary Science(2022) 9:35

Declarations
Competing interests
The authors declare that they have no known competing financial interests or
personal relationships that could influence the work reported in this paper.
Author details
1
Construction and Building Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime
Transport (AASTMT), B 2401 Smart Village, Giza 12577, Egypt. 2 Construction
and Building Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT),
2033 Elhorria Heliopolis, Cairo, Egypt. 3 Department of Hydraulics and Hydrology, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi
Malaysia (UTM), 81310 Skudia, Johor, Malaysia.
Received: 23 December 2021 Accepted: 8 June 2022

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