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Morphometric Determination and Digital Geological Mapping by RS and GIS Techniques in Aseer–Jazan Contact, Southwest Saudi Arabia

Mohd Yawar Ali Khan
Mohamed ElKashouty
Ali Mohammad Subyani
1 and
Fuqiang Tian
Department of Hydrogeology, Faculty of Earth Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Department of Hydraulic Engineering, School of Civil Engineering, Tsinghua University, Beijing 100084, China
Authors to whom correspondence should be addressed.
Water 2023, 15(13), 2438;
Submission received: 4 June 2023 / Revised: 23 June 2023 / Accepted: 28 June 2023 / Published: 1 July 2023


The hydrological characteristics of the watershed in the southern Aseer and northern Jazan regions of Saudi Arabia (SA) were identified by integrated remote sensing (RS) and geographic information system (GIS) techniques using Shuttle Radar Topography Mission (SRTM) and Landsat data. For this purpose, the Wadi Ishran, Wadi Baysh, Wadi Itwad, Wadi Tabab, and Wadi Bayd drainage basins were extracted. Wadi Ishran is the largest, and Wadi Tabab is the smallest. Stream order and bifurcation ratio show that the Itwad and Bayd basins are permeable and of high aquifer potentiality. The multisupervised classification found seven rock units that were spread out in different ways across the basins. The areas with the highest vegetation were in the southeast, the centre, and the northwest. The bands’ ratios show more iron-rich sediments in the northeast and southwest. This paper’s outcomes serve as the basis for planning and managing groundwater resources. It finds potential groundwater zones, determines the risk of flooding, and chooses places where harvesting can be undertaken.

1. Introduction

Natural resources, including land and water, have diminished and deteriorated over the past few decades due to different natural and anthropogenic processes. The extreme pressure on water resources results from population growth and rapid urbanization. The need for readily available freshwater of adequate quality for drinking and other applications is growing substantially. Groundwater and surface water are worsening [1,2,3,4,5,6]. Water consumption and demand in the industrial, municipal, and agricultural sectors in SA have skyrocketed as a result of recent developments [7,8]. Ouda et al. [9] stated that the supply–demand gap was 11,423 million m3/year during 2010. Groundwater is the primary water resource in typical arid regions such as Aseer and Jazan, southwest of SA (Figure 1). Projects associated with rapid development impact groundwater quality. Geologic contacts, structural elements, and mineralogy are identified using remote sensing data [10,11]. In SA, the Aseer–Jazan contact region is significant for agricultural and tourism. RS data can help distinguish potential zones for further exploration from noninteresting regions and provide information for a larger inaccessible area [12,13]. Image processing techniques, for example, principal component analysis (PCA), band rationing, and different classification techniques such as the maximum likelihood classifications (MLC) that are used in this study, can distinguish lithology’s distinctive spectral patterns [14,15].
The drainage basins analysis was used in planning and management procedures in mountainous areas (current study area). The description and evaluation of the stream network within drainage basins were qualitative estimates. The latter transformed into a quantitative analysis of the stream system, which provide numerical data of practical value.
Morphometric analysis defined the relief, linear, and aerial features of a watershed [16]. For planning and management, the mathematical analysis of drainage basin attributes offers valuable inputs for describing and understanding a variety of hydrological processes [17]. Strahler [18] and Horton [19] established the connection between morphometric parameters and basin hydrology. In order to use land and water resources sustainably and to reduce demand, watershed management is essential [20]. Markose et al. [21] used morphometric analysis to analyse and plan the drainage basin. In the past, many researchers investigated watershed hydrology and water resource management using morphometric techniques [16,20]. Benukantha et al. [22] stated that the morphometric analysis based on GIS is the most time-saving, effective, and precise method for management and planning implementation and watershed characterization since the advent of RS and GIS. The morphometric investigation was optimally processed and quantified using ARCGIS topographical techniques [23,24]. In the absence of hydrological data, the morphometric outputs deliver important hydrological features data [25,26,27]. The selection of suitable locations for land and water conservation programs and other natural resource planning is made using morphometric parameters [28,29,30].
GIS, RS, geological, hydrological, and hydrogeological parameters improved land use priority and water resources management and planning. The morphometric analysis of drainage basins helps to comprehend aspects of linear, areal, and relief parameters [31]. Geomorphologists confirm an excellent correlation between surface runoff features and geographic and geomorphic features for watersheds [32,33]. The determination of hydrological parameters significantly impacts the water situation and was considered the main priority in water management, reservoirs sites, rainwater harvesting best locations, and drainage basin management. These parameters are significant determinants of water dynamics within watersheds, providing valuable land and water resource management knowledge in regions lacking hydrology [30,34]. The watershed morphometric parameters estimation represents the fluctuation of the flooding risks [35].
SA controls 80% of the land area of the Arabian Peninsula. Its blessed land has many natural features, such as the Aseer valleys and streams flowing into rivers and drainage basins. They form green slopes and fields around the valleys, as if you were living in farms and gardens where rivers flow (Figure S1:; accessed on 21 September 2022). Tathleeth Valley is one of many valleys in Wadi Aseer, starting from the Aseer Mountains in the south and flowing north towards Wadi Al-Dawasir for over 500 km. The water shortage for domestic and drinking purposes enhances the water resources planning and management in SA. The watersheds and streams constitute the main aquifer recharge. The aquifer storage in SA declined because of a decrease in rainfall, dwellers’ expansion in the desert and urban areas, agricultural activity, domestic use, industrial activity, and the growth of the population [31]. Aquifer exploration and exploitation in desert land were the main objectives in Saudi Vision 2030. The fundamental issue was that a substantial percentage of residential water—about 90%—was desalinated Red Sea water [36,37].
This research aimed to detect the hydrological features of the watersheds and the relationship between the morphometric parameters of the Aseer and Jazan watersheds to reveal their impact on the hydrological process. ArcGIS software was used to implement RS- and GIS-assisted morphometric features. The findings of this study could aid in basin management and soil and water resource planning. The morphometric investigation was the first step in determining drainage basin dynamic features, prioritizing sub-basin growth, improving soil erosion, and managing water resources [38,39,40]. The aquifer potential areas with high groundwater recharge were identified through RS and GIS application in the morphometric evaluation. They promote agricultural and civilization development, raising per capita income and decreasing food imports.

2. Study Area

Most of the study area is situated along the Aseer–Jazan border, SA’s southwestern part, between 17°0′00″ and 18°0′00″ N and 42°0′00″ E and 42°0’00” (Figure 1).
The study area’s elevation ranges from 30 m to 3000 m above mean sea level (msl), with an average of 1530 m. Figure 2 shows the two-year (2020 and 2021) average temperature and precipitation of the study area. The warmest (mean temperature is 23.8 °C) and the coldest (mean temperature is 13.6 °C) months are June and January, respectively, making the climate warm and temperate (; accessed on 14 August 2022).
Wet oceanic winds are brought by the southwest monsoon, which causes rainfall in the region [12]. Rainfall occurs more frequently in the summer than in the winter, owing to wet oceanic winds carried by the southwestern monsoon [13]. The driest (average rainfall of 13 mm) and the wettest (average rainfall of 24 mm) months are October–December and March–August, respectively. The study area’s mild climate makes it a famous tourist place for local people. As a result, schemes related to fast development and tourism have an impact on groundwater quality.

2.1. Hydrogeology

The majority of the aquifer in the study area is composed of alluvium and jointed and fractured hard rocks and is recharged by rainfall infiltration. Because of the varying porosity and permeability of the shallow aquifers formed from the alluvium and jointed and fractured hard rocks, the aquifer storage coefficient varies. A decrease in groundwater flow is established when fine sediments are deposited in fractures, faults, and joints in the hard rocks. This allows for more seepage of runoff and aquifer recharge. By excavating downward through the rocks, water is pumped from these aquifers through the wells.
With total dissolved solids (TDS) mostly under 2000 mg/L, these aquifers reveal the outstanding quality of the water for irrigation. The concentration of TDS in Wadi Ishran and Wadi Tabab ranges from 228 to 2338 mg/L, respectively. The lack of lineaments and rock–water interactions might be the possible reasons for the high TDS concentration in these regions. The low TDS concentration in the Itwad, Baysh, and Bayd Basins (varying between 347 and 1216 mg/L) is attributed to a larger number of lineaments, resulting in a greater recharge. The nitrate (NO32−) contamination in most groundwater is less than 10 mg/L, excluding the wells located in the Itwad, Tabab, and Baysh Wadis, which exceeded the prescribed limit (45 mg/L) for drinking water. The majority of the aquifer was suitable for irrigation (TDS < 2000 ppm) and drinking (TDS < 1000 ppm and NO3 < 45 mg/L) purposes. The morphometric watershed evaluation improves aquifer potentiality delineation to increase boreholes, thereby increasing agricultural investment.

2.2. Water Demand vs. Supply

With little access to fresh water, SA is situated in a dry zone. SA has no permanent rivers or lakes because of the region’s high evaporation rates and low rainfall [41]. Aquifers in the nation hold about 3,958,000 million cubic meters (MCM) of water [42]. According to [42,43], the average annual recharge rate is 3850 MCM, which impacts the planning and management of water resources. The recharge is an annual groundwater yield from the aquifer systems, which are sustainable and renewable [44].
In SA, there is a much greater demand for water than what can be sustainably produced from conventional (surface water and groundwater) and non-conventional (desalinated water and treated wastewater) water resources. The leading cause of this gap is a lack of groundwater. Over time, there has been an increase in water demand, particularly in the agricultural and industrial sectors (Figure 3a). Figure 3b shows that SA’s population is projected to double by 2030, increasing the water supply from 6400 MCM/year to 10,158 MCM/year from 2010 to 2030, respectively (Figure 3c). Water needs have grown significantly over time, but groundwater yields have remained constant (Figure 3c).
Figure 3d demonstrates how the predictable gap between water supply and demand will narrow in 2025 and 2030 as agricultural practice declines. The decrease in per capita income will influence policymaking and people. This research aims to locate substitute water sources to lessen the gap and boost financial support for the agricultural industry.

3. Materials and Methods

The SRTM-Digital Elevation Model (SRTM-DEM) provided us with accurate data for preparing morphometric parameters [45,46]. The data gaps of the DEM map were preprocessed by filling using interpolation procedures and a sink–fill algorithm to reduce errors. Two SRTM-DEM images were obtained from the USGS website ( (accessed on 23 July 2022)). They (Dem 42 and Dem 43) were mosaicked to represent the regions of Aseer and Jazan that were investigated. The two images from Landsat-8 Enhanced Thematic Mapper Plus (ETM+8) were acquired (on path 167/row 047 and path 167/row 048). LC81670472018326LGN00 and LC81670482018326LGN00 were the LANDSAT SCENE IDs. The images include 11 bands and a low cloud cover (0.02). The images were corrected for quick atmospheric, wavelengths, contrast stretching, and UTM projection WSG84. SRTM-DEM is the best source map because it is more accurate vertically and horizontally and can provide more accurate data for morphometric analyses [20,47,48]. The morphometric parameters for the drainage basins are stream order (u), stream length (Lu), stream number (Nu), bifurcation ratio (Rb), mean stream length (Lum), drainage perimeter (P), drainage area (A), Rho coefficient (ρ), length–area relation (Lar), compactness coefficient (Cc), Lg, Dd, drainage texture (T), stream frequency (Fs), and constant of channel maintenance (C). These morphometric parameters are categorized according to their linear, areal, and relief hydrological characteristics [29,49]. They influence the characteristics of surface runoff and erosion; thus, they are utilized to differentiate and configure drainage basins [50]. Figure S2 represents a detailed flowchart of the watershed extraction methodology; using the formulas in Table 1, Table 2, Table 3 and Table 4 the morphometric parameters were determined.
The geological map was and geo-referenced based on satellite image coordinates, and rock units were digitized. Different types of digitized geology are valuable data for supervised image classification accuracy evaluation, MLC. ETM+8 satellite images were used to extract land cover classes. The software utilized for all the processes are Envi 5.1, Global Mapper 16, Erdas 2014, and ArcMap 10.2. The normalized difference vegetation index (NDVI) was calculated from satellite images to indicate the presence of green vegetation (hydrogeology). The digital number (DN) of satellite images was converted into reflectance values using the following equation [15]:
Band specific reflectance multiplication band × DN values + reflectance additive band
It corrected for sun angle by metadata included in the satellite images information as:
Reflectance/sin (sun elevation)
The NDVI was calculated by the following formula:
NDVI = (NIRRed)/(NIR + Red)
where NIR is the near-infrared band and Red is the red band.
The base map of the region of interest was created using the ETM+ images. Digital image processing was performed by utilizing ENVI version 5.1. The data were preprocessed to create a mosaic of two images. Stream networks, lineaments, geology, DEM, and NDVI were extracted using the ArcGIS 10.3 software packages. Envi v 5.1 was used to extract the lineaments, followed by trend analysis (RockWork v 16), handling extraction lineaments (Arc GIS 10.2), and automatic extraction lineaments (using PCI Line). PCI carries the most information and is appropriate for extraction of lineaments (PCI Geomatica, Markham, ON, Canada). The automatic lineaments extraction was based on the performance of the application and image data [51].

4. Results and Discussion

The five investigated basins (Figure 1) covering an area of 12,462 km2 are mainly composed of hard rocks and alluvial deposits. The hydrological parameters have been determined and are discussed in the following sections. The development of the watershed depends on exposed geology, geological structures, and precipitation. The remote sensing application identifies various geological units, the distribution of vegetation, and iron-bearing sediments. The drainage basins of the watershed are typically dendritic, indicating that hard rocks constitute most of the covered area.

4.1. Drainage Basins

Five drainage basins were delineated; Wadi Ishran, Wadi Baysh, Wadi Itwad, Wadi Tabab, and Wadi Bayd (Figure 1). The Wadi with the largest drainage basin is Ishran (5022.9 km2), followed by Baysh (4110 km2); Tabab is the smallest Wadi. The stream with the longest length is Wadi Baysh (2823.2 km), followed by Wadi Ishran (2063.4 km). The watersheds extracted by GIS are as follows.

4.1.1. Ishran Basin

The largest drainage basin is Ishran Wadi (area 5022.9 km2). The principal stream channel attains the sixth order, and structural lineaments are oriented NE–SW (Figure 4a). The dug wells are concentrated in the southwestern region (Figure 4b), where there are few to no lineaments. The high lineament density increases in the northern and northwestern regions (Figure 4b), distinguished by high rainfall infiltration and excellent aquifer potential. It is recommended to dig more wells in the northern and northwestern regions to increase the income per capita. Lineaments are oriented NE–SW (Figure S3c). The total length of lineaments is 292.2 km, with a concentration of 428 lineaments (Figure S3a), most of which are located in the northern and northwestern regions of the Ishran basin.

4.1.2. Baysh Basin

Wadi Baysh is the largest in southwest SA’s Jazan Province, while the northern portion of the basin is in Aseer Province. It is in the Tihama–Aseer coastal region [51]. The mainstream flows toward the Red Sea from the northeast to the southwest. The stream attains the sixth order (Figure 5a). There were 770 lineaments with a total length of 551 km (Figure S3b). They were primarily located in the northeast and corresponded to dug wells (Figure 5b). The structural lineaments were in the NE–SW and E–W directions (Figure 5c).

4.1.3. Wadi Itwad

There were 295 lineaments with a total length of 201 km (Figure S3c). The stream order is sixth (Figure 6a). The structural lineaments ran from northeast to southwest. The stream channel order is 5th (Figure 6c).

4.1.4. Wadi Tabab

The stream order is fourth (Figure 7a) and has the lowest discharge rate of the five basins. The number of lineaments was 257, with total length of 162.4 km (Figure S3d). The trend of the structural lineaments was NE–SW and E–W. (Figure 7b). The concentration of lineaments was due to the northeastern portion, which does not match the wells currently dug. To increase agricultural productivity, it is planned to add new dug wells in the region’s northeast. Trends of structural weakness were found in the NE–SW, N–S, and E–W directions (Figure 7c).

4.1.5. Wadi Bayd

The valley ended at 705 m and is fed by the El Hamda, Joan, Qalyta, Elahra, Habab, and Batyeh streams [52]. The basin is 778 km2 in size, and the stream channel reaches fifth order (Figure 8a). Figure 5e depicts approximately 21 lineaments with a total length of 13 km, the shortest of the five basins. They trend NE–SW, N–S, and E–W and are concentrated in the northeastern portion (Figure 8b,c).

4.2. Morphometric Analysis

The flooding in the Aseer–Jazan basins affects urbanization in the region. Since the Jazan–Abha highway intersects the Bayd, Baysh, and Tabab basins, the morphometric parameters must be studied in five basins.

4.2.1. Stream Order (u)

This estimates the stream’s position in the hierarchy of its tributaries; the stream subdivisions indicate stream size, flow rate, and watershed area [53]. Based on Strahler’s [18] application, the land was subdivided. Ishran and Baysh basins are represented by a sixth-order stream with a dendritic drainage pattern. They are the main streams through which all water and sediment discharge pass [53]. Itwad and Bayd watersheds are fifth-order streams, while the Tabab basin is fourth-order (Table 1). When stream order falls, hydraulic conductivity and infiltration rise [54]. Ishran and Baysh basins have a higher discharge rate than Itwad, Bayd, and Tabab watersheds. Ishran and Baysh have a lower permeability and infiltration capacity than the Tabab basin. Due to geomorphology and structural geology, stream order decreases as drainage basin size decreases (Figure S4). The stream number increases as the stream order declines (Table 1).

4.2.2. Stream Number (Nu)

The five basins contain 3170 streams, with the Ishran basin having the highest concentration (40%) and the Tabab watershed having the lowest concentration (5%) (Table 1). The Nu represents drainage basins’ erosive and developmental stages [55]. A large Nu (Ishran and Baysh) indicates an eroded landscape, whereas a smaller Nu (Wadi Tabab) indicates a mature landscape. Nu decreases alongside stream order (Table 1). This study also confirmed Horton’s law of Nu [19]; there is an inverse geometric correlation between stream order (u) and the logarithmic scale of Nu throughout the study region (Figure 9a and Figure 10). This inverse relationship is consistent with research conducted in India’s Varaha River basin and the upper Rihand watersheds [56,57]. The final variation is determined by the watershed’s physiographic variation and structural condition [58]. The decrease in Nu with increasing u (Figure 9a) indicates the presence of an eroding landform [59]. In Wadi Tabab, a lower in-stream number indicates a higher hydraulic conductivity.

4.2.3. Stream Length (Lu)

This identifies the nature of surface water (runoff) [20]. Low- and high-length streams are steep and gentle, respectively. Lu and drainage basin slopes were consistent with one another [60]. This reflects the basin area sharing for the specified stream order [18]. Stream lengths increase as stream order decreases (Table 1). Table 1 includes the total and average stream lengths for each order. Baysh basin had the most extended total stream lengths (LuT), which accounted for 2823 km for six orders (Table 1), although the minimum LuT for Bayd basin is 294 km. The log Lu versus u exhibits deviations from linear behaviour (Figure 11), which reflects lithological and topographical alterations [61]. The relationship resembles the watershed in the Huehuetan river sub-basin, Mexico [62]. The LuT is greatest in first-order streams and decreases with increasing stream order (Table 1). Wadi Baysh was distinguished by its long stream lengths and gentle slope, and as a result, it has a high aquifer potential, whereas Wadi Bayd has a low aquifer potential.

4.2.4. Mean Stream Length (Lum) and Stream Length Ratio (Lur)

These were determined based on the equations in Table 1. Lum ranged from 0.4 (Ishran) to 148.2 km (Baysh) of the fifth order (Table 1). Lum is dependent on topography and watershed area [60]. Stream length ratio (Lur) in Wadi Baysh ranged from 0.03 (fourth order) to 10.24 (fifth order) (Table 1), which can be attributed to variations in slope and topography [55,63]. Consequently, they impact the discharge and erosion of the watershed [64]. In Wadi Baydh, the highest Lur value (10.24) was attributed to an intense erosional process [20]. The stream orders with the greatest stream length ratio in Wadis Ishran, Itwad, Tabab, and Bayd are the sixth (3), fourth (6.3), third (4.58), and fifth (3.47), respectively (Table 1). They indicate that the areas of the sixth, fourth, third, and fifth stream orders were permeable and gentler than those of the other stream orders. The range of the mean stream length ratio (mean Lur) from 1.26 to 3.6 (Table 1) indicates the influence of lithology, lineaments, joints, fractures, and faults in watersheds.

4.2.5. Bifurcation Ratio (Rb)

Table 2 illustrates the equation. It is governed by geological characteristics, including terrain, lineaments, slope, and discharge rate [65]. Natural watershed Rb varied between 3 and 5 [14]. The average bifurcation ratio (R) varied from 3.8 (Bayd) to 5.3 (Tabab) (Table 2). The rise in Rb demonstrates structural impact [66]. Ishran and Baysh basins have the highest Rb values for the fourth and fifth orders (Rb values of 11 and 9, respectively), indicating steep slope, high overland flow, and maximum discharge [38,67]. When Rb reaches its maximum, it causes flash floods and soil degradation [68]. The watersheds (Baysh, Itwad, and Bayd) become more permeable, with fewer geological disturbances (2 < Rb < 5) [69,70]. If Rb is greater, the watershed (Ishran and Tabab) is elongated, whereas if Rb decreases, the basin becomes circular [71].

4.2.6. Rho Coefficient (ρ)

ρ is a crucial parameter that helps in determining the storage capacity of a drainage network and, consequently, the highest degree of drainage development in a given watershed [18]. Changes in ρ are determined by climatic, geologic, biological, geomorphologic, and anthropogenic factors [72]. ρ varied between 0.2 (Ishran basin) and 0.8 (Itwad basin) (Table 2). Wadis Baysh, Itwad, and Bayd have the highest ρ, indicating greater hydrologic storage progress through peak discharge. Wadis Ishran and Tabab have minimal ρ, indicating less hydrological storage capacity during floods. The latter two basins are vulnerable to flooding during intense precipitation due to drainage channels with inadequate ability to transport more surface runoff [20].

4.2.7. Drainage Area (A) and Perimeter (P)

The size and value of storm hydrographs were influenced by watershed areas [73]. The basins’ area and perimeter varied between 638.5 to 5023 km2 and 204 to 482 km, respectively (Table 3). If the watershed size is small, rainwater reaches the main channel more quickly than in larger basins. Due to their smaller basin area, the Tabab and Bayd watersheds pose the greatest risk of flooding. The intersection of both basins with the Jazan–Abha highway increases the risk of flooding. LuT versus watershed area (Figure S5) demonstrates that erosion is the dominant factor [74].

4.2.8. Circularity Ration (Rc)

This is estimated using the formula in Table 3. It is defined as the ratio of the watershed area to the area of the circle with the same perimeter as the watershed [75]. It varied from 0 (line) to 1 (circle) and delineated the geomorphological development of basin streams based on the basin’s geology. The Rc ranged between 0.17 and 0.27, with a mean of 0.21 (Table 3), which is less than one. This demonstrates that the basins are composed of homogeneous, permeable lithology and are elongated [76].

4.2.9. Compactness Coefficient (Cc)

Convergent values for the Cc ranged from 1.9 to 2.4. (Table 3). Lower Cc values in the Ishran and Baysh basins indicate greater elongation and less erosion. Itwad and Bayd basins have the highest Cc, reflecting less elongation and greater erosion. The Cc of a watershed corresponds directly with its infiltration capacity [68].

4.2.10. Length Area Relation (Lar)

Lar varies from 67 in the Tabab basin to 232 in the Ishran basin (Table 3). Lar explains the connection between stream length and basin area [74].

4.2.11. Drainage Density (Dd)

The result was determined by the equation in Table 4. The Dd represents the scale of the landform, runoff, and sediment–water outputs [77]. Lower Dd values indicate that streams are widely spaced and have low erosion [68]. This suggests that the underlying geology of the five basins is permeable (Dd 1.5), with less runoff and greater infiltration (Table 4). A total of 47% of the study area has a low Dd (Figure 12b), reflecting resistant geology and sound water leakage. This corresponds to a high concentration of lineaments (Figure 12a), so they are permeable and facilitate the leakage of precipitation. They are the most promising regions with excellent aquifer potential. The spatial distribution of the dug wells in the last area (Figure 12a) confirms the ideal hydrological parameters. On the other hand, 26% of the investigated area has high Dd (Figure 12b), indicating low subsoil permeability and high runoff rather than infiltration. The majority of the last site coincides with a low concentration of lineaments, which increases surface runoff rather than infiltration. They possess a low to extremely low aquifer potential.

4.2.12. Length of Overland Flow (Lg)

This influences watersheds’ physiographic and hydrologic development, as shown in Table 4’s equation. It is affected by precipitation intensity, leakage rate, soil types, geology, and grass density. Lg is the length of water flow over the ground before it becomes concentrated into permanent drainage channels or incised stream channels [78]. Higher Lg values (Baysh and Tabab basins) are associated with low relief, long flow paths, decreased runoff, and increased infiltration (Table 4). Lower Lg values (the rest of the basins) indicate high relief, steep ground slopes, short flow paths, increased runoff, and decreased infiltration [58].

4.2.13. Stream Frequency (Fs)

This is influenced by seepage rate, hydraulic conductivity, and topography. The utilized equation is found in Table 4. Fs is proportional to Dd; a lower Fs indicates less Dd, resulting in less runoff and flooding [63]. Fs is dependent upon precipitation, geography, and Dd. Fs values ranged from 0.25 to 0.26 (Table 4), indicating that less than one stream develops per km2. It is characterized by less rocky terrain, less erosion, and an exceptionally high infiltration capacity [59].

4.2.14. Infiltration Number (IF)

According to the equation in Table 4, the relationship between IF and leakage rate is inverse. The IF ranged between 0.10 and 0.17, indicating lower values. The watersheds were distinguished by increased infiltration and decreased runoff. The findings are consistent with those of a previous study [79], which found low IF values in the Araniar River basin in India, indicating higher infiltration rates and favourable groundwater recharge conditions.

4.2.15. Drainage Texture (T)

Depending on the infiltration capacity, lithology, and relief features of a particular terrain, the drainage texture (T) is a measure of the channel spacing closeness (Table 4). The T represents drainage system spacing [68]. The T is affected by precipitation, vegetation density, soil types, geomorphic development stages, infiltration capacity, and relief [80]. The T is very low (<2), indicating a coarse texture (Table 4). It clarified the groundwater’s increased permeability and enhanced recharge potential [80,81].

4.2.16. Constant of Channel Maintenance (C)

This determines the minimum required area for constructing a drainage channel. It is estimated in Table 4 using an equation. The calculated C ranged from 1.5 to 1.7 for the Baysh, Itwad, and Tabab basins and 2.4 to 2.7 for the Ishran and Bayd basins. The average C required to support 1 km of stream channels is two (Table 4). This indicates that a large area is needed to maintain 1 km of stream channel in these basins due to the increased surface rock permeability that promotes a higher infiltration rate. The Baysh, Itwad, and Tabab basins are characterized by low resistance and low infiltration capacity of bare soils; sparse vegetation and mountainous (steep-slope) terrain led to high overland flow and high flood potential [59]. Wadis Ishran and Bayd were obliterated by elevated C values (2.43 to 2.65), which indicate erosional sheeting [19,53,82].

4.3. Aquifer Potentiality

Based on selected morphometric parameters, the five basins are divided into three categories ranging from 1 (low permeable area) to 3 (high permeable location) (Table S1). Exploration of high aquifer recharge zones is a top priority in the Aseer–Jazan region, where the groundwater is the only water source. Selected morphometric parameters (Table S1) indicate the aquifer potential zones’ water leakage rate and runoff characteristics [26,83,84,85,86]. The Dd of the five investigated Wadis is less than 1.5, indicating suitable subsoil hydraulic conductivity with dense vegetation [18]. Accordingly, Wadis Baysh and Tabab have the lowest Dd (0.37 and 0.24, respectively), which indicates a good aquifer potential and encourages us to conduct significantly more hydrogeological and hydrological research. The five watersheds have poor Dd and coarse textures (Table S1), making them excellent locations for rainwater collection and wastewater recycling. The current study area has the highest precipitation intensity (200–600 mm/y) in Saudi Arabia. Rainfall and collected water recharge the groundwater directly, expanding the aquifer potential area. Wadis Baysh, Itwad, and Tabab have excellent aquifer potential, according to Lg (Table S1). Wadis Baysh, Itwad, and Bayd are located in a bifurcation zone (Table 3 and Table 4) characterized by infiltration and high aquifer potential. According to the morphometric parameters in Table S2, Wadis Bayd and Itwad have a very good to good aquifer potential (Table S2).

4.4. Remote Sensing

4.4.1. Multispectral Classification

It is simple to distinguish geological materials based on satellite images due to their different photo characteristics. Multispectral classification aims to classify distinct geologies using the spectral information of multiple bands. When a sample set is used in supervised classification algorithms, the classification is referred to as “supervised.” To determine which cluster an individual pixel’s value most closely matches, a classifier analyses the DNs of all pixels across all bands in the input image and compares them to the values of the clusters in the training set. The ML classifier takes into account more than just the centres of the clusters; it also takes into account the clusters’ size, orientation, and shape. To do this, we compute a statistical distance between the clusters using their means and covariance matrices [87]. The ML algorithm is used for classification on the assumption that the histogram of the image follows a normal distribution. It was made clear by Lillesand and Kiefer [88] that the ML classifier is only necessary for land cover classifications with residual uncertainty between overlapping classes. Using the available geological maps, all categories are connected to available features. To compile a set of statistics describing each rock unit’s spectral response pattern, 123 supervised training or areas of interest (AOIs) are selected and controlled to represent seven categories (rock types). It is convenient to include texture, shadow, tonal, and pattern distinctions for each class based on the number of AOIs. The selected AOIs’ spectral signatures are analysed, and statistical parameters are computed for all but the panchromatic band. One of the best image processing methods for distinguishing the primary rock units in the study area was found to be multispectral supervised classification (Figure 13). The digitally classified geological map (Figure 13) serves as a good base map for further analysis in the present study. The map depicts seven classes representing the major rock units in the area under investigation.
The rock units range from the Proterozoic to the Cenozoic (Figure 13). The majority of the investigated area is covered by Proterozoic rocks, which are present as metavolcanic and metasediments and are primarily represented by the Baysh and Abha groups and the Sabya Formation [89]. The various lithologies included granodiorite, diorite, gabbro, and basalt. Multiple tectonic activities, exacerbated by the opening of the Red Sea, affected the area from the Cambrian to the Quaternary (Oligo–Miocene). Elongated grabens and horsts formed by NW–SE and NW–SE trending faults are the most notable structural features of the region [90,91]. The study area’s most prominent wetland features generally extend eastward from the west, following the principal fracture system [92]. The maximum geology areal distribution in Wadi Ishran comprises granite, granodiorite, chlorite sericite schist, and amphibolite schist (1249 km2, 68%), while the minimum geology areal distribution comprises andesite and diabase (7 km2, 0.4%) (Figure 14a). The predominant exposed geology in Wadi Baysh consists of greenstone and schistose greenstone (708.3 km2, 56%), followed by granite and granodiorite (252 km2, 20%) (Figure 14b). The geology with the lowest area distribution comprises amphibolite, schist, and similar rocks (11 km2, or 0.8%). (Figure 14b). The total area of Wadi Ishran (1835 km2) was larger than that of Wadi Baysh (1266 km2). In the Itwad watershed, greenstone and schistose greenstone make up 67% of the exposed geology (1151 km2; 67%), whereas the remaining geology accounts for 3–10%. (Figure 15a). In Wadi Tabab, the exposed geology consists primarily of marble, quartzite, chlorite sericite schist, and amphibolite schist (109 km2; 87%), followed by granite and syenite (15 km2; 12%) (Figure 15b). Greenstone, schistose greenstone, amphibolite, schist, and similar rocks are the predominant exposed geology in Wadi Bayd (392 km2, 73%), whereas granite and granodiorite are the least abundant (0.33 km2, 0.1%) (Figure 16).

4.4.2. Band Composite and NDVI

The composite maps (bands 1–7 and 1–11) are displayed in Figure 17a,b. They distinguish between distinct geological types. Green vegetation in arid and semiarid regions during the dry season is an excellent indicator of shallow groundwater. In karstic terrains and hard rock, groundwater occurs in weathered and fractured zones. Therefore, during the dry season, green vegetation is indicative of shallow groundwater and corresponds primarily with lineaments. Green vegetation reflects a great deal of near-infrared (NIR) light but very little red band (RED). Since this difference has a large and positive value, much larger than the other land cover indices, it follows that this is the simplest vegetation index.
The higher reflectance in the green band causes vegetation to appear green because the NIR domain is not visible to the human eye. The strong absorption of chlorophyll pigment accounts for the low reflectance and transmittance in the visible spectrum. In the NIR (0.7–1.30 m), absorption effects are weaker, whereas reflectance and transmittance are greater. Details of the spectral signature are determined by pigment type, leaf cell structure, leaf water content, leaf orientation, and leaf thickness along the sun–sensor path. Seasonal and intraseasonal changes in plant and tree growth as a result of precipitation and temperature naturally contribute to the overall variability in vegetation development and, by extension, reflectance characteristics. Compared to outcrops of water, bare soils, dried-out vegetation, etc., the NIR and RED bands have a larger difference in reflectance (expressed in DNs) when observing green vegetation. Vegetation index images are pictures with different values for each pixel. Widely employed, the NDVI is normalized by dividing the difference by the sum, bringing the range of NDVI values to −1 and 1 (equation above). Figure 18 shows that the southeastern, central, and northwestern parts have very high vegetation rates.

4.4.3. Landsat Oli 8 Band Ratio (BR) and Colour Composite Images

The most prevalent form of alteration is the transformation of feldspars and ferromagnesian minerals into clays and other hydroxyl-bearing minerals. Due to the absorption of short-wave infrared (SWIR) light in these minerals’ spectra, they can be detected by remote sensing techniques [93]. Many ore occurrences also include sulphide minerals such as pyrite (FeS2), which breaks down into sulphuric acid, as well as other sulphates such as ferric hydroxides and complex sulphates, which are both highly coloured and exhibit crystal field absorption in the visible and near-infrared range (VNIR) [93,94].
This suite of alteration features was instrumental in defining a variability of hydrothermal ore deposits [93]. It is well known that ferric iron shows prominent absorption characteristics at approximately 0.82 m and 0.35 m, whereas ferrous iron absorbs at wavelengths of 1.0, 4.8–2.0, and 0.55–0.45. Hydroxyl-bearing minerals such as clays have a prominent absorption characteristic between 1.9, 2.35, and 2.5 [95]. Detecting the minerals above has served as a guide for ore deposit prospecting. Band ratio 4/2 is helpful for mapping ferric iron oxides due to their absorption in the blue region and high reflectance in the red area. Band ratio 5/6 was utilized for mapping ferrous minerals due to their high reflectance in this ratio. This study used the band ratio 6/7 to map kaolinite, montmorillonite, and clay minerals. All these features have a high reflectance on band 6 of the ETM+8 image and a low reflectance on band 7. The colour-density slice version of the band ratio images, in which the greyscale has been replaced by the colours, is displayed in the histogram of the threshold anomalies for the three band ratios of iron oxides and clay minerals. For ferric iron oxides, ferrous iron oxides, and clay minerals, RGB colours correspond. The threshold values can be estimated based on the statistical results of the band rationing 4/2, 5/6, and 6/7 in the Envi 5.1 program. According to one of the following equations, the background (threshold) values and, consequently, the expected anomalies of each band ratio image could be determined.
TH = M + 3x   Sd at confidence 98%
TH = M + 2x   Sd at confidence 98%
TH = M +    Sd at confidence 98%
TH is the reflectance value of the threshold pixel, M is the reflectance value of the average pixel, and SD is the standard deviation.
Table S3 presents a summary of the results. The band ratios 4/2, 5/6, and 6/7 produce the most accurate results for identifying ferrugination, ferromagnesian, and OH-bearing and carbonate minerals, respectively (Figure 19). This is because the Fe–O charge transfer transition exhibits a broad absorption band at wavelengths less than 0.55 m. It is responsible for the intense red hue of rocks abundant in iron oxides and hydroxides (Figure 19), which are prevalent in the northeastern and southwestern regions (Figure 19).

5. Conclusions and Recommendation

Recent developments in the SA have dramatically increased water demand and consumption in the industrial, agricultural, and municipal sectors. This study demonstrates that RS and GIS techniques can assess the watersheds’ boundaries, distribution, and hydrological characteristics. The morphometric parameters aid in determining geological behaviours, the rate of water seepage, and runoff. Morphometric parameters influence surface runoff flow velocity, runoff volume, and water level. They were examined and evaluated to recognize the flooding and aquifer potential recharge. Five drainage basins were extracted, with Ishran being the largest and Tabab the smallest. To increase income/capita, a correlation between lineaments concentration and proposed dug wells was established. The C, Dd, T, and Fs indicate that the five sub-basins are permeable and have excellent aquifer potential. Consequently, Wadis Baysh and Tabab represent the most promising aquifer potential regions. In supervised classification, 123 AOIs were selected and managed to represent seven lithological properties (digitized geological map). The NDVI outputs indicate a very high rate of vegetation in the southeastern, central, and northwestern regions. The band ratios 4/2, 5/6, and 6/7 reflect iron-oxide and hydroxide-rich rocks in the northeastern and southwestern regions.
In the future, measuring some of the morphometric parameters in the field is recommended, along with comparing them with those calculated by RS and GIS techniques. The satellite images and SRTM (DEM) characteristics influence the accuracy of the outputs. The morphometric analysis selects the best aquifer potential area and declines the flooding risk. Using stream networks and structural information, planners and decision-makers might build sustainable watersheds and allocate artificial recharge systems for resource management in diverse catchment regions. The long-term growth of natural resource management in the watersheds near the Aseer–Jazan contact also benefits from the current research. To fully understand the multidimensional nature of the issue and obtain a comprehensive explanation, it is required to monitor additional factors that have been identified as influencing hydrologic processes, such as land use, climate, and soil type. The future of the current research is to assess the drainage basin health effect, implementation of rejuvenation mechanisms, and large-scale (nano-watershed) evaluation.

Supplementary Materials

The following supporting information can be downloaded at:, Figure S1. Rivers and streams flowing in Aseer; Figure S2. Flowchart for drainage basin extraction; Figure S3. Lineaments lengths (m) of five basins in Aseer; Figure S4. Watershed vs. area and stream order; Figure S5. Basin area vs. LuT; Table S1. Morphometric analysis and the weight of the basins for groundwater recharge, weight (1–3): 1 low permeable zone, 2 medium permeable zone, and 3 high permeable zone, Basins No. 1: Ishran; 2: Baysh; 3: Itwad; 4: Tabab; and 5: Bayd; Table S2. Aquifer potentiality level based on morphometric parameters; and Table S3. Results of reflectance from statistical outputs.

Author Contributions

Conceptualization, M.E. and M.Y.A.K.; methodology, M.E.; software, M.E.; validation, M.E.; writing—original draft preparation, M.Y.A.K. and M.E.; writing—review and editing, M.Y.A.K., A.M.S. and F.T. All authors have read and agreed to the published version of the manuscript.


This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University Jeddah, under grant no. G: 307-145-1443. The authors, therefore, acknowledge with thanks DSR for technical and financial support.

Data Availability Statement

Not applicable.


This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University Jeddah, under grant no. G: 307-145-1443. The authors, therefore, acknowledge with thanks DSR for technical and financial support.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Adelodun, B.; Ajibade, F.O.; Ighalo, J.O.; Odey, G.; Ibrahim, R.G.; Kareem, K.Y.; Bakare, H.O.; Tiamiyu, A.O.; Ajibade, T.F.; Abdulkadir, T.S.; et al. Assessment of socioeconomic inequality based on virus-contaminated water usage in developing countries: A review. Environ. Res. 2021, 192, 110309. [Google Scholar] [CrossRef]
  2. Pandey, B.; Pathak, J.; Singh, P.; Kumar, R.; Kumar, A.; Kaushik, S.; Thakur, T.K. Microplastics in the Ecosystem: An Overview on Detection, Removal, Toxicity Assessment, and Control Release. Water 2022, 15, 51. [Google Scholar] [CrossRef]
  3. Agbasi, J.C.; Chukwu, C.N.; Nweke, N.D.; Uwajingba, H.C.; Khan MY, A.; Egbueri, J.C. Water pollution indexing and health risk assessment due to PTE ingestion and dermal absorption for nine human populations in Southeast Nigeria. Groundw. Sustain. Dev. 2023, 21, 100921. [Google Scholar] [CrossRef]
  4. Khan MY, A.; ElKashouty, M.; Abdellattif, A.; Egbueri, J.C.; Taha, A.I.; Al Deep, M.; Shaaban, F. Influence of natural and anthropogenic factors on the hydrogeology and hydrogeochemistry of Wadi Itwad Aquifer, Saudi Arabia: Assessment using multivariate statistics and PMWIN simulation. Ecol. Indic. 2023, 151, 110287. [Google Scholar] [CrossRef]
  5. Khan MY, A.; ElKashouty, M.; Khan, N.; Subyani, A.M.; Tian, F. Spatio-temporal evaluation of trace element contamination using multivariate statistical techniques and health risk assessment in groundwater, Khulais, Saudi Arabia. Appl. Water Sci. 2023, 13, 123. [Google Scholar] [CrossRef]
  6. Khan MY, A.; ElKashouty, M.; Zaidi, F.K.; Egbueri, J.C. Mapping Aquifer Recharge Potential Zones (ARPZ) Using Integrated Geospatial and Analytic Hierarchy Process (AHP) in an Arid Region of Saudi Arabia. Remote Sens. 2023, 15, 2567. [Google Scholar] [CrossRef]
  7. Food and Agriculture Organization of the United Nation (FAO). Saudi Arabia irrigation in the Middle East regions in figure. Aquatat Survey 2008. In Land FAO and Water Division Report; Freken, K., Ed.; FAO: Rome, Italy, 2009; Volume 34, pp. 325–337. [Google Scholar]
  8. Khan MY, A.; El Kashouty, M.; Gusti, W.; Kumar, A.; Subyani, A.M.; Alshehri, A. Geo-temporal signatures of physicochemical and heavy metals pollution in Groundwater of Khulais region—Makkah Province, Saudi Arabia. Front. Environ. Sci. 2022, 9, 699. [Google Scholar] [CrossRef]
  9. Ouda, O.K.M.; Shawesh, A.; Al-Olabi, T.; Younes, F.; Al-Waked, R. Review of domestic water conservation practices in Saudi Arabia. Appl. Water Sci. 2013, 3, 689–699. [Google Scholar] [CrossRef] [Green Version]
  10. Okada, K.; Ishii, M. Mineral and lithological mapping using thermal infrared remotely sensed data from ASTER simulator. In Proceedings of the International Geosciences and Remote Sensing Symposium Better Understanding of Earth Environment, Tokyo, Japan, 18–21 August 1993; Volume 93, pp. 126–128. [Google Scholar]
  11. Bedell, R.L. Geological mapping with ASTER satellite: New global satellite data that is a significant leap in remote sensing geologic and alteration mapping. Spec. Publ. Geo. Soc. Nev. 2001, 33, 329–334. [Google Scholar]
  12. Vincent, P. Saudi Arabia: An Environmental Overview; Taylor and Francis: London, UK, 2008. [Google Scholar] [CrossRef]
  13. Khan MY, A.; ElKashouty, M.; Subyani, A.M.; Tian, F.; Gusti, W. GIS and RS intelligence in delineating the groundwater potential zones in Arid Regions: A case study of southern Aseer, southwestern Saudi Arabia. Appl. Water Sci. 2022, 12, 3. [Google Scholar] [CrossRef]
  14. Crósta, A.P.; Filho, C.R.d.S. Searching for gold with ASTER. Earth Obs. Mag. 2003, 12, 38–41. [Google Scholar]
  15. Khan MY, A.; ElKashouty, M.; Tian, F. Mapping Groundwater Potential Zones Using Analytical Hierarchical Process and Multicriteria Evaluation in the Central Eastern Desert, Egypt. Water 2022, 14, 1041. [Google Scholar] [CrossRef]
  16. Dimple, D.; Rajput, J.; Al-Ansari, N.; Elbeltagi, A.; Zerouali, B.; Santos CA, G. Determining the Hydrological Behaviour of Catchment Based on Quantitative Morphometric Analysis in the Hard Rock Area of Nand Samand Catchment, Rajasthan, India. Hydrology 2022, 9, 31. [Google Scholar] [CrossRef]
  17. Yangchan, J.; Jain, A.K.; Tiwari, A.K.; Sood, A. Morphometric Analysis of Drainage Basin through GIS: A Case study of Sukhna Lake Watershed in Lower Shiwalik, India. Int. J. Sci. Eng. Res. 2015, 6, 1015–1023. [Google Scholar]
  18. Strahler, A.N. Quantitative Geomorphology of drainage basins and channel networks. In Handbook of Applied Hydrology; Chow, V.T., Ed.; McGraw-Hill: New York, NY, USA, 1964; pp. 439–476. [Google Scholar]
  19. Horton, R.E. Erosional Development of Streams and Their Drainage Basins. hydrophysical approach to quantitative morphology. Bull. Geol. Soc. Am. 1945, 56, 275–370. [Google Scholar] [CrossRef] [Green Version]
  20. Prakash, K.; Rawat, D.; Singh, S. Morphometric analysis using SRTM and GIS in synergy with depiction: A case study of the Karmanasa River basin, North central India. Appl. Water Sci. 2019, 9, 13. [Google Scholar] [CrossRef] [Green Version]
  21. Markose, V.J.; Dinesh, A.; Jayappa, K. Quantitative analysis of morphometric parameters of Kali River basin, southern India, using bearing azimuth and drainage (bAd) calculator and GIS. Environ. Earth Sci. 2014, 72, 2887–2903. [Google Scholar] [CrossRef]
  22. Benukantha, D.; Nagaraju, M.S.S.; Sahu, N.; Nasre, R.A.; Mohekar, D.S.; Srivastava, R.; Singh, S.K. Morphometric Analysis for Planning Soil and Water Conservation Measures Using Geospatial Technique. Int. J. Curr. Microbiol. App. Sci. 2019, 8, 2719–2728. [Google Scholar] [CrossRef]
  23. John Wilson, J.S.; Chandrasekar, N.; Magesh, N.S. Morphometric analysis of major sub-watersheds in Aiyar and Karai Pottanar Basin, central Tamil Nadu, India usingremote sensing and GIS techniques. Bonfring. Int. J. Ind. Eng. Manag. Sci. 2012, 2, 8–15. [Google Scholar]
  24. Prakash, K.; Singh, S.; Shukla, U.K. Morphometric changes of the Varuna river basin, Varanasi district, Uttar Pradesh. J. Geom. 2016, 10, 48–54. [Google Scholar]
  25. Perucca, P.L.; Angilieri, E.Y. Morphometric characterization of Del Molle basin applied to the evaluation of flash floods hazard, Iglesia Department, San Juan, Argentina. Quatern. Int. 2011, 233, 81–86. [Google Scholar] [CrossRef]
  26. Soni, S. Assessment of morphometric characteristics of Chakra watershed in Madhya Pradesh India using the geospatial technique. Appl. Water Sci. 2016, 7, 2089–2102. [Google Scholar] [CrossRef] [Green Version]
  27. Kaushik, P.; Ghosh, P. Morphometric analysis of Mej subbasin, Rajasthan, India, using remote sensing and GIS applications. Int. J. Create. Res. Thoughts 2018, 6, 1379–1392. [Google Scholar]
  28. Puno, G.R.; Puno, R.C.C. Watershed conservation prioritization using geomorphometric and land use-land cover parameters. Glob. J. Environ. Sci. Manag. 2019, 5, 279–294. [Google Scholar] [CrossRef]
  29. Bharath, A.; Kumar, K.K.; Maddamsetty, R.; Manjunatha, M.; Tangadagi, R.B.; Preethi, S. Drainage morphometry based sub-watershed prioritization of Kalinadi basin using geospatial technology. Environ. Chall. 2021, 5, 100277. [Google Scholar] [CrossRef]
  30. Benzougagh, B.; Meshram, S.G.; Dridri, A.; Boudad, L.; Baamar, B.; Sadkaoui, D.; Khedher, K.M. Identification of critical watersheds at risk of soil erosion using morphometric and geographic information system analysis. Appl. Water Sci. 2022, 12, 8. [Google Scholar] [CrossRef]
  31. Abboud, I.A.; Nofal, R.A. Morphometric analysis of wadi Khumal basin, western coast of Saudi Arabia, using remote sensing and GIS techniques. J. Afr. Earth Sci. 2017, 126, 58–74. [Google Scholar] [CrossRef]
  32. Bajabaa, S.; Masoud, M.; Al-Amri, N. Flash flood hazard mapping based on quantitative hydrology, geomorphology and GIS techniques (case study of wadi Al Lith, Saudi Arabia). Arab. J. Geosci. 2014, 7, 2469–2481. [Google Scholar] [CrossRef]
  33. Mahmoud, S.H.; Alazba, A.A. Geomorphological and geophysical information system analysis of major rainwater-harvesting basins in Al-Baha region, Saudi Arabia. Arab. J. Geosci. 2015, 8, 9959–9971. [Google Scholar] [CrossRef]
  34. Singh, S.; Singh, A.K.; Kumar, P.; Jaiswal, M.K. Morphotectonic analysis of the Bihar River, Madhya Pradesh, India. Proc. Indian Natl. Sci. Acad. 2021, 87, 163–174. [Google Scholar] [CrossRef]
  35. Bashar, B. Morphometric Parameters and Geospatial Analysis for Flash Flood Susceptibility Assessment: A Case Study of Jeddah City along the Red Sea Coast, Saudi Arabia. Water 2023, 15, 870. [Google Scholar] [CrossRef]
  36. Subyani, A.M. Hydrogeological and Hydrochemical Features of Wadi Adam, Makkah Al-Mukarramah Area. Earth Sci. 2005, 16, 1012–8832. [Google Scholar] [CrossRef]
  37. Khan, M.Y.A.; ElKashouty, M.; Bob, M. Impact of rapid urbanization and tourism on the groundwater quality in Al Madinah city, Saudi Arabia: A monitoring and modeling approach. Arab. J. Geosci. 2020, 13, 922. [Google Scholar] [CrossRef]
  38. Biswas, M.R.; Chakraborty, S. Watershed prioritization based on geo-morphometry and land use parameters–an approach to watershed development using remote sensing and GIS, Neora Watershed, Darjeeling and Jalpaiguri Districts, West Bengal, India. J. Appl. Geol. Geophys. 2016, 4, 36–49. [Google Scholar]
  39. Benzougagh, B.; Dridri, A.; Boudad, L.; Kodad, O.; Sdkaoui, D.; Bouikbane, H. Evaluation of natural hazard of Inaouene watershed river in northeast of Morocco: Application of morphometric and geographic information system approaches. Int. J. Innov. Appl. Stud. 2017, 19, 85–97. [Google Scholar]
  40. Meshram, S.G.; Meshram, C.; Abul Hasan, M.; Khan, M.A.; Islam, S. Morphometric deterministic model for prediction of sediment yield index for selected watersheds in upper Narmada Basin. Appl. Water Sci. 2022, 12, 153. [Google Scholar] [CrossRef]
  41. Al-Ibrahim, A.A. Excessive use of groundwater resources in Saudi Arabia: Impacts and policy options. Ambio 1991, 20, 34–37. [Google Scholar]
  42. MWE. Supporting Documents for King Hassan II Great Water Prize. 2012. Available online: (accessed on 16 November 2022).
  43. World Bank. A Water Sector Assessment Report on Countries of the Cooperation Council of the Arab State of the Gulf; Report No. 32539-MNA; World Bank: Washington, DC, USA, 2005. [Google Scholar]
  44. Ouda, O. Water demand versus supply in Saudi Arabia: Current and future challenges. Int. J. Water Resour. Dev. 2014, 30, 335–344. [Google Scholar] [CrossRef]
  45. Raj, N.J.; Prabhakaran, A.; Muthukrishnan, A. Quantitative stream network analysis for assessing form and hydrological processes of the watersheds of Kolli hills, Tamil Nadu, India. Arab. J. Geosci. 2021, 14, 2646. [Google Scholar] [CrossRef]
  46. Odiji, C.A.; Aderoju, O.M.; Eta, J.P.; Shehu, I.; Mai-Bukar, A.; Onuoha, H. Morphometric analysis and prioritization of upper Benue River watershed, Northern Nigeria. Appl. Water Sci. 2021, 11, 41. [Google Scholar] [CrossRef]
  47. Kaushik, P.; Ghosh, P. 3D DEM delineation of Chambal river basin from SRTM data using remote sensing and GIS technology. Int. J. Remote Sens. Geosci. 2015, 4, 1–6. [Google Scholar]
  48. Patel, A.; Katiyar, K.S.; Prasad, V. Performances evaluation of different open source DEM using diferential global positioning system (DGPS). Egypt. J. Remote Sens. Space Sci. 2016, 19, 7–16. [Google Scholar]
  49. Asfaw, D.; Workineh, G. Quantitative analysis of morphometry on Ribb and Gumara watersheds: Implications for soil and water conservation. Int. Soil Water Conserv. Res. 2021, 7, 150–157. [Google Scholar] [CrossRef]
  50. Torrefranca, T.; Otadoyc, R.E. GIS-based watershed characterization and morphometric analysis in Bohol Watersheds, Philippines. Geol. Ecol. Landsc. 2022. [Google Scholar] [CrossRef]
  51. Masoud, M.H.; Basahi, J.M.; Rajmohan, N. Impact of flash flood recharge on groundwater quality and its suitability in the Wadi Baysh Basin, Western Saudi Arabia: An integrated approach. Environ. Earth Sci. 2018, 77, 395. [Google Scholar] [CrossRef]
  52. Karim, A.A. Assessment of the Expected Flood Hazards of the Jizan-Abha Highway, Kingdom of SaudiArabia by Integrating Spatial-Based Hydrologic and Hydrodynamic Modeling. Glob. J. Res. Eng. 2019, 19, 27–35. [Google Scholar]
  53. Strahler, A.N. Quantitative Analysis of Watershed Geomorphology. Transactions. Am. Geophys. Union 1957, 38, 913–920. [Google Scholar] [CrossRef] [Green Version]
  54. Gajbhiye, S.; Mishra, S.K.; Pandey, A. Prioritizing erosion-prone area through morphometric analysis: An RS and GIS perspective. Appl. Water Sci. 2015, 4, 51–61. [Google Scholar] [CrossRef] [Green Version]
  55. Gutema, D.; Kassa, T.; Sifan, A.K. Morphometric Analysis to Identify Erosion Prone Areas on The Upper Blue Nile Using Gis (Case Study of Didessa and Jema Sub-Basin, Ethiopia). Int. Res. J. Eng. Technol. 2017, 4, 1773–1784. [Google Scholar]
  56. Subba Rao, N. A numerical scheme for groundwater development in a watershed basin of basement terrain: A case study from India. Hydrogeol. J. 2009, 17, 379–396. [Google Scholar] [CrossRef]
  57. Ghosh, M.; Gope, D. Hydro-morphometric characterization and prioritization of sub-watersheds for land and water resource management using fuzzy analytical hierarchical process (FAHP): A case study of upper Rihand watershed of Chhattisgarh State, India. Appl. Water Sci. 2021, 11, 17. [Google Scholar] [CrossRef]
  58. Biswas, S.S. Analysis of GIS Based Morphometric Parameters and Hydrological Changes in Parbati River Basin, Himachal Pradesh, India. J. Geogr. Nat. Disast. 2016, 6, 175. [Google Scholar] [CrossRef]
  59. Avijit, M. The significance of morphometric analysis to understand the hydrological and morphological characteristics in two different morpho climatic settings. Appl. Water Sci. 2020, 10, 33. [Google Scholar] [CrossRef] [Green Version]
  60. Dubey, S.K.; Sharma, D.; Mundetia, N. Morphometric Analysis of the Banas River Basin Using Geographical Information System, Rajasthan, India. Hydrology 2015, 3, 47–57. [Google Scholar] [CrossRef] [Green Version]
  61. Nageswara, R.K.; Swarna, L.P.; Arun, K.P.; Hari, K.M. Morphometric Analysis of Gostani River Basin in Andhara Pradesh State, India Using Spatial Information Technology. Int. J. Geomat. Geosci. 2010, 1, 179–187. [Google Scholar]
  62. López-Pérez, A.; Fernández-Reynoso, D.S. Watershed prioritization using morphometric analysis and vegetation index: A case study of Huehuetan river sub-basin, Mexico. Arab. J. Geosci. 2021, 14, 1852. [Google Scholar] [CrossRef]
  63. Magesh, N.; Jitheshlal, K.; Chandrasekar, N.; Jini, K. Geographical information system based morphometric analysis of Bharathapuzha river basin, Kerala, India. Appl. Water Sci. 2013, 3, 467–477. [Google Scholar] [CrossRef] [Green Version]
  64. Sreedevi, P.D.; Subrahmanyam, K.; Ahmed, S. The significance of morphometric analysis for obtaining groundwater potential zones in a structurally controlled terrain. Environ. Geol. 2004, 47, 412–420. [Google Scholar] [CrossRef]
  65. Yadav, S.K.; Singh, S.K.; Gupta, M.; Srivastava, P.K. Morphometric analysis of Upper Tons basin from Northern Foreland of Peninsular India using CARTOSAT satellite and GIS. Geocarto Int. 2014, 29, 895–914. [Google Scholar] [CrossRef]
  66. Nag, S.K.; Chakraborty, S. Influence of rock type and structure development of drainage network in hard rock terrain. J. Indian Soc. Remote Sens. 2003, 31, 25–35. [Google Scholar] [CrossRef]
  67. Rawat, K.S.; Mishra, A.K.; Tripathi, V.K. Hydro-morphometrical analyses of sub-himalyan region in relation to small hydro-electric power. Arab. J. Geosci. 2012, 6, 2889–2899. [Google Scholar] [CrossRef]
  68. Ajaykumar, K.K.; Tasadoq, H.J.; Sanjay, S.K.; Bhavana, N.U.; Rabindranath, N.S. Identification of erosion-prone areas using modified morphometric prioritization method and sediment production rate: A remote sensing and GIS approach. Geomat. Nat. Hazards Risk 2019, 10, 986–1006. [Google Scholar] [CrossRef] [Green Version]
  69. Chow, V.T. Handbook of Applied Hydrology; McGraw-Hill: New York, NY, USA, 1964. [Google Scholar]
  70. Kanhaiya, S.; Singh, B.P.; Singh, S.; Mittal, P.; Srivastava, V.K. Morphometric analysis, bed-load sediments and weathering intensity in the Khurar River Basin, Central India. Geol. J. 2019, 54, 466–481. [Google Scholar] [CrossRef] [Green Version]
  71. Harinath, V.; Raghu, V. Morphometric Analysis using Arc GIS Techniques A Case Study of Dharurvagu, South Eastern Part of Kurnool District, Andhra Pradesh, India. Int. J. Sci. Res. 2013, 2, 182–187. [Google Scholar]
  72. Pande, C.B.; Moharir, K.N.; Khadri SF, R. Watershed planning and development based on morphometric analysis and remote sensing and GIS techniques: A case study of semi-arid watershed in Maharashtra, India. In Groundwater Resources Development and Planning in the Semi-Arid Region; Springer: Cham, Switzerland, 2021; pp. 199–220. [Google Scholar]
  73. Gravelius, H. Flusskunde. Goschen Verlagshan dlung Berlin. In Morphometry of Drainage Basins; Zavoianu, I., Ed.; Elsevier: Amsterdam, The Netherlands, 1941. [Google Scholar]
  74. Hack, J.T. Studies in Longitudinal Stream Profiles in Virginia and Maryland; US Geological Survey Professional Paper 249-B; US Government Printing Office: Washington, DC, USA, 1957; pp. 45–97.
  75. Millar, J.P. High mountaineous streams Effect of geology on channel characteristics and bed material, Mem. New Maxico Bur. Mines Mineral. Resource 1958, 4, 1–51. [Google Scholar]
  76. Miller, O.M.; Summerson, C.H. Slope zone map. Geogr. Rev. 1960, 50, 194–202. [Google Scholar] [CrossRef]
  77. Chorley, R.J. Introduction to Physical Hydrology; Methuen and Co. Ltd.: Suffolk, UK, 1969; p. 211. [Google Scholar]
  78. Prasad, G. Environmental Geomorphology; Discovery Publishing House: New Delhi, India, 2008. [Google Scholar]
  79. Jasmin, I.; Mallikarjuna, P. Morphometric analysis of Araniar river basin using remote sensing and geographical information system in the assessment of groundwater potential. Arab. J. Geosci. 2013, 6, 3683–3692. [Google Scholar] [CrossRef]
  80. Smith, K.G. Standards for grading textures of erosional topography. Am. J. Sci. 1950, 248, 655–668. [Google Scholar] [CrossRef]
  81. Albaroot, M.; Nabil MAl Hamdi, S.A.; Mohammed, A.; Saleh, A.G. Quantification of Morphometric Analysis using Remote Sensing and GIS Techniques in the Qa’ Jahran Basin, Thamar Province, Yemen. Int. J. New Technol. Res. 2018, 4, 12–22. [Google Scholar]
  82. Schumm, S.A. Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geol. Soc. Am. Bull. 1956, 67, 597–646. [Google Scholar] [CrossRef]
  83. Singh, P.; Gupta, A.; Singh, M. Hydrological inferences from the watershed analysis of water resource management using remote sensing and techniques. Egypt J. Remote Sens. Space Sci. 2014, 17, 111–121. [Google Scholar] [CrossRef] [Green Version]
  84. Mohammed, O.A.; Sayl, K.N. Determination of Groundwater potential zone in arid and semi-arid regions: A review. In Proceedings of the 2020 13th International Conference on Developments in eSystems Engineering (DeSE), Virtual Conference, 14–17 December 2020; IEEE: New York, NY, USA, 2020; pp. 76–81. [Google Scholar]
  85. Mohammed, S.S.; Sayl, K.N.; Kamel, A.H. Ground water recharge mapping in Iraqi Western desert. Int. J. Des. Nat. Ecodyn. 2022, 17, 913–920. [Google Scholar] [CrossRef]
  86. Mohammed, O.A.; Sayl, K.N. A GIS-based multicriteria decision for groundwater potential zone in the west desert of Iraq. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2021; Volume 856, p. 012049. [Google Scholar]
  87. Lecia Geosystems & GIS Mapping Division. Erdas Field Guide, 7th ed; Erdas LLC.: Atlanta, GA, USA, 2003; 698p. [Google Scholar]
  88. Lillesand, T.M.; Kiefer, R.W. Remote Sensing and Image Interpretation, 4th ed.; John Wiley & Sons, Inc.: New York, NY, USA, 2000; 724p. [Google Scholar]
  89. Blank, R.; Johnson, P.; Gettings, M.; Simmons, G. Explanatory Notes to the Geologic Map of the Jazan Quadrangle; Deputy Minister for Mineral Resources: Jiddah, Saudi Arabia, 1985; p. 24.
  90. Mogren, S.; Batayneh, A.; Elawadi, E.; Al-Bassam, A.; Ibrahim, E.; Qaisy, S. Aquifer boundaries explored by geoelectrical measurements in the Red Sea coastal plain of Jazan area, South west Saudi Arabia. Int. J. Phys. Sci. 2011, 6, 3768–3776. [Google Scholar]
  91. Basahel, A.; Bahafzalla, A.; Mansour, H.; Omara, S. Primary structures and depositional environ of the Haddat Ash Sham sedimentary sequence, northwest of Jeddah, Saudi Arabia Arab Gulf. J. Sci. Res. 1983, 1, 143–155. [Google Scholar]
  92. Hussain, M.; Ibrahim, K. Electric resistivity, geochemical and hydrogeological of Wadi deposits, Western Saudi Arabia. J. King Abdel Aziz Univ. Earth Sci. 1997, 9, 55–72. [Google Scholar]
  93. Drury, S.A. Image Interpretation in Geology, 2nd ed.; Chapman & Hall: New York, NY, USA, 1993; xii 271–283p. [Google Scholar]
  94. Lillesand, T.M.; Kiefer, R.W. Remote Sensing and Image Interpretation, 3rd ed.; John Wiley & Sons: Hoboken, NJ, USA, 1994; pp. xvi + 750. [Google Scholar] [CrossRef]
  95. Gupta, R.P. Remote Sensing Geology; Springer: Berlin/Heidelberg, Germany, 1991; pp. xvi + 356. [Google Scholar] [CrossRef]
Figure 1. Delineation of drainage basins in southern Aseer and northwest Jazan.
Figure 1. Delineation of drainage basins in southern Aseer and northwest Jazan.
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Figure 2. Average monthly temperature and rainfall of the study area for the period of 2020–2021.
Figure 2. Average monthly temperature and rainfall of the study area for the period of 2020–2021.
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Figure 3. Current and predicted (a) water demand in different sectors, (b) population, (c) water supply capacity, and (d) gap between water demand and supply [44].
Figure 3. Current and predicted (a) water demand in different sectors, (b) population, (c) water supply capacity, and (d) gap between water demand and supply [44].
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Figure 4. Stream order (a), lineaments (b), and rose diagram (c) in Ishran basin.
Figure 4. Stream order (a), lineaments (b), and rose diagram (c) in Ishran basin.
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Figure 5. Stream order (a), lineaments (b), and rose diagram (c) in Baysh basin.
Figure 5. Stream order (a), lineaments (b), and rose diagram (c) in Baysh basin.
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Figure 6. Stream order (a), lineaments (b), and rose diagram (c) in Itwad basin.
Figure 6. Stream order (a), lineaments (b), and rose diagram (c) in Itwad basin.
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Figure 7. Stream order (a), lineaments (b), and rose diagram (c) in Tabab basin.
Figure 7. Stream order (a), lineaments (b), and rose diagram (c) in Tabab basin.
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Figure 8. Stream order (a), lineaments (b), and rose diagram (c) in Bayd basin.
Figure 8. Stream order (a), lineaments (b), and rose diagram (c) in Bayd basin.
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Figure 9. The relationship between ∑ Nu–u (a) and ∑ Lu–u (b) for five watersheds.
Figure 9. The relationship between ∑ Nu–u (a) and ∑ Lu–u (b) for five watersheds.
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Figure 10. The relationship between Log Nu and u for five watersheds.
Figure 10. The relationship between Log Nu and u for five watersheds.
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Figure 11. The relationship between log Lu and u for five watersheds.
Figure 11. The relationship between log Lu and u for five watersheds.
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Figure 12. (a) Lineaments and (b) drainage density map.
Figure 12. (a) Lineaments and (b) drainage density map.
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Figure 13. Supervised image classification of geology.
Figure 13. Supervised image classification of geology.
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Figure 14. Spatial distribution of the rock types in (a) Ishran and (b) Baysh basins.
Figure 14. Spatial distribution of the rock types in (a) Ishran and (b) Baysh basins.
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Figure 15. Spatial distribution of the rock types in (a) Itwad and (b) Tabab basins.
Figure 15. Spatial distribution of the rock types in (a) Itwad and (b) Tabab basins.
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Figure 16. Spatial distribution of the rock types in Bayd basin.
Figure 16. Spatial distribution of the rock types in Bayd basin.
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Figure 17. Composite, (a) 1–7 bands and (b) 1–11 bands.
Figure 17. Composite, (a) 1–7 bands and (b) 1–11 bands.
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Figure 18. NDVI of the study area.
Figure 18. NDVI of the study area.
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Figure 19. Iron-rich bearing rocks.
Figure 19. Iron-rich bearing rocks.
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Table 1. Stream number, stream length, mean stream, and stream length ratio of five basins.
Table 1. Stream number, stream length, mean stream, and stream length ratio of five basins.
Number of Streams (NuT) of Different Stream Order (u) Total Stream Lengths LuT (km)
1-Wadi Ishran998221461111127811707.7252.392.
2-Wadi Baysh81517733912103721393.5674.4457.3130.3148.29.52823.2
3-Wadi Itwad381821841 4863623.2310.543.360.677 1114.6
4-Wadi Tabab1352841 1684214.989.858.840.6 404.1
5-Wadi Bayd15534741 2015276138.496.331.527.3 293.5
∑ Nu24845421082943Total Nu = 3170∑Lu, km3939.31465.4747.9272.6252.910.7Total Lu = 6688.8
Mean stream length, Lum (km)Stream length ratio, Lur
1-Wadi Ishran1.711.1420.870.41.20.671.760.440.4631.26
2-Wadi Baysh1.713.8113.8614.481484.752.233.641.0410.240.033.44
3-Wadi Itwad1.643.792.4115.1577 2.310.646.35.08 3.6
4-Wadi Tabab1.593.2114.740.6 2.014.582.76 3.1
5-Wadi Bayd1.84.0713.767.8827.3 2.33.380.573.47 2.4
Note: Nu—number of segments of order “u”; Lum = Lu/Nu; Lu = total stream length of order ‘u’ in km; and Lur = Lum/Lum − 1 [18].
Table 2. Bifurcation ratio, mean bifurcation ratio, and Rho coefficient.
Table 2. Bifurcation ratio, mean bifurcation ratio, and Rho coefficient.
Bifurcation Ratio Rb
(Nu/Nu + 1)
Mean Rb Rho Coefficient
1-Wadi Ishran4.524.804.1811.
2-Wadi Baysh4.615.363.679.000.504.600.75
3-Wadi Itwad4.654.564.504.00 4.400.82
4-Wadi Tabab4.827.004.00 5.300.58
5-Wadi Bayd4.564.861.754.00 3.800.63
Note: Rbm = ∑Rb/no. of stream orders; and Rho coefficient (ρ) = Lur/Rb.
Table 3. Watershed geometry parameters.
Table 3. Watershed geometry parameters.
Basin No.Area “A” (Km2)Perimeter “P” (Km)Circularity Ratio (Rc)Compactness Coefficient (Cc)Length–Area Relation (Lar)
1-Wadi Ishran5022.9482.150.271.9232.65
2-Wadi Baysh4109.77463.550.242206.26
3-Wadi Itwad1913.26379.360.172.4130.37
4-Wadi Tabab638.52203.580.192.367.49
5-Wadi Bayd777.95237.420.172.475.98
Note: Rc = (4πA)/(P2); Cc = P 2 ( A π ) ; and Lar = 1.4 × A0.6.
Table 4. Drainage parameters of the study area.
Table 4. Drainage parameters of the study area.
Basin No.Drainage Density “Dd”; Dd = ∑LuT/A (km/km2)Length of Overland Flow “Lg”; Lg = l/2 Dd (km2/km)Stream Frequency “F”; F = ∑NuT/A (km2) Drainage Intensity “D”; Di = (F/Dd) Infiltration No. “If”; If = F × Dd Drainage Texture “T”; T = (Dd × F) Constant of Channel Maintenance “C”; C = l/Dd (km2/km)
1-Wadi Ishran0.410.210.250.620.10.12.43
2-Wadi Baysh0.690.340.250.370.170.171.46
3-Wadi Itwad0.580.290.250.440.150.151.72
4-Wadi Tabab0.630.320.260.420.170.171.58
5-Wadi Bayd0.380.190.260.680.10.12.65
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Khan, M.Y.A.; ElKashouty, M.; Subyani, A.M.; Tian, F. Morphometric Determination and Digital Geological Mapping by RS and GIS Techniques in Aseer–Jazan Contact, Southwest Saudi Arabia. Water 2023, 15, 2438.

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Khan MYA, ElKashouty M, Subyani AM, Tian F. Morphometric Determination and Digital Geological Mapping by RS and GIS Techniques in Aseer–Jazan Contact, Southwest Saudi Arabia. Water. 2023; 15(13):2438.

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Khan, Mohd Yawar Ali, Mohamed ElKashouty, Ali Mohammad Subyani, and Fuqiang Tian. 2023. "Morphometric Determination and Digital Geological Mapping by RS and GIS Techniques in Aseer–Jazan Contact, Southwest Saudi Arabia" Water 15, no. 13: 2438.

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