[go: up one dir, main page]
Next Article in Journal
Perceived Greenwashing and Its Impact on the Green Image of Brands
Previous Article in Journal
Coupling and Coordination Analysis of High-Quality Agricultural Development and Rural Revitalization: Spatio-Temporal Evolution, Spatial Disparities, and Convergence
Previous Article in Special Issue
Challenges of Using a Geographic Information System (GIS) in Managing Flash Floods in Shah Alam, Malaysia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 20
10.3390/su16209008
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Morphometric and Soil Erosion Characterization Based on Geospatial Analysis and Drainage Basin Prioritization of the Rabigh Area Along the Eastern Red Sea Coastal Plain, Saudi Arabia

by
Bashar Bashir
* and
Abdullah Alsalman
Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(20), 9008; https://doi.org/10.3390/su16209008
Submission received: 4 September 2024 / Revised: 11 October 2024 / Accepted: 16 October 2024 / Published: 17 October 2024
(This article belongs to the Special Issue Sustainable Resilience Planning for Natural Hazard Events)
Browse Figures
Figure 1
<p>(<b>a</b>) Political map of the Arabian Peninsula showing location of the Kingdom of Saudi Arabia, coastal regions (or zones) and location of the Makkah Province, and (<b>b</b>) a Google-based Landsat map showing the province of Makkah with the area of Rabigh in the north.</p> "> Figure 2
<p>Geological map of the Rabigh area. This map is modified after [<a href="#B42-sustainability-16-09008" class="html-bibr">42</a>].</p> "> Figure 3
<p>(<b>a</b>) Shaded relief map showing elevations and (<b>b</b>) slope map indicating slope levels in the Rabigh area.</p> "> Figure 4
<p>Methods for applying morphometric analysis.</p> "> Figure 5
<p>Rabigh area basins from 1 to 12 and their drainage systems.</p> "> Figure 6
<p>Priority ranking maps of the linear parameters applied in the present study including (<b>a</b>) drainage density; Dd), (<b>b</b>) drainage texture; Td), (<b>c</b>) bifurcation ratio; Rb), (<b>d</b>) stream frequency; F), and (<b>e</b>) overland flow length; Lg).</p> "> Figure 7
<p>Priority ranking maps of the relief parameters applied in the present study including (<b>a</b>) basin relief; H), (<b>b</b>) basin relative ratio; Rr), (<b>c</b>) relative relief ratio; Rrr), (<b>d</b>) ruggedness number; Nr), and (<b>e</b>) Melton ruggedness number; Nmr).</p> "> Figure 8
<p>Priority ranks and compound values map of the Rabigh area.</p> "> Figure 9
<p>Relative priority classes based on the compound values technique.</p> ">
Versions Notes

Abstract

:
Soil erosion is one of the most significant problems in global environmental development. Assigning, analyzing, and quantifying the main physical characteristics of drainage basins are powerful keys in identifying regions where there is a higher risk of soil erosion and where prompt mitigation actions are needed. Drainage basins and their drainage systems are ideally defined using the analysis morphometric parameters and their quantitative description. The present study aims to analyze morphometric parameters to prioritize drainage basins that are prone to erosion. Topographic sheets and remotely sensed digital elevation model (DEM) datasets have been prepared and analyzed using geospatial techniques to delineate drainage basins of different sizes and extract different ordered drainage systems. Based on the analysis of morphometric parameters, the Rabigh area was divided into 12 drainage basins, which significantly contribute to determining soil erosion priority levels. The present study selected and applied the most effective morphometric parameters to rank and prioritize the drainage basins of the study area after considering the crucial quantitative characteristics, such as linear, relief, and areal aspects. For each single basin, the compound factor was assigned from several morphometric parameters and applied to rank the Rabigh area. The results confirm that Basins 1, 4, 11, and 12 require a high level of soil erosion priority over an area of 2107 km2; however, Basins 3, 8, 9, and 10 have little degradation and a low level of soil erosion priority. Therefore, in the regions where high soil erosion is a factor, mitigation techniques such as terracing, filter strips, contouring, and other effective and useful structural and non-structural methods should be applied.

1. Introduction

The issue of soil erosion is becoming a major problem that has several negative repercussions, including decreased crop yields, deteriorated water quality, flooding, and land degradation due to unsuitability for agriculture [1,2]. Furthermore, it is a serious issue that might have a disastrous effect on livelihoods, the environment, and food security. It not only reduces the amount of fertile soil required for agriculture, but it also causes a decline in biodiversity, a decrease in agricultural production, and an increase in climate change vulnerability [3]. Economic growth and hydropower structures are significantly impacted by increasing sediment yield in reservoirs, which may reduce the capacity to generate electricity. Vital soil nutrients are reduced, and malnutrition is increased by limiting people’s access to reasonably priced and nutrient-dense food. In most developing nations, many human activities and natural disasters such as droughts and floods, and a lack of economic and technical development, etc., are often the cause of soil erosion [4].
Due to the importance of this issue, many workers have focused on using both effective general and specific methods to deal with the effects of soil erosion, considering the unique causes and effects in a particular place. Therefore, to assign and prioritize prone areas in a watershed for mitigation measures, a range of different techniques using different kinds of data can be employed, including field surveys and expert judgment, morphometric analysis, multi-criteria decision analysis, satellite imagery, hydrological modeling, digital elevation models (DEMs), etc. [1,2,5,6,7]. Every technique has advantages and disadvantages; thus, before choosing a candidate, factors including accuracy, method output, implementation costs and expertise, data availability, and limits should all be considered. In the present study, authors emphasize that while AHP is a widely accepted method for prioritization, it generally requires extensive fieldwork and expert input, making it both costly and time-consuming, especially in regions like the Rabigh area where field trips are logistically challenging. Field surveys are integral to AHP’s multi-criteria decision-making process but are often limited due to financial and temporal constraints [2,8,9]. Gathering information and defining some features through field works for the soil erosion studies is a very important task, but it is limited because it is costly and requires a long time [10]. In contrast, our study employs geospatial techniques such as morphometric analysis using high-resolution digital elevation models (ALOS PALSAR-DEM) and GIS tools, which have been successfully and sufficiently applied in various scientific studies to identify and prioritize areas prone to soil erosion [6,7,11]. These methods offer a practical, cost-effective alternative to AHP by leveraging remote sensing data, which eliminates the need for extensive on-site surveys and allows for timely and accurate analysis of watershed prioritization. Furthermore, recent studies suggest that expert judgment based on morphometric analysis and GIS is a robust and reliable technique, particularly in areas where field surveys are challenging or expensive [6,9]. This approach has been validated in several locations with similar environmental conditions, providing a suitable alternative to AHP for our study, ensuring methodological robustness while maintaining cost and time efficiency [5,7,9,12].
Drainage basins and their drainage systems are dynamic in nature and influenced by several significant factors including topography, local lithology, climate, vegetation, soil, and tectonic signals [13]. Drainage networks are among the most sensitive elements of a landscape, and their structure and the movements of the rivers are greatly influenced by topography [14]. The goal of drainage basin management study is to enhance production while minimizing or eliminating environmental hazards through the sustainable use of existing natural resources [15]. This suggests that a drainage basin is made up of social and natural processes, resulting in distinct and dynamic landscape hierarchies. Therefore, scientists may consider studying drainage basins as an ideal strategy for management and planning of natural resources [16]. Additionally, it has been applied as a method to achieve food security and as a comprehensive development idea [17,18]. For example, if we conserve soil, water, and vegetation, a drainage basin harmonizes the use of these resources and maximizes productivity with the least environmental harm [19,20,21,22]. Therefore, protecting the environment is one of the most important goals of drainage basin management plans [23,24].
It is very valuable to characterize and describe drainage basins and their drainage systems using morphometric analysis and quantitative description. These characteristics illustrate how the drainage system has evolved and how different soil erosion processes are related to one another. Many studies on drainage basin prioritization have been carried out using morphometric (i.e., areal, linear, and shape) parameters that have been determined using remote sensing and GIS techniques. Understanding the impact of drainage morphology could be attempted by analyzing the surface and subsurface data provided by drainage patterns and drainage parameters [25,26,27,28]. Therefore, the core of this paper is to apply morphometric parameters to understand in detail the hydrologic response, including surface runoff generation, infiltration capacity, and even groundwater potential. By running and processing the morphometric analysis, other drainage basin features, such as water travel time, time to peak, and severity of erosional processes, can be predicted more accurately and insightfully [15,27,28,29]. Significantly, this analysis may help in investigating the ungauged drainage basins with little information on soil, hydrology, geology, or geomorphology [28,30].
Drainage basins and/or sub-basins could be classified and ranked due to their importance according to process objectives. The authors of [31] applied this method in their work, explaining that prioritizing drainage basins is a technique for assigning water bodies into classes according to the degree of soil erosion and the drainage areas’ different conditions. Numerous related publications have examined how analysis of morphometric factors plays an important role in the drainage basin prioritizing process. For example, the author of [32] applied modern remote sensing and geographic information systems (GIS) techniques in examining drainage basin prioritization in Guhiya Basin in India; morphometric analysis was utilized by the authors in [33] to rank the nine sub-watersheds in the Piperiya watershed in order of importance for watershed management; authors in [34] define and discuss the drainage basins most vulnerable to soil erosion using morphometric analysis and prioritization in the Manot River catchment; the Dudhani River Basins in India were ranked due to soil erosion prioritization using powerful morphometric analysis [15]; the authors in [9] identified the soil erosion-prone regions of the Dabus watershed of the Blue Nile in Ethiopia using a combined technique of GIS and metamorphic analysis; the soil erosion vulnerability of the major Oued Amter Basin in northwest Morocco was assessed using morphometric and geospatial analysis [1]; and the authors of [2] applied morphometric analysis implications to the Rietspruit Sub-Basin in South Africa to assess the soil erosion susceptibility.
To establish the relevance of the Rabigh area as a case study in our research on morphometric and soil erosion characterization, it is crucial to recognize that even regions considered to be at low risk of erosion and flooding can display localized vulnerabilities due to specific environmental and anthropogenic factors. Although the Rabigh Zone is classified as low risk in broad assessments, its unique topographical features and land use patterns can significantly influence soil erosion processes, especially under extreme weather events or changes in land management practices [27,35]. Recent studies indicate that areas historically deemed stable can experience substantial erosion and flooding risks due to climate variability and urban expansion [36,37,38,39]. Given that Saudi Arabia is currently engaged in major projects to develop its coastal areas, such as the Neom project, examining natural hazards and developing predictive and mitigation strategies is vital for the success of these initiatives [30]. The Rabigh area holds great significance for the Kingdom, underscoring the need for proactive research to address potential natural disasters like floods and earthquakes. By focusing on this region, our study aims to provide a comprehensive analysis that identifies potential erosion hotspots and enhances our understanding of how geomorphological and anthropogenic changes can affect soil stability, ultimately guiding future land-use planning and disaster mitigation strategies [37].
Although this study focuses on the Rabigh area along the Eastern Red Sea Coast of Saudi Arabia, its findings have significant implications for regions facing similar geomorphological and environmental challenges globally. The morphometric and soil erosion characterization, coupled with drainage basin prioritization using geospatial analysis, presents a methodological framework that can be adapted to other arid and semi-arid regions worldwide. These areas, such as those in North and South Africa, the Middle East, and Central Asia, are increasingly vulnerable to land degradation, flash floods, and soil erosion exacerbated by climate change and unsustainable land use [1,2,35]. The integration of morphometric parameters to assess erosion risk and prioritize basins for intervention provides a critical tool for managing watersheds in environments with limited water resources and fragile ecosystems [9,35,40]. This study’s application of geospatial tools and morphometric analysis offers valuable lessons for global watershed management and soil conservation strategies. By identifying and prioritizing sub-basins based on erosion susceptibility, the research provides a replicable model for mitigating soil erosion risks, which are increasingly common in arid coastal regions worldwide. The results from the Rabigh area highlight the potential for adopting similar techniques in other vulnerable regions, such as the East Africa, Australia, and the Mediterranean, where sustainable land and water resource management are critical [5,9]. As climate change continues to reshape hydrological patterns, the insights from this study contribute to the growing body of international research focused on enhancing resilience and sustainability in water-scarce environments [3,27,41].
Given the significance of morphometric analysis in drainage basin management initiatives, the Rabigh area basins and their drainage morphology characteristics were the focus of this study. Therefore, the aim of this study was to employ an integrated methodology to categorize and rank basins in the Rabigh area due to soil erosion rate assessment. In addition, it aimed to improve the potentiality of the basins by combining datasets to present the major features of the drainage basins. This area has not yet undertaken any research to evaluate and rank the factors influencing the decline of drainage basins according to different standards. The results of this study will help policymakers allocate resources and prepare effective plans for future sustainability of the study area.

2. Study Area

The Rabigh area is located north of Jeddah City on the eastern shore of the Red Sea in Saudi Arabia (Figure 1). This area includes Rabigh City, which is home to several industrial factories and facilities (such as steel, petrochemical, power, and cement) and is in the province of Makkah, the principal commercial and industrial hub. Moreover, it is located between the industrial hubs of Tabuk Province to the north and the metropolitan regions of Jeddah. As a result, the Saudi government and planners are building projects in the area that need massive infrastructure and extra environmental studies. The study region is situated at latitudes 22°45′ N and 23° N on the eastern Red Sea coastal plain, and it is within the coverage area of the Makkah province (Figure 1a,b). Rabigh is located about 209 km north of Jeddah City, occupying about 23 km2 of shoreline (Figure 1). Geologically, quaternary deposits of bioclastic limestone with an average width of about 30 km encompass the study area’s coastal plain (Figure 2). The geological framework of the study area has been classified into several rock units including alkali–feldspar, basalt, chart, gabbro, granodiorite, gravel, sand, sandstone, diorite, amphibolite, syenogranite, sand, etc. Units including basalt, basaltic andesite, chart, marble, quartzite, tuff, and mafic agglomerate are located mainly at the eastern part of the study area and cover about 40% of the total area. Two main sets of faults and/or fractures, presenting NW–SE and NE–SW trends, fracture these units. Gravel with sand and silt unit are distributed along the entire western strip of the study area (Figure 2).
Some structural contacts are recorded between different units. For example, sandstone, silt, siltstone, shale, gypsum, and limestone units join gravel, sand, and silt unit by a NW–SE lineament. The same contact trend was recorded in the south part of the study area between (1) sandstone, silt, siltstone, shale, gypsum, and limestone units and volcanic units represented by basalt, and (2) sedimentary units made up of gravel, sand and silt units and peridotite, harzburgite, dunite, serpentinite, gabbro, and basalt. For more details about the geological framework of the study area, see Figure 2.
Topography of the study area surface varies due to the morphological characteristics, and this is reflected in the area topographical signatures, which are known for their quick changes. This area is laterally changing from east to west, where it is observed that it varies from −27 m along the coastal line to 1365 m at the most western part of study area (Figure 3a). The sloping characteristics provide percentage values from 0 up to 29, and the highest values are concentrated at the western, southern, and northwestern parts, respectively (Figure 3b).

3. Data and Methods

For the present study, a 12.5 m resolution Alos PALSAR digital elevation model data (DEM) from website of the Alaska Satellite Facility (https://search.asf.alaska.edu/#/) accessed on 27 May 2007 were obtained. Three different topographical sheets (maps), including codes GM-049C, GM-087C, and GM-084C, were additionally obtained from the Saudi geological survey (https://ngdp.sgs.gov.sa/ngp/) which were accessed on 3 July 2024. Despite being 1:25,000 in scale, the toposheets lacked some contours. Additionally, stream networks were absent and terminated in several locations. After being downloaded, the three topo map sheets were each georeferenced separately in ArcGIS after being transformed into TIFF files. The legends on the toposheets that were obtained in this way could lead to matching mistakes during digitization. To resolve this problem, four distinct polygon shapefiles that perfectly matched the toposheet boundaries were digitized. The mosaic tool in ArcGIS was then used to combine the georeferenced toposheets into a single composite toposheet that covered the whole study area. To access the basins stream networks, the combined toposheet has been digitized. As previously mentioned, the toposheet has many missing stream linkages that were later filled in using network analysis of the DEM.
ArcGIS software is an effective tool for prioritizing the basins and analyzing morphometric data. The data from the morphometric analysis of basins could be a key resource for managing water resources, preventing soil erosion, mapping susceptibility levels to landslides, assessing groundwater potential, and ranking basins in order of importance [6,27,43,44]. The first morphometric research was conducted in the middle of the 20th century with a traditional method based on labor-intensive hand analysis of topographic maps [25,45,46,47]. The traditional method of assessing river morphometrics is a labor-intensive and time-consuming process. However, more accurate and exact assessments may be carried out much more easily thanks to advancements in computational and geospatial technologies. When topographic maps are unavailable, morphometric parameters of a basin can be ascertained using satellite terrain data, such as the digital elevation model (DEM) [6]. Geographic information systems may easily incorporate digital elevation models [48]. These, in contrast to contours on topographic maps, offer continuous data. For the basin prioritizations in morphometric analysis, basic, derived, form, and relief characteristics were employed. The elevation, total area, total perimeter, length, and total number of streams, the most fundamental factors that defined the geometrical parameters, were digitally obtained using GIS software for each single basin independently. Figure 4 displays the general flowchart of the morphometric analysis process steps designed by the present work that can be used to prioritize different regions in the study area based on the severity of soil erosion determined by morphometric analysis. Significantly, creating mitigation plans for hotspot locations and accurately forecasting soil erosion depends on having a thorough grasp of each of these factors. By regulating the speed and flow of water, linear and relief morphometric factors have a direct link with soil erosion, whereas areal morphometric characteristics have an indirect association with soil erosion.

3.1. Morphometric Analysis

According to [49,50], the precise definition of a basin is necessary for the accurate identification of stream flow pathways and their contributing areas, which in turn is necessary for basin priority and its management techniques. In the present study, the stream network generation from DEM revealed a notable spatial deviation from ground truth, particularly close to the basin outlet. It takes a lot of time and work to perform morphometric analysis on the digital drainage network. Even with the aid of sophisticated computing tools like ArcGIS, processes like stream ordering for each segment, its naming, and merging and dividing stream segments at suitable locations take a long time [6,51]. The stream networks that emerged from the 12.5 m Alos PALSAR digital elevation model do not follow the actual natural river network. The original DEM was reconditioned and burned-in using the digital stream networks from the toposheets. Next, using Arc GIS hydrology tools, the restored DEM was utilized to draw the boundaries of the drainage network, stream order network, and watershed. The flow directions process in the study area was ascertained by using the eight directions flow direction technique. Water can move from one raster cell to its eight adjacent cells in accordance with this procedure. The algorithm determines which adjacent cell has the steepest downslope and based on the flow directions, assigns an integer code between 1 and 255. The Rabigh coastal area landscape was divided into 12 (from 1 to 12) different basins utilized greater than the fourth order (Figure 5).
An important stage in defining drainage networks is determining the cell threshold value, which establishes the quantity of raster cells required to initiate a stream grid [48]. Drainage density and threshold value are negatively correlated; a higher threshold value corresponds to fewer streams. Figure 5 displays the stream order map of the research area. ArcGIS software was used to compute the Rabigh area and its 12-basins, basin perimeter, basin length, main channel length, and length of stream orders. Despite the enhanced ability of the GIS tools to alter data and speed up computation, morphometric studies still pose challenges for most academics. In the present study, Table 1 presents the morphometric parameters ascertained by the ArcGIS toolbox, together with their equations and authors. The main objective of the present study was to rank the proposed basins according to their vulnerability to soil erosion hazard. When prioritizing basins, the morphometric parameters were chosen including relief morphometric parameters like relief ratio and ruggedness number, areal morphometric parameters like drainage density, drainage texture, stream frequency, form factor, elongation ratio, and circularity ratio, and linear morphometric parameters like mean bifurcation ratio and average length of overland flow. A compound value was assigned based on the significance of each parameter in relation to the soil erosion risk. Higher weighting indicates a higher significance of the characteristics with respect to the incidence of soil erosion. The compound value technique tested in this work has been applied by several authors in various scientific papers [7,27,45,52,53].

3.2. Compound Values Analysis

This technique of soil erosion priority is valuable and a very effective strategy which has been applied successfully by several authors (e.g., [9,52,57]). Assessment compound values (CP), which combine several morphometric factors into a single value, are an average rank used to prioritize basins. Each morphometric parameter rank is added together, and the result is divided by the total number of parameters that were chosen (N). The rankings are determined by examining how each characteristic relates to soil erosion. Following the estimation of the compound values, the basins can be ranked according to their values. The highest priority for erosion control or other management measures is given to the basins with the highest compound values. In the present study, using computed CP, the basins were ranked in order of soil erosion rates using the following processing: (1) assigning the morphometric parameters that had direct or an inverse relationship with the danger of erosion, (2) providing each parameter with a rank according to how important it was to the erosion risk, (3) using the equation (CP = ∑ i = 1 n r a n k   o f   p a r a m t e r s / N ) to determine the compound value for every single basin, and (4) assigning every single basin a specific rank as low, moderate, or high priority to address soil erosion.

4. Results

Results of the morphometric analysis of the Rabigh area revealed significant insights into the characteristics of various basins concerning their susceptibility to soil erosion. The study categorized the Rabigh area into 12 distinct basins based on hydrological tools in the ArcGIS software. In this section, authors aim to highlight and summarize the maximum and minimum values of the morphometric parameters. The rest of the results of all the basins are clearly tabulated in Table 2 and Table 3.

4.1. Basic Morphometric Paramters

The study area comprises a total of 12 drainage basins, with individual basin areas ranging from 62 km2 (Basin 2) to 1341 km2 (Basin 10), collectively covering an area of 5799 km2 within the Rabigh region (Table 2). The perimeter of the study basins varies from 62 km for Basin 2 to 306 km for Basin 12, with the total perimeter of the Rabigh area amounting to 1757 km (Table 2). In this study, Basin 3 is classified as a sixth-order basin, while six basins (Basin 4, Basin 5, Basin 8, Basin 9, Basin 10, and Basin 12) are fifth-order, and five basins (Basin 1, Basin 2, Basin 6, Basin 7, and Basin 11) are fourth-order (Figure 5). The total number of streams across the study area is 2392 km, distributed across different stream orders as follows: 1827 first-order, 417 second-order, 110 third-order, 29 fourth-order, 8 fifth-order, and 1 sixth-order streams. Among the basins, Basin 2 has the lowest stream count (28), whereas Basin 10 has the highest stream count (571) (Table 2).

4.2. Linear Morphometric Paramters

In this study, the average length of overland flow varies between 4.27 km in Basin 10 and 5.17 km in Basin 1 (Table 3). The bifurcation ratio, an indicator of the degree of branching in the drainage network, ranges from 2.8 in Basin 2 to 5.1 in Basin 1 (Table 3). The drainage density, which measures the total length of streams per unit area, exhibits values from 0.88 in Basins 4 and 8, located in the central part of the Rabigh area, to 1.03 in Basin 1, situated in the northern region (Table 3). Stream length analysis revealed a wide range of values, from 21.11 km in Basin 7 to 246.17 km in Basin 10, representing the minimum and maximum values, respectively (Table 3). Stream frequency, defined as the number of streams per unit area, ranges from 0.36 in Basin 1 to 0.48 in Basin 5 (Table 3). Additionally, drainage texture, which indicates the density of the drainage lines in relation to the basin perimeter, varies from 0.4 in Basin 2 to 2.1 in Basin 10 (Table 3). All the results related to these linear morphometric parameters for each basin are summarized in Table 3.

4.3. Areal Morphometric Paramters

As shown in Table 2, Basin 7 has the shortest length in the Rabigh area, measuring 19 km, while Basin 12 has the longest length at 89 km. The stream frequency, which represents the number of streams per unit area, varies from 0.1 to 0.6 across the study area (Table 3). The circularity ratio, a measure of the basin’s shape, ranges from 0.1 in Basin 12 to 0.4 in Basin 8 (Table 3). Similarly, the elongation ratio, which indicates the degree to which a basin approaches a circular shape, ranges from 0.3 in Basin 12 to 0.91 in Basin 9 (Table 3 and Figure 5). The infiltration number, an indicator of the infiltration capacity of the basin, ranges from a minimum value of 0.08 in Basins 3 and 9 to a maximum value of 0.35 in Basin 12 (Table 3). These variations reflect the differing hydrological characteristics across the basins. All results of the previous section are tabulated in Table 2 and Table 3.

4.4. Relief Morphometric Paramters

The basin relief values in the study area range from 91 m in Basin 2 to 1392 m in Basin 1, representing the minimum and maximum reliefs, respectively. When considering the ratio of basin relief to the longest basin dimension, the northern regions, particularly Basins 1 and 3, exhibit the highest values, while Basin 11 records the lowest (Table 3; Figure 5). The basin relief ratio varies from 4.2 in Basin 11 to 52.5 in Basin 1, indicating significant variability in the topographic steepness across the study area. Similarly, the relative relief ratio ranges from 145 in Basin 2 to 992 in Basin 1, highlighting differences in the elevation and terrain ruggedness between basins. The ruggedness number and Melton ruggedness number parameters show maximum values of 14.4 and 6.9, respectively, for Basin 1, indicating highly rugged terrain. Conversely, the minimum values are recorded in Basin 2 (0.8) and Basin 4 (1.2), reflecting relatively smoother topography. The remaining results for these two key parameters are summarized in Table 3.

4.5. Basin Prioritization

The 12 basins of the Rabigh study area were ranked from 1 to 12 based on individual morphometric parameters. For example, using the values of the drainage density (Dd) parameter, the basins were ranked as follows: Basin 1, Basin 4, Basin 2, Basin 11, Basin 7, Basin 6, Basin 5, Basin 10, Basin 9, Basin 12, Basin 3, and Basin 8, respectively (Table 4). The same ranking strategy was applied for the other morphometric parameters. Table 4 presents the rankings for each parameter. The compound values for the basins, ranging from 1 to 12, are as follows: 4.8, 5.9, 8.7, 5.0, 6.1, 6.5, 7.1, 8.8, 8.8, 8.4, 4.9, and 3.0, respectively (Table 4). Based on these compound values, the basins were prioritized from rank 1 to 12 as follows: Rank 1: Basin 12; Rank 2: Basin 1; Rank 3: Basin 11; Rank 4: Basin 4; Rank 5: Basin 2; Rank 6: Basin 5; Rank 7: Basin 6; Rank 8: Basin 7; Rank 9: Basin 10; Rank 10: Basin 3; Rank 11: Basin 8; and Rank 12: Basin 9, respectively (Table 4).

5. Discussion

5.1. Analysis of Morphometric Paramters

Morphometric characteristics of the Rabigh area present insights into the nature of surface development and help in understanding variations in rate and severity of soil erosion, making it imperative to evaluate the risk of soil erosion and create efficient soil erosion management strategies [27,28,48,55]. Therefore, the present study aimed to investigate four different morphometric parameters including basic geometries, linear, areal, and relief parameters to define and assign areas regions with a high risk of soil erosion. Additionally, it is essential to comprehend each of these factors in order to predict soil erosion with accuracy and create mitigation plans for hotspot locations. In the present study, we opted to use equal weightage values to simplify the approach and ensure consistency across the parameters. While equal weighting is a valid method in morphometric studies, it is crucial to evaluate how changes in weight assignments could affect the results. Recent studies emphasize the need for sensitivity analysis as a means of validating the robustness of ranking methods, particularly in geospatial and hydrological research [9,58]. Sensitivity analysis allows for an in-depth understanding of how rank variations influence watershed prioritization, helping to ensure the reliability of the ranking process. By not conducting this analysis, the study may overlook potential variations in ranking outcomes, which could alter the prioritization of different-level risk areas. Therefore, we acknowledge this limitation and propose that future research should incorporate sensitivity analysis to test the resilience of different weightage scenarios, thereby strengthening the robustness of the prioritization process.
Significantly, while areal morphometric factors have indirect interactions with soil erosion, linear and relief morphometric characteristics have direct relationships with soil erosion by regulating the speed and flow of water [1,6]. Similarly, authors in [2,6,7] suggested that higher areal parameters in a basin provide basins which are more circular in shape, have smaller perimeters, and experience slower rates of soil erosion. Basins that exhibit higher values for all linear and relief morphometric parameters, including drainage density, overland flow length, stream frequency, basin relief, roughness number, and relief ratio are more prone to erosion due to their greater runoff and steeper slopes. This conclusion aligns with the discoveries of [9,16,59], whose authors also discussed basins with similar characteristics experiencing high rates of soil erosion. The present study investigated the links of the selected morphometric parameters with soil erosion in the basins over different locations, including Kalvari Basin in Iran, the western coast of the Red Sea in Saudi Arabia, Gidabo Basin along the Rift valley in Ethiopia, Oued Amter Basin in Morocco, and Burdwan district in India [1,10,27]. This suggests that it is expected that different basins will have different specific morphometric factors that are important for soil erosion assessment. For example, the values of stream length recorded in the present study differ from those recorded in Wadi Al-Lith Basin in Saudi Arabia. Similarly, values of the drainage density in the present study, which range from 0.88 to 1.03, are higher than those of Wadi Al-Lith (0.67–1), but lower than those of Tabuk Basin in Saudi Arabia (1.11–1.30) [27,60]. Consequently, while the circulation ratio is reasonably constant, the results of other morphometric parameters differ across each basin, indicating variations in outcomes. Several variables, including rainfall intensity and pattern, slope characteristics, drainage patterns associated with topography, geology, and soil, as well as land use and land cover, can all have an impact on this variability. Furthermore, compared to linear and relief characteristics, areal factors such as the circularity and elongation ratios and form factor parameters generally have a vague explanation to express the soil erosion process. As illustrated in Figure 5, the Rabigh area is subdivided into 12 basins according to stream flow and flow direction. Morphometric parameters, which are tabulated in Table 1, were applied and analyzed for ranking each single basin and identifying regions at high risk of soil erosion [2,5,6,7,11].

5.2. Basic Geometries Analysis

Basic morphometric parameters, which provide details about the total size, form, and other physical properties of the basins, are the most fundamental information used to describe the basins. The area of the study basins ranges from 62 km2 to 1341 km2 for Basins 2 and 10, respectively, totaling 5799 km2 in the Rabigh area (Table 2). The total perimeter of the Rabigh area is 1757 km. Individual basin values range from 62 km for Basin 2 to 306 km for Basin 12 (Table 2). Most authors suggest that basins large in area have high signals of soil erosion [2,10,11]. This could occur because runoff from longer overland flow lengths can completely dissolve soil particles before joining another stream. To rank basins, the highest and lowest perimeter values can be used. Authors in [61] discussed basins with higher values of applied morphometric parameters severely impacted by soil erosion risk. In the present study, although Basin 10 has high values of the applied parameters, it is less severely impacted by soil erosion in overall rank compound value than basins showing lower values. Similarly, authors in [9] discussed the same findings. Therefore, this paper suggests that this is not generally the case for all the basins of the Rabigh area. The results of the present study show that Basins 2 and 12 are classified last and first, respectively, due to their perimeter values (Table 2). On the other hand, Basins 2 and 12 are ranked as moderate and high soil erosion risk areas, respectively, based on the total average rank of all specified characteristics. Authors in [9,62] focused on the significance of determining additional elements that contribute to soil erosion, such as land use practices, soil type, slope, and climate. This shows that since other factors can potentially influence high or low rates of soil erosion, only total area and perimeter parameters are insufficient to predict soil erosion rates in basins. Therefore, as suggested by authors in [9,62], experts could compare basins using a range of morphometric criteria, including basic, linear, relief, and areal factors.
Additionally, other basic geometries like stream order, stream number, stream length, and max/min elevation can be used to determine which basins are most vulnerable to soil erosion, to evaluate the influence of physical basins characteristics on soil erosion, and determine which basins should be the focus of the development of mitigation measures [62]. Stream order factor is a scale of the degree of the drainage system’s complexity. Basins with dense drainage regions, longer stream lengths, and steeper slopes are generally linked to more complicated drainage networks, which are indicated by higher stream orders. Authors in [9,25,63] recognized the hierarchical classification of streams according to their tributary linkages as the stream order system. First-order streams are the shortest unbranched streams, while second-order stream segments are formed when two first-order streams converge. In this process, two s-order streams merge to create a third-order stream segment, and so on. In the present study, just 1 basin (B 3) is sixth-order, 6 basins (B 4, B 5, B 8, B 9, B10, and B 12) are fifth order, and 5 basins (B 1, B 2, B 6, B 7, and B 11) are fourth order (Figure 5). When studying the characterization of a basin, additional fundamental factors that are inversely associated with stream order parameter are considered. To clarify this issue, the present study suggests that when two or more lower-order streams converge to generate higher-order streams, the number of streams reduces as stream order increases. The correlations between stream number, stream order, and stream length in each of the research area’s basins are tabulated in Table 2. In the present study, stream number factors are 1827, 417, 110, 29, 8, and 1 for the 1st, 2nd, 3rd, 4th, 5th, and 6th orders, respectively. The total stream number in the Rabigh area is 2392. Basins 2 and 10 recorded the minimum and maximum stream numbers as 28 and 571, respectively. Given that stream length significantly affects soil erosion, it is crucial to take this into account. Using this factor, ArcGIS is able to extract and adapt its measurements precisely. This is because longer streams allow silt to settle out of the water column and be transferred downstream, since they have more time to gather and transport material due to their slower water flow. Generally, as stream order increases, the overall length of the stream decreases; first-order streams are the longest, and last-order streams are the shortest [6,16].

5.3. Linear Parameters Analysis

There are more streams and channels in basins with drainage densities, greater stream length ratios, and bifurcation ratios, which may lead to an increased risk of soil erosion [27]. Similarly, basins of higher stream frequencies and longer overland flow lengths are indicative of basins with higher volume and velocity, which, given their drainage patterns and flow characteristics, imply a larger potential for soil erosion [26,64,65,66,67]. In the present study, the values of the bifurcation ratio range from 2.8 to 5.1 for Basins 2 and 1, respectively. Basin 1 (B 1) has high bifurcation ratios, indicating more dendritic drainage patterns (Figure 6). This may result in increased erosive power and faster water collection. However, a small portion of the Rabigh area to the northwest is less likely to experience soil erosion based on the rank of Rb. Consequently, authors in [2,27,68,69] applied remotely sensed data and geospatial and morphometric analysis to test and evaluate the soil erosion risk. Based on their results, land managers can utilize these metrics along with other data to identify which regions should receive priority for erosion control activities and how best to distribute resources to reduce soil erosion and safeguard the landscape. All bifurcation values are tabulated in Table 2.
In the present study, the drainage density parameter plays a vital role in assigning parts with high erosion risks. The results of this parameter range from 0.88 for Basins 4 and 8 in the middle part of the Rabigh area, respectively, to 1.03 for Basin 1 in the north part of Rabigh area. Authors in [6,9,70] discussed that mountainous topography, scarce vegetation, and weak or impermeable underlying material are the causes of high drainage density where low relief and highly permeable underlying material beneath dense vegetation are the causes of the low drainage density. Therefore, Basins 4 and 8 (Table 3), with their highly permeable soil, low elevation, minimal runoff, and high infiltration capacity, have a lower Dd value of 0.88 km/km2. Since this morphometric parameter measures the total length of streams and channels per unit area of a basin, it can also show how effectively a basin drains water. The stream frequency parameter (F) is a key to test the number of streams in a basin. F values range from 0.36 (B 1) to 0.48 (B 5) as minimum and maximum values of these morphometric parameters (Table 3). Generally, the present study suggests a positive correlation between F and Dd, with greater Dd frequently translating into higher Fs because of the existence of more stream channels. The drainage texture parameter is assigned by different values in the study area. It ranges from 0.4 for Basin 2 to 2.1 for Basin 10. According to the scale that has been applied by [55], drainage texture can be divided into five categories: extremely coarse (<2), coarse (2–4), intermediate (4–6), fine (6–8), and very fine (>8). Therefore, and according to all values of the Td, which are tabulated in Table 3, within the Rabigh area, all basins have extremely coarse drainage textures; the basins with the lowest and greatest drainage texture values are B 2 and B 10, respectively. When the slope is gentle (B 2), water tends to percolate and pass through a more intricate and sinuous network of streams (high Dt) and easily penetrates soils, providing conditions for slower runoff and a lesser soil erosion risk. Climate, rainfall, vegetation cover, geology, soil type, slope, infiltration rate, sub-basin size, and perimeters are all recommended to be considered when evaluating drainage texture in basins, as these variables have a major influence on drainage density. Additionally, the results of the average length of overland flow parameter range from 4.2 to 5.1 for Basins 10 and 1, respectively, as minimum and maximum values of this parameter in the study area. In addition to having shorter flow routes, more runoff, and less infiltration, Low Lg basins are more likely to experience soil erosion, particularly after severe rainfall. Table 3 and Figure 6 indicate that Basin 1 shows the highest values of Dd and Lg, 1.03 km2 and 5.1 km, respectively. This demonstrates how fast surface runoff causes regions with steep slopes and impermeable soils to frequently have high drainage densities and stream frequencies [71]. Overland flow length and drainage density do, however, have an indirect link. This implies that the length of overland flow decreases with increasing drainage density and vice versa. In general, decision-makers can use linear metrics such drainage density, stream frequency, and length of overland flow to access information for developing and implementing suitable soil conservation strategies. These characteristics also offer crucial information on the hydrological behavior of a watershed and its susceptibility to soil erosion, allowing for the development of focused and effective mitigation measures. Combining these factors with additional morphometric parameters allows land managers to create targeted, seasonally appropriate programs to control soil degradation and address the underlying causes of erosion [1,5,6].

5.4. Areal Parameters Analysis

Basins can be characterized and signals of vulnerability to soil erosion can be identified using four effective areal morphometric parameters, total basin length, form factor, circularity ratio, and elongation ratio. The distance between a basin’s exit and the basin’s division along the main channel is known as the basin length (Lb). As tabulated in Table 2, the shortest basin distance in the Rabigh area was recorded for Basin 7 as 19 km. Consequently, the longest basin length was observed for Basin 12 as 89 km. Table 2 presents all basin lengths for all studied basins. Generally, longer basins will have longer lag time, which means that following a rainfall event, it will take longer for water to reach the basin’s outlet, while lag durations between shorter and longer basins are shorter [6,11]. This could be because water must travel a lesser distance to reach the outlet from the furthest reaches [3,9]. In the same direction, a numerical value known as form factor (Ff) is used to characterize a basin’s form. Additionally, circular basins have intermediate Ff values near one, short-wide basins have the biggest Ff values, and elongated basins are longer and have reduced Ff values [72]. In other words, the form factor parameter is an effective factor to test the elongation shape of a given basin. Significantly, authors in [6,9,70] suggested that longer-lasting, flatter peak flows are present in basins with greater Ff values. The present study recorded a range of values of the Ff parameter (Table 2); they vary between 0.1 and 0.6 as minimum and maximum values and tend to primarily show elongated basins. Another crucial parameter in examining basin form is the circularity ratio (Rc), which sheds light on a drainage basin’s hydrological system. In the present study, Rc parameter provides values between 0.1 and 0.4 for Basins 12 and 8, respectively, supporting the suggested idea of the elongated forms of the basins. The circularity ratio is recognized as a calculated parameter which is extracted by comparing the drainage basin’s maximum length to the diameter of a circle the same size as the basin [61,73,74]. High Cr values indicate circular basins with moderate to high relief and porous surfaces, resulting in less time for ground penetration of surface runoff. On the other hand, impermeable surfaces with low relief and a prolonged surface runoff concentration time are indicated by lower Cr levels [6,9]. Additionally, the circularity ratio can be used to show the stages of basins; low, medium, and high values correspond to the young, mature, and old stages, respectively. Furthermore, the elongation ratio (Re) offers additional insight into the geometry of the basin [61]. The values of the elongation ratio parameter vary between 0.3 for Basin 12 and 0.91 for Basin 9 (Table 3 and Figure 5). Authors in [27,48,55] stated that basins can be categorized according to this parameter values as more elongated (< 0.5), less elongated (0.7–0.8), oval (0.8–0.9), circular (0.9–0.1), and elongated (0.5–0.7) based on the elongation ratio. Because of shorter concentration times, basins become more circular when Re values rise, and vice versa. Morphometric analysis has been used in earlier research in several Ethiopian basins to prioritize watershed management [16,45,75,76]. For example, authors in [45] discussed the significant areal morphometric parameter in describing and understanding the research area in the Ethiopian Genale Dawa Basin to prioritize soil erosion control. Similarly, in studying the Gidabo Basin in Ethiopia’s Southern Rift Valley, authors in [16] demonstrated the strong relationship between soil erosion and the applied four aerial morphometric parameters. Additionally, authors in [76] discovered that, in the Didessa and Jema Sub-Basins of Ethiopia, there were strong relationships between soil erosion and basin length, basin perimeter, and stream order.

5.5. Relief Parameters Analysis

Relief characteristics are very important factors providing insights into the topography and aid in assessing variations in topographic activity and evolution. The development and implementation of basin management plans is facilitated by the utilization of basin relief, a crucial morphometric characteristic that investigates the drainage patterns and topography of basins. Understanding relief parameters can help identify places in danger of soil erosion more effectively [75,77]. Relief parameters include basin relief, relative relief, relief ratio, and are physical attributes of a basin that can be used to determine how susceptible it is to erosion [5,6]. It is also further highlighted how crucial it is for basin managers, farmers, and environmental specialists to comprehend relief characteristics and soil erosion relationships to create efficient plans for mitigating and preventing soil erosion. The basin relief parameter (H) could be recognized as a vertical distance between a basin’s greatest and lowest elevation. In a basin, the link between soil erosion and basin relief is complicated and varies based on several factors. The results of this parameter range from 91 m to 1392 m for Basins 2 and 1, respectively, as minimum and maximum reliefs of the study area. Therefore, Basin 1 in the northern part of the study area exhibits substantial basin relief, defined as the variation in elevation between a basin’s highest and lowest points. When topography, slope, and drainage density are coupled, this high basin relief may exacerbate soil erosion. To reduce soil erosion and protect soil resources, this area might need to implement target management techniques. This could be because steeper slopes connected to higher relief may result in faster surface runoff, which may then increase erosion and sediment transport [77]. According to the ratio of basin relief to longest dimension (Rr), some of the study area’s northern regions, like B 1 and B 3, have the highest Rr, while B 11 is recorded as the lowest basin with its Rr value (Table 3 and Figure 7). Basin relief ratio values range from 4.2 to 52.5 for Basins 11 and 1, respectively. This metric gauges a basin’s overall steepness and is directly associated with the basin’s vulnerability to soil erosion. Soil erosion is more likely to occur in watersheds with high Rr values than in those with low Rr values. The relative relief ratio (Rr) parameter provides different values ranging from 145 to 992 for Basins 2 and 1, respectively. Similarly, the ruggedness number (Nr) and Melton ruggedness number (Nmr) parameters are significant indicators describing the relief characteristics of the Rabigh area. They give values of 14.4 and 6.9 for Basin 1, respectively, as maximum values. On the other hand, they show values of 0.8 and 1.2 for Basins 2 and 4, respectively, as minimum values. The rest results of these two effective parameters are tabulated in Table 3.
Basin 1 shows the highest values for all relief morphometric parameters including basin relief, basin relief ratio, relative basin relief ratio, ruggedness number, and Melton ruggedness number as 1392, 52.5, 992, 14.4, and 6.9, respectively. Nevertheless, this correlation varies depending on the field of study. Several previous studies have demonstrated that in other study areas, basins that exhibit the greatest values for one or more relief parameters in one study area may not exhibit the highest values for all relief parameters in other study areas [9,16,34]. Overall, several different research regions have different relationships between relief parameters and soil erosion because of a variety of factors, such as climate, human activity, land use/cover, geological and geomorphological characteristics, and human activity [27,53]. To clarify that, while a research location with resistant soils may demonstrate a reduced link between relief parameters and erosion, a study area with loose, erodible soils may experience higher rates of erosion even with lower relief factors [2,6]. Furthermore, rainfall in low-relief places may penetrate more readily, minimizing erosion. Thus, comprehending these factors and how they relate to relief criteria is crucial in creating soil erosion control plans that work in the targeted research regions [78].

5.6. Basin Prioritization Based on Compound Value

Assessment of basins with a high risk of soil erosion can be carried out with the help of morphometric parameter-based basin prioritizing techniques. The level of basin erosion vulnerability cannot be adequately explained by a single morphometric measure. This could be because soil erosion is a complicated process that is affected by several variables, such as land use, climate, geology, and management techniques. As a result, a more complete, effective, and successful indicator of a basin’s erosion risk is the compound value of morphometric characteristics [1,7,34,45,53]. It is determined by adding together the values of several morphometric parameters and is ranked based on how significant they are [63,71]. Although overall compound values can be used to lessen soil erosion, it is important to assess how well the basin’s future soil erosion rates are predicted.
In the present study, the 12 basins were ranked from 1 to 12 according to the analysis of the applied morphometric parameters. Authors in this work suggest scaling the ranks or levels from 1 to 12 as the highest and lowest levels of soil erosion priority according to the compound value. Therefore, we could say a basin with a rank of 12 has the lowest level of soil erosion priority; a basin ranked 11 is more exposed to soil erosion than a basin with a rank of 12; a basin of rank 10 is more exposed to soil erosion than a basin of rank 11; and so on (Table 4 and Figure 8). This work selected this strategy because no standard values of these ranks have been applied before. Although some authors classified ranks according to various values, none gives reasons for choosing these values of classification. For example, authors in [9] classified Dabus watershed into four classes based on compound values as very high (≤5.5), high (5.5–6.51), medium (6.51–7.5), and low (≥7.5). On the other hand, some authors such as those of [11,76] divided the upper basins and Didessa and Jema Basins into three categories: high, medium, and low. These categories were based on compound values of ≤6.5, 6.5 to 7.5, and ≥7.5 and ≥2.55, 2.55 to 3.55, and ≥3.55, respectively. Therefore, we can clearly see that the ranges of priority classification could be different from area to area and from study to study. The highest rank of the compound value strategy in the Rabigh area is 3, which was recorded for Basin 9 (Table 4 and Figure 8). Additionally, and to put results in a precise scale, the present paper assigns the 12 ranks into three levels of soil erosion risk levels. Each class comprises four ordered ranks. A high level of soil erosion priority indicates basins that are highly exposed to erosion risks (basins ranked from 1 to 4), a moderate level of soil priority covers basins that are moderately exposed to soil erosion risk (basins ranked 5, 6, 7, and 8), and finally, a low level describes the last four basin types in the original rank scale (basins ranked from 9 to 12). Figure 9 presents the map of soil erosion priority classes.
Based on the results of the present study, Basins 1, 4, 11, and 12 were categorized as high soil erosion priority areas according to them having more steep slopes, high relief signals, little vegetation, low infiltration rates, and high runoff amounts. These basins cover 2107 km2 of the total Rabigh area (36.33%) and they were delineated at the northern, middle, and southern parts of the study area, respectively (Figure 9). In basins with high susceptibility to erosion, immediate soil and water conservation measures such as bench terracing, contour binding, grass waterways, and gully control structures are required to prevent topsoil loss. These processes, which slow down water flow, manage gully erosion, and hold onto sediment, can stop water runoff and soil erosion actions [9,11,79]. The next four basins, which cover areas with moderate conditions of soil erosion risk according to results of the present study, are Basins 2, 5, 6, and 7. They were recorded mainly in the middle part of the study Rabigh area and cover 763.31 km2 (13.16%) of the total Rabigh area. To reduce the high levels of soil erosion in these moderate rank basins, strip and mixed cropping are necessary steps. The last class of the soil erosion priority is the low class, which is represented by Basins 3, 8, 9, and 10. They occupy about 5014 km2 (50%) of the total of the study Rabigh area (Figure 9). Therefore, the results of the present study state that a moderate level of soil erosion priority exists in about half the study Rabigh area. In medium-priority basins, agronomical techniques including mulching, contour farming, and strip cropping can be used to prevent soil erosion.
Depending on the distinct features and circumstances of each basin, different soil and water conservation strategies should be employed. The priority areas determined by morphometric characteristics and specific area data must be taken into consideration by relevant authorities and local experts. Experts should first examine the location and offer recommendations based on their expertise and experience in employing morphometric characteristics to correlate the specified area. Through the consideration of morphometric characteristics and professional guidance and information, authorities can proficiently execute soil and water conservation strategies aimed at safeguarding and perpetuating these important resources.

6. Conclusions

Soil erosion, whether natural or human-caused, can negatively impact a nation’s economy by reducing agricultural output and affecting water resource projects. Along the Red Sea coast, increased soil erosion leads to silt deposition, causing problems both upstream and downstream, including in the Rabigh coastal area. This study aimed to understand soil erosion patterns in the Rabigh region by ranking and categorizing basins with high erosion potential. Using GIS and mathematical methods, key parameters such as shape, elevation, slope, and stream networks were analyzed to assess each sub-watershed’s susceptibility to erosion.
This study identified the basins most vulnerable to soil erosion by analyzing various morphometric parameters. A compound value, representing the overall erosion potential, was calculated by summing key parameters. These include basic geometries like total area, perimeter, stream length, and stream order; areal factors such as basin length, circularity ratio, form factor, and elongation ratio; linear aspects like stream length ratio, drainage density, bifurcation ratio, and drainage texture; and relief parameters such as basin relief, relief ratio, relative relief, ruggedness number, and Melton ruggedness number.
The study area was classified into three priority levels—high, medium, and low—based on compound values for soil erosion risk. Basins 1, 4, 11, and 12, covering areas of 400 km2, 745 km2, 142 km2, and 818 km2, respectively, were identified as high-priority areas for soil erosion prevention. Basins 3, 8, 9, and 10, covering 617 km2, 240 km2, 728 km2, and 1341 km2, were categorized as low-priority areas. Basins 2, 5, 6, and 7, with areas of 62 km2, 400 km2, 193 km2, and 107 km2, were assigned a moderate erosion priority. Immediate management measures are recommended for high-priority basins to mitigate soil loss.
GIS and remote sensing can be combined to identify erosion-prone areas and estimate soil erosion rates at different scales. However, the study’s use of 12.5 m resolution data may have reduced the accuracy of morphometric parameters, potentially underestimating erosion-prone regions. To improve precision, future research should use higher-resolution DEM data, which can provide more accurate physical measurements for reliable sediment yield simulations and capture fine-scale features like gullies, ridges, and ephemeral streams. This would enhance understanding of erosion patterns. After assessing each basin’s erosion potential, land managers can develop targeted conservation strategies. Applying a priority ranking approach across arid regions can also offer valuable insights into soil erosion risks and is adaptable to other regions with poorly defined geological hazards and climatic conditions.

Author Contributions

Conceptualization, B.B. and A.A.; methodology, B.B.; software, B.B. and A.A.; validation, B.B.; formal analysis, B.B. and A.A.; investigation, B.B. and A.A.; resources, A.A.; data curation, B.B.; writing—original draft preparation, B.B. and A.A.; writing—review and editing, A.A.; visualization, B.B. and A.A.; supervision, B.B. and A.A.; project administration, B.B. and A.A.; funding acquisition, B.B. and A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Researchers Supporting Project, Grant number (RSP2024R296), King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The investigated data were obtained from (https://earthexplorer.usgs.gov/) and (https://ngdp.sgs.gov.sa/ngp/) websites.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. El Brahimi, M.; Mastere, M.; Benzougagh, B.; El Fellah, B.; Fartas, N.; Ladel, L.; Sbihi, A.; Turyasingura, B.; Alsulamy, S.; Khedher, K.M.; et al. Assessing soil erosion vulnerability through geospatial morphometric analysis in the Oued Amter Basin (Northwest Morocco). Euro-Mediterr. J. Environ. Integr. 2024, 9, 1157–1180. [Google Scholar] [CrossRef]
  2. Dzwairo, R.; Singh, S.K.; Patel, A. Soil erosion susceptibility assessment through morphometric analysis and morphotectonic implications in Rietspruit sub-basin, South Africa. Environ. Dev. Sustain. 2024, 1–12. [Google Scholar] [CrossRef]
  3. Polovina, S.; Radić, B.; Ristić, R.; Milčanović, V. Application of Remote Sensing for Identifying Soil Erosion Processes on a Regional Scale: An Innovative Approach to Enhance the Erosion Potential Model. Remote Sens. 2024, 16, 2390. [Google Scholar] [CrossRef]
  4. Kabite, G.; Gessesse, B. Hydro-geomorphological characterization of Dhidhessa River Basin, Ethiopia. Int. Soil Water Conserv. Res. 2018, 6, 175–183. [Google Scholar] [CrossRef]
  5. Asfaw, D.; Workineh, G. Quantitative analysis of morphometry on Ribb and Gumara watersheds: Implications for soil and water conservation. Int. Soil Water Conserv. Res. 2019, 7, 150–157. [Google Scholar] [CrossRef]
  6. Singh, W.R.; Barman, S.; Tirkey, G. Morphometric analysis and watershed prioritization in relation to soil erosion in Dudhnai Watershed. Appl. Water Sci. 2021, 11, 151. [Google Scholar] [CrossRef]
  7. Benzougagh, B.; Meshram, S.G.; Dridri, A.; Boudad, L.; Baamar, B.; Sadkaoui, D.; Khedher, K.M. Identification of critical watershed at risk of soil erosion using morphometric and geographic information system analysis. Appl. Water Sci. 2022, 12, 8. [Google Scholar] [CrossRef]
  8. Saaty, T.L. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 2008, 1, 83–98. [Google Scholar] [CrossRef]
  9. Duressa, A.A.; Feyissa, T.A.; Tukura, N.G.; Gudeta, B.G.; Gechelu, G.F.; Bibi, T.S. Identification of soil erosion-prone areas for effective mitigation measures using a combined approach of morphometric analysis and geographical information system. Results Eng. 2024, 21, 101712. [Google Scholar] [CrossRef]
  10. Saha, S.; Gayen, A.; Pourghasemi, H.R.; Tiefenbacher, J.P. Identification of soil erosion-susceptible areas using fuzzy logic and analytical hierarchy process modeling in an agricultural watershed of Burdwan district, India. Environ. Earth Sci. 2019, 78, 649. [Google Scholar] [CrossRef]
  11. Mohammed, A.; Adugna, T.; Takala, W. Morphometric analysis and prioritization of watersheds for soil erosion management in Upper Gibe catchment. J. Degrad. Min. Lands Manag. 2018, 6, 1419–1426. [Google Scholar] [CrossRef]
  12. Saha, S.; Das, J.; Mandal, T. Investigation of the watershed hydro-morphologic characteristics through the morphometric analysis: A study on Rayeng basin in Darjeeling Himalaya. Environ. Chall. 2022, 7, 100463. [Google Scholar] [CrossRef]
  13. Khalifa, A.; Çakir, Z.; Owen, L.A.; Kaya, Ş. Morphotectonic analysis of the East Anatolian Fault, Turkey. Turk. J. Earth Sci. 2018, 27, 110–126. [Google Scholar] [CrossRef]
  14. Hema, H.; Govindaiah, S.; Srikanth, L.; Surendra, H. Prioritization of sub-watersheds of the Kanakapura Watershed in the Arkavathi River Basin, Karnataka, India- using Remote sensing and GIS. Geol. Ecol. Landsc. 2021, 5, 149–160. [Google Scholar] [CrossRef]
  15. Hugonnet, R.; McNabb, R.; Berthier, E.; Menounos, B.; Nuth, C.; Girod, L.; Farinotti, D.; Huss, M.; Dussaillant, I.; Brun, F.; et al. Accelerated global glacier mass loss in the early twenty-first century. Nature 2021, 592, 726–731. [Google Scholar] [CrossRef]
  16. Abdeta, G.C.; Tesemma, A.B.; Tura, A.L.; Atlabachew, G.H. Morphometric analysis for prioritizing sub-watersheds and management planning and practices in Gidabo Basin, Southern Rift Valley of Ethiopia. Appl. Water Sci. 2020, 10, 158. [Google Scholar] [CrossRef]
  17. Worku, T.; Tripathi, S.K. Watershed Management in Highlands of Ethiopia: A Review. OAlib 2015, 2, 1–11. [Google Scholar] [CrossRef]
  18. German, L.; Mansoor, H.; Alemu, G.; Mazengia, W.; Amede, T.; Stroud, A. Participatory integrated watershed management: Evolution of concepts and methods in an ecoregional program of the eastern African highlands. Agric. Syst. 2007, 94, 189–204. [Google Scholar] [CrossRef]
  19. Kerr, J. Watershed Management: Lessons from Common Property Theory. [Online]. 2007. Available online: http://www.thecommons.org (accessed on 23 March 2024).
  20. Xue, L.; Alemu, T.; Gani, N.D.; Abdelsalam, M.G. Spatial and temporal variation of tectonic uplift in the southeastern Ethiopian Plateau from morphotectonic analysis. Geomorphology 2018, 309, 98–111. [Google Scholar] [CrossRef]
  21. Yin, L.; Wang, L.; Li, J.; Lu, S.; Tian, J.; Yin, Z.; Liu, S.; Zheng, W. YOLOV4_CSPBi: Enhanced Land Target Detection Model. Land 2023, 12, 1813. [Google Scholar] [CrossRef]
  22. Zhang, K.; Li, Y.; Yu, Z.; Yang, T.; Xu, J.; Chao, L.; Ni, J.; Wang, L.; Gao, Y.; Hu, Y.; et al. Xin’anjiang Nested Experimental Watershed (XAJ-NEW) for Understanding Multiscale Water Cycle: Scientific Objectives and Experimental Design. Engineering 2022, 18, 207–217. [Google Scholar] [CrossRef]
  23. Palanisami, K.; Kumar, D.S. Impacts of Watershed Development Programmes: Experiences and Evidences from Tamil Nadu. Agric. Econ. Res. Rev. 2009, 22, 387–396. [Google Scholar]
  24. Iqbal, H.S.M. Watershed Prioritization using Morphometric and Land Use/Land Cover Parameters of Dudhganga Catchment Kashmir Valley India using Spatial Technology. J. Geophys. Remote Sens. 2014, 3, 2169-0049. [Google Scholar] [CrossRef]
  25. Strahler, A.N. Hypsometric (area-altitude) analysis of erosional topography. Bull. Geol. Soc. Am. 1952, 63, 1117–1142. [Google Scholar] [CrossRef]
  26. Reddy, G.P.O.; Maji, A.K.; Gajbhiye, K.S. Drainage morphometry and its influence on landform characteristics in a basaltic terrain, Central India—A remote sensing and GIS approach. Int. J. Appl. Earth Obs. Geoinf. 2004, 6, 1–16. [Google Scholar] [CrossRef]
  27. Bashir, B.; Alsalman, A. Geospatial Analysis for Tectonic Assessment and Soil Erosion Prioritization: A Case Study of Wadi Al-Lith, Red Sea Coast, Saudi Arabia. Appl. Sci. 2023, 13, 12523. [Google Scholar] [CrossRef]
  28. Khalifa, A.; Bashir, B.; Alsalman, A.; Bachir, H. Morphometric-Hydro Characterization of the Coastal Line between El-Qussier and Marsa-Alam, Egypt: Preliminary Flood Risk Signatures. Applied Sciences 2022, 12, 6264. [Google Scholar] [CrossRef]
  29. Altaf, F.; Meraj, G.; Romshoo, S.A. Morphometric Analysis to Infer Hydrological Behaviour of Lidder Watershed, Western Himalaya, India. Geogr. J. 2013, 2013, 178021. [Google Scholar] [CrossRef]
  30. Bashir, B.; Alsalman, A. Morpho-Hydrological Analysis and Preliminary Flash Flood Hazard Mapping of Neom City, Northwestern Saudi Arabia, Using Geospatial Techniques. Sustainability 2024, 16, 23. [Google Scholar] [CrossRef]
  31. Pandey, A.; Chowdary, V.M.; Mal, B.C. Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resour. Manag. 2007, 21, 729–746. [Google Scholar] [CrossRef]
  32. Khan, M.A.; Gupta, V.P.; Moharana, P.C. Watershed prioritization using remote sensing and geographical information system: A case study from Guhiya, India. J. Arid. Environ. 2001, 49, 465–475. [Google Scholar] [CrossRef]
  33. Chandniha, S.K.; Kansal, M.L. Prioritization of sub-watersheds based on morphometric analysis using geospatial technique in Piperiya watershed, India. Appl. Water Sci. 2017, 7, 329–338. [Google Scholar] [CrossRef]
  34. Gajbhiye, S.; Mishra, S.K.; Pandey, A. Prioritizing erosion-prone area through morphometric analysis: An RS and GIS perspective. Appl. Water Sci. 2014, 4, 51–61. [Google Scholar] [CrossRef]
  35. Salih, A.; Hassaballa, A.A.; Ganawa, E. Mapping desertification degree and assessing its severity in Al-Ahsa Oasis, Saudi Arabia, using remote sensing-based indicators. Arab. J. Geosci. 2021, 14, 192. [Google Scholar] [CrossRef]
  36. Azaiez, N.; Alleoua, A.; Baazaoui, N.; Qhtani, N. Assessment of Soil Loss in the Mirabah Basin: An Overview of the Potential of Agricultural Terraces as Ancestral Practices (Saudi Arabia). Open J. Soil Sci. 2020, 10, 159–180. [Google Scholar] [CrossRef]
  37. Alharbi, O.A. Geomorphologic Features Characteristic of the Rabigh Coastal Area of the Eastern Red Sea, Saudi Arabia, Using Field Studies and Sentinel 2 Imagery. J. King Abdulaziz Univ. Mar. Sci. 2020, 30, 37–55. [Google Scholar] [CrossRef]
  38. Alsaihani, M.; Alharbi, R. Mapping of Soil Erosion Vulnerability in Wadi Bin Abdullah, Saudi Arabia through RUSLE and Remote Sensing. Water 2024, 16, 2663. [Google Scholar] [CrossRef]
  39. Mallick, J.; Alashker, Y.; Mohammad, S.A.D.; Ahmed, M.; Hasan, M.A. Risk assessment of soil erosion in semi-arid mountainous watershed in Saudi Arabia by RUSLE model coupled with remote sensing and GIS. Geocarto Int. 2014, 29, 915–940. [Google Scholar] [CrossRef]
  40. Borrelli, P.; Alewell, C.; Alvarez, P.; Anache, J.A.A.; Baartman, J.; Ballabio, C.; Bezak, N.; Biddoccu, M.; Cerdà, A.; Chalise, D.; et al. Soil erosion modelling: A global review and statistical analysis. Sci. Total. Environ. 2021, 780, 146494. [Google Scholar] [CrossRef]
  41. Rahman, M.R.; Shi, Z.H.; Chongfa, C. Soil erosion hazard evaluation—An integrated use of remote sensing, GIS and statistical approaches with biophysical parameters towards management strategies. Ecol. Modell. 2009, 220, 1724–1734. [Google Scholar] [CrossRef]
  42. Brown, M.W.H. Geologic Map of the Southern Hijaz Quadrangle; Kingdom of Saudi Arabia: Reston, VA, USA, 1963. [Google Scholar]
  43. Sreedevi, P.D.; Subrahmanyam, K.; Ahmed, S. The significance of morphometric analysis for obtaining groundwater potential zones in a structurally controlled terrain. Environ. Geol. 2005, 47, 412–420. [Google Scholar] [CrossRef]
  44. Salvi, S.S.; Mukhopadhyay, S.; Ranade, S.D.; Rajagopalan, A. Morphometric Analysis of River Drainage Basin/Watershed using GIS and RS: A Review. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET) 2017, 5, 503–508. [Google Scholar]
  45. Tukura, N.G.; Akalu, M.M.; Hussein, M.; Befekadu, A. Morphometric analysis and sub-watershed prioritization of Welmal watershed, Ganale-Dawa River Basin, Ethiopia: Implications for sediment erosion. J. Sediment. Environ. 2021, 6, 121–130. [Google Scholar] [CrossRef]
  46. 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]
  47. Schumm, S.A. Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Bull. Geol. Soc. Am. 1956, 67, 597–646. [Google Scholar] [CrossRef]
  48. Khalifa, A.; Bashir, B.; Alsalman, A.; Naik, S.P.; Nappi, R. Remotely Sensed Data, Morpho-Metric Analysis, and Integrated Method Approach for Flood Risk Assessment: Case Study of Wadi Al-Arish Landscape, Sinai, Egypt. Water 2023, 15, 1797. [Google Scholar] [CrossRef]
  49. Ahmed, S.A.; Chandrashekarappa, K.N.; Raj, S.K.; Nischitha, V.; Kavitha, G. Evaluation of Morphometric Parameters Derived from ASTER and SRTM DEM—A study on Bandihole Sub-watershed Basin in Karnataka. J. Indian Soc. Remote Sens. 2010, 38, 227–238. [Google Scholar] [CrossRef]
  50. Jamal, S.; Ali, A. A comparative study of automatic drainage network extraction using ASTER GDEM, SRTM DEM and Cartosat-1 DEM in parts of Kosi basin, Bihar, India. J. Umm Al-Qura Univ. Eng. Archit. 2023, 14, 45–56. [Google Scholar] [CrossRef]
  51. Singh, P.; Gupta, A.; Singh, M. Hydrological inferences from watershed analysis for water resource management using remote sensing and GIS techniques. Egypt. J. Remote Sens. Space Sci. 2014, 17, 111–121. [Google Scholar] [CrossRef]
  52. Ahmed, F.; Rao, K.S. Prioritization of sub-watersheds based on morphometric analysis using remote sensing and geographic information system techniques. Int. J. Remote Sens. GIS 2015, 4, 51–65. [Google Scholar]
  53. Haokip, P.; Khan, M.A.; Choudhari, P.; Kulimushi, L.C.; Qaraev, I. Identification of erosion-prone areas using morphometric parameters, land use land cover and multi-criteria decision-making method: Geo-informatics approach. Environ. Dev. Sustain. 2022, 24, 527–557. [Google Scholar] [CrossRef]
  54. Miller, V.C. A Quantitative Geomorphic Study of Drainage Basin Characteristics in the Clinch Mountain Area, Virginia and Tennessee; Department of Geology Columbia University: New York, NY, USA, 1953. [Google Scholar]
  55. Pareta, K.; Pareta, U. Quantitative Geomorphological Analysis of a Watershed of Ravi River Basin, H.P. India. Int. J. Remote Sens. GIS 2012, 1, 47–62. [Google Scholar]
  56. Melton, F.A. Aerial Photographs and Structural Geomorphology. J. Geol. 1959, 67, 351–370. [Google Scholar] [CrossRef]
  57. Rahaman, S.A.; Ajeez, S.A.; Aruchamy, S.; Jegankumar, R. Prioritization of Sub Watershed Based on Morphometric Characteristics Using Fuzzy Analytical Hierarchy Process and Geographical Information System—A Study of Kallar Watershed, Tamil Nadu. Aquat. Procedia 2015, 4, 1322–1330. [Google Scholar] [CrossRef]
  58. Patel, A.; Ajaykumar, K.; Dhaloiya, A.; Rao, K.V.R.; Rajwade, Y.; Saxena, C.K. Application of Remote Sensing and GIS for Morphometric Analysis: A Case Study of Burhanpur Watershed. In Surface and Groundwater Resources Development and Management in Semi-Arid Region: Strategies and Solutions for Sustainable Water Management; Springer International Publishing: Cham, Switzerland, 2023. [Google Scholar] [CrossRef]
  59. Abrahams, A.D. Channel Networks: A Geomorphological Perspective. Water Resour. Res. 1984, 20, 161–188. [Google Scholar] [CrossRef]
  60. Khan, M.Y.A.; ElKashouty, M. Watershed prioritization and hydro-morphometric analysis for the potential development of Tabuk Basin, Saudi Arabia using multivariate statistical analysis and coupled RS-GIS approach. Ecol. Indic. 2023, 154, 110766. [Google Scholar] [CrossRef]
  61. Kumar, A.; Singh, S.; Pramanik, M.; Chaudhary, S.; Maurya, A.K.; Kumar, M. Watershed prioritization for soil erosion mapping in the Lesser Himalayan Indian basin using PCA and WSA methods in conjunction with morphometric parameters and GIS-based approach. Environ. Dev. Sustain. 2022, 24, 3723–3761. [Google Scholar] [CrossRef]
  62. Shekar, P.R.; Mathew, A.; Arun, P.S.; Gopi, V.P. Sub-watershed prioritization using morphometric analysis, principal component analysis, hypsometric analysis, land use/land cover analysis, and machine learning approaches in the Peddavagu River Basin, India. J. Water Clim. Chang. 2023, 14, 2055–2084. [Google Scholar] [CrossRef]
  63. Arulbalaji, P.; Padmalal, D. Sub-watershed Prioritization Based on Drainage Morphometric Analysis: A Case Study of Cauvery River Basin in South India. J. Geol. Soc. India 2020, 95, 25–35. [Google Scholar] [CrossRef]
  64. Khalifa, A. Application of Remote sensing techniques in discrimination of rock units and preliminary assessment of tectonic activity using ASTER and ALOSE-PALSAR data at Gabal Delihimmi, Central Eastern Desert, Egypt. Egypt. J. Geol. 2023, 67, 287–298. [Google Scholar] [CrossRef]
  65. Said, S.; Siddique, R.; Shakeel, M. Morphometric analysis and sub-watersheds prioritization of Nagmati River watershed, Kutch District, Gujarat using GIS based approach. J. Water Land Dev. 2018, 39, 131–139. [Google Scholar] [CrossRef]
  66. Bhatt, S.; Ahmed, S.A. Morphometric analysis to determine floods in the Upper Krishna basin using Cartosat DEM. Geocarto Int. 2014, 29, 878–894. [Google Scholar] [CrossRef]
  67. Mesa, L.M. Morphometric analysis of a subtropical Andean basin (Tucumán, Argentina). Environ. Geol. 2006, 50, 1235–1242. [Google Scholar] [CrossRef]
  68. Waikar, M.L.; Nilawar, A.P. Morphometric Analysis of a Drainage Basin Using Geographical Information System: A Case study. Int. J. Multidiscip. Curr. Res. 2014, 2, 179–184. [Google Scholar]
  69. Soni, S. Assessment of Morphometric Characteristics of Chakrar Watershed in Madhya Pradesh India Using Geospatial Technique; Springer: Cham, Switzerland, 2017. [Google Scholar] [CrossRef]
  70. Choudhari, P.P.; Nigam, G.K.; Singh, S.K.; Thakur, S. Morphometric based prioritization of watershed for groundwater potential of Mula river basin, Maharashtra, India. Geol. Ecol. Landsc. 2018, 2, 256–267. [Google Scholar] [CrossRef]
  71. Farhan, Y.; Anaba, O.; Salim, A. Morphometric Analysis and Flash Floods Assessment for Drainage Basins of the Ras En Naqb Area, South Jordan Using GIS. J. Geosci. Environ. Prot. 2016, 4, 9–33. [Google Scholar] [CrossRef]
  72. Das, B.C.; Islam, A.; Sarkar, B. Drainage Basin Shape Indices to Understanding Channel Hydraulics. Water Resour. Manag. 2022, 36, 2523–2547. [Google Scholar] [CrossRef]
  73. Makrari, S.; Sharma, G.; Taloor, A.K.; Singh, M.S.; Sarma, K.K.; Aggarwal, S.P. Assessment of the geomorphic indices in relation to tectonics along selected sectors of Borpani River Basin, Assam using Cartosat DEM data. Geosystems Geoenvironment 2022, 1, 68. [Google Scholar] [CrossRef]
  74. Kalifa, A.; Çakir, Z.; Owen, L.A.; Kaya, Ş. Evaluation of the relative tectonic activity of the adıyaman fault within the arabian-anatolian plate boundary (Eastern Turkey). Geol. Acta 2019, 17, 1–17. [Google Scholar] [CrossRef]
  75. Muluneh, T.; Mamo, W. Morphometric Analysis of Didessa River Catchment in Blue Nile Basin, Western Ethiopia. Sci. Technol. Arts Res. J. 2014, 3, 191. [Google Scholar] [CrossRef]
  76. Debelo, G.; Tadele, K.; Koriche, S.A. 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]
  77. Tufa, F.G. Morphometric Analysis of Kito and Awetu Sub Basins Jimma, Ethiopia. Am. J. Water Sci. Eng. 2018, 4, 80. [Google Scholar] [CrossRef]
  78. Arabameri, A.; Saha, S.; Chen, W.; Roy, J.; Pradhan, B.; Bui, D.T. Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques. J. Hydrol. 2020, 587, 125007. [Google Scholar] [CrossRef]
  79. Liu, X.; Li, H.; Zhang, S.; Cruse, R.M.; Zhang, X. Gully erosion control practices in Northeast China: A review. Sustainability 2019, 11, 5065. [Google Scholar] [CrossRef]
Sustainability 16 09008 g001
Figure 1. (a) Political map of the Arabian Peninsula showing location of the Kingdom of Saudi Arabia, coastal regions (or zones) and location of the Makkah Province, and (b) a Google-based Landsat map showing the province of Makkah with the area of Rabigh in the north.
Figure 1. (a) Political map of the Arabian Peninsula showing location of the Kingdom of Saudi Arabia, coastal regions (or zones) and location of the Makkah Province, and (b) a Google-based Landsat map showing the province of Makkah with the area of Rabigh in the north.
Sustainability 16 09008 g001
Sustainability 16 09008 g002
Figure 2. Geological map of the Rabigh area. This map is modified after [42].
Figure 2. Geological map of the Rabigh area. This map is modified after [42].
Sustainability 16 09008 g002
Sustainability 16 09008 g003
Figure 3. (a) Shaded relief map showing elevations and (b) slope map indicating slope levels in the Rabigh area.
Figure 3. (a) Shaded relief map showing elevations and (b) slope map indicating slope levels in the Rabigh area.
Sustainability 16 09008 g003
Sustainability 16 09008 g004
Figure 4. Methods for applying morphometric analysis.
Figure 4. Methods for applying morphometric analysis.
Sustainability 16 09008 g004
Sustainability 16 09008 g005
Figure 5. Rabigh area basins from 1 to 12 and their drainage systems.
Figure 5. Rabigh area basins from 1 to 12 and their drainage systems.
Sustainability 16 09008 g005
Sustainability 16 09008 g006
Figure 6. Priority ranking maps of the linear parameters applied in the present study including (a) drainage density; Dd), (b) drainage texture; Td), (c) bifurcation ratio; Rb), (d) stream frequency; F), and (e) overland flow length; Lg).
Figure 6. Priority ranking maps of the linear parameters applied in the present study including (a) drainage density; Dd), (b) drainage texture; Td), (c) bifurcation ratio; Rb), (d) stream frequency; F), and (e) overland flow length; Lg).
Sustainability 16 09008 g006
Sustainability 16 09008 g007
Figure 7. Priority ranking maps of the relief parameters applied in the present study including (a) basin relief; H), (b) basin relative ratio; Rr), (c) relative relief ratio; Rrr), (d) ruggedness number; Nr), and (e) Melton ruggedness number; Nmr).
Figure 7. Priority ranking maps of the relief parameters applied in the present study including (a) basin relief; H), (b) basin relative ratio; Rr), (c) relative relief ratio; Rrr), (d) ruggedness number; Nr), and (e) Melton ruggedness number; Nmr).
Sustainability 16 09008 g007
Sustainability 16 09008 g008
Figure 8. Priority ranks and compound values map of the Rabigh area.
Figure 8. Priority ranks and compound values map of the Rabigh area.
Sustainability 16 09008 g008
Sustainability 16 09008 g009
Figure 9. Relative priority classes based on the compound values technique.
Figure 9. Relative priority classes based on the compound values technique.
Sustainability 16 09008 g009
Table 1. Applied morphometric parameters and their equations.
Table 1. Applied morphometric parameters and their equations.
ParametersEquationsReferences
Basic geometries
Basin area (A in km2)A = Projected total basin area[47]
Basin perimeter (P in km) [47]
Stream order Hierarchical rank [46]
Stream numbers (Ns)Nso = N1 + N2 + N3 +…. +Nn[48]
Stream length (Ls in km)Lso = L1 + L2 + L3 + …. + Ln[46,47]
Min. and max. elevations
(H and h in m) 		GIS processing [5,7]
Linear parameters
Average length of overland
Flow (Lg in km)		Lg = 0.5 × Dd[2,46]
Bifurcation ratio (Rb)Rb = Nso/Nso + 1; where streams number values of any estimated order and Nso + 1 is the stream number of the following higher order[27,47]
Mean stream length (SLm in km)Ls/Ns[47]
Drainage texture (Td in Km⁻1)Td = Ns/P [46]
Drainage density (Dd in km/km2)Dd = Ls/A[25]
Stream frequency (F in number/Km)F = Nso/A[46]
Areal parameters
Basin length (Lb in km)Lb = distance between basin outlet and the furthest point[47]
Form factor (Ff)Ff = A/Lb2[46]
Shape factor (Fsh)Fsh = 1/Ff[25]
Circularity ratio (Rc)Rc = 4πA/P2[54]
Elongation ratio (Re)Re = 1.129 × (√A/Lb) [47]
Infiltration number (Nif)Nif = F × Dd[25,55]
Relief parameters
Basin relief (H)H = hmax. − hmin.[25]
Basin relief ratio (Rr) Rr = H/Lb[47]
Relative relief ratio (Rrr)Rrr = H × (100/P)[56]
Ruggedness number (Nr)Nr = Dd × (H/1000)[25]
Melton ruggedness number (Nmr) Nmr = H/0.5 A[6,51]
Table 2. Basic geometries of the studied basins in the Rabigh area.
Table 2. Basic geometries of the studied basins in the Rabigh area.
Basin GeometriesBasin
123456789101112
Area (A in km2) 400626187454001931072407281341142818
Perimeter (P in km) 14062147181121956683175270107306
Basin length (Lb in km)26.520.53335.541.528.5173133542888
Stream numbers
Order 11172018424414556366923444341238
Order 223535533714101858981155
Order 3721016103341423414
Order 4114421123613
Order 5--211--111-1
Order 6--1---------
Stream length (Ls in km)415602366563761831022126581146142757
Elevation (Min. in m)−27−20−22−14−13−11−1625−2125−15−14
Elevation (Max. in m)13657113654563961618251554812681041344
Table 3. Results of the morphometric parameters of the Rabigh area basins. Lg: average length of overland flow; Rb: bifurcation ratio; SLm: mean stream length; Td: drainage texture; Dd: drainage density; F: stream frequency; Ff: form factor; Fsh: shape factor; Rc: circularity ratio; Re: elongation ratio; Nif: infiltration number; H: basin relief; Rr: basin relief ratio; Rrr: relative relief ratio; Nr: ruggedness number; and Nmr: Melton ruggedness number.
Table 3. Results of the morphometric parameters of the Rabigh area basins. Lg: average length of overland flow; Rb: bifurcation ratio; SLm: mean stream length; Td: drainage texture; Dd: drainage density; F: stream frequency; Ff: form factor; Fsh: shape factor; Rc: circularity ratio; Re: elongation ratio; Nif: infiltration number; H: basin relief; Rr: basin relief ratio; Rrr: relative relief ratio; Nr: ruggedness number; and Nmr: Melton ruggedness number.
Morphometric
Parameters		Basin
123456789101112
Lg5.174.825.024.404.684.724.764.414.514.274.984.62
Rb 5.122.833.053.473.653.883.313.083.964.653.493.98
SLm81.0221.11203.45188.53102.7847.0530.9268.80165.99246.1740.64190.24
Td1.050.441.601.761.590.770.751.211.772.110.521.01
Dd1.030.961.000.8800.930.940.9450.8830.900.850.990.92
F0.360.450.380.420.480.380.950.880.900.850.990.92
Ff0.570.140.560.590.230.240.30.240.660.460.180.1
Fsh1.756.751.761.684.284.123.2441.512.145.469.45
Rc0.250.190.350.280.330.260.300.420.290.230.150.1
Re0.850.430.840.870.540.550.620.560.910.770.480.36
Nif0.250.240.080.120.130.210.130.140.080.120.390.35
H13929113874704091729849056912431191358
Rr52.504.4441.9513.269.8665.2415.7917.1323.164.2615.43
Rrr992.13145.62942.34259.56335.47180.45147.54584.66324.91459.73110.49443.49
Nr14.410.8713.934.133.831.620.934.325.1310.611.1812.56
Nmr6.942.934.481.262.041.771.824.081.561.851.673.31
Table 4. Ranks and classes of the studied basins based on their vulnerability to soil erosion.
Table 4. Ranks and classes of the studied basins based on their vulnerability to soil erosion.
BasinsDdTdRbFLgHRrRrrNrNmrCompound ValueRelative Soil Erosion Priority
111015121111244.82
2391073722795.95
311412211117711118.710
4239426666654
5756107555576.16
6685964444106.57
7511111581212857.18
8121248128988928.811
996769109910128.812
10878381210101288.49
114113124333334.93
1210221101111131
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bashir, B.; Alsalman, A. Morphometric and Soil Erosion Characterization Based on Geospatial Analysis and Drainage Basin Prioritization of the Rabigh Area Along the Eastern Red Sea Coastal Plain, Saudi Arabia. Sustainability 2024, 16, 9008. https://doi.org/10.3390/su16209008

AMA Style

Bashir B, Alsalman A. Morphometric and Soil Erosion Characterization Based on Geospatial Analysis and Drainage Basin Prioritization of the Rabigh Area Along the Eastern Red Sea Coastal Plain, Saudi Arabia. Sustainability. 2024; 16(20):9008. https://doi.org/10.3390/su16209008

Chicago/Turabian Style

Bashir, Bashar, and Abdullah Alsalman. 2024. "Morphometric and Soil Erosion Characterization Based on Geospatial Analysis and Drainage Basin Prioritization of the Rabigh Area Along the Eastern Red Sea Coastal Plain, Saudi Arabia" Sustainability 16, no. 20: 9008. https://doi.org/10.3390/su16209008

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop