Wadi-Rahjan catchment of Western Saudi Arabia is the study area (Figure 1). Geographically, the catchment is located within latitude ranges of 21˚11'0''N to 21˚20'0''N and longitude of 40˚2'0''E to 40˚12'0''E. The catchment area accounts for about 157 km² with 359 to 2021 elevation above mean sea level. Physiographically, Wadi-Rahjan consists of hills, urban masses, and barren areas. The climate is semi-arid with an extensive hot summer season and a short winter characterized by rainfalls. Annually, the average temperature and precipitation are shown in Figure 2 and Figure 3.

Figure 4 shows a flowchart of the methodology used to estimate runoff with the soil conservation service curve number (SCN-CN) method, remote sensing (RS), and geographic information system (GIS). The land use/land cover (LULC), digital

elevation model (DEM), and soil texture maps were obtained from easily accessible RS data and processed with the ArcGIS 10.3 software. The United States’ Geological Survey’s Dem data with a 30-m resolution was employed to outline the Wadi-Rahjan area using the ArcGIS 10.3 (Figure 5). It also produced a slope map (Figure 6). The LULC map was developed with the Landsat ETM+ satellite imagery, downloaded from the US Geological Survey’s website. The climatic research unit (CRU) supplied the rainfall data with 1900 to 2019 records. The study’s soil data was obtained from Saudi’s main soil map, ministry of environment, water and agriculture.

Soil The texture of the soil indicates the percentage of sand, silt, and clay particles within a given area (Saxton et al., 1986) . Studying the soil water relation

and soil hydraulic properties is important when attempting to understand the soil-water relationship (Cosby et al., 1984; Saxton et al., 1986) . For soil data to be utilized in the SCS-CN model, the natural resource conservation service (NRCS) categorized soils according to their hydrologic soil groups (HSGs) into four separate soil categories: A, B, C, and D, which are respectively graded according to their capacity for infiltrating water and rate of water transmission through the soil (NRCS, 2009) (Table 1). HSG-A represents high permeability with low runoff potential. HSG-D, on the other hand, implies a very shallow or high clay content with high runoff potential. HSG-B and HSG-C are intermediate classes (NRCS, 2009; Sekar & Randhir, 2007) .

A significant thematic input in the study analysis is LULC. It specifies the current state of land use and pattern (Bhange & Deshmukh, 2018) . As LULC shifts rapidly, remote sensing satellite mapping is used widely (Bhange & Deshmukh, 2018) . LULC is a frequently used method for estimating the influence of watershed cover on infiltration and runoff, and it includes most types of natural vegetation, terraced bare soil, and rocks (Taher, 2015) . The Landsat ETM+ satellite imagery with a resolution of 30 m was used for the investigation. The LULC for the research region was obtained using supervised classification in the ERDAS Imagine program. Further real field land use data were used to support the supervised classification.

The United States’ Department of Agriculture’s SCS initiated the SCS-CN, specifically for rural areas (Mishra & Singh, 2003; USDA, 1972) . The SCS-CN technique is based on the link between rainfall and runoff depth under the curve number (CN) concept. CN helps convert the rainfall frequency distribution into a runoff frequency distribution. The CN has no dimension and is defined as 1 ≤ CN ≤ 100. Low CNs denote dry antecedent soil moisture conditions (AMCI), whereas average CNs denote a normal condition (AMCII), and high CNs denote a wet soil condition (AMCIII). Normal condition CN can be changed to wet and dry conditions through the Equation (1) and Equation (2) shown below (Hawkins et al., 1985) :

CN I = 4 .2 × CN II 10 − ( 0 .058 × CN II ) (1)

CN I I I = 23 × C N I I 1 0 + ( 0 .13 × C N I I ) (2)

where CN_I is a CN applied for dry conditions, CN_II is a CN applied for normal conditions, and CN_III is a CN applied for wet conditions. The range of AMC for each class is listed in Table 2. The composite CN (CN_c) can be used to estimate direct runoff in a catchment that consists of several soil types and land use using Equation (3) (Hawkins et al., 1985) :

CN c = ∑ i = 1 n CN i × A i A (3)

where CN_i is the CN value of the sub-region, A_i is the area of the sub-region, and A is the catchment area.

The SCS-CN model developed by the USDA (USDA, 1972) is responsible for many challenges associated with runoff generation, incorporating them under the CN parameter (Soulis & Valiantzas, 2012) . CN captures the potentiality of runoff as regards LULC, HSG, and rainfall. Using the SCS-CN technique, the runoff Equation (4) is obtained as follows:

Q = { ( P − λ S ) 2 P + ( 1 − λ ) S P > λ S 0 P ≤ λ S (4)

where Q is the direct runoff depth (mm), P is rainfall depth (mm), S is potential maximum retention after runoff begins (mm), and λ is surface runoff abstraction (dimensionless). Many studies on various geographical regions in various nations have shown that the values of λ are between 0.1 and 0.3 (Shrestha, 2003) . Studying the West Bank attachment, Shadeed and Almasri (2010) suggested λ = 0.2. Shrestha (2003) , in his sensitivity breakdown, uncovered that λ = 0.2 value is appropriate for mountainous agricultural lands, which have comparable features to the study location. Substituting λ = 0.2 in Equation (4), it becomes:

Q = ( P − 0.2 S ) 2 P + 0.8 S (5)

When runoff starts, the potential maximum retention (S) is found from Equation (6):

S = 25400 C N − 254 (6)

The catchment’s LULC map was grouped into four categories: agriculture, urban, bare soil, and rocks (Figure 7). Figure 7 shows the catchment area was predominantly rocky, followed by bare soils, urban masses, and agriculture, respectively. Both rocky and bare soils are most dominant, encompassing 62% (98 km²) and 35% (55 km²), respectively. Urban masses cover 2% area (about 2.5 km²) found in the central part of the catchment area. For agricultural areas, they have barely 1% (1.5 km²) of the catchment area. According to Zhao et al. (2004) , a good relationship between vegetation cover and runoff, where a high runoff was traced to deforestation and urbanization. The study featured a similar trend, which established a strong link between rainfall and runoff due to low vegetation cover.

Wadi-Rahjan has three soil textures, namely, sandy, loam, clay, and sandy clay loam. By soil categories and infiltration rates, the catchment area studied is grouped into three HSGs—Groups B, C, and D (Figure 8). HSG-D represents a clay soil region of about 35%, covering 56 km². This explains that a larger part of the study area is a good fit for water retention and runoff estimation. With about 25% dominance, the HSG-C has lesser land coverage. The C-class represents a

portion mixed with both clay and loam soils. HSG-B region accounts for about 40%, predominantly loamy soil texture. With the HSG portions, it’s worth concluding that soil texture within the study area contributes to significant runoff.

The CN_II values related to AMC-II were derived through cross-mapping the HSGs and LULC maps with the ArcGIS 10.3 software (Figure 9). The CN values obtained for CN_I and CN_III linked with AMC-I and AMC-III, respectively, were derived by Equation (1) and Equation (2), as shown in Figure 10 and Figure 11. The 90 to 94 group is considered the highest CN_II, covering 59% (93 km²), where between 74 and 80 CN group accounts for 1 km² of the entire study area recording less runoff. The CN category between 80 and 85 represents low-to-moderation runoff potential, covering 2 km², whereas the high-moderate runoff (CN class 85 to 90) accounts for 39% (about 61 km²). These indicators point to the Wadi-Rahjan catchment’s potency to record higher runoff for remarkably high CN values. A weighted CN (or CNc) was estimated, dividing the total CN value and related area by the total sub-catchment areas with Equation (3). As a result, the weighted CN_I, CN_II, and CN_III values were 80, 90, and 97, respectively.

Potential maximum retention (S) is obtained in the ArcGIS 10.3 software with Equation (6), following the CN map. S value (Figure 12) falls between 16 to 89 mm. Within the catchment, the least S values are found in developed areas and rock with typical low retention capacity. Also, bare areas around municipalities had far less retention capacity caused by improper use. Agricultural regions record the largest values with their high retention capacity. Noteworthily, the catchment area is mostly dominated by the S range from 16 to 50 mm.

The primary measures used or direct runoff computation is rainfall and CN. The SCS-CN model was employed in the GIS environment to develop the runoff potential map (Figure 13). The annual runoff ranged from 194 to 295 mm, with the highest percentage of runoff water occurring between 289 and 295 mm (35%) and 24% occurring between 269 and 288 mm. The 194 to 268 mm runoff water range was harvestable around the southern and eastern regions of the catchment. The results corroborated Choi and Ball (2002) assertion that fine-particle-rich soils have a lower infiltration rate than those with larger particles. As a result, outflow can occur more easily on the soils with these characteristics.

The CN values calculated are tested by pairing the rainfall and runoff data (Figure 14). This rainfall-runoff union is strongly correlated with a correlation

coefficient R² valued at 0.90. The SCS-CN model results matched the findings of Peng and You (2006) who found that study areas with a coefficient of runoff greater than 0.5 showed a better simulation effect.