Introduction

Environmental challenges are a source of concern for governments around the world. As a result, both industrialized and developing countries have sponsored and approved numerous treaties, The goal of these environmental laws is to conserve and protect the environment for future generations as well (Kefi M et al.2012). Wherefore, land degradation is a major environmental issue affected by a lot of elements such as climate change and inappropriate human practices (Mihi et al. 2019). Land degradation is producing major social, economic, and environmental concerns around the world, particularly in arid and semi-arid climate zones (Arar and Chenchouni 2014). Therefore, land loss is essentially the outcome of the mismanagement and depletion of natural assets, mainly from unfit farming techniques, and overgrazing (Dengiz et al.2015).

Water and soil are both important components. Water erosion is causing significant quantity and quality damage in the dry regions; it leads to the degradation of the pedological surface layers and the movement of its contents’ ingredient (Antoni et al. 2006). However, there are a number of studies which suggest that the Mediterranean region is vulnerable to climate change, with rising aridity speeding up erosion caused by water (Souadi 2011; Alsafadi et al. 2022). Although, soil is a nonrenewable resource that provides a diverse range of environmental benefits and products (Pal 2016; Saleem et al. 2020). However, the most prevalent kind of land damage is soil loss, and its severity is impacted by land use and management methods all over the world (Rahman et al. 2009; Xu et al. 2011). Therefore, soil loss has a detrimental impact on natural resources, reducing agriculture output and causing water quality degradation (Wang 2007).

In many Mediterranean areas, soil loss caused by rainfall and runoff is a common occurrence, where erosion formed has reached an irreversible point in some regions of the Mediterranean, and there is no more soil in others. Any soil loss of greater than 1t/ha year−1 can be regarded as inescapable within a period of time 50–100 years with a slow soil formation rate (Morgan 1992). Variation of separation and soil particle diffusion geographically and temporally by runoff from the ground surface causes these phenomena (Djoukbala et al. 2019). Whereas, soil erosion accounts for around 85% of the decline of agricultural areas, resulting in a 17% reduction in productivity (AbdelRahman and Arafat 2020). Hence, water soil loss is a serious problem that represents serious hazards to the environment and living in the area of EL-Mador Valley Basin.

Evaluating the geographical distribution of soil loss is the initial step toward estimating the consequences of water on soil erosion. Hence, we employed the Revised Universal Soil Loss Equation (RUSLE) model, which is a soil erosion empirical model depending on the Universal Soil Loss Equation (Wischmeier and Smith 1978). Which is one of the most often applied and utilized methods in the Mediterranean region and involves applying each parameter of the RUSLE equation to georeferenced data (Benkadja et al. 2015). And then RUSLE is intended for usage in a runoff area or on a hillside. The RUSLE model can calculate an average yearly classification of soil loss for a study area for a variety of simulations that entail various cropping systems, management strategies, and soil erosion monitor techniques (Angima et al. 2003).

GIS maps can be used to integrate datasets and apply mathematical formulae to the quantitative data of erosion factors. However, estimating soil loss is done on a cell level, a combination of GIS, RS, and RUSLE is a helpful tool (Millward and Mersey 1999). Therefore, the use of GIS and RS techniques permits for the estimate of soil loss and its geographical distribution to be evaluated at a lower charge and with greater accuracy in broader regions (Wang et al. 2003). Considered the main goal of that research is to measure the annual rate of soil loss and determine its geographical distribution in the EL-Mador Valley Basin using the RUSLE model through GIS.

Study area

EL-Mador Valley Basin is located in southwest Matrouh City, northwestern coast of Egypt. The study area extends between latitudes 31°12 ʹ 25"–31° 22 ʹ20" North and longitudes 27° 2ʹ 30"–27° 10 ʹ 30" East. It covers an area of approximately 105.61 Km2 (Fig. 1). This basin bordered Wadi El-Raml to the east, the Wadi Maged to the west, the plains lands to the south, and the Mediterranean Sea to the north. Various geological formations can be found in the study area, beginning with the Tertiary to the Quaternary. Tertiary formations consist of Marmarica formations in the south (Middle Miocene era), and Elhagif formations in the Middle (Pliocene era). Quaternary deposits sand, gravel, and recent costal deposits in the north. The climate is Mediterranean in character (hot and dry summers, mild and rainy winters). The annual rainfall rate between 140.50 and 211.66 mm, More than 70% of the annual rainfall arises from November to February, with no rain falling throughout the summer. The mean annual temperature varied from 15.00 to 27.63 °C, and the hottest month is June, July, and August of the summer time (E.M.A., 2018). Elevation ranges from − 1 m in the north to 182 m in the south. The population's primary economic activities are tourism and agriculture. Olives, figs, and agricultural products are one of the basin's most vital crops.

Fig. 1
figure 1

Site map of EL-Mador Valley Basin

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Materials and methods

The study uses a variety of spatial datasets from multiple sources to achieve its goals. Table 1 illustrates the datasets and their related sources. Therefore, these data are combined and calculated using the GIS, RS, and RUSLE for the mapping of the areas vulnerable to estimation soil water erosion.

Table 1 Information about the data utilized in this study
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Water erosion predicting models are helpful for studying soil water erosion and developing a soil loss response. In that research, the RUSLE was utilized to calculate soil losses (Renard et al. 1991). The RUSLE is the most extensively used model in this subject around the world and many studies utilize it. It has been widely utilized to quantify soil loss damage, evaluate soil loss hazards, and advise expansion projects for soil erosion control under various land cover situations (Boggs et al. 2001; Angima et al. 2003).

The RUSLE model depicts how soil loss as a result of rainwater and surface runoff is influenced by climate, soil, terrain, and land use (Fig. 2). Therefore, RUSLE uses five main parameters to determine the estimated average annual erosion (rainfall pattern, type of soil, topography, crop management, and conservation practices) (Renard et al. 1997). Hence, parameters are estimated independently using GIS and RS, and then merged to create an annual soil loss map that estimates erosion according to Eq. (1) (Wischmeier and Smith 1965)

$$A= R\times K\times LS\times C\times P,$$
(1)

where A = average annual soil loss (ton/ha/year), R = rainfall erosivity parameter (MJ/ha/mm/year), K = soil erodibility parameter (ton/MJ/mm), LS = slope length and topographic parameter, C = crop management parameter, and P = conservation practices parameter.

Fig. 2
figure 2

Diagram of the study for estimating water erosion using RUSLE model through GIS

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RUSLE parameters’ estimation and spatial data processing

Rainfall erosivity parameter (R)

The rainfall erosivity parameter (R) is among the most critical elements influencing the rate of soil loss variety of sites around the world (Hermassi et al. 2017). Further, the R parameter is the yearly average of the sum of rainfall kinetic energy events, which quantifies the impact of rainfall and runoff (Chatterjee et al. 2014). However, the R parameter is a criterion that assesses the effect of rainfall as well as the quantity and rate of runoff caused by rainfall events (Xu et al. 2009). Therefore, the R parameter is also influenced by patterning, geographic distribution, intensity, momentums, and kinetic energy, as well as the size of droplets (Carollo et al. 2018). In that research has been utilized the following equations by Wischmeier and Smith (1978) [Eq. (2)]

$$R=\sum_{i=1}^{12}1.735\times {10}^{(1.5\times {\mathrm{log}}_{10}({p}_{i}^{2}/p)-0.08188)},$$
(2)

where R is rainfall erosivity factor (MJ mm ha−1 h−1 year−1, Pi is monthly rainfall (mm), and P is annual rainfall (mm).

However, Fig. 3 displays the map of R parameter values that range between 378.654 and 302.392 MJ mm ha−1 h−1 per year, The distribution map of R parameter showed the northern part (high R value) and the southern part (low R value).

Fig. 3
figure 3

Distribution map of rainfall erosivity parameter (R)

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Soil erodibility parameter (K)

Soil erodibility parameter (K) depicts the surface soil proneness to rains and runoff eroding the soil (Kim 2006; Ganasri et al. 2015). The following are the primary soil characteristic that influences the K parameter: soil texture variables (silt, sand, clay %), then percent of organic matter, structure, permeability, and other elements that impact the soil type (Wischmeier et al. 1971). Moreover, the K parameter is a metric for the geographical variability of probable soil erosion on the basis of compositional and structural properties. The soil data utilized in this study came from the Digital Soil Map of the World (DSMW-2007) at 1:5.000.000 scale https://data.apps.fao.org/map/catalog. Hence, the values of the K parameter in this study were estimated utilizing the methods presented by Neitsch et al. (2011) [Eq. (3–7)]

$${K}_{usle}={f}_{csand \,\times } \, {f}_{cl-si \,\times } \, {f}_{orgc \, \times } \, {f}_{hisand }$$
(3)
$${f}_{\mathrm{csand}}=\left(0.2+0.3\times \mathrm{exp}\left[-0.256\times {m}_{\mathrm{s}}\times \left(1-\frac{{m}_{\mathrm{silt}}}{100}\right)\right]\right)$$
(4)
$${f}_{\mathrm{cl}-\mathrm{si}}={\left(\frac{{m}_{\mathrm{silt}}}{{m}_{\mathrm{c}}+{m}_{\mathrm{silt}}}\right)}^{0.3}$$
(5)
$${f}_{\mathrm{orgc}}=\left(1-\frac{0.25\times \mathrm{orgC}}{\mathrm{orgC}+\mathrm{exp}\left[3.72-2.95\times \mathrm{orgC}\right]}\right)$$
(6)
$${f}_{\mathrm{hisand}}=\left(1-\frac{0.7\times \left(1-\frac{{m}_{\mathrm{s}}}{100}\right)}{\left(1-\frac{{m}_{\mathrm{s}}}{100}\right)+\mathrm{exp}\left[-5.51+22.9\left(1-\frac{{m}_{\mathrm{s}}}{100}\right)\right]}\right),$$
(7)

where Kusle is the erodibility parameter, Ms is the sand %, Msilt is the silt%, Mc is the clay%, and Orgc is the organic matter%.

Furthermore, for each soil type, a K value was estimated, indicating that the K value for each soil type was consistent across the study area, and the K parameter varies from 0 to 1(Table 2). Figure 4 displays the map of K parameter values that range between 0.11775 and 0.17071 ton MJ− 1 mm−1.

Table 2 Assessment of K parameter in EL-Mador Valley Basin
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Fig. 4
figure 4

Distribution map of soil erodibility parameter (K)

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Topographic parameter (LS)

The topographic parameter (LS) is among the most important elements in estimating water erosion. Where the impact of terrain on soil loss is indicated by this element by calculating the runoff speed (Ganasri and Ramesh 2016). Furthermore, the relation of soil loss to slope degrees of hills is influenced by vegetation coverage amount and soil characteristics. This means that the LS parameter increases in proportion to the length and steepness of the hill slope (Koirala et al. 2019).

The topographic parameter LS is composed of two parameters: slope length (L) and slope degree (S) that have a substantial impact on runoff and, therefore, water soil erosion in the EL-Mador Valley Basin (Biswas and Pani 2015). The L refers to the impact of slope length on erosion, and as the slope length rises, so does the amount of soil lost per unit area. The S indicates the result of slope steepness on erosion, the degree of soil loss rises faster with slope steepness than with slope length (Ganasri and Ramesh 2016). Therefore, the topography of the study valley basin is featured steep slopes which reach 21 degrees, as illustrated in Fig. 5. And it is commonly represented by combining these two elements into one LS factor (Perovic et al. 2016).

Fig. 5
figure 5

Distribution map of slope values

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In this study, the LS parameter was estimated in GIS software by utilizing a digital elevation model (DEM) with 30 m resolution utilizing (SRTM) https://earthexplorer.usgs.gov. Where flow accumulation and slope degrees were generated and the LS parameter was then mapped using Eq. (8) (Wischmeier and Smith 1978)

$$LS={\left(Flow \, Accumulation\times \frac{Cell \, Size}{22.13}\right)}^{0.4}\times {\left(\frac{\mathrm{sin} \, slope}{0.0896}\right)}^{1.3}.$$
(8)

In this regard, LS values in the EL-Mador Valley Basin are between 0 and 210.071, as shown in Fig. 6.

Fig. 6
figure 6

Distribution map of topographic parameter (LS)

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Crop management parameter (c)

The crop management parameter (c) is the proportion of soil erosion caused by a specific crop in the foundation soil (Morgan 1994). Hence, the C parameter is engaged to clarify the result of agricultural crops and other management practices on the degree of eroding; it is used to measure how efficient different soil and crop management strategies are in avoiding soil degradation (Renard et al. 1997). After topography, the C parameter is considered the second most important element in managing soil loss (Benchettouh et al. 2017). The C parameter represents the amount of preserving the soil provided by vegetation cover, where vegetation has a major role in slowing down the water erosion process (Gyssels et al. 2005; Wang et al. 2013). Moreover, soil erosion reduces constantly with an increase in vegetation cover. Where vegetation cover is a complicated criterion for controlling soil erosion, because it dissipates raindrop kinetic energy, delays surface runoff, and improves infiltration capacity. As a result, the C parameter is a relation between bare-soil erosion and erosion found under a cropping system (Hu et al. 2015; Sujatha and Sridhar 2018).

One of the most widely utilized tools for determining the C parameter is the Normalized Difference Vegetation Index (NDVI) (Lin et al. 2002). In this work, the NDVI data produced by Satellite Landsat 8 OLI/TIRS (USGS data) were taken in February 2022 with a 30 m resolution was utilized to identify the C parameter and illustrate how variations in plant cover affect water soil erosion. Where the NDVI indices are referred to as the rate of the difference between the near-infrared band (NIR) and the red band (RED) and the sum of these two bands (Rouse Jr. et al.1973) using Eq. (9)

$$\mathrm{NDVI}=\frac{\mathrm{NIR}-\mathrm{RED}}{\mathrm{NIR}+\mathrm{RED}}.$$
(9)

The geographical distribution of NDVI values illustrated in Fig. 7 ranges from 0.045 to 0.439. In the current evaluation, the C parameter values were estimated utilizing the NDVI index (Van der Knijff et al. 1999), which is given by Eq. (10)

Fig. 7
figure 7

Distribution map of NDVI values

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$$C=\mathrm{exp}\left[(-2.5\times \mathrm{NDVI}/(1-\mathrm{NDVI})\right].$$
(10)

The C parameter varies between approximately 0 for dense vegetation and 1 for bare land. where a lower C parameter indicates a very strong vegetation cover cause and effect no soil erosion, while higher values of C parameter represent that there is no vegetation cover and cause soil erosion (Erencin 2000). In the present assessment, the geographical distribution of C parameter values ranged from 0.142 to 0.890 (Fig. 8).

Fig. 8
figure 8

Distribution map of crop management parameter (C)

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Conservation practices’ parameter (P)

Conservation practices’ parameter (P) measures the influence of conservation practices that decrease the quantity and volume of runoff (Wischmeier and Smith 1978). Hence, the P parameter is the soil erosion percentage with special support practice to the corresponding loss with tillage on the slopes, The P parameter represents the percent of soil erosion related to each cultivation technique based on its capacity to prevent erosion of the soil and decrease the size and velocity of the flowing water and facilitates infiltration (Rabia 2012). Whatever, the P parameter is one of the significant variables that help to minimize soil erosion by reducing the risk of it happening (Mahala 2018; Das et al. 2018).

To create a P parameter map, the Landsat Satellite image (Landsat 8 OLI/TIRS) was utilized to categorize land use/land cover (LULC) in the EL-Mador Valley Basin (Fig. 9). Arc GIS 10.8 software was used to merge bands of the satellite image in the study area which was taken in 2022 into a single file using the Composite band tool, and then, a directed classification process was used the supervised classification method (Barrett et al. 2014). In addition, the P parameter was calculated from the slope and LULC. In GIS, the slope map generated from the DEM was combined with LULC (Wischmeier and Smith 1978). In addition, the P parameter values were allocated to the various classes, and the geographical distribution of the P parameter was plotted (Table 3).

Fig. 9
figure 9

Land use/land cover (LULC) map of EL-Mador Valley Basin

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Table 3 Conservation practices P values for different land use in EL-Mador Valley Basin
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In this scope, the P parameter has a range of values between 0 and 1. When a value approaches 0, it means best conservation practices and relates to locations that are resistant to water erosion. When a value approaches 1, it means bad conservation practices and responsible for locations with steep slopes and means a lack of human support practices (Mahala 2018; Das et al. 2018). In the current assessment, the geographical distribution of P parameter values ranged from 0.05 to 0.75. In addition, the highest value was found in the study area in middle part of the study area where steep hills can be found (Fig. 10). Furthermore, the P parameter values vary according to the integration of slope classes and current agriculture practice.

Fig. 10
figure 10

Distribution map of conservation practice parameter (P)

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Results and discussion

RUSLE is an empirical formula-based predictive model for the five elements that control water soil erosion including the rainfall erosivity (R), the soil erodibility (K), the topographic (LS), crop management (c), and the conservation practices (P) (Prasannakumar et al. 2012). However, in ArcGIS10.8 software, the RUSLE parameters in the study area were expressed using raster layers. To measure, analyze, and create maps of water soil erosion hazard and severity for EL-Mador Valley Basin, those raster layers were multiplied extracted for R, K, LS, C, and P parameters of the RUSLE model and merged into the raster calculator tool in the ArcGIS 10.8 spatial analyst tools.

In the present assessment, Fig. 11 shows the annual rate of water soil erosion risk in the EL-Mador Valley Basin ranged from 0 to > 2500 ton ha−1 year−1, with a spatial average of 54.80 to ha−1 year−1. In this scope, the raster output of water soil erosion was divided into five hazard categories: Low (56.56%), Moderate (13.88%), High (8.09%), Severe (8.82%), and Extreme (12.65%), as shown in Table 4.

Fig. 11
figure 11

Distribution map of soil erosion values in EL-Mador Valley Basin

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Table 4 Classification of water soil erosion in EL-Mador Valley Basin
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The geographic distributions of high, severe, and extreme soil loss risk are generally found in the east, west, and central regions in the study area. However, these areas are more vulnerable to soil loss as a result of the geographic integration of rainfall kinetic energy, runoff, and steep slopes. In addition, the study showed that categories with high, severe, and extreme values of water soil erosion are sloping lands. Where the slopes in these places reach 21 degrees, as illustrated in Fig. 5. Moreover, many research have referred to the effect of slopes on topsoil movement, and these results are compatible with them (Thomas et al. 2018; Kayet et al. 2018).

Soil water erosion hazard evaluation is essential for managing land use and soil conservation strategy to decrease the damaging effects (Yue-qing et al. 2008; Baskan et al. 2010). In addition, the adoption of a system that combines RUSLE as an erosion model, RS, and GIS could increase the accuracy of findings across wide areas. However, this strategy enables decision­makers and supervisors to intervention approaches to prevent water soil erosion in areas where the risk is high. It also allows for the administration of soil used by facilities for the purpose of preserving from water erosion.

Conclusions

Soil erosion is a popular occurrence that has a negative impact on the land. Soil erosion refers to a loss of production caused by nutrient loss, a reduction in soil thickness, and in severe circumstances, full soil loss. As a result, it is essential to evaluate the erosion's possible to take preventative actions against damages (Morgan 1997). In addition, soil erosion is difficult to prevent, but it can be decreased with appropriate land-use management and support techniques that can stabilize the area’s topsoil. In this study, the RUSLE model merged with RS and GIS was utilized to estimate average annual soil erosion in the EL-Mador Valley Basin for the purpose of evaluating soil erosion and protecting soil and natural resources. Hence, the data on soil erosion in the study area will assist in the prioritization of soil conservation strategies. This study reveals that the annual rate of water soil erosion risk in the EL-Mador Valley Basin ranged from 0 to > 2500 ton ha­1 year­1, with a spatial average of 54.80 ton ha­1 year­1, These results are remarkably similar to those calculated using the RUSLE model in Mediterranean regions. Moreover, the study closed through the geographical distribution of soil loss risks classifications in the study area that the eastern, western, and central parts of the study area have the high, severe, and extreme values. In addition, it was observed the essence of the slope factor in speeding soil erosion in these areas. As a result, soil protection measures must be implemented to limit the risk of soil erosion and different soil management strategies should be prioritized in agricultural areas particularly on steep slopes. Meanwhile, the resulting soil loss map is an essential requirement for quick usage by local decision-makers for soil management planning.

Concluding remarks

The current study focuses on was mainly focused on predicting water erosion using RUSLE by integrating remote-sensing data and GIS and reviewing the results of the study by conducting of field survey and verify the results in the EL-Mador Valley Basin, which indicated the importance of this study in that region that is exposed to the risk of water erosion, according to the following: The outputs of the soil erosion risk map in the study area were divided into five hazard categories: Low (56.56%), Moderate (13.88%), High (8.09%), Severe (8.82%), and Extreme (12.65%). In addition, this study helps plans should be put into place to lessen the anticipated high soil erosion zone by working together with the local government and local farmers to reduce or eliminate soil erosion. Hence, local people will benefit in the long run.