Introduction

Soil is the most important vital natural resource that supports crucial ecosystem functions and provides several crucial environmental resources (Kouli et al. 2009; Alexakis et al. 2013). Soil erosion is a movement of sediments and organic matter from one place to another by a transport agent which is the runoff in the case of water erosion (Ellison 1946; Foucault and Raoult 1995; Enters 1998). It is considered as a major environmental problem since it negatively threatens natural resources and the environment (Rahman et al. 2009). It leads to the loss of essential soil elements, a scarcity of land resources, and endangers the richness of species and the equilibrium of the ecosystems. This causes a decline in global agricultural production and economic development (Pimentel and Burgess 2013). These harmful consequences are both at the level of the source where it could lead to desertification and at the level of the sediment reception areas where it causes siltation of hydraulic structures which would lead to flooding (Van Pelt et al. 2017). The consequences of soil degradation range from reduced soil fertility to the evacuation of entire regions (Roy et al. 2005).

This phenomenon is accentuated especially in regions with an arid to semiarid climate. The negative effects of soil erosion on the environment are now a topic of concern to researchers and scientists around the world (Pal and Chakrabortty 2019; Saha et al. 2020), and obviously, the quantitative water erosion mapping has attracted a lot of attention (Tuo et al. 2018; Vaezi et al. 2017). Many methods have been developed for the quantification and calculation of the erosion rate either measured directly in the field or estimated by soil analyses or empirical models and equations that take into account the impact of all variables of soil erosion (Lu et al. 2004; Moukhchane 2002; Prasannakumar et al. 2012; Tian et al. 2009). Among these are the universal soil loss equation (USLE) (Wischmeier and Smith 1978), the European Soil Erosion Model (EUROSEM) (Morgan et al. 1990), the Soil and Water Assessment Tool (SWAT) (Engel et al. 1993), the Mediterranean Desertification and Land Use (MEDALUS) (Kirkby et al. 1998), the Revised Universal Soil Loss Equation (RUSLE) which is an improved version of the USLE model (Renard 1997; Renard et al. 1991), etc. Thanks to the easy access to various input data and its precision, the RUSLE model remains the most widely used tool for soil erosion quantification studies around the world (Khan and Govil 2020; Mahala 2018) and remains a very good evaluation model that can easily be integrated into a GIS environment (Nehaï and Guettouche 2020; Pradeep et al. 2015).

Remote sensing and GIS are particularly powerful tools for the study of natural hazards (Abuzied et al. 2016a, 2016b). They are essential tools in interactive decision support systems for natural hazard management operations (Abuzied and Alrefaee 2019; BouKheir et al. 2006; Shrimali et al. 2001; Wachal and Hudak 2000).

In Morocco, as everywhere in the world, this phenomenon depends on several natural physico-climatic factors (e.g., rainfall, vegetation, lithology, topography, soil erodibility, etc.) and anthropogenic factors (e.g., cultivation on slopes, deforestation, hydraulic and civil engineering).

The Tifnout Askaoun watershed, located in southern Morocco between the high-altitude mountains of the High Atlas and the Anti-Atlas, is exposed highly to erosion because of different aspects (shape, high variable rainfall, rivers discharge, steep slope, and poorly developed soils) (Tairi et al. 2019; Bouchaou et al. 2008). Given this background, the purpose of this study is to map the annual erosion rate in the Tifnout Askaoun watershed, using the Revised Universal Soil Loss Equation in a GIS environment. Results should also allow to highlight the most threatened sub-catchments by the phenomenon of water erosion that requires priority intervention.

Materials and methods

Study area

The “Tifnout Askaoun” zone is located between the latitudes North 30° 35′ and 31° 05′ and the longitudes West 7° 37′and 8° 11′. This zone includes, in the north, the large Tifnout valley, which represents the southern flank of the western High Atlas Mountains, and the south Askaoun zone which represents the Siroua mountain of the Anti-Atlas. This highly mountainous region is mainly drained to the north by the Assif Tifnout and to the south by the different wadis of Askaoun. It is characterized by very rugged topography with altitudes ranging from 732 m.a.s.l. in the southern part of the Tifnout valley to 4065 m.a.s.l. near the summit of Toubkal (Fig. 1).

Fig. 1
figure 1

Geographical location with elevation variation of the study area

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Geology

The Tifnout Askaoun watershed includes two main mountain ranges (Fig. 2):

  • In the east

Fig. 2
figure 2

a Simplified geological map of Morocco (Western High Atlas, Meseta domain, and the Anti-Atlas) at the northern margin of the WAC (adapted from Hoepffner et al. 2005; Ouabid et al. 2017; EL Haibi et al. 2020). b Zoom of the contact area between the high Atlas and the Anti-Atlas and the location of the study area. c Simplified geological map of the Tifnout Askaoun watershed

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The Siroua Massif forms the link between the High Atlas and the Anti-Atlas, which explains the confusion often made in its identification with one or the other of these two chains. It is a mountainous area that is difficult to access, with an average altitude of close to 2000 m and with a summit of 3304 m (mountain of Siroua). This was visited at the beginning of the twentieth century (Gentil 1905). It is located in the central zone of the Anti-Atlas and belongs to the Pan-African Neoproterozoic domain. It is made up of a Pan-African basement and Upper to Terminal Neoproterozoic volcanic cover, as well as a much more recent Cretaceous and Neogene cover (Belkacim et al. 2017). This massif is cut to the south by the Anti-Atlas Major Fault (AAMF) (Choubert 1947) (Fig. 2).

  • In the north

The Moroccan High Atlas is located north of the South Atlas Fault (SAF) which boards, in the south, the Anti-Atlas mountain constituting the northern limit of the West African Craton (WAC) (Ennih and Liégeois 2001; Taib et al. 2020) (Fig. 2a and b). The High Atlas chain contains the highest peaks in all of North Africa (Toubkal 4167 m), with interior plateaus and basins and deep valleys like the Tifnout valley. The heart of the chain is of Paleozoic age where Georgian and Acadian lands outcrop in the western part of the study area with sandstone and quartzite lithologies (Michard et al. 2010; Missenard 2006). The Quaternary lands are negligible except to the south of the watershed in the form of alluvium at the bottom of the wadis (Fig. 2c).

Datasets

The process of soil degradation that affects our study area results from the interaction of several factors. To conduct this study, we used the following documents:

  • Monthly and annual rainfall data taken from the Tropical Rainfall Measuring Mission (TRMM) for a period of 12 years (1998–2009). Date were collected from the NASA database, which can be downloaded free of charge from https://disc.gsfc.nasa.gov/.

  • A Landsat 8 OLI satellite image (date_acquisition = 2018-08-23, (path/row 202/35), downloadable for free from the site of US Geological Survey (USGS) http://earthexplorer.usgs.gov/. Interpreted and classified to define land use.

  • A digital elevation model (30 m): a product of the Ministry of Economy, Trade and Industry, Japan (METI), and the National Aeronautics and Space Administration (NASA); downloadable for free from www.jspacesystems.or.jp/ersdac/GDEM/E/4.html.

  • The results of granulometry analysis and organic carbon of the soils sampled as of October 28, 2018, in the study area.

Pre-processing of the datasets

Before compiling all the factors, the raw datasets were georeferenced with Universal Transverse Mercator (UTM) projection using WGS 1984 datum. The boundary of the Tifnout Askaoun watershed was delineated from the DEM using the spatial analyst tool of ArcGIS (10.4.1). And it was used for subsetting the Landsat image, TRMM images, and projection of the soil samples. All the processing of the Landsat image was achieved using ENVI 5.3 software.

Methods

The RUSLE model was used in the GIS platform to map the spatial distribution of the soil loss rate in the Tifnout Askaoun watershed. This model uses five parameters considered as essential water erosion factors related to precipitation, soil characteristics, topography, and land use (Eq. 1 and Fig. 3):

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

where:

A:

is the annual loss of soil in (tons haˉ1yr ˉ1).

R:

is the rainfall erosivity factor in (MJ.mm. haˉ1h ˉ1yr ˉ1).

K:

is the soil erodibility factor in (t haˉ1 h ˉ1 haˉ1 MJˉ1mmˉ1).

LS:

is the topographic factor (dimensionless).

C:

is the cropping management factors (dimensionless).

P:

is the practice support factor (dimensionless).

Fig. 3
figure 3

The methodological framework for implementing the RUSLE model for soil erosion

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The RUSLE model is one of the best models for quantifying the rate of soil erosion, characterized by ease of use and satisfactory efficiency (Chafai et al. 2020). All the data entered into the geographic information system (GIS) enabled us to obtain the map of the risk of erosion in the Tifnout Askaoun watershed. According to several studies, the RUSLE model gives reliable results at the scale of watersheds all over the world and in particular in the Mediterranean countries (Bonn 1998; Smith 1999).

Generation of RUSLE factors

The model (RUSLE) has long been applied to watersheds to estimate annual soil losses. It is an easily applicable model as these input parameters can be generated from the available institutional data. The data used for the elaboration of the different parameters are a satellite image Landsat 8 OLI; a digital elevation model (DEM) of 30-m resolution; monthly and annual rainfall data provided by the TRMM data and results of granulometry analysis, and organic carbon of soil samples collected during the field trip on January 15, 2019, in the study area. We describe below the different factors used in the model.

Rainfall erosivity (R) factor

The R factor represents the susceptibility of detachment and displacement by the transport of soil particles by raindrops (Teng et al. 2018). The effect of the R factor is increased by the intense rains and the accumulation of moderate rains (Wischmeier and Smith 1978). In the absence of the availability of the rainfall data necessary for the calculation of the R factor, we used the data provided by the Tropical Rainfall Measuring Mission (TRMM) between 1998 and 2009 provided with open access on the NASA website. This satellite is a joint mission between NASA and the Japan Aerospace Exploration Agency (JAXA), aiming to measure tropical rainfall. In general, the values estimated by the TRMM show a significant correlation with those measured on the ground, which explains their wide use in the world. To calculate the R factor values ​​for the study area, we used the Rango–Arnoldus formula (Djoukbala et al. 2018; Rango and Arnoldus 1987; Sadiki et al. 2004). This formula is the most used and which requires only introducing monthly and annual precipitation whose expression is (Benselama et al. 2018):

$$ \mathit{\log}R=1.74\ast \mathit{\log}\sum \left(\frac{pi^2}{p}\right)+1.29 $$
(2)

where Pi is the monthly precipitation and P is the annual precipitation in mm.

Soil erodibility (K) factor

The K factor represents the influence of different soil properties on the slope’s susceptibility to erosion. It is defined as the “average annual loss rate of rainfall” for a “standard condition of bare soil,” which is recently increased with no conservation practice (Morgan 2005). The K factor essentially represents the soil loss that would occur on the USLE unit plot, with 22.1 m long, 1.83 m wide, and a slope of 9% (López-Vicente et al. 2008). Erodibility is closely related to the infiltration capacity of the soil, its structural stability, and its percentage of organic matter (Roose 1994). A very important role in the erodibility factor, the soil becomes easily erodible when the silty fraction increases with respect to clay and sand which constitute the more erodible fraction. Thus, in structurally stable soil with high organic matter content, the runoff rates decrease and, consequently, the rate of erosion (Kacem et al. 2018).

To develop the K factor map, a total of 24 soil samples were taken from the study area on October 28, 2018, and analyzed for their characteristics in the Laboratory of Applied Geology and Geo-Environment of the Faculty of Sciences at the University Ibn Zohr in Agadir, Morocco.

K factor values ​​for the study area were calculated from the Williams equation (Nyesheja et al. 2019; Sharpley and Williams 1990; Williams and Singh 1995):

$$ {\displaystyle \begin{array}{c}K\ rusle={f}_{csand}\ast {f}_{cl- si}\ast {f}_{orgC}\ast {f}_{hisand}\ast 0.1317\\ {}{f}_{csand}=0.2+0.3\mathit{\exp}\left(0.0256\ast Sa\ast \left(1-\frac{Si}{100}\right)\right)\end{array}} $$
(3)
$$ {\displaystyle \begin{array}{c}{f}_{cl- si}={\left(\frac{Si}{Cl+ Si}\right)}^{0.3}\\ {}{f}_{orgC}=\left(1-\kern0.5em \frac{0.25\ast C}{C+\mathit{\exp}\left(3.72-2.95C\right)}\right)\\ {}{f}_{hisand}=\left(1-\frac{0.7\ast SN}{SN+\mathit{\exp}\Big(-5.51+22.9 SN}\right)\end{array}} $$

where:

Sa:

is sand %

Si:

is silt %

Cl:

is clay%

C:

is organic carbon %

SN:

is \( 1-\left(\frac{Sa}{100}\right). \)

Slope length (LS) factor

This topographic factor influences strongly the importance of water erosion by its shape, inclination, and length. The LS factor is calculated by the combination of inclination and slope length. Several formulas allow the evaluation of this factor from the numerical model of the ground, with a resolution of 30 m (David 1988; Kalman 1967; Wischmeier and Smith 1978).

To calculate the LS factor, we use the Mitasova equation where the adopted parameters, slope, and flow accumulations were computed from the digital elevation model (DEM) (Benavidez et al. 2018; Mitasova et al. 1996):

$$ {\displaystyle \begin{array}{c} LS\kern1.25em =\left( Pow\left( Flow\ accumulation\ast cell\ size\right)/\mathrm{22.1,0.6}\right)\\ {}\ast Pow\left(\mathit{\sin}(slope)\ast 0.01745/0.09,1.3\right)\end{array}} $$
(4)

where LS is the length–slope steepness factor, cell size is the size of grid cell (for this study 30 m), and sin slope is the slope degree value in sin.

Cover and management (C) and conservation practice (P) factors

The C and P factors are interdependent and can be extracted from land cover and land use map. In this study, to map the land cover of the study area, we processed a Landsat 8 OLI image (path/row 202/35) that was acquired in August 23, 2018. In their collected form from theUSGS website, the Landsat 8 OLI data are geometrically corrected, ortho-rectified, and radiometrically calibrated (see USGS site). The characteristics of the used data are given in Table 1.

Table 1 Characteristics of the used Landsat 8 OLI data
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Commonly, several algorithms have been used to correct the atmospheric effects on satellite data, including Dark object subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). In this study, due to its simplicity and satisfactory results, we used the FLAASH algorithm to eliminate the effects of atmospheric scattering by subtracting from each band the value of the darkest pixel. Afterward, in order to map land cover and land use, we applied a supervised classification on three first components obtained from the principal component analysis (PCA) transformation, which commonly contain most information. The classification was achieved by using the maximum likelihood algorithm. All steps were achieved using ENVI software.

In order to evaluate the accuracy assessment of supervised classification outputs, the confusion table is widely used to express the proportionate reduction in error generated by a classification process compared with the error of a completely random classification (Aydda et al. 2019; Congalton 1991).

This table compares the ground truth data (real data) and classified data through assessing various statistics accuracies, including overall accuracy, user’s accuracy, producer’s accuracy, and Kappa coefficient.

Herein, we used Google Earth satellite images archive of the year 2019 to validate the obtained land cover and land use classified map. Practically, the obtained map was overlapped on Google Earth images to check the validity of each class. The overall accuracy of the obtained map is about 84%, and the Kappa coefficient is about 0.80, indicating a satisfactory result for best classification (Table 2).

Table 2 Thematic evaluation of the land cover map
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Cover and management (C)

Vegetation can intervene against surface water erosion in two main ways. First, it can prevent the ablation of the substrate. Then, it can promote the sedimentation by retaining the eroded sediments to the upstream part (Rey et al. 2004). Vegetation reduces also the energy of the surface runoff by acting as an interception of raindrops because of the aerial parts of the plants. This interception is a function of the density of the vegetative area and the structure of the plant cover. The vegetation also reduces surface runoff, by increasing the infiltration of the water (Cerdà 1998; Cosandey et al. 2000; Geddes and Dunkerley 1999).

The normalized difference vegetation index, called NDVI, is constructed from the red (R) and near-infrared (PIR) bands. The normalized vegetation index highlights the difference between the visible band of red and the near-infrared:

$$ NDVI=\frac{\left( PIR-R\right)}{\left( PIR+R\right)} $$
(5)

This index is sensitive to the vigor and quantity of vegetation. NDVI values ​​range from − 1 to + 1, with negative values ​​for surfaces other than plant covers, such as snow, water, or clouds, where red reflectance is greater than near-infrared. For bare soils, the reflectance is in the same order of magnitude in the red and the near-infrared, and then the NDVI has values ​​close to 0. The vegetal formations have values ​​of NDVI positive, generally between 0.1 and 0.7. The highest values ​​correspond to the more dense vegetation cover.

Numerous empirical relationships or equations have been established to relate the values of NDVI to the values of factor C (Phinzi and Ngetar 2019). These equations have been applied in several studies around the world, namely, regression equation to calculate the C factor (Dutta et al. 2015; Moses 2017; Uddin et al. 2016) who used De jong (1994).

To map the C factor, we use a formula established by De Jong (1994), revised in 1998 (De Jong et al. 1998), where the NDVI factor is generated from the Landsat 8 OLI image:

$$ C=0.431-0.805\ast NDVI $$
(6)

Conservation practice (P) factor

P factor describes the relationship between soil erosion and the conservation practices adopted in the field. These control practices reduce the rate of soil degradation by reducing the potential for runoff erosion by influencing the drainage, concentration, velocity, and hydraulic forces of flows. In the absence of conservation measures, the value of P is 1.0 (Benavidez et al. 2018; Dutta et al. 2015; Phinzi and Ngetar 2019).

To map the P factor, values were assigned for each land use class, according to Table 3.

Table 3 P values assigned for each land cover and land use classes
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Results

R factor

The mean annual and monthly precipitation derived from TRMM data was used to map the R factor which is characterized by medium to high values ranging from 48.66 to 143.032 (MJ.mm.hˉ1 hˉ1 yearˉ1) and an average of 94.58 (MJ.mm.hˉ1 hˉ1 yearˉ1) for the whole study area (Fig. 4). The highest rainfall erosivity (R) values are observed in the high-altitude zone north of the study area, near the summit of Toubkal, and to the southeast in the mountain of Siroua in Askaoun.

Fig. 4
figure 4

Rainfall erosivity map (R)

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K factor

The results of granulometric analyses of soil samples in the study area show the dominance of the sand followed by silt and low clay content, which gives a sandy silt texture for the majority of the analyzed samples. The dominant silt fraction compared to that of clays facilitates soil erosion in the Tifnout Askaoun watershed (Table 4). To evaluate the spatial variability of the K factor, several different interpolation methods were applied in ArcGIS 10.4.1 environment (such as Spline, inverse distance weighted (IDW)), but the ordinary kriging method based on Gaussian function was proved to be the most effective one for the production of the final erodibility map.

Table 4 Physical properties of soil samples and calculation of Fc sand, Fcl-si, F org C, F hisand, and K values (sampling date: October 28, 2018)
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The kriging interpolation technique is the most used. It has the advantage of taking into account the distances between the data (the measurement points), the distances between the data and the target (the points for which we want to estimate the measurement), and the spatial structure (Alexakis et al. 2013; Bouderbala et al. 2019).

The results obtained for the K factor of the Tifnout Askaoun watershed vary from 0.03 (t ha MJˉ1 mmˉ1) for the most resistant soils to 0.05 (t ha MJˉ1 mmˉ1) for the most erodible soils with an average of 0.044 (t ha MJˉ1 mmˉ1). The K factor map shows that the highest K values ​​are located in the south east near the Askaoun zone, in the north east near the Ifni lake, and in the south around the Mokhtar Soussi and Aoulouz dams (Fig. 5).

Fig. 5
figure 5

Soil erodibility map (K)

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LS factor

To mapping, the slope length (LS) factor by the Mitasova equation, the slope steepness values, and flow accumulation derived from DEM were used. The slope length factor (LS) varies from 0 to 1648.08 with an average value of 15.09 (Fig. 6). It is very strong at the high altitudes of the High Atlas Mountains where it exceeds 1500 near Lake Ifni.

Fig. 6
figure 6

Slope length (LS) map

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C factor

The identification of crop types at the plot level is difficult from the satellite image; NDVI has been used as a substitute of Landsat image for estimating C factor in the Tifnout Askaoun watershed (Fig. 7). The values obtained for the C factor range from 0 to 0.53 with an average of 0.23 (Fig. 8).

Fig. 7
figure 7

NDVI map of the Tifnout Askaoun watershed

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Fig. 8
figure 8

Cover and management (C) map

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The higher values of C factor are observed in the more unprotected soil. Areas without vegetation cover to the northeast and southeast present a high potential risk of soil erosion concerning C factor, while areas of dense vegetation cover to the south around the Mokhtar Soussi and Aoulouz dams show low sensitivity to soil erosion.

P factor

From the land use map, we noticed that 58% of the study area is occupied by the barren land class, followed by an open forest class with 28%. Agricultural and fallow land occupies 5 and 8%, respectively (Fig. 9). The values of P are assigned for each land use class according to Table 3. The map of the P factor shows that the majority of the study area displays values between 0.9 and 1, indicating the dominance of the barren land except in the south at Aouzioua (Fig. 10).

Fig. 9
figure 9

Land use and land cover map

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Fig. 10
figure 10

Conservation practice (P) map

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Evaluation of the soil loss

Overlaying the raster data layers representing erosion factors R, K, C, LS, and P in a GIS environment results in a map of the distribution of the annual soil losses in the Tifnout Askaoun watershed which is a mountainous area in southern Morocco (Fig. 11). The composite map highlights six classes which are given in Table 5. The result obtained indicates an estimated annual erosion rate ranging from 0 to 152.33 and an average of about 14.44 t/ha/year for the entire watershed studied. The results show that more than 20% of the investigated area has an erosion rate greater than 25 t/ha/year. Areas with a very high risk of erosion are located in the north east of the study area (High Atlas), while low-risk areas are located in the south and south east (Anti-Atlas) and along the Tifnout Valley.

Fig. 11
figure 11

Map of RUSLE soil erosion in Tifnout Askaoun watershed

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Table 5 Annual soil erosion rate distribution in the Tifnout Askaoun watershed
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Discussion

The tolerance thresholds for erosion in a temperate humid climate vary between 2.5 and 12.5 t/ha/year (Klingebiel and Montgomery 1966; USDA 1951), and this tolerance is lower in Mediterranean countries such as Morocco because of the pedogenesis which is much slower. Therefore, the values of soil losses cited above greatly exceed what pedogenesis can produce under current climatic conditions.

The soil loss rate obtained is higher than the tolerance limit of soil loss for High Atlas set between 5 and 10 t/ha/year (El-Ghanam and El-Ghozoli 2003; Ouassou et al. 2006; Snoussi 1988).

The comparison of our results to the study carried out by Gourfi et al. (2018) covering the whole of Morocco shows relative reliability of the model applied. It demonstrated an annual average rate greater than 20 t/ha/year for the whole country with a resolution of 1 km. Sedimentation and siltation calculations in the two dams (Mokhtar Soussi and Aoulouz) of the study area indicate that the two reservoirs are threatened by a very high rate of siltation (Table 6).

Table 6 Sedimentation observed in the dams of the study area
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The inequality of the distribution of soil losses in the different zones of the Tifnout Askaoun watershed is due to the variability of the different factors from one place to another. We subdivided the study area into 23 sub-catchments and noted that those located in the north and on the southern flank of the High Atlas Mountains show a higher erosion rate compared to those located along the Tifnout Valley and in the Askaoun zone (Fig. 12). The catchment (W1) of Toubkal is threatened by a very strong water erosion of an average of 48.05 (t/ha/year) because of the scarcity of the vegetation cover and the steep slope. We note that the w1, w2, w4, w5, and w7 sub-basins in the High Atlas show a risk of erosion from high to very high. To test the correlation between the input factors and the result obtained by the RUSLE model, we have calculated the characteristics of each catchment (Table 7).

Fig. 12
figure 12

Subdivision of the study area according to the calculated erosion rate. Wx indicates sub-catchment in the study area

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Table 7 Statistics of calculated soil erosion and adopted factors
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In general, all factors of the revised universal equation (topography, erodibility, climatology, and vegetation cover) do not show the same correlation with the annual erosion value (A) calculated by the RUSLE equation (Table 8) and (Fig. 13).

Table 8 Correlation matrix of soil erosion rate calculated by RUSLE and adopted factors
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Fig. 13
figure 13

Regression of LS, R, C, and K factors by A soil erosion

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The correlation matrix shows that the factor LS indicates the best correlation with the sensitivity to water erosion with a correlation coefficient of r = 0.72. This means that the LS dominates and controls the process of water erosion, followed by the R and C factors. The K factor shows a negative correlation r = 0.11 explained by the impossibility of sampling the soil in some inaccessible locations.

The priority for intervention against erosion in each sub-catchment is to prioritize an action plan against water erosion. Prioritizing the action plan in the sub-catchments requires a strategy that includes two main conditions, namely:

  • The average annual soil loss rate calculated in each sub-catchment

  • The protection of the Mokhtar Soussi and Aoulouz dams against the acceleration of their siltation, by considering the distance between the outlet of each sub-basin and the reservoir of the dam concerned

In this context, a decision matrix has been developed to define the intervention priority levels for each sub-catchment. Five classes have been designed ranging from non-urgent to very urgent action (Tables 9 and 10).

Table 9 Decision matrix for priority intervention in each sub-catchment
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Table 10 Priority classes of the 23 sub-basins of the Tifnout Askaoun catchment
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Among the 23 sub-catchments of the study area, it turns out that sub-basin number 5 located to the north of the study area shows a very high priority to develop and implement a more urgent action plan, given that its distance from the Mokhtar Soussi dam is less than 20 km and its average annual soil loss is estimated at 26.03 t/ha/year (Fig. 14).

Fig. 14
figure 14

Priority (from low to very high) map of the sub-catchments of the study area. The number indicates the sub-catchment

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The high-priority watershed sub-basins cover 71% of the study area; there are 11 sub-catchments (4, 6, 7, 8, 11, 12, 13, 14, 18, 19, 23) classified as urgent priority with average soil losses between 11.34 and 22.15 t/ha/year and distances between 0 and 24.98. They are mainly elongated in the central part of the watershed.

Despite the high erosion values ​​estimated for some sub-watershed, the intervention priority matrix assigned non-urgent priority to these because of the great distances from the reservoirs of the dams in the study area, but this does not prevent these sub-basins from benefiting from intervention against the phenomenon of water erosion.

Conclusions

It emerges from the application of the RUSLE model in the Tifnout Askaoun watershed that the R climate of the region has a variant aggressiveness of 48.66 to 143,032 units/year with an average of 94.58 units/year and an erodibility of soils K from 0, 03 to 0.05 with an average of the order of 0.04 and a topographic factor LS varying from 0 to 1648.08 with an average of 17.05 which is due to the nature of the very rugged terrain and the steep slopes of the High Atlas and Anti-Atlas mountains.

The Landsat image allowed us through the NDVI to map the C factor which varies from 0 to 0.53 with an average of 0.23 and which characterizes the protection conferred on the soil by the vegetation cover and on the basis on the mapping of the land cover; the values obtained for the P factor vary from 0.9 to 1 with an average of 0.85. This estimated rate of erosion represents a major risk for water reservoirs storage of the Mokhtar Soussi and Aoulouz dams. The result obtained by superimposing the five parameters of the RUSLE equation in a GIS environment indicates an annual average soil erosion of 14.44 t/ha/year for the studied area.

The results obtained show that the High Atlas Mountains, characterized by an active alpine neo-tectonics which accelerates erosion, show a greater rate of soil degradation especially in areas with marl–limestone lithology. However, the Anti-Atlas Mountains with stable Pan-African tectonics present medium erosion except at the high altitudes where the alteration of the magmatic rocks is intensely producing granitic sand easily transported.

The proposed intervention priority matrix enabled us to distinguish a sub-catchment area of very high intervention priority (sub-catchments area N°5) and eleven sub-catchments showing high priority (sub-catchments N°, 4, 6, 7, 8, 11, 12, 13, 14, 18, 19, and 23). The use of TRMM data will be a valuable method solution to solve the problem of precipitation data in Morocco.

Nevertheless, it is advisable to keep a critical sense with regard to these results because the RUSLE model in its primary objective was intended to be used in areas with low slope and only applies to sheet erosion since the source of energy is rain, so it does not apply to linear erosion. These results can be combined with those obtained by other methods of evaluating water erosion. For more precision, other methods are interesting, particularly radio-isotopic methods, SWAT model (Soil Water Assessment Tools), and the SAM model.