1. Introduction

2. Materials and Methods

2.2. Methodology and data source processing

The cartographic documents are generally produced at local scales. However, these data are often without recent updating, incomplete and sometimes non-existent, such as geographical reference data on topography, precipitation, land cover, etc... Similarly, georeferenced digital data on a small scale are mostly scarce or obsolete. On the other hand, the use of online databases on a global scale has become available these last years. Furthermore, the spatial analysis and modeling are facilitated mainly because of the existence of satellite data which has become very easy to have continuously. Indeed, the raster mode with a simple data structure and a regular shape of the grid is very convenient to keep geometric properties common to all layers of information, thus facilitating the combination of different layers, since all numeric values are dependent on the same base unit, the pixel.

In addition, these data need to be tested in several case studies on a global scale. That is why, one of the objectives of this study is to assess the potential of existing digital data for spatialized modeling in a GIS of soil water erosion in the current Tunisian context. It can be in some cases considered as an alternative to overcome the problem of lack of data.

The choice of RUSLE model (Equation 1) in this study was motivated by the fact that this model does not differ conceptually from the original soil loss equation (USLE) of Wischmeier and Smith [26]. On the contrary, it improves the quality of the environmental parameters which define the role of each factor. Also, the input data for this model is easier to access compared to other more recent models, which require more sophisticated data.

The RUSLE is written as: A=R*K*LS*C*P

(1)

Where:

A is the soil loss per unit area, expressed in the units of K and the period selected for R;

R is the rainfall and runoff erosivity factor;

K is the soil erodibility factor;

LS is the slope length and slope steepness factor;

C is the crop management factor;

P is the support practice factor.

RUSLE model was used to estimate the yearly average of soil loss due to water erosion, by taking into account the five factors indicated in equation (1). The latter has been applied using GIS and geospatial data composed of: (1) Climatic data including a 31-year average annual precipitation downloaded from the NASA-POWER meteorological parameters, taken from NASA's MERRA-2 assimilation model and necessary for computing the rainfall and Runoff Erosivity factor (R). (2) Soil map of Tunisia extracted from FAO Digital Soil Map of the Word (DSMW), essential for calculating the soil erodibility factor (K). (3) Global Digital Elevation Model (ASTER GDEM), which is used to calculate the topographic factor (LS). (4) LULC map of Tunisia extracted from ESRI 2020 Global Land Cover and used to calculate and map the vegetative cover factor (C) as well as the support and management practice factor (P).

Each of the factors was derived separately in raster format based on rainfall pattern, soil type, topography and LULC data in the context of soil water erosion modeling, in order to generate global scale maps. However, the current study, that deal with the estimation of soil erosion by water, did not consider development stage of the crops. Furthermore, the season variability effects were not taken into account.

The geospatial data with different types and origins are listed in Table 1. Figure 4 shows the flowchart of the method used for the estimation of each factor and for the analysis of soil loss based on the RUSLE model and GIS technique.

The following parts of this article make a brief description of the various factors showed in equation (1) and present the results obtained after modeling and analysis of geospatial data.

2.2.1. Rainfall and runoff erosivity factor (R-factor)

The estimation of the R factor requires knowledge of the kinetic energies and the average intensity over 30 min of the raindrops that fall each time, over a long period of up to 30 years [44]. But this direct method requires precipitation records at high resolutions to calculate R-factor. Other authors have developed alternative formulas when these data are not available. The equations most used to calculate the R factor using only annual precipitation are those of Renard and Freimund [45] (Equations 2 and 3), whose expressions are:

R = 0.0483. Pi^1.61               if 𝑃_𝑖 < 850 𝑚𝑚

(2)

R = 587,8 -1,219 Pi +0,004105. Pi ²        if 𝑃_𝑖 > 850 𝑚𝑚

(3)

Where: R is the measure of rainfall erosivity and Pi is the average annual precipitation (mm).

The estimation of rainfall and runoff erosivity using rainfall data with long-time intervals has been conducted by many authors for different regions of the world [36,46]. To calculate the average annual R-factor values in this study, a 31-year average annual data has been used and an interpolation of this data was applied to have a representative rainfall distribution map which is used as input for calculation of R-factor, knowing that the rainfall data available for the study area is not homogenous. This parameter, expressed in (MJ.mm.ha−1. h−1.year−1), takes into consideration the influence of climatic aggressiveness on soil loss [47].

To generate the R-factor for the whole Tunisia, a 31-year average annual precipitation from 1990 to 2020 required for this study, was downloaded freely, as a Comma-Separated Values (CSV) file, from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources known as POWER project which was initiated in 2003. It provides access to solar and meteorological data sets for the entire globe and particularly over regions where surface measurements are sparse or nonexistent. The meteorological parameters are based upon the MERRA-2 assimilation model. The monthly and annual precipitation data are derived from NASA's Global Precipitation Measurement (GPM). and an interpolation of this data was applied to have a representative rainfall distribution map which was used as input for calculation of R-factor. Since the mean annual precipitation in Tunisia does not exceed 850 mm, equation 3 was used to calculate the rainfall-runoff erosivity factor, taking into account the bioclimatic map of Tunisia which has been developed for climatically homogeneous areas. The Inverse Distance Interpolation (IDW) method was used to minimize the calculation error and above all to optimize the processing time on the ArcGIS software. It is a method that consists in creating, from the aggregated points and the corresponding information, a value grid with a certain continuity.

2.2.2. Soil erodibility (K-factor)

The soil erodibility K, expressed in [t. h. MJ−1.mm−1], determines the resistance of different types of soil to erosion, knowing that soils are more or less sensitive to water erosion. This K factor is determined according to the characteristics of the soil: infiltration capacity, retention texture and susceptibility to particle removal. The infiltration and the high cohesion of the materials increase the soil resistance to removal and gullying of particles. The high rate of sand stabilizes the structure of the soil and makes it less sensitive to climatic aggression. Similarly, the organic matter improves the physical and chemical properties (cohesion, structural stability, porosity) of the soil, increasing the ability to retain water, and strengthens resistance to erosion [48]. Thus, the higher the percentage of sand, the more the soil is permeable, which implies a low value of the K-factor and vice versa. Bolline and Rousseau (1978) [49] established the classification, in Table 2 interpreting the soil susceptibility index.

Table 1. Description of the data sources.

Table 1. Description of the data sources.

Erodibility (K)	Type of soil
K < 0.10	Soil highly resistant to erosion
0.10 à 0.25	Soil fairly resistant to erosion
0.25 à 0.35	Soil moderately resistant to erosion
0.35 à 0.45	Soil with low erosion resistance
>0.45	Soil with very low resistance to erosion

However, soil erodibility is not constant, as it varies not only with soil type, but also with seasons and cultivation techniques. The soil map of Tunisia was prepared using FAO Digital Soil Map of the Word Shapefile (DSMW), where the soil data layer has been clipped, according to the country boundaries relative to the study area, in the ArcGIS Desktop environment. K-factor was calculated using Williams (1995) [50] formula (Equations 4) and FAO Digital Soil Map.

The raster dataset of the FAO/UNESCO Digital Soil Map of the World (DSMW) has a spatial resolution of 5 * 5 arc minutes and is in geographic projection.

$$K=f_{csand}\ast f_{cl-si}\ast f_{orgC}\ast f_{hisand}$$

(4)

Where:

${\mathrm{f}}_{\mathrm{c}\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}$ is a factor that gives low soil erodibility factor for soils with high coarse-sand content and high value for soil with little sand.

${\mathrm{f}}_{\mathbf{c}\mathbf{l}-\mathbf{s}\mathbf{i}}$is a factor that gives low soil erodibility factor for soils with high clay to silt ratios.

${\mathrm{f}}_{\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{C}}$ is a factor that reduces soil erodibility for soils with extremely high sand content.

${\mathrm{f}}_{\mathrm{h}\mathrm{i}\mathrm{s}\mathrm{a}\mathrm{n}\mathrm{d}}$ is a factor that reduces soil erodibility factor with high coarse-sand contents and high value for soil with little sand.

The factors are calculated using these equations (Equations 5, 6, 7 and 8)

$$f_{csand}=0.2+0.3\mathrm{e}\mathrm{x}\mathrm{p}[-0.256{\mathrm{m}}_{\mathrm{s}}\left(1-\frac{{\mathrm{m}}_{\mathrm{s}\mathrm{i}\mathrm{l}\mathrm{t}}}{100}\right)]$$

(5)

$$f_{cl}-f_{si}={\left(\frac{{\mathrm{m}}_{\mathrm{s}\mathrm{i}\mathrm{l}\mathrm{t}}}{{\mathrm{m}}_{\mathrm{c}}-{\mathrm{m}}_{\mathrm{s}\mathrm{i}\mathrm{l}\mathrm{t}}}\right)}^{0.3}$$

(6)

$$f_{orgC}=1-\frac{0.25\mathrm{*}\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{C}}{\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{C}+\mathrm{e}\mathrm{x}\mathrm{p}[3.72-2.95\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{C}]}$$

(7)

$$f_{hisand}=1-\frac{0.7\left(1-\frac{{\mathrm{m}}_{\mathrm{s}}}{100}\right)}{1-\frac{{\mathrm{m}}_{\mathrm{s}}}{100}+\mathrm{e}\mathrm{x}\mathrm{p}\left[-5.57+22.9\left(1-\frac{{\mathrm{m}}_{\mathrm{s}}}{100}\right)\right]}$$

(8)

Where, m_s is the percent sand content (0.05-2.0) mm diameter particles,

m_silt is the percent silt content (< 0.002) mm diameter particles,

m_c is the percent clay content (0.05-2.0) mm diameter particles,

orgC is the percent organic carbon of the layer (%).

2.2.3. Topographic factor (LS-factor)

The two parameters that constitute the topographic factor are slope gradient and slope length factor were estimated, here in this study, through an ASTER Global Digital Elevation Model (ASTER GDEM) with a 30 m resolution downloaded freely from (https://search.earthdata.nasa.gov/downloads/) [73]. The DEM of Tunisia’s territory is ranged from -27 m to 1549 m (Figure 2). The latter was incorporated into GIS to determine accurately the slope gradient (S) and slope length (L), taking into account that the effect of slope length and degree of slope interaction should always be considered together [51]. So, to create the slope length and steepness (LS-factor), equivalent to topographic factor and relief factor, respectively, flow direction and flow accumulation maps were created after the filling process of DEM data by the use of the GIS extension Arc Hydro Tools. The flow accumulation was determined for each cell by the flow that flows through that cell. The greater the flow accumulation value, the easier the area will form runoff and vice versa. The slope map in degree, required to estimate the LS factor, shows that most of the slope of Tunisia is ranged from 0 to 10° which represent about 95,54 % from total area (Figure 9).

In 1985, Moore and Burch [52] developed the equation below (Equation 9) to compute the length-slope factor:

$$LS={\left(\frac{\mathrm{A}\mathrm{s}}{22.13}\right)}^{\mathrm{m}}\times {\left(\frac{\mathrm{s}\mathrm{i}\mathrm{n}\mathsf{\theta }}{0.0896}\right)}^{\mathrm{n}}$$

(9)

The equation (9) has been applied by many researchers [51,53,54]. The exponent value m can be taken equal to 0.4 while the value of n can be taken equal to 1.3 [53,54,55].

Where:

𝐿𝑆: is slope steepness–length factor.

𝐴𝑠: is the flow accumulation (in meters).

𝜃: is the slope angle (in radians).

𝑚 = 0.4 – 0.6 and n = 1.2 – 1.3.

In general, as the length of the slope increases, the total soil erosion and the soil erosion per unit area increase due to the gradual accumulation of runoff water in the downhill direction of the slope. As the steepness of the slope increases, the velocity and erosivity of the runoff increase.

2.2.4. Vegetative cover factor (C-factor)

It is a dimensionless factor presenting the effectiveness of the vegetation cover in relation to the susceptibility of the soil to erosion [56]. Vegetation cover and its spatial distribution play an important role in reducing the effects of runoff by amortizing the impact of rainwater on an area [15].

According to several studies, this RUSLE factor that can range from zero for water and a very well protected soil with very strong cover effects, to 1 for a surface that produces a lot of runoffs (bare soil) and leaves the soil very susceptible to water erosion [57]. Several authors consider that C-factor is around 0.01 (1/100) under dense forest, 0.05 (5/100) under grasslands and 0.24 (24/100) under crops (Table 3). Other researchers have adopted the calculation of C-factor by new simplified approaches: use of remote sensing techniques such as the classification of satellite images [58,59] and vegetation indices [60] or use of the Land Use/Land Cover (LULC) map and class assignment for each entity [61]. In fact, C-factor reflect the effect of LULC, cropping and management practices on the rate of soil erosion [62,63,64,65].

C-factor was determined, in this study based on the literature, by assigning a value for each type of LULC extracted from ESRI 2020 Global map of LULC, derived from ESA Sentinel-2 imagery at 10 m resolution and downloaded freely from https://livingatlas.arcgis.com/landcover/ for all land masses on the planet. Two Individual GeoTIFF scenes (32S_20200101-20210101 and 32R_20200101-20210101) covering the Tunisian territory, have been downloaded from Esri 2020 Land Cover Downloader application. These scenes were joined to form a mosaic image and the values of the C-factor (Table 3) for 7-class LULC types covering the study area were used in the data analysis to build the C-factor map for the year 2020 at 10 m resolution, performing the analysis in ArcGIS and then converted to a grid with 30 x 30 m spatial resolution.

2.2.5. Support and management practice factor (P-factor)

This factor, which is dimensionless, represents soil protection based on anti-erosion cultivation techniques that reduce runoff speed and thus reduce the risk of water erosion. It varies according to the landscaping carried out, namely cultivation on a level curve, in alternating strips or on terraces, reforestation in benches, ridging and ridging [15].

The P-factor is between 0 and 1, in which the value 0 represents a very good environment of resistance to erosion and the value 1 shows an absence of anti-erosion practice [72]. Wischmeier and Smith (1978) [15] established the classification according to the slope in percent (Table 4), showing that this factor can be distributed according to two zones: the cultivated zones and the other zones.

3. Results and discussion

4. Conclusions

References

1. Wang, B.; Zheng, F.; Römkens, M.J.M.; Darboux, F. Soil erodibility for water erosion: A perspective and Chinese experiences. Geomorphology 2013, 187, 1–10. [Google Scholar] [CrossRef]
2. Keesstra, D.S.; Bouma, J.; Wallinga, J.; Tittonell, P.; Smith, P.; Cerdà, A.; Montanarella, L.; Quinton, J.N.; Pachepsky, Y.; van der Putten, W.H.; et al. The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil 2016, 2, 111–128. [Google Scholar] [CrossRef]
3. Hessel, R.; Wyseure, G.; Panagea, I.S.; Alaoui, A.; Reed, M.S.; van Delden, H.; Muro, M.; Mills, J.; Oenema, O.; Areal, F.; van den Elsen, E.; Verzandvoort, S.; Assinck, F.; Elsen, A.; Lipiec, J.; Koutroulis, A.; O’Sullivan, L.; Bolinder, M.A.; Fleskens, L.; Kandeler, E.; Montanarella, L.; Heinen, M.; Toth, Z.; Hallama, M.; Cuevas, J.; Baartman, J.E.M.; Piccoli, I.; Dalgaard, T.; Stolte, J.; Black, J.E.; Chivers, C.A. Soil-Improving Cropping Systems for Sustainable and Profitable Farming in Europe. Land 2022, 11, 780. [Google Scholar] [CrossRef]
4. Gao, J. and Wang, H. Temporal analysis on quantitative attribution of karst soil erosion: a case study of a peak-cluster depression basin in Southwest China. Catena 2018, (172): 369–377.
5. Karydas, C. G.; Panagos, P. Modelling monthly soil losses and sediment yields in Cyprus, International Journal of Digital Earth 2016, Vol. 9, No. 8, 766–787.
6. Buryak, Z.A.; Narozhnyaya, A.G.; Gusarov, A.V.; Beylich, A.A. Solutions for the Spatial Organization of Cropland with Increased Erosion Risk at the Regional Level: A Case Study of Belgorod Oblast, European Russia. Land 2022, 11, 1492. [Google Scholar] [CrossRef]
7. Sun, L.; Wan, S.; Luo, W. Biochars Prepared from Anaerobic Digestion Residue, Palm Bark, and Eucalyptus for Adsorption of Cationic Methylene Blue Dye: Characterization, Equilibrium, and Kinetic Studies. Bioresour. Technol. 2013, 140, 406–413. [Google Scholar] [CrossRef] [PubMed]
8. Gao, J. Wetland and Its Degradation in the Yellow River Source Zone. In Landscape and Ecosystem Diversity, Dynamics and Management in the Yellow River Source Zone; Springer: Berlin, Germany, 2016; pp. 209–232. [Google Scholar]
9. Kefi, M. ; Yoshino, K; Setiawan, Y. Assessment and mapping of soil erosion risk by water in Tunisia using time series MODIS data. Paddy Water Environ 2012, 10:59–73.
10. Sidi Almouctar, M.A.; Wu, Y.; Zhao, F.; Dossou, J.F. Soil Erosion Assessment Using the RUSLE Model and Geospatial Techniques (Remote Sensing and GIS) in South-Central Niger (Maradi Region). Water 2021, 13, 3511. [Google Scholar] [CrossRef]
11. Ganasri, B.P.; Ramesh, H. Assessment of Soil Erosion by RUSLE Model Using Remote Sensing and GIS - A Case Study of Nethravathi Basin. Geosci. Front. 2016, 7, 953–961. [Google Scholar] [CrossRef]
12. El Jazouli, A.; Barakat, A.; Khellouk, R.; Rais, J.; El Baghdadi, M. Remote Sensing and GIS Techniques for Prediction of Land Use Land Cover Change Effects on Soil Erosion in the High Basin of the Oum Er Rbia River (Morocco). Remote. Sens. Appl. Soc. Environ. 2019, 13, 361–374. [Google Scholar] [CrossRef]
13. Spalevic, V.; Barovi´c, G.; Vujaˇci´c, D.; Curovi´c, M.; Behzadfar, M.; Djurovi´c, N.; Dudi´c, B.; Billi, P. The Impact of Land Use Changes on Soil Erosion in the River Basin of Miocki Potok, Montenegro. Water 2020, 12, 2973. [Google Scholar] [CrossRef]
14. Kastridis, A.; Kamperidou, V. Influence of Land Use Changes on Alleviation of Volvi Lake Wetland (North Greece). Soil Water Res. 2016, 10, 121–129. [Google Scholar] [CrossRef]
15. Wischmeier, W.H.; Smith, D.D. Predicting Rainfall-Erosion Losses—A Guide to Conservation Planning. Agriculture Handbook No. 537; U.S. Department of Agriculture: Washington, DC, USA, 1978; p. 58. [Google Scholar]
16. Renard, K.G.; Foster, G.R.; Weesies, G.A.; McCool, D.K.; Yoder, D.C. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agricultural handbook No. 703; U.S. Department of Agriculture: Washington, DC, USA, 1997; p. 407. [Google Scholar]
17. Kirkby, M.J.; Irvine, B.J.; Jones, R.J.A.; Govers, G.; Boer, M.; Cerdan, O.; Daroussin, J.; Gobin, A.; Grimm, M.; Le Bissonnais, Y.; et al. The PESERA coarse scale erosion model for Europe. I.Model rationale and implementation. Eur. J. Soil Sci. 2008, 59, 1293–1306. [Google Scholar] [CrossRef]
18. Karydas, C.G.; Panagos, P. The G2 erosion model: An algorithm for month-time step assessments. Environ Res. 2018, 161:256-267.
19. Morgan, R.P.C.; Quinton, J.N.; Smith, R.E.; Govers, G.; Poesen, J.W.A.; Auerswald, K.; Chisci, G.; Torri, D.; Styczen, M.E. The European Soil Ersoion Model (EUROSEM): A dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf. Processes Landf. 1998, 23, 527–544. [Google Scholar] [CrossRef]
20. Gavrilovic, Z.; Stefanovic, M.; Milovanovic, I.; Cotric, J.; Milojevic, M. Torrent classification-Base of rational management of erosive regions. IOP Conf. Ser. Earth Environ. Sci. 2008, 4, 012039. [Google Scholar] [CrossRef]
21. Dragiˇcevi´c, N.; Karleuša, B.; Ožani´c, N. A. Review of the Gavrilovi´c method (erosion potential method) application. Gradevinar. 2016, 68, 715–725. [Google Scholar]
22. Batista, P.V.G.; Davies, J.; Silva, M.L.N.; Quinton, J.N. On the evaluation of soil erosion models: Are we doing enough? Earth-Sci. Rev. 2019, 197, 102898. [Google Scholar] [CrossRef]
23. Igwe, P.U.; Onuigbo, A.A.; Chinedu, O.C.; Ezeaku, I.I.; Muoneke, M.M. Soil Erosion: A Review of Models and Applications. Int. J. Adv. Eng. Res. Sci. 2017, 4, 237341. [Google Scholar]
24. Karydas, C. G.; Panagos, P.; Gitas, I. Z. A Classification of Water Erosion Models According to Their Geospatial Characteristics. International Journal of Digital Earth 2014, Vol. 7, No. 3, 229–250. [Google Scholar] [CrossRef]
25. Kefi, M.; Yoshino, K.; City, T. Evaluation of the Economic Effects of Soil Erosion Risk on Agricultural Productivity Using Remote Sensing: Case of Watershed in Tunisia. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 2010, 38, 930. [Google Scholar]
26. Wischmeier, W.H. , Smith D.D. Predicting Rainfall-Erosion Losses from Cropland East of the Rocky Mountains: Guide for Selection of Practices for Soil and Water Conservation. Washington, D.C., USDA, Agricultural Research Service. 1965.
27. Bera, A. Assessment of soil loss by universal soil loss equation (USLE) model using GIS techniques: a case study of Gumti River Basin, Tripura, India. Model Earth Syst Environ. 2017, 3:29.
28. Elaloui, A.; Marrakchi, C.; Fekri, A. et al. USLE-based assessment of soil erosion by water in the watershed upstream Tessaoute (Central High Atlas, Morocco). Model Earth Syst Environ. 2017, 3:873–885.
29. Pham, T.G.; Degener, J.; Kappas, M. Integrated universal soil loss equation (USLE) and geographical information system (GIS) for soil erosion estimation in A Sap basin: Central Vietnam. Int Soil Water Conserv Res. 2018, 6:99–110.
30. Renard, K.G.; Foster, G.R.; Weesies, G.A.; McCool, D.K. Predicting soil erosion by water. A guide to conservation planning with the revised universal soil loss equation (RUSLE). Agric. Handbook 703. US Govt Print Office, Washington, DC. 1993.
31. Maqsoom, A.; Aslam, B.; Hassan, U.; Kazmi, Z.A.; Sodangi, M.; Tufail, R.F.; Farooq, D. Geospatial Assessment of Soil Erosion Intensity and Sediment Yield Using the Revised Universal Soil Loss Equation (RUSLE) Model. ISPRS Int. J. Geo-Inf. 2020, 9, 356. [Google Scholar] [CrossRef]
32. Xue, J.; Lyu, D.; Wang, D.; Wang, Y.; Yin, D.; Zhao, Z.; Mu, Z. Assessment of Soil Erosion Dynamics Using the GIS-Based RUSLE Model: A Case Study of Wangjiagou Watershed from the Three Gorges Reservoir Region, Southwestern China. Water 2018, 10, 1817. [Google Scholar] [CrossRef]
33. Tang, Q. , Xu, Y., Bennett, S.J., Li, Y. Assessment of soil erosion using RUSLE and GIS: a case study of the Yangou watershed in the Loess Plateau, China. Environmen. Earth Sci. 2015, 73 (4), 1715–1724.
34. Ganasri, B.P.; Ramesh, H. Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geosci. Front 2016, 7(6), 953–961. [Google Scholar] [CrossRef]
35. Ghosal, K.; Bhattacharya, S.D. A review of RUSLE Model. J. Indian Soc. Remote Sens. 2020. 1–19.
36. Tzioutzios, C.; Kastridis, A. Multi-Criteria Evaluation (MCE) Method for the Management of Woodland Plantations in Floodplain Areas. Int. J. Geo-Inf. 2020, 9, 725. [Google Scholar] [CrossRef]
37. Rawat, K.S.; Mishra, A.K.; Bhattacharyya, R. Soil Erosion Risk Assessment and Spatial Mapping Using LANDSAT-7 ETM+, RUSLE, and GIS—A Case Study. Arab. J. Geosci. 2016, 9, 288. [Google Scholar] [CrossRef]
38. Van Remortel, R.D.; Maichle, R.W.; Hickey, R.J. Computing the LS factor for the Revised Universal Soil Loss Equation through array-based slope processing of digital elevation data using a C++ executable. Computers & Geosciences 2004, 30: 1043–1053.
39. Efthimiou, N.; Lykoudi, E.; Psomiadis, E. Inherent Relationship of the USLE, RUSLE Topographic Factor Algorithms and Its Impact on Soil Erosion Modelling. Hydrol. Sci. J. 2020, 65, 1879–1893. [Google Scholar] [CrossRef]
40. Biswas, S.S.; Pani, P. Estimation of Soil Erosion Using RUSLE and GIS Techniques: A Case Study of Barakar River Basin, Jharkhand, India. Modeling Earth Syst. Environ. 2015, 4, 42. [Google Scholar] [CrossRef]
41. Panagos, P.; Borrelli, P.; Meusburger, K. A new European slope length and steepness factor (LS-factor) for modeling soil erosion by water. Geosciences 2015, 5 (2), pp. 117-126.
42. Yang, Q.; Guo, M.; Li, Z.; Wang, C. Extraction and analysis of China soil erosion topographic factor Soil Water Convers. China 2013, pp. 17-21.
43. National Institute of Meteorology. Climatological Report for summer 2021 in Tunisia: Hottest summer on record since 1950. Climate Product Department-Deputy Direction of Climatology 2021, pp. 1-16. Available online: https://www.meteo.tn/ (accessed on 18 September 2022).
44. Sadiki, A.; Bouhlassa, S.; Auajjar, J.; Faleh, A.; Macaire, J.J. Utilisation d’un SIG pour l’évaluation et la cartographie des risques d’érosion par l’Equation universelle des pertes en sol dans le Rif oriental (Maroc): cas du bassin versant de l’oued Boussouab. Bull l’Inst Sci Rabat Sect Sci Terre 2004, 26:69–79.
45. Renard, K.G.; Freimund, J.R. Using Monthly Precipitation Data to Estimate the R Factor in the Revised USLE. Journal of Hydrology 1994, 157, 287–306. [Google Scholar] [CrossRef]
46. Pangali Sharma, T.P.; Zhang, J.; Khanal, N.R.; Prodhan, F.A.; Nanzad, L.; Zhang, D.; Nepal, P. A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal. Int. J. Geo-Inf. 2021, 10, 247. [Google Scholar] [CrossRef]
47. Thapa, P. Spatial Estimation of Soil Erosion Using RUSLE Modeling: A Case Study of Dolakha District, Nepal. Env. Syst. Res. 2020, 9, 15. [Google Scholar] [CrossRef]
48. El Hage Hassan, H. Les apports d'un SIG dans la connaissance des évolutions de l'occupation du sol et de la limitation du risque érosif dans la plaine de la Bekaa (Liban): exemple d'un secteur du Bekaa el Gharbi. 2011. Doctoral thesis. Orleans.
49. Bolline, A.; Rousseau, P. Erodibilité des sols de moyenne en haute Belgique. Utilisation d’une méthode de calcul du facteur K de l’équation universelle de perte en terre. Bull. Soc. Géogr. de Liège 1978, 14, 4. pp. 127–140. [Google Scholar]
50. Williams, J.R. Chapter 25: The EPIC model. In V.P. Singh (ed.) Computer models of watershed hydrology. Water Resources Publications 1995, p. 909-1000.
51. Moore, I.D.; Wilson, J.P. Length-slope factors for the Revised Universal Soil Loss Equation: Simplified method of estimation. Journal of soil and water conservation 1992, Sep 1;47(5):423-428.
52. Moore, I.D.; Burch, G. J. Physical Basis of the Length Slope Factor in the Universal Soil Loss Equation. Soil Science Society America Journal 1986, 50: 1294–1298.
53. Moore, I.D.; Gessler, P.E.; Nielsen, G.A.; Peterson, G.A. Soil attribute prediction using terrain analysis. Soil science society of america journal 1993, Mar;57(2):443-52.
54. Jain, M.K.; Kothyari, U.C. Estimation of soil erosion and sediment yield using GIS. Hydrological Sciences Journal. 2000, Oct 1;45(5):771-86.
55. Van der Knijff, J. M.; Jones, R.J.A.; Montanarella, L. Soil erosion risk: assessment in Europe. 2000.
56. Garouani, A.; Chen, H.; Lewis, L.; Triback, A.; Abahrour, M. Cartographie de l'utilisation du sol et de l'érosion à partir d'images satellitaires et du SIG IDRISI au Nord Est du Maroc, Télédétection 2008, vol 8, n°3, p 193-201.
57. Negese, A.; Fekadu, E.; Getnet, H. Potential Soil Loss Estimation and Erosion-Prone Area Prioritization Using RUSLE, GIS, and Remote Sensing in Chereti Watershed, Northeastern Ethiopia. Air, Soil and Water Research 2021,14.
58. Karydas, C.G.; Sekuloska, T.; Silleos, G.N. Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete. Environmental Monitoring and Assessment 2009, Feb;149:19-28.
59. Lazzari, M.; Gioia, D.; Piccarreta, M.; Danese, M.; Lanorte, A. Sediment yield and erosion rate estimation in the mountain catchments of the Camastra artificial reservoir (Southern Italy): a comparison between different empirical methods. Catena 2015, p323–339. [Google Scholar] [CrossRef]
60. Vatandaşlar, C.; Yavuz, M. Modeling cover management factor of RUSLE using very high-resolution satellite imagery in a semiarid watershed. Environmental Earth Sciences 2017, Jan;76:1-21.
61. Borrelli, P.; Marker, M.; Panagos, P.; Schutt, B. Modeling Soil Erosion and River Sediment Yield for an Intermountain Drainage Basin of the Central Apennines, Italy. Catena 2014, 114: 45–58.
62. Sekiyama, A.; Mihara, M. Determining C factor of universal soil loss equation (USLE) based on remote sensing. International Journal of Environmental and Rural Development. 2016, p72. [Google Scholar]
63. Shawul, A.A.; Chakma, S. Spatiotemporal detection of land use/land cover change in the large basin using integrated approaches of remote sensing and GIS in the Upper Awash basin, Ethiopia. Environmental Earth Sciences 2019, vol. 78, no. 5.
64. Sewnet, A. Land use/cover change at Infraz watershed by using GIS and remote sensing techniques, northwestern Ethiopia. International Journal of River Basin Management 2015, vol. 14, no. 2, pp. 133–142.
65. Chakilu, G.G.; Moges, M.A. Assessing the land use/cover dynamics and its impact on the low flow of Gumara watershed, upper Blue Nile basin, Ethiopia. Hydrology: Current Research 2017, vol. 8, no. 1.
66. Guo, Q.K.; Liu, B.Y.; Yun, X.I.E.; Liu, Y.N.; Yin, S.Q. Estimation of USLE crop and management factor values for crop rotation systems in China. Journal of Integrative Agriculture 2015, 14(9), 1877–1888. [Google Scholar] [CrossRef]
67. Hurni, H. Erosion-productivity-conservation systems in Ethiopia.1985, 654-674.
68. Tiruneh, G.; Ayalew, M. Soil loss estimation using geographic information system in enfraz watershed for soil conservation planning in highlands of Ethiopia. International Journal of Agricultural Research, Innovation and Technology (IJARIT). 2015, 5(2355-2020-1587):21-30.
69. Ewunetu, A.; Simane, B.; Teferi, E.; Zaitchik, B.F. Land cover change in the blue nile river headwaters: farmers’ perceptions, pressures, and satellite-based mapping. Land 2021, 10(1), 68. [Google Scholar] [CrossRef]
70. Erdogan, H.E.; Erpul, G.; Bayramin, I. Use of USLE/GIS methodology for predicting soil loss in a semiarid agricultural watershed. Turkey: Department of Soil Science, University of Ankara 2006.
71. Swarnkar, S.; Malini, A.; Tripathi, S.; Sinha, R. Assessment of uncertainties in soil erosion and sediment yield estimates at ungauged basins: an application to the Garra River basin, India. Hydrology and Earth System Sciences 2018, Apr 24;22(4):2471-85.
72. Lufafa, A.; Tenywa, M.M.; Isabirye, M.; Majaliwa, M.J.G.; Woomer, P.L. Prediction of soil erosion in a Lake Victoria basin catchment using a GIS-based Universal Soil Loss model. Agricultural systems 2003, 76(3), 883–894. [Google Scholar] [CrossRef]
73. The ASTER Global Digital Elevation Model V003. Available online: https://search.earthdata.nasa.gov/downloads (accessed on 12 February 2022).
74. FAO. Digital Soil Map of the World (DSMW)|Land & Water|Food and Agriculture Organization of the United Nations Land & Water|Food and Agriculture Organization of the United Nations. Available online: http://www.fao.org/land-water/land/landgovernance/land-resources-planning-toolbox/category/details/en/c/1026564/ (accessed on 2 April 2022).