Assessment of the Erosion and Outflow Intensity in the Rif Region under Different Land Use and Land Cover Scenarios
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Intensity of Erosion and Outflow Assessment
2.2.1. IntErO Model Description and Implementation
2.2.2. Land Cover Change Scenarios and Effect of Geomorphological Conditions
2.3. Evolutionary Trends of Erosion in the Basin and Its Qualitative Assessment
- -
- Data collection: The first step is to collect data on the many factors that influence the risk of soil erosion, including rainfall, soil type, lithology, LULC, and vegetation cover. This data can be collected through surveys, remote sensing, or existing databases.
- -
- Data preparation involves digitizing and arranging the data obtained in a geographic information system (GIS) to process and prepare them for analysis.
- -
- Erosion risk assessment: The next step is to assess the erosion risk by summarizing the information on the different aspects using a matrix technique. Under the matrix approach, each element is given a score based on how much it contributes to erosion risk. These scores are then added together to provide an overall erosion risk score for each area (Table 4 and Table 5).
- -
- Erosion risk mapping uses erosion risk scores to show where there is a high risk of erosion. This allows you to prioritize protection measures and visualize the areas at risk of erosion.
- -
- Selection of conservation measures: Appropriate soil conservation measures can be selected for each erosion-prone area based on the erosion risk mapping. These measures may include terracing, contour plowing, improved plant cover, soil management techniques, and engineering structures.
- -
- Finally, the selected protective measures are implemented, monitored, and tested for their effectiveness in minimizing erosion risk.
3. Results and Discussion
3.1. Erosion Potential Assessment
3.2. Effect of Basin Geomorphological Conditions
3.3. Soil Erosion Scenarios
3.4. Quantitative Analysis of Erosion Trends in the Basin
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sadiki, A.; Faleh, A.; Navas, A.; Bouhlassa, S. Using magnetic susceptibility to assess soil degradation in the Eastern Rif, Morocco. Earth Surf. Process. Landf. 2009, 34, 2057–2069. [Google Scholar] [CrossRef]
- Okacha, A.; Salhi, A.; Arari, K.; El Badaoui, K.; Lahrichi, K. Soil erosion assessment using the RUSLE model for better planning: A case study from Morocco. Model. Earth Syst. Environ. 2023, 9, 3721–3729. [Google Scholar] [CrossRef]
- Sadiki, A.; Faleh, A.; Navas, A.; Bouhlassa, S. Assessing soil erosion and control factors by the radiometric technique in the Boussouab catchment, Eastern Rif, Morocco. CATENA 2007, 71, 13–20. [Google Scholar] [CrossRef]
- Ouallali, A.; Bouhsane, N.; Bouhlassa, S.; Moukhchane, M.; Ayoubi, S.; Aassoumi, H. Rapid magnetic susceptibility measurement as a tracer to assess the erosion–deposition process using tillage homogenization and simple proportional models: A case study in northern of Morocco. Int. J. Sediment Res. 2023, 38, 739–753. [Google Scholar] [CrossRef]
- Khodabin, G.; Lightburn, K.; Hashemi, S.M.; Moghada, M.S.K.; Jalilian, A. Evaluation of nitrate leaching, fatty acids, physiological traits and yield of rapeseed (Brassica napus) in response to tillage, irrigation and fertilizer management. Plant Soil 2022, 473, 423–440. [Google Scholar] [CrossRef]
- Qasim, W.; Wan, L.; Lv, H.; Zhao, Y.; Hu, J.; Meng, F.; Lin, S.; Butterbach-Bahl, K. Impact of anaerobic soil disinfestation on seasonal N2O emissions and N leaching in greenhouse vegetable production system depends on amount and quality of organic matter additions. Sci. Total. Environ. 2022, 830, 154673. [Google Scholar] [CrossRef]
- Liu, T.; Xu, X.; Yang, J. Experimental study on the effect of freezing-thawing cycles on wind erosion of black soil in Northeast China. Cold Reg. Sci. Technol. 2017, 136, 1–8. [Google Scholar] [CrossRef]
- Kader, S.; Raimi, M.O.; Spalevic, V.; Iyingiala, A.-A.; Bukola, R.W.; Jaufer, L.; Butt, T.E. A concise study on essential parameters for the sustainability of Lagoon waters in terms of scientific literature. Turk. J. Agric. For. 2023, 47, 288–307. [Google Scholar] [CrossRef]
- Xinbao, Z.; Higgitt, D.; Walling, D. A preliminary assessment of the potential for using caesium-137 to estimate rates of soil erosion in the Loess Plateau of China. Hydrol. Sci. J. 1990, 35, 243–252. [Google Scholar] [CrossRef]
- Ritchie, J.C.; Ritchie, C.A. Bibliography of Publications of 137 Cesium Studies Related to Erosion and Sediment Deposition; 1011-4289; International Atomic Energy Agency (IAEA): Vienna, Austria, 1995; pp. 125–201. [Google Scholar]
- Preiss, N.; Mélières, M.A.; Pourchet, M. A compilation of data on lead 210 concentration in surface air and fluxes at the air-surface and water-sediment interfaces. J. Geophys. Res. Atmos. 1996, 101, 28847–28862. [Google Scholar] [CrossRef]
- Walling, D.E.; He, Q.; Blake, W. Use of 7Be and 137Cs measurements to document short-and medium-term rates of water-induced soil erosion on agricultural land. Water Resour. Res. 1999, 35, 3865–3874. [Google Scholar] [CrossRef]
- Sepulveda, A.; Schuller, P.; Walling, D.E.; Castillo, A. Use of 7Be to document soil erosion associated with a short period of extreme rainfall. J. Environ. Radioact. 2008, 99, 35–49. [Google Scholar] [CrossRef]
- Fesenko, S.; Prudnikov, P.; Isamov, N.; Emlyutina, E.; Titov, I. Dynamics of 137Cs concentration in fodder in the long-term after the Chernobyl accident. Biol. Bull. 2022, 49, 2359–2368. [Google Scholar] [CrossRef]
- Cabrera, M.; Sanabria, R.; González, J.; Cabral, P.; Tejeda, S.; Zarazua, G.; Melgar-Paniagua, E.; Tassano, M. Using 137Cs and 210Pbex to assess soil redistribution at different temporal scales along with lithogenic radionuclides to evaluate contrasted watersheds in the Uruguayan Pampa grassland. Geoderma 2023, 435, 116502. [Google Scholar] [CrossRef]
- Lizaga Villuendas, I.; Latorre, B.; Gaspar, L.; Navas, A. Effect of historical land-use change on soil erosion in a Mediterranean catchment by integrating 137Cs measurements and WaTEM/SEDEM model. Hydrol. Process. 2022, 36, e14577. [Google Scholar] [CrossRef]
- Tan, M.L.; Gassman, P.W.; Yang, X.; Haywood, J. A review of SWAT applications, performance and future needs for simulation of hydro-climatic extremes. Adv. Water Resour. 2020, 143, 103662. [Google Scholar] [CrossRef]
- Hao, Z.; Wu, D. Data Preprocessing of Soil Attributes for Ecohydrological Applications Using SWAT Model at Xin’anjiang Upstream Watershed, China. Ecohydrol. Hydrobiol. 2023, 23, 198–210. [Google Scholar] [CrossRef]
- Briak, H.; Moussadek, R.; Aboumaria, K.; Mrabet, R. Assessing sediment yield in Kalaya gauged watershed (Northern Morocco) using GIS and SWAT model. Int. Soil Water Conserv. Res. 2016, 4, 177–185. [Google Scholar] [CrossRef]
- Ouallali, A.; Briak, H.; Aassoumi, H.; Beroho, M.; Bouhsane, N.; Moukhchane, M. Hydrological foretelling uncertainty evaluation of water balance components and sediments yield using a multi-variable optimization approach in an external Rif’s catchment. Morocco. Alex. Eng. J. 2020, 59, 775–789. [Google Scholar] [CrossRef]
- Renard, K.G. Predicting Soil Erosion by Water: A guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE); US Department of Agriculture, Agricultural Research Service: Beltsville, MD, USA, 1997.
- Williams, J.; Berndt, H. Sediment yield prediction based on watershed hydrology. Trans. ASAE 1977, 20, 1100–1104. [Google Scholar] [CrossRef]
- Mitasova, H.; Mitas, L. Modeling Soil Detachment with RUSLE 3D Using GIS; University of Illinois at Urbana-Champaign: Champaign, IL, USA, 1999. [Google Scholar]
- Gavrilović, S. Inženjering o Bujičnim Tokovima i Eroziji; Izgradnja: Belgrade, Serbia, 1972. [Google Scholar]
- Bezak, N.; Borrelli, P.; Mikoš, M.; Jemec Auflič, M.; Panagos, P. Towards multi-model soil erosion modelling: An evaluation of the erosion potential method (EPM) for global soil erosion assessments. CATENA 2024, 234, 107596. [Google Scholar] [CrossRef]
- Tangestani, M.H. Comparison of EPM and PSIAC models in GIS for erosion and sediment yield assessment in a semi-arid environment: Afzar Catchment, Fars Province, Iran. J. Asian Earth Sci. 2006, 27, 585–597. [Google Scholar] [CrossRef]
- Ahmadi, M.; Minaei, M.; Ebrahimi, O.; Nikseresht, M. Evaluation of WEPP and EPM for improved predictions of soil erosion in mountainous watersheds: A case study of Kangir River basin, Iran. Model. Earth Syst. Environ. 2020, 6, 2303–2315. [Google Scholar] [CrossRef]
- Sestras, P.; Mircea, S.; Roșca, S.; Bilașco, Ș.; Sălăgean, T.; Dragomir, L.O.; Herbei, M.V.; Bruma, S.; Sabou, C.; Marković, R.; et al. GIS based soil erosion assessment using the USLE model for efficient land management: A case study in an area with diverse pedo-geomorphological and bioclimatic characteristics. Not. Bot. Horti Agrobot. Cluj-Napoca 2023, 51, 13263. [Google Scholar] [CrossRef]
- Ouallali, A.; Aassoumi, H.; Moukhchane, M.; Moumou, A.; Houssni, M.; Spalevic, V.; Keesstra, S. Sediment mobilization study on Cretaceous, Tertiary and Quaternary lithological formations of an external Rif catchment, Morocco. Hydrol. Sci. J. 2020, 65, 1568–1582. [Google Scholar] [CrossRef]
- Spalevic, V.; Barovic, G.; Vujacic, D.; Curovic, M.; Behzadfar, M.; Djurovic, N.; Dudic, 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]
- Youssef, B.; Bouskri, I.; Brahim, B.; Kader, S.; Brahim, I.; Abdelkrim, B.; Spalević, V. The contribution of the frequency ratio model and the prediction rate for the analysis of landslide risk in the Tizi N’tichka area on the national road (RN9) linking Marrakech and Ouarzazate. CATENA 2023, 232, 107464. [Google Scholar] [CrossRef]
- Tahouri, J.; Sadiki, A.; Karrat, L.h.; Johnson, V.C.; Chan, N.w.; Fei, Z.; Kung, H.T. Using a modified PAP/RAC model and GIS-for mapping water erosion and causal risk factors: Case study of the Asfalou watershed, Morocco. Int. Soil Water Conserv. Res. 2022, 10, 254–272. [Google Scholar] [CrossRef]
- Elbadaoui, K.; Mansour, S.; Ikirri, M.; Abdelrahman, K.; Abu-Alam, T.; Abioui, M. Integrating Erosion Potential Model (EPM) and PAP/RAC Guidelines for Water Erosion Mapping and Detection of Vulnerable Areas in the Toudgha River Watershed of the Central High Atlas, Morocco. Land 2023, 12, 837. [Google Scholar] [CrossRef]
- Diani, K.; Ettazarini, S.; Hahou, Y.; El Belrhiti, H.; Allaoui, W.; Mounir, K.; Gourfi, A. Chapter 11—Identification of soil erosion sites in semiarid zones: Using GIS, remote sensing, and PAP/RAC model. In Handbook of Hydroinformatics; Eslamian, S., Eslamian, F., Eds.; Elsevier: Amsterdam, The Netherlands, 2023; pp. 169–183. [Google Scholar] [CrossRef]
- Huang, W.; Ho, H.C.; Peng, Y.; Li, L. Qualitative risk assessment of soil erosion for karst landforms in Chahe town, Southwest China: A hazard index approach. CATENA 2016, 144, 184–193. [Google Scholar] [CrossRef]
- Drzewiecki, W.; Wężyk, P.; Pierzchalski, M.; Szafrańska, B. Quantitative and qualitative assessment of soil erosion risk in Małopolska (Poland), supported by an object-based analysis of high-resolution satellite images. Pure Appl. Geophys. 2014, 171, 867–895. [Google Scholar] [CrossRef]
- Kader, S.; Jaufer, L.; Bashir, O.; Raimi, M.O. A Comparative Study on the Stormwater Retention of Organic Waste Substrates Biochar, Sawdust, and Wood Bark Recovered from Psidium Guajava L. Species. Comp. Study Stormwater Retent. Org. Waste Substrates 2023, 69, 105–112. [Google Scholar] [CrossRef]
- Chikh, H.A.; Habi, M.; Morsli, B. Influence of vegetation cover on the assessment of erosion and erosive potential in the Isser marly watershed in northwestern Algeria—Comparative study of RUSLE and PAP/RAC methods. Arab. J. Geosci. 2019, 12, 154. [Google Scholar] [CrossRef]
- Mohamed-Chérif, F.; Ducruet, C. Regional integration and maritime connectivity across the Maghreb seaport system. J. Transp. Geogr. 2016, 51, 280–293. [Google Scholar] [CrossRef]
- Sort, X.; Alcaniz, J. Contribution of sewage sludge to erosion control in the rehabilitation of limestone quarries. Land Degrad. Dev. 1996, 7, 69–76. [Google Scholar] [CrossRef]
- Ozcelik, M. Comparison of the environmental impact and production cost rates of aggregates produced from stream deposits and crushed rock quarries (Boğaçay Basin/Antalya/Turkey). Geoheritage 2022, 14, 18. [Google Scholar] [CrossRef]
- Taoufik, M.; Loukili, I.; Hadi, H.E.; Baghdad, B. Soil erosion risk assessment in an extraction area: Case of abandoned quarries in the Akreuch region (Morocco). In Proceedings of the 2020 IEEE International conference of Moroccan Geomatics (Morgeo), Casablanca, Morocco, 11–13 May 2020; pp. 1–5. [Google Scholar] [CrossRef]
- Costa, J.P.R.; Gomes, G.J.; Fernandes, G.; Magarinos, D.M.; Fonseca, A.; Pires, P.J. Ferronickel slag as free-draining rockfill dike material: A novel waste solution for mining regions. J. Mater. Cycles Waste Manag. 2023, 25, 128–143. [Google Scholar] [CrossRef]
- Elaloui, A.; Khalki, E.M.E.; Namous, M.; Ziadi, K.; Eloudi, H.; Faouzi, E.; Bou-Imajjane, L.; Karroum, M.; Tramblay, Y.; Boudhar, A. Soil erosion under future climate change scenarios in a semi-arid region. Water 2022, 15, 146. [Google Scholar] [CrossRef]
- López, S.; Wright, C.; Costanza, P. Environmental change in the equatorial Andes: Linking climate, land use, and land cover transformations. Remote Sens. Appl. Soc. Environ. 2017, 8, 291–303. [Google Scholar] [CrossRef]
- Henriques, C.; Zêzere, J.L.; Marques, F. The role of the lithological setting on the landslide pattern and distribution. Eng. Geol. 2015, 189, 17–31. [Google Scholar] [CrossRef]
- Abdolmaleki, M.; Mokhtari, A.R.; Akbar, S.; Alipour-Asll, M.; Carranza, E.J.M. Catchment basin analysis of stream sediment geochemical data: Incorporation of slope effect. J. Geochem. Explor. 2014, 140, 96–103. [Google Scholar] [CrossRef]
- Efthimiou, N.; Lykoudi, E.; Karavitis, C. Comparative analysis of sediment yield estimations using different empirical soil erosion models. Hydrol. Sci. J. 2017, 62, 2674–2694. [Google Scholar] [CrossRef]
- Raissis, F.; Theochari, A.-P.; Baltas, E. Soil transportation assessment of an eastern Mediterranean basin in Greece using GIS techniques. Euro-Mediterr. J. Environ. Integr. 2022, 7, 361–376. [Google Scholar] [CrossRef]
- Roy, P.; Pal, S.C.; Chakrabortty, R.; Saha, A.; Chowdhuri, I. A systematic review on climate change and geo-environmental factors induced land degradation: Processes, policy-practice gap and its management strategies. Geol. J. 2023, 58, 3487–3514. [Google Scholar] [CrossRef]
- Chalise, D.; Kumar, L.; Spalevic, V.; Skataric, G. Estimation of sediment yield and maximum outflow using the IntErO model in the Sarada river basin of Nepal. Water 2019, 11, 952. [Google Scholar] [CrossRef]
- Pavlova-Traykova, E.; Grigorova-Pesheva, B.; Petrova, K. Soil loss assessment by applying intero model. For. Sci. 2023, 59, 41–46. [Google Scholar]
- Sestras, P.; Mircea, S.; Cîmpeanu, S.M.; Teodorescu, R.; Roșca, S.; Bilașco, Ș.; Rusu, T.; Salagean, T.; Dragomir, L.O.; Marković, R. Soil erosion assessment using the intensity of erosion and outflow model by estimating sediment yield: Case study in river basins with different characteristics from Cluj County, Romania. Appl. Sci. 2023, 13, 9481. [Google Scholar] [CrossRef]
- Spalevic, V. Impact of Land Use on Runoff and Soil Erosion in Polimlje. Ph.D. Thesis, Faculty of Agriculture of the University of Belgrade, Belgrage, Serbia, 2011. [Google Scholar]
- Spalevic, V. Assessment of soil erosion processes by using the ‘IntErO’model: Case study of the Duboki Potok, Montenegro. J. Environ. Prot. Ecol. 2019, 20, 657–665. [Google Scholar]
- Mohammadi, M.; Khaledi Darvishan, A.; Spalevic, V.; Dudic, B.; Billi, P. Analysis of the impact of land use changes on soil erosion intensity and sediment yield using the intero model in the talar watershed of Iran. Water 2021, 13, 881. [Google Scholar] [CrossRef]
- Rajabi, A.M.; Yavari, A.; Cheshomi, A. Sediment yield and soil erosion assessment by using empirical models for Shazand watershed, a semi-arid area in center of Iran. Nat. Hazards 2022, 112, 1685–1704. [Google Scholar] [CrossRef]
- Berteni, F.; Barontini, S.; Grossi, G. Evaluating soil erosion by water in a small alpine catchment in Northern Italy: Comparison of empirical models. Acta Geochim. 2021, 40, 507–524. [Google Scholar] [CrossRef]
- Tričković, N.; Rončević, V.; Živanović, N.; Grujić, T.; Stefanović, L.; Jovanović, N.; Zlatić, M. Ecological and Economic Effects of Applying the Future Agricultural Production Structure Model (FAPSMS): The Case Study of the Barička River Basin. Sustainability 2023, 15, 8434. [Google Scholar] [CrossRef]
- Rončević, V.; Zlatić, M.; Todosijević, M. Environmental and economic effects of investments in sustainable land management in the basin of Šutilovac stream. Glas. Sumar. Fak. 2019, 19, 213–232. [Google Scholar] [CrossRef]
- Lamane, H.; Moussadek, R.; Baghdad, B.; Mouhir, L.; Briak, H.; Laghlimi, M.; Zouahri, A. Soil water erosion assessment in Morocco through modeling and fingerprinting applications: A review. Heliyon 2022, 8, e10209. [Google Scholar] [CrossRef]
- Griesbach, J.; Ruiz Sinoga, J.; Giordano, A.; Berney, O.; Gallart, F. Directives pour la Cartographie et la Mesure des Processus d’Erosion Hydrique dans les Zones Cotieres Mediterraneennes. 1998. Available online: https://www.academia.edu/76126325/Directives_pour_la_cartograophie_et_la_mesure_des_processus_d_erosion_hydrique_dans_les_zones_cotieres_mediterraneennes (accessed on 17 June 2023).
- Gavrilovic, Z. Use of an Empirical Method(Erosion Potential Method) for Calculating Sediment Production and Transportation in Unstudied or Torrential Streams. In Proceedings of the International Conference on River Regime, Wallingford, UK, 18–20 May 1988; Hydraulics Research Limited: Wallingford, UK, 1988; pp. 411–422. [Google Scholar]
- Sabri, E.; Spalevic, V.; Boukdir, A.; Karaoui, I.; Ouallali, A.; Mincato, R.L.; Sestras, P. Estimation of soil losses and reservoir sedimentation: A case study in Tillouguite sub-basin (High Atlas-Morocco). Agric. For./Poljopr. Sumar. 2022, 68, 207–220. [Google Scholar] [CrossRef]
- Aït Brahim, L.; Sossey Alaoui, F.; Siteri, H.; Tahri, M. Quantification of soil loss in the Nakhla watershed (northern Rif). Sécheresse-Sci. Chang. Planétaires 2003, 14, 101–106. [Google Scholar]
- Damnati, B.; Ibrahimi, S.; Benhardouze, O.; Benhardouze, H.; Reddad, H.; Radakovitch, O. Quantification de l’érosion par le 137Cs et le 210Pb: Cas de deux bassins versants au Nord-Ouest du Maroc (Région de Tanger-Tétouan). Notes Mémoires Serv. Géologique Maroc 2012, 575, 74–80. [Google Scholar]
- Ed-daoudy, L.; Lahmam, N.; Benmansour, M.; Afilal, H.; Ben harra, A.; Damnati, B. Hydric erosion rates in Raouz watershed, Morocco: RUSLE, GIS, and remote sensing. Remote Sens. Appl. Soc. Environ. 2023, 32, 101056. [Google Scholar] [CrossRef]
- Kader, S.; Novicevic, R.; Jaufer, L. Soil Management in Sustainable Agriculture: Analytical Approach for the Ammonia Removal from the Diary Manure. J. Agric. For. 2022, 68, 69–78. [Google Scholar] [CrossRef]
- Kostadinov, S.; Braunović, S.; Dragićević, S.; Zlatić, M.; Dragović, N.; Rakonjac, N. Effects of erosion control works: Case study—Grdelica Gorge, the South Morava River (Serbia). Water 2018, 10, 1094. [Google Scholar] [CrossRef]
- Manojlović, S.; Antić, M.; Šantić, D.; Sibinović, M.; Carević, I.; Srejić, T. Anthropogenic impact on erosion intensity: Case study of rural areas of pirot and dimitrovgrad municipalities, Serbia. Sustainability 2018, 10, 826. [Google Scholar] [CrossRef]
LULC | Area (km2) | % | LULC | Area (km2) | % |
---|---|---|---|---|---|
Dam | 1.1 | 3.1 | Dense forest | 0.3 | 0.9 |
Quarrying areas | 1.4 | 4.2 | Moderately dense forest | 1.1 | 3.1 |
Bare lands | 0.5 | 1.5 | Clear forest on dense scrublands | 1.5 | 4.5 |
Rock outcrop | 1.5 | 4.4 | Dense reforestation | 1.1 | 3.3 |
Agricultural land | 4.8 | 13.9 | Clear reforestation | 0.04 | 0.1 |
Association: agricultural and built land | 3.0 | 8.8 | Dense scrubland | 8.9 | 25.8 |
Clear scrubland | 91 | 26.4 |
Parameters | Symbols | Values | Units |
---|---|---|---|
The watershed’s length | F | 34.4 | km2 |
The length of the main watercourse | O | 25.7 | km |
The quickest path from the mouth to the fountainhead | Lv | 12.3 | km |
The entire length of the primary watercourse, including tributaries of classes I and II | Lm | 7.3 | km |
A set of parallel lines indicates the length of the river basin. | ΣL | 110.1 | km |
The more significant portion of the river basin | Lb | 9.3 | km |
The region of the shorter river basin | Fv | 23 | km2 |
The watershed’s length | Fm | 11.4 | km2 |
Length of contour lines | Liz | 50-100-150-200-250-300-350-400-450-500-550 | km |
The area between two adjacent contour lines | fiz | 0.3-2.23-3.9-4.32-5.8-6.44-5.06-3.13-1.84-0.91-0.38-0.09 | km |
The first contour line’s altitude | h0 | 50 | m |
Equidistance | Δh | 50 | m |
The lowest elevation in the river basin | Hmin | 12 | m |
The highest elevation in the river basin | Hmax | 572 | m |
The portion of the river basin comprises a highly porous rock product (limestone, sand, and gravel). | fp | 0.14 | |
The portion of the river basin comprises moderately permeable rocks (slates, marls, and brownstone). | fpp | 0.18 | |
The portion of the river basin comprises low-permeability rocks (thick clay, compact eruptive). | fo | 0.68 | |
The area of the river basin is covered with forests | fš | 0.64 | |
The portion of the river basin covered in grasslands, meadows, and orchards | ft | 0 | |
The section of the river basin is bare land, plowland, or ground devoid of grass vegetation. | fg | 0.36 | |
The torrential rain’s volume | hb | 61.13 | mm |
Incidence | Up | 100 | years |
Annual average air temperature | t0 | 17.5 | °C |
Annual precipitation average | Hgod | 677.02 | mm |
Soil product categories and associated types | Y | 1.1 | |
River basin planning, river basin planning coefficient | Xa | 0.53 | |
Numeral equivalents of visible and exposed erosion processes | φ | 0.32 |
Erosion Intensity | Erosion Type | Z | Mean Value Z |
---|---|---|---|
Excessive | Deep | 1.51 | 1.25 |
Mixed | 1.21–1.50 | ||
Surface | 1.01–1.20 | ||
Strong | Deep | 0.91–1 | 0.85 |
Mixed | 0.81–0.90 | ||
Surface | 0.71–0.80 | ||
Medium | Deep | 0.61–0.70 | 0.55 |
Mixed | 0.51–0.60 | ||
Surface | 0.41–0.50 | ||
Low | Deep | 0.31–0.40 | 0.3 |
Mixed | 0.25–0.30 | ||
Surface | 0.20–0.24 | ||
Very low | Deep | 0.01–0.19 | 0.1 |
Mixed | |||
Surface |
Lithofacies Classes | Density of Recovery | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Classes of Slope | a-1 | b-2 | c-3 | d-4 | Land Use | 1 (<25%) | 2 (25–50%) | 3 (50–75%) | 4 (>75%) | 5 (dam) |
1 | 1: EN | 1: EN | 1: EN | 2: EB | 1 | 2: M | 1: A | 1: A | 1: A | 0 |
2 | 1: EN | 1: EN | 2: EB | 3: EM | 2 | 3: B | 2: M | 1: A | 1: A | 0 |
3 | 2: EB | 2: EB | 3: EM | 4: EA | 3 | 4: MB | 4: MB | 3: B | 2: M | 0 |
4 | 3: EM | 3: EM | 4: EA | 4: EA | 4 | 4: MB | 4: MB | 3: B | 2: M | 0 |
5 | 4: EA | 4: EA | 5: EX | 5: EX | 5 (dam) | 0 | 0 | 0 | 0 | 0 |
Matrix 1: Degrees of erodibility EX: Extreme, EA: High, EM: Medium, EB: Moderate, EN: Low | Matrix 2: Degree of protection of the soil MB: Very low, B: Low, M: Medium, A: High |
Erodibility Classes | Erosion Forms | |||||||
---|---|---|---|---|---|---|---|---|
Soil Protection Classes | 1-EN | 2-EB | 3-EM | 4-EA | 5-EX | Erosive States | Minor Forms of Erosion (SP, L, D) | Major Forms of Erosion (M, C, Cx, Lx) |
1 (A) | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 3 |
2 (M) | 1 | 1 | 2 | 3 | 4 | 2 | 1 | 3 |
3 (B) | 1 | 2 | 3 | 4 | 4 | 3 | 2 | 4 |
4 (MB) | 4 | 4 | 5 | 5 | 5 | 4 | 2 | 4 |
5 (Dam) | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 4 |
Matrix 3: Erosive states 5: Very High, 4: High, 3: Notable, 2: Low, 1: Very low | Matrix 4: Erosion trends (*): (1), (2), (3), (4) |
Parameters | Symbols | Values | Units |
---|---|---|---|
The river basin shape coefficient. | A | 0.41 | |
Watershed development coefficient. | m | 0.59 | |
The average breadth of a river basin. | B | 3.7 | km |
The river basin A’s symmetry. | a | 0.67 | |
The density of the basin’s river network. | G | 3.2 | km/km2 |
The tortuousness of the river basin. | K | 1.68 | |
Average river basin elevation. | Hsr | 255.19 | m |
The river basin’s average elevation difference. | D | 243.19 | m |
Decrease in the typical river basin. | Isr | 479.65 | % |
The elevation of the river basin’s local erosion base. | Hleb | 560 | m |
Coefficient of the river basin relief’s erosion energy. | Er | 73.6 | |
Coefficient of permeability in the area. | S1 | 0.86 | |
Coefficient of the cover of vegetation. | S2 | 0.74 | |
Water retention in influx is presented analytically. | W | 0.7704 | m |
Water flow’s potential for energy during torrential downpours. | 2gDF½ | 405.14 | m km s |
Maximum outflow in the river basin. | Qmax | 81.51 | m3 s−1 |
Coefficient of temperature. | T | 1.36 | |
Coefficient of erosion in the river basins. | Z | 1.424 | |
Erosion material produced in the river basin. | Wgod | 169,137.6047 | m3/god |
Coefficient for the retention of deposits. | Ru | 0.224 | |
Actual losses in soil. | Ggod | 37,886.82 | m3/god |
Actual losses of soil per km2. | Ggod/km2 | 1101.36 | m3/km2 god |
Parameters | Symbols | Values | Units |
---|---|---|---|
The river basin shape coefficient. | A | 0.41 | |
Watershed development coefficient. | m | 0.59 | |
The average breadth of a river basin. | B | 3.7 | km |
The river basin A’s symmetry. | a | 0.67 | |
The density of the basin’s river network. | G | 3.2 | |
The tortuousness of the river basin. | K | 1.68 | |
Average river basin elevation. | Hsr | 255.19 | m |
The river basin’s average elevation difference. | D | 243.19 | m |
Decrease in the typical river basin. | Isr | 479.65 | % |
The elevation of the river basin’s local erosion base. | Hleb | 560 | m |
Coefficient of the river basin relief’s erosion energy. | Er | 73.6 | |
Coefficient of permeability in the area. | S1 | 0.86 | |
Coefficient of the cover of vegetation. | S2 | 0.69 | |
Water retention in influx is presented analytically. | W | 0.7704 | m |
Water flow’s potential for energy during torrential downpours. | 2gDF½ | 405.14 | m km s |
Maximum outflow in the river basin. | Qmax | 75.72 | m3/s |
Coefficient of temperature. | T | 1.36 | |
Coefficient of erosion in the river basins. | Z | 1.056 | |
Erosion material produced in the river basin. | Wgod | 10,7971.3 | m3/god |
Coefficient for the retention of deposits. | Ru | 0.224 | |
Actual losses in soil. | Ggod | 24,185.57 | m3/god |
Actual losses of soil per km2. | Ggod/km2 | 703.06 | m3/km2 god |
Parameters | Symbols | Values | Units |
---|---|---|---|
The river basin shape coefficient. | A | 0.41 | |
Watershed development coefficient. | m | 0.59 | |
The average breadth of a river basin. | B | 3.7 | km |
The river basin A’s symmetry. | a | 0.67 | |
The density of the basin’s river network. | G | 3.2 | |
The tortuousness of the river basin. | K | 1.68 | |
Average river basin elevation. | Hsr | 255.19 | m |
The river basin’s average elevation difference. | D | 243.19 | m |
Decrease in typical river basin decrease. | I.sr | 479.65 | % |
The elevation of the river basin’s local erosion base. | Hleb | 560 | m |
Coefficient of the river basin relief’s erosion energy. | Er | 73.6 | |
Coefficient of permeability in the area. | S1 | 0.86 | |
Coefficient of the cover of vegetation. | S2 | 0.74 | |
Water retention in influx is presented analytically. | W | 0.7704 | m |
Water flow’s potential for energy during torrential downpours. | 2gDF½ | 405.14 | mkms |
Maximum outflow in the river basin. | Qmax | 81.51 | m3/s |
Coefficient of temperature. | T | 1.36 | |
Coefficient of erosion in the river basins. | Z | 1.366 | |
Erosion material produced in the river basin. | Wgod | 158,888.9 | m3/god |
Coefficient for the retention of deposits. | Ru | 0.224 | |
Actual losses in soil. | Ggod | 35,591.11 | m3/god |
Actual losses of soil per km2. | Ggod/km2 | 1034.62 | m3/km2god |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ouallali, A.; Kader, S.; Bammou, Y.; Aqnouy, M.; Courba, S.; Beroho, M.; Briak, H.; Spalevic, V.; Kuriqi, A.; Hysa, A. Assessment of the Erosion and Outflow Intensity in the Rif Region under Different Land Use and Land Cover Scenarios. Land 2024, 13, 141. https://doi.org/10.3390/land13020141
Ouallali A, Kader S, Bammou Y, Aqnouy M, Courba S, Beroho M, Briak H, Spalevic V, Kuriqi A, Hysa A. Assessment of the Erosion and Outflow Intensity in the Rif Region under Different Land Use and Land Cover Scenarios. Land. 2024; 13(2):141. https://doi.org/10.3390/land13020141
Chicago/Turabian StyleOuallali, Abdessalam, Shuraik Kader, Youssef Bammou, Mourad Aqnouy, Said Courba, Mohamed Beroho, Hamza Briak, Velibor Spalevic, Alban Kuriqi, and Artan Hysa. 2024. "Assessment of the Erosion and Outflow Intensity in the Rif Region under Different Land Use and Land Cover Scenarios" Land 13, no. 2: 141. https://doi.org/10.3390/land13020141