Water erosion risk assessment in the Kasilian watershed with ICONA model and GIS/RS techniques

Document Type : Original Article

Authors

1 Department of Forest, Range and Watershed Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources University of Tehran, Karaj, Iran

Abstract

Extended abstract
Introduction
Awareness of the soil erosion risk in watersheds makes it possible to identify critical areas and prioritize management and conservation programs. Due to the lack of accurate and acceptable information about the small amount of soil erosion in watersheds, it is often necessary to estimate the sensitivity or potential of different areas of the watershed in terms of the severity of soil erosion. The use of new RS/GIS techniques along with modeling processes such as soil erosion accelerates the recognition, control and management of natural resources. Among the many methods for predicting erosion using RS/GIS, the ICONA model simulation results are widely accepted. The ICONA model is one of the simplest and most flexible qualitative methods for assessing erosion risk map. This model is an erosion risk assessment method that uses qualitative decision rules and hierarchical organization of the four main inputs. The erosion risk map prepared with the ICONA model can be used as a reliable framework for erosion risk assessment. This model with its flexibility can be used in decision making to solve the problems of soil erosion and degradation in specific conditions of each country or study area. The purpose of this study is to apply the ICONA qualitative model and the use of RS/GIS techniques to assess the potential for erosion risk in the Kasilian watershed (representing large areas of mountainous and forested parts of northern Alborz in Iran).
Materials and methods
In this study, with the aim of investigating the risk of erosion and identifying areas sensitive to water erosion in the Kasilian watershed, remote sensing and GIS techniques were used in the framework of the ICONA model. The ICONA model consists of seven stages. This model starts with 4 layers of slope, geology, vegetation and land use and by preparing two layers of soil erodibility and soil protection maps, has identified and evaluated the potential for erosion risk in different land uses.
Study area
Kasilian watershed with an area of ​​6750 hectares in the geographical range of Alborz. It lies within the latitudes of 35° 58′ 45′′ to 36° 07′ 45′′ north, and longitudes of 53° 01′ 30′′ to 53° 17′ 30′ east and south of Savadkooh city. It is located in Mazandaran province. This area represents large areas of mountainous and forested parts of northern Alborz in Iran. Land use of Kasilian watershed mainly includes forests, pastures, agriculture, residential and rock outcrops.
 
 
Results and discussion
Due to its morphological and topographic characteristics, Kasilian watershed is an area with high density and elongation with a large area of ​​very high slope class (20-35%), formations sensitive to water erosion (Shemshak formation), soil with heavy to semi texture heavy and hydrological group of soil is C. Therefore, erosion is predicted and observed in sensitive areas of the study area. Findings of the study in the area show that high vegetation has moderated the slope effect, in general, the highest level of erosion risk class is in the middle class. At the same time, the southern and higher parts of the forest lands of Kasilian watershed are in high risk of erosion due to reduced vegetation and slope. The findings also showed that the risk of erosion is very high in rangelands with high slope and low protection. The rock outcrop lands that exist in the upper elevations of the study area have a slope of more than 35%. These lands are in a very low erosion risk class. In fact, higher slopes can also provide a natural protection against soil erosion.
Conclusion
In total, 26.26% is in the medium erosion risk class, 25.44% in the low erosion risk class, 18.83% in the very low erosion risk class, 18.55% in the high erosion risk class and 10.92% is of very high erosion risk class. Rangelands and parts of agricultural lands rank first in the extent of the erosion risk class. The southern parts of the forest lands (with reduced vegetation cover) are in the high erosion risk class. This model can be used as a reliable framework for erosion risk assessment and can identify areas prone to erosion with a minimum parameter, cost and time reduction, accuracy and good flexibility.

Keywords

References

References
Persian References:
-Alijanpour Shelmani, A., Vaezi, A.R. and Tabatabaei, M.R., 2019. Comparison of soil aggregate stability in two very humid and semi-arid regions (Case Study: Kasilian and Said Abad Chai watersheds). 16th Iranian Soil Congress, University of Zanjan, Iran, August, v. 27-29.
-Chatrsimab, Z., Gharagozlou, A., Bolouri, S. and Mirdar Harijani, F., 2016. Erosion risk Map assessment using ICONA model and RS/GIS, case study: Aghagir Watershed. International First Conference the Silk Road, Isfahan University.
-Chatrsimab, Z., Barati, S., Mirdar Harijani, F. and rahmany, M., 2017. Erosion risk assessment using ICONA model and RS/GIS, case study: Salaj Anbar Watershed. International Conference on Natural Resources Management in Developing Countries.
-Entezari, M. and Khodadadi, F., 2017. Taleghanrood Watershed Soil Erosion Risk Assessment ICONA Model, Journal Invironment Hazard, v. 13(6), p. 31-48.  doi. 10.22111/JNEH.2017.3142
-Fazl Ola, R., 2006. Rainfall-runoff simulation model using previous rainfall index (Case study: Kasilian and Imameh watersheds), PhD thesis, Department of Hydrology and Water Resources, Faculty of Water Sciences and Engineering, Shahid Chamran University, 252 p.
-Hosseini Pazhouh, N., Ahmadali, K.H. and Shokoohi, A.R., 2018. Assessment of standardized precipitation and standardized precipitation-evapotranspiration indices for wet period detection. Gorgan University of Agriculture and Natural Resources. Journal of Soil Conservation Research, v. 25(6), p. 207-222.
Doi. 10.22069/JWSC.2019.15175.3036
-Karimi, L. and Amin, S., 2012. Soil Erosion risk assessment using ICONA model and RS/GIS in Watershed Sivand Dam. 16th Symposium of Geological Society of Iran, Shiraz university, Iran
-Kazemi, M., Nohegar, A. and Lagzian, F., 2012. Assessment Impacts Land use on Soil erosion using RS and GIS Techniques, Case study: Tang Bostanak Shiraz. 9th National Conference on Watershed Management Sciences and Engineering of Lorestan university. Iran.
-Mirzaei, N., Kavian, A. and Chobin, B., 2018. Water erosion risk assessment with ICONA model (Case Study: Gorganroud watershed). 6th International Congress on Development and Promotion of Fundamental Science and Technology in Society, dpcongress.ir, Iran.
https://civilica.com/doc/916766/ , COI: DPFSTS06_013
-Mohammadi, S., Karimzadeh, H.R. and Alizadeh, M., 2018. Soil erosion local estimate by RUSLE model in IRAN. J Ecohydrology, v. 5(2), p. 551-569.
Doi:  10.22059/IJE.2018.239777.706
-Moghim, H., Raoufat, M.R. and Khalili, A., 2013. Soil Erosion risk assessment using ICONA model and RS/GIS in Watershed area of Mulla Sadra Dam. 9th National Conference on Watershed Management Sciences and Engineering of Iran. Yazd university, Iran.
-Naderi, F., Karimi, H. and Naseri, B., 2011. Soil erosion potential zoning in Aseman Abad watershed by erosion index. Journal of Watershed Management Researches, v. 89, v. 44-51. https://www.sid.ir/en/journal/ViewPaper.aspx?ID=314264
-Roozkhash, B., 1996. GIS application in Flood Warning Systems and Flood Hazard Zones Desing in Kasilian Watershed, M.Sc. Thesis, Tehran University, 146 p.
-Sedighi, M., 2011. Soil Erosion risk assessment using ICONA model and RS/GIS, case study: Tang Sorkh Shiraz Watershed. Science and Research Branch, Islamic Azad University, Tehran, Iran.
 
English References:
-Bayramin, I., Dengiz, O., Baskan, O. and Parlak, M., 2003. Soil Erosion risk assessment with ICONA model: Case study: Beypazari area. Turk. J. Agric, For 27, p. 105-116. https://dergipark.org.tr/en/pub/tbtkagriculture/issue/11640/138624.
-Chander, G., Markham, B. and Helder, D., 2009. Summary of Current Radumetric Calibration Coefficients for Landsat MSS, TM, ETM+ and EO-A Ali Sensors. Remote Sensing of the Environment, v. 113, p. 893-903.
https://ntrs.nasa.gov/search.jsp?R=20090027884 2020-04-16T17:01:26+00:00Z
-Esmaeeli Gholzom, H. and Gholami, V., 2012. A comparison between natural forests and reforested lands in terms of runoff generation potential and hydrologic response, case study: Kasilian Watershed, Soil Water Res, v. 4, p. 166-173.
https://doi.org/10.17221/18/2012-SWR
-Gaatib, R. and Larabi, A., 2014. Integrated evaluation of soil hazard and risk management in the Oued Beht watershed using remote sensing and GIS techniques: impacts on El Kansra Dam Siltation (Morocco). J. Geogr. Inf. Syst, v. 6, p. 677-742.  ID:52287,12 pages 10.4236/jgis.2014.66056
-Holm, A.M., Cridland, S.W. and Roderick, M.L., 2003. The use time – integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of Western Australia. Remote Sens. Environ, v. 85, p. 145-158. DOI: 10.1016/S0034-4257(02)00199-2. v. 25(6), p. 207-222. Doi. 10.22069/JWSC.2019.15175.3036
-ICONA, 1991. Plan Nacional De Restauracion Hydrologico- Forestal Para Control De La Erosion. Ministrio de Agricultura, Pescay Alimentacion,
 Madrid.
-ICONA, 1997. Guidelines for Mapping and Measurement of Rainfall-induced Erision Processes in the Mediterranean Coastal Areas. Priorityaction programme regional activity centre, Split, Croatia.
-Kefi, M., Yoshino, K., Zayani, K. and Isoda, H., 2009. Estimation of Soil Loss by using Combination of Erosion Model and GIS: Case of Study Watersheds in Tunisia. Journal of Arid Land Studies, v. 19(1), p. 287-290.
-Luo, Z., Deng, L. and Yan, C., 2014. Soil erosion under different plant cover types and its influencing facors in Napahai Catchment, Shangri – La County, Yunnan province, China. Int. J. Sustain. Dev. World Ecol, https://doi.org/10.1080/13504509.2014.924448
-Oruk, E.O., Eric, N.J. and Ogogo, A.U., 2012. Influence of soil textural properties and land use cover type on soil erosion of a characteristic ultisols in Betem, Cross River Sate, Nigeria. J. Sustain. Dev. 5
DOI:10.5539/jsd.v5n7p104.
-Okou, F.A.Y., Tente, B., Bachmann, Y. and Sinsin, B., 2016. Regional erosion risk mapping for decision support: A case study from West Africa. Elsevier Land Use Policy, v. 56, p. 27-37.
http://dx.doi.org/10.1016/j.landusepol.2016.04.036.
-Okou, F.A.Y., Assogbadji, A.E., Bachmann, Y. and Sinsin, B., 2014. Ecological factors influencing physical soil degradation in the Atacora Mountain chain in Benin: West Africa. Mt. Res. Dev. 34, p. 157-166, DOI: 10.1659/MRD-JOURNAL-D-13-00030.1.

-Olivares, B., Verbist, K., Vargas, D., Lobo, R. and Silva, O., 2011. Evaluation of the USLE Model to Estimate Water Erosion in an Alfosol. J Soil Sci. Plant Nutr, v. 11(2), p. 71-84.

http://dx.doi.org/10.4067/S0718-95162011000200007

-Reis, M., Bolat, N. and Savaci., G., 2017. Soil Erosion Risk Assessment Using GIS and ICONA, A Case Study: in Kahramanmaras. Turkey Journal of agricultural faculty of gaziosmanpasa university, v. 34(1), p. 64-75.
doi:10.13002/jafag4208, 2017.
-Reed, B.C., Brown, J.F., Vander Zee, D., Loveland, T.R., Merchant, J.W. and Ohlen, D.O., 1994. Measuring phonological variability from satellite imagery. J. Veg. Sci, v. 5, p. 703-714.
https://doi.org/10.2307/3235884.
-Stanchi, S., Freppaz, M., Godone, D. and Zanini, E., 2013. Assessing the susceptibility of alpine soils to erosion using soil physical and site indicators. Soil Use Manag, v. 29, p. 586-596. https://doi.org/10.1111/sum.12063
-Stroosnijder, L., 2003. Technologies for improving green water use efficiency in West Africa. In: Water Conservation Technologies for Sastainable Dryland Agriculture in Sub-Saharan Africa Symposium and Workshop, Bloemfontein, South Africa, p. 92-102.
-Tehrany, M.S., Pradhan, B. and Jebur, M.N., 2013. Remote sensing reveals eco-environmental changes in urban areas of Klang Valley, Malaysia: contribution from object based analysis. J. Indian Soc. Remote Sens, v. 41, p. 981-991. doi:10.1007/s12524-013-0289-9.
-Tehrany, M.S., Pradhan, B. and Jebur, M.N., 2014. A comparative assessment between object and pixel – based classification approaches for land use/land cover mapping using SPORT 5 imagery. Geocarto Int, v. 29, p. 351-369.
https://doi.org/10.1080/10106049.2013.768300.
-Thiam, A.K., 2003. The causes and spatial pattern of land degradation risk in southern Mauritania using multitemporal AVHRR-NDVI imagery and field data. Land Degradation & Development, v. 14, p. 133-142.
https://doi.org/10.1002/ldr.533
-Tucker, C.J., Townshend, J.R.G. and Goff, T.E., 1985. African land-cover classification using satellite data. Science, v. 227, p. 369-375. DOI:10.1126/science.227.4685.369
-Verieling, A., Sterk, G. and Vigiak, O., 2006. Spatial evaluation of soil erosion risk in the West Osambara Mountains, Tanzania. Land Degrad. Dev, v. 17, p. 301-319.
https://doi.org/10.1002/ldr.711
-Wolka, K., Tadesse, H., Garedew, E. and Yimer, F., 2015. Soil erosion risk assessment in the Chaleleka wetland watershed, Central Rift Valley of Ethiopia. Environ. Syst. Res, v. 4, p.1-12. https://doi.org/10.1186/s40068-015-0030-5.
-Zaz, S. and Romshoo, S., 2012. Assessing the geoindicators of land degradation in the Kashmir Himalayan region India. Nat.Hazards, v.64, p.1219-1245. DOI 10.1007/s11069-012-0293-3.

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