مدلسازی نرخ فرسایش خاک و تولید رسوب با مدل RUSLE/SDR در حوضه آبریز دیزگران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران

2 استادیار گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران

3 کارشناس ارشد دفتر آبخیزداری و حفاظت خاک، سازمان جنگل‌ها، مراتع و آبخیزداری کشور، تهران

چکیده

یکی از رایج‌‌ترین انواع تخریب خاک که اثرات بسیار مخرب بر محیط زیست و زندگی بشر دارد، پدیده فرسایش خاک آبی  است. این پدیده کیفیت خاک را در محل کاهش می‌‌دهد و ممکن است باعث ایجاد مشکلات رسوب در پایین دست یا مخازن شود. شدت فرسایش خاک معمولا با استفاده از مدل‌‌های مختلف تخمین زده می‌‌شود. مدل معادله جهانی تلفات خاک (RUSLE ) به دلیل سادگی، نیاز کمتر به داده و سازگاری با شرایط مختلف محیطی رایج ترین مدل فرسایش خاک است. مطالعات در سراسر دنیا نشان می‌‌دهد که این مدل تجربی برای تخمین تلفات سالانه خاک در مقیاس حوضه  بسیار کارآمد است. برای تخمین نرخ تولید رسوب بر اساس نرخ فرسایش خاک حاصل از مدل RUSLE، از نسبت تحویل رسوب (SDR ) استفاده می‌‌شود. حوضه آبریز دیزگران واقع در مرز استان‌‌های کردستان و کرمانشاه در غرب ایران، یکی از حوضه‌‌های کوهستانی کشور و دارای توپوگرافی ناهموار و شرایط پوشش گیاهی و آب و هوایی است که آن را مستعد فرسایش خاک شدید می‌‌کند. هدف این تحقیق برآورد میانگین سالانه فرسایش خاک و تولید رسوب در حوضه آبریز دیزگران و بررسی همبستگی عوامل مختلف RUSLE با نرخ فرسایش برای درک بهتر تاثیر هریک از عوامل موثر بر این پدیده است. در این تحقیق فاکتور فرسایندگی باران از روش رگرسیون خطی WTLS براساس مدل ارتفاعی رقومی  ایجاد شد. با توجه به نتایج مدل RUSLE/SDR، نرخ متوسط فرسایش خاک در منطقه 45.09 تن در هکتار در سال و نرخ متوسط تولید رسوب 19.42 تن در هکتار در سال در حوضه محاسبه گردید. به ترتیب 3/1 % و 66/4 % از مساحت حوضه در کلاس‌‌های فرسایش خیلی کم و کم قرار گرفت. 18/16 %  از منطقه در کلاس فرسایش متوسط و به ترتیب 18/36 % و 68/41 % از سطح منطقه در کلاس‌‌های فرسایش شدید و بسیار شدید قرار گرفت. همچنین نتایج حاکی از این است که دو عامل توپوگرافی (با ضریب همبستگی 79 درصد) و پوشش گیاهی (با ضریب همبستگی 47 درصد) به ترتیب بیشترین همبستگی را با نرخ فرسایش خاک داشته و بنابراین به ترتیب مهم‌‌ترین  نقش را در تغییر نرخ فرسایش در منطقه دارند. نتایج این تحقیق نشان می‌‌دهد همانطور که از حضور اشکال مختلف فرسایش در بازدید میدانی از حوضه پیداست، این منطقه درگیر فرسایش شدید می‌‌باشد. از نتایج این تحقیق می‌‌توان برای انجام اقدامات مدیریتی در جهت کاهش نرخ فرسایش خاک در نقاط با نرخ بالای فرسایش و حفاظت و مدیریت حوضه آبریز دیزگران استفاده نمود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling the rate of soil erosion and sediment yield using the RUSLE / SDR model in the Dizgaran watershed

نویسندگان [English]

  • Somayeh Ebrahimzadeh 1
  • Meysam Argany 2
  • Farshad Mirdar Harijani 3
1 PhD student, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran
2 Assistant Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran.
3 MSc. Watershed and Soil Conservation Office, Forest, Range and Watershed Management of Iran, Tehran, Iran
چکیده [English]

Water soil erosion is one of the most common types of soil degradation, which has very destructive effects on the environment and human life. This phenomenon reduces the quality of the soil in the site and may cause sedimentation problems in the downstream or reservoirs. The severity of soil erosion is usually estimated using different models. The Revised Universal Soil Loss Equation (RUSLE) is the most common soil erosion model due to its simplicity, less need for data, and adaptability to different environmental conditions. Studies around the world show that this experimental model is very efficient for estimating annual soil losses at the watershed scale. Sediment Delivery Ratio (SDR) is used to estimate the rate of sediment yield based on the soil erosion rate obtained from RUSLE. Dizgaran watershed, located on the border of Kurdistan and Kermanshah provinces, western Iran, is one of the mountainous areas of the country and has rough topography, vegetation cover, and weather conditions that make it prone to severe soil erosion. This research aims to estimate the annual average rate of soil erosion and sediment yield in the Dizgaran watershed and also to investigate the correlation between different RUSLE factors and the erosion rate to better understand the effect of each factor on the erosion rate. In this research, the rainfall-runoff erosivity (R) factor was created using the WTLS linear regression method based on the Digital Elevation Model (DEM). According to the results of the RUSLE/SDR model, the average rate of soil erosion was 45.09 tons per hectare per year and the average rate of sediment production was 19.42 tons per hectare per year in the watershed. 1.3% and 4.66% of the area of the area were placed in very low and low erosion classes, respectively. 16.18% of the area was in the moderate erosion class, and 36.18% and 41.68% of the area were in the severe and very severe erosion classes, respectively. Also, the results indicate that the two factors of topography (with a correlation coefficient of 79%) and cover and management (with a correlation coefficient of 47%) have the highest correlation with the soil erosion rate, respectively, and therefore play the most important role in changing the erosion rate in the watershed. The results of this research show that this watershed is involved in severe erosion, as can be seen from the presence of different forms of soil erosion in the field investigation. The results of this research can be used to carry out management measures to reduce the rate of soil erosion in the points with a high rate of erosion and to protect and manage the Dizgaran watershed.

کلیدواژه‌ها [English]

  • soil erosion
  • sediment yield
  • RUSLE
  • SDR
  • WTLS
  • Dizgaran watershed

مراجع

رفاهی، حسینقلی. (1375). فرسایش آبی و کنترل آن، انتشارات دانشگاه تهران، 551 ص.
علیزاده، امین. (1380). اصول هیدرولوژی کاربردی، انتشارات آستان قدس رضوی، 735 ص.
Ahmad, W. S., Jamal, S., Taqi, M., El-Hamid, H. T. A., Norboo, J. (2022). Estimation of soil erosion and sediment yield concentrations in Dudhganga watershed of Kashmir Valley using RUSLE and SDR model. Environment, Development and Sustainability, 1-24.
Alewell, C., Borrelli, P., Meusburger, K., Panagos, P. (2019). Using the USLE: Chances, challenges, and limitations of soil erosion modeling. International soil and water conservation research, 7(3), 203-225.
Amore, E., Modica, C., Nearing, M. A., Santoro, V. C. (2004). Scale effect in USLE and WEPP application for soil erosion computation from three Sicilian basins. Journal of Hydrology, 293(1-4), pp 100-114.
Amsalu, T., Mengaw, A. (2014). GIS-based soil loss estimation using rusle model: the case of jabi tehinan woreda, ANRS, Ethiopia. Natural Resources, 2014. doi.org/10.4236/nr.2014.511054.
Anache, J. A., Bacchi, C. G., Alves-Sobrinho, T. (2014). Modeling of (R) USLE C-factor for pasture as a function of normalized difference vegetation index. Eur Int J Sci Technol, 3(9), 214-221.
Ashiagbor, G., Forkuo, E. K., Laari, P., Aabeyir, R. (2013). Modeling soil erosion using RUSLE and GIS tools. Int J Remote Sens Geosci, 2(4), 1-17.
Azizian, A., Koohi, S. (2021). The effects of applying different DEM resolutions, DEM sources, and flow tracing algorithms on LS factor and sediment yield estimation using USLE in Barajin River basin (BRB), Iran. Paddy and Water Environment, 19, 453-468.
Belasri, A., Lakhouili, A. (2016). Estimation of soil erosion risk using the universal soil loss equation (USLE) and geo-information technology in Oued El Makhazine Watershed, Morocco. Journal of Geographic Information System, 8(01), 98.
Benavidez, R., Jackson, B., Maxwell, D., Norton, K. (2018). A review of the (Revised) Universal Soil Loss Equation ((R) USLE): With a view to increasing its global applicability and improving soil loss estimates. Hydrology and Earth System Sciences, 22(11), 6059-6086.
Bhattacharya, R. K., Chatterjee, N. D., Das, K. (2020). Estimation of erosion susceptibility and sediment yield in the ephemeral channel using RUSLE and SDR model: Tropical Plateau Fringe Region, India. Gully erosion studies from India and surrounding regions, 163-185.
Biesemans, J., Van Meirvenne, M., Gabriels, D. (2000). Extending the RUSLE with the Monte Carlo error propagation technique to predict long-term average off-site sediment accumulation. Journal of Soil and Water Conservation, 55(1), 35-42. 
Bizuwerk, A., Taddese, G.,  Getahun, Y. (2003). Application of GIS for modeling soil loss rate in Awash River basin, Ethiopia. International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia, 1-11.
Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P. (2015). Modeling soil erosion at European scale: towards harmonization and reproducibility. Natural Hazards and Earth System Sciences, 15(2), 225-245.
De Rosa, P., Cencetti, C., Fredduzzi, A. (2016). A GRASS tool for the Sediment Delivery Ratio mapping. PeerJ Preprints, 4, e2227v1.
De Vente, J.,  Poesen, J. (2005). Predicting soil erosion and sediment yield at the basin scale: scale issues and semi-quantitative models. Earth-science reviews, 71(1-2), 95-125.
Drzewiecki, W., Wężyk, P., Pierzchalski, M.,  Szafrańska, B. (2014). Quantitative and qualitative assessment of soil erosion risk in Małopolska (Poland), supported by an object-based analysis of high-resolution satellite images. Pure and Applied Geophysics, 171, 867-895.
Ebrahimzadeh, S., Motagh, M., Mahboub, V.,  Harijani, F. M. (2018). An improved RUSLE/SDR model for the evaluation of soil erosion. Environmental Earth Sciences, 77(12), pp 1-17.
Ganasri, B. P., Ramesh, H. (2016). Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geoscience Frontiers, 7(6), 953-961.   
Gitas, I. Z., Douros, K., Minakou, C., Silleos, G. N.,  Karydas, C. G. (2009). Multi-temporal soil erosion risk assessment in N. Chalkidiki using a modified USLE raster model. EARSel proceedings, 8(1), 40-52.
Ghosal, K., Das Bhattacharya, S. (2020). A review of the RUSLE model. Journal of the Indian Society of Remote Sensing, 48, 689-707. 
Guduru, J. U., Jilo, N. B. (2023). Assessment of rainfall-induced soil erosion rate and severity analysis for prioritization of conservation measures using RUSLE and Multi-Criteria Evaluations Technique at Gidabo watershed, Rift Valley Basin, Ethiopia. Ecohydrology and Hydrobiology, 23(1), 30-47.
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295-309.
Jahun, B. G., Ibrahim, R., Dlamini, N. S., Musa, S. M. (2015). Review of soil erosion assessment using the RUSLE model and GIS. Journal of Biology, Agriculture and Healthcare, 5(9), 36-47.
Merchant, J. W. (2000). Remote sensing of the environment: an earth resource perspective. Cartography and Geographic Information Science, 27(4), 311-311.
Kamuju, N. (2016). Soil erosion and sediment yield analysis using prototype and enhanced SATEEC GIS system models. International Journal of Advanced Remote Sensing and GIS, 5(1), 1471-1482.
Kanito, D., Bedadi, B., Feyissa, S. (2023). Sediment yield estimation in GIS environment using RUSLE and SDR model in Southern Ethiopia. Geomatics, Natural Hazards and Risk, 14(1), 2167614.
Karaburun, A. (2010). Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed. Ozean Journal of Applied Sciences, 3(1), 77-85. 
KUO, K. T., SEKIYAMA, A., MIHARA, M. (2016). Determining the C factor of universal soil loss equation (USLE) based on remote sensing. International Journal of Environmental and Rural Development, 7(2), 154-161.
Luca, F., Buttafuoco, G., & Terranova, O. (2018). GIS and Soil. In Comprehensive geographic information systems (Vol. 3, pp. 37-50). Elsevier Inc..
Luvai, A., Obiero, J., Omuto, C. (2022). Soil loss assessment using the revised universal soil loss equation (RUSLE) model. Applied and Environmental Soil Science, 2022. doi.org/10.1155/2022/2122554.
Mahboub, V. (2012). On weighted total least-squares for geodetic transformations. Journal of geodesy, 86(5), pp 359-367.
Ma, L., Chi, X., Zuo, C. (2012, August). Evaluation of interpolation models for rainfall erosivity on a large scale. In 2012 First International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-5). IEEE.
Markose, V. J., Jayappa, K. S. (2016). Soil loss estimation and prioritization of subwatersheds of Kali River basin, Karnataka, India, using RUSLE and GIS. Environmental monitoring and assessment, 188(4), pp 225.
Merritt, W. S., Letcher, R. A., Jakeman, A. J. (2003). A review of erosion and sediment transport models. Environmental modeling and software, 18(8-9), 761-799.
Moisa, M. B., Babu, A., Getahun, K. (2023). Integration of geospatial technologies with RUSLE model for analysis of soil erosion in response to land use/land cover dynamics: a case of Jere watershed, Western Ethiopia. Sustainable Water Resources Management, 9(1), 13.
NRCS: USDA, (2002).Technical Guide to RUSLE. State Office of Michigan, Michigan.
Ochoa‐Cueva, P., Fries, A., Montesinos, P., Rodríguez‐Díaz, J. A., Boll, J. (2015). Spatial estimation of soil erosion risk by land‐cover change in the Andes of southern Ecuador. Land degradation and development, 26(6), 565-573.           
Oğuz, I., Susam, T., Kocyigit, R., Bicak, H., Demirkiran, O., Demir, S. (2019). Estimation of soil erosion and river sediment yield in a rural basin of North Anatolia, Turkey. Applied Ecology and Environmental Research, 17(4).
Okacha, A., Salhi, A., Arari, K., El Badaoui, K., Lahrichi, K. (2023). Soil erosion assessment using the RUSLE model for better planning: a case study from Morocco. Modeling Earth Systems and Environment, 1-9.  doi.org/10.1007/s40808-023-01731-4.
Ozsoy, G., Aksoy, E., and Karaata, E. U. (2013). Estimating soil loss of Doganci Dam watershed, northwest Turkey, and lifetime analysis of Doganci Dam using multi-year remotely sensed data and GIS techniques. Soil-Water Journal, 2(1), 927-934.
Ozsoy, G., and Aksoy, E. (2015). Prediction of soil loss differences and sediment accumulation at the Nilufer Creek watershed, Turkey, using multiyear satellite data in a GIS. Geocarto international, 30(8), 843-857.
Prasuhn, V., Liniger, H., Gisler, S., Herweg, K., Candinas, A., and Clément, J. P. (2013). A high-resolution soil erosion risk map of Switzerland as a strategic policy support system. Land use policy, 32, pp 281-291.
Prasuhn, V., Liniger, H., Gisler, S., Herweg, K., Candinas, A., Clément, J. P. (2013). A high-resolution soil erosion risk map of Switzerland as a strategic policy support system. Land use policy, 32, 281-291.
Renard, K. G. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Government Printing. ISBN: 0-16-048938-5.
Rostamian, R., Jaleh, A., Afyuni, M., Mousavi, S. F., Heidarpour, M., Jalalian, A., Abbaspour, K. C. (2008). Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrological Sciences Journal, 53(5), 977-988.
Shoshany, M., Goldshleger, N., Chudnovsky, A. (2013). Monitoring of agricultural soil degradation by remote-sensing methods: A review. International Journal of Remote Sensing, 34(17), 6152-6181.
Subarna, D., Purwanto, M. Y. J., Murtilaksono, K. Wiweka (2014). The relationship between monthly rainfall and elevation in the Cisangkuy watershed Bandung regency. International Journal of Latest Research in Science and Technology, 3(2), 55-60. ISSN (Online):2278-5299.
Teh, S. H. (2011). Soil erosion modeling using RUSLE and GIS on Cameron Highlands, Malaysia for hydropower development (Doctoral dissertation). https://skemman.is/handle/1946/7751.
Thomas, J., Joseph, S., Thrivikramji, K. P. (2018). Assessment of soil erosion in a monsoon-dominated mountain river basin in India using RUSLE-SDR and AHP. Hydrological Sciences Journal, 63(4), 542-560.
TPS, FAO. (2015). Status of the world’s soil resources (SWSR)—Main report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, 650.  
USDA, (1972). Sediment sources, yields, and delivery ratios, In National Engineering Handbook. Section 3 Sedimentation, USDA, Washington DC. https://directives.sc.egov.usda.gov/ViewRollUp.aspx?hid=17092&sf=1
Van der Knijff, J. M., Jones, R. J. A., Montanarella, L. (2000). Soil erosion risk: assessment in Europe. https://www.preventionweb.net/files/1581_ereurnew2.pdf
Wischmeier, W. H. (1959). A rainfall erosion index for a universal soil‐loss equation. Soil Science Society of America Journal, 23(3), 246-249.
Wischmeier W. H., and Smith, D. D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science, and Education Administration.
Zhu, H. Y., and Jia, S. (2004). Uncertainty in the spatial interpolation of rainfall data. Progress in Geography, 23(2), 34-42.

فایل ها

سابقه مقاله

  • تاریخ دریافت: 12 تیر 1400
  • تاریخ بازنگری: 08 بهمن 1402
  • تاریخ پذیرش: 19 اسفند 1402
  • تاریخ اولین انتشار: 19 اسفند 1402
  • تاریخ انتشار: 01 فروردین 1403

هم رسانی

ارجاع به این مقاله

آمار