Assessment of Annual Soil Erosion Losses in the Alashtar Watershed in Lorestan Province
Document Type : Original Article
Authors
1
Kharazmi university. Tehran
2
Department of Remote Sensing of Geographical Information Systems, Faculty of Geographical Sciences, Kharazmi University, Tehran.iran
3
Department of physical geography .Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
4
PhD student in geomorphology, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
10.22034/gmpj.2024.473166.1516
Abstract
Introduction
Soil erosion is a spatiotemporal phenomenon influenced by variable processes, necessitating multiple observations over time and space. These measurements inherently contain a level of uncertainty and are costly and time-consuming. With the advancement of spatial technologies, Geographic Information Systems (GIS), interpolation methods, and the increasing range of environmental data and remote sensing, soil erosion models play a crucial role in designing and implementing soil conservation management and strategies. The most significant empirical models for estimating annual soil erosion losses include the USLE and its modified version, RUSLE, widely used globally for soil erosion estimation. These models consider factors such as raindrop erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices, with most parameters now readily obtainable through high-quality remote sensing data.
Methodology
The study area, the Alashtar watershed plain, spans 80305 hectares in the northern part of Lorestan province and Selseleh county. Geomorphologically, it lies within the elevated or folded Zagros unit, characterized by thrust faulting and multiple fractures. The watershed predominantly features a mountainous landscape, with hills and mountains covering 65.39% of the area. The minimum elevation is 1500 meters, the maximum is 3600 meters, and the average elevation is 2100 meters. According to the De Martonne method, the watershed has a Mediterranean climate with an average annual rainfall of 506 millimeters.
The study area's aquifer is free-flowing, with all wells situated in the alluvial aquifer. In the mountainous part of the Alashtar watershed, carbonate formations, fracture systems, and weathering in the form of snow have developed karstic aquifers, which are the source of the Kehman River. The Kehman River, after originating from the southern heights of Green and hydrating the Alashtar plain, joins the Simreh and then the Karkheh rivers. The Alashtar plain, at the center of the watershed, is a graben surrounded by the Green, Varkhash, Mahab, Sarakheh, Darikanan, and Nashate heights, with geological formations dating back to the Mesozoic and Cenozoic eras. The predominant Jurassic-Cretaceous limestone rocks cover a significant portion of the Alashtar watershed, serving as the primary recharge units (karstic water sources). The soils of Alashtar belong to the brown soil group with a clay concentration horizon, characterized by very deep, dark brown to reddish-brown soils with a heavy texture, containing 3-15% coarse gravel in the surface layers and relatively high amounts of hard limestone grains in the sublayers.
The LAMPT model is basically based on the global soil erosion equation, which calculates the net rate of soil erosion. The environmental parameters of the model, including climatic data, land cover and geomorphology, were obtained from meteorological departments, natural resources and Sentinel-2 sensor satellite images during the target year (2023). To complete the data, field surveys were conducted and training data samples were also selected to prepare the land use map. The basis of LAMPT model is RUSLE model, in this model Sediment delivery ratio index (SDR) is used to show the spatial patterns of distribution and performance of pure soil sediments at the basin level. This model integrates the general characteristics of the basin landscape (land use classes, landform parameters, soil types, land cover and management) and precipitation values to simulate the gross soil erosion rate, SDR and net sediment yield at the basin level.
Results and Discussion
The findings indicate that the average annual soil loss across the watershed is 9.42 tons per hectare, which is consistent with the sediment yield measured at the Sarab-e-Seyyed Ali station (watershed outlet) with an average annual rate of 10.1 tons. The model demonstrates adequate precision in estimating soil erosion and resulting sedimentation. An assessment of soil erosion losses across different land use classes, derived from Sentinel-2 satellite images, shows that 60% of the Alashtar watershed area comprises pastures. The soil erosion rates in pastures with weak, moderate, and dense canopy cover are 17.7, 11.3, and 9.1 tons per hectare per year, respectively. Given the extent of pastures in the Alashtar watershed and the high erosion rates, particularly in those with weak canopy cover, land use planning and optimal utilization of pastures are essential to reduce soil erosion rates in this area.
Conclussion
In this research, the LAMPT model based on climate, land cover and geomorphological data was used to estimate the annual net rate of soil erosion in the Alashtar watershed. Estimating the net rate of soil erosion during 1402 using this model showed that the average annual soil loss at the basin level is 9.42 tons per hectare per year. The classification of the soil erosion map shows that 35% of the basin has an annual soil loss of more than 10 tons. In agricultural uses, rainfed fields have the highest rate of soil erosion during the year with the amount of 7.5 tons per hectare during the year. Considering the area of pastures in the Elashtar catchment area, the high rate of soil erosion in them, especially pastures with weak canopy, planning land use and optimal use of pastures is necessary to reduce the rate of soil erosion in this basin. The evaluation of the accuracy of the LAMPT model in comparison with the annual average sedimentation rate at the sediment measuring station of Sarab Said Ali (outlet of the basin) showed that the LAMPT model has a high accuracy in estimating the soil erosion and the sediment caused by it, and its difference with the ground data is small, so the use of This model is recommended to calculate soil erosion and its annual losses. According to the findings of the research, it is suggested that the northern and northeastern sub-basins of the basin should be prioritized for protection and watershed management measures. In addition to this, pastures with weak crown cover should be given special attention in terms of land use for watershed management and pasture management.
Soil erosion is a spatiotemporal phenomenon influenced by variable processes, necessitating multiple observations over time and space. These measurements inherently contain a level of uncertainty and are costly and time-consuming. With the advancement of spatial technologies, Geographic Information Systems (GIS), interpolation methods, and the increasing range of environmental data and remote sensing, soil erosion models play a crucial role in designing and implementing soil conservation management and strategies. The most significant empirical models for estimating annual soil erosion losses include the USLE and its modified version, RUSLE, widely used globally for soil erosion estimation. These models consider factors such as raindrop erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices, with most parameters now readily obtainable through high-quality remote sensing data.
Methodology
The study area, the Alashtar watershed plain, spans 80305 hectares in the northern part of Lorestan province and Selseleh county. Geomorphologically, it lies within the elevated or folded Zagros unit, characterized by thrust faulting and multiple fractures. The watershed predominantly features a mountainous landscape, with hills and mountains covering 65.39% of the area. The minimum elevation is 1500 meters, the maximum is 3600 meters, and the average elevation is 2100 meters. According to the De Martonne method, the watershed has a Mediterranean climate with an average annual rainfall of 506 millimeters.
The study area's aquifer is free-flowing, with all wells situated in the alluvial aquifer. In the mountainous part of the Alashtar watershed, carbonate formations, fracture systems, and weathering in the form of snow have developed karstic aquifers, which are the source of the Kehman River. The Kehman River, after originating from the southern heights of Green and hydrating the Alashtar plain, joins the Simreh and then the Karkheh rivers. The Alashtar plain, at the center of the watershed, is a graben surrounded by the Green, Varkhash, Mahab, Sarakheh, Darikanan, and Nashate heights, with geological formations dating back to the Mesozoic and Cenozoic eras. The predominant Jurassic-Cretaceous limestone rocks cover a significant portion of the Alashtar watershed, serving as the primary recharge units (karstic water sources). The soils of Alashtar belong to the brown soil group with a clay concentration horizon, characterized by very deep, dark brown to reddish-brown soils with a heavy texture, containing 3-15% coarse gravel in the surface layers and relatively high amounts of hard limestone grains in the sublayers.
The LAMPT model is basically based on the global soil erosion equation, which calculates the net rate of soil erosion. The environmental parameters of the model, including climatic data, land cover and geomorphology, were obtained from meteorological departments, natural resources and Sentinel-2 sensor satellite images during the target year (2023). To complete the data, field surveys were conducted and training data samples were also selected to prepare the land use map. The basis of LAMPT model is RUSLE model, in this model Sediment delivery ratio index (SDR) is used to show the spatial patterns of distribution and performance of pure soil sediments at the basin level. This model integrates the general characteristics of the basin landscape (land use classes, landform parameters, soil types, land cover and management) and precipitation values to simulate the gross soil erosion rate, SDR and net sediment yield at the basin level.
Results and Discussion
The findings indicate that the average annual soil loss across the watershed is 9.42 tons per hectare, which is consistent with the sediment yield measured at the Sarab-e-Seyyed Ali station (watershed outlet) with an average annual rate of 10.1 tons. The model demonstrates adequate precision in estimating soil erosion and resulting sedimentation. An assessment of soil erosion losses across different land use classes, derived from Sentinel-2 satellite images, shows that 60% of the Alashtar watershed area comprises pastures. The soil erosion rates in pastures with weak, moderate, and dense canopy cover are 17.7, 11.3, and 9.1 tons per hectare per year, respectively. Given the extent of pastures in the Alashtar watershed and the high erosion rates, particularly in those with weak canopy cover, land use planning and optimal utilization of pastures are essential to reduce soil erosion rates in this area.
Conclussion
In this research, the LAMPT model based on climate, land cover and geomorphological data was used to estimate the annual net rate of soil erosion in the Alashtar watershed. Estimating the net rate of soil erosion during 1402 using this model showed that the average annual soil loss at the basin level is 9.42 tons per hectare per year. The classification of the soil erosion map shows that 35% of the basin has an annual soil loss of more than 10 tons. In agricultural uses, rainfed fields have the highest rate of soil erosion during the year with the amount of 7.5 tons per hectare during the year. Considering the area of pastures in the Elashtar catchment area, the high rate of soil erosion in them, especially pastures with weak canopy, planning land use and optimal use of pastures is necessary to reduce the rate of soil erosion in this basin. The evaluation of the accuracy of the LAMPT model in comparison with the annual average sedimentation rate at the sediment measuring station of Sarab Said Ali (outlet of the basin) showed that the LAMPT model has a high accuracy in estimating the soil erosion and the sediment caused by it, and its difference with the ground data is small, so the use of This model is recommended to calculate soil erosion and its annual losses. According to the findings of the research, it is suggested that the northern and northeastern sub-basins of the basin should be prioritized for protection and watershed management measures. In addition to this, pastures with weak crown cover should be given special attention in terms of land use for watershed management and pasture management.
Keywords
احمدی، ح. ژئومورفولوژی کاربردی، جلد اول( فرسایش آبی)، انتشارات دانشگاه تهران، چاپ هشتم، 1391.
اصغری سراسکانرود.ص، بلواسی مهدی، زینالی بتول، بلواسی ایمانعلی، داودی علی. بررسی خطرپذیری فرسایش خاک در حوضه آبخیز دوآب لرستان با استفاده از تحلیل شبکه و فنآوریهای سنجش از دور و GIS. پژوهش های فرسایش محیطی. ۱۳۹۳; ۴ (۲) :صص۷۲-۸۹.
آرخی، ص و نیازی، ی (1389)؛ بررسی کاربرد GIS و RS برای تخمین فرسایش خاک و بار رسوب با استفاده از مدل RUSLE حوضه بالادست سد ایلام، مجله پژوهشهای حفاظت آبوخاک، جلد هفدهم، شماره دوم، صص 1-26.
بهاروند. سی، ابراهیمی. ب، زیودار. م و حقی آبی. ا.ح، (1392)، خلاصه گزارش تعیین میزان تبادل هیدرولیکی رودخانه و سفره آب زیرزمینی دشت الشتر با استفاده از شبکه جریان، سازمان مجری: دانشگاه آزاد واحد خرم آباد.
حاصلی. م، جلالیان.ح، ارزیابی و پهنهبندی مخاطرهی فرسایش خاک در حوضهی آبریز الشتر . تحلیل فضایی مخاطرات محیطی. ۱۳۹۳; ۱ (۴) ،صص۹۱-۱۰۴
سازمان تحقیقات کشاورزی وزارت کشاورزی، .1370مطالعات اجمالی خاکشناسی و طبقه بندی اراضی استان لرستان، مؤسسه تحقیقات خاک و آب
سپهر، ع و هنرمند، سعیده (1391)؛ تهیه نقشه خطر فرسایش واقعی خاک با استفاده از مدل کرین اصلاحشده حوضه آبخیز جهرم، جغرافیا و مخاطرات محیطی، دوره 1، شماره 3، صص 57-72.
شرکت سهامی آب منطقهای استان لرستان، .1382گزارش طرح جامع مدیریت خشکسالی استان لرستان، دانشگاه صنعتی امیر کبیر.
غلامی.ح، فتحی زاد حسن، صفری عطا، بی نیاز مهدی. برآورد فاکتور فرسایندگی باران با استفاده از الگوریتمهای زمین آمار ( مطالعهی موردی: استان ایلام). پژوهش های فرسایش محیطی. ۱۳۹۴; ۵ (۴) ،صص۱-۱۶.
فاطمی. ب، رضایی. ی (1396)، مبانی سنجش از دور، انتشارات آزاده، چاپ پنجم، تهران.
مصطفایی، ج و طالبی، ع،1393،نگاهی آماری به وضعیت فرسایش آبی در ایران، مجله ترویج و توسعه آبخیزداری، دوره دوم، شماره پنجم،صص 9-16.
کرم. امیر، ص. آمنه و حجه فروش نیا. شیلا، (1389)، برآورد و پهنه بندی فرسایش خاک در حوضه ماملو (شرق تهران ) با استفاده از روش های معادله اصلاح شده جهانی فرسایش خاک و فرآیند تحلیل سلسله مراتبی، مجله پژوهش های دانش زمین، سال اول، شماره دوم، صص 73-86.
معتمدی،م.زنگنه اسدی،م.عجم.ح،(1402).بررسی میزان فرسایش خاک با استفاده از مدل RUSLE و روش پسیاک اصلاح شده(مطالعه موردی: حوضه آبریز کال اسماعیل دره شهرستان شاهرود استان سمنان). پژوهش های ژئومورفولوژی کمی، دوره 11، شماره 4،بهار 1402،صص 147-165
مقیم، حسن، سلیمانپور سید مسعود، و محسنی، مهرسا(1401) برآورد فرسایش خاک با استفاده از مدلهای RUSLE, MPSIAC و روش تحلیل منطقهای در حوضه سد چشمه عاشق نیریز استان فارس، نشریه ترویج و توسعه آبخیز داری، سال دهم، شماره38، صص 17-9
نور،ح.عرب خدری،م.(1402). برآورد فرسایش خاک و نسبت تحویل رسوب با استفاده از مدل RUSLE در پایگاه تحقیقات حفاظت خاکسنگانه.مدل سازی و مدیریت آب و خاک.3(1).صص 42-53
Allafta, H., & Opp, C. (2022). Soil erosion assessment using the RUSLE model, remote sensing, and GIS in the Shatt Al-Arab basin (Iraq-Iran). Applied Sciences, 12(15), 7776.
Alijani, B. (2008). Effect of the Zagros Mountains on the spatial distribution of precipitation. Journal of mountain science, 5(3), 218-231.
Amellah, O., & el Morabiti, K. (2021). Assessment of soil erosion risk severity using GIS, remote sensing and RUSLE model in Oued Laou Basin (north Morocco). Soil science annual, 72(3).
Auerswald, K., Fiener, P., Martin, W., Elhaus, D., 2014. Use and misuse of the K factor equation in soil erosion modeling: an alternative equation for determining USLE nomograph soil erodibility values. Catena 118, 220–225. https://doi.org/10.1016/j. catena.2014.01.008.
Ciesiolka, C. A., Yu, B., Rose, C. W., Ghadiri, H., Lang, D., & Rosewell, C. (2006). Improvement in soil loss estimation in USLE type experiments. Journal of Soil and Water Conservation, 61(4), 223-229.
Dash, S. S., & Maity, R. (2023). Effect of climate change on soil erosion indicates a dominance of rainfall over LULC changes. Journal of Hydrology: Regional Studies, 47, 101373.
Diwediga, B., Le, Q. B., Agodzo, S. K., Tamene, L. D., & Wala, K. (2018). Modelling soil erosion response to sustainable landscape management scenarios in the Mo River Basin (Togo, West Africa). Science of the total environment, 625, 1309-1320.
Gayen, A., Saha, S., & Pourghasemi, H. R. (2020). Soil erosion assessment using RUSLE model and its validation by FR probability model. Geocarto International, 35(15), 1750-1768.
Hadi Allafta, Christian Opp , (2022). Soil Erosion Assessment Using the RUSLE Model, Remote Sensing, and GIS in the Shatt Al-Arab Basin(Iraq-Iran). Appl. Sci. 2022, 12, 7776. https://doi.org/10.3390/ app12157776)
Jain, M. K., & Kothyari, U. C. (2000). Estimation of soil erosion and sediment yield using GIS. Hydrological Sciences Journal, 45(5), 771-786.
Karthick, P., Lakshumanan, C., & Ramki, P. (2017). Estimation of soil erosion vulnerability in Perambalur Taluk, Tamilnadu using revised universal soil loss equation model (RUSLE) and geo information technology. International Research Journal of Earth Sciences, 5(8), 8-14.
Li, P., Tariq, A., Li, Q., Ghaffar, B., Farhan, M., Jamil, A., ... & Freeshah, M. (2023). Soil erosion assessment by RUSLE model using remote sensing and GIS in an arid zone. International Journal of Digital Earth, 16(1), 3105-3124.
Mutua, B. M., Klik, A., & Loiskandl, W. (2006). Modelling soil erosion and sediment yield at a catchment scale: the case of Masinga catchment, Kenya. Land degradation & development, 17(5), 557-570.
Nearing, M.A., 2000. Evaluating soil erosion models using measured plot data: accounting for variability in the data. Earth Surf. Process. Landf. 25, 1035–1043.
Nyssen, J., Poesen, J., Moeyersons, J., Deckers, J., Haile, M., & Lang, A. (2004). Human impact on the environment in the Ethiopian and Eritrean highlands—a state of the art. Earth-science reviews, 64(3-4), 273-320.
Pal, S. C., & Chakrabortty, R. (2019). Modeling of water induced surface soil erosion and the potential risk zone prediction in a sub-tropical watershed of Eastern India. Modeling Earth Systems and Environment, 5, 369-393.
Panagos, P., Borrelli, P., Robinson, D.A., 2015b. Common agricultural policy: tackling soil loss across Europe. Nature 526, 195.
Pandey, A., Chowdary, V. M., & Mal, B. C. (2007). Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water resources management, 21, 729-746.
Parysow, P., Wang, G., Gertner, G., & Anderson, A. B. (2003). Spatial uncertainty analysis for mapping soil erodibility based on joint sequential simulation. Catena, 53(1), 65-78.
Quinton, J.N., 2004. Erosion and sediment transport. In: Wainwright, J., Mulligan, M. (Eds.), Environmental Modelling: Finding Simplicity in Complexity. John Wiley & Sons Ltd, pp. 187–196..
Renard, K. G., & Freimund, J. R. (1994). Using monthly precipitation data to estimate the R-factor in the revised USLE. Journal of hydrology, 157(1-4), 287-306.
Samani, A. N., Ahmadi, H., Jafari, M., Boggs, G., Ghoddousi, J., & Malekian, A. (2009). Geomorphic threshold conditions for gully erosion in Southwestern Iran (Boushehr-Samal watershed). Journal of Asian Earth Sciences, 35(2), 180-189.
Stroosnijder, L., 2005. Measurement of erosion: is it possible? Catena 64, 162–173. https://doi.org/10.1016/j.catena.2005.08.004.
Toy, T. J., Foster, G. R., & Renard, K. G. (2002). Soil erosion: processes, prediction, measurement, and control. John Wiley & Sons.
Wahla, S. S., Kazmi, J. H., Sharifi, A., Shirazi, S. A., Tariq, A., & Joyell Smith, H. (2022). Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models. Geocarto International, 37(27), 14963-14982.
Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration.
Wischmeier, W. H., Johnson, C. B., & Cross, B. V. (1971). A soil erodibility nomograph for farmland and construction sites.
Deore, S.J. 2006. Prioritization of Micro-watersheds of Upper Bhama Basin on the Basis of Soil Erosion Risk Using Remote Sensing and GIS Technology. Ph.D. Thesis. Department of Geography. University of Pune, 147p.
