| تعداد نشریات | 285 |
| تعداد نشریات سازمان | 83 |
| تعداد شمارهها | 3,110 |
| تعداد مقالات | 34,666 |
| تعداد مشاهده مقاله | 49,824,884 |
| تعداد دریافت فایل اصل مقاله | 80,170,702 |
بررسی تغییرپذیری ماهانة فرسایش خاک در آبخیز معرف کسیلیان با استفاده از مدل RUSLE | ||
| پژوهش های آبخیزداری | ||
| مقاله 5، دوره 37، شماره 3 - شماره پیاپی 144، مهر 1403، صفحه 54-75 اصل مقاله (3.32 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2023.362691.1545 | ||
| نویسندگان | ||
| فاطمه سارونه1؛ عبدالواحد خالدی درویشان* 2؛ وحید موسوی3 | ||
| 1دانشآموختة کارشناسی ارشد گروه مهندسی آبخیزداری، دانشکدة منابعطبیعی، دانشگاه تربیت مدرس، نور | ||
| 2دانشیار گروه مهندسی آبخیزداری، دانشکدة منابعطبیعی، دانشگاه تربیت مدرس، نور | ||
| 3استادیار گروه مهندسی آبخیزداری، دانشکدة منابعطبیعی، دانشگاه تربیت مدرس، نور | ||
| چکیده | ||
| مقدمه و هدف یکی از رایجترین انواع نابودی خاک، فرسایش خاک بهوسیلة عاملهای باران و رواناب است. فرسایش خاک باعث کاهش کیفیت خاک در محل فرسایش میشود و رسوب بهوجود آمده از فرسایش نیز باعث ایجاد مشکلاتی در درون و خارج از آبخیز میشود. بهدلیل هزینة زیاد اندازهگیری فرسایش خاک و تولید رسوب، مدلهای متنوعی برای برآورد شدت این متغیرها در مقیاسهای مختلف مکانی و زمانی پیشنهادشده است. از این میان معادلة جهانی اصلاحشدة هدررفت خاک (RUSLE) هم بهدلیل وجود دادههای لازم برای عاملهای ورودی مدل و هم امکان اجرا بهشکل توزیعی، بهطور گسترده در همة نقاط جهان استفادهشده است. مواد و روشها این پژوهش با هدف برآورد فرسایش خاک با استفاده از مدل RUSLE در مقیاس ماهانه در سال 2021 برای آبخیز معرف کسیلیان انجام شد. ابتدا نقشة توزیعی پنج عامل مدل RUSLE تهیه شد. از آنجایی که متغیرهای پوششگیاهی و بارش در مقیاس ماهانه تغییر قابل توجهی داشتند، تغییرات زمانی عاملهای مزبور منجر به پویایی سامانة آبخیز و تغییرات ماهانه فرسایش خاک شد. همچنین، عاملهای فرسایشپذیری خاک، پستیبلندی و مدیریت زمین بهعنوان عاملهای ایستا در نظر گرفته شد. سرانجام عاملهای پنجگانه مدل در نرمافزار Arc GIS در یکدیگر ضرب شدند و نقشههای توزیعی فرسایش در مقیاسهای ماهانه، فصلی و سالانه تهیه شد. نتایج و بحث با توجه به نقشههای توزیعی ماهانه، فصلی و سالانة فرسایش خاک، بیشترین و کمترین فرسایش ماهانة خاک بهترتیب 1/13 و 0/13 تن در هکتار در ماههای نوامبر و آوریل رخ داد. میانگین شدت فرسایش خاک در فصلهای بهار، تابستان، پاییز و زمستان بهترتیب 1/32، 2/74، 2/99 و 1/52 تن در هکتار بهدست آمد. بنابراین، کمترین فرسایش فصلی خاک بهترتیب در بهار و زمستان و بیشترین آن بهترتیب در پاییز و تابستان بود. سرانجام میتوان گفت فرسایش خاک در نیمة دوم سال بیشتر از نیمة اول بود. میانگین شدت فرسایش خاک در آبخیز معرف کسیلیان 8/56 تن در هکتار در سال برآورد شد. نتیجهگیری و پیشنهادها نتایج نشان داد فرسایش در بخش گستردهای از آبخیز بررسیشده، کم بود و فقط دامنههای با شیب زیاد بهویژه با پوشش گیاهی کم شامل مراتع و زمینهای زراعی رهاشده، مستعد فرسایش تشدیدی خاک بودند. بر اساس نتایج این پژوهش فرسایش خاک بهدلیل تبدیل کاربریهای جنگل و مرتع به زمینهای کشاورزی و باغی و حتی مسکونی، افزایش یافت. سرانجام پیشنهاد میشود که با استفاده از راهکارهای مدیریتی و اقدامهای حفاظت خاک بهویژه در دامنهها و مناطق با شیب زیاد از تغییر کاربری زمین جلوگیری شود. | ||
| کلیدواژهها | ||
| تغییر کاربری زمین؛ حفاظت خاک؛ فرسایشپذیری خاک؛ مدلهای توزیعی؛ مدل RUSLE | ||
| عنوان مقاله [English] | ||
| Investigating the Monthly Variability of Soil Erosion in the Kasilian Representative Watershed Using RUSLE Model | ||
| نویسندگان [English] | ||
| Fatemeh Sarouneh1؛ Abdulvahed Khaledi Darvishan2؛ Vahid Moosavi3 | ||
| 1Former M.Sc. Student, Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran | ||
| 2Associate Professor, Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran | ||
| 3Assistant Professor, Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran | ||
| چکیده [English] | ||
| Introduction and Goal One of the most common types of soil degradation is soil erosion under the influence of rain and runoff factors. Soil erosion reduces the quality of the soil in the place of erosion, and also the sediment resulting from erosion causes problems inside and outside the watershed. Due to the high cost of measuring soil erosion and sediment yield, various models have been developed to estimate the intensity of these variables in different spatial and temporal scales. Among these, the revised universal soil loss equation (RUSLE) has been widely used in all parts of the world for reasons including the availability of data required for model input factors and the possibility of implementing it in a distributed format. Materials and Methods The present study was conducted in order to estimate soil erosion using the RUSLE model on a monthly scale for 2021 in the Kasilian Watershed. First, a distribution map of the five factors of the RUSLE model was prepared. Since the variables of vegetation cover and precipitation have a significant change on a monthly scale, therefore, the temporal changes of the mentioned factors lead to the dynamics of the watershed system and the monthly changes of soil erosion. Furthermore, soil erodibility factors, topography and land management were considered as static factors. Finally, the five factors of the model were multiplied together in Arc GIS software and erosion distribution maps were prepared on monthly, seasonal and annual scales. Results and Discussion According to the monthly, seasonal and annual distribution maps of soil erosion, the highest and lowest monthly soil erosion occurred in November and April with values of 1.13 and 0.13 tons per hectare, respectively. Also, the average intensity of soil erosion in spring, summer, autumn and winter seasons was 1.32, 2.74, 2.99 and 1.52 tons per hectare respectively. Therefore, spring and winter seasons respectively had the least and autumn and summer seasons had the highest contribution in the annual soil erosion. It can also be said that more soil erosion has occurred in the second half of the year compared to the first half. Finally, the average intensity of soil erosion in the Kasilian Watershed was estimated at 8.56 tons per hectare per year. Conclusion and Suggestions The results showed that a large part of the study watershed has low erosion and only the steep slopes, especially with low vegetation, including rangelands and abandoned agricultural lands, are prone to accelerated soil erosion. Specifically, soil erosion has increased due to the conversion of forest and rangeland into agricultural and orchard and even residential land. Finally, it is suggested to prevent the land use change by using land management solutions and soil conservation measures, especially in high slopes. | ||
| کلیدواژهها [English] | ||
| Distributed models, land use change, RUSLE model, soil conservation, soil erodibility | ||
| مراجع | ||
|
Alewell C, Borrelli P, Meusburger K, Panagos P. 2019. Using the USLE: Chances challenges and limitations of soil erosion modelling. In Soil Water Conserve Research, 7(3): 203-225. 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. Ansari A, Tayfur G. 2023. Comparative analysis of estimation of slope-length gradient (LS) factor for entire Afghanistan. Geomatics, Natural Hazards and Risk, 14(1): 2200890. Baiati Khatibi M. 2016. Assessment and predicting of soil erosion risk at semi-arid mountain: Integration of the USLE Model and GIS Technique for soil conservation planning (Case study: Sareskandarchy Catchement), Eastern Slope. Geography and Planning, 19(54): 61-81. (In Persian). Behera M, Sena DR, Mandal U, Kashyap PS, Dash SS. 2020. Integrated GIS-based RUSLE approach for quantification of potential soil erosion under future climate change scenarios. Environmental Monitoring and Assessment, 192(11): 1-18. Donovan M. 2022. Modelling soil loss from surface erosion at high-resolution to better understand sources and drivers across land uses and catchments; A national-scale assessment of Aotearoa, New Zealand. Environmental Modelling and Software, 147, 105228. Durigon VL, Carvalho DF, Antunes MAH, Oliveira PTS, Fernandes MM. 2014. NDVI time series for monitoring RUSLE cover management factor in a tropical watershed. International Journal of Remote Sensing, 35(2): 441-453. Ganasri BP, 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. Gayen A, Saha S. 2017. Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: A study on Pathro river basin, Jharkhand, India. Modeling Earth System and Environment, 3, 1123-1139. Ghavimipanah MH, Gholami L, Ghavimipanah MR. 2022. Estimation of soil erosion using RUSLE Model and determination of direct and Indirect damages in Kan Watershed. Journal Watershed Management Science, 16(56): 42-52. (In Persian). Gupta S, Kumar S. 2017. Simulating climate change impact on soil erosion using RUSLE model - A case study in a watershed of mid-Himalayan landscape. Journal of Earth System Science, 126, 43. Haji Kh, Esmaali-Ouri A, Mostafazadeh R, Nazarnejad H. 2014. Preparation and assessment of soil erosion map of Rozechai Urmia Watershed using GIS and RUSLE model. The 2th National Conference on Protection of Natural Resources and Environment, Ardabil, pp. 1-7. (In Persian). Haji Kh, Esmali-Ouri A, Mostafazadeh R, Nazarnejad H. 2018. Determining soil erosion rate in different landuses using RUSLE model in the Rozechai Watershed Urmia Iran. Journal of Conservation and Exploitation of Natural Resources, 7(1): 173-189. (In Persian). Hajigholizadeh M, Melesse AM, Fuentes HR. 2018. Erosion and sediment transport modelling in shallow waters: A review on approaches models and applications. International Journal of Environmental Research and Public Health, 15, 518. (In Persian). Hoyos N. 2005. Spatial modeling of soil erosion potential in a tropical watershed of the Colombian Andes. Catena, 63(1): 85-108. Islam MR, Jaafar WZW, Hin LS, Osman N, Karim MR. 2020. Devel-opment of an erosion model for Langat river basin Malaysia adapting GIS and RS in RUSLE. Applied Water Science, 10(7): 1-11. Khaledi Darvishan A, Faraji J, Gholami L, Khorsand M. 2021. Spatio-temporal variation of soil erosion in Khamsan representative watershed using RUSLE. Watershed Engineering and Management, 13(3): 534-547. (In Persian). Khorsand M, Khaledi-Darvishan A, Gholamali Fard M. 2015. The sensitivity of the annual erosion estimation map of the RUSLE Model to the methods of preparing the agricultural management factor map (C) in the Khamsan watershed. The 6th National Conference on Sustainable Agriculture and Natural Resources, Tehran, pp. 1-9. (In Persian). Koirala P, Thakuri S, Joshi S, Chauhan R. 2019. Estimation of soil erosion in Nepal using a RUSLE modeling and geospatial tool. Geosciences, 9, 147. Madadi A, Pasban A, Nezafat takle B. 2023. Investigating and evaluating the amount of soil loss in the land uses of the Atashgah watershed using the RUSLE Model and Landsat satellite images (OLI meter). Journal of Environmental Science Studies, 8(2): 6612-6625. (In Persian). Merchán L, Martínez-Graña AM, Alonso Rojo P, Criado M. 2023. Water erosion risk analysis in the Arribes del Duero Natural Park (Spain) using RUSLE and GIS techniques. Sustainability, 15, 1627. Mohammadi Sh, Balouei F, Haji Kh, Khaledi Darvishan A, Karydas CG. 2021. Country-scale spatio-temporal monitoring of soil erosion in Iran using the G2 model. International Journal of Digital Earth, 14(8): 1019-1039. Mohammadi Sh, Karimzadeh H, Habashi K. 2022. Assessment soil erosion and deposition in the Menderjan Watershed using USPED and RUSLE models in the environment of geographical information system (GIS). Desert Ecosystem Engineering, 6(17): 43-56. (In Persian). Mohammadi Sh, Karimzadeh H, Pourmanafi S, Soltani S. 2018. Spatial and temporal evaluation of soil erosion using RUSLE model Landsat satellite image time series (Case study: Menderjan, Isfahan). Journal of Range and Watershed Management, 71(3): 759-774. (In Persian). Noor H, Arabkhedri M. 2023. Prediction of soil erosion and sediment delivery ratio using RUSLE at Sanganeh soil conservation research station. Water and Soil Management and Modelling, 3(1): 42-53. (In Persian). Nwaogu C, Okeke OJ, Assuah Adu S, Babine E, Pechanec V. 2018. Land use-land cover change and soil-gully erosion relationships: A study of Nanka South-Eastern Nigeria using geoinformatics. In Dynamics in GIscience 4. Springer International Publishing, pp. 305-319. Olika G, Fikadu G, Gedefa B. 2023. GIS based soil loss assessment using RUSLE Model: A case of Horo district, western Ethiopia. Heliyon, 9, e13313. Othman AA, Ali SS, Salar SG, Obaid AK, Al-Kakey O, Liesenberg V. 2023. Insights for estimating and predicting reservoir sedimentation using the RUSLE-SDR approach: A case of Darbandikhan Lake Basin, Iraq–Iran. Remote Sensing, 15, 697. Panagos P, Ballabio C, Borrelli P, Meusburger K, Klik A, Rousseva S, Tadić M P, Michaelides S, Hrabalíková M, Olsen P, Aalto J, Lakatos M, Rymszewicz A, Dumitrescu A, Beguería S, Alewell Ch. 2015. Rainfall erosivity in Europe. Science of the Total Environment, 511: 801-814. Pasban A, Abedini M, frotan M. 2022. Evaluation and analysis of the impact of land use on soil erosion using the RUSLE experimental model (Case study: Balikhlochai Watershed, Ardabil Province). Geography and Human Relationships, 5(3): 238-258. (In Persian). Rawat KS, Singh SK. 2018. Appraisal of soil conservation capacity using NDVI model-based C factor of RUSLE model for a Semi-Arid ungauged watershed: A case study. Water Conservation Science and Engineering, 3(1): 47-58. Renard KG, Freidmund JR. 1994. Using monthly precipitation data to estimate the R-factor in the RUSLE. Journal of Hydrology, 157(1-4): 287-306. Rezaei M, Qargharechi Sh, GHaneei Motlagh GHR, Ayubi ShA. 2016. Estimation of soil erosion in Ziarat Basin using RUSLE model. 10th Iran Soil Science Congress, Karaj, pp. 1161-1163. (In Persian). Risse LM, Nearing MA, Laflen JM, Nicks AD. 1993. Error assessment in the universal soil loss equation. Soil Science Society of America Journal, 57(3): 825-833. Saadati H, Gholami SA, Sharifi F, Ayubzadeh SA. 2016. Investigating the effects of land use change on the surface runoff of the simulation model. Journal Natural Resources of Iran, 59(2): 301-313. (In Persian). Sadeghi SH, Moatamednia M, Moradi H. 2014. Variability of main unit hydrograph components of Kasilian Watershed in different effective precipitation time bases. Journal of Water and Soil Conservation, 21(4): 247-260. (In Persian). Saha M, Sauda SS, Real HRK, Mahmud M. 2022. Estimation of annual rate and spatial distribution of soil erosion in the Jamuna basin using RUSLE model: A geospatial approach. Environmental Challenges, 8, 100524. Sartip F, Radmanesh F, Zarei H, Salari jazi M. 2018. Automatic calibration of the continuous HMS-SMA Rainfall-Runoff model using the metaheuristic algorithm (Case study: Kasilian Basin). Irrigation Sciences and Engineering, 41(3): 15-28. (In Persian). Serbaji MM, Bouaziz M, Weslati O. 2023. Soil water erosion modeling in Tunisia using RUSLE and GIS integrated approaches and Geospatial Data. Land, 12(3): 548. Shi ZH, Cai CF, Ding SW, Wang TW, Chow TL. 2004. Soil conservation planning at the small watershed level using RUSLE with GIS: a case study in the three gorge area of China. Catena, 55(1): 33-48. Shinde V, Tiwari KN, Singh M. 2010. Prioritization of micro watersheds on the basis of soil erosion hazard using remote sensing and geographic information system. International Journal of Water Resources and Environmental Engineering, 2(3): 130-136. Tian P, Zhu Z, Yue Q, He Y, Zhang Z, Hao F, Liu M. 2021. Soil erosion assessment by RUSLE with improved P factor and its validation: Case study on mountainous and hilly areas of Hubei Province China. International Soil and Water Conservation Research, 9(3): 433-444. Troeh FR, Hobbs JA, Donahue RL. 1980. Soil and water conservation for productivity and environmental production. Prentice-Hall, 780 p. Wischmeier WH, Smith DD. 1978. Predicting rainfall erosion losses: A guide to conservation planning. Agriculture Handbook No. 537 p. USDA, Washington, DC. Zabihi M, Sadeghi SHR, Vafakhah M. 2014. Spatial analysis of rainfall erosivity index patterns at different time scales in Iran. Journal of Watershed Engineering and Management, 7(4): 422-457. (In Persian). Zhang K, Chao L, Wang Q, Huang Y, Liu R, Hong Y, Tu Y, Qu W, Ye J. 2019. Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China. Water Science and Engineering, 12(2): 85-97. | ||
|
آمار تعداد مشاهده مقاله: 768 تعداد دریافت فایل اصل مقاله: 332 |
||