فهرست مطالب

پژوهش های فرسایش محیطی - سال هشتم شماره 4 (پیاپی 32، زمستان 1397)
  • سال هشتم شماره 4 (پیاپی 32، زمستان 1397)
  • تاریخ انتشار: 1397/12/10
  • تعداد عناوین: 6
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  • علیرضا جلالی فرد، محسن حسینعلی زاده*، چوقی بایرام کمکی، مجید عظیم محسنی صفحات 1-18
    فرسایش پایپینگ از جمله فرسایش های آبی است که عموما در سازندهایی با ظرفیت نفوذپذیری کم و کانی های قابل انحلال زیاد به وجود می آید. تحقیق حاضر در محدوده ای به وسعت تقریبی 105 هکتار در اراضی لسی استان گلستان انجام شد که تعداد 102 پایپینگ در آن به ثبت رسید. هدف این مطالعه، پیش بینی وجود یا عدم وجود پایپینگ و پیش بینی خطر آن از طریق مدل سازی خصوصیات فیزیکی و شیمیایی خاک بر اساس رگرسیون لجستیک است؛ در این راستا، ابتدا به صورت میدانی تمامی پایپینگ های محدوده سرشماری و از داخل و خارج خاک نمونه برداری صورت گرفت. سپس خصوصیات مورفومتری آنها نیز ثبت شد. سپس داده های استخراج شده، توسط مولفه های آماری تجزیه و تحلیل شد. برای مقایسه ی فرسایش پایپینگ در دو کاربری زراعت و مرتع، از آزمون خی دو و آنالیز مولفه های اصلی استفاده شد. نتایج آزمون خی دو نشان داد که شکل دامنه، شکل پایپینگ و بافت خاک به کاربری اراضی وابسته نیست. همچنین برای مقایسه ی خصوصیات فیزیکی و شیمیایی خاک در داخل و مجاور پایپینگ، از روش های آنالیز واریانس طرح تکراری چندگانه و آنالیز مولفه های اصلی استفاده شد. ویژگی های فیزیکی و شیمیایی خاک در داخل و خارج پایپینگ در سطوح مختلف معنی داری (95% و 99%) با ایجاد پایپینگ رابطه ی معنی داری داشته است. با استفاده از آنالیز مولفه های اصلی، مولفه های فقدان تراکم خاک، چسبندگی خاک و خصوصیات الکترولیتی بیشترین تاثیر را در رخداد پایپینگ داشته اند. درنهایت از رگرسیون لجستیک دوگانه و چندگانه به ترتیب در مدل سازی وجود یا عدم وجود و خطر تشکیل پایپینگ استفاده شد. تجزیه و تحلیل رگرسیون لجستیک دوگانه حاکی از آن بود که از بین متغیرهای موردمطالعه، متغیرهای کاربری اراضی، مقاومت خاک، جهت جغرافیایی، شکل شیب، درصد شیب و اسیدیته خاک بیشترین تاثیر را در رخداد پایپینگ به خود اختصاص داده اند. بر اساس رگرسیون لجستیک چندگانه نیز مهم ترین متغیرهای دخیل در خطر تشکیل پایپینگ به کاربری اراضی، مقاومت خاک، فاصله از آبراهه و جهت جغرافیایی اختصاص یافت.
    کلیدواژگان: ایکی آغزلی، خصوصیات فیزیکی و شیمیایی خاک، رگرسیون لجستیک، لس، مدل سازی پایپینگ
  • محمد حسن صادقی روش* صفحات 19-40
    بیابان زایی یک از بزرگ ترین چالش های زیست محیطی زمان ما به شمار می رود. این پدیده مسئله ای جهانی است و پیامدهای جدی آن بر تنوع زیستی، ایمنی محیط زیست، ریشه کنی فقر، ثبات اجتماعی- اقتصادی و توسعه ی پایدار در سراسر جهان تاثیرگذار است. علی رغم اثرات جدی زیست محیطی، اقتصادی و اجتماعی این پدیده، کوشش های اندکی در زمینه ی ارائه ی راهبردهای بهینه کنترل و کاهش آن صورت گرفته است؛ بنابراین، مقاله ی حاضر با هدف ارائه ی راهبردهای بهینه ی بیابان زدایی به صورت نظام مند و در قالب یک مدل تصمیم گیری گروهی انجام شد. به این منظور در ابتدا در چارچوب روش تصمیم گیری چند شاخصه و با استفاده از تکنیک آنتروپی شانون، ارجحیت شاخص ها برآورد شد. سپس با ایجاد ساختار رجحانی و رتبه بندی معیارها و راهبردها با کاربرد روش میانگین رتبه های بس سون، به رتبه بندی فواصل و تعیین اولویت راهبردها با استفاده از مدل ارسته پرداخته شد. نتایج حاصل نشان داد که معیارهای «ابزارهای علمی و تکنولوژیکی» (C5) و «تناسب و سازگاری با محیط زیست» (C7) به ترتیب با ضریب اهمیت 2628/0 و 2587/0، در بالاترین درجه ی اهمیت قرار دارد و از میان راهبردها، راهبرد توسعه و احیای پوشش گیاهی (23A) با رتبه ی کلی R(m)=46/5 مهم ترین راهبرد در فرایند بیابان زدایی منطقه ی مطالعاتی است و راهبردهای جلوگیری از تبدیل و تغییر نامناسب کاربری اراضی (18A) با رتبه ی کلی R(m)=53/5 و کنترل چرای دام (20A) با رتبه ی کلی R(m)=69 به ترتیب در اولویت های بعدی قرار می گیرد؛ از این رو، پیشنهاد شد که در طرح های کنترل و کاهش اثرات بیابان زایی و احیای اراضی تخریب یافته، نتایج و رتبه بندی به دست آمده قابل توجه قرار گیرد.
    کلیدواژگان: تصمیم گیری چند شاخصه، تصمیم گیری گروهی، مقایسه زوجی، میانگین رتبه های بس سون، وزن نسبی
  • پروین غلامی*، محمدمهدی حسین زاده صفحات 41-64
    فرسایش بستر و کناره، تغییرات کانال رود و در نهایت آسیب پذیری کانال، مسئله ای اجتماعی، محیطی و اقتصادی است که اغلب خسارات جبرا ن ناپذیری را به ساکنان و تاسیسات حاشیه ی رودخانه وارد می سازد. در این تحقیق به منظور پیش بینی و ارزیابی میزان پایداری و ناپایداری کناره و بستر و شناسایی مناطقی با ریسک بالای آسیب پذیری کانال در رودخانه ی مسیل موچان، باز ه ای از این رودخانه در حد فاصل روستاهای قلعه تا سرسختی به طول تقریبی 65/5 کیلومتر انتخاب شد. پس از مشاهدات میدانی، 8 مقطع در طول بازه ی مورد مطالعه انتخاب و نمونه برداری و اندازه گیری های موردنیاز صورت گرفت. با استفاده از داده های برداشت شده شامل مقدار دبی، تنش برشی مرزی و بحرانی، ضریب مانینگ، عرض کانال فعال، عرض دره، محاسبه ی مساحت پهنه های حفاظت شده و فاقد تولید رسوب در حوضه ی مورد مطالعه، محاسبه ی شیب کانال، عرض کانال در دبی لبالبی، عمق کانال در دبی لبالبی، عرض دبی سیلاب و مشخص کردن نوع رسوبات بستر، پارامترهای موردنیاز از جمله کلاس های خطر فرسایش کناره ای و فرسایش کانال در مدل رودخانه ای شوم، محاسبه و در نتیجه آسیب پذیری کانال در 8 مقطع رودخانه ی مسیل موچان انجام شد. نتایج نشان داد که تمامی مقاطع این رودخانه در معرض آسیب پذیری متوسط تا زیاد قرار دارد؛ مقاطع 1، 2، 3، 4 و 5 دارای ریسک آسیب پذیری متوسط است و مقاطع 6، 7 و 8 ریسک آسیب پذیری بالایی دارد.
    کلیدواژگان: آسیب پذیری کانال، پایداری کانال، رودخانه مسیل موچان، فرسایش رودخانه
  • پیمان محمدی احمدمحمودی* صفحات 65-81
    برای محاسبه ی قدرت فرسایندگی بارش، داده های فضایی بارندگی با وضوح بالا برای ارزیابی فرسایش باران ضروری است. سنجنده های باران، توزیع فضایی نامنظم و ناهماهنگی فضایی حاصل از بارش را در برآورد مقدار بارندگی به خوبی نشان نمی دهند؛ زیرا بارش را به صورت نقطه ای اندازه گیری می کنند. برآورد مقدار بارش از داده های ماهواره ای، راه حلی جایگزین برای این مشکل است که امکان برآورد مقدار بارش و توزیع فضایی آن را در مناطق بزرگ فراهم می کند. هدف از این پژوهش، ارزیابی قابلیت داده های بارشی ماهواره ی TRMM در برآورد بارش و پایش نرخ فرسایندگی در سطح ایران است. در این پژوهش از داده های ماهانه ی ماهواره ی TRMM طی سال های 2000، 2005، 2010 و 2015، به منظور برآورد نرخ و ترسیم نقشه ی بارش و فرسایندگی با استفاده از شاخص فورنیه در سطح کشور استفاده شده است. برای ارزیابی دقت و صحت داده های TRMM نیز از آمار بارش ماهانه ی 45 ایستگاه زمینی هم زمان با داده های TRMM استفاده شده است. نتایج پژوهش در ارزیابی بارش و نرخ فرسایندگی نشان می دهد که به طورکلی، بیشترین نرخ بارش و فرسایندگی متعلق به ناحیه ی خزری، مناطق مرتفع زاگرس و البرز است و کم بارش ترین مناطق بارشی و نرخ فرسایندگی به ایران مرکزی، شرق و جنوب شرق کشور اختصاص دارد. سایر مناطق کشور نیز نرخ فرسایندگی بینابینی دارد. نتایج ارزیابی دقت داده های TRMM در قیاس با ایستگاه های زمینی نشان می دهد که ضریب R2 برای سال های پایش شده به ترتیب 86/0، 77/0، 73/0 و 82/0 است که از این منظر، این داده ها جایگزین مناسبی برای ایستگاه های زمینی محسوب می شود. نتایج ضریب RMSE برای سال های پایش شده نیز به ترتیب برابر است با 152، 205، 213 و 273 که از این نظر، اختلاف بین داده های زمینی و ماهواره ای به دلیل قدرت تفکیک مکانی ضعیف داده های TRMM و ماهیت متفاوت برداشت با ایستگاه های زمینی نسبتا زیاد است.
    کلیدواژگان: داده های فضایی، فرسایش، مخاطرات محیطی، مدیریت اراضی
  • شهروز شجاعی*، محمدرضا نورا، شهرام حبیبی مود صفحات 82-100
    با توجه به روند افزایشی فرسایش خاک و تولید رسوب در کشور، توسعه ی روش های ارزیابی کمی و کیفی فرسایش و رسوب  ضروری است. هدف از این تحقیق، ارزیابی فرسایش و رسوب  در حوضه ی آبریز گابریک  است که با استفاده از دو مدل تجربی MPSIAC و FSM و روش اندازه گیری مستقیم انجام شد. بدین منظور، نقشه ی واحدهای هیدرولوژیک منطقه به عنوان نقشه ی پایه تهیه شد. در مدل MPSIAC، وضعیت فرسایش و تولید رسوب در هر واحد کاری بر حسب شدت و ضعف 9 عامل محیطی و در روش FSM، تعیین شدت فرسایش با استفاده از محاسبه ی 7 عامل محیطی ارزیابی شد. سپس لایه های مربوطه در محیط نرم افزار Arc GIS تهیه شد. در روش اندازه گیری مستقیم رسوب با استفاده از وسایل نمونه برداری و نرم افزار CIVIL 3D، به تخمین میزان رسوب دهی حوضه پرداخته شد. بر اساس نتایج،   میزان رسوب کل حوضه با اعمال ضریب مربوطه با استفاده از دو مدل MPSIAC و FSM، به ترتیب 4  و 3/6 تن در هکتار در سال برآورد شد و براساس ضریب رسوب دهی حوضه (R)، از لحاظ کیفی بخش اعظم حوضه در کلاس فرسایشی متوسط قرار گرفت. در روش اندازه گیری مستقیم با اعمال ضریب حجمی و آنالیز داده های صحرایی، میزان رسوب تجمعی ایستگاه معادل 9/6 تن در هکتار در سال برآورد شد. به طور کلی، با مقایسه ی نتایج به دست آمده می توان چنین استنباط کرد که مقادیر رسوب تولیدی به دست آمده از مدلFSM  نسبت به MPSIAC، با روش اندازه گیری مستقیم مطابقت بیشتری دارد.
    کلیدواژگان: تخمین فرسایش و رسوب دهی، حوضه آبریز گابریک، روش مستقیم، مدل MPSIAC، مدل FMS
  • حسین شهاب آرخازلو*، شکراله اصغری صفحات 101-121
    تعیین خطر فرسایش آبکندی، نیازمند برآورد شدت این خطر و تعیین توزیع خطر فرسایش در سطح حوضه ی آبخیز است. در این پژوهش سه حوضه ی آبخیز در مناطق ارتاداغ، ملااحمد و سرچم استان اردبیل انتخاب شد. با استفاده از روابط دو مدل REGEM[1] و REGEM سازگار شده با شرایط منطقه (AREGEM[2])، سه خروجی مدل شامل عامل تنش برشی ، فرسایش خاک (Kd) و حجم آبکندها (V) در آبکندهای منتخب محاسبه شد. سپس با استفاده از درون یابی IDW، توزیع این سه عامل در سطح حوضه ها صورت گرفت. در نهایت با استفاده از دو شاخص نسبت تراکم (Dr) و مجموع کیفیت (Qs)، طبقات شدت فرسایش به دست آمده از دو مدل ارزیابی و با یکدیگر مقایسه شد. نتایج نشان داد بین مقادیر فرسایش برآورد شده با مدل AREGEM و طبقات حساس به فرسایش آبکندی، انطباق بیشتری وجود دارد. بنابراین، مدل AREGEM کیفیت پهنه بندی بیشتری دارد و توانایی این مدل در تعیین طبقات حساس به فرسایش آبکندی بیشتر است. همچنین در بین سه منطقه ی مورد مطالعه، مدل AREGEM در منطقه ی ارتاداغ بیشترین کارایی را در تعیین توزیع فرسایش آبکندی دارد. از بین سه خروجی به دست آمده از مدل نیز استفاده از حجم آبکندها در تعیین دقیق تر توزیع فرسایش آبکندی، کارایی بیشتری دارد.
    کلیدواژگان: اصلاح مدل، سامانه اطلاعات جغرافیایی (GIS)، ارزیابی خطر
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  • Alireza Jalalifard, Mohsen Hosseinalizadeh*, Chooghi Bairam Komaki, Majid Azimmohseni Pages 1-18
    Introduction
     Piping erosion is one of the common water erosions usually occurs in soil with low infiltration and soluble minerals that finally changes landscape. Different factors control its occurrence and development. To predict piping occurrence/hazard through modeling, effective factors should be prioritized and considered as inputs of the model. The present study has performed through field survey for piping erosion across the Iky Aghzly watershed with an area of 105 ha lead to recording of about 102 pipes (≤1 pipe per ha). This study aims to detect the locations of piping erosion via the modeling of logistic regression.
    Methodology
    In order to perform this research, all of the piping erosions in the study area were surveyed and their morphometric characteristics were recorded, and the sampling of the topmost 5 cm of soil was taken from inside and outside of the pipes. Then the principal comparison analysis (PCA) was used. To compare piping erosion in rangeland and cropland, X2 test and PCA were used. Additionally, the PCA and multivariate ANOVA were used to evaluate the main differences of the physicochemical soil properties of inner and outer sides of the pipes. Finally, the binary and multivariate logistic regression was applied to predict the existence or lack of piping's extension hazard, respectively.
    Results
    In this study, the danger of piping for humans and animals was calculated based on the depth of the pipes. Based on the results, a very high risk category (pipes with more than four meters depth) is mostly located in rangeland land use. Respectively, the amount of low risk, moderate risk, high risk and very high risk classes in the study area was 31, 29, 23 and 19. The results of comparison of piping erosion in rangeland and cropland showed that the shape of the hillslope, the shape of the piping and the soil texture are not dependent on the land use. There is also a significant relationship between the land use and direction of the piping extension at the significant level of 0.009. Based on the PCA, among few quantitative affective factors in the erosion of the piping, about 8 components were selected which contains almost 89 percent of the pipes in the study area. Comparison of the physicochemical properties of the soils showed that their means were significantly different in both inside and outside of the piping. To predict two state of the existence or lack of piping via binary logistic regression, with a quantitative and qualitative predictive parameter, a suitable model (coefficient of determination, r2, of 0.5) was calculated. Based on a multivariate logistic regression and considering the quantitative and qualitative predictive parameters, a suitable regression model with a coefficient of determination of 0.6 was found to model the risk of piping.
    Discussion & Conclusions
    According to the results of t-test for each component, it was determined that the land use of rangeland and cropland are significantly different only in piping coordinates. Since the amount of this component is higher in rangeland than that in cropland, this difference can be explained by the diameter and depth of the pipings in the rangeland, as well as the short proximity from the ridge. By comparing the physicochemical properties of the interior and exterior of pipings, it was found that the standard deviation of the data in the organic matter is very low, and the lowest difference is significant in the inside and outside of the piping. The results also showed that soil resistance, organic matter and bulk density decreased, as well as pH, EC, ESP, SAR, and porosity were increased. Based on binary logistic results, for the probability of a piping occurrence in a specific location to be determined, type of land use, soil resistance, geographic direction, shape of the hillslope, slope percentage, and pH is needed. In general, the factors affecting the formation of piping can be identified as biological activity and physical properties of soil such as texture (silty), low bulk density and high porosity with water penetration and severe surface runoff in the study area. The effective factors of the piping formation are land use, proximity from the waterway and soil resistance, and they are significant at 1% level. Using the logistic model in this study, the most important factors controlling piping were determined as the shape of the concave hillslope and topography of the pipe location as well as proximity from the waterways.
    Keywords: Iky Aghzly, Physicochemical properties of soil, logistic regression, Loess, Piping Modeling
  • Mohammad Hassan Sadeghi Ravesh* Pages 19-40
    Nowadays, Desertification is one of the greatest environmental challenges. It is a global issue and its serious consequences affect on biodiversity, environmental safety, poverty eradication, economic and social stability and sustainable development around the world. Despite the serious environmental, social and economic impact of desertification phenomenon, few studies have been done in providing optimal alternatives. This paper tries to provide systematic and optimal alternatives in a group decision-making model. At the first in the framework of Multiple Attribute Decision-making (MADM), indices preference was determined using shannon entropy, then alternatives priority was evaluated by ORESTE model. The results showed that alternative of vegetation cover development and reclamation (A23) with general rating of = 46.5 is the most important alternative in de-desertification prosess in the study area, and alternatives of prevention of unsuitable land use changes (A18) and Livestock grazing Control (A20) were in the next priority with general rating of 53.5 and 69, respectively. Therefore, it is suggested that the obtained results and ranking should be considered in projects of controlling and reducing the effects of desertification and rehabilitatyion of degraded lands plans.
    Keywords: Besson ranks average, Group decision-making, Local Priority, Multiple Attribute Decision-making (MADM), Pirewise comparison
  • Parvin Gholami*, Mohammad Mehdi Hosienzadeh Pages 41-64
    Introduction
    Rivers are one of the dynamic landforms of natural landscapes that are subject to changes due to environmental variables and human interactions during different times and locations. Changes in the river, erosion of the bed and river bank are natural processes of alluvial rivers that destroy the surrounding agricultural land and damage human installations around the river. Human activities, such as the degradation of basin vegetation and the uncontrolled harvesting of river water, cause more pressure on the  river and the destruction of the plant covered area along the river and the change of the river ecosystem. Therefore, the erosion of bed and bank and channel changes, finally, the vulnerability of  the channel, as social and economic issue, often causes irreparable damage to residents and facilities in riverside. Curitit (2014) predicts the erosion and sustainability of the riverbank in Stony Clove Creek in Catskills, using the analysis of the Bank Assessment for Non-point. Source Consequences of Sediment (BANCS). This study showed that the results obtained through the BANCS method may lead to improved  management of the future of the Stony Clove Basin. Hosseinzadeh et al. (2005) studied the efficiency of the Rasgen classification system in the rivers of Babol and Talar in the Caspian Sea coastal plain. The results of this study showed that the prediction of the river type using this method can not be substituted as real research on the river and can lead to failure of management plans.
    Methodology
    The study area is a reach of 5.65 km in the Masil-Muchan river. The river originates from the northern and eastern mountains of Amarat village and joins the Shara River, one of the main branches of the Gharache river. The average discharge of Masil-Muchan river is 0.23 cubic meters per second and its Sediment discharge rate is 0.76 cubic meters per day. This river is located in Astaneh city, Markazi province. The channel vulnerability model, by dividing the average shear boundary stress (erosion forces) and shear stresses for each cross section of river, calculates the ratio of erodability both quantitatively and qualitatively, and based on that, the amount of damage to the channel is determined. The calculated data for the first stage for this model included active channel width, valley width, calculated area of ​​protected areas and no sediment production in studied basin, channel slope, channel width in bankfull, channel depth in bankfull, boundary and critical shear stress, Manning coefficient and flood width are considered. The required data for the second stage of the channel vulnerability model is as follows:
    Determining the type of sediment substrate: sediment types are classified into three general categories: a) non cohesive; b) cohesive deposits; and c) concrete bedrock, which the type of sediment was determined on the basis of field observations.
    Determination of bank erosion class: In this model, bank erosion classes are divided into three erosion classes: low, moderate and high erosion, that their type at each cross section was determined on the basis of field observations and comparison with the photograph.
    3) Determination of erosion class of the channel in Schumm class:  According to Schumm river model, the rivers are classified into six classes. The type of erosion class of river channel, was determined using field observations and cross-sectional profiles, and comparing it with Schumm river classes at each cross section of the river, then the river erosion class number entered the vulnerability model. Depending on type of Schumm erosion class and bank erosion, the degree of erosion is different in both classes. In the third stage, using the data provided, the ratios and classes of effective parameters, the rate of channel vulnerability is calculated in all cross sections of river. the ratios and classes of effective in rate of channel vulnerability are as follows: 1) risk of channel erodability rate, 2) risk of channel entrenchment, 3) At the end is calculated channel vulnerability.
    Results
    In this study, the channel vulnerability model was used to investigate and determine the Instability and vulnerability of the channel in Masil Muchhan river. With this aim, 8 cross sections were selected in a reach of 5.55 km between villages of Ghaleh and Sarsakhti and calculated shear stress and channel erosive forces on the basis of field data and some of the effective parameters in the channel,  and at the end was determined vulnerability of the river by describing all parameters as a table in the main article. Based on the calculations, it was found that all cross sections of the Masil Muchhan river are subject to moderate to high vulnerability; cross sections 1, 2, 3, 4, and 5 have a moderate vulnerability and cross sections 6, 7 and 8 have a high vulnerability risk that Is described the causes of erodibility difference, vulnerability and cross-sectional profile of all cross sections is expressed in the original article.
    Discussion & Conclusions
    The results of the channel vulnerability calculations showed that all cross sections of the Masil Muchan river are subject to erodibility and thus vulnerability. Of the 8 cross-sections that were evaluated, 5 cross-sections (sections 1, 2, 3, 4, and 5) have a medium vulnerability risk and three cross sections (6, 7, and 8) are at high vulnerability risk. The results of vulnerability were investigated in two levels: the results of vulnerability in two levels showed that at the level of morphometric indices of the channel, all of these three cross sections showed a high degree of slope and depth of bankfull. These parameters increase the flow velocity at bankfull discharge in these cross sections of river. In addition to the above, all three sections have the highest critical shear stress, which, in case of bankfull discharge, causes the highest amount of shear stress and thus erodibility. In these sections, despite the high Wet perimeter and low amount of hydraulic radius, which reduces the flow of power, but the high shear stress and flow velocity in bankfull discharge, increases the power of river and ultimately creates a high risk of vulnerability. Angle of repose on all banks was obtained by a similar number and therefore not considered as an effective parameter in the channel vulnerability. As a result, the largest amount of erosion occurs for banks which are more sloping. In all sections except sections 5 and 6, the left banks have more erodibility due to greater slope.
    Keywords: Channel vulnerability, River erosion, Masil Mochan River, Channel Stability
  • Peyman Mohammadi Ahmad Mohammadi* Pages 65-81
    Introduction
        In order to calculate the erosive power of rainfall, high-resolution precipitation data are necessary for rainfall erosion evaluation. However, collecting the required data on kinetic energy of the rainfall particles and precipitation rates with short-term temporal resolution is a time-intensive task, particularly in developing countries, and the collected data are difficult to process. Rain sensors provide valuable information on the rate and intensity of rainfall, but fail to adequately represent irregular and inconsistent spatial distribution of the precipitation when evaluating the precipitation rate as those perform point measurements of precipitation. Under such circumstances, evaluation of precipitation rate from satellite data provides an alternative approach to the problem, which makes it possible to estimate precipitation rate and its spatial distribution across large areas. All around the world, several research works have been performed to estimate soil erodibility factor using the precipitation product of TRMM sensor, while no research has used such products for erosion and erodibility studies in Iran. Given that a limiting factor for estimating rainfall erosive power across large areas in Iran has been the lack of required data on precipitation intensity or precipitation rate, the present research can provide an approach to address such limitations. This study is aimed at monitoring the precipitation and hence evaluating and monitoring soil erodibility factor using precipitation products of TRMM sensor and comparing the results with those of terrestrial stations
    Methodology
        In this research, in order to use Modified Fournier Index (MFI) to estimate corrosive rate during 2000, 2005, 2010, and 2015, monthly precipitation products data of TRMM3B24 sensor was retrieved from http://apdrc.soest.hawaii.edu for all months of the considered years. Then, using the Fournier index and the equation proposed by Renard and Ferimvend, the erosive rate for the entire country was extracted for the four years considered in this study. In order to verify and evaluate the accuracy of the precipitation products of TRMM sensor, the monthly precipitation data collected from 45 terrestrial stations were used, and interpolation technique was used to develop precipitation and erosion maps based on the terrestrial data. Accuracy of the precipitation products of TRMM sensor was verified based on root-mean-square error (RMSE) and coefficient of determination (CD) of the annual precipitation at the pixel position of the synoptic stations.
    Results
    Results of evaluating annual precipitation rates indicated among the monitored years, the 2010 had experienced the lowest level of precipitation, while the 2000 was the year with the highest precipition level. A review on rainfall erosion maps indicated that, in 2000, the areas of the country with the highest erosion rates corresponded to the Middle Zagros and Caspian areas. The country’s erosion map in 2005 closely resembled that in 2000, the erosion rates in the southern Kerman and northern Hormozgan were significantly lower than 2005. In 2010, when mean annual precipitation exhibited a low, the Chabahar Area exhibited the lowest erosion rate across the country, because of the intrusion of a seasonal air mass in June. In 2015, once again, maximum erosion rate across the country corresponded to those in 2000 and 2005, as determined by increased precipitation rate. A comparison between annual precipitation data collected from TRMM sensor and synoptic stations showed that, during all of the four years, an adequately good R2 value was established between the data from TRMM and that from terrestrial stations. The highest value of R2 between the TRMM and terrestrial stations data was obtained for 2000 (0.86), while the lowest R2 value was that of 2015 (0.73). The obtained value of RMSE showed that, in 2000 (the year with the highest precipitation rate among the monitored years), the value of RMSE was 152 mm. For this year, the difference between the peak participation estimated from the two methodologies was 406 mm, which was mainly related to the four stations with the highest precipitation rates. In 2005, the difference between the peak participation estimated based on the data from the two sources was 543 mm, with a RMSE of 205 mm. Also in this year, the difference between minimum precipitation values was highest, as compared to the other years considered in this study. In 2010, the difference between the peak participation estimated based on the data from the two sources was 533 mm, with the RMSE reduced to 129 mm.
    Discussion & Conclusions
    Spatial resolution includes terrestrial dimensions of each pixel of the image and determines accuracy of the image. Terrestrial dimensions of each pixel of the precipitation products of TRMM is 25 km by 25 km. Given the nature and definition of the pixel, it is the smallest spatial unit with its most important characteristic being the consistency across the entire pixel. Accordingly, the 625-km2 area of each pixel of an image from TRMM takes only one numerical value (DN) which is an average value of the precipitation across the entire 625-km2 area. Therefore, accuracy of each pixel in recording the precipitation depends on the variations of precipitation across the mentioned area. Terrestrial stations provide point estimations of precipitation, and interpolation technique is used to prepare erosion and precipitation maps from terrestrial stations. Accordingly, the higher the number of points included in the process of interpolation, the more flexible will be the resultant interpolated map. Given the fundamental differences between precipitation measurements by satellite sensors and classic terrestrial stations, it is difficult to firmly express that the data from terrestrial stations shall be taken as reference data and the remote sensing data shall be verified against the terrestrial data, because even in point measurements, there are cases where the precipitation condition in points near the measurement point is significantly different from that at the measurement point.
    Keywords: Spatial Data, Erosion, Environmental Hazards, Land Management
  • Shahrooz Shojaei*, Mohammadreza Noura, Shahram HabibiMood Pages 82-100
    Introduction
         Soil erosion, as one of the most important environmental problems in the world, has a devastating effect on all life, natural resources and it's under human management. Considering that one of the important goals in the management of drainage of basins such as Gabric basin is preventing soil erosion, and also one of the important factors when designing dam or sedimentation structures, is estimating sediment production in the drainage basin, the estimation and calculation of the actual sediment deposited in the constructed dams at the basin outlet, and comparing it with the results of the empirical models, is the best method for estimating the sediment yield in the basins lacking sediment station such as Gabric basin.Therefore, if the amount of estimated sediment is closer to its actual value, it will definitely perform better at the time of designing the dam or planning in the basin in terms of cost and observance of technical and economic principles. In this regard, this study aimed at evaluating empirical models of MPSIAC and F.S.M. and direct measurement method for estimating sediment yield and erosion in the Gabric basin.
    Methodology
         In this research, in order to determine the data and estimate the score of each of the required parameters by three methods of MPSIAC and FSM methods and direct measurement, the existing basic maps and reports, the information of meteorological stations of the studied area, field studies, 42 sheets of digital topographic map with 1: 25000 scale for the study of stratigraphy, lithology, geology of the area, pedology maps, vegetation cover, land use map and area DEM (using ArcGIS 10.2 software) have been used.
    In the MPSIAC model, using the sum of the scores obtained in the basin unit map for 9 important factors influencing soil erosion and sediment production, the layers of these factors were prepared in ArcGIS software, and then the map of sedimentation rate (R ) gains to the basin . In the FSM mqqodel, after determining the score of 7 factors in the basin unit's map and preparing the layers of these factors, the maps of these 7 factors were introduced and, with their multiplication, the erodability index (FSM Index), and, using the relation of the FSM method, the erosion rate is estimated in terms of tons per kilometer for the basin. Finally, by applying the coefficient of sediment delivery ratio obtained by the MPSIAC method, the sediment production rate of the basin and sub-basins is calculated.
    In the direct measurement method for measuring the sediments behind the checkdems and Gabric dam, more than 110 boreholes were drilled with mechanical excavator at a surface of about one hectare of sediment. Also, the estimation of the sediment content behind the 70 founded checkdems was done using an Agro device, a GPS device and also meter. In this method, the CIVIL 3D 2015 software has been used to estimate the volume of reservoir sediments (Shojaei, 2018).
    Results
          Using MPSIAC model, the sedimentation rate (R) in the Gabric basin was 68.6 and the specific sedimentation for the basin was equal to 3.1 tons per hectare per year. On the other hand, due to the fact that the soil texture in most parts of the Gabric basin is moderated to light, the equation of medium to coarse-grained soils is used to calculate the sedimentation coefficient (SDR). This amount was estimated 0.32 for the studied basin. The total sediment in the Gabric basin, due to the major contribution of each formation in the production of bed load, is 11378 tons per year and the total sediment due to the specific sediment (suspension load of 3.1 and bed load of 9 tons per hectare per year and weight percentage of bed load of 0.3) was estimated 4 tons per hectare per year.
    Using the FSM model to estimate the erosion rate in the Gabric Basin, the erodability rate is 168.9 and the sediment delivery ratio is 0.32, and produced sediment is 6.6 tons per hectare per year.
    In the direct measurement method, the total estimated sediment content at the hydrometric station and Gabric dam during the execution time of the project is equal to 214637.9 m3,  respectively, the amount of sediment during the same time interval through the MPSIAC and FSM methods was estimated to be 9.33103 and 193288.4 m3. In direct measurement method, by applying volumetric coefficient and field data analysis, the cumulative sediment concentration of the station and the dam site was estimated to be 6.9 tons per hectare per year.
    Discussion & Conclusions
    By comparing the results obtained from the two empirical models with the direct measurement method, it can be concluded that the produced sediment yields from the FSM model are closer and superior to the MPSIAC model compared with the direct measurement of erosion and sediment, which could indicate a better match between the FSM model and direct measurements of erosion and sedimentation in this region. In addition, based on these results, it can be argued that in terms of quality, most of the basin is in a moderate erosion class.
    Keywords: Estimation of sedimentation, Erosion, Gabric drainage Basin, Direct measurement, MPSIAC model, FMS model
  • Hossein Shahab Arkhazloo*, Shokrollah Asghari Pages 101-121
    Introduction
    Gully erosion is the most severe type of water erosion, which causes large damage in the watersheds. To determine the risk of this type of erosion, it is necessary to estimate its severity and specify the distribution of the risk of erosion in the watershed. One of the models used to estimate the gully erosion is REGEM model, but accurate estimation of erosion requires adaptation of the model to regional conditions. In this study, from REGEM and Adapted REGEM (AREGEM) models, they were used to estimate the gully erosion severity of Ardebil province. Also, the distribution of the two models' outputs where mapped in the three watershed and compared to the chosen ones of optimal model.
    Methodology
    In this research, three watersheds were selected in Orta Dagh, Mollahammad and Sarcham of Ardebil province. Respectively 28, 33 and 20 small gullies were selected from three watersheds. In order to estimate the gully erosion with the AREGEM model, the equations of REGEM base model were used. For this purpose, regression relations between the measured values of the gully dimensions and the estimated values by REGEM model were established and coefficients of equations were corrected. Using the equations of REGEM and AREGEM models, three outputs of the model including shear stress , soil erosion factor (Kd) and gully volume (V) for selecting gullies were calculated and with using of IDW interpolation method, obtained the distribution of these three factors in the three watersheds. The outputs of the models were considered as an indicator of gully erosion severity and the watersheds were divided into four levels of erosion intensity. Finally, using two indicators of density ratio (Dr) and quality sum (Qs), the erosion classes obtained from the two models were evaluated and compared.
    Results
    The results showed that using the REGEM model, the average volume of gullies were in the Orta Dagh region 15.6, Mollahammad 17.2 and Sarcham 16.3 cubic meters while the AREGEM model was 16.1, 17.8 and 16.8 Cubic meter respectively. For the three areas of Orta Dagh, Mollahammad and Sarcham, the average of shear stress with REGEM model was 31.5, 32.2 and 24.8, respectively, and with AREGEM model 32.1, 33.3 and 20.6 respectively. The soil erosion factor (Kd) with REGEM model was 62.1%, 63.3% and 69.1% for the three regions, with AREGEM 1/62, 64.2% and 70.1%, respectively. In general, there is not much difference between the mean values of the outputs obtained from the two models, but the distribution map of the outputs in the two models differed greatly. Visually, in all three regions, the estimated erosion volume by AREGEM model has better compliance with the gully density of watersheds, while estimates of the REGEM base model in all three regions were not a good predict of gullies distribution. Also, the comparing of shear stress and soil erosion factors maps, showed similar results in three regions. Therefore, in order to have a more accurate comparing of the two models' outputs distribution map, the semi-quantitative index of the density ratio (Dr) and the quantitative index of quality sum (Qs) were used. Generally, in all three areas, for maps derived from the AREGEM model, with the increase in the classes of all three outputs of the model, the index of the density ratio (Dr) had an incremental trend, while for the REGEM model this trend was not regular. This indicates that as the erosion rate estimated by the AREGEM outputs increases, the density of the gully in those classes also increases, so there is a greater relation between the estimated erosion values with the AREGEM model and the sensitive classes to gully erosion. Also, the use of the Qs index showed that, the AREGEM model has a higher zoning quality and the ability of this model to determine the classes of sensitivity to gully erosion is more abundant.
    Discussion & Conclusions
    The use of the REGEM base model to determine the distribution of gully erosion is not accurate and this model needs to be modified and applied according to the conditions of the study area. For this purpose, the Adapted REGEM (AREGEM) model was presented and used. Also it has been observed that between three outputs obtained from the model, the use of gully volume is more effective in determining the exact distribution of gully erosion. Among the three studied regions, the AREGEM model in the Orta Dagh region has the highest efficiency for determining the distribution of gully erosion and then, respectively in the Mollahammad and Sarcham.
    Keywords: Model correction, GIS, Risk assessment