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هیدروژیومورفولوژی - سال سوم شماره 11 (تابستان 1396)
  • سال سوم شماره 11 (تابستان 1396)
  • تاریخ انتشار: 1396/08/07
  • تعداد عناوین: 8
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  • علمی - پژوهشی
  • احد حبیب زاده، شهرام روستایی، محمدرضا نیکجو، عطاءالله ندیری صفحات 1-20
    نهشته های کواترنر به عنوان منابع اصلی تامین کننده ی آب شیرین برای بشر محسوب شده و همیشه تحت تاثیر فعالیت های انسانی از قبیل کشاورزی، صنعت، غیره قرار داشته اند. مدل مفهومی نشان دهنده ی ابعاد و جهت و چگونگی گسترش نهشته ها است. در پژوهش حاضر مدل مفهومی- چینه ای نهشته های کواترنر دشت تسوج واقع در شمال دریاچه ی ارومیه تهیه شده است. مدل بر اساس 28 لوگ زمین شناسی چاه های مشاهده ای و 78 سونداژ ژئوالکتریکی در نرم افزار GMS تهیه شده است. بر اساس مقاطع ژئوالکتریک 5 کلاس چینه ای برای نهشته های کواترنر دشت تسوج تفکیک گردید، که شامل نهشته های آبرفتی Qal، نهشته های خشک Q3، نهشته های آبرفتی دانه متوسط (احتمالا حاوی آب) Q2، نهشته های آبرفتی ریزدانه (احتمالا حاوی آب) Q1و رس Qmf هستند. نتایج نشان می دهد، نهشته های کلاس Q2از نفوذپذیری متوسط برخوردار بوده دارای سفره ی آب زیرزمینی شیرین هستند، گسترش عمده ی این واحد در نواحی شرقی و جنوب شرقی می باشد. بیشترین فراوانی نهشته های Q3و Qal در ارتفاع 1320 متر بوده ولی ضخامت بالا در ارتفاع 1550 متر با ماکزیمم 190 متر است. این نهشته ها از یک گسترش افقی سرتاسر برخوردار بوده لکن گسترش عمودی آنها بیشتر در نواحی شمالی دشت به خصوص شمال شرقی است. از خصوصیات این واحد نفوذپذیری شدید، عدم وجود ناخالصی های رسی در آن می باشد.
    کلیدواژگان: سونداژ ژئوالکتریکی، شمال دریاچه ارومیه، نهشته ای کواترنر، مدل مفهومی
  • بختیار فیضی زاده صفحات 21-38
    تصاویر رقومی سنجش از دور از قابلیت بالایی در مدیریت منابع طبیعی برخوردارند که یکی از مهم ترین آنها آشکارسازی تغییرات پوشش و کاربری اراضی است. در حال حاضر با استفاده از تکنیک های پردازش تصویر و مقایسه چندزمانه داده های سنجش از دور می توان تغییرات کاربری اراضی را در طی دوره های زمانی مشخص نموده و با کسب آگاهی از نسبت تغییرات، تغییرات پوشش و کاربری اراضی آتی را پیش بینی نموده و نسبت به مدیریت آنها اقدام نمود. تحقیق حاضر نمونه ای از کاربرد داده های سنجش از دور در آشکارسازی تغییرات کاربری اراضی و مدلسازی اثرات آن در فرسایش است. در این تحقیق از تصاویر ماهواره ای TM ،ETM+ سال های 2015-2002-200-1989 استفاده شده و تغییرات کاربری اراضی در طی سه دوره ارزیابی شده است. پردازش تصاویر ماهواره ای در سه مرحله ی پیش پردازش، پردازش و پس پردازش انجام شد. در ادامه ی طبقه بندی تصاویر ماهواره ای انجام شده و نتایج برای استخراج نقشه های تغییرات و انجام اقدامات لازم به محیط GIS انتقال یافته و با استفاده از تحلیل های مکانی GIS تغییرات کاربری اراضی مورد مدلسازی قرار گرفت. نتایج پژوهش نشان می دهد که در سه دوره یاد شده ضمن افزایش اراضی باغی، تخریب و تبدیل اراضی مرتعی خوب به مراتع ضعیف و اراضی دیم در سطح قابل توجی صورت گرفته است که نقش مهمی در افزایش آسیب پذیری منطقه ی مورد مطالعه در مقابل فرسایش خاک داشته است.
    کلیدواژگان: مدل مفهومی سنجش از دور، کاربری اراضی، آشکارسازی تغییرات، سد علویان
  • غلامعباس فلاح قالهری، الهام کدخدا صفحات 39-57
    تعامل عمیق، پیچیده و مداوم بارش با سایر عناصر و عوامل اقلیمی، سبب تغییر و تنوع این عنصر در بعد زمان و مکان شده است. یکی از رویکردهای مطالعاتی جدید در اقلیم شناسی، توصیف تنوع مکانی بارش بر اساس آماره های مکانی است. هدف مطالعه ی حاضر آن است که با استفاده از روش های آمار مکانی، رفتار عمومی بارش دشت مشهد در امتداد مکان ارائه گردد. در این راستا از داده های بارش روزانه 34 ایستگاه همدید، اقلیم شناسی و باران سنجی طی دوره ی آماری (1392-1342 هجری خورشیدی) استفاده شده است. در ابتدا بر اساس روش های آمار کلاسیک، پراکندگی مکانی و زمانی بارش مورد مطالعه و سپس سه مشخصه ی میانگین مرکزی، فاصله استاندارد و توزیع جهت دار مورد بررسی قرار گرفتند. نتایج و بررسی ها، نشان داد که مرکز ثقل (گرانیگاه) بارش های سالانه نیم قرن اخیر دشت مشهد 83/3 کیلومتر جابجایی داشته است و توزیع جهت دار گویای بزرگی اثر شیب و جهت گیری آن برمیزان بارش است. همچنین فاصله استاندارد بارش دشت مشهد در دهه ی پنجم (1392-1382) نسبت به دهه ی اول (1352-1342) به میزان 57/1254 واحد تغییر نموده است. این عامل دلیلی بر ناپایداری روابط خطی عوامل مکانی در تولید بارش در دشت مشهد است. بر این اساس ناهمواری ها و ارتفاع بیشترین نقش را در الگوی مکانی بارش در دشت مشهد ایفا می کنند.
    کلیدواژگان: ساختار مکانی، مرکز ثقل بارش، مدلسازی مکانی، دشت مشهد
  • منوچهر فرج زاده اصل، علی اصغر هدایی، مریم ملا شاهی، ندا رجبی رستم آبادی صفحات 59-81
    فرسایش خاک یکی از مهم ترین مخاطرات طبیعی در هر کشور بشمار می آید که پیامدهایی چون کاهش حاصلخیزی، کاهش محصول و بیابان زایی به ویژه در مناطق خشک را به همراه دارد. هدف این تحقیق، مقایسه رسوب معلق در حوضه های آبخیز دریای خزر با اقلیم مرطوب و ایران مرکزی با اقلیم خشک کشور است. جهت انجام پژوهش از داده های باران سنجی، دبی سنجی و رسوب سنجی به همراه شیب، توپوگرافی، کاربری اراضی و سنگ شناسی استفاده شده است. برای تحلیل داده ها از روش های تحلیل آماری در نرم افزاSPSS استفاده شده است. نتایج حاصل از آزمون همبستگی نشان داد که رابطه و همبستگی قوی بین دو پارامتر بارش و رسوب وجود دارد. با توجه به نتایج حاصله از مدل رگرسیون چندمتغیره، بین متغیر های بارش، دبی و رسوب سالانه در حوضه های مورد مطالعه رابطه ی معنادار و مستقیم وجود داشته و مدل های نسبتا خوبی از روابط متغیر های بارش، دبی و رسوب معلق به دست آمد. بر اساس توزیع فضایی رسوب، در حوضه ی آبخیز ایران مرکزی بیشترین میزان رسوب در غرب حوضه در ایستگاه های قلعه ی شاهرخ و چمریز و کمترین میزان رسوب در شمال و جنوب حوضه مشاهده می شود. در حوضه ی آبخیز خزر، بیشترین میزان بار رسوبی در حوضه ی آبخیز قره سو و ران در ایستگاه قزاقلی سپس در حوضه ی سفیدرود در ایستگاه قره گونی مشاهده می گردد. کمترین میزان بار رسوبی نیز مربوط به حوضه ی آبخیز خزر در حوضه ی تالش و ایستگاه های جنوبی خزر است.
    کلیدواژگان: تحلیل زمانی - مکانی، رسوب، حوضه های آبخیز، فرسایش
  • وحید نورانی، صالح محسن زاده صفحات 83-103
    در این مقاله جهت برآورد میزان فرسایش و رسوب در زیرحوضه های حوضه ی آجی چای با توجه به نبود آمار کافی بار رسوب -که یکی از مسائل اساسی حوضه های کشور می باشد- از مدل تجربی پسیاک اصلاح شده استفاده شده است. از آنجائی که میزان رسوب حاصل از این مدل متوسط سالانه می باشد، لذا در مرحله ی اول جهت محاسبه رسوب برای هر سال چگونگی تغییرات فاکتورهای نه گانه مدل فوق نسبت به زمان مورد بررسی قرار داده شده است. فاکتورهای که ماهانه (مثل بارش و رواناب) و یا سالانه (مثل پوشش گیاهی و کاربری اراضی) دستخوش تغییر هستند، نقش مستقیمی در محاسبه رسوب برای هر سال دارند. در مرحله ی دوم برای ریزمقیاس کردن رسوب سالانه ی حاصل از مدلسازی مرحله ی اول و با توجه به اینکه نمی توان رسوب سالانه را به نسبت مساوی برای تمام ماه های سال توزیع کرد، با استفاده از روش آبشاری میزان رسوب سالانه به ماهانه ریزمقیاس گردید. نتایج حاصل نشان می دهد بین بار رسوب برآورد شده با مدل پسیاک اصلاح شده و ریزمقیاس شده با مدل آبشاری، با نتایج مشاهداتی و ثبت شده همبستگی بالایی وجود دارد. از طرفی همانطوری که انتظار می ر فت از بین عوامل نه گانه مدل، دو عامل فرسایش رودخانه ای و فرسایش سطحی به ترتیب 6/13 و 4/13 بیشترین امتیاز را دارند همچنین کاربری اراضی و پوشش گیاهی با امتیازهای 4/13 و 5/11 نقش خود را در تولید و یا مهار رسوب به خوبی نشان می دهند. میزان رسوبدهی سالانه در کل حوضه 92/1 تن در هکتار می باشد که زیرحوضه 22 با توجه به شیب تند و پوشش گیاهی کاملا ضعیف با 88/3 تن در هکتار در سال بیشترین و زیرحوضه 1-14 با 0/1 تن در هکتار در سال کمترین مقدار تولید رسوب را در حوضه به خود اختصاص دادند.
    کلیدواژگان: رسوب دهی، مدل پسیاک اصلاح شده، ریزمقیاس کردن آبشاری، آجی چای
  • ابوالقاسم امیراحمدی، مهناز ناعمی تبار، بهار گلکار استادی صفحات 105-125
    منطقه ی باجگیران به دلیل شرایط جغرافیایی از جمله مناطق مستعد برای وقوع زمین لغزش است. هدف اصلی از این پژوهش اولویت بندی عوامل موثر بر وقوع زمین لغزش و پهنه بندی خطر وقوع زمین لغزه در منطقه می باشد. بدین منظور بعد از انجام مطالعات کتابخانه ای و تهیه ی نقشه ی پراکندگی لغزش منطقه از ده پارامتر تاثیرگذار شامل ارتفاع، بارندگی، شیب، جهت شیب، شکل شیب، فاصله از آبراهه، فاصله از جاده، فاصله از گسل، پوشش زمین و لیتولوژی استفاده شد و ماتریس آنتروپی برای این عوامل محاسبه و در محیط Gis پهنه بندی خطر زمین لغزش در منطقه انجام شد. اولویت بندی عوامل موثر با استفاده از شاخص آنتروپی نشان داد که لایه های شیب، جهت شیب، لیتولوژی، فاصله از گسل و ارتفاع بیشترین نقش را در وقوع زمین لغزش در منطقه دارند. پهنه بندی حساسیت زمین لغزش با مدل مذکور نشان می دهد که42 % زمین لغزه ها در محدوده ی خطر زیاد، 31% در محدوده ی خطر متوسط، 27 % در محدوده ی خطر کم واقع شده است. نتایج نشان می دهد بیشترین درصد لغزش های رخ داده در منطقه، در پهنه خطر زیاد که توسط مدل آنتروپی مشخص شده بود، قرار گرفته است. این امر حاکی از آن است که مدل پیشنهادی مدلی مناسب برای تعیین خطر حساسیت وقوع زمین لغزش در منطقه است.
    کلیدواژگان: زمین لغزش، پهنه بندی، مدل آنتروپی، Gis، منطقه باجگیران
  • کیوان محمدزاده*، سیران بهمنی، محمدحسین فتحی صفحات 127-148
    این پژوهش با هدف شناسایی عوامل مؤثر در ایجاد پدیده ی زمین لغزش و تعیین مناطق دارای پتانسیل زمین لغزش درکرانه های جنوبی اهرچای از روستای نصیرآباد تا سد ستارخان حوضه ی جنوبی اهرچای با استفاده از روش رگرسیون لجستیک انجام شده است. به همین منظور از تصویر سنجده Resourcesat ، 2014 ماهواره IRS استفاده شد. فاکتورهای موثر وقوع زمین لغزش در محیط GIS آماده و سپس با لایه ی پراکنش زمین لغزش ها قطع داده شده و نقشه ی پهنه بندی خطر زمین لغزش در روش فوق تولید شد. نتایج نشان داد که روش رگرسیون لجستیک نتایج بهتری را در بررسی پتانسیل وقوع زمین لغزش در منطقه ی مورد مطالعه دارد. بر اساس نقشه ی تهیه شده بخش های غربی و جنوبی و محدوده ی شمال شرق منطقه ی مورد مطالعه از نظر وقوع زمین لغزش بیشترین پتانسیل وقوع زمین لغزش را دارد. با توجه به اطلاعات به دست آمده، 19/17درصد از اراضی محدوده ی مورد مطالعه با پتانسیل متوسط به بالا (34 درصد زمین لغزش ها) و 3 درصد از مساحت منطقه ی مورد مطالعه در محدوده با پتانسیل خیلی زیاد که بیش از 18 درصد زمین لغزش ها در آن به وقوع پیوسته است قرار دارد.
    کلیدواژگان: اهر چای، رگرسیون لجستیک، ماهواره IRS، زمین لغزش
  • حمیدرضا باباعلی، رضا دهقانی صفحات 149-168
    سیل یکی از بلایای طبیعی مهمی است که همه ساله باعث ایجاد خسارت های مالی و جانی فراوانی به جوامع مختلف می گردد. به همین دلیل محققان سعی نموده اند که تغییرات کمی این پدیده را حتی المقدور به طور دقیق مورد بررسی قرار دهند. در این پژوهش جهت تخمین دبی سیلابی ایستگاه کهمان الشتر واقع در استان لرستان از مدل شبکه ی عصبی موجک استفاده شد و نتایج آن با سایر روش های هوشمند از جمله شبکه ی عصبی مصنوعی مقایسه گردید. برای این منظور از پارامتر حداکثر بارش 24 ساعته در مقیاس زمانی روزانه با تاخیرهای مختلف در طی دوره ی آماری (1391-1380) به عنوان ورودی و دبی حداکثر روزانه به عنوان پارامتر خروجی مدل ها انتخاب گردید. معیارهای ضریب همبستگی، ریشه ی میانگین مربعات خطا و میانگین قدرمطلق خطا برای ارزیابی و عملکرد مدل ها مورد استفاده قرار گرفت. نتایج نشان داد هر دو مدل قابلیت خوبی در تخمین دبی سیلابی دارند، لیکن از لحاظ دقت، مدل شبکه ی عصبی موجک عملکرد بهتری نسبت به شبکه ی عصبی مصنوعی از خود نشان داده است.
    کلیدواژگان: دبی سیلابی، شبکه ی عصبی موجک، شبکه ی عصبی مصنوعی، الشتر
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  • Ahad Habibzadeh, Shahram Roostaei, Mohhamad Reza Nikjoo, Atta Allah Nadiri Pages 1-20
    Quaternary deposits as the major sources of fresh water for humans have often been influenced by anthropogenic activities such as agriculture, industry, and the like. The Tasuj plain is located in 45°18 to 45° 32 E and 38°20 to 38°24 N in north of Lake Urmia in East Azarbyjan province. This plain is one of the sub-basins of Lake Urmia which is surrounded by 12 major plains. The Tasuj basin is about 558 km2. This includes 302 km2 of the Tasuj plain and 256 km2 of Mount Mishu. The study area is surrounded by Lake Urmia (south), Mount Mishu (north), the Salmas Plain (west), and the Shabestar Plain (east). The highest elevation of the Tasuj basin is 3,133 m above the sea level (amsl) at the Peak of Mount Alamdar and the lowest elevation is 1,274 m near Lake Urmia. In the Tasuj basin, only a few seasonal rivers, originating from Mount Mishu, may appear. These seasonal rivers can flood the Tasuj plain in wet seasons. The seasonal rivers are the Amestejan, Angoshtejan, Almas, Chehregan, Tiran, Cheshmekonan, Sheikhvali, Sheikhmarjan, and Ghelmansara.
    MethodologyThe conceptual model represents the dimensions, directions, and circumstances of the distribution of the deposits. This research was based on the stratigraphy, the conceptual model of Quaternary deposits of the Tasuj plain, north of Lake Urmia. This simulation was carried out using the GMS software, based on 28 geological logs of observation wells and 78 geoelectrical sounding per geoelectrical sections. The application menus of this software including GIS, TINs, Solids, Boreholes, 3D Gride, and 3D scatter point were used in the research.
    Results and DiscussionQuaternary deposits of the Tasuj plain were divided into 5 classes of strata, including (Qal), (Q3), (Q2), (Q1), and (Qmf). The results showed that Q2 deposits had an average permeability and contained fresh water aquifer in the eastern and southeastern areas. Although Q3 and Qal were located in 1320 m above sea levels (asml), the highest thickness (i.e., 190 m) was shown in 1550 m asml. These deposits spread horizontally in the whole area, but its vertical expansion was more in the northern and, particularly, in the north eastern areas. Q3 and Qal classes were characterized by high permeability and lack of clay.
    ConclusionThe results of this study indicated that the conceptual-stratigraphic model has high efficiency in identifying the Quaternary deposits. The 3D-capable model can expand the point wise characteristics and thickness of Quaternary deposits in the study area using interpolation method. Quaternary deposits of the Tasuj plain were characterized as alluvial deposits (Qal), dry deposits (Q3), medium grain alluvial deposits (possibly water bearing) (Q2), fine grain alluvial deposits (possibly water bearing) (Q1), and clay (Qmf). In addition, the conceptual-spatial model of the quaternary deposits of the Tasuj aquifer showed that aquifer bedrock in the Galemaraghoosh-Shikhvaly was lower than other areas along the coast. There might be a buried deep valley from Almas to Tasuj, Galemaraghoosh.
    Keywords: Quaternary deposits, North of Lake Urmia, Conceptual model, Geoelectrical sounding
  • Bakhtiar Feizizadeh Pages 21-38
    IntroductionThe modification of the Earth’s terrestrial surface by human activities is commonly known as the land use/land cover change (LULCC) around the globe. Although the modification of the land by humans to obtain livelihoods and other essentials has been a common practice for thousands of years, the extent, intensity, and rate of LULCC are far greater now than they were in the past. These changes are driving forces for local, regional, and global level unprecedented changes in the ecosystems and environmental processes. The empirical studies conducted by researchers from diverse disciplines have found that changes in the land use/land cover is a key to many diverse applications such as agriculture, environment, ecology, forestry, geology, and hydrology.
    Satellite Remote Sensing and GIS are the most common methods for the quantification, mapping, and detection of the patterns of the LULCC, because of their accurate geo-referencing procedures, digital formats suitable for computer processing, and repetitive data acquisition. Technically speaking, the remote sensing based digital satellite images have a high capability for natural resource's management operations. Land use/land cover change detection is considered as one of the most important applications in the domain of the remote sensing satellite images. Related to this applicability, it will be possible to apply multi-temporal satellite images for the detection of the land use change. Based on the results obtained from the change detection operation and modeling of the further land use changes, one will be capable to makes better decision for natural resource's management. Based on this statement, the main objective of this research is to represent the applicability of the satellite images for the detection of the land use changes, particularly on the upper areas of the Allavian dam of the Sofi-chai basin.
    Dataset and methodsThe study area was the upper area of the Allavian Dam in Maragheh. The research was carried out based on the digital interpretations of the Landsat images (ETM and TM) of the years 1989, 2000, 2002, and 2015. Based on these images, the land use changes of this region were separately detected for 3 periods. It should be noted that the widely practiced operations such as image preprocessing, classification, and post processing with those related techniques were considered in this study. Indeed, it is widely known that preprocessing before the the change detection phenomenon is very important in order to establish a more direct relationship between the acquired data and the biophysical phenomena. Accordingly, atmospheric and geometric correction were applied as the first step on satellite images. In doing so, the LSLC classes were determined based on the spatial resolution of the satellite images. Then, image enhancement methods were applied to detect each LULC class on the satellite image. Next, GPS based training data was collected in the field operation and integrated with the satellite images. In addition, the supervised maximum likelihood was applied to derive LULC map for each year. The validation step was also part of this section for the accuracy assessment based on kappa coefficient and error matrix.
    Results and ConclusionAfter developing LULC maps, the results were transformed into GIS environment for the following steps and GIS analysis. The results indicated a significant changes in LULC of the study area. They also indicated that orchards cover had increased throughout the study periods but rich range lands widely converted into poor range lands because of losing the significant canopy of the native plants. Increasing the trend of the orchards area may be in relation with the population growth and this factor can be affected by ( have an effect on) range land degrading. The water supply out of Allavian dam might be another reason for increasing the orchard’s area. The results also acknowledged the capability of the remote sensing for the LULC and change detection analysis. The results of this research are of great importance for decision making authorities in governmental departments such as the ministry of agriculture and natural resources for the purposes of planning and decision making.
    Keywords: Remote sensing, Land use, Change Detection, Allavian dam
  • Gholamabbas Fallah Ghalhari, Elham Kadkhoda Pages 39-57
    IntroductionThe meaningful, complex, and ongoing connection between the rainfall and other climatic elements causes diversity in space and time. A new approach in climatology is to describe the spatial variability of the rainfall based on the spatial statistics. Unlike the classical statistics, the spatial statistics shows the statistical data on a map. Therefore, the attention and emphasis on the spatial differences and the identification of the specific and unique points or homogeneous regions will be provided. Modeling of the rainfall behavior is one of the main foundations in any climate research. In this regard, two major efforts are of interest to climatologists. One of them is the precipitation zoning. The other one is the analysis of the spatial temporal variations of the precipitation. This analysis is important for weather forecasting and a wide range of decision makers, including hydrologists, farmers, and industrialists.
    MethodologyUsing statistical methods, the present study aimed to introduce the fundamentals of the spatial data and the general precipitation behavior of Mashhad’s plain along the space. In this regard, the study used the daily precipitation data of 34 synoptic stations, climatology, and rain gauge during the survey period, 1963-2013. The study initially analyzed the spatial and temporal distributions of the precipitation based on the classical statistical methods. Then, it focused on the central average, standard distance, and directional distribution. In this research, the universal Moran method was used to calculate the spatial autocorrelation data. In addition, the central mean method was used to calculate the basin rainfall gradient. Finally, directional distribution was used to calculate the trend and direction of the precipitation distribution.
    DiscussionThe results showed that the gravity center, the centroid, of the annual rainfall during the last half-century sustains a displacement of 3.83 km where the distribution arrow demonstrates the magnitude of the tilt and orientation on the amount of the precipitation.
    Also, the standard distance of the precipitation in Mashhad, in the fifth decade (2003-2013) compared to the first decade (1963-1973), changed to 1254.57. This change can be one of the reasons of the instability of the linear relationships of the spatial factors and the rainfall in the plains of Mashhad.
    ConclusionThe results showed that the center of the rainfall gravity of Mashhad plain was displaced over 3.3 km during the 50-year period. In addition, there was a change of 0.269 degrees in the direction of the distribution of the precipitation in the fifth period compared to the first period. Since, this shift was negative to areas with spatial dependence, it indicated a general drop in the rainfall in the Mashhad plain. The results also showed that the roughness and height might be two important factors affecting the spatial patterns of the rainfall in Mashhad Plain.
    Keywords: Spatial structure, Center of precipitation concentration, Spatial modelling, Mashhad plain
  • Manuchehr Farajzadeh, Ali Asghar Hodaei, Maryam Mollashahi, Neda Rajabi Rostam Abadi Pages 59-81
    IntroductionSoil erosion as one of the most important natural hazards of each country usually results in reduced fertility, crop reduction, and desertification, particularly in arid and semi-arid areas. Two-thirds of Iran is located in the arid and semi-arid areas and one of its climatic features is flood. Consequently, soil erosion is one of its environmental problems. Nowadays, since soil is important for the life of products and is directly related to the balance of the ecosystem and the water cycle, its protection and fertility are two important factors that shouldnt be ignored. The purpose of this study was to compare the suspended sediment in two drainage basins of the Caspian Sea, with a humid climate, and central Iran, with an arid climate.
    MethodologyFor research surveys, pluviometersdata, sediment and discharge assessment, slope, topography with land use, and lithology were used. Maps were obtained from survey organization, geological survey and mineral exploration, and Natural Rescues of Iran. To this end, land use maps, based on the land use type, were classified into six categories including urban area, forests land, pasture land, agricultural land, swamp land, and arid land, without vegetation cover. In addition, the geological maps, based on the stone resistance and amount of sediment production, were classified into ten categories including the hardest stones, very hard stones, so hard stones, enough hard stones, mediocre stones, enough soft stones, partly soft stones, powder stones, loose stones, and so loose stones. Finally, the data was analyzed using the SPSS software.
    Results and DiscussionThe results indicated a high and significant correlation between the rainfall and sediment. There was also a direct and significant correlation between the rainfalls, discharge, and yearly sediment of the field. In addition, a fairly good model was achieved from the rainfall, discharge, and sediments variables.
    Considering the distribution of the sediment in central Iran, the highest sediment volume was seen in the west of the basin at Shahrokh, Chamriz station. The lowest sediment volume, in contrast, was seen in its north and south. In the Caspian basin, the highest sediment volume was seen in Gharasou and Ran basin at Ghezaghli station. The second highest sediment volume was seen in Gharaghoni station at Sefidrood basin. The lowest sediment volume was seen in Talesh basin and in the southern stations of the Caspian Sea.
    Keywords: Spatiotemporal analysis, Sediment, Drainage basin, Erosion
  • Vahid Nourani, Saleh Mohsenzadeh Pages 83-103
    IntroductionIn this study, the MPSIAC model was used to consider the effects of the dominant factors in sediment production in order to estimate the rate of the erosion and sediment load in sub-basins of the Aji Chay River. Since the sediment rate of this model is the annual average, the variations of the nine fold factors of this model was examined in order to calculate the sediment for each year. Then, the annual and monthly sediment rates were quantified using a cascading method.
    MethodologyIn order to estimate the sediment production and the relationship between the degree of the sediment yield and the amount of production, equation (1) which was based on determining the scores of the factors considered in the PSIAC model and obtaining their total scores in each hydrological unit was used
    38.77e0.0353R = Equation(1): QS
    Qs=sediment yield (m3/km2/year) R= sedimentation rate
    The PSIAC model specifies some variations for each factor, which is somewhat selective and requires an expert judgment. Johnson and Gombard (1982) have made the nine-fold factors for this method as numerical equations.
    The estimated sediment rate using MPSIAC method is based on the annual average. Therefore, the variations of the factors of MPSIAC model were examined and compared to estimate the sediment for each year. Due to the fact that sediment is not the same throughout the year, it was not possible to equally consider annual sediment for all months of the year. Thus, for the purpose of the quantification of the monthly sediment, the cascading micro-scale was used through verifying the existing data and filling the deficiencies of the data. In the process of disintegration, the sediment, which was the annual sediment in the initial intervals, was sequentially broken into smaller surfaces with specific coefficients and calibrated.
    Equation(2): SNij = Sij
    Equation(3): SijNky = Sk
    Results and discussionIn this paper, the annual sediment rate was estimated using remote sensing, GIS techniques, and the application of the experimental model of MPSIAC in hydrological units and its zoning in the area. Then, by inserting the DEM into the GIS environment and by modifying the ups and downs, the flow direction, the network of waterways, and the primary and secondary sub-basins were produced. As a result, the production rate of the sediment and the scores of the each of the factors in the sub-basins were calculated using the equations presented in the MPSIAC model. The results showed that there was a high correlation between the estimated sediment load with the MPSIAC model and the observed and recorded results.
    The results of the MPSIAC model for the estimated sediment rate were based on the annual average, so the existing data and nine-fold factors of MPSIAC model, which were time-consuming, were used for the monthly sedimentation. To measure the amount of the precipitation and runoff for different months of each statistical year and to study the amount and manner of changes in vegetation and land use in the studied area, the annual precipitation and annual erosion were calculated for each statistical year. Then, sub-scaling was done through the calculation of the sub-scale coefficients of annual to monthly sediment.
    ConclusionThe estimated sediment rate using MPSIAC model and observational and measured data of the sediment in the hydrometric stations of the Aji Chay basin has high accuracy and acceptable correlation. In addition, by comparing and verifying the available and measured data in the hydrometric stations of the AjiChay basin at low scales with extractive data of this method, it turns out that the sediment values can be estimated at low scales by specifying the sub-scale coefficients and calculating the sediment for each year.
    Keywords: Sedimentation, MPSIAC model, GIS, Cascade sub-scaling, Aji Chay River
  • Abolghasem Amirahmadi, Mahnaz Naemitabar, Bahar Gholkarostadi Pages 105-125
    IntroductionLandslide is one of the natural phenomena causing many financial losses and casualties in Iran every year (Kamranzadeh, 2014, p.101). This phenomenon occurs when the force of the materials’ weight is higher than the soil's sheer force (Memarian et al., 2006, p. 105). The Shannon entropy is a function of probability distribution and a standard for measuring uncertainty in the information content of a parameter. In addition, by considering the frequency of the occurrence of the subgroups of that parameter, it shows the heterogeneity level. As a result, it calculates the effect of each parameter on the results of the system (Hosseinpour Mil Arghadan et al., 2014). The purposes of the present study are the selection of criteria and standards, preparation of the digital factors layers, preparation of the landslide hazard zonation map, diagnosis of the high risk points via the Shannon entropy, presentation of the strategies appropriate for preventing possible risks, and solutions to reduce damages in the study area. Bajgiran is the central district of Bajgiran Country and a part Doulatkhaneh Rural District of Ghouchan Township. According to climatological divisions, Bajgiran has a moderate mountainous climate. Moreover, geologically and structurally, it is part of Kopeh Dagh Sedimentary Basin. In terms of stratigraphy, outcrops from the Jurassic rock units to the present era can be observed in the study area.
    Materials and methodsIn the present study, first of all, the factors affecting the occurrence of the landslides including height, precipitation, slope, slope direction, slope shape, distance from the waterway, distance from the road, distance from the fault, land cover, and lithology were identified, and the mentioned maps were digitized in GIS. To this end, using the topographic map on a scale of 1:50000, the Digital Elevation Model Map (DEM), factors of slope degree, slope direction, slope shape, height level, distance from the waterway, and distance from the road were prepared. In addition, using the land-use map on a scale of 1:25000, information layers of land use were extracted. Then, to draw the lithological map, the distance from the fault of the geological map on a scale of 1:50000 was used. Finally, to draw the precipitation map, the statistics of the rain gauge stations of five Daroungar, Mohammad Taghi Beig, Aman Gholi, Kikan, Hey Hey Ghouchan, and Bahman Jan Stations were used.
    Results and DiscussionAfter converting the criteria into integers and the formation of the initial matrix, the values of Pij and K were respectively calculated via equations 1 and 2. To calculate Ej for each criterion, equation 2 was used. The results are indicated in table 2. In this equation, the value of E is a function of n. For each n, where Pi is equal, the value of E becomes maximum which is statistically calculated via probability distribution of Pi. Then, the uncertainty or the degree of the deviation of each criterion (dj), obtained from the fraction of the value of Ej, from 1 were calculated per each indices effective on landslides of the study area (table 2). After that, using equation 5, the weight of each parameter, used in the entropy matrix of landslides (Wj), including height (0.02113), precipitation (0.031142), shape of slope (0.0116110), slope (0.011342), distance from the waterway (0.045161), distance from the road (0.113401), distance from the fault (0.099871), land use (0.997110), and lithology (0.095148) were obtained. After that, the regional model of the landslide hazard degree in the area was obtained via equation 6. Hi is the landslide hazard degree in the area (equation 7).
    ConclusionThe aim of the present study was to prioritize the factors affecting the occurrences of landslides and to zone their sensitivity in Bajgiran Region via the Shannon entropy. The results of the study showed that the most important factors affecting landslides in the study area were slope layers, slope direction, lithology, distance from the fault, and height. After weighing the parameters and formatting the entropy matrix, the zonation mappings were conducted. To this end, the information layers were prepared in Arc GIS and converted into Raster formats. With regard to the zoning maps obtained from the entropy model, 15 landslides have occurred in the area, of which 9 landslides have occurred in a high risk zone (42%), 4 landslides in a moderate risk zone (31%), and 2 landslides in a low risk zone (27%). Regarding the slope factor, it can be said that most of the landslides have occurred in slopes with 60% because of the lack of the soil-formation process prone to the slippery movements. In case of the slope direction, most of the landslides have occurred in northern domains and in heights with 1600 m high. This result is compatible with the faults and calcareous, marl, and Pyura Chilensis organizations of the area. The results of the present study also show that the entropy model has appropriate performance in identifying risk areas and their zonation. In addition, the results can be used in decision making and management of the land use and urban planning.
    Keywords: Landslide, Zoning, The entropy model, GIS, Bajgiran Region
  • Keyvan Mohammadzadeh, Seiran Bahmani, Mohammad Hossein Fathi Pages 127-148
    IntroductionIranian territory has the main prerequisites for the occurrence of a wide range of landslides due to its mountainous topography, tectonic activities, high seismicity, and different geological and climatic conditions. Therefore, reducing the effects of natural disasters, particularly landslides, is one of the key challenges for land-use planners and policymakers in this field. In this study, the southern side of the Ahar Chai basin from Nasirabad Village to Sattarkhan Dam is evaluated for the probability of the landslide occurrence. This region is highly susceptible to landslide occurrence because of the extensive manipulation and its natural conditions. Indeed, the occurrence of the large shallow landslides in this region is an indication of this susceptibility. In this study, Linear Regression Model has been used to prepare the landslide zonation.
    MethodologyThe study area was the southern sides of the Ahar Chai River, from Nasirabad village in Varzaghan to the Sattarkhan Dam, with an area of 128 km2. In order to study the potential of the landslide occurrence in this region, nine main factors including slope, slope direction, lithology, land use, precipitation, distance from the fault, distance from the river, distance from the road, and vegetation were identified. The model which was used in this study was Logistic Regression. This model is one of the predictive statistical methods for dependent variables in which zero and one respectively indicate the occurrence and non-occurrence of landslides. In addition, instead of being linear, the regression of the variables is S-shaped or logistic curve and the estimations are in the range of zero-one. Indeed, values close to zero indicate the low probability of the occurrence and values close to one indicate the high probability of the occurrence.
    DiscussionIn Logistic Regression model, after entering the data into the Logistic Regression model and using the effective parameters in Idrisi software, the coefficients of the model were extracted. A value of 965, which represents a very high correlation between the independent and dependent variables, was obtained for the ROC index. After determining the validity of the Logistic Regression model, using the above indicators, landslide sensitivity zonation map was prepared. In the present model, the land use factor with the highest coefficient was the best predictive variable in determining the probability of the landslide occurrence in this region. In addition, the SPI index and the distance from the fault had respectively the second and third highest coefficients. After zoning the landslide, the slip area was calculated for each class and its results showed that zones with highest risk had the lowest area percentage and these areas were located in the western slopes.
    ConclusionThe results showed that while land use, lithology factors, and SPI index with positive coefficients had higher correlation, the other factors with negative coefficients had lower correlation. Based on the map, the western, southern, and the north-eastern parts of the region have the highest potential for landslide occurrence. Furthermore, the high value of the ROC index and its proximity to number one indicates that landslides in the study area have a strong correlation with the probability values derived from the Logistic Regression Model. In addition, the assessment of the SCAI scaling hazard zonation map shows that there is a high correlation between the hazard map with the existing slip points and the field observations of the area. It can be said that, in addition to the natural factors, some human factors including unstructured road construction may play an important role in the occurrence of the landslides. It is also necessary to avoid making changes in the ecosystems and land use. Finally, any policies to construct structures should be commensurate with the geomorphologic and geological conditions.
    Keywords: Ahar hay, logistic regression, IRS Satellite, Land slide
  • Hamidreza Babaali, Reza Dehghani Pages 149-168
    IntroductionFlood is one of the hazardous natural disasters that causes loss of life and financial problems every year. Therefore, scientists have tried to assess the quantitative variability of this phenomenon as much as possible. In this study, the recorded data in Kahman Aleshtar watershed area, which is located in Lorestan province, was used to investigate the precision of the different flood peak discharge prediction models. In addition, the wavelet and artificial neural network models were selected for the modeling of the flood peak discharge and the results were compared to examine the accuracy of the studied models.
    MethodlogyDaily flood peak discharges of the basin in Kahman station, which were applied for the calibration and validation of the models, were selected and observed. For this purpose, maximum daily precipitation rate, at a daily scale and between the years 2001-2012, and flood peak discharge were respectively used as the input and output parameters. The wavelet-based neural network which was based on the combination of the wavelet theory and neural networks were created. Indeed, it has the benefits and features of the neural networks and the charm, flexibility, strong mathematical foundations, and the analysis of the multi-scale wavelets. The combination of the wavelet theory with the neural network concepts for the creation of the wavelet neural network and feed-forward neural shock can be a good alternative for estimating the approximate nonlinear functions. Feed-forward neural network with sigmoid activation function is in the hidden layer. While at the nerve shocked wavelet, the wavelet functions as the activation of the hidden layer feed-forward networks are considered, in both these networks and scale wavelet, the transformation parameters are optimized with their weight. Artificial neural networks inspired by the brain's information processing systems, designed and emerged into. To help the learning process and with the use of the processors called neurons, there was an attempt to understand the inherent relationships among the data mapping, the input space, and the optimal space. The hidden layer or layers, the information received from the input layer, and the output layer are the processing and disposal.
    Based on the artificial neural network structure, its major features are high processing speed and the ability to learn the pattern, the ability to extend the model after learning, the flexibility against unwanted errors, and no disruption to the error on the part of the connection due to the weight distribution network.
    The first practical application of the synthetic networks with the introduction of the multilayer perceptron networks was consultation. For training this network, back propagation algorithm is used. The basis of this algorithm is based on the error correction of the learning rule. That consists of two main routes. By adjusting the parameters in the MLP model, error signal and input signal occurs. Determining the number of the layers and neurons is the most important issue in simulation with the artificial neural network. The criteria of the correlation coefficient, the root mean square error, and the mean absolute error were used to evaluate and compare the performance of the models.
    ResultsThe results showed that both models in a structure, consisting of 1 to 4 delay, gives better results than any other structure. In addition, based on the results of the evaluation criterion, the model which was used to wavelet neural network model, was the most accurate (R=0.921), and the lowest root mean square error RMSE=0.005m3/s and the lowest average absolute error MAE=0.003m3/s the validation phase is capable.
    Conclusions Wavelet neural network model outperformed the artificial neural network. Consequently, it can be effective in forecasting the daily flood peak discharge. It can also facilitate the development and the implementation of the surface water management strategies. Finally, predicting the piver flow process is a major step in water engineering studies and water resource's management.
    Keywords: Flood peak discharge, Wavelet neural networks, Artificial neural network, Aleshtar