فهرست مطالب

فصلنامه پژوهش های فرسایش محیطی
سال ششم شماره 4 (پیاپی 24، زمستان 1395)

  • تاریخ انتشار: 1395/11/11
  • تعداد عناوین: 6
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  • مهدی حیات زاده *، محمدرضا اختصاصی، حسین ملکی نژاد، علی فتح زاده، حمیدرضا عظیم زاده صفحات 1-22
    از آنجاکه تغییر کاربری اراضی بر بسیاری از فرآیندهای طبیعی نظیر فرسایش خاک و تولید رسوب، سیلاب و تخریب خصوصیات فیزیکی شمیایی خاک اثر می گذارد؛ بنابراین نیاز است تا جنبه های مختلف تغییر کاربری اراضی و روند گذشته و آینده آن در مطالعات و تصمیم گیری های کلان کشور قابل توجه قرار گیرد. طبقه بندی کاربری اراضی با استفاده از تصاویر ماهواره ای، یکی از مهمترین کاربردهای سنجش از دور است و الگوریتم های زیادی برای این منظور توسعه یافته است. این مطالعه ابتدا کارایی الگوریتم های طبقه بندی ماشین بردار پشتیبان (SVM)، شبکه عصبی پرسپترون چندلایه (MLP) و روش شبکه نگاشت خود سازمانده (SOM) را در طبقه بندی تصاویر ماهواره ای لندست بررسی می کند. سپس کارایی این الگوریتم ها در طبقه بندی با یکدیگر مقایسه می شود. نتایج نشان می دهد که الگوریتم SVM با دقت کل 95/0 و ضریب کاپای 93/0 نسبت به دو روش دیگر برتری دارد. در این تحقیق برای پیش بینی وضعیت کاربری اراضی آینده (2025)، از مدل LCM بر پایه ی تحلیل زنجیره ی مارکوف- سلول های خودکار استفاده شد؛ بدین منظور از نقشه های کاربری اراضی 2002- 1993، برای واسنجی و از نقشه ی 2002- 2014، برای ارزیابی مدل استفاده شد. همچنین برای ایجاد نقشه ی تناسب اراضی، از متغیرهای مکانی فاصله از جاده، فاصله از مناطق مسکونی، فاصله از آبراهه ی اصلی، ارتفاع و شیب به عنوان عوامل موثر بر تغییرات کاربری استفاده شد. نتایج حاصل از ارزیابی مدل CA-Markov برای دوره ی 2002- 2014، با میزان توافق برابر 94/0 و شاخص کاپا (K location) که توانایی مدل را در پیش بینی موقعیت سلول ها نشان می دهد برابر 92/0 است و از کارایی مناسب این رویکرد در شبیه سازی نقشه ی کاربری اراضی آینده ی حوضه حکایت می کند. نتایج بررسی تغییرات کاربری تا سال 2025، بیانگر تغییر کاربری باغ های منطقه به میزان 60 درصد و تبدیل شدن آن به کاربری شهری است؛ به عبارت دیگر، پیش بینی می شود که تا سال 2025 ، حدود 420 هکتار از باغ ها در منطقه ی مورد مطالعه از بین خواهد رفت. از این رو باتوجه به شرایط خاص این حوضه و قرار گرفتن در محدوده ی اقلیمی خشک و فراخشک، لزوم تمرکز فعالیت های مدیریت و اصلاح اراضی بر این نوع کاربری ها افزایش می یابد.
    کلیدواژگان: الگوریتم ماشین بردار، زنجیره مارکوف، سلول های خودکار، تغییر کاربری اراضی
  • ابوالحسن فتح آبادی *، اکرم قندی، حامد روحانی، سید مرتضی سیدیان صفحات 23-46
    به منظور مدیریت و کاهش خطرات ناشی از وقوع زمین لغزش نیاز است تا مناطق مختلف از نظر این خطر، پهنه بندی شود؛ بدین منظور در این تحقیق با استفاده از روش های شبکه عصبی مصنوعی، رگرسیون لجستیک، نسبت فراوانی، شاخص آماری و دمپستر شفر، به پهنه بندی خطر زمین لغزش در حوضه ی چهل چای استان گلستان پرداخته شد. پس از تهیه ی نقشه ی پراکنش زمین لغزش ها، نقشه ی فاکتورهای مستقل موثر در وقوع زمین لغزش شامل شیب، جهت شیب، فاصله از جاده، فاصله از رودخانه، کاربری اراضی، انحنای کل، انحنای دشت، انحنای پروفیل، ارتفاع، شاخص رطوبت توپوگرافیکی، زمین شناسی و فاصله از گسل تهیه شد. برای آموزش و آزمون مدل های مختلف، 91 زمین لغزش مشاهداتی به دو گروه تقسیم بندی شد: آموزش و تست. آموزش، شامل 80 درصد کل زمین لغزش ها (73 زمین لغزش) و تست، شامل 20 درصد زمین لغزش ها (18 زمین لغزش) است. نتایج نشان داد، مقدار مساحت حاصل از زیر منحنی ROC داده های تست برای روش های شبکه عصبی (86/0)، رگرسیون لجستیک (77/0)، دمپستر شفر (77/0)، نسبت فراوانی (72/0) و شاخص آماری (71/0) است. به طور کلی هم از نظر مساحت زیر منحنی ROC و هم از نظر تعداد زمین لغزش های مشاهداتی در کلاس های مختلف حساسیت، بهترین عملکرد مربوط به روش های چند متغیره ی شبکه عصبی مصنوعی و رگرسیون لجستیک بود و در بین روش های دو متغیره نیز روش دمپستر شفر، عملکرد بهتری نسبت به سایر روش های دو متغیره داشت.
    کلیدواژگان: زمین لغزش، چهل چای، پهنه بندی، چند متغیره، دو متغیره، منحنی ROC
  • مهندس مریم ادهمی، مهندس محسن ذبیحی، مهندس سعید زارع نقده، رئوف مصطفی زاده * صفحات 47-67
    تحلیل منطقه ای توزیع رسوب معلق از موارد اساسی در اجرای پروژه های حفاظت آب و خاک است که امکان برآورد رسوب را در آبخیزهای فاقد آمار فراهم می سازد. برای این منظور تعیین مناطق همگن برای ارائه ی نتایج نزدیک به واقعیت، نیازمند خوشه بندی صحیح آبخیزها در واحدهای همگن است. در همین راستا، هدف پژوهش حاضر مقایسه ی تکنیک های مختلف روش سلسله مراتبی اعم از Single linkage، Ward و β-Flexible و انتخاب بهترین تکنیک برای تعیین مناطق همگن در حوضه ی رودخانه های قره سو و گرگانرود استان گلستان است. در هر کدام از روش های خوشه بندی آبخیزها، به عنوان پیش فرض 2، 3، 4 و 5 خوشه در نظر گرفته و با شاخص های اعتبارسنجی Pseudo-F و Dunn بررسی شد. نتایج نشان داد که از بین تکنیک های مختلف مورد استفاده و براساس شاخص های سنجش کیفیت خوشه بندی، روش Single linkage عملکرد بهتری را ارائه داد. با توجه به اینکه مقادیر رسوب معلق از داده های اندازه گیری شده ی دبی و منحنی های سنجه رسوب به دست آمده ، خوشه بندی دارای صحت بیشتری است؛ لذا روش مناسب خوشه بندی می تواند در اتخاذ روش های صحیح مدیریت در حوضه های آبخیز، به خصوص در مسائل رسوب گذاری و فرسایش موثر باشد.
    کلیدواژگان: تحلیل منطقه ای، خوشه بندی، رسوب معلق، مناطق همگن، گرگانرود و قره سو
  • خدیجه بهلکه، مهدی عابدی *، قاسمعلی دیانتی تیلکی صفحات 68-80
    مراتع کوهستانی فرسایش خاک و تخریب بالایی دارند و گیاهان بالشتکی نقش مهمی در حفاظت خاک این مناطق ایفاء می کنند. در مورد چگونگی تاثیر این گیاهان در حفاظت از خاک، اطلاعات کمی وجود دارد. این مطالعه در پی بررسی تاثیر جهت های مختلف جغرافیایی بر بهبود خردزیستگاهی گونه ی اسپرس است. برای این منظور، نوسان دمای روزانه در دو جهت شمالی و جنوبی در زیر و بیرون بوته محاسبه شد. همچنین رطوبت خاک زیر و بیرون بوته در جهت های مختلف، با استفاده از دستگاه TDR در دو بازه زمانی اندازه گیری شد. برای تعیین مهم ترین عامل تاثیرگذار بر بهبود خردزیستگاه، از بین عوامل فصل و جهت جغرافیایی، از مدل خطی ترکیبی عمومی و برای مقایسه ی اثر جهت و فصل بر رطوبت زیر و بیرون بوته، از آزمون تی غیرجفتی استفاده شد. به این ترتیب، نوسان دمای روزانه در دامنه ی جنوبی و شمالی در بیرون بوته (1/7 و 9/3 درجه ی سانتی گراد) بیش از زیر بوته (2/2، 9/1 درجه ی سانتی گراد) است. براساس نتایج مدل خطی ترکیبی عمومی، در بیرون بوته عامل فصل و جهت (05/0 > P ، 0/6F = و 01/0 > P ، 3/16F = ) بیشترین تاثیر معنی دار را بر رطوبت خاک داشت؛ به طوری که در انتهای بهار، رطوبت در دامنه ی شمالی افزایش و در دامنه ی جنوبی کاهش معنی دار داشت. از طرف دیگر در زیر بوته، فصل مهم ترین عامل است (01/0 > P ، 1/31F = ) و رطوبت در انتهای فصل بهار کاهش معنی دار داشت. نتایج این تحقیق میزان تغییرات رطوبت را به عنوان یکی از مهم ترین عوامل موثر در فرسایش خاک، بررسی کرد و اهمیت بوته های اسپرس را در حفظ این رطوبت نشان داد. همچنین میزان تاثیر جهت های مختلف جغرافیایی و فصل رویش گیاهان را در تغییرات رطوبت مقایسه کرد که این امر خود می تواند در تحلیل چگونگی نقش بوته ها در حفظ رطوبت خاک مناطق کوهستانی استفاده شود.
    کلیدواژگان: اسپرس، بهبود خردزیستگاه، دما، رطوبت، گونه پرستاربالشتکی
  • احمد نوحه گر، محمد کاظمی *، سید جواد احمدی، حمید غلامی، رسول مهدوی صفحات 81-103
    در راستای کنترل فرسایش، رسوب و حفاظت خاک، شناخت منابع تولید رسوب و تعیین سهم نسبی هر یک از منابع برای تعیین فعالیت های مدیریتی مناسب نقش به سزایی دارد. کارآیی روش منشایابی با ردیاب ها یا انگشت نگاری به عنوان روشی موفق و موثر، برای تعیین منابع رسوبات به اثبات رسیده است. هدف از تحقیق حاضر، تعیین سهم سازندها و کاربری های اراضی مختلف بر فرسایش و رسوب است که با استفاده از مدل ترکیبی هوگس صورت گرفت؛ بدین منظور 43 نمونه ی سطحی جمع آوری شد که از میان آنها ذرات کمتر از 63 میکرون به عنوان هدف آزمایش قرار گرفتند. به منظور اندازه گیری عناصر ژئوشیمیایی از دستگاه ICP-AES و برای اندازه گیری ایزوتوپ های استرانسیوم (87Sr و 86Sr) و نئودیمیوم (143Nd و 144Nd) از دستگاه ICP-MS استفاده شد. برای یافتن بهترین پاسخ در حل این مدل، از سه روش الگوریتم ژنتیک، بهینه سازی لوکال و شبیه سازی مونت کارلو استفاده شد و برای تعیین ضریب کارآیی مناسب مدل، از شاخص GOF. عناصر کربن، مس، سیلیکون و تیتانیوم به عنوان ترکیب بهینه ی ردیاب ها برای تفکیک سهم واحدهای کاربری اراضی و عناصر استرانسیوم، تیتانیوم، مس و نسبت ایزوتوپی نئودیمیوم 144/143 به عنوان بهترین ترکیب بهینه برای تفکیک سهم سازندهای زمین شناسی انتخاب شدند. نتایج نشان داد روش بهینه سازی لوکال، با شاخص GOF 94/99 درصد در واحد سازندها و روش بهینه سازی الگوریتم ژنتیک، با شاخص GOF 84/97 درصد در واحد کاربری اراضی بیشترین مقادیردقت را دارند. بیشترین سهم منابع تولید رسوب در واحد سازندها به ترتیب مربوط به سازندهای آسماری و کواترنر معادل 51/84 و 37/5 درصد و بیشترین سهم منابع تولید رسوب در واحد کاربری های اراضی مربوط به کاربری های مراتع و جنگل ها، به ترتیب معادل 04/63 و 81/31 درصد است. سازندهای پابده گورپی و بختیاری به ترتیب با امتیاز 24/0 و 27/0 و اراضی زراعی و جنگلی به ترتیب با امتیاز 022/0 و 55/0 نسبت به بقیه ی سازندها و کاربری های اراضی کمترین اهمیت نسبی را به خود اختصاص دادند و برای مدیریت اراضی در اولویت قرار ندارند.
    کلیدواژگان: مدل های ترکیبی، ردیاب، الگوریتم ژنتیک، اهمیت نسبی، تنگ بستانک
  • محمد عباسی *، علی نجفی نژاد، واحد بردی شیخ، مجید عظیم محسنی صفحات 104-124
    خصوصیات فیزیکی و شیمیایی خاک در شیب و کاربری های مختلف اراضی تغییر می کند. ارزیابی و تجزیه و تحلیل این تغییرات در زمینه ی ویژگی های خاک، رواناب و رسوب به برنامه ریزی و مدیریت صحیح منجر می شود. تحقیق حاضر، در حوزه ی آبخیز کچیک شهرستان مراوه تپه در استان گلستان اجرا شده است. پنج کاربری اراضی شامل جنگل طبیعی، جنگل دست کاشت، مرتع و زراعت هندوانه و گندم درو شده است که در چهار طبقه شیب 12- 3، 18-12، 25-18، 40-25 درصد و پلات های 4 مترمربعی انتخاب شد. رواناب و رسوب در هر مرحله ی جمع آوری و مقدار رسوب، حجم رواناب، مواد مغذی (فسفر، نیتروژن و مواد آلی)، ظرفیت تبادل کاتیونی، رطوبت پیشین و وزن مخصوص خاک در آزمایشگاه اندازه گیری شده است. نتایج نشان داد با تغییر کاربری از جنگل به زراعت، رواناب و رسوب به ترتیب 7/3 و 22 برابر شده و وزن مخصوص خاک، به میزان 52/0 گرم بر سانتی متر مکعب افزایش یافته است. میزان نیتروژن، فسفر، ماده آلی، رطوبت پیشین خاک و ظرفیت تبادل کاتیونی از کاربری جنگل به زراعت، به ترتیب 7/2، 3، 8/5، 2/3 و 7/2 برابر کاهش یافته است. شیب در کاربری های مختلف، اثرات متفاوتی را در میزان متغیرهای اندازه گیری شده ایجاد کرده است. طبق نتایج به ترتیب ماده آلی، فسفر و ظرفیت تبادل کاتیونی بیشترین تاثیر را در میزان رواناب و رسوب نشان داده اند. با توجه به نقش محوری پوشش گیاهی در کاهش رواناب و رسوب، مدیریت صحیح اراضی و اصلاح کاربری می تواند در کاهش اثرات منفی ناشی از آنها تاثیر زیادی داشته باشد.
    کلیدواژگان: مواد مغذی، پلات، اراضی لسی، هدررفت خاک
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  • Mehdi Hayatzadeh *, Dr Mohammad Reza Ekhtesasi, Dr Hossein Malekinezhad, Dr Ali Fathzadeh, Dr Hamid Reza Azimzadeh Pages 1-22
    Introduction
    Since the land use change affects many natural processes including soil erosion and sediment yield, floods and soil degradation and the chemical and physical properties of soil, so, different aspects of land use changes in the past and future should be considered particularly in the planning and decision-making. One of the most important applications of remote sensing is land use classification using satellite images. For this purpose, many algorithms have been developed. Since the generate maps with sufficient efficiency is the main purpose of processing images and thematic maps, therefore, selection of appropriate classification algorithm can play an important role in this regard. In recent years, it has been of interest for researchers to study land use changes, modeling and predicting these changes for the future due to the good performance of GIS systems and satellite data. Markov chain and cellular automata model is one of the current models to simulate land use map. This model, which is a combination of cellular automata model and Markov chain, is able to simulate land use changes with multiple features.
    THEORETICAL FRAMEWORK: To evaluate the effectiveness of algorithms for mapping land use classification for past and present, three of Landsat satellite images were chosen from the years 1993 (TM), 2002 (ETM) and 2014 (Landsat8) with almost an identical period. In this study, the middle of summer (July) was chosen as the time criterion for the selected images to minimize the effects of cloud cover and snow and also to improve the accuracy of training samples. At this time, the canopy of vegetation is maximum and cloud cover is minimum (Shahkooeei et al., 2014). One of the main steps is the prediction of the future land use map of basin by using Markov-cellular automata.
    Methodology
    In this study, firstly, the efficiencies of support vector machine (SVM), artificial neural network (MLP) and network self-organizing map (SOM) classification algorithms have been evaluated in the classification of Landsat satellite images. In addition, the efficiencies of the mentioned algorithms were compared. In the second step, for the prediction of future land use (2025), LCM model based on Markov chain and cellular automata was used. For this purpose, land use maps of 1993-2002 were applied for the calibration, and the 2002-2014 ones were implemented for model evaluation. Also, for producing the land suitability map, spatial variables involving distance from the road, distance from residential areas, distance from the mainstream, elevation and slope as the effective factors influencing on land use changes, were considered.
    Results
    The results showed that SVM algorithm was superior with the total accuracy of 0.95 and kappa coefficient of 0.93 than the other two methods. In addition, the results of CA-Markov model for the period of 2002-2014 with the agreement of the 0.94 and Kappa index (Klocation) which showed the ability of the model to predict the position of the cells equal to 0.92, suggested a good performance of this approach to simulate future land-use map of the basin.
    CONCLUSIONS & SUGGESTIONS: The estimated land use changes in 2025 attributes the exchanging of 60 percent of the gardens to urban areas. In other words, it is anticipated that approximately 420 hectares of orchards areas would be lost by 2025 in the study area. So, due to certain conditions of being in drylands, land management and land use changes must be further considered in comparison to those ones in the past.
    Keywords: support vector machine, Markov chain, cellular automata, land use change
  • Aboalhasan Fathabadi *, Akram Ghandi, Hamed Rouhani, Seyed Morteza Seyedi Pages 23-46
    Introduction
    In the last decades, due to human interventions and the effect of natural factors, the occurrence of landslide increased especially in the north of Iran, where the amount of rainfall is suitable for the landslide occurrence. In order to manage and mitigate the damages caused by landslide, the potential landslide-prone areas should be identified.
    In landslide susceptibility mapping, using the independent conditioning factors, the probability of the spatial occurrence of landslide in an area is estimated (1, 2). There are different qualitative and quantitative approaches to prepared landslide sustainability maps. Quantitative approaches can be divided into three categories: Statistical, probabilistic and distribution-free methods (3). Statistical methods include bivariate and multivariate methods. In bivarite statistical methods, each individual thematic data layer is crossed with the landslide inventory maps, and the weight values, indicating the importance of each parameter class in the landslide occurrence, are assigned to each factor class (4). In contrast, in multivariate methods, the relative contribution of each conditioning factor to landslide occurrence is calculated (5). Each method of mapping has advantages and disadvantages, and there is no one method accepted universally for the effective assessment of landslide hazards.
    Methodology
    In this study, artificial neural network, Logistic regression, frequency ratios, statistical index and Dempster–Shafer methods were used for landslide susceptibility mapping in Chehel Chay watershed in Golestan province. This watershed covers an area of about 256.83 km2 between longitude 36°59′ and 37°13′E and between the latitude 55° 23′ and 55° 38′ N, with the elevation ranging from 179.3 in the northern part to over 2928.3 in the southern part. The mean annual precipitation is 766.5 mm and the dominant land use in this watershed is forest.
    The first step in the land-slide susceptibility assessment is mapping the existing landslides. In this study, using air photograph, as in previous studies, Google Earth and field surveys landslide inventory map were constructed. As landslides inventory maps constructed, using geology, topographic and land use maps thematic layers of 12 landslide conditioning factors including slope angle, slope aspect, curvature, profile curvature, plan curvature, altitude, distance from roads, distance from rivers, lithology, distance from faults, land use and topographic wetness index were prepared. To train and validate different methods, the landslide inventory was randomly split into a training dataset of 80% (73 landslide locations), for estimating the artificial neural network and logistic regression parameters and bivariate models weights, and a testing dataset of 20% (18 landslides locations). By translating bivariate methods weights to thematic layers and implementing the artificial neural network and logistic regression to all the study area, pixels landslide sustainability maps were prepared. Additionally, to evaluate landslide susceptibility maps areas under the ROC curve, the percentage of observed test landslide in each landslide susceptibility class and the area of very high susceptibility class were used.
    Results
    Results showed that the area under the prediction curve for artificial neural network, logistic regression, Dempster–Shafer, frequency ratio and statistical index were 0.86, 0.77, 0.77, 0.72, and 0.71, respectively. Frequency ratio, artificial neural network, Logistic regression, statistical index, and Dempster–Shafer had the least area of very high susceptibility class, respectively. The percentage of landslide pixels coincided with the sites falling in the very high susceptibility classes for Dempster–Shafer, Artificial neural network, Logistic regression, statistical index and frequency ratio, were 0.72, 0.52, 0.32, 0.22, 0.09 respectively. With respect to the area under prediction curve, the percentage of landslide pixels coincided with the sites falling in the very high susceptibility class; multivariate methods including artificial neural network and logistic regression outperformed the other bivariate methods; also Dempster–Shafer had better performance than the other bivariate models. A similar result was obtained by Kavzoglu et al. (2015) and Pradhan and Lee (2010). On the contrary, Ozdemir and Altural (2013), Lee and Pradhan (2007) and Park (2011) concluded that bivariate models had better performance than multivariate methods. Using forward logistic regression, the factors of slope angle, plan curvature, elevation, distance from roads, distance from rivers, lithology and land use were selected as the most important factors. As distance from the road, fault and river increased, the occurrence of landslide and the weight of bivarite methods decreased.
    CONCLUSIONS & SUGGESTIONS: In this study, the capability of bivarite and multivariate methods in landslide sustainability mapping in Chel-Chay watershed was evaluated. Results showed that with respect to the area under the prediction curve, the percentage of landslide pixels coincided with the sites falling in the very susceptibility class, and regarding the area of very high susceptibility class, multivariate methods had better performance.
    Keywords: landslide, Chehel-chay, Zonation, Multivariate methods, bivariate methods, ROC Curve
  • Eng. Maryam Adhami, Eng. Mohsen Zabihi, Eng. Saieed Zare Naghadeh, Dr. Raoof Mostafazadeh * Pages 47-67
    Introduction
    The assessment of watershed sediment load is necessary for controling soil erosion and reducing the potential of sediment production. Different estimates of sediment amounts along with the lack of long-term measurements limits the accessibility to reliable data series of erosion rate and sediment yield. Therefore, the observed data of suspended sediment load could be used to estimate soil loss in the catchment upstream. Hence, one of the valid methods to estimate soil erosion is using of the recorded data of hydrometery stations in combination with catchment characteristics that will provide accurate predictions. For this purpose, recognition of similar sub-watersheds according to climatic, physiographic, geologic land use could be useful in the erosion control operations.
    THEORETICAL FRAMEWORK: To estimate the exact amount of sediment in the ungauged areas, clustering is introduced as a key step. Various methods and techniques have been used to determine the best number of clusters. However, application of different clustering methods and selection of the best one is rarely found. To this aim, the objective of present study is to determine the most important variables in sediment production using Single linkage, Ward and β-Flexible methods for the clustering of sub-watersheds of Gorganroud and Qareh-Sou river basins in Golestan Province.
    Methodology
    The Gorganroud and Qareh Sou Watersheds are located at the North-Eastern part of Iran. The seventeen hydrometric stations were selected with a 24-year (1986–2010) recorded data of discharge and suspended sediment load. The Grubbs and Beck method was used to perform the verity in order to verify the outlier discharge measured data. The correlation method was used to fill the missing data in time series. The normality of discharge and suspended sediment data were tested using Kolmogrov-Smirnov test and verified for choosing the well-set trend analyses method. The linear regression and Mann-Kendal Taw methods were used for the data with normal and non-normal distribution in trend analysis, respectively. Auto Correlation Function (ACF) test method was used to determine the internal consistency between the data series.
    A set of 38 factors from the five main groups of categories were investigated to determine the sediment yield controlling independent variables. Principal Component Analysis (PCA) was used to determine the most effective variables. In order to detect the best classification method, three classification techniques (Single linkage, Ward’s, and β-flexible methods) were examined in the study area. The Single Linkage also called nearest neighbor is a simple clustering method. The object pairs forms clusters hierarchically starting from the most similar pairs according to the similarity in a descending order. Ward’s algorithm is one of the frequently used techniques for the regionalization studies of hydrology and climatology factors. A generalized hierarchical method, β-Flexible, formed the group calculating the external object. The distance from a point to the group was computed in this method.
    Many indices have been developed to examine the validity of clustering techniques based on finding an optimal partitioning. In the present study, Pseudo F and Dunn’s Indices were used to assess the accuracy of clustering algorithms. Accurate clustering means having non-overlapping partitions. One of the most commonly used criteria for the selection of group number is the maximization of pseudo-F statistics. This statistics is based on multivariate normal distribution of data.
    Results
    All data series of 17 sub-watersheds in Gorganroud and Qareh Sou basins were tested with different clustering alghorithms. Two data series showed autocorrelation, detected by the ACF test. Two data sets had trends according to the Kendal’s test. Therefore, 13 sub-watersheds remained for the final classification. Some 38 independent variables were calculated and screened with PCA. The variables with similar effects on sediment yield, were grouped in 7 components. The selected components were chosen according to the amount of variance. The results of PCA and the selected representative variables in each component have been given in Table 1.
    CONCLUSIONS & SUGGESTIONS: The results showed that the Single linkage method presented a better performance considering the accuracy criterion. The suspended sediment values were determined using measured discharge and available Sediment Rating Curves; therefore, the identified clusters as the reliable and appropriate watershed grouping methods which could be regarded as a useful tool in the management of watersheds particularly in the context of erosion and sedimentation.
    Keywords: Clustering, Gorganroud, Qareh-Sou, Homogenous Region, Regional Analysis, Suspunded Sediment Load
  • Khadijeh Bhalkeh, Mehdi Abedi *, Ghasem Ali Dinati Tilaki Pages 68-80
    Introduction
    Mountainous habitats are characterized with low temperature, limited growing seasons, high mortality rate due to freezing and high radiance which limit occurrence of species. Such habitats also face early grazing and soil erosion. The most dominant shrubs in such habitats are cushions which have important roles in mountainous areas with high erosion potentials. However, their roles in soil conservations are not clear. Cushions can create microclimate in harsh conditions in the high altitudes by modifying soil moisture and temperature which could improve species establishments. In addition, exposure can also change soil moisture conditions and influence on temperature fluctuations. The aim of this study is to investigate the effect of exposures on improving microclimate of Onobrychis cornuta.
    THEORETICAL FRAMEWORK :In harsh conditions considering SGH (Stress gradient hypothesis), it is expected that shrubs facilitate establishments of species. Shrubs act as nurses in the disturbed conditions. However, the role of cushions has not been studied yet and it is needed to know how such woody species modify habitats and also how exposures influence on the microhabitat conditions.
    Methodology
    The study area is located in Golestan national park and Alme-Gharatikan site with the altitude of 1800 m. The most dominant species are perennial grasses of Festuca valesiaca, shrubs of Onobrychis cornuta and perennial forbs of Cephalaria microcephala. Two exposures were selected where the North exposure has deep and developed soil compared to the South exposure with more stones. Minimum, maximum, mean and Diurnal Temperature Fluctuations (DTF) at the two exposures (North and South) were calculated for both under patches and open area using thermometers. Soil moisture was also measured by TDR instrument in the patches and open area during two time intervals. For the determination of the most important factors affecting soil moisture including exposure, time and their interactions, GLMM was applied and the compared means were tested by T-test.
    Results
    In the southern exposure, temperature fluctuation was 2-3.5 C (mean= 2.2 C) for under cushions and in the open plots was 2-11.5 C (mean= 7.1). Therefore, in the open plots, fluctuations are higher than under shrubs. In the North exposure, fluctuation is 0.5- 3.5 C (mean= 2.1) for under shrubs and ranged from 0.5 to 10.5 C (mean= 6.5). Maximum DFT belongs to the open plot of the South exposure (7.1 C) and the lowest DFT was observed below shrubs in the North exposure (3.9 C). The minimum and maximum temperatures were observed in the southern exposure. Thus, the Diurnal Temperature Fluctuations in the open area (7.1, 3.9 ºC) were greater than the patches (2.2, 1.9 ºC) in the South and North exposures, respectively. According to GLMM results, the time and exposures (F= 6.0; P CONCLUSIONS & SUGGESTIONS: This study indicates the importance of cushions in moisture maintains and temperature modifications influencing soil conservation and understory species survival. This could be used for the cushion roles in maintaining moisture in the mountain habitats. In general, cushions keep moisture for longer time in terms of the distances between them. High moisture availability reduces soil erosion which is critical in mountainous habitats. The moisture reduction in the South exposures and late spring is higher. Therefore, cushions play more ecological roles in such conditions. Cushions also modify temperature fluctuations which benefit species occurrence in these habitats. This modification is more important in the southern exposure which, due to earlier snow melting, species face longer freezing periods. In such conditions, cushions can play the nursing roles for species conservation. In addition, such microclimatic conditions facilitate the occurrence of species in such habitats and conserve them in the freezing and cold winters. Cushions’ role in Iran, due to overgrazing and high erosion, is more considerable, and in the conservational plans, these species should be considered. Considering the present results, this study suggests examining the role of different shrub functional types.
    Keywords: Onobrychis cornuta, microhabitat modification, temperature, moisture, nurse speciescushion
  • Ahmad Nohegar, Mohammad Kazemi *, Javad Ahmadi, Hamid Gholami, Rasol Mahdavi Pages 81-103
    Introduction
    Many catchment erosion studies focus on formation and land use as the primary source of sediment. It is important to improve information on sediment sources, especially in large catchments and sediment source information which can support catchment management decisions. Erosion control projects need to be understood as the relative contributions of different sediment sources from catchments. Fingerprinting methods identify soil erosion sources where geologic variations or different land uses span watershed boundaries. Sediment fingerprinting studies often rely on the collection of sediment from different sources within a catchment. Few studies have focused on using the Hughes mixed model to identify sediment sources. This model can quickly process a large number of samples from the main samples based on Monte Carlo simulation. The main objectives of this research were to determine the contribution of sediment sources by applying a fingerprinting mixing model in a Tange Bostanak drainage catchment.
    Material and Methods.Case Study Our study area was located in the Tange Bostanak catchment (30°16′ to 30°25′ N and 52°03′ to 52°13′ E),in the Southern Zagros Mountains, 80 km Northwest of Shiraz, Iran. The drainage area of the Tange Bostanak catchment is 81.73 km2.
    Sediment source samples were collected throughout the study catchment from each of the three principal source types (cultivated land, pasture, forest, gardens and also six formations in catchment). 43 representative samples were collected from these potential sources at different locations within the study catchment. Samples were initially oven-dried to 40 °C in order to remove the bias associated with the grain-size effects, only the
    Results And Discussion
    C, N, Cu, Ti, Si and Sr were identified by the Kruskal–Wallis test to discriminate the potential sediment sources in land use and Nd, Si, C, N, Ti and Nd144/Nd143 were identified by the Kruskal–Wallis test to discriminate the potential sediment sources in the formations. In stepwise multivariate discriminant function analysis, four tracers(C, Cu, Si, Ti) were capable of correctly distinguishing the land use source type. Four tracers (Nd143/144, Cu, Si, Ti) verified the ability to discriminate between geology information source categories. The results on geology information showed that the mean relative contributions related to the areas of Asmary (84.51%) and Quaternary (5.37%) were highest, respectively in Local optimization with 99.94 GOF index. For land uses, the results showed that the GOF index with 97.84 associated with GA optimization were the greatest. The relative contributions related to the areas of range lands (63.04%) and forest (31.81%) were the highest, respectively. Pabedeh Gorpi and Bakhtyari information with 0.24 and 0.27 were the lowest relative importance; also cultivation and forest land uses with 0.022 and 0.55 were the lowest relative importance, respectively. This study suggested that the future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the Hughes (relying on iterations involving all data, and not only their mean values) models with the optimization using genetic algorithms to best predict the relative contribution of sediment sources. Comparing the applications in this catchment, the Hughes mixed model appears a more robust method in Tange Bostanak catchment using the GA optimization method.
    Keywords: Fingerprinting, Mixing model, Relative contribution, GA, Tange Bostanak Watershed
  • Mohammad Abbasi *, Dr Ali Najafi Nejad, Dr Vahed Berdi Sheikh, Majid Azim Mohseni Pages 104-124
    Introduction
    Soil erosion and its issues are among the most important environmental challenges. In many areas, soil erosion affected valuable natural resources and soil fertility. The recognition of the factors affecting runoff and soil erosion, and the determination of their issues are essential for soil and water management conducive to sustainable development. The assessment and analysis of the changes associated with the soil characteristics; runoff and sediment lead to proper planning and management.
    THEORETICAL FRAMEWORK: Review of former studies shows that the land use change is one of the main effective factors regarding the erosion intensity whose effect is sometimes more than the rainfall intensity and slope. For quantifying the effects of land use change and slope on runoff, sediment and soil nutrients, the present study was carried out with the aim of comparing the chemical and physical properties of soil in different land uses and slope classes, and of investigating their effects on runoff and sediment.
    Methodology
    This research was accomplished in Kechik watershed on loess formation located in Maravetape township, Golestan province.Rainfall simulations were done in 4-square-meter plots on five land use including forest (Natural forest and Reforestation), rangeland, farmland (Watermelon and Harvested Wheat) and four-slope classes including 3-12%, 12-18%, 18-25% and 25-40%. Based on the three-nested design, 96 simulation experiments and samplings were done. Output runoff and sediment and soil samples were collected. Nutrient amount (Phosphorus, Nitrogen, and Organic matter), Cation Exchange Capacity, Antecedent moisture content and Soil density were measured at the laboratory.
    Results
    The result showed a change in the type of land use (natural forest, forestation, rangeland, watermelon farmland and harvested wheat) due to the increase in runoff from 12.8, 40, 25.2, and 40.7 to 54.9 lit., respectively. The average sediment changed from 57.5, 230.9, 119.1, and 1369.6 to 1190.1 regarding the land uses, respectively. The reduction of phosphorus was found from 13.4, 8.8, 10.8, and 6.3 to 4.4 in different land uses. The organic matter reduced from 6.4, 4, 3.2, and 1.6 to 1.1 in different land uses, respectively. The cation exchangeable capacity reduced from 16.8, 10.3, 9.1, and 6.2 to 7.6 c mol kg-1 in different land uses, respectively. The soil moisture changed from 22.2, 6.4, 12.8, and 6.8 to 6.7 in different land uses, respectively. The soil density in different land uses increased from 1.05, 1.16, 1.25, and 1.63 to 1.57 gr cm3, respectively.
    Discussion
    According to the results, organic matter is the only variable that has significant difference affected by slope changing in all of the land uses. Also, the investigation of the relation between measured variables of runoff and sediment showed that the organic matter, phosphorus, cation exchange capacities have the highest effects on runoff and sediment, respectively. The increase in runoff in farmland comparing to the forest shows the importance of forests in reducing runoff, flood management, and ground water recharge. On the other hand, sediment production in farmlands 20 times more than in forests shows the important of forest in preventing sediment production, filling the reservoir of dams, and losing valuable soil.
    Keywords: Nutrients, Simulation, Loess lands, Soil loss