0 تأثیر سیستم ریشه‌ای علف و تیور بر پارامترهای مقاومت برشی ساحل رودخانه ‌(مطالعۀ موردی: رودخانۀ کر) Effect of Vetiver grass root system on river bank shear strength parameters, case study: Kor river https://ije.ut.ac.ir/article_67500.html 10.22059/ije.2018.234283.615 0 فرسایش کناره‏ای در رودخانه‏‏ها خسار‌ت‌های زیادی را به اراضی مجاور تحمیل می‌کند و تغییر ریخت‏شناسی رودخانه‏‏ها را به همراه دارد. تحقیق حاضر با هدف ارزیابی کارایی علف وتیور در افزایش مقاومت برشی خاک و کاهش فرسایش سواحل رودخانه‏‏ها صورت گرفته است. تغییرات پارامترهای ساختاری علف وتیور شامل RAR، RDR، RDDI و RLD در اعماق مختلف و همچنین توسعۀ عرضی و عمقی ریشه بررسی شد. با بررسی سه فاصلۀ مختلف بین بوته‏های وتیور، فاصلۀ 30 سانتی‏متری بین بوته‏ها به عنوان فاصلۀ بهینه مشخص شد. نتایج نشان داد علف وتیور سبب افزایش بیش از 104 درصدی چسبندگی و حدود 83 درصدی زاویۀ اصطکاک داخلی در مقایسه با حالت بدون ریشه شده است. مقایسۀ بین پارامترهای مختلف نشان داد به‌ترتیب پارامترهای RAR و RDR مناسب‏ترین پارامتر برای محاسبۀ چسبندگی و زاویۀ اصطکاک داخلی خاک در حضور ریشۀ وتیور هستند. همچنین، با انجام مقایسه‏ای بین وتیور و برخی گونه‏های درختی منطقه از نظر مقاومت کششی ریشه، مدت زمان لازم برای استقرار گیاه، مقاومت در برابر خشکسالی و قابلیت خودترمیمی مشخص شد که علف وتیور در مقایسه با سایر گونه‏های گیاهی متداول، مناسب‏تر است و می‏تواند به عنوان جایگزین مناسبی برای محافظت سواحل رودخانه در برابر فرسایش استفاده شود. 1 River bank erosion imposes sever damages to the adjacent lands annually and changes the river morphology considerably. In this study, the effectiveness of the Vetiver grass root system in enhancement of the soil shear strength and reducing the river bank erosion is examined. Several experiments were carried out to find the effect of Vetiver grass root system on soil cohesion and internal friction angle. Also, variations of morphological characteristics of Vetiver grass root system including RAR, RDR, RDDI and RLD in different depths have been investigated as well as the lateral and vertical distribution of roots. Three different spacing between Vetiver grass plants have been studied and the optimum distance has been proposed. The results showed that Vetiver grass root system increases the soil cohesion and internal friction factor upto 104% and 83%, respectively. Also, it was found that soil cohesion and internal friction factor are best correlated with RAR and RDR, respectively. Finally, an attempt was made to compare the Vetiver grass with several native trees. It was concluded that Vetiver grass is more suitable than common plants and can be considered as an alternative for river bank protection against erosion. 727 738 حسین حمیدی‌فر Hossein Hamidifar استادیار بخش مهندسی آب، دانشگاه شیراز ایران hamidifar@shirazu.ac.ir مهدی بهرامی Mehdi Bahrami استادیار گروه مهندسی آب، دانشگاه فسا ایران mehdibahrami121@gmail.com محمدجواد امیری Mohaamdjavad Amiri استادیار گروه مهندسی آب، دانشگاه فسا ایران amiriboogar@yahoo.com پوشش گیاهی حفاظت سواحل رودخانه رسوب روش بیولوژیک فرسایش کناره [1]. Dang MH, Umeda S, Yuhi M. Long-term riverbed response of lower Tedori River, Japan, to sediment extraction and dam construction. Environmental Earth Sciences, 2014; 72(8): 2971-2983.‏##[2]. Balaban SI, Hudson-Edwards KA, Miller JR. A GIS-based method for evaluating sediment storage and transport in large mining-affected river systems. Environmental Earth Sciences, 2015; 74(6): 4685-4698.##[3]. Ahmadian-Yazdi M. Effect of vegetation on Tajan-Harirood meander bank erosion. MSc. Thesis, 2000; Gorgan University of Agriculture and Natural Resources. [Persian]##[4]. Samadi A, and Amiri-Tokaldani E. River bank mass erosion: Process and Mechanism, University of Tehran Press; 2015. p. 504.##[5]. Shirdeli A, Shafai-Bajestan M, Ciacar H. Investigation of the effects of tamarisk and tamarisk aphyllaroots on the stability of seistan river banks. 7th International River Engineering Conference, Shahid Chamran University, 2007; 13-15 Feb 2007, Ahwaz [Persian]##[6]. Lin D, Liu W, Lin S. Estimating the effect of shear strength increment due to root on the stability of makino bamboo forest slopeland, Journal of GeoEngineering, 2011; 6(2): 73-88##[7]. Dumlao MR, Ramananarivo S, Goyal V, DeJong JT, Waller J, Silk WK. The role of root development of Avena fatua in conferring soil strength. American journal of botany, 2015; 102(7): 1050-1060.##[8]. Ghestem M, Veylon G, Bernard A, Vanel Q, Stokes A. Influence of plant root system morphology and architectural traits on soil shear resistance. Plant and soil, 2014; 377(1-2), 43-61.‏##[9]. Islam MS. Application of Vetiver (Vetiveria zizanioides) as a bio-technical slope protection measure–some success stories in Bangladesh.‏ Proceedings of the 6th International Conference on Vetiver, 2015; 5-8 May, Danang City, Vietnam.##[10]. Abdullah MN, and Osman N. Soil-root Shear Strength Properties of Some Slope Plants. Sains Malaysiana, 2011; 40(10): 1065–1073##[11]. Genet M, Stokes A, Fourcoud T, Norris JE. The influence of plant diversity on slope stability in a moist evergreen deciduous forest. Ecological Engineering, 2010;36: 265-275.##[12]. Schwarz M, Preti F, Giadrossich F, Lehmann P, Or D. Quantifying the role of vegetation in slope stability: A case study in Tuscany (Italy). Ecological Engineering 2010; 36: 285-291.##[13]. Ebrahimi N, Shirdeli A, Nik-khah-Javan E, Hossein M. Effects of river bed vegetation on flow hydraulics and bed forms. Journal of Watershed Management and Engineering, 2015; 8(2): 182-192. [Persian]##[14]. Hemphill RW, Bramley ME. Protection of river and canal banks. CIRIA, Butter Worths, London; 1989.##[15]. Shafaei-Bajestan M, Salimi M. Effects of tamaricaceae and popoluse roots on in situ soil shear strength of Karoon river banks. Journal of Agriculture and Natural Resources Science and Technology, 2002; 6(4): 27-40. [Persian]##[16]. Shirdeli A. Study of bioengineering methods for river bank stabilization, Journal of Watershed Science and Engineering, 2012; 7(23): 53-62. [Persian]##[17]. Dastoorani M, Rajabi-Mohammadi F. Study of mechanical and hydrological effects of river bank vegetation on bank stability, Case Study: Hana River, 3rd National Seminar on water resources management, University of Agriculture and Natural Resources Science, 2011; 10-12 September, Sari, Iran. [Persian]##[18]. Troung P, Van T, Pinners E. Vetiver system applications (Technical Reference). 2008.##[19]. Niknejad D. Biological control of river bank erosion by Vetivergrass. 11th National Seminar on Irrigation and Vapor reduction. Kerman, Shahid Bahonar University, 2010; 8-10 February, Kerman, Iran. [Persian]##[20]. Xie FX. Vetiver for highway stabilization in Jian Young country: demonstration and extension. In; proceedings of the Interventional vetiver workshop, Fuzhow, China; 1997.##[21]. Xia HP, Ao HX, Liu SZ, He DQ. Application of the Vetiver grass bioengineering technology for the prevention of highway slippage in southern China. Proc. Ground and Water Bioengineering for Erosion Control and Slope Stabilization, Manila, Philippines April 1999.##[22]. Ke C, Feng Z, Wu X, Tu F. Design Principles and Engineering Samples of Applying Vetiver Eco-engineering Technology for Steep Slope and River Bank Stabilisation. In Proceedings of the Third International Vetiver Conference (ICV3), 2003; Guangzhou, China.##[23]. Sy M. The vetiver: from nursery to the protection of infrastructures. Proceedings of the Third International Conference on Vetiver and Exhibition, 2003; Guangzhou, China.##[24]. Hengchaovanich D, and Nilaweera NS. An assessment of strength properties of vetiver grass roots in relation to slope stabilization. In International Conference on Vetiver, 1996; Chain Kai, Thailand.##[25]. Sangab-Zagros consulting Engineers. Evaluation report of geology and morphology of the Kor river. Fars Regional Water Organization; 2008. [Persian]##[26]. Shirani H, Haj-Abbasi M, Afyooni M, Hemmat E. Effect of tillage and manure on maize root morphology. Soil and Water Journal, 2008; 23(1): 101-107. [Persian]##[27]. Davoudi M, Fatemi-Aqda M. Effect of Diameter and Density of Willow Roots on Shear Resistance of Soils, Geosciences, 2008; 71: 143-148. [Persian]##[28]. Shariata Jafari M, Davoudi M, Safaei M and Partoi A. Invetigating the effect of Diospyros lotus root system in soil reinforcement using RDR and RDDI indices. Journal of Watershed Engineering and Management, 2014; 6(2): 107-114. [Persian]##[29]. Davoudi M, Fatemi-Aqda M, Noroozi H, Shah-Alipoor GH. Effect of tree root diameter on soil shear strength, 4th seminar on engineering geology and environment, 2004; 24-26 February, Tarbiat Modares University, Tehran, Iran. [Persian]##[30]. Vannoppen, W., Poesen, J., De Baets, S., Vanmaercke, M., Peeters, P., & Vandevoorde, B. Effectiveness of plant roots in controlling rill and gully erosion: A case study on vegetation communities on river dikes. In Proceedings of the 4th International Conference on Soil Bio- and Eco-Engineering, 11-14 July 2016,Sydney, Australia.##[31]. Day SD, Seiler JR, Persaud N. A comparison of root growth dynamics of silver maple and flowering dogwood in compacted soil at differing soil water contents. Tree Physiology, 2000; 20(4): 257-263.##[32]. Goldsmith W. Soil strength reinforcement by plants, International Erosion Control Association (IECA); 2006. p. 5-16.##[33]. da Silva EV, Bouillet JP, de Moraes Gonçalves JL, Junior CHA, Trivelin, PCO, Hinsinger P, Jourdan C, Nouvellon Y, Stape JL, Laclau, JP.. Functional specialization of Eucalyptus fine roots: contrasting potential uptake rates for nitrogen, potassium and calcium tracers at varying soil depths. Functional Ecology, 2011; 25: 996–1006.##[34]. Maleki S, Naghdi R, Abdi E, Nikooi M. Study of Tuska root effects on soil armoring as a bioenginnering tools. Iranian Forest Journal. 2013; 6 (1): 49-58. [Persian]##

0 مقایسۀ کارایی مدل‌های شبکۀ عصبی مصنوعی، منطق فازی و جنگل تصادفی در برآورد پارامتر قابلیت انتقال آبخوان دشت ملکان Comparing Performans of Fuzzy Logic, Artificial Neural Network and Random Forest Models in Transmissivity Estimation of Malekan Plain Aquifer https://ije.ut.ac.ir/article_67501.html 10.22059/ije.2018.239914.707 0 آبخوان دشت ملکان به عنوان یکی از آبخوان‏های حوضۀ دریاچۀ ارومیه، به مدیریت صحیح کمی و کیفی نیاز دارد. روش‏های مختلفی از جمله انجام آزمون پمپاژ، روش‏های آزمایشگاهی، استفاده از ردیاب‏ها و روش‏های ژئوفیزیکی برای ارزیابی پارامترهای هیدروژئولوژیکی و مدیریت مناسب آبخوان‏ها وجود دارد. هر چند تعبیر و تفسیر داده‏های به‌دست‌آمده از آزمون پمپاژ، بهترین روش تخمین پارامترهای هیدروژئولوژیکی آبخوان است، اما این روش‏ پرهزینه، وقت‏گیر و نتایج آن مختص به مناطق محدودی خواهد بود. با توجه به اینکه مدل‏های هوش مصنوعی توانایی‏هایی در برآورد پارامترهای هیدروژئولوژیکی نشان داده‏اند، در تحقیق حاضر کارایی مدل‏های شبکه‏های عصبی مصنوعی، منطق فازی و جنگل تصادفی در برآورد پارامتر قابلیت انتقال آبخوان دشت ملکان بررسی شده‏ است. پارامترهای ژئوفیزیکی و هیدروژئولوژیکی مرتبط با قابلیت انتقال، از جمله مقاومت عرضی، هدایت الکتریکی، ضخامت آبخوان و هدایت هیدرولیکی به عنوان مهم‏ترین ورودی در این مدل‏ها در نظر گرفته شده ‏است. بر اساس نتایج به‌دست‌آمده از بین مدل‏های شبکۀ عصبی و فازی و جنگل تصادفی، مدل جنگل تصادفی ‌دقت و توانایی بیشتری در شبیه‏سازی داشته‏ است. نتایج به‌دست‌آمده از شبیه‏سازی ( 96/0 = AUC، 001/0 = MSE و 986/0= R2) و تعیین مهم‏ترین پارامترهای تأثیرگذار در پیش‏بینی قابلیت انتقال، گویای برتری این مدل نسبت به مدل‏های شبکه‏های عصبی مصنوعی و منطق فازی در بحث پیش‏بینی است. 1 Estimating aquifer hydrogeological parameters is essential for the studies or management of groundwater resources. There are several different methods such as pumping test, simulation or modeling of groundwater, geophisical modeling to estimate these parameters. Although analysis and evaluation of pumping test data is the best way to achieve this purpose, it is costly, time consuming and the gained results are from limited points. Malekan plain aquifer is one of the marginal plains of Urmia Lake which suffered more ground water declination and Salinization in last decades and it needs qualitative and quantitative management. In this study, artificial neural networks, fuzzy logic and random forest models have been used to estimate the transmission of aquifers and the performance each of these models has been investigated. Inputs of presented models included related geophysical and hydrogeological variables to transmissivity such as transverse resistivity (Rt), electric conductivity (EC), alluvium thickness (B), and hydraulic conductivity (k). Based on the results of all models, random forest model has higher accuracy and ability to predict transmissivity parameter. According to this model, electrical conductivity (EC), aquifer environment (A) and hydraulic gradient (H) parameters were identified as the most important parameters to predict the transmissivity, respectively. 739 751 حسین نوروزی قوشبلاغ hossein norouzi دانشجوی دکتری هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز، تبریز، ایران ایران hosseinnorouzi168@yahoo.com عطا الله ندیری Ata Allah Nadiri دانشیار هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز، تبریز، ایران ایران nadiri@tabrizu.ac.ir اصغر اصغری مقدم Asghar Asghari Moghaddam استاد هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز، تبریز، ایران ایران moghaddam@tabrizu.ac.ir مریم قره‌خانی Maryam Norouzi دانشجوی دکتری هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز، تبریز، ایران ایران m.gharekhani90@gmail.com آب‏های زیرزمینی جنگل تصادفی دشت ملکان قابلیت انتقال هوش مصنوعی [1]. Chow VT. 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0 تعیین دبی زیست‌محیطی بومی سفیدرود Determination of the Environmental Flow Requirements for the SefidRud River, IRAN https://ije.ut.ac.ir/article_67502.html 10.22059/ije.2018.240350.711 0 دبی زیست‌محیطی توصیفی از زمان‌بندی، کیفیت و کمیّت آب مورد نیاز برای پایداری اکوسیستم آبی است. هدف مقالۀ حاضر، ارزیابی روش‌های متداول تعیین دبی زیست‌محیطی و ارائۀ روش بومی سفیدرود است. دبی زیست‌محیطی سفیدرود با کمک روش‌های هیدرولوژیکی، هیدرولیکی و شبیه‌سازی زیستگاه بررسی شد و نتایج به‌دست‌آمده به صورت یک پیشنهاد بومی در غالب درصدی از میانگین دبی سالانه بیان شد. طبق ابلاغیۀ وزارت نیرو، شیوۀ تعیین دبی زیست‌محیطی روش تنانت (هیدرولوژیکی) است. این روش به همراه روش محیط ترشده (هیدرولیکی) با سه ایدۀ متفاوت به کار گرفته شد و در روش شبیه‌سازی زیستگاه نیز فیل‌ماهی به عنوان گونۀ شاخص انتخاب و دبی زیست‌محیطی مطابق با شرایط زندگی گونۀ هدف بررسی شد. مزایا و محدودیت‌های روش‌های بررسی‌شده معرفی شد. بر اساس نتایج به‌دست‌آمده از مقادیر محاسباتی و ضعف‌ها و قوت‌های آنها به‌ترتیب 20‌ و 30‌ درصد میانگین دبی سالانه برای حفظ سلامت رودخانه در شرایط ضعیف تا نسبتاً خوب پیشنهاد شد. در نهایت، روش ترکیبی شبیه‌سازی زیستگاه و بیشترین انحنای محیط خیس‌شده برای تعیین کمترین دبی زیست‌محیطی و بهبود شرایط اکوسیستم منطقه به عنوان روش بومی سفیدرود پیشنهاد شد. 1 Environmental Flows describes the timing, quality, and quantity of water flows required to sustain ecosystems, human well-being and livelihoods that depend upon them. A review of the present status of environmental flow methodologies revealed the existence of 4 differentiated methodologies. The main purpose of this paper is to assess common methods of environmental flow requirements. According to the standards of the Ministry of Energy of Iran, Tennant introduced as the base method for determining the environmental flows. This method together with wetted perimeter method with three different ideas is applied to the SefidRud. Also, the habitat simulation method, which is a part of the ecological method, is applied to SefidRud River too. Huso Huso fish was chosen as the species river target in this research study. The advantages and limitations of the above mentioned methods are presented as a part of results. 20% and 30% of the annual average flows are suggested for maintaining river health in poor and good conditions, respectively. The combined simulation method habitats and the maximum curvature of the wetted perimeter are recommended as a native method for SefidRud River to determine the minimum ecological flow and improve the ecosystem of the studied river. 753 762 فاطمه فتاح‌پور Fatemeh Fattahpour کارشناس ارشد مهندسی منابع آب، دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران ایران fattahpour135@alumni.ut.ac.ir کیومرث ابراهیمی Kumars Ebrahimi استاد مهندسی منابع آب، دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران ایران ebrahimik@ut.ac.ir سوگند بیات Sogand Bayat دانشجوی کارشناسی ارشد مهندسی منابع آب، دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران ایران sogand.bayat@ut.ac.ir تنانت حقابۀ زیستی رودخانه گونۀ شاخص محیط خیس‌شده [1]. Naiman R J, Bunn SE, Nilsson C, Petts GE, Pinay G, Thompson LC. Legitimizing fluvial systems as users of water: an overview. Environmental Management. 2002; 30:455-467.##[2]. Arthington A. Saving Rivers in the Third Millennium. Ecology and the Environment Book; 2012.##[3]. Dunbar MJ, Acreman MC, Gustard A, Elliott CRN. Overseas Approaches to Setting River Flow Objectives; 1998.##[4]. Acreman M, Dunbar MJ. Defining environmental river flow requirements- a review. J. Hydrology and Earth system Sciences. 2004; 8(5): 861-876.##[5]. Liu J, Liu Q, Yang H. Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality. Ecological Indicators. 2016; 60: 434-441.##[6]. Tharme RE. A global persprctive on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. Published online in Wiley InterScience. 2003; 19:397-441.##[7]. Richter BD, Baumgartner JV, Wigington R, Braun DP. How much water does a river need. Freshwater Biology. 1997; 37: 231-249.##[8]. Office of Standard and Technical Criteria. Planning and Budget Organization of Iran. Drinking Water Standards. Tehran; 1992. (In Persian)##[9]. Tharme RE, King JM. Development of the Building Block Methodology for Instream Flow Assessment, and Supporting Research on the Effects of the Different magnitude Flows on Riverine Ecosystems. Water Research Commission; 1998.##[10]. Smakhtin VU, Revenga C, Doll P. A pilot Global Assessment of Environment Water Requirement and Scarcity. International water Resources Association. 2004; 307-317.##[11]. Mann JL. Instream flow methodologies: An evaluation of the Tennant method for higher gradient streams in the national forest system lands in the western US. Master Thesis. Department of Forest, Rangeland, and Watershed Stewardship,colorado state university, Colorado; 2006. 143 p.##[12]. Watt SP. A methodology for environmental protection of Ontario watercourses with respect to the permit to take water program; 2007. 134p.##[13]. Zolfaghari S, Ghanbarpour M, Habibnejad M, Afkhami M. Evaluation and assessment of environmental flow using hydrological methods (Case study: Shadegan wetland). Journal of Watershed Management Science and Engineering. 2009; 3(8): 67-70. (in Persian)##[14]. Poff N, Richter B, Arthington A, Bunn S, Naiman R, Kendy E, et al. The ecological li, its of hydrologic alteration (ELOHA), A new framework for developing regional environmental flow standards. J. Freshwater Biol. 2010; 55(1): 147-170.##[15]. Jushi KD, Jha DN, Alam A, Srivastava SK, Kumar V, Sharma AP. Environmental Flow requirements of river sone: impact of low discharge on fisheries. Current Science. 2014; 107(3): 478.##[16]. Mostafavi S, Yasi M. Evaluation of Environmental Flowsin Rivers Using Hydrological Methods (case study: The Barandozchi River- Urmia Lake Basin). J. water Soil. 2015; 29(5), 1219-1231. (in Persian)##[17]. Bayat S, Ebrahimi K., Assessment of Different Environmental Flow Methods. 2th National Iraninan Conference on Hydrology, Shahrekord, Iran, 2017. (in Persian)##[18]. Orth DJ, Maughan OE. Evaluation of the “Montana Method” for recommending instream flows in Oklahoma streams. Pro. Okla. Acad. Sci. 1981; 61:62-66.##[19]. Tennant DL. Instream flow regimes for fish, wildlife, recreation and environmental resources. Instream Flow Needs. Volume II. American Fisheries Society. Bethesda MD. 1976; 359-373.##[20]. King JM, Tharme RE, Villiers DE. Environmental flow assessments for rivers: manual for the Building Block Methodology. Water Research Commission Technology Transfer Report No. TT131/00. Pretoria, South Africa; 2003.##[21]. Peres DJ, Cancelliere A, Environmental Flow Assessment Based on Different Metrics of Hydrological Alteration. Water Resources Management. 2016; 30(15).##[22]. Annear TC, Conder AL. Relative bias of Several fisheries instream flow methods. North American Journal of Fisheries Management. 1984; 4(4B): 531-539.##[23]. Poff NL, Allan JD, Bain MB, Kar JR, Prestegaard kl. The natural flow regime: a paradigm for river conservation and restoration. Bio science, 1997; 47: 769-784.##[24]. Gippel CJ, Stewardson MJ. Use of wetted perimeter in defining minimum environmental flows. Regulated Rivers: Research and Management. 1998; 14: 53-67.##[25]. Jowett IG. Instream flow methods: a comparison of approaches. Regulated rivers. 1997; 13: 115-127.##[26]. Ahmadipour Z, Yasi M. Comparison of echo-hydrologic-hydraulic methods in the assessment of river ecological flow (Nazlou river, Urmia lake basin), Hydraulic science journal. 2015; 9(2): 69-82. (in Persian)##[27]. Amini M, Shokouhi A. Analytical solution Determination of the fracture point of the contaminated-discharge environment in the hydraulic method of determining the minimum environmental flow. "Journal of Hydraulic. 2015; 9(1): 27-43. (in Persian)##

0 کاربرد الگوریتم ژنتیک در بهینه‌سازی عملکرد سیستم استنتاج فازی-عصبی تطبیقی به منظور پیش‌‏بینی بیشترین دمای هوا (مطالعۀ موردی: شهر اصفهان) Application of Genetic Algorithm to Optimize the Performance of Adaptive Neural - Fuzzy Inference System in order to predict maximum of air temperature (Case study: Isfahan city) https://ije.ut.ac.ir/article_67503.html 10.22059/ije.2018.241766.726 0 الگوریتم‏های موجود برای آموزش سیستم استنتاج فازی- عصبی تطبیقی (ANFIS) باوجود کاربرد فراوان، نقایصی همچون به‌دام‌افتادن در بهینۀ محلی دارند. در پژوهش حاضر، کاربرد الگوریتم‏های بهینه‏سازی ژنتیک (GA)، ازدحام ذرات (PSO)، کلونی مورچگان برای محیط‌‏‌‌های پیوسته (ACOR) و تکامل تفاضلی (DE)، در توسعه و بهبود عملکرد ANFIS ‌بررسی شد. به‌‏عنوان مطالعۀ موردی، بیشترین دمای ماهانۀ شهر اصفهان در بازۀ زمانی 64 ساله (1330-1393)، شبیه‏سازی و تحلیل شد. به این منظور، ابتدا با استفاده از آنالیز حساسیت، مناسب‌‏ترین ورودی‏ها برای هر یک از افق‏های پیش‏بینی (یک ماه، یک تا سه سال) انتخاب شد. سپس، بیشترین دما به‏وسیلۀ مدل‏های هیبریدی ANFIS-GA، ANFIS-PSO، ANFIS-DE، ANFIS-ACOR و مدل ANFIS پیش‏بینی شد. در ادامه، عملکرد هر یک از مدل‏ها با استفاده از شاخص‏های آماری R2، RMSE و MAE ارزیابی شد. نتایج نشان داد مدل ANFIS-GA، به‏عنوان مناسب‏ترین مدل، دقت عملکرد ANFIS را در پیش‏بینی افق‌‏های یک ماه و یک تا سه سال آینده در R2 به‌ترتیب به مقدار 06/0، 07/0، 08/0 و 12/0 و در RMSE به میزان 09/0، 09/0، 16/0 و 1/0 بهبود داده است. پس ‏از آن، به‌ترتیب ANFIS-DE و ANFIS-PSO مناسب‏ترین دقت را داشتند. از سوی دیگر، ANFIS با بیشترین خطا و کمترین R2، به‏عنوان ضعیف‏ترین مدل شناخته شد. نتایج ‌نشان داد مدل‏های هیبریدی پیشنهادی، با استفاده از تکنیک جست‌وجوی سراسری و جلوگیری از به‌دام‌افتادن در بهینۀ محلی، عملکرد ANFIS را به‏طور مطلوبی بهبود داده‏اند. مدل‏های پیشنهادی پتانسیل زیادی به‏منظور استفاده در سایر مسائل مرتبط با هیدرولوژی و منابع آب دار‌ند. 1 In this study, the use of genetic optimization algorithm (GA),Particle Swarm(PSO), the ant colony for continuum (ACOR)and differential evolution (DE),to develop and improve the performance of ANFIS were investigated. the monthly maximum temperatures in Isfahan during the period of 64 years (1951-2014), was simulated and analyzed. At first in a sensitivity analysis, the best entries for each prediction period (1 month, 1, 2 and 3 years) were selected. Then, the maximum temperature hybrid models by ANFIS-GA,ANFIS-PSO,ANFIS-DE,ANFIS-ACOR and ANFIS were examined. The performance of each model with regard to R2, RMSE and MAE were evaluated. The results showed that the ANFIS-GA, as the most appropriate model, increased ANFIS performance in R2 to by 0.06, 0.07, 0.08 and 0.12 and RMSE by 0.09, 0.09, 0.16 and 0.1, respectively, in 1 month and 1, 2 and 3 year. After, ANFIS-DE and ANFIS-PSO, respectively, had the best forecasting accuracy. On the other hand, ANFIS showed highest error and lowest R2, as the weakest model. The results showed that the proposed models, which use global search techniques and avoid being trapped in local optimum, could improve the performance of ANFIS favorably.Therefore, these models can be used in other areas related to hydrology and water resources. 763 775 مهران منوچهری‌‌نیا mehran manoochehri nia دانشجوی کارشناسی ارشد مهندسی و مدیریت منابع آب، دانشکدۀ مهندسی عمران، دانشگاه سمنان ایران mehran3560@gmail.com آرمین آزاد armin Azad دانشجوی کارشناسی ارشد مهندسی و مدیریت منابع آب، دانشکدۀ مهندسی عمران، دانشگاه سمنان ایران armin.azad1@yahoo.com سعید فرزین Saeed Farzin استادیار گروه مهندسی آب و سازه‌های هیدرولیکی، دانشکدۀ مهندسی عمران، دانشگاه سمنان ایران saeed.farzin@semnan.ac.ir حجت کرمی Hojat Karami استادیار گروه مهندسی آب و سازه‌های هیدرولیکی، دانشکدۀ مهندسی عمران، دانشگاه سمنان ایران hkarami@semnan.ac.ir الگوریتم‏های تکاملی هیبریدی الگوریتم ژنتیک جواب بهینۀ محلی دمای هوا سیستم استنتاج فازی- عصبی [1]. Asakareh H. ARIMA modeling of annual mean temperature of Tabriz city. 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0 ارزیابی تغییرات جریان آینده تحت سناریوهای مختلف تغییر اقلیم برحوضه آبریز رودخانۀ گاماسیاب Assessment of future runoff trends under multiple climate change scenarios in the Gamasiab river basin https://ije.ut.ac.ir/article_67504.html 10.22059/ije.2018.242453.732 0 تغییر اقلیم با توجه به ظرفیت محدود اکوسیستم‏ها می‏تواند موجب بروز مشکلاتی در تأمین آب قابل‏ دسترس برای رفع نیاز بشر شود و بر توانایی منطقه‏ای در مواجهه با بلایای طبیعی مرتبط با آب تأثیر بگذارد. یکی از مشخصه‏های طبیعی رودخانۀ گاماسیاب احتمال وقوع سیلاب و خطرات ناشی از آن است. بنابراین مطالعات هیدرولوژیکی تحت شرایط تغییر اقلیم برای ساماندهی و مدیریت آن ضروری است. لزوم استفاده از مدل‏های سری CMIP5 در پژوهش‏های جدید با توجه به دقت زیاد آنها و کمبود کارهای پژوهشی به وسیلۀ این مدل‏ها در کشور ما، سبب شد در پژوهش حاضر از خروجی‏های چهار مدل سری CMIP5 و دو سناریوی RCP2.6 و RCP8.5 برای آیندۀ نزدیک (2020-2049 میلادی) و آیندۀ دور (2070-2099 میلادی) استفاده شود. نتایج نشان می‏دهد مقدار بارندگی سالانۀ حوضه در پنج ایستگاه بررسی‌شده با توجه به سناریوهای مد نظر و دوره‏های زمانی مختلف بین 6/31- تا 8/52 درصد تغییر خواهد کرد. میانگین کمترین و بیشترین دمای ماهانه در ایستگاه کرمانشاه به ترتیب حداکثر تا C°75/2 و C°15/2 و در ایستگاه همدان به ترتیب حداکثر تا C°43/3 و C° 26/4 با توجه به سناریوهای مختلف افزایش می‏یابد. در این پژوهش از مدل SWAT برای شبیه‏سازی هیدرولوژیکی جریان استفاده شد. نتایج ضمن تأیید کارایی مدل SWAT در شبیه‏سازی دبی حوضه، نشان داد میزان تغییرات رواناب حاصل از خروجی مدل CSIRO-Mk تحت سناریوهای مختلف نسبت به دورۀ مشاهداتی از 3/42 – تا 8/17 درصد متغیر خواهد بود. نتایج تحقیق ضرورت به‌کارگیری تدابیر لازم به‌منظور سازگاری با تغییر اقلیم در سیاست‏های آتی مدیریت حوضۀ گاماسیاب تأکید می‌کند. 1 One of the natural characteristics of the Gamasiab River is the probability of occurrence of the flood and its hazard. Hydrological studies under climate change conditions are required to organize and manage it. Because of the necessity of using CMIP5 series models in new researches due to their high accuracy and lack of research using these models in our country, in the present study, four models of CMPI5 series and two scenarios RCP2.6 and RCP8.5 were used for the near future (2049-2020 AD) and the far future (2070-2099 AD). The results show the annual rainfall in five stations would vary from 52.8 to -31.6 percent according to the scenarios and different time periods. The average minimum and maximum monthly temperature at Kermanshah station increases to 2.75 ° C and 2.15 C°, and at Hamadan station, increases to 3.43 C° and 4.26 C°, respectively according to different scenarios. The SWAT model was used to simulate the hydrologic regime. The results while confirming the effectiveness of the SWAT model for simulating of river discharge, showed that changes in runoff rate using the output of the CSIRO-k3.6.0 model under different scenarios would indicate a change from 17.8 to -42.3 percent 777 789 شهاب‌الدین زارع‌زاده مهریزی Shahab oddin Zarezade Mehrizi دانش‌آموخته دکتری آبخیزداری، گروه مهندسی منابع طبیعی، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران ایران shahabazar13@gmail.com اسداله خورانی Asadollah Khoorani دانشیار، گروه جغرافیا، دانشکدۀ علوم انسانی، دانشگاه هرمزگان، بندرعباس، ایران ایران agroclimatologist@gmail.com جواد بذرافشان Javad Bazrafshan دانشیار، گروه مهندسی آبیاری و آبادانی، دانشگاه تهران، کرج، ایران ایران jbazr@ut.ac.ir ام‌البنین بذرافشان Omolbanin Bazrafshan استادیار، گروه مهندسی منابع طبیعی، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران ایران bazrafshan1361@gmail.com تغییر اقلیم حوضۀ گاماسیاب شبیه‏سازی [1]. 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0 ارزیابی وضعیت هیدروژئوشیمی آبخوان دشت سلماس با استفاده از روش‌های آماری چندمتغیره Hydrogeochemistry evaluation of Salmas plain aquifer using multivariate statistical methods https://ije.ut.ac.ir/article_67505.html 10.22059/ije.2018.242803.737 0 برای درک بهتر فرایندهای هیدروژئوشیمیایی در آبخوان دشت سلماس، روش‏های گرافیکی و آماری چند‏متغیره برای تفسیر نتایج به‌دست‌آمده از آنالیز نمونه‏ها به کار برده شدند. نتایج دیاگرام پایپر و دیاگرام بسط داده‌شدۀ دورو نشان‏ می‏دهند تیپ غالب آب‏ زیرزمینی بی‏کربنات کلسیم – منیزیم است و در قسمت‏های انتهایی و جنوب ‏شرقی دشت تیپ مختلط نیز دیده‏ می‌شود. آنالیز خوشه‏ای سلسله‌مراتبی (HCA)، پنج گروه مختلف آب ‏زیرزمینی را نشان‏ می‏دهد (HC1 تا HC5). این روش به خلاف روش‏های گرافیکی، توانایی نشان‌دادن تأثیر غلظت نیترات در طبقه‏بندی نمونه‏ها را دارد. نمودارهای استیف مربوط به گروه‏های HC1 تا HC5، سه منشأ اصلی برای آب‏ زیرزمینی در دشت سلماس را آشکار می‏کنند، به طوری که سه گروه HC1 تا HC3 آب‏هایی هستند که از سنگ‏های آهکی و دولومیتی نشئت گرفته‏اند. در گروه HC4، Clˉ و Na+ غالب هستند و آب‏های شور را نشان می‏دهند. گروه HC5 آب‏هایی هستند که تحت تأثیر انحلال ساده یا اختلاط قرار گرفته‏اند. براساس نتایج تحلیل عاملی (FA)، سه عامل اصلی مؤثر بر هیدروشیمی آبخوان دشت سلماس شناسایی شدند که 03/85 درصد از واریانس کل داده‏ها را شامل می‏شوند. عامل‏های اول و دوم زمین‏زاد هستند و عامل سوم انسان‏زاد است. با توجه به عامل نخست انحلال کانی‏های تبخیری در قسمت‏های شمالی، شمال ‏غربی دشت و جاهایی که سازندهای الیگو-میوسن (OMrb)، میوسن (Mur) و پلیوسن (PLQc) برونزد دارند در هیدروشیمی آب ‏زیرزمینی کارکرد اصلی را دارد. عامل دوم حاصل تعادل آب-سنگ است که در زمینۀ انحلال سنگ‏های حاوی ترکیبات کلسیم و منیزیم است و مرتبط به مناطق تغذیه است و عامل سوم ناشی از فعالیت‏های انسانی و استفاده از کودهای کشاورزی است. 1 For better understand of hydrogeochemical processes in the Salmas plain aquifer, this study adopted graphical methods and multivariate statistical techniques to analyze groundwater samples. The results of the Piper diagram and expanded Durov diagram reveals that the major groundwater type is Ca-(Mg)-HCO3 and mixing groundwater type exists in southeast of the Salmas plain. Hierarchical Cluster Analysis (HCA) identified five classes of groundwater type (HC1 to HC5). The hierarchical cluster analysis is able to show the influence of nitrate concentration in classification while the graphical methods cannot. The Stiff diagrams of five classes (HC1 to HC5) show three different sources of groundwater samples.The HC1 to HC3 classes indicate groundwater with limestone and dolomite origin. In HC4 class, Na+ and Clˉ are the dominate ions in groundwater samples and shows saline waters. The HC5 shows mixing groundwater. Using Factor Analysis (FA), we identified three factors that accounted for 85.03% of the total variance of the dataset. Factors 1 and 2 are reflected the natural hydrogeochemical processes and factor 3 is anthropogenic in the Salmas plain 791 800 کیوان نادری Keyvan Naderi دانشجوی دکتری هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز ایران keiwan.naderi@yahoo.com عطا الله ندیری Ata Allah Nadiri دانشیار، دانشکدۀ علوم طبیعی، دانشگاه تبریز ایران nadiri@tabrizu.ac.ir اصغر اصغری مقدم Asghar Asghari Moghaddam استاد، دانشکدۀ علوم طبیعی، دانشگاه تبریز ایران moghaddam@tabrizu.ac.ir مهدی کرد Mehdi Kord استادیار، دانشکدۀ علوم، دانشگاه کردستان ایران m.kord@uok.ac.ir آب‏ زیرزمینی آنالیز خوشه‏ای سلسله‌مراتبی تحلیل عاملی هیدروژئوشیمی [1]. Hounslow AW. Water Quality Data Analysis and Interpretation. 1nd ed. Florida: CKC press; 1995.##[2]. Jalali L, Moghaddam AA. Detection of hydrogeochemical status and salinity trend in Khoy plain aquifer by statistical and hydrochemical methods. Journal of Environmental Studies. 2013; 39(2): 113-122 [Persian].##[3]. Aghazadeh N, Moghaddam AA. 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0 ارزیابی کیفی و کمی سفره‌های آب زیرزمینی با به‌کارگیری روش WQI و آزمون من-کندال (مطالعۀ موردی: دشت سرخون-استان هرمزگان) Using the WQI method and the Man-Kendall test to assess the qualitative and quantitative status of groundwater aquifers (case study: Sarkhoon plain, Hormozgan province) https://ije.ut.ac.ir/article_67506.html 10.22059/ije.2018.244156.761 0 نبود دسترسی پایدار به منابع آبی، منازعات اجتماعی و اختلالات اقتصادی را به دنبال خواهد داشت. این موضوع در حال حاضر یکی از مسائل جدی حوضۀ آبخیز سرخون در استان هرمزگان است. از جمله اقداماتی که برای حل این مسئله می‏تواند مفید واقع شود، بررسی روند تغییر در خصوصیات کمی و کیفی منابع آبی این حوضه است. به این منظور، از داده‏های بارندگی و داده‏های مربوط به کیفیت و کمیت آب‏های زیرزمینی استفاده شده است. برای انجام این کار، از روش WQI به منظور تعیین کلاس کیفی آب و از آزمون من-کندال برای مشاهدۀ روند در متغیرهای بررسی‌شده استفاده شده است. نتایج بیان‌کنندۀ کاهش معنا‏دار و قطعی کیفیت و افت تراز آب زیرزمینی در دشت سرخون است. روند به‌دست‌آمده برای افت سطح ایستابی دشت با ضریب واریانس ۰۳/۰ و آمارۀ S من-کندال ۲۷۷- در سطح اطمینان ۹/۹۹ درصد معنا‏دار گزارش می‏شود. شیب خط به‌دست‌آمده برای افت تراز و افزایش مقدار WQI، که بیان‌کنندۀ کاهش کیفیت آب است، به‏ترتیب ۳۳/۰- و ۴۲/۳ بوده است. مقادیر گزارش‌شده برای شیب خط بیان‌کنندۀ افت ۳۳ سانتی‏متری و افزایش ۴/۳ واحدیWQI به ازای هر سال است. درنهایت، بر اساس درک و شناخت جامع به‌دست‌آمده از شرایط منطقۀ مطالعه‌شده، اقداماتی از جمله دریافت عوارض منابع طبیعی به منظور تعادل‏بخشی به کیفیت و کمیت منابع آب زیرزمینی منطقۀ مطالعه‌شده پیشنهاد شده است. 1 Lack of sustained access to water resources will lead to social disputes and economic disruption. This issue is currently one of the serious problems in Sarkhoon watershed, Hormozgan province. In this regard, the WQI method and the Man-Kendall test have been used to determine the water quality class andto observe the trend in the studied variables, respectively. The results showed that a significant and definite decrease in the quality and level of groundwater in the Sarkhoon plain. The obtained trend for declining water level in the plain had a coefficient of variance of 0.03 and S-Mann-Kendall statistic of -277 at 99.9% significance level. The slope of trend obtained for water level drop and the increase in WQI values, which indicates a decrease in water quality, were -33.3 and -42.3, respectively. The reported values for the slope of trend represent a decrease of 33 centimeters and an increase of 3/4 WQI unit per year. 801 811 بهزاد عادلی Behzad Adeli دانشجوی دکتری آبخیزداری، دانشگاه هرمزگان ایران adeli2info@gmail.com حنانه محمدی کنگرانی hanane kangarani دانشیار، گروه مرتع و آبخیزداری دانشگاه هرمزگان ایران kangarani@ut.ac.ir امیر سعدالدین amir sadodin دانشیار، گروه مرتع و آبخیزداری دانشگاه گرگان ایران amir.sadoddin@gmail.com ام‌البنین بذرافشان omolbanin bazrafshan استادیار، گروه مرتع و آبخیزداری دانشگاه هرمزگان ایران bazrafshan1361@gmail.com محسن آرمین mohsen armin استادیار، گروه مهندسی منابع طبیعی (آبخیزداری) و پژوهشکدۀ منابع طبیعی و زیست دانشگاه یاسوج ایران mohsenarmin2007@gmail.com آب زیرزمینی آزمون من-کندال تحلیل روند دشت سرخون روش WQI [1]. Adamowski, J.,Chan H. F. A wavelet neural network conjunction model for groundwater level forecasting." Journal of Hydrology. 2011; 407(1): 28-40.##[2]. Nazari, R., Joodavi, A. Applied Flow and contaminant Transport Modeling in Aquifer. 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0 اولویت‌بندی سیل‌خیزی زیرحوزه‌های آبخیز مهارلو در استان فارس با استفاده از پارامترهای مورفومتریک و مدل تصمیم‌گیری VIKOR Prioritization of Flood Inundation sub-watersheds of Maharlo Watershed in Fars Province Using Morphometric Parameters and VIKOR Decision Making Model https://ije.ut.ac.ir/article_67507.html 10.22059/ije.2018.244246.763 0 هدف از تحقیق حاضر، اولویت‏بندی مکانی سیل‏خیزی زیرحوزه‏های آبخیز مهارلو با استفاده از پارامترهای مورفومتریک و مدل تصمیم‏گیری VIKOR است. به این منظور، 13 پارامتر مورفومتری شامل درجۀ شیب، تراکم زهکشی، فراوانی آبراهه، ثابت‌نگه‌داشت آبراهه، بافت زهکشی، عدد ناهمواری، ضریب گردی، ضریب فشردگی، نسبت ناهمواری، طول جریان، ضریب فرم، ضریب کشیدگی و ضریب شکل و یک پارامتر اقلیم شامل بارندگی انتخاب شد. برای تعیین وزن پارامترها از مدل فرایند تحلیل سلسله‌مراتبی (AHP) استفاده شد‌. نتایج وزن‏دهی پارامتر‏ها با استفاده از مدل AHP نشان داد پارامترهای مورفومتری درجۀ شیب و تراکم زهکشی و پارامتر اقلیمی بارندگی به‏ترتیب با مقدار (206/0، 165/0 و 134/0) بیشترین وزن و تأثیر را در رخ‏داد سیل منطقۀ مطالعه‌شده داشتند، در حالی ‏که کمترین وزن و در پی آن حداقل تأثیر مربوط به ضریب شکل (012/0) بود. همچنین، به‏منظور اولویت‏بندی 53 زیرحوضۀ آبخیز مهارلو از مدل تصمیم‏گیری VIKOR استفاده شد. نتایج نشان داد زیرحوزۀ 34 براساس اولویت‏بندی سیل‏خیزی رتبۀ اول (082/0) ، زیرحوضۀ 31 رتبۀ دوم (110/0) و زیرحوضۀ 12 رتبۀ سوم (129/0) را به‏ خود اختصاص داده‏اند که باید برای انجام عملیات مدیریتی در اولویت قرار گیرند، در حالی ‏که زیرحوضۀ 42 آخرین رتبه [1] را در اولویت‏بندی سیل‏خیزی داشت‌ که بیان‌کنندۀ حساسیت بسیار کم آن به وقوع سیل است. 1 The current study aims to prioritize of spatial flooding of Maharlo Sub-watersheds using morphometric parameters and VIKOR decision model. So, to select 13 morphometric parameters including slope, drainage density, stream frequency, constant of channel maintenance, drainage texture rate, ruggedness number, circularity ratio, compactness coefficient, relief ratio, stream length, form factor, elongation ratio, and coefficient of shape, and 1 climatic parameter including rain have been used to determine the weight of the parameters of the AHP. The results showed that the morphometric parameters such as slope and drainage density, and the climatic parameter of the rain, with values of (0.206, 0.165 and 0.134) had the most weight and effect on flood event, while the least weight and consequently the least effect on the coefficient is related to shape (0.122) factor. Also, for prioritizing used from 53 sub-watersheds based on VIKOR decision model. The results showed that sub-watershed 34 based on prioritization of flood first ranked (0.082), The second sub- watershed 31 (0.110) and sub- watershed 12 third ranked (0.129) given specialty, which must have to be prioritized for management operations. While sub-watershed 42 has the last ranked (1) in prioritization of flood, which is indicate low sensitivity to flood events. 813 827 مهدیس امیری Mahdis Amiri دانشجوی کارشناسی ارشد مدیریت مناطق بیابانی، دانشکدۀ کشاورزی، دانشگاه شیراز ایران mahdisamiri94@gmail.com حمیدرضا پورقاسمی Hamidreza Pourghasemi استادیار بخش مهندسی منابع طبیعی و محیط زیست، دانشکدۀ کشاورزی، دانشگاه شیراز ایران hamidreza.pourghasemi@yahoo.com علیرضا عرب‌عامری Alireza Arabameri دانشجوی دکتری ژئومورفولوژی دانشگاه تربیت مدرس ایران alireza.ameri91@yahoo.com اولویت‏بندی سیل‏خیزی پارامترهای مورفومتری حوزۀ آبخیز مهارلو فرایند تحلیل سلسله‌مراتبی مدل VIKOR [1]. Badri B, Zare R, Honarbakhsh A, Atashkhar, F. Prioritization of flood potential Beheshtabad Sub- watershed. 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0 ارزیابی منطقه‌ای مخاطرۀ سیل در مقیاس زیرحوضه با استفاده از سنجش از دور و مدل منطق فازی (مطالعۀ موردی: حوضۀ آبخیز مرند) Regional Flood Hazard assessment at the Sub-basin Scale Using Remote Sensing & Fuzzy logic https://ije.ut.ac.ir/article_67508.html 10.22059/ije.2018.245661.775 0 ازجمله مهم‌ترین مخاطرات تهدیدکنندۀ جوامع بشری سیل است. در پژوهش حاضر با یکپارچه‌سازی مدل منطقی برآورد رواناب اوج، به ارزیابی مخاطرۀ سیل حوضۀ آبخیز مرند در مقیاس زیرحوضه با استفاده از سنجش از دور و GIS پرداخته شده است. پس از تعیین ضریب رواناب با استفاده از لایه‏های پوشش/کاربری اراضی تهیه‌شده از تصاویر ماهوارۀ Sentinel 2A، نقشۀ شیب تهیه‌شده از DEM 30 متری سنجندۀ ASTER و گروه‏های هیدرولوژیک خاک، با استفاده از میزان تعیین‌شده که با محاسبۀ زمان تمرکز زیرحوضه‏ها تعیین شد، رواناب اوج برای همۀ زیرحوضه‏ها محاسبه شد. در ادامه، با استفاده از تابع عضویت خطی در مدل منطق فازی، یکپارچه‌سازی دو لایۀ رواناب اوج‏ تهیه شده و لایۀ ارتفاع، بین صفر و یک فازی‏سازی شدند و سپس با اعمال همپوشانی ضربی وزن‏های مشخص براساس شاخص مخاطرۀ سیل (FHI)، به هریک از این دو لایه و سپس جمع نتایج آنها، نقشۀ توزیع مخاطرۀ سیل تهیه شد. با کلاس‏بندی نقشۀ مخاطرۀ تهیه‌شده در پنج کلاس شامل بسیار کم‏خطر، کم‌خطر، متوسط، پرخطر و بسیار پرخطر با نتایج به‌دست‌آمده از سیستم اطلاعات جغرافیایی مشارکتی یا PGIS و ورود این اطلاعات به ماتریس درهم‌ریختگی میزان دقت نقشه‏های تهیه‌شده 83/87 درصد تعیین شد. 1 Flood is among the most important environmental hazards, broadly threatening human societies and their assets. In this research, by integrating the Rational model to estimate peak runoff into Marand basin flood hazard on a sub-basin scale, assessments are accomplished using remote sensing and GIS. After determining the runoff coefficient land cover/use layers were taken from satellite images of the Sentinel 2A, and the slope map was derived from the ASTER DEM 30m and soil hydrological groups, using the specified amount of rainfall hamely intensity in mode of 1-hour peak runoff was calculated. Using linear membership function in fuzzy logic model, integrating prepared this peak runoff and elevation lines between zero and one were fuzzy and then by applying multiple weight tangles to each of these two layers we collected their results, and the flood hazards distribution map was prepared. With the implementation of prepared risk map in fifth grade the classes include very low-risk, low risk, medium, high and very high risk with the results of GIS partnership or PGIS and entering this information into the confusion matrix. The accuracy of prepared maps was determined to be about 87.83%. 829 841 سیدمحمد موسوی SeidMohamad Mousavi کارشناسی ارشد سنجش از دور و GIS، دانشگاه تبریز ایران mohamad.mousavi368@gmail.com شهرام روستائی Shahram Roostaei استاد گروه سنجش از دور و GIS، دانشکدۀ برنامه‌ریزی و علوم محیطی، دانشگاه تبریز ایران roostaei@tabrizu.ac.ir هاشم رستم‌زاده Hashem Rostamzadeh استادیار گروه آب و هواشناسی، دانشکدۀ برنامه‌ریزی و علوم محیطی، دانشگاه تبریز ایران hrostamzadeh@gmail.com حوضۀ آبخیز مرند مخاطرۀ سیل منطق فازی PGIS Sentinel 2A [1]. Chang LF, Lin CH, Su MD. Application of geographic weighted regression to establish flood-damage functions reflecting spatial variation. Water Sa. 2008 Feb;34(2):209-16.##[2]. Ahmadi Ilekhchi, A, Haj Abasi, M, Jalalian, A. The Effect of Rural Areas and Land Use Change on Runoff Production, Journal of Agricultural Science and Natural Resource. 2002; 6; 25-36(Persian).##[3]. Malekian, A, Oftadegan khozani, A, Ashourzad, Gh. Flood Hazard Zoning in Watershed Scale using Fuzzy Logic (Case study: Akhtar Abad Watershed). Journal of Natural Geography Research. 2012; 4:44; 131-152 (Persian).##[4]. Beroshke, A, Sokouti, R, Montaseri, M, Ghahremani, A, Investigating the phenomenon of flood and its zoning using satellite imagery. Seventh International River Engineering Workshop. University of Shahid Chamran. 2006, pp 8(Persian).##[5]. Nyarko BK. Application of a rational model in GIS for flood risk assessment in Accra, Ghana. Journal of Spatial Hydrology. 2002 Jun 7;2(1).##[6]. Moel HD, Alphen JV, Aerts JC. Flood maps in Europe-methods, availability and use.##[7]. Ghanavati, E, Flood risk zoning in Karaj using fuzzy logic. Geography and environmental hazards. 2013; 8; 113-131(Persian).##[8]. Nazmfar, H, Beheshti Javid, A, Fathi, M H, Potential flooding and flood risk zonation using fuzzy logic model (Case study: ghuri chay river catchment). Second International Conference on Environmental Hazards, 7th and 8th of November, Kharazmi Faculty of Tehran, 2013: pp 9(Persian).##[9]. Dastourani, M, Hayatzadeh, M, Fathzadeh, A, Hakimzadeh, M A. Investigating the Efficiency of Empirical Relationships in Estimating Flood peak in desert areas of Central Iran. Geography and Development Magazine. 2014; 36; 145-160##[10]. Ghanavati, A, Babaei Aghdam, F, Hemmati, T, Rahimi, M. Flood Potential Zoning Using Fuzzy Logic Model in GIS Environment (Case Study of Khayavchi Meshkinshshahr River Basin), Hydrogeomorphology Journal. 2015; 3; 121-135(Persian).##[11]. Beheshti Javid, A, Amani, S, Shahi Boyaghchi, M. Assessing the flood potential of the Karnawah River using the fuzzy logic model, the first international congress on land, space and clean energy. 2015: pp7 (Persian).##[12]. Sadeghi Goghari, M, Eskandari Damaneh, H, AZareh, A, Flood risk zoning using fuzzy logic. Case study, Isfahan, 7th International Conference on Integrated Management of Crisis, Tehran, Permanent Secretariat of the International Conference on Integrated Management of the Crisis. 2015: pp 10(Persian).##[13]. Bhatt GD, Sinha K, Deka PK, Kumar A. Flood hazard and risk assessment in Chamoli District, Uttarakhand using satellite remote sensing and GIS techniques. International Journal of Innovative Research in Science, Engineering and Technology. 2014 Aug;3(8):9.##[14]. Asumadu-Sarkodie S, Owusu PA, Jayaweera MP. Flood risk management in Ghana: A case study in Accra. Advances in Applied Science Research. 2015 May 4;6(4):196-201.##[15]. Asare-Kyei D, Forkuor G, Venus V. Modeling flood hazard zones at the sub-district level with the rational model integrated with GIS and remote sensing approaches. Water. 2015 Jul 6;7(7):3531-64.##[16]. 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WSEAS Transactions on Information Science and Applications. 2009 Mar 1;3(6):477-89.##[21]. Feizizadeh, B, Jafari, F, Nazmfar, H. Application of Remote Sensing Data in Detection of Land Use Change, Fine Arts Magazine. 2008; 34; pp 20 (Persian).##[22]. Mahdavi, M, Applied Hydrology, Vol. 2, Tehran University Press. 2013 (Persian).##[23]. Modeling the spatial distribution of lightning and thunder storms using satellite images in the northwest of the country. Master thesis, Department of Natural Geography, Tabriz University. 2007(Persian).##[24]. Valizadeh Kamran, Kh, Nasiri Ghaleh Bin, S, Investigation of Thunderstorm Rainfall in the Highlands of the Northwest of Iran, First National Conference on Geography, Urban Development and Sustainable Development, Tehran, Koomesh Environmental Society, Aviation University. 2013 (Persian).##[25]. Alizadeh, A, Principle of Applied Hydrology. Ferdowsi University of Mashhad Press, Iran, 2015, 650 (Persian).##[26]. 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0 تعیین نقش جنگل‏‌های بلوط در حفاظت از کیفیت آب بر پایۀ تابع تولید خدمات در حوضۀ زوجی تنگ‌شول فارس Determining of the Oak forests’ role on protecting water quality based on the service function in Tang-e Shool, Fars https://ije.ut.ac.ir/article_67553.html 10.22059/ije.2018.249562.800 0 آب‏‌های سطحی از مهم‌ترین منابع تأمین آب‏‌های آشامیدنی هستند که تحت تنش‏‌های محیطی و انسانی قرار دارند. تحقیقات زیادی اثر مثبت پوشش جنگلی را بر کیفیت آب نشان داده‌‌اند، ولی نبود اطلاعات کمی و اقتصادی در زمینۀ تأثیر جنگل بر کیفیت آب، موجب شده است تا این مسئله فقط به طور کیفی بیان شود. اطلاعات کمی و به‌ویژه تأثیر آن بر هزینۀ پالایش آب در ایران بسیار کم است و یا موجود نیست. به این منظور، در پژوهش حاضر زیرحوضه‏‌های جنگلی با درصدهای تاج‏ پوشش مختلف در حوضۀ تنگ‌شول کامفیروز فارس انتخاب و شاخص کیفیت (WQI) آب با استفاده از متغیرهای فیزیکی‌ـ شیمیایی رواناب‏‌های حاصل از بارندگی تعیین شد. همچنین، برای نخستین‌بار تابع تولید خدمت کیفیت آب در جنگل با درنظرگرفتن سایر ویژگی‏‌های جنگل به‏ طور کمی برآورد شد. نتایج نشان داد شاخص کیفیت آب در درصدهای تاج پوشش بیشتر از طبقۀ 20ـ 30، در مقیاس کیفی خوب قرار دارند، همچنین با افزایش هر یک درصد تاج‏ پوشش جنگلی، شاخص کیفیت به میزان 8/0 درصد بهبود می‌‌یابد و افزایش هر یک درصد مساحت زیرحوضۀ جنگلی، سبب کاهش 14/0درصدی کیفیت آب‏‌ می‏‌شود. سازند آغاجاری نیز نسبت به آسماری اثر منفی و خاک شنی لومی نسبت به خاک لومی و رسی اثر مثبتی بر کیفیت آب در منطقه دارند. یافته‌های پژوهش افزایش تاج‏ پوشش جنگلی برای حفاظت از کیفیت آب تا 30 درصد، هزینه‏‌های تصفیه را به کمترین حد می‌رساند و آب‏‌های تأمین‌‌شده از این جنگل‏‌ها‏‌ می‏‌تواند مستقیم به مصرف شرب انسانی برسد. 1 Surface water is an important source for drinking water, which are under environmental and anthropogenic stresses. There are several evidences confirming that forest cover positively affects water quality. However, little quantitate information is available regarding the impacts of forest canopy cover on purification of drinking water quality in Iran. To achieve this, we selected several forested catchments with different ground canopy cover in Tang-e-Shool forest located in the Fars province and Water Quality Index (WQI) were measured by using physicochemical parameters. The production function of water quality service was also estimated by taking into account other characteristics of the forested micro catchments. Our results showed that the WQI in 30% crown cover is in a good quality scale. Also with an increase of 1% in the crown cover, WQI will improve by 0.8% and an increase of 1% in the area of the micro catchment causes a decline of 0.14 percent of WQI. Also, Aghajari Formation has a negative effect on WQI compare to Asmari. According to this study increasing the crown cover to protect water quality by up to 30%, will minimize treatment costs and the waters provided by these forests can be used for human drinking purposes. 843 853 طوبی روستا Touba Rousta دانشجوی دکتری اقتصاد و مدیریت جنگل، گروه جنگل‌داری و اقتصاد جنگل، دانشکدۀ منابع طبیعی دانشگاه تهران، کرج ایران rousta.t@ut.ac.ir علی اکبر نظری سامانی Ali akbar Nazari samani دانشیار گروه احیای مناطق خشک و کوهستانی، دانشکدۀ منابع طبیعی دانشگاه تهران، کرج ایران aknazari@ut.ac.ir سید مهدی حشمت‌الواعظین Seyed Mahdi Heshmatolvaezin دانشیار گروه جنگل‌داری و اقتصاد جنگل، دانشکدۀ منابع طبیعی دانشگاه تهران، کرج ایران mheshmat@ut.ac.ir منصور زیبایی Mansour Zibaei استاد بخش اقتصاد کشاورزی، دانشکدۀ کشاورزی دانشگاه شیراز، شیراز ایران zibaei@shirazu.ac.ir پدرام عطارد Pedram Attarod دانشیار گروه جنگل‌داری و اقتصاد جنگل، دانشکدۀ منابع طبیعی دانشگاه تهران، کرج ایران attarod@ut.ac.ir سید کاظم بردبار Seyed Kazem Bordbar استادیار پژوهشی مرکز تحقیقات و آموزش کشاورزی فارس، سازمان تحقیقات آموزش و ترویج کشاورزی ایران ایران sbordbar86@gmail.com پارامترهای فیزیکی‌ـ شیمیایی جنگل‏‌های بلوط درصد تاج پوشش شاخص کیفیت آب [1]. 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0 مروری بر روش‏های حذف فلزات سنگین از محیط‏های آبی A review of methods for removing heavy metal from aqueous media https://ije.ut.ac.ir/article_67554.html 10.22059/ije.2018.249854.804 0 ‌مقالۀ حاضر به طور خلاصه به تعریف فلزات سنگین از جنبه‏های مختلف، منابع تولید و آثار منفی آنها بر سلامتی انسان، استانداردها و قوانین تنظیمی مراجع مختلف برای رعایت حدود مجاز و در نهایت نیز به بررسی روش‏های تصفیه، جداسازی و حذف این عناصر پرداخته است. در خصوص روش‏های جداسازی، محدودۀ وسیعی از فرایندها و معایب و مزایای هریک از انواع قدیمی تا فناوری‏های نوین، بررسی شده که شامل رسوب‏دهی شیمیایی، انعقاد لخته‏سازی، شناورسازی، تبادل یونی، تصفیۀ الکتروشیمیایی، فیلتراسیون غشایی و جذب سطحی می‌شوند. در این میان، به جذب سطحی به عنوان یک رویکرد ساده ولی کارآمد، به‌طور ویژه پرداخته شده است و انواع جاذب‏ها شامل کربن فعال، نانولوله‏های کربنی، گرافن اکساید، جاذب‏های زیستی مطالعه ‌شده‌‏اند. امروزه، مواد نانو با توجه به ویژگی‏های منحصربه‌فردی همچون مساحت سطح زیاد، سایت‏های فعال فراوان و ظرفیت جذب بسیارخوب، کاربردهای چشم‏گیری در تصفیۀ محیط‏های آب و فاضلاب از خود نشان داده‏اند. در این میان، نانوذرات مغناطیسی اکسیدهای آهن به عنوان جاذب‏های مقرون‏به‌صرفه، با راندمان بالا و دوستدار محیط‏ زیست بررسی شده‏اند. 1 The present paper briefly describes heavy metals from various aspects, the sources of production and the negative effects of these metals on human health, the standards and regulations of various agencies to comply with the permissible limits and finally, it also explores the methods of purification, separation and removal of these elements. Regarding treatment methods, a wide range of processes and disadvantages and advantages of each of the old types to the new technologies has been investigated, including chemical precipitation, coagulation-flocculation, flotation, ion exchange, electrochemical treatment, membrane filtration and adsorption. In this regard, absorption is considered as a simple but efficient approach, and has been specifically addressed and sorbents have been studied including active carbon, carbon nanotubes, graphene oxides and biosorbents. Today, nanoscale materials have shown significant applications in the treatment of aquatic environments, due to their unique features such as high surface area, large active sites and high absorption capacity. In this regard, magnetic nanoparticles of iron oxides are considered as cost-effective, high-efficiency and environmentally friendly adsorbents. 855 874 محمدحسین فاتحی Mohamad Hosein Fatehi دانشجوی دکتری دانشکدۀ مهندسی شیمی و نفت دانشگاه صنعتی شریف ایران mhfd63@yahoo.com جلال شایگان Jalal Shayegan استاد دانشکدۀ مهندسی شیمی و نفت دانشگاه صنعتی شریف ایران shayegan@sharif.edu محمد ذبیحی Mohamad Zabihi استادیار دانشکدۀ مهندسی شیمی دانشگاه صنعتی سهند تبریز ایران zabihi@sut.ac.ir جذب سطحی روش‏های حذف فلزات سنگین محیط‏های آبی نانوذرات مغناطیسی [1]. IUPAC Technical Report, “Heavy Metals”— A Meaningless Term? Pure and Applied Chemistry. 2002; 74(5): 793–807.##[2]. U.S. Environmental Protection Agency. EPA’s Terms of Environment, 2000.##[3]. World Health Organization. Adverse Health effects of Heavy Metals in Children, 2011.##[4]. Chowdhury S, Jafar Mazumder M.A, Al-Attas O, Husain T. Heavy metals in drinking water: Occurrences, implications, and future needs in developing countries. 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0 مروری بر اصول زیست‌پالایی نیترات از آب با روش نیترات‌زدایی فاز جامد Nitrate removal of water and wastewater by solid phase denitrification https://ije.ut.ac.ir/article_67555.html 10.22059/ije.2018.249987.805 0 از سال‏ها پیش آلودگی نیترات در آب و خاک از نگرانی‏های اساسی در مسائل زیست‌محیطی دنیا بوده است. ترکیبات حاوی نیتروژن رهاشده در محیط زیست می‏تواند سبب بروز مشکلات جدی مانند یوتریفیکاسیون رودخانه‏ها و موجب بیماری خطرناکی به نام متهموگلوبینمیا و سایر اختلالات در سلامتی انسان شود. یکی از اهداف مهم تصفیۀ فاضلاب، حذف نیتروژن است که از طریق فرایندهای فیزیکی، شیمیایی و بیولوژیکی انجام می‏شود که روش‏های بیولوژیکی حذف نیتروژن مؤثرتر و اقتصادی‏تر‌ند. دنیتریفیکاسیون از فرایندهای اصلی حذف نیترات در آب‏هاست که فرایندی بدون نیاز به اکسیژن محسوب می‏شود و در آن باکتری‏ها از نیترات به عنوان گیرنده برای به‏دست‌آوردن انرژی به منظور رشد استفاده می‏کنند. دنیتریفیکاسیون فاز جامد یک فناوری نوظهور است که در سال‏های اخیر مورد توجه قرار گرفته است که در آن از پلیمرهای زیست‌تخریب‌پذیر به عنوان منبع کربنی و حامل بیوفیلم برای میکروارگانیسم‏های نیترات‌زدا استفاده می‏شود. این روش راهکاری امیدوارکننده و ارزان برای حذف نیترات از آب و فاضلاب است و در آینده می‏توان توجه بیشتری معطوف به حذف هم‌زمان نیترات و سایر آلاینده‏ها از آب‏ به وسیلۀ دنیتریفیکاسیون فاز جامد شده و از این طریق سلامت محیط و انسان تضمین شود. 1 Years ago nitrate pollution in water and soil has been a major concern in the world's environmental issues. Nitrogen-containing compounds in the environment can cause new problems, such as river eutrofication and a dangerous disease called methemoglobinemia and other disorders in human health. One of the most important wastewater treatment goals is the removal of nitrogen, which is carried out by chemical, physical and biological processes that biological methods of nitrogen removal are more efficient and economical. Nitrification is one of the main processes for the removal of nitrate in water, a process that requires no oxygen, in which bacteria use from nitrate as an electron receiver to obtain energy for growth. Solid-phase denitrification process is an emerging technology which has received increasing attention in recent years. It uses biodegradable polymers as both the carbon source and biofilm carrier for denitrifying microorganisms this process is a promising technology for the removal of nitrate from water and wastewater. In the future, more attention can be devoted to the simultaneous removal of nitrate and other pollutants from water by Solid-phase denitrification, thereby ensuring the health of the environment and human. 875 889 مهدی ضرابی Mehdi Zarabi استادیار دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران mzarabi@ut.ac.ir بهاره کریمی دونا BAHAREH KARIMI DOUNA دانشجوی کارشناسی ارشد زیست‌فناوری صنعت و محیط زیست، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران baharehkarimidouna72@gmail.com اشرف السادات حاتمیان زارمی Ashraf sadat Hatamian zaremi استادیار دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران hatamian_a@ut.ac.ir آلودگی نیترات تصفیۀ فاضلاب دنیتریفیکاسیون فاز جامد میکروارگانیسم‏های نیترات‏زدا [1]. Choi J, Maruthamuthu. S, Lee H, Hyun Ha T, Bae J. Nitrate removal by electro-bioremediation technology in Korean soil. Journal of Hazardous Materials. 2009;168: 1208–1216##[2]. Bahadoran Z, Mirmiran P, Ghasemi A, Kabir A, Azizi F, Hadaegh F. 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0 تدوین مدل بهینه ‏سازی فازی برای بهره برداری تلفیقی از آب سطحی و آب زیرزمینی (مطالعۀ موردی: دشت آستانه-کوچصفهان) Developing Fuzzy Optimization Model for Conjunctive Use of Surface and Ground Water, Case Study: Astaneh-Koch Esfahan Plain https://ije.ut.ac.ir/article_67557.html 10.22059/ije.2018.250098.807 0 در تحقیق حاضر، مدل بهینه‏سازی تماماً فازی با درنظرگرفتن عدم قطعیت‏ها برای برداشت تلفیقی آب سطحی و زیرزمینی برای تأمین نیاز کشاورزی ارائه شده است. تراز آب زیرزمینی دشت آستانه-کوچصفهان با نرم‌افزار GMS شبیه‏سازی و نتایج آن به‌صورت روابط رگرسیونی افت – برداشت به‌عنوان قید مدل بهینه‏سازی استفاده شد. در ادامه، مدل بهینه‏سازی فازی به دو روش کومار و جایالاکیشمی ابتدا به حالت صریح تبدیل شده و با نرم‏افزار GAMS اجرا شد. نتایج بیشترین افت را در هر دو روش 25/1 متر در ماه فروردین برای ناحیۀ راست کانال سنگر و برای ناحیۀ چپ آن 25/1 متر در ماه مرداد نشان داد. بیشترین کمبود در روش کومار، در ناحیۀ چپ سنگر مربوط به سال 1381 بود که 82/59 درصد از نیازها تأمین شد، در حالی که در ناحیۀ راست 76/56 درصد از نیازها در سال 1393 تأمین شد. در روش جایالاکیشمی در بدترین شرایط، بیشترین کمبود در سال 1377 و 1393 بود که 5/66 و 96/60 درصد از نیازها به‌ترتیب برای ناحیۀ چپ و راست سنگر تأمین شد و نیز در روش کومار، در شرایط بیشترین کمبود مجموع چپ و راست سنگر در سال 1377، تأمین نیازها معادل حدود 9/65 درصد بود. در حالی که، در روش جایالاکیشمی این مقدار 5/66 درصد در سال 1377 بوده و در وضع موجود این درصد تأمین در بدترین شرایط 54 درصد است. مدل بهینه‏سازی فازی ارائه‌شده با درنظرگرفتن عدم قطعیت‏ها نسبت به مدل‏های کلاسیک برتری دارد و می‏تواند برای مدیریت تلفیقی تأمین آب کشاورزی به کار رود. 1 In this study, an entirely fuzzy optimization model is presented for conjunction use of surface and groundwater. Groundwater level in Astaneh-Koch Esfahan Aquifer was simulated using the GMS Model, while its results were used as a constraint in optimization model. Then, Kumar and Jayalakishimi fuzzy optimization methods were solved by applying the GAMS software. Maximum water supply shortage in Kumar method for left-side of Sangar was in 2009 that 58.36% of demands was satisfied. Also this value in the right-side was calculated about 56.76% in 2008. In the Jayalakishimi method, the maximum water supply shortage was obtained in 1998 and 2014 that 66.5% and 60.96% of demands for left and right-side are satisfied, respectively. On the other hand, for this method, in the situation of total maximum shortage for left and right sides of Sangar channel, supply percentage of water needs was about 65.9% in 1998, while for the Jayalakishimi method, it was obtained about 66.5% in 1998. Also in the current situation, the supply percentage in the worst conditions is 54%. Regarding consideration of uncertainties, the proposed fuzzy optimization model can be applied to manage the conjunctive water supply for agriculture. 891 905 سامی قوردویی میلان Sami Ghordoyee Milan کارشناسی ارشد مهندسی منابع آب، گروه مهندسی آبیاری و زهکشی، پردیس ابوریحان، دانشگاه تهران ایران s.milan@ut.ac.ir عباس روزبهانی Abbas Roozbahani استادیار گروه مهندسی آبیاری و زهکشی، پردیس ابوریحان، دانشگاه تهران ایران roozbahany@ut.ac.ir محمد ابراهیم بنی حبیب Mohammad Ebrahim Banihabib دانشیار گروه مهندسی آبیاری و زهکشی، پردیس ابوریحان، دانشگاه تهران ایران banihabib@ut.ac.ir سامان جوادی Saman Javadi استادیار گروه مهندسی آبیاری و زهکشی، پردیس ابوریحان، دانشگاه تهران ایران javadis@ut.ac.ir روش جایالاکیشمی روش کومار‌ سفیدرود شبیه‏سازی آب زیرزمینی MODFLOW [1]. Todd KD, Mays LW. Groundwater hydrology. John Wiley & Sons, Inc, NJ. 2005.##[2]. Buras N. Conjunctive operation of dams and aquifers. Journal of the Hydraulics Division. 1963; 89(6):111-31.##[3]. Abadi A, Kholghi A, Bozorg Hadad O, Mohammadi K. Providing operational rules for real-time management of surface water and underground water resources. MSc Thesis.2010. University of Tehran, Pardis-Karaj. [Persian]##[4]. Bazargan-Lari, MR, Kerachian R, Mansoori A. A Conflict-Resolution Model for the Conjunctive Use of Surface and Groundwater Resources that Considers Water-Quality Issues: A Case Study. Environmental management. 2009; 43(3):470-82.##[5]. Karamouz M, Tabari. MMR, Kerachian R. Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water International. 2007; 32(1):163–176.##[6]. Mohammad Rezapour Tabari M, Ebadi T, Maknon R. Development of a Smart Model for Groundwater Level Prediction Based on Aquifer Dynamic Conditions. Water and Wastewater Journal. 2010; Volume: 21(4): 70-80. [Persian]##[7]. Azari, A, Radmanesh F. Simulation-Multi-Purpose Optimization for Integrated Water Resources Management in Surface Water and Underground Water Interactions Using Genetic Algorithm (Case Study: Dasht Daz), MSc Thesis.2013; Shahid Chamran University of Ahvaz. [Persian]##[8]. Safavi H.R, Alijanian M.A. Optimal Crop Planning and Conjunctive Use of Surface Water and Groundwater Resources Using Fuzzy Dynamic Programming. Journal of irrigation and drainage engineering © ASCE. 2011; 383-397.##[9]. Safavi HR, Chakraei I, Kabiri-Samani A, Golmohammadi MH. Optimal reservoir operation based on conjunctive use of surface water and groundwater using neuro-fuzzy systems. Water resources management. 2013; 27(12):4259-75.##[10]. Safavi HR, Enteshari S. Conjunctive use of surface and ground water resources using the ant system optimization. Agricultural Water Management. 2016; 173:23-34.##[11]. Tabari MMR. Conjunctive Use Management under Uncertainty Conditions in Aquifer Parameters. Water Resources Management. 2015; 29(8):2967-86.##[12]. Chang L-C, Chu H-J, Chen Y-W. A fuzzy inference system for the conjunctive use of surface and subsurface water. Advances in Fuzzy Systems. 2013; 2013:2.##[13]. Rezaei F, Safavi HR, Mirchi A, Madani K. F-MOPSO: an alternative multi-objective PSO algorithm for conjunctive water use management. Journal of Hydro-environment Research. 2017; 14:1-18.##[14]. Safavi H, Rezaei F. Conjunctive use of surface and ground water using fuzzy neural network and genetic algorithm. Iranian Journal of Science and Technology Transactions of Civil Engineering. 2015; 39(C2):365.##[15]. Rezaei F, Safavi HR, Zekri M. A hybrid fuzzy-based multi-objective PSO algorithm for conjunctive water use and optimal multi-crop pattern planning. Water Resources Management. 2017; 31(4):1139-55.##[16]. Langeroudi M.R, Kerachian R. .Developing Operating Rules for Conjunctive Use of Surface and Groundwater Considering the Water Quality Issues. KSCE Journal of Civil Engineering. 2014; 18(2):454-461.##[17]. Chen, Y. W., Chang, L. C., Huang, C. W. and Chu, H. J. Applying genetic algorithm and neural network to the conjunctive use of surface and subsurface water. Water resources management.2013; 27: 4731-4757.##[18]. Sahoo, B., Lohani, A. K. and Sahu, R. K. Fuzzy multiobjective and linear programming based management models for optimal land-water-crop system planning. Water resources management. 2006;20: 931-948.##[19]. Li, M., Fu, Q., Singh, V. P., Ma, M. and Liu, X. An intuitionistic fuzzy multi-objective non-linear programming model for sustainable irrigation water allocation under the combination of dry and wet conditions. Journalof Hydrology. 2017;##[20]. Asaadi Mehrabani, M. Banihabib, M. Roozbahany A. Fuzzy Linear Programming Model for the Optimization of Cropping Pattern in Zarrinehroud Basin. Iran Water Resources Research. 2018; 14(1), 13-24. [Persian]##[21]. Continuation of the study of the plains with a quantitative and qualitative quantitative and qualitative study network of Astaneh-Kochi Esfahan 1389-1390, [Persian]##[22]. Pandam Consulting Engineers. Improvement of irrigation and drainage network in Gilan aquifer. 1383; Volume Four. [Persian]##[23]. Harbaugh AW, Banta ER, Hill MC, McDonald MG. MODFLOW-2000, the U.S. Geological Survey modular groundwater model.2000; Report No. 00–92, U.S. Geological Survey, Denver.##[24]. Klir GJ, Yuan B. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A. Zadeh: World Scientific Publishing Co., Inc.; 1996.##[25]. Zadeh, L.A. Fuzzy sets. J. of Information and Control, 1965; 8(3), 338-353.##[26]. Kumar A, Kaur J, Singh P. Fuzzy optimal solution of fully fuzzy linear programming problems with inequality constraints. 2010.##[27]. Kumar A, Kaur J, Singh P. A new method for solving fully fuzzy linear programming problems. Applied Mathematical Modelling. 2011; 35(2):817-23.##[28]. 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0 مدل‏ سازی جریان در یک قوس از رودخانۀ طبیعی بر اساس مدل‏ های مختلف آشفتگی (مطالعۀ موردی: رودخانۀ دوآب صمصامی) Flow modeling in a bend of a natural river based on different turbulence models(case study:Doab Samsami River) https://ije.ut.ac.ir/article_67558.html 10.22059/ije.2018.252171.827 0 مدل‏سازی جریان در رودخانه‏ها با توجه به مسیر پیچان‏رودی آنها بسیار پیچیده بوده و اغلب مستلزم استفاده از یک مدل عددی قوی برای پیش‏بینی آثار آشفتگی است. همچنین، انتخاب نوع مدل آشفتگی می‌تواند در شبیه‏سازی و بررسی خصوصیات جریان مؤثر ‏باشد. انواع مختلفی از مدل‏های آشفتگی در مدل عددی SSIIM قابل استفاده است که در تحقیق حاضر با هدف بررسی کارایی مدل‏های آشفتگی، از سه نوع مختلف مدل آشفتگی k-Ԑ یعنی نوع استاندارد، بر اساس سرعت آب و RNG به منظور شبیه‏سازی خصوصیات جریان در نقاط مختلف از مقطع عرضی 45 درجه از یک قوس تند واقع در رودخانۀ دوآب صمصامی از سرشاخه‏های کارون بزرگ استفاده شد. با مقایسۀ مقادیر اندازه‌گیری‌شدۀ مؤلفه‏های سرعت، نتایج به‌دست‌آمده از مدل‌ها مشخص شد. مدل k-Ԑ استاندارد در تعیین مؤلفۀ قائم سرعت و نوع مدل k-Ԑ بر اساس سرعت آب برای مؤلفۀ طولی سرعت دقت بیشتری دارد که در مجموع قابلیت کلیۀ مدل‏های آشفتگی یادشده مناسب ارزیابی می‌شود. همچنین، درنظرگرفتن دقیق ناهمواری‏های بستر و زبری کنارۀ‏ کانال رودخانه در مدل عددی می‌تواند بر افزایش صحت نتایج مدل تأثیر بسزایی داشته باشد. 1 Flow modeling in rivers is very complicated due to their meandering path. Therefore, the use of an accurate numerical model for predicting flow pattern and the effects of flow turbulence is necessary. Furthermore, choosing the type of turbulence model can be effective in simulating and studying flow properties. Different types of turbulence models can be used in SSIIM numerical model. In this study, with the aim of investigating the efficiency of turbulence models, three different kindes of k-Ԑ turbulence model,including the standard type, based on water velocity and RNG, are used to simulate flow characteristics in different points of a 45 degrees cross-section from a steep bend located on the Doab Samsami river. Comparing the measured values of the velocity component with the results of the model indicated that the standard k-Ԑ model for determining the vertical component of velocity and k-Ԑ model based on water velocity for the longitudinal component of the velocity are best models in accuracy, respectively and generally the ability of all mentioned the turbulence models evaluated well. Moreover, exact consideration of bed roughness and roughness of the channel bank in the numerical model can have a significant effect on the accuracy of the model results. 907 916 افشین هنربخش afshin honarbakhsh دانشیار گروه مرتع و آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد‌ ایران afshin.honarbakhsh@gmail.com روح الله کریمیان کاکلکی Rouhollah Karimian kakolaki دانشجوی دکتری تخصصی علوم و مهندسی آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد‌ ایران karimian.roh@gmail.com غلامرضا شمس قهفرخی gholamreza shams ghahfarokhi استادیار گروه مهندسی عمران، دانشکدۀ فنی و مهندسی، دانشگاه شهرکرد‌ ایران g.shams@eng.sku.ac.ir علیرضا داوودیان دهکردی alireza davoudian dehkordi استاد گروه پترولوژی، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد‌ ایران alireza.davoudian@gmail.com مهدی پژوهش mehdi pajouhesh استادیار گروه مرتع و آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد‌ ایران drpajoohesh@gmail.com مدل k-Ԑ مدل عددی SSIIM مؤلفۀ طولی سرعت مؤلفۀ قائم سرعت RNG [1]. Yousefi, H, Golshan,M , Pirnia, A. Evaluation of HEC-HMS Hydrological Model in estimating Flood Hydrograph of Dry and Humid regions. Journal of Ecohydrology, 2018; 5(1): 319 – 330. .)Persian(.##[2]. Yousefi, H, Ehara,S ,Noorollahi, Y. Modifying the analysis made by water quality index using multi-criteria decision making methods. Journal of African Earth sciences, 2018; 138, 309 – 318.##[3]. Yousefi, H, Tavakkoli-Moghaddam, R, Oliaei, MTB, Mohammadi, M. Solving a bi-objective vehicle routing problem under uncertainty by a revised multichoice goal programming approach. International Journal of Industrial Engineering Computations, 2017; 8(3):283-302##[4]. Xiufang Z. Pingyi W. and Y. Chengyu. Experimental study on flow turbulence distribution around a spur dike with different structure. Journal of Procedia Engineering,2015; 28: 772-775.##[5]. Hao, Z. Hajime, N. kenji, K. and B. Yasyuki. Experiment and simulation of turbulent flow in local scour around a spur dyke. Journal of Sediment Research, 2014; 24: 33-45.##[6]. Lai, Y. G. and Greimann, B.P. Predicting contraction scour with two-dimensional depth averaged model. Journal of Hydraulic Research, 2010; 48(3): 383-387.##[7]. Khosravi, G. The numerical simulation of flow and sediment transport with model CCHE2D (Case Study: meander downstream Minab). Master's thesis, University of Hormozgan, Bandarabbas, Iran. 2012. (Persian).##[8]. Fathi, M., A. Honarbakhsh, M. Rostami and D. Davoodian Dehkordi. simulating the flow pattern with a two-dimensional numerical model in a range of natural meanders, Case Study: Khoshkerood Farsan River, Chaharmahal and Bakhtiari, Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Sciences. 2012; 62(1): 95-108. (Persian).##[9]. Andersson. B, Andersson.R, Hakansson. L, Mortensen. M, Sudiyo. R and vanWachem. B. Computational fluid dynamics for engineers. Cambridge University Press, 2012.##[10]. Rodi, W. and Leschziner M. A. Calculation of Strongly Curved Open Channel Flow. Journal of the Hydraulic Division,1978; 105(HY10) : 1297-1333.##[11]. Shettar, A.S., and Murthy, K.K.A numerical study of division of flow in open channels, Journal of Hydraulic Research, Delft, The Netherlands,1996; 34)5( :651-675.##[12]. Han, S.S. Characteristics of flow around 90 open channel bends. PhD. Thesis. Dept. of Building,Civil and Environmental Engineering, Concordia University, Montreal, Quebec.2010.##[13]. Van Balen, W., Uijttewaal, W.S.J., and Blanckaert, K. Large eddy simulation of a mildly curved open channel flow", J. Fluid Mech.2009; 630(1): 413-442.##[14]. SafarzadehGandshamin, A., and Salehi Neishabouri, A. A. Numerical study of turbulent flow pattern and qualitative study of sediment transport and erosion in lateral drainage from the river. Journal of Modarres Technical and Engineering,2007; 25(1) : 1-18.( Persian).##[15]. Omid Beygi, M. A. Laboratory study and numerical simulation of three dimensional flow pattern in lateral drainage of the river in the presence of submerged panels. Msc Thesis. Dept. of Agriculture, Tarbiat Modarres University.2010.( Persian).##[16]. Mozaffari, J., Samadi, A., Mohseni Movahhed, S. A., Davoud-Maghami, D. Comparison of RSM and LES Turbulence Models on Sharp Bend. Journal of Ferdowsi Civil Engineering, 2015; 27(1): 77-86. (Persian).##[17]. Zhang, M. L., and Shen, Y. M. Three-dimensional simulation of meandering river based on 3-D RNG k-ɛ turbulence model. Journal of Hydrodynamics,2008; 20(4) : 448-455.##[18]. Yu, L. R. Flow and transport simulation of Madeira River using three depth-averaged two-equation turbulence closure models. Water Science and Engineering, 2012. 5(1): 11-25##[19]. Wu, W. CCHE2D Sediment Transport Model (Version 2.1). Tech Report No. NCCHETR- 2001-3, NCCHE, University of Mississippi, 2009. USA, P: 12.##[20]. Cea, L., Pena, L., J. Puertas, J., M. E. Vazquez-Cendon, M. E., and Pena, E. Application of several depth-averaged turbulence models to simulate flow in vertical slot fishways. Journal of Hydraulic Engineering, 2007;133(2):160–172.##[21]. Launder, B. E., and Spalding, D. B. The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering, 1974; 3(2): 269-289.##

0 مکانیسم آلوده‌شدن آبخوان دشت کاشان با تأکید بر آلودگی نیترات و نیتریت Mechanism of N- Nitrate pollution of Kashan plain aquifer https://ije.ut.ac.ir/article_67560.html 10.22059/ije.2018.252177.828 0 در تحقیق حاضر، آلودگی آبخوان دشت کاشان از نظر نیترات و نیتریت و مکانیسم آن بررسی شد. به این منظور، 42 نمونه آب از آبخوان تهیه و آنالیز شد. نتایج پژوهش نشان داد کلراید و سولفات، آنیون‏های غالب و سدیم و کلسیم، کاتیون‏های غالب‌اند. نتایج بررسی حد مجاز متغیرهای TDS، pH، Na+، K+، Cl-، SO42-، NO3-، NO2-، Ca2+ و Mg2+، نشان داد به‏ترتیب 61/97، 47/40، 100، 0، 61/97، 23/95، 09/38، 24/95، 34/83 و 23/95 درصد از منابع آب منطقه براساس استانداردهای WHO و ISIRI، وضعیت غیرمجاز دارند. همچنین، نیترات با Na+، K+، Ca2+، Mg2+، Cl-، HCO3-، SO42-، NO2-، TDS و EC به‏ترتیب همبستگی 68/0، 50/0، 63/0، 52/0، 64/0، 34/0-، 32/0، 64/0، 64/0 و 65/0 دارد. سایر نتایج نشان داد نیترات، ‌دامنۀ تغییرات 86/1 تا 1034 و میانگین 76/118 میلی‏گرم بر لیتر دارد. همچنین، 49/40، 42/21 و 09/38 درصد از نمونه‏ها از نظر نیترات به‏ترتیب، کمی‏آلوده، آلوده و بسیار آلوده‌اند. به‏منظور بررسی دقیق‏تر آلودگی آبخوان طبق توصیۀ WHO، نتایج ترکیبی نیترات و نیتریت نشان داد 23/95 درصد از نمونه‏ها، غلظت بیش از حد مجاز دارند. بنابراین، فقط بخش‏های جزئی از آبخوان در جنوب، جنوب غرب و غرب، وضعیت قابل قبولی دارند. 1 In this research, the hydrogeochemical situation of Kashan aquifer, its pollution to nitrate and nitrite and their mechanism were studied. For this purpose, 42 water samples from the aquifer were prepared and analyzed. The results showed that chloride and sulfate are predominant anions and sodium and calcium are dominant cations. The results about the permitted range of TDS, pH, Na+, K+, Cl-, SO42-, NO3-, NO2-, Ca2+ and Mg2+ variables showed that respectively, 97.61%, 40.47%, 100%, 97.61%, 95.23%, 38.09%, 95.24%, 83.34% and 95.23% of region water resources have unauthorized status according to WHO and ISIRI standards. Correlation between NO3- with Na+, K+, Ca2 +, Mg2+, Cl-, SO42-, NO2-, TDS and EC was 0.68, 0.50, 0.63, 0.52, 0.64, -0.34, 0.32, 0.64, 0.64, respectively. other results showed that nitrate varied between 1.86 to 1034 and had an average of 118.76 mg/lit. Also, 40.49%, 21.42% and 38.9% of the samples for nitrate were slightly polluted, polluted and highly polluted, respectively. For more precise investigation of aquifer contamination, based on WHO recommendation, the combined results of nitrate and nitrite showed that 95.23% of the samples had a non-allowable concentration. Therefore, only small parts of the aquifer in the south, southwest and west have acceptable situation. 917 929 محمد میرزاوند Mohammad Mirzavand دانشجوی دکتری علوم و مهندسی آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین دانشگاه کاشان ایران mmirzavand23@yahoo.com هدی قاسمیه Hoda Ghasemieh دانشیار دانشکدۀ منابع طبیعی و علوم زمین دانشگاه کاشان ایران h.ghasemieh@kashanu.ac.ir سید جواد ساداتی نژاد Seid Javad Sadatineghad دانشیار دانشکدۀ علوم و فنون نوین دانشگاه تهران ایران jsadatinejad@ut.ac.ir رحیم باقری Bagheri Nik Ghojogh استادیار دانشکدۀ علوم زمین دانشگاه صنعتی شاهرود ایران r.bagheri@shahroodut.ac.ir ایان داگلاس کلارک Ian Douglas Clark استاد، دانشکدۀ علوم محیطی و زمین، دانشگاه اتاوا، اتاوا، کانادا ایران idclark@uottawa.ca آبخوان کاشان شور شدن مکانیسم آلودگی نیترات نیتریت ]1]. Singh ET, Gupta A, Singh NR. Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data. Environmental Science and Pollution Research International. 2013; 20:2421-30.##]2]. Karami GH, Jafari H, Ghanaatian H. Contamination of groundwater resources in the agricultural land of Magan plain, Semnan province. Journal of Advanced Applied Geology. 2016; 21:45-55 [Persian].##[3]. Pawar N., Sheikh I. Nitrate pollution of ground waters from shallow basaltic aquifers, Deccan Trap Hydrologic Province, India. Environmental Geology. 1995;25(3):197–204.##[4]. Gordon B, Callan P, Vickers C. WHO guidelines for drinking-water quality. World Health Organization (WHO). 4th ed. Geneva. Switzerland. 2008;38(3): 564 p.##[5]. ISIRI. Drinking water physical and chemical specification. Institute of Standard and Industrial Research of Iran, Tehran. 1053. 5th revision. 2013;26p [Persian].##[6]. Amarlooei A, Nazeri M, Sayeh miri K, Nourmoradi H, Sayehmiri K, Khodarahmi F. Investigation on the concentration of nitrate and nitrite in Ilam ground waters. Scientific Journal of Ilam University of Medical Sciences (sjimu). 2014;22(4):34-41 [Persian].##[7]. Khodai K, Mohammadzadeh H, Nasseri H, Shahsavari AH. Evaluating of nitrate contamination in Dezful-Andimeshk plain and identifying of nitrate sources using 15N and 18O Isotopes. Iranian Journal of Geology. 2012; 22(6):93-111 [Persian].##]8]. Majumdar D, Gupta N. Nitrate pollution of ground water and associated human health disorders. Indian Journal of Environmental Health. 2000;42(1):28–39.##[9]. Jafari Malekabadi A, Afyuni M, Mousavi S.F, Khosravi A. Nitrate concentration In groundwater In Isfahan province. Journal of Water and Soil Science (JWSS). 2004;8(3):69-82 [Persian].##]10]. Khosravi Dehkordi A, Afyuni M, Mousavi S.F. Investigation of nitrate concentration changes of groundwater in Zayandehroud margin in Isfahan province. Journal of Environmetal Studies. 2006;32(39):33-40 [Persian].##[11]. Selek Z, Yetis A. Assessment of nitrate contamination in a transnational groundwater basin: a case study in the Ceylanpinar Plain, Turkey. Environ Earth Science. 2017;76:698: 1-11.##[12]. Mirzavand M, Ghazavi R, Sadatinejad S., Ghasemieh H, Vali A. Investigation of Kashan aquifer situation using electric resistance method with Shelomberje arrangement. Desert Ecosystem Engineering Journal. 2014;3(4):43-56 [Persian].##[13]. Mirzavand M, Ghazavi R. A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resources Management. 2015;29(4):1315–1328.##]14]. Clark ID. Groundwater geochemistry and isotopes. Taylor & Francis Group; 2015. 471 p.##]15]. http://www.aqion.de/site/92.##[16]. Amiri V, Nakhaei M, Lak R, Kholghi M. Assessment of seasonal groundwater quality and potential saltwater intrusion: a study case in Urmia coastal aquifer (NW Iran) using the groundwater quality index (GQI) and hydrochemical facies evolution diagram (HFE-D). Stoch Environ Res Risk Assess. 2016;30(5):1473–1484.##]17]. Mehdinia SM, Nikravesh SH. Determining contamination of drinking water distribution network in Damghan city. Journal of Water & Wastewater. 2002;43:60-1 [Persian].##[18]. Menció A, Mas-pla J, Otero N, Regàs O, Boy-roura M, Puig R, et al. Nitrate pollution of groundwater ; all right … , but nothing else ? Science of Total Environment. 2015; 539:241-251.##]19]. Xing L, Guo H, Zhan Y. Groundwater hydrochemical characteristics and processes along flow paths in the North China plain. Journal of Assian Earth Science. 2013;70-71:250-64.##

0 بهبود برآورد هیدروگراف سیلاب با استفاده از مدل شمارۀ منحنی اصلاح‏شده (رابطۀ غیرخطی Ia-S) Improvement of Estimation of Flood Hydrograph Using Modified Curve Number (non-linear Ia-S) Model https://ije.ut.ac.ir/article_67659.html 10.22059/ije.2018.252799.832 0 مدل شمارۀ منحنی (SCS-CN) در حالت متداول بر اساس رابطۀ خطی بین جذب اولیه (Ia) و بیشترین پتانسیل نگهداشت (S) حوضۀ آبریز است، ولی این مدل برای درنظرگرفتن رابطۀ غیرخطی Ia-S اصلاح ‌شده است. هدف از مطالعۀ حاضر مقایسۀ مدل‌های متداول شمارۀ منحنی و شمارۀ منحنی اصلاح‌شده (رابطۀ غیرخطی Ia-S) در برآورد هیدروگراف سیلاب در پنج حوضۀ آبریز گالیکش، نوده، تمر، وطنا و کچیک (کاربرد ۳۷ رویداد بارش-رواناب در محاسبات و انتخاب ۱۴ رویداد برای مقایسۀ نتایج در مرحلۀ صحت‌سنجی) است. برای مقایسۀ نتایج از معیارهای ریشۀ میانگین مربعات خطا (RMSE)، نش-ساتکلیف (NSE) و خطای برآورد دبی اوج (PEP) استفاده شده است. بررسی معیارهای RMSE و NSE و PEP نشان می‌دهد کاربرد مدل شمارۀ منحنی اصلاح‌شده (رابطۀ غیرخطی Ia-S) در همۀ رویدادهای مرحلۀ صحت‌سنجی موجب بهبود برآورد هیدروگراف و دبی اوج سیلاب نسبت به کاربرد مدل متداول شمارۀ منحنی (SCS-CN) شده است. بنابراین، نتایج نشان می‌دهد در حوضه‌های آبریز مطالعه‌شده مدل شمارۀ منحنی اصلاح‌شده (رابطۀ غیرخطی Ia-S) موجب بهبود درخور توجه کارایی مدل متداول شمارۀ منحنی (SCS-CN) شده است. 1 The Curve Number Model (SCS-CN) is in conventional mode is based on the linear relationship between initial absorption (Ia) and potential maximum retention (S) of the catchment but this model has been modified to consider non-linear Ia-S relation. The objective of this study is to compare the conventional curve number and modified curve number (non-linear Ia-S relation) models in flood hydrograph estimation in five Galikesh, Nodeh, Tamer, Vatana and Kechik catchments (37 rainfall-runoff events in calculation and selection of 14 events for results comparison in validation step). The root mean square error (RMSE), Nash-Sutcliff (NSE) and peak discharge estimation error (PEP) criteria were used for results comparison. Investigation of RMSE and NSE and PEP criteria shows that the application of modified curved number model (non-linear Ia-S) in all events of validation step improves the estimations of flood hydrograph and peak discharge in comparison with conventional curve number model (SCS-CN), therefore the results indicated that in studied catchments, the modified curve number model (non-linear Ia-S) has improved the conventional curve number SCS-CN model. 931 939 ساناز دایی Sanaz Daei کارشناس ارشد مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران sanazdaei826@yahoo.com میثم سالاری جزی Meysam Salari Jazi استادیار، گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران meysam.salarijazi@gmail.com خلیل قربانی Khalil Ghorbani دانشیار، گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران ghorbani.khalil@yahoo.com مهدی مفتاح هلقی Mahdi Meftah Halaghi دانشیار، گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران meftah_20@yahoo.com سیلاب شمارۀ منحنی اصلاح‌شده (رابطۀ غیرخطی Ia-S) شمارۀ منحنی (SCS-CN)‌ هیدروگراف [1].King K W and Balogh, J. C. Curve numbers for golf course watersheds. Transactions of the ASABE. 2008; 51(3):987-996.##[2].Tramblay Y, Bouvier C, Martin C, Didon-Lescot J. F, Todorovik D, Domergue J. M. Assessment of initial soil moisture conditions for event-based rainfall–runoff modelling. Journal of Hydrology. 2010;387(3):176-187.##[3].Chung W. H, Wang I. T, Wang R. Y. Theory-based SCS-CN method and its applications. Journal of Hydrologic Engineering. 2010; 15(12):1045-1058.##[4].Ponce V. M, Hawkins R. H. Runoff curve number: Has it reached maturity. Journal of hydrologic engineering. 1996; 1(1):11-19.##[5].Soulis K. X, Valiantzas J. D. SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds-the two-CN system approach. Hydrology and Earth System Sciences. 2012; 16(3): 1001-1015.‏##[6].Romero P, Castro G, Gomez J. A, Fereres E. Curve number values for olive orchards under different soil management. Soil Science Society of America Journal. 2007; 71(6):1758-1769.##[7].Hawkins R. H. Asymptotic determination of runoff curve numbers from data. Journal of Irrigation and Drainage Engineering. 1993; 119(2):334-345.##[8].Ebrahimian M, Nuruddin A.A, MohdSoom M.A.B, Sood, A.M. Application of NRCS-curve number method for runoff estimation in a mountainous watershed. Caspian J. Environ. Sci. 2012; 10: 103-114.##[9].Cazier D.J, Hawkins R.H.Regional application of the curve number method. Water today and tomorrow. Proc. ASCE Irrigation and Drainage Division Special Conf., ASCE. New York. 1984; 710.##[10].Woodward D.E, Hawkins R.H, Hjelmfelt J.R, Jr A.T, Mullem J.A, Quan, Q.D. Runoff curve number method: Examination of the initial abstraction ratio. Proc. world water Environ. Res. Congress.2003; 1-10.##[11].Shi Z.H, Chen L.D, Fang N.F, Qin, D.F, Cai C.F. Research on SCSCN initial abstraction ratio using rainfall runoff event analysis in the three Gorges Area, China. Catina. 2009;77(1): 1-7.##[12].Singh M. Simulating rainfall changes effects on runoff and soil erosion in submontane Punjab. M.Sc. Thesis. Punjab Agricultural University. Ludhiana. 2014.##[13].Bales J, Betson R. P. The curve number as a hydrologic index. Rainfall Runoff Relationship. 1981; 371-386.##[14].Hauser V. L, Jones O. R. Runoff curve numbers for the Southern High Plains. Transactions of the ASAE. 1991; 34(1):142-148.##[15].Gao Y, Zhu, B, Miao C. Y. Application of SCS model to estimate the volume of rainfall runoff in sloping field of purple soil. Chinese Agricultural Science Bulletin. 2006; 22(11): 396-400.##[16].Mishra S K, and Singh V. P. Another look at SCS-CN method. Journal of Hydrologic Engineering. 1999; 4(3): 257-264.##[17].Mishra S K, Jain M K, Singh V P. Evaluation of the SCS-CN-based model incorporating antecedent moisture. Water resources management. 2004; 18(6): 567-589.##[18].Mishra S. K, Sahu R K, Eldho T I, Jain M K. An improved Ia- S relation incorporating antecedent moisture in SCS-CN methodology. Water Resources Management. 2006; 20(5): 643-660.##[19].Jiao P Xu D, Wang S, Yu Y, Han S. Improved SCS-CN method based on storage and depletion of antecedent daily precipitation. Water ResourManag. 2015; 29:4753–4765.##[20].Sahu R. K, Mishra S. K, Eldho T. I. An improved AMC‐coupled runoff curve number model. Hydrological processes. 2010;24(20): 2834-2839.##[21].Karn A L, Lal M, Mishra S K, Chaube U C , Pandey A. Evaluation of SCS-CN Inspired models and their comparison. Journal of Indian Water Resources Society. 2016; 36(3): 19-27.##[22].Mishra S. K, Singh V. P. Soil conservation service curve number (SCS-CN) methodology. 42th ed. Springer Science and Business Media.. 2013.##[23].Mishra S.K, Singh V.P. SCS-CN-based hydrologic simulation pack-age. In: Singh V.P,Frevert D.K, editors. Mathematical Models in SmallWatershed Hydrology and Applications. Water Resour. Publ, P.O. Box2841, Littleton, Colorado 80161. 2002. pp. 391-464.##[24].Mishra S.K, Singh V.P, Sansalone J.J, Aravamuthan V. A modiﬁedSCS-CN method: characterization and testing. J. Water Resour. Manage. 2003;17: 37-68.##[25].Nash J. E, Sutcliffe J. V. River flow forecasting through conceptual models.part I- A discussion of principles. Journal of Hydrology. 1970; 10(3): 282-290.##[26].Adib A, Salarijazi M, Vaghefi M, Shooshtari M. M, Akhondali A. M. Comparison between GcIUH-Clark, GIUH-Nash, Clark-IUH, and Nash-IUH models. Turkish Journal of Engineering and Environmental Sciences. 2010; 34(2): 91-104.##[27].Adib A, Salarijazi M, Najafpour K. Evaluation of synthetic outlet runoff assessment models. Journal of Applied Sciences and Environmental Management. 2010; 14(3): 13-18.##[28].Adib A, Salarijazi M, Shooshtari M M, Akhondali A M. Comparison between characteristics of geomorphoclimatic instantaneous unit hydrograph be produced by GcIUH based Clark Model and Clark IUH model. Journal of Marine Science and Technology. 2011; 19(2): 201-209.##[29].Eidipour A, Akhondali A. M, Zarei H, Salarijazi M. Flood hydrograph estimation using GIUH model in ungauged karst basins (Case study: Abolabbas basin). TUEXENIA. 2016;36(3): 26-33.##

0 به‌کارگیری مدل PHABSIM در تبیین رژیم اکولوژیکی رودخانه به‌منظور برآورد جریان زیست ‏محیطی و مقایسه با روش‏های هیدرولوژیکی (مطالعۀ موردی: رودخانۀ قره‏سو) Application of the PHABSIM model in Explaining the Ecological Regime of the River in order to Estimate the Environmental Flow and Compare with Hydrological Methods (Case Study: Gharasoo River) https://ije.ut.ac.ir/article_67660.html 10.22059/ije.2018.253183.834 0 اختصاص آب به محیط‌زیست که با عنوان حق‌آبه زیست‌محیطی مطرح می‌شود، برای حفظ اکوسیستم رودخانه و پایین‌دست آن بسیار حیاتی و مهم می‌باشد. عدم تخصیص مناسب جریان زیست‌محیطی، موجب اختلال در فعالیت‌های حیاتی موجودات آب‌زی، کاهش ارتباطات بین اکوسیستم‌ها و دسترسی به مناطق مناسب جهت تخم‌ریزی و مهاجرت آبزیان گردیده است. در مطالعه حاضر روش‌های هیدرولوژیکی تنانت، تسمن و آرکانزاس به منظور برآورد حداقل جریان زیست‌محیطی و مدل شبیه‌سازی زیستگاه PHABSIM جهت تأمین حداقل شرایط زیستگاه برای گونه شاخص سیاه ماهی C.capoeta gracilis در رودخانه قره سو منتهی به خلیج گرگان، مورد محاسبه و ارزیابی قرار گرفتند. آمار مورد نیاز برای محاسبات هیدرولوژیکی نیز از ایستگاه هیدرومتری سیاه‌آب در طول دوره آماری 44 ساله ( 1394-1350) استفاده شد. اندازه گیری و ثبت متغیرهای محیطی، داده‌های مربوط به مقاطع عرضی رودخانه شامل فاصله هر مقطع از مقطع پایین‌دست در اواخر خرداد ماه 1396، در4 ناحیه اصلی و مقاطع کنترل در هر ناحیه از پایین‌دست (مصب رودخانه) به سمت بالادست و در طول رودخانه قره‌سو انجام شد. بر اساس ارزیابی اکولوژیکی، روش تنانت نیاز آب زیست‌محیطی رودخانه قره‌سو را 30 درصد متوسط دبی سالانه برای فصول بهار و تابستان و 10 درصد متوسط دبی سالانه برای فصول پاییز و زمستان به ترتیب مقادیر 57/0 و 19/0 متر مکعب بر ثانیه و روش‌های تسمن 44 درصد، آرکانزاس 64 درصد و تکنیک شبیه‌سازی زیستگاه 85 درصد میانگین جریان سالانه در رودخانه قره‌سو، به ترتیب مقادیر 856/0، 22/1 و 63/1 متر مکعب بر ثانیه، جریان زیست‌محیطی را برآورد می‌نمایند. با بررسی الزامات برآورد جریان زیست‌محیطی در رودخانه مطالعه شده، با توجه به شرایط دینامیک اکولوژیکی و زیستگاهی رودخانه، مدل شبیه‌سازی زیستگاه بسیار کاراتر از روش‌های هیدرولوژیکی ظاهر شده و پاسخ آن‌ها به مسئله تخصیص رژیم اکولوژیکی جریان می‌تواند منطقی و حافظ بقای محیط اکولوژیکی باشد. 1 Lack of appropriate allocation Environmental Flow, has been disrupts the vital activities of aquatic organisms, reduces communication between ecosystems, access to suitable areas for spawning and migrating aquatic. In this research, the environmental demand of Gharasoo River at the siahab station hydrometry was investigated at the entrance to the Gorgan Gulf. Were evaluated of In order to obtain the ecological requirement of Gharasoo River in Golestan province hydrological methods of Tenant, Tessman, Arkansas, Physical Habitat Simulation Model (PHABSIM) for species Capoeta capoeta gracilis. The findings of this research show method of Tenant by taking 30 % annual average flow for the spring and summer seasons, 10 % annual average flow for autumn and winter seasons suggests respectively quantities 0/57 and 0/19 cms. Methods of Tessman, Arkansas and Physical Habitat Simulation model Provide estimates environmental water requirement in equal order 0/856, 1/22, 1/63 cms. Also, there was a lot of difference among between the results of estimating the minimum required water requirement for the river using hydrological methods and providing minimum habitat conditions for indicator species using habitat simulation method and as regards the ecological and habitat conditions of the river are a completely dynamic situation. 941 955 محمدحسن نادری mohammad hasan naderi دانش‌آموختۀ کارشناسی ارشد مهندسی منابع آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان‌ ایران naderigau@gmail.com مهدی ذاکری نیا mehdi zakerinia دانشیار گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان‌ ایران a_zakerinia@yahoo.com میثم سالاری جزی meisam salarijazi استادیار گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان‌ ایران meysam.salarijazi@gmail.com جریان زیست‏محیطی رودخانۀ قره‏سو روش‏های هیدرولوژیکی شبیه‏سازی زیستگاه مطلوبیت زیستگاه [1]. Yan Y, Yang Z, Liu Q, Sun T. 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Ecohydrology. 2013; 6(4):507-510.##[8]. Poff NL, Richter BD, Arthington AH, Bunn SE, Naiman RJ, Kendy E, et al. The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshwater Biology. 2010; 55(1):147-70.##[9]. Tharme RE. A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River research and applications. 2003;19(56):397-441.##[10]. Shokoohi A, Hong Y. Using hydrologic and hydraulically derived geometric parameters of perennial rivers to determine minimum water requirements of ecological habitats (case study: Mazandaran Sea Basin—Iran). Hydrological Processes. 2011; 25(22):3490-3498.##[11]. Bahukandi KD, Ahuja NJ. 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0 بررسی کارایی مدل ریزمقیاس‌نمایی آماری (SDSM) در شبیه‌سازی و پیش‌بینی پارامترهای اقلیمی (مطالعۀ موردی: ایستگاه سینوپتیک کرج) Evaluation of the Efficiency of Statistical Downscaling Model (SDSM) In Simulation and Forecast of Climatic Parameters (Case Study: Karaj Synoptic Station) https://ije.ut.ac.ir/article_67661.html 10.22059/ije.2018.254290.847 0 افزایش جمعیت جهان، استفادۀ بیش ‏از ‏حد از سوخت‏های فسیلی، تغییر کاربری اراضی، گسترش روزافزون فعالیت‏های صنعتی برای تأمین رفاه و نیازهای جمعیت کرۀ زمین، موجب شده است تا پس از انقلاب صنعتی به‏تدریج تغییرات مشهودی در اقلیم کرۀ زمین به‏وجود آید که بارزترین آن افزایش متوسط دمای‏ کرۀ زمین و تبعات آن است. برای بررسی سیستم‏های اقلیمی در مقیاس‏های جهانی از مدل‏هایی با عنوان GCM استفاده می‏شود؛ این مدل‏ها، رفتار فیزیکی سیستم زمین، جوّ و اقیانوس را به شکل ریاضی شبیه‏سازی می‏کنند. در پژوهش حاضر برای ریزمقیاس‌نمایی و بررسی تغییرات اقلیم در منطقۀ کرج، از مدل SDSM استفاده شد. نتایج معیارهای آماری ارزیابی کارایی مدل رگرسیون خطی چندمتغیره نشان داد توانایی این مدل در شبیه‏سازی بارندگی و دما ایستگاه کرج نسبتاً قابل ‏قبول و با داده‏های مشاهداتی مطابقت دارد. نتایج شبیه‏سازی‏، به‏طور متوسط در سناریوی A2، در دوره‏های اول (1999ـ 2020)، دوم (2021ـ 2050) و سوم (2051ـ 2080) دربارۀ بارندگی به‌ترتیب حدود 1/0، 1/0 و 2/0 میلی‏متر نسبت به دورۀ پایه کاهش و در مورد دما به‌ترتیب حدود 1/0، 4/0 و 2/0 سانتی‏گراد نسبت به دورۀ پایه افزایش را نشان می‏دهد. تحت سناریوی B2 در دوره‏های زمانی یادشده دربارۀ بارندگی به‌ترتیب حدود صفر، 1/0 و 2/0 میلی‏متر کاهش و در مورد دما به‌ترتیب حدود 2/0، 1/0 و 1/0 سانتی‏گراد نسبت به دورۀ پایه افزایش را نشان می‏دهد. تغییرات در میزان بارندگی، سبب ایجاد تغییرات مهمی در کیفیت و کمیت منابع آب خواهد شد که نیاز به برنامه‏ریزی برای بهره‏برداری بهتر از منابع آب را نشان می‌دهد. 1 The increase in the world's population, the use of more than fossil fuels, landuse change, the increasing expansion of industrial activities to provide the welfare and needs of the planet's population has led to gradual changes in the climate after the Industrial Revolution The Earth is the most significant of which is the increase in the average temperature in Korea, the increase of extreme climatic phenomena such as floods,storms,rising sea levels, melting of polar ice and Drought.In this research, SDSM model was used for quantitative estimation and investigation of climate change in Karaj region. The simulation results, on average, in the scenario A2, in the first periods(2020-1999),second(2021-2050) and third(2051-2080) for rainfall were about 0.1, 0 and 0.2mm in comparison with the base period and in the case of temperature is about 0.1, 0.4 and 0.2C, respectively, relative to the base period of increase. Under scenario B2, the time periods mentioned for rainfall were about 0, 0.1 and 0.2 millimeters, respectively, and about 0.2,0.1 and 0.1 centigrade, respectively Shows the increase relative to the base period.Changes in rainfall will lead to significant changes in the quality and quantity of water resources, which require careful planning in order to utilize water resources. 957 968 حسین یوسفی Hossein Yousefi دانشیار دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران hosseinyousefi@ut.ac.ir لیلی امینی leili amini دانشجوی کارشناسی ارشد اکوهیدرولوژی، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران leili.amini@ut.ac.ir لیلا قاسمی leila ghasemi دانشجوی کارشناسی ارشد اکوهیدرولوژی، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران ghasemi.leila@ut.ac.ir نسیبه امرایی nasibeh amrai دانشجوی کارشناسی ارشد اکوهیدرولوژی، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران nasibeh.amrai@ut.ac.ir تغییر اقلیم ریزمقیاس‌نمایی سناریوهای اقلیمی شبیه‏سازی مدل SDSM [1]. Azizabadi farahan M, Bakhtiari B, Ghaderi K, Rezapour M. Assessment the Effect of Climate Change on Severity- Duration-Frequency Curves Of Drought in Ghareh Sou Basin Using Detailed Functions, Journal of Iranian soil and soil research. 2017; 47(4): 743-754. [Persian]##[2]. Rezaei M, Nehtani M, Abkar A, Rezaei M, Mirkazehi Rigi M. Erformance evaluation of statistical downscaling model (Sdsm) in the prediction of temperature parameters in the dry climate and Hyper (Case Study: Kerman and Bam). Journal of Watershed Management. 2014;5:10. [Persian]##[3]. Shiaae beighi A, Abbaspour M, Soltanieh M, Hosseinzadeh F, Abedi Z. Assessment of climate change and its impact on the performance and fuel consumption of Iran's thermal power plants in the next decade. Journal of Science and technology of the environment. 2014;16(2). [Persian]##[4]. Yaaghubi, M. Masahbovani, A. Compare and evaluate different sources of uncertainty in studying the effects of climate change on runoff of semi-arid basins (Case Study: Heart River Basin large-Yazd), Iranian Water Resources Research. 2015;11(3):113-130. [Persian]##[5]. Tirgarfakheri, F. Arezumandi, L. Applications of downscaling in GCMS to create a map of rainfall in the southern coast of the Caspian, Third International Symposium on Environmental and Water Resources Engineering. 2015;1-10. [Persian]##[6]. Taei Samiromi S, Moradi H, Khodagholi M. Simulation and forecasting of climatic variables by multiple linear model Sdsm and General Circulation Models (Case Study: Watershed Bar Nishapur). Journal of humans and the environment. 2014;28:1393. [Persian]##[7]. Sayari N, Alizadeh A, Bannayan Awal M, Farid Hossaini A, Hesami Kermani M.R. Comparison of two GCM models (HadCM3 and CGCM2) for the prediction of climate parameters and crop water use under climate change (Case Study: Kashafrood Basin). Journal of Water and Soil. 2011; 25(4): 912-925. [Persian]##[8]. Tavakol-Davani,H. Nasserib ,a M. Zahraie, B. Improved statistical downscaling of daily precipitation using SDSM platform and data-mining methods. International Journal of Climatology,1-18. [Persian]##[9]. SadatAshofteh P ,MasahBoani A. Effect of Climate Change on Maximum Discharge: Case Study, Aydoghmoos Basin, East Azarbaijan, Journal of Agricultural Sciences and Technology. 2010. 14; 25-39. [Persian]##[10]. Arun Mondal n, Deepak K, Sananda K. Change in rainfall erosivity in the past and future due to climate change in the central part of India, International SoilandWaterConservationResearch4,pp. 2016;186–194.##[11]. Zhang Y, You Q, Chen Ch, Ge J. Impacts of climate change on streamflows under RCP scenarios: A case study in Xin River Basin, China. Atmospheric Research. 2016; 178–179##[12]. Garnaud C, Sushama L. Biosphere-climate interactions in a changing climate over North America. Journal of Geophysical Research: Atmospheres. 2014;1091-1108.##[13]. Wetterhall F, Bardossy A, Chen D, Halldin S, Xu C-Y. Daily precipitation-downscaling techniques in three Chinese regions. WATER RESOURCES RESEARCH. 2006; (42)11423,1-13.##[14]. Wilby, R.L. Dawson, C.W. Barrow,E.M. sdsm -a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software. 2002;17,147-159.##[15]. Dibike, Y.b. and P. Coulibaly. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. Journal of Hydrology. 2005;307: 145-163.##[16]. Zia Hashmi,M.shamseldin,A. Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess. 2011;25,475-484.##[17].Tavasoli A. Intra-Storm runoff coefficient simulation using the components of rainfall in the watershed bar Nishapur, Journal of Watershed Management Sciences and Engineering. 2010;10(4): 21-33. [Persian]##[18]. Moriasi D N, Arnold JG, Van Liew M. W, Bingner R. L, Harmel, R. D, and Veith, T. L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE. 2007; 50(3), 900-855.##[19]. Taei Samiromi S, Moradi H, Khodagholi M. Selection of general circulation model and appropriate scenario for studying the effects of climate change in watershed Bar Neishabour. The 2nd National Conference on Climate Change and its Impact on Agriculture and the Environment, Orumiyeh, Collections of articles. 2013; 28-32. [Persian]##

0 مدل ریاضی اثر پایین‌آمدن سطح آب رودخانه بر جریان غیر دائمی آب زیرزمینی در آبخوان نشتی به روش تفکیک متغیرها Modelling the effect of water fall in the river level on unsteady groundwater flow in leaky aquifer by separation of variables https://ije.ut.ac.ir/article_67662.html 10.22059/ije.2018.254655.852 0 به‌منظور مدل‌سازی جریان آب زیرزمینی می‏توان از روش‏های عددی و تحلیلی بهره‌‏مند شد. در پژوهش حاضر با به‌کارگیری مدل ریاضی و استفاده از روش تفکیک متغیرها آثار پارامترهای مختلف بر آبخوان نشتی بررسی شده است. این آبخوان در مجاورت رودخانه واقع و دچار افت سطح جریان در مرز می‏شود. مقایسۀ تغییرات هد هیدرولیکی نشان می‌دهد با گذشت زمان، تغییرات سطح آب در آبخوان کمتر می‌شود و آبخوان خود را با شرایط جدید وفق می‏دهد. با افزایش ضریب هدایت هیدرولیکی، سطح جریان در آبخوان کاهش می‌یابد و کاهش هدایت هیدرولیکی، تأثیر بیشتری نسبت به افزایش آن بر آبخوان می‏گذارد. همچنین، با افزایش تغذیۀ سطحی، هد هیدرولیکی سطح جریان افزایش می‏یابد و با گذشت زمان این تغییرات مشهود‏تر است. تغییرات دبی خروجی بیشتر از تغییرات دبی ورودی است و بعد از گذشت حدود 60 روز آبخوان حالت ثباتی به خود می‏گیرد که این دو مقدار ورودی و خروجی تثبیت می‏شود. علاوه بر این‏ها، نتایج مدل‌سازی روابط ارائه‌شده در تحقیق حاضر با نتایج مدل نرم‌‏افزار مادفلو مقایسه شده است. این مقایسه نشان داد حل تحلیلی ارائه‌شده در پژوهش حاضر بسیار کارآمد است. 1 In order to model groundwater flow, numerical and analytical methods can be utilized. In this paper, the effects of different parameters on leaky aquifer were investigated using mathematical model and separation of variables method. This aquifer is located adjacent to the river and the flow rate falls across the border. Comparison of hydraulic head changes shows that over the time the water level changes decreases in the aquifer and the aquifer adapts itself to the new conditions. Groundwater level decrease with rises in hydraulic conductivity. Reducing hydraulic conductivity has a greater effect than increasing it on the aquifer. Also, the groundwater head rises by increasing the recharge rate and over time, these changes are more evident. Outflow changes are greater than inflow changes. In addition, the presented analytical solution is compared with those results obtained from MODFLOW. This comparison showed that the analytical solution presented in this research is very efficient. 969 976 ایرج سعیدپناه Iraj Saeedpanah استادیار گروه عمران دانشکدۀ مهندسی، دانشگاه زنجان ایران saeedpanah@znu.ac.ir سمیه محمدزاده روفچائی Somayeh Mohammadzade Roofchaee دانشجوی کارشناسی ارشد مهندسی عمران، دانشگاه زنجان ایران somayeh.mohamadzade@znu.ac.ir آبخوان نشتی اندرکنش آب سطحی- آبخوان‌ پایین‌آمدن سطح آب تفکیک متغیرها [1]. Winter, T. C. Relation of streams, lakes, and wetlands to groundwater flow systems. Hydrogeology Journal. 1999; 7:28–45##[2]. Yang, Y.S., Wang, L. A review of modeling tools for implantation of the EU water framework directive in handling diffuse water pollution. Water Resources Management. 2010; 24:1819–1843##[3]. Courbis, A. L., Vayassade, B., Martin, C., Didon-Lescot, J.F. Modelling and simulation of a catchment in order to evaluate water resources. Global NEST Journal. 2008; 10(3): 301-309.##[4]. Ma, S., Kassinos, S.C., Kassinos, D.F., Akylas, E. Modeling the impact of water withdrawal schemes on the transport of pesticides in the Kouris Dam (Cyprus). Global NEST Journal. 2008; 10(3): 350-358.##[5]. Boufadel, M. C., Peridier, V. Exact analytical expressions for the piezometric profile and water exchange between stream and groundwater during and after a uniform rise of the stream level. Water resources research. 2002; 38(7): 1-6.##[6]. Hussein, M., Schwartz, F.W. Modeling of flow and contaminant transport in coupled stream–aquifer systems. Journal of Contaminant Hydrology. 2003; 65: 41–64.##[7]. Singh, S.K. Aquifer response to sinusoidal or arbitrary stage of semipervious stream. Journal of Hydraulic Engineering. 2004; 130(11): 1108-1118.##[8]. Kim, K.Y., Kim, T., Kim, Y., Woo, N.C. A semi-analytical solution for groundwater responses to stream-stage variations and tidal ﬂuctuations in a coastal aquifer. Hydrological Process. 2007; 21(5): 665–674.##[9]. Bansal, R. K., Das, S. K. Analytical solution for transient hydraulic head, flow rate and volumetric exchange in an aquifer under recharge condition. Journal of Hydrology and Hydromechanics. 2009; 57(2): 113-120.##[10]. Guo, H. P., Jiao, J.J., Li, H. L. Groundwater response to tidal fluctuation in a two-zone aquifer. Journal of Hydrology. 2010; 381:364–371.##[11]. Telogloua L.S, Bansal, R k. Transient solution for stream–unconfined aquifer interaction due to time varying stream head and in the presence of leakage. Journal of Hydrology. 2012; 428: 68–79.##[12]. Kashaigili, J. J., Mashauri D. A., Abdo, G. Groundwater management by using mathematical modeling: case of the Makutupora groundwater basin in dodoma Tanzania. Botswana Journal of Technology. 2003; 12(1):19–24.##[13]. Palma, H. C., Bentley, L. R. A regional-scale groundwater ﬂowmodel for the Leon–Chinandega aquifer, Nicaragua. Hydrogeology Journal. 2007; 15:1457–72.##[14]. Budge, T.J., Sharp, Jr. JM. Modeling the usefulness of spatial correlation analysis on karst systems. Ground Water. 2009; 47(3):427–37.##[15]. Xu, X., Huang, G., Zhan, H., Qu, Z., Huang, Q. Integration of SWAP and MODFLOW-2000 for modeling groundwater dynamics in shallow water table areas. Journal of Hydrology. 2012; 412:170–181.##[16]. Saeedpanah I, GolmohamadiAzar R, New Analytical Solutions for Unsteady Flow in a Leaky Aquifer between Two Parallel Streams. Water Resources Management. 2017; 31(7): 2315–2332.##[17]. Srivastava, Kirti;Serrano, Sergio E.; Workman, S. R. Stochastic modeling of transient stream aquifer interaction with the nonlinear Boussinesq equation. Journal of Hydrology. 2005; 328: 538-547.##

0 پیش‏بینی وضعیت خشکسالی طی دورۀ 2018-2037 تحت رویکرد تغییر اقلیم (مطالعۀ موردی: ایستگاه‌های ایلام و دهلران) Prediction of drought condition during 2018-2037 period under Climate Change Approach (Case study: Ilam and Dehloran Stations) https://ije.ut.ac.ir/article_67663.html 10.22059/ije.2018.256186.866 0 خشکسالی به‏عنوان دوره‏ای مشخص می‏شود که در آن بارندگی نسبت به شرایط نرمال منطقه، کاهش یافته است. برای ارزیابی خشکسالی شاخص‏هایی وجود دارد. پدیدۀ خشکسالی تحت تأثیر تغییر اقلیم می‏تواند تغییرات غیرقابل پیش‏بینی داشته باشد. در ‌تحقیق حاضر ابتدا با استفاده از داده‏های بارندگی ماهانه، خشکسالی دورة پایه 1998ـ 2017 در سری‏های زمانی 3، 6، 12 و 24 ماهه در ایستگاه‏های هواشناسی ایلام و دهلران واقع در استان ایلام ارزیابی شد. سپس، با استفاده از داده‏های روزانۀ بارش، دمای کمینه، دمای بیشینه و تابش با استفادۀ مدل ریزمقیاس LARS-WG 5.5 و تحت مدل گردش عمومی Hadcm3 و سناریوهای اقلیمی A2 و B1، مقادیر بارش ماهانۀ دورة آتی (2018-2037) ‌بررسی شد. سپس، با استفاده از مقادیر بارش ماهانه، شاخص خشکسالی SPI برای دورة آینده و در سری‏های زمانی 3، 6، 12 و 24 ‌ارزیابی شد. نتایج ارزیابی خشکسالی در دورة پایه در ایستگاه ایلام نشان داد ‌دورۀ 2008-2014 نسبتاً مرطوب بوده است. همچنین، در ایستگاه دهلران در اوایل دوره، ترسالی را نشان داده است. درضمن، مدل LARS-WG در پیش‏بینی بارش در منطقۀ مطالعه‌شده عملکرد خوبی را نشان داد. ارزیابی خشکسالی در دورة آینده بر اساس سناریوهای A2 و B1 نشان داد در ایستگاه ایلام بین سال‏های 2025 تا 2035 دورة تقریباً خشکی و نیز، در ایستگاه دهلران از سال 2019 تا 2021 دورۀ کامل خشکی خواهد بود. همچنین، نتایج نشان می‌دهد با افزایش دورة آماری، تداوم دوره‏های خشکسالی و ترسالی بیشتر و شدت آنها کمتر خواهد بود. 1 Drought phenomena may cause unpredictable changes under influence of climate change and there are indexes for its evaluation. In this research, firstly, base period’s drought (1998-2017) was evaluated in 3, 6, 12 and 24-month time series in synoptic stations of Ilam and Dehloran, located in Ilam province, through using monthly precipitation data. Then, monthly precipitation of future period (2018-2037) were studied through using daily data of precipitation, minimum temperature, maximum temperature and radiation via using downscaling LARS-WG Model under Hadcm3 General Circulation Model and A2 and B1 Regional Scenarios. Then, SPI drought index was evaluated for future period in desired time series. Results of drought evaluation in base period in Ilam Station represented that 2008-2014 period had been a relatively humid period. It also represented timid period in Dehloran Station at beginning of period. Evaluation of drought in future period based on A2 and B1 Scenarios presented there will be a mostly drought period in Ilam Station between 2025 to 2035. Also, there will be a complete drought period in Dehloran Station from 2019 to 2021. Also, results represented that duration of drought and timid periods are increasing and their severity will be decreased by increase of statistical period. 977 991 اقبال نوروزی Eghbal Norozi دانشجوی کارشناسی ارشد مهندسی اکوهیدرولوژی، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران eghbalnorozi@ut.ac.ir نورالدین رستمی Noredin Rostami استادیار، دانشکدۀ کشاورزی، دانشگاه ایلام ایران n.rostami@ilam.ac.ir محمدحسین جهانگیر Mohammad Hossein Jahangir استادیار، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران mh.jahangir@ut.ac.ir تغییر اقلیم شاخص SPI مدل LARS-WG مدل Hadcm3 [1]. Van Pelt SC, Swart RJ. Climate change risk management in transnational river basin: The Rhine. Water Resource Management, 2011; 25(1): 3837-3861.##[2]. IPCC (Intergovernmental Panel on Climate Change). Summary for policy makers. In: IPCC. Climate change: The physical Science basic, Contribution of working group first to the Fourth assessment report of the intergovernmental panel on climate change, Cambridge university press, 2007: 450p.##[3]. Quevauviller P. Adapting to climate change: reducing water-related risks in Europe – EU policy and research considerations. Environmental Science and Policy, 2011; 14(7): 722-729.## [4]. Parvaneh B, Dargahian F, Shiravand H. Prediction of Drought in Lorestan province during 2011-2030 by downscaling 4 GCM models. Quarterly Geographical Journal of Territory, 2015; 12 (45):1-13. [Persian].##[5]. Golmohammadi M, Massah Bavani A. The Perusal of Climate Change Impact on Drought Intensity and Duration, Journal of Water and Soil, 2011; 25(2): 315-326. [Persian].##[6]. Kiem AS, Austin EK. Drought and the future of rural communities: Opportunities and challenges for climate change adaptation in regional Victoria, Australia. Global Environmental Change, 2013; 23:1307-1316.##[7]. Philip GO, Babatunde JA, Gunner L. Impacts of climate change on hydro-meteorological drought over the Volta Basin, West Africa. Global and Planetary Change, 2017; 155; 121-132.##[8]. Vidal JP, Wade S. A multimodel assessment of future climatological droughts in the United Kingdom. International Journal of Climatology, 2009; 29(14): 2056-2071.##[9]. Babaeian E, Nagafineik Z, Zabolabasi F, Habibie M, Adab H, Malbisei S. Climate Change Assessment over Iran During 2010-2039 by Using Statistical Downscaling of ECHO- G Model, 2010; 7(16): 135-152. [Persian].##[10]. Abdul Hosseini M, Eslamian S, Musavi SF. Analysis of variation of drought socio-economic characteristics and the effect of climate change, First National Conference on Meteorology and Water Management, Tehran, University of Technology, Department of Irrigation Engineering. 2010: 1-10. [Persian].##[11]. Dastorani MT, Massah Bavani A, Poormohammadi S, Rahimian MH. Assessment of potential climate change impacts on drought indicators (case study: Yazd Station, Central Iran), Journal of Desert, 2011; 16(2):159-167. [Persian].##[12]. Abbasi F, Asmari M. Forecasting and Assessment of Climate Change over Iran During Future Decades by Using MAGICC-SCENGEN Model, Journal of Water and Soil, 2011; 25(1): 70-83. [Persian].##[13]. Vrochidou AE, Tsanis IK, Grillakis MG, Koutroulis AG. The impact of climate change on hydro meteorological droughts at a basin scale. Journal of Hydrology, 2013; 476(8): 290-301.##[14]. Salehpour jam A, Mohseni Saravi M, Bazrafshan J, Khalighi S. Investigation of Climate Change Effect on Drought Characteristics in the Future Period using the HadCM3 model (Case Study: Northwest of Iran), Journal of Range and Watershed Management, 2015; 67(4): 537-548. [Persian].##[15]. Nikbakht Shahbazi A. Standard Precipitation Index (SPI) analysis in Karoon 3 Watershed under climate change, Journal of Science Water Engineering, 2013; 3(8): 83-98. [Persian].##[16]. Hoseinizade A, Seyed Kaboli H, Zarei H, Akhond Ali AM. The Intensity and Return Period of Drought under Future Climate Change Scenarios in Dezful Iran, Journal of Irrigation Science Engineering, 2016; 39(1): 33-43. [Persian].##[17]. Sajjad Khan M, Coulibaly P, Dibike Y. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 2006; 319(1-4): 357-382.##[18]. McKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, 1996; 17(22):179-183.##[19]. Thom HCS. A note on the gamma distribution, Weather Review, 1958; 86(4):117-122.##[20]. Abramowitz, M., Stegun, I.A. Handbook of Mathematical Functions. Dover Publications, 1965; New York.##[21]. Edwards DC, McKee TB. Characteristics of 20th century drought in the United States at multiple time scales. Colorado State University, Climatology Report Number 97–2, Fort Collins, Colorado, 1997.##[22]. Pirnia A, Golshan M, Bigonah S, Solaimani Karim. Investigating the drought characteristics of Tamar basin (upstream of Golestan Dam) using SPI and SPEI indices under current and future climate conditions, Journal of Ecohydrology, 2018; 5(1); 215-228. [Persian].##[23]. Bazrafshan O, Mohseni Saravi M, Malekian A, Moeini A. A study on drought characteristics of Golestan Province using Standardized Precipitation Index (SPI). Iranian Journal of Range and Desert Reseach, 2011; 18 (3):395-407. [Persian].##

0 مقایسه و پهنه بندی کیفیت منابع آب زیرزمینی دشت بجنورد طی دوره‏ های خشکسالی و ترسالی با استفاده از شاخص ‏های SPI، RAI و PN Assessment and zoning of groundwater quality of Bojnord plain during drought and wet periods with using SPI, RAI and PN indices. https://ije.ut.ac.ir/article_67664.html 10.22059/ije.2018.257381.875 0 یکی از پیامدهای مهم تغییر اقلیم، افزایش شدت خشکسالی است. خشکسالی یک پدیدۀ طبیعی تکرارشونده است که با کمبود منابع آب در دسترس در یک محدودۀ جغرافیایی بزرگ و در یک دورۀ زمانی مشخص، ارتباط دارد. در سال‏های اخیر، افزایش فراوانی وقوع پدیده‏های حدی اقلیمی مانند سیل و خشکسالی، همراه با شواهد گرمایش جهانی سبب افزایش توجه به موضوع‌های اقلیمی شده است. در تحقیق حاضر ابتدا با احصای آمارهای اقلیمی دو ایستگاه بجنورد و اسدلی وضعیت خشکسالی دشت بجنورد با توجه به شاخص‏های خشکسالی SPI، RAI و PN ‌بررسی شده است. سپس، با استفاده از نمودارهای پایپر و ویلکاکس کیفیت منابع آب زیرزمینی طی دوره‏های خشکسالی و ترسالی مقایسه شده است. نتایج بررسی نشان می‌دهد دشت بجنورد طی سال‏های اخیر در وضعیت خشکسالی قرار داشته است. همچنین، نتایج نشان می‏دهد کیفیت منابع آب در دورۀ ترسالی بیشتر آب‏های ترکیبی و شیرین بوده، اما در دورۀ خشکسالی رخسارۀ آب بیشتر از نوع ترکیبی و شورمزه بوده است. ‌در ضمن، نتایج نشان می‏دهد در دورۀ خشکسالی بیش از 50 درصد چاه‏های نمونه در وضعیت بسیار شور و نامناسب برای کشاورزی قرار داشته‌اند. 1 One of the major consequences of climate change is the increasing severity of drought. Drought is a recurring natural phenomenon that is associated with a shortage of available water resources in a large area over a given period of time. In recent years, the increasing frequency of occurrence of extreme climatic phenomena such as floods and droughts, along with global warming evidence, has led to an increase in attention to climatic issues. In this research, by preparing climatic statistics of two stations in Bojnourd and Assadi, the drought condition of Bojnoord plain has been investigated according to drought indices SPI, RAI and PN. Then, using Piper and Wilcox diagram, the quality of groundwater resources has been compared during drought and wet periods. The results of the survey indicate that the Bojnourd plain has been in drought situations in recent years. The results show that the quality of water resources during the wet period was mostly mixed and sweet water, but in the drought period, the water type was more than the mixed and saline water. The results also show that in the drought period more than 50 percent of wells in saltwater conditions were unsuitable for agriculture. 993 1005 حسین یوسفی Hossein Yousefi دانشیار، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران hosseinyousefi@ut.ac.ir عبدالرضا کاشکی Abdolreza Kashki استادیار، دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری ایران r.kashki@yahoo.com مختار کرمی mokhtar karami استادیار، دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری ایران m.karami08@yahoo.co.uk احمد حسین زاده ahmad hosseinzadeh دانشجوی دکترای اقلیم‏شناسی شهری، دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری ایران ahmad.ut@gamil.com الیاس ریحانی elyas reyhani دانشجوی کارشناسی ارشد اکوهیدرولوژی، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران elyas.reyhani@ut.ac.ir آب‏های زیرزمینی پهنه ‏بندی خشکسالی دشت بجنورد [1]. Kelanki M and Karandish F. Forecasting the long-term effects of climate change on climatic components in the region wet, journal of Water and Irrigation Engineering,2015,20(5),131-148.[Persian]##[2]. Navare A. An Analysis on International Society Practice toward the Climate Change. Environmental research.2015; 5 (9), 47-58.[Persian]##[3]. Jamali Z, Khoorani A. Impacts of climate change on extreme precipitation events in arid (Bandar Abbas) and semi-arid (Shahrekord) stations in Iran. Natural Environment Change. 2015 Jul 1; 1(1):85-94. [Persian]##[4]. farajzadeh m and ahmadian g. Temporal and Spatial Analysis of Drought with use of SPI Index in Iran. Journals management system.2014; 3(4), 1-16. [Persian]##[5]. HOSEINIZADE A, SEYED KH, ZAREI H, AKHON AA. The Intensity and Return Period of Drought under Future Climate Change Scenarios in Dezful, Iran. Journal of irrigation science and engineering.2016; 39(1), 33-43.[Persian]##[6]. ASADZADEH F, KAKI M, SHAKIBA S, RAEI B. Impact of drought on grounwater quality and groundwater level in QORVEH-CHARDOLI plain. Iran-water resources research.2016; 12(3), 153-165.[Persian]##[7]. Jahangir M, Norozi e. Numerical comparison of RAI and PNPI meteorological indices to assess and quantify the drought situation in Khuzestan province. Journal of echohydrology, 2017; 4(3), 923-930.[Persian]##[8]. Naserzade M, Ahmadi E. Evaluation of the performance of meteorological drought indicators in drought evaluation and zoning in Qazvin province.Researches in Geographical Sciences; 2013, 12(27): 141-162.[Persian]##[9]. Borna R ,Azimi F ,Saeidi dehaki N. Comparison of SIAP, PN, RAI indicators In the study of drought in Khuzestan province With emphasis on Abadan and Dezful stations, Natural History Quarter,2010;3(9),77-88.[Persian]##[10]. ANSARI H, DAVARI K, Sanaeinezhad SH. Drought monitoring with new precipitation and evapotranspiration index based on Fuzzy Logic. Journal of Water and Soil,2010;24(1),38-52.##[11]. Bazrafshan O, MOHSENI SM, Malekian A, Moeini A. A study on drought characteristics of Golestan province using standardized precipitation index (SPI). Journal of rangeland and desert research, 2011; 18(3), 395-407.##[12]. Yosefi H, Nohegar A, KHosravi Z, Farahani M. Management and zonation Drought using SPI and RDI indices (case study: Markazi province),Journal of echohydrology,2015;2(3):337-344. [Persian]##[13]. Pramudya Y ,Onishi T. Assessment of the Standardized Precipitation Index (SPI) in Tegal City, Central Java, Indonesia, IOP Conf. Series, Earth and Environmental Science, 2018 Mar;129(1):12-19.##[14]. Roshan M, Karimi V, Abjar J. Investigation of meteorological drought Indices in Mazandaran synoptic Stations,2011;2(5),15-25.[Persian]##[15]. Moasedi A, Qabaei M. Modification of Standardized Precipitation Index (SPI) Based on Relevant Probability Distribution Function, Journal of Water and Soil, 2011; 25(5):1206-1216.[Persian]##[16]. Khandouzi F ,Zangane A ,Zamani A ,Zhahamat Y. Survey of Hydro-geochemical Quality and Health of Groundwater in Ramian, Golestan Province, Iran, Journal of Health Research in Community,2015,1(3):41-52.[Persian]##[17]. Mooney PH. Toward a class analysis of Midwestern agriculture. Rural Sociology. 1983 Dec 1; 48(4):563.##[18]. Sikdar P, Sarkar S, Palchoudhury k, S. Geochemical evolution of groundwater in the Quaternary aquifer of Calcutta and Howrah, India, Journal of Asian Earth Sciences,2008;19(5),579-594.##[19]. Nadiri A , Asghari A , Frank T-C, Fijani E. Hydrogeochemical analysis for Tasuj plain aquifer, Iran, Journal of Earth System Science,2013;122(4),1091-1105.##[20]. Giglo B, Farid B, Najafi Nejad A, Moqani V, Qiasi A. Evaluation of water quality variation of Zarringol river, journal of Water and Soil Conservation Studies,2013;20(1),77-95.[Persian]##

0 مروری بر اثر نانوسیالات در کاهش میزان هدررفت آب و بهبود مشخصه‌های حرارتی در برج‌های خنک‌کن A review of the effect of nanofluids to reduce water loss and improve thermal properties in cooling towers https://ije.ut.ac.ir/article_67720.html 10.22059/ije.2018.257411.874 0 در بیشتر کارخانه‏ها، برج‏های خنک‏کن جزء کاربردی‏ترین تجهیزاتی هستند که برای حذف حرارت اضافی در فرایند و پس‏دادن آن به محیط استفاده می‌شوند. مقالۀ حاضر یک مرور اجمالی بر روش‏های نوین در حوزۀ تأثیر انواع نانوسیالات بر بهبود خواص حرارتی و کاهش میزان هدررفت آب است. نانوسیالات می‏توانند خواص حرارتی همچون ظرفیت گرمایی ویژه و ضریب رسانش حرارتی را بهبود بخشند و سبب افزایش ویسکوزیته و چگالی در مقایسه با سیال پایه شوند. پراکنده‌شدن نانوذره سبب افزایش کشش سطحی نانوسیال شده و مقاومت در برابر تبخیرشدن آب افزایش می‌یابد. بنابراین، در مقالۀ حاضر تأثیر نانوسیالات روی‏اکساید/آب، گرافیت نانوپروس/آب، آلومینا/آب، تیتانیوم اکساید/آب و مس اکساید/آب را با غلظت‏های متفاوت بر بهبود عملکرد برج‏های خنک‏کن بررسی می‌شود. در تحقیقات مشاهده‏شده با استفاده از نانوسیالات مشخصه‏هایی از جمله میزان خنک‏کردن، مشخصۀ برج‏ خنک‏کن، ضریب انتقال حرارت حجمی و بازده بهبود می‏یابد. بهترین نمونۀ گزارش‌شده در مقالات انجام‌شده، استفاده از نانوسیال روی‏اکساید/آب با غلظت‏های 02/0 و 05/0 wt% ‌است که ویژگی مشخصۀ حرارتی را به‌ترتیب به میزان 5/21 و 5/22 درصد در مقایسه با آب خالص بهبود داده ‏بود. در ادامه، نتایج آنالیز حساسیت در تحقیقات صورت‌گرفته تحلیل و بررسی می‏شود. 1 In most of the factories, one of the most important and practical devices is the type of cooling towers which are used to release extra heat from processes in various industries to the environment. This study is an overview of novel ways on the effect of different type’s nanofluids on the thermal performance and reduce the amount of flowing water down the cooling tower. Nanofluids can improve thermo physical properties such as heat capacity& thermal conductivity coefficient and increase density & viscosity in comparison to base fluid. The dispersion of the nanoparticle increases the surface tension of the nanofluid and increases the resistance to water evaporation.So in this paper study influence of ZnO/water, nonporous graphene, AL2O3/ water, TiO2/water, CuO/water with the different concentration in order to improve cooling tower performance. It was found that by using nanofluids, cooling range, cooling tower characteristic (TC), volumetric heat transfer coefficient and efficiency are enhanced in comparison to water. For example, TC enhanced by 21.5% and 22.5% for ZnO/water nanofluid with concentration of 0.02 wt% and 0.05 wt%, respectively. In continue, results of sensitivity analyses that has been carried out in investigations, are discussed. 1007 1015 فاطمه راضی آستارایی Fatemeh Razi Astaraei استادیار دانشکدۀ علوم و فنون نوین دانشگاه تهران ایران razias_m@ut.ac.ir سید علی موسوی seyed ali Mousavi دانشجوی کارشناسی ارشد گروه مهندسی انرژی‌های نو و محیط‏ زیست، دانشکدۀ علوم و فنون نوین، دانشگاه تهران ایران s.a.mousavi74@ut.ac.ir برج خنک‏کن عملکرد حرارتی نانوسیال هدررفت آب [1]. Askari S, Lotfi R, Seifkordi A, Rashidi AM, Koolivand H. A novel approach for energy and water conservation in wet cooling towers by using MWNTs and nanoporous graphene nanofluids. Energy conversion and management. 2016 Feb 1;109:10-8.##[2]. Zhai Z, Fu S. Improving cooling efficiency of dry-cooling towers under cross-wind conditions by using wind-break methods. Applied Thermal Engineering. 2006 Jul 1;26(10):1008-17.##[3]. Imani-Mofrad P, Saeed ZH, Shanbedi M. Experimental investigation of filled bed effect on the thermal performance of a wet cooling tower by using ZnO/water nanofluid. Energy Conversion and Management. 2016 Nov 1;127:199-207.##[4]. Xie X, Zhang Y, He C, Xu T, Zhang B, Chen Q. Bench-Scale Experimental Study on the Heat Transfer Intensification of a Closed Wet Cooling Tower Using Aluminum Oxide Nanofluids. Industrial & Engineering Chemistry Research. 2017 May 12;56(20):6022-34.##[5]. Goodarzi, M., Kiasat, M., Influence of nanoparticle immersed in water on Thermal performance of wet cooling tower. 1th tajhizatconf., Dec. 2013. Tehran, Iran. [Persian].##[6]. Babu JR, Kumar KK, Rao SS. State-of-art review on hybrid nanofluids. Renewable and Sustainable Energy Reviews. 2017 Sep 1;77:551-65.##[7]. Esfe MH, Alirezaie A, Rejvani M. An applicable study on the thermal conductivity of SWCNT-MgO hybrid nanofluid and price-performance analysis for energy management. Applied Thermal Engineering. 2017 Jan 25;111:1202-10.##[8]. 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0 بررسی تأثیر افت سطح آب زیرزمینی روی فرایند فرسایش خاک و تشکیل پدیدۀ گردوغبار Investigating of Groundwater Head-Loss Impact on Soil Erosion Process and Formation of Dust Phenomenon https://ije.ut.ac.ir/article_67721.html 10.22059/ije.2018.241942.728 0 ‌حفر بی‏رویۀ چاه‏های عمیق، سبب افت سطح آب زیرزمینی و مسائل ناشی از آن شده است که مهم‏ترین آن نابودی پوشش گیاهی دشت‏ها است که آب مورد نیاز خود را از رطوبت موجود در زمین می‏گیرند. هدف از تحقیق حاضر، تجزیه و تحلیل کاهش رطوبت خاک سطحی و منبع تولید گردوغبار در حوضۀ هامون- هیرمند از استان سیستان و بلوچستان است. به منظور رسیدن به این اهداف، از نرم‏افزار WEAP برای شبیه‏سازی افت جریان زیرسطحی استفاده شده و سپس با اعمال سناریوهای مدیریتی منابع آب، مقدار افت جریان زیرسطحی تا سال 2031 شبیه‏سازی شد. با استفاده از روش تحلیل سلسله‌مراتبی بهترین گزینه از بین سناریوها انتخاب شد. منطقۀ مطالعه‌شده تحت این سناریو در پایان سال هدف، 29 سانتی‏متر افت جریان زیرسطحی دارد و مقدار کل نیاز تأمین‌نشده برای معیارهای مختلف همچون شرب، کشاورزی و زیست‌محیطی برابر 83/1804 میلیون مترمکعب است. سرعت باد در منطقۀ مطالعه‌شده بیش از سرعت آستانۀ حرکت ذرات است (بیش از 400 سانتی‌متر بر ثانیه)، بنابراین منطقه توان تولید گردوغبار دارد. برای تجزیه و تحلیل کاهش رطوبت خاک سطحی از مدل HYDRUS-1D در مقالۀ حاضر استفاده شده است و نتایج به‌دست‌آمده نشان از کاهش مقدار رطوبت خاک در هر سال دارد. درضمن، ارتباط بین افت جریان زیرسطحی و کاهش مقدار رطوبت خاک در تولید گردوغبار بررسی شد و این نتیجه به دست آمد که کاهش رطوبت خاک سطحی ارتباط نزدیکی با افت جریان زیرسطحی به اندازۀ حداکثر 83/93 سانتی‌متر و همچنین توان تولید گردوغبار متناسب با آن را دارد. 1 The purpose of this research is to analyze the decrease of surface moisture and source of dusts in Hamoon-Hirmand Basin of Sistan and Baluchestan province. In order to achieve the objectives of this paper, WEAP software was used to simulate subsurface flow drops and then, by applying water resource management scenarios, the subsurface flow loss rate was simulated by 2031. Using the hierarchical analysis method, the best option among the scenarios was selected and the area under study under this scenario at the end of the year is 29 cm below the subsurface flow rate and the total unsecured amount for various criteria such as drinking, agriculture And the environment is equal to 804.183 million cubic meters. The wind speed in the study area is greater than the velocity threshold of the particles (more than 400 cm / s), so the area is capable of producing dust. The HYDRYS-1D model has been used to analyze the soil moisture content reduction in this paper. The obtained results indicate a decrease in soil moisture content in each year. It was concluded that the reduction of soil moisture content is closely related to the subsurface flow rate and the potential for dust 1017 1035 غلامرضا عزیزیان gholamreza azizyan استادیار، دانشکدۀ مهندسی شهید نیکبخت، گروه مهندسی عمران، دانشگاه سیستان و بلوچستان ایران g.azizyan@eng.usb.ac.ir سید آرمان هاشمی منفرد Seyed Arman Hashemi Monfared دانشیار، دانشکدۀ مهندسی شهید نیکبخت، گروه مهندسی عمران، دانشگاه سیستان و بلوچستان ایران hashemi@eng.usb.ac.ir امیرحسین جوان محصل Amirhosein Javan Mohasel فارغ‌التحصیل کارشناسی ارشد، دانشکدۀ مهندسی شهید نیکبخت، گروه مهندسی عمران، دانشگاه سیستان و بلوچستان ایران ajavan.civil@gmail.com محسن دهقانی درمیان Mohsen Dehghani Darmian دانشجوی دکتری، دانشکدۀ مهندسی شهید نیکبخت، گروه مهندسی عمران، دانشگاه سیستان و بلوچستان ایران mohsen.dehghani@pgs.usb.ac.ir افت سطح آب زیرزمینی حوضۀ هامون‌ـ ‏هیرمند رطوبت خاک گردوغبار [1]. Subramanya, K. Engineering Hydrology. 1nd ed. Mashhad: Hashemi, S, R; 2003. [Persian]##[2]. Kardvani, P. The desert (salt) of central Iran and its neighboring areas. 1nd ed. Tehran: Tehran University Press; 2007.[Persian]##[3]. Elmore, A. Kaste, J. Okin, G. Fantle, M. Groundwater influences on atmospheric dust generation in deserts. Journal of Arid Environments. 2008; 72(1): 1753– 1765.## [4]. Maki T, Hara K, Iwata A, Lee KC, Kawai K, Kai K, Kobayashi F, Pointing SB, Archer S, Hasegawa H, Iwasaka Y. Variations in airborne bacterial communities at high altitudes over the Noto Peninsula (Japan) in response to Asian dust events. Atmospheric Chemistry and Physics. 2017 Oct 9;17(19):11877-97.## [5]. Maki T, Kurosaki Y, Onishi K, Lee KC, Pointing SB, Jugder D, Yamanaka N, Hasegawa H, Shinoda M. Variations in the structure of airborne bacterial communities in Tsogt-Ovoo of Gobi desert area during dust events. Air Quality, Atmosphere & Health. 2017 Apr 1;10(3):249-60.##[6]. Bi J, Huang J, Shi J, Hu Z, Zhou T, Zhang G, Huang Z, Wang X, Jin H. 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Application of management scenario in prediction of groundwater fluctuations with MODFLOW conceptual and mathematical model (Case Study: Khazal Plain - Nahavand). Journal of Ecohydrology. 2016; (3): 303-319. [Persian]##[17]. Mohammadi, A. Delbari, M. Chari, M, M. Comparison of SWAP and HYDRUS-1D models in simulating water movement and salt concentration in soil. National Conference on Irrigation and Reduction of Evaporation.2013 ;( 12):12-22. [Persian]##[18]. Asar, A. Derakhshannegad, Z. Soltanimohammadi, A. Goshe, M. Soil moisture simulation with HYDRUS-1D model in wheat cultivation conditions. Journal of Irrigation Science and Engineering. 2014 ;( 37):81-92. [Persian]##[19]. X. Guan, J. Huang1, Y. Zhang1, Y. Xie1 and J. Liu , The relationship between anthropogenic dust and population over global semi-arid regions. Atmos Chem. Phys., , 5159–5169, 2016.##[20]. Song H, Wang K, Zhang Y, Hong C, Zhou S. Simulation and evaluation of dust emissions with WRF-Chem (v3. 7.1) and its relationship to the changing climate over East Asia from 1980 to 2015. Atmospheric Environment. 2017 Oct 1;167:511-22.##[21]. H. Shen, J.Abuduwail, A. Samat. L. Ma, A review on the research of modern aeolian dust in Central Asia, Arab J Geosci (2016) 9: 625.##[22]. Nakhaei S. Water resources allocation considering the effects of climate change in the catchment area (Case study: Sarbaz River). Master's Thesis in Civil Engineering. 2017 July. [Persian]##[23]. Alizadeh A. Investigation of groundwater drawdown effect on salinity in close sub-basins with different qualities and surface/groundwater management to control this phenomena. Master's Thesis in Civil Engineering. 2015 Sep. [Persian]##[24].Ajamzade, A. WEAP model application guide. 1nd ed. Tehran: Islamic Azad University; 2015. [Persian]##[25]. Ramrodi, H, A. Ajdarimoghadam, M. Management of water resources of Pearsarab Uorki region of Sistan and Baluchestan province in case of increasing water requirements by assessing WEAP model. Research in Civil Engineering, Architecture, Urbanism and Sustainable Environment.2015; 1-13. [Persian]##[26]. Shahidi, A. Ahmadi, M. Video tutorial model HYDRUS. 1nd ed. Tehran: Kalk Zarin; 2014. [Persian]##[27]. Water Resources Development Report. 1nd ed. Tehran: Sistan and Baluchestan Regional Water Company; 2015. [Persian]##[28]. Khosravi, M. Long-Term Spatial Analysis of Lake Hamoon. Iranian Water Resources Research Journal.2010 ;( 6):68-79. [Persian]##[29]. Mirakzehi K, Pahlavan-Rad MR, Shahriari A, Bameri A. Digital soil mapping of deltaic soils: A case of study from Hirmand (Helmand) river delta. Geoderma. 2018 Mar 1;313:233-40.##[30]. Ashrafi ZN, Ghasemian M, Shahrestani MI, Khodabandeh E, Sedaghat A. 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0 مدیریت تقاضای مصرف آب با رویکرد اقتصادی در شمال استان سیستان و بلوچستان Integrated management of water demand by economic approach in Northern Sistan and Baluchestan province https://ije.ut.ac.ir/article_67722.html 10.22059/ije.2018.248647.797 0 در کشورهای کم‌آب مانند ایران، توجه به تمامی منابع آبی اهمیت زیادی دارد و این امر در قالب مدیریت یکپارچۀ منابع آبی قابل اجرا خواهد بود. مطالعۀ حاضر عوامل مؤثر بر تقاضای آب در مصارف کشاورزی و خانگی و میزان تقاضای آب و کشش‏های درآمدی و قیمتی در بخش خانگی در شمال استان سیستان و بلوچستان را بررسی کرده است. به این منظور، در بخش خانگی از تابع مطلوبیت استون‌ـ گری استفاده شده و در بخش کشاورزی با به‌کارگیری مدل لاجیت، عوامل کمی و کیفی مؤثر بر مدیریت بهینۀ منابع آب بررسی شده است. تخمین ضرایب تابع تقاضای خانگی برای آب آشامیدنی در شهرستان‌های زابل و زهک نشان داد اگر در هر ماه یک نفر به جمعیت شهر اضافه شود، به متوسط ماهانۀ مصرف سرانۀ خانگی آب 00079/0 مترمکعب افزوده می‏شود. همچنین، اگر به صورت ماهانه یک ریال به قیمت واقعی آب اضافه (کم) شود، متوسط ماهانۀ مصرف سرانۀ خانگی آب 54/2 مترمکعب کاهش (افزایش) می‏یابد و اگر در هر ماه یک درجۀ سانتی‌گراد متوسط کمترین دمای روزانه افزایش (کاهش) یابد، متوسط ماهانۀ مصرف سرانۀ خانگی آب 306/0 مترمکعب افزایش (کاهش) می‏یابد. همچنین، تقاضای سرانه در حالت کلی برابر 5/61 مترمکعب در سال و اضافۀ مصرف سرانه در این حالت 8/26 مترمکعب است. همچنین، کشش‏های قیمتی و درآمدی تقاضای خانگی آب در این حالت به‌ترتیب برابر با 282/0 و 373/0 هستند که نشان‏دهندۀ کم‌کشش بودن تقاضای آب نسبت به قیمت است. کشش درآمدی نیز کمتر از یک بوده و بیان‌کنندۀ ضروری‌بودن کالای آب است. نتایج تخمین مدل مصرف بهینۀ آب کشاورزی نیز نشان داد بیشترین تأثیر مربوط به متغیر سابقه و تجربۀ کاری است و سپس به‌ترتیب متغیرهای استفاده از کودها و سموم شیمیایی، نوع کانال‏های ارتباطی، روش آبیاری، آموزش و سطح تحصیلات بیشترین تأثیر را در استفادۀ بهینه از منابع آب کشاورزی دارند. 1 This study survived the factors affecting on water demand in agricultural and domestic consumption, amount of water demand and income and price elasticities in household sector in the north of Sistan and Baluchestan. For this purpose, we used the Stone-Greay utility function in the household sector and considered quantitative and qualitative factors influencing the optimal water resources management in agriculture using the Logit model. The results of water demand function estimation in household sector showed that the demand for per capita in general condition was equivalent to 61.5 cubic meters per year and the extera consumption per capita in this case was 26.8 cubic meters. Also, income and price elasticities of household water demand in this case were equal to 282.0 and 373.0 respectively, indicating that the demand for water was less tangible than price; Income stretch is less than one, indicating the necessity of water. The results of estimation of the optimum water consumption model showed that the most effect was on the work experience and history variables and then the variables of using fertilizers and chemical pesticides, type of communication channels, irrigation methods, education and education level had the most effect on optimal use of agricultural water resources. 1037 1049 جواد شهرکی Javad Shahraki دانشیار دانشکدۀ علوم زیست‌محیطی و کشاورزی پایدار، دانشگاه سیستان و بلوچستان ایران j.shahraki@eco.usb.ac.ir علی رهنما Ali Rahnama دانشجوی دکتری اقتصاد کشاورزی، دانشکدۀ علوم زیست محیطی و کشاورزی پایدار، دانشگاه سیستان و بلوچستان ایران ali.rahnama65@gmail.com حمیده خاکسار آستانه Hamideh Khaksar Astaneh هیئت علمی گروه اقتصاد گردشگری جهاد دانشگاهی خراسان رضوی ایران letter.i2017@gmail.com تابع مطلوبیت استون- گری شمال سیستان و بلوچستان مدل لاجیت مدیریت تقاضای مصرف آب‌ [1]. Mousavi SN, Kavoosi Kalashami M. 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0 تعیین میزان تبخیر و تعرق پتانسیل واقعی و بهترین مدل برآورد نیاز آبی زعفران در تربت حیدریه Determine of the Actual and Potential Evapotranspiration and Appropriate Model for Determining Water Requirement of Saffron (Case study: Torbat Heydarieh) https://ije.ut.ac.ir/article_67723.html 10.22059/ije.2018.252321.830 0 ‌گیاه نیمه‌گرمسیری زعفران، به دلیل نیاز آبی کم و درآمد زیاد، جایگاه ویژه‏ای در الگوی کشت مناطق خشک و نیمه‏خشکی همچون منطقۀ خراسان ‏رضوی دارد. در تحقیق حاضر، به تعیین میزان تبخیر و تعرق پتانسیل و واقعی و مناسب‏ترین مدل برآورد تبخیر و تعرق زعفران در منطقۀ تربت ‏حیدریه (قطب تولید زعفران جهان) پرداخته شد. به این منظور، روش‏های مختلف فائوپنمن‏مانتیث، بلانی‏کریدل، هارگریوزسامانی، تورنت‏وایت، جنسن‏هیز و تورک ارزیابی شدند. بررسی مقایسۀ نتایج به‌دست‌آمده از روش فائو با سایر روش‏ها با استفاده از آزمون کای‏اسکوئر انجام شد. نتایج مقایسۀ روش‏های مختلف با روش فائوپنمن‏مانتیث به‏عنوان روش استاندارد و مبنا نشان داد روش‏های بلانی‏‏کریدل، جنسن‏‏هیز و هارگریوزسامانی به‏ترتیب دقت بیشتری نسبت به سایر روش‏ها داشتند. مقدار نیاز آبی سالانۀ زعفران در اقلیم منطقۀ تربت ‏حیدریه با استفاده از روش استاندارد فائوپنمن‏مانتیث، 1731 مترمکعب در هکتار به‏دست آمد. از آنجا ‏که در معادلۀ‏ هارگریوزسامانی برای محاسبۀ تبخیر و تعرق فقط به دو عامل دما و تشعشع خورشیدی نیاز است و تعیین عوامل یادشده در بیشتر ایستگاه‏های هواشناسی امکان‏پذیر است، پیشنهاد می‌شود در برآوردهای تخمینی اولیه و سریع نیاز آبی گیاه زعفران از این روش استفاده شود. 1 The semi-tropical plant of saffron, due to low water requirement and high income, has a special place in the cultivating pattern of arid and semi-arid regions such as Torbat Heydarieh region of Khorasan Razavi. In this research was carried out to determine the potential and actual evapotranspiration rate and the most suitable model for estimating evapotranspiration of saffron in Torbat Heydarieh (the world's saffron producing pole). The results of comparison of different methods with FAO method as standard and basic method showed that Blaney-Criddle, Genesis and Hargreaves methods were more accurately than other methods. A comparison of the results of the FAO method with other methods was performed using Chi-square test. The amount of annual water requirement of saffron in the climate of Torbat Heydarieh using the FAO method was 1731 m3 / ha. Since in the Hargreaves-Samani equation, for the calculation of evapotranspiration, only two factors are necessary for temperature and solar radiation, and it is possible to determine the factors in most weather stations, the overall result of this research is that, in the estimation of initial and Rapid need for a saffron plant in the region can be used. 1051 1061 پروین علی اکبری PARVIN ALIAKBARI کارشناسی ارشد مهندسی منابع آب، پژوهشگر پژوهشکدۀ زعفران دانشگاه تربت حیدریه ایران p_aliakbari89@yahoo.com امیر سالاری amir salari استادیار گروه تولیدات گیاهی، دانشکدۀ کشاورزی، پژوهشگر پژوهشکدۀ زعفران دانشگاه تربت حیدریه ایران salari.1361@yahoo.com عباس خاشعی سیوکی Abbas KhasheiSiuki دانشیار گروه مهندسی آب، دانشکدۀ کشاورزی دانشگاه بیرجند ایران abbaskhashei@birjand.ac.ir فائوپنمن‏مانتیث قطب تولید زعفران جهان نیاز آبی [1]. Ehsanzadoh P, Yadollahi A.A, Maibodi A.N.M. 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