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0 تحلیل عدم قطعیت تغییرات متغیرهای اقلیمی بارش و دما تحت تأثیر تغییر اقلیم (مطالعۀ موردی: استان خراسان جنوبی) Uncertainty analysis of temperature and precipitation variation influenced by climate change (Case Study: Southern Khorasan Province) https://ije.ut.ac.ir/article_63204.html 10.22059/ije.2017.63204 0 در این مطالعه آثار تغییر اقلیم روی متغیرهای اقلیمی بارندگی، کمترین و بیشترین دما بررسی شده است. بررسی روند تغییرات کمترین دما‌، بیشترین دما و بارندگی به‌همراه تحلیل عدم قطعیت خروجی مدل‏های گردش عمومی از جمله اقدامات انجام‌شده است. به‌منظور بررسی آثار تغییر اقلیم، از خروجی 15 مدل گردش عمومی تحت سه سناریوی A1B، A2 و B1 استفاده شد. به‌منظور ریزمقیاس‌‏نمایی داده‏های بزرگ‌مقیاس از مدل LARS-WG استفاده شد. بدین‌منظور آمار مشاهداتی هفت ایستگاه سینوپتیک موجود در سطح استان طی سال‏های 1990 تا 2010 میلادی به‌عنوان دورۀ پایه به مدل معرفی شد. پس از ریزمقیاس‌نمایی خروجی مدل‏های اقلیمی با انتخاب برترین و سازگارترین مدل‏ها، مقادیر شبیه‏سازی‌شده برای دما و بارش به‌صورت سالانه در مدل‏های منتخب تهیه شد. بدین‌ترتیب مجموعه خروجی مدل‏های اقلیمی برای هر متغیر و به‌صورت سالانه به‌عنوان ورودی فرمان بوت‌استرپ ایجاد شد و باند عدم قطعیت خروجی مدل‏های اقلیمی در سطح 99 درصد به‌صورت سالانه ارزیابی شد. نتایج مربوط به دقت مدل‏های گردش عمومی نشان داد بیشتر مدل‏ها توانمندی زیادی در شبیه‏سازی رفتار بارندگی ندارند؛ ولی عملکرد این مدل‏ها در شبیه‏سازی تغییرات کمترین و بیشترین دما بسیار خوب برآورد شد. نتایج تحلیل روند در سطح ایستگاهی و استانی نشان‏دهندۀ کاهش بارش و افزایش دما خواهد بود. با مقایسۀ تغییرات دما در آینده نسبت به دورۀ پایه می‏توان این انتظار را داشت که کمترین و بیشترین دما به‌ترتیب حدود 6/0 درجه کاهش و 2 درجه افزایش را ‌داشته باشند. نتایج تحلیل عدم قطعیت نشان ‏می‏دهد منابع عدم قطعیت شایان توجهی در شبیه‏سازی مؤلفه‏های هواشناسی دارند. همچنین با تحلیل این عدم قطعیت‏ها در خصوص بارندگی می‏توان گفت که با گذر زمان، شدت تغییرات بارندگی افزایش می‏یابد. این نتیجه در خصوص کمترین و بیشترین دما نیز صادق است. 1 For survey of climate change effects In this research fifteen GCMs models are used. By using downscaling method of LARS-WG large scale projections of GCMs output subgrided from high resolutions to local coordinate. For this aim, observation data (1990-2010) of synoptic stations in province are collected and was assumed as base period. Trend was fulfilled by Man-Kendal as well as uncertainty was carried out by bootstrapping function. Annual simulations of rainfall and temperature were used as entrance to Bootstrap. Confidence interval for each station was determined in 0.09 levels. Results about performance of GCMs showed that almost all models haven’t high ability to simulation of behavior of precipitation pattern. However performance of these models for simulation of variation of least and most temperature was very good. Results of trend analysis for stations and province showed that decrease of rainfall and increase of average temperature. By comparison of variation of temperature in future than historical period it is founded that minimum and maximum temperature will have 0.6 decrease and 2 increase respectively. also uncertainty analysis showed that there are significant sources of uncertainty in simulation of meteorological components. Also annual precipitation variations in future are more severe than historical period. 943 953 علی شهیدی Ali Shahidi دانشیار گروه علوم و مهندسی آب، دانشکدۀ کشاورزی، دانشگاه بیرجند Iran ashahidi@birjand.ac.ir سید محمد تاجبخش mohammad tajbakhsh استادیار گروه مرتع و آبخیزداری، دانشکدۀ منابع طبیعی و محیط زیست، دانشگاه بیرجند Iran tajbakhsh.m@gmail.com عباس خاشعی سیوکی Abbas Khasheie دانشیار گروه علوم و مهندسی آب، دانشکدۀ کشاورزی، دانشگاه بیرجند Iran abbaskhasheei@birjand.ac.ir حسین خزیمه نژاد Hossein Khozeymehnejad استادیار گروه علوم و مهندسی آب، دانشکدۀ کشاورزی، دانشگاه بیرجند Iran hkhozeymeh@birjand.ac.ir احمد جعفرزاده Ahmad Jafarzadeh دانشجوی دکتری مهندسی منابع آب، گروه علوم و مهندسی آب، دانشکدۀ کشاورزی، دانشگاه بیرجند Iran mnt.jafarzadeh@chmail.ir Bootstrapping LARS-WG Man-Kendal pricipitation [1]. Mani A, Tsai FT. Ensemble Averaging Methods for Quantifying Uncertainty Sources in Modeling Climate Change Impact on Runoff Projection. Journal of Hydrologic Engineering. 2016 Dec 1:04016067..##[2]. Eghdamirad S, Johnson F, Woldemeskel F, Sharma A. Quantifying the sources of uncertainty in upper air climate variables. Journal of Geophysical Research: Atmospheres. 2016 Apr 27;121(8):3859-74.##[3]. Leedale J, Tompkins AM, Caminade C, Jones AE, Nikulin G, Morse AP. Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty. Geospatial health. 2016 Mar 31;11(1s).##[4].Delghandi M, Moazenzadeh R. Assessment of temporal and special variation of Iran’s rainfall and temperature under climate change conditions. By considering of uncertainty of AOGCM and emission scenarios. Iranian journal of ECO HYDROLOGY. 2016 Dec 16;3(3). 321-331. [Persian]##[5].Kaboli H. Uncertainty of daily maximum rainfall esission scenarios of greenhouse gases on 2040. (case study:Razavi Khorasan province).Iranian journal of ECO HYDROLOGY. 2016 Feb25;2(4). 455-465. [Persian]##[6]. Fakhri M, Farzaneh MR, Eslamian S, Khordadi MJ. Confidence interval assessment to estimate dry and wet spells under climate change in Shahrekord Station, Iran. Journal of Hydrologic Engineering. 2012 Aug 7;18(7):911-8.##[7]. Farzaneh MR, Eslamian S, Samadi SZ, Akbarpour A. An appropriate general circulation model (GCM) to investigate climate change impact. International Journal of Hydrology Science and Technology. 2012 Jan 1;2(1):34-47.##[8].Etemadi H, Samadi S, Sharifikia M. Simulation of the Future Climatic Changes in Jask Area and Its Impact on Hara Forests. Geography and develop. 2014 Jul 28;13(41). 87-104. [persian]##[9].Ghermezcheshmeh B, Rasuli A, Rezaei-Banafsheh M, Massah A, Khorshiddoost A. Uncertainty analyzing of Neural Network in downscaling of HadCM3 data with bootstrap confidence interval method. 2014 Aug 2;7(3). 306-316. [Persian]##[10]. Chen J, Brissette FP, Chaumont D, Braun M. 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Handbook of statistical methods in meteorology. Handbook of statistical methods in meteorology.. 1953.##[15]. Yue S, Pilon P, Cavadias G. Power of the Mann–Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series. Journal of hydrology. 2002 Mar 1;259(1):254-71.##[16]. Semenov MA, Barrow EM, Lars-Wg A. A stochastic weather generator for use in climate impact studies. User’s manual, Version. 2002 Aug;3.##[17].Jafarzadeh A, Khashei-siuki A, Shahidi A. Assessment two methods of statistical downscaling LARS-WG and SDSM in estimates of climate parameters variation (Case study: Birjand plain). Journal of water and soil conservation. 2016 July 19;23(4). [Persian]##[18]. Duan K, Mei Y. A comparison study of three statistical downscaling methods and their model-averaging ensemble for precipitation downscaling in China. Theoretical and applied climatology. 2014 May 1;116(3-4):707-19.##[19]. Wilby RL, Tomlinson OJ, Dawson CW. 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0 پیش‏ بینی رواناب با استفاده از مدل‏ های هوشمند Runoff prediction using intelligent models https://ije.ut.ac.ir/article_63228.html 10.22059/ije.2017.63228 0 پیش‏بینی رواناب رودخانه‏ها به‌دلیل اهمیت زیاد آن در برنامه‏ریزی‏ها، بهره‏برداری از مخازن و همچنین مدیریت آب‏‏های سطحی همواره مورد توجه مسئولان، برنامه‏ریزان و مهندسان آب و منابع آبی بوده است. از طرفی، به‌دلیل تغییرات زمانی و مکانی موجود، روابط غیرخطی و عدم قطعیت و بسیاری از عوامل دیگر پیش‏بینی رابطۀ بارش‌ـ رواناب بسیار مشکل است، اما امروزه استفاده از سامانه‏های هوشمند در پیش‏بینی چنین پدیده‏های پیچیده‌ای می‏تواند مفید و مؤثر باشد. در این پژوهش سعی شده است با استفاده از داده‏های هواشناسی و هیدرومتری طی دورۀ زمانی 1349-1350 تا 1390-1391 رواناب در حوضۀ آبخیز امامه با استفاده از مدل‏های شبکۀ عصبی پرسپترون چندلایه، تابع پایۀ شعاعی و سیستم عصبی فازی تطبیقی تخمین زده شود. نتایج نشان داد از بین مدل‏های یادشده سیستم عصبی فازی تطبیقی عملکرد بسیار زیادی داشته است و به‌خوبی می‏تواند رواناب را پیش‏بینی کند به‌طوری‏که با توجه به خطاها ساختار 54 با هشت ورودی شامل بارندگی و دبی تا تأخیر دو روز و دما، تبخیر و تعرق و رطوبت نسبی همان روز که دارای تابع عضویت گوسی و جداسازی از نوع خوشه‏ای با خطای MSE، RMSE و MAE به‌ترتیب 001/0، 025/0 و 008/0 در مرحلۀ آموزش و 001/0، 026/0 و 008/0 در مرحلۀ آزمایش به‌عنوان بهترین مدل حوضۀ امامه بوده است. 1 River runoff prediction because of its high importance in planning, reservoir operation and management of surface water has always attracted the attention of officials, planners and water engineers and water resources. On the other hand because of availab temporal and spatial changes, non-linear relationships and uncertainty, and many other factors to predict rainfall-runoff relationship is very difficult. But todays the use of intelligent systems can be useful for predicting such complex phenomena. In this study, using meteorological and hydrometric data for the period 1970-1971 to 2011-2012 to estimate runoff in the watershed Amameh using MLP, RBF, and ANFIS were used. The results showed that out of models ANFIS has the best function and can predict runoff very well. So that according errors, the structure model number 54 with eight inputs including rainfall and runoff to delay for two days and temperature, evaporation and relative humidity and cluster seperation and its errors was 0.001, 0.025 and 0.008 in training stage and 0.001, 0.026 and 0.008 in test stage was the best model in Amameh Watershed. 955 968 محبوبه معتمدنیا mahboobeh moatamednia دکتری علوم و مهندسی آبخیزداری Iran mmoatamednia@yahoo.com احمد نوحه گر Ahmad Nohegar استاد گروه آموزش، برنامه ‏ریزی و مدیریت محیط زیست، دانشکدۀ محیط زیست، دانشگاه تهران، کرج Iran nohegar@ut.ac.ir آرش ملکیان Arash Malekian دانشیار گروه احیای مناطق خشک و کوهستانی، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج Iran malekian@ut.ac.ir مریم صابری Maryam Saberi مدرس دانشگاه فنی و حرفه ‏ای، یزد Iran maryam.saberia@gmail.com کمال کریمی Kamal Karimi رئیس ادارۀ منابع طبیعی شهرستان بافق Iran karimikamal5@yahoo.com Amameh Representative Watershed Intelligent Models Rainfall-runoff relationship [1].Danandehmehr A, Majdzadeh Tabatabai MR. 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Investigating efficiency fuzzy logic to predict daily river flow, Iran-Watershed Management Science & Engineering. 2012; 5(17): 7-14. [In Persian].##[8]. Nohegar A, Motamednia M, Malekian A. Daily river flood mresentative watershed, Physical Geography Research Quarterly. 2016; 48(3): 367-383. [In Persian].##[9].Mahdavi M, Applied Hydrology, First volume, fourth edition, Tehran university press, 2003: P. 364. [In Persian].##[10].Imrie CE, Durucan S, Korre A. River fow prediction using artificial neural networks: generalisation beyond the calibration range, J. of Hydrology. 2000; 233: 138-153 pp.##[11].Food I, Kartman N. Neural network in civil engineering: principal and understanding, J. of computing in civil engineering. 1996; 8 (2): 131-148.##[12].Kaastra I. Boyd MS. Forecasting futures trading volume using neural networks, The J. of Futures Markets.1995; 15(8): 953-970.##[13].Gharaei-Manesh S, Fathzadeh A, Taghizadeh-Mehrjardi R. Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran, Cold Regions Science and Technology, 2016; 122: 26–35 pp.##[14].Kakaei Lafdani E, Moghaddamnia A, Ahmadi A. Daily suspended sediment load prediction using artificial neural networks and support vector machines, J. of Hydrology, 2013; 478: 50–62.##[15].Mahjouri N, Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling, Applied Soft Computing, 2014; 38: 329–345 pp.##[16].Dawson CW, Wilby R.L., 2001. Hydrological modeling using artificial neural network, Progress in Physical Geography. 2001; 25: 80–108.##[17].Tokar A S, Markus M. Precipitation rainfall-runoff modeling using artificial neural network and conceptual models, J.of Hydrologic Engineering. 2000; 5(2):156-161 pp.##[18].Dibike Y, Solomatine D. River flow forecasting using artificial neural networks. J. of Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere. 2001; 26: 1–8.##[19].Mendez MC, Wenceslao G, ManuelPF, José Manuel LP, Roman L. Modelling of the monthly and daily behaviour of the runoff of the Xallas river using Box-Jenkins and neural networks methods. J. of Hydrology. 2004; 1685-1694.##[20].Melesse AM, Ahmad S, McClain ME, Wang X, Lim YH. Suspended sediment load prediction of river systems: An Artificial Neural Networks Approach, Agricultural Water Management. 2011; 98(5): 855-866.##[21].Zounemat-Kermani M, Teshnehlab M. Using adaptive neuro-fuzzy inference system for hydrological time series prediction, Applied Soft Computing. 2008; 8(2): 928-936.##

0 برآورد مقدار بهینۀ آبدهی ویژه و تخمین تغذیۀ آب زیرزمینی آبخوان آزاد‌دشت گلگیر، استان خوزستان Assessment the Specific Yield Optimal Value and Groundwater Recharge Estimate of Unconfined Aquifer on Golgir plain, Khuzestan Province https://ije.ut.ac.ir/article_63229.html 10.22059/ije.2017.63229 0 روش نوسانات سطح آب (WTF) بر پایۀ صعود سطح آب بر اثر تغذیۀ آب زیرزمینی استوار است. به‌کارگیری این روش، مستلزم تخمین مناسب مقدار آبدهی ویژه در محدودۀ نوسانات سطح آب زیرزمینی است. در این مقاله، مقدار آبدهی ویژه از روش لاگ چاه‏های حفاری (DWL)، روش پوشش خط مستقیم (ESL) و روش سونداژ الکتریکی قائم (VES) تخمین زده شد. به‌منظور ارزیابی و انتخاب روش مناسب‏، مقدار آبدهی ویژه از داده‏های هیدرولوژیکی و هیدروژئولوژیکی هشت‌ساله (1385 تا 1392) دشت گلگیر با ضریب همبستگی برای رابطۀ بین تغذیه‌ـ بارندگی و تغذیه‌ـ جریان پایه در روش پوشش خط مستقیم (ESL) به‌ترتیب با 79/0 r2= و 90/0 r2= به‌دست آمد. همچنین، با توجه به ضریب همبستگی مناسب روش ESL، مقدار بهینۀ آبدهی ویژه برای دشت گلگیر برابر 12/0 تخمین زده شد. ضریب همبستگی به‌دست‌آمده از رابطۀ بین میزان تغذیۀ آب‏ زیرزمینی، جریان پایه و بارندگی در روش‏های DWL، ESL و VES نیز دقت زیاد روش ESL را نشان داد که روش ESL با 90/0=r2 بهترین تخمین مقدار آبدهی ویژه را دارد. بنابراین، با توجه به اهمیت مقدار تغذیه در مدیریت و برنامه‏ریزی منابع آب ‏زیرزمینی و تأثیر زیاد مقدار آبدهی ویژه در روش WTF، تخمین تغذیۀ آب‏ زیرزمینی با استفاده از مقدار بهینۀ آبدهی ویژه کمک شایانی به محققان علوم آب می‏کند. 1 The water table fluctuation (WTF) method is based on rises of a water table are caused by recharging groundwater. To apply this method, an appropriate estimate of the specific yield in the zone of the groundwater level fluctuation is required. In this paper, the specific yield are estimated these methods include Drilling Well Logs (DWL), Envelope Straight Line (ESL) and Vertical Electrical Sounding (VES) method. To evaluate and choosing the best method, specific yields value by hydrologic and hydrogeological data during eight years (2006 to 2013) of Golgir plain with correlation coefficient between recharge-rainfall and recharge-baseflow in ESL method respectively was obtained r2=0.79 and r2=0.90. Also, attention to appropriate correlation coefficient in ESL method the specific yield optimized amount equal 0.12 for Golgir plain estimated. The coefficient correlation obtained from the relation between recharge groundwater value, base flow, and rainfall by using DWL, ESL and VES methods indicated that ESL method with r2=90 is the best method to estimate the specific yield. Hence, attention to importance of recharge value in planning and management of groundwater and high affection of specific yield value in WTF method, groundwater recharge estimation by using specific yield optimal value help to the water science researchers. 969 981 سعید ترک قشقایی‌نژاد Saeed Tourk Qashqai Nejad دانشجوی دکتری آب‌های زیرزمینی، دانشکدۀ علوم زمین، دانشگاه شهید چمران اهواز Iran saeed.tourkqashqai@gmail.com منوچهر چیت‌سازان Manouchehr Chitsazan استاد، دانشکدۀ علوم زمین، دانشگاه شهید چمران اهواز Iran manouchehrchitsazan@gmail.cm Golgir plain Groundwater recharge Specific Yield WTF method 1-                  Sophocleous M. Groundwater recharge estimation and regionalization: the Great Bend Prairie of central Kansas and its recharge statistics. Journal of Hydrology. 1992; 137(1):113-140.##2-                  Pour Mohamadi S, Dastoorani MT, Jafari H, Rahimian MH, Goodarzi M, Masmarian Z, et al. Hamedan Tuyserkan groundwater balance analysis by using MODFLOW mathematical models. 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Using groundwater levels to estimate recharge. Hydrogeology journal. 2002; 10(1):91-109.##8-                  Rabinowitz DD, Gross GW, Holmes CR. Environmental tritium as a hydrometeorologic tool in the Roswell basin, New Mexico, I. Tritium input function and precipitation-recharge relation. Journal of Hydrology. 1977; 32(1-2):3-17.##9-                  Crosbie RS, Binning P, Kalma JD. A time series approach to inferring groundwater recharge using the water table fluctuation method. Water Resources Research. 2005; 41(1), Issue 1.##10-              Mohammadi Z.Using geostatistical methods to estimate the spatial distribution of hydraulic conductivity in the Golgir plain and the results of a mathematical model. Khuzestan Department of Water and Power; 2012. [Persian].##11-              Darvishzadeh A. Geology of Iran, Danesh-E-Emrouz pub., Teheran; 1991. [Persian].##12-              Khuzestan Department of Water and Power. Studies on recognition of groundwater in Golgir plain region. 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Department of Water Resources; 1967.##19-              Manghi F, Mortazavi B, Crother C, Hamdi MR. Estimating regional groundwater recharge using a hydrological budget method. Water resources management. 2009; 23(12):2475-2489.##20-              Varni M, Comas R, Weinzettel P, Dietrich S. Application of the water table fluctuation method to characterize groundwater recharge in the Pampa plain, Argentina. Hydrological Sciences Journal. 2013; 58:7, 1445-1455.##21-              McWhorter DB, Sunada DK. Ground-water hydrology and hydraulics. 1st ed. Colorado. Water Resources Publication; 1977.##22-              Chen, J, Hubbard S, Rubin Y. Estimating the hydraulic conductivity at the South Oyster Site from geophysical tomographic data using Bayesian techniques based on the normal linear regression model. Water Resources Research. 2001; 37(6):1603-1613.##23-              Mazáč O, Kelly WE, Landa I. A hydrogeophysical model for relations between electrical and hydraulic properties of aquifers. Journal of Hydrology. 1985; 79(1):1-19.##24-              Torabi H, Dehghani R. The trend analysis of Cham Anjir basin change slightly by using non-parametric tests. Journal of Ecohydrology. 2016; 3(3):415-425. [Persian].##25-              Ashrafzadeh A, Aghajani M. Specific yield estimation in without statistic by using runoff regional analysis. Journal of Ecohydrology. 2017; 4(2):331-343. [Persian].##26-              Archie GE. The electrical resistivity log as an aid in determining some reservoir characteristics. Transactions of the AIME. 1942; 146(01):54-62.##27-              Koefoed O, Patra HP, Mallick K. Geosounding principles. Elsevier Science & Technology; 1979.##

0 مدل‌سازی جامدات محلول با استفاده از روش‌های هیبریدی محاسبات نرم (مطالعۀ موردی: حوضۀ آبریز نازلوچای) Moldeling Of Dissolved Solids By Using Hybrid Soft Computing Methods (Case Study: Nazluchay Basin) https://ije.ut.ac.ir/article_63230.html 10.22059/ije.2017.63230 0 رودخانه‏ها اهمیت بسیار زیادی در تأمین آب آشامیدنی و کشاورزی دارند. در این مطالعه، قابلیت روش‏‏های منفرد و هیبریدی‌ـ موجکی شبکه‏های عصبی، سامانۀ استنتاجی عصبی‌ـ فازی تطبیقی و برنامه‏ریزی بیان ژن برای مدل‌سازی میزان جامدات محلول حوضۀ آبریز نازلوچای ارزیابی شدند. به این منظور از داده‏های کیفیت آب با طول دورۀ آماری 19 ساله (1372-1390)، چهار ایستگاه هیدرومتری واقع در حوضۀ آبریز نازلوچای استفاده شد. پس از بررسی صحت داده‏ها و ایستگاه‏های منتخب، با استفاده از تبدیل موجک دابچیز نوع چهارم، سیگنال‏های داده‏های مربوط به آن آنالیز شد. در مدل‌سازی از 80 درصد داده‏ها برای آموزش و 20 درصد داده‏ها برای آزمون مدل‏ها استفاده شده است. ارزیابی عملکرد مدل‏های به‌کار‌رفته بر اساس آزمون‏های آماری مختلف، ضریب همبستگی، ریشۀ میانگین مربعات خطا و میانگین قدر مطلق خطا انجام گرفت. نتایج بیان‌کنندۀ عملکرد قابل قبول همۀ روش‏های منفرد و هیبریدی‌ـ موجکی شبکۀ‏ عصبی مصنوعی، سامانۀ استنتاجی عصبی‌ـ فازی تطبیقی و برنامه‏ریزی بیان ژن برای مدل‌سازی میزان جامدات محلول در حوضۀ آبریز نازلوچای است؛ ولی به‌ترتیب اولویت WGEP، GEP، WANFIS، ANFIS-SC،WANN، ANFIS-GP و ANN عملکرد بهتری دارند. همچنین مدل هیبریدی برنامه‏ریزی بیان ژن‌ـ موجکی با داشتن کمترین میزان RMSE به مقدار 078/21 بهترین عملکرد را در بین سایر مدل‏های منفرد و هیبریدی دارد. 1 Rivers has important roles in providing drinking and agricultural water supply. In this study, single and hybrid-wavelet methods of artificial neural networks, adaptive neuro fuzzy inference system and Gene expression programming were validated total dissolved solids modelling of Nazluchay Basin. Therefore, water quality data with 19 years length (1993-2011), four hydrometric stations at Nazluchay Basin, were used. After validating of data and selected stations, the data were analyzed by using Daubechies-4 wavelet transform. For modelling 80% of data for training and 20% of data for testing of the model were used. The evaluation of models performance is applied based on different statistical tests, correlation coefficient, and mean root of error squares and mean absolute error. The results indicate acceptable performance of all single and hybrid-wavelet methods of artificial neural networks, adaptive neuro fuzzy inference system and Gene expression programming for modeling the total dissolved solids in the Nazluchay basin. Based on WGEP, GEP, WANFIS, ANFIS-SC, WANN, ANFIS-GP and ANN have best performance, respectively. In addition Gene expression programming-wavelet hybrid model with the minimum RMSE amounted 21.078 has best performance compared with other single and hybrid models. 983 996 سروین زمان زاد قویدل Sarvin Zamanzad Ghavidel دانشجوی دکتری مهندسی منابع آب دانشگاه ارومیه Iran snzghavidel@gmail.com مجید منتصری Majid Montaseri دانشیار گروه مهندسی آب دانشگاه ارومیه Iran sarvinghavidel1@gmail.com هادی ثانی خانی Hadi Sanikhani استادیار گروه مهندسی آب دانشگاه کردستان Iran sn_ghavidel@yahoo.com gene expression Wavelet transform Dissolved Solids Nazluchay 1. Rajaee T, Jafari H. Prediction of Water Sodium Absorption Ratio (SAR) using ANN and Wavelet Conjunction Model (Case Study: Rudbar Station of Sefidrud River). Journal pf water and soil. 2016; 26(2.2): 189-205.##2. Banejad H, Kamali M, Amirmoradi K, Olyaie E. Forecasting Some of the Qualitative Parameters of Rivers Using Wavelet Artificial Neural Network Hybrid (W-ANN) Model (Case of study: Jajroud River of Tehran and Gharaso River of Kermanshah). Iran. J. Health & Environ. 2012; 6(3).[Persian]##3. Guang-ming Z, Hong-wei L, Xiang-can J, XU M. Assessment of the water quality and nutrition of the Dongting lake with wavelet neural network. Journal of Hunan University. 2005; 32:91-94.##4. Sengorur B, Dogan E, Koklu R, Samandar A. Dissolved oxygen estimation using artificial neural network for water quality control. Fresenius Environmental Bulletin. 2006; 15:1064–1067.##5. Noorani V, Salehi K. Modeling of rainfall - runoff using fuzzy neural network and adaptive neural networks and fuzzy inference methods compare. Prosceedings of 4th National Congress on Civil Engineering. 2008; Tehran. [Persian]##6. Zhou HC, Peng Y, Liang G-H. The research of monthly discharge predictor-corrector model based on wavelet decomposition. 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Optimization of wavelet-ANFIS and wavelet-ANN hybrid models by Taguchi method for groundwater level forecasting. Arab. J. Sci. Eng. 2013b; DOI 10.1007/s13369-013-0762-3.##13. Ghavidel S.Z.Z, Montaseri M. Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin. Stochastic environmental research and risk assessment. 2014; 28(8): 2101-2118.##14. Yarar A. A hybrid wavelet and neuro-fuzzy model for forecasting the monthly streamflow data. Water Resour Manag. 2014; 28:553–565.##15. Alizadeh MJ, Kavianpour MR. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Marine Pollution Bulletin. 2015; 98(1-2):171-178.##16. Özger M, Burak Kabataş M. Sediment load prediction by combined fuzzy logic-wavelet method. Journal of Hydroinformatcs. 2015; 17 (6): 930-942.##17. Ravansalar M, Rajaee T, Ergil M. Prediction of dissolved oxygen in River Calder by noise elimination time series using wavelet transform. Journal of Experimental & Theoretical Artificial Intelligence. 2015; DOI:10.1080/0952813X.2015.1042531.##18. Shafaei M, Kisi O. Lake Level Forecasting Using Wavelet-SVR Wavelet-ANFIS and Wavelet-ARMA Conjunction Models. Water Resources Management. 2015; DOI:10.1007/s11269-015-1147-z.##19. National Geographical Organization.The Gazetter Of Rivers In The I.R Of Iran, Orumiyeh Lake Watershed. National Geographical Organization Publication. 2016; First Volume, p 67 and 77.##20. Toufani P, Mosaedi A, Fakheri Fard A. Prediction of Precipitation Applying Wavelet Network Model (Case study: Zarringol station, Golestan province, Iran). Journal of Water and Soil. 2011; 25(5): 1217-1226.##21. Jain SK, Das A, Srivastava DK. Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management, ASCE. 1999; 125(5) 263-271.##22. Caudill M. 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0 ارزیابی چند‌معیارۀ تغییرات مکانی شاخص فقر آب در تعدادی از حوضه ‏های آبخیز استان اردبیل Multi-criteria evaluation of water poverty index spatial variations in some watersheds of Ardabil Province https://ije.ut.ac.ir/article_63231.html 10.22059/ije.2017.63231 0 منابع آب وجه مشترک اهداف و چالش‏های توسعۀ پایدار است و کمبود آن یکی از معضلات بزرگ چندبعدی قرن حاضر است که می‏تواند سرمنشأ بسیاری از تحولات مثبت و منفی جهان قرار گیرد. اخیراً لزوم ارائۀ شاخص‏های جامع و چندبعدی برای ارزیابی وضعیت موجود و پیش‏بینی وضعیت آیندۀ منابع آب سطحی اهمیت ویژه‏ای پیدا کرده است. به این منظور، شاخص فقر آب برای ارزیابی دسترسی به منابع آب ارائه شده است. در این پژوهش مقدار شاخص فقر آب با درنظرگرفتن معیارهای منابع، دسترسی، مصارف، محیط زیست و ظرفیت اجتماعی- اقتصادی در مقیاس زیرحوضه در استان اردبیل محاسبه شد. در ادامه، با رویکردهای مختلف وزن‏دهی (وزن یکسان به معیارها و هر بار تأکید بر یکی از معیارها در وزن‏دهی) مقدار شاخص فقر آب ارزیابی شد. سپس، حوضه‏های مختلف بر اساس وزن‏دهی‏های مختلف از نظر شاخص فقر آب اولویت‏بندی شدند. نتایج نشان داد محدودۀ تغییرات مقادیر شاخص فقر آب، در حالت‏های مختلف وزن‏دهی بین 22 تا 65 در حوضه‏های مطالعه‌شده متغیر است. در وزن‏دهی یکسان به معیارهای شاخص فقر آب، حوضۀ شمس‏آباد با مقدار 29 ‌فقر آبی بیشتر و حوضۀ پل‏سلطان با مقدار 58 ‌فقر آبی کمتری در مقایسه با دیگر حوضه‏های مطالعه‌شده در استان دارند. مقدار متوسط شاخص فقر آب برای کل حوضه‏های مطالعه‌شده در استان اردبیل 43 به‌دست آمد که طبق طبقه‏بندی مرکز اکولوژی و هیدرولوژی والینگفورد، فقر آبی شدیدی دار‌د. شاخص فقر آب نشان‏دهندۀ تأثیر ترکیبی عوامل مؤثر بر کمبود و تنش منابع آبی است که امکان اولویت‏بندی و تدوین نسخه‏های مدیریتی برای مناطق مختلف را فراهم می‏‌کند. باید توجه داشت که تعیین و تحلیل شدت کمبود و تنش منابع آب در مناطق مختلف بر اساس شرایط منابع آب منطقۀ بررسی‌شده، قابلیت محاسبۀ شاخص، وجود داده‏ها و نوع معیارهای انتخابی متفاوت خواهد بود. 1 Water resources scarcity is one of the biggest multidimensional issue of current century that could be the source of many positive and negative aspects of developments.The need for a comprehensive and multi-dimensional indicators to assess the condition and to predict the future status of surface water resources has become increasingly important. To this end, Water Poverty Index(WPI) is proposed to assess the availability of water resources.In this study, the amount of WPI, taking into account the resources, access, cost, environmental and socio-economic capacity criteria were calculated in Ardabil Province subwatersheds. Then, the different weighting approaches (equal weight to all criteria and one-at-a-time emphasizing on different criteria), were used to examine the WPI values and the sub-watersheds of the study area were ranked in terms of water poverty degree. The results showed that the values of water poverty index ranges from 22 to 65, acording to the different weighting approached in the study area.Considering equally weighted Water Poverty Index, the Shamsabad watershed had a higher water poverty index (29), while the Polesoltan watershed had the best condition with respect to water poverty condition compared to other watershed in the Ardabil Province. 997 1009 رقیه آسیابی هیر Roghayeh Asiabi Hir دانش ‏آموختۀ کارشناسی ارشد آبخیزداری، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی Iran roghaye.asiabi@gmail.com رئوف مصطفی‌زاده Raoof Mostafazadeh استادیار گروه منابع طبیعی، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی Iran raoofmostafazadeh@uma.ac.ir مجید رئوف Majid Raoof دانشیار گروه مهندسی آب، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی Iran majidraoof2000@gmail.com اباذر اسمعلی عوری Abazar Esmali Ouri دانشیار گروه منابع طبیعی، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی Iran esmaliouri@uma.ac.ir Water shortage Spatial variations Water Poverty Index Prioritization Multi-criteria weighting [1].Rezayan A, Rezayan AH. 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Evaluation of the state of water resources using modified water poverty index: a case study in the Srepok river basin, Vietnam-Cambodia. International Journal of River Basin Management, 2010;8(3-4): 305- 317.##[17]. Curtis V, Cairncross S, Yonli R.Review: domestic hygiene and diarrhea-pinpointing the problem. Tropical Medicine and International Health, 2000; 5(I): 22-32.##[18]. Cullis J, Oregan D. Targeting the water-poor through water poverty mapping. Water Policy, 2004;6: 397- 411.##[19]. World Health Organization/United Nations Childrens Fund (WHO/UNICEF). Joint monitoringprograme for water supply and sanitation. Global Water Supply and Sanitation Assessment Report.2000.##[20]. Howard G, Bartram J. Domestic water quantity, level and health. World Health Organization. 2003.##[21]. Han H, Zhao L. Rural income poverty in Western China is water poverty. China and World Economy, 2005;13(5): 76- 88.##[22]. Sullivan CA, Meigh JR, Giacomello AM. 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0 تحلیل کارکرد و موقعیت دست‌اندرکاران سازمانی در شبکۀ مدیریت اجرایی منابع آب دشت گرمسار Analysis of the role and position of organizational stakeholders in the executive management network of water resources in Garmsar plain https://ije.ut.ac.ir/article_63232.html 10.22059/ije.2017.63232 0 تحلیل دست‏اندرکاران سازمانی، از جمله الزامات مطالعات طرح‏های مدیریت یکپارچه و مشارکتی منابع آب است. هدف این تحقیق، بهره‏گیری از الگوی تحلیل شبکه‏ای برای شناخت کارکرد و موقعیت سازمان‏های مرتبط با سطح اجرایی مدیریت منابع آب در دشت گرمسار و تحلیل قابلیت‏های این شبکه برای ایجاد و استقرار نظام یکپارچه و مشارکتی منابع آب است. در این‏ زمینه، تعداد 29 سازمان به‏عنوان مرز شبکه شناسایی شدند. این سازمان‏ها بر اساس کارکرد و موقعیتی که در شبکۀ مدیریت منابع آب دارند به سه زیرگروه توسعه‏ای، حفاظتی و واسطه‏ای تقسیم شدند. همچنین میزان انسجام و پایداری این شبکه بر اساس شاخص‏های سطح کلان شبکه شامل اندازه، تراکم، تمرکز، میزان دوسویگی پیوندها و شاخص‏های سطح میانی (زیرگروه‏ها) شبکه شامل مرکز- پیرامون و شاخص E-I، ‌بررسی شده است. بر اساس نتایج شاخص‏ها در سطح کلان شبکه میزان تراکم پیوند تبادل اطلاعات و همکاری در حد ضعیف هستند و توزیع مناسبی بین زیرگروه‏ها ندارد. دوسویگی پیوندهای شبکه 48/50 درصد و کوتاه‏ترین فاصلۀ میان دو کنشگر در این شبکه 815/1 است. بر این اساس، حدود نیمی از روابط یک‏سویه است و سرعت گردش اطلاعات در این شبکه در حد متوسط تا پایین است. بر اساس نتایج شاخص‏های سطح میانی شبکه میزان تراکم کنشگران مرکزی 2/82 درصد و تراکم بین کنشگران پیرامونی 6/5 درصد است. نتایج به‌دست‌آمده ضرورت کاهش تمرکز در شبکه و تقویت روابط کنشگران واسطه‏ای و پیرامونی برای دستیابی به مدیریت یکپارچه و مشارکتی منابع آب را تأکید می‌کند. 1 Organizational stakeholders network Analysis can be considered in integrated and participatory water resources management approaches. The aim of this research was to use the network analysis model for understanding the role and position of organizations related to the executive level of water resources management In Garmsar plain. Accordingly, 29 organizations were identified as the network boundaries. Based on the role and position in the network management of water resources, these were divided into three developmental, protective and intermediate groups. The coherence and sustainability of this network were studied based on network level indicators. According to the results of indicators at the network level, the link density of information exchange and cooperation is poor and does not benefit from a proper distribution among the subgroups. Based on the subgroup level indicators of the network, the density rate of the central actors is equal to 82.2%, while the density between the periphery actors accounts for 5.6%. The results also emphasize the need to reduce centralization within the network and to strengthen the relations between intermediate and peripheral actors to achieve the integrated and participatory management of water resources. 1011 1024 وحید جعفریان Vahid Jafarian دانشجوی دکتری بیابان‌زدایی دانشکدۀ کویرشناسی دانشگاه سمنان Iran jjafarian1393@yahoo.com محمد رضا یزدانی Mohammad Reza Yazdani دانشیار دانشکدۀ کویرشناسی دانشگاه سمنان- سمنان Iran m_yazdani@semnan.ac.ir محمد رحیمی Mohammad Rahimi دانشیار دانشکدۀ کویرشناسی دانشگاه سمنان- سمنان Iran mrahimi@sun.semnan.ac.ir مهدی قربانی Mehdi Ghorbani استادیار دانشکدۀ منابع طبیعی دانشگاه تهران Iran mehghorbani@ut.ac.ir network analysis Organizational stakeholders (executives) Garmsar plain Integrated Management of Water Resources Cooperation Madani K. Water management in Iran: what is causing the looming crisis? Journal of Environmental Studies and Sciences. 2014;4(4):315-28.##Ernstson H، Sörlin S، Elmqvist T. 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0 ارزیابی عملکرد الگوریتم خفاش در بهینه‌سازی پارامترهای مدل غیرخطی ماسکینگام برای روندیابی سیلاب Evaluation of the performance of bat algorithm in optimization of nonlinear Muskingum model parameters for flood routing https://ije.ut.ac.ir/article_63233.html 10.22059/ije.2017.63233 0 در این پژوهش، الگوریتم خفاش به‌عنوان الگوریتمی مبتنی بر سرعت و مکان خفاش‏ها در بهینه‏سازی پارامترهای مدل غیرخطی ماسکینگام برای روندیابی سیلاب استفاده شده است. به‌منظور بررسی کارایی این الگوریتم، مطالعۀ موردی سیل ویلسون و همچنین یک سیل تاریخی از منطقۀ لیقوان به‌منظور روندیابی سیلاب و محاسبۀ پارامترهای مدل ماسکینگام انتخاب شد. مجموع مربعات انحرافات و مجموع قدر مطلق انحرافات بین دبی‏های روندیابی‌شده و مشاهداتی، به‌عنوان توابع هدف در نظر گرفته شد. بر اساس نتایج به‏دست‏آمده از روندیابی سیل ویلسون با استفاده از الگوریتم خفاش، مقادیر این توابع هدف به‌ترتیب برابر 14/35 و 76/22 مترمکعب بر ثانیه است. نتایج روندیابی سیل لیقوان با الگوریتم خفاش نیز نشان داد مجموع مربعات انحرافات، مجموع قدر مطلق انحرافات و تفاوت بین دبی‏های اوج مشاهداتی و روندیابی‌شده به‌ترتیب برابر 24/7، 23/6 و صفر متر‌مکعب بر ثانیه است. در تحقیق حاضر، عملکرد الگوریتم خفاش با الگوریتم‏های تکاملی نظیر الگوریتم ژنتیک، ازدحام ذرات و هارمونی مقایسه شد. نتایج بیان‌کنندۀ برتری روش خفاش برای محاسبۀ دقیق پارامترهای مدل ماسکینگام و پیش‏بینی دقیق سیلاب است. بنابراین، از دستاوردهای تحقیق حاضر می‏توان به معرفی روش الگوریتم خفاش برای حل مسائل مرتبط با هیدرولوژی و مدیریت منابع آب اشاره داشت به‏گونه‏ای که در بسیاری از این مسائل با توابع هدف غیرخطی و قیود پیچیده مواجهیم که الگوریتم یادشده پاسخ‌های با‌کیفیت در کمترین زمان ممکن را دارد. 1 In this study, bat algorithm is used as an algorithm based on velocity and location of bats to optimize the parameters of Muskingum's nonlinear model for flood routing. The case study of Wilson flood as well as a historical flood from Lighvan area were selected for flood routing and calculating the parameters of Muskingum's model, with the aim of examining the efficiency of this algorithm. The sum of squares of deviations and the sum of the absolute values of deviations between routed and observational flows were considered as the objective functions. According to the results obtained from the Wilson flood routing using the bat algorithm, the values of these objective functions are equal to 35.14 and 22.76 m3 per second, respectively. The results of routing of Lighvan flood by using bat algorithm also indicated that the sum of squared deviations, the sum of absolute values of deviations, and the difference between observed and routed peak flows are equal to 7.24, 6.23 and 0 m3/s, respectively. In the present study, the performance of the bat algorithm was compared with evolutionary algorithms such as genetic, particle swarm, and harmony algorithms. 1025 1032 محمد احترام Mohammad Ehteram دانشجوی دکتری، دانشکدۀ مهندسی عمران، دانشگاه سمنان Iran eh.mohammad@yahoo.com حجت کرمی Hjoat Karami استادیار، دانشکدۀ مهندسی عمران، دانشگاه سمنان Iran hkarami@semnan.ac.ir سید فرهاد موسوی Sayed-Farhad Mousavi استاد، دانشکدۀ مهندسی عمران، دانشگاه سمنان Iran fmousavi@profs.semnan.ac.ir سعید فرزین Saeed Farzin استادیار، دانشکدۀ مهندسی عمران، دانشگاه سمنان Iran saeed.farzin@semnan.ac.ir سعید سر کمریان Saeed Sarkamaryan دانشجوی دکتری، دانشکدۀ مهندسی عمران، دانشگاه چمران Iran saeid.sarkamaryan@gmail.com Flood routing Bat algorithm Muskingum model Optimizationو Wilson flood [1]. Das, A.. Parameter estimation for Muskingum models. Journal of Irrigation and Drainage Engineering. ASCE. (2004); 130(2): 140-147.##[2]. Ponce, V.M. and Lugo, A.. Modeling looped ratings in Muskingum-Cunge routing. Journal of Hydrologic Engineering (ASCE). (2001); 6(2): 119-124.##                                                   ##[3]. Chu, H.J. and Chang L.C.. Applying particle swarm optimization to parameter estimation of the nonlinear Muskingum model. Journal of Hydrologic Engineering (ASCE). (2009); 14(9):1024-1027.##[4]. Al-Hummed, J.M. and Essen, I.I. Approximate methods for the estimation of Muskingum flood routing parameters. Water Resources Management. (2006); 20: 979-990.##[5]. Geem, Z..Parameter Estimation for the Nonlinear Muskingum Model using the BFGS Technique. Irrigation and Drainage Engineering. ASCE. (2006); 132(5): 474-478.##[6]. Wang, G.T. and Chen, S. A semianalytical solution of the Saint-Venant equations for channel flood routing. Journal ofWater Resources Research. (2003); 39(4): 1-10.##[7]. Mohan, S. Parameter estimation of nonlinear Muskingum models using Genetic Algorithm. Hydraulic Engineering (ASCE). (1997);132(2): 137-142.##[8]. Xu, D.M., Qiu, L. and Chen, S.Y. Estimation of nonlinear Muskingum model parameter using differential evolution. Journal of Hydrologic Engineering (ASCE). (2011);17: 348-353.##[9]. Kim, J.H., Geem, Z.W. and Kim, E.S.. Parameter estimation of the nonlinear Muskingum model using Harmony Search, Journal of The American Water Resources Association. (2001);37: 1131-1138.##[10]. Orouji, H., Bozorg-Haddad, O., Fallah-Mehdipour, E. and Mariño, M.A.,. Flood routing in branched river by genetic programming. Water Management. (2012); 167(2): 115-123.##[11]. Ouyang, A., Liu, L. and Li, K.. GPU-based variation of parallel invasive weed optimization algorithm for 1000D functions. Natural Computation (ICNC). 10th International Conference. (2014);19-21 August. Xiamen.##[12]. Yang, X.S. and Gandomi, A.H. Bat algorithm: A novel approach for global engineering optimization. Engineering Computations. (2012); 29(5): 464–483.##[13]. Ahmadianfar, I., Adib, A., and Salarijazi, M. Optimizing multireservoir operation: Hybrid of bat algorithm and differential evolution. J. Water Resour. Plann. Manage. (2015);, 10.1061/(ASCE)WR.1943-5452.##[14]. Yang, X.S. A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NISCO 2010), J. R. Gonzalez, et al. Eds., Springer, Berlin, (2010) 284: 65-74.##[15]. Yang, X.S. Meta-heuristic optimization with applications: Demonstration via bat algorithm. Proc. 5th Bioinspired Optimization Methods and Their Applications (BIOMA2012), Bohinj, Slovenia, pp. (2012); 23–34.##[16]. Yoon, J.W. and Padmanabhan, G. Parameter estimation of linear and nonlinear Muskingum models. Water Resources Planning and Management (ASCE). (1993); 119(5): 600-610.##[17]. Ghafari, A., Fakheri, A.Flood routing based on hydraulic model and hydrologic model. Water and soil. (2011); 201 (3):48-70 (In Persian).##[18]. Barati, R. Discussion of ‘Parameter estimation of the nonlinear Muskingum model using parameter-setting-free harmony search’ by Z. W. Geem. J. Hydrology. (2012);.1943-5584##[19]. Barati, R. Application of Excel solver for parameter estimation of the nonlinear Muskingum models. KSCE J. Civil. Engineering., (2013);17(5), 1139–1148.##[20]. Barati, R. Closure to ‘Parameter estimation of nonlinear Muskingum model using Nelder-Mead simplex algorithm’ by R. Barati. J. Hydrol. Eng. (2013);, 367–370.##[21]. Easa, S. M. Multi-criteria optimisation of the Muskingum flood model: A new approach. Proc. ICE Water Manage., (In Persian). (2014). 16(4):214-228##[22]. Easa, S. M. Versatile Muskingum flood model with four variable parameters. Proc. ICE - Water Manage., (2014);168(3): 139–148.##

0 مدل‌سازی جریان خروجی زیرحوضه ‏های کارون بزرگ در شرایط اقلیمی آینده Flow Modelling in Great Karun Sub-basins in terms of Future Climate https://ije.ut.ac.ir/article_63234.html 10.22059/ije.2017.229255.502 0 تغییر اقلیم با تغییر در چرخۀ هیدرولوژی، جریان خروجی از حوضه را تحت تأثیر قرار می‏دهد. دانستن میزان تغییرات احتمالی در مقادیر بارش و رواناب خروجی حوضه به برنامه‏ریزی و مدیریت بهتر منابع آب کمک خواهد کرد. تغییرات بارش ناشی از تغییر اقلیم با مدل‏های گردش عمومی جوّ تحت سناریوهای مختلف شبیه‌سازی می‏شود. بررسی تغییرات رواناب به کاربرد مدل‏های بارش‌ـ رواناب نیاز دارد. هدف از این پژوهش، مدل‌سازی جریان خروجی بخشی از حوضۀ کارون، که از تغییر اقلیم به‌وجود آمده، است. بنابراین، دما و بارش حوضۀ آبخیز کارون بزرگ برای سال‏های 2011 تا 2030 و 2046 تا 2065، با استفاده از دو مدل گردش عمومی جوّ و فرایند کوچک‌مقیاس‏سازی تحت دو سناریوی A2 و B1 شبیه‏سازی شد. سپس جریان خروجی سه زیرحوضۀ اندیمشک، اهواز و یاسوج به‏وسیلۀ مدل بارش رواناب IHACRES و با استفاده از مقادیر بارش و دمای پیش‏بینی‌شده تحت دو سناریوی A2 و B1 شبیه‏سازی شد. مقایسه‌ها نشان داد در دوره‏های آتی تحت هر دو سناریو، مقادیر بارش، بیشترین و کمترین دما افزایش خواهند داشت. نتایج شبیه‏سازی رواناب نیز نشان داد در حوضه‏های مطالعه‌شده میزان رواناب دوره‏های آتی تحت هر دو سناریو، در فصل‏های بهار و تابستان، کاهش و در پاییز و زمستان، افزایش خواهد یافت. 1 Climate change affects runoff flow of the basin by changing in hydrological cycle parameters. Knowing the possible changes in the amount of precipitation and runoff of the basin will help to better planning and management of water resources. Precipitation changes due to climate change can be simulated using atmospheric general circulation models under different scenarios. Assessment of runoff needs using precipitation- runoff models. The aim of this research is flow modelling in some parts of the Great Karun Basin as a result of possible changes in future climate. For this purpose, temperature and precipitation changes of the Great Karun Basin are simulated for years 2011-2030 and 2046-2065 using two general circulation models and downscaling process under B1 and A2 scenarios. Then, the output flow of Andimeshk, Ahwaz and Yasouj sub-basins was predicted by IHACRES rainfall- runoff model and using precipitation and temperature data predicted under B1 and A2 scenarios. Compare revealed that, the amount of precipitation, maximum temperature and minimum temperature will increase in future periods under both scenarios. The results of flow simulation also show that the runoff of future periods under both scenarios will decrease in spring and summer and increase in autumn and winter in study area. 1033 1047 مرضیه کیهان‌پناه Marziyeh kayhanpanah دانش‏ آموختۀ علوم و مهندسی آبخیزداری، دانشگاه شهرکرد Iran m.kayhanpanah@gmail.com رفعت زارع بیدکی Rafat Zare Bidaki استادیار دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد Iran zare.rafat@nres.sku.ac.ir جواد بذرافشان Javad Bazrafshan دانشیار دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران Iran bazrafshan@ut.ac.ir climate change simulation General Atmospheric circulation Model Statistical Downscaling 1. Wilby R. L, Harris I. A frame work for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames. UK. Water Resources Research. 2006; 42: 7.##2. Mitchell T.D. 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0 ارزیابی ویژگی‏ های هیدروشیمیایی و کیفیت آب چشمه‏ ها و چاه‏ های محدودۀ دریاچۀ زریوار Assessment of hydrochemical characteristics and water quality of springs and wells in Zarivar Lake zone https://ije.ut.ac.ir/article_63235.html 10.22059/ije.2017.63235 0 این پژوهش با هدف بررسی ویژگی‏های فیزیکوشیمیایی، هیدروژئوشیمیایی، رخساره‏های هیدروشیمیایی، تعادل ترمودینامیکی، مکانیسم‌های کنترل‌کنندۀ وضعیت شیمیایی آب هفت چشمه و 15 چاه محدودۀ دریاچۀ زریوار بر اساس 15 متغیر کیفیت آب طی سال‌های 1377 تا 1392 و همچنین مقایسۀ کیفیت آب چشمه‏ها و چاه‌ها و تغییرات فصلی آنها انجام شد. تحلیل‏ها و مقایسات آماری بر اساس نمودارهای پایپر، شولر، دورو، لودویگ-لنگلایر، ویلکوکس و گیبس، نسبت‌های یونی مختلف، شاخص‏های اشباع و آزمون‏های ویلکاکسون و من-ویتنی انجام گرفت. یون‌های فراوان شامل بی‌کربنات، کلسیم و منیزیم است که دلیل آن انحلال سنگ‌های کربناته در منطقۀ تغذیۀ آب‌های زیرزمینی است. نسبت کلسیم به منیزیم در آب همۀ چشمه‏ها و چاه‌ها به‌دلیل انحلال کانی‏های سیلیکاته بین دو تا نُه، سختی آب بیشتر از 300 میلی‏گرم در لیتر کربنات کلسیم یا به‌بیانی خیلی سخت بود. دو رخسارۀ هیدروشیمیایی اصلی شامل کلسیم- منیزیم- بی‌کربنات و کلسیم- منیزیم- بی‌کربنات- سولفات بود که نتیجۀ فرایندهای تغییردهندۀ شیمی آب طی مسیر جریان و سنگ‏شناسی تشکیلات زمین‏شناسی زیرین منطقه است. نسبت‌های یونی Mg/Ca در برابر Cl و نمودارهای گیبس بیان می‌کند که اهمیت فراوان مکانیسم‏های تعامل سنگ و آب، تبادل کاتیونی و انحلال کانی‏های کربنات و سیلیکات در تعیین کیفیت شیمیایی آب چشمه‏ها و چاه‌های منطقه بود. 1 This study was done to investigate the physicochemical and hydrogeochemical properties, hydrochemical features, thermodynamic equilibrium, controlling mechanisms of 7 springs and 15 wells water chemistry in Zarivar Lake in Kurdistan province by the 15 water quality data variables from 1998 to 2013 and also compare the quality of water in springs and wells and their seasonal variation. Piper, Schoeller, Durov, Ludwig-Langelier, Wilcox and Gibbs diagrams, calculating different ionic ratios, saturation indices, Wilcoxon and Mann-Whitney tests were used. Abundant ions are bicarbonate, calcium and magnesium, and the dissolution of carbonate rocks in the ground water recharge area caused to increase them. The ratio of Ca/Mg in all springs and wells duo to dissolution of silicate minerals are between 2 and 9. Total hardness is greater than 300 mg/l based on CaHCO3 or very hard water. Two main hydrochemical facies are Ca-Mg-HCO3 and Ca-Mg-HCO3-SO4 that are the result of changing water chemistry processes along the flow path and lithology of underlying geological formation. Ion ratios of Mg/Ca to Cl and Gibbs diagram showed the dominant mechanisms of interaction between rock and water, cation exchange and dissolution of carbonate and silicate minerals in determination of the chemical quality of springs and wells water. 1049 1060 شیرکو ابراهیمی محمدی Shirko Ebrahimi Mohammadi استادیار گروه مهندسی مرتع و آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه کردستان Iran shirkoebrahimi@uok.ac.ir Groundwater Saturation index Gibbs Ion ratio Marivan [1] Walton WC. Groundwater resources evaluation. Mc Graw Hill Book Co, New York; 1970.##[2] Joshi BK. Hydrology and nutrient dynamic of spring of almora–binsar area, indian central himalaya: landscapes, practices, and management. Water Resources. 2006; 33(1): 87–86.##[3] Martos-Rosillo S, Moral F. Hydrochemical changes due to intensive use of groundwater in the carbonate aquifers of Sierra de Estepa (Seville, southern Spain). Journal of Hydrology. 2015; 528: 249–263.##[4] Zheng Q, Ma M, Wang Y, Yan Y, Liu L, Liu L. Hydrochemical characteristics and quality assessment of shallow groundwater in Xincai River Basin, Northern China. Procedia Earth and Planetary Science. 2017; 17: 368-371.##[5] Niu N, Wang H, Loáiciga HA, Hong S, Shao W. Temporal variations of groundwater quality in the Western Jianghan Plain, China. Science of the Total Environment. 2017; 578(1): 542-550.##[6] Malki M, Bouchaou L, Hirich A, Brahim YA, Choukr-Allah R. Impact of agricultural practices on groundwater quality in intensive irrigated area of Chtouka-Massa, Morocco. Science of the Total Environment. 2017; 574: 760–770.##[7] Ebadati N. Qualitative trend of groundwater resources Eyvanakey plain. Iranian Journal of Ecohydrology. 2015; 2(4): 383-394. [Persian].##[8] Najafzadeh H, Zehtabian Gh, Khosravi H, Golkarian A. The Effect of Climatic and Geology Parameters on Groundwater Resources Quantitative and Qualitative (Case Study: Mahvelat). Iranian Journal of Ecohydrology. 2015; 2(3): 235-336. [Persian].##[9] Zaree A, Amiri MJT. Assessing the spatial and zoning of drinking and irrigation water quality using the geostatistics technique and GIS. Iranian Journal of Ecohydrology. 2016; 3(4): 505-516. [Persian].##[10] Department of Natural Resources in Kurdistan Province, Implementation - detailed studies of Zarivar watershed, Marivan, Volume VII (Groundwater), 2007. P. 49.##[11] Department of Natural Resources in Kurdistan Province, Implementation - detailed studies of Zarivar watershed, Marivan, Volume III (Geology and Geomorphology), 2007. P. 63.##[12] Fantong WY, Fouépé AT, Serges I, Djomou LB, Banseka HS, Anazawa K, SMA A, Mendjo JW, Aka FT, Ohba T, Hell JV, Nkeng GE. Temporal pollution by nitrate (NO3), and discharge of springs in shallow crystalline aquifers: Case of Akok Ndoue catchment, Yaounde (Cameroon). African Journal of Environmental Science and Technology. 2013; 7(5): 175-191.##[13] Maya AL, Loucks MD. Solute and isotopic geochemistry and groundwater flow in the central Wasatch range, Utah. Journal of Hydrology. 1995; 172: 31–59.##[14] Katz BG, Coplen TB, Bullen TD, Davis JH. Use of chemical and isotopic tracer to characterize the interactions between groundwater and surface water in mantled karst. Groundwater. 1997; 35(6): 1014–1028.##[15] Dehghani F, Rahnamayi R, Malekooti J, Saadat S. Evaluation of calcium to magnesium ratio in some country irrigation water. Journal of Water Research in Agriculture. 2013; 23(1): 117-129.##[16] Memon M, Soomro M, AkhtarKazi MS, Memon S. Drinking water quality assessment in Southern Sindh (Pakistan). Environmental Monitoring and Assessment. 2011; 177(1): 39-55.##[17] Piper AMA. Graphical procedure in the geochemical interpretation of water analysis. EOS Trans. Am. Geophys. Union. 1994; 25: 914–928.##[18] Azizi M. Hydrogeology and hydrogeochemistry of Marivan and Ghezelchesoo plain. Ms.C thesis. Tarbiat Modares University. Basic science faculty. 2013.##[19] Elliott T, Andrews JN, Edmunds WM. Hydrochemical trends, palaeorecharge and groundwater ages in the fissured chalk aquifer of the London and Berkshire Basins, UK. Applied Geochemistry. 1999; 14: 333–363.##[20] McIntonsh JC, Walter LM. Paleowaters in Silurian–Devonian carbonate aquifers: geochemical evolution of groundwater in the Great Lakes region since the Late PleistoceneGeochimica et Cosmochimica Acta. 2006; 70: 2454–2479.##[21] Ansari AMD, Deodhar A, Kumar US, Khatti VS. Water quality of few springs in outer Himalayas – A study on the groundwater bedrock interactions and hydrochemical evolution. Groundwater for Sustainable Development. 2015; 1: 59–67.##[22] White WB. Geomorphology and Hydrology of Karst Terrains. Oxford University Press, New York, 1988.##[23] Wilcox LV. Classification and use of irrigation waters, US Department of Agriculture, Washington Dc, 1995.##[24] Langmuir D. Aqueous environmental geochemistry. Prentice Hall Inc. Upper Saddle River, NJ, 1997.##

0 تأثیر ورمی کمپوست و کود شیمیایی اوره بر تغییرات ماهانۀ رواناب در مقیاس کرت Influence of vermicompost and urea chemical fertilizer on monthly changes in runoff at plot scale https://ije.ut.ac.ir/article_63236.html 10.22059/ije.2017.63236 0 در مطالعۀ حاضر تغییرات ماهانۀ کمیت و کیفیت رواناب تحت تأثیر استفادۀ تلفیقی از کود ورمی کمپوست و کود شیمیایی اوره در شمال ایران بررسی شد. بدین‌منظور 18 کرت 5 ×1 متر روی اراضی کشاورزی با شیب 14 درصد تحت شرایط بارش طبیعی در سال 1393 به‌مدت پنج ماه برای اندازه‌گیری رواناب نصب شدند. در مجموع رواناب‏های به‌دست‌آمده از 12 رگبار از دی‌ماه 1393تا اردیبهشت 1394 بررسی شد. تیمارهای بررسی‌شده شامل تیمار شاهد (بدون کود آلی و شیمیایی)، 100 درصد ورمی کمپوست، 100 درصد کود شیمیایی اوره، 100 درصد ورمی کمپوست + 50 درصد کود شیمیایی اوره، 75 درصد ورمی کمپوست + 50 درصد کود شیمیایی اوره،50 درصد ورمی کمپوست + 50 درصد کود شیمیایی اوره است. نتایج نشان داد پارامترهای تولید رسوب، غلظت نیترات، EC و pH رواناب تحت تأثیر تیمارهای تلفیقی ورمی کمپوست و کود اوره قرار نگرفته است، اما استفاده از ورمی کمپوست به کاهش معنا‏دار حجم رواناب در سطح اطمینان 99 درصد در ماه نخست آزمایش منجر شده است. در مجموع، نتایج نشان‌دهندۀ اثرگذاری مثبت ورمی کمپوست بر کمیت رواناب است، اما این اثرگذاری بر مدیریت کمیت و کیفیت رواناب در اراضی شیب‏دار محدود بوده است. 1 This study aims to investigate monthly changes in runoff quality and quantity under the influence of Vermicompost and urea in Northern Iran. For this purpose, 18 runoff measurement plots of 1× 5 m were installed on 14% slopes under natural rainfall during 5 months (from January 2014 until April 2015). In total 12 rainfall events and their runoff were considered. Treatments in this study included control (without organic and chemical fertilizers), 100% Vermicompost, 100% Urea fertilizer, 100% Vermicompost + 50% Urea chemical fertilizer, 75% Vermicompost + 50% Urea chemical fertilizer, 50% Vermicompost + 50% Urea chemical fertilizer. The results showed that sediment yields, nitrate amount, pH and EC of runoff were not influenced due to applying Vermicompost and Urea fertilizer. But using Vermicompost reduced volume of runoff overfirst month of the experiment (sig=0.002) which shows the positive effect of Vermicompost on runoff quantity but it has a limited impact on runoff management on steep terrain. 1061 1070 مریم رضایی پاشا Maryam Rezaei Pasha دانشجوی دکترای علوم و مهندسی آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری Iran pasha.m65@gmail.com کاکا شاهدی Kaka Shahedi دانشیار گروه آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری Iran kaka.shahedi@gmail.com قربان وهاب‌زاده Qorban Vahabzadeh دانشیار گروه آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری Iran vahabzadeh@yahoo.com عطااله کاویان Ataollah Kavian دانشیار گروه آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری Iran a.kavian@sanru.ac.ir مهدی قاجار Mehdi Ghajar Sepanlou دانشیار گروه خاک‌شناسی، دانشکدۀ علوم زراعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری Iran sepanlu@yahoo.com پاسکال جوکئت Pascal Jouquet پژوهشگر مؤسسۀ تحقیقات اکولوژی و علوم محیطی، فرانسه‌ Iran pascal.jouquet@ird.fr Agricultural Runoff Volume Northern Iran Runoff quality vermicompost [1]. 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Seventh National Conference on Watershed Management Science and Engineering. Isfahan University of Technology. Iran. 2012 (Persian).##[6]. Hazbavi Z., Sadeghi S H R, Younesi H A. Analysis and assessing affectability of runoff components from different levels of polyacrylamide. Journal of Water and Soil resources Conservation. 2013; 2(2):1-13(Persian).##[7]. Bertol J O, Eduardo Rizzi N, Favaretto , do Carmo M. Phosphorus loss by surface runoff in no-till system under mineral and organic fertilization. Scientia Agricola (Piracicaba, Braz.). 2010; 67(1): 71-77.##[8]. Mafakhery S, Omidbeigy R, Sefidkon F, Rejali F. Effect of biofertilizers on physiological and morphological characteristics and on essential oil content in Dragonhead (Dracocephalum moldavica). Iranian journal of Horticultural Sciences. 2012; 42(3):245-254 (Persian).##[9]. Lazcano C, Dominguez J. Chapter 10 the Use of Vermicompost in Sustainable Agriculture: Impact on Plant Growth and Soil Fertility. ISBN 978-1-61324-785-3. 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0 توسعۀ مدلی برای محاسبۀ شاخص‎ پایداری کمی و کیفی منابع آب زیرزمینی Development of a Model for Calculation of Sustainability Index of Groundwater Resources https://ije.ut.ac.ir/article_63237.html 10.22059/ije.2017.63237 0 امروزه یکی از دغدغه‏‎های مهم در بسیاری از کشورهای جهان، تأمین آب به‌منظور توسعۀ پایدار است. برای مدیریت مؤثر منابع آب زیرزمینی به‌منظور توسعۀ پایدار از یک سو به ابزار مناسب برای مدل‎سازی و از سوی دیگر به معیاری برای محاسبۀ پایداری نیاز است. این تحقیق وضعیت پایداری آبخوان با استفاده از مدل ترکیبی را که شامل مدل‎ هیدرولوژیکی SWAT، مدل جریان آب زیرزمینی MODFLOW و مدل انتقال آلاینده MT3DMS می‌شود، در حوضۀ مطالعاتی اصفهان‌ـ برخوار بررسی می‎کند. خروجی مدل SWAT به‌عنوان ورودی مدل MODFLOW و خروجی مدل MODFLOW به‌عنوان ورودی مدل MT3DMS استفاده می‌شود. ارتفاع و غلظت آب در هر سلول مدل کمی و کیفی (MODFLOW و MT3DMS) به‌عنوان ورودی MATLAB برای محاسبۀ شاخص پایداری (با استفاده از سه معیار عملکرد اطمینان‎پذیری، برگشت‎پذیری و آسیب‎پذیری) تحت سه سناریوی مدیریتی (ادامۀ برداشت روند فعلی، افزایش 30 درصدی برداشت از آبخوان و کاهش 30 درصدی برداشت از آبخوان) استفاده می‌شود. نتایج نشان‌دهندۀ شاخص پایداری طی دورۀ شبیه‎سازی برابر 052/0 و به‌ترتیب تحت سناریوی اول، دوم و سوم برابر 040/0، 033/0 و 050/0 است. نتایج نشان می‎دهد با کاهش 30 درصدی بهره‎برداری از آبخوان، شاخص پایداری کمی و کیفی آبخوان در بیشتر نقاط به‌طور شایان توجهی بهبود خواهد یافت. 1 Nowadays, one of the most important concerns in many countries is water supply for sustainable development. So for efficient management of groundwater resources, on the one hand, we need an appropriate tools for modeling, on the other hand, we need measure sustainability to check the performance of scenarios. This paper analyzes the sustainability condition using an integrated modeling framework that consists in sequentially a watershed agriculturally based hydrological model (Soil and Water Assessment Tool, SWAT) with a groundwater flow model developed in MODFLOW, and with a TDS mass-transport model in MT3DMS in Esfahan-Borkhar in Iran. SWAT model outputs are used as MODFLOW inputs to simulate changes in groundwater flow and storage and impacts on stream–aquifer interaction. MODFLOW output (groundwater velocity field from MODFLOW) are used as MT3DMS inputs for assessing the fate and transport of TDS. Heads and concentrations of each cell of model (MODFLOW and MT3DMS) are used to calculate the developed Sustainability Index (whith 3 performance criteria, Reliability, Resilience and Vulnerability) under three scenarios. The results indicate that SI in simulation period is 0.052 and under first, second and third scenarios are respectively 0.040, 0.033 and 0.050. 1071 1087 اصغر کمالی Asghar Kamali دانشجوی کارشناسی ارشد، دانشکدۀ محیط زیست دانشگاه تهران Iran asg.kamali@gmail.com محمد حسین نیک‌سخن Mohammad Hossein Niksokhan دانشیار، دانشکدۀ محیط زیست دانشگاه تهران Iran niksokhan@ut.ac.ir sustainability index SWAT MODFLOW MT3DMS MATLAB USGS. How much water is there on, in, and above the Earth? [Internet]. 2016. Available from: https://water.usgs.gov/edu/earthhowmuch.html.##Wikipedia. Water distribution on Earth [Internet].2017. Available from: https://en.wikipedia.org/wiki/Water_distribution_on_Earth.##Moreaux M, Reynaud A. 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0 بهینه‌سازی روش DRASTIC با استفاده از هوش مصنوعی برای ارزیابی آسیب‌پذیری آبخوان‏ چند‏گانۀ دشت ورزقان Optimization of DRASTIC method using ANN to evaluating of vulnerability of multiple Varzqan aquifer https://ije.ut.ac.ir/article_63238.html 10.22059/ije.2017.63238 0 با توجه به افزایش جمعیت و توسعۀ فعالیت‏های کشاورزی و معدنی در دشت ورزقان که سبب افزایش مقادیر نیترات تا پنج برابر استاندارد سازمان بهداشت جهانی (WHO) شده، ارزیابی آسیب‏پذیری و حفاظت از منابع آب زیرزمینی در این منطقه اهمیت زیادی دارد. در این پژوهش، آسیب‏پذیری آبخوان چندگانۀ دشت ورزقان در برابر آلودگی به کمک روش DRASTIC در محیط ArcGIS بررسی شده و بهینه‏سازی روش DRASTIC با استفاده از مدل ANN صورت گرفته است. برای اجرای روش DRASTIC از پارامترهای مؤثر در ارزیابی آسیب‏پذیری سفرۀ آب زیرزمینی شامل عمق سطح ایستابی، تغذیۀ خالص، جنس محیط آبخوان، نوع خاک، شیب توپوگرافی، مواد تشکیل‌دهندۀ ناحیۀ غیراشباع و هدایت هیدرولیکی استفاده شده که به‌صورت هفت لایۀ جداگانه برای آبخوان آزاد و تحت فشار تهیه و بعد از رتبه‌دهی و وزن‌دهی و تلفیق این هفت لایه شاخص DRASTIC محاسبه شد که براساس نتایج به‌دست‌آمده شاخص DRASTIC برای آبخوان آزاد 92- 164 و برای آبخوان تحت فشار 48-93 برآورد شد. به‌منظور بهینه‌سازی روش DRASTIC، از مدل شبکۀ عصبی مصنوعی استفاده و به این منظور داده‌های ورودی )پارامترهای (DRASTIC و خروجی (شاخص آسیب‏پذیری) و مقادیر نیترات مربوط به آن به دو دستۀ آموزش و آزمایش تقسیم شد و پس از آموزش مدل، با استفاده از مقادیر نیترات نتایج مدل در مرحلۀ آزمایش ارزیابی شد. نتایج نشان داد مدل شبکۀ عصبی مصنوعی به‌کار گرفته‌شده، قابلیت بهبود نتایج روش  DRASTICاولیه را دارد. برای صحت‏سنجی نتایج روش کلاسیک و مدل هوش مصنوعی استفاده‌شده در این پژوهش، از داده‏های غلظت نیترات و ضریب همبستگی آن با شاخص آسیب‏پذیری در منطقه استفاده شد. مدل ANN با داشتن ضریب تعیین (R2) و شاخص همبستگی (CI) بیشتر نسبت به روش DRASTIC و همچنین توانایی ارزیابی یکپارچۀ آبخوان چندگانه و حذف خطای نظر کارشناسی اعمال‌شده در روش کلاسیک، روش بهتری برای ارزیابی آسیب‏پذیری آبخوان چندگانۀ دشت ورزقان است. 1 Due to population growth and agricultural development and mining activities in the plain Varzeqan where nitrate concentration exceeds from 5 times the standard World Health Organization (WHO). So, Evaluation of vulnerability and protection of groundwater resources are very important in this area. The DRASTIC method uses seven effective environmental parameters on assessment of aquifer vulnerability such as Depth to groundwater level, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone and hydraulic Conductivity, as seven layers were prepared separately for unconfined and confined aquifer by corresponded the rate and weighting. The DRASTIC index value was evaluated for unconfined, confined aquifer 92-163 and 48-93 respectively. The artificial neural network model was used to optimize the DRASTIC method. In these model the DRASTIC parameters were considered as input, and conditioned DRASTIC index were used as output, and the data were divided into two categories of train and test. After model training, the model results were evaluated by the nitrate concentration through coefficient of determination (R2) and correlation index (CI) creteria. The results showed that artificial neural network model show high capability to improve the results of general DRASTIC and reduce subjectivity of model, especially in multiple aquifer. 1089 1103 عطا الله ندیری Ata Allah Nadiri استادیار گروه علوم زمین، دانشکدۀ علوم طبیعی، دانشگاه تبریز Iran nadiri@tabrizu.ac.ir زهرا صدقی Zahra Sedghi دانشجوی کارشناسی ارشد هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز Iran sedghizahra93@gmail.com نعیمه کاظمیان Naeimeh Kazemian کارشناس شرکت آب و فاضلاب استان آذربایجان شرقی Iran naimeh_kazemain@yahoo.com Artificial Neural Network DRASTIC Multiple Aquifer vulnerability 1. Vrba J, and Zaporozec A. Guidebook on mapping groundwater vulnerability. International Contributions to Hydrogeology. 1994;Verlag Heinz Heise GmbH and Co, KG.##2. Babiker I.S, Mohamed M.A.A, Hiyama T, and Kato K. A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Science of the Total Environment. 2005; 345(1-3), pp 127-140.##3. Aller L, Bennett T, Lehr J. H, Petty R. J, & Hackett G. DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings. Ada Oklahoma: U.S. Environmental Protection Agency. 1987; 600/2-87-035.##4. Panagopoulos G, Antonakos A, & Lambrakis, N. Optimization of DRASTIC model for groundwater vulnerability assessment, by the use of simple statistical methods and GIS. Hydrogeology Journal.2006; 14: 894-911.##5. Shukla S, Mostaghimi S, Shanholt V. O, Collins, M.C. & Ross B. B. A county-level assessment of ground water contamination by pesticides. GroundWater Monitoring & Remediation. 2000; 20: 104-119.##6. Secunda S, Collin M.L, & Melloul A.J. Groundwater vulnerability assessment using a composite model combining DRASTIC with extensive agricultural land use in Israel’s Sharon region. Journal of Environmental Management. 1998; 54: 39-57.##7. Dixon B. Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool. Journal of Applied Geography. 2005b; 25: 327-347.##8. Dixon, B. Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. Journal of Hydrology. 2005a; 309: 17-38.##9. Nadiri A.A, Asghari Moghaddam A, Sadeghi F, Aghaee H. Investigation of Arsenic Anomalies in Water Resources of Sahand Dam. Journal of Environmental Studies. 2012; 38(3).##10. Nadiri A.A, Asghari Moghaddam A, Abghari H. Supervised Committee Fuzzy Logic Model for Estimation of Aquifers Transmissivity Case study: Tasuj Plain. Water and Soil Science. 2014.##11. Fijani E, Nadiri A.A, Asghari Moghaddam A, Tsai F, & Dixon B. Optimization of DRASTIC Method by Supervised Committee Machine Artificial Intelligence to Assess Groundwater Vulnerability for Maragheh-Bonab Plain Aquifer, Iran. Journal of hydrology. 2013; 530: 89-100.##12. Nadiri A.A, Gharekhani M, Khatibi R, Sadeghfam S. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Science of The Total Environment. 2017a; 574: 691-706.##13. Nadiri A.A, Gharekhani M, Khatibi R, AsghariMoghaddam A. Assessment of Groundwater Vulnerability Using Supervised Committee to Combine Fuzzy Logic Models. Journal of EPSR (Environment Pollution Science Research ). 2017b; 564-653.## 14. Javanshir G Nadiri A.A, Sadeghfam S, Novinpour E. Introducing a new method to aquifer vulnerability assessment of Moghan plain based on combination of DRASTIC, SINTACS and SI methods. Ecohydrology.1395; Page 491-503. [Persian].##15. Gharekhani M. Optimization of groundwater vulnerability assessment methods using artificial intelligence models, Case study: Ardabil aquifer. MS. Thesis, Tabriz University , IRAN.1394. [Persian]##16. Yekom Consulting Engineers. Detailed, Reports and semi comprehensive groundwater studies of plains of East Azarbaijan Regional Water Company in ArcGIS media. Studies of groundwater study area Ahar-Varzeqan. 1388; page 208. [Persian].## 17. Mehrpartou M, Amini Fazl A, and Radfar J. Geologic map of Varzeghan. scale 1:100000.1371. [Persian]. 18. Consulting Engineers Water Frespand. Providing balance and water cycle of Ahar –Varzeqan in the study area. Department of Energy, East Azerbaijan Regional Water company. 1383. [Persian].##19.Saadati H. Groundwater and Surface water quality studies of Varzeqan area. MS. Thesis, Tabriz University, IRAN, 1390. [Persian].##20. Gogu R.C, & Dassargues A. Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environmental Geology. 2000; 39: 549-559.##21. Almasri M. N. Assessment of intrinsic vulnerability to contamination for Gaza coastal aquifer, Palestine. Journal of Environmental Management. 2008; 88: 577-593.##22. Stigter T. Y, Ribeiro L, & Carvalho Dill A. M. M. Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal, Hydrogeology Journal. 2006; 14: 79-99.##23. Soper R. C. Groundwater vulnerability to agrochemicals: A GIS-based DRASTIC model analysis of Carrol, Chariton, and Saline Counties, Missouri, USA. Master science thesis, University of Missouri-Columbia. 2006.##24. Anil K.J, Mao J, & Mohiuddin K.M. Artificial neural network: a tutorial. IEEE. 1996.##25. Hornik K, Stimchcombe M, & White H. multilayer feed forward network are Universal approximators, Neural Networks.1989; 2: 359-366.##26. Nadiri A.A,. Groundwater level prediction using artificial neural networks model in the Metro area in Tabriz. MS. Thesis, Tabriz University , IRAN. 1386. [Persian].##27. ASCE, Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Network in hydrology, part I and II. 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0 مدل ترکیبی تصمیم‌گیری چندمعیاره در احیای راهبردی یک رودخانۀ فصلیِ شهری Using a hybrid Multiple Criteria Decision Making model for the strategic restoriation of a seasonal-urban river https://ije.ut.ac.ir/article_63240.html 10.22059/ije.2017.63240 0 هدف از این تحقیق، تعیین راهبرد برتر برای احیای یک رودخانۀ فصلی شهری است. برای تعیین راهبردها و معیارهای تأثیرگذار از جلسات توفان فکری و روش SWOT استفاده شده است. سپس، راهبردها با استفاده از مدل هیبریدی تحلیل سلسله‌مراتبی و شباهت به گزینۀ ایده‏آل اصلاح‌شده رتبه‏بندی ‌شده و راهبرد برتر انتخاب شد. در این پژوهش دو هدف اساسی احیای رودخانۀ فصلی یعنی «احیای کمی و کیفی جریان آب پایه و تغذیۀ آبخوان آن» و «ایمن‏سازی و کاهش خطر سیلاب» به‌صورت مجزا در نظر گرفته شد. نتایج تحقیق نشان داد بهترین راهبرد برای احیای رودخانه‏های شهری و فصلی با هدف احیای کمی و کیفی جریان آب پایه و تغذیۀ آبخوان، راهبرد «تعادل‌بخشی منابع و مصارف آب رودخانه و آبخوان» است. از طرف دیگر، در هدف ایمن‏سازی و مدیریت سیلاب تنها راه حل مدیریتی مطرح‌شده در بین گزینه‏های موجود یعنی راهبرد «مدیریت سیلاب‌‏دشت شامل کنترل توسعه و ساخت» به‌عنوان راهبرد برتر شناخته شد. نتایج این تحقیق نشان می‌دهد در احیای رودخانۀ فصلی شهری راهبردهای مدیریتی جایگاه بالاتری را نسبت به راهبردهای سازه‏ای از آن خود کرده‏اند؛ همچنین از چارچوب پیشنهادی این تحقیق می‏توان در مدیریت راهبردی احیای رودخانه‏های شهری و فصلی دیگر استفاده کرد. 1 The aim of this study is to determine the best strategy for restoration of a seasonal-urban river. Brainstorming sessions were held and SWOT method was used to determine the strategies and effective criteria. Then, the strategies were ranked by using the hybrid model of Analytical Hierarchy Process and Modified Technique for Order of Preference by Similarity to Ideal Solution and the best strategy was selected. In this study, two basic goals of river's restoration were considered separately which are restoring the quality and quantity of water flow and recharging its aquifer and securing and reduce the risk of floods. The results showed that the best strategy for restoring the quality and quantity of aquifer and water flow is balancing the sources and uses of river and groundwater. On other hand, for flood risk management, only management strategy (flood plain management including control of development and construction) was recognized as the best strategy. The results showed that for restoration of a seasonal-urban river, the management strategies have higher priority compared to structural strategies. The proposed framework of this paper can be used in the strategic management of river restoration in other seasonal-urban rivers. 1105 1116 محمد ابراهیم بنی حبیب Mohammad Ebrahim Banihabib دانشیار گروه مهندسی آبیاری و زهکشی دانشگاه تهران، پردیس ابوریحان Iran banihabib@ut.ac.ir مارینا عزتی امینی Marina Ezzati amini دانش‌آموختۀ کارشناسی ارشد مهندسی منابع آب دانشگاه تهران، پردیس ابوریحان Iran marina_ezzati_amini@yahoo.com محمدهادی شبستری Mohammad Hadi Shabestari دانش‌آموختۀ کارشناسی ارشد مهندسی منابع آب دانشگاه تهران، پردیس ابوریحان Iran shabestari.hadi@alumni.ut.ac.ir Strategic Management Multi-Criteria Decision Making river restoration flood safety Hohensinner S, Lager B, Sonnlechner C, Haidvogl G, Gierlinger S, Schmid M, et al. Changes in water and land: the reconstructed Viennese riverscape from 1500 to the present. Water History. 2013;5(2):145-72.##RESTORE. River by Design: Rethinking development and river restoration. Environment Agancy, Horizon House, Deanery Road. 2013.##Palmer, M. A., E. S. Bernhardt, J. D. Allan, P. S. Lake, G. Alexander, S. Brooks, J. Carr et al. Standards for ecologically successful river restoration. Journal of applied ecology 42, no. 2. 2005;208-217.##Bradshaw A.D. Underlying principles of restoration. Canadian Journal of Fisheries and Aquatic Sciences. 1996;53:3–9.##Woolsey S, Capelli F, Gonser T, Hoehn E, Hostmann M, Junker B, et al. A strategy to assess river restoration success. Freshwater Biology. 2007;52(4):752-69.##Zhao Y, Yang Z, Xu F. Theoretical framework of the urban river restoration planning. Environmental Informatics Archives. 2007;5:241-7.##Vietz, Geoff J., Ian D. Rutherfurd, Tim D. Fletcher, and Christopher J. Walsh. Thinking outside the channel: Challenges and opportunities for protection and restoration of stream morphology in urbanizing catchments. Landscape and Urban Planning 145. 2016:34-44.##Banihabib ME, Jamali FS. Determining approaches for controlling debris flows in an urban river. The third national conference on flood management and engineering with the approach of urban floods. 2015. [Persian]##Ataei M. Multi criteria decision making. Shahrood University of Technology. 2009. [Persian]## Shabestari MH, Banihabib ME. Ranking of Agricultural Water Demand Management Strategies in Arid Regions by Hybrid Model of AHP and M-TOPSIS. Journal of Water Research in Agriculture. 2015;29(1):101-115. [Persian]## Ezzati M, Banihabib ME. Algorithm to determine the best strategy for flood management. The third national conference on flood management and engineering with the approach of urban floods. 2015. [Persian]## Azarnivand A, Banihabib ME. Planning and strategic management of water resources in Lake Urmia basin in accordance with sustainable development. Second National Conference on Agricultural Sustainable Development and Healthy Environment. 2013. [Persian]##Banihabib ME, Laghabdoost A. Flood Management Options Using Analytical Hierarchy Process and Evaluation and Mixed Criteria. Journal of Irrigation and Water engineering. 2013;14(4):72-82. [Persian]##Aczél, J, Saaty T. Procedures for synthesizing ratio judgments. Journal of mathematical Psychology. 1983;27(1):93-102.##Saaty TL, Vargas LG. Inconsistency and rank preservation. Journal of Mathematical Psychology. 1984;28(2):205-214.##The program of restoration and balance of groundwater resources of the country. Department of Water and Abfa. Office of operating systems and water conservation. 2014. [Persian]##Documents of multi-sectoral water resources management. the basic steps necessary to achieve the goals predicted in documents of sectoral and multi-sectoral development. 2005. [Persian]##Journal of Iranian Water Policy Research Institute. 2013;4:5-13. [Persian]##

0 تحلیل حساسیت مدل احتساب‌کنندۀ رطوبت خاک برای شبیه‏ سازی پیوسته در حوضۀ بهشت ‏آباد Sensitivity of the SMA HEC-HMS Model for Continuous Hydrological Modeling in Beheshtabad Basin https://ije.ut.ac.ir/article_63241.html 10.22059/ije.2017.63241 0 آنالیز حساسیت ابزار مناسبی برای نمایش اختلاف خروجی‏ها درنتیجۀ تغییر در پارامترهای مدل است. از کاربردهای این نوع تحلیل یافتن پارامترهای حساس برای کالیبره‌کردن و شناسایی ورودی‏های مهم در سیستم است. هدف از این پژوهش، تحلیل حساسیت پارامترهای مدل هیدرولوژیکی HEC-HMS(SMA)4.2 و بررسی واسنجی پارامترهای مدل SMA (احتساب‌کنندۀ رطوبت خاک) به‏عنوان بخشی از مدل HEC-HMS است. در تحقیق حاضر ضمن واسنجی و اعتبار‌سنجی مدل HEC-HMS(SMA) حساسیت دستی و خودکار پارامترهای مدل در حوضۀ آبخیز بهشت‏آباد تحلیل ‏شده است. برای شبیه‏سازی از آمار دبی، بارش، دما و تبخیر و تعرق ایستگاه بهشت‏آباد طی دورۀ آماری 1998 تا 2015 به‏صورت آمار روزانه استفاده شد، از داده‏های 13 سال این بازۀ زمانی برای واسنجی و از چهار سال آخر برای اعتبارسنجی استفاده شد. نتایج واسنجی و اعتبارسنجی داده‏ها به‌ترتیب مقدار ضریب راندمان و ریشۀ میانگین مربعات خطا 696/0، (m3/s) 2/13 برای واسنجی و مقدار ضریب راندمان و ریشۀ میانگین مربعات خطا 63/0، (m3/s)7 برای اعتبار‌سنجی به‏دست ‏آمده، تحلیل حساسیت خودکار با نرم‏افزار HEC-HMS4.2 صورت گرفت. طبق نتایج پارامترهای ذخیرۀ خاک، ذخیرۀ کششی و ثابت افت بیشترین حساسیت را در واسنجی مدل داشتند که اهمیت این عوامل را در فرایند مدل‏سازی پیوسته در حوضۀ مد نظر نشان می‏دهد. 1 Sensitivity analysis is a common tool to study how changes due to uncertainty in the inputs or parameters affect on the output of the simulating model. The aim of this study is to perform sensitivity analysis and model calibration for the HEC-HMS model with soil moisture accounting (SMA) algorithm. In this research manual and automatic parameter sensitivity analysis was used to calibrate and validate the model in the Beheshtabad Basin. The data (1998 to 2015) was separated into 2 parts, the first 13 years daily data set including discharge, rainfall, temperature and evapotranspiration were used for calibration. While, the second period of 2012 to 2015 was used to test the model validation. The evaluation was based on model efficiency coefficient and root mean square error indexes. The model efficiency coefficient values 0.696 and 0.63 were resulted for both calibration and validation respectively.The root mean square error were found to be 13.2 m3/s and 7 m3/s for corresponding stages.The results of performed sensitivity analysis in both form of automatic and manual have showed the parameters soil storage, tension storage and recession constant have highest sensitivity. This is an indicator for the importance of these factors in continuous modeling in specified catchment. 1117 1127 الهام کیانی سلمی Elham Kianisalmi دانشجوی کارشناسی ارشد آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد Iran elham_kianisalmi@yahoo.com افشین هنربخش Afshin Honarbakhsh دانشیار گروه آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد Iran afshin.honarbakhsh@gmail.com خدایار عبدالهی Khodayar Abdollahi استادیار گروه آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد Iran abdollahikh@yahoo.com Sensitivity analysis Soil Moisture Accounting model efficiency coefficient recession constant 1. Bennett T. Development and application of a continuous soil moisture accounting algorithm for the Hydrologic Engineering Center-Hydrologic Modeling System, HEC-HMS: MSc Thesis Dept. of Civil and Environmental Engineering, Univ of California, Davis, Calif.1998.##2. Silva D, Weerakoon. Srikantha Hearth. Modeling of Event and Continuous Flow Hydrographs with HEC–HMS: Case Study in the Kelani River Basin, Sri Lanka. Journal of Hydrology.2014:19:800-806.##3. Munyaneza O, Mukubwa A, Maskey S, Wenninger J, Uhlenbrook S. Assessment of surface water resources availability using catchment modeling and the results of tracer studies in the meso-scale Migina Catchment, Rwanda. Hydrology and Earth System Sciences. 2013:10:15375–15408.##4. Enroe E.M. Guidelines for continuous simulation of streamflow in Johnson County, Kansas, with HEC-HMS. Ph.D. Department of Civil, Environmental and Architectural Engineering, University of Kansas.2010.##5. Garcia A, Sainz A, Revillaa JA, Álvareza C, Juanesa J. A, Puentea A. Surface water resources assessment in scarcely gauged basins in the north of Spain. Journal of Hydrology.2008:356: 312-326.##6. Dawdy D R, O’Donnell T. Mathematical model of catchment behavior. ASCE Hydraulic Div.1965: 91: 123-137.##7. JamesL D, Burges S. J. Selection, calibration, and testing of hydrologic models, in Hydrologic Modeling of Small Watersheds. Edited by C.T. Haan, H.P. Johnson, D.L. Brakensiek, and American Society of Agricultural Engineers. Monograph.1982: 5:437-472.##8. Fleming M, Neary V. Continuous hydrologic modeling study with the hydrologic modeling system. J.Hydrol. Eng. 2004:9(3):175-183.##9. United States Department of Agriculture (USDA).Urban Hydrology for Small Watersheds.1986:55.##10. US Army Corps of Engineers Institute for Water Resources (USACE).HEC-HMS Technical Reference Manual, Davis, C.A.2000.##11. Haan C.T. Statistical methods in hydrology. Second Edition, Iowa State Press, 2002:496.##12. Rezaeian Zadeh M, Abghari H, Singh V, Jamshidi H, Moradzadeh A. Improvement of Continuous Hydrologic Models and HMS SMA Parameters Reduction.2010.##13. McCuen RH, Modeling hydrologic change. Statistical methods. Lewis Publishers.2003:433.##14. Clark C O, Storage and the unit hydrograph: Transactions: American Society of Civil Engineers, 1945:1419-1488.##15. Saghafian B, Tajrishi M, Taheri Shahraini H, Jalali M. Modeling spatial variability of daily rainfall in southwest of Iran. Scientia Iranica. 2003:10: 164-174.##16. Rostamian R, Jalali A, Afyuni M, Mousavi SF, Heidarpour M, Jalalian A and Abbaspour KC. Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrological Sciences–Journal–des Sciences Hydrologiques. 2008:53(5): 977-988,##18. Kamali B, MousaviS J, & Abbaspour K C. Automatic calibration of HEC-HMS using single-objective and multi-objective PSO algorithms. Hydrological processes.2012.##19. RezaeianZadeh, M. Hydrologic Simulation of Khosrow Shirin Watershed One of Mollasadra Dam Sub basins Using Stanford Watershed Model- IV (SWM-IV). MSc thesis, Dept. of Water Structures Engineering, University of Shiraz.2009.##20. Waikhom RS, Manoj K, Jain. Continuous Hydrological Modeling using Soil Moisture Accounting Algorithm in Vamsadhara River Basin, India. Journal of Water Resource and Hydraulic Engineering. 2015: 4: 398-408##21. Nash and Sutcliffe. River flow forecasting through conceptual models, Part I: A discussion of principles”. J. Hydrology, 1970:10(3):282-290.##22. Bhuiyan H, McNairn H, Powers J, Merzouki A. Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model. j. Hydrology, 2017.##

0 پیش‌بینی ساعتی و روزانۀ ارتفاع موج دریا در منطقۀ چابهار Hourly and daily prediction of sea wave height In the Chabahar area https://ije.ut.ac.ir/article_63242.html 10.22059/ije.2017.230390.529 0 امواج ناشی از باد به‌دلیل انرژی و تأثیر زیاد بر فعالیت‏های دریایی، اهمیت زیادی دارند. با توجه به اثرگذاری امواج دریا بر فعالیت‏های دریایی، تأثیر عوامل مختلف بر این متغیر در منطقۀ‏ چابهار بررسی شد. در این پژوهش از روش الگوریتم جست‌وجوی گرگ (WSA) برای پیش‏بینی ارتفاع موج در دو بازۀ زمانی ساعتی و روزانه، استفاده ‏شده است. به این منظور از اطلاعات ارتفاع امواج طی سال‏های آماری 1386 تا 1390، برای پیش‏بینی روزانه و آمار ماه‏های بهمن و اسفند 1385 برای پیش‏بینی ساعتی استفاده شد. نتایج به‌دست‌آمده از الگوریتم WSA با نتایج الگوریتم‏های ژنتیک (GA) و جست‌وجوی هارمونی (HS) مقایسه شد. نتایج نشان داد الگوریتم جست‌وجوی گرگ در هر دو بازۀ ساعتی و روزانه عملکرد بهتری داشته است، به‏طوری ‏که ضریب تبیین (R2)، جذر میانگین مربعات خطا (RMSE)، شاخص توافق ویلموت (d) و میانگین قدر مطلق خطا (MAE) به‏ترتیب برابر 9497/0، 0704/0، 987/0 و 0483/0 برای پیش‏بینی ساعتی و 8558/0، 1742/0، 9599/0 و 1138/0 برای بازۀ روزانه به‌دست آمد. مقایسۀ نتایج به‏دست‏آمده، بیان‌کنندۀ عملکرد مناسب الگوریتم جست‌وجوی گرگ در پیش‏بینی ارتفاع موج برای این منطقه بوده است. 1 The waves is important, because of it’s energy and high impact in maritime activities. Considering the effect of wave on marine activities in Chabahar, different factors influencing the wave height were considered in the present study. In this paper, the Wolf Search Algorithm (WSA) was used to predicting wave height in two categories, daily and hourly. For this purpose, the daily data of the year 2007-2011 and hourly data consisting of two month data of the year 2006 were employed. The results of the WSA were compared with Genetic Algorithm (GA) and Harmony Search Algorithm (HS). The WSA had a better performance for both hourly and daily data. So that R2, RMSE, d And MAE predict 0.9497, 0.0704, 0.987 and 0.0483 for hourly prediction and 0.8558, 0.1742, 0.9599 and 0.1138 for daily prediction respectively. The results show the high ability of evolutionary algorithms in wave height prediction in this Region. 1129 1140 سعید اکبری فرد Saeid Akbarifard دانشجوی دکتری مهندسی منابع آب، گروه هیدرولوژی و منابع آب، دانشکدۀ مهندسی علوم آب، دانشگاه شهید چمران اهواز Iran akbarifard_saeid@yahoo.com حیدر زارعی Heydar Zarei استادیار گروه هیدرولوژی و منابع آب، دانشکدۀ مهندسی علوم آب، دانشگاه شهید چمران اهواز Iran zareih@scu.ac.ir ابراهیم زلقی Ebrahim Zalaghi دانشجوی دکتری مهندسی منابع آب، گروه هیدرولوژی و منابع آب، دانشکدۀ مهندسی علوم آب، دانشگاه شهید چمران اهواز Iran ezallaghi@yahoo.com Sea Wave height prediction Wolf Search Algorithm Chabahar [1]. Derakhshan S, Gharabaghi A, Chenaghlu MR. Prediction of sea waves specification by experimental methods in Bushehr. 1st national congrees on civil engineering. Sharif University. Tehran. 2004; 1-9. [Persian]##[2]. Khalili N. Forecasting precipitation with artificial neural networks. M.Sc Thesis. Water engineering Department. Ferdowsi university of Mashhad. 2006; [Persian]##[3]. Lari K, Pourmandi-Yekta A, Mehdipour F. Wind waves prediction by the statistical model based on neural network in Bushehr Province. 4thinternational conference on coasts. port and marine structures. Bandar Abbas. 2000; 1-7. [Persian]##[4]. Pierson WJ, Moskowitz L. A proposed spectral form for fully developed wind seas based on the similarity theory of SA Kitaigorodskii. Journal of geophysical research. 1964; 69(24):5181-5190.##[5]. Hasselmann K, Barnett TP, Bouws E, Carlson H, Cartwright DE, Enke K, et al. Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Research project. Deutsches Hydrographisches Institut; 1973.p. 7-91.##[6]. Imani H, Kamranzadeh B. Scrutiny results of numerical simulation significant wave height in Chabahar. Sixth International Conference on Offshore Industries. Iranian Offshore Engineering Society. Tehran. 2015; 1-8. [Persian]##[7]. Zhang S, Song Z, Li Y. An advanced inversion algorithm for significant wave height estimation based on random field. Ocean Engineering. 2016; 15(127):298-304.##[8]. Taleghani M, Amirteymuri AR. Wave height predicted in Caspian Sea using artificial neural networks. Journal of Operational Research in Its Applications (Applied Mathematics). 2008; 5(18):39-47. [Persian]##[9]. Zamani A, Azimian A. Wave height prediction in Caspian Sea by neural network. 9th conference of Fluid dynamics. Shiraz University. 2004; 1-11. [Persian]##[10]. Abed-Elmdoust A, Kerachian R. Wave height prediction using the rough set theory. Ocean Engineering. 2012; 1(54):244-250.##[11]. Amani-Dashlejeh J, Bonakdar, L. Using neural network in prediction of wave height and period with different return period in South Bandar Abbas. 10th Marine industries conference. Khoramshahr. 2008; 1-11. [Persian]##[12]. Krishna Kumar N, Savitha R, Al Mamun A. Regional ocean wave height prediction using sequential learning neural networks. 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0 ارزیابی مدل SEBS در برآورد تبخیر‌ـ تعرق واقعی با استفاده از تصاویر ماهواره‌ای MODIS در مقیاس منطقه‌ای (مطالعۀ موردی: دشت سیستان) Evaluating SEBES Model to Estimate Actual Evapotranspiration using ‎MODIS Sensor Data in Regional Scale (Case Study: Sistan Plain)‎ https://ije.ut.ac.ir/article_63243.html 10.22059/ije.2017.63243 0 ‌هدف از انجام این پژوهش، برآورد تبخیر و تعرق واقعی دشت سیستان با استفاده از فناوری سنجش از راه دور و تصاویر ماهواره‌ای سنجندۀ مودیس و بررسی کارایی مدل بیلان انرژی سطحی SEBS در برآورد تبخیر و تعرق واقعی روی محدودۀ ‌مطالعه‌شده است. برای این منظور شارهای سطحی بیلان انرژی برای هر پیکسل تصویر محاسبه و مقدار تبخیر و تعرق واقعی به‌صورت باقی‌ماندۀ معادلۀ توازن انرژی در سطح برآورد شد. سپس نتایج برآورد‌شده با نتایج اطلاعات زمینی دو نقطه شامل ایستگاه‌های سینوپتیک زهک و ایستگاه مخزن چاه نیمۀ 1 مقایسه شد. نتایج نشان داد مطابق نقشۀ توزیع مکانی تبخیر‌ـ تعرق لحظه‌ای، بیشترین میزان تبخیر برای سطح پیکره‌های آبی از جمله مخازن چاه نیمه و قسمتی از تالاب هامون است. این میزان برابر 13/1 میلی‌متر بر ساعت است. مناطق کشاورزی دشت سیستان نیز با داشتن توزیع میزان تبخیر بین 5/0 تا 1 سهم زیادی در تبخیر- تعرق منطقه دارد. مقایسه و ارزیابی نتایج مدل SEBS با دو نقطۀ زمینی با معیارهایی مانند میانگین درصد اختلاف مطلق، ریشۀ میانگین مربع اختلافات و معیار ضریب همبستگی نشان می‏دهد مدل SEBS ‌عملکرد خوبی در سطح زمین و آب دارد. 1 Obviously, in any planning for qualitative and quantitative management of water resources, ‎estimating the water balance and values of the input and output components that will play an ‎important role. Several studies in the field of evapotranspiration most complex component of ‎the water balance in the world and many models offered and developed‏.‏‎ Remote Sensing due to ‎the superiority of meteorological methods based on measuring point and water balance is more ‎in the works. In this study, performance of Surface Energy Balance (SEBS) model to estimate ‎actual evapotranspiration in Sistan plain is studied. For this purpose data from Zehak ‎meteorological stations and the technology of remote sensing and MODIS sensor images used. ‎Surface flux of energy balance is calculated for each image pixel and actual evapotranspiration ‎were estimated by the remaining amount of the energy balance at the level. The results were ‎compared with results of two point ground-based data consist of the hay grown on the sidelines ‎of Zahak synoptic station and water level of reservoir of Chahnime1. Model showed good ‎performance for both land and water based on correlation coefficient value with 0.78 and 0.89 ‎respectively.‎ 1141 1150 مسعود محمدابراهیم Masoud Mohammad Ebrahim دانشجوی کارشناسی ارشد مهندسی منابع آب، دانشکدۀ آب و خاک، دانشگاه زابل Iran zahradehqan66@gmail.com ام البنی محمدرضاپور Omolbani Mohammadrezapour استادیار گروه مهندسی آب، دانشکدۀ آب و خاک، دانشگاه زابل Iran nmohammadrezapour@yahoo.com هادی اکبرزاده مقدم سه‌قلعه Hadi Akbarzadeh seghaleh دانش‌آموختۀ کارشناسی ارشد مهندسی آب، دانشگاه زابل Iran hadi3castle@gmail.com evapotranspiration remote sensing SEBS Penman-Monteith Sistan Plain‎ 1-      Brutsaert W. Hydrology: an introduction: Cambridge University Press.2005.##2-      Sun Z, Wei B, Su W, Shen W, Wang C, You D, Liu Z. Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China. Mathematical and Computer Modelling.2011. 54(3): 1086-1092.##3-      Liu S, Sun R, Sun Z, Li X, Liu C. Evaluation of three complementary relationship approaches for evapotranspiration over the Yellow River basin. Hydrological processes, 2006.20(11): 2347-2361.##4-      McCabe M.F, Wood E.F. Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sensing of Environment, 2006. 105(4): 271-285.##5-      Brunsell N.A. 2011. Characterizing the multi–scale spatial structure of remotely sensed evapotranspiration with information theory.##6-      Batra N, Islam S, Venturini V, Bisht, G Jiang, L. Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains. Remote Sensing of Environment. 2006. 103(1): 1-15.##7-      Kustas WP, Choudhury BJ, Moran MS, Reginato RJ, Jackson RD, Gay LW, Weaver, H.L. Determination of sensible heat flux over sparse canopy using thermal infrared data. Agricultural and Forest Meteorology. 1989. 44(3): 197-216.##8-      Su ZB. 2002. A Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes from point to continental scale. Paper presented at the Spectra Workshop.##9-        Bastiaanssen W, Pelgrum H, Wang J, Ma Y, Moreno J, Roerink G,Van der Wal T. A remote sensing surface energy balance algorithm for land (SEBAL).: Part 2: Validation. Journal of hydrology. 1998. 212: 213-229.##10-    Allen RG, Tasumi M, Trezza R. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of irrigation and drainage engineering, 2007.133(4): 380-394.##11-  Tasumi M, Allen RG. Satellite-based ET mapping to assess variation in ET with timing of crop development. Agricultural Water Management. 2007.88(1): 54-62.##12-  Van der Kwast J, Timmermans W, Gieske A, Su Z, Olioso A, Jia L, Elbers J, Karssenberg D, De Jong S, de Jong S. Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain). Hydrology and Earth System Sciences Discussions.2009. 6(1): 1165-1196.##13-  Evans R, Hulbert S, Murrihy E, Bastiaanssen, W Molloy R. Using satellite imagery to measure evaporation from storages–solving the great unknown in water accounting. Paper presented at the Irrigation and Drainage Conference 2009.##14-  Jin X, Wan L, Su Z. Research on evaporation of Taiyuan basin area by using remote sensing. Hydrology and Earth System Sciences Discussions. 2005. 2(1): 209-227.##15-  Jia L, Xi G, Liu S, Huang C, Yan Y, Liu G. Regional estimation of daily to annual regional evapotranspiration with MODIS data in the Yellow River Delta wetland. Hydrology and earth system sciences. 2009. 13(10): 1775-1787.##16-  Muthuwatta LP, Bos M, Rientjes T. Assessment of water availability and consumption in the Karkheh River Basin, Iran—using remote sensing and geo-statistics. Water Resources Management. 2010. 24(3): 459-484.##17-  Elhag M, Psilovikos A, Manakos I, Perakis K. Application of the SEBS water balance model in estimating daily evapotranspiration and evaporative fraction from remote sensing data over the Nile Delta. Water Resources Management. 2011. 25(11): 2731-2742.##18-  Akbarzadeh H, Haghighatgo P, Bagheri M.H. Estimates of Evaporation from Surface Water Bodies with SEBAL Algorithm using Remote Sensing Techniques (Case Study: Chahnimeh’s Fresh Water Reservoirs of Sistan). Iranian Journal of Irrigation and Drainage. 2015. 3( 9): 511-522.##19-  Noroozi AA, Jalali N, Miri M, Abbasi M. Estimating rice leaf area index at North Iran. Journal of water and Soil Resources Conservation. 2012. 3(2): 29-40.##20-  Monteith JL. Principles of environmental physics. Edward Arnold Press. Fourth Edition, 2014. 403 pp.##21-  Timmermans WJ, Kustas WP, Anderson, MC, French AN. An intercomparison of the surface energy balance algorithm for land (SEBAL) and the two-source energy balance (TSEB) modeling schemes. Remote Sensing of Environment. 2007. 108(4): 369-384.##

0 ارزیابی کارکرد تغییرپذیری اقلیم و تغییر کاربری اراضی در تغییرات کیفیت آب رودخانۀ هراز (استان مازندران) Investigating the contribution of climate variability and land use change in water quality changes of Haraz River (Mazandaran Province) https://ije.ut.ac.ir/article_63245.html 10.22059/ije.2017.63245 0 شناخت روند پارامترهای کیفی آب و عوامل مؤثر بر تغییرات آن، همانند آنالیز کمی منابع آب، از ملزومات مدیریت پایدار و تأمین سلامت حوضه‏های آبخیز است. با توجه به اهمیت منابع آب سطحی برای مصارف مختلف در حوضۀ آبخیز هراز، ضروری است تا درک صحیحی از کیفیت این منابع و شناخت عوامل تأثیرگذار بر آن صورت گیرد. به این منظور برای ارزیابی کیفیت آب سطحی رودخانۀ هراز، داده‏های 12 متغیر کیفیت آب به همراه پارامترهای هیدرو‌اقلیمی با استفاده از آزمون ناپارامتریک من-کندال (Mann-Kendall) طی دورۀ آماری 1370-1394 آنالیز شدند. برای تعیین آثار احتمالی تغییرات کاربری اراضی، این تغییرات نیز با استفاده از GIS برای سال‏های 1370، 1385 و 1394 ‌ارزیابی شدند. نتایج نشان داد بیشتر سری‏های زمانی کیفیت آب روند افزایشی معنا‏داری طی دوره داشته‏اند که بیان‌کنندۀ کاهش شدید کیفیت آب رودخانۀ هراز است. با توجه به نتایج آزمون من-کندال (آنالیز روند پارامترهای هیدرو‌اقلیمی، تعیین نقطۀ تغییر ناگهانی، آنالیز همبستگی کندال) و همچنین روند تغییرات کاربری اراضی، نتیجه گرفته شد که هر دو عوامل اقلیمی و تغییرات کاربری اراضی می‏توانند در کاهش کیفیت منابع آب تأثیرگذار باشند به‏طوری که افزایش دما و کاهش بارندگی از یک‌طرف می‏تواند به کاهش پارامترهایی نظیر کلسیم، بی‌کربنات و سختی کل (TH) منجر شود و از طرف دیگر، این تغییرات به‌همراه تغییرات کاربری اراضی می‏تواند دلیل اصلی افزایش بیشتر سری‏های زمانی مانند کل مواد محلول (TDS)، هدایت الکتریکی (EC)، سدیم، کلرید، نسبت جذب سدیم (SAR) و غیره باشد. 1 To analyze surface water quality in the Haraz River, 12 water quality variables accompanied by hydro-climatologic parameters analyzed by Mann-Kendall Non-parametric test during the period of 1991-2015. To detect probable effects of land use changes, these changes also analyzed by GIS in 1991, 2006 and 2015 years. The results showed that most of time series of water quality have significant increasing trend that represents severe reduction in water quality of Haraz river. According to the results of Mann-Kendall test (trend analysis of hydro-climatic parameters, detection of abrupt change point and Kendall τ correlation test) and also land use changes trend, it was concluded that both climatic and land use change can be effective on reducing the quality of water resources so that temperature increase and precipitation decrease on one hand can be led to decrease of some parameters such as Ca2+, HCO3- and TH, and on the other hand, these changes together with land use changes can be the main reason of increase in most of parameters such as TDS, EC, Na+, Cl-, SAR etc. 1151 1163 عبدالله پیرنیا Abdollah Pirnia دانشجوی دکتری آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری Iran abd.god62@gmail.com کریم سلیمانی Karim Solaimani استاد دانشگاه علوم کشاورزی و منابع طبیعی ساری، دانشکدۀ منابع طبیعی ساری، ساری Iran solaimani2001@yahoo.co.uk محمود حبیب نژاد روشن Mahmoud Habibnejad roshan استاد دانشگاه علوم کشاورزی و منابع طبیعی ساری، دانشکدۀ منابع طبیعی ساری، ساری Iran roshanbah@yahoo.com علی اصغر بسالت پور Ali asghar Besalatpour استادیار دانشکدۀ کشاورزی، دانشگاه ولی‌عصر‏(عج‏) رفسنجان، رفسنجان Iran a_besalatpour@yahoo.com Trend Analysis Water quality Climate variability Land Use Change Haraz River Basin [1]. 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0 تأثیر ذرات رسوبی در حمل عناصر سنگین در بازۀ میانی رودخانۀ دز Role of suspended particulates on heavy elements transport in the middle part of Dez River https://ije.ut.ac.ir/article_63246.html 10.22059/ije.2017.233850.609 0 این پژوهش با هدف پایش و شناسایی وضعیت رسوب معلق و عناصر سنگین حمل‌شده در قسمتی از رودخانۀ دز پایین‏تر از سد دز تا محل اتصال شطیط و سهم جریانات ورودی از دو سرشاخۀ آن در استان خوزستان انجام گرفت. در مجموع، 38 نمونه در سه زمان شامل یک دورۀ کم‏آبی و دو رویداد سیلاب از ایستگاه‏های دوکوهه روی بالارود، دهنوباقر روی کهنک و دزفول و حرمله روی دز برداشت شد. پس از جدا‌کردن رسوب، تعدادی از نمونه‏ها به آزمایشگاه ارسال و عناصر سنگین با استفاده از دستگاه پلاسمای جفتی انتقالی اندازه‏گیری شد. نتایج بیان‌کنندۀ آن است که هیچ‌یک از عناصر از دیدگاه کشاورزی آلاینده نیست؛ ولی از دیدگاه حفاظت محیط زیست، نمونه‏های رسوب معلق آنالیز‌شده از نظر سه فلز سنگین کروم، کبالت و نیکل به‏ترتیب با دامنۀ مقادیر 88ـ 96، 20ـ 22 و 76ـ 91 قسمت در میلیون آلودگی داشتند. غلظت عناصر سنگین یادشده در ایستگاه پایاب حرمله بیش از سرشاخه‏ها بود که احتمالاً ناشی از ترسیب ذرات درشت‏تر رسوب بوده است. به‏عنوان نتیجۀ کلی می‏توان گفت که رسوب معلق بازه‏های بین ایستگاه دوکوهه تا حرمله و دهنوباقر تا حرمله از نظر سه فلز یادشده آلوده بوده‏اند. 1 This study was performed in order to monitor and recognize the condition of transported suspended particulates and absorbed heavy metals in Dez river between Shahid Abbaspour Dam and Shotate river Junction and the effect of inflow from two tributaries in Kouzestan province, Iran. We took 38 samples in three steps including a base flow period and two flood events from four gauging sites including Dokouheh on Balaroud, Zourabad on Kohnak, Dezful on Dez upstream and Harmalah on Dez downstream. After sediment filtration, some samples were analyzed by Inductively Coupled Plasma to determine 12 heavy metals. The results showed that the river suspended sediment classified as not polluted by heavy metals for agriculture purpose at least for studies samples. However, the amount of three heavy metals including Cobalt (20-22 ppm), Chrome (88-96 ppm) and Nickel (76-91 ppm) are more than environmental safe thresholds. In addition, the concentration of those heavy metals in downstream station is more than tributaries which is probably due to deposition of coarser sediment particles. Therefore, it can be concluded that the suspended sediments between Dokouheh to Haramalah reach and Zourabad to Haramalah reaches are polluted by three mentioned heavy metals. 1165 1174 محمود عرب خدری Mahmood Arabkhedri پژوهشکدۀ حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران‌ Iran mahmood.arabkhedri@gmail.com فریدون سلیمانی Feridoun Soleimani مرکز تحقیقات کشاورزی و منابع طبیعی استان خوزستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اهواز‌ Iran frsolaimani@gmail.com سعید نبی‏ پی لشکریان Saeed Nabipay Lashkarian پژوهشکدۀ حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران‌ Iran snabipay@gmail.com فریدون سلطانی Feridoun Soltani سازمان آب و برق خوزستان، اهواز Iran soltani19@yahoo.com Heavy metals Non-source pollutants Suspended sediment Sediment management Environment Protect Agency (EPA). 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Journal of Science and Technology of Agriculture and Natural Resources. 2000; 4 (4): 31-42. [Persian]##International Atomic Energy Association (IAEA). Guidelines for using FRNs to assess soil erosion and effectiveness of soil conservation strategies. IAEA TECDOC-1741. IAEA publication. 2014; 213 p.##Kiani Harchegani M, Sadeghi SHR. Spatial variations of relationship between heavy metals transportation and particle size distribution of suspended sediments. Journal of Water and Soil Conservation. 2013; 20(1): 169-184.##Rajabzadeh Sekkeh M, Saeedi M. The role of sediments and river suspended materials on absorbent of Copper, Zinc and Cadmium in laboratory scale-Case study: Jajroud river, The proceeding of the 4th Iranian civil engineering congress, University of Tehran, 2010; 8p. [Persian]##Sadeghi SHR, Kiani Harchegani M, Younesi, HA. 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0 تصحیح دبی حداکثر سالانه بر اساس انتخاب مناسب‌ترین تابع توزیع احتمال در جنوب ایران Correction annual maximum discharge based on appropriate probability distribution function in south of Iran https://ije.ut.ac.ir/article_63249.html 10.22059/ije.2017.63249 0 ‌پژوهش حاضر با هدف انتخاب بهترین تابع توزیع آماری برای دبی‏های حداکثر سالانه در استان‏های جنوبی کشور ایران (سیستان و بلوچستان، فارس، کرمان، بوشهر و هرمزگان) از داده‏های روزانۀ دبی 108 ایستگاه هیدرومتری (طول دورۀ آماری 1362‌ـ 1391) استفاده و داده‏های یادشده با 65 تابع توزیع آماری موجود برازش داده شد و پس از انجام آزمون‏های نکویی برازش با استفاده از محاسبات آماری، بهترین تابع توزیع آماری برای دبی‏های حداکثر سالانه تعیین شد و در نهایت مقادیر بزرگی دبی با دورۀ بازگشت‏های مختلف محاسبه و با نتایج به‌دست‌آمده از توابع توزیع مرسوم شامل لوگ پیرسون سه‌پارامتره، لوگ نرمال سه‌پارامتره و توزیع ویکبای مقایسه شد. نتایج نشان داد در همۀ استان‏های جنوبی کشور، تابع توزیع ویکبای با 2/43 درصد رتبۀ اول، تابع توزیع لوگ پیرسون سه‌پارامتره با فراوانی 6/13 درصد رتبۀ دوم و توزیع آماری لوگ نرمال سه‌پارامتره با فراوانی 5/6 درصد رتبۀ سوم بهترین توزیع آماری را به خود اختصاص داده‌اند. شاخص میانگین خطای اریب (MBE) در برآورد دبی حداکثر سالانه نشان داد در دورۀ بازگشت‏های 2، 5 و 10 سالۀ توزیع آماری ویکبای و در دورۀ بازگشت‏های 25، 50 و 100 سالۀ توزیع آماری لوگ پیرسون سه‌پارامتره برآورد بهتری داشته است. همچنین با مقایسۀ شاخص جذر میانگین مربع خطا (RMSE) و متوسط قدر مطلق درصد خطا (MAPE) در دو توزیع آماری ویکبای و لوگ پیرسون سه‌پارامتره مشخص شد که در دورۀ بازگشت‏های مختلف توزیع آماری ویکبای برآورد بهتری از لحاظ این شاخص داشته است. بنابراین، در مناطق جنوبی کشور، کاربرد توزیع ویکبای در تحلیل فراوانی وقوع سیلاب به‏منظور پیش‏بینی دقیق‏تر مقادیر دبی حداکثر سالانه در دورۀ بازگشت‏های مختلف، می‏تواند راهگشا باشد. 1 In this research for choosing the best distribution function for the annual maximum discharge (AMD) in the southern provinces of Iran; The daily discharge of 108 hydrometric stations (1983-2012) were used; Data were fitted with 65 probability distribution functions. After the goodness of fit tests using the statistical calculations, the best distributions function for the AMD was determined and eventually the discharge amounts were calculated with different return periods and compared with the result of the common distribution functions like log- Pearson (III), log- normalIII and Weakby. The result shown, the Waekby distribution functions with the 2.43% and the first rank, the log- PearsonIII with the frequency of 6.13% and the second rank and log- normal III with the frequency of the 5.6% and the third rand, gained the best statistical distribution. The MBE in AMD estimation showed that in 2.5 and 10- year return period, the Weakby statistical distribution and in the 25.5 and 100-year return period. The log- Pearson III statistical distribution has a better estimation. Comparing the RMSE with MAPE in both Weakby and log PearsonIII statistical distribution, it is found that Weakby statistical distribution has a better estimation in the different return periods in this index. 1175 1185 حسین صادقی مزیدی Hosein sadeghi Mazidi دانشجوی دکتری، گروه مرتع و آبخیزداری، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس Iran hossien_fasa@yahoo.com ام‌البنین بذرافشان Ommolbanin Bazrafshan استادیار، گروه مرتع و آبخیزداری، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس Iran bazrafshan1361@gmail.com عبدالرضا بهره مند Abdolreza Bahremand دانشیار، گروه مرتع و آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان Iran abdolreza.bahremand@yahoo.com آرش ملکیان Arash Malekian دانشیار، گروه مرتع و آبخیزداری، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج Iran malekian@ut.ac.ir Annual maximum discharge Waekby distribution log- Pearson (III) goodness of fit tests  [1]. Pacione M. The principles and practice of applied geography. 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0 مدل‌سازی پاسخ جریان آب زیرزمینی در آبخوان نشتی ساحلی به نوسانات جزر و مد به روش جداسازی متغیرها و تبدیل فوریه Modelling the Groundwater Flow Response to Tidal Fluctuation in a Coastal Leaky Aquifer by separation of variables and Fourier transformation https://ije.ut.ac.ir/article_63252.html 10.22059/ije.2017.235150.631 0 دراین مقاله روابط جدیدی برای مدل‌سازی تحلیلی پاسخ جریان آب زیرزمینی در آبخوان نشتی ساحلی به نوسانات جزر و مد به روش جداسازی متغیرها و تبدیل فوریهارائه شده است. نتایج محاسبات حل تحلیلی ارائه‌شده در این تحقیق نشان می‏دهد تأثیر جزر و مد در فواصل نزدیک به ساحل بیشتر است و با دور‌شدن از ساحل، مقدار نوسانات سطح آب زیرزمینی کمتر می‏شود. با توجه به بررسی انجام‏‌شده، افزایش مقدار قابلیت انتقال سبب افزایش هد هیدرولیکی سطح آب زیرزمینی می‏شود. این تغییرات مادامی که قابلیت انتقال کمتر از  است، محسوس‏تر است اما با چند‌برابر شدن مقدار قابلیت انتقال، میزان افزایش هد هیدرولیکی سطح آب زیرزمینی کمتر است. همچنین با افزایش مقدار تغذیۀ هد سطح آب زیرزمینی افزایش می‏یابد. تأثیر افزایش مقدار قابلیت انتقال و تغذیه بر افزایش هد هیدرولیکی در فواصل نزدیک به ساحل کمتر و در اواسط مسیر در فاصلۀ 30 تا 75 متری از مرز جزر و مدی بیشتر است. علاوه بر این‌ها، نتایج مدل‌سازی روابط جدید ارائه‌شده در این تحقیق با نتایج مدل نرم‌‏افزار مادفلو مقایسه شد. این مقایسه نشان داد حل تحلیلی ارائه‌شده در این پژوهش بسیار کارآمد است. 1 In this paper, groundwater response to tidal effects in a Coastal Leaky Aquifer is simulated via analytical solutions. The solutions are obtained by application of separation of variables method and Fourier transform method. It is shown that those points of the aquifer near to the coastal shore are much more influenced by the tidal fluctuations in the boundary than the rest of the aquifer. The amplitude of tidal change is significant at points of the aquifer near to the coastal shore and gets smaller with distance from the boundary. In addition, it is shown that groundwater level increases with rises in transmissivity. This phenomenon is more significant when transmissivity is less than 400 m2/hr . Also, the groundwater head rises with rises in recharge rate. The effect of variations in transmissivity and recharge rate on hydraulic head is more significant at points located between 30 and 75 meters and less significant at points near to the coastal shore. The presented analytical solution is compared and verified with those results obtained from MODFLOW. 1187 1197 ایرج سعیدپناه Iraj Saeedpanah استادیار گروه عمران دانشکدۀ مهندسی، دانشگاه زنجان Iran saeedpanah@znu.ac.ir سمیه محمدزاده روفچائی Somayeh Mohammadzade Roofchaee دانشجوی کارشناسی ارشد مهندسی عمران، دانشگاه زنجان Iran somayeh.mohamadzade@znu.ac.ir Analytical solution Coastal leaky aquifer Groundwater Flow Tide Method of separation of variables and Fourier transformation [1] Sophocleous M. Interactions between groundwater and surface water: the state of the science. Hydrogeology Journal. 2002; 10: 52–67.##[2] Dong L, Chen J, Fu C. Jiang H. Analysis of groundwater-level fluctuation in a coastal confined aquifer induced by sea-level variation. Hydrogeology Journal. 2012; 20: 719–726.##[3] Chuang M.H, Yeh H.D. An analytical solution for the head distribution in a tidal leaky confined aquifer extending an infinite distance under the sea. Advances in Water Resources. 2007; 30(3): 439-445.## [4] Jacob C. E, Flow of groundwater; In: Engineering Hydraulics. New York: John Wiley;1950.##[5] Ferris J.G. Cyclic fluctuations of water level as a basis for determining aquifer transmissibility. International Assoc. of Scientific Hydrology. 1951; 33, 148-155.##[6] Van Der Kamp G. Tidal fluctuations in a confined aquifer extending under the sea, 24th International Geological Conference, Montreal, Quebec, Canada. 1972; 11: 101–106.##[7] Tang Z.H, Jiao J.J. A two-dimensional analytical solution for groundwater flow in a leaky confined aquifer system near open tidal water. Hydrological Process. 2001; 15(4):573–585.##[8] Li H, Jiao J.J. Tide-induced groundwater fluctuation in a coastal leaky confined aquifer system extending under the sea. water resources research. 2001; 37(5): 1165–1171.##[9] Li H, Jiao J.J. Tidal groundwater level fluctuations in L-shaped leaky coastal aquifer system. Journal of Hydrology. 2002; 268(1- 4): 234-243.##[10] Li H, Jiao J. J. Tide-induced seawater–groundwater circulation in a multi-layered coastal leaky aquifer system. Journal of Hydrology. 2003; 274(1- 4):211–224.##[11] Li H, Li G, Cheng J, Boufadel M. C. Tide-induced head fluctuations in a confined aquifer with sediment covering its outlet at the sea floor. water resources research. 2007; 43(3): W03404, Doi:10.1029/2005WR004724.##[12] Guo Q. N, Li H. L, Boufadel M. C, xia Y, Li G. Tide-induced groundwater head fluctuation in coastal multi-layered aquifer systems with a submarine outlet capping. Advances in Water Resources. 2007; 30(8):1746–1755.##[13] Guo H. P, Jiao J. J, Li HL. Groundwater response to tidal fluctuation in a two-zone aquifer. Journal of Hydrology. 2010; 381(3- 4):364–371.##[14] Huang C.S, Yeh H.D, Chang C.H. A general analytical solution for groundwater fluctuations due to dual tide in long but narrow islands. water resources research. 2012; 48(5): W05508, Doi:10.1029/2011WR011211.##.[15] Saeedpanah I, GolmohamadiAzar R, New Analytical Expressions for Two-Dimensional Aquifer Adjoining with Streams of Varying Water Level. Water Resources Management. 2017; 31(1): 403–424.##[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] Sun H. A two-dimensional analytical solution of groundwater response to tidal loading in an estuary. water resources research. 1997; 33(6): 1429-1435.##[18] Li L, Barry D.A, Cunningham C, Stagnitti F, Parlange J.Y. A two dimensional analytical solution of groundwater response to tidal loading in an estuary and ocean. Advances in Water Resources. 2000; 23(8): 825-833.##

0 پتانسیل‏ سنجی منابع آب زیرزمینی با رویکردی ترکیبی به الگوریتم بهینه‏ سازی ازدحام ذرات و سیستم اطلاعات مکانی (مطالعۀ موردی: دشت مهران، ایلام) Potential Evaluation of Underground Water Resource with the Hybrid Approach to Particle Swarm Optimization Algorithm and Geospatial Information Systems (Case Study: Mehran, Ilam) https://ije.ut.ac.ir/article_63259.html 10.22059/ije.2017.63259 0 یکی از مسائل مهم در زمینۀ مدیریت صحیح منابع آب زیرزمینی، شناسایی پتانسیل این منابع به‌منظور برنامه‏ریزی و تصمیم‏گیری صحیح دربارۀ بهره‏برداری از آنهاست. هدف این پژوهش، پتانسیل‏سنجی منابع آب زیرزمینی با رویکردی ترکیبی به الگوریتم بهینه‏سازی ازدحام ذرات (PSO) و سیستم اطلاعات مکانی (GIS) در دشت مهران است. به این منظور و برای شناسایی پتانسیل منابع آب زیرزمینی در این منطقه، 13 فاکتور مختلف تأثیرگذار بر میزان نفوذ آب در داخل زمین و تشکیل منابع آب زیرزمینی شامل شیب، ارتفاع، تراکم زهکشی، تراکم خطواره، نقشۀ T، نقشۀ K، نقشۀ Recharge، نقشۀ کاربری زمین، نقشۀ سنگ‌شناسی، نقشۀ Sy، نقشۀ عمق آب زیرزمینی، نقشۀ تراکم چاه و نقشۀ هم‌کلر، شناسایی و نقشۀ آنها تهیه و طبقه‏بندی شد. سپس، با استفاده از الگوریتم PSO، هر یک از نقشه‏ها وزن‏دهی و پس از آن با استفاده از روش همپوشانی وزن‏دار در محیط GIS با یکدیگر ترکیب شدند و در انتها، دو نقشۀ نهایی پتانسیل آب زیرزمینی، یک بار در حالتی که معادلۀ بهینه‏سازی برابر با نقشۀ تراکم چاه قرار گرفت (PSO_chah) و بار دیگر برای حالتی که معادلۀ بهینه‏سازی برابر نقشۀ آبدهی ویژه قرار گرفت (PSO_Sy)، به‌دست آمد. در این زمینه، نقشۀ PSO_chah، 56/2 درصد از منطقه و نقشۀ PSO_Sy، 40/2 درصد از منطقه را به‌عنوان مناطق با پتانسیل بسیار زیاد از نظر منابع آب زیرزمینی مشخص کردند. 1 One of very important problems in correct managing of groundwater resources is finding potential of this resources to correct planning and deciding for use of them. The purpose of this research is potential evaluation of groundwater resources with the hybrid approach to particle swarm optimization algorithm and geographic information systems in Mehran plain. In this regard and due to evaluation of groundwater resources potential in this area, 13 various factors which have a great impact on level of water permeability in ground and groundwater resources formation Including the slope, height, drainage density, fault density, T map, K map, recharge map, landuse map, lithology map, Sy map, depth of groundwater map, well density map and Cl map, were prepared and classified. Then, by PSO algorithm, each map was assigned weight and with overlay method in GIS combined with each other and at the end 2 final groundwater potential map were obtained, once when that optimization equation equal to the well density map (PSO_chah), and once again when that optimization equation equal to the Sy map (PSO_Sy). In this context, PSO_chah map, 2.56% and PSO_Sy map, 2.40% of area determined as areas with very high potential in case of groundwater resources. 1199 1213 ساسان محمودی جم Sasan Mahmoudi Jam دانشجوی کارشناسی ارشد آب و سازه‌های هیدرولیکی، دانشکدۀ مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی Iran sasanjam774@yahoo.com سید حسین قریشی نجف‌آبادی Seyed Hossein Ghoreyshi Najaf Abadi استادیار دانشکدۀ مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی Iran h_ghoreishi@sbu.ac.ir علیرضا وفایی‌نژاد Alireza Vafaeinejad استادیار دانشکدۀ مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی Iran a_vafaei@sbu.ac.ir علی مریدی Ali Moridi استادیار دانشکدۀ مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی Iran moridi1978@gmail.com صفا خزایی Safa Khazaee استادیار دانشکده و پژوهشکدۀ پدافند غیرعامل، دانشگاه جامع امام حسین‌(ع) Iran khazai.s@gmail.com Potential Evaluation Underground Water Particle Swarm Optimization Algorithm Geospatial Information Systems [1]        Vafaeinejad A.R, yousof zadeh J, yousofi H, mohammadi varzaneh N. Management of water distribution in irrigation networks and Allocation of cropping pattern with the help of geographic information systems and linear programming (case study: land downstream of the Aghchay dam). Journal of Echo Hydrology. 2014, 123-132. (In Persian).##[2]        Vafaeinejad A.R, Cropping pattern optimization by using TOPSIS method and genetic algorithm based on GIS capabilities (case study: Land of the plain, Isfahan). Journal of Echo Hydrology. 2016, 69-82. (In Persian).##[3]        Chenini I, Ben Mammou A. Groundwater recharge study in arid region: An approach using GIS techniques and numerical modeling. Computers & Geosciences. 2010, 801–817.##[4]        Tweed S.O, Leblanc M, Webb J.A, Lubczynski M.W. Remote sensing and GIS for mapping groundwater recharge and discharge areas in salinity prone catchments. SE Australia. Hydrogeol. 2007, 75–96.##[5]        Entekhabi D, Moghaddam M. Mapping recharge from space: roadmap to meeting the grand challenge. Hydrogeol. 2007, 105–116.##[6]        Das D. Satellite remote sensing in subsurface water targeting.In: Proceeding ACSM-ASPRS Annual Convention. 1990, 99–103.##[7]        Hoffmann J, Sander P. Remote sensing and GIS in hydrogeology. Hydrogeol. 2007, 1–3.##[8]        Rahman A. A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. Appl. Geograph. 2008, 32–53.##[9]        Gupta M, and Srivastava P.K. Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh,Gujarat, India. Water Int. 2010, 233–245.##[10]     Saud M.Al. Mapping potential areas for groundwater storage in Wadi Aurnah Basin, western Arabian Peninsula, using remote sensing and geographic information system techniques. Hydrogeol. 2010, 1481–1495.##[11]     Elewa H.H. Qaddah A.A. Groundwater potentiality mapping in the Sinai Peninsula, Egypt, using remote sensing and GIS-watershed-based modeling. Hydrogeol. 2011, 613–628.##[12]     Konkul J. Rojborwornwittaya W. Chotpantarat S. Hydrogeologic characteristics and groundwater potentiality mapping using potential surface analysis in the Huay Sai area, Phetchaburi Province, Thailand. Geosci. 2014, 89–103.##[13]     Mon'em M.J, Noori M.A. Application of PSO optimization algorithm in distribution and optimized delivery of water in irrigation networks. Journal of Irrigation and Drainage of iran. 2010, 82-73. (In Persian).##[14]     Ying chun, Ge, XinLi, Chunlin Huang, Zhuotong Nan. A Decision Support System for irrigation water allocation along the middle reaches of the Heihe River Basin. Northwest China. Environmental Modelling & Software. 2013, 182-192.##[15]     Li Y, Sun H, Zhang C, Li G. Sites Selection of ATMs Based on Particle Swarm Optimization. International Conference on Information Technology and Computer Science. 2009, 526-530##[16]     Haupt RL, Haupt SE. Practical Genetic Algorithms. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2003.##[17]     Engelbrecht, Andries P. Computational intelligence: an introduction. wiley, 2007.##[18]     Mohammadi varzaneh N, Vafaeinejad A.R, The allocation of water in irrigation networks with the help of decision support system based on Geographic Information System (GIS) and particle swarm algorithm (PSO) (case study: Agricultural land of Ghortan). Journal of Echo Hydrology. 2015, 39-49. (In Persian).##

0 برآورد رواناب رویداد مبنا در حوضۀ کوهستانی با استفاده از مدل توزیعی‌ـ فیزیکی GSSHA Event-Oriented Runoff Estimation in Mountainous Basin by GSSHA Physically- Distributed Model https://ije.ut.ac.ir/article_63263.html 10.22059/ije.2017.236526.656 0 مدل‌های توزیعی‌ـ فیزیکی در شبیه‌سازی فرایندهای رواناب سطحی در حوضه‏هایی با شرایط پیچیدۀ فیزیکی به جواب‌هایی با اعتمادپذیری بیشتر منجر می‏شوند. در این مطالعه مدل‌سازی فرایند بارش‌ـ رواناب در حوضۀ آبریز کوهستانی زیارت با استفاده از مدل توزیعی‌ـ فیزیکی GSSHA بررسی شده است. به این منظور نقشه‏های مدل رقوم ارتفاعی، نوع خاک و کاربری اراضی تهیه شده و سه رویداد برای واسنجی و دو رویداد برای صحت‏سنجی در نظر گرفته شد. معیارهای دقت برازش نش‌ـ ساتکلیف (NSE)، درصد خطای برآورد حجم (PEV)، درصد خطای برآورد دبی اوج (PETP) و درصد خطای برآورد زمان دبی اوج (PEP) و معیار بصری برای تحلیل نتایج استفاده شد. میانۀ معیارهای PEV، PEP و PETP در مراحل واسنجی و صحت‌سنجی به‌ترتیب 3/25 و 5/61، 5/5 و 8/11 و 8/4 و 0 بوده که نشان‌دهندۀ کم‌برآوردی حجم، دقت مناسب در دبی اوج و دقت بسیار خوب در زمان دبی اوج است. نیز بررسی بصری هیدروگراف‌های شبیه‌سازی و معیار NSE تأیید‌کنندۀ دقت مدل در شبیه‏سازی شکل هیدروگراف رواناب است. نتایج نشان می‏دهد اگر‏چه پارامتر رطوبت اولیۀ خاک در مرحلۀ صحت‏سنجی بر اساس یک تخمین اولیه در نظر گرفته شده، در مجموع دقت مدل در برآورد مشخصه‌های رواناب مناسب است. 1 The physically-distributed models lead to more reliable results for surface runoff process simulation in basins with complicated physical condition. In this study, rainfall-runoff modeling in Ziarat mountainous basin is investigated using GSSHA physically-distributed model. Digital elevation model, soil type and land use maps prepared and three and two events are considered for calibration and validation. Fitness precision criteria including Nash-Sutcliffe (NSE), Percentage Error in Volume (PEV), Percentage Error in Time to Peak (PETP) and Percentage Error in Peak (PEP) beside visual criterion used for results analysis. Median of PEV, PEP and PETP for calibration and validation steps were (25.3 and 61.5), (5.5 and 11.8) and (4.8 and 0) that indicated underestimation for volume, suitable precision for peak and excellent precision for time to peak estimations. Also, evaluation of simulated hydrographs using visual and NSE criteria confirmed model precision for hydrograph simulation. The results show although soil initial moisture selected based on initial estimation in validation step but the overall precision of model in runoff characteristics estimations is suitable. 1215 1225 علی شریفی Ali Sharifi دانشجوی کارشناسی ارشد مهندسی منابع آب، گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان Iran alisharifi761@gmail.com میثم سالاری جزی Meysam Salarijazi استادیار گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان Iran meysam.salarijazi@gmail.com خلیل قربانی Khalil Ghorbani دانشیار گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان Iran ghorbani.khalil@yahoo.com Physically-Distributed Rainfall-runoff GSSHA Ziarat [1]. Panday, Sorab; Huyakorn, Peter S. A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow. Advances in water Resources, 2004, 27.4: 361-382.‏##[2]. Refsgaard, Jens Christian; Knudsen, Jesper. Operational validation and intercomparison of different types of hydrological models. Water Resources Research, 1996, 32.7: 2189-2202.‏##[3]. Refsgaard, Jens Christian; Storm, Borge; Refsgaard, Anders. Recent developments of the SystemeHydrologiqueEuropeen (SHE) towards the MIKE SHE. IAHS Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, 1995, 231: 427.‏##[4]. Eidipour, Amin, et al. Flood Hydrograph Estimation Using GIUH Model in Ungauged Karst Basins (Case Study: Abolabbas Basin).‏##[5]. Adib, Arash; Salarijazi, Meysam; Najafpour, Khosro. Evaluation of synthetic outlet runoff assessment models. Journal of Applied Sciences and Environmental Management, 2010, 14.3##[6]. Adib, Arash, et al. Comparison between GCIUH-Clark, GIUH-Nash, Clark-IUH, and Nash-IUH models. Turkish Journal of Engineering and Environmental Sciences, 2010, 34.2: 91-104.‏##[7]. Adib, Arash, et al. 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Prediction of runoff and soil moistures at the watershed scale: Effects of model complexity and parameter assignment. Water Resources Research, 2003, 39.3.‏##[13]. Niedzialek, Justin M.; Ogden, Fred L. Physics-based distributed rainfall-runoff modeling of urbanized watersheds revisited with GSSHA. In: World Water & Environmental Resources Congress 2003. 2003. p. 1-10.‏##[14]. Downer, Charles W.; Ogden, Fred L. GSSHA: Model to simulate diverse stream flow producing processes. Journal of Hydrologic Engineering, 2004, 9.3: 161-174.‏##[15]. Habib, Emad; Aduvala, Ananda V.; Meselhe, Ehab A. Analysis of radar-rainfall error characteristics and implications for streamflow simulation uncertainty. Hydrological sciences journal, 2008, 53.3: 568-587.‏##[16]. Paudel, Murari. An examination of distributed hydrologic modeling methods as compared with traditional lumped parameter approaches. 2010.‏##[17]. ELhassan, Almoutaz A., et al. 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0 پایش خشکسالی هواشناسی به‌منظور حفظ پایداری در سناریوهای واداشت تابشی منطقۀ مطالعاتی (حوضۀ آبریز سد دویرج) Meteorological drought monitoring in order to sustainability in RCP scenarios Case study: Doiraj watershed https://ije.ut.ac.ir/article_63267.html 10.22059/ije.2017.230628.535 0 پدیدۀ تغییر اقلیم آب و هوایی موجب تکرار حوادث غیرمترقبه نظیر خشکسالی و سیل می‌شود و خسار‌ت‌های بسیاری به زندگی انسان و اکوسیستم‏های طبیعی وارد می‏کند. هدف از این پژوهش، حفظ پایداری حوضۀ سد دویرج شهرستان دهلران در سناریوهای واداشت تابشی در برابر حوادث تغییر اقلیم است. دورۀ مشاهداتی در این پژوهش (1987‌ـ 2015) و دورۀ آتی (2016‌ـ 2044) است. به این‌منظور از ترکیب وزنی پنج مدل گزارش پنجم (AR5) تحت سناریوی rcp8.5 برای ارزیابی تغییرات بارش و دما در دورۀ آتی استفاده شد. از روش وزن‌دهی MOTP برای کاهش عدم قطعیت مدل‏های GCM استفاده شد و ریزمقیاس‌سازی به روش عامل تغییر انجام ‏شد. پایش خشکسالی هواشناسی در بازه‏های ماهانه، فصلی و سالانه با روش زنجیرۀ‏ مارکوف و شاخص‏های خشکسالی  SIAP, SPI ,Z scoreو BMDI و تحلیل فراوانی محاسبه شد. نتایج بیان‌کنندۀ افزایش میانگین درازمدت بارش و دمای ماهانه به‌میزان 14 درصد و 2/1 درجۀ سانتی‏گراد نسبت به دورۀ پایه است. تحلیل عدم قطعیت بارش‏ها با زنجیرۀ مارکوف احتمال وقوع ماه بدون بارش بعد از ماه بدون بارش دیگر در فصول زمستان، بهار و پاییز به‌ترتیب 56، 63 و 52 درصد است و احتمال وقوع بارش بعد از یک ماه خشک در فصول یادشده به‌ترتیب 44، 35 و 47 درصد است نیز بیشترین احتمال وقوع ماه‏های با بارش، مربوط به ماه آوریل است. بر اساس تحلیل نمایه‏های خشکسالی سال 2017-2018 نسبت به سال 2016-2017 مرطوب‏تر و در کل دورۀ آتی سال‏های 2024-2025 و 2025-2026 مرطوب‏ترین سال‏ها بر اساس این پژوهش‌اند. تحلیل فراوانی بارش حوضۀ سد دویرج بارش با دورۀ بازگشت 50 سال را 61/727 میلی‏متر در یک سال برآورد کرده است. 1 The aim of this study is to preserve the sustainability of the Doiraj watershed in RCP scenarios. The observation and future period in this study is (1987-2015) and (2044-2016). For this purpose, the combined weight of 5 models of Fifth Report (AR5), rcp8.5 scenario, used to assess changes in temperature and precipitation in the coming period. MOTP weighting method to reduce uncertainty of GCM models were used Meteorological drought monitoring in monthly, Seasonal and yearly intervals using Markov chain, frequency analysis and drought indexes SIAP, SPI, Z score and BMDI was calculated. The results showed that long-term average monthly rainfall and temperature at a rate of 14 percent and 2.1 degrees Celsius as compared to the baseline. Markov chain probability of uncertainty precipitation showed, two months without precipitation in winter, spring and autumn, respectively 56, 63 and 52 percent and the chance of precipitation after a month of dry seasons, respectively 44, 35 and 47 percent. Based on the analysis of the indices during the years 2017-2018 than in 2016-2017 wetter and future years in the period 2024-2025 and 2025-2026 wettest years on the basis of this research. 1227 1239 مریم حافظ پرست Maryam Hafezparast دکتری منابع آب، استادیار گروه مهندسی آب، دانشگاه رازی Iran maryam.hafezparast@gmail.com زهره پورخیراله Zohreh Pourkheirolah دانشجوی کارشناسی ارشد مهندسی منابع آب، دانشگاه رازی Iran zohre217p@gmail.com Meteorological drought AR5 Drought indexes Markov Chain Frequency Analysis [1]-Mansouri B, Ahmadzadeh, Hojat. Masah Bavani, Alireza. Morid Said. Delavar, Saeeid. The effects of climate change on water resources basin model SWAT. Journal of Agricultural Science and Technology. 2014; 28(6): 1191-1203. [Persian].##[2]- Li Z, Liu WZ,Zhang X C, and Zheng F. Impact of land use change and climate variability on hydrology in an agriculture cathment on the Loess Plateau of China. Journal of Hydrology.2009; 377: 35-42.##[3]- Masah Bavani A. 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0 ارزیابی اثر تغذیۀ مصنوعی بر تعادل‌بخشی آبخوان با استفاده از شاخص پایداری Assessment of artificial recharge on aquifer restoring using sustainability index https://ije.ut.ac.ir/article_63270.html 10.22059/ije.2017.234332.616 0 برگشت تراز آب زیرزمینی به تراز مطلوب با استفاده از راهکارهای تعادل‏بخشی از اهداف اصلی سیاست‌گذاران آب کشور است. یکی از راهکارهای طرح یادشده، تغذیۀ مصنوعی است که به‌منظور کمی‌کردن میزان اثربخشی آن، با استفاده از شاخص‏های پایداری در آبخوان ارزیابی شد. برای نخستین‌بار پایداری سیستم آب زیرزمینی، با استفاده از ترکیب سه شاخص اعتمادپذیری، آسیب‏پذیری و مطلوبیت و با درنظرگرفتن اثر سناریوی تغذیۀ مصنوعی ارزیابی شد. به این‌منظور با استفاده از مدل MODFLOW اثر تغذیۀ مصنوعی شوراب سیوجان بر وضعیت آبخوان بیرجند طی یک دورۀ نُه‌ساله شبیه‏سازی و پیش‌بینی وضعیت آبخوان تا افق 1404 در شرایط نرمال اقلیمی در سه سناریوی برداشت آب انجام گرفت. نتایج شبیه‏سازی نشان داد سیستم آبخوان در بخش انتهایی، که چاه مشاهده‏ای خوسف قرار دارد، با توجه به مسیر حرکت تغذیۀ آب زیرزمینی، اختلاف کم بین تراز مطلوب و تراز آب زیرزمینی و ضخامت کم ناحیۀ اشباع، بیشترین پایداری را به‌میزان 55 درصد داشته است. بررسی شاخص‏ها در منطقه تحت تأثیر تغذیۀ مصنوعی در آبخوان نشان می‏دهد اجرای طرح تغذیۀ مصنوعی توانسته بین 21 تا 25 درصد با توجه به سناریوی کاهش، ثابت‌بودن برداشت و افزایش برداشت مقدار شاخص پایداری سیستم را بهبود دهد. شاخص ارائه‌شده در این تحقیق با توجه به قابلیت توزیعی‌بودن و امکان بررسی اثر سناریوهای مختلف، می‏تواند در سایر آبخوان‏ها و در تصمیم‏گیری به کار برده شود. 1 One of the strategies, is conducting artificial recharge, which is required to be evaluated using sustainability indexes in order to quantify the effects. In this research, a new approach is utilized for evaluation of groundwater system sustainability, combining the indexes, reliability, vulnerability and desirability considering the effect of artificial recharge scenario. First, the effect of Shurab Sivjan artificial recharge project on Birjand aquifer is simulated for a nine-year period, projected to 1404 Hijri (2025 Gregorian) with normal climatic condition, using MODFLOW model. Simulation results show that aquifer system sustainability is higher in the downstream parts, where the Khusf observation well is located, justifying by the groundwater recharge flow direction, low difference between optimum and measured groundwater level, and thin saturated thickness with 55 percent. Evaluation of indexes over the aquifer shows that the artificial recharge project could enhance the system sustainability between 21 to 25 percent according to reduce, constant and increment discharge scenarios. The proposed index in this research can be utilized for the other aquifers as well as in decision-making, because of its ability to define in distributed manner and possibility of evaluating the effects of different scenarios. 1241 1253 حمید کاردان مقدم Hamid Kardan Moghaddam دانشجوی دکتری منابع آب پردیس ابوریحان، دانشگاه تهران Iran hkardan@ut.ac.ir محمد‌ابراهیم بنی‌حبیب Mohammad Ebrahim Banihabib دانشیار گروه مهندسی آبیاری و زهکشی پردیس ابوریحان، دانشگاه تهران Iran banihabib@ut.ac.ir سامان جوادی Saman Javadi استادیار گروه مهندسی آبیاری و زهکشی پردیس ابوریحان، دانشگاه تهران Iran javadis@ut.ac.ir sustainability index reliability vulnerability desirability Artificial recharge [1]. Campos-Gaytan JR, Kretzschmar T, Herrera-Oliva CS. Future groundwater extraction scenarios for an aquifer in a semiarid environment: case study of Guadalupe Valley Aquifer, Baja California, Northwest Mexico. Environmental monitoring and assessment. 2014;186(11):7961-85.##[2]. Du S, Su X, Zhang W. 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0 بررسی و تحلیل اقتصادی و محیط ‏زیستی توسعۀ نیروگاه‏ های برقابی کوچک Economic and Environmental Analysis of the Small Hydropower Plants Development https://ije.ut.ac.ir/article_63271.html 10.22059/ije.2017.63271 0 حفظ محیط ‏زیست و پایان‏پذیر‌بودن منابع فسیلی برای تولید انرژی، توجه بشر را به استفاده از منابع جایگزین و تجدیدپذیر معطوف کرده است. از این‌رو، توسعۀ نیروگاه‏های برقابی از جمله راهکارهای به‌کار گرفته‌شده در کشورهای مختلف است. با توجه به تقسیم‏بندی نیروگاه‏های برقابی به کوچک و بزرگ‌مقیاس، بررسی عوامل اقتصادی و ملاحظات محیط‏ زیستی تأثیر زیادی در انتخاب مقیاس بهینۀ نیروگاه‏های برقابی دارند. در این مطالعه از روش تحلیلی و بررسی تطبیقی برای تعیین آثار اقتصادی و محیط‏ زیستی احداث نیروگاه‏های برقابی کوچک و مقایسۀ آنها با نیروگاه‏های برقابی بزرگ استفاده شده است. نتایج بیان‌کنندۀ آن است که نیروگاه‏های برقابی کوچک علاوه بر امکان بهره‏برداری از کمترین پتانسیل آبی، از نظر اقتصادی و محیط‏ زیستی نیز نسبت به نیروگاه‏های برقابی بزرگ‌مقیاس برتری‏ دارند. برخی از مزیت‏های مهم نیروگاه‏های برقابی کوچک که سبب تمایز آنها از نیروگاه‏های برقابی بزرگ و توسعۀ آنها شده است، عبارت‏اند از: کم‌بودن هزینه‏های سرمایه‏گذاری، کوتاهی زمان ساخت، کاهش انتشار گازهای گلخانه‏ای، پراکندگی مناسب واحدها، ظرفیت مناسب انتقال فناوری و قابلیت سرمایه‏گذاری توسط بخش خصوصی. این مزیت‏ها موجب شده است که به نیروگاه‏های برقابی کوچک به‌عنوان جایگزین مناسبی برای نیروگاه‏های برقابی بزرگ توجه شود. 1 The necessity to preserve the environment and the depletion of fossil fuels for energy production have concerned human attention to the use of alternative and renewable sources. Hence, hydropower plants development is one of the solutions that are using in countries. Since the hydropower plants division into small and large scale, economic and environmental factors plays an important role in choosing the optimal scale of hydropower plants. In this study, an analytical and comparative method use in order to determine the economic and environmental effect of the construction of small hydropower plants and their comparison with large hydropower plants. The results indicate that small hydropower plants; in addition to being able to use the minimum potential of water; are economically and environmentally advantages of in comparison with large hydropower plants. Some of the main advantages of small hydropower plants that differentiate them from large hydropower plants are: low investment costs, shorter construction time, reduced greenhouse gas emissions, the proper dispersal of units, enabling capacity for technology transfer and private sector investment capability. These advantages have led small hydropower to be considered as a good alternative to large hydropower plants. 1255 1268 حسن جنگ آور Hassan Jangavar دانشجوی دکتری اقتصاد نفت و گاز، دانشکدۀ اقتصاد، دانشگاه علامه طباطبائی، تهران Iran hjangavar@gmail.com یونس نوراللهی Younes Noorollahi دانشیار، دانشکدۀ علوم و فنون، دانشگاه تهران Iran noorollahi@ut.ac.ir علی امامی میبدی Ali Emami Meybodi دانشیار، دانشکدۀ اقتصاد، دانشگاه علامه طباطبائی، تهران Iran emami@atu.ac.ir Small hydropower plants large hydropower plants Environment factors economic factors Renewable Energies [1].Renewable energy policy network for the 21st century (REN21). Renewables 2016: global status report. 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