ORIGINAL_ARTICLE
Identifying and prioritizing of appropriate sites for artificial recharge using Hierarchical Fuzzy TOPSIS (Case study: Fatoieh Plain)
Nowadays, groundwater depletion and the lack of replace water sources, one of the biggest problems is Nowadays, groundwater depletion and lack of enough water to replace it is one of the biggest problems considered all over the world. To solve this crisis as one of the major solutions, we can consider the artificial recharge of aquifers. Fatoieh plain of Hormozgan province is located in the central city district Bastak. Therefore groundwater is the major and only reliable source of water and due to severe drawdown; it is needed for artificial recharge of ground water aquifer of the region. Therefore, in order to identify areas suitable for artificial recharge some of the indicators consisting of nine hydrogeology and geographic parameters were chosen and presented in the form of maps in GIS environment. Meanwhile, we used fuzzy TOPSIS method and hierarchical fuzzy TOPSIS network analysis combined with GIS to integrate of different information layers and to obtain the most suitable sites for the implementation of artificial recharge of the Fatoieh plain. The results showed good potential for artificial recharge in southern parts of the plain. Meanwhile, the fuzzy TOPSIS network analysis presented better and acceptable results.
https://ije.ut.ac.ir/article_64816_c4cd8aa60951c7e29bab3ba56ba1a1c0.pdf
2018-03-21
1
10
10.22059/ije.2017.224297.409
Artificial recharge
Analytical Network Fuzzy TOPSIS
Hierarchical
Arash
Malekian
malekian@ut.ac.ir
1
دانشیار دانشکدۀ منابع طبیعی، دانشگاه تهران
LEAD_AUTHOR
Mohammad
Pour Reza
arshmalek@yahoo.com
2
دانشجوی کارشناسی ارشد دانشگاه آزاد اسلامی واحد لارستان
AUTHOR
Alizadeh A. The principles of Applied Hydrology, 13th edition. Mashhad: Astan Quds Razavi; 2005. [Persian].
1
[2]. Ravi Shankar MN, Mohan G. A GIS based hydrogeomorphic approach for identification of sitespecific artificial-recharge techniques in the Deccan volcanic province. J. Earth Syst. Sci. 2005. No, 114. pp. 505-514.
2
[3]. Todd DK, Mays LW. Groundwater hydrology. 3nd, John Wiley and Sons publishers; 2005. P. 636. [4]. Salari S. Solid waste disposal site selection areas suitable for using GIS. M.Sc. thesis. Facutly of water scince Engineering, Shahid Chamran University of Ahvaz. 2012. [Persian].
3
[4]. Sener B, Süzen ML, Doyuran V. Landfill site selection by using geographic information systems. Environmental Geology. 2006. 49 (3):376-388.
4
[5]. Ghayoumian J, et al. Application of GIS techniques to determine areas most suitable for artificial groundwater recharge in a coastal aquifer in southern Iran. Journal of Asian Earth Sciences. 2007. 30, 364 -374.
5
[6] Torfi H. Plain Kharan Feasibility Study of artificial recharge techniques using remote sensing and GIS. M.A. thesis. Faculty of Sciences. Shahid Chamran University of Ahvaz. 2009. [Persian].
6
[7]. Karimi E, Zare M, Karimi M, Bahrami Z. Areas suitable of site selection for artificial recharge using GIS and hierarchical analysis method. 1st National symposium on Geology of Iran. Shiraz. 2011. [Persian].
7
[8]. Sepand S. Feasibility study of artificial recharge in the range of Lali. MSc thesis. Faculty of Sciences. Shahid Chamran University of Ahvaz. 2008. [Persian].
8
[9]. Ebrahimi F. Artificial recharge of site selection in the township Shahrood. M.A. thesis. Shahrood University. 2011. [Persian].
9
[10]. NajafAbadi AM. Areas suitable of site selection for artificial recharge of groundwater in two ways Boolean logic and fuzzy basin Shahrekord plain. M.Sc. thesis. Shahrekord University. 2010. [Persian].
10
[11]. Bowen WM. AHP: Multiple Criteria Evaluation. In: Klosterman, R, Brail R and Bossard EG, Editor. Spreadsheet Models for Urban and Regional Analysis. New Brunswick: Center for Urban Policy Research. 1993. pp. 333-357.
11
ORIGINAL_ARTICLE
Investigating the temporal variation and meteorological drought effect on groundwater resources in Kerman plain using SPI and GRI indices
Whereas climatic parameters find importance due to their impacts on water resources, therefore this research was done carried out for investigating of meteorological drought and its impacts on Kerman groundwater resources in the eighteen- year’s period (1997-2014). In this regard Standardized Precipitation Index (SPI) values in Kerman meteorological station and Groundwater Resource Index (GRI) of plain in different time scales (1, 3, 6, 9, 12, 18, 24 and 48 months) was calculated. The results of correlation test between GRI index in different time scales and SPI index in the time period under research without any delay and with a time lag of one to twelve months showed that 6 and 18 months GRI index have significantly related to 48 month SPI index with 0.628 and 0.631 Pearson coefficients, respectively and also 48 months GRI index was correlated to 48 months SPI index with 6 month lag time and its Pearson coefficient was 0.686. The results of regression model showed that up to 57 percent of variations in GRI index explained and justified by SPI index. This is due to other factors such as uncontrolled exploitation of groundwater resources on the loss of groundwater and groundwater index linked. Iso-decline maps showed that the decline in groundwater levels across the plain, an average of 20 meters and rising in ground water levels in the range of Kerman city by 14 meters.
https://ije.ut.ac.ir/article_64817_4c99b310829a4bf77c70be6f946e8636.pdf
2018-03-21
11
22
10.22059/ije.2017.225328.434
Correlation and Regression analysis
Kerman Plain
Standardized Precipitation Index
Groundwater Resource Index
Sedigheh
Mohammadi
mohamadisedigeh@gmail.com
1
Assistant professor, Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
LEAD_AUTHOR
Farzin
Naseri
fnnaseri@yahoo.com
2
1. Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran.
AUTHOR
Hamid
Nazaripour
h.nazaripour@kgut.ac.ir
3
Department of Environment, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran.
AUTHOR
Mishra AK, Singh VP. A review of drought concepts. J Hydrol. 2010; 391(1-2): 202–216.
1
[2]. Keneth HF. Climate Variation Drought and Desertification. W. M. O. Annual Report. Jevenva; 2003.
2
[3]. Karang Li. Drought early Warning and Impact Assessment in China. Proceeding of an Export Group meeting; 2004.
3
[4]. Kordavani P. Drought and ways to cope with it in Iran. 1nd ed. Tehran. Tehran University Press; 2001. [Persian]
4
[5]. Li Bailing, Rodell M. Evaluation of a model-based groundwater drought indicator in the conterminous U.S. J. Hydrol. 2015; 526(1-2): 78-88.
5
[6]. Peters E, Bier G, Van Lanen HAJ, Torfs PJJF. Propagation and spatial distribution of drought in a groundwater catchment. J. Hydrol. 2006; 321(1-4): 257-275.
6
[7]. Malins D, Metternicht G. Assessing the spatial extent of dry land salinity through fuzzy modeling. Ecol. Modell. 2006; 193(3-4): 387-411.
7
[8]. Abdinejad GhA. Research in to Effective Factors on Desertification and Drought. Jungle and Range. 2009; 78(1): 8-10. [Persian]
8
[9]. Tallaksen LM, Van Lanen HAJ. Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater. 1nd ed. Netherlands. Elsevier Press; 2004.
9
[10]. Mckee T B, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. Proprints. 8th Conference of Applied Climatology. California: Anaheim; 1993.
10
[11].Ghare Sheykhloo AH, Khosravani Shiri Z, Arabali A. Monitoring and zonation of drought for optimized water resources management. Third Confrence of water resources management. Tabriz: Tabriz university; 2009. [Persian]
11
[12]. Mendicino G, Senatore A, Versace P. A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate. J. Hydrol. 2008; 357(1-2): 282-302.
12
[13]. Adhikary SK, Das SK, Saha GC, Chaki, T. Groundwater drought assessment for barind irrigation project in northwestern Bangladesh. 20th International Congress on Modelling and Simulation. Adelaide: Australia; 2013.
13
[14]. Seeboonruang U. Impact assessment of climate change on groundwater and vulnerability to drought of areas in Eastern Thailand. Environ. Earth Sci. 2015; 75(1):42-62.
14
[15]. Hao Z, Hao F, Singh V, Xia Y, Ouyang W, Shen X. A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices. Adv. Water Resour. 2016; 92(4): 240-247.
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[16]. Šebenik U, Brilly M, Šraj M. Drought Analysis using the Standardized Precipitation Index (SPI). Acta Geogr Slov. 2017; 57(1): 31-49.
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[17]. Seyf M, Mohamadzade H, Mosaedi A. Evaluating the impacts of drought on groundwater resources in Fasa aquifer using SPI, GRI and SECI. Water Resources Engineering. 2013; 5(13): 45-59. [Persian]
17
[18]. Chaman pira GH, Zehtabian Gh, Ahmadi H, Malekian A. Research in to drought effects on groundwater resources for optimized Utilization management, case study: Plain Alashtar. Watershed Engineering and Management. 2015; 6(1): 10-20. [Persian]
18
[19]. Ahmadi Akhoorme M, Nohegar A, Soleimani Motlagh M, Taie Semiromi,M. Groundwater drought investigating using SWI and GRI indices (Case study: Marvdasht Kharameh Aquifer). Journal of irrigation and water engineering. 2015; 6(21): 105-118. [Persian]
19
[20]. Bakhtiare Enayat B, Malekian A, Salajegheh A. Time and Lag Correlation Analysis between Climate Drought and Hydrological Drought in Hashtgerd Plain. Iranian Journal of Soil and Water Research. 2016; 46(4):609-616. [Persian]
20
[21]. Mahdavi M. Applied Hydrology. Vol. 1. 4nd ed. Tehran: University of Tehran Press;2003.
21
[22]. Mohamadi S, Salajegheh A, Mahdavi M, Bagheri R. An investigation on spatial and temporal variations of groundwater level in Kerman plain using suitable geostatistical method (During a 10-year period), Iranian Journal of Range and Desert Reseach. 2012; 19 (1): 60-71. [Persian]
22
[23].Wilhite DA, Glantz MH. Understanding the drought phenomenon: The role of definitions. Water Int. 1985;10: 111–120.
23
[24]. Tabatabaei, S.M.F. Living things. 384 pp.Enteshar Sahami. 2006.
24
[25]. Shahidasht AR, Abbasnejhad, A. Survey of groundwater resources in Kerman province plain. Journal of applied geology, 2011; 7 (2): 131-146.
25
ORIGINAL_ARTICLE
Evaluate the Water Quality and Trend of changes in quality parameters of Kashkan basin
In this paper Kashkan basin from upstream to downstream water quality changes were evaluated. First, the process of long-term qualitative changes in two branches Kashkan (Khorramabad and Horod) was evaluated. Mann-Kendall test was used to determine Routing data. For examination of water quality AqQA software Schoeller diagram extracted and analyzed for each station. Information needed to assess water quality in terms of agriculture was transferred to the diagram Will Cox and Classification of water was determined. At all stations trend of discharge, negative and to further Water Quality Indicators the trend was positive. The results showed that in both branch water quality parameters Decreased. Water quality changes after joining the two branch Khorramabad and Herod At the station Poldokhtar was investigated. At the station Poldokhtar Many parameters were In a range between the value of them In two branches before joining But in some of them also was violated this law and increased At the station Poldokhtar. Due to the reduction in flow rate and increase of quality parameters, the basic premise was proven.
https://ije.ut.ac.ir/article_64818_d463a937e984df9cefb5661be579e0e7.pdf
2018-03-21
23
36
10.22059/ije.2017.228466.490
Routing
Kashkan
quality
Mann-Kendall.
Hasan
Torabi Poudeh
torabi.ha@lu.ac.ir
1
دانشیار، گروه مهندسی آب، دانشگاه لرستان
LEAD_AUTHOR
Parastoo
Hamezadeh
swallow_kaspian@yahoo.com
2
دانشجوی دکتری سازههای آبی، دانشگاه لرستان
AUTHOR
[1]. Pal DK, Bhattacharyya T, Ray SK, Chandran P, Srivastava P, Durge SL, et al. Significance of soil modifiers (Ca-zeolites and gypsum) in naturally degraded Vertisols of the Peninsular India in redefining the sodic soils. Geoderma. 2006; 136(1):210-228.
1
[2]. Nazarian1 S, Farid gigolo B, Chemical Quality Survey and Trends of Water Quality Parameters at Nodeh Station of Gorganroud River, Golestan Province of Iran. Irrigation & Water Engineering. 2015; 5(19): 80-92. (Persian)
2
[3]. Khadam IM, Kaluarachchi JJ.Water quality modeling under hydrologic variability and parameter uncertainty using erosion-scaled export coefficients. Journal of Hydrology.2006; 330(1):354-367.
3
[4]. Elshorbagy A, Lindell O. Object-oriented modeling approach to surface water quality management. Environmental Modelling & Software. 2006; 21(5): 689-698.
4
[5]. Hashemi SE, Mousavi SF, Taheri SM, Ghareh-Chahi A. Analysis of Groundwater Quality Acceptability for Drinking purposes in Nine Cities in Isfahan Province Using Fuzzy Inference System. Iran-Water Resources Research. 2010; 6(3): 25-34. (Persian)
5
[6]. Yang X, Wei J. GIS-based spatial regression and prediction of water quality in river networks: a case study in Iowa. Journal of Environmental Management. 2010; 91(10): 1943-1951.
6
[7]. SolaimaniSardo M, Vali AA, Ghazavi R, Saidi Goraghani HR. Trend Analysis of Chemical Water Quality Parameters; Case study Cham Anjir River. Irrigation & Water Engineering. 2013; 3(12):95-105. (Persian)
7
[8]. Javid AH, Mirbagheri SA, Karimian A. Assessing Dez Dam reservoir water quality by application of WQI and TSI indices. Iranian Journal of Health and Environment. 2014; 7(2): 133-142. (Persian)
8
[9]. Ebadati N. Trend assessment of changes in water quality plain Eyvanakey. Iranian journal of Ecohydrology.2016; 2(4):383-394. (Persian)
9
[10]. Asadzadeh F, Kaki M, Shakiba S, Raei B. Impact of Drought on Groundwater Quality and Groundwater Level in Qorveh-Chardoli Plain. Iran-Water Resources Research.2016; 12(3): 153-165. (Persian)
10
[11]. Solgi E, Sheikhzadeh H. Study of Water Quality of Aras River Using Physico-Chemical Variables. Iran-Water Resources Research. 2016; 12(3): 207-213. (Persian)
11
[12]. Sadeghi SH, Allbuali A, Ghazavi R. Investigation of Temporal and Spatial Trends of Water Quality Parameters Change Using Geostatistic Methods in Kashan Plain. Journal of Water and Soil Science. 2016; 20(76): 73-82. (Persian)
12
[13]. Yousefi H, Mohammadi A, Noorollahi Y, Sadatinejad SJ. Qualitative Evaluation of Surface Water Resources of Hiv basin. Iranian journal of Ecohydrology.2016; 3(2):141-149. (Persian)
13
[14]. Mann HB. Nonparametric tests against trend. Econometrica: Journal of the Econometric Society.1945; 245-259.
14
[15]. Kendall MG. Rank Correlation Methods; Griffin & Co, London. ISBN 0-85264-199-0; 1975.
15
[16]. Alizadeh A. Applied hydrology; Imam Reza University Press. 1999. (Persian)
16
ORIGINAL_ARTICLE
Modeling of stage-discharge relationship in compound channels using multi-stage gene expression programming
In flood conditions at the alluvial rivers with compound sections, due to momentum exchange between main channel and floodplains, flow discharge prediction by traditional methods is very erroneous. In this paper, a new method known as multi-stage gene expression programming has been used for computation of flow discharge in straight compound channels. For modeling, three dimensionless parameters of relative depth, coherence and relative calculated flow discharge, and one parameter of relative measured flow discharge were selected as inputs and output, respectively. Using 402 stage-discharge dataset from 31 laboratory and field compound channels, explicit relationships were developed for flow discharge prediction. The mean absolute errors of this method were obtained as 10.2% and 11.6%, respectively for training and testing phases. Hence compared with the Manning's formula (with mean absolute error of 19.3%), the proposed method is quit outperform. Hence, application of this method is recommended for flood flow discharge in rivers having compound channel forms. Also, by incorporating this new idea with the computation procedures of the river water surface profiles and flood routing, the design of flood alleviation schemes will be improved.
https://ije.ut.ac.ir/article_64822_2101ad319789bff2d1adeb63e985fa23.pdf
2018-03-21
37
48
10.22059/ije.2017.228922.497
Bankfull channel
Flow discharge
Flooded rivers
Gene expression programming algorithm
Optimization
Abdolreza
Zahiri
zahiri.areza@gmail.com
1
دانشیار گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان
LEAD_AUTHOR
Mohammad Ali
Shabani
ma_shabani30@yahoo.com
2
North Khorasan Water Regional Co.
AUTHOR
Ackers, P. Hydraulic design of two-stage channels. Journal of Water and Maritime Engineering. 1992 Dec ; 96: 247-257.
1
[2]. Jiang, B,, Yang, K,, and Cao, S. An analytical model for the distributions of velocity and discharge in compound channels with submerged vegetation. PLoS ONE. 2015 Jul 10; 10(7): 1-17.
2
[3]. Yang, Z., Gao, W. and Huai, W. Estimation of discharge in compound channels based on energy concept. Journal of Hydraulic Research. 2102 Aug 31; 50(1): 105-113.
3
[4]. Conway, Ph., O’Sullivan, J.J., and Lambert, M.F. Stage–discharge prediction in straight compound channels using 3D numerical models. Proceedings of the Institution of Civil Engineers, Water Management. 2013 Jun; 166(1): 3-15.
4
[5]. Wark, J.B., Samuels, P.G., and Ervine, D.A. A practical method of estimating velocity and discharge in compound channels. International Conference on River Flood Hydraulics. London, 1990 Sep; 163-172.
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[6]. Shiono, K. and Knight, D.W. Turbulent open-channel flows with variable depth across the channel. Journal of Fluid Mechanics. 1991 Jun; 222: 617-646.
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[7]. Hu, C., Ju, Z., and Guo, Q. Flow movement and sediment transport in compound channels. Journal of Hydraulic Research. 2010 Mar 18; 48(1): 23-32.
7
[8]. Wormleaton, P.R. and Merrett, D.J. 1990. An improved method of calculation for steady uniform flow in prismatic main channel/floodplain sections. Journal of Hydraulic Research. 1990 Apr; 28(2): 157-174.
8
[9]. Bousmar, D., and Zech, Y. Momentum transfer for practical flow computation in compound channels. Journal of Hydraulic Engineering. 1999 Jul 1; 125(7): 696-70.
9
[10]. Naik, B. and Khatua, K.K. Water surface profile computation in nonprismatic compound channels. Aquatic Procedia. 2015 Jun 25; 4: 1500-1507.
10
[11]. Zahiri, A., Azamathulla, H.Md. Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels. Neural Computing & Applications. 2014 Feb; 24(2):413-420.
11
[12]. Zahiri, A., Dehghani, A.A. and Azamathulla, H.Md. "Chapter 4. Application of gene-expression programming in hydraulics engineering". Handbook of Genetic Programming Applications, A.H. Gandomi, A.H. Alavi and C. Ryan (eds). Springer. 2015; 71-98.
12
[13]. Huthoff, F., Roose, P.C., Augustijn, D.C.M., and Hulscher, S.J.M.H. Interacting divided channel method for compound channel flow. Journal of Hydraulic Engineering. 2008 Aug; 134(8):1158-1165.
13
[14]. Liu, W., and James, C. S. Estimating of discharge capacity in meandering compound channels using artificial neural networks. Canadian Journal of Civil Engineering. 2000 Nov 2; 27(2): 297-308.
14
[15]. Zahiri, A., and Dehghani, A.A. Flow discharge determination in straight compound channels using ANN. World Academy of Science, Engineering and Technology. Italy, 2009 Oct 28-30; 58: 12-15.
15
[16]. Unal, B., Mamak, M., Seckin, G., and Cobaner, M. Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels. Advances in Engineering Software. 2010 Feb 1; 41: 120-129.
16
[17]. Parsaeei, A., Yonesi, H. and Najafian, S. Predictive modeling of discharge in compound open channel by support vector machine technique. Earth System Environment. 2015 May 9; 1:1-6.
17
[18]. Azamathulla, H.Md., and Zahiri, A. Flow discharge prediction in compound channels using linear genetic programming. Journal of Hydrology. 2012 Aug 6; 454-455C: 203-207.
18
[19]. Chow, V.T. Open channel hydraulics. McGraw-Hill. London. 1959.
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[20]. Chadwick, A., and Morfett, J., and Borthwick, M. Hydraulics in civil and environmental engineering. CRC Press, Fourth Edition. 2004.
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[21]. Martin, L.A. and Myers, R.C. Measurement of overbank flow in a compound river channel. Journal of Institution of Water and Environment Management. 1991 Dec; 91(2): 645-657.
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[22]. Ferreira, C. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems. 2001 Feb 25; 13(2): 87-129.
22
[23]. Sattar, M.A. Gene expression models for the prediction of longitudinal dispersion coefficients in transitional and turbulent pipe flow. Journal of Pipeline Systems Engineering and Practice. 2014 Feb; 5(1): 04013011.
23
[24]. Gandomi A.H., and Alavi, A.H. Multi-stage genetic programming: A new strategy to nonlinear system modeling. Information Sciences. 2011 Jul 23; 181(23): 5227-5239.
24
[25]. Blalock, M.E. and Sturm, T.W. Minimum specific energy in compound channel. Journal of Hydraulic Division. 1981 Nov; 107: 699–717.
25
[26]. Knight, D.W. and Demetriou. J.D. Flood plain and main channel flow interaction. Journal of Hydraulic Division. 1983 Aug 1; 109(8):1073-1092.
26
[27]. Knight, D.W. and Sellin, R.H. J. The SERC flood channel facility. Journal of Institution of Water and Environment Management. 1987 Jan 23; 1(2): 198-204.
27
[28]. Lambert, M.F. and Sellin, R.H.J. Discharge prediction in straight compound channels using the mixing length concept. Journal of Hydraulic Research. 1996 Mar 18; 34: 381-394.
28
[29]. Myers, R.C. and Lyness. J.F. Discharge ratios in smooth and rough compound channels. Journal of Hydraulic Engineering. 1997 Mar 1; 123(3): 182-188.
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[30]. Lambert, M.F., and Myers, R.C. Estimating the discharge capacity in straight compound channels. Water, Maritime and Energy. 1998 Jan; 130:84-94.
30
[31]. Haidera, M.A., and Valentine, E.M. A practical method for predicting the total discharge in mobile and rigid boundary compound channels. International Conference on Fluvial Hydraulics. Belgium. 2002 Sep 4-6; 153-160.
31
[32]. Lai, S.H. and Bessaih, N. Flow in compound channels. 1st International Conference on Managing Rivers in the 21st Century. Malaysia. 2004 Sep 21-23; 275-280.
32
[33]. Atabay, S., and Knight, D.W. 1-D modelling of conveyance, boundary shear and sediment transport in overbank flow. Journal of Hydraulic Research. 2006 Nov; 44(6): 739-754.
33
[34]. Bousmar, D., Wilkin, N., Jacquemart, H. and Zech, Y. Overbank flow in symmetrically narrowing floodplains. Journal of Hydraulic Engineering. 2004 Apr; 130(4): 305-312.
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[35]. Fernandes, J.N., Leal, J.B. and Cardoso, A.H. Analysis of flow characteristics in a compound channel: comparison between experimental data and 1-D numerical simulations. Proceedings of the 10th Urban Environment Symposium. Sweden 2010 Jun 9-11; 249–262.
35
[36]. Knight, D.W., Shiono, K., and Pirt, J. Predictions of depth mean velocity and discharge in natural rivers with overbank flow. International Conference on Hydraulics and Environmental Modeling of Coastal, Estuarine and River Waters. UK. 1989 Sep 19-21; 419-428.
36
[37]. Tarrab, L., and Weber, J.F. Transverse mixing coefficient prediction in natural channels. Computational Mechanics. 2004 Jun; 13: 1343-1355.
37
ORIGINAL_ARTICLE
Study the qualitative Canada of the Sardabrood River in Mazandaran with the use the qualitative indicator of Canada׳s water
The rivers are considered as a small part of flowing water and one of the basic sources of the world׳s irrigation, drinking water industry and other uses. The aims of this study are division of water quality in three stations (Zawat, Walt and Roodbarak) which are situated in Sardabrood river by using qualitative water index (CWQI) and the Aquachem software. For this aim to calculate the indicator, calcium, magnesium, potassium, sodium, Chloride, Sulfate, The electrical conductivity (EC) and acidity (PH) based on the results, Zawat station is in a rather good rating for potation and in an excellent rating for irrigation and Walt and Roodbarak had bad ratings. All the Roodbarak stations are in bad conditions for raising aquatic amusement and are needed to be filtered and refined. Concerning to the analysis, the best and worst qualitative water is related to the Zawat and Walt and Roodbarak stations. The type of water by the Aquachem was Bicarbonate- Sodium (NaHCO3).
https://ije.ut.ac.ir/article_64823_bd1015f0947b85768f1e66d80381968d.pdf
2018-03-21
49
58
10.22059/ije.2017.229927.520
quality of water resources
Canadian Water
river
Aquachem
Fahimeh
Khadempour
fahimkhadempour@yahoo.com
1
University of Shahid Bahonar kerman
AUTHOR
Nasrin
Sayari
nasrin_sayari@yahoo.com
2
University of Shahid Bahonar Kerman
LEAD_AUTHOR
[1]. Hushmand A, Syed cable H, Delqandi M. Review changes to the water quality index (WQI) and the effective parameters (period Mlasany- Karun River Ahwaz), Conference and Exhibition of Environmental Engineering, Tehran University, Iran. 2008. (In Persian).
1
[2]. Delbari M, Afrasiabi P, Salari M. Zoning quality parameters (salinity and alkalinity) using geostatistical methods case study: Kerman Plain. Journal of Water Resources Engineering, 2012; 6:11-24. (In Persian).
2
[3]. Enriqu S, Manuel F, Colmenarejo J, Angel R, Garcl L, Borja R. Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution. Ecological Indicators, 2007; 7:315-328.
3
[4]. Ebrahimpur S, Mohammadzadeh H. Water quality assessment and zoning lake using qualitative indicators NSFWQI, OWQI. CWQI. Journal of Environmental Research, 2013; 4(7): 137-146. (In Persian).
4
[5]. Simoes F, Moreira A, Bisinoti M.C, Gimenez S, Santos M. Water quality index as a simple indicatorm of aquaculture effects on aquatic bodies. Ecological Indicators. 2008; 38: 476- 480.
5
[6]. Shamsaei A, Oreei Zareh A, Sarang. The comparison of water indices and zoning quality in karoon and dezrivers. Journal of Water and Wastewater, 2005; 55: 39-48. (In Persian).
6
[7]. Sadeghi M, Bay A, Bay N, Soflaie N, Mehdinejad M.H, Mallah M. The effect oagriculture drainage on water quality of the zaringol in golestan province by the water quality index. Journal of Research in Environmental Health, 2015; 1(3): 177-185. (In Persian).
7
[8]. Khorramabadi Shams G, Yusefzadeh A, Godini H, Hoseinzadeh E, Khoshgoftar M, Yusefzadeh A. Evaluation of river water quality using NSFWQI and GIS: A case study of Khorramrood river in khorramabad, Iran. Journal of Lorestan University of Medical Sciences, 2014; 3(3):101-111. (In Persian).
8
[9]. Fataei A, Seyyedsharifi S.A, Seiiedsafaviyan S.T, Nasrollahzadeh S. Water quality assessment based on WQI and CWQI indexes in balikhlouriver. Iran. Journal of Basic and Applied. 2013; 3(3):263- 269. (In Persian).
9
[10]. Sayari N, Abbas Zadeh M, Taji H, Hatamei B. Karunriver water quality monitoring and dose using the index. CWQI First National Conference on Sustainable Management of Soil Resources and Environment, Kerman University, Iran. 2014. (In Persian).
10
[11]. Sedaghat M, Esmaeelpour Alamdari Z, Sayari N. Water quality study using the canadian water quality index (Case study: Tajan river). The First National Conference on Sustainable Management of Soil Resources and the Environment, Kerman University, Iran. 2014. (In Persian).
11
[12]. Myrmshtaqy S. M, Amirnejad R, Khaledian M.R. Sefidrud river water quality study and mapping of them using qualitative indicators NSFWQI and OWQI. Journal of Wetlands, 2011; 3(9): 23-34.
12
[13]. Singh G, Kant Kamal R. Application of water quality index for assessment of surface water quality status in Goa. Current World Environment, 2014; 9(3): 994- 1000.
13
[14]. Nor Azalina R, Mohd Hafiz Z, Rosmina A. Salak river water quality identification and classification according to physico-chemical characteristics. Procedia Engineering Journal, 2012; 50:69-77.
14
[15]. The Canadian Water Quality Index 1.0 Technical Report. (http://www.ccme.ca/ceqg-_rcqe/ea2.html). 2001.
15
[16]. Piper A. M. A graphic procedure in the geochemical interpretation of water analysis. Trans. American Geophysical Union, 1944; 25 (6): 914-928.
16
[17]. Fetter C.W. Applied Hydrogeology. 2nd ed. Macmillan Publishing Company, New York, 310p. 1988.
17
ORIGINAL_ARTICLE
Select the Most Suitable Inputs to the Artificial Neural Network Model by Using the ACO Algorithm
the performance criteria have used in this study is including mean square error (MSE), sum square error (SSE), Nash_Sutcliffe and correlation coefficient. The result indicated the best ANNa model is ANNa2 with MSE equal 0.0017. Inputs in this model are Total Cation, Total Hardness and Calcium. The best ANNb model is ANNb3 with MSE equal 0.0012. Inputs in this model are Sodium adsorption ratio, pH, Total Hardness and Calcium. Also, the results indicated that using ACO algorithm for finding the best input parameters had increased neural network performance, in ANNb models than ANNa for validation network and for test network we see with increases inputs the performance of network increases. According to results we can say that against try and error for finding the best inputs, we can use the parameter that those had a high correlation between target parameter as first step. But parameters that have high correlation between target parameter, necessarily don't the best inputs. But the parameter that those had a high correlation between target parameter as inputs of neural network. Also, we find that the ACO algorithm can be used as a method of input variable selection and that improved the performance of neural network.
https://ije.ut.ac.ir/article_64824_69b3a7a4a209f29819d858eede62dcc5.pdf
2018-03-21
59
68
10.22059/ije.2017.230717.538
Artificial Neural Network
Ant colony Algorithm
Gadarchay River
Input Variable Selection
Mohammad Javad
Zeinali
mj.zeynali@yahoo.com
1
دانشجوی دکتری منابع آب، گروه علوم و مهندسی آب، دانشگاه بیرجند
AUTHOR
Ali
Shahidi
shahidi@birjand.ac.ir
2
Associate Professor Department of Water Engineering College of Agriculture University of Birjand
LEAD_AUTHOR
Khoshnazar A, Nasrabadi T and Abbasimaedeh P. Evaluating the efficiency of artificial neural network in prediction of electrical conductivity of Zarrinehroud river. Journal of Human and Environment. 2013; 10(22):1-16. [Persian]
1
[2]. Banejad H, Kamali M, Amirmoradi K and Olyaie E. Forecasting some of the qualitative parameters of rivers using wavelet artificial neural network hybrid (w-ann) model (case study: Jajroud river of Tehran and Gharaso river of Kermanshah). Journal of Health and Environment, 2013; 6(3): 277-294. [Persian]
2
[3]. Barzegar R, Adamowski J and Asghari Moghaddam A. Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-chay river, Iran. Stochastic Environmental Research and Risk Assessment, 2016; 30(7):1797-1819.
3
[4]. Sattari MT, Rezazadeh Joudi A and Kusiak A. Estimation of water quality parameters with data-driven model. Journal American Water Works Association. 2016; 108:4.
4
[5]. Kanda EK, Kipkorir EC and Kosgei JR. Dissolved oxygen modelling using artificial neural network: a case of river nzoia, lake victoria basin, kenya. Journal of Water Security, 2016; 2:1-7.
5
[6]. Seght Foroosh A, Monjezi M and Khademi Hamidi J. Optimization of blasting operation using hybrid Neural Network-Ant Colony (Case Study: Delkan Iron Mine). Journal of Modeling and Engineering. 2017; DOI: 10.22075/JME.2017.2449. [Persian]
6
[7]. Faghih H. Evaluating artificial neural network and its optimization using genetic algorithm in estimation of monthly precipitation data (case study: Kurdistan region). Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources). 2010. 14(51): 27-44. [Persian]
7
[8]. Socha K and Blum C. An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Journal of Neural Computing and Applications. 2007; 16: 235-247.
8
[9]. Emami Skardi MJ, Afshar A, Saadatpour M and Solis SS. Hybrid ACO–ANN-based multi-objective simulation–optimization model for pollutant load control at basin scale. Environmental Modeling and Assessment. 2015; 20(1): 29-39.
9
[10]. Valdez F, Castillo O and Melin P. Ant colony optimization for the design of modular neural networks in pattern recognition. In Neural Networks (IJCNN), International Joint Conference. 2016; 163-168.
10
[11]. Zho G. Ant colony optimization training feed-forward neural network based on elitist selection strategy. Boletín técnico. 2017; 55(1): 200-206.
11
[12]. Zeynali MJ, Nikbakht S, Mohammadezapour O. Prediction input flows to Mollasadra reservoir by using artificial neural network. 5th Iranian Water Resources Management Conference. Shahid Beheshti University. 29 Jul 2013. [Persian]
12
[13]. Zeynali MJ, Mohammadrezapour O and Forughi F. Comparison of imperialist competitive algorithm (ICA) and ant colony algorithm (ACO) for optimizing exploitation of Doroudzan reservoir with application of chain constraints approach. Journal of Water and Soil Conservation. 2016; 22(6): 231-243. [Persian]
13
ORIGINAL_ARTICLE
Morphometric parameter extraction and analysis for watershed periodization over the Naka Roud Catchment
The aim of this research is watershed prioritization using morphometric parameters and Multiple Criteria- Decision- Making (MCDM) by Geographic Information System (GIS) and Remote Sensing (RS) techniques. For this purpose, First digital elevation model (DEM) was developed of study area using low frequency radar data, then, 17 watershed extraction for prioritization in ArcGIS10.2. After preprocessing and preparation of digital elevation model 14 morphometric parameters extracted including 5 Shape morphometric Parameters (Form factor, Elongation Ratio, Circularity Ratio, Compression ratio), 2 linear parameters (bifurcation ratio, Stream length), 5 areal parameters (Drainage Density, Drainage texture rate, Constant of channel maintenance, Stream frequency, Penetration ratio) and 3 topographic parameters (Relief ratio, Ruggedness number, Slope). The results of the evaluation of morphometric parameters by using AHP model showed that bifurcation ratio, Slope and Drainage Density With the most points (0.227, 0.174, 0.135)points Were in very much Acute condition and other watershed also were in very Acute condition And requires watershed management practices to protect water and soil resources
https://ije.ut.ac.ir/article_64825_7c7a12d1a600be94ae16bec251856979.pdf
2018-03-21
69
83
10.22059/ije.2017.231263.550
"morphometric parameters"
" Multiple Criteria- Decision- Making"
" prioritization"
"Naka Roud Catchment"
Mohammad
Sharifikia
sharifikia@modares.ac.ir
1
دانشیار، گروه سنجش از دور، دانشگاه تربیت مدرس، تهران
LEAD_AUTHOR
Siavosh
Shayan
shayan@modars.ac.ir
2
Physical gegrapgy
AUTHOR
Mojtaba
Yamani
myamani@ut.ac.ir
3
faculty member of Geomorpholoyg dept.
AUTHOR
Alireza
Arab Ameri
alireza.ameri91@yahoo.com
4
Department of physical Geography / Tarbiat modares Univ.
AUTHOR
[1]. Horton RE. Drainage basin characteristics. Trans. Am. Geophys. Union. 1932; 13: 350–361.
1
[2]. Patel D, Gajjar C, Srivastava P. Prioritization of Malesari Mini-Watersheds through Morphometric Analysis: A Remote Sensing and GIS Perspective. Environmental Earth Sciences. 2013; 69: 2643-2656.
2
[3]. Chopra R, Dhiman RD, Sharma PK. Morphometric Analysis of Sub-Watersheds in Gurdaspur District, Punjab Using Remote Sensing and GIS Techniques. Journal of the Indian Society of Remote Sensing. 2005; 33: 531-539.
3
[4]. Horton RE. Erosional development of streams and their drainage basins: Hydrophysical approach to quantitative morphology. Geol Soc Am Bull. 1945; 56: 275-370.
4
[5]. Strahler A. Quantitative Geomorphology of Drainage Basins and Channel Networks. In: Chow, V., Ed., Handbook of Applied Hydrology, McGraw Hill, New York. 1964; 5: 439-476.
5
[6]. Miller V. A Quantitative Geomorphic Study of Drainage Basin Characteristics in the Clinch Mountain Area, Virginia and Tennessee. Project NR 389-402, Technical Report 3, Columbia University, Department of Geology, ONR, New York. 1953.
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[7]. Schumm S. Evolution of Drainage Systems and Slopes in Badlands at Perth Amboy, New Jersey. Geological Society of America Bulletin. 1956; 67: 597-646.
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[8]. Kouli M, Vallianatos F, Soupios P, Alexakis D. GIS-Based Morphometric Analysis of Two Major Watersheds, Western Crete, Greece. Journal of Environmental Hydrology. 2007; 15: 1-17.
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[9]. Vittala SS, Govindaiah S, Honne GH. Prioritization of sub-watersheds for sustainable development and management of natural resources: An integrated approach using remote sensing, GIS and socio-economic data. Current Sci. 2008; 95: 345-354.
9
[10]. Jang T, Vellidis G, Hyman JB, Brooks E, Kurkalova LA, Boll J, Cho J. Model for Prioritizing best management practice implementation: sediment load reduction. Environ. Manage. 2013; 51. 209–224.
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[11]. Iqbal M, Sajjad H, Bhat FA. Morphometric Analysis of Shaliganga Sub Catchment, Kashmir Valley, India Using Geographical Information System. Int J Eng Trends Technol. 2013; 4: 10-21.
11
[12]. Mohd I, Haroon S, Bhat FA. Morphometric Analysis of Shaliganga Sub Catchment, Kashmir Valley, India Using Geographical Information System, International. Journal of Engineering Trends and Technology. 2013; 4 (1).
12
[13]. Mark D. Relations between Field-Surveyed Channel Network and Map-Based Geomorphometric Measures, Inez Kentucky. Annals of the Association of American Geographers. 1983; 73: 358-372.
13
[14]. Markose V, Dinesh A, Jayappa K. Quantitative Analysis of Morphometric Parameters of Kali River Basin, Sothern India, Using Bearing Azimuth and Drainage (bAd) Calculator and GIS. Environmental Earth Sciences. 2014; 72: 2887-2903.
14
[15]. Farhan Y, Anbar A, Enaba O, Al-Shaikh N. Quantitative Analysis of Geomorphometric Parameters of Wadi Kerak, Jordan, Using Remote Sensing and GIS, Journal of Water Resource and Protection. 2015; 7: 456-475.
15
[16]. Singh P, Thakur J, Singh U. Morphometric Analysis of Morar River Basin, Madhya Pradesh, India, Using Remote Sensing and GIS Techniques. Environmental Earth Sciences. 2013; 68: 1967-1977.
16
[17]. Asgharpoor M. Multi Criteria Decision Making, Tehran University press, Tehran. 2010. [Persian]
17
[18]. Javed A, Khanday MY, Ahmed R. Prioritization of watersheds based on morphometric and landuse analysis using RS and GIS techniques. Journal of the Indian society of Remote Sensing. 2009; 37. 261-274.
18
[19]. Thakkar A, Dhiman S. Morphometric analysis and prioritization of miniwatersheds in a Mohr watershed, Gujarat using remote sensing and GIS techniques. Journal of the Indian society of Remote Sensing. 2007; 35 (4). 313–321.
19
[20]. Aher P, Adinarayana J, Gorantiwar SD. Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: A remote sensing and GIS approach. Journal of Hydrology. 2014; 511. 850-860.
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[21]. Iqbal M, Sajjad H. Watershed Prioritization using Morphometric and Land Use/Land Cover Parameters of Dudhganga Catchment Kashmir Valley India using Spatial Technology, J Geophys Remote Sens. 2014; 3:1.
21
[22]. Rahmati O, Tahmasebipour N, Pourghasemi HR. Sub-watershed flooding prioritization using morphometric and correlation analysis (Case study: Golestan Watershed). Ecohydrology. 2015; 2. 151-161. [Persian]
22
[23]. Fallah M, Mohammadi M, Kavian K. Prioritization of Sub-watershedsusing Morphometric and LandUse change Analysis. Ecohydrology. 2015; 2. 261-274. [Persian]
23
[24]. Asadi Nalivan O, Saghazadeh N, Salahshur Dastgerdi M, Bay M. Sub-basin prioritization suing morphometric analysis and GIS for Watershed Management Measures (Case study: Maraveh Tappeh watershed, Golestan). Ecohydrology. 2015; 1. 90-103. [Persian]
24
[25]. Melton MA. Correlations structure of morphometric properties of drainage systems and their controlling agents. Journal of Geology. 1958; 66. 442-460.
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[26]. El-Santawy MF. A VIKOR Method for Solving Personnel Training Selection Problem. International Journal of Computing Science. 2012; 1 (2): 9-12.
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[28]. Sargaonkar A, Rathi B, Baile A. Identifying potential sites for artificial groundwater recharge in sub-watershed of River Kanhan, India. Environmental Earth Sciences. 2010;6: 1-10.
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[29]. Badri SA. models of rural planning. Pamphlets practical lesson in geography and rural planning. 2003 Payame noor university. [Persian]
29
[30]. Kaliraj S, Chandrasekar N, Magesh NS. Morphometric analysis of the River Thamirabarani sub-basin in Kanyakumari District, South west coast of Tamil Nadu, India, using remote sensing and GIS. Environ Earth Sci, 2014; 73:1–27.
30
[31]. Teixeira J, Chamine´ HI, Espinha Marques J, Carvalho JM, Pereira AJ, Carvalho MR, Fonseca PE, Pe´rez-Alberti A, Rocha F. A comprehensive analysis of groundwater resources using GIS and multicriteria tools (Caldas da Cavaca, Central Portugal). Environmental issues. Environ Earth Sci, 2015; 73(6): 2699–2715.
31
[32]. Ahmed F, Srinivasa Rao K. Prioritization of Sub-watersheds based on Morphometric Analysis using Remote Sensing and Geographic Information System Techniques. International Journal of Remote Sensing and GIS, 2015; 4(2): 51-65.
32
[33]. Chandniha SK, Kansal ML. Prioritization of sub-watersheds based on morphometric analysis using geospatial technique in Piperiya watershed, India. Applied Water Science (Springer). 2014. this article is published with open access at Springerlink.com.
33
[34]. GajbhiyeS, Sharma SK, Meshram C. Prioritization of Watershed through Sediment Yield Index Using RS and GIS Approach. International Journal of u- and e- Service, Science and Technology. 2014; 7: 47-60.
34
ORIGINAL_ARTICLE
A new approach to determine probable land subsidence areas
(Case study: The Salmas plain aquifer)
This research introduces a general framework (GARDLIF) for evaluate land subsidence potential of any area in the aquifers. This methodology evaluated in the Salmas aquifer. In the first, raster layers of the GARDLIF factors were prepared. Then, the layers were reclassified based on the GARDLIF framework criteria and modeling was carried out. The land Subsidence vulnerability map of the Salmas aquifer shows that the most land subsidence potential is related to the north-east of aquifer (Gharagheshlagh) which was consistent with the results of prior studies. In order to assessment the effectiveness of each factors in output layer, map removal sensitivity analysis was performed. The most changes in vulnerability index are associated with remove of the discharge (D) and land use (L) maps that average changes are 1.62 and 1.5, respectively. Due to high rating of aquifer media (A) and impact of aquifer thickness (I) in most parts of the aquifer, vulnerability index extensively changes by remove A and I factors. Based on sensitivity analysis, the least important factor is the Distance of fault (F) that its average variability index is 0.4%.
https://ije.ut.ac.ir/article_64826_c022cf11467f784c179588d86cb1d85b.pdf
2018-03-21
85
97
10.22059/ije.2017.233252.601
Aquifer
Land Subsidence
Salmas plain
GARDLIF
Keyvan
Naderi
keiwan.naderi@yahoo.com
1
Earth science department, faculty of natural science, University of Tabriz, Tabriz, Iran
AUTHOR
Ata Allah
Nadiri
nadiri@tabrizu.ac.ir
2
Assistant Professor of Natural Faculty, University of Tabriz
LEAD_AUTHOR
Asghar
Asghari Moghaddam
asgharimoghaddam@tabrizu.ac.ir
3
Geology department, faculty of natural science, university of Tabriz, Tabriz, Iran
AUTHOR
Mehdi
Kord
m.kord@uok.ac.ir
4
Earth Science, faculty of natural science, university of Kurdistan, Sanandej, Iran
AUTHOR
Alkhamis R, Kariminasab S, Aryana F. Investigating the effect of land subsidence due to groundwater discharge on well casing damage. Journal of water. 2006; 60: 77-87 (Persian).
1
[2]. Handbook SE. Subsidence engineering. National Coal Board. Production department. London; 1975.
2
[3]. Trinh MT, Fredlund DG. Modeling subsidence due to ground water extraction in the Hannoi city area. journal of geology technology. 2000; 37: 621-637.
3
[4]. Pacheco J, Arzate J, Rojas E, Arroyo M, Yutsis V, Ochoa G, Delimitation of ground failure zones due to land subsidence using gravity data and finite element modeling in the Queretaro valley, Mexico. Journal of engineering geology. 2006;16: 143-160.
4
[5]. Larson KJ, Basagaoglu H, Marino MA. prediction of optimal safe ground water yield and land subsidence in the Los Banos-Kettleman city area, California, using a calibrated numerical simulation model. Journal of hydrology. 2001; 242: 79-102.
5
[6]. Lashkaripour GR, Ghafoori M, Rostami Barani HR. An investigation on the mechanism of earth-fissure and land subsidence in the western part of Kashmar plain. Geological Studies. 2009; 1(1): 95-111.
6
[7]. Moghtased-Azar K, Mirzaei A, Nankali HR, Tavakoli F. Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurement) and methodological data in the surrounding areas of Lake Urmia. Nonlinear Processes in Geophysics. 2012; 19:675-683.
7
[8]. Sedighi M, Arabi S, Nankali HR, Amighpey M, Tavakoli F, Soltanpour A, et al. Subsidence detection using In-SAR and Geodetic measurement in the Nourth-west of Iran. Fringe 2009 Workshop, ESA communication, ESRIN, Frascati, Italy.
8
[9]. Hafezimoghadas N. Ghafoori M. Enviromental Geology. 1nd ed. Shahrood: Shahrood University of Technology Press, Iran; 2007 (Persian).
9
[10]. Bouwer H. Groundwater Hydrology. translated by: Lotfi-Sadigh A. 13. Tabriz: Sahand University of Technology Press; 1993 (Persian).
10
[11]. Alizadeh A. Principles of applied hydrology. 9nd ed. 35. Mashhad, Iran: Imam Reza university Press; 1996 (Persian).
11
[12]. Poland JF, Davis GH. Land subsidence due to withdrawn of fluids. Engineering Geology. 1969; 2:187-269
12
[13]. Scanlon B, Healy R, Cook P. Choosing Appropriate Techniques for Quantifying Groundwater Recharge. Journal of Hydrology. 2002; 10(1): 18-39.
13
[14]. Rosen L. A study of the DRASTIC methodology with emphasis on Swedish conditions. Ground Water. 1994;32(2):278.
14
[15]. Babiker IS, Mohamed MA, Hiyama T, 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):127-40.
15
ORIGINAL_ARTICLE
Assessment of precipitation data from Asfazari national database in runoff evaluating and regional drought monitoring
Due to high spatiotemporal resolution of gridded data, reanalysis precipitation databases, have many applications in climate prediction, climate change modeling, water resources management and hydrological modeling, especially in areas without observational data. Therefore, in the present study, hydrological simulation was evaluated by SWAT model and spatial data mining for drought monitoring with SPI and SDI indexes over Maharlu Lake and assessing the temporal sense of Asfazari national database by observational stations as a reference on the spatial extent. The results showed high accuracy of Asfazari database in simulating of runoff in comparison with simulated runoff with observation database. The coefficient of determination and Nash efficiency presented the average accuracy of 0.6 in the simulation. During the cold and rainy seasons, the performance of this database is higher than is the warm season. During rainy months of year, the correlation coefficient between observation and Asfazari is about 0.85, and the POD index is more than 0.9. Furthermore, subsequently the accuracy of monitoring drought by Asfazari is too high, so it can be said that the Asfazari database can be used as a reliable database in runoff simulating and drought monitoring, especially in areas with poor or no sufficient precipitation data.
https://ije.ut.ac.ir/article_64827_fe69e2b34586fbb6c98befc04ae6510f.pdf
2018-03-21
99
110
10.22059/ije.2017.235625.643
reanalysis database
Maharlu Lake
SPI and SDI Indexes
SWAT Model
Mohammad Reza
Eini
mohammad.eini@ut.ac.ir
1
Water Resources Engineering, Abouraihan College, University of Tehran
AUTHOR
Saman
Javadi
javadis@ut.ac.ir
2
استادیار گروه مهندسی آب، پردیس ابوریحان، دانشگاه تهران
LEAD_AUTHOR
Majid
Delavar
m.delavar@modares.ac.ir
3
Associate Assistant, Department of Water Resources Engineering, Tarbiat Modares University
AUTHOR
Mohammad
Darand
m.darand@uok.ac.ir
4
Associate Professor of Climatology University of Kurdistan Faculty of Natural Resources Department of Climatology Iran
AUTHOR
Sorooshian S, AghaKouchak A, Arkin P, Eylander J, Foufoula-Georgiou E, Harmon R, et al. Advancing the Remote Sensing of Precipitation. Bulletin of the American Meteorological Society. 2011;92(10):1271-2.
1
[2]. Miao C, Ashouri H, Hsu K-L, Sorooshian S, Duan Q. Evaluation of the PERSIANN-CDR Daily Rainfall Estimates in Capturing the Behavior of Extreme Precipitation Events over China. Journal of Hydrometeorology. 2015;16(3):1387-96.
2
[3]. Zhu Q, Xuan W, Liu L, Xu Y-P. Evaluation and hydrological application of precipitation estimates derived from PERSIANN-CDR, TRMM 3B42V7, and NCEP-CFSR over humid regions in China. Hydrol Processes. 2016;30(17):3061-83.
3
[4]. Darand M, Zand Karimi S. Evaluation of the accuracy of the Global Precipitation Climatology Center (GPCC) data over Iran. Journal of Iran Geophysical. 2016;11(3)-95:103. [Persian]
4
[5]. Fuka DR, Walter MT, MacAlister C, Degaetano AT, Steenhuis TS, Easton ZM. Using the Climate Forecast System Reanalysis as weather input data for watershed models. Hydrol Processes. 2014;28(22):5613-23.
5
[6]. Auerbach DA, Easton ZM, Walter MT, Flecker AS, Fuka DR. Evaluating weather observations and the Climate Forecast System Reanalysis as inputs for hydrologic modelling in the tropics. Hydrol Processes. 2016;30(19):3466-77.
6
[7]. Dile YT, Srinivasan R. Evaluation of CFSR climate data for hydrologic prediction in data-scarce watersheds: an application in the Blue Nile River Basin. JAWRA Journal of the American Water Resources Association. 2014;50(5):1226-41.
7
[8]. Monteiro JAF, Strauch M, Srinivasan R, Abbaspour K, Gücker B. Accuracy of grid precipitation data for Brazil: application in river discharge modelling of the Tocantins catchment. Hydrol Processes. 2016;30(9):1419-30.
8
[9]. Darand M, Amanollahi J, Zandkarimi S. Evaluation of the performance of TRMM Multi-satellite Precipitation Analysis (TMPA) estimation over Iran. Atmospheric Research. 2017;190:121-7.
9
[10]. HajiHosseini H, HajiHosseini MR, Morid S, Delavar M. Assessment of changes in hydro-meteorological variables upstream of Helmand Basin during the last century using CRU data and SWAT model. Iran-water resources research 2013;1(2)38-52. [Persian]
10
[11]. Masoudian A, Keykhosravi M, RayatPisheh, F. Intruduction and evaluation Asafzari database with GPCC, GPCP, CMAP. Geographical Research 2015; 2(1)19:73-88. [Persian]
11
[12].Darand M, Zerafati O, Kefayatmotlagh R, Samandar R. Comparison between global and regional precipitation data bases with base station Asfazari precipitation Iran. Geographical Research. 2015;3(1) 30:2. [Persian]
12
[13]. Raziei T, Bordi I, Pereira LS. An Application of GPCC and NCEP/NCAR Datasets for Drought Variability Analysis in Iran. Water Resources Management. 2011;25(4):1075-86.
13
[14]. Katiraie-Boroujerdy P-S, Nasrollahi N, Hsu K-l, Sorooshian S. Quantifying the reliability of four global datasets for drought monitoring over a semiarid region. Theoretical and Applied Climatology. 2016;123(1):387-98.
14
[15]. Adjei KA, Ren L, Appiah-Adjei EK, Odai SN. Application of satellite-derived rainfall for hydrological modelling in the data-scarce Black Volta trans-boundary basin. Hydrology Research. 2015;46(5):777-91.
15
[16]. Fekete BM, Vörösmarty CJ, Roads JO, Willmott CJ. Uncertainties in Precipitation and Their Impacts on Runoff Estimates. Journal of Climate. 2004;17(2):294-304.
16
[17]. Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, et al. Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology. 2010;395(3):199-215.
17
[18]. Seyyedi H, Anagnostou EN, Beighley E, McCollum J. Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications. Hydrol Earth Syst Sci. 2014;18(12):5077-91.
18
[19]. Thiemig V, Rojas R, Zambrano-Bigiarini M, De Roo A. Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology. 2013;499:324-38.
19
[20]. Casse C, Gosset M, Peugeot C, Pedinotti V, Boone A, Tanimoun BA, et al. Potential of satellite rainfall products to predict Niger River flood events in Niamey. Atmospheric Research. 2015;163:162-76.
20
[21]. Neitsch, SL, Arnold JG, Kiniry JR, Srinivasan R, Williams JR. Soil and Water Assessment Tool, User Manual, Version 2012. Grassland, Soil and Water Research Laboratory, Temple, Tex; 2011.
21
[22]. Mckee TB, Doesken NJ, Kleist J. Drought monitoring with multiple timescales. Preprints, Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc. 1993;179-184.
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[23]. Kao S-C, Govindaraju RS. A copula-based joint deficit index for droughts. Journal of Hydrology. 2010;380(1):121-34.
23
[24]. Eghtedari M., Bazrafashan J., Shafiee M., Hejabi S. Prediction of Streamflow Drought Using SPI and Markov Chain in Kharkheh’s Basin. Journal of Water and Soil Conservation. 2016;23(2)130-115. [Persian]
24
ORIGINAL_ARTICLE
Risk assessment of climate change impacts on groundwater level (Case study: Gotvand Aghili aquifer)
In the present study the impacts of climate change on groundwater levels in the Gotvand Aghili aquifer was investigated. For this purpose, groundwater was simulated using the MODFLOW model in the GMS framework for a period of 2002-2012. After calibration of model the values of RMSE for steady and unsteady conditions were 0.751 and 0.852m respectively, and average correlation coefficient of 0.82 was obtained for the verification of model. Then outputs of 10 AOGCM models under the RCP8.5 emission scenarios, the latest assessment report of IPCC, were used to simulate the climate parameters and study their impact of groundwater levels in future. For this purpose, the periods of 2000-1971 and 2024-2015 were selected as the base forecast periods respectively. The 5 climate scenarios (at the risk of 0.1, 0.25, 0.5, 0.75 and 0.9) were used for simulation of climate parameters for the future period. Then groundwater levels were predicted for the future period under these scenarios. Based on the results under the risk level of 0.1 the aquifer will experienced the biggest drop of 1.8m and under scenario with risk level of 0.9, the groundwater level will increased 0.48 meters during the future 10 years of 2024 to 2015.
https://ije.ut.ac.ir/article_64828_795b4667abba8d3c00dc59d2156d8c4e.pdf
2018-03-21
111
122
10.22059/ije.2017.235715.645
climate change
Groundwater level
risk assessment
Gotvand Aghili aquifer
Saeid
Hamzeh
saeid.hamzeh@ut.ac.ir
1
استادیار گروه سنجش از دور و GIS، دانشکدۀ جغرافیا، دانشگاه تهران
LEAD_AUTHOR
Zahra
Bagherpour
zahra.bagherpour9092@gmail.com
2
Water Resource Engineering, Malayer University
AUTHOR
Mehdi
Delghandi
delghandi@gmail.com
3
Shahrood University of Technology
AUTHOR
Hamid
Kardan Moghaddam
hkardan@ut.ac.ir
4
University of Tehran
AUTHOR
[1]. Doll P, Hoffmann-Dobreva H, Portmanna F.T, Siebertb S, Eickerc A, Rodell M, et al. Impact of water withdrawals from groundwater and surface water on continental water storage variations. Journal of Geodynamics. 2012; 59–60: 143–156.
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[2]. Bell A, Zhu T, Xie H, Ringler C. Climate-water interactions-Challenges for improved representation in integrated assessment models. Energy Economics. 2014; 46:510-521.
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[4]. IPCC. Climate change. The science of climate change. Contribution of working group I to the second assessment report of the intergovernmental panel on climate change. Eds. Houghton, J.T., Filho, L.G.M., Callander, B.A., Harris, N., Attenberg, A. and Maskell K.. Cambridge University Press, Cambridge. 2001; b:572.
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[5]. IPCC, Summary for policymakers.In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 2014; 1-32.
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[6]. Panda D.K, Mishra A, Kumar A. Trend quantification in groundwater levels of Gujarat in western India. Hydrological Sciences Journal. 2012; 57 (7): 1325–1336.
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[7]. Erturk A, Ekdal A, Gürel M, Karakaya N, Guzel C, Gönenç E. Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed. Science of the Total Environment. 2014; 499: 437-447.
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[8]. Naderianfar M, Ansari H, Ziaie A, davary K. Evaluating the groundwater level fluctuations under different climatic conditions in the basin Neyshabour. Irrigation & Water Engineering. 2011; 3(1): 22-37. [Persian].
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[9]. Altafi Dadgar M, Mohammadzade H, Nassery H. Simulation of bojnourd aquifer groundwater flow with emphasis on climate change using mathematical model. National Conference on Water Flow and Pollution. University of Tehran. 2012. [Persian].
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[10]. Ministry of Energy. 2014. Studies on providing balance sheet for water resources of Great Karoon Basin area, Volume VI (Reports of balance sheet studies on Aghili-Gotvand area), Consulting Engineers of Saman Waterway. 2014. [Persian].
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[11]. Shamsai A. Hydraulic flow in porous media: application of mathematical models – computer models (Volume 3). 2nd ed. Tehran: Amirkabir University of Technology (Tehran Polytechnic); 2004. [Persian].
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[12]. Chitsazan M, Kashkouli H. (Translation). Quantitative solutions in hydrogeology and ground water modeling. Writing Neren Kresic. 1nd ed. Ahvaz: Shahid Chamran University; 2002. [Persian].
12
[13]. Pinder G.F, Cooper H.H. A numerical technique for calculating the transient position of the saltwater front. Wat. Resour. Res. 1970; 6(3): 875-882.
13
[14]. Delghandi M. Risk assessment of climate change impact on wheat yield and provide solutions to its compatibility (Case Study: Southern Khuzestan). Thesis Ph.D. Shahid Chamran University of Ahvaz. Iran. 2012. [Persian].
14
[15]. Ruiz-Ramos M, Minguez MI. Evaluating uncertainty in climate change impacts on crop productivity in the Iberian Peninsula. Climate Research. 2010; 44: 69-82.
15
ORIGINAL_ARTICLE
Evaluation and zoning the desertification destructive effects using IMPDA model and clustering (case study: Normashir and Rahmatabad plain)
Practical activities in controlling desertification must be based on current desertification situation and its severity. Based on this theory, in this study, the destructive ability of desertification was analyzed for the Bam Normashir and Rahmatabad (Kerman Province) plain aquifer using the IMDPA model and geographical information system (GIS). In this study, using the quantitative (water table depth) and qualitative (Electrical conductivity (EC), Cl and SAR) indicators as well as the soil erosion magnitudes in the studied locations, severity classes of degradation were evaluated. Then, the destruction map of the studied region was prepared by taking into consideration the maximum limitation in qualitative indices. Cluster analysis Ward and K-means method were utilized for testing the models. The results indicated that the desertification destructive in 90.81% and 8.61% of the aquifer is at severe hazard and very severe hazard class, respectively. Overall, 62% of the model results (IMDPA) was reported significant with clustering.
https://ije.ut.ac.ir/article_64829_95b3b7d7f2588deb2dd8cac563ec1c9d.pdf
2018-03-21
123
134
10.22059/ije.2017.231963.568
Bam Normashir and Rahmatabad
Clustering
Ground waters
IMDPA
quantitative degradation hazard
Saman
Maroufpoor
saman.maroofpoor@gmail.com
1
دانشجوی دکترای مدیریت و برنامهریزی منابع آب، گروه آبیاری و آبادانی، دانشگاه تهران
AUTHOR
Ahmad
Fakheri Fard
affard312@yahoo.com
2
استاد گروه علوم و مهندسی آب دانشگاه تبریز
LEAD_AUTHOR
Jalal
Shiri
j_shiri2005@yahoo.com
3
استادیار گروه علوم و مهندسی آب دانشگاه تبریز
AUTHOR
[1]. Roux P, Preez C.C, Strydo M.G. Significance of soil modifiers in naturally degraded Vertisols of the Peninsular Indian in redefining the sodic soils. Journal of Geoderma. 2007; 136(1-2): 210-228.
1
[2]. Zarei H, Akhondali E. The evaluation of quality trend of water resource in Abolabads reservoir river basin and irrigation and drainage network. Network Management Articles National Conference of Irrigation and Drainage, martyr Chamran University. 2006; 3: 1626-1620. (In Persian)
2
[3]. The Department of Environment and Conservation (NSW).2007. Guidelines for the Assessment and Management of groundwater Contamination. Published by: Department of Environment and Conservation NSW, Website:www.environment.nsw.gov.au.
3
[4]. Vahabzadeh E. Understanding the environment: planet earth live. Sixth Edition. University of Mashhad Press. 2009.
4
[5]. Mahdavi M. Applied Hydrology. Volume II Tehran University Press. 2005.
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[6]. Rizzo D.M, Mouser J.M. Evaluation of Geostatistics for Combined Hydrochemistry and Microbial Community Fingerprinting at a Waste Disposal Site. 2000; 1-11.
6
[7]. Thapinta A, Hudak P. Use of geographic information systems for assessing groundwater pollution potential by pesticides in Central Thailand. Journal of Environmental International. 2003; 29: 87-93.
7
[8]. Tabatabaeefar SM, Zehtabian Gh.R, Rahimi M, Khosravi H, Nikoo Sh. The impact assessment of temporal variation of climatological and groundwater condition on desertification intensity in Garmsar Plain. Journal of Desert Management. 2013; 2:39-48. (In Persian)
8
[9]. Nateghi S, Zehtabian Gh.R, Ahmadi H. Evaluation of desertification intensity in Segzi Plain using IMDPA model. J. Range Water Manage. 2009; 62(3):419-430. (In Persian)
9
[10]. Zolfaghari F. Identification of present desertification status in Sistan plain using IMDPA method. M.Sc thesis, Faculty of Natural Resources. University of Zabol. 2010. (In Persian)
10
[11]. Niko S.H. Assessment of potential desertification, land degradation to identify the effective factors by using IMDPA method (Case study: Damghan region). A thesis of Ph.D. in combating desertification, University of Tehran. 2011; 233p. (In Persian)
11
[12]. Pahlavanravi A, Moghaddamnia A.R, Hashemi Z, Javadi M.R, Miri, A, Evaluation of desertification intensity with wind erosion criterion using MICD and FAO-UNEP models in Zahak region of Sistan. Iranian Journal of Range and Desert Reseach. 2012; 19(4): 624- 639. (In Persian)
12
[13]. Mesbahzadeh T, Ahmadi H, Zehtabian Gh, Sarmadian F, Moghimi Nezhad F. Calibration of IMDPA model with regarding to land criteria to present regional model for desertification intensity (Case study: Abuzaidabad, Kashan). Journal of Range and Watershed Management. 2013; 66(3): 469-476.
13
[14]. Shokoohi E.S, Zehtabian Gh.R, Tavili A. Study of desertification status using IMDPA model with emphasis on water and soil criteria (Case study: Khezr Abad - Elah Abad of Yazd plain). Journal of Range and Watershed Management. 2013; 65(4): 517-528.
14
[15]. Silakhori A, Ownegh M, Saad Eddin A, Fylhksh A. Comparing the efficiency of the Iranian model of desertification potential assessment (IMDPA) and MICD models, case study: Sabzevar area. Journal of Preceding studies soil and water conservation. 2014; 4(21): 1-28. (In Persian)
15
[16]. Vly A.A, Mousavi S.H, Ahmadi S.M. Assessment of desertification in Masjed Soleiman basin by using IMDPA model. Journal of Engineering desert ecosystem. 2015; 4(9):43-56. (In Persian)
16
[17]. Ahmadi, H. Iranian Model of Desertification Potential Assessment. Faculty of Natural Resources, University of Tehran.2004. (In Persian)
17
[18]. Ward Jr. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Asssociation. 1963; 58 (301): 236-244.
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[19]. Macqueen J. Some methods for classtification and analysis ofmultivariate observation. In Proceeding of the 5th Berkeley symposiumon mathematical statistics and probability. Berkeley, CA: University of California. 1967; 1: 297-2871.
19
[20]. Moameni M. clustering data. 2nd ed. Tehran: 2014. (In Persian)
20
[21]. Rousseeuw P.J. Silhouette: A graphical aid to the interpretation and validation of cluster analasis, Journal of Computational and Applied Mathematics.1987; 20: 53-65.
21
[22]. Zakeri nazhad R, Masoudi M, Fallah Shamsi SR, Afzali SF. Evaluation of severity of desertification, groundwater assessment criteria and using geographic information system (Case study: Zarindasht plain). Journal of Irrigation and Water. 2008; 2(0):8-87. (In Persian)
22
[23]. Masoudi M, Barzegar S. Assessment and Mapping of Qualitatative and Quantitative Severity Degradation of Groundwater Resourses using the Modified IMDPA Desertification Model and GIS (Case Study: Firuz-abad Plain of Fars province). Journal of Irrigation and Water. 2015; 5(20):88-95. (In Persian)
23
[24]. Johnson C.W, Gebhardt K.A. Predicting sediment yields from saga brush rangeland in, Proceedings of the workshop on estimating erosion and sediment yield on rangeland Department of agriculture ARM-W. 1982. 26:145-156.
24
ORIGINAL_ARTICLE
Evaluation of irrigation networks in rising of groundwater levels, Behbahan plain case study
In the past few years, due to inappropriate management of surface and groundwater resource exploitation in the Behbahan plain, the water table has risen and caused to some problems such as the phenomenon of flooding in the urban area. Development of irrigation and drainage networks in a wide zone of the study area is one of the most effective factors causing this phenomenon is. In order to evaluate this, the water level of the observation wells as well as the amount of input water to irrigation networks were investigated in a long-term hydrological period (since 2003-2004 to 2015-2016) in the region. Using these data, the representative hydrograph, the iso-depth contour, iso-piez contour, and fluctuation maps of Behbahan plain were investigated in different parts of it. The results showed that the fluctuations in groundwater level of Behbahan aquifer and its storage depend on the input water from the irrigation networks in the study area. Among the irrigation networks, Boneh-basht irrigation network has more effective role in feeding of Behbahan aquifer and rising groundwater level in comparison with the northern and southern Maroon irrigation networks.
https://ije.ut.ac.ir/article_64830_0868e420dbe9add67156a82e358de003.pdf
2018-03-21
135
148
10.22059/ije.2017.234088.611
Behbahan plain
Irrigation networks
Rising of groundwater level
Flooding
Hassan
Daneshian
daneshianhassan@yahoo.com
1
PhD candidate Hydrogeology, Department of Geology, Faculty of Earth Sciences, Shahid Chamran University, Ahvaz, Iran
AUTHOR
Nasrallah
Kalantari
nkalantari34@gmail.com
2
Faculty of earth sciences, Shahid Chamran University, Ahvaz,Iran
LEAD_AUTHOR
Ghandehary A, Gord Noshahri A, Barati R, Hasani KH. Localized increase of ground water in metropolitan cities; opportunities and challenges. Journal of water and sustainable development. 2014; 2:75-82 (in Persian).
1
[2]. Lashkaripour GH, Ghafoori M, Soueizi Z., Peyvandi Z. Gord Noshahri A, Barati R, Hasani Kh. Decreasing of groundwater level and land subsidence in the Mashhad plain. 9th Conference of Geological Society of Iran. 2005; 123-131 (in Persian).
2
[3]. Mohammadi-Behzad HR, Kalantari N, Shaban M, Ghafari HR. Application of GIS in assessment of hydrogeological drought (Khovayes plain case study in the northern Khuzestan. Conference of Geomatics. 2010; National Cartographic Center, Iran (in Persian).
3
[4]. Mohammadi F, Zarei H, Esmaeili ST, Bakhshi M. The causes of rising of the groundwater level in Abbas plain. The first National Conference of the evaluation of implementation of the plan for agricultural development in 550 thousand hectares. 2014; Governorship Khuzestan Province, Iran (in Persian).
4
[5]. Kaiser R, Skillern FF. Deep trouble: Options for managing the hidden threat of aquifer depletion in Texas. Tex. Tech L. Rev.. 2000; 32: 250-304.
5
[6]. Lutz, A., Minyila, S., Saga, B., Diarra, S., Apambire, B. and Thomas, J., 2014. Fluctuation of groundwater levels and recharge patterns in Northern Ghana. Climate, 3(1), pp.1-15.
6
[7]. Asghari-Moghadam A, Ranjbar M, Jahedan N, Ghareh-Baghlou L. Rising the groundwater level and its impact on reduced quality in Naghadeh aquifer. 3th Conference of Water Resources Management. Tabriz, Iran; 2008 (in Persian).
7
[8]. Mozhdeganifar N, Rahnama MB. Examined observation wells in Kerman and Ekhtiarabad area associated with raising the level of groundwater in parts of Kerman city. 10th Seminar of irrigation and reduce evaporation. 2009; kerman, Iran (in Persian).
8
[9]. Karami R, Lashkaripour GH , Ghafouri M, Hafezi-Moghadads N. Investigating the causes of the rising in the Behbahan area (Case study: Cement factory in Behbahan city). Journal of Advanced Applied Geology. 2012; 6:74-79 (in Persian).
9
[10]. Al-Sefry SA, Şen Z. Groundwater rise problem and risk evaluation in major cities of arid lands–Jedddah Case in Kingdom of Saudi Arabia. Water Resources Management. 2006; 20(1):91-108.
10
[11]. Brassington FC, Rushton KR. A rising water table in central Liverpool. Quarterly Journal of Engineering Geology and Hydrogeology. 1987; 20(2):151-8.
11
[12]. Vázquez-Suñé E, Sánchez-Vila X, Carrera J, Marizza M, Arandes R, Gutierrez LA. Rising groundwater levels in Barcelona: evolution and effects on urban structures. Groundwater in the Urban Environment: Problems, Processes and Management. Balkema. 1997; 267-72.
12
[13]. Ghandehary A, Gord Noshahri A, Barati R, Hasani K. Local groundwater rise under metropolitans; opportunities and challenges. Journal of Water and Sustainable Development. 2014; 2:75-82 (in Persian).
13
[14]. Abu-Rizaiza, OS. Threats from groundwater table rise in urban areas in developing countries. Water international, 1999; 24(1):46-52.
14
[15]. Frans V, Yen D, Rijsberman, M. Impact of groundwater on urban development in The Netherlands. In Impacts of Urban Growth on Surface Water and Groundwater Quality: Proceedings of an International Symposium Held During IUGG 99, the XXII General Assembly of the International Union of Geodesy and Geophysics, at Birmingham, UK. 1999; 259: 13-15.
15
[16]. Al-Senafy M, Hadi K, Fadlelmawla A. Al-Fahad K, Al-Khalid A, Bhandary H. Causes of Groundwater Rise at Al-Qurain Residential Area, Kuwait. Procedia Environmental Sciences. 2015; 25:4-10.
16
[17]. Bob M, Rahman, N, Elamin A, Taher S. Rising groundwater levels problem in urban areas: a case study from the Central Area of Madinah City, Saudi Arabia. Arabian Journal for Science and Engineering. 2016; 41(4):1461-1472.
17
[18]. Abedi-Koupaei J, Golabchian M. Estimation of Hydrodynamic Parameters of Groundwater Resources in Kouhpayeh- Segzi Watershed Using MODFLOW. Journal of Sciences & Technology Agricultural & Natural Resource, Water and Soil Sciences. 2015; 72:281-292 (in Persian).
18
[19]. Shahsavari AA, Khodaei K. Preparation of groundwater flow model of the Behbahan plain aquifer using GIS. 9th Conference of Geological Society of Iran. 2005; 61-70 (in Persian).
19
[20]. Aghanabati A. Geology of Iran. Publications of Geological Survey & Mineral Explorations of Iran. Tehran, Iran; 2006 (in Persian).
20
[21]. Shokri Koochak S, Behnia A. Monitoring and Prediction of Khuzestan Province, Iran Drought Using SPI drought Index and Markov Chain. Journal of irrigation sciences and engineering. 2013; 3:3-11 (in Persian).
21
ORIGINAL_ARTICLE
Estimation and determination of spatial pattern of Apple tree water requirement in Iran
Evaluation of water requirement in designing irrigation systems and water supply in agriculture is important. The aim of this study was to estimate and determine the spatial pattern of water requirement of apple tree using weather stations statistics from 1985 to 2013 in apple tree cultivation areas in Iran. Initially, annual reference evapotranspiration was estimated based on the FAO, Penman-Monteith model, In the following, the stages of growth the summer apple Kohanz crab and autumn apple Red-Delicious were identified. Eto calculation annual results and water requirement of growth stages were identified using ArcGIS10.2 as a spatial pattern for each variety. The results showed that the annual ETo in the North-Eastern and Southern regions of the apple cultivation area cultivar reaches more than 2000 mm. The summer apple Kohanz crab has less water requirement than autumn apple Red-Delicious varieties and the middle stage of growth in apple trees have the highest water requirement. The spatial changes in evapotranspiration and water requirement apple trees are due to altitude factor. From the north to the south and from the West to the East, the apple cultivation areas in Iran increases the amount of water requirement.
https://ije.ut.ac.ir/article_64831_372b51898d990ac43049e0be0eca3dcc.pdf
2018-03-21
149
160
10.22059/ije.2017.237443.665
apple tree
Reference Evapotranspiration
Spatial pattern
Water requirement
Hamzeh
Ahmadi
hamzehahmadi2009@gmail.com
1
Geography Department, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevr. Iran.
AUTHOR
Gholam Abbas
Fallah Ghalhari
g.fallah@hsu.ac.ir
2
Assisstant Professor, Faculy of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar
LEAD_AUTHOR
Masoud
Goodarzi
mgoodarzi@scwmri.ac.ir
3
Assistant Professor, Soil Conservation and Watershed Management Research Institute, Tehran
AUTHOR
Ashofteh P, Omidhadad B. Assessments based on the risk of water demand for products under climatic conditions using AOGCM models. Pasture and Watershed, Iranian Journal of Natural Resources, 2015; 68(3): 457- 441. [In Persian]
1
[2]. Sayari A, Alizadeh M, Bannayan M, Hossaini F, Hesami Kermani MR. Comparison of two GCM models (HadCM3 and CGCM2) for the prediction of climate parameters and crop water use under limate change (Case Study: Kashafrood Basin). Journal of Water and Soil, 2011; 25(4): 912-925. [In Persian]
2
[3]. Hosseinpanahi F, Kafi M, Parsa M, Nasiri Mahallati M, Banyayan M. Evaluation of yield and yield components of resistant and susceptible wheat cultivars under moisture stress conditions using the Penman-Monteith, FAO model. Journal of Environmental Stresses in Crop Sciences, 2011;4 (1):63-47. [In Persian]
3
[4]. Zakerinia M, Ghorbani K, HezarJarribi A. Crop water demand assessment for cropping pattern in irrigation network with ArcET (Case study: Droodzan basin, Fars Province). Water and Soil Conservation Research Journal, 2014;21 (2): 208-191. [In Persian]
4
[5]. Bakhtiari B, Liaghat AM, Khalili A. Effect of measurements time scales of meteorological variables on the estimation of crop reference water requirement in Kerman region. Iranian Journal of Irrigation and Drainage, 2010; 23(1):89-83. [In Persian]
5
[6].Goyal MR, Harmsen EW, editors. Evapotranspiration: principles and applications for water management, Torento; Apple Academic Press Inc; 2013.
6
[7]. Janick J. Horticultural Reviews, Wild apple and fruit trees of central Asia. John Wiley & Sons; New york: 2003.
7
[8]. Farajzadeh M, Rahimi M, Kamali GA, Mavrommatis T. Modelling apple tree bud burst time and frost risk in Iran. Meteorological Applications, 2010; 17(1): 45-52.
8
[9]. Kamali Gh, Rahimi M, Mohammadian N, Mahdavian A. Prediction of flowering time of Golden apple cultivar based on cumulative chilling requirments for preventing frost damage in Golmakan area of Khorasan. Journal of Humanities Research, University of Isfahan, 2007, 1 (22): 171 - 182. [In Persian]
9
[10]. Valashedi RN, Sabziparvar AA. Evaluation of winter chill requirement models using the observed apple tree phenology data in Kahriz (Urmia, Iran). Iranian Horticultural Science, 2015; 47 (3): 570-561. [In Persian]
10
[11]. Erez A, Fishman S.The dynamic model for chilling evaluation in peach buds. In IV International Peach Symposium,1997; 465: 507-510.
11
[12]. Ashraf B, Mousavibaghi M, Kamali Gh, Davari K. Prediction of water requirement of sugar beet during 2011-2030 by using simulated weather data with LARS-WG downscaling model. Journal of Water and Soil, 2011; 25 (5):1196 - 1184. [In Persian]
12
[13]. Mirmousavi SH, Akbari H, Akbarzadeh Y. Calibration of refrence evapotranspiration(ETo) estimate and estimation of water requirement for Olive plant in Kermanshah province. Journal of Geography and Environmental Sustainability, 2012; 2(3): 64 -45. [In Persian]
13
[14]. Jahanbakhsh S, Khorshiddoust AM, Mirhashemi H, Khorrami H, Tadayoni M. Trend changes of the reference crop water requirement and its associated meteorological variables in East Azerbaijan. Journal of Water and Soil, 2014; 28 (2): 306 -296. [In Persian]
14
[15]. FallahGhalhari GA, Ahmadi H. The estimation of phenological thresholds of Saffron cultivation in Isfahan province based on the daily temperature statistics, Saffron Agronomy and Technology, 2015;3 (1):65-49. [In Persian]
15
[16]. FallahGhalhari GA, Rahchamani M, Biranvand F. Estimation of Sesame plant water requirement in Sabzevar climate. Quarterly Journal of Arid areas Research, 2014; 5 (21): 14-1. [In Persian]
16
[17]. Ahmadi H, FallahGhalhari GA, and Shaemi A. Estimating and evaluating the trends of annual refrence eevapotranspiration based on Influential climatic parameters in the North- East of Iran, Journal of Water and Soil Science, 2016; 26 (2&3): 269-257. [In Persian]
17
[18]. Xu CY, Gong L, Jiang T, Chen D, Singh VP. Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. Journal of Hydrology, 2006; 327(1): 81-93.
18
[19]. Diodato N, Ceccarelli M, Bellocchi G. GIS-aided evaluation of evapotranspiration at multiple spatial and temporal climate patterns using geoindicators. Ecological Indicators, 2010; 10(5):1009-1016.
19
[20]. Kousari MR, Ahani H. An investigation on reference crop evapotranspiration trend from 1975 to 2005 in Iran. International Journal of Climatology, 2012; 32(15): 2387-2402.
20
[21]. Gao G, Xu CY, Chen D, Singh VP. Spatial and temporal characteristics of actual evapotranspiration over Haihe River basin in China. Stochastic environmental research and risk assessment, 2012; 26(5): 655-669.
21
[22]. Shen Y, Li S, Chen Y, Qi Y, Zhang S. Estimation of regional irrigation water requirement and water supply risk in the arid region of Northwestern China 1989–2010. Agricultural Water Management, 2013; 128: 55-64.
22
[23]. Surendran U, Sushanth CM, Mammen G, Joseph EJ. Modelling the crop water requirement using FAO-CROPWAT and assessment of water resources for sustainable water resource management: A case study in Palakkad district of humid tropical Kerala, India. Aquatic Procedia, 2015; 4: 1211-1219.
23
[24]. Ji XB, Chen JM, Zhao WZ, Kang ES, Jin BW, Xu SQ. Comparison of hourly and daily Penman-Monteith grass- and alfalfa-reference evapotranspiration equations and crop coefficients for maize under arid climatic conditions, Agricultural Water Management, 2017; 192: 1–11.
24
[25]. Mansouri Z, Menani MR. Assessment of the water needs of Apricot and Olive crops under arid climatic conditions: Case study of Tinibaouine region (North-East of Algeria), 2017; 12(30): 46-52. [In Persian]
25
[26]. Allen RG, Pereira LS, Raes D, Smith M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 1998; 300(9):D05109.
26
[27].Vaziri J, Salamat A, Entesari MR, Meschee M, Heidari N, Dehghanisaniyeh H. Evapotranspiration of plants: Instructions for calculating required water for plants. National Iranian Irrigation and Drainage Committee Press, Tehran: First Edition; 1999. [In Persian]
27
ORIGINAL_ARTICLE
Quantitative evaluation of the effects of watershed management operations on carbon sequestration and storage in order to reduce climate change (Case study: Parood Watershed- One of the sub basins of Shahrood Basin)
In order to evaluate the effects of watershed management practices on carbon sequestration and storage, Vegetation, litter and soil samples in each treatment were taken by systematic-randomly method from 10 areas of the basin that represents the variation of soil and vegetation along 10 transects and 100 plots (Except for small earth dams and biomechanical treatments which there were 2 small earth dams and 3 biomechanical treatments in this basin so samples were taken from all of them respectively along three and one transects). Also soil samples were taken at each transects randomly in two depths (0-10 cm and 10-50 cm), soil samples were tested in soil laboratory. Results indicate that in all treatments most amount of sequestered carbon was occurred in the soil (about 99% of whole carbon stocks in the ecosystem). Finally the results showed natural rangeland that have good conditions in terms of vegetation and soil have the most carbon stock in two soil depths and overall (soil + biomass + litter) with 647.84 ton/ha up to a depth of 50 cm from the soil surface and mortar stone dams with 169.35 ton/ha have the lowest carbon stock in this basin.
https://ije.ut.ac.ir/article_64832_a1cab1a141abdcb94afda8536d17f1b9.pdf
2018-03-21
161
172
10.22059/ije.2017.240880.717
Watershed management
carbon stock
Parood
Mohammad
Tahmoures
tahmoures@ut.ac.ir
1
Faculty of Natural Resources, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
LEAD_AUTHOR
Mohammad
Jafari
jafary@ut.ac.ir
2
Faculty of Natural Resources, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
AUTHOR
Hasan
Ahmadi
ahmadi@ut.ac.ir
3
Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Hosein
Azarnivand
4
استاد گروه احیای مناطق خشک و کوهستانی، دانشکدۀ منابع طبیعی، دانشگاه تهران
AUTHOR
Ali Akbar
Nazari Samani
5
دانشیار گروه احیای مناطق خشک و کوهستانی، دانشکدة منابع طبیعی، دانشگاه تهران
AUTHOR
Lal R. Soil carbon stocks under present and future climate with specific reference to European eco regions, Jour. Nutrient Cycling in Agro ecosystems. 2008; 81(2): 113-127.
1
[2]. Stockmann U, Adams MA, Crawford JW, Field DJ, Henakaarchchi N, Jenkins M, et al. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ. 2013; 164(1), 80–99.
2
[3]. Selim HM, Newman, A, Zhang, L, Arceneaux, A, Tubaña, B, Gaston, LA. Distributions of organic carbon and related parameters in a Louisiana sugarcane soil. Soil Tillage Res. 2016; 155, 401–411.
3
[4]. Teng J, Xiang T, Huang Z, Wu J, Jiang P, Meng C, Li Y, Fuhrmann JJ. Spatial distribution and variability of carbon storage in different sympodial bamboo species in China. J. Environ. Manag. 2015;168, 46–52.
4
[5]. Brown J, Angerer J, Salley S, Blaisdell R and Stuth J. Improving estimates of rangeland carbon sequestration potential in the U.S. Southwest. Rangeland Ecology & Management. 2012; 63:147–154.
5
[6]. Chen LF, He ZB, Du J, Yang JJ, Zhu X. Patterns and environmental controls of soil organic carbon and total nitrogen in alpine ecosystems of northwestern China. Catena. 2016; 137, 37–43.
6
[7]. Chang X F, Wang S P, Zhu X X, Cui SJ, Luo CY, Zhang ZH, Wilkes A. Impacts of management practices on soil organic carbon in degraded alpine meadows on the Tibetan Plateau. Jour. Biogeosciences Discuss. 2014;11: 417– 440.
7
[8]. Zhiming Qi, Patricia NS, Bartling R, Derner D, Gale H, Dunn Liwang Ma. Development and evaluation of the carbon–nitrogen cycle module for the GPFARM-Range model. Computers and Electronics in Agriculture. 2012; 83:1–10.
8
[9]. Li Q, Yu P, Li G, Zhou D, Chen X. Overlooking soil erosion induces underestimation of the soil Closs in degraded land. Quaternary Int. 2014; 349, 287– 290.
9
[10]. Naseri S, Jafari M, Tavakoli H, Arzani, H. Effect of mechanical control practices on soil and vegetation carbon sequestration (Case study: Catchment Basin of Kardeh- Iran). Jour. Biodiversity and Environmental Sciences. 2014; 5(2): 122 -135.
10
[11]. Naseri S, Tavakoli H, Jafari M, Arzani H. Impacts of Rangeland Reclamation and Management on Carbon Stock in North East of Iran (Case Study: Kardeh Basin, Mashhad, Iran). Journal of Rangeland Science. 2016; 6(4), 320-333.
11
[12]. Lashanizand M, Parvizi y, Shahrokhvandi SR, Rafiee B. Comparative evaluation of carbon sequestration in relation to watershed management practices and reclamation operations (Case Study: Rimele, Romeshkan flood spreading and Abkhandari Koohdasht), Iranian Journal of Range and Desert Reseach. 2013; 20 (2), 397-402.
12
[13]. Derner JD, Schuman GE. Carbon sequestration and rangelands: Asynthesis of Land management and precipitation effects. Journal of Soil and Water Conservation. 2015; 62(2): 77-85.
13
[14]. Hill MJ, Britten R, McKeon GM. A scenario calculator for effect of grazing land management on carbon stock in Australian rangelands. Environ. Model. And Software. 2013; (18):627-644.
14
[15]. Zhao B, Li Z, Li P, Xu G, Gao H, Cheng Y, et al. Spatial distribution of soil organic carbon and its influencing factors under the condition of ecological construction in a hilly-gully watershed of the Loess Plateau, China. Geoderma. 2017; 296, 10–17.
15
[16]. Regional Water Company of Qazvin province report. The part of water resources management. 2015; 19p [In Persian].
16
[17]. Zhang L, Xie Zh, Zhao R, Wang Y. The impact of land use change on soil organic carbon and labile organic carbon stocks in the Long zhong region of Loess Plateau. Jour. Arid Land. 2012; 4(3): 241−250.
17
[18]. Li Z, Liu C, Dong Y, Chang X, Nie X, Liu L, et al. Response of soil organic carbon and nitrogen stocks to soil erosion and land use types in the Loess hilly–gully region of China. Soil & Tillage Research. 2017; 166, 1-9.
18
[19]. Ajami M, Heidari A, Khormali F, Gorji M, Ayoubi S. Environmental factors controlling soil organic carbon storage in loess soils of a subhumid region, northern Iran. Geoderma. 2016; 281, 1–10.
19
[20]. Abegaz A, Winowiecki LA, Vågen TG, Langan S, Smith JU. Spatial and temporal dynamics of soil organic carbon in landscapes of the upper Blue Nile Basin of the Ethiopian Highlands. Agric. Ecosyst. Environ. 2016; 218, 190–208.
20
ORIGINAL_ARTICLE
Prediction of water resource status affected by climate change by ANFIS model and General Circulation Model (case study: Ziarat basin of Gorgan)
In this research, the effect of climate change on rainfall, runoff, temperature and water resource for Ziarat basin of Golestan province, was assessed. General ccirculation model, HadCM3, was used under three scenarios as A1B, A2 and B1 for 3 future time duration as 2011-2030, 2046-2065 and 2080-2099, respectively. In order to down scaling of HadCM3 output, ANFIS model was used. The predicted results showed increasing temperature as 0.32-1.77oC and decreasing precipitation as 1.6-31.46 mm for future durations. Then, the mentioned results, curve number map and physiographic parameters, basin and waterway slope, gained from Arc-GIS, as importing data to calibrated HEC-HMS model was made in order to simulate the discharge of climate future durations. Results presented the runoff volume and peak of discharge have decreased in all three scenarios for horizon 2020, 2055 and 2090 and for all mentioned horizons, the most of decreasing was related to A2 scenario. Percentage decrease in peak and discharge volume was obtained 1.72 and 1.83, 3.06 and 3.07, 4.43 and 4.48 for mentioned periods, respectively. Consequently, based on extracting information from models, no enough storage would happen in dams. Finally, some solution for this problem was presented.
https://ije.ut.ac.ir/article_64833_e3c1e07e56d8f535cf489a2e2ec44a1c.pdf
2018-03-21
173
187
10.22059/ije.2017.241377.720
climate change
Rainfall-runoff
Hadcm3
ANFIS
HEC-HMS
Seyed Hamed
Shakib
hshakib@buqaen.ac.ir
1
Msc, Faculty of Civil Engineering, Bozorgmehr University of Qaenat, Qaen, Iran
AUTHOR
Saeed
Farzin
saeed.farzin@semnan.ac.ir
2
استادیار، گروه مهندسی آب و سازه های هیدرولیکی، دانشکدۀ مهندسی عمران، دانشگاه سمنان
LEAD_AUTHOR
Tirgar Fakheri F, Alijani B, Zeaiean Firuzabadi P. Akbari M. Simulation of Snowmelt Runoff Under Climate Change Scenarios in Armand Basin. Iranian Journal of Ecohydrology. 2017; 4(2): 357-368. [In Persian]
1
[2]. Dehghani N, Ghasemieh H, Sadatinejad, S. Ghorbani, K. Evaluating the impact of climate change on runoff using hydrological model (Case study: Bazoft-Samsami Watershed). Iranian Journal of Ecohydrology. 2017; 4(1): 89-102. [In Persian]
2
[3]. IPCC. Climate change. Contribution of Working Group II to the third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. 2007.
3
[4]. Hashemin nasab F, Mousavi baygi M, Bakhtiari B, Davari K. Prediction the Rainfall Changes with Downscaling LARS-WG and HadCM3 models in Kerman during the next 20 years (2030-2011). The Iranian Society of Irrigation & Water Engineering. 2013; 3(12): 43-58. [In Persian]
4
[5]. Neshat A, Sajadi Bami Y. The prediction of the climate change effect on the temperature parameter by the General Circulation Models HadCM3: a case study of Kerman and Bam. Water Engineering. 2017; 9(30): 51-62. [In Persian]
5
[6]. Rezaee M, Nahtaj M, Moghadamniya A, Abkar A, Rezaee M. Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor). Water Engineering. 2015; 8(24): 25-40. [In Persian]
6
[7]. Farzin, S, Karami H, Doostmohammadi M, Ghanbari A, Zamiri E. The performance of Artificial Neural Network in prediction and analysis of hydrological processes (Case study: Water shortage in Nazloo-chai watershed, West Azerbaijan province). Iranian journal of Ecohydrology. 2017; 3(4): 631-644. [In Persian]
7
[8]. Mollaie A. Determination of curve number for estimating of rang of volume by GIS. 6 th International River Engineering Conference, Ahvaz. 2002; 1139-1144. [In Persian]
8
[9]. Garmei R, Faridhosseini A. Optimization Parameters of Rainfall-Runoff Model of HEC-HMS through PSO Algorithm. Iranian Journal of Soil and Water Research. 2015; 46(2): 255-264. [In Persian]
9
[10]. Khoshravesh M, Raeni M, Nikzad-Tehrani E, Koulaian A. The impacts of Urbanization and impervious surfaces on runoff of sardaabrud basin, kalardasht, using HEC-HMS rainfall-runoff model. Iranian Journal of Irrigation and Drainage. 2015; 1(9): 209-220. [In Persian]
10
[11]. Halid H, Ridd P. Modeling inter-annual variation of a local rainfall data using a fuzzy logic technique. Proceedings of the International Forum on Climate Prediction. James Cook University,Australia. 2002; 166–170.
11
[12]. Maria C, Haroldo F, Ferreira. Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. Journal of Hydrology. 2005; 301: 146–162.
12
[13]. Chen YN, Li WH, Xu CC, Hao XM. Effects of climate change on water resources in Tarim River Basin, Northwest China. Journal of Environmental Sciences. 2007; 19(4):488-93.
13
[14]. Tolika K, Anagnostopoulou C, Maheras P, Vafiadis M. Simulation of future changes in extreme rainfall and temperature conditions over the Greek area: a comparison of two statistical downscaling approaches. Global and Planetary Change. 2008; 63(2):132-151.
14
[15]. Koutroulis AG, Tsanis IK, Daliakopoulos IN, Jacob D. Impact of climate change on water resources status: A case study for Crete Island, Greece. Journal of hydrology. 2013; 479 :146-158.
15
[16]. Nepal S. Impacts of climate change on the hydrological regime of the Koshi river basin in the Himalayan region. Journal of Hydro-environment Research. 2016; 10: 76-89.
16
[17]. Inouye AM, Lach DH, Stevenson JR, Bolte JP, Koch J. Participatory Modeling to Assess Climate Impacts on Water Resources in the Big Wood Basin, Idaho. Springer International Publishing. 2017.p. 289-306.
17
[18]. Seiller G, Roy R, Anctil F. Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources. Journal of Hydrology. 2017; 547: 280-95.
18
[19]. Mohammadi M., Karami H., Farzin S, Farokhi A. Prediction of Monthly Precipitation Based on Large-scale Climate Signals Using Intelligent Models and Multiple Linear Regression (Case Study: Semnan Synoptic Station). Iranian journal of Ecohydrology. 2017; 4(1): 201-214. [In Persian]
19
[20]. Massah Bavani A, Morid S. Impacts of Climate Change on Water Resources and Food Production: A Case Study of Zayandeh - Rud Basin, Esfahan, Iran. Iran Water Resources Research. 2005; 1(1): 40-47. [In Persian]
20
[21]. Azaranfar A, Abrishamchi A. Assessment of the impact of climate change on precipitation and temperature in the Zayandeh Roud river basin by using the general rotation model outputs. Second Conference on water resource management, Isfahan University of technology. 2006. [In Persian]
21
[22]. Modirian R, Babaeian I, Karimian M. The Optimum Configuration of RegCM3 Model for Simulation of Precipitation and Temperature at Autumn Seasonal over Khorasan Region in 1991-2000. Physical Geography Researches Journal. 2010; 41(70): 107-120. [In Persian]
22
[23]. Massah Bavani A, Morid S. Impact of Climate Change on the Water Resources of Zayandeh Rud Basin. Journal of Water and Soil Science. 2013; 9(4): 17-28. [In Persian]
23
[24]. Javidan N, Bahremand A. Sensitivity Test of Parameters Influencing Flood Hydrograph Routing with a Diffusion-Wave Distributed using Distributed Hydrological Model, Wet Spa, in Ziarat Watershed. Journal of Water and Soil. 2016; 30(3): 685-697. [In Persian]
24
[25]. Soltani J, Moghaddamnia A, Piri J, Mirmoradzehi J. Performance Comparison of Integrated Models of NN-ARX and ANFIS with GA-GT to Daily Pan Evaporation Estimation Under Arid and Hot Climate of Baluchistan. Journal of Water and Soil. 2013; 27(2): 381-393. [In Persian]
25
[26]. Ashofteh PS, Bozorg-Haddad O, Loáiciga HA. Development of adaptive strategies for irrigation water demand management under climate change. Journal of Irrigation and Drainage Engineering. 2017;143(2): 04016077.
26
[27]. Khajeh S, Paimozd S, Moghaddasi M. Assessing the Impact of Climate Changes on Hydrological Drought Based on Reservoir Performance Indices (Case Study: ZayandehRud River Basin, Iran). Water Resources Management. 2017; 11: 1-6.
27
[28]. Salehpour JA, Mohseni SM, Bazrafshan J, Khalighi S. Investigation of Climate Change Effect on Drought Characteristics in the Future Period using the HadCM3 model (Case Study: Northwest of Iran). Journal of Range and Watershed Management. 2015; 537-548.
28
[29]. Mohammadi M, Karami H, Farzin S, Farokhi A. Prediction of Monthly Precipitation Based on Large-scale Climate Signals Using Intelligent Models and Multiple Linear Regression (Case Study: Semnan Synoptic Station). Iranian journal of Ecohydrology. 2017; 4(1): 201-214. [In Persian]
29
[30]. Momeni M, Rezaee N. Aras Dam Reservoir Operation Model by Using of Dynamic Programming. Journal of Industrial Management. 2008; 1(1): 139-152. [In Persian]
30
[31]. Farshadmehr M, Moghaddas M, Meftahe Halaghi M. Linking Drought Monitoring Systems to Management Measures for Zarrinehrood Dam Operation (Case Study: Zarrinehrood Basin). Iranian Journal of Soil and Water Research. 2015; 46(3): 423-430. [In Persian]
31
[32]. Emadi A, Khademi M. Reservoir Operation Rule Curve of Doroodzan Dam using Yield Model. Journal of Water and Soil. 2012; 25(5): 1058-1068. [In Persian]
32
ORIGINAL_ARTICLE
Investigation of Heteorological Drought in Southern basins of Urmia lake (Case study: Zarrineh rud and Simeneh rud)
In recent years, Urmia Lake faced with declining water level and shrinkig area of Lake due to reduced water inflow from sub basins. The Simeneh Rud and Zarrineh Rud Rivers comprise 51.6 % of the long term total surface water inflow into the Lake. The main aim of this research is investigation climate behavior of basins in view of metrological drought during recent four decates. In this case drought condition was monitored by using Standardized Precipitation Index (SPI) in deferent time scaled in 31 meteorological stations in Southern basins of Urmia Lake. Spatial distribution of drought indices using by geostatics methods including ordinary kriging, Co-Kriging and inverse distance method (IDW) were calculated, and regional drought map zoning were provided for wet and drough years.The results show that studid area experienced two wet period and one drouth period during recent four decates. After domination of 3 years drough and again govering normal condition over basins, drying Urmia Lake was continued because more factors, mainly human activities, beside of climate effects. In many years, the ordinary kriging model was more appropriate than the Co-Kriging and IDW for mapping regional drought.
https://ije.ut.ac.ir/article_64834_d242ffcc347bcd7745ba77b10c7a3c46.pdf
2018-03-21
189
202
10.22059/ije.2018.245903.781
SPI
Urmia Lake
Zarrineh Rud
Simeneh Rud
Geostatics metod
Majid
Montaseri
barg.thesis1@gmail.com
1
urmia university
LEAD_AUTHOR
Amir
Nourjou
shiraz.barg@gmail.com
2
Water Engineering Department, Urmia University,Urmia
AUTHOR
Javad
Behmanesh
javad.golkar.136810@gmail.com
3
Water Engineering Department, Urmia University, Urmia
AUTHOR
Mehdi
Akbari
javad.golkar.136811@gmail.com
4
Agricultural Engineering Research Institute (AERI), Karaj
AUTHOR
Madani K, AghaKouchak A, Mirchi A. Iran’s Socio-economic Drought: Challenges of a Water-Bankrupt Nation. Iranian Studies. 2016; 49(6): 997-1016.
1
[2]. Tabari H, Abghari H, Hosseinzadeh Talaee P. Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran. Hydrol. Process. 2012; 26: 3351–3361.
2
[3]. Zahedie Gharahaghaj M, GHavidel Rahimi Y. Determine of the threshold of drought and calculate the reliable precipitation in the Lake Urmia basin. Physical Geography Research Quarterly. 2007; 59:21-34. [Persian]
3
[4]. Palmer WC. Meteorological Drought. U.S, Weather Bureau Technical paper. 1965; 45: 1-58.
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[5]. Wilhite DA, Glantz MH. Understanding the drought phenomenon, The role of definitions. Water International. 1985; 10: 111-120.
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[6]. Noohi K, Asgari A. Study of Drought and Return Priod Drought in Qum Region. Agricultural Aridity and Drought, Scientific and Extension Quarterly, Hahad Agriculture. 2006; 15: 47-64.
6
[7]. Barua S, Ng AWM, Perera BJC. Comparative Evaluation of Drought Indices: A Case Study on The Yarra River Catchment in Australia. Journal of Water Resources Planning and Management. 2011; 137(2): 215-226.
7
[8]. Moreira EE, Coelho CA, Paulo AA, Pereira LS, Mexia JT. SPI-based Drought Category Prediction Using Loglinear Models. Journal of Hydrology. 2008; 354: 116-130.
8
[9]. McKee TB, Doesken NJ, Kleist J. Drought monitoring with multiple time scales. In Proceedings of the 9th Conference on Applied Climatology. AMS: Boston, MA. 1995: 233–236.
9
[10]. Hayes MJ. Drought Indices. National Drought Mitigation Center ,Noaa, Press..2001; 11p.
10
[11]. Keyantash J, Dracup JA. The Quantification of Ddrought: An Evaluation of Drought Indices. Bulletin of the American Meteorological Society. 2002; 83(8): 1167-1180.
11
[12]. Khalighi Sigaroudi Sh, Sadeghi Sangdehi A, Awsati Kh, GHavidel Rahimi Y. The Study of Drought and Wet Year Assessment models for Stations in Mazandaran province. Iranian Journal of Rangland and Desert. 2009; 16(1): 44-54. [Persian]
12
[13]. Ensafimoghadam T. An Investigation and assessment of climatological indices and determination of suitable index for climatological droughts in the Salt Lake Basin of Iran. Iranian Journal of Rangland and Desert. 2007; 14(2): 271-288. [Persian]
13
[14]. Shokoohi A, Morovati R. An investigation on the Urmia Lake Basin drought using RDI and SPI indices. Watershed Engineering and Management. 2014; 6(3):232-246. [Persian]
14
[15]. Alipour A, Hashemi M, Hosseini SA, Pazhooh F. Assessment and comparison of multiple index climatic droughts and determine the best Index in central Iran. ECO Hydrology. 2017; 4(1): 133-147. [Persian]
15
[16]. Jahangir MH, Khoshmashraban M,Yousefi H. Drought monitoring with Standard Precipitation Index (SPI) and drought forecasting with Multi-layers perceptron (Case study: Tehran and Alborz Provinces).Ecohydrology.2015;2 (4):417-428. [Persian]
16
[17]. Amirataee B, Montaseri M, Yasi M. Comparison of Inherent Performance of Seven Drought Indices in Drought Mitigation Using a Monte Carlo Simulation Approach. Journal of Civil and Environmental Engineering. 2015; 43(1): 25-39. [Persian]
17
[18]. Montaseri M, Amirataee B. Comprehensive stochastic assessment of meteorological drought indices. Int. J. Climatol. 2016; 31: 162–173.
18
[19]. Wamwling A. Accuracy of geostatistical prediction of yearly precipitation in Lower Saxony. Journal of Environmetrics. 2003; 14(7): 699-709.
19
[20]. Zheng X, Basher R. Thin-Plate Smoothing Spline Modeling of spatial climate data and its application to mapping South Pacific Rainfalls. Journal of Monthly Weather Review. 1995; 123: 3086-3102.
20
[21]. Tabios GQ, Salas JD. A comparative analysis of technique for spatial interpolation of precipitation. Water Resources Bulletin. 1985; 21(3): 365-380.
21
[22]. Mozafari GA, Khosravi Y, Abbasi E, Tavakoli F. Assessment of Geostatistical Methods for Spatial Analysis of SPI and EDI Drought Indices. World Applied Sciences Journal. 2011; 15 (4): 474-482.
22
[23]. Abbaspour M, Nazaridoust A. Determination of environmental water requirements of Lake Urmia, Iran: an ecological approach. International Journal of Environmental Studies. 2007; 64(2): 161-169.
23
[24]. Bars RL. Hydrology: An Introduction to Hydrologic Science. Addison-Wesley Publishing Co., New York, USA; 1990
24
[25]. Adeloye AJ, Montaseri M. Preliminary Stream flow Data Analyses Prior to Water Resources Planning Study. Hydrological Sciences Journal. 2002; 47(5): 679-692.
25
[26]. McKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology. AMS: Boston, MA:1993; 179–184.
26
[27]. Mishra AK, Singh VP, Desa VR. Drought Characterization: A Probabilistic Approach. Stochastic Environmental Research and Risk Assessment. 2009; 23(1): 41-55.
27
[28]. Cacciamani C, Morgillo A, Marchesi S, Pavan V. Monitoring, Forecasting Drought on a Redional Scale Emilia-Romangna Region. Netherlands: Springer-Verlag. 2005; 62(1): 29-48.
28
[29]. Edwards DC, McKee TB. Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report umber 97–2, Colorado State University, Fort Collins, Colorado; 1997.
29
[30]. Wu H, Svobod MD, Hayes MJ, Wilhite DA, Wen F. 2007. Appropriate application of the standardized precipitation index in arid locations and dry seasons. International Journal of Climatology. 200; l(27): 65–79.
30
[31]. Issaks EH, Srivastava RM. Applied geostatistics. Newyork,Oxford University Press; 1989: 561 pp.
31
[32]. Hassani Pak AA. Geostatistics. 2nd ed. Tehran university pub; 2010. [Persian]
32
[33]. Thiessen AH. 1911. Precipitation averages for large areas. Monthly Weather Review. 1911; 39(7): 1082-1084.
33
[34]. Hassanzadeh E, Zarghami M, Hassanzadeh Y. Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resources Management. 211; 26(1): 129-145.
34
[35]. Litaor MI, Reichmann O, Belzer M, Auerswald K, Nishri A, Shenker M. Spatial Analysis of Phosphorus Sorption Capacity in a Semiarid Altered Wetland. Journal of Environ. Qual. 2003; 32: 335–343.
35
[36]. Akhtari R, Mahdian MH, Morid S. Assessment of Spatial Analysis of SPI and EDI Drought Indices in Tehran Province. Iran_Water Resources Research. 2006; 3:27-37. [Persian]
36
ORIGINAL_ARTICLE
The Application of Intelligent Techniques for Predicting Daily Flow at Telvar Basin River
River flow, which temporarily and spatially changes, is a major hydrological variable in water resource planning. In research, on water resources, the perdition of the river flow based on historical data is a main step for watershed management. In this study three intelligent techniques including wavelet artificial neural networks, gene expression programming, and support vector machine (SVM- LS) were compared in river daily flow perdition at the Telvar basin. Daily recorded data from 2002-2012 were used in the modeling procedure. Data were divided to train (75%) and test (25%) groups. Results indicated that all three modeling methods have high performance in predicting daily flow using the two-day lag data. The correlation coefficients for wavelet artificial neural networks, gene expression programming, and support vector machine (SVM- LS) were 0.9, 0.94, and 0.92 respectively. Therefore, it can be concluded that the gene expression programming has slightly better results in comparison with the other two modeling methods. The accuracy of the gene expression programming model increased due to the increasing of the lag data from 2 to 4 and five days.
https://ije.ut.ac.ir/article_64835_997b89899b7a1ac368730b95d91cd2c9.pdf
2018-03-21
203
213
10.22059/ije.2018.223015.386
Flow estimation
Gene Expression Planning
SVM model
Telvar River
Wavelet Neural Network
Motalleb
Byazidi
m.byzdi@gmail.com
1
Department of Water Science
LEAD_AUTHOR
Farrokh
Asadzadeh
f.asadzadeh@urmia.ac.ir
2
استادیار گروه علوم خاک دانشگاه ارومیه
AUTHOR
Mehri
Kaki
mehrikaki67@gmail.com
3
کارشناسی ارشد مهندسی منابع آب، دانشگاه تبریز
AUTHOR
[1]. Kisi O. Least squares support vector machine for modeling daily reference evapotranspiration. Irrigation Science; 2012.
1
[2]. Ghorbani MA, Kisi O, Aalinezhad MA. Probe into the chaotic nature of daily stream flow time series by correlation dimension and largest Lyapunov methods Applied Mathematical Modelling. 2010; 34: 4050–4057.
2
[3]. Whigham PA, Crpper PF. Modelling Rainfall-Runoff Using Genetic Programming. 2001. 33:707-721.
3
[4]. Nikbakhtshabazi A. Application of support vector machine in river flow forecasting, Iranian Hydraulic Conference; 2009 [Persian]
4
[5]. Yu PS, Chen ST, Chang IF. Support vector regression for real-time flood stage forecasting Hydrology. 2006; 328: 704-716.
5
[6]. Okkan U. Wavelet neural network model for reservoir inflow prediction. Journal of ScientiaIranica; 2012, December 1445–1455
6
[7]. Sayagavi V.G, Charhate Sh, Magar R. 2016. Estimation of Discharge Using LS-SVM and Model. Journal of Water Resources and Ocean Science. 2016; 5(6): 78-86.
7
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25
ORIGINAL_ARTICLE
Investigating the drought characteristics of Tamar basin (upstream of Golestan Dam) using SPI and SPEI indices under current and future climate conditions
To predict climate change and its effect on drought future situation in Tamar Basin, first daily output data of CanESM2 model downscaled and predicted by SDSM model and also RCP 2.6 and RCP 8.5 scenarios in the 2020-2049 period. Then, drought conditions evaluated by predicted data and SPI and SPEI indices in the future. Trend analysis of temperature and precipitation variables also carried out by Mann-Kendall non-parametric test. The results of trend analysis showed that precipitation changes is negligible and increase of temperature in most time series is significant. The performance of SDSM model to predict temperature and precipitation data is also very suitable and its outputs showed that temperature and precipitation have increased rather than that in the baseline period. The results of SPI index indicated that in both the periods the most droughts and wets have occurred at the late and first half of the two periods respectively. Evaluation of drought by SPEI index showed more severe drought rather than SPI index and according to increase of temperature trend in the baseline period and also temperature increase in the future can say the results of SPEI index are more actual and logical than the results of SPI index.
https://ije.ut.ac.ir/article_64836_a71bb0977326a89a1cac79548c072a89.pdf
2018-03-21
215
228
10.22059/ije.2018.239226.689
Drought
SPI and SPEI indices
SDSM model
Fifth Assessment Report of IPCC
Tamar Basin
Abdollah
Pirnia
abd.god62@gmail.com
1
دانشجوی دکتری آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری
AUTHOR
Mohammad
Golshan
golshan.mohammad@yahoo.com
2
Department of Watershed Management, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari
AUTHOR
Samira
Bigonah
bigonah.2008@gmail.com
3
Graduated in Watershed Management, Faculty of Natural Resources, University of Yazd, Yazd, Iran
AUTHOR
Karim
Solaimani
solaimani2001@yahoo.co.uk
4
University Academic member
LEAD_AUTHOR
Li B, Su H, Chen F, Wu J, Qi J. The changing characteristics of drought in China from 1982 to 2005. Natural Hazards, 2013; 68:723–743
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4
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7
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8
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9
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42
ORIGINAL_ARTICLE
Designing and construction of a portable rainfall simulator
Rain simulation is one of the important methods for measuring hydrological and soil erosion processes. Rapid evaluation and high repeatability capabilities are the advantages of using the rainfall simulator. The weight of constructed simulator is approximately 20 kg, can simulate rainfall at 28 to 95 mm / h intensity on the plot area of one up to three square meters. The droplet diameter was measured using flour pellet method and stain method. Flour pellet method was selected due to higher determination factor, and then its results were applied to continue the study. The highest uniformity coefficient (96.6%) was obtained at the intensity of 8.3 cm/h and the lowest coefficient (90.6%) was obtained at 9.5 cm/h intensity, which is higher than the acceptable level. The average diameter of the droplets is from 0.97 mm at intensity of 2.8 cm/h to 1.22 mm at 7.1 cm/ h, which lies within the range of natural precipitation. Also, the range of velocity variations is from 3.58 to 4.21 m/s. According to the mentioned specifications, a suitable portable rainfall simulator was designed with acceptable accuracy for runoff, permeability, erosion and sedimentation studies in field.
https://ije.ut.ac.ir/article_64837_7e087f0dfc8edbf193f926552c6817fd.pdf
2018-03-21
229
239
10.22059/ije.2018.239387.695
Velocity and kinetic energy of rainfall
Christiansen uniformity coefficient
Flour pellet method
Nozzle
Soheila
Aghabeigi Amin
saghabeigi@yahoo.com
1
استادیار، گروه منابع طبیعی، پردیس کشاورزی و منابع طبیعی، دانشگاه رازی، کرمانشاه
LEAD_AUTHOR
Mahmood
Arabkhedri
mahmood.arabkhedri@gmail.com
2
Water & Soil Conservation Dep., SCWMRI
AUTHOR
Sawatsky l, Dick w, Cooper D, Keys M. Design of a rainfall simulator to measure erosion of reclaimed surface, earth and environmental. AGRA Earth and Environmental Limited, The 20th annual mine reclamation symposium.1996.
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2
[3]. Mahmoodabadi M and Arabkhedri M. Rainfall and Erosion Simulation Laboratory Soil Conservation and Watershed Management Research Institute: Characteristics, Capabilities and Applications. 2011; 1(3): 1-11.( In Persian)
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[4]. Navas EL, Alberto E, Maehin J and Galhn Z A. Design and operation of a rainfall simulator for field studies of runoff and soil erosion, Soil technology. 1990; 3: 385-397.
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[5]. Francisco J P, Latorre R, Castro L and Delgado A. A comparison of two variable intensity rainfall simulators for runoff studies. Soil and Tillage Research. 2010; 107: 11–16.
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[6]. Jahanbakhshi F, Ekhtesasi MR, Talebi A and Piri M. Investigation of permeability of three geological formations in different precipitation intensities using rainfall simulator (Case study: Shirkooh Yazd). 11th national conference on watershed management sciences and engineering of Iran. 2016; April 19-21. Yasooj. Iran. (In Persian)
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20
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33
ORIGINAL_ARTICLE
Investigating carbon footprint of drinking water supply system in sepidan
Human environmental impacts on the earth can be examined in various fields. One of the areas of great importance is carbon dioxide emissions by human. One of the thousands of human activities that lead to carbon dioxide emissions into the environment is the supply of water. This paper examines the amount of carbon dioxide released by the drinking water supply system in Sepidan. This project took place within a year's time. The results of the surveyes show that in Sepidan, from one well with an energy consumption of 81318 kwh, one spring with an energy consumption of 102234 kwh, and the building of water and sewage organization with an energy consumption of 5491 kwh, amounting to 1067, 48,769 and 3293 kilograms of carbon dioxide release per year. Also, the results showed that 38 grams of carbon dioxide is produced for the supply of one liter of drinking water in the Sepidan. The results of study showed that the supply of water from the spring at a higher altitude point than areas of the city due to the specific topography of the Sepidan, Which is in the form of sloping, requires much less energy, and releases less contamination or carbon Dioxide.
https://ije.ut.ac.ir/article_64838_1b938b402f2684b6befae4de1763c028.pdf
2018-03-21
241
249
10.22059/ije.2018.240963.718
carbon dioxide
drinking water
energy consumption
Ahmad
Hajinezhad
hajinezhad@ut.ac.ir
1
Tehran university,Tehran,Iran
AUTHOR
Hossein
Yousefi
hosseinyousefi@ut.ac.ir
2
مدیر گروه علوم و فناوریهای محیطی، دانشکده علوم و فنون نوین دانشگاه تهران
LEAD_AUTHOR
Omid
Bahmeh
omid.bahmeh@ut.ac.ir
3
Tehran university,Tehran,Iran
AUTHOR
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19
[20]. Sami Kashkooli B,Bahrami M,Ansari jaberi M, Optimal Operation of Water Supply Pumping Stations Using the Association of Artificial Honey Bee Society (ABC) Algorithm, Water and Soil Conservation Research Journal, 2017,23( 5):175-189. [Persian]
20
[21]. Rajabpour R., Afshar H, 2008. Optimized operation of serial pump stations using PSO,2017,27[6]:3-14. [Persian]
21
[22]. Feldman M. Aspects of energy efficiency in water supply systems in:Proceedings of the 5th IWA water loss reduction specialist conference. South Africa:2009, 85-89.
22
ORIGINAL_ARTICLE
Monitoring the status of Bakhtegan Lake and surrounding areas using satellite imagery and computational intelligence
Multilispectral picture classification is one of the most important techniques for separating earth units.The phenomenon of global warming,expansion damming,water storage behind dams and excessive utilization of existing water for human uses has caused the drying of lakes, including Lake Bakhtegan. For this purpose, Landsat images of 1991, 2000, 2010, and 2017 were collected in Bakhtegan Lake and surrounding areas. These images were categorized based on educational samples in four classes of water, septicity, mountain and urban areas after pre-processing and corrections required by the supervised maximum likeness.The same image was then sorted by multi-layer perceptron neural network method in the above classes. Finally, for both methods, the error matrix was extracted and the overall accuracy and kappa coefficient were calculated.For the year 1991, the maximum probability and neural network method was 87% and 93%, and the kappa coefficient was calculated to be 0.86 and 0.90, respectively. . Therefore, due to the higher accuracy of Negative Network, images of the years 2000, 2010 and 2017 were categorized by this method.After classification, in order to evaluate it, Google Earth was considered as the test sample for each information class and the overall accuracy and kappa coefficient were 89% and 0.85, respectively.
https://ije.ut.ac.ir/article_64839_8ea1dfb6ae014f6e666236435596fa70.pdf
2018-03-21
251
263
10.22059/ije.2018.244595.767
Classification
Maximum Likelyhood
Training Samples
neural network
Multilayer Perceptron
Ghazal
Torabi
ghazaltorabi70@gmail.com
1
RS & GIS, Faculty of Environment and Energy,Islamic Azad University, Research Branch, Tehran, Iran
AUTHOR
Hossein
Aghamahammadi zanjirabad
aghamohammadi@srbiau.ac.ir
2
Assistant Professor, Remote Sensing and Spatial Information Systems, Faculty of Environment and Energy, Islamic Azad University, Research Branch, Tehran, Iran
LEAD_AUTHOR
Saeed
Behzadi
behzadi.iau@gmail.com
3
Assistant Professor, Remote Sensing and Spatial Information Systems, Faculty of Environment and Energy, Islamic Azad University, Research Branch, Tehran, Iran
AUTHOR
Khosravani Z, Khajedin J, Mohebbi M, Safianian A. Check satellite imagery capability P5 and P6 in preparation of desert areas map, M.Sc degree Agriculture. Faculty of Agriculture and Natural Resources. Isfahan University of Technology.2008 [Persian].
1
[2].Alavipanah SK. Application of remote sensing in earth sciences. 4nd ed. Tehran:Tehran University. 2009. [Persian].
2
[3]. Khoshnudi N.www.irna.ir.. 2017;May 09. [Persian].
3
[4]. Teimuri I, Purahmad A, Habibi L, Salarvandian F. Determination of environmental liability of Tashk and Bakhtegan lakes using C-Fuzzy classification method. Natural Geography Research.2011; 77. 21-37. [Persian].
4
[5]. Zahedifard N, Khajeddin SJD. Application of digital data of TM sensor in preparation of land use map of Bazoft watershed basin. Agricultural Science and Technology.2004; 8(2). 91-105. [Persian].
5
[6]. Ghasemlu N, Mohammadzade A, Sahebizoj MR. The classification of large scale satellite images using artificial neural network methods and comparing them with the most similarity and least distances from the mean. Geomatics National Conference. Tehran: National Cartographic Center. 2009. [Persian].
6
[7]. Fatemi SB, Rezaee Y. Basics of remote sensing. 2nd ed. Tehran: Azadeh; 2014. [Persian].
7
[8]. Rahimzadegan M, Mobashri MR, Valadanzoj MJ, Maghsudimerani Y. Provides a method for classifying AVIRIS hybridization data by extracting attributes and combining classifiers. Iran Remote sensing and GIS.2014; 1(1). 99-114. [Persian].
8
[9]. Jafari M, Zahtabian GhR, Ehsani AH. Investigating the effect of thermal bonding and satellite-controlled satellite sorting algorithms on land use planning(case study: Kashan). Research on Range and Desert of Iran. 2015; 20(1). 72-87. [Persian].
9
[10]. Shafiee M, Sarkargarardakani A, Vahidnia MH. Comparison of classification with random forestry algorithms and neural networks on simulated hyperspectral images. Geomatics National Conference. Tehran: National Cartographic Center. 2017. [Persian].
10
[11]. Yaghubzade M, Akbarpur A. Investigation of the effect of satellite image categorization algorithms on the runoff and flood excursion maximum flood number using RS and GIS. Geography and development. 2011; 9(22). 5-22. [Persian].
11
[12]. Ahmadpur A, Soleimani K, Shokri M, Ghorbanipashakalaee J. Comparison of the efficiency of three common methods of supervised satellite data classification in coarse shear study. Remote Sensing and GIS in Natural Resources Science. 2011; 2(2). 69-81. [Persian].
12
[13]. Saberi A, Esmaeili A, Bagheri H. Improved ASTER image classification using ACO and GA algorithms. Geomatics National Conference. Tehran: National Cartographic Center. 2014. [Persian].
13
[14]. Alimoradi N, Jamali AA, Mazraemolaee M, Khajepur H. Investigation of land use change process using LCM model and landsat satellite image and future forecasting using neural network (MLP) (Borujerd County). Geomatics National Conference. Tehran: National Cartographic Center. 2017. [Persian].
14
[15]. Khezriahmadabad M, Bameri M, Bashghare M, Arkhi S. Monitoring land use change using satellite images and RS and GIS techniques (Case Study: Baharestan). Geomatics National Conference. Tehran: National Cartographic Center. 2017. [Persian].
15
[16]. Ghasemlu N, Mohammadzade A, Sahebi MR, Valadanzoj MJ. The classification of large-scale satellite images using artificial neural network methods and comparing them with the max-likelyhood and min distance from the mean. Geomatics National Conference. Tehran: National Cartographic Center. 2008. [Persian].
16
[17]. Yusefi S, Taze M, Mirzaee S, Moradi HR, Tavangar Sh. Comparison of different satellite image classification algorithms for land use mapping (case study: Noor city). Remote Sensing and GIS in Natural Resources Science. 2014; 5(3). 67-76. [Persian].
17
[18]. Bolhasani K, Zareei H, Kabolizade M. Investigating and evaluating the changes in vegatation in recent decades using RS and GIS. Geomatics National Conference. Tehran: National Cartographic Center. 2017. [Persian].
18
[19]. Giacinto G, Roli F, Bruzzone L. Combination of neural and statistical algorithms for supervised classification of remote sensing images. Pattern Recognition Letters. 2000; 21(5). 385-397.
19
[20]. Hepner N, George F. Artificial neural network classification using a minimal training set: comparision to conventional supervised classification. Photogrammetric Engineering and Remote Sensing. 1990; 56(4). 65-78.
20
[21]. Dennison F, Roberts D, Peterson S. Spectral shape based temporal composition algorithms for MODIS surface reflectance data. Remote Sensing of Enviroment. 2007; 109(4). 510-522.
21
[22]. Neagoen V, Neghina M, Datcu M. Neural network techniques for automated land-cover change detecion in multispectral satellite time series imagery. Mathematical Models and Methods In Applied Sciences. 2012; 1(6). 130-139.
22
[23]. Zeraati M, Matinfar HR, Alavipanah SK. Investigating and evaluating quantitative and qualitative methods of land use and land cover changes in Kashan region using remote sensing images analysis TM and ETM+. Geomatics National Conference. Tehran: National Cartographic Center. 2014. [Persian].
23
ORIGINAL_ARTICLE
Investigation of the Effect of Wind Breaks in Decreasing Reservoir Evaporation Using Fluent (Case Study: Chahnimeh of Sistan)
Evaporation depends on various factors including temperature, wind speed, humidity, water salinity, water depth, etc. Early studies have shown that wind was found to be the most important factor in evaporation in Chahnimeh region, Sistan and Baluchistan, Iran. Using windbreaks is a method to control the wind speed, which contributes to reduced evaporation. The data were collected from the Zahak Meteorological Station of Zabol, Iran. This article aimed to simulate the wind flow passing over the windbreak using the FLUENT numerical model and determine the effect of windbreak geometry and distance between windbreaks on input wind speed. Therefore, the geometry of the problem was determined in GAMBIT to solve the flow field. Meanwhile, computational field meshing and type of ruling boundary were determined. After preparing the network and determining the boundaries of the flow field, the file was analyzed in FLUENT and the analytical conditions of the fluid, its characteristics, and the specification of boundary conditions were applied on the geometry. Accordingly, the problem was solved. The results showed that if 2m height windbreaks are vertically installed at north-west direction, the evaporation is effectively reduced
https://ije.ut.ac.ir/article_64930_281030ee962202b0ce9b8f8bf6348161.pdf
2018-03-21
265
278
10.22059/ije.2017.236320.652
Evaporation Control
Windbreak
modeling
FLUENT
Chahnimeh
Seyed Arman
Hashemi Monfared
hashemi@eng.usb.ac.ir
1
استادیار، دانشکدۀ مهندسی عمران، دانشگاه سیستان و بلوچستان
LEAD_AUTHOR
Mehdi
Rezapoor
mehdi_civil666@yahoo.com
2
دانشگاه علوم دریایی چابهار
AUTHOR
Tahmineh
Zhian
tahmineh.zhian@yahoo.com
3
دانشجوی کارشناسی ارشد
AUTHOR
[1]. Najafi M, Azimi V, Shayan Nezhad M. Estimation of accuracy of intelligent methods and analysis of sensitivity of evapotranspiration of reference plant to meteorological parameters in two different climates. Journal of Eco Hydrology. 2014;1(1):17-24. [Persian].
1
[2]. Nazari R, Kaviani A. Estimation of Potential Evapotranspiration and Peptic Evaporation Methods with Lysymmetric Values in a Semi-Dry Climate (Case Study: Ghazvin Flat). Journal of Eco Hydrology. 2016;3(1):19-30. [Persian].
2
[3]. Skidmore EL, Hagen LJ. Evaporation in sheltered areas as influenced by windbreak porosity. Agricultural Meteorology. 1970 Jan 1;7:363-74.
3
[4]. Lomas J, Schlesinger E. The influence of a windbreak on evaporation. Agricultural meteorology. 1971 Jan 1;8:107-15.
4
[5]. Raine JK, Stevenson DC. Wind protection by model fences in a simulated atmospheric boundary layer. Journal of Wind Engineering and Industrial Aerodynamics. 1977 Jun 1;2(2):159-80.
5
[6]. Wilson JD. On the choice of a windbreak porosity profile. Boundary-Layer Meteorology. 1987 Jan 1;38(1):37-49.
6
[7]. Heisler GM, Dewalle DR. 2. Effects of windbreak structure on wind flow. Agriculture, ecosystems & environment. 1988 Aug 1;22:41-69.
7
[8]. Agriculture, Ecosystems and Environment, 22/23 (1988) 15-16 15Elsevier Science Publishers B.V., Amsterdam -- Printed in The Netherlands
8
[9]. Brandle JR, Finch S. How windbreaks work. University of Nebraska – Lincoln 1991 January.
9
[10]. Wang H, Takle ES. A numerical simulation of boundary-layer flows near shelterbelts. Boundary-Layer Meteorology. 1995 Jul 1;75(1-2):141-73
10
[11]. Richardsona GM, Richards PJ. Full-scale measurements of the effect of a porous windbreak on wind spectra. Journal of wind engineering and industrial aerodynamics. 1995 Feb 1;54:611
11
[12]. Smith DM, Jarvis PG, Odongo JC. Sources of water used by trees and millet in Sahelian windbreak systems. Journal of Hydrology. 1997 Nov 1;198(1):140-53.
12
[13]. Cleugh HA. Effects of windbreaks on airflow, microclimates and crop yields. Agroforestry systems. 1998 Apr 1;41(1):55-84.
13
[14]. Vigiak O, Sterk G, Warren A, Hagen LJ. Spatial modeling of wind speed around windbreaks. Catena. 2003 Jul 1;52(3):273-88.
14
[15]. Helfer F, Zhang H, Lemckert C. Evaporation reduction by windbreaks: Overview, modelling and efficiency. Urban Water Security Research Alliance; 2009.
15
[16]. Lin XJ, Barrington S, Choiniere D, Prasher S. Effect of weather conditions on windbreak odour dispersion. Journal of Wind Engineering and Industrial Aerodynamics. 2009 Dec 31;97(11):487-96.
16
[17]. Yeh CP, Tsai CH, Yang RJ. An investigation into the sheltering performance of porous windbreaks under various wind directions. Journal of Wind Engineering and Industrial Aerodynamics. 2010 Nov 30;98(10):520-32..
17
[18]. Lee KH, Ehsani R, Castle WS. A laser scanning system for estimating wind velocity reduction through tree windbreaks. Computers and electronics in agriculture. 2010 Jul 31;73(1):1-6.
18
[19]. Bitog JP, Lee IB, Hwang HS, Shin MH, Hong SW, Seo IH, Kwon KS, Mostafa E, Pang Z. Numerical simulation study of a tree windbreak. Biosystems engineering. 2012 Jan 31;111(1):40-8..
19
[20]. Giannoulis A, Mistriotis A, Briassoulis D. Design and analysis of the response of elastically supported wind-break panels of two different permeabilities under wind load. Biosystems Engineering. 2015 Jan 31;129:57-69.
20
[21]. He Y, Jones PJ, Rayment M. A simple parameterisation of windbreak effects on wind speed reduction and resulting thermal benefits to sheep. Agricultural and Forest Meteorology. 2017 May 28;239:96-107.
21
[22]. Towhidi A, Ghafari Ghahroudi H. ANSYS FLEUNT Guideline. 1st Vol, Cultural and Art Institute of Dybaran, Tehran: 2nd Ed, 2015 March. [Persian].
22
[23]. Mianeh Ro M. Determining Desertification in vulnerable areas of Iran based on climatic indicators using mathematical models. Master's Degree Thesis of Climatology, Islamic Azad Unievrsity, Shahr-e Ray Branch, 2001, p 145. [Persian].
23
ORIGINAL_ARTICLE
Evaluation the effectiveness of the use of urban wastewater on management of desertification hazard
(a case study: Barabad desert area of Sabzevar)
The issue of sustainable development emphasizes the undesirable effects of development in ecosystems . One of the issues raised in the ecohydrology of arid areas is the management of urban wastewater. The purpose of this research is to investigate the possibility of using Sabzevar wastewater in the restoration of Drake-Brabid desert region. In assessing the efficacy of wastewater quality and its effects on the ecosystem , the quality of wastewater was compared with standard values in the uses of agricultural sectors, artificial discharge, crop pattern, irrigation, livestock and wildlife. For determination the possibility of wastewater re-use Delphi technique was evaluated by effective factors. The SWOT model was used to integrate, analyze information, decide and propose a strategy tailored to the design. The results showed that the total score of the obtained external factors was 2.992 and 2.917for internal factors. respectively, which resulted in the invasive pattern as a result of the proposed design. The presence of natural reeds with the potential for refining, water storage in the Kalchoor River and the high adaptability of desert species are factors that allow us to use wastewater and revive its ecosystem.
https://ije.ut.ac.ir/article_64952_9e1973224b4871b4f870596b0d679240.pdf
2018-03-21
279
292
10.22059/ije.2018.228766.494
natural hazard
Desertification
desert eco-hydrolory wastewater
sewage treatment plants of Sabzevar
SWOT Model
Abbasali
Vali
vali@kashanu.ac.ir
1
desert combating department,natural resources college,university of Kashan.
LEAD_AUTHOR
Hassan
Barabadi
hassan.barabadi@yahoo.com
2
desert combating department,natural resources college,university of kashan,kashan,iran
AUTHOR
Abolghasem
Amirahmadi
amirahmadi1388@gmail.com
3
climatology and geomorphology department,Hakim Sabzevary university
AUTHOR
[1]. Deputy Planning and Supervision strategy of Presidential. Environmental regulations on the reuse of wastewater and Return water. 2010; 535: 8. (In Persian)
1
[2]. Newsletters of Global network of Confronting Desertification. 2009. [Persian]
2
[3]. Mariolakos I. Water Resources Management in the Framework of Sustainable Development. Desalination. 2007; 213: 147-151.
3
[4]. Salehi A, Tabari M, Mohammadi J, Aliarb A. Effect of irrigation with municipal effluent on soil and growth of Pinus eldarica Medw trees. Iranian Journal of Forest and Poplar Research. 2008; 16(2): 186-196. (In Persian)
4
[5]. Masoudi Ashtiani S, Parsinejad M, Abbasi F. The Effect of Urban Wastewater Utilization at Sorghum Irrigation on Some Soil Physical Properties, Soil Researches Journal (Soil and Water Sciences). 2011; 25(3): 243-253. (In Persian)
5
[6]. Joya MH. Investigation of Industrial Wastewater and Household Wastewater and Its Impact on Aquifer of Yazd-Ardakan plain. Collection Articles of The first scientific seminar on water resources studies. Ministry of Energy. Tehran. 1990; 8p. (In Persian)
6
[7]. Danesh SH, Alizadeh A. Application of Wastewater in Agriculture, Opportunities and Challenges, Collection Articles of the First National Seminar on position Recovered waters and Wastewater in Water Resources Management, Mashhad. 2008; 12P. (In Persian)
7
[8]. Khanjani MJ, Rashidi A, Hashemipour SM. Use of treated wastewater Sewage in Cultivation of Pistachio. Collection Articles of the 2nd Watershed Management and Water and Soil Management Conference. Kerman, Iran. 2005; 12 p. (In Persian)
8
[9]. Rezazadeh S, Ghanavi Z. Investigating the Challenges and Solutions of the Use of Wastewater from the Sewage Treatment System of Qazvin City in Irrigation Traditional gardens of Qazvin.Collection Articles of the Second National Seminar on position Recovered waters and Wastewater in Water Resources Management, Mashhad. 2010; 11 P. (In Persian)
9
[10]. Niknam R, Yousefpour AE, Hajian MH, Rashidi Sharif Abadi A. Economic Estimation of Application Wastewater Treatment Plant of Kerman city for irrigation of agricultural lands with environmental considerations. Collection Articles of the 4th Water Resources Management Conference, Tehran, Amirkabir University. 2011; 14 P. (In Persian)
10
[11]. Shahriari A, Nouri S, Abedi Kupai J, AySalah F. Effect of irrigation with treated wastewater on growth of Nitraria schoberi under greenhouse conditions, Journal of Science and Technology of greenhouse crops. 2010; 1(4): 13-21. (In Persian)
11
[12]. Feizi M. Effect of Treated Wastewater on Accumulation of Heavy Metals in Plants and Soil. International Workshop on Wastewater Reuse Management. ICID-CIID. Seoul. Korea. 2001:137-146.
12
[13]. Alaton I, Tanik A, Ovez S, Iskender G, Gure M, Orhon D. Reuse potential of urban wastewater treatment plant effluents in Turkey: a case study on selected plants. Desalination. 2007; 215(1): 159-165.
13
[14]. Al-Omran AM, Al-Wabel MI, El-Maghraby SE, Nadeem ME, Al-Sharani S. Spatial variability for some properties of the wastewater irrigated soils. Journal of the Saudi Society of Agricultural Sciences. 2012; 12:167-175.
14
[15]. Gatta G, Libutti A, Gagliardi A, Beneduce L, Brusetti L, Borruso L, et al. Treated agro-industrial wastewater irrigation of tomato crop: Effects on qualitative/quantitative characteristics of production and microbiological properties of the soil. Agricultural Water Management. 2015; 149: 33–43.
15
[16]. Dadrasi Sabzevar A, Khosroshahi M. The effects of the use of low quality flood on desert area. Iranian journal of Range and Desert Reseach. 2010; 17(1): 127-148. (In Persian)
16
[17]. First Consulting Engineers. The Project of Operation plan from salty, low salty and unconventional water in the level of Watershed of Country. Report No.6: Appropriate policies and strategies of salty, low salty and unconventional water. 2007. (In Persian)
17
[18]. Alizadeh A. Principles of Applied Hydrology. 13nd ed. Mashhad: Imam Reza University; 2001. [Persian]
18
[19]. Environmental Protection Organization. Environmental standards and standards, Environmental Protection Organization publications. 1999. (In Persian)
19
[20]. Irrigation and Drainage National Committee of Iran. A review of standards and practices for the use of wastewater for irrigation. 2011; 30 p. (In Persian)
20
[21]. Ayers RS, Westcot DW. Water Quality for Agriculture. FAO Irrigation and Drainage Paper. FAO. Rome, Italy. 1985; 29.
21
[22]. Institute of Standards and Industrial Research of Iran. National Iranian Standard, Physical and Chemical Properties of Drinking Water, Physical and Chemical Properties of Drinking Water, 1997; Standard 1053(5). (In Persian)
22
[23]. Water and Soil Pollution Check Office of Environmental Protection Organization. Qualitative Study Guide water resources. Internal Publications. 2001. (In Persian)
23
[24]. Dadrasi Sabzevar A, Khosroshahi M, Barabadi H. Feasibility assessment of use o f refined urban wastewater for reclamation of arid lands (Case study: The Urban wastewater treatmentt plantt of Sabzevar). Desert Management. 2014; 3: 37-49. (In Persian)
24
[25]. Synoptic Station of Sabzevar City. Meteorological data; 2017. (In Persian)
25
[26]. Barabadi H, Feasibility of desert areas recovery with using wastewater treatment plant East of Isfahan (Case study: Segzi plain of Isfahan), National conference on defense and security of desert and desert regions of Iran (with development, defense and sustainable development approach), Strategic Defense Research Center, Tehran. 2016. (In Persian)
26
ORIGINAL_ARTICLE
Analysis of multiple parameters pollution map based on land use map and using K-mean clustering technique in Qazvin aquifer
One of the main approaches to control and prevent the aquifers from contamination is to identify the critical contaminated areas in relation to the water application. Most of the researchers considered only one type of contamination or one type of water application. In this research, using cluster analysis and water application, a multi-parameter groundwater quality classification map is developed. Three water quality parameters including arsenic, total suspended solids, and nitrate for three different applications including agricultural, industrial and drinking purposes are used to develop a classified contamination map for an aquifer in central Iran, Qazvin aquifer. The optimal number of clusters is five and it is determined using Davies-Bouldin Index. Based on the standard of World Health Organization for drinking water and considering the three selected quality parameters, the results show that the most suitable class of the aquifer, Class C1, with 22 percent of total area is mostly in northern areas of the aquifer with minimum human activity. In central areas with increase in industrial and agricultural activities, the lower classes, C4 and C5, with 35 percent of total area are appeared.
https://ije.ut.ac.ir/article_64953_28d4dda16945789ae416306a5dd921d2.pdf
2018-03-21
293
305
10.22059/ije.2017.230936.541
Groundwater pollution
Multi-variable Qualitative Analysis
Clustering
Pollution distribution
Qazvin Aquifer
Saman
Javadi
javadis@ut.ac.ir
1
استادیار گروه مهندسی آب، پردیس ابوریحان، دانشگاه تهران
LEAD_AUTHOR
Mehdi
Hashemy
mehdi.hashemy@ut.ac.ir
2
Assistant professor of Irrigation and drainage department, Aburaihan campus, university of Tehran
AUTHOR
Mehdi
Sokhtezari
sokhtezari@yahoo.com
3
M.Sc. Graduate Civil Engineering
AUTHOR
1- Neshat A, Pradhan B, Javadi S. Risk assessment of groundwater pollution using monte carlo approach in an agriculture region: an example from Kerman plain, Iran. Computers, Environment and urban system. 2015; 50(1): 66-73.
1
2- Al-adamat R.A.N, Foster I.D.L, Baban S.M.J. Groundwater vulnerability and risk mapping for the Basaltic aquifer of the Azraq basin of Jordan using GIS and Remote sensing and DRASTIC, Applied Geography. 2003; 23(4): 303-324.
2
3- Samani S, Kalantari N, Rahimi, M.H. Evaluation of groundwater quality by Cluster analysis technique in Avan aquifer, Journal of Water resources engineering. 2011; 4: 75-85. [Persian].
3
4- Aghdar H, Mohammadyari F. Assessment of groundwater quality using Cluster analysis method in Mehran and Dehloran aquifer. The first international conference on new achievements in Agriculture, natural resources and environmental sciences. 2014. [Persian].
4
5- Ouyang Y, Jia Zh, Cui L. Estimating impacts of land use on groundwater quality using trilinear analysis. Environmental monitoring and assessment. 2014; 186(9): 5353-5362.
5
6- Ghiasi N, Arabkhedri M, Ghafari A, Hatami H. Survey on the effect of some morphometric characteristics of basins on peak discharge with different return periods (Case study north Albors basins). Research and development journal. 2004; 62: 2-10. [Persian].
6
7- Goulet V, Rocourt J, Jacquet C. Cluster of listeriosis cases in France. Euro surveillance weekly. 2002; 27(6).
7
8- Kim K J, Ahn H. A recommender system using GA K-means clustering in an online shopping market. Expert Syst. Appl. 2008; 34 (2): 1200–1209.
8
9- Usman N, Toriman M.E, Juahir H. Assessment of Groundwater Quality Using Multivariate Statistical Techniques in Terengganu. Science and Technology, 2014; 4(3): 42-49.
9
10- Zou H, Zou Z, Wang X. An Enhanced K-Means Algorithm for Water Quality Analysis of the Haihe River in China. Int. J. Environ. Res. Public Health. 2015; 12: 1400-1413.
10
11- Azhar S.C, Aris A.Z, Yussof M.K, Ramli M.F, Juahir H. Classification of river water quality using multivariate analysis. Procedia Environmental Sciences. 2015; 30: 79-84.
11
12- Oorkavalan G, Chidambaram S.M, Mariappan V, Kandaswamy G, Natarajan S. Cluster Analysis to Assess Groundwater Quality in Erode District, Tamil Nadu, India. Circuits and Systems, 2016; 7: 877-890.
12
13- Fianko J.R, Osae S, Adomako D, Achel D.J. Relationship between land use and groundwater quality in six districts in the eastern region of Ghana. Environmental Monitoring and Assessment. 2009; 153(4): 139-146.
13
14- Yongjun J, Daoxian Y, Shiyou X, Linli L, Gui Zh, Raosheng H. Groundwater quality and land use change in a typical karst agricultural region: a case study of Xiaojiang watershed, Yunnan. Journal of geographical Sciences. 2006; 16(4): 405-414.
14
15- Lerner D, Harris B. The relationship between land use and groundwater resources and quality. Land Use Policy. 2009; 26(1): 265-273.
15
16- Announcement. Hydrogeology section- the report of Quality and Quantity Modelling study in Qazvin aquifer. Qazvin Regional Water Company. 2012: 23-26. [Persian].
16
17- Han J, Kamber M. Data mining concepts and techniques. San Francisco, U.S.A, Morgan Kaufman Publisher. 2006: 110.
17
18- Hoppner F, Klawonn F, Kruse R, Runkler T. Fuzzy cluster analysis. Sussex, England: Wiley and Sons. 1999: 146.
18
19- Feil B. Fuzzy Clustering in Process of Data Mining. Ph.D. thesis, Department of Process Engineering, University of Veszprem Hungary. 2006.
19
20- Kim D.W, Lee K.H, Lee D. On cluster validity index for estimation of the optimal number of fuzzy clusters. Journal of Pattern Recognition Society. 2004; 37: 209-225.
20
21- Hashemy S.M. Spatial and Temporal Clustering in Irrigation network using classic and fuzzy technique. M.Sc. thesis, Tarbiat Modares University. 2008. [Persian].
21
22- Davies D.L, Bouldin D.W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1979; 1(4): 224–227.
22
ORIGINAL_ARTICLE
Variability of Sediment Rating Equations in the Length of some Rivers of Kermanshah, Kurdistan, Zanjan and Gilan Provinces
The study of sediment transport capacity and sediment transport process has especially important in rivers hydraulic and its morphology. Given that, the changes studies of corrective coefficient in various methods of sediment estimation can be useful, therefore, the present study for the changes comparison of corrective coefficients from up-stream to down-stream Gharasu river (Kermanshah), Negel and Sarvabad rivers (Kurdistan) and Ghezelozan river (Kurdistan, Zanjan and Gilan) was conducted using LQMLE, Smearing, CF1, CF2, normal and FAO hydrology methods. The results showed that from up-stream to down-stream river, the erodibility in up-stream watershed of stations decreased with rerducing equation coefficient and the sediment transport increased with the enhancement equation power. The reducing and decresing equation power and coefficient in down-stream direct of various cross sections of rivers can be added the areas with less slope to up-stream watershed. In studied stations, the applied hydrologic methods (expect FAO method) estimated the sediment rate less than observed sediment rate. Also, the comparison of observed and estimated sediment rates showed that the down-watershed stations had the estimated sediment difference less than toward up-watershed stations.
https://ije.ut.ac.ir/article_64954_de2a2f0dcf7d617d81680eadb3a3c624.pdf
2018-03-21
307
318
10.22059/ije.2017.233029.596
Bed Load
Calibration
Erodibility
Sediment Rating Curve
Suspended Sediment
jabar
Hadi Ghoroghi
j.hadi88@gmail.com
1
Total Office Expert of Natural Resources in Kurdistan Proviance, Kamiaran, Mochesh Foresty, Iran
AUTHOR
Leila
Gholami
gholami.leily@yahoo.com
2
استادیار، گروه مهندسی آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری
LEAD_AUTHOR
Adris
Karami
adris.karamy@gmail.com
3
M. Sc. Graduate Student, Tehran Payam Noor University, East Center, Tehran. Iran
AUTHOR
Ahmadi H, Malekian A, Abedi R. The most Appropriate Statistical Method for Suspended Sediment Estimation of Rivers (Case Study: Roodak Station of the Jajrood Basin). Quarterly J Envir Erosion Res. 2012; 2:78-88. (In Persian)
1
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[6] Telvari AR. The suspended sediment with some watershed charactrestices in Karkheh and Dez brenchez in Lrestan proviance. Pajohesh and Sazandegi Journal. 2002; 15(1):47-56. (In Persian)
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[7] Pavanelli D, Bigi A. Suspended sediment concentration for three Apennine monitored basins particle size distribution and physical parameters. In: The Agro Environment Congress, Venice, Italy. 2004; 537-544.
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[8] Arabkhedri M. The study of suspended sediment in Iran watersheds. Iran Water Resource Research, 2005; 2:61-60. (In Persian)
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[9] Endreny TA, Hassett JM. Robustness of pollutant loading estimators for sample size reduction in a suburban watershed. Int J River Basin Manag. 2005; 3(1):53-66.
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[10] Ghomshi M. Podeh T Application evalation of sediment load equations in Khosastan Rivers. Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources), 2003; 6(1): 13-30. (In Persian)
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[11] Feiznia S, Ghafari G, Karimizade K, Tabatabayizade MS. Determination of the Most Suitable Method for Estimation of Suspended Sediment in Hydrometric Stations Upland of Latian and Taleghan Dams. Journaral of natural environment (Iranian journal of natural resources), 2011; 64(3):231-242. (In Persian)
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[12] Mirzaei MR, Arabkhedri M, Feiznia S, Ahmadi H. A Comparison of methods of estimation of suspended sediment. Iranian Journal of Natural Resources, 2005; 58(2):301-315. (In Persian)
12
[13] Arabkhedri M, Hakiamkhani Sh, Varvani J. The validity of extrapolation methods in estimation of annual mean suspended sediment yield (17 hydrometric stations). Journal of Agricultural Scinence and Natural Resources, 2004; 11(3):123-131. (In Persian)
13
[14] Zanganeh ME, Mosaedi A, Meftah Halghi M, Dehghan AA. Determination of suitable method for estimating suspended sediments discharge in Arazkoose hydrometric station (Gorganrood Basin). Journal of Water and Soil Conservation, 18(2):85-104. (In Persian)
14
[15] Mosaedi A, Mohammadi Ostadkelayeh A, Najafinejad A, Yaghmaiee F. Optimization of the relations between flow discharge and suspended sediment discharge in selected hydrometric stations of Gorganroud river. Iranian Journal of Natural Resources, 2006; 59(2):331-342. (In Persian)
15
[16] Zoratipour A, Mahdavi M, Khalighi Sigaroudi Sh, Salajegheh A, Shams Almaali N. Assessment of the effect of classification on the improved estimation of suspended sediment load using hydrological methods (Case study: Taleghan Basin). Journal of the Iranian Natural Resources, 2009; 61(4):809-819. (In Persian)
16
[17] Ulke A, Tayfur G, Ozkul S. Predicting suspended sediment loads and missing data for Gediz river, Turkey. J Hydrol Eng. 2009; 14(9):954-965.
17
[18] Kakaei Lafdani E, Moghaddam Nia A, Ahmadi A. Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol. 2013; 478(25):50-62.
18
[19] Zhu YM, Lu XX, Zhou Y. Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the upper Yangtze catchment, China. Geomorphology, 2007; 84(1):111-125.
19
[20] Melesse AM, Ahmad S, McClain ME, Wang X, Lim YH. Suspended sediment load prediction of river systems: An artificial neural network approach. Agricul Water Manag. 2011; 98(5):855-866.
20
[21] Tabatabaei MR, Solaimani K, Habibnejad Roshan M, Kavian A. Estimation of daily suspended sediment concentration using artificial neural networks and data clustering by self-organizing map (Case Study: Sierra Hydrometry Station- Karaj Dam Watershed). Journal of Watershed Management Research, 2015; 5(10):98-116. (In Persian)
21
[22] Khazaii Moghani S, Najafi Nezhad A, Aziam Mohseni M, Shaikh B. Forecasting suspended sediment discharge by using time series transfer function model in selected stations of Gorganrood, Golestan Province. Journal of Water and Soil Conservation, 2013; 21(3):185-202. (In Persian)
22
[23] Varvani J, Najafi Nejad A, Mirmoini Karahroudi A. Improving of sediment rating curve using minimum variance unbiased estimator. Gorgan, J0urnal of Agricultural Sciences and Natural Resources, 2008; 15(1):150-161. (In Persian)
23
[24] Sadeghi SHR, Fazli S, Khaledi Darvishan AV. Efficency assessment of sediment rating curve in Khamsan representative watershed. 6th National Conference of Watershed Management Sciences and Engineering, 28-29 April 2010, 8 P. (In Persian)
24
[25] Sadeghi, SHR, Mizuyama T, Miyata S, Gomi T, Kosugi K, Fukushima T, Mizugaki S, Onda Y. Development, evaluation and interpretation of sediment rating curves for a Japanese small mountainous reforested watershed. Geoderma, 2008; 144:198-211.
25
[26] Hadi Ghoroghi J Khaledi Darvishan AV. Performance evaluation of suspended sediment load prediction models in North and West of Iran (Case study: Gharasoo and Tajan rivers). Iranian Water Research Journal, 2014; 9(2):73-78.
26
[27] Kavian A, Moradian M, Darabi H, Safari A. The modification coefficients comparison of sediment curve equation in sub-humid and semi-arid rivers. Extant ion and Development of Watershed Management, 2013; 2(7):15-20. (In Persian)
27
[28] Najafinejad A, Mardian M, Varvani J, Sheikh VB. Performance evaluation of correction factors in optimization of sediment rating curve (Case Study: Kamal Saleh Dam Watershed, Markazi Province, Iran). Journal of Water and Soil Conservation, 2011; 18(2):105-122. (In Persian)
28
[29] Dastranj A, Khazai M, Kazemi M, Falah S, Adeli B. Assessment corrective methods for estimating suspended sediment (Case Study: Beshaar Watershed). Quarterly Journal of Environmental Erosion Research, 2015; 4(3):47-57. (In Persian)
29
[30] Nohegar A, Kazemi M, Ahmadi SJ, Gholami H, Mahdavi R. Determine the most appropriate corrective method to estimate suspended sediment load (Case Study: Tange Bostanak Watershed). Journal of Natural Ecosystems of Iran, 2017; 7(3):67-82. (In Persian)
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[31] Walling DE, Webb BW. The Reliability of suspended sediment load data, in: erosion and sediment transport (Proc. of Florence Symp. June 1981, IAHS. Public. 1981; 133:177-194.
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[32] Jones KR, Berney O, Carr DP, Barret EC. Arid zone hydrology for agricultural development. FAO Irrigation and Drainage Paper, 1981; 37:271-284.
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[33] Thomas RB. Estimating total suspended sediment yield with probability sampling. Water Resour Res. 1985; 21:1381-1388.
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[34] Duan N. Smearing estimate, a nonparametric retransformation method. Harvard University Press, Cambridge, Mass, 1983; 456p.
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[35] Koch RW, Smillie GM. Comment on river loads underestimated by rating curves. Water Resour Res, 1986; 22(13):2121-2122.
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[36] Hadi Ghoroghi H, Habibnejad Roshan M, Khaledi Darvishan AV. Efficiency of different data separation methods to increase the accuracy of sediment rating curve; Case Study a part of the Sefidrood watershed. The Iranian Society of Irrigation and Water, 2013; 2(2):97-111. (In Persian)
36
[37] Khaledi Darvishan, AV, Hadi Ghoroghi J, Gholami L, Katebi Kord A. The changes study of sediment rating coefficient in Gharasoo River of Kermanhah province. The 6th National Conference of Iran Water Resources Management, 23-25 Aprial 2016. Kurdisatn University, Sanandaj, Iran. 2016. (In Persian)
37
[38] Hadi Ghoroghi J, Khaledi Darvishan AV. the estimation of suspended sediment yield models in north and west of Iran (Case of study: Gharasoo and Tajan Rivers). Journal of Iran Water Research, 2015; 9(2):73-78. (In Persian)
38
ORIGINAL_ARTICLE
Performance of HEC-HMS Hydrological Model in Simulation of Flood Hydrograph in arid and humid watersheds
In these studies the ability of hydrological model HEC-HMS was used for simulation the six flood hydrograph in Kasilian watershed with 67.8 km2 area with semi-humid to the humid climate and in Kardeh watershed with 93.2 km2 area with completely dry climate. Rainfall hyetographs data with minute time step and related flood hydrographs were entered to model; so floods hydrographs were simulated with 30 minute time step. Parameters sensitivity analysis by trial and error methods showed that the CN parameter have high sensitivity than the others. The CN parameter sensitivity in the arid area is higher than humid area. The R2, NS and CP coefficients with cubic meters per second unit were used for model performance. The values of these coefficients in Kasilian watershed for simulated floods were between 0.84 to 0.93, 0.78 to 0.81, and 96.13 to 188, respectively. In Kardeh watershed these coefficients calculated between 0.76 to 0.89, 0.71 to 0.75 and 113.4 to 216.7, respectively. The results showed that the model performances are acceptable in both watersheds. But in the humid watershed the model had high performance for watershed management implement works than the dry watershed
https://ije.ut.ac.ir/article_64955_361acd6ff381ce05b1e281ef1ac87cfe.pdf
2018-03-21
319
330
10.22059/ije.2018.240802.715
Flood
simulation
Hydrograph
Kasilian
Kardeh
Hossein
Yousefi
hosseinyousefi@ut.ac.ir
1
مدیر گروه علوم و فناوریهای محیطی، دانشکده علوم و فنون نوین دانشگاه تهران
LEAD_AUTHOR
Mohammad
Golshan
golshan.mohammad@yahoo.com
2
Department of Watershed Management, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari
AUTHOR
Abdollah
Pirnia
abd.god62@gmail.com
3
Department of Watershed Management, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari
AUTHOR
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50
ORIGINAL_ARTICLE
The Lazarar Principle and the Right to the Environment; Case Study: Lake Urmia
Lake Urmia, despite its unique environmental characteristics, has been exposed to drying as a result of the interference of natural and human factors, and contrary to the role it could play in reducing the environmental conditions of the region, the Lake Urmia threatens the health of the people and the loss of their basic rights to life,including the right to clean air,the optional choice of residence, etc. In spite of these problems, what is necessary is the consideration of the fundamental question of how can we use the jurisprudential foundations to justify the right to the environment of Lake Urmia? Where is the rule of “Lazarar” as one of the indisputable rules of jurisprudence in proving this right,and can this rule be used in cases where the absence of an affair or quitting can cause harm? The current paper, understanding this necessity,explores the concept of the right to the environment and the concept of the Lazarar principle,and has tried to clarify the relation between these two concepts, to clarify the position of the argumentative principle in proving the environmental rights of Lake Urmia,and in this way, to provide strategies for protecting the lake and providing environmental protection around it.
https://ije.ut.ac.ir/article_64956_2af585e4a1f2713f485d1b00c41ab3b2.pdf
2018-03-21
331
341
10.22059/ije.2018.245768.778
right
Right to the environment
The Lazarar principle
Lake Urmia
reza
nikkhah
rnsj_nikkhah@yahoo.com
1
university of urmia
LEAD_AUTHOR
seyed mehdi
salehi
drmahdisalehi@yahoo.com
2
university of urmia
AUTHOR
hosein
javadi
javad.golkar.1368@gmail.com
3
University of Urmia
AUTHOR
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