ORIGINAL_ARTICLE
Introducing a new method to aquifer vulnerability assessment of Moghan plain based on combination of DRASTIC, SINTACS and SI methods
Increasing growth of population leads to increase in human activities such as agriculture and industry, which these activities increasing and irregular usage of fertilizers, pesticides and insecticides result in soil and groundwater pollution. Meanwhile, vulnerability assessment can play important role in the management of polluting activities. In this research a new method has been proposed for the vulnerability assessment of Moghan plain as one of the most important provider of agricultural products and animal husbandry in the Ardebil province. This method incorporates the weighted combination of three common vulnerability assessment methods DRASTIC, SINTACS and SI which. Comparison of obtained results from the proposal method with field nitrate concentration data of this area which sampled from 21 tube wells in the study area in the autumn 1394 indicates that the proposed method has a higher correlation index than other methods. According to the result of proposed method, 41, 46 and 13 percent of the study area have been located in areas with low, medium and high vulnerability respectively.The proposed methodology in this study could be used for vulnerability assessment of other aquifers.
https://ije.ut.ac.ir/article_60345_637ecce01db18f3af1d1278856ac395b.pdf
2016-12-21
491
503
10.22059/ije.2016.60345
Golnaz
Javanshir
golnazjavanshir@gmail.com
1
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Iran
AUTHOR
Ata Allah
Nadiri
nadiri@tabrizu.ac.ir
2
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Iran
LEAD_AUTHOR
Sina
Sadeghfam
s.sadeghfam@gmail.com
3
Civil Engineering Department, Engineering Faculty, University of Maragheh, Iran
AUTHOR
Esfandiar
Abbas Novinpour
e.a.nvinpour@gmail.com
4
Department of Geology, Faculty of Sciences, University ofUrmia, Iran
AUTHOR
1- Maarofi S, Soleymani S, Ghobadi M, Rahimi Gh, Maarofi H. Malayer aquifer vulnerability assessment using models DRASTIC, SI and SINTACS. Soil and Water Research Journal.2011:19(3).[ Persian]
1
2- Vrba J A, and Zaporozec A. Guidebook on Mapping Groundwater Vulnerability. International Contribution for Hydrogeology. Hannover, Germany. 1994: 131 p.
2
3- Rangzan K, Firuzabadi P, Mirzaee L, and Alijani F. Interpolation varamin plain aquifer vulnerability by the DRASTIC models, experimental evaluation of unsaturated region in GIS environment. Iranian Geology Journal. 2008; 6: 21-32. [Persian].
3
4- Aller L, Bennet T, Leher JH, Petty RJ, Hackett G. DRASTIC: A Standardized system for evaluating groundwater pollution potential using hydro-geological settings. Kerr Environmental Research Laboratory, U.S. Environmental Protection Agency Report. 1987; (EPA/600/2-87/035).
4
5- Lee S. Evaluation of waste disposal site using the DRASTIC system in Southern Korea. Environmental Geology. 2003; 44: 654-664.
5
6- Antonakos A, Lambrakis N. Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates based on the DRASTIC model, an example from NE Korinthia, Greece. Journal of Hydrology. 2007; 333, 288-304.
6
7- Harter T, Walker L. Assessing vulnerability of Groundwater. US Natural Resources Conservation Service.
7
8- Sadeghiravesh M, Zehtabian Gh. Khezrabad aquifer vulnerability assessment using models DRASTIC. Environmental Journal. 2013;55
8
9- Corniello A, Ducci D, Napolitano P. Comparison between parametric methods to evaluate aquifer pollution vulnerability using a GIS: An example in the Piana Campana. Engineering Geology and the Environment, Balkema, Rotterdam, The Netherlands. 1997; P: 1721-1726.
9
10- Dixon B. Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool. Journal of Applied Geography. 2005b; 25, 327-347.
10
11- Asghari Moghaddam A, Fijani E, Nadiri AA. Groundwater Vulnerability Assessment Using GIS-Based DRASTIC Model in the Bazargan and Poldasht Plains. Journal of Environmental Studies. 2010; 35, 52
11
12-Meteorological Organization Ardabil. 2002.
12
13- Panagopoulos G, Antonakos A, Lambrakis N. Optimization of DRASTIC model for groundwater vulnerability assessment, by the use of simple statistical methods and GIS. Hydrology Journal(published online). 2005.
13
14- Civita M, Massimo. Legenda unificata per le Carte della vulnerabilita dei corpi idrici sotterranei/ Unified legend for the aquifer pollution vulnerability Maps, Studi sulla Vulnerabilita degli Acquiferi”, Pitagora Edit, Bologna. 1990.
14
15- Stigter TY, Ribeiro L, Carvalho Dill AMM. Evaluation of anintrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeology. 2006; 14: 79-99.
15
16- Nadiri AA, Garekhani M, Khatibi R, Sadeghfam S, Asghari Moghaddam A. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Science of the Total Environment. 2017; 574 (2017) 691–706.
16
17- Fijani E, Nadiri AA, Asghari Moghaddam A, Tsai F, Dixon B. Optimization of DRASTIC Method by Supervised Committee Machine Artificial Intelligence to Assess Groundwater Vulnerability for Maragheh-Bonab Plain Aquifer, Iran. Journal of Hydrology. 2013; 530, 89-100.
17
ORIGINAL_ARTICLE
Assessment spatial variability and Mapping of drinking and agricultural water quality using geostatisticsand GIStechniques
Quality properties of groundwater is one of the major components which can used for water resources management. In this study, spatial variability of groundwater quality for drinking uses
(Schoeller standards) and agricultural uses (Wilcox standards) was investigated during 1387-1392.In this research, we use information of 24 observation wells in Fasa plain, to investigate the spatial variability of water quality parameters for different category by using geostatistics technique in GIS software and associated areas of different classof water quality were also determined. Afterwards, the change in the area by using parametric (linear regression) and non-parametric (Spearman) statistical tests for determined period was evaluated. The results showed that the areas with suitable class for drinking and agricultural uses based on the both statistical methods were decreased, while the areas with unsuitable class were increased. The categories of drinking water quality evaluated by spatial variability of the study area fall under unsuitable class, Bad class and temporarily acceptable class which these areas have been increased and are not significant at 95% level. The categories of agricultural water quality evaluated by spatial variability of the study area fall under good class, average class and bad class which the areas with average quality class have been decreased and the areas with bad quality class have been increased,these variables are significant at 95% level based on the both statistical methods.
https://ije.ut.ac.ir/article_60354_904b3e57e14984d511e6a6af9a99f905.pdf
2016-12-21
505
516
10.22059/ije.2016.60354
Geostatistics
Schoeller
Wilcox
Groundwater quality
parametric and non-parametric tests
Abdol Rassoul
Zarei
ar_zareiee@fasau.ac.ir
1
Department of Natural Resources Science, College of Agriculture, Fasa University
AUTHOR
Mohammad Javad
Amiri
amiriboogar@yahoo.com
2
Department of Water Engineering, College of Agriculture, Fasa University
LEAD_AUTHOR
منابع
1
[1] Todd, DK. Ground water hydrology.2nd Ed. John Wiley and Sons, 552 p; 1980.
2
[2] Nazarizadeh F, Ershadian B, Zandvakili K, Noori-Emamzadehi MR. Investigation spatial variability of groundwater quality in Balarood plain at Khuzestan province.1st conference on optimum utilization of water resources. 2006;Shahrekord.[Persian].
3
[3] ZehtabianGh, Janfaza E, Mohammad asgari H,Nematollahi, MJ. Modeling of ground water spatial distribution for some chemical properties (Case study in Garmsar watershed). Iranian journal of Range and Desert Reseach. 2010; 17(1): 61-73.[Persian].
4
[4] Jahanshahi A, RouhiMoghaddam E, Dehvari A. Investigating groundwater quality parameters using GIS and geostatistics (Case study: Shahr-Babakplain aquifer). 2014; 24(2): 183-197.[Persian].
5
[5] Zahedifar M, Moosavi SAA, Rajabi M. 2013. Zoning the groundwater chemical quality attributes of Fasa plain using geostatistical approaches. 2013; 27(4): 812-822.[Persian].
6
[6]Zarei A, Bahrami M. Evaluation of quality and quantity changes of underground water in Fasa plain, Fars (2006 - 2013). Irrigation & Water Engineering.2016; 20(24): 103-113. [Persian].
7
[7]Mahdavi M, Applied hydrology. 8th edition. Tehran: University of Tehran; 2013.
8
[8] Fetouani S, SbaaM, Vanclooster M,Bendra B. Assessing groundwater quality in the irrigated plain of Triffa (North-east Morocco).Agricultural Water Management. 2008; 95: 133-142.
9
[9] Freeze RA, Cherry T. Groundwater, Prentice-Hall, Inc, Englewood Cliffs, New Jersey, 604 p. 1979.
10
[10] Habibi V, Ahmadi A,Fattahi MM. Modeling spatial variability of ground water chemical properties usinggeostatistical methods. Iran-Watershed Management Science & Engineering. 2009; 3(7): 23-34.[Persian].
11
[11] Sheikh Goodarzi M, Mousavi SH, Khorasani N.Imulating spatial changes in groundwater qualitative factors using geostatistical methods (Case study: Tehran - Karaj Plain). Journal of Natural Environment, Iranian Journal ofNatural Resources. 2012; 65(1): 83-93.[Persian].
12
[12] Maria PM, Luis R. Nitrate probability mapping in the northern aquifer alluvial system of the river Tagus (Portugal) using Disjunctive krriging. Science of the Total Environment. 2010; 408(5): 1021-1034.
13
[13] Shabani M. Investigation the variation of groundwater qualityinArsanjan plain. PhysicalGeography. 2009; 1(3): 71-82. [Persian].
14
[14] Rezaei M, Davatgar N, Tajdari K, Abolpour B. Investigation the spatial variability of some important groundwater quality factors in Guilan, Iran. Journal of Water and Soil. 2010; 24(5): 932-941. [Persian].
15
[15] Maghami Y, Ghazavi R, Vali AA, Sharafi S. Evaluation of spatial interpolation methods for water quality zoning using GIS Case study, Abadeh Township. Geography and Environmental Planning Journal. 2011; 42(2): 171-182. [Persian].
16
[16]Moghaddam AR, Ghallehban-Tekmedash M, Esmaili K. Investigation of temporal and spatial trend of water quality parameters in view of weather fluctuations using GIS; Mashhad Plain. Journal of Water and Soil Conservation. 2013; 20(3): 211-225. [Persian].
17
[17] Mohammad Aghaei M. Spatial variability of quality parameters and assessment of heavy metal danger in Qom plain. M.S. Thesis, Zabol University. Iran. [Persian].
18
[18] Gaus I, Kinniburgh DG, Talbot JC, Webster R. Geostatistical analysis of arsenic concentration in groundwater in Bangladesh using disjunctive krriging. Environmental Geology. 2003; 44: 939-948.
19
[19] Safari M. Determination of optimum groundwater network monitoring using geostatistics method. M.S. Thesis, TarbiatModaresol University. Iran. [Persian].
20
[20] Mohammadi M, Mohammadi-Ghaleni M, Ebrahimi K. Spatial and temporal variations of groundwater quality of qazvin plain. Iran Water Research Journal. 2011; 5(8): 41-52.[Persian].
21
[21] Goovaerts P. Geostatistics for natural resources evaluation, Oxford University Press, New York, 483 p. 1997.
22
ORIGINAL_ARTICLE
Evaluation of nitrate concentration and vulnerability of the groundwater by GODS and AVI methods (case study: Kordkandi-Duzduzan Plain, East Azarbaijan province)
The aim of this study is to assess the concentration of nitrate in groundwater resources of the Kordkandi-Duzduzan plain and to evaluate the vulnerability of the groundwater using AVI and GODS methods. Kordkandi-Duzduzan plain is located in East Azarbaijan Province in the northwest of Iran, which groundwater is important for drinking and agriculture in the region due to scarcity of suitable surface waters. As well as, intensive agricultural activities and excessive use of agricultural fertilizers have caused groundwater nitrate contamination. Therefore, it is required to assess the quality of the groundwater regarding nitrate and to determine the vulnerability of the aquifer. For this purpose, 22 water samples were collected from shallow and deep water wells in July 2015 and analyzed. The lowest concentration of nitrate with 3.31 mg/L was from the northof the plain and adjacent of the central ranges which was due to high depth of groundwater level and fine grain of the sediments. The highest nitrate concentration with 37.23 mg/L was from the southeast of the area which can be attributed to the course sediments of this area. The results showed that anthropogenic activities are the main reason for the presence of nitrate in the groundwater. These activities can be caused by overuse of fertilizers by farmers or leakage from domestic sewage systems in the area. The moderate correlation (r = 0.497) between nitrate concentration of bicarbonate also support the use of nitrogen fertilizers in agricultural lands. In order to initial estimation of the vulnerability, simple AVI and GODS methods were used. According to the AVI method, northwest and east of the plain and based on GODS method, northwest and southeast of the plain were determined as the highest contamination potential in comparison with other parts of the plain.
https://ije.ut.ac.ir/article_60355_1014757aba7d68dc8edf0732564bc804.pdf
2016-12-21
517
531
10.22059/ije.2016.60355
groundwater
Contamination potential
Kordkandi-Duzduzan plain
Nitrate source
Shahla
Soltani
shahla.soltani93@ms.tabrizu.ac.ir
1
MSc. student in Hydrogeology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
AUTHOR
Asghar
Asghari Moghaddam
moghaddam@tabrizu.ac.ir
2
Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
LEAD_AUTHOR
Rahim
Barzegar
rm.barzegar@yahoo.com
3
Ph.D. student in Hydrogeology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
AUTHOR
Naeimeh
Kazemian
naimeh_kazemian@yahoo.com
4
East Azarbaijan Province Water and Waste Water Company, Tabriz, Iran
AUTHOR
[1]. Asghari Moghaddam A, Adigozalpour A. Investigation of Aluminum, Iron, Manganese, Chromium and Cadmium Concentrations in Groundwater of Oshnavieh Plain. Ecohydrology. 2016;3(2):167-179. [Persian]
1
[2]. Hooshangi N, Alesheikh AA, Nadiri AA, Asghari Moghaddam A. Evaluation and comparison of geostatistical and fuzzy interpolation methods in estimation of groundwater arsenic, Case study: Khoy plain aquifer. Ecohydrology. 2015;2(1):63-77. [Persian]
2
[3]. Krapac IG, Dey WS, Roy WR, Smyth CA, Storment E, Sargent, SL, et al. Impacts of swine manure pits on groundwater quality. Environmental Pollution. 2002;120(2): 475-492.
3
[4]. Criss RE, Davisson ML. Fertilizers, water quality, and human health. Environmental Health Perspective. 2004;112(10): A536-A536.
4
[5]. Postma D, Boesen C, Kristiansen H, Larsen F. Nitrate reduction in an unconfined sandy aquifer: water chemistry, reduction processes, and geochemical modeling. Water Resour Res. 1991;27: 2027-2045.
5
[6]. Widory D, Kloppmann W, Chery L, Bonnin J, Rochdi H, Guinamant JL. Nitrate in groundwater: an isotopic multi-tracer approach. Journal of Contaminant Hydrology. 2004;72: 165–188.
6
[7]. Hajinezhad A, Servati P, Yousefi H.Effect of The Landfill Leachate to quality of Groundwater of Bojnourd City With the Approach Standard Landfill Design or Replacement of Anaerobic Digester. Ecohydrology. 2015;2(3):301-310. [Persian]
7
[8]. Hajinezhad A, Ziaee Halimehjani E. Study landfill development in Rasht And latex management in order to reduce pollution Anzali Lagoon. Ecohydrology. 2015;2(1):11-22. [Persian]
8
[9]. Di HJ, Cameron KC. Nitrate leaching and pasture production from different nitrogen sources on a shallow stony soil under flood-irrigated dairy pasture. Australian Journal of Soil Research. 2002;40(2): 317-334.
9
[10]. Kraft GJ. Stites W. Nitrate impacts on groundwater from irrigated-vegetable systems in a humid north-central US sand plain. Agriculture, Ecosystem and Environment. 2003;100(1): 63-74.
10
[11].Heydarikochi E. Verification of changes in the nitrate with amount of raining in drinking water of fasa' s villages during years of 2007-2008.Journal of Fasa University of Medical Sciences. 2011;1(2):43-48.
11
[12]. Hamilton PA, Helsel DR. Effects of agriculture on groundwater quality in five regions of the United States. Ground Water.1995;33: 217–226.
12
[13]. Evans TA, Maidment DR. A spatial and statistical assessment of the vulnerability of Texas groundwater to nitrate contamination.Center for Research in Water Resources. 1995; Online Report 95-2.
13
[14]. Nolan BT, Hitt KJ, Ruddy CB. Probability of nitrate contamination of recently recharged ground waters in the conterminous United States.Environmental Sceince and Technology.2006;36: 2138-45.
14
[15]. Debernardi L, De Luca DA, Lasagna M. Correlation between nitrate concentration in groundwater and parameters affecting aquifer intrinsic vulnerability. Environmental Geology. 2008;55: 539–558.
15
[16]. Keskin T. Nitrate and heavy metal pollution resulting from agricultural activity: a case study from Eskipazar (Karabuk, Turkey). Environmental Earth Sciences. 2010;61: 703-721.
16
[17]. EPA. Drinking Water Standards. U. S. EPA. New York. 1995.
17
[18]. Gharekhani M, Nadiri AA, Asghari Moghaddam A, Sadeghi Aghdam F. Optimization of DRASTIC Model by Support Vector Machine and Artificial Neural Network for Evaluating of Intrinsic Vulnerability of Ardabil Plain Aquifer. Ecohydrology. 2015;2(3):311-324. [Persian]
18
[19]. Vrba J, Zoporozec A. Guidebook on mapping groundwater vulnerability. International Contributions to Hydrogeology.Verlag Heinz Heise GmbH and Co. KG. 1994.
19
[20]. Barzegar R, Asghari Moghaddam A, Baghban H. A supervisedcommittee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: acase study from Tabriz plain aquifer, Iran. Stochastic Environmental Research andRisk Assessment. 2016;30(3):883–899.
20
[21]. Babiker IS, Mohamed MAA, 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(1-3): 127-140.
21
[22]. Antonakos AK, Lambrakis NJ. Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates, based on the drastic model, an example from NE Korinthia, Greece. Journal of Hydrology. 2007;333(7): 288–304.
22
[23]. AsghariMoghaddam A, Barzegar R. Investigation of Nitrate Concentration Anomaly Source and Vulnerability of Groundwater Resources of Tabriz Plain Using AVI and GOD Methods. Water and Soil Science.2015;24(4): 11-27. [Persian]
23
[24]. Fakhri MS, AsghariMoghaddam A, Najib M, Barzegar R. Investigation of nitrate concentrations in groundwater resources of Marand plain and groundwater vulnerability assessment using AVI and GODS methods. Journal of Environmental Studies.2015;41 (1): 49-66. [Persian]
24
[25]. Samani S, Kalantari N, Rahimi MH. Nitrate contamination of groundwater and assessment potential and sources of pollution in the Evan plain.Water and Soil Resources Conservation.2012;1(3): 29-38. [Persian]
25
[26]. Nas B, Berktay A. Groundwater contamination by nitrates in the city of Konya,(Turkey): A GIS perspective.Journal of Environmental Management. 2006;79(1): 30-37.
26
[27]. Belmonte-Jimenez SI, Campos-Enriquez JO, Alatorre-Zamora MA. Vulnerability to contamination of the Zaachila aquifer, Oaxaca, Mexico.Geophysical international. 2005;44(3): 283-300.
27
[28]. Vias JM. Andreo B. Perles MJ. Carrasco F. A comparative study of four schemes for groundwater vulnerability mapping in a diffuse flow carbonate aquifer under Mediterranean climatic conditions.Environmental Geology. 2005;47(4): 586-595.
28
[29]. Fraga CM, Fernandes LFS, Pacheco FAL, Reis C, Moura JP. Exploratory assessment of groundwater vulnerability to pollution in the Sordo River Basin, Northeast of Portugal. Rem Revista Escola de Minas. 2013;66(1): 49–58.
29
[30]. Alizadeh Z. Investigation of hydrogeology and hydrogeochemistry of Bilverdi-Duzduzan plain aquifers. MSC dissertation.2008.p.206. [Persian]
30
[31]. APHA. Standard methods for the examination of water andwastewater, 17th edn. APHA, Washington, DC.1995.
31
[32]. Van Stempvoort D, Ewert L, Wassenaar L. AVI: A Method for groundwater protection mapping in the prairie provinces of Canada. PPWD pilot project, Sept. 1991- March 1992. Groundwater and Contaminants Project, Environmental Sciences Division, National Hydrology Research Institute. 1992.
32
[33]. Gogu R, Dassargues A. Current trend and future challenge in groundwater vulnerability assessment using overlay and index methods. Environ Geo. 1999;39(6): 549-559.
33
[34]. Kazakis N, Voudouris K. Comparison of three applied methods of groundwater vulnerability mapping: A case study from the Florina basin, Northern Greece. Proceedings of 9th International Hydrogeological Congress, Kalavrita, Greece. Advances in the Research of Aquatic Environment, Springer. 2011.p.359–367.
34
[35]. Foster SSD. Fundamental concepts in aquifer vulnerability, pollution risk and protection strategy. In: van Duijvenbooden W, van Waegeningh HG (eds) Proceedings and information in vulnerability of soil and ground-water to pollutants, vol 38. TNO Committee on Hydrological Research, The Hague. 1987.p.69–86.
35
[36]. Paez G. Evaluacion de la vulnerabilidad a la contaminacion de las agues subterraneas en el Valle del Cauca, InformeEjecutivo, Corporeginal del Valle del Cauca, Cauca, Colombia. 1990; 352(3): 95-120.
36
[37]. World Health Organization. Guidelines for drinking-water quality. 2014.
37
[38]. Kraft GI, Sites W, Mechanic Dj. Impact of arrigated vegetable agriculture in a humid North-Central U.S. sand plain aquifer. Ground Water. 1999;37(13): 572-580.
38
[39]. Gillardet J, Dupre B, Louvat P, Allegre CJ. Global silicate weathering and CO2 consumption rates deduced from the chemistry of large rivers. Chemical Geology. 1999;159(5): 3–10.
39
[40]. Jalali M. Geochemistry characterization of groundwater in an agricultural area of Razan, Hamadan, Iran. Environmental Geology. 2009;56: 1479-1488.
40
[41]. Han G. Liu CQ. Water geochemistry controlled by carbonate dissolution: a study of the river waters draining karst-dominated terrain, Guizhou province, China. Chem Geol. 2004;204: 1–21.
41
[42]. Bohlke JK. Groundwater recharge and agricultural contamination.Hydrogeology Journal.2002;10: 153–179.
42
[43]. Schoonen M, Brown CJ. The hydrogeochemistry of the Peconic River watershed: A quantitative approach to estimate the anthropogenic loadings in thewatershed, Geology of the Long Island and Metropolitan New York: SUNY Stony Brook, Long Island Geologist. 1994;24(2): 117-123.
43
ORIGINAL_ARTICLE
Evaluation of IHACRES hydrological model for simulation of daily flow
(case study Polrood and Shalmanrood rivers)
Due to the lack of information on most stations and economic constraints for collecting observed data. Identification of a suitable hydrological model can help in the management of water resources. The IHACRES model doesn't require complex data for input, so this model is superior to other models. In this research the efficiency of IHACRES model for simulation of daily flow for Polrood and Shalmanrood rivers which is located in a humid area are (Guilan Province) evaluated. According to results, R2 (Coefficient of Determination) are between 0.60 and 0.70 and also low APRE (Average Parameter Relative Error) results for both rivers that for Polrood is (APRE= 0.367) and for Shalmanrood is (APRE= 0.058). The results of the evaluation showed that the IHACRES model has low deflection for simulation of daily flow amounts and has good ability for simulation of flow in humid area that has high flow. The efficiency of IHACRES model in predicting daily flow was found to be fairly good.
https://ije.ut.ac.ir/article_60356_268c73bedf68cb38b0023a97b99935f4.pdf
2016-12-21
533
543
10.22059/ije.2016.60356
Model
hydrology
IHACRES
daily flow
Ebrahim
Amiri
eamiri57@yahoo.com
1
Department of Water Engineering, Islamic Azad University, Lahijan Branch, Lahijan.
LEAD_AUTHOR
Mir Makan
Roudbari Mousavi
moein.roodbari@yahoo.com
2
M.Sc. student of Engineering and Water Resources Management, Civil Engineering Department, Islamic Azad University Lahijan Branch, Lahijan
AUTHOR
1.Ashofte, P. Masah Bovani, A. 2008. Analysis uncertainty impacts of climate change on flood regime Bayesian approach; Case Study Aidoghmoush Basin, East Azerbaijan, thesis, Tehran University.
1
2. Beven, K. J. 2000. Rainfall-Runoff Modeling. John Willey and Sons Ltd, England, 200 pp.
2
3. Booij, M. J. 2002. Appropriate modeling of climate change impacts of river flooding, Ph.D. Thesis, University Twente, Netherlands, 179 pp.
3
4.Carla Carcano, E., Bartolini, P., Muselli, M., and Piroddi, L. 2008. Jordanrecurrent neural network versus IHACRES in modelling daily streamflows. J.Hydrol. 362: 291-307.
4
5. Croke B. F. W. and Jakeman A. J. 2008. Use ofthe IHACRES rainfall-runoff model in arid andsemiarid regions. In: Weather H. S. SorooshianS. and Sharma K. D. (Eds.), HydrologicaModeling in Arid and Semi-arid Areas.Cambridge University Press, Cambridge. pp.41-48.
5
6. Day.P.J and Croke.B.F.W.(2003), Evalution of streamflow predictions by the IHACRES rainfall-runoff model in two South African catchments, Enviromental Modeling & software 18.705-712
6
7. Dooge, J.C.I. 1973. Linear Theory of Hydrologic Systems. Technical Bulletin No 1468. United States Department of Agriculture,Washington DC. 327.
7
8.Doosti, M. Shahedi, K. 2012. IHACRES in the semi-conceptual model to simulate daily flow (Case study: Tamar Basin )
8
9. Eyad Abushandi & Broder Merkel. Modelling Rainfall Runoff Relations Using HEC-HMS and IHACRES for a Single Rain Event in an Arid Region of Jordan. Water Resour Manage (2013) 27:2391–2409
9
10.Goodarzi, M. Masah, A. 2012. Comparison of hydrologic models IHACERS, SWAT. SIMHYD Gharehsou in simulating runoff, water management and irrigation Journal 2 (1), 40-25.
10
11.KheyrFam , H. Sadeghi, H.R. 2011. Using the model to estimate daily discharge IHACRES some watersheds in Golestan Province Journal of Watershed Management
11
12. Littlewood, I. G. K., J. R. Parker and D. A. Post. 1997. IHACRES Catchment-Scale Rainfall-Streamflow Modelling (PC version), Center for Ecology and Hydrology, The Australian National University, 95 p.
12
13.Littlewood, L.G., Clarke, R.T., Collischonn, W., and Croke, B.F.W. 2007.Predicting daily Streamflow using rainfall forecasts, a simple loss module andunit hydrographs: Two Brazilian catchments.Environmental Modelling andSoftware, 22: 1229-1239
13
14. Motovilov, Y.G., L. Gottschalk, K. Engeland and A. Rohde. 1999. Validation of adistributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 98-99: 257-277.
14
15. Sadeghi, S.H.R., B. Yasrebi, and F. NoorMohammadi. 2005. Development and analysis of monthly precipitation runoff relationships for Haraz Watershed in Mazandaran Province. Journal of Agricultural Sciences and Natural Resources of Khazar, 3(1): 1-12. (In Persian)
15
16.Sadeghi, H.R. Moradi, H. 2004. Effectiveness of different methods of statistical analysis on rainfall-runoff modeling, Journal of Agricultural Sciences and Natural Resources, Gorgan
16
17. Sedaghat, A. Fatahi, A. 2008. Drought early warning indicators in Iran. Journal of Geography and Development, University of Sistan and Baluchestan. The sixth volume. Numbers 11: 76-59
17
18. Wondimu Tolcha, Istvan Waltner. Performance Assessment of the IHACRES Model in the Upper Catchment of Dawa Sub-basin, Borna Rangeland, Ethiopia. Engineering and Applied Sciences. Vol. 1, No. 2, 2016, pp. 13-19. doi: 10.11648/j.eas.20160102.11
18
19. Ye, W., A.J. Jakeman and P.C. Young. 1998. Identification of improved rainfallrunoff models for an ephemeral low-yielding Australian catchment. Environmental Modelling and Software, 13: 59-74.
19
20. Zarei, M., Ghanbarpour, M., Habibnezhad Roshan, M., and Shahedi, K. 2010. Calibration and evaluation of IHACRES hydrological model to simulate runoff. J. Water Soil Agric. Sci. Tech. 25: 104-114.
20
21. Zarei, M., Ghanbarpoor, M.R. 2009. River flow simulations using rainfall-runoff models IHACRES (Kasilian River case study). Of watershed Iran. Number 8: 20-11
21
22. Zlatunova, D., G. Gergov and I.G. Littlewood. 2002. Preliminary assessment of a unit hydrograph-based continuous simulation model for bulgarian rivers, Proceedings International Environmental Modelling and Software Society Conference, iEMSs. Lugano, Switzerland. Vol. I, 405-409 pp.
22
ORIGINAL_ARTICLE
Investigation of effect of basin’s physiographic and climatic parameters in seasonal river flow simulation
Physiographic characteristics and climatic conditions are factors which contributing to river flow regime and understanding of relations between these factors and river flow in a basin result in its application for the ungauged sub-basins river flow prediction. In this research the relation between physiographic and climatic parameters of Golestan province and rivers flow were examined by application of M5 regression tree model, k-nearest neighbors (KNN) model and multiple linear model (MLR). Daily recorded data for 28 years (1984-2011) including rainfall, temperature and river flow, belonging to hydrometry and meteorological stations of 39 sub-basins were used to extract seasonal series. The average of R and RMSE criteria in different seasons were 0.768 and 0.800 for M5 model, 0.885 and 0.501 for KNN model and 0.693 and 1.205 for MLR model which revealed better results for KNN model. In addition, according to R and RMSE, the accuracy of modeling results in different seasons were respectively as winter, autumn, spring and summer. In other words, the results of predicted river flows in the wet seasons were more accurate than dry seasons. Moreover, the MBE criterion indicated that the KNN model led to underestimation for spring and winter and overestimation for summer and autumn, M5 model led to underestimation in spring and overestimation in other seasons and MLR model had underestimation in winter and overestimation in other seasons.
https://ije.ut.ac.ir/article_60357_2dd4180edc54d3ee4b8a326d9a82f212.pdf
2016-12-21
545
555
10.22059/ije.2016.60357
Keywords: River Flow
ungauged basins
M5 Decision Tree Model
KNN Model
MLR Model
Zahra
Naeimi Kalourazi
zahranaeimi.70@gmail.com
1
M.Sc. Graduate, Dept. of Water Resources Engineering, Gorgan University of Agricultural Sciences and Natural Resource
AUTHOR
Khalil
Ghorbani
ghorbani.khalil@yahoo.com
2
Deptartment of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources
LEAD_AUTHOR
Meysam
Salarijazi
meysam.salarijazi@yahoo.com
3
Deptartment of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources
AUTHOR
Amir ahmad
Dehghani
a.dehghani@gau.ac.ir
4
Deptartment of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources
AUTHOR
[1].Govindaraju RS. Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering. 2000; 5(2): 115-123.
1
[2].Salajegheh A, Fathabadi A, Gholami H. Predict river discharge using the nearest neighbor. 5th national conference on science and management engineering Iran. Gorgan University of Agricultural Sciences and Natural Resources. 2010. [Persian].
2
[3].Lohani AK, Kumar R, Singh RD. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology. 2012; 442: 23-35.
3
[4].Kisi Ö. Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish Journal of Engineering and Environmental Sciences. 2005; 29(1): 9-20.
4
[5].Nabizadeh M, Mosaedi A, Hesam M, Dehghani AA. Comparing the performance of Fuzzy based models in stream flow on Lighvan River. J. of Water and Soil Conservation. 2012; 19(1): 117-134. [Persian].
5
[6].Parviz L, Kholghi M, Malmir M. Comparison of Methods temporal resolution and artificial neural networks in anticipation of a seasonal river flow. Journal of Iran Water Research. 2008; 2(2): 9-17. [Persian].
6
[7].Zarezadeh-Mehrizi M, Bozorg Haddad O. Inflow Simulation and Forecasting Optimization Using Hybrid ANN-GA Algorithm. Journal of Water and Soil. 2010; 24(5): 942-954. [Persian].
7
[8].Seyedian SM, Soleimani M, Kashani M. Predicting streamflow data-driven model and time series. Iranian Journal of Eco Hydrology. 2015; 1(3): 167-179. [Persian].
8
[9].Ahmadi F, Radmanesh F, Mirabbasi Najaf abadi R. Comparison between Genetic Programming and Support Vector Machine Methods for Daily River Flow Forecasting (Case Study: Barandoozchay River). Journal of Water and Soil. 2014; 28(6): 1162-1171. [Persian].
9
[10].Sanikhani H, Dinpajuh Y, Ghorbani MM. River flow modeling using K- nearest neighborhood and intelligent methods. Journal of Water and Soil Science. 2015; 25(1): 219-233. [Persian].
10
[11].Firat M, Gungor M. River flow estimation using adaptive neuro-fuzzy inference system. Journal of Mathematics and Computers in Simulation. 2006; 75(3-4): 87-96
11
[12].Negaresh H, Tavousi T, Mehdinasab M. Modeling the Production of Runoff in Kashkan River Catchment Based on the Statistical Methods. Journal of Research in urban ecology. 2014; 3(6): 81-92. [Persian].
12
13. Zare Abyaneh H, Bayat Varkeshi M. Evaluation of Artificial Intelligent and Empirical Models in Estimation of
13
Annual Runoff. Journal of Water and Soil. 2010; 25(2): 365-379. [Persian].
14
[14].Eskandarinia AR, Nazarpour H, Ahmadi MZ, Teimouri M, Moshfegh MZ. Examine the effect of antecedent precipitation in the river flow estimates by artificial neural network (case study: Bakhtiari River). Journal of watershed management. 2011; 2(3): 51-62. [Persian].
15
[15].Khedmati H, Manshouri M, Heydarizade M, Sedghi H. Zonation and Estimation of Flood Discharge in Unguaged Sites Located in South-East Basins of Iran Using a Combination of Flood Index and Multi-Variable Regression Methods (Sistan and Baluchistan, Kerman, Yazd and Hormozgan Provinces). J. Water Soil. 2010; 24: 3: 593-609. [Persian].
16
[16].Akbari M, Van Overloop PJ, Afshar A. Clustered K nearest neighbor algorithm for daily inflow forecasting. Water resources management. 2011; 25(5):1341-57.
17
[17].Ghorbani Kh, Sohrabian E, Salarijazi M. Evaluation of hydrological and data mining models in monthly river discharge simulation and prediction (Case study: Araz-Kouseh watershed). Journal of Water and Soil Conservation. 2016; 23(1): 203-217. [Persian].
18
[18]. Ghorbani Kh, Meftah Halaghi M, Sohrabian E. Evaluation of hydrological and data-based models in estimation of daily runoff in Galikesh watershed. Int. J. Hydrology Science and Technology. 2016; 6(1): 27-44.
19
[19].Naeimi Kalourazi Z, Ghorbani Kh, Salarijazi M, Dehghani A. A. Estimation of monthly discharge using climatic and physiographic parameters of ungauged basins. Journal of Water and Soil Conservation. 2016; 23(3): 207-224. [Persian].
20
ORIGINAL_ARTICLE
Trend analysis, modeling and uncertainty estimation of streamflow recession (Case Study: Bashar River of Kohgiloyeh and Boyer Ahmad Province)
Streamflow recession indicates the river network balance between revenue and losses of river. Recession curve expreses the storage- output relationship for the catchment. The aim of this study was trend anaylysis, modeling of Streamflow recession and uncertainty estimation in Shahmokhtar station on the Bashar River in Kohgiluyeh and Boyer Ahmad province. Based on the results of the Mann-Kendall, discharge trend at studied station was very little increasing, but there was no significant trend. After determination of parts, the Maillet, Baronz, Boussinesq, Horton, Drouge and exponential reservoir models were fitted. In this regard, initially the different parts of the recession lamb (in multireservoir models) were determined and the parameters of Maillet, Barnes, Boussinesq, Horton, Coutagne, Drogue and exponential reservoir models were estimated. To calibrate the coefficients of models, in addition to overlaying the estimated and observed hydrographs, the sum of square error criteria was used. Comparing the results also showed that models of Drouge, Barnes, Horton, Boussinesq exponential reservoir and Maillet could be fitted well, respectively.
https://ije.ut.ac.ir/article_60358_96e94d8bb472dbf566cbef5711243533.pdf
2016-12-21
557
567
10.22059/ije.2016.60358
Streamflow recession
Hydrograph
Bashar River
trend
Kohgiluyeh and Boyer ahmad
Mehdi
Bahrami
mehdibahrami121@gmail.com
1
Department of Water Engineering, Faculty of Agriculture, Fasa University, Fasa, Iran
LEAD_AUTHOR
Abolhasan
Fathabadi
fathabadi@gmail.com
2
Faculty of Natural Resources, Gonbad Kavoos University
AUTHOR
Ali
Hojati
alihojati@gmail.com
3
MSc. of Civil Engineering, Water Structures
AUTHOR
Moore RD. Storage-outflow modelling of streamflow recessions, with application to a shallow-soil forested catchment. Journal of Hydrology. 1997; 198: 260-270.
1
Griffiths GA, Clausen B. Streamflow recession in basins with multiple water storages. Journal of Hydrology. 1997; 190: 60-74.
2
Tallaksen LM. A review of baseflow recession analysis. Journal of Hydrology. 1995; 165: 349-370.
3
Brodie RS, Hostetler S. A review of technique for analyzing basflow from stream hydroghraphs. 2007.
4
Wittenberg H. Nonlinear analysis of low flow recession curves. FRIENDS: Flow Regimes from International and Experimental Network Data. IAHS Publ. 1994; 221:61.
5
Sujono J, Shikasho S, Hiramatsu K. A comparison of techniques for hydrographic recession analysis. Hydrological Processes. 2004; 18: 403-413.
6
Dewandel B, Lachassagne P, Bakalowicz M, Weng PH, Al-Malki A. Evaluation of aquifer thickness by analysing recession hydrographs. Application to the Oman ophiolite hard-rock aquifer. Journal of Hydrology. 2003; 274:248-269.
7
Von Storch VH. Misuses of statistical analysis in climate research, in H. V. Storch and A. Navarra (eds), Analysis of Climate Variability: Applications of Statistical Techniques, Springer-Verlag Berlin, 1995; 11–26.
8
Hamed KH, Rao AR. A modified Mann-Kendall trend test for autocorrelated data, Journal of Hydrology. 1998; 204: 182–196.
9
Yue S, Pilon P, Phinney B, Cavadias G. The influence of autocorrelation on the ability to detect trend in hydrological series, Hydrology Processes. 2002; 16: 1807–1829.
10
Sen PK. Estimates of the regression coefficient based on Kendall’s tau, J. American Statist. Assoc. 1968; 63: 1379–1389.
11
Chapman TG. A comparison of algorithms for stream flow recession and baseflow separation. Hydrology Processes. 1999; 13: 701–714.
12
Amit H, Lyakhovsky V, Katz A, Starinsky A, Burg A. Interpretation of spring recession curves. Groundwater. 2002; 40: 543-551.
13
Nathan RJ, McMahan TA. Evaluation of automated techniques for baseflow and recession analysis. Water Resources Research. 1990; 26 (7):1465-1473.
14
Vogel, RM, Kroll CN. Regional geohydrologic-geomorphic relationships for the estimation of low flow statistics. Water Resources Research. 1992; 28 (9): 2451-2458.
15
Chapman TG. Modelling stream recession flows. Environmental Modelling and Software. 2003; 18 (8-9): 683-692.
16
Rees HG, Holmes MGR, Young AR, Kansakar SR. Recession based hydrological models for estimating low flow in ungauged catchment in the Himalayas. Hydrology and Earth System Sciences. 2004; 8(5): 891-902.
17
ORIGINAL_ARTICLE
Improving the Performance of ANN Model, Using Wavelet Transform and PCA Methodfor Modeling and Predict Biochemical Oxygen Demand (BOD)
In recent decades, the developments of artificial intelligence to predict hydrologic models have been widely used. In this study, the ability of artificial neural network(ANN) models for modeling and predict the biological oxygen demand (BOD) is located on the Karun River in West Iran were evaluated. To improve the simulation results, wavelet analysis was used as a hybrid model. BOD index monthly time series Karun River in Mollasani station for 13 years (2002-2014) and the use of auxiliary variables dissolved oxygen (DO), river flows and monthly temperature was simulated. Thebest of inputs of model by the Principal Component Analysis method (PCA) was selected. To evaluate and compare the performance of models, Root Mean Square Error(RMSE) criteria, Coefficient of Determination (R2) and Akaike's Information Criterion (AIC) were used. The results showed that ANN has a margin of error of 0.0412 and the coefficient of determination 0.76 and application of wavelet transform on input data model improves the results to error of 0.0273 and the coefficient of determination 0.89.
https://ije.ut.ac.ir/article_60359_ae448931c115491dc57eba56bc37f18c.pdf
2016-12-21
569
585
10.22059/ije.2016.60359
BOD
Wavelet transform
PCA
Karun River
Freidon
Radmanesh
freidon_radmanesh@yahoo.com
1
Associate Professor of Hydrology and Water Resources, Faculty of Water Sciences Eng. Shahid Chamran University, Ahvaz, Iran.
LEAD_AUTHOR
Amir
Pourhaghi
pourhaghiamir@yahoo.com
2
Ph.D. Student of Water Resources Engineering, Faculty of Water Sciences Eng. Shahid Chamran University, Ahvaz, Iran.
AUTHOR
Abazar
Solgi
a-solgi@phdstu.scu.ac.ir
3
Ph.D. Student of Water Resources Engineering, Faculty of Water Sciences Eng. Shahid Chamran University, Ahvaz, Iran.
AUTHOR
منابع
1
[1] Farhadian M, Haddad O, Seifollahi-Aghmiuni S, Loáiciga H. Assimilative Capacity and Flow Dilution for Water Quality Protection in Rivers. Journal of Hazardous, Toxic, and Radioactive Waste(ASCE). 2014;19(2):04014027-1-8.
2
[2] Dogan E, lent Sengorur B, Koklu R. Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. Journal of Environmental Management. 2009;90:1219-35.
3
[3] Chapman D. Water Quality Assessments. ed f, editor. London: Chapman and Hall Ltd; 1992.
4
[4] Radwan M, Willems P, El-Sadek A, Berlamont J. Modelling of dissolved oxygen and biochemical oxygen demand in river water using a detailed and simplified model. Int J River Basin Manage. 2003;1(2):97-103.
5
[5] Lopes JF, Dias JM, Cardoso AC, Silva CIV. The water quality of the Ria de Aveiro lagoon, Portugal: from the observations to the implementation of a numerical model. Mar Environ Res 2005;60:594-628.
6
[6] Delzer GC, McKenzie SW. Fıve-Day Bıochemıcal Oxygen Demand. Edition T, editor. USGS TWRI Book9-A7 U.S. Geological Survey TWRI Book; 1999.
7
[7] Suen JP, Eheart JW, Asce M. Evaluation of neural networks for modelling nitrate concentration in rivers. Journal Water Resources Planning Management. 2003;129:505-10.
8
[8] Xiang SL, Liu ZM, Ma LP. Study of multivariate linear regression analysis model for ground water quality prediction. Guizhou Sci. 2006;24:60-2.
9
[9] Wu HJ, Lin ZY, Guo SL. The application of artificial neural networks in the resources and environment. Resour Environ Yangtze Basin 2000;9:237-241.
10
[10] Cobaner M, Unal B, Kisi Ö. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydrometeorological data. Journal of Hydrology. 2009;367:52-61.
11
[11] Kişi Ö. Evolutionary fuzzy models for river suspended sediment concentration estimation. Journal of Hydrology. 2009;372(1–4):68-79.
12
[12] Ahmed AAM, Shah MA. Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King SaudUniversity - Engineering Sciences. 2015:1-7.
13
[13] Sarkara A, Pandeyb P. River Water Quality Modelling using Artificial Neural Network Technique. Aquatic Procedia 2015;4:1070-1077.
14
[14] Safavi HR. Prediction of River Water Quality by Adaptive Neuro Fuzzy Ineerence System(ANFIS). Journal of Enviromental Studies 2010;36(53):1-10.
15
[15] Christos S A, Papaspyros. J.N.E., Tsihrintzis VA. An artificial neural network model and design equations for BOD and COD removal prediction in horizontal subsurface flow constructed wetlands. Chemical Engineering Journal. 2008;143(1–3):96-110.
16
[16] Najah A, Elshafie A, Karim O, Jaffar O. Prediction of Johor river water quality parameters using artificial neural networks. European Journal of Scientific Research 2009;28(3):422-435.
17
[17] Asadollahfardi G, Taklify. A, Ghanbari A. Application of Artificial Neural Network to Predict TDS in Talkheh Rud River. Journal of Irrigation and Drainage Engineering(ASCE). 2012;138(4):363–370.
18
[18] Wen X, Fang J, Diao M, Zhang C. Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China. Environmental monitoring and assessment 2013;185(5):4361-4371.
19
[19] Parmar KS, Bhardwaj R. Wavelet and statistical analysis of river water quality parameters. Applied Mathematics and Computation. 2013;219:10172-10182.
20
[20] Jouanneau S, Recoules L, Durand MJ, Boukabache A, Picot V, Primault Y, et al. Methods for assessing biochemical oxygen demand (BOD): A review. Water Research. 2014;49:62-82.
21
[21] Liang S, Han S, Sun Z. Parameter optimizationmethod for the water quality dynamic model based on data-driven theory. Marine Pollution Bulletin. 2015;98(1–2):137-147.
22
[22] Olyaie E, Banejad H, Samadi MT, AR Rahmani AR, and Saghi MH. Performance Evaluation of Artificial Neural Networks for Predicting Rivers Water Quality Indices (BOD and DO) in Hamadan Morad Beik River. water and soil science. 2010;20.1(3):200-210.
23
[23] Bierkens MFB. Modeling water table fluctuations by means of a stochastic differential equation. Journal of Water Resources Research. 1988;34(10):2485-2499.
24
[24] Shafaei M, Fakheri Fard A, Darbandi S, and Ghorbani M. Prediction Daily Flow of Vanyar Station Using ANN and Wavelet Hybrid Procedure. Journal of Irrigation & Water Engineering. 2014;4(24):113-29.
25
[25] Polikar R. Fundamental Conceptand an Overview of the Wavelet Theory Wavelet Tutorial Rowan university: Glassbord, NJ.08028; 1996.
26
[26] Sifuzzaman M, Islam MR, and Ali MZ. Application of Wavelet Transform and its advantages Compared to Fourier Transform. Journal of Physical Sciences. 2009;13:121-134.
27
[27] Thuillard M. A review of wavelet networks, wavelets, fuzzy wavelets and their application. ESITin: Presented in Conference,. 2000.
28
[28] Okkan U. Wavelet neural network model for reservoir inflow prediction. Scientia Iranica. 2012;19(6):1445–1455.
29
[29] Alizadeh MJ, Kavianpour MR. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Marine Pollution Bulletin. 2015;98(1–2):171-178.
30
[30] Riad S, Mania J, Bouchaou L, Najjar Y. Rainfall-runoff model usinganartificial neural network approach. Mathematical and Computer Modelling. 2004;40(7–8):839-846.
31
[31] Solgi A. Stream flow forecasting using combined Neural Network Wavelet model and comparsion with Adaptive Neuro Fuzzy Inference System and Artificial NeuralNetwork methods(Case Study: Gamasyab River, Nahavand). [Persian]. IRAN: Shahid Chamran University of Ahvaz,Iran.; 2014.
32
[32] McCulloch WS, Pitts W. A logic calculus of the ideas imminent in nervous activity. Bull Math Biophys. 1943;5:115-33.
33
[33] Rosenblatt F. Priciples of Neurodynamics: Perceptrons and the Theory of Brain Mechanics. Spartan1962.
34
[34] Gallant SI. Neural Network Learning and Expert Systems: The MIT press; 1993.
35
[35] Smith M. Neural Networks for Statistical Modelling. Van Nostrand Reinhold.1994. 235p.
36
[36] Singh KP, Basant A, Malik A, Jain G. Artificial neural network modeling of the river water quality-A case study. Ecological Modelling 2009;220:888–895.
37
[37] Govindaraju RS. Artificial Neural Networks in Hydrology. II: Hydrologic Applications. Journal of Hydrologic Engineering. 2000;5(2):124-137.
38
[38] Apostolopoulou MS, Sotiropoulos DG, Livieris IE, Pintelas P. A memoryless BFGS neural network training algorithm. 7th IEEE International Conference on. 2009:216 - 221.
39
[39] Minsky M, Papert S. Perceptrons. Cambridge: MIT Press,; 1969.
40
[40] Jones AJ, Tsui A, de Oliveira AG. Neural models of arbitrary chaotic systems: construction and the role of time delayed feedback in control and synchronization.. Complex Int 2002;9:1-9.
41
[41] Nourani V, Kisi Ö, KomasiM. Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology 2011;402:41–59.
42
[42] Mallat SG. A wavelet tour of signal processing. 2, editor: San Diego; 1998. 557 p.
43
[43] Hutcheson G, and Nick S. The multivariate social scientist: Introductory statistics using generalized linear models. Thousand Oaks, CA,Sage Publications. 1999.
44
[44] Johnson RA, and Wichern DW. Applied multivariate statistical analysis 3rd Ed, editor. Englewood Cliffs, SA1982. 590 p.
45
[45] Caliendo C, Parisi A. Principal component analysis applied to crash data on multilane roads. Third international SIIV Congress; 20-22 September; Bari, Italy: ANCONA SIIV 2005. p. 1-7.
46
[46] Cattel RB. The scree test for the number ofthe factor. Multivariate Behavioral Research. 1996;1(2):245-76.
47
[47] Singh KP, Malik A, Mohan D, Sinha S. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India) a case study. Water Research. 2004;38(18):3980-3992.
48
[48] Nourani V, Komasi M, Mano A. A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling. Water Resour Manage 2009;23:2877–2894.
49
[49] Solgi A, Nourani V, Pourhaghi A. Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neuro Fuzzy Inference System (Case Study: Varayneh Station, Nahavand). Advances in Civil Engineering. 2014;2014:1-12.
50
ORIGINAL_ARTICLE
Remediation of cadmium contaminated water by Populus nigraSawdust as a low-cost biosorbent:
Process optimization by using response surface methodology
In this study, the removal of Cd(II) from aqueous solutions have been carried out using Populusnigra saw dust as low-cost, readily available biosorbent. Various physico-chemical parameters such as pH, initial metal ion concentration, and adsorbent dosage level and contact time were studied at room temperature to optimize the conditions for maximum adsorption. The central composite design was carried out with aqueous solution of cadmium with various concentrations ranging from 5-25 mgl-1. The range of variation for the other variables including pH, sawdust dosage and contact time are 2-10, 5-50 gl-1, and 5-105 minutes, respectively. A good agreement between predictive model for cadmium removal by sawdust and experimental results was observed (R2= 0.9283 and RMSE=2.93%). The maximum removal of 96.25% was achieved at cadmium concentration 38.75 mgl-1, pH of 6.5, saw dust dosage of 10 gl-1 and contact time of 80 min as the optimal conditions. The highly efficient and the rapid uptake of Cd(II) by low cost saw dust indicated that it could be an excellent alternative for the removal of cadmium by sorption process from contaminated aqueous solutions.
https://ije.ut.ac.ir/article_60360_75ce539bf4e64f3e544afbfc77c1e686.pdf
2016-12-21
587
596
10.22059/ije.2016.60360
Bioadsorbent
Heavy metals
modeling
Response Surface Methodology
water pollution
Farrokh
Asadzadeh
f.asadzadeh@urmia.ac.ir
1
Department of Soil Science, Urmia University, Urmia, Iran
LEAD_AUTHOR
Mahdi
Maleki Kaklar
k.mahdi.maleki@gmail.com
2
Department of Chemical Engineering, University of Zanjan, Zanjan, Iran
AUTHOR
Mohsen
Barin
m.barin@urmia.ac.ir
3
Department of Soil Science, Urmia University, Urmia, Iran
AUTHOR
منابع
1
Calace N, Di Muro A, Nardi E, Petronio BM, Pietroletti M. Adsorption isotherms for describing heavy-metal retention in paper mill sludges. Industrial & engineering chemistry research. 2002;41(22):5491-7.
2
Larous S, Meniai AH, Lehocine MB. Experimental study of the removal of copper from aqueous solutions by adsorption using sawdust. Desalination. 2005;185(1):483-90.
3
Rao KS, Mohapatra M, Anand S, Venkateswarlu P. Review on cadmium removal from aqueous solutions. International Journal of Engineering, Science and Technology. 2010;2(7): 81-103.
4
Institute of Standards and Industrial Research of Iran. Drinking water- Physical and chemical specifications. ISIRI, 1053. 2008; 5th Revision.
5
Ayyappan R, Sophia AC, Swaminathan K, Sandhya S. Removal of Pb (II) from aqueous solution using carbon derived from agricultural wastes. Process Biochemistry. 2005;40(3):1293-9.
6
Li Q, Zhai J, Zhang W, Wang M, Zhou J. Kinetic studies of adsorption of Pb (II), Cr (III) and Cu (II) from aqueous solution by sawdust and modified peanut husk. Journal of Hazardous Materials. 2007;141(1):163-7.
7
Hasan SH, Srivastava P. Batch and continuous biosorption of Cu 2+ by immobilized biomass of Arthrobacter sp. Journal of environmental management. 2009;90(11):3313-21.
8
Davarnejad R, Panahi P. Cu (II) and Ni (II) removal from aqueous solutions by adsorption on Henna and optimization of effective parameters by using the response surface methodology. Journal of Industrial and Engineering Chemistry. 2016; 33:270-5.
9
Arous O, Gherrou A, Kerdjoudj H. Removal of Ag (l), Cu (II) and Zn (ll) ions with a supported liquid membrane containing cryptands as carriers. Desalination. 2004;161(3):295-303.
10
10. Shukla A, Zhang YH, Dubey P, Margrave JL, Shukla SS. The role of sawdust in the removal of unwanted materials from water. Journal of Hazardous Materials. 2002;95(1):137-52.
11
11. Semerjian L. Equilibrium and kinetics of cadmium adsorption from aqueous solutions using untreated Pinus halepensis sawdust. Journal of Hazardous Materials. 2010;173(1):236-42.
12
12. Zheng W, Li XM, Wang F, Yang Q, Deng P, Zeng GM. Adsorption removal of cadmium and copper from aqueous solution by areca- a food waste. Journal of Hazardous Materials. 2008;157(2):490-5.
13
13. Oyedeji OA, Osinfade GB. Removal of copper (II), iron (II) and lead (II) ions from mono-component simulated water effluent by adsorption on coconut husk. African Journal of Environmental Science and Technology. 2010; 4 (6):382-387.
14
14. Šćiban M, Radetić B, Kevrešan Ž, Klašnja M. Adsorption of heavy metals from electroplating wastewater by wood sawdust. Bioresource Technology. 2007;98(2):402-9.
15
15. Crist RH, Martin JR, Crist DR. Interaction of metal ions with acid sites of biosorbents peat moss and Vaucheria and model substances alginic and humic acids. Environmental science & technology. 1999;33(13):2252-6.
16
16. Seki K, Saito N, Aoyama M. Removal of heavy metal ions from solutions by coniferous barks. Wood Science and Technology. 1997;31(6):441-7.
17
17. Reddad Z, Gerente C, Andres Y, Le Cloirec P. Adsorption of several metal ions onto a low-cost biosorbent: kinetic and equilibrium studies. Environmental science & technology. 2002;36(9):2067-73.
18
18. Titi OA, Bello OS. An overview of low cost adsorbents for copper (II) ions removal. Journal of Biotechnology & Biomaterials. 2015; 177(5): 1-13.
19
19. Akunwa NK, Muhammad MN, Akunna JC. Treatment of metal-contaminated wastewater: A comparison of low-cost biosorbents. Journal of environmental management. 2014;146:517-23.
20
20. Salazar-Rabago JJ, Leyva-Ramos R. Novel biosorbent with high adsorption capacity prepared by chemical modification of white pine (Pinus durangensis) sawdust. Adsorption of Pb (II) from aqueous solutions. Journal of environmental management. 2016;169:303-12.
21
21. Taty-Costodes VC, Fauduet H, Porte C, Delacroix A. Removal of Cd (II) and Pb (II) ions, from aqueous solutions, by adsorption onto sawdust of Pinus sylvestris. Journal of Hazardous Materials. 2003; 105(1):121-42.
22
22. Memon SQ, Memon N, Solangi AR. Sawdust: A green and economical sorbent for thallium removal. Chemical Engineering Journal. 2008;140(1):235-40.
23
23. Naiya TK, Chowdhury P, Bhattacharya AK, Das SK. Saw dust and neem bark as low-cost natural biosorbent for adsorptive removal of Zn (II) and Cd (II) ions from aqueous solutions. Chemical Engineering Journal. 2009;148(1):68-79. 20.
24
24. Yu B, Zhang Y, Shukla A, Shukla SS, Dorris KL. The removal of heavy metals from aqueous solutions by sawdust adsorption-removal of lead and comparison of its adsorption with copper. Journal of hazardous materials. 2001; 84(1):83-94.
25
25. Kumar NM, Ramasamy R, Manonmani HK. Production and optimization of L-asparaginase from Cladosporium sp. using agricultural residues in solid state fermentation. Industrial Crops and Products 2013;43:150-8.
26
26. Alkhatib MF, Mamun AA, Akbar I. Application of response surface methodology (RSM) for optimization of color removal from POME by granular activated carbon. International Journal of Environmental Science and Technology. 2015;12(4):1295-302.
27
27. Aghaeinejad-Meybodi A, Ebadi A, Shafiei S, Khataee A, Rostampour M. Degradation of antidepressant drug fluoxetine in aqueous media by ozone/H2O2 system: process optimization using central composite design. Environmental technology. 2015;36(12):1477-88.
28
28. Yu B, Zhang Y, Shukla A, Shukla SS, Dorris KL. The removal of heavy metal from aqueous solutions by sawdust adsorption -removal of copper. Journal of Hazardous Materials. 2000; 80(1):33-42.
29
29. Levenspiel O. Chemical engineering reaction. Wiley-Eastern Limited, New York. 1972.
30
30. Hashem A, Adam E, Hussein HA, Sanousy MA, Ayoub A. Bioadsorption of Cd (II) from contaminated water on treated sawdust: adsorption mechanism and optimization.Journal of Water Resource and Protection 2013; 5: 82-90.
31
ORIGINAL_ARTICLE
Assessing the conservation impacts of climate change based on temperature projected on 21 century (Case study: Arazkoseh and Nodeh stations)
Assessing the potential impacts of 21st-century climate change on species distributions and ecological processes requires climate scenarios with sufficient spatial resolution. In this study we projected future changes in maximum temperature and minimum temperature under CMIP3 SRES and CMIP5 RCPs scenarios with two station-based datasets (Arazkoseh and Nodeh) of the eastern Golestan province. Change scenarios (2046-2065 and 2080-2099) are compared to the reference period (1986-2005).Therefore, 8 GCM models under 6 emission scenarios are downscaled by LARS-WG and SDSM. The results indicated that the largest increase in temperature among the old emission scenarios and new emission scenario are projected by A1B and RCP8.5, respectively. The variation between model projections is considerable. The uncertainty range is large for the change in warm seasonal period. For the two future periods, the downscaling methods produce seasonal increases in the temperature with an almost ordinal order of summer, spring, winter and autumn. Also, results show that temperature indices based on seasonal maxima are generally projected to increase more than minima. In general, uncertainty generates large spread ranges of estimated climate change impacts, therefore due to wide ranges of temperatures projection, to provide a complete picture of possible climate change impact studies that focus on a single or a few of climate models open to the charge of cherry-picking.
Keywords: .
https://ije.ut.ac.ir/article_60361_0c02160d36b05f695406750170e218d3.pdf
2016-12-21
597
609
10.22059/ije.2016.60361
Emission scenario
Representative Concentration Pathways
LARS-WG
SDSM
Uncertainty
Maryam
Ahmadvand Kahrizi
ahmadvandk.maryam@gmail.com
1
MSc Student of Watershed Management, Gonbad Kavous University, Iran
AUTHOR
Hamed
Rouhani
rouhani.hamed@yahoo.com
2
Dep. Of Range and Watershed Management, Gonbad KavousUniversity, Iran
LEAD_AUTHOR
[1]. IPCC. Climate change 2013. The physical science basis. Summary for policy makers. Contribution of Working Group I to the Intergovernmental Panel on Climate Change. Cambridge University Press. 2013; 18.
1
[2]. Foster G, Rahmstorf S. Global temperature evolution 1979-2010. Environmental Research Letters. 2011; 6 (4): 044022.
2
[3]. Gillett N P, Arora V K, Flato G M, Scinocca J F, Salzen k. Improved constraints on 21st- century warming derived using 160 years of temperature observations. Geophysical Research Letters. 2012; 39 (1): L01704.
3
[4]. Huber M, Knutti R. Anthropagonic and natural warming inferred from changes in earth's energy balance. Nature Geosciences. 2011; 5 (1): 31-36.
4
[5]. Wilby R L, Charles S P, Zorita E, Timbal B, Whetton P and Mearns L O. Guidelines for use of climate scenarios developed from statistical downscaling methods. 2004. IPCC Task Group on data and scenario support for Impact and Climate Analysis (TGICA). hhttp://ipcc-ddc.cru.uea.ac. uk/guidelines/dgm_no2_v1_09_2004. Pdfi.
5
[6]. IPCC. Climate change 2007. The Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel science basis of climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 2007; 996.
6
[7]. Wilby R L, Wigley T M L, Conway D, Jones P D, Hewitson B C, Main J, Wilks D S. Statistical downscaling of general circulation model output: a comparison of methods. Water Resources Research. 1998; 34: 2995_3008.
7
[8]. Tabor K, Williams J W. Globally downscaled climate projections for assessing the conservation impacts of climate change. Ecological Applications. 2010; 20 (2): 554–565.
8
[9]. Knight F H. Risk, Uncertainty, and Profit. Boston: Houghton Mifflin. 1921.
9
[10]. Khan M S, Coulibaly P. Climate change impact study on water resources with uncertainty estimates using Bayesian neural network. McMaster University, PhD Thesis, Canada. 2006.
10
[11]. Dibike Y B, Coulibaly P. Temporal neural network for downscaling climate variability and extremes. Neural Networks. 2006; 19 (2): 135-144.
11
[12]. Hashemi M Z, Shamseldin A Y. Comparison of SDSM and LARS-WG simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess. 2011; 25 (4): 475-484.
12
[13]. Samadi S, Catherine A M, Wilson E, Moradkhanim H. Uncertainty analysis of statistical downscaling models using Hadley Centere Coupled Model. Theoretical and Applied Climatology. 2013; 114 (4): 673- 690.
13
[14]. Saraf V R, Regulwar D G. Assessment of climate change for precipitation and temperature using ststistical downscaling methods in upper Godavari River Basin, India. Journal of Water Resource and Protection. 2016; 8 (1):31-45.
14
[15]. Daneshfaraz R, Razaghpoor H. Assessment of climate on evapotranspiration of West Azerbaijan province. Journal of Geographic Space. 2014; 14 (46): 199-211. (Persian).
15
[16]. Abbasnia M, Tavosi T, Khosravi M, Toros H. Uncertainty analysis of future changes in daily maximum temperatures over Iran by GIS. Journal of Geographic information. 2015; 25 (97): 29-43. (Persian).
16
[17]. Ahmadvand Kahrizi M, Rouhani H, Heshmatpour A, Seyedian M. Evaluation SDSM downscaling model to predict temperature (Case Study: Arazkuseh stations and nodes). Conference semi-arid hydrology, University of Kurdistan. 2015. (Persian).
17
[18]. Jafarzadeh M, Rouhani H, Heshmatpour A, Kashani M. Detecting trend of meteorological series across the Gorganrood Basin in the last three decades. Journal of Watershed Management Research. 2016; 7 (13): 230-240.(Persian).
18
[19]. Ashofteh P, Massah Bouani A R. Impact of climate change on maximum discharges, Case study of Aidoghmoush Basin, East Azerbaijan. Soil and Water Sciences. 2010; (14) 53: 25-39. (Persian).
19
[20]. Sanikhani H, Dinpajoh Y, Pour Yusef S, Ghavidel S Z, Solati B. The impact of climate change on runoff in watersheds, (Case study: Ajichay watershed in East Azerbaijan province, Iran). Journal of Water and Soil. 2014; 27 (6): 1225-1234. (Persian).
20
[21]. Farajzadeh M. Climate change effects on river discharge,Case study Sheshpir River. Journal of Geography and Environmental Planning. 2013; 49 (1): 17-32. (Persian).
21
[22]. Taei Semiromi S, Moradi H R, Khodagholi M. Simulation and forecasting of climatic variables by multiple linear model SDSM and General Circulation Models, Case study: Watershed Nishabur. Journal of Humans and the Environment. 2014; 12 (1): 1-15. (Persian).
22
[23]. Hamidianpour M, Baaghideh M, Abbasnia M. Assessment of the precipitation and temperature changes over south east Iran using downscaling of general circulation models outputs. Physical Geography Researches. 2016; 48 (1): 107-123. (Persian).
23
[24]. Ribalaygua J, Pino M R, Portoles J, Roldan E, Gaitan E, Chinarro D, Torres L. Climate change scenarios for temperature and precipitation in Aragon (Spain). Science of the Total Environment. 2013; 463: 1015-1030.
24
[25]. Majhi S, Pattnayak KC, Pattnayak R. Projections of rainfall and surface temperature over Nabarangpur district using multiple CMIP5 models in RCP4.5 and 8.5 scenarios. International Journal of Applied Research. 2016; 2 (3): 399- 405.
25
[26]. Wobus C, Flanner M, Sarofim M C, Moura M C P, Smith S J. Future Arctic temperature change resulting from a range of aerosol emissions scenarios. Earth's Future. 2016; 4: 270-281.
26
[27]. Nosouhian S, Ghobadinia M, Tabatabaei S H, Khaleghi H. Effect of climate change on temperature and precipitation in Shahrekord and Boroojen plan during 2020-2049. Iran's National Meteorological Conference. 2013. (Persian).
27
[28]. Noori M, Sharifi M B, Zarghami M. Effects of climate changes on inflow of reservoirs in the uncertainty condition, Case study: Bostan and Golestan dams in the Gorganroud catchment). Iranian Journal of Irrigation and Drainage. 2015; 9 (2): 367-380. (Persian).
28
[29]. Lakzaianpoor GH, Mohammadrezapoor O, Malmir M. Evaluating the effects of climatic changes on runoff of Nazloochaei River in Uremia lake catchment area. Journal of Geography and Development. 2016; 14 (42): 183-198. (Persian).
29
[30]. Aung M T, Shrestha S, Weesakul S, Shrestha P K. Multi- model climate change projections for Belu River Basin, Myanmar under Representative Concentration Pathways. Journal of Earth Science & Climatic Change. 2016; 7(1): 1-13.
30
[31]. Dousti M, Habibnezhad Roshan M, Shahedi K, Miryaghoubzade M H. Study of climate of Tamar river basin Golestan province in terms of climate change by LARS-WG model. Journal of Earth and Space Physics. 2013; 39 (4): 177-189. (Persian).
31
[32]. Basheer A K, Lu H, Omer A, Ali A B, Abdelgader A M S. Impact of climate change under CMIP5 RCP scenarios and the streamflow in the Dinder river and ecosystem habitats in Dinder National Paek, Sudan. Hydrology and Earth System Sciences.2016; 20 (4): 1331-1353.
32
[33]. Martinez-Meyer E. Climatechange and biodiversity: some considerations in forecasting shifts inspecies' potential distributions. Biodiversity Informatics. 2005; 26 (2).
33
[34]. Pierce ES. Where are all the Mycobacterium avium subspeciesparatuberculosis in patients with Crohn's disease?. PLoS Pathog. 2009Mar 27;5 (3): e1000234.
34
[35]. Beaumont NJ, Austen MC, Atkins JP, Burdon D, Degraer S, Dentinho TP, Derous S, Holm P, Horton T, Van Ierland E, Marboe AH. Identification, definition and quantification of goods and services provided by marinebiodiversity: implications for the ecosystem approach. Marine PollutionBulletin. 2007; 54 (3): 253-65.
35
ORIGINAL_ARTICLE
Impressionability of Suspended Sediment from Land use changes in Dinevar Watershed of Kermanshah Province
The devastating impact of land use change in the watershed is increasing the generation rate of sediment and entrance to rivers and sedimentation in reservoirs. According to this, studying on impact of land use change on hydrological process is necessary. Aim of this study was investigating the impact of land use change on suspended sediment during 1994 to 2010 by SWAT model in Dinevar watershed. SUFI-2 program was used for SWAT calibration and validation. The results of NS and R2 indicators above of 50 and 60 percent for both calibration and validation steps respectively imply to the model efficiency to hydrology data simulating in Dinevar watershed. The analysis of land use over a period of 16 years showed that the greatest change was for agricultural land more than 30 percent and the minimum was occurred for residential area and roads (3.25%). The results of the suspended sediment study showed the significant effect of land use change on sediment generated in the study period. So that the maximum amount of suspended sediment in 1994, 18.1 grams per liter, but this amount in 2010 reached to 65/12 gram per liter.
https://ije.ut.ac.ir/article_60362_d3c2388b39b85e85405649e7745d4c49.pdf
2016-12-21
611
621
10.22059/ije.2016.60362
Dinevar
SUFI-2 program
Hydrologic models
SWAT Model
Soheila
Aghabeigi Amin
saghabeigi@yahoo.com
1
Departmentof Natural Resources, Faculty of Agricultural, RaziUniversity, Kermanshah, Iran
LEAD_AUTHOR
Ali Reza
Ildromi
ildromi@yahoo.com
2
Departmentof Rangeland and Watershed, Facultyof Natural Resources, Malayer university, Malayer, Iran
AUTHOR
Hamid Reza
Noori
hamidwatershed@yahoo.com
3
استادیار گروه مرتع و آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه ملایر
AUTHOR
Afsane
Haghighi Kermanshahi
afsaneh.haghighikermanshah@gmail.com
4
M.Sc. Student, Department of Rangeland and Watershed, Faculty of Natural Resources, Malayer university, Malayer, Iran
AUTHOR
Shi, P.J., Yuan, Y., Zheng, J., Wang, J.A., Ge, Y. and Qiu, G.Y. The effect of land use/cover change on surface runoff in Shenzhen region, China, Catena. 2007; 69:31-35.
1
Alansi, A.W., Amin, M.S.M., Abdul Halim, G., Shafri, H.Z.M., and Aimrun, W. Validation of SWAT model for stream flow simulation and forecasting in Upper Bernam humid tropical river basin. Hydrology Earth System. 2009; 6: 7581-7609.
2
Saadati, H., Gholami, S.A. Sharifi, F. and Ayoubzadeh, S. A. An investigation of the effects of land use change on simulating surface runoff using SWAT mathematical model (Case Study: Kasilian Catchment Area), Iranian Journal of Natural Resources. 2006; 59(2): 301-313. (In Persian)
3
Ghafari, G., Ghodosi, J., Ahmadi, H. The effect of land use changes on watershed hydrology reactions, Journal of water and soil conservation. 2009; 16(1):163-180. (In Persian)
4
Rezazade, M. S., Ganjali khani, M. and Kermani, M. Z. N. Comparing the performance of semi-distributed hydrological model SWAT and integrated model HEC - HMS in the simulation flow rate (Case study: Ab bakhsha watershed), Ecohydrology journal. 2015; 2:(4), 479-467. (In Persian)
5
Vafakhah, M., Javadi, M. R. and Najafi Majd, J. The impact of land use change on the runoff in the watershed of the river Chalus, Ecohydrology journal.2015; 2:(2), 211-220. (In Persian)
6
Mohamadi, M., Moradi, H. R., Pourghasemi, H. R. and Farazjoo, H. The impact of land use change on the runoff by using WetSpa, Ecohydrology journal. 2015; 2:(4), 357-369. (In Persian)
7
Rostamian, R., Mosavi, F., Heidarpour, M., Efyoni, M. and AbaspourK. Runoff and sediment estimation by using SWAT 2000 in Behesht Abad watershed. Journal of Science and Technology of Agriculture and Natural Resources. 2008; 12 (46): 517-531.(In Persian)
8
Talebizadeh, M., Morid, S., Ayyoubzadeh, S.A., and Ghasemzadeh, M.Uncertainty Analysis in Sediment Load Modeling Using ANN and SWAT Model. Water Resour Manage. 2009; DOI 10.1007/s11269-009-9522-2.(In Persian)
9
Alavinia M. and Nasiri S.Simulation sediment yield using SWAT Model, 8th International River Engineering Conference Shahid Chamran University. 2010; 26-28 Jan, Ahwaz. (In Persian)
10
Kazemi Khaledi, H.Sediment estimation using WEPP model and comparing with SWAT (Case study: Ammame watershed), M.Sc. thesis, Tarbiat modares university.2009; 123p.(In Persian)
11
Bazrkar, M. Mathematical model SWAT application in nutrients simulation to determine the contribution of pollution sources in Ilam dam basin, M.Sc. thesis, Tarbiat modares university.2011; 154p.(In Persian)
12
Qiu, L.J., Zheng, F.L, and Yin, R.Sh. SWAT- based runoff and sediment simulation in a small watershed, the loessial hilly-gullied region of China: capabilities and challenges, International Journal of Sediment Research. 2012; 27: 226-234.
13
Kimwaga, R. J., Bukirwa, F., Banadda,N., Wali, U.G., Nhapi,I., and Mashauri D.A. Modeling the Impact of Land Use Changes on Sediment Loading Into Lake Victoria Using SWAT Model: A Case OF Simiyu Catchment Tanzania, The Open Environmental Engineering Journal. 2012; 5: 66-76.
14
Mukundan, R., Radcliffe, D.E., and Risse, L. M. Spatial resolution of soil data and channel erosion effects on SWAT model predictions of flow and sediment, Jurnal of soil and water conservation. 2012; 65 (2): 92-104.
15
Ayana. A.B., Edossa.D.C. and Kositsakulchai, E. Simulation of Sediment Yield using SWAT Model in Fincha Watershed, Ethiopia Kasetsart Journal. 2012; 46 : 283 - 297.
16
Santos, J., Nunes, J., sampaio, E., Moreira, M., Lima, J., Jacinto, R. and Corte-RealJ. Climate and Landuse Change Impacts on hydrological processes and soil erosion in a dry Mediterranean agro-forested catchment, southern Portugal. Hydrology and earth system sciences discussions. 2014; 16: 714-730.
17
Abbaspour, K. C., C. A. Johnson and M. Th. Van Genuchten. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure, Vadose Zone. 2004; 3: 1340-1352.
18
Schuol, J., Abbaspour,K.C.,Srinivasan, Y.H., and A., Zehnder. Modeling blue and green water availability in Africa. Water resources research. 2008; 32: 18.
19
Ayana. A.B., Edossa.D.C. and Kositsakulchai, E. Simulation of Sediment Yield using SWAT Model in Fincha Watershed, Ethiopia Kasetsart Journal. 2012; 46 : 283 - 297.
20
Abbaspour, K. C., Yang, J., Maximov, I., Siber,R., Bogner, K., Mieleitner, J. et al. Modeling of hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal Hydrology. 2007; 333: 413-430.
21
Franchini, M., Bernini, A., Barbetta, S., Moramarco, T. Forecasting discharges at the downstream end of a river reach through two simple Muskingum based procedures, Journal of Hydrology. 2011; 399: 335–352.
22
Song, X., Kong, F., Zhu, Z. Application of Muskingum routing method with variable parameters in ungauged basin, Water Science and Engineering. 2011; 4(1): 1-12
23
Justification studies of watershed management and renewable natural resources of Dinevar watershed. Department of natural resources of Kermanshah province, Saman Ab e Sarzamin consulting engineers.2010; 230p.(In Persian)
24
Anwar, N.S. Simulated impact of land use dynamics on hydrology during a 20-year-period of Beles Basin in Ethiopia, Student thesis, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering,2010; 1-33.
25
Tolson, B. A.and Shoemaker, C. A. Watershed modeling of the Cannonsville basin using SWAT2000: Model development, calibration and validation for the prediction of flow, sediment and phosphorus transport to the Cannonsville reservoir. Technical Report, School of Civil and Environmental Engineering, Cornell University. 2004; 159pp.
26
Chu, T. W. and Shirmohammadi, A. Evaluation of the SWAT model’s hydrology component in thepiedmont physiographic region of Maryland Transion ASAE. 2004; 47(4): 1057-1073.
27
Spruill, C.A., Workman, S.R. and Taraba, J.L. Simulation of daily and monthly stream discharge from small watershed using the SWAT model. Soil and Water Division of ASAE , 2000; 98(5): 1431-1440.
28
Wang, X. and Melesse, A.M. Evaluation of the SWAT model’s snowmelt hydrology in a northwestern Minnesota watershed, Transactions of the ASAE. 2005; 48(4): 1-18.
29
Saadati, H., S.A. Gholami, F. Sharifi and S.A. Ayoubzadeh. An investigation of the effects of land use change on simulating surface runoff using SWAT mathematical model (Case Study: Kasilian Catchment Area), Iranian Journal of Natural Resources. 2006; 59(2): 301-313. (In Persian)
30
Wang, S., Kang, S., Zhang L. and Li, F. Modelling hydrological response to different land-use and climate change scenarios in the Zamu River basin of northwest China. Journal of Hydrological Processes.2008; 22: 2502-2510.
31
ORIGINAL_ARTICLE
Microclimate Changes of Cushion Species OnobrychisCornuta Affected by Fire in Golestan National Park Grasslands
Cushions as dominant species of mountainous habitats facilitate surrounding species. The aim of this study is the investigation of fire on OnobrychisCornutamicrohabitats in the mountainous grasslands. Fire occurredin summer 2013. Therefore, in order to study the temperature fluctuations, thermometers were established under burned and unburned patches since 15 March 2016 for 31 days (half hours intervals). Parameters related to temperature were recorded with iButton thermometers and then were compared usingt-test between control and burned shrubs in two time intervals. Except for the min temperature, other parameters related to soil temperatures including max, DTF and mean temperatures significantly increased (P≥0.01). DTF in burned patches (2.5-14.5 ºC) was higher than control shrubs (1.0-3.5 ºC). Soil moisture was also measured by TDR instrument in the both burned and unburned shrubs during two time intervals. For determination of the most important factors affecting soil moisture including fire, time and their interactions, GLMM were applied and compare mean were tested by T-test. Considering GLMM results, time (F= 22.4; P ≤ 0.01) had highest impact on soil moisture. Soil moisture in both control and burned sites declined which was only significant at the first sampling time (P≥0.05).
https://ije.ut.ac.ir/article_60363_07a5bbfbbb3486011a36aff4c6d13097.pdf
2016-12-21
623
630
10.22059/ije.2016.60363
Nurse Shrub
Facilitation
temperature
moisture
GLMM
Khadijeh
Bhalkeh
khadijeh.bahalkeh@gmail.com
1
MSc. Student, Rangeland Management Department, Faculty of Natural Resources, TarbiatModares University, Iran.
AUTHOR
Mehdi
Abedi
mehdi.abedi@modares.ac.ir
2
Rangeland Management Department, Faculty of Natural Resources, TarbiatModares University, Iran.
LEAD_AUTHOR
Ghasemali
Dianati Tilaki
abedimail@gmail.com
3
دانشیار گروه مرتعداری، دانشکدۀ منابع طبیعی، دانشگاه تربیت مدرس، نور، ایران
AUTHOR
[1]. Callaway RM, Brooker R, Choler P, Kikvidze Z, Lortie CJ, Michalet R, et al.Positive interactions among alpine plants increase with stress. Nature. 2002; 417(6891): 844-848.
1
[2]. Bruno JF, StachowiczJJ, Bertness MD. Inclusion of facilitation into ecological theory. Trends in Ecology & Evolution. 2003; 18(3): 119-125.
2
[3]. Brooker RW, Maestre FT, Callaway RM, Lortie CL, Cavieres LA, Kunstler G, et al. Facilitation in plant communities: the past, the present, and the future. Journal of Ecology. 2008; 96(1): 18-34.
3
[4]. Forbis TA. Seedling demography in an alpine ecosystem. American Journal of Botany. 2003; 90(8): 1197-1206.
4
[5]. Cavieres LA, BadanoEI, Sierra‐Almeida A, Gómez‐Gonzalez S, Molina‐Montenegro MA. Positive interactions between alpine plant species and the nurse cushion plant Laretiaacaulisdo not increase with elevation in the Andes of central Chile. New Phytologist. 2006; 169(1): 59-69.
5
[6]. Moro M, Pugnaire F, Haase P, Puigdefábregas J. Effect of the canopy of Retamasphaerocarpa on its understorey in a semiarid environment. Functional Ecology. 1997a; 11(4): 425-431.
6
[7]. Moro M, Pugnaire F, Haase P, Puigdefábregas J. Mechanisms of interaction between a leguminous shrub and its understory in a semi‐arid environment. Ecography. 1997b; 20(2): 175-184.
7
[8]. Abedi M, Bartelheimer M, Poschlod P. Effects of substrate type, moisture and its interactions on soil seed survival of three Rumex species. Plant and soil. 2014; 374(1-2): 485-495.
8
[9]. BadanoEI, Bustamante RO, Villarroel E, MarquetPA, Cavieres LA. Facilitation by nurse plants regulates community invisibility in harsh environments. Journal of Vegetation Science.2015; 26(4), 756-767.
9
[10]. Michalet R and Pugnaire FI. Facilitation in communities: underlying mechanisms, community and ecosystem implications. Functional Ecology. 2016; 30(1), 3-9.
10
[11]. Pugnaire FI, Armas C, Maester FT. Positive plant interactions in the Iberian Southeast: mechanisms, environmental gradients, and ecosystem function. Journal of Arid Environments. 2011; 75(12): 1310-1320.
11
[12]. Michalet R, Brooker RW, Lortie CJ, Maalouf JP, Pugnaire FI. Disentangling direct and indirect effects of a legume shrub on its understorey community. Oikos. 2015; 124(9), 1251-1262.
12
[13]. Kos M, Poschlod P.Seeds use temperature cues to ensure germination under nurse-plant shade in xeric Kalahari savannah. Annals of Botany. 2007; 99(4): 667-675.
13
[14]. Sher Y, Zaady E, Ronen Z, Nejidat A. Nitrification activity and levels of inorganic nitrogen in soils of a semi-arid ecosystem following a drought-induced shrub death. European Journal of Soil Biology. 2012; 53: 86-93.
14
[15]. Cavieres LA, Quiroz CL, Molina-Montenegro MA, Muñoz AA, Pauchard A. Nurse effect of the native cushion plant Azorellamonantha on the invasive non-native Taraxacumofficinale in the high-Andes of central Chile. Perspectives in Plant Ecology, Evolution and Systematics. 2005; 7(3): 217-226.
15
[16]. DeBano LF, Ritsema CJ, Dekker LW.The role of fire and soil heating on water repellency. Soil water repellency: occurrence, consequences and amelioration. 2003; 193-202.
16
[17]. Neary DG, Klopatek CC, DeBano LF, Ffolliott PF.Fire effects on belowground sustainability: a review and synthesis. Forest ecology and management. 1999; 122(1): 51-71.
17
[18]. Certini G. Effects of fire on properties of forest soils: a review. Oecologia. 2005; 143(1): 1-10.
18
[19]. Thomaz EL, Fachin PA.Effects of heating on soil physical properties by using realistic peak temperature gradients. Geoderma. 2014; 230: 243-249.
19
[20]. Jankju M. Role of nurse shrubs in restoration of an arid rangeland: effects of microclimate on grass establishment. Journal of Arid Environments. 2013; 89: 103-109.
20
[21]. Kolahchi N, MohseniSaravi M, Tavili A, Asadian G. Investigation of Interception and its Importance in Ecohydrology Studies in Rangeland Plants. Journal of Ecohydrology. 2014; 1(1): 1-10. [Persian].
21
[22]. Yousefi S, MatinkhahSH, Rohani F, Nael N. Anabasis aphylla&Pteropyrumaucheri Canopy Cover Effect on Generating Stemflow in Arid Regions. Journal of Ecohydrology. 2014;1(2): 133-142.[Persian].
22
[23]. Akhani H. Plant biodiversity of Golestan National Park, Iran. Stapfia. 1998; 53: 411p.
23
[24]. Abedi M, Arzani H, Shahriary E, Tongway D, Aminzadeh M. Assessment of patches structure and function in arid and semi-arid Rangelands. Environmental Studies. 2007; 40: 117-126.[Persian].
24
[25]. Cavieres LA, BadanoEI, Sierra-Almeida A, Molina-Montenegro MA. Microclimatic modifications of cushion plants and their consequences for seedling survival of native and non-native herbaceous species in the high Andes of central Chile. Arctic, Antarctic, and Alpine Research. 2007; 39(2): 229-236.
25
[26]. Arzani H and Abedi M. Rangeland Assessment: Vegetation measurement. University of Tehran. 2015; Press,217p.[Persian].
26
[27]. Kos M, Poschlod P.Seeds use temperature cues to ensure germination under nurse-plant shade in xeric Kalahari savannah. Annals of Botany. 2007; 99(4): 667-675.
27
[28]. Arroyo M, Cavieres L, Penaloza A, Arroyo-Kalin M. Positive associations between the cushion plant Azorellamonantha (Apiaceae) and alpine plant species in the Chilean Patagonian Andes, Plant Ecology. 2003; 169(1): 121-129.
28
[29]. Akhalkatsi M, AbdaladzeO, Nakhutsrishvili G, SmithWK.Facilitation of seedling microsites by Rhododendron caucasicum extends the Betulalitwinowii alpine treeline, Caucasus Mountains, Republic of Georgia. Arctic, Antarctic, and Alpine Research. 2006; 38(4): 481-488.
29
[30]. Billings WD. Adaptations and origins of alpine plants. Arctic and alpine research. 1974; 129-142.
30
[31]. Poschlod P, Abedi M, Bartelheimer M, Drobnik J, Rosbakh S, Saatkamp A. Seed ecology andassembly rules in plant communities In: Van der Maarel E and Franklin J, editors. Vegetation ecology.2th.John Wiley & Sons, Ltd.2013. pp. 164-202.
31
[32]. Keeley JE, Bond WJ, Bradstock RA, Pausas JG, Rundel PW. Fire in Mediterranean ecosystems: ecology, evolution and management, Cambridge University: Press.522 p. 2011.
32
[33]. Iverson LR, Hutchinson TF. Soil temperature and moisture fluctuations during and after prescribed fire in mixed-oak forests, USA. Natural Areas Journal. 2002; 22: 296–304.
33
[34]. Vermeire LT, Wester DB, Mitchell RB, Fuhlendorf SD.Fire and grazing effects on wind erosion, soil water content, and soil temperature. Journal of Environmental Quality. 2005; 34(5): 1559-1565.
34
[35]. Luna B, Moreno J, Cruz A, Fernández-González F.Heat-Shock and seed germination of a group of Mediterranean plant species growing in a burned area: an approach based on plant functional types. Environmental and experimental botany. 2007; 60(3): 324-333.
35
[36]. Zaki E, Abedi M, Erfanzadeh, Naghinejad AR. Response of different plant functional groups to Aerosol and aqueous smokes treatments. Plant researches. 2106; 20. (In press).[Persian].
36
[37]. Michalet R.highlighting the multiple drivers of change in interactions along stress gradients. New Phytologist. 2007; 173(1): 3-6.
37
[38]. Melgoza G, Nowak RS, Tausch RJ.Soil water exploitation after fire: competition between Bromus tectorum (cheatgrass) and two native species. Oecologia. 1990; 83(1): 7-13.
38
[39]. Smith SD, Nowak RS. Ecophysiology of plants in the Intermountain lowlands. In: Osmond CB, Pitelka LF and Hidy GM, editors. In Plant biology of the Basin and Range. Springer-Verlag, New York, USA.. Springer Berlin Heidelberg.1990.p.179-241.
39
[40]. Silva JS, Rego FC, Mazzoleni S.Soil water dynamics after fire in a Portuguese shrubland. International Journal of Wildland Fire. 2006; 15(1): 99-111.
40
[41]. Mauchamp A, Janeau JL. Water funneling by the crown of Flourensiacernua, a Chihuahuan Desert shrub. Journal of Arid Environments. 1993; 25: 299–306.
41
[42]. Stoof CR, Wesseling JG, Ritsema CJ. Effects of fire and ash on soil water retention. Geoderma. 2010; 159(3): 276-285.
42
[43]. SharrowSH, Wright HA. Effects of fire, ash, and litter on soil nitrate, temperature, moisture and tobosagrass production in the rolling plains. Journal of Range Management. 1977; 266-270.
43
ORIGINAL_ARTICLE
The performance of Artificial Neural Network in prediction and analysis of hydrological processes (Case study: Water shortage in Nazloo-chai watershed, West Azerbaijan province)
Precipitation is one of the hydrological processes that play an important role in controlling water resources management. Shortage of rain causes some problems such as lack of drinking water. Due to the importance of the issue of water shortage, using modern methods to predict hydrological processes will play an important role in planning and management of water resources. Therefore, in this study, monthly shortage of water in Nazloo-chai watershed was predicted using Artificial Neural Network (ANN) and improved wavelet-neural network (IWNN) models, for the past 39 years (1973-2012). Performance of these two models was evaluated using statistical indicators including correlation coefficient (R), determination coefficient (R2) and root mean square error (RMSE). According to the results of IWNN model, the obtained correlation coefficient was 0.960 and 0.945 for testing and training modes, respectively, and this model has greater ability for predicting the shortage of water in comparison with ANN. Accordingly, the amount of monthly water shortage in this watershed was predicted for 2013 to 2020. Results indicated that shortage of water still remains as in the past years. The average water shortage was estimated nearly as 2.95 million cubic meters (MCM) in the next 7 years, while, this parameter for the past 39 years was 4.04 MCM. Therefore, it is required to take necessary measures for future years, and with careful management plans for exploitation of water resources (agriculture, industry, urban, etc.), it is possible to reduce water shortage in the coming years.
https://ije.ut.ac.ir/article_60365_a0db40a0548fe4f77ca21eecf0848d83.pdf
2016-12-21
631
644
10.22059/ije.2016.60365
Water shortage
Artificial Intelligence
Wavelet algorithm
De-noising
Optimized network
Saeed
Farzin
saeed.farzin@semnan.ac.ir
1
Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
LEAD_AUTHOR
Hojat
Karami
hkarami@semnan.ac.ir
2
Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
AUTHOR
Mahsa
Doostmohammadi
mahsa_doost70@yahoo.com
3
M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
AUTHOR
Anese
Ghanbari
a.ghanbari_88@yahoo.com
4
M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
AUTHOR
Elham
Zamiri
elham.zamiri90@yahoo.com
5
M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
AUTHOR
مراجع
1
1. Water and Wastewater Department of Energy Office Planning. Report begin updating the master plan studies of water in watersheds grade 2 Tehran. 2008. [In Persian]
2
2. Menhaj M. Computational Intelligence (first volume: Foundations of Neural Networks). Amirkabir University Publishers. 2014. Page 716. [In Persian]
3
3. Hall T, Brooks HE, Doswell III CA. Precipitation forecasting using a neural network. Weather and forecasting. 1999 Jun;14(3):338-45.
4
4. Sahai AK, Soman MK, Satyan V. All India summer monsoon rainfall prediction using an artificial neural network. Climate dynamics. 2000 1;16(4):291-302.
5
5. Ramirez M.C.V, Velho H.F, Ferreira N.J. Artificial neural network technique for rainfall forecasting applied to the Sa˜o Paulo region. Journal of Hydrology. 2005; 301,146–162.
6
6. Sahoo GB, Ray C. Flow forecasting for a Hawaii stream using rating curves and neural networks. Journal of hydrology. 2006 5;317(1):63-80.
7
7. Bustami R, Bessaih N, Bong C, Suhaili S. Artificial neural network for precipitation and water level predictions of Bedup River. IAENG International Journal of computer science. 2007 1; 34(2):228-33.
8
8. Yang ZP, Lu WX, Long YQ, Li P. Application and comparison of two prediction models for groundwater levels: A case study in Western Jilin Province, China. Journal of arid Environments. 2009; 73(4):487-92.
9
9. Nastos PT, Moustris KP, Larissi IK, Paliatsos AG. Rain intensity forecast using artificial neural networks in Athens, Greece. Atmospheric Research. 2013 31; 119:153-60.
10
10. Antonopoulos VZ, Gianniou SK, Antonopoulos AV. Artificial neural networks and empirical equations to estimate daily evaporation: application to lake Vegoritis, Greece. Hydrological Sciences Journal. 2016 Jan 16(just-accepted).
11
11. Faghih H. Evaluating artificial neural network and its optimization using genetic algorithm in estimation of monthly precipitation data (Case Study: Kurdistan Region). JWSS - Isfahan University of Technology. 2010; 14 (51):27-44. [In Persian]
12
12. Safshekan F, PirMoradian N, Afshin Sharifan R. Simulation of rainfall-runoff hydrograph according to the pattern of rainfall and the use of artificial neural network in kasilian basin. Iran-Watershed Management Science & Engineering. 2011; 5(15):1-10. [In Persian]
13
13. Fatahi A, Delavar M, Noohi K. North Karun river flow forecasting using artificial neural network. Geographical Research Publishers. 2012; 51-78. [In Persian]
14
14. Khazaei M, Mirzaei MR. Comparison prediction performance of monthly discharge using ANN and time series. Watershed Engineering and Management. 2013; 5(2): 74-84. [In Persian]
15
15. Rahmati E, Montazeri M, Gandomkar A, Lashanizand M. Evaporation Predict Using Climate Signals and Artificial Neural Network in Dez Basin. Geographical Research Journal. 2015 30(2):261-274. [In Persian]
16
16. Jahangir M, Khoshmashraban M, Yousefi H. Drought monitoring and forecasting network using standard precipitation index and multilayer perceptron Neural Network (Case Study: Tehran and Alborz provinces). Iranian Journal of Ecohydrology. 2016; 417-428. [In Persian]
17
17. Haghizadeh A, Mohammadlou M, Noori F. Simulation of rainfall-runoff process using multilayer perceptron and adaptive neuro-fuzzy interface system and multiple regressions (Case study: Khorramabd watershed). Iranian Journal of Ecohydrology. 2015; 233-243. [In Persian]
18
18. Hajiabadi R, Farzin S, Hassanzadeh Y. Intelligent Models Performance Improvement Based on Wavelet Algorithm and Logarithmic Transformations in Suspended Sediment Estimation. Journal of Water and Soil. 2016;30(1):112-124. [In Persian]
19
19. Lotfollahi-Yaghin MA, Koohdaragh M. Examining the function of wavelet packet transform (WPT) and continues wavelet transform (CWT) in recognizing the crack specification. KSCE Journal of Civil Engineering. 2011; 497-506. [In Persian]
20
20. Sifuzzaman M, Islam MR, Ali MZ. Application of wavelet transform and its advantages compared to Fourier transform.2009; 121-134.
21
21. Kim TW, Valdés JB. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering. 2003 Nov; 8(6):319-28.
22
22. Kişi Ö. Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering. 2009 19; 14(8):773-82.
23
23. Shafaei M, Kisi O. Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models. Neural Computing and Applications. 2016: 1-4.
24
24. Hassanzadeh Y, Abdi Kordani A, Fakheri Fard A. Drought Forecasting Using Genetic Algorithm and Conjoined Model of Neural Network-Wavelet. Journal of Water and Wastewater. 2012; 23(3): 48-59. [In Persian]
25
25.shafaei M, Fakhei Fard A, Darbandi S, ghorbani M. Predicrion Daily Flow of Vanyar Station Using ANN and Wavelet Hybrid Procedure. Journal of Irrigation and Water.2014; 113-128. [In Persian]
26
26. Roshangar K, Zarghaami M, Tarlaniazar M. Forecasting Daily Urban Water Consumption using Conjunctive Evolutionary Algorithm and Wavelet Transform Analysis, A Case Study of Hamedan City, Iran. Journal of Water and Wastewater. 2015; 26(4): 110-120. [In Persian]
27
27. Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. International journal of forecasting. 1998; 14(1):35-62.
28
28. French MN, Krajewski WF, Cuykendall RR. Rainfall forecasting in space and time using a neural network. Journal of hydrology. 1992 Aug 15;137(1-4):1-31.
29
29. Silverman D, Dracup JA. Artificial neural networks and long-range precipitation prediction in California. Journal of applied meteorology. 2000 Jan;39(1):57-66.
30
30. Choubin B, Khalighi-Sigaroodi S, Malekian A, Kişi Ö. Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrological Sciences Journal. 2016 Apr 25; 61(6):1001-9.
31
31. De Vos NJ. Rainfall-run off modelling using artificial neural networks. Doctoral dissertation, TU Delft, Delft University of Technology. 2003.
32
32. Safshekan F, Pirmoradian N, Afshin Sharifan R. Simulation of rainfall-runoff according to the pattern of rainfall and the use of artificial neural network. Iran-Watershed Management Science & Engineering. 2011. [In Persian]
33
33. Shahhossein Dastjerdi S, Shahnoushi N, Darijani A, Davari K. Application of artificial neural network models in simulation of drought severity (A Case of Torshakli Station in Golestan Province). Third National Conference on Integrated Water Resource Management.2012. [In Persian]
34
34. Gopalakrishnan K. Effect of training algorithms on neural networks aided pavement diagnosis. International Journal of Engineering, Science and Technology. 2010;2(2):83-92.
35
35. Legates DR, McCabe GJ. Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water resources research. 1999;35(1):233-41.
36
36. Erfanian M, Bayazi M, Abghari H, Esmali Ouri A. Monthly simulation of streamflow and sediment using the SWAT in Nazlochai and prioritization of critical regions. Journal of Watershed Engineering and Management. 2016;552-562. [In Persian]
37
37. Ahmadi L. Water allocation in Nazloo plain, Urima, using Weap and Vensim. Thesis for the degree of Master of Science in civil engineering. Semnan University. 2016. [In Persian]
38
38. Noori R, Farokhnia A, Morid S. Riahi Madvar H. Effect of input variables preprocessing in artificial neural network on monthly flow prediction by PCA and wavelet transformation. Journal of Water and Wastewater. 2009 20(1): 13-22. [In Persian]
39
39. Rajaee T, Ebrahimi H. Application of wavelet-neural network model for forecasting groundwater level time series with non-stationary and nonlinear characteristics. J. of Water and Soil Conservation. 2016;22(5): 99-115. [In Persian]
40
40. Nikmanesh MR. Prediction of monthly average discharge using the hybrid model of artificial neural network and wavelet transforms (Case study: KorRiver-Pol-e-Khan Station). J. of Water and Soil Conservation. 2015;22(3): 231-239. [In Persian]
41
41. Rajaee T, Jafari H. Prediction of water sodium absorption ratio (SAR) using ANN and wavelet conjunction model (case study: Rudbar Station of Sefidrud River). 2016;26(2-2):189-205. [In Persian]
42
ORIGINAL_ARTICLE
The Application of RUSLE Model in Spatial DistributionDetermination of Soil loss Hazard
The modeling can provide a quantitative approach and consistency in the estimationsoil erosion and sediment yield by a wide range of conditions. In this study, the integration method of revised universal soil loss equation model, geographic information system and remote sensing techniques were used in order to identify the spatial distribution of soil erosion and sediment yield in the Talar watershed. Parameters of rainfall erosivity, soil erodibility, slope length and slope gradient and vegetation cover were calculated in order to provision RUSLE map. The amount of soil loss was calculated from 0 to 9201 tons per hectare per year for the total basin and classification of erosion areas showed that erosion class of low, medium, high and very high with value of 33.12, 27.62, 21.13 and 18.13 percent respectivelycovered the total watershed. The linear regression analysis showed that in the between parameters of RUSLE model, the slope length and slope gradient parameter with value of 0.93 have the most correlation with the soil loss map. Also sw3 sub-watershed with value of 5580.33 tons per hectare per year and the sw4 sub-watershed with value of 19.59 percent have the highest and lowest Erosion hazard and Sediment yield respectively in the between sub-watersheds. The results showed that conservation and management measures can be useful to control and also reduce soil erosion and sediment yield in the Talar watershed.
https://ije.ut.ac.ir/article_60368_28a77f778b15cd845baad6df022a2ffc.pdf
2016-12-21
645
658
10.22059/ije.2016.60368
Experimental model, Geographic information system
remote sensing
Talar watershed
Maziar
Mohammadi
maziarmohammadi68@yahoo.com
1
دانشجوی دکتری، گروه علوم و مهندسی آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه تربیت مدرس
AUTHOR
Moghadase
Fallah
fallah_moghadase@yahoo.cm
2
کارشناس ارشد آبخیزداری، گروه مهندسی آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی
AUTHOR
Ataollah
Kavian
ataollah.kavian@yahoo.com
3
دانشیار، گروه مهندسی آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری
LEAD_AUTHOR
Leila
Gholami
l.gholami@sanru.ac.ir
4
استادیار، گروه مهندسی آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری
AUTHOR
Ebrahim
Omidvar
ebrahimomidvar@gmail.com
5
استادیار، گروه مهندسی مرتع و آبخیزداری، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه کاشان
AUTHOR
منابع
1
[1]. Eswaran H, Lal R, Reich P. Land degradation: an overview. In: Bridges EM, Hannam ID, Oldeman LR, Penning de Vries FWT, Scherr SJ, Sombatpanit S (eds) Response to land degradation. Science, Enfield, 2001. P. 20-35.
2
[2]. Turner B, Clark W, Kates R, Richards J, Matthews J, Meyer W. The earth as transformed by human action. Cambridge: Cambridge Univ. Press.1990.
3
[3]. Lal R. Soil erosion impact on agronomic productivity and environment quality: critical reviews. Plant Sci. 1998;17:319-464.
4
[4]. Grepperud S. Soil conservation and government policies in tropical area: does aid worsen the incentives for arresting erosion. Agric econ. 2012;12:120-140.
5
[5]. Ganasri B, Ramesh H.Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin. Geoscience Frontiers. 2015;1-9.
6
[6]. Prasannakumar V, Shiny R, Geetha N, Vijith H. Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala, India. Environ Earth Sci. 2011;64:965–972.
7
[7]. Nearing M, Foster G,Lane L. A process-based soil erosion model for USDA water erosion prediction project. Transactions of ASAE. 1989;32(5):1587–1593.
8
[8]. Knisel W. A field scale model for chemicals, runoff, and erosion from agricultural management systems. us department of agriculture research service : US Department of Agriculture Research Service. 2010.
9
[9]. Morgan R, Quinton J, Rickson R. Structure of the soil erosion prediction model for the European community. Proceedings of International Symposium Water Erosion, Sedimentation and Resource Conservation, 9–13 October, 1990 Dehradun, India. Central Soil andWater Conservation Research and T. CSWCRTI, Dehradun, India. 1990; p.49-59.
10
[10]. Arnold J, Srinivasan R, Muttiah R, Williams J. Large area hydrologic modeling and assessment part I: Model development1. J Am Water Res Assoc. 1998;34(1):73-89.
11
[11]. Lazzari M, Gioia D, Piccarreta M, Danese M, Danese A. Sediment yield and erosion rate estimation in the mountain catchments of the Camastra artificial reservoir (Southern Italy): a comparison between different empirical methods. Catena. 2015;127:323e339.
12
[12]. Renard K, Foster G, Weesies G, McCool D, Yoder D. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss EquationRUSLE). US Department of Agriculture (Ed.), Agricultural Handbook. US Department of Agriculture, Washington. 1997;703:1–251.
13
[13]. Wischmeier W, Smith D. Predicting rainfall erosion losses-a guide to conservation planning. Agriculture Handbook No. 537. US Department of Agriculture Science and Education Administration, Washington, DC, USA, 1978. p. 163.
14
[14]. Vipul Shinde K, Tiwari S, Manjushree S. Prioritization of micro watersheds on the basis of soil erosion hazard using remote sensing and geographic information system. International Journal of Water Resources a Environmental Engineering. 2010;2(3):130-136.
15
[15]. Pandey A., Chowdary V, Mal B. Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resources Management. 2007;21(4):742-746.
16
[16]. kouli M, Soupios P, Vallianatos F. Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece. Environ Geol. 2009;57(3):483-497.
17
[17]. Kefi M, Yoshino K. Evaluation of the economic effects of soil erosion risk on agricultural productivity using remote sensing: case of watershed in Tunis. International Archives of the Photogrammetry, Remote Sensing Spatial Information Sci. 2010.
18
[18]. Chen T, Niu R, Wang Y, Li P, Zhang L, Du B. Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques. Environ MonitoringAssess. 2011;179:605-617.
19
[19]. Prasannakumar V, Vijith H, Abinod S, Geetha N. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geoscience Frontiers. 2012;3(2):209-215.
20
[20]. Farhan Y, Nawaiseh S. Spatial assessment of soil erosion risk using RUSLE and GIS techniques. Environ Earth Sci. 2015;74(6):4649-4669.
21
[21]. Zandi J, Soleimani K, Habibnejad Roshan M. Prioritizing of areas of soil erosion control using techniques of multi-criteria evaluation and GIS. Geography and Development. 2013;31:93-105.(In Persian).
22
[22]. Rahimi K, Mazbani M. Assess of erosion changes Sivand watershed during 1988 and 2009 using of the model RUSLE. Environmental Erosion Researchs. 2013;9:1-18. (In Persian).
23
[23]. Rakhbin M, Nohegar A, Kamali A, Habib Ellahian M. Estimates of erosion and sediment yield in the watershed Lavrfyn (Hormozgan eparchy) using remote sensing (RS), Geographic Information System (GIS) and experimental models RUSLE. Geographical Research. 2014;3(114):89-104.(In Persian).
24
[24]. Jahad Engineering services. Comprehensive study of Haraz Watershed. Compilation Reports., Studies office and evaluations watersheds.2001. (In Persian).
25
[25]. Vaezi A, Bahrami H, Sadeghi S, Mahdian M. Spatial variability of soil erodibility factor (K) of the USLE in North West of Iran. J Agric Sci Tech. 2010;12:241-252.
26
[26]. Pradhan B, Chaudhari A, Adinarayana J, Buchroithner M. Soil erosion assessment and its correlation with landslid events using remote sensing data and GIS. Environ Monitoring Assess. 2011;171:153-161.
27
[27]. Hichey R. Slope Angle and Slope Length Solutions for GIS. Cartogeraphy. 2002;29: 582-591.
28
[28]. Van Remortel R, Maichle R, Hickey R. Computing the LS factor for the revised universal soil oss equation through array-based slope processing of digital elevation data using C++ executable. Computers and Geosciences. 2004;30:1043-1053.
29
[29]. Wischmeier W, Smith D. Predicting rainfall erosion losses-a guide to conservation planning. Agriculture Handbook No. 537. US Department of Agriculture Science and Education Administration, Washington, DC, USA, 1978. p. 163.
30
[30]. De Jong S. Aplication of Reflective Remote Sensing for Land Degradation Studies. University of Utrecht. 1994.
31
[31]. De Jeng S. Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data. catena. 1999;37:291-308.
32
[32]. Kigira F, Gathenya J, Home P. Modeling the influence of land use/land cover changes on sediment yield and hydrology in thika river catchment Kenya, Using Swat Model. Nile Basin Water Sci Eng J. 2012;3(3):56-72.
33
[33]. Lin C, Lin W, Chou W. Soil erosion prediction and sediment yield estimation: the Taiwan experience. Soil Till Res. 2002;68:143-152.
34
[34]. Fallah suraki M, Kavian A, Omidvar E. Zoning of soil erosion hazard in the Haraz watershed model RUSLE. 2 National Conference on climate change and engineering sustainable agriculture and natural Resources,, Tehran- September 17.2015. (In Persian).
35
[35]. Renard K, Foster G, Weesies G, McCool D, Yoder D. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss EquationRUSLE). US Department of Agriculture (Ed.), Agricultural Handbook. US Department of Agriculture, Washington. 1997;703:1–251.
36
[36]. Khatiby M, Karami F, Rajabi M, Nikju M. Assessment of the soil erosion hazard in the sararkandr chay watersheds, the Sahand east hillsides using of USLE and GIS. Journal of Geography and Urban Planning. 2012;40(16):1-23.(In Persian).
37
[37]. Rezai P, Faridy P, Ghorbani M, Kazemi M. Estimate of soil erosion using of RUSLE model and identify the most effective factor in watershed Gabric-southeast Hormozgan province. Quantitative geomorphology researchs 2014;1:97-113.(In Persian).
38
[38]. Dabral P, Baithuri N, Pandey A. Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Res Manag. 2011;22(12):1783-1798.
39
[39]. Kamaludin K, Lihan T, Ali Rahman Z, Mustapha M, Idris W, Rahim S. Integration of remote sensing, RUSLE and GIS to model potential soil loss and sediment yield (SY). Hydrol Earth System Sci. 2013;10:4567–4596.
40
[40]. Getachew H, Melesse A. Effects of Land Use Change on Sediment and Water Yields in Yang Ming Shan National Park, Taiwan. Environments. 2015;2:32-42.
41
[41]. Huang T, Lo K. Effects of land use change on sediment and water yields in yang ming shan national park, Taiwan. Environments. 2012;2:32-42.
42
[42]. Khoi D, Suetsugi T. Impact of climate and land-use changes on hydrological processes and sediment yield—a case study of the Be River catchment, Vietnam. Hydrol Sci J. 2014;59(5):1097-1108.
43
ORIGINAL_ARTICLE
Compare Learning Function in Neural Networks for River Runoff Modeling
Accurate prediction of river flow is one of the most important factors in surface water recourses management especially during floods and drought periods. In fact deriving a proper method for flow forecasting is an important challenge in water resources management and engineering. Although, during recent decades, some black box models based on artificial neural networks (ANN), have been developed to overcome this problem and the accuracy privilege to common statistical methods (such as auto regression and moving average time series method) have been shown. In these research only attended change number of hidden layer and number of neurons for to approach to the best structure in neural network, and complex in proper network designand can’t be simply used by other investigators. In this study examined 15 the neural network learning function and the results indicated in the structure of the network with one hidden layer (ANN1),learnlv1, learnh and learnis by MSE=0.000158, 0.000185 and 0.000188, have been better performance than the other learning functions. And in the structure of the network with two hidden layer (ANN2),learnh, learnsomb and learncon learning function by MSE=0.000154, 0.000173 and 0.000176 have been better performance than the other learning functions.But on the other hand by ten times run this two models, learnsom and learngdm learning functions in ANN1 model and learnh and learnos in ANN2 model had most frequency among the best learning functions and thus it is better that the number of hidden layer not more than one, when we use back propagation network (that its learning function is learngdm). Because in this way we have more chance to achieve ideal response. But if we are going to increase network performance byincreasing the number of hidden layer, it is better that use the default of network and learngdm carefully.
https://ije.ut.ac.ir/article_60374_d77fee960ebb0f0651213b9a3074e2f7.pdf
2016-12-21
659
667
10.22059/ije.2016.60374
Artificial Neural Networks
Learning Function
Prediction
Performance Criteria
Mohammad Javad
Zeynali
mj.zeynali@yahoo.com
1
Ph.D. Student. Department of Science and Water Engineering University of Birjand
AUTHOR
Seyed Reza
Hashemi
srezahashemi@yahoo.com
2
Department of Science and Water Engineering University of Birjand
LEAD_AUTHOR
منابع
1
[1].Tokar AS, Markus M. Precipitation – runoff modeling using artificialneural network and conceptual models. Journal of Hydrologic Engineering. 2000;4:150-161.
2
[2].Razavi SS, Karamuoz M. in Prediction monthly river flows by using artificial aeural network. 10th studentsConferenceonCivil Engineering. Amirkabir University of Technology. 22 Oct 2003. [Persian]
3
[3].Fathi P, Mohammadi Y, Homayi M. Intelligent modeling of monthly flow time series into vahdat dam in sanandaj city. Journal of Water and Soil. 2009; 23(1):209-220. [Persian]
4
[4].Dorum A, Yarar A, FaikSevimli M, and Onucyildiz M. Modelling the rainfall-runoff data of susurluk basin. Expert Systems with Applications. 2010. 37: 6587-6593.
5
[5].Chua HC, and Wong SW. Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. JournalHydrology. 2011. 399: 394-409.
6
[6].Patil S,Valunjkar, S. Study of different rainfall-runoff forecasting algorithms for better water consumption. International Conference on Computational Techniques and Artificial Intelligence. 2012. 327-330.
7
[7].Zeynali MJ, Nikbakht S, Mohammadezapour O. Prediction Input Flows to Mollasadra Reservoir by Useing Artificial Neural Network. 5th Iranian water resources management conference. ShahidbeheshtiUniversity.29 jul 2013. [Persian]
8
[8].Braddock RD,Kremmer ML, Sanzogni L. Feedforward artificial neural network model forforecasting rainfall-runoff. Journal of Environmental Sciences. 1998. 9:419-432.
9
[9]. Kia M. Soft Computing in MATLAB.Qian academic publishing. [Persian]
10
[10].www.mathwork.com
11
[11].Demuth H,Beale M. Neural network toolbox for use with MATLAB. Sixth printing Revised for Version 4. Pp:680.
12
[12].Hahangeer AR, Raeini M, Ahmadi MZ. Comparison of artificial neural networks (ANN) simulation of rainfall-runoff process with HEC-HMS model in Kardeh watershed. Journal of Water and Soil. 2008. 22(2):72-84. [Persian]
13
[13]. Kumar S, Merwade V, Kam J, Thurner K. Streamflow trends in Indiana: effects of long term persistence, precipitation and subsurface drains. Journal of Hydrology. 2009.374(1): 171-183.
14
[14].Cybenko G. Approximation by superposition of a sigmoidal function. Mathematics of control, signals and systems 2.4. 1989. 303-314.
15
[15].Hornik K, Stinchcombe M, White H. Multilayer feed-forward networks are universal approximators. Neural Networks. 1989. 2(5):359-366.
16
[16].Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: the state of the art. International Journalof Forecasting. 1998. 14(1):35-62.
17
[17].Noori R, Abdoli MA, Ghasrodashti AA, JaliliGhazizade M. Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: a case study of Mashhad.Environmental Progress & Sustainable Energy. 2009. 28 (2):249-258.
18
[18].Nikmanesh MR. Prediction of Monthly Average Discharge Using the Hybrid Model of Artificial Neural Network and Wavelet Transforms (Case Study: Kor River Pol-e-Khan Station). Journal of Water and Soil Conservation. 2015. 22(3):231-239. [Persian]
19
[19].Noori R, Farokhnia A, Morid S, RiahiMadvar H. Effect of Input Variables Preprocessing in Artificial Neural Network on Monthly Flow Prediction by PCA and Wavelet Transformation. Journal of Water & Wastewater. 2008. 20(69):1-22
20
ORIGINAL_ARTICLE
Comparison between estimated annual soil lossusing RUSLE model with data from the erosion pins and plots in Khamsan representative watershed
The presentstudy aimed to compare the annual soil loss prediction of RUSLE model with the soil erosion measurements using erosion pins and plots in Khamesan representative watershed, Kurdistan Province. For this purpose, the distributed annual soil loss was estimated by RUSLE model. The suspended sediment samples were then collected daily for one year (2015/7 to 2016/6) in hydrometry station at the watershed outlet. Soil erosion was also measured in pins and plot located in North, West and East aspects of control subwatershed at the same period. The sediment delivery ratio (SDR) was then calculated through dividing total sediment load and erosion of the watershed resulted from three methods of RUSLE, erosion pins and plots. Results indicated that in plot method, the erosion generalized to the whole watershed (0.06 t ha-1 y-1) was much lower than reality and therefore, SDR was overestimated (655%). In erosion pin method, the erosion generalized to the whole watershed (76.79 t ha-1 y-1) was much more than reality and therefore, SDR was underestimated (0.51%). Whereas in RUSLE method, SDR was estimated more acceptably (2.21%) and estimated soil erosion by model (18.53 t ha-1 y-1) was clearly closer to reality. Therefore, generalizing the results of erosion pins and plots considering only the area ratio, can not be a suitable estimate of erosion to the whole watershed. Investigating watershed topography showed that low-slope area in the middle and downstream probably is the main factor of sediment trapping and decreasing sediment transport ratio to the watershed outlet.
https://ije.ut.ac.ir/article_60376_9c08cb5c531807a2ef1c2d438a1aa1c9.pdf
2016-12-21
669
680
10.22059/ije.2016.60376
Sediment delivery
Sediment transport
Sediment trapping
Soil loss
Mohsen
Khorsand
khorsand.1988@yahoo.com
1
Sc. Student of Watershed Management, Faculty of Natural Resources, TarbiatModares University, Noor, Iran
AUTHOR
Abdulvahed
Khaledi Darvishan
a.khaledi@modares.ac.ir
2
Department of Watershed Management Engineering, Faculty of Natural Resources, TarbiatModares University, Noor, Iran
LEAD_AUTHOR
Mehdi
Gholamalifard
gholamalifard@gmail.com
3
Department of Watershed Management Engineering, Faculty of Natural Resources, TarbiatModares University, Noor, Iran
AUTHOR
منابع
1
[1] Rahman, M.R., Shi, Z.H., andChongfa, C., 2009. Soil erosion hazard evaluation-an integrated use of remote sensing, GIS and statistical approaches with biophysical parameters towards management strategies. Ecological Modelling, 220(13): 1724-1734.
2
[2] Refahi, H. (1996). Water erosion and its control. Tehran University Press.167P. (In Persian).
3
[3] Wischmeier, W.H., and Smith, D.D., 1978. Predicting rainfall erosion losses - A guide to conservation planning. Agricultural Handbook No. 537. United States Department of Agriculture Washington DC, 58 P.
4
[4] Oliveira, P.T.S., Wendland, E., and Nearing, M.A. 2013. Rainfall erosivity in Brazil: A review. Catena, 100: 139-147.
5
[5]Gitas, I.Z., Douros, K., Minakou, C., Silleos, G.N., andKarydas, C.G., 2009. Multi-temporal soil erosion risk assessment in N. Chalkidiki using a modified USLE raster model. EARSeLeProceedings, 8(1): 40-52.
6
[6]Renard, K.G., Foster, G.R., Weesies, G.A., and McCool, D.K., 1996. Predicting soil erosion by water. A guide to conservation planning with the revised universal soil loss equation (RUSLE). Agricultural. Handbook 703. US Govt Print Office, Washington, DC, 383 P.
7
[7] Lu, H., Moran, C.J., and Prosser, I.P., 2006. Modelling sediment delivery ratio over the Murray Darling Basin. Environmental Modelling and Software, 21(9): 1297-1308.
8
[8] Kasai, M., Marutani, T., Reid, L.M., andTrustrum, N.A., 2001. Estimation of temporally averaged sediment delivery ratio using aggradational terraces in headwater catchments of the Waipaoa River, North Island, New Zealand. Earth Surface Processes and Landforms, 26(1): 1-16.
9
[9]Vrieling, A., 2006. Satellite remote sensing for water erosion assessment: A review. Catena, 65(1): 2-18.
10
[10]Weifeng, Z., andBingfang, W., 2008. Assessment of soil erosion and sediment delivery ratio using remote sensing and GIS: a case study of upstream Chaobaihe River catchment, north China. International Journal of Sediment Research, 23(2): 167-173.
11
[11] Jain, S.K., Kumar, S., and Varghese, J., 2001. Estimation of soil erosion for a Himalayan watershed using GIS technique. Water Resources Management, 15(1): 41-54.
12
[12] Pandey, A., Chowdary, V.M., and Mal B.C., 2007. Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resources Management, 21(4): 729-746.
13
[13]Kouli, M., Soupios, P., andVallianatos, F. 2009. Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece. Environmental Geology, 57(3): 483-497.
14
[14]Bahadur, K.K., 2009. Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa Watershed, Nan Province, Thailand. Environmental Geology, 57(3): 695-705.
15
[15]Prasannakumar, V., Vijith, H., Abinod, S., andGeetha, N., 2012. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geoscience Frontiers, 3(2): 209-215.
16
[16]Farhan, Y., Zregat, D. and Farhan, I., 2013. Spatial estimation of soil erosion risk using RUSLE approach, RS, and GIS techniques: a case study of Kufranja Watershed, Northern Jordan. Journal of Water Resource and Protection, 5(12): 1247.
17
[17] Abdul Rahaman, S., Aruchamy, S., Jegankumar, R. and Abdul Ajeez, S., 2015. Estimation of Annual Average Soil Loss, Based on RUSLE Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol II-2/W2: 207-214.
18
[18]Zandi, J.,Habibnezhad, M. and Solaimani, K., 2013. Assessment of Soil Erosion Risk Map and its relationship with environmental factors (case study: Vazrud watershed, Mazandaran).Rangeland Watershed Management,66(3):401-415. (In Persian).
19
[19]Rahimi, Kh., and Mazbani, M., 2013. Assess changes Sivand basin erosion during the years 1988 to 2009 using the model RUSLE. Journal of Environmental Erosion Research, 3(1):1-18. (In Persian).
20
[20]Rokhbin, M., Nohegar, A., Kamali, A.R., and Habibllahian, M.H., 2014. Evaluating the Amount of Erosion and Sediment in Lavarefin Watershed (Hormozgan Province) By Using Remote sensing (RS), Geographic Information System (GIS), and Empirical Model (RUSLE). GeographicalResearch, 29(114):89-104.
21
[21]Rezai, P.,Faridi, P., Ghorbani, M.,Kazemi, M., 2014. Soil erosion using models and identify the most effective factor in watershed RUSLE Gabric-southeast province.Quantitative geomorphological researches, 3(1):97-113. (In Persian)
22
[22]Sadeghi, S.H.R., Gholami L., and Khaledi Darvishan A.,2008. Compare Estimation Methods of delivery Chhlgzy watershed dam winter storm in Kurdistan. Journal of Water and Soil (Agricultural Scienes and Technology),22(1):141-150. (In Persian)
23
[23]Gholami, L., Sadeghi, S.H.R., and Khaledi Darvishan, A.,2009. Modeling Storm-Wise Sediment Delivery Ratio Model in Chehelgazi Watershed by using Climatic and Hydrologic Characteristics. Agricultural Sciences and Natural Resources,16(2): 253-260. (In Persian)
24
[24] Chang, M. 2006. Forest hydrology: an introduction to water and forest. Second Edition, Iowa State University, 474 P.
25
[25] Ramos-Scharron, C.E., and MacDonald, L.H. 2007. Development and Application of a GIS-Based Sediment Budget Model, Environmental Management, 84(2): 157-172.
26
[26] General Office of Natural Resources, Kurdistan Province, 2014.Final Report of Watershed Management Studies of Khamsan RepresentativeWatershed,124 P.
27
[27]Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K. and Yoder D.C., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE) (Agricultural Handbook 703). US Department of Agriculture, Washington, DC, 404 P.
28
[28] Cooper, K., 2011. Evaluation of the Relationship between the RUSLE R-Factor and Mean Annual Precipitation. http://www.engr.colostate.edu/~pierre/ce_old/Projects/linkfiles/CooperR-factor-Final.pdf (last access: 15 January 2015), 2011.
29
[29].Patil, R.J., and Sharma, S.K., 2013. Remote Sensing and GIS based modeling of crop/cover management factor (C) of USLE in Shakker river watershed. International Conference on Chemical, Agricultural and Medical Sciences (CAMS-2013) Dec. 29-30, 2013 Kuala Lumpur, Malaysia, 4 P.
30
[30] Durigon, V.L., Carvalho, D.F., Antunes, M.A.H., Oliveira, P.T.S. and Fernandes, M.M., 2014. NDVI time series for monitoring RUSLE cover management factor in a tropical watershed. International Journal of Remote Sensing, 35(2): 441-453.
31
[31] Troeh, F.R., Hobbs, J.A. and Donahue, R.L., 1980. Soil and water conservation for productivity and environmental protection. 3rd Edition. Prentice-Hall, Inc. 624 P.
32
[32] Mahdavi, M. (2011) Applied Hydrology. Vol. 2, 7thEdition, Tehran University Press, 437 P. (In Persian).
33
[33] Walling, D.E., Collins, A. L., Sichingabula, H.M., and Leeks, G.J.L., 2001. Integrated assessment of catchment suspended sediment budgets: a Zambian example. Land Degradation and Development, 12(5): 387-415.
34
[34] Romkens, M.J., Helming, K., and Prasad, S.N., 2002. Soil erosion under different rainfall intensities, surface roughness, and soil water regimes. Catena, 46(2): 103-123.
35
[35] Hartanto, H., Prabhu, R., Widayat, A.S., andAsdak, C., 2003. Factors affecting runoff and soil erosion: plot-level soil loss monitoring for assessing sustainability of forest management. Forest Ecology and Management, 180(1): 361-374.
36
[36]Khaledi Darvishan, A., Sadeghi, S.H., Homaee, M., and Arabkhedri, M., 2014. Measuring sheet erosion using synthetic color-contrast aggregates. Hydrological Processes. 28(15): 4463-4471.
37
[37]Mutchler, C. and Larson, C., 1971. Splash Amounts from Waterdrop Impact on a Smooth Surface. Water Resources Research, 7: 195-200.
38
[38]Zachar, D., 1982. Soil Erosion. Elsevier. Bratislava, Czechoslovakia, 548 P.
39
[39]Auerswald, K., 1993. Influence of Initial Moisture and Time since Tillage on Surface Structure Breakdown and Erosion of a Loessial Soil. Catena Supplement, 24: 93-101.
40
[40]Kinnell, P.I.A., 2005. Raindrop-Impact-Induced Erosion Processes and Prediction: A Review. Hydrological Processes, 19: 2815-2844.
41
[41] Ghahramani, A. Ishikawa, Y., Gomi, T. Shiraki, K., and Miyata, Sh., 2011. Effect of Ground Cover on Splash and Sheetwash Erosion over a Steep Forested Hillslope: A Plot-Scale Study. Catena, 85: 34-47.
42
[42]Parsons, A.J. and Stone, P.M., 2006. Effects of Intra-Storm Variations in Rainfall Intensity on Interrill Runoff and Erosion. Catena, 67: 68-78.
43