ORIGINAL_ARTICLE
Examination of relationship between teleconnection indexes on temperature and precipitation components (Case Study: Karaj Synoptic Stations)
Over the recent decades, human knowledge on earth's climate and his concern about climate change in the future has increased, which has contributed to precise identification of the factors influencing earth's climate. One of the climate phenomena, the change of which causes great anomalies of climate, particularly on temperature and precipitation patterns in many parts of the world, is teleconnection, and it is very important to reveal the relationship between them and climatic parameters for a better understanding of volatility and climate variability in every area. In this study, the relationship between large-scale and well-known patterns such as the Southern Oscillation Index (SOI), North Atlantic oscillation (NAO), Pacific North America (PNA), Multivariate ENSO Index (MEI) and (PDO) with 9 temperature and precipitation variables in the Karaj synoptic station in a monthly basis were analyzed over the 26-year period (2010-1985). First, the normality of the data series based on the Kolmogorov - Smirnov was confirmed. In order to examine the relationship between large-scale patterns with temperature and precipitation variables, the Pearson correlation coefficient was used. The correlations were assessed on a monthly basis without delay and a delay of one month. The results showed that there is a relationship between the NAO index and the temperature and precipitation variables mostly in autumn and winter months and the impact on the autumn months is higher than the winter months. SOI index is more related with the precipitation variables; this index was clearly shown to play a greater role in autumn and winter months, while the MEI index shows a higher correlation with the temperature variables and not particular relation was shown with precipitation variables for this index. Role and impact of this index, in particular on temperature parameters of April and May in spring and December in late autumn, is stronger. The relationship of PDO index with temperature and precipitation variables in May is observed more in the middle of spring. PNA index is effective only on temperature variables, showing higher relationship in December and February. The obtained results are important for greater understanding of the temperature and precipitation variability.
https://ije.ut.ac.ir/article_62414_cf613f8e8ab4b19a58595d9ed1289307.pdf
2017-09-23
641
651
10.22059/ije.2017.62414
ENSO
precipitation
climate indexes
teleconnection
temperature
Massoud
Goudarzi
massoudgoodarzi@yahoo.com
1
Assistant Professor, Soil Conservation and Watershed Management Research Institute SCWMRI, Tehran
LEAD_AUTHOR
Hamzeh
Ahmadi
massoudgoodarzy@gmail.com
2
PhD Student, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar
AUTHOR
Seyed Asaad
Hosseini
hosseini.asad8@gmail.com
3
PhD, Faculty of Human Science, Mohaghegh Ardabili University, Ardabil
AUTHOR
منابع
1
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[12]. Rasanen, T A, Kummu, M, 2013, Spatiotemporal influences of ENSO on precipitation and flood pulse in the Mekong River Basin, Journal of Hydrology, 476: 154-168.
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[13]. Hamedani Azmoodehfar, M. Azarmsa, S.A, 2013, Assessment the Effect of ENSO on Weather Temperature Changes Using Fuzzy Analysis (Case Study: Chabahar). PCBEE Procedia, 5: 508 – 513.
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[14]. Daneshmand, H., Tavousi, T., Khosravi, M., Tavakoli, S, 2015, Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran. Journal of the Saudi Society of Agricultural Sciences, 14(1): 33-40.
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[15]. Sun, X. Thyer, M. Renard, B, Lang M. 2014. A general regional frequency analysis framework for quantifying local-scale climate effects: A case study of ENSO effects on Southeast Queensland rainfall, Journal of Hydrology, 512: 53-68.
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[16]. Beck, F., Bárdossy, A., Seidel, J., Müller, T., Sanchis, E. F., Hauser, A, 2015, Statistical analysis of sub-daily precipitation extremes in Singapore. Journal of Hydrology: Regional Studies, 3: 337-358.
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[25]. Masoudian, S A, 2005, Study of Iran rainfall associated with ENSO, geography and regional development, (4): 73-82.[Persian]
26
ORIGINAL_ARTICLE
Simulation and prediction of drought using Cellular Automata and Markov methods in Najaf Abad plain
The governing factors of drought are non-linearly correlated. Therefore, researcher needs to apply nonlinear methods such as CA to model and predict the drought. CA and its derivatives are among novel methods of drought simulation that rarely used for predicting the drought. While such methods have simple structures, they provide high visual capabilities for drought monitoring. This paper investigates drought in Najaf Abad plain using Markov, CA Markov and Landsat satellite images. First, satellite image time series of transpiration were classified for 1995, 2008 and 2015, and the land zonation of drought condition was estimated. Then, the drought in 2020 was predicted using CA Markov. The Kappa index is 0.63 and the agreement between actual and predicted map (M (m)) is 0.85. Our findings showed that our proposed model can suitably predict the drought. In addition, the drought distribution map showing the possibility of changes in 2020, suggests that if the situation continues and no changes in the type of cultivation and cropping pattern happen, all areas in danger of drought in 2015, will face drought more intensely and more widely, in 2020.
https://ije.ut.ac.ir/article_62415_89d40bca232d8729a39d45bdc1eaef21.pdf
2017-09-23
653
662
10.22059/ije.2017.62415
Drought
evapotranspiration
Najaf Abad Basin
Markov and cellular automation system
Roza
Ebrahimian
roza_ebrahimian@yahoo.com
1
Graduate Student of RS and GIS, Faculty of Environment and Energy, Islamic Azad University, Science and Research Branch, Tehran
AUTHOR
Ali Asghar
Aleshikh
alesheikh@kntu.ac.ir
2
Professor, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
Abolfazl
Masodian
porcista@yahoo.ie
3
Professor, Geography and Humanities, Isfahan University, Isfahan
AUTHOR
[1]. Askarizade M, Behniafar A, Zabol Abasi F, Malbosi Sh. Regionalization of drought with index percentage of normal (PN) and deciles (DC) in Khorasan Razavi Province. Journal of human settlements planning. 2015;3(7):27. [Persian]
1
[2]. Fathi merj A, Heidarian A. Evaluation of meteorological drought, agricultural and hydrological using GIS in Khozestan province. Iran-Watershed Management Science & Engineering. 2010;7(23):19-32. [Persian]
2
[3]. Samadi H, Ebrahimi A. Drought and surface water and groundwater resources. Drought Impacts and its Mitigation Approaches. First ed. Sahrekord University: Soroosh; 2010.p. 115. [Persian]
3
[4]. Niknam H, Ajdari moghadam M, Khosravi M. Neuro-fuzzy model patterns and telecommunications to predict drought Case Study: Zahedan. 4th international congress of the Islamic World Geographers. Zahedan. 2010. [Persian]
4
[5]. Reza zade R, Mir Ahmadi M. Cellular Automation is a new method for simulation of urban growth. Journal of Technology of Education. 2008;4(6):47-55. [Persian]
5
[6]. Alam J, Rahman M, Sadaat A. Monitoring Meteorological and Agricultural Drought dynamics in Barind Region Bangladesh using Standard Precipitation Index and Markov Chain Model. Journal of Geomatics and Geosciences. 2013;3(3):511-524.
6
[7]. Avilés A, Célleri R, Solera A, Paredes J. Probabilistic Forecasting of Drought events using Markov Chain- and Bayesian Network-Based Models: A Case Study of an Andean Regulated River Basin. Journal of water. 2016;8(37): 1.
7
[8]. Edalat Gostar M, Farzadian M, Amiri N. Stochastic models for prediction of drought in the county of Shiraz. National Conference on management of Water Crisis. Marvdasht. 2008. [Persian]
8
[9]. Hasan Zade E, Abdi Kordani A, Fakheri fard A. Prediction of drought using genetic algorithms and neural network model. Journal of water and wastewater. 2009;23(3):48-59. [Persian]
9
[10]. Rostami A, Razmkhah H, Fatahi M. Monitoring and development of artificial neural network model to predict drought using SPI index case study: Kohgiluyeh and Boyer. Faculty of Agriculture, Islamic Azad University of Shiraz. 2011:1. [Persian]
10
[11]. Darzi F, Safavi H, Mamanposh A. Modeling of return flow from Nekooabad network to Najaf Abad basin. Second Conference on Water Resources of Iran. Isfahan. 2006. [Persian]
11
[12]. Phedge N, Muralikrishna I V, Chalapatirao K V. Study of cellular Automata Models for urban growth. www. GIS Development.net.
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[13]. OSullivan D. Exploring spatial process dynamics using irregular Cellular Automaton models. Journal of Geographical Analysis. 2001;33(1):1-18.
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[14]. White R, Engelen G. High resolution integrated modeling of the spatial Dynamics of urban and regional systems, Computers, environment and urban System. 2000;24:383-400.
14
[15]. OSullivan D, Torrens P. Cellular models of urban systems. In: Bandini S, Worsch T, editor. Theory and Practical Issues on Cellular Automata. First ed. London: Springer; 2001.p. 108-116.
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[16]. Ahadnejad M, Rabet A. Evaluation and forecast of Haman impacts based on land use changes using multi temporal satellite imagery and GIS: a case study on Zanjan. Journal of the Indian Society of Remote Sensing. 2009;37(4):659–669.
16
[17]. Hadavi F. Evaluation of physical development of the Zanjan city for optimized planning using GIS techniques. 1th National conference and exhibition Geomatics and conference of International Remote sensing. Tehran. 2011. [Persian]
17
ORIGINAL_ARTICLE
Prioritization of potential areas for construction of underground dam using geometric average method in geographical information system
Exploitation of underground water resources is a way to modulate the shortage of seasonal water. In this regard, proper site selection for constructing underground dam to reserve waters is a challenging issue. Thus, using an approach with minimum error is essential. The purpose of this paper was optimum selection of potential sites for constructing underground dam in Hamedan-Bahar catchment basin using a new GIS-based approach to reduce the error. For this purpose, the efficient criteria of a proper site to make underground dam were first recognized. These criteria include density of drainages, separation of the aqueducts, spring and wells, geological appropriate, slope, fault density, proximity to roads and villages and special conditions of land use. Then, different evidence layers were weighted in [0, 1] range using logistic function in geographic information system. Finally, the weighted evidence layers were integrated using geometric average function. Thus, a model representing favorable areas for making underground dam was generated. The results obtained introduced less than 10% of the streams (respecting the whole streams in the study area) as suitable, and demonstrated that the method applied can be used efficiently to delimit the study area and to recognize suitable streams for construction of underground dam.
https://ije.ut.ac.ir/article_62494_7934babc3fe5e2d688eb76b7f87b8a93.pdf
2017-09-23
663
672
10.22059/ije.2017.62494
Multi-Criteria Decision Making
Integration
Hamedan-Bahar catchment basin
Locating
weighting criteria
Mahyar
Yousefi
m.yousefi.eng@gmail.com
1
Assistant Professor, Faculty of Engineering, Malayer University, Malayer, Iran
AUTHOR
Behnoosh
Farokhzadeh
b.farokhzadeh@malayeru.ac.ir
2
Assistant Professor, Department of Watershed and Rangeland Management, Malayer University, Malayer, Iran
AUTHOR
Samira
Basati
3
MA Student in Watershed Management, Malayer University, Malayer, Iran
AUTHOR
منابع
1
[1]. Kheirkhah Zarkesh MM, Naseri HR, Daodi MH, Salami H. Using the Analytic Hierarchy Process in the prioritization of right places for underground dam construction (Case study: northern slopes of the mountains karkas - natanz). Research and development on natural resources. 2009; (79): 93-101. (In Persian)
2
[2]. PBO. Underground dams, new technique for underground water resource development, water resources and research project studies the optimal utilization of existing water facilities. 1993; No 8. 65p (In Persian)
3
[3]. Dorfeshan F, Heidarnejad M, Bordbar A, Daneshian H. Locating Suitable Sites for the Construction of Underground Dams through Analytic Hierarchy Process. International Conference on Earth, Environment and Life Sciences Dec.23-24,2014 Dubai(UAE).
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[4]. Forzieri G, Gardenti M, Cuparrini F, Castelli F. A methodology for the Pre-Selection of Suitable Sites for Surface and underground Small in arid areas: A Case Study in the region of kidal, Mali. Physics and chemistry of the Earth. 2007; 33: 74-85
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[5]. Jamali IA, Mortberg U, Olofsson B, Shafique M. A Spatial Multi-Criteria Analysis Approach for Locating Suitable Sites for Construction of Subsurface Dams in Northern Pakistan. Water Resour Manage. 2014; 28: 5157-5174.
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[6]. Rezaei P, Rezaei K, Nazari-Shirkouhi, S, Jamalizadeh Tajabadi M R. Application of Fuzzy Multi-Criteria Decision Making Analysis for Evaluating and Selecting the Best Location for Construction of Underground Dam. Acto Polytechnica Hungalica. 2013; 10(70).
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[7]. Salahaldin A, Foad, Al, Sarkawt G, Nadhir, Al. Evaluation of Selected Site Location for Subsurface Dam Construction Within Lsayi Watershed Using GIS and RS Garmiyan Area Kurdistan Region. Journal of Water Resource and Protection. 2014; 6: 972-987.
8
[8]. Pirmoradian R, Behbahani MR, Nazaryfar MH, Velayati S. Initial locating of suitable area for underground dam construction in Eyvanakey plain. The first national conference on water resources and agricultural challenges. Iran, Islamic Azad University Khorasgan. 2013.
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[9]. Eisavi V, Cearami J, Ali-Mohammadi A, NikNezhad A. Comparison of AHP and Fuzzy-AHP decision making approaches in initial locating of suitable area for underground dam construction in Taleghan area. Journal of Earth Sciences. 2012; 22(85): 27-34. (In Persian)
10
[10]. Mohebi tafreshi A, Kheirkhah Zarkesh M, mohebi tafreshi G. Application of GIS and RS techniques as decision support systems for locating suitable sites for underground dam construction (Case Study; Qazvin Province). Journal of Watershed Management Science and Engineering. 2014; 8(26): 35-50. (In Persian)
11
[11]. NikNezhad A. Locating underground dam (Case study: Coat basin). Master's thesis, University of Tarbiyat Modares. Tehran. 2011. (In Persian)
12
[12]. Farokhzadeh B, Attaeian B, Akhzari D, Razandi Y, Bazrafshan O. Combination of Boolean Logic and Analytical Hierarchy Process Methods for Locating Underground Dam Construction. ECOPERSIA. 2015 Sep 1; 3(3):1065-75.
13
[13]. Yousefi M, Carranza E.J.M. Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping. Computers & Geosciences. 2015; 83: 72–79.
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[14]. Yousefi M, Carranza E.J.M. Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Computers & Geosciences. 2015; 74: 97-109.
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[15]. Yousefi M, Kamkar-Rouhani A. Principle of Mineral Potential Modeling Techniques (In Geographic Information System), Amirkabir University Press; 2010. (in Persian)
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[16]. Porwal A. Mineral Potential Mapping with Mathematical Geological Models, Ph.D. Thesis, University of Utrecht, The Netherlands, ITC (International Institute for Geo-Information Science and Earth Observation) Publication No. 130, Enschede. 2006. 289pp.
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[17]. Zimmermann H.J. Fuzzy Set Theory – and Its Applications, Kluwer Academic Publishing, Dordrecht. 1991; 399pp.
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[18]. Moien A. A justification report on extending the ban on exploitation of groundwater resources in the Hamedan-bahar basin, Office of Water resources, Office of water supply, Hamedan Regional Water Authority. 2009. (In Persian)
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[19]. Yousefi M, Carranza EJM. Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences. 2015; 79: 69-81.
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[20]. Wang JQ, Zhang Z.H. Multi-criteria decision-making method with incomplete certain information based on intuitionistic fuzzy number, Control and Decision. 2009; 24: 226–230.
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[21]. Wang JQ, Zhang ZH. Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multi-criteria decision making problems, Journal of Systems Engineering and Electronics. 2009; 20: 321–326.
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[22]. Wang YM, Chin KS, Yang JB. Measuring the performances of decision making units using geometric average efficiency. Journal of the Operational Research Society. 2007; 58: 929–937.
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28
ORIGINAL_ARTICLE
Spatial distribution of snow water equivalent modeling based on topography and climatic factors (Case Study: Sohravard watershed, Zanjan Province)
It is inevitable to obtain necessary data including snow depth, snow density and snow water equivalent (SWE) in order to manage water resources in mountains areas. On the other hand, due to financial constraints, unfair weather and impassability of mountainous areas, measurement is limited to the points, and its generalization to larger areas is associated with large errors. A method for predicting the SWE is investigation the relationship between the SWE and effective factors. Therefore, in this research, mountainous Sohravard watershed located in Zanjan Province was selected as the case study. The required data and maps including Digital Elevation Model (DEM), slope, aspect, northern, eastern, profile curvature, plan curvature, topography position index and solar radiation maps were extracted. Then, during the peak of snowfall in the area, snow depth of 150 points and snow density of 18 points were measured using Latin Hypercube and random sampling methods, respectively. The calculation of upwind slope was carried out for the measured snow points. In the next step, the quantitative relation between the SWE and effective factors was determined by fitting a regression relationship. The efficiency of the created models was evaluated by statistical criteria including mean bias error, mean absolute error, root mean square error and correlation coefficient(R). The results showed that SWE in the studied watershed could be estimated by using stepwise regression. As the results show, although climate factor of upwind slope requires high computing, its incorporation in the model can lead to increased model efficiency in the SWE estimation. The SWE had the highest significant correlation equal to 0.607 with the elevation, and the lowest significant correlation equal to 0.204 to the northern part of the study area. Correlation coefficient between the dependent variable SWE and independent variable upwind slope shows that 300 meters distance is the most effective distance of the interaction of wind and terrain in creation of wind sheltering and wind deflation. Coefficient of variation in snow depth and snow density measurements is 54.14% and 7.89%, respectively.
https://ije.ut.ac.ir/article_62495_913740584666c9fa7dae02ff1d482949.pdf
2017-09-23
673
686
10.22059/ije.2017.62495
Latin hypercube sampling
Snow water equivalent
Stepwise regression
upwind slope
Hojatolah
Ganjkhanlo
hojat.ghanjkhanlo@yahoo.com
1
PhD Student, Faculty of Natural Resources, Tarbiat Modarres University
AUTHOR
Mehdi
Vafakhah
vafakhah@modares.ac.ir
2
Associate Professor, Faculty of Natural Resources, Tarbiat Modarres University
LEAD_AUTHOR
Ali
Fathzadeh
afathzadeh@gmail.com
3
Associate Professor, College of Agriculture & Natural Resources, Ardakan University
AUTHOR
Hossein
Zeinivand
hzeinivand@gmail.com
4
Associate Professor, Department of Watershed Management Engineering, Lorestan University
AUTHOR
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[26].Mizukami N, Perica S, Hatch D. Regional approach for mapping climatologically snow water equivalent over the mountainous regions of the Western United States. Journal of Hydrology. 2011; 40 :72-82.
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[28].Sharifi M.R,Akhoond-Ali A,Porhemmat J,Mohammadi J. Effect of elevation, slope, aspect and on Snow depth in samsami basin. (Technical Report) Iran Journal- Water Resources Research. 2007; 3(3):69-72.[Persian].
28
[29] Marofi s, Tabari H, ZareAbyeane H. Predicting Spatial Distribution of Snow Water Equivalent Using Multivariate Non Linear Regression and Computational Intelligence Methods. Water Resources Management. 2011; 25:1417-1435.
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[30].Taghizadeh–Mehrjardi R, Gharaei-Manesh S, Fathzadeh A. Predicting of spatial distribution snow depth using regression kriging method in the region of Yazd Sakhvidi. Journal of Watershed Management Science and Engineering. 2015; 9(28):41-48.
30
[31]. Gharaei-Manesh S,Fathzadeh A,Taghizadeh–Mehrjardi R. Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran. Cold Regions Science and Technology. 2016; 122:26-35
31
[32]. Taghizadeh –Mehrjardi R,Sarmadian F,Tazeh M, Omid M,Toomanian N, Rosta M-J.
32
Comparison of different Sampling methods for digital soil mapping in Ardakan region. Journal of Management and Watershed Engineering.2015; 4(6):353-363.[Persian].
33
[33].Pare-Zanganeh A,Hasaniha H. Detiled study ofSohravard-Ghydar watershed.2002;307p..[Persian].
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[34].Pahlavan –Rad M-R, Kormali F,Toomanian N,kiani F, Komaki-B CH. Digital soil mapping using Random Forest model in Golestan province. Journal of Soil and Water Conservation.2015; 6 (21):73-93 [Persian].
35
[35] Taghizadeh –Mehrjardi R,Sarmadian F, Omid M,Toomanian N, Rosta M- J, Rahimian M-H. Digital mapping of soil classes using of data mining techniques in Ardakan region of Yazd Province.Journal of Scientific and Agriculture. 2013; 37(2):101-115. [Persian].
36
[36].Marofi s, Tabari H, Zare-Abyeane H, Sharifi M.R,Akhoond-Ali. Mapping of Snow Water Equivalent in one of Karoon mountain sub basin (Cass Study- Samsami basin).Journal of Agriculture and Natural Resources.2010;16(3) :1-11.[Persian].
37
[37].Marofi s, Tabari H, Zare-Abyeane H, Sharifi M.R. Investigating the influences of wind on spatial distribution of snow accumulation in one of Karoon sub basin (Cass Study- Samsami basin). Journal of Sciences of Research of Irrigation and Water 2011: 1(1):31-44. [Persian].
38
[38].Shaban A, Faour G, Khawlie M, Abdallah C. Remote sensing application to estimate the volume of water in the form of snow on Mount Lebanon. Hydrological Sciences Journal. 2004; 49(4):643-653.
39
[39].Konosuke S, Tsutomu K, Yinsheng Z, Mamoru I, Yoshihiro I. Altitudinal Distribution of Snow Water Equivalent in the Tuul River Basin, Mongolia. International Workshop on Terrestrial Change in Mongolia.2006.
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[40].Bloschl G, Kirnbauer R, Gutknecht D, Distributed Snowmelt Simulation in an Alpine Catchment. Model Evaluation on the Basis of Snow Cover Patterns. Water Resources Research.1991; 27 (12) 3171-3179.
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[41].Molotch N, Colee M, Bales R, Dozier J. Estimating the spatial distribution of snow water equivalent in an alpine basin using binary regression tree models. the impact of digital elevation data independent variable selection. Hydrological Processes. 2005; 19(7):1459-1479.
42
ORIGINAL_ARTICLE
Investigation of relationship between meteorological and hydrological drought in Karkheh watershed
Drought is one of the natural disasters which is strongly influenced by climatic fluctuations and makes a set of complex problems in various parts. The current study aims for investigation of time relationship between meteorological and hydrological drought in Karkhe catchment. For this purpose, 26 rain gauge stations and 8 gauging stations were used. In part of research, for understanding delay trends in comparison with precipitation in reality, Pearson correlation coefficient between precipitation and discharge was calculated with different time delays. Two indices (SPI and SDI index) were selected for examining Meteorological drought and hydrological drought. Index values in short term step (1 to 3 months), medium-term (6 months) and long term (12 months) was calculated by DrinC software. The results showed that there is 99% direct significant correlation between precipitation-discharge and two SPI-SDI indices in four steps of time. However, the correlation coefficient is significant for any four time steps, but the value of correlation coefficient has more delay time in a mood with no delay time compared to other cases. Therefore, we can conclude that drought occurs simultaneously in the catchment Karkhe meteorologically and hydrologically. Also, due to the lack of consistent correlation coefficients and delay time in both cases, two aforementioned indices properly show drought conditions.
https://ije.ut.ac.ir/article_62496_f1b7e7a1a2d9d001e8f839b10e9c270e.pdf
2017-09-23
687
698
10.22059/ije.2017.62496
Drought meteorological
drought hydrological
correlation Pearson
basin Karkhe
Razieh
Koushki
r.koushki67@gmail.com
1
MSc. Graduate of Agricultural Meteorology, Faculty of Desert Studies, Semnan University
AUTHOR
Mohammad
Rahimi
r.koushki@semnan.ac.ir
2
Associate Professor, Department of Agricultural Meteorology, Faculty of Desert Studies, Semnan University
LEAD_AUTHOR
Mojtaba
Amiri
mojtabaamiri@semnan.ac.ir
3
Assistant Professor, Department of agricultural Meteorology, Faculty of Natural Resources, Semnan University
AUTHOR
Majid
Mohammadi
mohammady.wme@gmail.com
4
Assistant Professor, Department of agricultural Meteorology, Faculty of Natural Resources, Semnan University
AUTHOR
Jafar
Dastorani
jdastorani@gmail.com
5
Assistant Professor, Department of agricultural Meteorology, Faculty of Natural Resources, Semnan University
AUTHOR
منابع
1
[1]. Mozafari Gh. Unconformity in meteorological ang hydrological drought in two neighboring basin at north montain slope of shirkoh yazd. Journal of spatial planning. 2006;10 (2): 173-190. [Persian].
2
[2]. Khazayi M, Telvari A. Hydrological drought frequency distribution analysis. Journal of Geography and Urban Development. 2003; (2): 45-56. [Persian].
3
[3]. Eivazi M, Mosaedi A. Monitoring and spatial analysis of meteorological drought in golestan province using geostatistical methods., Journal of range and watershed management, iranian journal of natural resources. 2011; 64: 65-78. [Persian].
4
[4]. Alavi Nia H, Sadatinejad j, Abdullah Kh. Provide a model for prediction of hydrological drought in Karoon-1 basin. Environmental Erosion Research Journal. 2011; (1): 45-56. [Persian].
5
[5]. Rzayyklantry d. Check meteorological and hydrological drought in East Mazandaran. Gorgan University of Agricultural Sciences and Natural Resources. 2011 Master thesis.
6
[6]. Eskandari Damaneh H, Zehtabian GH, Khosravi H, Azareh. Analysis of temporal and spatial relationship between meteorological and hydrological drought in Tehran province. Jurnal Management System. Winter 2016:113-120. [Persian].
7
[7]. Babaei H, Araghinejad SH, HorfarA. Time interval identification of the occurrences of meteorological and hydrological droughts in Zayandeh-Rud basin, Arid Biom Scientific and Research Journal, 2011; 1 (3): 1-12. [Persian].
8
[8]. Mofidipoor N, Brady Sheikh V, Ownegh M, Sydaldyn A. the analysis of relationship between meteorological and hydrological droughts in Atrak. Watershed jwmr. 2011; 3 (5): 16-26. [Persian].
9
[9]. Azareh A, Rahdari MR, Sardoii ER, & Moghadam,FA. Investigate the relationship between hydrological and meteorological droughts in Karaj dam basin. European Journal of Experimental Biology. 2014; 4(3): 102-107.
10
[10]. Soleimani Sardou F, Bahramand A. Hydrological drought analysis using SDI index in Halilrud basin of Iran. Environmental Resources Research.2014; 2(1): 47-56.
11
[11]. Vardipour A, Azarakhsh M., Mosaedi A, Farzadmehr J. The relationship between meteorological and hydrological droughts Mashhad plain. The National Conference of Sciences and Environment Engineering, June 2014. Ahvaz Province. [Persian].
12
[12]. Tigkas D, Vangelis H, Tsakiris G. Drought and climatic change impact on streamflow in small watersheds. Science of the Total Environment. 2012; 440: 33-41.
13
[13]. Zhiyong Wu, Yun Mao, Xiaoyan Li, Guihua Lu, Qingxia Lin and Huating Xu. Exploring spatiotemporal relationships among meteorological, agricultural, and hydrological droughts in Southwest China. Stochastic Environmental Research and Risk Assessment 2016;( 30.3): 1033-1044.
14
[14]. Christopher N, Awange J, Corner R, Kuhn M and Okwuashi O On the potentials of multiple climate variables in assessing the spatio-temporal characteristics of hydrological droughts over the Volta Basin." Science of The Total Environment 2016; (557): 819-837.
15
[15]. Ndehedehe CE, Awange JL, Corner R J, KuhnM, Okwuashi O. On the potentials of multiple climate variables in assessing the spatio-temporal characteristics of hydrological droughts over the Volta Basin. Science of The Total Environment. 2016; 557: 819-837.
16
[16]. Moradi H, Sepahvand A, Khazaee, M. Meteorological and hydrological drought monitoring using SPI index modified and SDI (Case Study of Khorramabad), 5th National Conference of Iran' Watershed. May 2009, Gorgan. [Persian].
17
[17]. Salajegh A, Razavizade S, Khorasani N, Hamidifar M, Salajegh S. Land use Changes and its Effects on Water Quality (Case study: Karkheh watershed), Journal of Environmental Studies. 37(58), 81-86.
18
[18]. GHasemi M, Eslamian S,Soltani S. Monitoring and regionalization of reteorological drought in karkhe watershed using standardized precipitation index and precipitation deciles. Journal of Wateter of Research in Agriculture. 2008; 8 (3):35-23.
19
[19]. Kazemzadeh M, Malekian A. Spatial characteristics and temporal trends of meteorological and hydrological droughts in northwestern Iran. Natural Hazards, 2016;(1): 191-210.
20
[20]. Mair A, Fares A. Influence of groundwater pumping and rainfall spatio-temporal variation on streamflow. Journal of Hydrology. 2010; 393(3): 287-308.
21
[21]. Karamoz M, Araghynejad Sh, Advanced Hydrology. Amir Kabir University Press, 2005.
22
[22]. Bazrafshan J.1381. A few examples of performance evaluation in different climates of Iran meteorological drought indices, Masters Meteorology, School of Agriculture, University of Tehran.
23
[23].Nalbantis I, Tsakiris G. Assessment of hydrological drought revisited. Water Resources Management. 2009; 23(5): 881-897.
24
ORIGINAL_ARTICLE
Estimation of wetland-aquifer exchanges (Case Study: Kaniborazan wetland)
Surface water and groundwater interactions can occur between surface water bodies (such as rivers, lakes, and wetlands) and groundwater resources. Quantifying water exchange between a wetland and an underlying aquifer is an important task for studies in such fields as water budgets and environmental water requirements. In this study, the groundwater component from controlling factors on wetland water level is considered to determine the water requirements of Kaniborazan wetland, located at the southern part of Urmia Lake. The results of the present study indicate that during the assessment period of 1998-2015, Kaniborazan wetland has been always recharged from underlying groundwater resources considering the hydraulic gradient threshold of this wetland. These recharge values have been maximum for 1998, 2002, and 2015 with the annual value of 4.11, 5.09, and 3.78 MCM, respectively. The mean annual value of this wetland depth has been estimated to be less than 16 cm. The impacts of drainage system, channels, and traditional streams have been investigated on this wetland during the assessment period. The obtained results show that these sources have a significant effect on the water supply of the wetland. Based on the available data and information, for example for 2006 and 2015, not taking the impacts of drainage system, channels, and traditional streams on this wetland into account will lead to the wetland water volume reduction of about 15% and 30%, respectively.
https://ije.ut.ac.ir/article_62503_3521d96e0da1ca9a63481bdaf830de9d.pdf
2017-09-23
699
709
10.22059/ije.2017.62503
Aquifer
drainage system
Groundwater Resources
water exchanges
wetland
Hamed
Ketabchi
h.ketabchi@modares.ac.ir
1
Assistant Professor, Department of Water Resources Engineering, Tarbiat Modares University
LEAD_AUTHOR
Davood
Mahmoodzadeh
d.mahmoodzadeh@ut.ac.ir
2
PhD Student, Department of Civil Engineering, University of Tehran
AUTHOR
Reza
Farhoudi Hafdaran
reza.farhoudi@modares.ac.ir
3
MSc., Department of Water Resources Engineering, Tarbiat Modares University
AUTHOR
منابع
1
[1]. Rafiei Y, Malekmohamadi B, Abkar AA, Yavari A, Ramezani-Mehrian M, Zahrabi H. Assessment of wetlands environmental changes and protected areas using temporal images Landsat TM (Case Study: Neyriz Wetland). Journal of Environmental Study. 2011; 37(57), 65-76
2
[2]. Ayafat SA. Wetland Benefits, Compiled by: John Davies and Gordon Claridge, Supported by: IWRB, WA, AWB and Supervised by: Anoushirvan Najafi and Esmail Kahrom. 2000.
3
[3]. Ganjidoust H, Ayati B, Khara H, Khodaparast SH, Akbarzadeh A, Ahmadzadeh T, Shaban L, Nezami A , Zolfi Nejhad K. Investigation of environmental pollution in Shiah Keshim wetland. Environmental Sciences. 2009; 6(3), 117-132. [Persian].
4
[4]. Wang Y, Mitchell BR, Nugranad-Marzilli J, Bonynge G, Zhou Y, Shriver G. Remote sensing of land-cover change and landscape context of the National Parks: A case study of the northeast temperate network. Remote Sensing of Environment. 2009; 113(7), 1453-1461.
5
[5]. Jones DA, Hansen AJ, Bly K, Doherty K, Verschuyl JP, Paugh JI, Story SJ. Monitoring land use and cover around parks: A conceptual approach. Remote Sensing of Environment. 2009; 113(7), 1346-1356.
6
[6]. Choi J, Harvey JW. Quantifying time-varying ground-water discharge and recharge in wetlands of the northern Florida Everglades. Wetlands. 2000; 20(3), 500-511.
7
[7]. Tobias CR, Harvey JW, Anderson IC. Quantifying groundwater discharge through fringing wetlands to estuaries: Seasonal variability, methods comparison, and implications for wetland-estuary exchange. Limnology and Oceanography. 2001; 46(3), 604-615.
8
[8]. Walter DA, Masterson JP, LeBlanc, DR. Simulated pond-aquifer interactions under natural and stressed conditions near Snake Pond. Cape Cod, Massachusetts: US Geological Survey Water-Resources Investigations Report. 2002; 99-4174.
9
[9]. Rassam DW, Werner A. Review of Groundwater-surface water Interaction modelling approaches and their suitability for Australian conditions. E-water cooperative research centre. 2008.
10
[10]. Zapata-Rios X, Price RM. Estimates of groundwater discharge to a coastal wetland using multiple techniques: Taylor Slough, Everglades National Park, USA. Hydrogeology Journal. 2012; 20(8), 1651-1668.
11
[11]. Baratelli F, Flipo N, Moatar F. Estimation of stream-aquifer exchanges at regional scale using a distributed model: sensitivity to in-stream water level fluctuations, riverbed elevation and roughness. Journal of Hydrology. 2016; 542, 686-703.
12
[12]. Yousefi-sangani K, Mohammadzadeh H. Surface water and groundwater exchange and how to measure water leakage. Second national conferences on water. Behbahan Islamic Azad University. 2009. [Persian].
13
[13]. Water Engineering Research Institute of Tarbiat Modares University. Environmental flow determination of Urmia Lake basin wetlands and rivers, Hydrology studies report, wetland-aquifer exchanges section. East Azerbaijan Department of Environment. 2016. [Persian].
14
[14]. Water Research Institute. Synthesis report, Integrated water resources management of Urmia Lake basin. West Azerbaijan Regional Water Authority. 2006. [Persian].
15
[15]. Water budget updating study of Urmia Lake basin. Water and sustainable development Consulting Co. 2014. [Persian].
16
ORIGINAL_ARTICLE
Assessment of the economic and hydrological effects of the climate change on Kharrood Watershed
In the present study, first the behavioral patterns of precipitation climatic variable over the period 1985-2014 in Kharrood watershed was investigated. Then, in order to analyze the effects of climate change resulting from reduced rainfall under different scenarios (i.e. mild, moderate and severe) on hydrological (available water resources and economic value of irrigation water) and economic (agricultural products and farmers’ gross profit) variables, a biophysical-economic modeling system was used. The aforementioned modeling system includes the products yield function based on rainfall (biophysical part of model) and positive mathematical programming approach (economic part of model) solved in consecutive three stages and in the GAMS 24.1 software. The required data were collected referring to the rain gauge stations and the relevant agencies in Qazvin province. Behavioral pattern of precipitation showed that this climate variable reduced in Kharrood watershed after year 2001. The results of proposed model showed that climate change resulting from reduced rainfall under mild to severe scenarios decreases the available water resources from 11/3 to 23/0 %, increases the economic value of irrigation water from 7/08 to 15/22 %, decreases the total acreage of water crops from 5/14 to 16/39 % and decreases the farmers’ gross profit from 6/58 to 13/41 % compared to the base year. The highest decrease of the available water resources in Kharrod watershed was obtained under severe scenarios and at a rate of 15/29 million cubic meters. Finally, use of deficit irrigation techniques, determination of the rate of water charge for farmers on the basis of equality consideration, fallow-lands and provision of facilities to farmers in order to equip their lands with new irrigation systems were proposed in order to deal with the effects of climate change and protect water resources in this watershed.
https://ije.ut.ac.ir/article_62504_799aad51af5c0a14ab7c1c852d8548b2.pdf
2017-09-23
711
724
10.22059/ije.2017.62504
Behavioral pattern of precipitation
climate change
hydrological variables
sustainability of water resources
biophysical-economic model
Abozar
Parhizkari
abozar.parhizkari@yahoo.com
1
PhD Student of Agricultural Economics, Payam Noor University, Tehran, Iran
LEAD_AUTHOR
Saeed
Yazdani
syazdani@ut.ac.ir
2
Professor of Agricultural Economics, University of Tehran
AUTHOR
منابع
1
[1]. Parhizkari A. Determination economic value of irrigation water and farmer’s response to price and non-price policies in Qazvin province, the thesis submitted for the degree of M.Sc in the field of agricultural economics, University of Zabol, Iran, 2013; 130 P. [In Persian]
2
[2]. Ghayour H, Masodian A. The effects of global warming on the water cycle in nature, Journal of Geographical Researchs, 1996; 46(1): 53-69. [In Persian]
3
[3]. Parhizkari A, Sabuhi M. Analysis of the economic and welfare impacts of establishment irrigation water market in Qazvin province, Journal of Agricultural Economics and Development, 2013; 27(4): 338-350. [In Persian]
4
[4]. Mahmoodi A, Parhizkari A. Economic analysis of climate change impacts on crop yield, crop pattern, and gross margin for farmers (Case Study: Qazvin). Development of Agriculture and Rural Development, 2015; 1(2): 25-40. [In Persian]
5
[5]. Godarzi M, Salahi B, Hosein S.A. The effects of climate change on surface runoff changes (Case study: The lake's basin). Eco-hydrology Journal, 2015; 2(2): 175-189. [In Persian]
6
[6]. Mesmarian Z, Mesahebevani A, Javadi S. The impact of climate change on grandwater balance Shahrekord plain in future periods. Eco-hydrology Journal, 2016; 3(2): 234-242. [In Persian]
7
[7]. Nazari Poya H, Kordvani P, Faraji Rad AR. Assessing the impact of climate change on hydro dynamic parameters of the Ekbatan dam basin (Case study: Hamedan province). Eco-hydrology Journal, 2016; 3(2): 181-194. [In Persian]
8
[8]. Jung IIW, Chang H. Assessment of future runoff trends under multiple climate change scenarios in the Willamette River Basin, Oregon, USA. Journal of Hydrology, 2010; 16: 63-87.
9
[9]. Traynham L, Palmer R, Polebitski A. Impacts of future climate conditions and forecasted population growth on water supply systems in the Puget Sound region, Water Resours, 2011; 137(2): 318-326.
10
[10]. Ponce R, BlancoM, Giupponi C. The economic impacts of climate change on the Chilean agricultural sector: a non-linear agricultural supply model. Chilean Journal of Agricultural Research, 2014; 74(4): 404-412.
11
[11]. Esteve P, Varela-Ortega C, Blanco-Gutiérrez I, Downing T. A hydro-economic model for the assessment of climate change impacts and adaptation in irrigated agriculture, Ecological Economics, 2012; 120(2): 49-58.
12
[12]. Howitt RE, Medellin-Azuara J, MacEwan D, Lund R. Calibrating disaggregate economic models of agricultural production and water management. Science of the Environmental Modeling and Software, 2012; 38: 244-258.
13
[13]. Qureshi ME, Whitten S, Mainuddin M, Marvanek M, Elmahdi A. A biophysical and economic model of agriculture and water in the Murray-Darling Basin, Australia, Environmental Modelling and Software, 2013; 41: 98-106.
14
[14]. Agriculture Jihad Organization of Qazvin Province. 2015. The improvement of plant production. [In Persian]
15
[15]. Regional Water Company of Qazvin province. The part of water resources management. 2015; 19p [In Persian]
16
[16]. Medellan-Azuara J, Harou J, Howitt R. Predicting farmer responses to water pricing, rationing and subsidies assuming profit maximizing investment in irrigation technology. Science of the Agricultural Water Management, 2011; 108: 73-82.
17
[17]. Parhizkari A, Sabouhi M, Ahmadpour M, Badibarzin H. Simulation the Farmers’ Response to Irrigation Water Pricing and Rationing Policies (Case Study: Sistan Region). Journal of Agricultural Economics and Development, 2014; 28(2): 164-176. [In Persian]
18
[18]. Parhizkari A, Sabouhi M. Simulation farmers’ response to reducing available water policy, Journal of Water and Irrigation Management, 2013; 3(2): 59-74. [In Persian]
19
[19]. Parhizkari A, Sabuhi M, Ziaee S. Simulation water market and analysis of the effects irrigation water sharing policy on cropping patterns under conditions of water shortage, Journal of Agricultural Economics and Development, 2013; 27(3): 242-252. [In Persian]
20
[20]. Qureshi ME, Schwabe K, Connor J, Kirby M. Environmental water incentive policy and return flows, Water Resources Research, 2010; 46p.
21
[21]. Khanlari A, Keykha AA. Assessment the impact of climate change on the agricultural production in Mazandaran province, Master's thesis, Faculty of Agriculture, University of Zabol, 2010; 72p. [In Persian]
22
ORIGINAL_ARTICLE
Evaluation and comparison of frequency ratio, statistic index and entropy methods for groundwater potential mapping using GIS (Case Study: Jahrom Township)
Groundwater is considered one of the most valuable fresh water resources. The rapid increase in human population has increased the demand for groundwater supplies for drinking, agricultural, and industrial purposes. It is necessary to provide groundwater spring potential maps for implementing a successful groundwater determination, protection, and management program. The main objective of this study was to produce groundwater spring potential maps in the Jahrom region, using frequency ratio, statistic index and entropy methods. Twelve hydrological-geological-physiographical (HGP) factors influencing locations of springs were considered in this research and processed in ArcGIS environment. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), distance to roads, distance to rivers, distance to faults, lithology, land use and fault density. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 103 springs identified, 70 (≈70 %) locations were used for the spring potential mapping, while the remaining 33 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the statistic index model was calculated to be %91 and for frequency ratio and entropy the AUC to be %92 and %92.7, respectively.
https://ije.ut.ac.ir/article_62505_9cecd5a4b2a65aef5301b65fd2ed50ea.pdf
2017-09-23
725
736
10.22059/ije.2017.62505
Spring potential mapping
frequency ratio method
statistic index method
entropy method
Seyyed Vahid
Razavi Termeh
vrazavi70@gmail.com
1
MSc. Student, Faculty of Geodesy & Geomatics Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
Mohammad Saedi
Mesgari
mesgari@kntu.ac.ir
2
Associate Professor, Faculty of Geodesy and Geomatics Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran
AUTHOR
kazem
kazemi
kazem_k_1369@yahoo.com
3
MSc. Student, Faculty of Geodesy & Geomatics Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran
AUTHOR
منابع
1
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Shahid S, Nath S, Roy J. Groundwater potential modelling in a soft rock area using a GIS. International Journal of Remote Sensing. 2000; 21(9):1919-24.
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Oh H-J, Kim Y-S, Choi J-K, Park E, Lee S. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology. 2011; 399(3):158-72.
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Ganapuram S, Kumar GV, Krishna IM, Kahya E, Demirel MC. Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS. Advances in Engineering Software. 2009; 40(7):506-18.
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Pradhan B. Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Open Geosciences. 2009;1(1):120-9.
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Prasad R, Mondal N, Banerjee P, Nandakumar M, Singh V. Deciphering potential groundwater zone in hard rock through the application of GIS. Environmental geology. 2008; 55(3):467-75.
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Masetti M, Poli S, Sterlacchini S. The use of the weights-of-evidence modeling technique to estimate the vulnerability of groundwater to nitrate contamination. Natural Resources Research. 2007; 16(2):109-19.
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Arthur JD, Wood HAR, Baker AE, Cichon JR, Raines GL. Development and implementation of a Bayesian-based aquifer vulnerability assessment in Florida. Natural Resources Research. 2007; 16(2):93-107.
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Zhang Z, Cheng Q, editors. GIS Spatial statistical analysis of groundwater in GTA, Canada. Geoscience and Remote Sensing Symposium, 2002 IGARSS'02 2002 IEEE International; 2002: IEEE.
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Moghaddam DD, Rezaei M, Pourghasemi H, Pourtaghie Z, Pradhan B. Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal of Geosciences. 2015;8(2):913-29.
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Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A. Groundwater spring potential mapping using Shannon’s entropy and Random Forest models in the Bojnord watershed, Iran. Earth Science Informatics. 2015;8(1):171-86.
23
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34
ORIGINAL_ARTICLE
Hydrogeological drought management index (HDMI) as a tool for groundwater resource management under drought conditions (Case Study: Dayyer-Abdan district, Boushehr province)
Drought has major negative effects on water resources and its related environments. Sometimes the drought damages are irreparable. Groundwater drought is one of the most important droughts caused by insufficient groundwater recharge. This study aims to evaluate the effect of drought on groundwater resources of Dayyer-Abdan district, south of Boushehr province. Data and information such as rainfall, groundwater level, well discharge rates and groundwater quality data were used for this purpose. The Standardized Precipitation Index (SPI) and Groundwater Resource Index (GRI) were used to assess the drought situation. A new index called Hydrogeological Drought Management Index (HDMI) is introduced in this research. The HDMI index is a combination of Groundwater Resource Index (GRI), the Modified Standardized Electrical Conductivity Index (MSECI) and Standardized Well Discharge Index (SWDI). Based on the obtained results, average of GRI index is less than -1, indicating a moderate groundwater drought. Groundwater drought also has destroyed the groundwater quality. From the groundwater quality view point (MSECI index), the most critical zone of the aquifer is located in the southern part of the study area. From the aquifer potential view point (SWDI index), the most suitable groundwater zone is in the northern parts of the study area. The southern parts of the Dayyer-Abdan district (adjacent to the Persian Gulf) have the lowest amounts of HDMI index (less than -4). So exploitation of groundwater is not recommended in these areas.
https://ije.ut.ac.ir/article_62506_c12266562f80793915d316ffd8c7aebb.pdf
2017-09-23
737
748
10.22059/ije.2017.62506
Groundwater
Drought
hydrogeological drought management index (HDMI)
Dayyer-Abdan district
Muhammad
Faryabi
faryabi753@yahoo.com
1
Assistant Professor, College of Natural Resources, University of Jiroft, Jiroft, Iran
LEAD_AUTHOR
Jaber
Mozaffarizade
mozaffarizade-j@yahoo.com
2
PhD Candidate, Department of Earth Science, University of Shiraz, Shiraz, Iran
AUTHOR
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1
[2]. Abdollahi Kh. Assessing the spatial and temporal pattern of meteorological drought in Iran. Available at http://drought.iranhydrology.net.
2
[3]. Seif M, Mohammadzade H, Sayyad H. Evaluation of drought effect on groundwater resource in Fasa plain using standardized precipitation index and groundwater standardized electrical conductivity. Water Resource Engineering, 2012; 5(13): 55-72. [Persian].
3
[4]. Mendicino G, Senatore A, Versace P. A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate. Journal of Hydrology, 2008; 357: 282-302.
4
[5]. Nazemossadat MJ. Is it raining? Drought and excess rainfall in Iran and their relationship with the El Nino-southern oscillation. Shiraz University press, 120 p. [Persian].
5
[6]. Azizi Gh. Relation between recent drought and groundwater resources in the Qazvin plain. Geographical Reseach Journal, 2003; 35 (46): 131-143 [Persian
6
[7]. ].Nazemi Sh, Khara H. Investigation on drought effect on diversity, frequency and distribution of benthic fauna in Amirkelaye wetland. Iranian Scientific Fisheries Journal, 2005; 14 (3): 141-156 [Persian].
7
[8]. Shakiba W, Mirbagheri B, Kheiri A. Drought and its impact on groundwater resources in East of Kermanshah province using SPI index. Journal of Geography, 2010; 8(25): 105-124. [Persian].
8
[9]. Naserzadeh M, Ahmadi E. Meteorological drought indices in assessing the performance of the drought and its zoning in Qazvin. Applied Research of GIS (Geographical Sciences), 2012; 12(27): 141-162. [Persian].
9
[10]. Chamanpira Gh, Zehtabian Gh, Ahmadi H, Malekian A. Effect of drought on groundwater resources in order to optimize utilization management, case study: Alashtar plain. Watershed Engineering and Management, 2014; 6(1): 10-20 [Persian].
10
[11]. Khoshhal J, Ghayoor HA, Moradi M. A survey on the impact of groundwater drought in Dehgolan basin, Kurdistan province. Natural Geography Research, 2012; 79: 19-36 [Persian].
11
[12]. Karami F. Evaluation of Meteorological Drought Effects in the Reduction of Ground Watertable (Case study: Tabriz Plain). Journal of Geography and Planning, 2011; 16(31): 111-131[Persian].
12
[13]. Aleboali A, Ghazavi R, Sadatinezhad SJ. Study the effects of drought on groundwater resources using SPI index (A case study: Kashan plain). Desert Ecosystem Engineering Journal, 2016; 5(10): 13-22 [Persian].
13
[14]. Mckee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. 8th conference of applied climatology, Aneheim. 1993
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[15]..Scibek J, Allen DM. Comparing modeled responses of two high-permeability unconfined aquifers to predicted climate change. Global and Planetary Change, 2006; 50: 50-62.
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[17]. Mair A, Fares A. Influence of groundwater pumping and rainfall spatio-temporal variation of stream flow. Journal of Hydrology. 2010; 393 :287-308.
17
[18]. Shahid S, Hazarika MK.Groundwater drought in the northwestern district of Bangladesh. Water Resource Management, 2010; 24(10): 1989-2006.
18
Khan MA, Gadiwala MS. A Study of drought over Sindh (Pakistan) using standardized precipitation index (SPI) 1951 to 2010. Pakistan Journal of Meteorology, 2013; 9(18): 15-22.
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[19]. Barkey BL, Bailey RT. Estimating the impact of drought on groundwater resources of the Marshall Islands. Water, 2017; 9 (41): 1-12.
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[20]. Boushehr Regional Water Authority. Report of groundwater resource of Boushehr province. 2012. [Persian].
21
[21]. Bigonah S, Ekhtesasi M, Faryabi M. Evaluation of Drought effect on groundwater resources in Jiroft plain using GRI index. 9th symposium of watershed science and engineering, Yazd University. 2012. [Persian].
22
ORIGINAL_ARTICLE
Evaluation of the role of Sarcahan-Floodwater spreading in the artificial groundwater recharge
Despite the arid and semi-arid climate of Iran, a considerable volume of water becomes out of reach by annual flash flood events. Therefore, in order to solve the problem, Floodwater Spreading Systems have been implemented approximately over the past three decades. The aim of this study was to evaluate quantitative changes of the groundwater affected by the Sarcahan floodwater spreading which was implemented in Hormozgarn Province using Control Volume technique. Sarcahan floodwater spreading project has been implemented in an area of 2000 hectares. The well-hydrographs, rainfall histographs, and the fluctuation of groundwater at observation wells synchronizing with the frequency of flooding were used to evaluate the effect of the project on the groundwater. Examined observation wells showed a relative increase in the level of groundwater after the primarily flood controlling; in other words, the hydrograph of observation well located in the area of flooding has considerably increased by about 1.34 meters. Groundwater levels changes recorded at the observation wells are quite similar to the feeding/withdrawing ratio whereas in 2008 groundwater level increased by about 4.43 meters. Furthermore, in the Gahkom-Saadatabad the amount of rain more than 60 mm or continuous rainfall in large quantities can artificially recharge the aquifer. The results indicated that the Sarcahan project has not satisfactory effect on the groundwater according to predictions.
https://ije.ut.ac.ir/article_62507_4dda8331459bc79321acd690f7b77025.pdf
2017-09-23
749
761
10.22059/ije.2017.62507
Groundwater level
Artificial recharge
control volume
fluctuation
Sarcahan
Abazar
Mostafaei
abazar.mostafaei@gmail.com
1
PhD of Hydrogeology, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education & Extension Organization (AREEO), Tehran, Iran
LEAD_AUTHOR
Vahideh
Moradniya
vahidehmoradniya@gmail.com
2
MSc., Soil Conservation and Watershed Management Research Institute(SCWMRI), Agricultural Research, Education & Extension Organization (AREEO), Tehran, Iran
AUTHOR
Masoud
Godarzi
massoudgoodarzi@yahoo.com
3
Assistant Professor, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education & Extension Organization (AREEO), Tehran, Iran
AUTHOR
منابع
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[7]. Mostafaei A, Kalantari N, Kheirkhah M. Assessing the success of floodwater spreading projects using a fuzzy approach. Water Science and Technology. 2016;74(8):1980-1991.
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[9]. Saadati H, Khayyam M. Survey of Flood water Spreading on quantitative changes of Vegetation Cover and Groundwater Recharge by Remote Sensing and GIS in Tasouj Aquifer in East Azarbayjan.Territory. 2009;5(19): 1-10) (In Persian).
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[10]. Barkhordari J, Tireh Shabankareh K, Mehrjerdi MZ, Khalkhali M. Study of water spreading effects on quantitative and qualitative changes of pastural cover: A case study in station of Sarchahan water spreading (Hormozgan province). Watershed Researches in Pajouhesh & Sazandegi. 2009;82: 65-72 (In Persian).
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32
ORIGINAL_ARTICLE
Definition of Sarab Aquifer Hydrochemical Facies Distribution by means of Fuzzy C-Mean Clustering and Hierarchical Cluster Analysis Methods
In this research, clustering of a hydrochemical data set from Sarab plain aquifer has been carried out using Fuzzy C-Means (FCM) and Hierarchical Cluster Analysis (HCA) techniques and its application in delineation of hydrochemical facies has been studied. The statistical clusters analyze the spatial coherence indicating that that the clusters have a hydrogeological correspondence with aquifer hydrochemical facies. Groundwater samples were grouped into four classes using the fuzzy c-mean. The data set includes 49 water samples and 12 hydrochemical variables selected from the study area. The results obtained from both approaches presented cluster centers that can be used in order to identify the physical and chemical processes causing variations in the hydrochemistry variation of study area. The FCM method is potentially useful in establishing hydrochemical facies distribution. The results showed that the clustering scheme for partitioning water chemistry samples into homogeneous groups produced by FCM method is an important tool for determination of aquifer hydrochemical facies and the FCM method is more capable to investigate threshold data than HCA method which is characterized by sharp and abrupt variation.
https://ije.ut.ac.ir/article_62624_e3f0b56743a77aa47b404ebadf762674.pdf
2017-09-23
763
773
10.22059/ije.2017.62624
Groundwater
hydrochemical facies
Sarab Plain Clustering
Fuzzy logic
Meisam
Vadiati
meysam.vadiati@gmail.com
1
PhD Student, Faculty of Natural Science, University of Tabriz, Tabriz
LEAD_AUTHOR
Asghar
Asghari Moghaddam
moghaddam@tabrizu.ac.ir
2
Professor, Faculty of Natural Science, University of Tabriz, Tabriz
AUTHOR
Muhammad
Nakhaei
nakhaeimohammad@gmail.com
3
Professor, Faculty of Earth Science, Kharazmi University, Tehran
AUTHOR
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19
ORIGINAL_ARTICLE
Verification methods of Analytical Hierarchy Process (AHP) and Multivariate Regression (MR) in landslide zoning (Case Study: Valiasr Watershed in Ardabil Province)
Providing effective solutions to prevent and reduce the damage caused by landslides is inevitable. Predicting and zoning landslides are among these solutions. Accordingly, the present study was conducted to compare and assess the accuracy of Analytical Hierarchy Process (AHP) and Multivariate Regression (MR) methods in landslide hazard zoning in Valiasr Watershed with an area of 198 km-2 located in western Ardebil Province. Six factors including aspect, slope, elevation, lithology, land use and distance from the river were known as the most effective factors in landslide occurrence in the study area. Landslide zones were then obtained in five classes using two AHP and MR methods. Finally, landslide zoning maps were compared and evaluated by Density ratio index (Dr) and Quality sum index (Qs) to assess the accuracy of two studied methods. The results showed that distance from river, slope, land use, lithology and height were weighting in 0.426, 0.173, 0.145, 0.134, 0.089, and 0.033 in the AHP methods and 0.531, 0.109, 0.344, 0.273, 0.123 and 0.061 in the MR method, respectively. The amount of two Dr and Qs indices were calculated to be 5.51 and 0.44 respectively in the AHP method and 6.45 and 0.72 respectively in MR method which indicated that MR method having 28% disagreement with reality was more accurate than AHP method having 56% disagreement with reality for landslide hazard in the study area.
https://ije.ut.ac.ir/article_62626_2820fcaa0b07239e33e6f8ad594e5379.pdf
2017-09-23
775
789
10.22059/ije.2017.62626
Density ratio index
geographic information systems (GIS)
mass movements
quality sum index
Mohammad Hossein
Ghavimipanah
m.h.ghavimipanah1370@gmail.com
1
MSc. Student, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran
AUTHOR
Abdulvahed
Khaledi Darvishan
a.khaledi@modares.ac.ir
2
Assistant Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran
LEAD_AUTHOR
Mohammad Reza
Ghavimipanah
m.h.ghavimipanah1370111@gmail.com
3
PhD in Tectonic-Geology, Geology Expert in TaHa Consulting Engineers
AUTHOR
Petschko H, Brenning A, Bell R, Goetz J, Glade T. Assessing the quality of landslide susceptibility maps–case study Lower Austria. Nat. Hazards Earth Syst. Sci. 2014; 14(1): 95-118.
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Pourghasemi HR, Moradi HR, Aghda SF, Gokceoglu C, Pradhan B. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arabian J. Geosciences. 2014; 7(5): 1857-1878.
6
Shahabi H, Khezri S, Ahmad BB, Hashim M. Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena. 2014; 115: 55-70.
7
Sangchini EK, Emami SN, Tahmasebipour N, Pourghasemi HR, Naghibi SA, Arami SA, Pradhan B. Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran. Arabian J. Geosciences. 2016; 9(3): 1-15.
8
Das I, Sahoo S, Van Westen C, Stein A, Hack R. Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology. 2010; 114(4): 627-637.
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Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena. 2011; 85(3): 274-287.
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Vasudevan N, Ramanathan K. Geological factors contributing to landslides: case studies of a few landslides in different regions of India. Earth Environ. Sci. 2016; 30(1): 012011.
13
Chang ZF, Chen XL, An XW, Cui JW. Contributing factors to the failure of an unusually large landslide triggered by the 2014 Ludian, Yunnan, China, Ms= 6.5 earthquake. Nat. Hazards Earth Syst. Sci. 2016; 16: 497-507.
14
Mia MT, Sultana N, Paul A. Studies on the Causes, Impacts and Mitigation Strategies of Landslide in Chittagong city, Bangladesh. J. Environ. Sci. Nat. Resour. 2016; 8(2): 1-5.
15
Pourghasemi HR, Moradi HR, Aghda SF. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat. Hazards. 2013; 69(1): 749-779.
16
Motavali SA, Hosseinzade MM, Esmaeili R, Derafshi Kh. Landslide risk zoning assessment using Multivariate Regression (MR), logistic regression (LR), Analytical Hierarchy Process (AHP) and Fuzzy Logic (FL) (Case Study: Taleghan Watershed). Quantitative Geomorph. Res. 2014; 4(1): 1-20. (Persian)
17
Shiran k, Hajihashemijazi MR, Niknezhad SA, Rakhsha S. Landslide Risk Zoning Potential by Analytical Hierarchy Process (AHP) and Multivariate Regression (MR) (Case Study: Upstream of North Karoon Basin). Journal of Range and Watershed Management, Iranian J. Nat. Res. 2012; 65(3): 395-409. (Persian).
18
Alkhasawneh MS, Ngah UK, Tay LT, Isa NAM. Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network. Environ. Earth Sci. 2014; 72(3): 787-799.
19
Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian J. Geosciences. 2013; 6(7): 2351-2365.
20
Yalcin A. GIS-based Landslide Susceptibility Mapping Using Analytical Hierarchy Process and Bivariate Statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena. 2008; 72: 1-12.
21
Sharir K, Simon N, Roslee R. Regional assessment on the influence of land use related factor on landslide occurrences in Kundasang, Sabah. In: Proceedings of the Universiti Kebangsaan Malaysia. Sci. Technol. 2016; 1784(1): 0600151-5.
22
Giuseppe F, Simoni S, Godt JW, Lu N, Rigon R. Geomorphological control on variably saturated hillslope hydrology and slope instability. Water Resources Res. 2016; 52(6): 4590-4607.
23
Alkhasawneh MS, Ngah UK, Tay LT, Mat Isa NA, Al-batah MS. Determination of important topographic factors for landslide mapping analysis using MLP network. Scientific World J. 2013. 1-12.
24
Park S, Choi C, Kim B, Kim J. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ. Earth Sci. 2013; 68(5): 1443-1464.
25
Regmi AD, Yoshida K, Pourghasemi HR, DhitaL MR, Pradhan B. Landslide susceptibility mapping along Bhalubang—Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. J. Mountain Sci. 2014; 11(5): 1266-1285.
26
Kayastha P, Dhital MR, De Smedt F. Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comp. Geosciences. 2013; 52: 398-408.
27
Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Althuwaynee OF. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat. Hazards. 2013; 65(1): 135-165.
28
Gee MD. Classification of landslides hazard Zonation methods and a test of predictive capability. Bell, Davi, H (Eds.), Proceedings 6th International Symposium on Landslide. 1992; 48-56.
29
ORIGINAL_ARTICLE
Simulation of two-dimensional velocity distributions in rivers based on Chiu's theory (Case Study: Gorganrood River)
Solution of stream-wise flow velocity in two dimensions (in width and depth directions) in rivers is essential for many hydraulic features such as stage-discharge rating curve development, suspended sediment transport estimation and boundary shear stress calculation. In this paper, using Chiu's entropy theory, a simple method has been proposed for simulation of vertical and transverse profiles of flow velocity in the straight rivers. For calibration and validation of the proposed method, a new idea based on the optimum estimation of entropy parameter in rivers was used. The results of this study at Aghghalla hydrometric station located on Gorganrood River showed that velocity flow field obtained by the Chiu's theory has suitable accuracy compared to the field data. Further, statistical analysis of the results revealed that the mean absolute errors of this method for solution of flow velocities in calibration and validation stages are 5.2% and 3.5%, respectively. These errors are 5.9% and 6.04% respectively for the total river flow discharge prediction. According to little input data, the proposed method has more advantage than other existing methods.
https://ije.ut.ac.ir/article_62627_1802c9cd2f9e0b77193bd41350405fa5.pdf
2017-09-23
791
802
10.22059/ije.2017.62627
Chiu's theory
probability
Stage-discharge relationship
vertical and transverse velocity distribution
Abdolreza
Zahiri
zahiri.areza@gmail.com
1
Associate Professor, Water Engineering Department, College of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources
LEAD_AUTHOR
Firoozeh
Hashemi
hashemi.2714@yahoo.com
2
MSc. Graduate, Civil Engineering, Islamic Azad University, Bandar Abbas Branch
AUTHOR
Iman
Yousefabadi
iman.yousefabadi@yahoo.com
3
MSc. Graduate, Civil Engineering, Islamic Azad University, Bandar Abbas Branch
AUTHOR
منابع
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[18]. Cui, HJ, Singh, VP. Two-dimensional velocity distribution in open channels using the Tsallis entropy. Journal of Hydraulic Engineering. 2013 Mar 15; 18(3): 331–339.
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22
ORIGINAL_ARTICLE
Calibrating Priestley-Taylor coefficient to estimate free water surface evaporation (Case Study: Mahabad Dam Reservoir)
Evapotranspiration is one of the important components of basin water balance which cannot be measured directly at the basin scale. Therefore, it is inevitably estimated through indirect methods. In this regard, the Advection Aridity model, one of the widely used models of complementary relationship, has attracted lots of attentions. Due to the existence of Priestley-Taylor equation in the Advection Aridity model, it is required to calibrate Priestley-Taylor coefficient to increase the accuracy of the model. The current research aims at calibrating Priestley-Taylor coefficient in estimation of potential evaporation through Penman method to apply it in the Advection Aridity model in the studied area of Mahabad dam reservoir, Iran. The required data were collected for a period of 26 years (1986-2012) from Mahabad 1st order meteorological station which is located a short distance from Mahabad reservoir. The results showed that Priestley-Taylor coefficient undergoes monthly changes during a year and decreases during the warm months of the year. Therefore, it is better to use its monthly values in calculations. In the area under study, its minimum and maximum averages were 1.01 and 1.68, respectively. Moreover, the long term average of this coefficient during a period of 26 years has been calculated to be 1.25.
https://ije.ut.ac.ir/article_62628_e9a3cd2df9a768628f333fded58d71fc.pdf
2017-09-23
803
815
10.22059/ije.2017.62628
Priestley-Taylor coefficient
Penman equation
advection aridity model
Bouchet hypothesis
Mahabad dam reservoir
Anahita
Ghobadi
ghobadi86a@yahoo.com
1
MSc. Student, Department of Environmental Engineering, College of Environmental , West Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Peyman
Daneshkar Arasteh
arasteh1348@yahoo.com
2
Associate Professor, Water Sciences and Engineering Department, Faculty of Engineering and Technology, Imam Khomeini International University, Qazvin, Iran
LEAD_AUTHOR
Seyyed Mostafa
Khezri
khezri@sharif.edu
3
Associate Professor, Environment and Energy Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Valizadeh Kamran Kh, Jahanbakhsh S, Zahedi M, Rezaee Banafsheh M. Actual evapotranspiration and its relation to land use analysis in GIS case study Meshkinshar city. Journal of Geographic Space. 2012; 37: 39-54 [Persian].
1
Poormohamadi S, Dastourani MT, Cheraghi SAM, Mokhtari MH, Rahimian MH. Evaluation and estimation of water balance components in dry areas by using remote sensing and GIS (Case Study: Yazd Manshad watershed). Journal of Water and Wastewater. 2011; 22(3): 99-108.
2
Bouchet RJ. Evapotranspiration Reelle et Potentielle Signification Climatique. International Assosciaton of Hydrological Sciences. 1963; 62: 134-142.
3
Brutsaert W, Stricker H. An advection aridity approach to estimate actual regional evapotranspiration.Water Resources Research. 1979; 15(2): 443-449.
4
Priestley CHB, Taylor RJ. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review. 1972; 100(2): 81-92.
5
Arasteh PD, Tajrishy M. Calibrating Priestley Taylor model to estimate open water evaporation under regional advection using volume balance method case study Chahnimeh reservoir Iran. Journal of Applied Sciences. 2008; 8(22): 4097-4104.
6
Eichinger WE, Parlange MB, Strickler H. On the concept of equilibrium evaporation and the value of the Priestley Taylor coefficient. Water Resources Research.1996; 32(1): 161-164.
7
Lhomme JP. A theoretical basis for the Priestley Taylor coefficient. Boundary Layer Meteorology. 1997; 82(2): 179–191.
8
Castellvi F, Stockle CO, Perez PJ, Ibanez M. Comparison of methods for applying the Priestley Taylor equation at a regional scale. Hydrological Process. 2001; 15(9): 1609–1620.
9
Pereira AR. The Priestley Taylor parameter and the decoupling factor for estimating reference crop evapotranspiration. Agricultural and Forest Meteorology. 2004; 125(3-4): 305–313.
10
Fisher JB, De Biase TA, Qi Y, Mu M, Goldstein AH. Evapotranspiration models compared on a Sierra Nevada forest ecosystem. Environmental Modeling Software. 2005; 20(6): 783-796.
11
Komatsu H. Forest categorization according to dry canopy evaporation rates in the growing season: comparison of the Priestley Taylor coefficient values from various observation sites. Hydrological Processes. 2005; 19(19): 3873-3896.
12
Allen RG, Pereira LS, Raes D, Smith M. Crop evapotranspiration guidelines for computing crop water requirements. Irrigation and Drainage FAO56. Rome. FAO. 1998.
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Ulgen K, Hepbasli A. Solar radiation models Part 1: a review. Energy Sources. 2004; 26(5): 507-520.
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Kamali GH, Moradi I. Solar radiation: fundamental and applications in agriculture and renewable energy. Atmospheric & Meteorological Research Center (ASMERC) Tehran. 2004 [Persian].
16
Penman HL. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London Series A –Mathematical and Physical Sciences. 1948; 193: 193- 120.
17
Valiantzas J. Simplified versions for the Penman evaporation equation using routine weather data. Journal of Hydrology. 2006; 331(3-4): 690-702.
18
Szilagyi J, Parlange MB, Katul GG. Assessment of the Priestley-Taylor parameter value from ERA-Interim global reanalysis data. Journal of Hydrology and Environment Research. 2014; 2(1): 1-7.
19
Vourlitis GL, Hayashi M, De Nogueira SJ, Caseiro FT, Campelo JH. Seasonal variations in the evapotranspiration of a transitional tropical forest of Mato Grosso Brazil. Water Resources Research. 2002; 38(6): 30-1-30-11.
20
De Bruin HAR, Keijman JQ. The Priestley-Taylor evaporation model applied to a large shallow lake in the Netherlands. Journal of Applied Meteorology. 1979; 18(7): 898-903.
21
ORIGINAL_ARTICLE
Habitat utility modeling of organic (wild) pistachios (Pistacia Vera) using Maximum Entropy Method (MaxEnt) in Sarakhs Forest Area (Gonbadli in khorasan Province)
Organic (wild) Pistacia vera is a species of broadleaf forest. One of the most important natural forest habitats in the world is located at northwest and southwest of the city of Sarakhs, Khorasan Razavi Province, Iran. In addition, the unique ecological features of this habitat and the economic value of organic pistachio greatly impact the lives of people residing in this region and the country. Unfortunately, continuous overharvesting and drought-stricken development endanger natural life, regrowth and cultivation of this unique species. Understanding spatial distribution of this species plays a significant role in assessing regional protection and development; on the other hand, it can be helpful to recognize effective ecological factors on its habitat. Therefore, modeling this distribution is very important. In this paper, habitat modeling of this species is studied using maximum entropy method according to edaphic, climatic and physiographic data in the city of Sarakhs (in district of Gonbadli). The results of Jackknife test for surveying significance of variables shows that changes in soil properties such as gravel percentage, exchangeable sodium adsorption ratio, sodium content soil, gypsum, climatic factors (temperature and precipitation) and height above sea level are the most important factors affecting the distribution of habitats. The accuracy of model is assessed by kappa coefficient to be 0.72 and AUC 0.92. Moreover, the obtained results reveal that maximum entropy method is an appropriate method for habitat modeling.
https://ije.ut.ac.ir/article_62636_386719be60843e3674537e1a2cd9ea45.pdf
2017-09-23
817
824
10.22059/ije.2017.62636
Organic (wild) Pistaciavera
Maximum entropy
habitat modeling
Sarakhs (in district Gonbadli)
Mahdi
Zarabi
mzarabi@ut.ac.ir
1
Assistant Professor, Faculty of New Sciences and Technologies, University of Tehran
LEAD_AUTHOR
Rasoul
Haghdadi
rasoul.haghdadi@ut.ac.ir
2
MSc. Student in Ecohydrology, Faculty of New Sciences and Technologies, University of Tehran
AUTHOR
Hossein
Yousefi
hosseinyousefi@ut.ac.ir
3
Assistant Professor, Faculty of New Sciences and Technologies, University of Tehran
AUTHOR
E. Khosrojerdi, H. Dorodi and T. Namdost, "Physiographic factors on yield and quality of pistachio tree common in the forests of Khwaja Kalat, Khorasan Razavi," Forest Research & spruce OF IRAN, pp. 337-347, 2010. [ in persian]
1
M. Zohary, "Monographical study of the genus Pistacia," Palestinian Journal of Botany, vol. 5, pp. 187-228, 1952.
2
H. Sabeti, Forests Trees and Shrubs of Iran, Tehran, 1996.
3
A. Onay, V. Pirinc, H. Yildirim and D. Basaran, "In Vitro Micrografting of mature pistachio ( Pistachia vera var. siirt)," Plant Cell, Tissue and organ culture, vol. 77, pp. 215-219, 2004.
4
M. Irannezhad parizi, "Evaluation of natural habitats Pistachio in Iran," Research and Construction, Vols. 19, pp. 20-26, 1996. - [ in persian]
5
F. Jafar abadi, M. Abdolahi ezat abadi and M. Eslami, "The effect of the degradation of groundwater resources on the economic value of agricultural capital pistachio in Kerman province," Agricultural Economics Research, pp. 1-19, 2016. [in prsian]
6
F. Sadat hoseni, A. Darvish sefat and n. a. Zargham, "Capability of IRS-P6-LISS IV images for mapping large number of wild pistachio forests (Case study: forest Khvajhklat Khorasan)," Journal of Forest Iran, Iran Forestry Association, pp. 311-320, 2013.[in persian]
7
M. Ramezani, "The study of influencing factors on wild pistacia sp distribution in Khorasan province,". Research Institute of Forests and Renglands, 2006.
8
l. Khalasi ahvazi, M. Zare chahoki and S. z. Hosseni, "The geographic distribution of species habitat modeling Artemisia aucheri and Artemisia sieberi based on presence-based methods (ENFA and MaxEnt)," Renewable natural resources research, pp. 57-73, 1394.
9
M. Zare Chahouki, L. Khalasi Ahvazi and. H. Azrnivand, "Comparison of three modelin approaches for predictiong species distribution in mountainous scrub vegetation (Semnan rangelands, Iran)," Polish Journal of Ecology, pp. 105-117, 2012.
10
L. Khalasi ahwazi, M. Zare chahoki, H. Azarnivand and M. Soltani gerd faramarzi, "Habitat suitability modeling Eurotia ceratoides," Journal of Research Range, pp. 362-373, 2012, - [in Persian]
11
E. Pielou, "The use of point to plant distances in the study of the pattern of plant population," J.Ecology, vol. 47, pp. 607-613, 1959.
12
R. Anderson and E. Mart´ ınez-Meyer, "Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador," Biol. Conser, vol. 116, pp. 167-179, 2004.
13
C. Graham, S. Ferrier, F. Huettman, C. Moritz and A. Peterson, "New developments in museum based informatics and applications in biodiversity analysis," Trends Ecol. Evol, vol. 19, pp. 497-503, 2004.
14
J. Elith, C. Graham, R. Anderson, M. Dudík, S. Ferrier, A. Guisan, R. Hijmans, F. Huettmann, J. Leathwick, A. Lehmann, J. Li, L. Lohmann, B. Loiselle and G. Manion, "Novel methods improve prediction of species’ distribution from occurrence data," Ecography, vol. 29, pp. 129-151, 2006.
15
A. Peterson and J. Shaw, "Lutzomyia vectors for cutaneous leishmaniasis in southern Brazil: ecological niche models, predicted geographic distribution, and climate change effects.," Int. J. Parasitol, vol. 33, pp. 919-931, 2003.
16
M. Zare chahoki, H. Piri sahragard and H. Azarnivand, "Habitat distribution model plant species in rangelands of Qom Sultan Hoz with maximum entropy method," Scientific Journal of Range, pp. 212-221, 2014, -[in Persian]
17
L. Khalasi ahwazi, M. Zare chahoki and s. z. Hosseni, "The geographic distribution of species habitat modeling Artemisia aucheri and Artemisia sieberi based on presence-based methods (MaxEnt and ENTFA)," Journal of Renewable Natural Resources Research, Vols. 57-73, p. 19, 2016, -[in persian]
18
D. Lemke, P. Hulme, J. Brown and T. W, "Distribution modelling of Japanese honeysuckle (Lonicera japonica) invasion in the Cumberland Plateau and Mountain Region, USA," Forest Ecology and Management, pp. 139-149, 2011.
19
H. Piri Sahragard and M. Zare Chahouki, "An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province," Ecological Modelling, pp. 64-71, 2015.
20
S. Mahmoodi and M. Hakimian, Soil Fundamentals, Tehran: University of Tehran, 2008, - [Persian]
21
S. Phillips, K. Richardson, R. Scachetti-Pereira, J. Sober on, S. Williams, M. S. Wisz and N. E. Zammermann, "Novel methods improve predictions of species," Ecography, vol. 29, pp. 129-151, 2006.
22
ORIGINAL_ARTICLE
Investigation of the determination relationship of effective rainfall in high rainfall and low rainfall zones (Case Study: Rasht and Daran)
Given the vital importance of water in human life, recognizing the effective use of rainfall and crop water requirement and economic planning are very important. Identifying and applying appropriate method for estimating effective rainfall, especially in rainfed is highly important. The current study aims to determine the most suitable experimental method for estimating effective rainfall for sowing Wheat, Barley, Peas and Lentils in high rainfall zones (Rasht in Gilan) as compared to low rainfall zones (Daran in Isfahan). In this study, five different experimental methods are presented in order to determine the effective rainfall including: the Soil Conservation Service (SCS), reliable method, empirical method, United States Department of Agriculture (USDA), and percentage method. The results show that all methods used for determination of effective rainfall in Rasht are useful. However, these methods require to be pre-calibrated in order to determine the effective rainfall in Daran identified as a low rainfall area. Based on the obtained results, as regards prioritization of using the methods under study in these areas, SCS and USDA methods are proposed for Rasht, while the USDA and percentage methods are proposed for Daran. Since the amount and duration of rainfall is not controllable, effective strategies can be applied to increase precipitation efficiency and consequently effective precipitation; these strategies include: reduction of surface runoff, water storage for times of low rainfall, reduced water depth penetration, and planning for the cultivation of plants that are consistent with the precipitation regime.
https://ije.ut.ac.ir/article_62638_60caa08b836f993adafab0ef23bacebb.pdf
2017-09-23
825
836
10.22059/ije.2017.62638
Effective Rainfall
Runoff
Experimental
USDA
SCS
Shamim
Larijani
shamim_larijani@ut.ac.ir
1
PhD Candidate, Water Engineering, Department of Water Sciences and Engineering, Ferdowsi University of Mashhad
LEAD_AUTHOR
Mohammad
Salarian
mohammad.salarian@mail.um.ac.ir
2
MA Student in Water Engineering, Department of Water Sciences and Engineering, University of Tehran
AUTHOR
Amin
Alizadeh
alizadeh@gmail.com
3
Professor, Department of Water Sciences and Engineering, Ferdowsi University of Mashhad
AUTHOR
Teymour
Sohrabi
myousef@ut.ac.ir
4
Professor, Department of Water Sciences and Engineering, University of Tehran
AUTHOR
Rahimi J, Khalili A and Bazrafshan J. Estimation of effective precipitation for winter wheat in different regions of Iran using an Extended Soil-Water Balance Model. Desert. 2014; 19(2): 91-98.
1
Kardavani P. Drought and ways to cope with it, University of Tehran Press. Iran. 2001.
2
Mohan S, Simhadrirao B and Arumugam N. Comparative Study of Effective Rainfall Estimation Methods for Lowland Rice, Water Resources Management. 2001;10(1): 335-44.
3
Dastane N.G. Effective rainfall. FAO Consultant, Project Coordinator, Indian Agricultural Research Institute, New Delhi. 1978.
4
Tsai S. M, Chen S and Wang H. Y. A study on the practical model of planned effective rainfall for paddy fields in Taiwan. Journal of Marine Science and Technology. 2005 ; 13(2): 73-82.
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Chahoon j, Yontsand D and Melvin S. Estimating Effective Rainfall. 2001.
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Miller A and Thompson J C. Elements of Meteorology. Ohio. Merril Pub. 1970.
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Snyder R. L and Davis U. C. Drought Tips ; www.edis.ifas.ufl.edu/aeo78. 2001.
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Tsao I. S. Study on the calculation and estimation of effective rainfall on paddy field by using electronic computer. Taiwan water conservancy. 1971.
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Smajstrla A. G and Zazueta F. S. Estimating crop irrigation requirements for irrigation system design and consumptive use permitting. University of Florida Cooperative Extension Service, Institute of Food and Agriculture Sciences, EDIS. 1998.
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Rahman M.M, M.O.Islam and M.Hasanuzzaman. Study of effective rainfall for irrigated agriculture in south-eastern part of Bangladesh, World Journals of Agriculture. 2008; 4(4):453-457.
11
Adnan S and Khan A. H. Effective rainfall for irrigated agriculture plains of Pakistan. Pakistan Journal Meteorology. 2009; 6(11): 61-72.
12
Tavakoli A.R, Oweis T, Ashrafi Sh, Asadi H, Siadat H and Liaghat A. Improving rainwater productivity with supplemental irrigation in upper Karkheh river basin of Iran. International Center for Agricultural Research in the Dry Areas (ICARDA), Aleppo, Syria. 2010.123pp.
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Croissant R.L, Peterson G.A and Westfall D.G. Dryland Cropping Systems.Colorado State University, Cooperative Extension. 1998; Bulletin No. 0.516.
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Kousari M.R, Ekhtesasi M.R, Tazeh M, Naeini M.A.S and Zarch M.A.A. An investigation of the Iranian climatic changes by considering the precipitation, temperature, and relative humidity parameters. Theoretical and Applied Climatology. 2011; 103(3-4): 321-335.
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Modarres R, and Sarhadi A. Statistically-based regionalization of rainfall climates of Iran. Global and Planetary Change. 2011; 75(1): 67-75.
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Sadeghi S. H. R, Moatamednia M and Behzadfar M. Spatial and temporal variations in the rainfall erosivity factor in Iran. Journal of Agricultural Science and Technology. 2011; (13): 451-464.
17
Mohammadi H, Karimpour Reihan M. The effect of 1991-2001 droughts on ground water in Neishabour plain. Desert. 2008; (12) : 185-197.
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Javanmard S, Yatagai A, Nodzu M.I, BodaghJamali J and Kawamoto H. Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Advances in Geosciences. 2010; (25) :119-125.
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Ghaffari, A. The role of Dryland Agricultural Research Institute in drought mitigation in Iran. Options Méditerranéennes, A. 2010; (95): 273-278.
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Alizadeh A and Kamali G. Water Use of Plants in Iran, Astan Quds Publication. 2007.
21
Allen R.G, Pereira L.S, Raes D and Smith M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome.1998; 300(9): p.D05109.
22
SCS, U.S. Soil Conservation Service, National Engineering Handbook, Hydrology. 1972.
23
Roshan G. R. and Grab S. W. Regional climate change scenarios and their impacts on water requirements for wheat production in Iran. International Journal of Plant Production. 2012; 6(2): 239-266.
24
ORIGINAL_ARTICLE
Effects of consecutive storms on splash erosion components for two different rainfall intensities under laboratory conditions
Obviously, no erosion occurs unless detachment takes place first, either by raindrop or runoff. The Raindrop-Impact-Induced Erosion (RIIE) occurs when detachment is caused by raindrop energy. For this purpose, a set of laboratorial experiments were scheduled to examine the effects of consecutive storms on RIIE components (i.e. upward splash, downward splash, net splash, total splash and upward splash/downward splash). The experiments were conducted for two different rainfall intensities of 30 and 90 mm h−1 at slope of 5% under rainfall simulation and soil erosion for a soil collected from Kojour rangeland watershed in the Alborz Mountains, northern of Iran. The comparative analysis of the results showed that there was no significant difference between upward with downward splash under rainfall intensity of 30 as well as under rainfall intensity of 90 mm h−1 in consecutive storms (p ≤ 0.05) using One-Way ANOVA in SPSS Statistics 22 software. In addition, the obtained results indicated that RIIE components viz. total and upward and downward splash were significantly different (p ≤ 0.01) under rainfall intensities of 30 and 90 mm h−1 in the third, fourth and fifth consecutive storms. However, net splash and upward/downward splash proportion was not significantly different (p > 0.05) under rainfall intensities of 30 and 90 mm h−1 in different consecutive storms. The results also indicated a 2.5-fold increase in coefficient of variation of net splash and total splash under rainfall intensity of 30 mm h-1 then the rainfall intensity of 90 mm h-1.
https://ije.ut.ac.ir/article_62640_7bfa1b883d2066cbc2c989926bc79906.pdf
2017-09-23
837
846
10.22059/ije.2017.62640
Event Storm
Rainfall simulation
splash cup
net splash
Mahboobeh
Kiani-Harchegani
mahboobeh.kiyani20@gmail.com
1
PhD Graduate, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University and Member of Watershed Management Society of Iran
LEAD_AUTHOR
Seyyed Hamidreza
Sadeghi
sadeghi@modares.ac.ir
2
Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University
AUTHOR
[1]. Terry JP. A rain splash component analysis to define mechanisms of soil detachment and transportation. Aust. J. Soil Res. 1998; 36: 525–542.
1
[2]. Kinnell PIA. Raindrop impact induced erosion processes and prediction: A review. Hydrol. Process. 2005; 19: 2815–2844.
2
[3]. Kinnell PIA. The influence of raindrop induced saltation on particle size distributions in sediment discharged by rain-impacted flow on planar surfaces. Catena. 2009;78(1): 2-11.
3
[4]. Kinnell, PIA. A review of the design and operation of runoff and soil loss plots. Catena. 2016; 145, 257-265.
4
[5]. Ellison WD. Studies of raindrop erosion. Agri. Eng. 1944; 25 (4): 131–136.
5
[6]. Ekern PC. Raindrop impact as a force initiating soil erosion. Soil Sci. Soc. Am. Proc. 1950; 15: 7–10.
6
[7]. Morgan RPC. Field studies of rainsplash erosion. Earth Surf. Process. 1978; 3: 295–299.
7
[8]. Bryan RB. The influence of slope angle on soil entrainment by sheetwash and rainsplash. Earth Surf. Process. 1979; 4: 43–58.
8
[9]. Torri D, Poesen J. The effect of soil surface slope on raindrop detachment. Catena. 1992; 19: 561–578.
9
[10]. Fan JC, Wu MF. Effects of soil strength, texture, slope steepness and rainfall intensity on interrill erosion of some soils in Taiwan. In10th International Soil Conservation Organization meeting, Purdue University, USDA-ARS national soil erosion research laboratory. 1999; May.
10
[11]. Barry DA, Sander GC, Jomaa S, Heng BCP, Parlange JY, Lisle IG. Hogarth WL. Exact solutions of the Hairsine-Rose precipitation-driven erosion model for a uniform grain size soil. J. Hydrol. 2010; 389 (3–4): 399–405.
11
[12]. Parsons AJ, Lascelles B. Rainfall simulation in geomorphology. Earth Surf. Process. Land. 2000; 25(7): 679-689.
12
[13]. Legout C, Leguédois S, Le Bissonnais Y, Malam Issa O. Splash distance and size distributions for various soils. Geoderma. 2005; 124: 279–292.
13
[14]. Goebes P, Seitz S, Geißler C, Lassu T, Peters P, Seeger M, Nadrowski, K. Scholten T. Momentum or kinetic energy – How do substrate properties influence the calculation of rainfall erosivity? J. Hydrol. 2014; 517: 310–316.
14
[15]. Fu S, Liu B, Liu H, Xu L. The effects of slope on interrill erosion at short slopes. Catena. 2011; 84: 29-34.
15
[16]. Ma RM, Li ZX, Cai CF, Wang JG. The dynamic response of splash erosion to aggregate mechanical breakdown through rainfall simulation events in Ultisols (subtropical China). Catena. 2014; 121: 279-287.
16
[17]. Khalili Moghadam B, Jabarifar M, Bagheri M, Shahbazi E. Effects of land use change on soil splash erosion in the semi-arid region of Iran. Geoderma. 2015; 241-242: 210-220.
17
[18]. Saedi T, Shorafa M, Gorji M, Khalili Moghadam. B, Indirect and direct effects of soil properties on soil splash erosion rate in calcareous soils of the central Zagross, Iran: A laboratory study. Geoderma. 2016; 271: 1-9.
18
[19]. Yusefi A, Farrokhian Firouzi A, Khalili Moghadam BEvaluation of temporal variation of splash erosion in different slopes and agricultural and forest land uses. J. Soil Water Resour. Conserv. 2014; 3(3): 11-20. (In Persian)
19
[20]. Khaledi Darvishan A, Sharifi Moghadam E. Effects of aggregate diameter on soil splash under laboratorial conditions. Iran-Water. Manag. Sci. Eng. 2016; 10(32): 33-38. (In Persian)
20
[21]. Sadeghi SHR, Kiani Harchegani M, Asadi H. Variability of particle size distributions of upward/downward splashed materials in different rainfall intensities and slope. Geoderma. 2017; 290: 100-106.
21
[22]. Kiani Harchegani M, Sadeghi SHR, Asadi H. Comparative analysis of the effects of rainfall intensity and experimental plot slope on raindrop impact induced erosion (RIIE). J. Water Soil Res. 2016; 46 (4): 631–640. (In Persian)
22
[23]. Kiani Harchegani M, Sadeghi SHR, Asadi H. Changeability of concentration and particle size distribution of effective sediment in initial and mature flow generation conditions under different slops and rainfall intensities, Iranian J Water. Eng. Manag. 2016; Accepted. (In Persian)
23
[24]. Mahdavi M. Applied Hydrology, Tehran University Press. 2002; 2: 437. (In Persian)
24
[25]. Abdollahi Z, Sadeghi SHR, Khaledi Darvishan A. Variation of simulated rainfall characteristics by permuting intake discharge and water pressure. Iran. Water. Manag. Sci. Eng. 2016; 10(34): 51-62. (In Persian)
25
[26]. Khaledi Darvishan A, Sadeghi SHR, Homaee M, Arabkhedri M. Measuring sheet erosion using synthetic colorcontrast aggregates. Hydrol. Process. 2014; 28(15): 4463-4471.
26
[27]. Kiani Harchegani M, Sadeghi SHR, Asadi H. Inter-storm variability of coefficient of variation of runoff volume and soil loss during rainfall and erosion simulation replicates, J. Ecohydrol. 2017; 4(1): 191-199. (In Persian)
27
[28]. Agassi M, Bradford JM. Methodologies for interrill soil erosion studies. Soil Till. Res. 1999; 49:277–287.
28
[29]. Sadeghi SHR, Kiani Harchegani M. Effects of sand mining on suspended sediment particle size distribution in Kojour forest river, Iran. J Agri. Sci. Tech. 2012; 14: 1637-1646.
29
[30]. Bayazidi A, Oladi B, Abbasi N. Data analysis by SPSS (PASW) 18 software. Abed Publication. 2009; 1: 250 p. (In Persian)
30
[31]. Le Bissonnais Y. Aggregate stability and assessment of soil crustability and erodibility: I. Theory and methodology. Euro. J. Soil Sci. 1996. 47: 425-437.
31
ORIGINAL_ARTICLE
Climate change impact on annual precipitation and temperature of Zanjan province with uncertainties investigation
In this paper, climate change impact on annual precipitation and temperature series for Zanjan province was assessed and uncertainties were investigated. Spatial average of annual time series of precipitation and temperature for Zanjan province were calculated and then modelled using ARMA model and 100 annual precipitation and temperature series of length 30 years were generated for spatial average of Zanjan province. Using the models, future scenarios of 6 GCMs under 3 emission scenarios were downscaled and 100 annual precipitation and temperature series of length 30 years were generated for each of the scenarios. 90% bounds of the variable statistics for the current condition were compared with the 90% bounds of the corresponding values for all of the future scenarios and the uncertainties were investigated. Model validation showed that the models are adequate for generation of the annual temperature and precipitation series. In confidence level of 90%, it is expected that average temperature of Zanjan province increase from 0.6 to 3.2 ºC and average precipitation change from -25% to +15% in 2035-64 period. So, the uncertainty due to the GCMs structure and the emission scenarios are considerable and should be taken into account.
https://ije.ut.ac.ir/article_62641_45c1b5b7e7d21cf182ccd59d81d9f60c.pdf
2017-09-23
847
860
10.22059/ije.2017.62641
Annual model
ARMA
climate change
stochastic
Uncertainty
Mohammad Reza
Khazaei
m_r_khazaee@yahoo.com
1
Assistant Professor, Department of Civil Engineering, Payame Noor University, Iran
LEAD_AUTHOR
Motalleb
Bayazidi
m.byzedi@gmail.com
2
Assistant Professor, Department of Water Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
AUTHOR
Ahmad
Sharafati
asharafati@gmail.com
3
Assistant Professor, Technical and Engineering Department, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
1. IPCC. Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; 2013.
1
2. IPCC. Climate change 2001. Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press; 2001.
2
3. Devkota L.P. and D.R. Gyawali. Impacts of climate change on hydrological regime and water resources management of the Koshi River Basin, Nepal. Journal of Hydrology: Regional Studies, 2015;4: 502–515.
3
4. Rana AK,FosterT,Bosshard J, Olsson and Bengtsson L. Impact of climate change on rainfall over Mumbai using Distribution-based Scaling of Global Climate Model projections. Journal of Hydrology: Regional Studies, 2014;1: 107–128.
4
5. Khazaei MR, Zahabiyoun B, and Saghafian B. Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model. International Journal of Climatology, 2012;32: 1997-2006.
5
6. Ahmadvand Kahrizi M, Rouhani H. Assessing the conservation impacts of climate change based on temperature projected on 21 century (Case study: Arazkoseh and Nodeh stations). Ecohydrology, 2017;3(4): 597-609 (in Persian).
6
7. Khazaei MR. Climate change impact assessment on hydrological regimes of a mountainous river basin in Iran. Journal of Water and Soil Resources Conservation, 2016;5(3): 43-54 (in Persian).
7
8. Fowler HJ, Blenkinsop S, and Tebaldi C. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 2007;27: 1547-1578.
8
9. IPCC. General Guidelines on the use of Scenario Data for Climate Impact and Adaptation Assessment, version 2, 2007.
9
10. Kay AL, DaviesHN, Bell VA, and JonesRG. Comparison of uncertainty sources for climate change impacts: flood frequency in England. Climatic Change, 2009;92: 41-63.
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11. Semenov MA, Brooks RJ, Barrow EM,and RichardsonCW. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research, 1998;10: 95-107.
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12. Chapman T. Stochastic modelling of daily rainfall: the impact of adjoining wet days on the distribution of rainfall amounts. Environmental Modelling & Software, 1998;13: 317-324.
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13. Dubrovsky M, Buchtele J, and ZaludZ. High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modeling. Climatic Change, 2004;63: 145-179.
13
14. Khazaei MR, Ahmadi S, Saghafian B, and Zahabiyoun B. A new daily weather generator to preserve extremes and low-frequency variability. Climatic Change, 2013;119:631–645.
14
15. Reaney SM, andFowler HJ. Uncertainty estimation of climate change impacts on river flow incorporating stochastic downscaling and hydrological model parameterisation error sources, BHS 10th National Hydrology Symposium, Exeter, 2008.
15
16. Minville M, Brissette F, and Leconte R. Uncertainty of the impact of climate change on the hydrology of a nordic watershed. Journal of Hydrology, 2008;358:70-83.
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17. Semenov MA. Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agricultural and Forest Meteorology, 2007;144: 127-138.
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18. Wilby RL, DawsonCW, and BarrowEM. SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 2002;17: 147-159.
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19. Mavromatis T. and HansenJW. Interannual variability characteristics and simulated crop response of four stochastic weather generators, Agricultural and Forest Meteorology, 2001;109: 283-296.
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20. Hansen JWand MavromatisT. Correcting low-frequency variability bias in stochastic weather generators. Agricultural and Forest Meteorology,2001;109:297-310.
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21. Salahi B, Goudarzi M, HosseiniSA. Predicting the temperature and precipitation changes during the 2050s in Urmia Lake Basin. Watershed Engineering and Management, 2017;8(4): 425-438 (in Persian).
21
22. Rezaee M, Nahtaj M, Moghadamniya A, Abkar A, Rezaee M. Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor). Water Engineering, 2015;8(24): 25-40 (in Persian).
22
23. Semiromi ST, Moradi H, Khodagholi M. Predicted changes in some of climate variables using downscale model LARS-WG and output of HADCM3 model under different scenarios. Watershed Engineering and Management, 2015;7(2): 145-156 (in Persian).
23
24. Liu Y, Wu J,Liu Y, Hu BX,Hao Y, Huo X, et al. Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model. Quaternary International, 2015;358:137-145.
24
25. Salas JD, Delleur JW, Yevjevich V, Lane WL. Applied modeling of hydrologic time series. Water Resources Publications, Littleton, CO, p 484, 1980.
25
ORIGINAL_ARTICLE
Site selection of aquifers of Ghorveh pasture areas using satellite images
Over the recent years, given lack of surface water, population growth, and agricultural development, more attention has been paid to underground water, its management and extraction. This source is considered as one of the valuable resources of freshwater making it as one of the most valuable vital resources. Groundwater extraction, over the recent years, has given rise to decreased water level of this resource, that is, its level has decreased by an amount of 19.9m in the plain of Qorveh since 1986, 1987 until 2015. Therefore, the plain has become one of the forbidden plains out of 625 country plains. In recent years, integrating data of remote sensing (RS) and geographic information system (GIS) has increased for exploration of this valuable material. So that the combination of these two has become a point in the fields related to the issue of water. In this study, using (RS) and (GIS), data of potential areas of groundwater were identified. Layers created for site selection of aquifer in this study are as follows: geology, topography, slope, lineament, density of lineament, drainage, density of drainage, land use and vegetation of cover. Then, the final map was obtained using respective layers by means of fuzzy method in gis. The resulted map was divided into five groups of very good, good, average, weak and very weak. The results showed that the areas with good and very good potential with an area of 136461 hectare forming about half of the area of plain under study are located more in northern and eastern parts characterized by alluvial lands, vegetation and low slope, while regions with lower potential are located more in southern parts characterized by high slope areas covered with rock solid.
https://ije.ut.ac.ir/article_62643_9d46f4360c50f3538ef08aee103b3792.pdf
2017-09-23
861
871
10.22059/ije.2017.62643
GIS
RS
Site selection
Aquifer
Fuzzy
Yousef
Salehi
yoosefsalehi66@yahoo.com
1
MSc. in RS and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Zahra
Azizi
zazizi@srbiau.ac.ir
2
Assistant Professor, Department of GIS-RS, Faculty of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Hossein
Aghamohamadi
hossein.aghamohammadi@gmail.com
3
Assistant Professor, Department of GIS-RS, Faculty of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
منابع
1
[1]. Zehtabian G, Khosravi H, Ghodsi M. High Demand in a Land of Water Scarcity: Iran. In: Graciela SM, Courel MF, editor. Water and Sustainability in Arid Regions. 1th ed. Netherlands: Springer ; 2001.p. 75-86.
2
[2]. Alizadeh A. Principles of applied Hydrology. 35nd ed. Mashhad: Imam Reza University; 2012. [Persian].
3
[3]. Sedaghat M. Land and water resources (groundwater). 6nd ed. Tehran: Payame noor university; 2008. [Persian].
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[4]. Jha M, Kamii Y, Chikamori K. Cost-effective Approaches for Sustainable Groundwater Management in Alluvial Aquifer Systems. Water Resources Management. 2010; 23(2):219-233.
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[5]. Mehdipoor A, Mehdipoor S, Haj Seyed Ali Khani N. The history of the aqueduct and its impact on civilization Iranians. International Conference on the aqueduct. Kerman. Iran. 2005. [Persian].
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[6]. Joven PA, Yamaguchi S, Takada J. Characterization of Groundwater Potential of Agusan del Norte,Philippines, Spring Conference, Tokyo Institute of Technology. Japan. 2010.
7
[7]. Iran Water Resources Management Company. Assessment situation of groundwater resources in the country By the end year of 2014-2015. 2016. [Persian].
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[8]. Thilagavathi N, Subramani T, Suresh M, Karunanidhi D. Mapping of groundwater potential zones in Salem Chalk Hills,Tamil Nadu, India, using remote sensing and GIS techniques. Environmental Monitoring and Assessment. 2015; 187(4):1-17.
9
[9]. Pinto D, Shrestha S, Babel MS, Ninsawat S. Delineation of groundwater potential zones in the Comoro watershed, Timor Leste using GIS, remote sensing and analytic hierarchy process (AHP) technique. Applied Water Science. 2015; 6(22):1-17.
10
[10]. Yamani M, Alizadeh SH. Potential mapping of groundwater resources using Analytical Hierarchy Process(AHP) Case study Basin Abadeh- Oghli Fars. Hydrogeomorphology. 2015; (1):131-144. [Persian].
11
[11]. Mohammadnezhadarogh V, Sayyad A, Golmohammadzadeh B. Mapping of areas prone to water Ground using GIS and MIF(case study: Urmia city). Research quantitative geomorphology. 2013; 2(3):45-58. [Persian].
12
[12]. Report Map 1: 50,000 Geological Organization of Iran. [Persian].
13
[13]. Abshirini E, Rangzan K, Khorshidi S. Potential mapping of groundwater resources using Weighted index overlay method in GIS environment (Case Study: within the anticline Pabdeh). Conferences of Geomatics. Tehran. Iran.2008. [Persian].
14
[14]. Rahimi D. Potential mapping of groundwater resources (case study: plain of Shahrekurd) Geography and environmental planning. 2012;22(4):127-142. [Persian].
15
[15]. Nag sk. Application of Remote Sensing and GIS in Groundwater Exploration. In: Ahmad SH, Jayakumar R, Salih AB, editor. Groundwater Dynamics in Hard Rock Aquifers. Earth Sciences & Geography , 1th ed. India: Springer; 2008.p. 87-92.
16
[16]. Magesh NS, Chandrasekar N, Soundranayagam JP. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geoscience Frontiers. 2012;3(2):189-196.
17
[17]. Rezvani A. Use of aerial and satellite photographs in geography. 3nd ed. Tehran: Payame noor university; 2012. [Persian].
18
[18]. Ghodrati M. Applied Learning of ARC GIS 10.2. 1nd ed. Tehran: Simaye Danesh; 2014. [Persian].
19
[19]. French, N. H, Ijaz Hussain. Water Spreading Manual Range management Record, West Pakistan Range Improvement scheme, Lahur., Pakistan, 1964: No. 1, 44P.
20
[20]. Viskarami I, Payamani K, Shahkarami A. A., Sepahvand A. The Effects of Water spreading on Groundwater Resources in Kohdasht Plain. J. Sci. & Technol. Agric. & Natur. Resour., Water and Soil Sci., 2013: Vol. 17(65): 153-161.
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[21]. Senser, E., Davraz, A., and Ozcelik, M.. An integration of GIS and remote sensing in groundwater investigations: a case study in Burdur, Turkey. International Journal of hydrogeology. 2004: 13: 826-834.
22
ORIGINAL_ARTICLE
Investigating modern methods of water wells rehabilitation for use in Iran
One of the problems associated with long term use of wells is creation of deposits on well screen or gravel packs. The deposits, despite the presence of water in the context of the aquifer, prevent the penetration of water into the well. As a result, it greatly reduces the amount of water production of the well. Rehabilitation of wells is characterized by removing the deposits from the front and back of the well screen. So far, there have been some methods of well rehabilitation, nowadays, however, with the development of technology, more efficient methods have been developed causing less damage to the aquifer and the environment. In this study, new methods of well rehabilitation are investigated for implementation in Iran. A criterion for determining the efficiency of the methods is proposed. The method of air shock in combination to water jet is estimated to be more efficient with less time and cost consumption for implementation in Iran.
https://ije.ut.ac.ir/article_62644_e71a005a5eb189dd3189b085dc81d26c.pdf
2017-09-23
873
886
10.22059/ije.2017.62644
Well rehabilitation
design
well development
air shock
water jet
Alireza
Hadi
hrhadi@ut.ac.ir
1
Assistant Professor, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Hamed
Rahimi Sharbaf
h.rahimi.sh@gmail.com
2
MSc. Student, Tarbiat Modares University, Tehran, Iran
AUTHOR
Ali
Tahmasbi
aattrrgg@yahoo.com
3
Rural Water and Wastewater Company of Lorestan Province
AUTHOR
منابع
1
[1] Mansuy N. Water Well Rehabilitation – A Comprehensive Guide to Understanding Problems and Solutions. 4th ed. Florida: CRC Press - Lewis Publishers; 1999.
2
[2] Sonic Umwelttechnik GmbH. Well Rehabilitation with high-energetic Ultrasound. Sonic Information No. E1-09. 2014.
3
[3] Driscoll F G. Groundwater and Wells. 2nd ed. Minnesota: Johnson Screens; 1986.
4
[4] Thullner M, Van Cappellen P, Reginer P. Modeling the impact of microbial activity on redox dynamics in porus media. Geochimica et Cosmochimica Acta. 2005; 69(21):5005-5019.
5
[5] Mansuy N, Miller G P. Preventive Well Maintenance Reduces Costs. Subsurface Technologies Inc.2014.
6
[6] Petrauskas A. Increasing the efficiency of water well regeneration with ultrasound by using acoustic transducers consisting of elements in flexural vibration. 2009; 64(3):1392-2114.
7
[7] Frac-Packer. Accessed on 2000. http://www.flatwaterfleet.com/html/frac_packers.html.
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[8] Common well problems and basic rehabilitation approaches. Accessed on 18 July 2014. http://www.agr.gc.ca/eng/science-and-innovation/agricultural-practices/water/wells-and-groundwater/water-wells/well-rehabilitation/?id=1371571333366.
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[9] Smith S A. Recent innovations in well rehabilitation. Accessed on April 1994. http://www.groundwaterscience.com/resources/tech-article-library/94-recent-innovations-in-well-rehabilitation.html.
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[10] Reece R. Water well rehabilition technologies and well asset management. Utility Service Group; 2014.
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[11] AQUA FREED & AQUA GARD Case Studies. Accessed on Jun 2013. http://www.subsurfacetech.com/aqua-freed-case-studies.
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[12] Save thousands by jetting your well and not re-drilling. Accessed on 2000. http://www.mrbillspump.com/residential_and_commercial_water_well_jetting.php.
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[13] Developing and rehabilitating well screens in sand and gravel wells. Accessed on 2000. http://www.flatwaterfleet.com/html/jetting_tool.html.
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[14] Hydro fracturing. Accessed on 2009. www.highyieldwater.com/3.html.
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[15] Say goodbye to low-yielding wells with hydro-fracturing. Accessed on 2000. http://foglepump.com/hydro-fracting/2450777.
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[16] Well Rehabilitation with high-energetic Ultrasound. Sonic Umwelttecchnik. SONIC Information No. E1-09. 2009, Accessed on April 2017. www.sonic-umwelttechnik.de.
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[17] Results from practical application. Sonic Umwelttecchnik. SONIC Information No.E3. 2009. Accessed on April 2017. www.sonic-umwelttechnik.de.
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[18] Gipsou T C. Method and apparatus for downhole oil well production stimulation. 1994; US Patent No. 5297631.
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[19] Jansen J R, Taylor R W, Frazier W C. Method for improved water well production, 1996; US Patent No. 5579845.
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[20] Bi-Product – Residuals – Explosives Free. Water Well Solutions. 2012. Accessed on April 2017. http://www.wwssg.com
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[21] Mefford B, Hanna L J. Model tests of air burst and hydraulic back-flush cleaning efficiency for a cook cylindrical screen. Model Test Summary Report. 1997.
22
[22] Air impulse rehabilitiation results by project. Municipal Well & Pump. 2011. Accessed on April 2017. www.municipalwellandpump.co
23
ORIGINAL_ARTICLE
Investigating suspended sediment yield in Ziarat Drainage Basin, Gorgan in different seasons using sediment fingerprinting technique
Soil erosion is the most serious and irreversible threat to sustainable development. According to the increasing rate of soil erosion and sediment yield, the current study aimed to determine relative contribution of sediment sources in Ziarat catchment in fall, winter and spring seasons using sediment fingerprinting technique. In this regard, 43 samples from sediment sources including rangeland and cultivated land as surface erosion and stream bank and road verges as sub-surface erosion as well as 14 samples of suspended sediment at outlet of catchment were collected and concentration of geochemical tracers, organic carbon and 137Cs were measured. The optimum set of tracers was selected using the Kruskal-Wallis H test and discriminant function analysis. Finally, the relative contribution of sediment sources was determined through mixing model in different seasons. The results showed that the maximum relative contribution was related to surface erosion in winter and spring seasons. Sub-surface erosion in spring season with 60.4% also caused a large amount of sediment yield. The results of this study can be used to select the management strategies in soil erosion and sediment control of Ziarat catchment in different seasons.
https://ije.ut.ac.ir/article_62646_64c8d5ebd0300f5228f6d02413e5b311.pdf
2017-09-23
887
895
10.22059/ije.2017.62646
Sediment fingerprinting
seasonal erosion
137Cs
Ziarat catchment
Kazem
Nosrati
k_nosrati@sbu.ac.ir
1
Associate Professor, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Saeede
Jalali
jalali.sa.67@gmail.com
2
MA Student in Geomorphology, Shahid Beheshti University, Tehran, Iran
AUTHOR
منابع 1- Morgan, R. P. C, Duzant, J. H. Modified MMF (Morgan–Morgan–Finney) model for evaluating effects of crops and vegetation cover on soil erosion. Earth Surface Processes and Landform. 2008,3: 3(1): 90 - 106
1
2- Collins, A. L. Williams, L. J., Zhang, Y. S., Marius, M., Dungait, J. A. J., Smallman, D. J., Naden, P. S. Catchment source contributions to the sediment-bound organic matter degrading salmonid spawning gravels in a lowland river, southern England. Science of The Total Environment, 2001,3: 456: 181-195. 1. Russel M. A., Walling D. E., and Hodgkinson R. A. Suspende sediment sources in two small lowland agricultural catchments in UK. Journal of Hydrology, 2003:252, 1-24 2. Zapata F.. The use of environmental radionuclides as tracers in soil erosion and sedimentation investigation: Recent advances and future developments. Soil and tilage Research,2003: 69.3-13 3. Hakimkhani Sh.. Investigation use of tracers in sediment tracing fluviall sediment, Poldasht station. Master science thesis. 2007.[In persian].
2
Koiter, A.,J., Lobb, D.A., Owens. P.N., Petticrew, E.L., Tiessen, K., Li, S.,. The behavioural characteris of sediment properties and their implications for sediment fingerprinting as an approach for identifying sediment sources in river basins. Earth sci. Rev. 2013. 125, 24-42
3
Walling D.E., The evolution of sediment sources fingerprinting investigations in fluvial system. Soils Sediments 2013: 13:1658-1675
4
Mabit L., Benmansour M., Abril J.M., Walling D,E., Meusburger K., Lurian A.R., Bernard C., Tarjan S., Owens P.N., Blake W.H., Alewell C. Fallout Pb210 as a soil and sediment tracering in catchment sediment budget investigation : a review Earth Sci. Rev. 2014:138:335-351
5
Walling D. E.,. Tracing suspende sediment sources in catchments and rivers systems, Science of the Total Environment. 2005: 334:159-184
6
Carreras N. M., Krein A., Gallart F., Iffly J. F., Pfister L., Haffman L., Owens P. N.,. Assessment of different parameter for discriminating potentioal suspende sediment sources and provenance: A multi scale study in Luxembourg. Geomorphology. 2010: 11:118-129
7
Collins A.L., Walling D. E., Leeks G. J. L.,. Fingerprinting the origin of fluvial suspended sediment in larger river basin: Combining assessment of spatial provenance and source type. Geografiska Annaler. 1997: 79(a): 239-254
8
Nostrati, K., Ascribing soil erosion of hillslope components to river sediment yield. Journal of Environmental Management. 2017: 194: 63-72
9
Tiecher T., Caner L., Minella J.P.G., Pellegrini A., Capoane V., Rasche J.W.A., Schaefer G.L., Rheinhimer D. Tracing sediment sources in two paired agricultural catchment with different riparian forest and wetland proportion in southern Brazil. Geoderma.2017:258:225-239
10
Zhao G., Mu X., Han M., An Z., Gao P., Sun W., Xu W.,. Sediment yield and sources in dam-controlled watershed on the northern Loess Plateau. Catena 2017:149:110-119
11
Du. P., Des E Walling.,. Fingerprinting surficial sources: Exploring some potential problems associated with the spatial variability of material properties. Journal of Environmental Management. 2017: 194 : 4-15
12
Garzon- Garcia A., Laceby J.P., Olley J.M., Bunn S.E., Differentiating the sources of fine sediment organic matter and nitrogen in a subtropical Australian catchment. Science of the total environment. 2017: 575: 1384-1394
13
Collins, A. L., Williams , l., j., zhang y. s. marius m., dungait, j. a., smallman, d. j. et al.,. Catchment source contributions to the sediment-bound organic matter degrading salamonid apawing gravels in a lowland river, southern england. Science of the total environment, 2013: 456.181-195
14
Cooper, R., Krueger, T., Hiscock, K, M., and Rawlins, Barry G. High-temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach. Earth Surface Processes and Landforms, 2015:40: 78-92.
15
Devereux, O, H., Prestegaard, K. L., Needelman, B. A., and Gellis, Allen C. Suspended-sediment sources in an urban watershed, Northeast Branch Anacostia River, Maryland. Hydrological Processes, 2012:24:1391-1403.
16
Gruszowski, K. E., Foster, I. D. L., Lees, J. A., & Charlesworth, S. M. Sediment sources and transport pathways in a rural catchment, Herefordshire, UK. Hydrological Processes, 2013: 17: 2665-2681.
17
Nosrati k, Govers G, Semmenes B.X, and Ward E,J.,. A mixing model to incorporate uncertainty in sediment fingerprinting. CATENA, 2014: 217: 173-180.
18
Rowan, J. S., Black, S. and Franks, S. W. Sediment fingerprinting as an environmental forensics tool explaining cyanobacteria blooms in lakes. Applied Geography, 2013: 32: 832-843.
19
D Haen K, Verstraeten G., Dusar B,. Degryse B., Heax J., Walkens M. Unravelling changing sediment sources in a Mediterranean mountain catchment Bayesian fingerprinting approach. HydrologicalProcesses. 2013: 27:896-927.
20
Carter, J., Owens, P N., Walling, D, E., and Leeks, Graham J. L. Fingerprinting suspended sediment sources in a large urban river system. Science of The Total Environment, 2003: 14: 513-534.
21
Hakimkhani Sh., invetigatd of relative contribution of erosion typs in sediment yield. GhareAgaj, Maku, Journal of Natural Resource. 2001. 63-13-27,.[In persia].
22
Olley J., Burton J., Smolders K., Pantus F., and Pietsch T. The application of fallout radionnuclides to determine erosion process in water supply catchments of subtropical South-east Queensland, Australlia. Hydrological Processes, 2012:27:62-70
23
He, Q., and Walling, D. E. The distribution of fallout 137 Cs and 210 Pb in undisturbed and cultivated soils. Applied Radiation and Isotopes, 1997: 48(5), 677-690.
24
Ownes, P. N., Walling. D.E.,. Spatial variability of cesium-137 inventories a: reference sites: an example from two contrasting sites in England and Zimbabwe. Appl. Radiate. Isot. 1996:47,699-707.
25
Collins A.L., Walling D.E., Sichingabula H.M., Suspended sediment source fingerprinting in small tropical catchment and managment implications,. Applied Geography, 2001: 21:387-412.
26
Motha J.A., Wallbrink P.J., Hairsine P.B,. and Grayson R. B.. Determining the sources of suspende sediment in forested catchment in southeastern Australia. Water Resources Reasearch. 2003: 39(3): 1056-1070
27
Wallbrink, PJ, Murray, AS, Olley, JM, & Olive, LJ.. Determining sources and transit times of suspended sediment in the Murrumbidgee River, New South Wales, Australia, using fallout 137Cs and 210Pb. Water Resources Research, 1998:34(4), 879-887.
28
Caitcheon G. G, Olley J. M., Pantus F., Hancock G., and Leslie C.. The dominant erosion processes supplying fine sediment to three major rivers in tropical Australia. Geomorphology, 2012: 151-152. Pp.188-195
29
ORIGINAL_ARTICLE
Optimization of the number of rain gage stations based on interpolation methods and principal components analysis in Iran
Optimization of the number of synoptic stations in the estimation of rainfall is an important step in terms of reducing the maintenance cost and saving the data collection. The main objective of this study was to determine the optimal number of synoptic stations to estimate the amount of rainfall in Iran. Accordingly, the amount of rainfall of synoptic stations related to a common 14-year period was received from the National Weather Service and the performances of five different interpolation methods were evaluated. Based on the results of radial basis function (RBF), with a margin of error of 0.63, this method was selected as the most appropriate method in fitting the data. Studies show that eliminating the synoptic stations in PCA method increases the estimation error of RMSE from 0.48 to 0.52 related given that all synoptic stations were used; moreover, in the radial basis function, interpolation method decreases from 0.63 to 0.55 which indicates the suitability of this method in the optimization of synoptic stations. The results indicate that through removing 34 and 22 points from the network of synoptic stations in Iran respectively in the PCA method and interpolation method of radial basis, the resulting error will acceptable.
https://ije.ut.ac.ir/article_62648_1ec9d1abca9d43073a4822b11be7478e.pdf
2017-09-23
897
910
10.22059/ije.2017.62648
Optimization
interpolation
PCA
Validation
synoptic stations
Zahra
Gerkani Nezhad Moshizi
z.gerkani94@gmail.com
1
Graduate Student, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar -Abbas, Iran
AUTHOR
Fatemeh
Teimouri
f.teimouri93@gmail.com
2
Graduate Student, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar -Abbas, Iran
AUTHOR
Ommolbanin
Bazrafshan
bazrafshan1361@gmail.com
3
Assistant Professor, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar- Abbas, Iran
LEAD_AUTHOR
منابع
1
[1] Yousefi H, Nohegar A, Khosravi Z, Azizabadi Farahani M. Drought Modeling and Management Using SPI and RDI Indexes (Case study: Markazi provin). The Univercity of Tehrans Scientific Journals Database. 2015; 2(3): 337- 344. (In Persian)
2
[2] Mohammadi M, Karami H, Farzin S, Farokhi A. Prediction of Monthly Precipitation Based on Large-scale Climate Signals Using Intelligent Models and Multiple Linear Regression (Case Study: Semnan Synoptic Station), The Univercity of Tehrans Scientific Journals Database. 2017; 4(1): 201- 214. (In Persian)
3
[3] Sajal KA, Abdullah GY, Nitin M. Optimal design of rain gauge network in the Middle Yarra River catchment, Australia. Hydrol.Process.2014.
4
[4] Pardo-Igúzquiza E. Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. Journal of Hydrology. 1998; 210: 206–220.
5
[5] Xie, Y., bin Chen, T., Mei Lei, Jun Yang, Qing-jun Guo, Bo Song, Xiao-yong Zhou, 2011, Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis, Chemosphere, Vol. 82, No. 3, PP. 468- 476.
6
[6] Kassim AHM, Kottegoda NT. Rainfall network design through comparative kriging methods. Hydrological Sciences Journal. 1991; 36: 223–240.
7
[7] Mohd Aziz M, Yusof F, Mohd Daud Z, Yusop A, Afif M. Optimal design of rain gauge network in Johor by using geostatistics and particle swarm optimization. International Journal of GEOMATE. 2016; 11(25): 2422-2428.
8
[8] Adhikary SK, Yilmaz AG, Muttil N. Optimal design of rain gauge network in the Middle Yarra River catchment, Australia. HYDROLOGICAL PROCESSES. 2014; 29(3): 2582–2599.
9
[9] Mishra AK, Coulibaly P. Developments in hydrometric network design: a review. Reviews of Geophysics. 2009. 47: RG2001.
10
[10] Chebbi A, Bargaoui ZK, da Conceição Cunha M. Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results. Hydrol. Earth Syst. Sci.2013; 17: 4259–4268.
11
[11] Tsintikidis D, Georgakakos KP, Sperfslag JA, Smith DE, Carpenter TM. Precipitation Uncertainty and Raingage Network Design Within Folsum Lake watershed. Journal of Hydrologic Engineering.2002; 7(2): 175-184.
12
[12] Dimitris M, Metaxa G. 2006. Geostatiscal Analisis of Spatial Variability of Rainfall and Optimal Design of a Rainguage Network, Water Resources Management. 2006; 10: 107-127.
13
[13] Cheng KS, Wei C, Cheng YB, Yeh HC. Effect of spatial variation characteristics on contouring of design storm depth, Hydrol Process 2003; 17(9): 1755-69.
14
[14] Barca E, Passarella G, Uricchio V. 2008. Optimal Extension of the Rain Gauge Monitoring Network of the Apulian Regional Consortium for Crop Protection, Environ Monittoring Assessment. 2008; 145(58): 375-386.
15
[15] Karamouz M, Kerachian R, Akhbari M, Hafez B. Design of river water quality monitoring networks: a case study, Environ Model Assess 2009; 14(6): 705-14.
16
[16] Shafiei M, Ghahraman B, Saghafian B. 2013. Evaluation and optimization of raingauge network based on probability kriging (case study: Gorgan-Rud watershed). Iran-Water Resources Research. 2013; 9(2): 9-18. (In Persian)
17
[17] Adib A, Moslemzadeh M. Optimal Selection of Number of Rainfall Gauging Stations by Kriging and Genetic Algorithm Methods. International Journal of Optimization in Civil Engineering. 2016; 6(4):581-594.
18
[18] Feki H, Slimani M, Cudennec CH. 2016. Geostatistically based optimization of a rainfall monitoring network extension: case of the climatically heterogeneous Tunisia. Hydrology Research.2016; 48(1).
19
[19] Asakere H, Kriging interpolation method is used in the case of Iran, the interpolation of precipitation 12/26/1376. Journal of Geography and Development. 2004; 5(12): 25-42. (In Persian)
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[20] Gurunathan K, Ravichandran S. 1994. Analysis of water quality data using a multivariate statistical technique - a case study. IAHS Pub. 1994; 219.
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[21] Salah H. Geostatistical analysis of groundwater levels in the south Al Jabal Al Akhdar area using GIS. GIS Ostrava. 2009; 25: 1-10.
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[22] Taghizadeh R, Zareian M, Mahmudi SH, Heidari A, Sarmadian F. Evaluation methods temporal interpolation to determine the spatial variability of water quality characteristics of groundwater in Rafsanjan. Watershed Management Science and Engineering Iran. 2008; 2(5): 63-70. (In Persian)
23
[23] Bastin G, Lorent B, Duque C, Gevers M. Optimal estimation of the average areal rainfall and optimal selection of rain gauge locations, Water Resour Res. 1984; 20(4): 463-70.
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[24] Webster R and Oliver MA. Geostatistics for Environmental Scientists. John Wiley and Sons, Ltd., Chichester, UK.2001; 271.
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[25] Asghari moghadam A, Nurani V, Nadiri A. Predict when and where the groundwater level in the city of Tabriz metro area using neural kriging model. Iran Water Resources Research. 2008; 13(1): 14-24. (In Persian)
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[26] Isaaks, E.H., Srivastava, R.M., 1989, An Introduction to Applied Geostatistic, Oxford University Press New York, P.561.
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[27] Sun Y, Kang S, Li F and Zhang L. Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China. Environmental Modelling & Software. 2009; 24:1163-1170.
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[28] 28. Camdevyren HN, Demyr A, Kanik S, Keskyn. “Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs.” Ecol. Model. 2005; 181: 581–589.
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[29] Manly BFJ. Multivariate statistical methods: A primer, 2nd Ed., Chapman and Hall, London.1986.
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[30] Lu, W. Z., W. J. Wang, X. K. Wang, Z. B. Xu and A. Y. T. Leung. “Using improved neural network to analyze RSP, NOX and NO2 levels in urban air in Mong Kok, Hong Kong.” Environmental Monitoring and Assessment. 2003; 87: 235–254.
31
Petersen W. Process identification by principal component analysis of river water-quality data. Ecol. Model.2001; 138: 193-213
32
ORIGINAL_ARTICLE
Scrutiny of base flow separation using natural monthly discharge
In hydrology studies, the river regimes are considered to be natural while this assumption is not correct since the river water is used for developing agriculture, industry, and other needs, particularly in the middle basins. The requirement for river base flow separation is to have access to natural river regime on a daily basis, which, regarding the limitations of naturalization, natural river regime can be calculated on an annual or monthly basis. Therefore, the estimation of base flow has been faced with some difficulties. On the other side, due to the complex relation of surface water with groundwater, the estimation of base flow is too hard. Therefore, in this study, the removal process method was used for naturalizing monthly discharge of Belbar basin, and Eckhart RDF algorithm was used since it takes aquifer characteristics into account for calculating base flow and it has the ability to optimize its parameters. The constant parameters of return and the maximum base flow index for monthly natural river discharge in hydrometric stations of Belbar dam were optimized. Then, the monthly base flow was calculated and the results showed that the monthly natural river discharge can be used like the daily natural river discharge with the same accuracy.
https://ije.ut.ac.ir/article_62649_38c519cb67b9d71f4d3520546d3a2b31.pdf
2017-09-23
911
921
10.22059/ije.2017.62649
Base flow
recursive digital filter
natural discharge
rock aquifer
belbar dam
Amin
Eeidipour
amineidipour@gmail.com
1
Dezab Consulting Engineer, Ahvaz, Iran
LEAD_AUTHOR
Amir
Pourhaghi
amir_55_36@yahoo.com
2
PhD Student of Water Resources Engineering, Faculty of Water Sciences Engineering Shahid Chamran University, Ahvaz, Iran
AUTHOR
Akbar
Shokrolahi
a_shokrolahi@gmail.com
3
Dezab Consulting Engineer, Ahvaz, Iran
AUTHOR
Hossein
Yousefi
hosseinyousefi@ut.ac.ir
4
Assistant Professor, Faculty of New Sciences and Technologies, University of Tehran
AUTHOR
منابع
1
Bayazidi S, Aliabad HM, Zafaranizade M. Natural discharge of hydrometric stations with Cindex (remove Trend amended), the Ninth International Conference of Civil Engineering, University of Isfahan; 2012 [Persian]
2
White KE, Sloto RA. Base-flow frequency characteristics of selected Pennsylvania Streams. US Department of the Interior, US Geological Survey; 1990.
3
Holtschlag DJ, Nicholas JR. Indirect ground-water discharge to the Great Lakes. US Geological Survey; 1998.
4
Hoos AB. Recharge rates and aquifer hydraulic characteristics for selected drainage basins in middle and east Tennessee. US Geological Survey; Books and Open-File Reports [distributor]; 1990.
5
Rutledge AT. Computer programs for describing the recession of ground-water discharge and for estimating mean ground-water recharge and discharge from streamflow records: Update;1998.
6
Mau DP, Winter TC. Estimating Ground‐Water Recharge from Streamflow Hydrographs for a Small Mountain Watershed in a Temperate Humid Climate, New Hampshire, USA. Ground Water. 1997 Mar. 1;35(2):291-304.
7
Chapman TG. Comment on “Evaluation of automated techniques for base flow and recession analyses” by RJ Nathan and TA McMahon. Water Resources Research. 1991 Jul. 1;27(7):1783-4.
8
Nathan RJ, McMahon TA. Evaluation of automated techniques for base flow and recession analyses. Water Resources Research. 1990 Jul. 1;26(7):1465-73.
9
Lyne V, Hollick M. Stochastic time-variable rainfall-runoff modelling. InInstitute of Engineers Australia National Conference 1979 Sep. (Vol. 1979, pp. 89-93).
10
Eckhardt K. How to construct recursive digital filters for baseflow separation. Hydrological processes. 2005 Feb. 15;19(2):507-15.
11
Eckhardt K. A comparison of baseflow indices, which were calculated with seven different baseflow separation methods. Journal of Hydrology. 2008 Apr. 30;352(1):168-73.
12
Gonzales AL, Nonner J, Heijkers J, Uhlenbrook S. Comparison of different base flow separation methods in a lowland catchment. Hydrology and Earth System Sciences. 2009 Nov. 4;13(11):2055-68.
13
Smakhtin VU. Estimating continuous monthly baseflow time series and their possible applications in the context of the ecological reserve. Water Sa. 2001 Apr 1;27(2):213-7.
14
Shoshtari MM. Hydraulic groundwater, Ahvaz:Shahid Chamran UniversityPress; 2010 [Persian]
15
Beard L. R. HEC-4 Monthly Streamflow Simulation.1971.
16
ORIGINAL_ARTICLE
Numerical comparison of RAI and PNPI meteorological indices to assess and quantify the drought situation in Khuzestan province
Over the past few decades, the frequency and number of drought occurrence has been more than other natural disasters influencing human societies. Drought is a kind of natural disaster formed and extended smoothly and in a crawling way in comparison with other natural hazards like heavy precipitations and floodwaters. There are different and numerous indicators for quantitative expression of drought. Percentage Indicators of normal precipitation (PNPI) and precipitation abnormality (RAI) status and continuation of drought in 8 stations of Abadan, Ahvaz, Bandar-e Mahshahr, Bostan, Masjed Soleyman, Omidieh, Ramhormoz and Safi Abad in Khuzestan province during the statistical period (1990-2014) have been considered in this research. As the obtained results show, based on PNPI indicator, the most severe drought happened during period 1990-2014 in Bandar- e Mahshahr station with the amount of 20.39 in 2010. Also, the most severe wet period happened in Ahvaz station with the amount of 216.33 in 1997, based on PNPI indicator. Also, based on RAI indicator, the most severe drought happened in Bandar- e Mahshahr with the amount of 6.26 in 2010 during the period 1990-2014. Based on RAI indicator, the most severe wet period occurred in Ahvaz station with the amount of 8.6 in 1997.
https://ije.ut.ac.ir/article_62650_51d82e1c68aa9e60c241aca610c03ea5.pdf
2017-09-23
923
930
10.22059/ije.2017.62650
Drought
PNPI indicator
RAI indicator
Khuzestan Province
zoning
Mohammad Hossein
Jahangir
mh.jahangir@ut.ac.ir
1
Assistant Professor, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Eghbal
Norozi
eghbalnorozi@ut.ac.ir
2
MSc. Student, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
AUTHOR
منابع
1
[1] Movedat E, Maleki S, Classification and Spatial Measurement of Social - Physical damages of the Cities Against Earthquakes by Using VIKOR Technique and GIS, Case Study: Yazd City. Jornal of Management System. 2014; 4(11): 85-103. (In Prsian).
2
[2] Mosavi S H, AbasAli V, Maeiri M.Study of Drought severity and its severity in Semnan using DI index. Second National Conference on Drought Effects and Management Tools. Isfahan Agricultural and Natural Resources Research Center. 2009.(In Prsian).
3
[3] Heidari, H. Plantand.Drought, Reserch institute of forest and Regeland publications, 2006.
4
[4] Khosravi Y, Mozafari Kh. Error Analysis in the Evaluation of the SPI Drought Index Using Geostatistical Methods, Case Study of Bushehr Province. Jornal of Geography. 2015; 14(48): 189-212.(In Prsian).
5
[5] Evazi M, Masaedi A. Monitoring and Spatial Analysis of Meteorological Drought in Golestan Province using Geostatistical Methods. Jornal of Range and Watershed Management, Iranian jornal of Natural Resources. 2011; 64(1): 65-78.(In Persian).
6
[6] Ansari H, Davari K. Zoning drought using standard precipitation index(SPI) in GIS Environment, Case study of khorasan province. Jornal of Geogrohi.2007; Res. 60:97-108. (In Persian).
7
[7] Brna R, Azimi F, Saeidi Dehaki N, Comparison of RAI, PN and SIAP Indicators in the Study of Drought in Khuzestan Province with Emphasis on Abadan and Dezful Station. Jornal of Natural Geography Quarterly. 2010; 3(9): 77-88.(In Persian).
8
[8] Vafahkhah M, Rajabi M. Efficiency of Meteorological Drought Indices for Monitoring and Assessment of Drought in Bakhtegan, Tashk, and Maharlo Lakes Watershed. Jornal of Desert. 2005; 10(2): 369-383.(In Persian).
9
[9] Saeid M, Moghadasi M, Ghasemi H. Drought monitoring system design for Tehran province. Research project, 2004.(In Persian).
10
[10] Maleki S, Movedat E. Drought crisis zoning with SIAP, PNPI and TOPSIS profiles, Case Study of Yazd Province. Journal of Disaster Management and Mitigation 2016; 6(1): 59-70.(In Persian).
11
[11] Mohamadian A, Kouhi M, Adine baigi A, Rasouli G, Bazrafshan B. Comparison of Monitoring Using SPI, DI and PNI and Zoning Them, Case Study Northern Khorasan Province. Jornal of Water and Soil Coservation. 2010; 17(1); 177-184.(In Persian).
12
[12] Khosravi M, Movaghari A, Mansori Daneshvar M. Evaluation of PNI, RAI, SPI and SIP Indices for Drought Zoning of Iran by Comparing IDW Intrusion Detection and DEM Digital Elevation Model. Journal of Geography and Environmental Sustainability. 2012; 2(5): 53-70.(In Persian).
13
[13] Willeke K. Lin X.J. Grinshpun S.A. Improved aerosol collection by combined impaction and centrifugal motion. Aerosol Science and technology, 1998;28(5):439-456.
14
[14] Van Rooy M.P. A Rainfall anomaly index )RAI) independent of time and space.Notos,1965: No. 14: 43-48.
15
ORIGINAL_ARTICLE
Water resources and their role in attracting tourists (Case Study: Tehran’s Qanats)
Since Iranians introduced Qanat to the world, it has been widely spread around the world due to its usefulness providing people with groundwater. According to studies conducted in Iran, it is estimated that about 10,000 km qanats with 300,000 wells lie under Iranian urban areas. In order to utilize these water resources, sustainable development should be taken into account, which is exploitation of these resources with responsibility, reasonable and proper planning. Tourism is associated with many advantages including cultural exchange, flourished native businesses, increased rate of employment, and familiarity of communities with each other. Studying the history and structure of Qanats in Iran and other regions reveals the high technical knowledge of our ancestors. Many reasons contribute to the decline of Qanats in Iran including new methods of irrigation, climate changes and inappropriate management knowledge. However, based on previous successful experiences in reviving Qanats and turning them into tourist attractions in different parts of the world and also Iran, the high potential of this structure to attract a huge number of tourists is revealed; therefore, reviving and improving these structures in historical parts of Tehran is recommended.
https://ije.ut.ac.ir/article_62651_98c4170b9e58c1d5a2b30986848a909d.pdf
2017-09-23
931
941
10.22059/ije.2017.62651
Qanat
Tourism
Tehran
resuscitation of water resources
Nasrollah
Abadian
nasrollah.abadian@yahoo.com
1
PhD Student, Department of Urbanism, Emarat Branch, Islamic Azad University, Dubai, UAE
AUTHOR
Naser
Eghbali
naser.eqbali@yahoo.com
2
Associate Professor, Department of Geography, Tehran Central Branch of Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Nasim
Khanlou
hevak.valkyr@gmail.com
3
Assistant Professor, Department of Urbanism, Eastern Branch of Tehran, Islamic Azad University, Tehran, Iran
AUTHOR
منابع
1
1. Stiros SC. Accurate measurements with primitive instruments: the “paradox” in the qanat design. Journal of Archaeological Science. 2006;33(8):1058-64.
2
2. Maleki A, Khorsandi A. Qanat in Iran, The case study of Tehran qanats. 2005, [Persian].
3
3. Shams A. A rediscovered-new ‘Qanat’ system in the High Mountains of Sinai Peninsula, with Levantine reflections. Journal of Arid Environments. 2014;110:69-74.
4
4. Wilkinson TJ, Boucharlat R, Ertsen MW, Gillmore G, Kennet D, Magee P, et al. From human niche construction to imperial power: long-term trends in ancient Iranian water systems. Water History. 2012;4(2):155-76.
5
5. Hamidian A, Ghorbani M, Abdolshahnejad M, Abdolshahnejad A. RETRACTED: Qanat, Traditional Eco-technology for Irrigation and Water Management. Agriculture and Agricultural Science Procedia. 2015;4:119-25.
6
6. Carrión A, Fornes A. Underground medieval water distribution network in a Spanish town. Tunnelling and Underground Space Technology. 2016;51:90-7.
7
7. L.Khaniki M, S.Yazdi AA. Qanat Tourism. Yazd: Shahandeh; 2015.
8
8. Parsizadeh F, Ibrion M, Mokhtari M, Lein H, Nadim F. Bam 2003 earthquake disaster: On the earthquake risk perception, resilience and earthquake culture – Cultural beliefs and cultural landscape of Qanats, gardens of Khorma trees and Argh-e Bam. International Journal of Disaster Risk Reduction. 2015;14:457-69.
9
9. Abbasnejad A, Abbasnejad B, Derakhshani R, Hemmati Sarapardeh A. Qanat hazard in Iranian urban areas: explanation and remedies. Environmental Earth Sciences. 2016;75(19):1306.
10
10. Mahmoodi MR, Fadaei Nezhad S. Feasibility Study on the Establishment of Ecomuseums in Areas under the Influence of Qanats in Iran. Journal of Applied Environmental and Biological Sciences. 2015;5(11):72-80.
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