1 - Comparison of selected thermal indices in the northwest of Iran https://jnec.ut.ac.ir/article_55074.html 1 The present study compared simple thermal indices and the indices derived from energy budget models in the northwest of Iran. For this purpose, the air temperature, solar radiation, relative humidity, cloud, cover and wind speed of 13 meteorological stations in the northwest of Iran during the the period of 1986 to 2007 were selected for comparison. The results which were extracted using Bioklima and RayMan models, showed that the indices based on human energy balance had a significant correlation with each other (with R2 above 90%), and the lowest R2 (70%) was related to Subjective temperature index (STI). The indices based on relatively simple formulas had low correlation with Universal Thermal Climate Index (UTCI) and Physiologically Equivalent Temperature (PET). The probable reason for this lack of conformity was the lack of radiation factor in the equations. Furthermore, UTCI was very sensitive to the changes in air temperature, solar radiation, relative humidity and wind speed, especially. In this regard, it represented the response of the human body. The findings of this analysis indicated that UTCI and PET indices were the most suitable indices which could be used in determining thermal comfort conditions. 0 - 1 20 - - Hassan Farajzadeh PhD Candidate, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran Iran hassanfa2003@yahoo.com - - Mohammad Saligheh Associate Professor, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran Iran - - Bohloul Alijani Professor, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran Iran b.alijani@yahoo.com - - Andreas Matzarakis Professor, Albert-Ludwigs University Freiburg, 79085 Freiburg, Germany Iran human thermal comfort meteorological variables Northwest Iran thermal indices 1. ASHRAE, 1997. American Society of Heating, Refrigerating and Air Conditioning Engineers Handbook Fundamentals Volume, Chap. 8. Thermal Comfort, 8.1–8.28.##2. Basarin, B., Krzic, A., Lazic, L., Lukic, T., Dordevic, J., Petrovic1, B.J., Copic, S., Matic, D., Hrnjak, I., Matzarakis, A., 2014. evaluation of bioclimate conditions in two special nature reserves in Vojvodina (northern Serbia). Carpathian journal of earth and environmental sciences, 9 (4): 93-108.##3. Bedford, T., 1951. Equivalent temperature, what it is, how it's measured. Heating, Piping, Air conditioning.##4. Blazejczyk, K., 1994. New climatological- and -physiological model of the human heat balance outdoor (MENEX) and its applications in bioclimatological studies in different scales. ZeszytyIGiPZ PAN 28:27–58.##5. Blazejczyk, K., 2005. MENEX_2005-the updated version of man-environment heat exchange model.##6. Blazejczyk K, Epstein Y, Jendritzky G, Staiger H, Tinz B (2012) Comparison of UTCI to selected thermal indices. 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Farajzadeh, H., Matzarakis, A., 2009. Quantification of climate for tourism in the northwest of Iran. Meteorological Applications 16(4):545-555.##21. Farajzadeh, H., Matzarakis, A., 2012. Evaluation of thermal comfort conditions in Ourmieh Lake, Iran. Theoretical and Applied Climatology 107(3-4):451-45.##22. Fiala, D., Havenith, G., Broede, P., Kampmann, B., Jendritzky, G., 2012. UTCI-fiala, multi-node model of human heat transfer and temperature regulation. Int J Biometeorol 56(3):429–441.##23. Fiala, D., Lomas, K.J., Stohrer, M., 2001. Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions. Int J Biometeorol 45:143–159.##24. Fröhlich, D., Matzarakis, A., 2015. A quantitative sensitivity analysis on the behaviour of common thermal indices under hot and windy conditions in Doha, Qatar. Theoretical and Applied Climatology, 1-9.##25. Gagge, A.P., Fobelets, A.P., Berglund, L.A., 1986. standard predictive index of human response to the thermal environment. ASHRAE Trans. (United States), 92(CONF-8606125).##26. Gagge, A., 1971. An effective temperature scale based on a simple model of human physiological regulatory response. ASHRAE Trans 77(1):247-262.##27. Givoni, B., 1976. Man, Climate and Architecture. Appl. SCI. Publishers, London, 483.##28. Gulyas, A., Matzarakis, A., 2007. Selected examples of bioclimatic analysis applying the physiologically equivalent temperature in Hungary. ActaClimatologicaEtChorologica 40(41):37-4.##29. Havenith, G., Fiala, D., Błazejczy, K., Richards, M., Brode, P., Holmer, I., Rintamaki, H., Benshabat, Y., Jendritzky, G., 2012. The UTCI-clothing model. Int J Biometeorol 56(3):461–470.##30. Höppe, P.R., 1997. Aspects of human biometeorology in past, present and future. Int J Biometeorol 40(1):19–23.##31. Höppe, P.R., 1999. The physiological equivalent temperature- a universal index for the biometeorological assessment of the thermal environment. Int J Biometeorol 43:71-75.##32. Houghten, F.C., Yagloglou, C.P., 1923. Determination of the comfort zone. ASHVE Transactions, 29, 361.##33. ISO TR 11079, 1993. Evaluation of cold environments. Determination of required clothing insulation, International Organisation of Standardization,Geneva.##34. IUPS, 2003. Glossary of terms for thermal physiology. Third Edition, revised by The Commission for Thermal Physiology of the International Union of Physiological Sciences. Journal of Thermal Biology. 28(1):75-106.##35. Jendritzky, G., de Dear, R., Havenith, G., 2012. UTCI—Why another thermal index? International journal of biometeorology 56(3):421-428.##36. Landsberg, H.E., 1972. The Assessment of Human Bioclimate, a Limited Review of Physical Parameters. World Meteorological Organization, Technical Note (123), WMO(331), Geneva.##37. Masterson, J., Richardson, F.A., 1979. Humidex: a Method of Quantifying Human Discomfort Due to Excessive Heat and Humidity. Environment Canada, Downsview, Ontario.##38. Matzarakis, A., Endler, C., 2010. Climate change and thermal bioclimate in cities: impacts and options for adaptation in Freiburg, Germany. International journal of biometeorology, 54(4):479-483.##39. Matzarakis, A., Mayer, H., 1997. Heat stress in Greece. International journal. Biometeorol. 41: 34-39.##40. Matzarakis, A., Mayer, H., Iziomon, M.G., 1999. Applications of a universal thermal index: physiological equivalent temperature. International Journal of Biometeorology, 43(2):76-84.##41. Matzarakis, A., Rutz, F., Mayer, H., 2000. Estimation and calculation of the mean radiant temperature within urban structures. WCASP-50, WMO/TD No. 1026, 273-278.##42. Matzarakis, A., Rutz, F., Mayer, H., 2007. Modelling Radiation fluxes in simple and complex environments–application of the RayMan model. 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Biometeorol. 50:358-369.##49. Nastos, P.T., Matzarakis, Α., 2008. Variability of tropical days over Greece within the second half of the twentieth century. Theor. Appl. Climatol. 93:75-89.##50. Nastos, P.T., Matzarakis, A., 2012. The effect of air temperature and human thermal indices on mortality in Athens, Greece. Theoretical and Applied Climatology, 108(3-4):591-599.##51. Novak, M., 2013. Use of the UTCI in the Czech Republic. Geogr Pol 86:21-28.##52. Nowosad, M., Rodzik, B., Wereski, S., Dobek, M., 2013. The UTCI index in Lesko and Lublin and its circulation determinants. GeographiaPolonica 86(1):29-36.##53. Parsons, K., 2014. Human thermal environments: the effects of hot, moderate, and cold environments on human health, comfort, and performance. Crc Press.##54. Parsons, K.C., 2003. Human thermal environments: the effects of hot, moderate, and cold environments on human health, comfort and performance. Taylor and Francis, London, New York, 527.##55. Shady, M.R., 2013. 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1 - Assessing landscape change of Minab delta morphs before and after dam construction https://jnec.ut.ac.ir/article_55073.html 1 As special depositional environments which are adjacent to the seas, deltas have provided a field for human habitat establishment. Geomorphic features of deltas are in constant transformation due to their dynamic features. Constructing dams on rivers can intensify these changes and cause either negative or positive consequences. Minab delta in Hormozgan Province of Iran is a round or crescent-shaped delta which has Esteghlal Dam constructed on its creating river. Minab Dam is constructed upstream of Minab Delta in Hormozgan Province. The research aimed to derive landscape metrics of delta and assess their changes before and after the construction of dams. Landsat Satellite images of 1983 and 2013 provided the four classes as abandoned delta, active delta, subaqueous deltaic plain and aquatic environment, using maximum likelihood method, with a Kappa Coefficient accuracy of 86.55 and 88.42 for revealing changes quantitatively. To quantify landscape metrics, percentage of landscape (PLAND), Number of Patch (NP) and Mean Patch Size (MPS) metrics were computed. As indicated by the results, the NP metric increased in all the classes except active delta, and all classes showed a reduction in MPS metric. The amount of NP, Mean Nearest Neighbor (MNN) and Largest Patch Index (LPI) increased to the 22, 19 m and 1.43 percent, respectively, which clearly reveals landscape fragmentation, along with growing NP metric and a reduction in MPS. 0 - 21 29 - - Manouchehr Farajzadeh Professor, Department of Remote Sensing & GIS, Tarbiat Modares University, Tehran, Iran Iran farajzam@modares.ac.ir - - Mohammad Kamangar M.Sc. Remote Sensing & GIS, department of natural resources, Hormozgan University, Bandar-e-Abbas, Iran Iran mohamad.kamangar63@gmail.com - - Fahimeh Bahrami M.Sc. Watershed Management, Department of Natural Resources, Hormozgan University, Bandar-e-Abbas, Iran Iran watershed_scale@yahoo.com dam delta landscape Minab Morphology 1. Arshad, S., Morid, S., Hadi, M., 2007. Evaluate the morphological changes of rivers with RS, Agricultural Sciences and Natural Resources, 6, 180-194. dio: http://fa.journals.sid.ir/ViewPaper.aspx?ID=77273.##2. Azmoodeh Ardalan, A., Mosayebzadeh, M., 2003. Determining topography of water surface in Persian Gulf and Oman Sea integrating GPS observations and tide gages information, Journal of Technical Faculty, V.3, N.22, 177-188pp.##3. Berberoglu, S., Akin, A., 2009. Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. International Journal of Applied Earth Observation and Geoinformation, 11(1), 46-53. doi: http://dx.doi.org/10.1016/j.jag.2008.06.002.##4. Blodget, H.W., Taylor, P.T., Roark, J.H., 1991. Shoreline changes along the Rosetta-Nile Promontory: Monitoring with satellite observations. Marine Geology, 99(1–2), 67-77. doi: http://dx.doi.org/10.1016/0025-3227(91)90083-G.##5. Chen, X., Yu, S.X., Zhang, Y. P., 2013. Evaluation of Spatiotemporal Dynamics of Simulated Land Use/Cover in China Using a Probabilistic Cellular Automata-Markov Model. Pedosphere, 23(2), 243-255. doi: http://dx.doi.org/10.1016/S1002-0160(13)60013-2.##6. Chow, F.K., Weigel, A.P., Street, R.L., Rotach, M.W. and Xue, M., 2006. High-resolution large-eddy simulations of flow in a steep Alpine valley. Part I: Methodology, verification, and sensitivity studies. Journal of Applied Meteorology and Climatology, 45, 63–86.##7. Coleman, J.M., Roberts, H.H., Huh, O.K., 1986a. Deltaic landforms. Geomorphology from space: a global overview of regional landforms.##8. Coleman, J.M., Roberts, H.H., Huh, O.K., 1986b. Deltaic landforms: Washington, D.C., NASA.##9. Farajzade asl, M., 2011. Handbook of Morphology of deltas.##10. Farokhi, Z., Barani, Gh., Arshad, S., 2006. Assessment Alterations plan Dez River with GIS& RS, Iranian Hydraulic Conference, Bahonar University, Iran.##11. Kaltat, D., 2003. Servati, M., Physical Geography of the Sea and coast, SAMT publication, 218p.##12. Nagler, P.L., Glenn, E.P., Hinojosa-Huerta, O., 2009). Synthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, Mexico. Remote Sensing of Environment, 113(7), 1473-1485. doi: http://dx.doi.org/10.1016/j.rse.2008.06.018.##13. Nohegar, A., Hosinzade, M., 2003. Ocean dynamics and determinants of fluctuations in sea level in the northern deltas change case stydy: Strait of Hormuz. Geography and Environmental Planning Journal, 3, 125-142.##14. Overeem, I., Syvitski, J.P.M., 2009. Dynamics and vulnerability of delta systems. LOICZ reports & studies, 35(GKSS Research Center, Geesthacht), 56p.##15. Rangzan, K., Salehi, B., Salahshori, P., 2008. Changes Detection in the downstream region Karkheh before and after dam construction using multi-temporal images, Geomatics Conference, Tehran, Iran.##16. Richards, J.A., Jia, X., 2006. Remote Sensing Digital Image Analysis. 453.##17. Weng, Q., 2002. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management, 64(3), 273-284. doi: http://dx.doi.org/10.1006/jema.2001.0509.##

1 - Regional climate changes and their effects on monthly energy consumption in buildings in Iran https://jnec.ut.ac.ir/article_55069.html 1 This present research work was carried out to evaluate the energy consumption in a typical Iranian building based on the forecast of climatic variables. Thus, the LARS-WG model was validated for some northwest stations of Iran, including Tabriz, Ardebil, Oromieh, Kermanshah, Hamedan, Sannandaj, Qazvin, and Zanjan. The average monthly outdoor temperature was forecasted from 2011 to 2100. The relevant data were generated when this model was used in three phases, including calibration, meteorological data generation, and meteorological data analysis. In the model, HADCM3 general atmospheric circulation model data was extracted each day, and a special LARS-WG model-based scenario is compiled for each general atmospheric circulation model network. The results of this study showed a delay of one month in the future yearly temperature curve and an average increment of 4°C in all the eight Iranian cities. Furthermore, as a result of these expected changes, the future maximum and minimum outdoor temperatures will be higher in the winter and reduced in autumn. Another related result of this temperature variation is a decrease in the heating energy consumption in the months of February and March and an increment in the months of November and December. On the other hand, there will be an increment in the cooling energy consumption in the months of May and June and a decrement in the months of August and September. Generally, some kinds of parameters, like the thermal inertia of the buildings and number of air changes, were combined as design proposals to define future building constructions with the lowest energy consumption. Thus, with half changes in the air and in the heating season, the energy consumption is reduced to one quarter of the initial forecast value, and in the cooling season, the energy consumption will be slightly higher, reaching the energy consumption defined today. Finally, it can be concluded that it is now the right moment to define future building design criteria. 0 - 31 48 - - Gholamreza Roshan Assistance Professor, Department of Geography, Golestan University, Iran Iran ghr.rowshan@gmail.com - - Jose A Orosa Professor, Department of Energy and M.P., E.T.S.NyM, University of A Coruña, Spain Iran r.rowshan@yahoo.com Building climate change Energy consumption Iran 1. Arnfield, J. A., 2003. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology 23(1), 1-26.##2. Abassi, F., Malbusi, S., Babaeian, I., Asmari, M., Borhani, R., 2010. Climate Change Prediction of South Khorasan Province During 2010-2039 by Using Statistical Downscaling of ECHO-G Data. Journal of Water and Soil 24, 218-233 (in Persian).##3. Babaeian I, Najafi Z, ZabulAbbasi F, Habibi Nokhandan M, Adab H, Malbosi Sh., 2010. Climate Change Assessment over Iran During 2010 -2039 by Using Statistical Downscaling of ECHO- G Model.Geography and Development, 16: 135-152 (In Persian).##4. Babaeian, I., Kwon, W.T., Im, Es., 2004. Application of weather generator technique for climate change assessment over Korea. Korea Meteorological Research Institute, Climate Research lab., 98pp.##5. Bergeron, O., Strachan, I.B., 2012. Wintertime radiation and energy budget along an urbanization gradient in Montreal, Canada. International Journal of Climatology 32(1), 137-152.##6. Christen, A., Vogt, R., 2004. Energy and radiation balance of a central European city. International Journal of Climatology 24(11), 1395-1421.##7. Dubrovsky, M., Roll, M., 1996. 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A comparative analysis of short-term and long-term thermal comfort surveys in Iran. Energy and Buildings 34(6), 607-614.##14. Nazemosadat, M.J., Cordery, T.I., 2000. On the relationship between ENSO and autumn rainfall in Iran. International Journal of Climatology 20(1), 47–61.##15. Roodgar, M., Mahmoudi, M. M.G., Ebrahimi P, Molaei, D., 2011. Sustainability, architectural topology and green building evaluations of Kashan-Iran as a hot-arid region. Procedia Engineering. 21,811-819,##16. Rasco, P., Szeidl, L., Semenov, M.A., 1991. A serial approach to local stochastic models. Journal of Ecological Modeling 57, 27-41.##17. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M.. Miller, H.L., 2007. The Physical Science Basis. Change scenarios in the UK. Agricultural and forest meteorology 144:127- IPCC, Summary for Policymarkers, in: Climate Change 2007. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 1-18.##18. Semenov, M.A., Brooks, R.J., Barrow, E.M., Richardson, C.W., 1998. Comparison of the WGEN and LARS-WG stochastic weather generators in divers climates. Climate Research 10(2), 95-107##19. Semenov, M.A., Barrow, E.M., 2002. LARS-WG a stochastic weather generator for use in climate impact studies. User’s manual, Version3.0. http://www.rothamsted.ac.uk/mas-models/download/LARS-WG-Manual.pdf.##20. Semenov, M.A., 2007. Developing of high-resolution UKCUP02-based climate change scenarios in the UK. Agricultural and Forest Meteorology 144,127-138.##21. Sabziparvar, A.A., Mirmasoudi, S.H., Tabari, H., Nazemosadat, M.J., Maryanaji, Z., 2011. ENSO teleconnection impacts on reference evapotranspiration variability in some warm climates of Iran. International Journal of Climatology 31, 1710 –1723.##22. Sayari, N., Alizadeh, A., Bannayan, Awal, M., Farid Hossaini, A., Hesami Kermani, M.R., 2011. Comparison of Two GCM Models (HadCM3 and CGCM2) for the Prediction of Climate Parameters and Crop Water Use under Climate Change Case Study: Kashafrood Basin. Journal of Water and Soil 25, 912-925 (in Persian).##23. Thompson, C.S., Mullan, A.B., 1995. Weather Generators. NIWA Internal report 115- 120.##24. Tabari, H., Talaee, P. H., 2011. Analysis of trends in temperature data in arid and semi-arid regions of Iran. Global and Planetary Change 79, 1-10.## 25. Wilby, R.L., Conway, D., Jones, P.D., 2002. Prospects for downscaling seasonal precipitation variability using conditioned weather generator parameters, hydrological processes 16(6), 1215-1234.##26. Wilks, D.S., Wilby, R.L., 1999. The weather generation game: a review of stochastic weather models. Progress in Physical Geography 23(3), 329-357.##27. Wan, K.K.W., Li, D.H.W., Pan, W., Lam, J.C., 2012. Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications. Applied Energy 97,274-282.##28. Zarghami, M., Abdi, A., Babaeian, I., Hassanzadeh, Y., Kanani, R., 2011. Impact of climate change on runoffs in East Azerbaijan, Iran. Global and Planetary Change 78,137-146.##

1 - Comparison and evaluation of three methods of multi attribute decision making methods in choosing the best plant species for environmental management (Case study: Chah Jam Erg) https://jnec.ut.ac.ir/article_55068.html 1 Today, environmental crisis and loss of natural resources are the principle causes of the creation of environmental management systems. The optimal management of natural resources requires the assessment and classification of ecological and environmental potentials. Using this method, the abilities and restrictions of resources can be recognized, and their future trends can be predicted. Nebka landscape is the natural reaction of ecosystem against the stress of wind erosion and ecosystem tries to adjust wind stress by creating this landscape. Therefore, the development of Nebka is the best and most suitable method for quicksand stabilization in the study area, and the most adaptable type of Nebka species must be identified and selected for the development of the ecosystem. This important aim will not be achieved except through careful and scientific investigation of Nebka phenomenon. According to the present environmental conditions, a suitable method with high accuracy is required in order to evaluate and manage natural resources and environment for achieving sustainable development. The aim of this study was to select the best Nebka species for quicksand stabilization using their morphometric parameters analysis by multi attribute decision making (MADM) methods. The results of this study show that in three models, Haloxylon obtained the highest point. Therefore, it has the highest effect in stabilization of quicksand. For implementation of stabilization projects of mobile sands in the study area, development of Haloxylon Nebka systems have the highest importance and efficiency. The results of this study will be beneficial in systemic management of desert regions and stabilization projects of quicksand. 0 - 49 62 - - Mojtaba Yamani Professor of Physical Geography (Geomorphology), Faculty of Geography, University of Tehran, Tehran, Iran Iran myamani@ut.ac.ir - - Alireza Arabameri Ph.D Candidate of Geomorpholog, Faculty of Humanities, Department of Geography, Tarbiat Modares University, Tehran, Iran Iran alireza.ameri91@yahoo.com Chah Jam Erg multi attribute decision making (MADM) models Nebka plant species 1. Ahmadi, H. 1998. Applied Geomorphology, second volume wind erosion, University of Tehran.##2. Asgharpour, A.A., 2012. Multi CriteriaDecision Making, Second Edition, University of Tehran.##3. Bellman, R.E., Zadeh, L.A., 1970. Decision-making in a fuzzy environment, Management Sciences, 17, 141–164.##4. Bishop, S.R., Momiji, H., Carretero-Gonzalez, R., Warren, A., 2002. Modelling desert dune fields based on discrete dynamics. Discrete Dynamicsin Nature and Society, 7, 7–17.##5. Boender, C.G.E., Graan, J.G., Lootsma, F.A., 1989. Multi-attribute decision analysis with fuzzy pairwise comparisons, Fuzzy Sets and Systems, 29, 133–143.##6. Chang, Y.H., Yeh, C.H., 2002. A survey analysis of service quality for domestic airlines, European Journal of Operational Research, 139, 166–177.##7. Chen, C.T., 2000. Extensions to the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems, 114- 119.##8. Chen, S.J., Hwang, C.L., 1992. Fuzzy multiple attribute decision making methods and application, in: Lecture Notes in Economics and MathematicalSystems, Springer, New York.##9. Danin, A., 1996. Plants of desert dunes. Springer, 177,136 p.##10. Douseri, A., 1995. 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Multiple Attribute Decision Making: Methods and Application, Springer, New York.##18. Jain, R., 1978. A procedure for multi-aspect decision making using fuzzy sets, The International Journal of Systems Sciences, 8, 1–7.##19. Kacprzyk, J., Fedrizzi, M., Nurmi, H., 1992. Group decision making and consensus under fuzzy preferences and fuzzy majority, Fuzzy Sets andSystems, 49, 21–31.##20. Keeney, R., Raiffa, H., 1976. Decision with Multiple Objective: Preference and Value Tradeoffs, Wiley, New Work.##21. Lee, H.S. 1999. Optimal consensus of fuzzy opinions under group decision making environment, in: IEEE International Conference onSystems, Man and Cybernetics, Tokyo, Japan, 314–319.##22. Liang, G.S., 1999. Fuzzy MCDM based on ideal and anti-ideal concepts, European Journal of Operational Research, 112, 682–691.##23. Mousavi, S.H., Moyari, M., Sayf, A., Vali, A.A., 2012. Selection of the most appropriate plant species for stabilizing of sand dunes using AHP model (Case study: Najarabad Erg. Northeastern of troud, Journal of environmentalogy, 61, 105-116.##24. Nickling, W.G., Wolfe, S.A., 1994. The Morphology and Ongin of Nebkhas, Region of Mopti, Mali, West Africa,Journal of Arid Environments, 28, 13-30.##25. Nurmi, H., 1981. Approaches to collect decision making with fuzzy preference relations, Fuzzy Sets and Systems, 6, 249–259.##26. Olson, D.L., 2004. Comparison of weights in TOPSIS models, Mathematical and Computer Modeling, 40, 21-727.##27. Pradhan, B., 2011. An Assessment of the Use of an Advanced Neural Network Model with Five Different Training Strategies for the Preparation of Landslide Susceptibility Maps. Journal of Data Science, 9, 65-81.##28. Raj, P.A., Kumar, D.N., 1999. Ranking alternatives with fuzzy weights using maximizing set and minimizing set, Fuzzy Sets and Systems, 105, 365–375.##29. Refahi, H., 2004. 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1 - Variation of ionospheric slab thickness over South Africa https://jnec.ut.ac.ir/article_55070.html 1 Ionospheric slab thickness is defined as the ratio of TEC to maximum electron density of the F-region (NmF2), proportional to the square of the F2-layer critical frequency (foF2). It is an important parameter in that it is linearly correlated with scale height of the ionosphere, which is related to electron density profile. It also reflects variation of the neutral temperature. Therefore, ionospheric slab thickness is a significant parameter representative of the ionosphere. In this paper, the International Reference Ionosphere (IRI) model, South African Bottomside Ionospheric Model (SABIM), and measurements from ionosondes in the South African Ionosonde Network were combined within their own limitations to develop a map of foF2 values for the South African region. This parameter and vertical TEC values derived from the map using the IRI model were used to compute ionospheric slab thickness. Finally climatology of the slab thickness is described by diurnal and seasonal variations. 0 - 63 69 - - Mohammad Ali Sharifi Associate Professor, Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, North Kargar Ave, Tehran, Iran Iran sharifi@ut.ac.ir - - Saeed Farzaneh PhD. Candidate, Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, North Kargar Ave, Tehran, Iran Iran farzaneh@ut.ac.ir Fof2 ionospheric slab thickness IRI NmF2 South Africa 1. Bilitza, D., McKinnell, L., Reinisch, B., Fuller-Rowell, T., 2011. The international reference ionosphere today and in the future. J. Geod., 85, 909–920.##2. Bilitza, D., Reinisch, B.W., 2008. International Reference Ionosphere 2007: Improvements and new parameters, Adv. Space Res., 42, 599–609.##3. Bhuyan, P.K., Singh, L., Tyagi, T.R., 1986. Equivalent slab thickness of the ionosphere over 26◦N through the ascending half of a solar cycle, Ann. Geophys., 4, 131.##4. Bhonsle, R.V., Da Rosa, A.V., Garriott, O.K., 1965. Measurement of total electron content and equivalent slab thickness of the mid-latitude ionosphere, Radio Sc., 69D(7), 929.##5. Chuo, Y.J., 2007. The variation of ionospheric slab thickness over equatorial ionization area crest region, J. Atmos. Sol.-Terr. Phys., 69, 947–954.##6. Davies, K., Liu, X.M., 1997. Ionospheric slab thickness in middle and lowlatitudes, Radio Sci., 26(4), 997–1005.##7. Gulyaeva, T.L., Jayachandran, B., Krishnankutty, T.N., 2004. Latitudinal variation of ionospheric slab thickness, Adv. Space Res., 33, 862–865.##8. Huang, Y.N., 1983. Some result of ionospheric slab thickness observations at Lunping, J. Geophys. Res., 88, 5517.##9. Jin, S., Cho, J., Park, J., 2007. Ionospheric slab thickness and its seasonal variations observed by GPS, J. Atmos. Sol.-Terr. Phys., 69, 1864–1870.##10. McKinnell, LA., 2008. SABIM Model Version 3.0: A Bottomside Ionospheric Model for the South African Region. Technical Report for Grintek.##11. McKinnell, L.A., 2002. A neural network based ionospheric model for the bottomside electron density profile over Grahamstown, South Africa, PhD Thesis, Rhodes University.##12. McKinnell, L.A., Poole, A.W.V., 2004. Neural network-based ionospheric modelling over the South African region, South African Journal of Science, 100, 519–523.##13. Okoh, D.I., McKinnell, L.A., Cilliers, P.J., 2010. Developing an ionospheric map for South Africa. Ann. Geophys., 28, 1431–1439.##14. Poole, A.W.V., McKinnell, L.A., 2000. On the predictability of foF2 using neural networks, Radio Sci., 35, 225–234.##

1 - Estimation of the relative active tectonics in Shahriary basin (Central Iran) using geomorphic and seismicity indices https://jnec.ut.ac.ir/article_55072.html 1 Iran is well known for having countless historical and instrumental records of tectonic events. Shahriary catchment, as a study area, is part of the Zagros collision seismic province. This research aimed to introduce a new method which is useful in identifying the relative active tectonic events of an area. The research method was designed in order to calculate the relative active tectonic index (IRAT) using geomorphic and seismicity indices. Accordingly, IRAT was determined based on the river length–gradient index (SL), drainage basin asymmetry (AF), transverse topographic symmetry factor (TP), hypsometric integral (HI) and the drainage basin shape index (BS). The obtained results showed that areas with relatively high, moderate and low tectonic activities comprised 71%, 19% and 10% of the study area, respectively. In addition, the calculated mean seismicity in Shahriary was 4.8±0.2 Ms, with an acceleration gravity of 0.3 g (i.e., a high-risk zone). The resultant data confirmed the ability of seismicity indices to estimate IRATs. Therefore, application of the proposed method for assessing the IRAT of an area is confidently recommended in watershed management planning. 0 - 71 83 - - Alireza Habibi MSc. Researcher, Soil Conservation and Watershed Management Research Institute, Tehran, Iran Iran habibi1354@yahoo.com - - Mohammadreza Gharibreza Assistance Professor, Soil Conservation and Watershed Management Research Institute, Tehran, Iran Iran gharibreza4@yahoo.com geomorphic indices relative active tectonics seismic Shahriary 1. Alipoor, R., Poorkermani, M., Zare, M., El-Hamdouni, R., 2011. Active tectonic assessment around Rudbar Lorestan dam site, High Zagros Belt (SW of Iran). Geomorphology, 128, 1-14.##2. Azor, A., Keller, E.A., Yeats, R.S., 2002. 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Gharibreza, M., Masoumi, H., Habibi, A., 2009. Geology and Geomorphology of Shahriary Catchment. Tehran. Soil Conservation and Watershed Management Research Imstitute, 114.##8. Goorabi, A., 2007. Evidences of Active Geomorphology of Darake Basin. Geography Research, 60, 177-196.##9. Habibollahi, M., 2007. Analysis of Active Aectonic in Zayandehrod Upstream. Geography. Isfahan Isfahan University. M.Sc.##10. Hack, J.T., 1973. Stream-profiles analysis and stream-gradient index. Journal of Research of the U.S, 1(4), 421–429.##11. Hare, P.H., Gardner, T.W. 1985. Geomorphic indicators of vertical neotectonism along converging plate margins. Tectonic Geomorphology. Proceedings of the 15th Annual Binghamton Geomorphology Symposium. Boston, Allen and Unwin.##12. IIEES 1995. Seismic hazard zonation map of Iran. Seismology. Tehran, International Institute of Earthquake Engineering and Seismology.##13. Karami, F., 2009. Geomorphic assessment of the tectonic activity in the drainage basin saydabad chay. Physical Geography research, 69, 67-82.##14. Keller, E.A., Pinter, N. 2002a. Active Tectonics: Earthquakes, Uplift and Landscape. Active Tectonics: Earthquakes, Uplift and Landscape. Newjersey, Prentice Hall.##15. Khatib, M., 2008. Influence of Nehbandan Fault System on Nehbandan City Morphology. Geography & Development, 12, 5-24.##16. Khosravi, G., 2008. Active Tectonic Analysis and Impact on Drainage Network on Khuzestan Plain. Geography. NajafAbad Islamic Azad University. M.Sc.##17. Madadi, A., Rezaiee, M., Rajaiee, H., 2005. Analysis activity Neo- technique using geo-morphological. Geographical Research, 2(48), 123-138.##18. Mahmood, S., Gloaguen, R., 2012. Appraisal of active tectonics in Hindu Kush: Insights from DEM derived geomorphic indices and drainage analysis. Geoscience Frontiers, 3(4), 407-428.##19. Maroukian, H., Gaki-Papana, K., Karymbalis, E., Vouvalidis, K., Pavlopoulos, K., Papanastassiou, 2008. Morphotectonic control on drainage network evolution in thePerachora Peninsula, Greece. Geomorphology, 102, 81–92.##20. Mayer, L., 1990. Introduction to Quantitative Geomorphology. Newjersy, Prentice Hall, Englewood, Cliff.##21. Mohajer-Ashjai, A., Nowroozi, A.A., 1979. The Tabas earthquake of September 16, 1978 in eastcentral Iran: a preliminary field report. Geophysics Research Letters, 6, 689-692.##22. Mokhtari, D., 2005. Morph metric parameters used in determining the amount of activity faults (A case study: North fault Mishoo). Earth Sciences, 59, 70-83.##23. Molin, P., Pazzaglia, F.J., Dramis, F., 2004. Geomorphic expression of active tectonics in a rapidly-deforming forearc, sila massif, Calabria, southern Italy. American Journal of Science, 304, 559–589.##24. Negaresh, H., 2003. Earthquake, Cites, and Faults. Geography Researches, 1, 1-18.##25. Nowroozi, A.A., 1985. Empirical relations between magnitudes and fault parameters for earthquakes in Iran. Bulletin of the Seismological Society of America (BSSA), 75, 1327-1338.##26. Paul, A., Hatzfeld, D., Kaviani, A., Tatar, M., Péquegnat, C., 2010. Seismic imaging of the lithospheric structure of the Zagros mountain belt (Iran). Geomorphology, 330, 5-18.##27. Pike, R.J., Wilson, S.E., 1971. Elevation–relief ratio, hypsometric integral and geomorphic area-altitude analysis. Geological Society of America Bulletin, 82, 1079–1084.##28. Ramírez-Herrera, M., 1998. Geomorphic assessment of active tectonics in the Acambay Graben, Mexican volcanic belt. EarthSurface Processes and Landforms, 23, 317–332.##29. Silva, P.G., Goy, J.L., Zazo, C., Bardajm, T., 2003. Fault generated mountain fronts in Southeast Spain: geomorphologic assessment of tectonic and earthquake activity. Geomorphology, 250, 203–226.##30. Strahler, A.N., 1952. Hypsometric (area-altitude) analysis of erosional topography. 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1 - Impacts of climate change on extreme precipitation events in arid (Bandar Abbas) and semi-arid (Shahrekord) stations in Iran https://jnec.ut.ac.ir/article_55071.html 1 The aim of this paper is to project extreme precipitation events in an arid and a semiarid station. In order to project climate change based on general circulation models (GCMs), we have applied LARS-WG[1] downscaling tool. This stochastic weather generator down-scaled the climate of two synoptic stations using HADCM3 model and A2 emission scenario for 2040. We extracted extreme precipitation events, as daily 90th and 10th percentile for rainy days (considered if daily precipitation was greater than 1 mm), for based and projected data. The research outcomes showed an increase both in 90th percentile by 13 mm and in 10th percentile by 0.2 mm in arid station, Bandar Abbas. In the semiarid station, Shahrekord, the 90th percentile precipitation increased by 6.1 mm and the 10th percentile precipitation decreased by 3.4 mm. In total, for both stations, 90th percentile precipitations showed a more stable trend than the 10th percentile. 1. Long Ashton Research Station-Weather Generator 0 - 85 94 - - Zahra Jamali MSc Graduated on Combatting Desertification, Hormozgan University, Bandar Abbas, Iran Iran zahra_jamali1386@yahoo.com - - Asadollah Khoorani Assistance Professor, Geography Department, Hormozgan University, Bandar Abbas, Iran Iran khoorani@hormozgan.ac.ir 10th percentile 90th percentile Hadcm3 LARS_WG RMSE 1. Asakereh, H., 2012. Frequency Distribution Change of Extreme precipitation in Zanjan City. Geography and Environmental Planning Journal, vol. 45, No.1, 13-18 pp.##2. Benestad, S., Rasmus. E., 2006. Can we expect more extreme precipitation on the monthly time scale? Journal of Climate, Vol. 19: 630 637.##3. Becker. S., Hartmann. H., Zhsng. Q., Wu. Y., Tiang. T., 2007. Cyclicityanalysis of Precipitation regimes in the Yangtze River Basin, China. Int. J. Climatol. 28: 579–588.##4. Bartolini, G., Morabito, M., Crisci, A., Grifoni, D., Torrigianitonmaso, P., Martina, M.G., and Orlandini, S., 2008. Recent trends in orlandini. Simon 2008, Recent trends in indices of extremes. International Journal of Climatology. 28:1751-1760.##5. Alijani. B., O’Brien, J., Yarnal, B., 2007. Spatial analysis of precipitation intensity and concentration in Iran, Theor. Appl. Climatol. 94: 107-124.##6. Cryer, J.D., Chan, K.S., 2008. Time Series Analysis, Springer Texts in Statistics.##7. IPCC. 2007. Climate Change- the scientiﬁc basis. IntergovernmentalPanel on Climate Change. Cambridge University Press: Cambridge.##8. IPCC- TGCIA- Carter, T.R., Alfsen, K.E.B., Dai, P., Desanker, S.R., Gaffin, F., Giorgi, M., Hulme, M., Lal, L.J., Mata, L.O., Mearns, J.F.B., Mitchenll, T., Morit, R., Moss, D., Murdiyarson, J.D., Pabon-Caicedo, J., Palutikof, M.L., Parry, C., Rosenzweig, B., Seguin, R. Scholes, J., Whetton, P.H. 2007. Generate Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment, Intergovermental Panel on Climate Change, Task Group on Scenarios for Climate Impact Assessment, Version2: 66 pp.##9. IPCC, 2002. IPCC Workshop on Changes in Extreme Weather and Climate Events. Beijing, China, IPCC, 107 pp.##10. Racsko, P., Szeidl, L., Semenov, M., 1991. A serial approach to local stochastic weather models. Ecological Modeling 57, 27–41.##11. Rahimzadeh, F., Asgari, A. Fattahi, 2009. Variability of extreme temperature and precipitation in Iran during recent decades. Int. J. Climatol. 29: 329-343.##12. Semenov, M.A., Barrow, E.M. 1997. Use of a stochastic weather generator in the development of climate change scenarios. Climate Change 35, 397–414.##13. Sajjad Khan, M., Coulibaly, P., Dibike, Y., 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology 319, 357–382.##14. Semenov M.A., Brooks R.J., Barrow E.M., Richardson C.W. 1998. Comparison of the WGEN and LARS-WG stochastic weather generators in diverse climates. Climate Research10, 95-107.##15. Salon, S., Cossarini, G., Libralato, S., Gao, X., Solidoro, S., Giorgi, F., 2008. Downscaling experiment for the Venice lagoon. I. Validation of the present-day precipitation climatology. Clim Res 38:31–41.##16. SenRoy, Sh., Balling, J.R., Robert, C. 2004. Trends in extreme daily precipitation indices in India. Int. J. Climatol.24: 457-466.##17. Lane, M.E., kirshen P.H., Vogel R.M. 1999. Indicators of impact of global climate change on U.S water resources. ASCE, journal of Water Resource Planning and Management.125(4): 194-204.##18. Wilby, R.L., Wigley T.M.L., Conway D., Jones P.D., Hewitson B.C., Main J., Wilks D.S. 1998. Statistical downscaling of General Circulation Model output: A comparison of methods. Water Resources Research, 34, 2995–3008.##