Assessment of Climate Change Impacts on Groundwater Recharge for Different Soil Types-Guelph Region in Grand River Basin, Canada 2 2 Background: Global warming and climate change are widely indicated as important phenomena in the 21st century that cause serious impacts on the global water resources. Changes in temperature, precipitation and evaporation are occurring in regions throughout the world, resulting in changes including, runoff, streamflow and groundwater regimes, reduced water quantity and quality. Materials and Methods: Relying upon thirty years of base data (1965–1994), three global circulation models (GCM), namely GISS, GFDM and CCC, are utilized to assess impact of climate change to groundwater recharge rates between years 2010 to 2050 for the Guelph region of the Grand River Basin in Canada. The resulting groundwater recharge rates for alternative soil layers are used to assess water balance conditions, and ultimately, the percolation rate to the groundwater using the Visual-HELP model. Results: While the climate change impact assessment indicates that evaporation will increase and percolation will decrease during summer, increased percolation is indicated in winter due to additional freeze/thaw dimensions of climate change. The net effect is that the impact of climate change, based upon use of GCM models, is expected to increase groundwater recharge rate by 10% on average (7% for CCC, 10.6% for GISS and 12% for GFDM) in future. Discussion and Conclusions: According to the results of this research in the Guelph region, the monthly average percolation rate is higher with climate change; (i) the percolation rate is increased during winter due to freeze/thaw effects, while (ii) it is decreased during summer due to higher evaporation rate. 1 - 1731 1744 - - Homayoon Motiee Assistant Professor, Department of Water Engineering, Water and Environment Faculty, Shahid Beheshti University, Tehran, Iran Assistant Professor, Department of Water Iran h_motiei@sbu.ac.ir - - Edward McBean Professor of Water Resources, School of Engineering, University of Guelph, Guelph, Canada Professor of Water Resources, School of Engineerin Canada emcbean@uoguelph.ca Climate change GCM models Grand River Basin Groundwater Guelph Visual-HELP IPCC, Climate Change 2014: Impacts, adaptation and vulnerability. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge university press, Cambridge, United Kingdom, 2014; p. 688. ##Zierl B, Bugmann H. Global change impacts on hydrological processes in Alpine catchments. Wat Res Res. 2005; 41(2): 1-13. ##Penner JE, Lister D, Griggs DJ, Dokken DJM, Farland M. IPCC report about aviation and global atmosphere. ((Eds.), Cambridge university press, UK, 2014; p. 373. ##UNFCCC, Climate change: impact, vulnerabilities and adaptation in developing countries, United Nations framework convention on climate change (UNFCCC) press, 2010; p. 68. ##Goodarzi E, Dastorani M, Massah Bavani A, Talebi A. Evaluation of the Change-factor and LARS-WG methods of downscaling for simulation of climatic variables in the future (Case study: Herat Azam Watershed, Yazd - Iran), J Ecop. 2015; 3 (1): 833-846. ##Feizi V, Mollashahi M, Frajzadeh M, Azizi G. Spatial and Temporal trend analysis of temperature and precipitation in Iran. J Ecop. 2014; 2 (4): 727-742. ##McBean E, Motiee H, Assessment of impact of climate change on water resources: a long term analysis of the Great Lakes of North America, J. Hydrol. Earth Syst Sci. 2008; (12): 239-255. ##Maathuis H, Thorleifson LH. Potential impact of climate change on Prairie groundwater supplies: Review of current knowledge. Saskatchewan Research Council, Publication No. 11304-2E00, 2000; p. 93. ##Hansen J, Ruedy R, Glascoe J, Sato M. 19: GISS analysis of surface temperature change. J. Geo, Res., 1999; (104): 30997-31022. ## Manabe S, Stouffer RJ. Study of abrupt climate change by a coupled ocean-atmosphere model, Quarternary Science Reviews, 2001; (19): 285-299. ## Boer GJ, Lambert SJ. Multi-modeldecadalpotentialpredictabilityofprecipitationandtemperature, Geophys Res Lett. 2008; 35: L05706. ## Hengeveld HG. Projections for Canada’s climate future: a discussion of recent simulations with the Canadian global climate model, Environment Canada, 2000; p. 27. ## Mortsch L, Allen M, Klaassen J. Development of climate change scenarios for impact and adaptation studies in the Great Lakes – St. Lawrence Basin, International Joint Communication Report Environment Canada; 2005. ## Kulshreshtha S, Wheaton E. Climate change adaptation and food production in Canada: Some research challenges. WIT Trans Ecol Environ. 2013; 170: 101-112. ## Smith JV, Mc Bean E. The impact of climate change on surface water resources, Chapter in The Impact of Climate Change on Water in the Grand River Basin, Ontario, University of Waterloo. Waterloo, Ontario 1993; 25-52. ## Jyrkama MI, Sykes JF. The impact of climate change on spatially varying groundwater recharge in the Grand river watershed (Ontario), J Hydrol. 2007; 338: 237-250. ## Croley TE, Luukkonen CL. Potential effects of climate change on ground water in Lansing, Michigan. J Am Water Resour As. 2003; 39 (1): 149–163. ## Allen DM, Mackie DC, Wei M. Groundwater and climate change: a sensitivity analysis for the Grand Forks aquifer, southern British Columbia, Canada. Hydrogeol J. 2004; 12: 270-290. ## Dragoni W, Sukhija BS, Climate Change and Groundwater, Geological Society, London, Special Publications, 2008; 288: p. 1-12. ## Grand River Conservation Authority (GRCA), Low water response - areas of concern - Mill Creek., http://www.grandriver.ca/LowWater/mill.cfm, 2002. ## Grand River Conservation Authority (GRCA), state of the watershed report: Background Report on the Health of the Grand River Watershed 1996-97. Cambridge, Ontario. 1998; p. 143. ## Wood AW, Leung LR, Sridhar V, Lettenmaier DP.: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Climatic Change, 2004; 62: 189-216. ## Xu Z, Yang ZL. An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations. J Climate. 2012; 25: 6271-6286. ## Diaz-Nieto J, Wilby RL. A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom. Climatic Change. 2005; 2(3): 245-268. ## Anandhi A, Frei A, Pierson DC, Schneiderman EM, Zion MS, Lounsbury D, et al. Examination of change factor methodologies for climate change impact assessment, Wat Res Res. 2011; 47(3): p. W03501. ## Waterloo Hydrologic Inc. (WHI). User’s manual of Visual HELP, 2001; p. 335. ## Brooks RH, Corey AT, Hydraulic properties of porous media. Hydrology Papers, No. 3, Colorado State U., Fort Collins, Colorado, 1964; p. 27. ## Schroeder PR, Dozier TS, Zappi PA., McEnroe BM, Sjostrom JW, Peyton RL. The hydrologic evaluation of landfill performance (HELP) model: Engineering documentation for version 3. Environmental Protection Agency (EPA), 1994; p. 168. ## Berger K, The hydrologic evaluation of landfill performance (HELP) model, Institute of Soil Science, Hamburg, Germany, https://www.geo.uni-hamburg.de/en/bodenkunde/service/help-model.html; 2014. ## Presant EW, Wicklund RE, The soils of waterloo county, Report No 44 of the Ontario Soil Survey, Department of Soil Science, University of Guelph and The Ontario Department of Agricultural and Food, 1971; p. 104. ##

Land Use Planning Using a Quantitative Model and Geographic Information System (GIS) In Sistan Region, Iran 2 2 Background: Land use planning is a science that determines the type of land use through studying the ecological and socio-economic characteristics of the land. Materials and Methods: A systematic method known as the Makhdoom Model was used for the analysis of maps to evaluate the land use and natural resources for future sustainable land planning of an area in Sistan region, using GIS as a tool. For this purpose, the ecological capability maps of different land uses, including forest and range, agriculture, ecotourism, rural and urban development were initially prepared by overlaying geographical maps in GIS for the study area. Then, the prioritization of land uses was assessed using a quantitative model by considering the ecological and socio-economic characteristics of the study area. Results: The results indicated that the maximum area of the proposed uses (28.7%) was related to conservation, showing this land use had high potential in the study area. Also, the minimum area of proposed uses was related to dry farming. Discussion and Conclusions: This research proved that quantitative methods can be more useful than classic methods (qualitative). 1 - 1745 1759 - - Masoud Masoudi Associate Professor, Department of Natural Resources and Environment, Shiraz University, Iran Associate Professor, Department of Natural Iran masoudi@shirazu.ac.ir - - hamidreza jahantigh Former M.Sc. Student, Department of Natural Resources and Environment, Shiraz University, Iran Former M.Sc. Student, Department of Natural Iran hrjahantigh@gmail.com - - Parviz Jokar Former M.Sc. Student, Department of Natural Resources and Environment, Shiraz University, Iran Former M.Sc. Student, Department of Natural Iran parvizjokar2012@gmail.com Land use planning Modified GIS Sistan region Food and Agriculture Organization of the United Nations (FAO). Guidelines for Land Use Planning. Soil Resources, Management and Conservation Service, Rome.1993; p. 96. ##Makhdoom, M. Fundamental of Land Use Planning. Tehran University Press, Tehran. 2001; p. 289. (In Persian). ##National Environment Management Authority (NEMA). Land use and Environment, Report on the state of the Environment in Kenya. A publication of the National Environment Management Authority (NEMA), Kenya. 2004; p. 176. ##Jafari R, Bakhshandehmehr L. Quantitative mapping and assessment of environmentally sensitive areas to desertification in central Iran. Land Degrad. Develop. 2013; 27: 108-119. ##Barzani M, Khairulmaini OS. Desertiﬁcation risk mapping of the Zayandeh Rood Basin in Iran. J Earth Syst Sci. 2013; 122: 1269-1282. ##Nouri J, Sharifipour R. Ecological Capability Evaluation of Rural Development by Means of GIS. Iran J Environ Health Sci Eng. 2004; 1: 81-90. ##Pauleit S. Duhme F. GIS Assessment of Munich’s Urban Forest Structure for Urban Planning. J Arboric. 2000; 26: 133-141. ##Swanson E. Geographic information system (GIS) information enhanced land use planning. Michigan Center for Geographic Information Department of Information Technology. 2003. ##Bojo´rquez-Tapia L, Di´Az-Mondrago´ NS, Ezcurra E. GIS-based approach for participatory decision making and land suitability assessment. Int J Geogr Info Sci. 2001; 15: 129-151. ## Biswas A. Baran PB. Application of fuzzy goal programming technique to land use planning in agricultural system. Omega. 2005; 33: 391-398. ## Gandasasmita K, Sakamoto K. Practical application of a land resources information system for agricultural landscape planning. Landscape Urban Plan. 2007; 79: 38-52. ## Peel D, Lloyd M. Neo-traditional planning. Towards a new ethos for land use planning? Land Use Policy. 2007; 24: 396-403. ## Yousefi-Sahzabi A, Sasaki K, Djamaluddin I, Yousefi H, Sugai Y. GIS modeling of CO2 emission sources and storage possibilities. Energy Procedia. 2011; 4: 2831-2838. ## Feizizadeh B, Blaschke T. Land suitability analysis for Tabriz County, Iran: A multi-criteria evaluation approach using GIS. J Environ Plann Manage. 2013; 56: 1-23. ## Oyinloye M, Kufoniyi O. Application of IKONOS Satellite Images in Monitoring of Urban Land use Change in Ikeja, GRA, Lagos, Nigeria. IJESI. 2013; 2: 1-10. ## Santra A. Role of Remote Sensing in Forest Carbon Sequestration with Special Emphasis on Fossil Fuel Combustion. In: Kumar, R. (ed.) Fossil Fuels: Sources, Environmental Concerns and Waste Management Practices. New York, Nova Science Publishers, Inc. 2013. ## Masoudi M. Risk assessment and remedial measures of land degradation in parts of Southern Iran. Lambert Academic Publishing (LAP), Germany. 2010. ## Abu Hammad A, Tumeizi A. Land degradation: socioeconomic and environmental causes and consequences in the Eastern Mediterranean. Land Degrad Dev. 2010; 23: 216-226. ## Bocco G, Mendoza M, Velazquez A. Remote sensing and GIS-based regional geomorphological Mapping-a tool for land use planning in developing countries. Geomorphology. 2001; 39: 211-219. ## Prato T. Evaluating land use plans under uncertainty. Land Use Policy. 2007; 24: 165-174. ## Food and Agriculture Organization of the United Nations (FAO). A framework for land evaluation. Soils Bulletin 32, Soil resources development and conservation service land and water development division, Rome. 1976; p. 87. ## Amiri MJ, Salman Mahini A, Jalali SGH, Hosseini SM, Azari Dehkordi F. A Comparison of Maps Overlay Systemic Method and Boolean-Fuzzy Logic in the Ecological Capability Evaluation of No.33 and 34 Watershed Forests in Northern Iran. Environ Sci. 2010; 7: 109-124. (In Persian). ## Baja S, Chapman DM, Dragovich D. A conceptual model for defining and assessing land management units using a Fuzzy modeling approach in GIS Environment. Environ Manage. 2006; 29: 647-661. ## Babaie Kafaky S, Mataji A, Ahmadi Sani N. Ecological Capability Assessment for Multiple-Use in Forest Areas Using GIS-Based Multiple Criteria Decision Making Approach. Am J Environ Sci, 2009; 5: 714-721. ## Espejel I, Fischer DW, Hinojosa A, GarcõÂa C, Leyva C. Land-use planning for the Guadalupe Valley, Baja California, Mexico. Landscape Urban Plan. 1999; 45: 219-232. ## Pierce SM, Cowling RM, Knight AT, Lombard AT, Rouget M, Wolf T. Systematic conservation planning products for land-use planning: Interpretation for implementation. Biol Conserv. 2005; 125: 441-458. ## Jokar P. Mapping of land use planning based on modification and quantitative method of current model. (A case study: Jahrom Township). M.Sc. Thesis, Faculty of Agriculture, Shiraz University, 2015. ## Asadifard E. Landuse Planning in Firuzabad Township Based on Modifying Method of Current Model Using GIS. M.Sc. Thesis, Faculty of Agriculture, Shiraz University, 2015. ## Razaghi S. Assessment and comparison of land use planning in Sepidan region using models of Makhdom, MCE and EMOLUP. M.Sc. Thesis, Faculty of Agriculture, Shiraz University, 2016. ## Masoudi M, Jokar P. Suggestion the Proposed Model of EMOLUP, with New Approach in Land Use Planning (Step Two: Prioritizing for Different Land Uses). Environ Sci. 2016; 14(2): 23-36. (In Persian). ##

Heavy Metals Assessment of Surface Sediments in Mighan Wetland Using the Sediment Quality Index 2 2 Background: Sediments are integral part of wetlands providing a valuable key to recognize heavy metal fluctuations in the past. Materials and Methods: The surface sediment samples were taken from thirteen sites, then prepared and digested with percholoric acid and nitric acid at 1:4 ratio, followed by flame atomic absorption spectrophotometry analysis. Results: The average of total metal concentration in 13 sites were found to be 9.182, 9.514, 45.351 and 43.456 µg g-1 for Pb, Zn, Cu and Ni, respectively. Also, comparison of sediment quality indices, including contamination factor (Cf), contamination degree (Cd), and modified contamination degree (mCd)) showed that Cu contamination was significantly different from the other heavy metals, while Ni contamination was average, and Pb and Zn contaminations were low. Discussion and Conclusions: This research confirms that the Mighan wetland is polluted with heavy metals and their excessive accumulation in sediments. 1 - 1761 1770 - - Samar Mortazavi Department of Environmental Sciences, Malayer University Department of Environmental Sciences, Malayer Iran mortazavi.s@gmail.com - - Faezeh Saberinasab M.Sc. Student of Environmental Science, Malayer University, Malayer, Iran M.Sc. Student of Environmental Science, Malayer Iran saberinasabfaezeh@yahoo.com Hakanson index Mighan wetland Sediment index Ghrefat H, Yusuf N. Assessing Mn, Fe, Cu, Zn, and Cd pollution in bottom sediments of Wadi Al-Arab Dam, Jordan. Chemosphere. 2006; 65: 2114-2121. ##Ogunfowokan A, Oyekunle J, Olutona G, Atoyebi A, Lawal A. Speciation study of heavy metals in water and sediments from Asunle River of the Obafemi Awolowo University, Ile-Ife, Nigeria. Int J Environ Prot. 2013; 3(3): 6-16. ##Meiggs TO. The use of sediment analysis in forensic investigations and procedural requirements for such studies. Contaminants Sediments. 1980; 1: 297-308. ##Mansoori J, Khorasani N. Wetlands,the value of their on human. JES. 1985; 13(13): 63-100. (In Persian). ##Hajihossaini M, Varvani J, Abdi N. Compared of heavy metal concentration (Al, Pb, Zn, Ni) in surface sediment of Mighan wetland. Natl. Conf. Environment planning. Hamedan Iran. 2013. (In Persian). ##Majidi F, Hajihossaini M, Samaee A. Comprehensive Management Plan ecological of Mighan wetland. Head office of environmental protection in Markazi province.Markazi Environmental Protection Pub. 2014; p. 24. (In Persian). ##Algan O, Cagatay N, Sarikaya HZ, Balkis N, Sari E. Pollution monitoring using marine sediments, A case study on the Istanbul Metropolitan Area. Turkish J Eng Environ Sci. 1999; 23: 39-48. ##Hamzeh MA, Mahmudi Gharaie MH, Baskaleh G. Geochemical Investigation on the Sources and Influences of Heavy Metal Pollution in Fishing Harbours of Ramin and Beris. JOC. 2014; 5 (17): 21-31. (In Persian). ##Heidarian L, Dezfolinejad F, Sharifi S. Heavy metlas pollution in Gharasoo river. Conf. Environmental Engineering. Tehran. Iran. 2009. (In Persian). ## Kheirvar N, Dadollahi A. Heavy metlas concentrations in sediment and Shirbot fish (Barbusgrypus) in Arvandrood. Environ Sci Technol. 2010; 12(2): 123 - 131. ## Kadhum SA, Ishak MY, Zulkifli SZ, Hashim RB. Evaluation of the status and distributions of heavy metal pollution in surface sediments of the Langat River Basin in Selangor Malaysia. Mar Pollut Bull. 2015; 101: 391-396. ## Cheng Q, Wang R, Huang W, Wang W, Li X. Assessment of heavy metal contamination in the sediments from the Yellow River wetland National Nature Reserve (the Sanmenxia section), China. Environ. Sci Pollut Res Int. 2015; 22: 8586-8593. ## Bahador M, Moradi A, Naji A, Dehghani M. Anthropogenic impacts on heavy metals (Pb, Ni, Zn and Fe) concentration in surface sediments of Shoor River Estuary, Bandar Abbas. J Aqu Eco. 2015; 5 (2): 38-48. (In Persian). ## Mortazavi S, Attaeian B, Abdolkarimi S. Risk assessment and environmental geochemistry of Pb, Cu and Fe in surface sediments (case study: Hashilan wetland, Kermanshah, Iran). ECOPERSIA. 2016; 4(2): 1413-1426. ## Davoodpoor R, Hadian N. Birds of Mighan wetland in Arak. Sahi Pub. 2012; p. 160. (In Persian). ## Dalman O, Demirak A, Balc A. Determination of heavy metals (Cd, Pb) and trace elements (Cu, Zn) in sediments and fish of the Southeastern Aegean Sea (Turkey) by atomic absorption spectrometry. Food Chem. 2006; 95: 157-162. ## Ismail A. Heavy metal concentrations in sediments off Bintulu, Malaysia. Mar Pollut Bull. 1993; 26: 706-707. ## Naji A, Ismail A. Sediment quality assessment of Klang Estuary, Malaysia. Aquat Ecosyst Health Manag. 2012; 15: 287-293. ## Turekian KK, Wedepohl KH. Distribution of the elements in some major units of the earth's crust. Geol Soc Am Bull. 1961; 72: 175-192. ## Hakanson L. An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res. 1980; 14: 975-1001. ## Hafizur Rahman S, Khanam D, Mehedi Adyel T, Shahidul Islam M, Aminul Ahsan M, Ahedul Akbor M. Assessment of heavy metal contamination of agricultural soil around Dhaka Export Processing Zone (DEPZ) (Bangladesh): Implication of Seasonal Variation and Indices. Appl Sci. 2012; 2(3): 584-601. ## Abrahim G. Holocene sediments of Tamaki Estuary, Characterisation and impact of recent human activity on an urban estuary in Auckland, New Zealand. Thesis (PhD-Geology), University of Auckland. 2005. ## Sabzalizade S, Dehghan Madiseh S. Quantify heavy metal pollution in the Life-Bosif region (the Northwest of Persian Gulf) according to the enrichment factor in land. ISFJ. 2010; 19: 51-60. (In Persian). ## Bagheri H, Darvish Bastami K, Sharmad T, Bagheri Z. Assessment of heavy metal distribution in the Gorgan Bay. JOC. 2012; 3 (11): 65-72. (In Persian). ## Saeedi M, Li L, Karbassi A, Zanjani A. Sorbed metals fractionation and risk assessment of release in river sediment and particulate matter. Environ Monit Assess. 2013; 185: 1737-1754. ## Khosravi M, Bahramifar N, Ghasempour M. Survey of Heavy Metals (Cd, Pb, Hg, Zn and Cu) Contamination in Sediment of Three Sites Anzali Wetland. IJHE. 2011; 4 (2): 223-232. (In Persian). ## Jain C. Metal fractionation study on bed sediments of River Yamuna, India. Water Res. 2004; 38: 569-578. ## Karimi M, Ghasempoorshirazi S. Geochemical distribution and heavy metal pollution levels in the Kor river (south of marvdasht). Applied Geology. 2012. 8: 133-145. (In Persian). ## Ghazban F, Zare Khosh Eghbal M. Source of Heavy Metal Pollutions in the Sediments of the Anzali Wetland in Northern Iran. JES. 2011; 57(37): 1-12. ##

Digital Mapping of Topsoil Salinity Using Remote Sensing Indices in Agh-Ghala Plain, Iran 2 2 Background: Soil salinization is a world-wide land degradation process in arid and semi-arid regions that leads to sever economic and social consequences. Materials and Methods: We analyzed soil salinity by two statistical linear (multiple linear regression) and non-linear (artificial neural network) models using Landsat OLI data in Agh-Ghala plain located in north east of Iran. In situ soil electrical conductivity (EC) of 156 topsoil samples (depth of 0-15cm) was also determined. A Pearson correlation between 26 spectral indices derived from Landsat OLI data and in situ measured ECs was used to apply efficient indices in assessing soil salinity. The best correlated indices such as blue, green and red bands, intensity indices (Int1, Int2), soil salinity indices (Si1, Si2, Si3, Si11, Aster-Si), vegetation Indices (NDVI, DVI, RVI, SAVI), greenness and wetness indices were used to develop two models. Results: Comparison between two estimation models showed that the performance of ANN model (R2=0.964 and RMSE=2.237) was more reliable than that of MLR model (R2=0.506 and RMSE=9.674) in monitoring and predicting soil salinity. Out of the total area, 66% and 55.8% was identified as non-saline, slightly and very slightly saline for ANN and MLR models, respectively. Conclusions: This shows that remote sensing data can be effectively used to model and map spatial variations of soil salinity. 1 - 1771 1786 - - Seyedeh Zohreh Mousavi Ph.D Student of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran Ph.D Student of Watershed Engineering, Faculty Iran zmousavi2006@gmail.com - - Mahmood Habibnejad Professor of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran Professor of Watershed Engineering, Faculty Iran - - Ataollah Kavian Associate Professor of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran Associate Professor of Watershed Engineering, Iran - - Karim Solaimani Professor of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran Professor of Watershed Engineering, Faculty Iran solaimani2001@yahoo.co.uk - - Farhad Khormali Professor of Pedology, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran Professor of Pedology, Faculty of Water and Iran khormali@yahoo.com Artificial Neural Network Electrical conductivity Landsat OLI data Multiple linear regression Iran Akhtar A, Shahbaz Kh, Nisar H, Munir AH, Suad A. Characterizing soil salinity in irrigated agriculture using a remote sensing approach, Phys Chem Earth. 2013; 1-10. ##Akramkhanov A, Vlek PLG. The assessment of spatial distribution of soil salinity risk using neural network. Environ Monit Assess. 2012; 184: 2475-2485. ##Allbed A, Kumar L, Sinha P. Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques. J. Remote Sens. 2014; 6: 1137-1157. ##Amini M, Afyuni M, Fathianpourb N, Khademi H, Fluchler H. Continuous soil pollution mapping usingfuzzy logic and spatial interpolation. Geoderma. 2005; 124: 223-233. ##Aquastat. FAO's Information System onWater and Agriculture. 2016 [WWWDocument]. ##Asfaw E, Suryabhagavan KV, Argaw M. Soil salinity modeling and mapping using remote sensing and GIS: the case of Wenjisuger cane irrigation farm, Ethiopia. J Saudi Soc Agricul Sci. 2016; Accepted Manuscript. ##Baret F, Guyot G. Potentials and limits of vegetation induces for LAI and APAR assessment. Remote Sens Environ. 1991; 535: 161-173. ##Bierwirth PN, Brodie RS. Gama-ray remote sensing of Aeolian salt sources in the Murray-Darling Basin, Australia. Remote Sens. Environ. 2008; 112: 550-559. ##Bihamta MR, Zare-Chahouki A. Principales of statistics for the natural resources science. Tehran: Tehran University; 2008. (In persian). ## Buaziz M, Matschullat J, Gloaguen R. Improve remote sensing detection of soil salinity from a semi-arid climate in northeast Brazil. C.R. Geosci. 2011; 343: 795-803. ## Cai S, Zhang R, Liu L, Zhou DA. Method of salt-affected soil informationextraction based on a support vector machine with texture features. Math Comput Model. 2010; 51 (11-12): 1319-1325. ## Chang YM, Chang LC, Chang FJ. Comparison of static feedforward and dynamic-feedback neural networks for rainfall runoff modeling. J Hydro. 2004; 290: 297-311. ## Chuangye S, Hangxu R, Chong H. Estimating soil salinity in the Yellow River Delta, eastern China-An integrated approach using spectral and terrain indices with the Generalized Additive Model Pedosphere. 2016; 26 (5): 626-635. ## Clevers JGPW. The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sens Environ. 1988; 25 (1): 53-70. ## Clevers JGPW. The application of a weighted infrared vegetation index for estimating leaf area index by correcting soil moisture. Remote Sens Environ. 1989; 29: 23-37. ## Colombani N, Mastrocicco M, Giambastiani BMS. Predicting salinization trends in a lowland coastal aquifer: Comacchio (Italy). Water Resour Manag. 2015; 29: 603-618. ## Daliakopoulos IN,Tsanis IK, Koutroulis A, Kourglalas NN, Varouchakis AE, The threat of soil salinity: A European scale review. Sci Total Environ. 2016; 573: 727-739. ## Demuth H, BealeM. Neural Network Toolbox for use with MatLab.User's guide version 4. 2004. The Math Works, Inc. 3 Apple Hill Drive, Natick, MA 01760-2098. ## Douaoui AEK, Nicolas H, Walteer Ch. Detecting salinity hazards within a semiarid context by means of combining soil and remote sensing data. Geoderma. 2006; 134: 217-230. ## Draper NR, Smith H. Applied Regression Analysis, third ed. New York: John Wiley & Sons, INC; 1998. ## Farifteh J, Farshad A, George RJ, Assessing salt-affected soils using remotesensing, solute modelling, and geophysics. Geoderma. 2006; 130 (3-4): 191-206. ## Farifteh J, Vander MF, Atzberger C, Carranza EJM. Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptivemethods (PLSR and ANN). Remote Sens Environ. 2007; 110 (1): 59-78. ## Fernandez-Buces N, Siebe C, Cram S, Palacio JL. Mapping soil salinityusing a combined spectral response index for bare soil and vegetation: a casestudy in the former lake Texcoco, Mexico J Arid Environ. 2006; 65 (4):644-667. ## Ghassemi F, Jakeman AJ, Nix HA. Salinization of Land and Water Resources: Human Causes, Extent, Management and Case Studies. The Australian National University/CAB International, Canberra, Australia/Wallingford, Oxon, UK. 1995. ## Goldshleger N, Chudnovsky A, Ben-BinyaminR. Predicting salinity in tomato using soil reflectance spectral. Int J Remote Sens. 2013; 34: 6079-6093. ## Gorji T, Sertel E, Tanik A. Manitoring soil salinity via remote sensing technology under data scarce conditions: a case study from Torkey. Ecol Indicators. 2017; 74: 384-391. ## Haykin S. Neural networks: A comprehensive foundation. 2nd ed. New Jersey: Prentice–Hall Inc. 1999. ## Huete AR, Jackson RD, Post DF. Spectral response of a plant canopy with different soil backgrounds. Remote Sens Environ. 1985; 17: 37-53. ## Irons JR, Dwyer JL, Barsi JA. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sens Environ. 2012; 122: 11-21. ## JafariHaghighi M. Methods for soil analysis. Tehran: NedayeZoha; 2003. (In Persian). ## Khan NM, Rastoskuev VV, Shalina E, Sato Y. Mapping salt-affected soil using remote sensing indicators, A simple approach with the use of GIS Idrisi. 22nd Asian Conference on Remote Sensing, 5-9 November 2001, Singapore. ## LiD J, Man-chun W, Tivip T. Study on soil salinization information in arid region using remote sensing technique. Agr Sci China. 2011; 10 (3): 404-411. ## Li S, Chen X. A new bare-soil index for rapid mapping developing areas using landsat8 data. The International Archives of the Photogrammetry, Remote Sens. Spatial Inform. Sci. volume XL-4. ISPRS Technical Commission IV Symposium, Suzhou, China This contribution. 14- 16 May 2014. ## Metternicht GI. Categorical fuzziness: a comparison between crisp and fuzzyclassboundarymodelling for mapping salt-affected soils using Landsat TM data and a classification based on anion ratios. Ecol Model. 2003; 168 (3): 371-389. ## Metternicht GI, Zinck JA. Remote sensing of soil salinity: potentials and constraints. Remote Sens Environ. 2003; 85: 1-20. ## Noroozi AA, Homaee M, Farshad A. Integrated application of remote sensing andspatial statistical models to the identification of soil salinity: a case study fromGarmsarPlain, Iran J Environ Sci. 2012; 9: 59-74. ## Qu YH, Duan XL, Gao HY, Chen AP, An YQ, Song JL, Zhou HM, He T. Quantitative retrieval of soil salinity using hyperspectral data in the region ofInner Mongolia Hetao irrigation district. Spectrosc Spect Anal. 2009; 29(5):1362-1366. ## Rengasamy P. World salinization with emphasis on Australia. J Exp Bot. 2006; 57: 1017-1023. ## Richardson AJ, Wiegand CL. Distinguishing vegetation from soil background information. Eng Remote Sens. 1997; 43: 1541-1542. ## Rouse JW, Haas RH, Schelle JA, Deering DW, Harlan JC. Monitoring the vernal advancement or retro gradation of natural vegetation. NASA/GSFC, Type III, Final report, Green-belt, MD. 1974; p. 371. ## Shoshany M, Goldshleger N,Chudnovsky A. Monitoring of agricultural soil degradation by remote-sensing methods: A review. Int J Remote Sens. 2013; 34(17): 6152-6181. ## Sidik A, Zhao Sh, Wen Y. Estimating soil salinity in pingluo county of China using QuickBird data and soil reflectance spectra. Int J Appl Observ Geoinform. 2014; 26: 156-175. ## Soil and water research Institute, Study improvement of irrigation, drainage and agricultural development for Gorgan plain, Golestan Province in Iran. Gorgan; 2003. ## Taghizadeh-Mehrjardi R, Minasny B, Sarmadian F, Malone BP, Digital soil mapping of soil salinity in Ardakan region, central Iran. Geoderma. 2014; 213: 15-28. ## Taghizadeh-Mehrjardi R, Sarmadian F, Savaghebi Gh, Omid M, Toomanian N, Roosta MJ, Rahimian MH. Comparison of Neuro-fazzy, ANFIS, ANN and MLR models in soil salinity prediction (case study: Ardakan City). J Nat Res Iran. 2013; 66(2): 207-222. (In Persian). ## Tajgardan T, Ayoubi Sh, Shataii Sh, Khormali F. Mapping soil surface salinity using remote sensing data of ETM+ (Case study: North of AghGhala, Golestan Province). Water Soil Conserv. 2009; 16 (2): 1-17. (In Persian). ## Tomasella J, Hodnett MG, Rossato L. Pedotransfer functions for the estimation of soil water retention in Brazilian soils. J Soil Sci Soc AM. 2000; 49: 1100-1105. ## Urquhart EA, Zaitchik BF, Hoffiman MJ, Guikema, SD, Geiger EF. Remotely sensed estimates of surface salinity in the Chesapeake Bay: a statistical approach. Remote Sens Environ. 2012; 123: 522-531. ## Van Beek CL, TóthG. (Eds.), Risk Assessment Methodologies of Soil Threats in Europe, JRC Scientific and Policy Reports EUR. Luxembourg. Office for Official Publication of the European Communities 2012. ## Weng YL, Gong P, Zhu ZL. A spectral indexfor estimating soil salinity in the Yellow River Delta Regionof China using EO-1 Hyperion data. Pedosphere. 2010; 20:378-388. ## Wijaya A, Liesenberg V, Gloaguen R. Retrieval of forest attributesin complex successional forests of Central Indonesia: Modeling andestimation of bitemporal data. Forest Ecol Manag. 2010; 259(12): 2315-2326. ## Wilding LP. Spatial variability: Its documentation, accommodation, and implication to soil survey. In: Nielsen, D.R., and J. Bouma, (eds.), Soil Spatial Variability, Pudoc, Wagenigen, the Netherlands. 1985; 166-194. ## Yu R, Liu T, xu Y, Zhu C, Zhang Q, Quz. Analysis of salinization dynamics by remote sensing in Hetao Irrigation District of North China. agr. water manag. 2010; 97: 1952-1960. ## Zewdu Sh, Suryabhagavan KV, Balakrishnan M. Land-use/Land-coverDynamics in Sego Irrigation Farm South Ethiopia Using Geospatial Tools. J Saudi Soc Agr Sci. 2016; 15: 91-97. ##

On-Site and Off-Site Effects of Land Degradation in Albania 2 2 Background: This paper is devoted to preliminary assessment of the economic cost of land degradation in Albania resulting from unsustainable land use, based on comparing the costs of action for dealing with land degradation versus the costs of inaction. Materials and Methods: The causes of land degradation are divided into proximate and underlying ones, which interact with each other to result in different levels of land degradation. The economic impacts of land degradation on soil uses are valued according to their typology and their different impacts have been classified spatially into on-site and off-site effects, distinguished according to the economic values that are affected. Results: The results showed that the on-site costs of soil degradation are significant, but are not be a major concern in the short run. However, on the local scale, impacts will be more substantial for the affected areas. The off-site costs of soil degradation are substantial, however. In some cases, they may exceed the on-site costs, despite the fact that a large part of the off-site costs could not be quantified. Discussion and Conclusions: Some of these issues, especially the conservation of water resources and their sustainable management to reduce sedimentation in rivers and dams, and flood risk reduction, call for immediate conservation measures. 1 - 1787 1797 - - Gazmend Zeneli University of Marin Barleti, Tirana, Albania University of Marin Barleti, Tirana, Albania Albania gzeneli2000@gmail.com - - Semiha Loca University of Marin Barleti, Tirana, Albania University of Marin Barleti, Tirana, Albania Albania 1@gmail.com - - Abdulla Diku DIAVA Consulting, Tirana, Albania DIAVA Consulting, Tirana, Albania Albania 2@gmail.com - - Albana Lila Raiffeisen Bank Tirana, Albania Raiffeisen Bank Tirana, Albania Albania 3@gmail.com Cost Economic assessment Environmental impact Land use Sustainability Alcantara C, Kuemmerle T, Baumann M, Bragina EV, Griffiths P, Hostert P, Knorn J, Müller D, Prishchepov AV, Schierhorn F, Sieber A. Mapping the extent of abandoned farmland in Central and Eastern Europe using MODIS time series satellite data. Environ Res Lett. 2013; 8(3): 035035. ##Aw-Hassan A, Korol V, Nishanov N, Djanibekov U, Dubovyk O, Mirzabaev A. Economics of land degradation and improvement in Uzbekistan. In: Economics of Land Degradation and Improvement–A Global Assessment for Sustainable Development 2016; 651-682. Springer International Publishing. ##Barbier EB, Hochard JP. Does Land Degradation Increase Poverty in Developing Countries?. PloS one. 2016; 11(5):e0152973. ##Binaj A, Veizi P, Beqiraj E, Gjoka F, Kasa E. Economic losses from soil degradation in agricultural areas in Albania. Agric. Econ.–Czech. 2014; 1 (60): 6. ##Bockheim JG. Proposal to study economic and environmental benefits of reducing soil erosion in Albania. Land Tenure Center, University of Wisconsin-Madison; 1997 Sep. ##Bouza ME, Aranda-Rickert A, Brizuela MM, Wilson MG, Sasal MC, Sione SM, Beghetto S, Gabioud EA, Oszust JD, Bran DE, Velazco V. Economics of Land Degradation in Argentina. In: Economics of Land Degradation and Improvement–A Global Assessment for Sustainable Development 2016; 291-326. Springer International Publishing. ## Brune GM. Trap efficiency of reservoirs. Eos, Transactions American Geophysical Union. 1953; 34(3): 407-18. ##CNVP. Ulza Reservoir Bathymetry and Lifespan Analysis. Final Report. 2013; p. 122. (available at: http://www.profor.info/knowledge/innovative-financing-sustainable-forest-management-southwest-balkans) ## Deininger K, Savastano S, and Carletto C. Land fragmentation, cropland abandonment, and land market operation in Albania. World Development. 2012; 40(10): 2108-22. ## Ebinger J. Albania’s Energy Sector: Vulnerable to Climate Change. Europe and Central Asia Knowledge Brief; Volume No. 29. 2010. World Bank, Washington, DC. ## ELD Initiative. The rewards of investing in sustainable land management. Interim Report for the Economics of Land Degradation Initiative: A Global Strategy for Sustainable Land Management. 2013. ## FAO. Agro-Ecological Zoning and GIS application in Asia with special emphasis on land degradation assessment in drylands (LADA). Proceedings of a Regional Workshop, Bangkok, Thailand10–14 November 2003. FAO, website: ftp://ftp.fao.org/agl/agll/docs/misc38e.pdf. ## Gardi C, Panagos P, Van Liedekerke M, Bosco C, and De Brogniez D. Land take and food security: assessment of land take on the agricultural production in Europe. J Environ Plan. Manage. 2015; 58(5): 898-912. ## Gatzojannis S, Stefanidis P, Kalabokidis K. Freiburg Im Breisgau Abteilung Für Forstliche Biometrie. Mitteilungen der Abteilung für Forstliche Biometrie. 2001; 1: p. 41. ## Gebreselassie S, Kirui OK, Mirzabaev A. Economics of land degradation and improvement in Ethiopia. In: Economics of land degradation and improvement–a global assessment for sustainable development 2016; 401-430. Springer International Publishing. ## Grazhdani S, Shumka S. An approach to mapping soil erosion by water with application to Albania. Desalination. 2007; 213(1-3): 263-72. ## Karydas CG, Zdruli P, Koci S, Sallaku F. Monthly Time-Step Erosion Risk Monitoring of Ishmi-Erzeni Watershed, Albania, Using the G2 Model. Environ Model Assess. 2015; 20(6): 657-72. ##Keesstra S, Pereira P, Novara A, Brevik EC, Azorin-Molina C, Parras-Alcántara L, Jordán A, Cerdà A. Effects of soil management techniques on soil water erosion in apricot orchards. Sci. Total Environ. 2016a; 551: 357-66. ## Keesstra SD, Geissen V, Mosse K, Piiranen S, Scudiero E, Leistra M, Van Schaik L. Soil as a filter for groundwater quality. Curr. Opin. Environ. Sustainability. 2012; 4(5): 507-16. ##Kirui OK. Economics of land degradation and improvement in Tanzania and Malawi. In: Economics of Land Degradation and Improvement–A Global Assessment for Sustainable Development 2016; 609-649. Springer International Publishing. ## Kovacs AS, Fulop B, Honti M. Detection of hot spots of soil erosion and reservoir siltation in ungauged Mediterranean catchments. Energy Procedia. 2012; 18: 934. ## Laze K. Identifying and understanding the patterns and processes of forest cover change in Albania and Kosovo. Studies on the Agricultural and Food Sector in Central and Eastern Europe; 2014.74 ISBN 978-3-938584-78-1, http://nbnresolving.de/urn: nbn:de:gbv:3: 2-31678. ## MARDWA (Ministry of Agriculture, Rural Development and Water Administration- Albania). Agricultural statistic. 2015. (Available at: http://www.bujqesia.gov.al/files/pages_files/15-11-11-03-11-4814-02-25-01-33-30Statistikat_e_Vitit_2012.pdf.) ## Moussa B, Nkonya E, Meyer S, Kato E, Johnson T, Hawkins J. Economics of land degradation and improvement in Niger. In: Economics of Land Degradation and Improvement–A Global Assessment for Sustainable Development 2016; 499-539. Springer International Publishing. ## Müller D, Munroe DK. Changing rural landscapes in Albania: cropland abandonment and forest clearing in the postsocialist transition. Ann Assoc Am Geogr. 2008; 98(4): 855-76. ## Müller D, Sikor T. Effects of postsocialist reforms on land cover and land use in South-Eastern Albania. Appl Geogr. 2006; 26(3): 175-91. ## Nachtergaele F, Petri M, Biancalani R, Van Lynden G, Van Velthuizen H, Bloise M. Global land degradation information system (GLADIS). Beta version. An information database for land degradation assessment at global level. Land degradation assessment in drylands technical report. 2010; 17. ## Nkonya E, Anderson W, Kato E, Koo J, Mirzabaev A, Von Braun J, Meyer S. Global cost of land degradation. In: Economics of Land Degradation and Improvement–A Global Assessment for Sustainable Development 2016a; 117-165. Springer International Publishing. ##Nkonya E, Mirzabaev A, Von Braun J. Economics of Land Degradation and Improvement: An Introduction and Overview. In: Economics of Land Degradation and Improvement–A Global Assessment for Sustainable Development 2016b; 1-14. Springer International Publishing. ##Quillérou E, Thomas RJ. Costs of land degradation and beneﬁts of land restoration: A review of valuation methods and suggested frameworks for inclusion into policy-making. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 2012; 7(060): 1-12. (Available at http://www.cabi.org/cabreviews) ## Reed MS, Stringer LC, Dougill AJ, Perkins JS, Atlhopheng JR, Mulale K, Favretto N. Reorienting land degradation towards sustainable land management: Linking sustainable livelihoods with ecosystem services in rangeland systems. J Environ Manage. 2015; 151: 472-85. ## Requier-Desjardins M, Adhikari B, Sperlich S. Some notes on the economic assessment of land degradation. Land Degrad Dev. 2011; 22(2): 285-98. ## Rundel PW, Montenegro G, Jaksic F, editors. Landscape disturbance and biodiversity in Mediterranean-type ecosystems. Springer Science & Business Media; 2013. ## Scherr SJ. A downward spiral? Research evidence on the relationship between poverty and natural resource degradation. Food policy. 2000; 25(4): 479-98. ##Sow S, Nkonya E, Meyer S, Kato E. Cost, Drivers and Action Against Land Degradation in Senegal. In: Economics of Land Degradation and Improvement–A Global Assessment for Sustainable Development 2016; 577-608). Springer International Publishing. ## Stavi I, Lal R. Achieving zero net land degradation: challenges and opportunities. J Arid Environ. 2015; 112: 44-51. ##Stocking M, Murnaghan N. Land degradation–Guidelines for field assessment. Overseas Development Group, University of East Anglia, Norwich, UK. 2000: 120. ## Turner KG, Anderson S, Gonzales-Chang M, Costanza R, Courville S, Dalgaard T, Dominati E, Kubiszewski I, Ogilvy S, Porfirio L, Ratna N. A review of methods, data, and models to assess changes in the value of ecosystem services from land degradation and restoration. Ecol Model. 2016; 319: 190-207. ##Von Braun J, Gerber N, Mirzabaev A, Nkonya E. The Economic of Land Degradation–An Issue Paper, Global Soil Week 2012. Global Soil Forum. Institute for Advanced Sustainability Studies, Postdam. 2012; 1-30. ## Von Braun J, Gerber N, Mirzabaev A, Nkonya E. The Economics of Land Degradation. 2013. Working Paper 109. ZEF Working Paper Series, ISSN 1864-6638. University of Bonn ## Young A. Land degradation in South Asia: its severity, causes and effects upon the people. FAO; 1994. M-51ISBN 92-5-103595-4. ## Zdruli P, Lushaj S. The status of soil survey in Albania and some of its major environmental findings. In: Soil resourcesof Southern and Eastern Mediterranean countries. Bari: Zdruli P. (ed.), Steduto P. (ed.), Lacirignola C. (ed.), Montanarella L. (ed.). CIHEAM, 2001. p. 69-89 (OptionsMéditerranéennes: Série B. Etudes et Recherches; n . 34) ## Zdruli P, Lushaj S. Status of soil degradation in Albania. In: Soil in Central and Eastern European countries, in the New Independent States, in Central Asian countries and in Mongolia. European Commission, Joint Research Centre (Lahmar, R., Dosso, M., Ruellan, A. and Montanarella, L. (eds). 2000; EUR 19732 EN. p. 53-63 ## Zdruli P, Karydas CG, Dedaj K, Salillari I, Cela F,Lushaj S, Panagos P. High resolution spatiotemporal analysis of erosion risk per land cover category in Korçe region, Albania. Earth Science Informatics. 2016; 9(4): 481-95. ## Zdruli P, Lushaj Sh, Pezzuto A, Fanelli D, D'Amico O, Filomeno O, De Santis S, Todorovic M, Nerilli E, Dedaj K, Seferi B. Preparing a georeferenced soil databese for Albania at scale 2: 250,000 using the European soil bureau manual of procedures 1.1. In 7. International Meeting on: Soils with Mediterranean Type of Climate, Valenzano (Italy), 23-28 Sep 2001 2002. CIHEAM-IAMB. ## Zeneli G. Boosting bioenergy from woody biomass in Albania: Opportunities and impediments. In: Resource protection and bioenergy. Zeneli G. (ed.), UFO Press. Tirana, 2008; 133-155. ##

Effects of Rainfall Intensity-Duration-Frequency Curves Reformation on Urban Flood Characteristics in Semiarid Environment 2 2 Background: A design storm is a theoretical storm event based on rainfall intensities associated with frequency of occurrence and having a set duration. Estimating design storm via rainfall intensity–duration–frequency (IDF) curves is important for hydrological planning of urban areas. Material and Methods: The impact of changes in rainfall intensity–duration–frequency (IDF) curves on flood properties in an urban area of Zanjan city was investigated, using Storm Water Management Model (SWMM). For the IDF curve generation, Sherman and Ghahreman-Abkhezr methods were compared. Results: According to results, the estimated rainfall depth and, consequently the peak runoff rate for different return periods had decreased in the recent years, except for 2-year return period. Decrease in peak runoff rate was 30, 39, 41 and 42 percent for 5-10-20 and 50-year return periods, respectively. Based on the results, for peak runoff evaluated in 50-year return period using Sherman and Ghahreman-Abkhezr hyetograph, percent of flood that occurred before the peak runoff were 27 and 22 percent, respectively. Discussion and Conclusion: Design rainfall hyetograph showed that Sherman method gave larger rainfall intensity compared to Ghahreman-Abkhezr method. Estimated peak and total runoff volume follow trend of rainfall intensity. As Ghahreman-Abkhezr method use longer and newer rainfall data for creating IDF curves, we can conclude that climate change cause change in rainfall characteristics. The runoff modeling show that main urban drainage system had enough transfer capacity against the flood condition, but survey information indicated several inundations in some flat areas, curbs and gutters. Inappropriate design and obstruction of the runoff paths via urban garbage and sediments are some parameters that could lead to such local inundation. 1 - 1799 1813 - - Reza Ghazavi Associate Professor, Department of Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran Associate Professor, Department of Watershed Iran rghazavi@yahoo.com - - Ali Moafi Rabori Ph.D.Student, Department of Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran Ph.D.Student, Department of Watershed Management, Iran ali.moafi@ut.ac.ir - - Mohsen Ahadnejad Reveshty Associate Professor, Department of Geography, Faculty of Humanities, University of Zanjan, Zanjan, Iran Associate Professor, Department of Geography, Iran ahadnejad@gmail.com Design storm Flood Rainfall IDF curve Storm water SWMM Feizi V, Mollashahi M, Frajzadeh M, GhasemAzizi Gh. Temporal Trend Analysis of Temperature and Precipitation in Iran. ECOPERSIA. 2014; 2(4): 727-742. ##Aghabeigi AS, Moradi HR, Fattahi B. Sediment and Runoff Measurement in Different Rangeland Vegetation Types using Rainfall Simulator. ECOPERSIA. 2014; 2(2): 525-538. ##Choi K, Ball JE. Parameter estimation for urban runoff modelling, Urban Water. 2002; 4: 31-41. ##Hoang L, FennerRichard A, Skenderian M. A conceptual approach for evaluating the multiple benefits of urban flood management practices. Journal of Flood Risk Management. 2016; Online Version of Record published before inclusion in an issue. ##United Nations. World Urbanization Prospects: The 2005 Revision, ESA/P/WP/200, 2006; p. 210. ##Zoppou C. Review of urban storm water models, Environ Modell Softw J Elsevier. 2001; 16: 195-231. ##Huong HTL, Pathirana A. Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam, Hydrol Earth Syst Sc. 2013; 17: 379-394. ##Ahilan S, Guan M, Sleigh A, Wright N, Chang, H. The influence of floodplain restoration on flow and sediment dynamics in an urban river. Journal of Flood Risk Management. Online Version of Record published before inclusion in an issue. 2016. ##Lee ES, Lee DK, Kim SH, Lee KC. Design strategies to reduce surface water flooding in a historical district. Journal of Flood Risk Management. Online Version of Record published before inclusion in an issue. 2016. ##Morgan A, Branfireun B, Csillag F. An Evaluation of the Contributions of Urbanization and Climatic Change to Runoff Characteristics in the Laurel Creek Watershed, Ontario. Canadian Water Resources Journal. 2004; 29(3): 171–182. ##Alam N, Olsthoorn T. Sustainable Conjunctive Use of Surface and Ground Water: Modelling on the Basin Scale. Int J Naut Res Mar Sci. ECOPERSIA. 2011; 1 (1): 1-12. ##Ghahreman B, Abkhezr H. Reformation the relations of Intensity- duration-frequency of Iran. Journal of Sciences and Technology of Agriculture and Natural Resources. 2004; 8(2): 1-13. (In Persian). ##Prodanovic P, Simonovic SP. Development of rainfall intensity duration frequency curves for the city of London under the changing climate. Water Resources Research Report, London. 2007; p. 51. ##Semadeni -Davies A, Herne bring C, Svensson G, Gustafsson L. The impacts of climate change and urbanization on drainage in Helsingborg, Sweden: Combined Sewer System. J Hydrol. 2008; 350 (1-2): 100-113. ##Desramaut N. Estimation of Intensity Duration Frequency Curves for Current and Future Climates (Case study: Quebec Canada).M.Sc. Thesis, University of McGill, Canada, 2008; p. 83. ##Ayman GA, ElGamal M, ElMostafa A, ElBadry H. Developing Intensity-Duration-Frequency Curves in Scarce Data Region: An Approach using Regional Analysis and Satellite Data. Scientific Research, Engineering. 2011; 3: 215-226. ##Ibrahim HE. Developing rainfall intensity duration–frequency relationship for two regions in Saudi Arabia, J. King Saud University – Engineering Sciences. 2012; 24 (2): 131-140. ##Huber W, Dickinson R. Storm water management model, version 4: user’s manual, US Environmental Protection Agency Athens, 2nd Ed. 1992; p. 720. ##Gironas J, Roesner LA, Davis J. Storm water management model applications manual, EPA, United States. 2009: p. 180. ##Mahdavi M, Applied Hydrology. Volume (2). Tehran Press. Iran. 2007; p. 427. (In Persian) ##McCuen RH, Johnson PA, Ragan RM. Highway Hydrology: Hydraulic Design Series No: 2. Hydrology, Federal Highway Administration, Washington, DC FHWA-SA.1996: p. 357. ##American Society of Civil Engineering. Gravity Sanitary Sewer Design and Construction. ASCE Manual of Practice. 1982; 60: p. 275. ##Rossman LA. Storm water management model user’s manual, version 5.0, EPA. United States. 2009; p. 233. ##Natural resources conservation service. Conservation Engineering Division, Small Watershed Hydrology, WinTR–55 User Guide. 2009; p. 142. ##Butler D, Davies J. Urban Drainage, Chapter 7. System component and design, Taylor & Francis. 2011; p. 28. ##Behbahani SM. Surface water hydrology. 2nd Ed. Tehran Press. 2009; p. 488. (In Persian) ##Zaghloul NA, Abu Kiefa MA. Neural network solution of inverse parameters used in the sensitivity- calibration analyses of the SWMM model simulations, Adv Eng Softw J Elsevier. 2001; 32 (7): 587-595. ##Willems P. Revision of urban drainage design rules based on extrapolation of design rainfall statistics.12th International Conference on Urban Drainage, 11-16 September, Porto Alegre/RS- Brazil. 2011; 1-8. ##Moatamednia M, Nohegar A, Malekian A, KarimiZarchi K, Tavasoli A. Performance of Different Models for Curve Number Estimation (Case study: Bar Watershed in KhorasanRazavi Province, Iran). ECOPERSIA, 2015; 3(3): 1031-1049. ##Camorani G, Attilio C, Armando B. Effects of land use changes on the hydrologic response of reclamation systems, Phys. Chem. Earth J Elsevier. 2005; 30: 561-574. ##Watt WE, Waters D, McLean R. Climate Change and Urban Stormwater Infrastructure in Canada: Context and Case Studies. Report 2003-1. Meteorological Service of Canada, Waterloo, Ontario, Canada. 2003; p. 27. ##

Selenium Bio-accumulation and Bio-concentration Factors in some Plant Species in an Arid Area in Central Part of Iran 2 2 Background: Concentrations of Se in seven plant species (white mulberry, apricot, spindle tree, pistachio, wheat, barley, chives), and the associated soil samples were investigated in Shahrood and Damghan, Iran. Materials and Methods: Soil samples were taken from the surface zone (0-5 cm) and plough zone (5-20 cm) in 13 sampling locations. The collected soil and plant samples were taken to the laboratory, then digested usin USEPA's method and analyzed by Inductively Coupled Plasma Optical Emission Spectroscopy technique. Results: Since there was a significant correlation (r=0.688, p<0.01) between Se concentration in the two soil's depths, it was turned out that agricultural practices, through tillage and plough, had probably moved Se to the deeper parts of the soil in area in which agricultural activity was prevalent. The highest accumulation of Se was recorded in the chives with the average value of 0.35mg kg-1. Except for apricot, the concentrations of Se in top parts of the plants (e.g. leaf, grain, fruit) were higher than stem/stalk, implying the easy translocation of this element in the considered plant species. Conclusions: The highest values of bio-concentration factors were recorded in chives followed by spindle tree and wheat, whereas the lowest level was detected in pistachio. 1 - 1815 1827 - - Mohamad Sakizadeh Assistant Professor, Department of Environmental Sciences, Faculty of Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran Assistant Professor, Department of Environmental Iran msakizadeh@gmail.com - - Hadi Ghorbani Associate Professor, Department of Soil Sciences, Shahrood University of Technology, Shahrood, Iran Associate Professor, Department of Soil Sciences, Iran ghorbani1969@yahoo.com Agricultural activity Bio-concentration factor Soil pollution Mortazavi S, Attaeian B, Abdolkarimi, S. Risk Assessment and Environmental Geochemistry of Pb, Cu and Fe in Surface Sediments (Case Study: Hashilan Wetland, Kermanshah, Iran). ECOPERSIA 2016; 4(2): 1411-1424. ##Sayadi MH, Rezaei MR, RashkiGhaleno O, Afsari K, Poor Mollaei N. Natural and Concentration Factor Distribution of Heavy Metals in Sediments of ChahNimeh Reservoirs of Sistan, Iran. ECOPERISA 2015; 3(2): 1003-1012. ##Sakizadeh M, Faraji F, Pouraghniyayi MJ. Quality of Groundwater in an Area with Intensive Agricultural Activity Expo Health. 2016; 8: 93-105. ##Goldhaber SB. Trace element risk assessment: essentiality vs. toxicity. Regul Toxicol Pharmacol. 2003; 38: 232-242. ##Altansuvd J, Nakamaru YM, Kasajima S, Ito H. Yoshida H. Effect of long-term phosphorus fertilization on soil Se and transfer of soil Se to crops in northern Japan. Chemosphere 2014; 107: 7-12. ##Harris J, Schneberg KA, Pilon‑Smits EAH. Sulfur–Selenium–molybdenum interactions distinguish selenium hyperaccumulatorStanleyapinnata from non‑hyperaccumulator Brassica juncea (Brassicaceae). Planta 2014; 239: 479-491. ##Fordyce MF. Se Deficiency and Toxicity in the Environment, In: Alloway, B., Centeno, J.A., Finkelman, R.B., Fuge, R., Lindh, U., Smedley, P. E ssentials of medical geology, 2013; 375-416. ##Terry N, Zayed M, de Souza MP. Tarun A.S. Se in higher plants. Annu.Rev. Plant Physiol. Plant Mol Biol. 2000; 51: 401-32. ##Kabata-Pendias A. Trace elements in soils and plants. Taylor and Francis Group; 2011. ## De Temmerman L, Waegeneers N, Thiry C, Du Laing G, Tack F. Ruttens A. Selenium content of Belgian cultivated soils and its uptake by field crops and vegetables. Sci Total Environ. 2014; 468-489: 77-82. ## Auger J, Yang W, Arnault I, Pannier F, Potin-Gautier M. High-performance liquid chtomatographic–inductively coupled plasma mass spectrometric evidence for Se-“alliins” in garlic and onion grown in Se-rich soil, J Chromatogra. A 2004; 1032:103-107. ##Gojkovic Z, Garbayo I, Ariza JLG, Márová I, Vílchez C. Selenium bioaccumulation and toxicity in cultures of green microalgae. Algal Res. 2015; 7: 106-116. ## Ziaii M, Pouyan A, Ziaei M. A Computational Optimized Extended Model for Mineral Potential Mapping Based on W of E Method. Am J Appl Sci. 2009; 6: 200-203. ## Doulati Ardejani F, JodieriShokri B, Moradzadeh A, Shafaei SZ, Kakaei R. Geochemical characterization of pyrite oxidation and environmental problems related to release and transport of metals from a coal washing low-grade waste dump, Shahrood, northeast Iran. Environ Monit Assess. 2011; 183: 41-55. ## Hajizadeh Namaghi H, Karami GH, Saadat S. A study on chemical properties of groundwater and soil in ophiolitic rocks in Firuzabad, east of Shahrood, Iran: with emphasis to heavy metal contamination. Environ Monit Assess.174: 573-583. ## Nazemi L, Nazmara S, Eshraghyan MR, Nasseri S, Djafarian K, Yunesian M, Sereshti H, Moameni A, Shahtaheri SJ. Selenium status in soil, water and essential crops of Iran. Iran J Environ Healt. 2012; 9(11): 1-8. (In Persin). ## Kazemi GA. Temporal changes in the physical properties and chemical composition of the municipal water supply of Shahrood, northeastern Iran. Hydrogeol J. 2004; 12: 723-734. ## Bakhshi M. A short report on the climate and water resources of Shahrood [in Persian]. In: Proc. of Shahrood and development symposium, November 1998, Shahrood, Iran, 1998; 143-164. (In Persian). ## Kazemi GA, Mehdizadeh H. Trend, characteristics and chemical composition of atmospheric precipitation at Shahrood, Northeast Iran. In: Abstracts of the 33rd general assembly of the International Union of Geodesy and Geophysics (IUGG), Sapporo, Japan; 2003. ## Massadeh AM, Alomary AA, Mir S, Momani FA, Haddad HI, Hadad YA. Analysis of Zn, Cd, As, Cu, Pb, and Fe in snails as bioindicators and soil samples near traffic road by ICP-OES, Environ. Sci Pollut Res. 2016; 23: 13424-13431. ## Boussen S, Soubrand M, Bril H, Ouerfelli K, Abdeljaouad S. Transfer of lead, zinc and cadmium from mine tailings to wheat (Triticumaestivum) in carbonated Mediterranean (Northern Tunisia) soils. Geoderma 2013; 192: 227-236. ## Kabata-Pendias A, Mukherjee AB. Trace elements from soil to human. Springer 2007; 519. ## Smedley PL, Kinniburgh DG. A review of the source, behaviour and distribution of arsenic in natural waters. Appl Geochem. 2002; 17: 517-568. ## Broadley MR, White PJ, Bryson RJ, Meacham MC, Bowen HC, Johnson SE, et al. Biofortification of UK food crops with Selenium. Proc Nutr Soc. 2006; 65:169-81. ## Gupta UC, Gupta SC. Selenium in soils and crops, its deficiencies in livestock and humans: implications for management. Commun. Soil Sci Plan. 2000; 31(11–14): 1791-1807. ## Roca-Perez L, Gil C, Cervera ML, Gonzálvez A, Ramos-Miras J, Pons V, Bech J, Boluda R. Selenium and heavy metals content in some Mediterranean soils. J Geochem Explor. 2010; 107: 110-116. ## Ajwa HA, Bañuelos GS. Selenium uptake by plants from soils amended with inorganic and organic materials. J Environ Qual. 1998; 27 (5): 1218-1227. ## Pinheiro EFM, Pereira MG, Anjos LHC. Aggregate distribution and soil organic matter under different tillage systems for vegetable crops in a Red Latosol from Brazil, Soil Till Res. 2004; 77: 79-84. ## Eich-Greatorex S, Sogn TA, Øgaard AF, Aasen I. Plant availability of inorganic and organic selenium fertilizer as influenced by soil organic matter content and pH. Nutr Cycl Agroecosys. 2007; 79: 221-231. ## Johnson CC, Ge X, Green KA, Liu X. Selenium distribution in the local environment of selected villages of the Keshan Disease belt, Zhangjiakou District, Hebei Province, People's Republic of China Appl Geochem. 2000; 15(3): 385-401. ## Fernandez-Martınez A, Charlet L. Selenium environmental cycling and bioavailability: a structural chemist point of view. Rev Env Sci Biotech. 2009; 8(1): 81-110. ## Pappa EC, Pappas AC, Surai PF. Selenium content in selected foods from the Greek market and estimation of the daily intake. Sci Total Environ. 2006; 372: 100-8. ## Al-Ahmary KM. Selenium content in selected foods from the Saudi Arabia market and estimation of the daily intake. Arab J Chem. 2009; 2: 95-99. ## Dumont E, Vanhaecke F, Cornelis R. Selenium speciation from food source to metabolites: a critical review. Anal Bioanal Chem. 2006; 385: 1304-23. ## Sirichakwal PP, Puwastein P, Polngam J, Kongkachuichai R. Selenium content of Thai foods. J Food Compos Anal. 2005; 18: 47-59. ## Navarro-Alarcon M, Cabrera-Vique C. Selenium in food and the human body: A review. Sci Total Envir. 2008; 400: 115-141. ## Ip C, Ganther HE. Novel strategies in selenium cancer chemoprevention research. In: Burk RF, editor. Selenium in biology and human health. New York: Springer-Verlag; 1994. ## Eapen S, D’Souza SF. Prospects of genetic engineering of plants for phytoremediation of toxic metals. Biot Adv. 2005; 23:97-114. ## Adams ML, Lombi E, Zhao FJ, McGrath SP. Evidence of low selenium concentrations in UK bread-making wheat grain. J Sci Food Agric. 2002; 82: 1160-5. ## Bailey FC, Knight AW, Ogle RS, Klaine SJ. Effect of sulfate level on selenium uptake by Ruppiamaritima. Chemosphere 1995; 30: 579-591. ##Öborn I, Jansson G, Johnsson LA. Field study on the influence of soil pH on trace element levels in spring wheat (Triticumaestivum), potatoes (Solanumtuberosum) and carrots (Daucuscarota). Water Air Soil Pollut. 1995; 85: 835-40. ## Fitz WJ, Wenzel WW. Arsenic transformation in the soil-rhizosphereplant system, fundamentals and potential application of phytoremediation. J Biotechnol. 2002; 99: 259-278. ## Banuelos GS, Meek DW. Selenium accumulation in selected vegetable. J Plant Nutr. 1989; 12: 1255-1272. ## Wang S, Liang D, Wang D, Wei W, Fu D, Lin Z. Selenium fractionation and speciation in agriculture soils and accumulation in corn (Zea mays L.) under field conditions in Shaanxi Province, China Sci Total Environ. 2012; 427-428: 159-164. ## Bitterli C, Bañuelos GS, Schulin R. Use of transfer factors to characterize uptake of selenium by plants. J Geochem Explor. 2010; 107: 206-216. ## Száková J, Tremlová J, Pegová K, Najmanová J, Tlustoš P. Soil-to-plant transfer of native selenium for wild vegetationcover at selected locations of the Czech Republic. Environ Monit Assess. 2015; 187: 358. ##