University of Isfahan Geography and Environmental Planning 2008-5362 34 2 2023 06 22 Identification of Fault Lineaments and Earthquake Hazard Zoning Using Remote Sensing and GIS: A Case Study of Rumeshkan County, Lorestan Province Identification of Fault Lineaments and Earthquake Hazard Zoning Using Remote Sensing and GIS: A Case Study of Rumeshkan County, Lorestan Province 1 16 27026 10.22108/gep.2022.132279.1484 FA Siamak Baharvand Associate Professor, Department of Geology, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran Alireza Firoozfar Associate Professor, Department of Civil Engineering, Zanjan University, Zanjan, Iran , Salman Soori M.Sc., Geology Engineering, Zamin Kavan South Company, Tehran, Iran Journal Article 2022 01 16 Abstract As a natural hazard, earthquake has always caused destruction and loss of human life throughout history. Proper planning to prevent or reduce the destructive impact of earthquake hazard is of particular importance. In this study, to prevent and decrease the risk of this phenomenon, faults were detected and earthquake risk zoning was done in Rumeshkan County in Lorestan Province. For this purpose, first, the lineaments in the region were detected by using 2013 satellite images from Landsat 8 OLI sensors (row 37 and pass 166) and applying Directional Filters in ENVI software, as well as Lineament Extraction in Geomatica software. Afterwards, by comparing the lines with the constructed band combinations, the Digital Elevation Model (DEM) and geological map of the study area were investigated, the faults were separated, and their map was prepared in the Geographic Information System (ArcGIS). In this study, by using expert judgment method and AHP-Fuzzy, the factors that affected the risk of earthquakes in Rumeshkan County, including distance from fault, slope, geomorphology, lithology, and distance from epicenters of past earthquakes were weighted and the seismic hazard map of the region was prepared. According to the obtained results, 31.8, 34.3, 10.3, 14.6, and 9.0% of the area were in very low, low, medium, high, and very high hazard classes, respectively. Examination of the earthquake hazard sensitivity map showed that the highest sensitivity to seismic hazard was in the eastern parts of the county and that the central parts had very low and low risks; thus, settlement of population in the latter areas is recommended. Keywords: earthquake, Rumeshkan, remote asensing, GIS, AHP-fuzzy Introduction Earthquakes have always been among the most important natural hazards. Every year, a large number of people in the world are affected by the adverse effects of earthquake. To decrease human and economic losses, as well as their social consequences, it is necessary to gain an accurate knowledge of the risks of earthquakes in different places based on the current knowledge and the latest reliable technologies. Risk zoning is an important approach in the pre-crisis management process that greatly assists planners and managers to take measures to reduce earthquakes earthquake vulnerability. The main issues are the selection of vulnerability criteria and the way of combining them, as well as selecting an appropriate model that can best represent the rate of vulnerability. Today, with the increasing science advancement, satellites and satellite imagery have progressed; therefore, the use of remote sensing methods and satellite image processing appear to be highly efficient for identifying and studying geological phenomena, such as faults and lineaments. GIS is also one of the most powerful software in the field of environmental hazard mapping that contributes to efficient management of spatial and temporal data. Methodology Manual, automatic, and semi-automatic methods are used to extract tha data of lineaments and faults. In this study, a semi-automatic method was used, which is a combination of automatic and manual methods and is more reliable with a good speed. For this purpose, the 2013 Landsat 8 images taken by OLI sensor (row 37 and pass 166) were analyzed. In this study, first the lineaments in this area were extracted by applying Lineament Extraction Filter in Geomatica software. Then, by spatial highlighting and applying directional filters on 8-band images obtained from the Landsat 8 satellite image of OLI sensor, as well as creating band combinations, the faults in the area were manually identified. AHP-fuzzy was used for earthquake hazard zoning in Rumeshkan County. In addition to the fault map of the region, other factors, including slope, geomorphology, lithology, and distance from the epicenters of past earthquakes, were used. The fuzzy hierarchical integrated model has a high efficiency in earthquake risk zoning due to removing the inherent inaccuracy and uncertainty of decision makers' perceptions and reflecting their opinions as a definitive number. After weighting the factors, by combining the maps in the environment of Geographic Information System (GIS) software, the earthquake zoning map of the region was prepared. Discussion In this research, after examining the factors affecting the earthquake risk, a map of each of the factors was prepared in the environment of ArcGIS software. The results obtained from the analysis of each factor were as follows: The study of lithology and geomorphology of the region showed that due to the looseness of Quaternary alluvial sediments, this unit had the highest sensitivity to earthquake hazard and the highest weight was assigned to it. Investigation of the area slope according to the expert judgments indicated that the highest sensitivity to earthquake hazard was related to the highest slope degrees, which could be attributed to the low shear strength of materials at high slopes. The results obtained from the study of the faults in the region revealed that sensitivity to earthquakes increased by decreasing distance from the faults because earthquakes themselves were caused by the movement of faults. Also, the sensitivity increased as the distances from the epicenters of past earthquakes decreased. After examining the factors affecting earthquake risk, the map of each factor was fuzzified by using the fuzzy membership functions. For this purpose, to standardize the maps, the slope and lithology maps were respectively obtained through incremental linear and Gaussian membership functions to determine distances from the faults, epicenters of old earthquakes, and geomorphology of the faults. Considering the fact that each layer had a different impact on seismic hazard zoning, weighting of the layers and substrates was necessary. In the hierarchical analysis process, first, a pairwise comparison was done and the results were transferred to the Expert Choice software in order to calculate the weight of each factor. Based on the obtained findings, the fault layer had the most important role in preparing the seismic zoning map of the study area. According to the prepared lineament maps, the faults were mostly concentrated in the eastern parts of Rumeshkan County. Conclusion Most studies concerning fracture and fault modeling in each region depend on analysis of lineaments. In this research, a remote sensing technique (OLI sensor satellite imagery) was used to obtain an integrated digital map of the region's lineaments. The results showed that the use of a semi-automatic method was one of the fastest and most accurate approaches for extracting the maps of faults and fractures. In this study, 5 factors were selected as effective factors in the seismic hazard zoning of Rumeshkan City. Prioritization of the factors by using the hierarchical analysis showed that the factors of distance from fault, distances from epicenters of past earthquakes, lithology, slope, and geomorphology played the most important roles in preparing the seismic hazard map, respectively. According to the results of seismic hazard zoning, >23% of the area was in the high- and very high-risk classes, covering most of the eastern part of the study area. Combination of the residential area map with the earthquake risk zoning map also showed that the residential areas located in the eastern, western, and central parts were in high- and very high-risk classes, low- to medium-risk class, and very low- and low-risk classes, respectively. Chaqabol, the capital of the county, was located at the central part. References - Bimal, N., Yadav, O. P., & Murat, A. (2010). A fuzzy- AHP approach to prioritization of CIS attributes in target planning for automotive product development. Expert System with Application Journal, 37, pp. 6775- 6786. - Fayaz, M., Romshoo, S. A., Rashid, I., & Chandra, R. (2022). Earthquake Vulnerability Assessment of the Built Environment in Srinagar City, Kashmir, Himalaya, Using GIS. Natural Hazards and Earth System Sciences, pp. 1-35. https://doi.org/10.5194/nhess-2022-155 - Gannouni, S. and Gabtni, H. (2015). Structural Interpretation of Lineaments by Satellite Image Processing (Landsat TM) in the Region of Zahret Medien (Northern Tunisia). Journal of Geographic Information System, 7, pp. 119-127. doi: 10.4236/jgis.2015.72011 - Janardhana Raju, N., Reddy, T. V. K., & Munirathnam, P. (2006). Subsurface dams to harvest rainwater: A case study of the Swarnamukhi River Basin, southern India. Hydrology Journal, 14, pp. 526-531. - Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multiattribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics, 87(2), pp. 171-184. - Pudi, R., Martha, T. R., Roy, P., Vinod, K., & Rao, R. (2021). Mesoscale seismic hazard zonation in the Central Seismic Gap of the Himalaya by GIS-based analysis of ground motion, site, and earthquake-induced effects. Environ Earth Sci., 80, p. 613. https://doi.org/10.1007/s12665-021-09907-w - Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill, New York. - Varo, J., Sekac, T., & Jana, S. K. (2019). Earthquake hazard micro-zonation in Fiji islands: A research of Vitilevu Island. IJRTE, 8, pp. 2296-2307. - Vahidnia, M. H., Alesheikh, A. A., & Alimohammadi, A. (2009). Hospital site selection using fuzzy AHP and its derivatives. Journal of Environmental Management, 90(10), pp. 3048-3056. - Yassaghi, A. (2006). Integration of Landsat Imagery Interpretation and Geomagnetic Data on Verification of Deep-Seated Transverse Fault Lineaments in SE Zagros. International Journal of Remote Sensing, 56(12), pp. 152-167. Abstract As a natural hazard, earthquake has always caused destruction and loss of human life throughout history. Proper planning to prevent or reduce the destructive impact of earthquake hazard is of particular importance. In this study, to prevent and decrease the risk of this phenomenon, faults were detected and earthquake risk zoning was done in Rumeshkan County in Lorestan Province. For this purpose, first, the lineaments in the region were detected by using 2013 satellite images from Landsat 8 OLI sensors (row 37 and pass 166) and applying Directional Filters in ENVI software, as well as Lineament Extraction in Geomatica software. Afterwards, by comparing the lines with the constructed band combinations, the Digital Elevation Model (DEM) and geological map of the study area were investigated, the faults were separated, and their map was prepared in the Geographic Information System (ArcGIS). In this study, by using expert judgment method and AHP-Fuzzy, the factors that affected the risk of earthquakes in Rumeshkan County, including distance from fault, slope, geomorphology, lithology, and distance from epicenters of past earthquakes were weighted and the seismic hazard map of the region was prepared. According to the obtained results, 31.8, 34.3, 10.3, 14.6, and 9.0% of the area were in very low, low, medium, high, and very high hazard classes, respectively. Examination of the earthquake hazard sensitivity map showed that the highest sensitivity to seismic hazard was in the eastern parts of the county and that the central parts had very low and low risks; thus, settlement of population in the latter areas is recommended. Keywords: earthquake, Rumeshkan, remote asensing, GIS, AHP-fuzzy Introduction Earthquakes have always been among the most important natural hazards. Every year, a large number of people in the world are affected by the adverse effects of earthquake. To decrease human and economic losses, as well as their social consequences, it is necessary to gain an accurate knowledge of the risks of earthquakes in different places based on the current knowledge and the latest reliable technologies. Risk zoning is an important approach in the pre-crisis management process that greatly assists planners and managers to take measures to reduce earthquakes earthquake vulnerability. The main issues are the selection of vulnerability criteria and the way of combining them, as well as selecting an appropriate model that can best represent the rate of vulnerability. Today, with the increasing science advancement, satellites and satellite imagery have progressed; therefore, the use of remote sensing methods and satellite image processing appear to be highly efficient for identifying and studying geological phenomena, such as faults and lineaments. GIS is also one of the most powerful software in the field of environmental hazard mapping that contributes to efficient management of spatial and temporal data. Methodology Manual, automatic, and semi-automatic methods are used to extract tha data of lineaments and faults. In this study, a semi-automatic method was used, which is a combination of automatic and manual methods and is more reliable with a good speed. For this purpose, the 2013 Landsat 8 images taken by OLI sensor (row 37 and pass 166) were analyzed. In this study, first the lineaments in this area were extracted by applying Lineament Extraction Filter in Geomatica software. Then, by spatial highlighting and applying directional filters on 8-band images obtained from the Landsat 8 satellite image of OLI sensor, as well as creating band combinations, the faults in the area were manually identified. AHP-fuzzy was used for earthquake hazard zoning in Rumeshkan County. In addition to the fault map of the region, other factors, including slope, geomorphology, lithology, and distance from the epicenters of past earthquakes, were used. The fuzzy hierarchical integrated model has a high efficiency in earthquake risk zoning due to removing the inherent inaccuracy and uncertainty of decision makers' perceptions and reflecting their opinions as a definitive number. After weighting the factors, by combining the maps in the environment of Geographic Information System (GIS) software, the earthquake zoning map of the region was prepared. Discussion In this research, after examining the factors affecting the earthquake risk, a map of each of the factors was prepared in the environment of ArcGIS software. The results obtained from the analysis of each factor were as follows: The study of lithology and geomorphology of the region showed that due to the looseness of Quaternary alluvial sediments, this unit had the highest sensitivity to earthquake hazard and the highest weight was assigned to it. Investigation of the area slope according to the expert judgments indicated that the highest sensitivity to earthquake hazard was related to the highest slope degrees, which could be attributed to the low shear strength of materials at high slopes. The results obtained from the study of the faults in the region revealed that sensitivity to earthquakes increased by decreasing distance from the faults because earthquakes themselves were caused by the movement of faults. Also, the sensitivity increased as the distances from the epicenters of past earthquakes decreased. After examining the factors affecting earthquake risk, the map of each factor was fuzzified by using the fuzzy membership functions. For this purpose, to standardize the maps, the slope and lithology maps were respectively obtained through incremental linear and Gaussian membership functions to determine distances from the faults, epicenters of old earthquakes, and geomorphology of the faults. Considering the fact that each layer had a different impact on seismic hazard zoning, weighting of the layers and substrates was necessary. In the hierarchical analysis process, first, a pairwise comparison was done and the results were transferred to the Expert Choice software in order to calculate the weight of each factor. Based on the obtained findings, the fault layer had the most important role in preparing the seismic zoning map of the study area. According to the prepared lineament maps, the faults were mostly concentrated in the eastern parts of Rumeshkan County. Conclusion Most studies concerning fracture and fault modeling in each region depend on analysis of lineaments. In this research, a remote sensing technique (OLI sensor satellite imagery) was used to obtain an integrated digital map of the region's lineaments. The results showed that the use of a semi-automatic method was one of the fastest and most accurate approaches for extracting the maps of faults and fractures. In this study, 5 factors were selected as effective factors in the seismic hazard zoning of Rumeshkan City. Prioritization of the factors by using the hierarchical analysis showed that the factors of distance from fault, distances from epicenters of past earthquakes, lithology, slope, and geomorphology played the most important roles in preparing the seismic hazard map, respectively. According to the results of seismic hazard zoning, >23% of the area was in the high- and very high-risk classes, covering most of the eastern part of the study area. Combination of the residential area map with the earthquake risk zoning map also showed that the residential areas located in the eastern, western, and central parts were in high- and very high-risk classes, low- to medium-risk class, and very low- and low-risk classes, respectively. Chaqabol, the capital of the county, was located at the central part. References - Bimal, N., Yadav, O. P., & Murat, A. (2010). A fuzzy- AHP approach to prioritization of CIS attributes in target planning for automotive product development. Expert System with Application Journal, 37, pp. 6775- 6786. - Fayaz, M., Romshoo, S. A., Rashid, I., & Chandra, R. (2022). Earthquake Vulnerability Assessment of the Built Environment in Srinagar City, Kashmir, Himalaya, Using GIS. Natural Hazards and Earth System Sciences, pp. 1-35. https://doi.org/10.5194/nhess-2022-155 - Gannouni, S. and Gabtni, H. (2015). Structural Interpretation of Lineaments by Satellite Image Processing (Landsat TM) in the Region of Zahret Medien (Northern Tunisia). Journal of Geographic Information System, 7, pp. 119-127. doi: 10.4236/jgis.2015.72011 - Janardhana Raju, N., Reddy, T. V. K., & Munirathnam, P. (2006). Subsurface dams to harvest rainwater: A case study of the Swarnamukhi River Basin, southern India. Hydrology Journal, 14, pp. 526-531. - Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multiattribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics, 87(2), pp. 171-184. - Pudi, R., Martha, T. R., Roy, P., Vinod, K., & Rao, R. (2021). Mesoscale seismic hazard zonation in the Central Seismic Gap of the Himalaya by GIS-based analysis of ground motion, site, and earthquake-induced effects. Environ Earth Sci., 80, p. 613. https://doi.org/10.1007/s12665-021-09907-w - Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill, New York. - Varo, J., Sekac, T., & Jana, S. K. (2019). Earthquake hazard micro-zonation in Fiji islands: A research of Vitilevu Island. IJRTE, 8, pp. 2296-2307. - Vahidnia, M. H., Alesheikh, A. A., & Alimohammadi, A. (2009). Hospital site selection using fuzzy AHP and its derivatives. Journal of Environmental Management, 90(10), pp. 3048-3056. - Yassaghi, A. (2006). Integration of Landsat Imagery Interpretation and Geomagnetic Data on Verification of Deep-Seated Transverse Fault Lineaments in SE Zagros. International Journal of Remote Sensing, 56(12), pp. 152-167.

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University of Isfahan Geography and Environmental Planning 2008-5362 34 2 2023 06 22 Investigating the Factors Affecting the Ecological Footprint of Sari City Investigating the Factors Affecting the Ecological Footprint of Sari City 17 26 27073 10.22108/gep.2022.133118.1506 FA Maryam Nazari Master of Geography and Urban Planning, Shahid Beheshti University, Tehran, Iran Mohsen Kalantari Associate Professor of Geography and Urban Planning, Shahid Beheshti University, Tehran, Iran Journal Article 2022 04 10 Abstract Cities manifesting the world's most consuming ecosystem are responsible for a large part of the world's environmental problems. Knowledge of the ecological conditions prevailing in any regions is essential for achieving development. Ecological Footprint Index (EFI) is of great interest for assessing urban communities as a way to measure the levels of sustainability. In this research, the ecological footprint method, which is a quantitative model, was used to analyze the data and measure the sustainability of urban areas. To this goal, an attempt was made to study the EFI and biological capacity of the urban ecosystem of Sari City by using a descriptive-analytical method and relying on library resources. Ecological footprint in the consumption sector, including housing, services, and transportation, was calculated in 4 areas of Sari City. According to the results of data analysis, the ecological footprint of consumption in the mentioned city was equal to 0.94 global hectares and its biological capacity was 0.59 global hectares per person. Comparison of the biological capacity and ecological footprint of this city showed that it had an ecological deficit and was thus ecologically unstable. Among the footprints calculated in the consumption sector, transportation with the ecological footprint of 46.46969 ha had the most ecological footprint. Also, analyses of the ecological footprints in the 4 regions of Sari City showed that Region 1 had a more footprint than other regions, indicating that it followed a higher consumption pattern, but in general, all areas of Sari City were in an ecologically unstable situation according to the research results. Keywords: ecological footprint, sustainable development, urbanization capacity, Sari Introduction: Rapid population growth and consequent expansions of cities, as well as the urbanization process exceeding management and development of urban services, have led to an increasing use of natural resources and energy. The amount of ecological footprint of a society depends on the following factors: population size, average standard of living, average productivity of land ecosystem, efficiency of harvesting, processing, water resources, and use of other resources. By measuring and controlling each of these variables, the effects of resource utilization can be determined, the degree of sustainability of the urban system in relation to the natural ecosystem can be studied, and finally, appropriate policies and strategies can be applied to reduce the effects of ecological footprint and increase urban sustainability. It is important to note that analysis of ecological footprint varies according to the type of community, country, and the amount of technology used in that community. In other words, ecological footprint varies based on the level of development and land use in each country. Generally, the study of ecological footprint shows that the developed countries have a greater impact on natural areas. Methodology: Various social, economic, cultural, political, and environmental aspects, etc. have affected human life. One of the aspects of rapid urban development is increasing urban population and thus increasing use of the ecological resources of cities. The mismatch between the exploitation level of resources and ecological potential of a city has caused urban instability, which needs to be determined by measuring the ecological potential of exploitation so as to increase urban sustainability. In recent decades, there has been a large increase in the population of Sari City, which has caused its ecological instability due to the excessive use of land and ecological resources. Therefore, it is necessary to determine its ecological potential and level of utilization of resources. The present study tried to measure the ecological footprint, consumption, housing, and transportation in Sari City and determine its ecological status and sustainability. Thus, in addition to recognizing the current situation, the future of this city can be predicted and its problems can be solved in terms of each of the mentioned ecological indicators, as well as providing the necessary measures to prevent its possible natural hazards. Discussion: Ecological footprint is a computational tool for measuring population demand on nature. It is mainly used to assess ecological potential, ultimate ecological capacity, and sustainable development. The ecological footprint of a country or region involves the areas of bio-production (land and sea) that will be needed to consolidate current consumptions by using the dominant technology. The Ecological Footprint Index (EFI) includes several special functions in the areas of bio-production, such as land, agriculture, and forestry, both for wood production and carbon sequestration in geospatial pastures and water areas. The key concept for calculating ecological footprint and bioavailability by this index is using the same unit of hectare globally; thus, it is easy to evaluate and compare the studied areas with other areas globally. The ecological footprint method is a prelude to planning and one of the important and essential tools, which helps to achieve sustainability. The results of this research indicated that the ecological footprint of housing in Sari City was 1 hectare worldwide. Of 13980,29 hectares, 2071,55, 3840,81, 1602,64, and 620,66 hectares showed the global ecological footprints of the housing sector in the 1st, 2nd, 3rd, and 4th regions of Sari City, respectively. Among the 4 districts of the city, District 2 had the highest footprint in the housing sector with an ecological footprint of 3840,81 hectares; in other words, the citizens living in this district needed more lands to meet the needs of their housing sector. The ecological footprint of transportation is estimated with regard to urban areas. It is calculated by the sum of the ecological footprints of the Earth and the energy consumptions, including gasoline, diesel, CNG. Conclusion: Due to the nature of this research, library and field methods were used based on quantitative and qualitative data and information. At first, the ecological footprint indicators were developed for Sari City based on library methods. Then, the field information required for each indicator were collected and analyzed. Finally, the status of each indicator and the general situation of the city were determined in terms of ecological footprint and degree of sustainability. The ecological footprint in the city of Sari was 46969,24 hectares worldwide, of which 13955,3, 10736,77, 10563,51, and 11713,66 hectares were the global footprints of Zones 1, 2, 3 and 4, respectively. Ecological sustainability offers solutions that initially require revision in relation to agriculture, housing, energy, urban design, transportation, economy, family, consumer resources, forestry, deserts, and the core values of our lives. The study of the bodies and functions of cities, urban planning and designing, ecological design, ecological village, ecological city, and other forms of environmental designs are essential for achieving and promoting urban sustainability. According to the results obtained from the roles of the various parameters in the stability of Sari City, the most important issue for promoting this city was achieving sustainable development by preventing the pattern of consumerism and replacing it with productivity, as well as taking advantage of the opportunities with regard to the strengths and weaknesses. References: - Abedi, Z. (2017). From Ecological Footprint to Sustainable City. International Conference on Urban Economics. - Ahmad, M., Ahmed, Z., Yang, X., Hussain, N., & Sinha, A. (2021). Financial development and environmental degradation: Do human capital and institutional quality make a difference? Gondwana Research.‏ - Ahmed, Z. and Wang, Z. (2019). Investigating the impact of human capital on the ecological footprint in India: An empirical analysis. Environmental Science and Pollution Research, Vol. 26, No. 26, pp. 26782-26796.‏ - Ahmed, Z., Asghar, M. M., Malik, M. N., & Nawaz, K. (2020). Moving towards a sustainable environment: The dynamic linkage between natural resources, human capital, urbanization, economic growth, and ecological footprint in China. Resources Policy, Vol. 67, No. 101677.‏ - Alvarado, R., Ortiz, C., Jiménez, N., Ochoa-Jiménez, D., & Tillaguango, B. (2021). Ecological footprint, air quality and research and development: The role of agriculture and international trade. Journal of Cleaner Production, Vol. 288, No. 125589.‏ - Bautista-Puig, N., Aleixo, A. M., Leal, S., Azeiteiro, U., & Costas, R. (2021). Unveiling the Research Landscape of Sustainable Development Goals and Their Inclusion in Higher Education Institutions and Research Centers: Major Trends in 2000–2017. Frontiers in Sustainability, Vol. 2, No. 12.‏ - Casoli, E., Piazzi, L., Nicoletti, L., Jona-Lasinio, G., Cecchi, E., Mancini, G., & Ardizzone, G. (2020). Ecology, distribution, and demography of erect bryozoans in Mediterranean coralligenous reefs. Estuarine, Coastal, and Shelf Science, Vol. 235, No. 106573. - Danish, R. and Khan, S. U. D. (2020). Determinants of the ecological footprint: Role of renewable energy, natural resources, and urbanization. Sustainable Cities and Society, Vol. 54, No. 101996.‏ - Destek, M. A. and Sarkodie, S. A. (2019). Investigation of environmental Kuznets curve for ecological footprint: The role of energy and financial development. Science of the Total Environment, Vol. 650, pp. 2483-2489.‏ - Du, Y. W., Wang, Y. C., & Li, W. S. (2022). Emergy ecological footprint method considering uncertainty and its application in evaluating marine ranching resources and environmental carrying capacity. Journal of Cleaner Production, No. 130363. - Huang, Y., Haseeb, M., Usman, M., & Ozturk, I. (2022). Dynamic association between ICT, renewable energy, economic complexity and ecological footprint: Is there any difference between E-7 (developing) and G-7 (developed) countries? Technology in Society, Vol. 68, No. 101853. - Khakpour, B., Rahnama, M., & Damavandi, H. (2015). Application of ecological footprint method in assessing the sustainability of urban development (Case study: Sari City). First National Conference on Geography, Tourism, Natural Resources, and Sustainable Development. - Li, P., Zhang, R., & Xu, L. (2021). Three-dimensional ecological footprint based on ecosystem service value and their drivers: A case study of Urumqi. Ecological Indicators, Vol. 131, No. 108117.‏ - Lin, D., Hanscom, L., Murthy, A., Galli, A., Evans, M., Neill, E., & Wackernagel, M. (2018). Ecological footprint accounting for countries: Updates and results of the National Footprint Accounts, 2012–2018. Resources, Vol. 7, No. 3, p. 58.‏ - Liu, W., Yan, Y., Wang, D., & Ma, W. (2018). Integrate carbon dynamics models for assessing the impact of land use intervention on carbon sequestration ecosystem service. Ecological Indicators, Vol. 91, pp. 268-277.‏ - Saberifar, R. (2007). Sustainable Urban Development, Peak Noor. Humanities, Vol. 5, No. 2, pp. 108-115. - Salehi, I. (2007). The Role of Urban Planning Rules and Regulations in Realizing a Good City and Sustainable Urban Development (Case Study: Tehran). Journal of Environmental Studies, 32(40), 51-62. - Tan, F. and Lu, Z. (2016). Assessing regional sustainable development through an integration of nonlinear principal component analysis and Gram Schmidt orthogonalization. Ecological Indicators, Vol. 63, pp. 71-81.‏ - Taqvaee, M. and Safarabadi, A. (2013). Sustainable urban development and some effective factors for the study of the city of Kermanshah. Journal of Urban Sociological Studies (Urban Studies), Vol. 3, No. 6, pp. 1-22. - Wu, J. and Bai, Z. (2022). Spatial and temporal changes of the ecological footprint of China's resource-based cities in the process of urbanization. Resources Policy, Vol. 75, pp. 102-491. - Yang, X., Li, N., Mu, H., Zhang, M., Pang, J., & Ahmad, M. (2021). Study on the long-term and short-term effects of globalization and population aging on ecological footprint in OECD countries. Ecological Complexity, Vol. 47, No. 100946.‏ - Yu, H., Liu, X., Kong, B., Li, R., & Wang, G. (2019). Landscape ecology development supported by geospatial technologies: A review. Ecological Informatics, Vol. 51, pp. 185-192.‏ - Zafar, M. W., Zaidi, S. A. H., Khan, N. R., Mirza, F. M., Hou, F., & Kirmani, S. A. A. (2019). The impact of natural resources, human capital, and foreign direct investment on the ecological footprint: The case of the United States. Resources Policy, Vol. 63, No. 101428.‏ Abstract Cities manifesting the world's most consuming ecosystem are responsible for a large part of the world's environmental problems. Knowledge of the ecological conditions prevailing in any regions is essential for achieving development. Ecological Footprint Index (EFI) is of great interest for assessing urban communities as a way to measure the levels of sustainability. In this research, the ecological footprint method, which is a quantitative model, was used to analyze the data and measure the sustainability of urban areas. To this goal, an attempt was made to study the EFI and biological capacity of the urban ecosystem of Sari City by using a descriptive-analytical method and relying on library resources. Ecological footprint in the consumption sector, including housing, services, and transportation, was calculated in 4 areas of Sari City. According to the results of data analysis, the ecological footprint of consumption in the mentioned city was equal to 0.94 global hectares and its biological capacity was 0.59 global hectares per person. Comparison of the biological capacity and ecological footprint of this city showed that it had an ecological deficit and was thus ecologically unstable. Among the footprints calculated in the consumption sector, transportation with the ecological footprint of 46.46969 ha had the most ecological footprint. Also, analyses of the ecological footprints in the 4 regions of Sari City showed that Region 1 had a more footprint than other regions, indicating that it followed a higher consumption pattern, but in general, all areas of Sari City were in an ecologically unstable situation according to the research results. Keywords: ecological footprint, sustainable development, urbanization capacity, Sari Introduction: Rapid population growth and consequent expansions of cities, as well as the urbanization process exceeding management and development of urban services, have led to an increasing use of natural resources and energy. The amount of ecological footprint of a society depends on the following factors: population size, average standard of living, average productivity of land ecosystem, efficiency of harvesting, processing, water resources, and use of other resources. By measuring and controlling each of these variables, the effects of resource utilization can be determined, the degree of sustainability of the urban system in relation to the natural ecosystem can be studied, and finally, appropriate policies and strategies can be applied to reduce the effects of ecological footprint and increase urban sustainability. It is important to note that analysis of ecological footprint varies according to the type of community, country, and the amount of technology used in that community. In other words, ecological footprint varies based on the level of development and land use in each country. Generally, the study of ecological footprint shows that the developed countries have a greater impact on natural areas. Methodology: Various social, economic, cultural, political, and environmental aspects, etc. have affected human life. One of the aspects of rapid urban development is increasing urban population and thus increasing use of the ecological resources of cities. The mismatch between the exploitation level of resources and ecological potential of a city has caused urban instability, which needs to be determined by measuring the ecological potential of exploitation so as to increase urban sustainability. In recent decades, there has been a large increase in the population of Sari City, which has caused its ecological instability due to the excessive use of land and ecological resources. Therefore, it is necessary to determine its ecological potential and level of utilization of resources. The present study tried to measure the ecological footprint, consumption, housing, and transportation in Sari City and determine its ecological status and sustainability. Thus, in addition to recognizing the current situation, the future of this city can be predicted and its problems can be solved in terms of each of the mentioned ecological indicators, as well as providing the necessary measures to prevent its possible natural hazards. Discussion: Ecological footprint is a computational tool for measuring population demand on nature. It is mainly used to assess ecological potential, ultimate ecological capacity, and sustainable development. The ecological footprint of a country or region involves the areas of bio-production (land and sea) that will be needed to consolidate current consumptions by using the dominant technology. The Ecological Footprint Index (EFI) includes several special functions in the areas of bio-production, such as land, agriculture, and forestry, both for wood production and carbon sequestration in geospatial pastures and water areas. The key concept for calculating ecological footprint and bioavailability by this index is using the same unit of hectare globally; thus, it is easy to evaluate and compare the studied areas with other areas globally. The ecological footprint method is a prelude to planning and one of the important and essential tools, which helps to achieve sustainability. The results of this research indicated that the ecological footprint of housing in Sari City was 1 hectare worldwide. Of 13980,29 hectares, 2071,55, 3840,81, 1602,64, and 620,66 hectares showed the global ecological footprints of the housing sector in the 1st, 2nd, 3rd, and 4th regions of Sari City, respectively. Among the 4 districts of the city, District 2 had the highest footprint in the housing sector with an ecological footprint of 3840,81 hectares; in other words, the citizens living in this district needed more lands to meet the needs of their housing sector. The ecological footprint of transportation is estimated with regard to urban areas. It is calculated by the sum of the ecological footprints of the Earth and the energy consumptions, including gasoline, diesel, CNG. Conclusion: Due to the nature of this research, library and field methods were used based on quantitative and qualitative data and information. At first, the ecological footprint indicators were developed for Sari City based on library methods. Then, the field information required for each indicator were collected and analyzed. Finally, the status of each indicator and the general situation of the city were determined in terms of ecological footprint and degree of sustainability. The ecological footprint in the city of Sari was 46969,24 hectares worldwide, of which 13955,3, 10736,77, 10563,51, and 11713,66 hectares were the global footprints of Zones 1, 2, 3 and 4, respectively. Ecological sustainability offers solutions that initially require revision in relation to agriculture, housing, energy, urban design, transportation, economy, family, consumer resources, forestry, deserts, and the core values of our lives. The study of the bodies and functions of cities, urban planning and designing, ecological design, ecological village, ecological city, and other forms of environmental designs are essential for achieving and promoting urban sustainability. According to the results obtained from the roles of the various parameters in the stability of Sari City, the most important issue for promoting this city was achieving sustainable development by preventing the pattern of consumerism and replacing it with productivity, as well as taking advantage of the opportunities with regard to the strengths and weaknesses. References: - Abedi, Z. (2017). From Ecological Footprint to Sustainable City. International Conference on Urban Economics. - Ahmad, M., Ahmed, Z., Yang, X., Hussain, N., & Sinha, A. (2021). Financial development and environmental degradation: Do human capital and institutional quality make a difference? Gondwana Research.‏ - Ahmed, Z. and Wang, Z. (2019). Investigating the impact of human capital on the ecological footprint in India: An empirical analysis. Environmental Science and Pollution Research, Vol. 26, No. 26, pp. 26782-26796.‏ - Ahmed, Z., Asghar, M. M., Malik, M. N., & Nawaz, K. (2020). Moving towards a sustainable environment: The dynamic linkage between natural resources, human capital, urbanization, economic growth, and ecological footprint in China. Resources Policy, Vol. 67, No. 101677.‏ - Alvarado, R., Ortiz, C., Jiménez, N., Ochoa-Jiménez, D., & Tillaguango, B. (2021). Ecological footprint, air quality and research and development: The role of agriculture and international trade. Journal of Cleaner Production, Vol. 288, No. 125589.‏ - Bautista-Puig, N., Aleixo, A. M., Leal, S., Azeiteiro, U., & Costas, R. (2021). Unveiling the Research Landscape of Sustainable Development Goals and Their Inclusion in Higher Education Institutions and Research Centers: Major Trends in 2000–2017. Frontiers in Sustainability, Vol. 2, No. 12.‏ - Casoli, E., Piazzi, L., Nicoletti, L., Jona-Lasinio, G., Cecchi, E., Mancini, G., & Ardizzone, G. (2020). Ecology, distribution, and demography of erect bryozoans in Mediterranean coralligenous reefs. Estuarine, Coastal, and Shelf Science, Vol. 235, No. 106573. - Danish, R. and Khan, S. U. D. (2020). Determinants of the ecological footprint: Role of renewable energy, natural resources, and urbanization. Sustainable Cities and Society, Vol. 54, No. 101996.‏ - Destek, M. A. and Sarkodie, S. A. (2019). Investigation of environmental Kuznets curve for ecological footprint: The role of energy and financial development. Science of the Total Environment, Vol. 650, pp. 2483-2489.‏ - Du, Y. W., Wang, Y. C., & Li, W. S. (2022). Emergy ecological footprint method considering uncertainty and its application in evaluating marine ranching resources and environmental carrying capacity. Journal of Cleaner Production, No. 130363. - Huang, Y., Haseeb, M., Usman, M., & Ozturk, I. (2022). Dynamic association between ICT, renewable energy, economic complexity and ecological footprint: Is there any difference between E-7 (developing) and G-7 (developed) countries? Technology in Society, Vol. 68, No. 101853. - Khakpour, B., Rahnama, M., & Damavandi, H. (2015). Application of ecological footprint method in assessing the sustainability of urban development (Case study: Sari City). First National Conference on Geography, Tourism, Natural Resources, and Sustainable Development. - Li, P., Zhang, R., & Xu, L. (2021). Three-dimensional ecological footprint based on ecosystem service value and their drivers: A case study of Urumqi. Ecological Indicators, Vol. 131, No. 108117.‏ - Lin, D., Hanscom, L., Murthy, A., Galli, A., Evans, M., Neill, E., & Wackernagel, M. (2018). Ecological footprint accounting for countries: Updates and results of the National Footprint Accounts, 2012–2018. Resources, Vol. 7, No. 3, p. 58.‏ - Liu, W., Yan, Y., Wang, D., & Ma, W. (2018). Integrate carbon dynamics models for assessing the impact of land use intervention on carbon sequestration ecosystem service. Ecological Indicators, Vol. 91, pp. 268-277.‏ - Saberifar, R. (2007). Sustainable Urban Development, Peak Noor. Humanities, Vol. 5, No. 2, pp. 108-115. - Salehi, I. (2007). The Role of Urban Planning Rules and Regulations in Realizing a Good City and Sustainable Urban Development (Case Study: Tehran). Journal of Environmental Studies, 32(40), 51-62. - Tan, F. and Lu, Z. (2016). Assessing regional sustainable development through an integration of nonlinear principal component analysis and Gram Schmidt orthogonalization. Ecological Indicators, Vol. 63, pp. 71-81.‏ - Taqvaee, M. and Safarabadi, A. (2013). Sustainable urban development and some effective factors for the study of the city of Kermanshah. Journal of Urban Sociological Studies (Urban Studies), Vol. 3, No. 6, pp. 1-22. - Wu, J. and Bai, Z. (2022). Spatial and temporal changes of the ecological footprint of China's resource-based cities in the process of urbanization. Resources Policy, Vol. 75, pp. 102-491. - Yang, X., Li, N., Mu, H., Zhang, M., Pang, J., & Ahmad, M. (2021). Study on the long-term and short-term effects of globalization and population aging on ecological footprint in OECD countries. Ecological Complexity, Vol. 47, No. 100946.‏ - Yu, H., Liu, X., Kong, B., Li, R., & Wang, G. (2019). Landscape ecology development supported by geospatial technologies: A review. Ecological Informatics, Vol. 51, pp. 185-192.‏ - Zafar, M. W., Zaidi, S. A. H., Khan, N. R., Mirza, F. M., Hou, F., & Kirmani, S. A. A. (2019). The impact of natural resources, human capital, and foreign direct investment on the ecological footprint: The case of the United States. Resources Policy, Vol. 63, No. 101428.‏

https://gep.ui.ac.ir/article_27073_0dda66a7767df3c3b4ff750f5231e032.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 2 2023 06 22 Ancestral Tourism: A Novel Market of Isfahan Tourism Ancestral Tourism: A Novel Market of Isfahan Tourism 37 54 27097 10.22108/gep.2022.134558.1536 FA Neda Torabi Farsani Associate Professor in Tourism, Department of Museum and Tourism, Art University of Isfahan, Isfahan, Iran Leila Mirghadr Ph.D. Student of Tourism, Faculty of Tourism, Science and Culture University, Tehran, Iran. Hossein Sadeghi Shahdani Master’s Student in Tourism, Art University of Isfahan, Isfahan, Iran Journal Article 2022 07 31 Abstract Searching in family roots and traveling to discover genealogy with a leisure motivation is called tourism, which can not only create opportunities to introduce and preserve family heritage, but also diversify the tourism market. The present study pursued the following 3 objectives: 1) identification of the potentials of Isfahan City for promoting ancestral tourism, 2) introduction of suitable travel packages of ancestral tourism in Isfahan Province, and 3) identification of appropriate strategies for promoting ancestral tourism. This research was of an exploratory type with qualitative method associated with thematic analysis. The statistical population of the study consisted of the experts in the fields of history, tourism, and culture. The findings of this research illustrated that Isfahan City had the potential of promoting ancestral tourism due to the presence of famous families, who had migrated to this city throughout history, families, who were known for their professions, royal families, neighborhoods called other cities in Isfahan;, and immigrants and refugees throughout the history, as well as different past divisions of the country. In addition, the incoming and outgoing travelers and city tour packages could be introduced as the revenue-generating opportunities in the ancestral tourism sector in this province. Lastly, the 4 strategies of market penetration, product development, horizontal integration, and collaboration could be introduced as suitable strategies for the prosperity of ancestral tourism in Isfahan City. Keywords: ancestral tourism, Isfahan, genealogical tourism, roots tourism Introduction Nowadays, searching in family roots, as well as traveling and discovering genealogy with a leisure motivation is one of the aspects of tourism that can not only create opportunities to introduce and preserve family heritage, but also diversify the tourism market. Ancestral tourism has been proposed as one of the rapidly growing segments of the heritage tourism market (Basu, 2004; Santos and Yan, 2010), which was not noticed by academics in 2005 and was just introduced under the umbrella of cultural heritage tourism. The existing research literature demonstrate that the genealogical tourists are not a homogeneous group whose activities have been described by presenting a set of terms. Ancestral tourism is variously classified into genealogical tourism, heritage tourism, diaspora tourism, cultural tourism, and roots tourism (McCain and Ray, 2003, pp. 713-17; Timothy and Teye, 2004; Basu, 2004; Gaudry, 2007). It is noteworthy that Isfahan Province has been invaded by many foreigners throughout history and many ethnic groups, such as Georgians, Armenians, etc. have immigrated to Isfahan. In addition, Chaharmahal and Bakhtiari Province and Yazd Province have been parts of Isfahan. Historical evidence has demonstrated that Isfahan has a great potential to organize ancestral tours. Therefore, the current research sought to identify suitable travel packages for the development of ancestral tourism in Isfahan Province and identify suitable solutions and strategies for the development of this new niche market of tourism from the experts’ points of view. Methodology The present study pursued the following 3 objectives: 1) identifying the potentials of Isfahan City for promoting ancestral tourism, 2) introducing suitable travel packages of ancestral tourism in Isfahan Province, and 3) identifying appropriate strategies for promoting ancestral tourism. This research was of an exploratory type with a qualitative method based on thematic analysis. MAXQDA software was used as a tool for data analysis. The statistical population of the study included the experts in the fields of history, tourism, and culture. The data were gathered through snowball sampling or chain-referral sampling as a non-probability sampling technique. After each interview, the data were coded until the codes reached a saturation point in the 16th interview and no new codes were added to the previous codes. Results and discussion The research findings indicated that Isfahan had the potential of promoting ancestral tourism due to the presence of famous families, who had migrated to this city throughout history, families, who were known for their professions, royal families, neighborhoods called other cities in Isfahan, and immigrants and refugees throughout the history, as well as different past divisions of the country. In addition, the incoming and outgoing people and city tour packages could be introduced as the revenue-generating opportunities in the ancestral tourism sector in this province. Lastly, the 4 strategies of market penetration, product development, horizontal integration, and collaboration could be introduced as suitable strategies for the prosperity of ancestral tourism in Isfahan. Conclusion Ancestral tourism is a journey that is done with the motivation of discovering and searching family genealogies. The results of our data analysis indicated that famous families, such as Kazeruni, Homayi, Sheikh Baha'i, etc., who had migrated to this city throughout history live in Isfahan City. In addition, being the capital of Iran, especially during the Safavid dynasty, and the 34-year rule of Mass'oud Mirza Zell-e Soltan, the first son of Naser al-Din Shah Qajar, had made this city suitable for ancestral tours. There were also families in Isfahan, who were famous for their professions. Moreover, there existed some neighborhoods in Isfahan called other cities. The presence of immigrants and refugees throughout the history and different past divisions of the country were the other potentials that attracted visitors and genealogical tourists to Isfahan. The second purpose of the present study was to identify travel packages with the subject of genealogy. The results of data analysis using a qualitative method based on thematic analysis demonstrated that the design of packages for visits to this city and province with the subject of genealogy, including itinerary designs for the incoming and outgoing tourists, especially for Armenians, Georgians, and Iraqis, would not only help diversify the tourism market of the province, but also be a source of income for travel agencies and tour guides. It is worth mentioning most of the researches in the field of ancestral tourism (Pelliccia et al., 2018; Murdy et al., 2018) emphasized on incoming travel packages, while the city of Isfahan and Isfahan province had the potential of outgoing packages and city tours as well. The third goal of the current research was to identify appropriate strategies for the prosperity of ancestral tourism in Isfahan City. Ultimately, the 4 strategies of market penetration, product development, horizontal integration, and collaboration were introduced as suitable strategies for the prosperity of ancestral tourism in this city. Birtwistle (2005) also emphasized marketing strategy for ancestral tourism boom, which was confirmed by the results of this investigation. References - Agoes, A. and Par, M. M. (2016, May). Tourism Management in Cikondang Ancestral Hamlet. In Asia Tourism Forum 2016-the 12th Biennial Conference of Hospitality and Tourism Industry in Asia (pp. 78-84, Atlantis Press. - Alexander, M., Bryce, D., & Murdy, S. (2017). Delivering the past: Providing personalized ancestral tourism experiences. Journal of Travel Research, 56(4), 543-555. - Basu, P. (2004). My own island home: The Orkney homecoming. Journal of Material Culture, 9(1), 27-42. - Basu, P. (2005). “Roots Tourism as Return Movement: Semantics and the Scottish Diaspora.” In Emigrant Homecomings: The Return Movement of Emigrants 1600-2000, edited by M. Harper, 131-50, Manchester: Manchester University Press. - Basu, P. (2007). Highland homecomings: Genealogy and heritage tourism in the Scottish diaspora., London: Routledge. - Birtwistle, M. (2005). Genealogy tourism: The Scottish market opportunities. In M. Novelli (Ed.), Niche tourism: Contemporary issues, trends, and cases (pp. 59-72). Oxford: Elsevier. - Coles, T. E. and Timothy, D. J. (Eds.) (2004). Tourism, diasporas, and space London: Routledge. Fowler, S. (2003). Ancestral tourism. Insights, 2003, D31-D36 - Fowler, H. W. and Fowler, F. G. (1974). The concise Oxford dictionary of current English. Oxford: Oxford University Press. - Gaudry, L. (2007). What Clan Are You? An Exploration of Heritage and Ancestral Tourism with Canadian Scottish Descendents (Master's thesis, University of Waterloo), Ontario, Canada. - Garrod, B. and Fyall, A. (2000). Managing heritage tourism. Annals of tourism research, 27(3), 682-708. - Gergelyova, M. (2007). An investigation of the potential of genealogy tourism as a catalyst for regional development in County Galway. Unpublished thesis (Master of Arts in Heritage Studies), Galway-Mayo Institute of Technology. Galway, Ireland. http://hdl.handle.net/10759/314245 - Harraway, D.J. (1997), Modest Witness. London: Routledge - Higginbotham, G. (2012). Seeking roots and tracing lineages: Constructing a framework of reference for roots and genealogical tourism. Journal of Heritage Tourism, 7(3), 189-203. - Hjorthén, A. (2021). Old World Homecomings: Campaigns of Ancestral Tourism and Cultural Diplomacy (1945–66). Journal of Contemporary History, 56(4), 1147-1170. - Luke, C. (2013). Cultural sovereignty in the Balkans and Turkey: The politics of preservation and rehabilitation. Journal of Social Archaeology, 13(3), 350-370. - McCain, G. and Ray, N. M. (2003). Legacy tourism: The search for personal meaning in heritage travel. Tourism Management, 24(6), 713-717. - Maruyama, N. U., Weber, I., & Stronza, A. L. (2010). Negotiating identity. Experiences of Tourism Culture & Communication, 10(1), 1-14. - Murdy, S., Alexander, M., & Bryce, D. (2018). What pulls ancestral tourists ‘home’? An analysis of ancestral tourist motivations. Tourism Management, 64, 13-19. - Nash, C. (2005). Geographies of relatedness. Transactions of the Institute of British Geographers, 30(4), 449-462. - Russell, D. W. (2008). Nostalgic tourism. Journal of Travel & Tourism Marketing, 25(2), 103-116. - Stephenson, M. L. (2002). Travelling to the ancestral homelands: The aspirations and experiences of a UK Caribbean community. Current Issues in Tourism, 5(5), 378-425. - Pelliccia, A. (2018). In the family home: Roots tourism among Greek second generation in Italy. Current Issues in Tourism, 21(18), 2108-2123. - Scottish Parliament (2000). The Scottish Tourism Industry. The Information Center Research Notes. Database online: Available at www.scottishparliament.uk/SI/whats_happening/research/pdf-res-notes/rn00-77. pdf - Santos, C. A. and Yan, G. (2010). Genealogical tourism: A phenomenological examination. Journal of Travel Research, 49(1), 56-67. - Sim, D. and Leith, M. (2013). Diaspora tourists and the Scottish Homecoming 2009. Journal of Heritage Tourism, 8(4), 259-274. - Wilson, F. R., Pan, W., & Schumsky, D. A. (2012). Recalculation of the critical values for Lawshe’s content validity ratio. Measurement and Evaluation in Counseling and Development, 45(3), 197-210. Abstract Searching in family roots and traveling to discover genealogy with a leisure motivation is called tourism, which can not only create opportunities to introduce and preserve family heritage, but also diversify the tourism market. The present study pursued the following 3 objectives: 1) identification of the potentials of Isfahan City for promoting ancestral tourism, 2) introduction of suitable travel packages of ancestral tourism in Isfahan Province, and 3) identification of appropriate strategies for promoting ancestral tourism. This research was of an exploratory type with qualitative method associated with thematic analysis. The statistical population of the study consisted of the experts in the fields of history, tourism, and culture. The findings of this research illustrated that Isfahan City had the potential of promoting ancestral tourism due to the presence of famous families, who had migrated to this city throughout history, families, who were known for their professions, royal families, neighborhoods called other cities in Isfahan;, and immigrants and refugees throughout the history, as well as different past divisions of the country. In addition, the incoming and outgoing travelers and city tour packages could be introduced as the revenue-generating opportunities in the ancestral tourism sector in this province. Lastly, the 4 strategies of market penetration, product development, horizontal integration, and collaboration could be introduced as suitable strategies for the prosperity of ancestral tourism in Isfahan City. Keywords: ancestral tourism, Isfahan, genealogical tourism, roots tourism Introduction Nowadays, searching in family roots, as well as traveling and discovering genealogy with a leisure motivation is one of the aspects of tourism that can not only create opportunities to introduce and preserve family heritage, but also diversify the tourism market. Ancestral tourism has been proposed as one of the rapidly growing segments of the heritage tourism market (Basu, 2004; Santos and Yan, 2010), which was not noticed by academics in 2005 and was just introduced under the umbrella of cultural heritage tourism. The existing research literature demonstrate that the genealogical tourists are not a homogeneous group whose activities have been described by presenting a set of terms. Ancestral tourism is variously classified into genealogical tourism, heritage tourism, diaspora tourism, cultural tourism, and roots tourism (McCain and Ray, 2003, pp. 713-17; Timothy and Teye, 2004; Basu, 2004; Gaudry, 2007). It is noteworthy that Isfahan Province has been invaded by many foreigners throughout history and many ethnic groups, such as Georgians, Armenians, etc. have immigrated to Isfahan. In addition, Chaharmahal and Bakhtiari Province and Yazd Province have been parts of Isfahan. Historical evidence has demonstrated that Isfahan has a great potential to organize ancestral tours. Therefore, the current research sought to identify suitable travel packages for the development of ancestral tourism in Isfahan Province and identify suitable solutions and strategies for the development of this new niche market of tourism from the experts’ points of view. Methodology The present study pursued the following 3 objectives: 1) identifying the potentials of Isfahan City for promoting ancestral tourism, 2) introducing suitable travel packages of ancestral tourism in Isfahan Province, and 3) identifying appropriate strategies for promoting ancestral tourism. This research was of an exploratory type with a qualitative method based on thematic analysis. MAXQDA software was used as a tool for data analysis. The statistical population of the study included the experts in the fields of history, tourism, and culture. The data were gathered through snowball sampling or chain-referral sampling as a non-probability sampling technique. After each interview, the data were coded until the codes reached a saturation point in the 16th interview and no new codes were added to the previous codes. Results and discussion The research findings indicated that Isfahan had the potential of promoting ancestral tourism due to the presence of famous families, who had migrated to this city throughout history, families, who were known for their professions, royal families, neighborhoods called other cities in Isfahan, and immigrants and refugees throughout the history, as well as different past divisions of the country. In addition, the incoming and outgoing people and city tour packages could be introduced as the revenue-generating opportunities in the ancestral tourism sector in this province. Lastly, the 4 strategies of market penetration, product development, horizontal integration, and collaboration could be introduced as suitable strategies for the prosperity of ancestral tourism in Isfahan. Conclusion Ancestral tourism is a journey that is done with the motivation of discovering and searching family genealogies. The results of our data analysis indicated that famous families, such as Kazeruni, Homayi, Sheikh Baha'i, etc., who had migrated to this city throughout history live in Isfahan City. In addition, being the capital of Iran, especially during the Safavid dynasty, and the 34-year rule of Mass'oud Mirza Zell-e Soltan, the first son of Naser al-Din Shah Qajar, had made this city suitable for ancestral tours. There were also families in Isfahan, who were famous for their professions. Moreover, there existed some neighborhoods in Isfahan called other cities. The presence of immigrants and refugees throughout the history and different past divisions of the country were the other potentials that attracted visitors and genealogical tourists to Isfahan. The second purpose of the present study was to identify travel packages with the subject of genealogy. The results of data analysis using a qualitative method based on thematic analysis demonstrated that the design of packages for visits to this city and province with the subject of genealogy, including itinerary designs for the incoming and outgoing tourists, especially for Armenians, Georgians, and Iraqis, would not only help diversify the tourism market of the province, but also be a source of income for travel agencies and tour guides. It is worth mentioning most of the researches in the field of ancestral tourism (Pelliccia et al., 2018; Murdy et al., 2018) emphasized on incoming travel packages, while the city of Isfahan and Isfahan province had the potential of outgoing packages and city tours as well. The third goal of the current research was to identify appropriate strategies for the prosperity of ancestral tourism in Isfahan City. Ultimately, the 4 strategies of market penetration, product development, horizontal integration, and collaboration were introduced as suitable strategies for the prosperity of ancestral tourism in this city. Birtwistle (2005) also emphasized marketing strategy for ancestral tourism boom, which was confirmed by the results of this investigation. References - Agoes, A. and Par, M. M. (2016, May). Tourism Management in Cikondang Ancestral Hamlet. In Asia Tourism Forum 2016-the 12th Biennial Conference of Hospitality and Tourism Industry in Asia (pp. 78-84, Atlantis Press. - Alexander, M., Bryce, D., & Murdy, S. (2017). Delivering the past: Providing personalized ancestral tourism experiences. Journal of Travel Research, 56(4), 543-555. - Basu, P. (2004). My own island home: The Orkney homecoming. Journal of Material Culture, 9(1), 27-42. - Basu, P. (2005). “Roots Tourism as Return Movement: Semantics and the Scottish Diaspora.” In Emigrant Homecomings: The Return Movement of Emigrants 1600-2000, edited by M. Harper, 131-50, Manchester: Manchester University Press. - Basu, P. (2007). Highland homecomings: Genealogy and heritage tourism in the Scottish diaspora., London: Routledge. - Birtwistle, M. (2005). Genealogy tourism: The Scottish market opportunities. In M. Novelli (Ed.), Niche tourism: Contemporary issues, trends, and cases (pp. 59-72). Oxford: Elsevier. - Coles, T. E. and Timothy, D. J. (Eds.) (2004). Tourism, diasporas, and space London: Routledge. Fowler, S. (2003). Ancestral tourism. Insights, 2003, D31-D36 - Fowler, H. W. and Fowler, F. G. (1974). The concise Oxford dictionary of current English. Oxford: Oxford University Press. - Gaudry, L. (2007). What Clan Are You? An Exploration of Heritage and Ancestral Tourism with Canadian Scottish Descendents (Master's thesis, University of Waterloo), Ontario, Canada. - Garrod, B. and Fyall, A. (2000). Managing heritage tourism. Annals of tourism research, 27(3), 682-708. - Gergelyova, M. (2007). An investigation of the potential of genealogy tourism as a catalyst for regional development in County Galway. Unpublished thesis (Master of Arts in Heritage Studies), Galway-Mayo Institute of Technology. Galway, Ireland. http://hdl.handle.net/10759/314245 - Harraway, D.J. (1997), Modest Witness. London: Routledge - Higginbotham, G. (2012). Seeking roots and tracing lineages: Constructing a framework of reference for roots and genealogical tourism. Journal of Heritage Tourism, 7(3), 189-203. - Hjorthén, A. (2021). Old World Homecomings: Campaigns of Ancestral Tourism and Cultural Diplomacy (1945–66). Journal of Contemporary History, 56(4), 1147-1170. - Luke, C. (2013). Cultural sovereignty in the Balkans and Turkey: The politics of preservation and rehabilitation. Journal of Social Archaeology, 13(3), 350-370. - McCain, G. and Ray, N. M. (2003). Legacy tourism: The search for personal meaning in heritage travel. Tourism Management, 24(6), 713-717. - Maruyama, N. U., Weber, I., & Stronza, A. L. (2010). Negotiating identity. Experiences of Tourism Culture & Communication, 10(1), 1-14. - Murdy, S., Alexander, M., & Bryce, D. (2018). What pulls ancestral tourists ‘home’? An analysis of ancestral tourist motivations. Tourism Management, 64, 13-19. - Nash, C. (2005). Geographies of relatedness. Transactions of the Institute of British Geographers, 30(4), 449-462. - Russell, D. W. (2008). Nostalgic tourism. Journal of Travel & Tourism Marketing, 25(2), 103-116. - Stephenson, M. L. (2002). Travelling to the ancestral homelands: The aspirations and experiences of a UK Caribbean community. Current Issues in Tourism, 5(5), 378-425. - Pelliccia, A. (2018). In the family home: Roots tourism among Greek second generation in Italy. Current Issues in Tourism, 21(18), 2108-2123. - Scottish Parliament (2000). The Scottish Tourism Industry. The Information Center Research Notes. Database online: Available at www.scottishparliament.uk/SI/whats_happening/research/pdf-res-notes/rn00-77. pdf - Santos, C. A. and Yan, G. (2010). Genealogical tourism: A phenomenological examination. Journal of Travel Research, 49(1), 56-67. - Sim, D. and Leith, M. (2013). Diaspora tourists and the Scottish Homecoming 2009. Journal of Heritage Tourism, 8(4), 259-274. - Wilson, F. R., Pan, W., & Schumsky, D. A. (2012). Recalculation of the critical values for Lawshe’s content validity ratio. Measurement and Evaluation in Counseling and Development, 45(3), 197-210.

https://gep.ui.ac.ir/article_27097_ab0c117f85f74e4b4b07da7326e75d9f.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 2 2023 06 22 Climatic Zoning of the Southern Coastline of the Caspian Sea Using Multivariate Statistical Methods Climatic Zoning of the Southern Coastline of the Caspian Sea Using Multivariate Statistical Methods 55 74 26659 10.22108/gep.2022.127922.1409 FA Hadis Sadeghi Ph.D. in Climatology, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran Hosein Mohammadi Professor, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran Ali Akbar Shamsipour Associate Professor, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran Mostafa Karimi Assistant Professor, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iranan Journal Article 2021 04 03 Abstract Identifying homogeneous climatic zones plays an important role in the success of regional development programs. The climate system is composed of various elements, factors, and variables that together form the climatic components of a region. Multi-characteristic and multivariate methods can combine and overlap the types of elements and variables effective in constructing the climate with appropriate weights in the climatic zoning area. In the present study, climatic zoning of the Caspian region was performed using factor analysis and cluster analysis. For this purpose, a 30*30 matrix consisting of 30 meteorological stations and 30 climatic and environmental variables was formed. The results of factor analysis showed that the climate of the region is affected by 5 factors including precipitation-humidity, temperature, wind, sunlight, and environmental factors. These factors explained a total of 92.5% of the variance of the data. Then, cluster analysis was performed by the hierarchical integration method of Ward on the five mentioned factors. The results showed four climatic zones including humid, semi-humid, semi-arid, and arid in the study area. Keywords: Climate Zoning, Factor Analysis, Multivariate Analysis, Cluster Analysis, Caspian Coastline, Iran. Introduction Knowledge of climatic zones has long attracted the attention of many scientists and has led to the presentation of various methods of climatic classification such as De Marten, Koppen, Ivanov, Amberje, Selianinov, Hansen, and others. With significant computer advances in recent years, it has become possible to perform internal methods on large volumes of data and the use of new classification methods such as multivariate statistical methods (factor analysis and cluster analysis) have expanded to classify the interactions of a large number of climatic components. Identifying homogeneous climatic zones and the capabilities and limitations of the agricultural climate of each climatic zone can play an effective role in carrying out projects and planning. Methodology The study area in this research is the greenest and rainiest region of the country (i.e. the southern shores of the Caspian Sea). In this study, factor analysis with Varimax rotation has been used to identify the factors affecting the climate of the study area and the hierarchical clustering method has been used in its climatic zoning. For this purpose, out of 30 variables affecting agricultural activities, including three environmental variables of latitude, altitude, and distance from the sea, as well as 27 climatic variables including maximum, minimum, and average temperature, an average temperature of winter, spring, summer, and autumn were used. Number of days with a maximum temperature of 30 ° C and above and minimum temperature of 0 ° C and below, average sunny hours, number of full cloudy, partly cloudy, and sunny days, hours of radiation, average relative humidity and annual rainfall, average winter, spring, summer and autumn, total annual rainfall with more than 1 mm, number of days more than 1 mm, more than 5 mm, more than 10 mm and more than 20 mm, average evapotranspiration and average wind speed in 30 stations, and the synoptic meteorology of the region with a suitable statistical period between 2002 to 2018 were used on a daily time scale. ETO Calculator software and radiation amount were used using the Angstrom-Prescott function to calculate the reference evapotranspiration. Discussion The results of the Bartlett test showed that the data are suitable for factor analysis and the results can be generalized to the statistical population. The results also showed that the region's climate is the result of the interaction of 5 different factors and explains 92.5% of the total variance. Based on the results of factor scores of variables, variables of average annual rainfall, winter, spring, summer, and autumn, total annual rainfall on days with more than 1 mm, number of days with more than 1 and 5 mm and with heavy rainfall of more than 10 and 20 mm, the number of full and partly cloudy days and average relative humidity had the highest correlation coefficient with the first factor. Due to the fact that the naming of the factors is based on the highest values of correlation coefficients, it was named the precipitation-moisture factor. In the second factor, the variables of average minimum, maximum and average daily temperatures, average temperatures of winter, spring, summer, and autumn, and the number of days with a maximum temperature of 30 ° C and above had the highest factor load and weight. Therefore, the second factor was named the temperature factor. The third factor explains 6.5% of the total variance of the data and was named the wind factor, as the mean variable of wind speed had the highest correlation coefficient with this factor. The fourth factor explains only 5.8% of the variance of the data changes. Because the variable number of sunny days had the highest correlation coefficient with this factor, it was named the factor of sunshine. The fifth factor explains only 5.5% of the variance of the data changes. Because the variables of distance from the sea and latitude had the highest factor and weight in this factor, it was named an environmental factor. After performing factor analysis and identifying the main factors using the hierarchical clustering method by the Ward method, the studied stations are grouped into homogeneous categories and zones and climatic classification was performed. According to the cluster tree diagram obtained and the cutting location of the diagram at the interval of 8, 4 clusters were identified. According to the findings, four climatic zones including a humid climate zone located in the northern parts of Gilan province to the western and central plains of Mazandaran province, a semi-humid climate zone including the eastern and central parts of Mazandaran province to parts of the western and southern regions of Gilan province and western parts of Golestan province, semi-arid climate zone located in the southern parts of the southern shores of the Caspian Sea, and arid climate zone located in the eastern and northeastern parts of Golestan province for the region were identified. Conclusion The output of this study was four climatic clusters for the study area, which is different from the study of Nazmafar and Goldoust (2015) who in their research on the zoning of the north and northwest of the country, identified three climatic zones for the northern region. In their study, the first climate zone with the effect of precipitation factor was located in the southwest of the Caspian Sea and the second climate zone with the effect of temperature factor was located in a part of the southern shores of the Caspian Sea and the northern slopes of Alborz Mountain range. Therefore, the present study has provided more specific and accurate climatic zones. The findings of this study are consistent with the findings of Montazeri and Bai (2012). They showed in their research that Mazandaran province was located in two humid and semi-cold climates with low rainfall, Gilan province was located in two humid and semi-humid regions, and Golestan province was located in the climate zones of humid, semi-humid, cold, low rainfall, semi-cold, and low rainfall. Also, the findings of this study were consistent with the findings of Fallah Ghaleri et al. (2015) in the field of climatic zoning in Gilan province. References - Biabiany, E., Bernard, D., Page, V., & Paugam-Moisy, H. (2020). Design of an expert distance metric for climate clustering: The case of rainfall in the Lesser Antilles. Journal of Computers and Geosciences, 145, 1-15. - Carvalho, M., Melo-Gonçalves, P., Teixeira, J., & Rocha, T. (2016). Regionalization of Europe based on a K-Means Cluster Analysis of the climate change of temperatures and precipitation. Journal of Physics and Chemistry of the Earth, 94, 22-28. - Schmidt, G. (2019). The Ecological relevance of parameter choice in describing climate. The Ecological relevance of parameter choice in describing climate, 6, 1-26. - Tapiador, F., Moreno, R., & Navarro, A. (2019). Consensus in climate classifications for present climate and global warming scenarios. Journal of Atmospheric Research, 216, 26-36. - Yang, L., Bai, L., Song, B., & Liu, N. (2020). A new approach to develop a climate classification for building energy efficiency addressing Chinese climate characteristics. Energy, 195, 1-14. - Zhao, J., Xia, B., Han, J., & Liang, K. (2020). Technological adaption zone of passive evaporative cooling of China, based on clustering analysis. Journal of Sustainable Cities and Society, 66, 1-10. Abstract Identifying homogeneous climatic zones plays an important role in the success of regional development programs. The climate system is composed of various elements, factors, and variables that together form the climatic components of a region. Multi-characteristic and multivariate methods can combine and overlap the types of elements and variables effective in constructing the climate with appropriate weights in the climatic zoning area. In the present study, climatic zoning of the Caspian region was performed using factor analysis and cluster analysis. For this purpose, a 30*30 matrix consisting of 30 meteorological stations and 30 climatic and environmental variables was formed. The results of factor analysis showed that the climate of the region is affected by 5 factors including precipitation-humidity, temperature, wind, sunlight, and environmental factors. These factors explained a total of 92.5% of the variance of the data. Then, cluster analysis was performed by the hierarchical integration method of Ward on the five mentioned factors. The results showed four climatic zones including humid, semi-humid, semi-arid, and arid in the study area. Keywords: Climate Zoning, Factor Analysis, Multivariate Analysis, Cluster Analysis, Caspian Coastline, Iran. Introduction Knowledge of climatic zones has long attracted the attention of many scientists and has led to the presentation of various methods of climatic classification such as De Marten, Koppen, Ivanov, Amberje, Selianinov, Hansen, and others. With significant computer advances in recent years, it has become possible to perform internal methods on large volumes of data and the use of new classification methods such as multivariate statistical methods (factor analysis and cluster analysis) have expanded to classify the interactions of a large number of climatic components. Identifying homogeneous climatic zones and the capabilities and limitations of the agricultural climate of each climatic zone can play an effective role in carrying out projects and planning. Methodology The study area in this research is the greenest and rainiest region of the country (i.e. the southern shores of the Caspian Sea). In this study, factor analysis with Varimax rotation has been used to identify the factors affecting the climate of the study area and the hierarchical clustering method has been used in its climatic zoning. For this purpose, out of 30 variables affecting agricultural activities, including three environmental variables of latitude, altitude, and distance from the sea, as well as 27 climatic variables including maximum, minimum, and average temperature, an average temperature of winter, spring, summer, and autumn were used. Number of days with a maximum temperature of 30 ° C and above and minimum temperature of 0 ° C and below, average sunny hours, number of full cloudy, partly cloudy, and sunny days, hours of radiation, average relative humidity and annual rainfall, average winter, spring, summer and autumn, total annual rainfall with more than 1 mm, number of days more than 1 mm, more than 5 mm, more than 10 mm and more than 20 mm, average evapotranspiration and average wind speed in 30 stations, and the synoptic meteorology of the region with a suitable statistical period between 2002 to 2018 were used on a daily time scale. ETO Calculator software and radiation amount were used using the Angstrom-Prescott function to calculate the reference evapotranspiration. Discussion The results of the Bartlett test showed that the data are suitable for factor analysis and the results can be generalized to the statistical population. The results also showed that the region's climate is the result of the interaction of 5 different factors and explains 92.5% of the total variance. Based on the results of factor scores of variables, variables of average annual rainfall, winter, spring, summer, and autumn, total annual rainfall on days with more than 1 mm, number of days with more than 1 and 5 mm and with heavy rainfall of more than 10 and 20 mm, the number of full and partly cloudy days and average relative humidity had the highest correlation coefficient with the first factor. Due to the fact that the naming of the factors is based on the highest values of correlation coefficients, it was named the precipitation-moisture factor. In the second factor, the variables of average minimum, maximum and average daily temperatures, average temperatures of winter, spring, summer, and autumn, and the number of days with a maximum temperature of 30 ° C and above had the highest factor load and weight. Therefore, the second factor was named the temperature factor. The third factor explains 6.5% of the total variance of the data and was named the wind factor, as the mean variable of wind speed had the highest correlation coefficient with this factor. The fourth factor explains only 5.8% of the variance of the data changes. Because the variable number of sunny days had the highest correlation coefficient with this factor, it was named the factor of sunshine. The fifth factor explains only 5.5% of the variance of the data changes. Because the variables of distance from the sea and latitude had the highest factor and weight in this factor, it was named an environmental factor. After performing factor analysis and identifying the main factors using the hierarchical clustering method by the Ward method, the studied stations are grouped into homogeneous categories and zones and climatic classification was performed. According to the cluster tree diagram obtained and the cutting location of the diagram at the interval of 8, 4 clusters were identified. According to the findings, four climatic zones including a humid climate zone located in the northern parts of Gilan province to the western and central plains of Mazandaran province, a semi-humid climate zone including the eastern and central parts of Mazandaran province to parts of the western and southern regions of Gilan province and western parts of Golestan province, semi-arid climate zone located in the southern parts of the southern shores of the Caspian Sea, and arid climate zone located in the eastern and northeastern parts of Golestan province for the region were identified. Conclusion The output of this study was four climatic clusters for the study area, which is different from the study of Nazmafar and Goldoust (2015) who in their research on the zoning of the north and northwest of the country, identified three climatic zones for the northern region. In their study, the first climate zone with the effect of precipitation factor was located in the southwest of the Caspian Sea and the second climate zone with the effect of temperature factor was located in a part of the southern shores of the Caspian Sea and the northern slopes of Alborz Mountain range. Therefore, the present study has provided more specific and accurate climatic zones. The findings of this study are consistent with the findings of Montazeri and Bai (2012). They showed in their research that Mazandaran province was located in two humid and semi-cold climates with low rainfall, Gilan province was located in two humid and semi-humid regions, and Golestan province was located in the climate zones of humid, semi-humid, cold, low rainfall, semi-cold, and low rainfall. Also, the findings of this study were consistent with the findings of Fallah Ghaleri et al. (2015) in the field of climatic zoning in Gilan province. References - Biabiany, E., Bernard, D., Page, V., & Paugam-Moisy, H. (2020). Design of an expert distance metric for climate clustering: The case of rainfall in the Lesser Antilles. Journal of Computers and Geosciences, 145, 1-15. - Carvalho, M., Melo-Gonçalves, P., Teixeira, J., & Rocha, T. (2016). Regionalization of Europe based on a K-Means Cluster Analysis of the climate change of temperatures and precipitation. Journal of Physics and Chemistry of the Earth, 94, 22-28. - Schmidt, G. (2019). The Ecological relevance of parameter choice in describing climate. The Ecological relevance of parameter choice in describing climate, 6, 1-26. - Tapiador, F., Moreno, R., & Navarro, A. (2019). Consensus in climate classifications for present climate and global warming scenarios. Journal of Atmospheric Research, 216, 26-36. - Yang, L., Bai, L., Song, B., & Liu, N. (2020). A new approach to develop a climate classification for building energy efficiency addressing Chinese climate characteristics. Energy, 195, 1-14. - Zhao, J., Xia, B., Han, J., & Liang, K. (2020). Technological adaption zone of passive evaporative cooling of China, based on clustering analysis. Journal of Sustainable Cities and Society, 66, 1-10.

https://gep.ui.ac.ir/article_26659_e6ff0af89d2685ff4115113db187c69c.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 2 2023 06 22 Assessing Peasant Farmers’ Challenges for Achieving Sustainable Rural Development in Iran (Case study: Zanjan Province) Assessing Peasant Farmers’ Challenges for Achieving Sustainable Rural Development in Iran (Case study: Zanjan Province) 75 90 27214 10.22108/gep.2022.130710.1460 FA Zoleikha Naderkhani Ph.D. Student in Geography and Rural Planning, University of Isfahan, Isfahan, Iran Yusef Ghanbari Associate Professor, Department of Geography and Rural Planning, University of Isfahan, Isfahan, Iran Journal Article 2021 09 28 Abstract: The peasant exploitation system is an undeniable fact in the rural economy of Iran. Many rural families’ incomes are based on this agricultural system. Despite many problems, peasant farmers are considered as the most important factor for achieving agricultural and rural developments due to their knowledge of the countryside and agriculture. In this research, we studied the problems and challenges of peasant farmers in Zanjan Province. This research was an applied one in terms of purpose and descriptive and analytical in nature. The data required for the research were collected by using a survey method (questionnaire, interview, and observation). The statistical population of this study included 40 villages from 8 cities with peasant cultivation. Finally, 340 questionnaires were randomly distributed among the rural households by using Cochran's formula and the exploratory factor analysis was used for analysis. Cronbach's alpha coefficient for the entire research questionnaire was obtained to be 873., which confirmed its overall reliability. The results of factor analysis showed that the 5 economic, managerial, infrastructure-environmental, social and local community factors were finally able to explain 66% of the variance of the rural farmers’ challenges. In the above-mentioned factors, farmers’ low incomes from cultivation (773.), lack of timely and appropriate government support of the farmers (715.), destruction and change of using orchards and agricultural lands (746.), the farmers’ lack of self-confidence and skills (695.), and low quality and dispersion of agricultural lands (609.) were the most important and influential challenges for the agricultural operators, respectively. Based on the results, it could be said that most of the challenges and rural problems in Zanjan Province could be solved with principled management and purposeful and practical planning. Keywords: agriculture, sustainable development, peasant exploitation, Zanjan Province 1.Introduction: The features of agriculture convert it into a unique instrument for progress. Agriculture can be contributive in other sectors of the economy and actualize faster development, reduce poverty, and cause stability in environmental issues. It can be an economic source for providing investment opportunities in the private sector and an initial driving force in the related industries (Iravani and Varmarzyari, 2008). Land exploitation systems determine the human-environment correlations for proper land use, which in turn make natural grounds and sources to be advantageous in the socio-economic dimensions and stability of agriculture. Considering a specific system in agricultural land exploitation without considering technological advances, industrial ranking, socio-economic-strategic opportunities, employment issues, probability of absorbing skilled farmers, etc. is not a rational procedure (Motiei-Langroudi & Shamsaei, 2009). Land exploitation in an area is the result of a set of historic events, interactions of economic forces with the environment, and social values. Despite initial distribution from a geographical area and exploitation manner therein, the present concern for ecological and cultural aspects is evident. All these provide a proper opportunity to assess the relationship between land exploitation and the environment (Sing & Dillon, 1995). Peasant exploitation system in Iran is a basic principle in rural agriculture. The incomes, employments, and livelihoods of many rural families are based on this agricultural system. Yet, despite the existence of many problems, peasant farmers are considered as the most important factor for achieving agricultural and rural developments due to their knowledge of the countryside and agriculture. Agriculture forms the basis of Iran's rural economy and rural economy is heavily dependent on and affected by agriculture. This sector with its existing system has major problems for various reasons, such as increasing farmers’ ages and inabilities to farm, lack of desire of the young generation to live in the village and continue agricultural activities, migration from villages and abandonment of lands, lack of knowledge and agricultural tools, lack of attention and proper management, and lack of organization, which should be considered and treated properly. Methodology: This research aimed at analyzing challenges of the peasant farmers in Zanjan Province with the aim of removing barriers to rural development. It was an applied research in terms of purpose and descriptive-analytical in nature. The statistical population included the farmers' households working in peasant exploitation systems. 340 households were selected as the sample size by using Cochran's formula. All the questionnaires were prepared by the villagers from rural areas and the researchers. The research data were collected through library and field methods (questionnaire and interview). The questions were based on a 5-point Likert spectrum for the indicators of 10 economic factors, 7 managerial factors, 6 environmental-infrastructure factors, 7 social factors, and 4 local community factors. The data were analyzed by using factor analysis technique in SPSS software. Factor analysis was applied to analyze the interrelationships between a large number of variables and explain them based on their common sub-dimensions. The KMO value was specified to confirm validity of the questionnaire (0.847). Discussion: The result of applying factor analysis to the peasant farmers’ challenges was reduction of the 34 factors to 5 final factors. The number of the extracted factors, along with the special values, indicated the share of each factor in the total variance of the variables and the larger the value was, the greater the importance and role of that factor could be. In this study, a total of 5 factors were able to explain about 66% of the total variance of the peasant farmers’ challenges. The economic (16.995%) and local community (6.718%) factors had the highest and lowest shares in explaining the total variance of the variables, respectively. The managerial, environmental-infrastructure, and social factors with the values of 15.831, 14.2292, and 12.994% were able to explain the total variance as well. According to the research findings and from the rural people’ points of view, the 5 resulted factors as the most important factors in the challenges of peasant exploitation were as follows: 1) economic factor; 2) managerial factor; 3) environmental-infrastructural factor; 4) social factor; and 5) local community factor. The economic challenges included the farmers’ low and unpleasant incomes from cultivation, presence of brokers and intermediaries in buying and selling products, and lack of guaranteed purchase of products and price stability in the market; the management challenges included lack of timely and appropriate governmental support and inputs for farmers, weak product processing and packaging industries, poor performance of institutions in relation to reconstruction, and protection and monitoring of natural resources; the environmental-infrastructure challenges included destruction and change of using orchards and agricultural lands, long distances from villages to cities and service centers, and excessive use of groundwater and chemical fertilizers and toxins; the social challenges included the farmers lack of self-confidence and skills, lack of access to the information and statistics required by the farmers, and lack of job opportunities for women and youths in rural communities; the local community challenges included traditional farming methods in rural areas and unwillingness to change the methods of cultivation and low quality and fragmentation of agricultural lands. Conclusion: In Iran, the first need for cultivation by peasants to be able to continue and return to their main positions in the Iranian economy is providing a legal framework and technical infrastructure with proper management and planning. To organize this system, assistance from Ministry of Jihad Agriculture, Property and Deeds Registration Organization, and Engineering System and Land Owner Organization should be sought for peasant agriculture and the duties of each of the mentioned organizations should be clearly defined. Considering that the exploiters in Iran do not have any guilds and organizations, a special guild or enterprise should be formed for the exploiters so that they can use these enterprises as a database for their regional conditions, potentials, and shortcomings. It can provide farmers with the requirements they need and they can solve their problems by referring to it in cases they are in trouble. Cooperatives can also be used as an institution to educate the exploiters. In other words, cooperatives can form an organization for exploitation of water and soil resources. They can provide the required services, including education and promotion, as well as the inputs needed by the users. To reduce the farmers’ risks in Iran, a credit-financial platform providing credit lines and facilities should be created for farmers. Other measures to be taken for the peasant exploiters are establishment of institutions and enterprises, to which the villagers and old farmers can entrusted their lands after improving them based on infrastructural conditions. These lands should be leased to farmers for rent so that the exploiters do not have to sell their lands and they can have their incomes from renting them. A fundamental problem in Iranian agriculture is fragmentation of lands and inability to implement integration plans and new programs with new systems for cultivation and irrigation. To solve this problem, financial and psychological supports, as well as a proper plan, must be provided. Various and continuous measures should be taken to guide and inform rural people so that they can be assured. References: - Iravani, H. and Varmarzyari, H. (2008). Global Development Report: Agriculture for Development. University of Tehran Press, Tehran. - Sing, J. and Dillon, S. S. (1995). Agricultural Geography. Translated by Siavash Dehghanian, Avaz Koucheki, & Ali Kolahi Ahari, Ferdowsi University, Mashhad. - Baumgartner, H. and Homburg, C. (1995). Applications of structural equation modeling in marketing research: A review. International Journal of Research in Marketing, Vol. 13, 139-161. https://doi.org/10.1016/0167-8116(95)00038-0 - Cirella, G. T. and Tao, L. (2010). The index of sustainable functionality: An application for measuring sustainability. International Journal of Human and Social Sciences, 5(5), 279-285. 10.5281/zenodo.1330369 - Davidova, S. and Thomson, K. (2014). Family farming in Europe: Challenges and prospects. Directorate General for Internal Policies, Policy Department B: Structural and Cohesion Policies, European Parliament. http://www.europarl.europa.eu/ - Duffy, P. (2018). Small-farm settlement landscapes in transition. Irish Geography, 50(2), 12-19. 10.2014/igj.v50i2.1320 - Jouzi, Z., Azadi, H., Taheri, F., Zafarshani, K., Gebrehiwot, K., Van Passel, S., & Lebailly, F. (2017). Organic farming and small-scale farmers: Main opportunities and challenges. Ecological Economics, Vol. 132, 144-154. https://doi.org/10.1016/j.ecolecon.2016.10.016 - Lynch, H., Uchanskim, M., Patrick, M., & Wharton, C. (2018). Small-farm sustainability in the south west: Challenges, opportunities, and best practices for local farming in Arizona and New Mexico. Food Studies, 8(2), 45-56. - Sing, J. and Dillon, S. S. (1995). Agricultural Geography. Translated by Siavash Dehghanian, Avaz Koucheki, & Ali Kolahi Ahri, Ferdowsi University, Mashhad, 534-535. Abstract: The peasant exploitation system is an undeniable fact in the rural economy of Iran. Many rural families’ incomes are based on this agricultural system. Despite many problems, peasant farmers are considered as the most important factor for achieving agricultural and rural developments due to their knowledge of the countryside and agriculture. In this research, we studied the problems and challenges of peasant farmers in Zanjan Province. This research was an applied one in terms of purpose and descriptive and analytical in nature. The data required for the research were collected by using a survey method (questionnaire, interview, and observation). The statistical population of this study included 40 villages from 8 cities with peasant cultivation. Finally, 340 questionnaires were randomly distributed among the rural households by using Cochran's formula and the exploratory factor analysis was used for analysis. Cronbach's alpha coefficient for the entire research questionnaire was obtained to be 873., which confirmed its overall reliability. The results of factor analysis showed that the 5 economic, managerial, infrastructure-environmental, social and local community factors were finally able to explain 66% of the variance of the rural farmers’ challenges. In the above-mentioned factors, farmers’ low incomes from cultivation (773.), lack of timely and appropriate government support of the farmers (715.), destruction and change of using orchards and agricultural lands (746.), the farmers’ lack of self-confidence and skills (695.), and low quality and dispersion of agricultural lands (609.) were the most important and influential challenges for the agricultural operators, respectively. Based on the results, it could be said that most of the challenges and rural problems in Zanjan Province could be solved with principled management and purposeful and practical planning. Keywords: agriculture, sustainable development, peasant exploitation, Zanjan Province 1.Introduction: The features of agriculture convert it into a unique instrument for progress. Agriculture can be contributive in other sectors of the economy and actualize faster development, reduce poverty, and cause stability in environmental issues. It can be an economic source for providing investment opportunities in the private sector and an initial driving force in the related industries (Iravani and Varmarzyari, 2008). Land exploitation systems determine the human-environment correlations for proper land use, which in turn make natural grounds and sources to be advantageous in the socio-economic dimensions and stability of agriculture. Considering a specific system in agricultural land exploitation without considering technological advances, industrial ranking, socio-economic-strategic opportunities, employment issues, probability of absorbing skilled farmers, etc. is not a rational procedure (Motiei-Langroudi & Shamsaei, 2009). Land exploitation in an area is the result of a set of historic events, interactions of economic forces with the environment, and social values. Despite initial distribution from a geographical area and exploitation manner therein, the present concern for ecological and cultural aspects is evident. All these provide a proper opportunity to assess the relationship between land exploitation and the environment (Sing & Dillon, 1995). Peasant exploitation system in Iran is a basic principle in rural agriculture. The incomes, employments, and livelihoods of many rural families are based on this agricultural system. Yet, despite the existence of many problems, peasant farmers are considered as the most important factor for achieving agricultural and rural developments due to their knowledge of the countryside and agriculture. Agriculture forms the basis of Iran's rural economy and rural economy is heavily dependent on and affected by agriculture. This sector with its existing system has major problems for various reasons, such as increasing farmers’ ages and inabilities to farm, lack of desire of the young generation to live in the village and continue agricultural activities, migration from villages and abandonment of lands, lack of knowledge and agricultural tools, lack of attention and proper management, and lack of organization, which should be considered and treated properly. Methodology: This research aimed at analyzing challenges of the peasant farmers in Zanjan Province with the aim of removing barriers to rural development. It was an applied research in terms of purpose and descriptive-analytical in nature. The statistical population included the farmers' households working in peasant exploitation systems. 340 households were selected as the sample size by using Cochran's formula. All the questionnaires were prepared by the villagers from rural areas and the researchers. The research data were collected through library and field methods (questionnaire and interview). The questions were based on a 5-point Likert spectrum for the indicators of 10 economic factors, 7 managerial factors, 6 environmental-infrastructure factors, 7 social factors, and 4 local community factors. The data were analyzed by using factor analysis technique in SPSS software. Factor analysis was applied to analyze the interrelationships between a large number of variables and explain them based on their common sub-dimensions. The KMO value was specified to confirm validity of the questionnaire (0.847). Discussion: The result of applying factor analysis to the peasant farmers’ challenges was reduction of the 34 factors to 5 final factors. The number of the extracted factors, along with the special values, indicated the share of each factor in the total variance of the variables and the larger the value was, the greater the importance and role of that factor could be. In this study, a total of 5 factors were able to explain about 66% of the total variance of the peasant farmers’ challenges. The economic (16.995%) and local community (6.718%) factors had the highest and lowest shares in explaining the total variance of the variables, respectively. The managerial, environmental-infrastructure, and social factors with the values of 15.831, 14.2292, and 12.994% were able to explain the total variance as well. According to the research findings and from the rural people’ points of view, the 5 resulted factors as the most important factors in the challenges of peasant exploitation were as follows: 1) economic factor; 2) managerial factor; 3) environmental-infrastructural factor; 4) social factor; and 5) local community factor. The economic challenges included the farmers’ low and unpleasant incomes from cultivation, presence of brokers and intermediaries in buying and selling products, and lack of guaranteed purchase of products and price stability in the market; the management challenges included lack of timely and appropriate governmental support and inputs for farmers, weak product processing and packaging industries, poor performance of institutions in relation to reconstruction, and protection and monitoring of natural resources; the environmental-infrastructure challenges included destruction and change of using orchards and agricultural lands, long distances from villages to cities and service centers, and excessive use of groundwater and chemical fertilizers and toxins; the social challenges included the farmers lack of self-confidence and skills, lack of access to the information and statistics required by the farmers, and lack of job opportunities for women and youths in rural communities; the local community challenges included traditional farming methods in rural areas and unwillingness to change the methods of cultivation and low quality and fragmentation of agricultural lands. Conclusion: In Iran, the first need for cultivation by peasants to be able to continue and return to their main positions in the Iranian economy is providing a legal framework and technical infrastructure with proper management and planning. To organize this system, assistance from Ministry of Jihad Agriculture, Property and Deeds Registration Organization, and Engineering System and Land Owner Organization should be sought for peasant agriculture and the duties of each of the mentioned organizations should be clearly defined. Considering that the exploiters in Iran do not have any guilds and organizations, a special guild or enterprise should be formed for the exploiters so that they can use these enterprises as a database for their regional conditions, potentials, and shortcomings. It can provide farmers with the requirements they need and they can solve their problems by referring to it in cases they are in trouble. Cooperatives can also be used as an institution to educate the exploiters. In other words, cooperatives can form an organization for exploitation of water and soil resources. They can provide the required services, including education and promotion, as well as the inputs needed by the users. To reduce the farmers’ risks in Iran, a credit-financial platform providing credit lines and facilities should be created for farmers. Other measures to be taken for the peasant exploiters are establishment of institutions and enterprises, to which the villagers and old farmers can entrusted their lands after improving them based on infrastructural conditions. These lands should be leased to farmers for rent so that the exploiters do not have to sell their lands and they can have their incomes from renting them. A fundamental problem in Iranian agriculture is fragmentation of lands and inability to implement integration plans and new programs with new systems for cultivation and irrigation. To solve this problem, financial and psychological supports, as well as a proper plan, must be provided. Various and continuous measures should be taken to guide and inform rural people so that they can be assured. References: - Iravani, H. and Varmarzyari, H. (2008). Global Development Report: Agriculture for Development. University of Tehran Press, Tehran. - Sing, J. and Dillon, S. S. (1995). Agricultural Geography. Translated by Siavash Dehghanian, Avaz Koucheki, & Ali Kolahi Ahari, Ferdowsi University, Mashhad. - Baumgartner, H. and Homburg, C. (1995). Applications of structural equation modeling in marketing research: A review. International Journal of Research in Marketing, Vol. 13, 139-161. https://doi.org/10.1016/0167-8116(95)00038-0 - Cirella, G. T. and Tao, L. (2010). The index of sustainable functionality: An application for measuring sustainability. International Journal of Human and Social Sciences, 5(5), 279-285. 10.5281/zenodo.1330369 - Davidova, S. and Thomson, K. (2014). Family farming in Europe: Challenges and prospects. Directorate General for Internal Policies, Policy Department B: Structural and Cohesion Policies, European Parliament. http://www.europarl.europa.eu/ - Duffy, P. (2018). Small-farm settlement landscapes in transition. Irish Geography, 50(2), 12-19. 10.2014/igj.v50i2.1320 - Jouzi, Z., Azadi, H., Taheri, F., Zafarshani, K., Gebrehiwot, K., Van Passel, S., & Lebailly, F. (2017). Organic farming and small-scale farmers: Main opportunities and challenges. Ecological Economics, Vol. 132, 144-154. https://doi.org/10.1016/j.ecolecon.2016.10.016 - Lynch, H., Uchanskim, M., Patrick, M., & Wharton, C. (2018). Small-farm sustainability in the south west: Challenges, opportunities, and best practices for local farming in Arizona and New Mexico. Food Studies, 8(2), 45-56. - Sing, J. and Dillon, S. S. (1995). Agricultural Geography. Translated by Siavash Dehghanian, Avaz Koucheki, & Ali Kolahi Ahri, Ferdowsi University, Mashhad, 534-535.

https://gep.ui.ac.ir/article_27214_2aef7edc97b1d940f4f2128ffecc8ccc.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 2 2023 06 22 Designing a Conceptual Model of Greenwashing Stimuli in Eco-Lodges: A Foundation Data Study in Isfahan Province1 Designing a Conceptual Model of Greenwashing Stimuli in Eco-Lodges: A Foundation Data Study in Isfahan Province1 91 112 26924 10.22108/gep.2022.133953.1530 FA Mahnaz Doosti-Irani PhD candidate of Tourism, Department of tourism, Faculty of Human sciences, Science and Arts university, Yazd, Iran. Mehdi Basouli Assistant Professor, Department of tourism, Faculty of Human sciences, Science and Arts university, Yazd, Iran Mir Mohammad Asadi Assistant Professor, Department of tourism, Faculty of Human sciences, Science and Arts university, Yazd, Iran. Journal Article 2022 06 05 Abstract Due to the negative effects of greenwashing on the environment and tourism stakeholders, tourism industry is in dire need of extensive guidelines on environmental claims. To establish such guidelines, it is first necessary to know the stimuli of greenwashing in this industry. The present study was conducted with the aim of identifying greenwashing stimuli in eco-lodges and designing its conceptual model. This study, which was an applied research, was done by using foundation data theory in Isfahan Province. To collect the data, a review of the research literature was done and in-depth interviews with the experts were followed. The condition for entering the statistical population of the research was specialization in the field studies of green marketing, greenwashing, tourism, and eco-lodges, as well as having at least 3 years of relevant work experience. Sampling was purposeful and continued until the data and theoretical saturations were achieved. The results showed that greenwashing stimulants in eco-lodges could be divided into 3 categories: causal factors (motivation to take advantage of green benefits), underlying factors (weakness of internal and external environments), and moderators (environmental feedback). The results also showed that environmental feedback, in addition to being moderating (based on the effect of causal factors on greenwashing), directly and indirectly (through underlying factors) affected greenwashing. Overall, the results and suggestions of the present study can give new insights to planners and industry officials to control greenwashing in eco-lodges. Keywords: Greenwashing Stimulants, Eco-lodges, Environmental sustainability, Green marketing, Foundation data Introduction The need for appropriate green management measures (Gavrilović & Maksimović, 2018), the growing demand for green services (de Freitas et al., 2020; Gupta et al., 2019), the desire to pay more for green products, the use of green brands as a competitive advantage (de Freitas Netto et al., 2020), etc. would lead firms to develop green marketing strategies and show their corporate images, besides giving social responsibility to consumers. However, in the midst of such green claims, it is difficult to say who really cares about the future of the planet and who only benefits from a sense of responsibility for the community. Previous research has shown that about 98% of the products claim to be environmentally friendly, while misleading consumers in some way. They are considered a form of greenwashing (Du, 2015). While greenwashing has a negative effect on the green image (Chen et al., 2019), word of mouth (Zareie, Siyahsarani Kajouri & Farsizade, 2014), and green trust in the brand (Karimi Sarme, Esmaeilpour Mobasher Amini, 2019; Khan pour, 2019), continuation of this trend can have consequences for green jobs in tourism, tourists, local community and other tourism stakeholders, thus affecting the future of this industry. Therefore, this industry is in dire need of extensive guidelines on environmental claims. To establish such guidelines, it is first necessary to know the stimuli of greenwashing in this industry. Therefore, the present study was conducted with the aim of identifying greenwashing stimuli in eco-lodges and designing their conceptual model. Materials and Methods To collect the data, a review of the research literature and in-depth interviews with 12 experts were done. Sampling was purposeful and continued until saturation. To analyze the data, the systematic version of the foundation data theory was used. To evaluate the reliability of the results, review and verification methods, inconsistency rate, and Kappa index were applied to provide a rich description. Discussion The results showed that greenwashing stimulants could be divided into 3 categories: causal, underlying, and moderating factors. Causal factors indicate the motivation to take advantage of green benefits, which leads to improving the image, increasing market share (more sales), attracting capital, gaining stakeholders’ trust, economic efficiency, and competitive advantage. Underlying factors indicate the weakness of internal and external environments. Weakness of the internal environment include the two dimensions of weakness of individual psychological stimuli and environmental knowledge. The weakness of the external environment include the two dimensions of the lack of information of the relevant departments and weakness of supervision. Finally, moderators point to environmental feedback, which include the 3 indicators of online tourist interactions about environmental performance of the brands, tourist ranking of the environmental performance of eco-lodges, and tourist demand because the eco-lodge was green. The results also showed that environmental feedback, in addition to being moderating (in the effect of causal factors on greenwashing), directly and indirectly (through underlying factors) affected greenwashing. Conclusion No studies were found to identify the mentioned stimuli in tourism. The results and suggestions of the present study can give new insights for planners and industry officials to control greenwashing in eco-lodges. Considering the possible effects of the incentives to use green benefits on greenwashing, it is suggested that the industry officials and planners explain the long-term consequences of greenwashing to the relevant business owners. Also, due to the weakness of the indoor environment, the following suggestions will be made: Focusing on increasing the knowledge and awareness of the managers and staff of eco-lodges about the environmental hazards of the planet, developing clear, uniform, and consistent standards for implementation of green strategies in eco-lodges, and focusing on individual psychological stimuli. Due to the weakness of the external environment, it is suggested that the information of the relevant departments be strengthened and supervision be increased. It is also suggested that efforts be made to strengthen environmental feedback in order to moderate the effects of causal and underlying factors on greenwashing. Considerations: This article was taken from the first author's doctoral dissertation in the field of tourism, Department of Tourism Management, Faculty of Human Sciences, Science and Arts university, Yazd, Iran. References - Aggarwal, P., & Kadyan, A. (2014). Greenwashing: The darker side of CSR. Indian Journal of Applied Research, 4(3), 61-66. - Aji, H. M., Sutikno, B. (2015). The extended consequence of greenwashing: Perceived consumer skepticism. Int. J. Bus. Info. 10 (4) 433. - Alarie, C. (2017). The Investigation of CEO Leadership Style as a Driver of Greenwashing and a Case Study Analysis to Provide Empirical Evidence for the Delmas and Burbano’s Drivers to Greenwashing Framework (Doctoral dissertation, Concordia University). - Alexis, P. (2017). Over-tourism and anti-tourist sentiment: an exploratory analysis and discussion. Ovidius University Annals, Economic Sciences Series, 17(2), 288-293. - Alipour, H., Malazizi, N., & Rezapouraghdam, H. (2018, June). Complementing sustainability through green marketing: from tourism operator’s perspective. In 8th advances in hospitality and tourism marketing and management conference (p. 636). - Berno, T. & Bricker, K. (2001). Sustainable tourism development: the long road from theory to practice. International Journal of Economic Development 3(3), 1-18. - Blome, C., Foerstl, K., & Schleper, M. C. (2017). Antecedents of green supplier championing and greenwashing: An empirical study on leadership and ethical incentives. Journal of Cleaner Production, 152, 339–350. - Chen, H., Bernard, S., & Rahman, I. (2019). Greenwashing in hotels: A structural model of trust and behavioral intentions. Journal of cleaner production, 206, 326-335. Cristobal-Fransi, E., Daries, N., Ferrer-Rosell, B., Marine-Roig, E., & Martin-Fuentes, E. (2020). Sustainable tourism marketing. - De Freitas Netto, S. V., Sobral, M. F. F., Ribeiro, A. R. B., & da Luz Soares, G. R. (2020). Concepts and forms of greenwashing: a systematic review. Environmental Sciences Europe, 32(1), 1-12. - De Jong, M. D., Huluba, G., & Beldad, A. D. (2020). Different Shades of Greenwashing: Consumers’ Reactions to Environmental Lies, Half-Lies, and Organizations Taking Credit for Following Legal Obligations. Journal of business and technical communication, 34(1), 38-76. - Delmas, M. A., & Burbano, V. C. (2011). The drivers of greenwashing. California Management Review, 54, 64–87. - Du, X. (2015). How the market values greenwashing? Evidence from China. Journal of Business Ethics, 128, 547–574. - Gavrilović, Z., & Maksimović, M. (2018). Green innovations in the tourism sector. Strategic Management, 23(1), 36-42. - Ghaith, A., Abdel-Wahab, M., Abdel-ate, A. A., & Qoura, O. (2019, a). Profiling of Egyptian Eco-lodge Guests. International Journal of Heritage, Tourism and Hospitality, 13(2), 54-69. - Ghaith, A., Abdel-Wahab, M., Abdel-ate, A. A., & Qoura, O. (2019, b). Service Quality and Guest Satisfaction in Egyptian Eco-lodge. International Journal of Heritage, Tourism and Hospitality, 13(2), 36-53. - Gupta, A., Dash, S., & Mishra, A. (2019). All that glitters is not green: Creating trustworthy ecofriendly services at green hotels. Tourism Management, 70, 155-169. - Kumar, R., & Kumar, R. (2013). Green marketing: Reality or greenwashing. Asian Journal of Multidisciplinary Studies, 1(5), 47-53 - Lyon, T. P., & Montgomery, A. W. (2015). The means and end of greenwash. Organization & Environment, 28, 223–249. - Lyon, T. P., Maxwell, J. W. (2011). Greenwash: Corporate environmental disclosure under threat of audit. J. Econ. Manag. Strat. 20(1), 3-41. - Mafi, M., Pratt, S., & Trupp, A. (2019). Determining ecotourism satisfaction attributes–a case study of an ecolodge in Fiji. Journal of Ecotourism, 1-23. - Majláth, M. (2016). How Does Greenwashing Effect the Firm, the Industry and the Society-the Case of the VW Emission Scandal. Proceedings of FIKUSZ 2016, 111. - Majláth, M. (2017). The effect of greenwashing information on ad evaluation. European Journal of Sustainable Development, 6(3), 92-92. - Pimonenko, T., Bilan, Y., Horák, J., Starchenko, L., & Gajda, W. (2020). Green Brand of Companies and Greenwashing under Sustainable Development Goals. Sustainability, 12(4), 1679. - Porritt, J. (2007). Capitalism as If the World Matters. London: Earthscan. - Rahman, I. (2017). The Interplay of Product Involvement and Sustainable Consumption: An Empirical Analysis of Behavioral Intentions Related to Green Hotels, Organic Wines and Green Cars. Sustain. Dev. - Rani, M. R. J., & Ravi, P. (2020). Factors Influencing Green Tourism–A Conceptual Approach. Studies in Indian Place Names, 40(18), 1144-1153. - Sharpley, R. (2010). The myth of sustainable tourism. CSD Center for Sustainable Development. - Siano, A., Vollero, A., Conte, F., & Amalibe, S. (2017). “More than words”: Expanding the taxonomy of greenwashing after the Volkswagen scandal. Journal of Business Research, 71, 27–37. - Smith, V. L., & Font, X. (2014). Volunteer tourism, greenwashing and understanding responsible marketing using market signalling theory. Journal of Sustainable Tourism, 22(6), 942-963. - Telfer, D. and Sharpley, R. (2008) Tourism and Development in the Developing World. London: Routledge. - Yu, E. P. Y., Van Luu, B., & Chen, C. H. (2020). Greenwashing in environmental, social and governance disclosures. Research in International Business and Finance, 52, 101192. - Zanasi, C., Rota, C., Trerè, S., Falciatori, S. (2017). An Assessment of the Food Companies Sustainability Policies through a Greenwashing Indicator. International Journal on Food System Dynamics. 61-81. - Zhang, K., Pan, Z., & Janardhanan, M. (2022). Relationship between the Degree of Internationalization and Greenwashing of Environmental Responsibilities in China-Based on the Legitimacy Perspective. Sustainability, 14(5), 2794. Abstract Due to the negative effects of greenwashing on the environment and tourism stakeholders, tourism industry is in dire need of extensive guidelines on environmental claims. To establish such guidelines, it is first necessary to know the stimuli of greenwashing in this industry. The present study was conducted with the aim of identifying greenwashing stimuli in eco-lodges and designing its conceptual model. This study, which was an applied research, was done by using foundation data theory in Isfahan Province. To collect the data, a review of the research literature was done and in-depth interviews with the experts were followed. The condition for entering the statistical population of the research was specialization in the field studies of green marketing, greenwashing, tourism, and eco-lodges, as well as having at least 3 years of relevant work experience. Sampling was purposeful and continued until the data and theoretical saturations were achieved. The results showed that greenwashing stimulants in eco-lodges could be divided into 3 categories: causal factors (motivation to take advantage of green benefits), underlying factors (weakness of internal and external environments), and moderators (environmental feedback). The results also showed that environmental feedback, in addition to being moderating (based on the effect of causal factors on greenwashing), directly and indirectly (through underlying factors) affected greenwashing. Overall, the results and suggestions of the present study can give new insights to planners and industry officials to control greenwashing in eco-lodges. Keywords: Greenwashing Stimulants, Eco-lodges, Environmental sustainability, Green marketing, Foundation data Introduction The need for appropriate green management measures (Gavrilović & Maksimović, 2018), the growing demand for green services (de Freitas et al., 2020; Gupta et al., 2019), the desire to pay more for green products, the use of green brands as a competitive advantage (de Freitas Netto et al., 2020), etc. would lead firms to develop green marketing strategies and show their corporate images, besides giving social responsibility to consumers. However, in the midst of such green claims, it is difficult to say who really cares about the future of the planet and who only benefits from a sense of responsibility for the community. Previous research has shown that about 98% of the products claim to be environmentally friendly, while misleading consumers in some way. They are considered a form of greenwashing (Du, 2015). While greenwashing has a negative effect on the green image (Chen et al., 2019), word of mouth (Zareie, Siyahsarani Kajouri & Farsizade, 2014), and green trust in the brand (Karimi Sarme, Esmaeilpour Mobasher Amini, 2019; Khan pour, 2019), continuation of this trend can have consequences for green jobs in tourism, tourists, local community and other tourism stakeholders, thus affecting the future of this industry. Therefore, this industry is in dire need of extensive guidelines on environmental claims. To establish such guidelines, it is first necessary to know the stimuli of greenwashing in this industry. Therefore, the present study was conducted with the aim of identifying greenwashing stimuli in eco-lodges and designing their conceptual model. Materials and Methods To collect the data, a review of the research literature and in-depth interviews with 12 experts were done. Sampling was purposeful and continued until saturation. To analyze the data, the systematic version of the foundation data theory was used. To evaluate the reliability of the results, review and verification methods, inconsistency rate, and Kappa index were applied to provide a rich description. Discussion The results showed that greenwashing stimulants could be divided into 3 categories: causal, underlying, and moderating factors. Causal factors indicate the motivation to take advantage of green benefits, which leads to improving the image, increasing market share (more sales), attracting capital, gaining stakeholders’ trust, economic efficiency, and competitive advantage. Underlying factors indicate the weakness of internal and external environments. Weakness of the internal environment include the two dimensions of weakness of individual psychological stimuli and environmental knowledge. The weakness of the external environment include the two dimensions of the lack of information of the relevant departments and weakness of supervision. Finally, moderators point to environmental feedback, which include the 3 indicators of online tourist interactions about environmental performance of the brands, tourist ranking of the environmental performance of eco-lodges, and tourist demand because the eco-lodge was green. The results also showed that environmental feedback, in addition to being moderating (in the effect of causal factors on greenwashing), directly and indirectly (through underlying factors) affected greenwashing. Conclusion No studies were found to identify the mentioned stimuli in tourism. The results and suggestions of the present study can give new insights for planners and industry officials to control greenwashing in eco-lodges. Considering the possible effects of the incentives to use green benefits on greenwashing, it is suggested that the industry officials and planners explain the long-term consequences of greenwashing to the relevant business owners. Also, due to the weakness of the indoor environment, the following suggestions will be made: Focusing on increasing the knowledge and awareness of the managers and staff of eco-lodges about the environmental hazards of the planet, developing clear, uniform, and consistent standards for implementation of green strategies in eco-lodges, and focusing on individual psychological stimuli. Due to the weakness of the external environment, it is suggested that the information of the relevant departments be strengthened and supervision be increased. It is also suggested that efforts be made to strengthen environmental feedback in order to moderate the effects of causal and underlying factors on greenwashing. Considerations: This article was taken from the first author's doctoral dissertation in the field of tourism, Department of Tourism Management, Faculty of Human Sciences, Science and Arts university, Yazd, Iran. References - Aggarwal, P., & Kadyan, A. (2014). Greenwashing: The darker side of CSR. Indian Journal of Applied Research, 4(3), 61-66. - Aji, H. M., Sutikno, B. (2015). The extended consequence of greenwashing: Perceived consumer skepticism. Int. J. Bus. Info. 10 (4) 433. - Alarie, C. (2017). The Investigation of CEO Leadership Style as a Driver of Greenwashing and a Case Study Analysis to Provide Empirical Evidence for the Delmas and Burbano’s Drivers to Greenwashing Framework (Doctoral dissertation, Concordia University). - Alexis, P. (2017). Over-tourism and anti-tourist sentiment: an exploratory analysis and discussion. Ovidius University Annals, Economic Sciences Series, 17(2), 288-293. - Alipour, H., Malazizi, N., & Rezapouraghdam, H. (2018, June). Complementing sustainability through green marketing: from tourism operator’s perspective. In 8th advances in hospitality and tourism marketing and management conference (p. 636). - Berno, T. & Bricker, K. (2001). Sustainable tourism development: the long road from theory to practice. International Journal of Economic Development 3(3), 1-18. - Blome, C., Foerstl, K., & Schleper, M. C. (2017). Antecedents of green supplier championing and greenwashing: An empirical study on leadership and ethical incentives. Journal of Cleaner Production, 152, 339–350. - Chen, H., Bernard, S., & Rahman, I. (2019). Greenwashing in hotels: A structural model of trust and behavioral intentions. Journal of cleaner production, 206, 326-335. Cristobal-Fransi, E., Daries, N., Ferrer-Rosell, B., Marine-Roig, E., & Martin-Fuentes, E. (2020). Sustainable tourism marketing. - De Freitas Netto, S. V., Sobral, M. F. F., Ribeiro, A. R. B., & da Luz Soares, G. R. (2020). Concepts and forms of greenwashing: a systematic review. Environmental Sciences Europe, 32(1), 1-12. - De Jong, M. D., Huluba, G., & Beldad, A. D. (2020). Different Shades of Greenwashing: Consumers’ Reactions to Environmental Lies, Half-Lies, and Organizations Taking Credit for Following Legal Obligations. Journal of business and technical communication, 34(1), 38-76. - Delmas, M. A., & Burbano, V. C. (2011). The drivers of greenwashing. California Management Review, 54, 64–87. - Du, X. (2015). How the market values greenwashing? Evidence from China. Journal of Business Ethics, 128, 547–574. - Gavrilović, Z., & Maksimović, M. (2018). Green innovations in the tourism sector. Strategic Management, 23(1), 36-42. - Ghaith, A., Abdel-Wahab, M., Abdel-ate, A. A., & Qoura, O. (2019, a). Profiling of Egyptian Eco-lodge Guests. International Journal of Heritage, Tourism and Hospitality, 13(2), 54-69. - Ghaith, A., Abdel-Wahab, M., Abdel-ate, A. A., & Qoura, O. (2019, b). Service Quality and Guest Satisfaction in Egyptian Eco-lodge. International Journal of Heritage, Tourism and Hospitality, 13(2), 36-53. - Gupta, A., Dash, S., & Mishra, A. (2019). All that glitters is not green: Creating trustworthy ecofriendly services at green hotels. Tourism Management, 70, 155-169. - Kumar, R., & Kumar, R. (2013). Green marketing: Reality or greenwashing. Asian Journal of Multidisciplinary Studies, 1(5), 47-53 - Lyon, T. P., & Montgomery, A. W. (2015). The means and end of greenwash. Organization & Environment, 28, 223–249. - Lyon, T. P., Maxwell, J. W. (2011). Greenwash: Corporate environmental disclosure under threat of audit. J. Econ. Manag. Strat. 20(1), 3-41. - Mafi, M., Pratt, S., & Trupp, A. (2019). Determining ecotourism satisfaction attributes–a case study of an ecolodge in Fiji. Journal of Ecotourism, 1-23. - Majláth, M. (2016). How Does Greenwashing Effect the Firm, the Industry and the Society-the Case of the VW Emission Scandal. Proceedings of FIKUSZ 2016, 111. - Majláth, M. (2017). The effect of greenwashing information on ad evaluation. European Journal of Sustainable Development, 6(3), 92-92. - Pimonenko, T., Bilan, Y., Horák, J., Starchenko, L., & Gajda, W. (2020). Green Brand of Companies and Greenwashing under Sustainable Development Goals. Sustainability, 12(4), 1679. - Porritt, J. (2007). Capitalism as If the World Matters. London: Earthscan. - Rahman, I. (2017). The Interplay of Product Involvement and Sustainable Consumption: An Empirical Analysis of Behavioral Intentions Related to Green Hotels, Organic Wines and Green Cars. Sustain. Dev. - Rani, M. R. J., & Ravi, P. (2020). Factors Influencing Green Tourism–A Conceptual Approach. Studies in Indian Place Names, 40(18), 1144-1153. - Sharpley, R. (2010). The myth of sustainable tourism. CSD Center for Sustainable Development. - Siano, A., Vollero, A., Conte, F., & Amalibe, S. (2017). “More than words”: Expanding the taxonomy of greenwashing after the Volkswagen scandal. Journal of Business Research, 71, 27–37. - Smith, V. L., & Font, X. (2014). Volunteer tourism, greenwashing and understanding responsible marketing using market signalling theory. Journal of Sustainable Tourism, 22(6), 942-963. - Telfer, D. and Sharpley, R. (2008) Tourism and Development in the Developing World. London: Routledge. - Yu, E. P. Y., Van Luu, B., & Chen, C. H. (2020). Greenwashing in environmental, social and governance disclosures. Research in International Business and Finance, 52, 101192. - Zanasi, C., Rota, C., Trerè, S., Falciatori, S. (2017). An Assessment of the Food Companies Sustainability Policies through a Greenwashing Indicator. International Journal on Food System Dynamics. 61-81. - Zhang, K., Pan, Z., & Janardhanan, M. (2022). Relationship between the Degree of Internationalization and Greenwashing of Environmental Responsibilities in China-Based on the Legitimacy Perspective. Sustainability, 14(5), 2794.

https://gep.ui.ac.ir/article_26924_4873f4e70b9ac188f5324f60ecf399d7.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 2 2023 06 22 Estimating the Accuracy of the TanDEM-X Digital Elevation Model in the Simulation of Flood Hydraulic Characteristics (Case Study: Atrak River Basin) Estimating the Accuracy of the TanDEM-X Digital Elevation Model in the Simulation of Flood Hydraulic Characteristics (Case Study: Atrak River Basin) 113 134 27148 10.22108/gep.2022.134293.1533 FA ٍEsmaeel Parizi Post-doctoral Researcher, Department of Physical Geography, University of Tehran, Tehran, Iran , Seiyed Mossa Hosseini Associate Professor, Department of Physical Geography, University of Tehran, Tehran, Iran Journal Article 2022 07 16 Abstract Hydraulic modelling of floods plays an important role in flood management and the related risk reduction. The case study in this research was a 20-km reach of the Atrak River in the upstream of Maraveh Tappeh City, which is one of the most hazardous regions of Iran from flood viewpoint. The aim of this research was to estimate the accuracy of the TanDEM-X digital elevation model with a resolution of 12 meters in simulating flood hydraulic characteristics. To achieve this aim, the HEC-RAS 2D model was used in steady conditions to simulate floods with a return period of 5, 10, 25, 50, 100, and 200 years. The results indicated that the inundation area varied in the range of 4.40 km2(return period of 5 years) and5.93 km2 (return period of 200 years). In the return period of 200 years, the mean flow depth and velocity increased by 67.9 and 49.5% compared to the return period of 5 years, respectively. The sensitivity test also indicated that the maximum sensitivities of the inundation area, mean flow depth, and mean flow velocity to Manning’s coefficient were4.65, 4.84, and -12.23%, respectively. The validation results of the HEC-RAS 2D model by using the inundation area extracted from Landsat-8 OLI satellite images for a return period of 10 years showed that the fit percentage indicator was 86%. The results of this study indicated an initial effort for hydraulic modelling of flood characteristics with the TDX elevation digital model. Keywords:HEC-RAS 2D model, Frequency analysis, Hydraulic modelling, Landsat-8 satellite images Introduction Floods are among the most common and destructive natural disasters worldwide, imposing various adverse effects in different countries (Bui et al., 2018). These include fatalities, damage to infrastructures, people displacement, and environmental damages (Rahmati et al., 2020). Over the last decade, floods have affected millions of people worldwide and caused a damage of more than US$ 400 billion (Aerts, 2020). In Asia, more than 90% of human casualties resulting from natural disasters stem from flood events (Smith, 2003). Among several countries in Asia, Iran faces destructive floods each year due to its vast extent and heavy precipitations in most basins (Jahangir et al., 2019). Over the past 60 years, more than 3,700 flood events have been reported in Iran, while during the last decade, the damage caused by flooding has increased by 250% (Norouzi and Taslimi, 2012). Iran has recently experienced immense floods because of poor watershed management and climate change (Pouyan et al., 2021). In 2019, flooding events affected 25 out of 31 provinces, resulting in more than 77 human casualties and damage of US$ 2.2 billion (Khosravi et al., 2018). Even though we do not have an accurate answer to how climate change may impact flooding events, such as the ones that occurred in 2019(Sherpa and Shirzaei, 2021), a recent study has suggested that Iran will probably experience a higher frequency of floods in the future (Vaghefi et al., 2019). In addition, the growth of urbanization and increasing deforestation will make the condition worse (Arabameri et al., 2019). Methodology In the current study, the long-term (1977-2017) data of maximum discharge in the hydrometric station of Qazanqaya were used for the frequency analysis of Flood Peak Discharge (FPD). The stationarity in the time series of annual maximum peak discharge was checked before fitting the distribution. For computing FPD in the various return periods for the hydrometric station of Qazanqaya, the annual maximum discharge records were fitted via EasyFit software. Three goodness-of-fit criteria, including Anderson-Darling, Kolmogorov-Smirnov, and Chi-square, were adopted to select the best-fitted distribution. Finally, flood discharges with 5-, 10-, 25-, 50- 100-, and 200-yr return periods were estimated for the hydrometric station based on the corresponding best-fitted distribution. This study simulated 2D steady flow in a return period of 5-200 years using HEC-RAS 5.0 software (U.S. Army Corps of Engineering, 2016). Due to the complex numerical schemes, 2D diffusive wave equations could provide greater stability and faster calculation times (Li et al., 2020) and were thus used in this study to simulate 2D steady flows in a return period of 5 to 200 years. The peak flow discharges in the return periods of 5-200 years estimated from frequency analysis in the hydrometric station of Qazanqaya were considered as the upstream boundary conditions in the hydraulic model. Furthermore, the downstream boundary conditions were considered as normal depth conditions obtained based on the energy slope. Manning’s roughness coefficients of the main channel and floodplain were estimated based on the land cover mapand USGS method (Arcement and Schneider, 1989). In the previous studies, modification of Normalized Difference Water Index (NDWI) has been successfully done to map the flooding areas (Li et al., 2018). Hence, based on the date of the flood events, which were recorded in the hydrometric station of Qazanqaya, the flooded area was extracted from Landsat-8 OLI images. On the other hand, the fit percentage indicator proved to be useful for the validation of flood inundation models (Khojeh et al., 2022). A value of closer to 100% could denote a better agreement in flood extent modeling by TDX Digital Elevation Model. Discussion The results of hydraulic modelling indicated that the inundation area varied in the range of 4.40 square kilometers (return period of 5 years) and5.93 square kilometers (return period of 200 years). On the other hand, in the return period of 200 years, the mean flow depth and velocity increased by 67.9 and 49.5% compared to the return period of 5 years, respectively. The validation results of the HEC-RAS 2D model by using the inundation area extracted from Landsat-8 OLI satellite images for a 10-yr return period indicated that the fit percentage indicator was 86%, indicating a high agreement of flood modeling results based onthe TDX digital elevation model. Conclusion The results of the frequency analysis and estimation of flood peak discharges with a return period of 5 to 200 years in the Atrak River Basin showed that this basin with peak discharges between 487.8 m3/s (5-year flood) and 1605.6 m3/s (200-year flood) could be considered as one of the most dangerous basins in Iran, which could cause a lot of human and financial losses, especially for floods with a high return period. Although HEC-RAS 2D modeling based on the TDX digital elevation model with a resolution of 12 m indicated that this digital elevation model with an accuracy of 86% (14% error) was probably better than digital elevation models, such as SRTM, ASTER, and ALOS, with a resolution of 30 m , its validation for other flood-prone areas of Iran was necessary. References - Aerts, J. C. J. H. (2020). Integrating agent-based approaches with flood risk models: A review and perspective. Water Security, 11, 100076. - Arabameri, A., Rezaei, K., Cerdà, A., Conoscenti, C., & Kalantari, Z. (2019). A comparison of statistical methods and multi-criteria decision-making to map flood hazard susceptibility in Northern Iran. Science of the Total Environment, 660, 443-458. - Arcement, G. J., & Schneider, V. R. (1989). Guide for selecting Manning's roughness coefficients for natural channels and flood plains. Available from the US Geological Survey, Books, and Open-File Reports Section, Box 25425, Federal Center, Denver, CO 80225-0425, Water-Supply Paper, 2339, 1989, p. 38, Fig. 22, Tab 4, Ref. 23. - Bales, J. D., & Wagner, C. R. (2009). Sources of uncertainty in flood inundation maps. Journal of Flood Risk Management, 2(2), 139-147. - Brunner, G.W. (2016). HEC-RAS River Analysis System, 2D Modeling User’s Manual, Version 5.0. Davis, CA. - Bui, D. T., Panahi, M., Shahabi, H., Singh, V. P., Shirzadi, A., Chapi, K., & Ahmad, B. B. (2018). Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific Reports, 8(1), 1-14. - Costabile, P., Costanzo, C., Ferraro, D., Macchione, F., & Petaccia, G. (2020). Performances of the new HEC-RAS,Version 5 for 2-D hydrodynamic-based rainfall-runoff simulations at basin scale: Comparison with a state-of-the art model. Water, 12(9), 2326. - Dong, Y., Zhao, J., Floricioiu, D., Krieger, L., Fritz, T., & Eineder, M. (2021). High-resolution topography of the Antarctic Peninsula combining the TanDEM-X DEM and Reference Elevation Model of Antarctica (REMA) mosaic. The Cryosphere, 15(9), 4421-4443. - Falter, D., Vorogushyn, S., Lhomme, J., Apel, H., Gouldby, B., & Merz, B. (2013). Hydraulic model evaluation for large-scale flood risk assessments. Hydrological Processes, 27(9), 1331-1340. - Golshan, M., Jahanshahi, A., & Afzali, A. (2016). Flood hazard zoning using HEC-RAS in GIS environment and impact of manning roughness coefficient changes on flood zones in Semi-arid climate. Desert, 21(1), 24-34. - Grohmann, C. H. (2018). Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM, and ALOS AW3D30. Remote Sensing of Environment, 212, 121-133. - Gu, X., Zhang, Q., Singh, V. P., & Shi, P. (2017). Changes in magnitude and frequency of heavy precipitation across China and its potential links to summer temperature. Journal of Hydrology, 547, 718-731. - Hamed, K. H. (2008). Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis. Journal of hydrology, 349(3-4), 350-363. - Jahangir, M. H., Reineh, S. M. M., & Abolghasemi, M. (2019). Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather and Climate Extremes, 25, 100215. - Janizadeh, S., Pal, S. C., Saha, A., Chowdhuri, I., Ahmadi, K., Mirzaei, S., & Tiefenbacher, J. P. (2021). Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management, 298, 113551. - Karamouz, M., & Mahani, F. F. (2021). DEM uncertainty based coastal flood inundation modeling considering water quality impacts. Water Resources Management, 35(10), 3083-3103. - Khojeh, S., Ataie-Ashtiani, B., & Hosseini, S. M. (2022). Effect of DEM resolution in flood modeling: Acase study of Gorganrood River, Northeastern Iran. Natural Hazards, 1-21. - Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., & Bui, D. T. (2018). A comparative assessment of decision tree algorithms for flash flood susceptibility modeling at Haraz Watershed, northern Iran. Science of the Total Environment, 627, 744-755. - Leitao, J. P., Boonya-Aroonnet, S., Prodanović, D., & Maksimović, Č. (2009). The influence of digital elevation model resolution on overland flow networks for modeling urban pluvial flooding. Water Science and Technology, 60(12), 3137-3149. - Li, J., Yang, X., Maffei, C., Tooth, S., & Yao, G. (2018). Applying independent component analysis on Sentinel-2 imagery to characterize geomorphological responses to an extreme flood event near the non-vegetated Río Colorado terminus, Salar de Uyuni, Bolivia. Remote Sensing, 10(5), 725. - Li, J., Zhao, Y., Bates, P., Neal, J., Tooth, S., Hawker, L., & Maffei, C. (2020). Digital Elevation Models for topographic characterization and flood flow modeling along low-gradient, terminal dryland rivers: A comparison of spaceborne datasets for the Río Colorado, Bolivia. Journal of Hydrology, 591, 125617. - Modarres, R., Sarhadi, A., & Burn, D. H. (2016). Changes of extreme drought and flood events in Iran. Global and Planetary Change, 144, 67-81. - Muench, R., Cherrington, E., Griffin, R., & Mamane, B. (2022). Assessment of Open Access Global Elevation Model Errors Impact on Flood Extents in Southern Niger. Frontiers in Environmental Science, 547. - Muthusamy, M., Casado, M. R., Butler, D., & Leinster, P. (2021). Understanding the effects of Digital Elevation Model resolution in urban fluvial flood modeling. Journal of Hydrology, 596, 126088. - Norouzi, G., & Taslimi, M. (2012). The impact of flood damages on the production of Iran’s Agricultural Sector. Middle East J Sci. Res., 12, 921-926. - Papaioannou, G., Loukas, A., Vasiliades, L., & Aronica, G. T. (2016). Flood inundation mapping sensitivity to riverine spatial resolution and modeling approach. Natural Hazards, 83(1), 117-132. - Phillips, J. D. (1988). Incorporating fluvial change in hydrologic simulations: Acase study in coastal North Carolina. Applied Geography, 8(1), 25-36. - Pinos, J., & Timbe, L. (2019). Performance assessment of two-dimensional hydraulic models for generation of flood inundation maps in mountain river basins. Water Science and Engineering, 12(1), 11-18. - Pouyan, S., Pourghasemi, H. R., Bordbar, M., Rahmanian, S., & Clague, J. J. (2021). A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports, 11(1), 1-19. - Rahmati, O., Darabi, H., Panahi, M., Kalantari, Z., Naghibi, S. A., Ferreira, C. S. S., & Haghighi, A. T. (2020). Development of novel hybridized models for urban flood susceptibility mapping. Scientific Reports, 10(1), 1-19. - Rangari, V. A., Umamahesh, N. V., & Bhatt, C. M. (2019). Assessment of inundation risk in urban floods using HEC RAS 2D. Modeling Earth Systems and Environment, 5(4), 1839-1851. - Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Tridon, D. B., Bräutigam, B., & Moreira, A. (2017). Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS Journal of Photogrammetry and Remote Sensing, 132, 119-139. - Saksena, S., & Merwade, V. (2015). Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. Journal of Hydrology, 530, 180-194. - Sheikh, V. (2014). Analysis of hydroclimatic trends in the Atrak River Basin, North Khorasan, Iran (1975–2008). Environmental Resources Research, 2(1), 1-14. - Sherpa, S. F., & Shirzaei, M. (2022). Country-wide flood exposure analysis using Sentinel-1 synthetic aperture radar data: Case study of 2019 Iran flood. Journal of Flood Risk Management, 15(1), e12770. - Shi, X., Qin, T., Nie, H., Weng, B., & He, S. (2019). Changes in major global river discharges directed into the ocean. International Journal of Environmental Research and Public Health, 16(8), 1469. - Smith, K. (2003). Environmental hazards: Assessing risk and reducing disaster. Routledge. - Srinivas, V. V., Tripathi, S., Rao, A. R., & Govindaraju, R. S. (2008). Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering. Journal of Hydrology, 348(1-2), 148-166. - Tamiru, H., & Wagari, M. (2022). Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia. Modeling Earth Systems and Environment, 8(2), 2291-2303. - Tayefi, V., Lane, S. N., Hardy, R. J., & Yu, D. (2007). A comparison of one- and two-dimensional approaches to modeling flood inundation over complex upland floodplains. Hydrological Processes: An International Journal, 21(23), 3190-3202. - U.S. Army Corps of Engineering. (2016). HEC-RAS 5.0 Hydraulic Reference Manual. U.S. Army Corps of Engineers, Institute for Water Resources, Hydrologic Engineering Center, Davis, CA, USA, CPD-68. - Utlu, M., & Özdemir, H. (2020). How much spatial resolution do we need to model a local flood event? Benchmark testing based on UAV data from Biga River (Turkey). Arabian Journal of Geosciences, 13(24), 1-14. - Vaghefi, S. A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A., Yang, H., &Abbaspour, K. C. (2019). The future of extreme climate in Iran. Scientific Reports, 9(1), 1-11. - Wessel, B. (2016). TanDEM-X Ground Segment–DEM Products Specification Document. German Space Center. - Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., & Roth, A. (2018). Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data. ISPRS Journal of Photogrammetry and Remote Sensing, 139, 171-182. - Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025-3033. - Xu, K., Fang, J., Fang, Y., Sun, Q., Wu, C., & Liu, M. (2021). The Importance of Digital Elevation Model Selection in Flood Simulation and AProposed Method to Reduce DEM Errors: A Case Study in Shanghai. International Journal of Disaster Risk Science, 12(6), 890-902. - Zhang, K., Gann, D., Ross, M., Biswas, H., Li, Y., & Rhome, J. (2019a). Comparison of TanDEM-X DEM with LiDAR data for accuracy assessment in a coastal urban area. Remote Sensing, 11(7), 876. - Zhang, K., Gann, D., Ross, M., Robertson, Q., Sarmiento, J., Santana, S., & Fritz, C. (2019b). Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sensing of Environment, 225, 290-306. Abstract Hydraulic modelling of floods plays an important role in flood management and the related risk reduction. The case study in this research was a 20-km reach of the Atrak River in the upstream of Maraveh Tappeh City, which is one of the most hazardous regions of Iran from flood viewpoint. The aim of this research was to estimate the accuracy of the TanDEM-X digital elevation model with a resolution of 12 meters in simulating flood hydraulic characteristics. To achieve this aim, the HEC-RAS 2D model was used in steady conditions to simulate floods with a return period of 5, 10, 25, 50, 100, and 200 years. The results indicated that the inundation area varied in the range of 4.40 km2(return period of 5 years) and5.93 km2 (return period of 200 years). In the return period of 200 years, the mean flow depth and velocity increased by 67.9 and 49.5% compared to the return period of 5 years, respectively. The sensitivity test also indicated that the maximum sensitivities of the inundation area, mean flow depth, and mean flow velocity to Manning’s coefficient were4.65, 4.84, and -12.23%, respectively. The validation results of the HEC-RAS 2D model by using the inundation area extracted from Landsat-8 OLI satellite images for a return period of 10 years showed that the fit percentage indicator was 86%. The results of this study indicated an initial effort for hydraulic modelling of flood characteristics with the TDX elevation digital model. Keywords:HEC-RAS 2D model, Frequency analysis, Hydraulic modelling, Landsat-8 satellite images Introduction Floods are among the most common and destructive natural disasters worldwide, imposing various adverse effects in different countries (Bui et al., 2018). These include fatalities, damage to infrastructures, people displacement, and environmental damages (Rahmati et al., 2020). Over the last decade, floods have affected millions of people worldwide and caused a damage of more than US$ 400 billion (Aerts, 2020). In Asia, more than 90% of human casualties resulting from natural disasters stem from flood events (Smith, 2003). Among several countries in Asia, Iran faces destructive floods each year due to its vast extent and heavy precipitations in most basins (Jahangir et al., 2019). Over the past 60 years, more than 3,700 flood events have been reported in Iran, while during the last decade, the damage caused by flooding has increased by 250% (Norouzi and Taslimi, 2012). Iran has recently experienced immense floods because of poor watershed management and climate change (Pouyan et al., 2021). In 2019, flooding events affected 25 out of 31 provinces, resulting in more than 77 human casualties and damage of US$ 2.2 billion (Khosravi et al., 2018). Even though we do not have an accurate answer to how climate change may impact flooding events, such as the ones that occurred in 2019(Sherpa and Shirzaei, 2021), a recent study has suggested that Iran will probably experience a higher frequency of floods in the future (Vaghefi et al., 2019). In addition, the growth of urbanization and increasing deforestation will make the condition worse (Arabameri et al., 2019). Methodology In the current study, the long-term (1977-2017) data of maximum discharge in the hydrometric station of Qazanqaya were used for the frequency analysis of Flood Peak Discharge (FPD). The stationarity in the time series of annual maximum peak discharge was checked before fitting the distribution. For computing FPD in the various return periods for the hydrometric station of Qazanqaya, the annual maximum discharge records were fitted via EasyFit software. Three goodness-of-fit criteria, including Anderson-Darling, Kolmogorov-Smirnov, and Chi-square, were adopted to select the best-fitted distribution. Finally, flood discharges with 5-, 10-, 25-, 50- 100-, and 200-yr return periods were estimated for the hydrometric station based on the corresponding best-fitted distribution. This study simulated 2D steady flow in a return period of 5-200 years using HEC-RAS 5.0 software (U.S. Army Corps of Engineering, 2016). Due to the complex numerical schemes, 2D diffusive wave equations could provide greater stability and faster calculation times (Li et al., 2020) and were thus used in this study to simulate 2D steady flows in a return period of 5 to 200 years. The peak flow discharges in the return periods of 5-200 years estimated from frequency analysis in the hydrometric station of Qazanqaya were considered as the upstream boundary conditions in the hydraulic model. Furthermore, the downstream boundary conditions were considered as normal depth conditions obtained based on the energy slope. Manning’s roughness coefficients of the main channel and floodplain were estimated based on the land cover mapand USGS method (Arcement and Schneider, 1989). In the previous studies, modification of Normalized Difference Water Index (NDWI) has been successfully done to map the flooding areas (Li et al., 2018). Hence, based on the date of the flood events, which were recorded in the hydrometric station of Qazanqaya, the flooded area was extracted from Landsat-8 OLI images. On the other hand, the fit percentage indicator proved to be useful for the validation of flood inundation models (Khojeh et al., 2022). A value of closer to 100% could denote a better agreement in flood extent modeling by TDX Digital Elevation Model. Discussion The results of hydraulic modelling indicated that the inundation area varied in the range of 4.40 square kilometers (return period of 5 years) and5.93 square kilometers (return period of 200 years). On the other hand, in the return period of 200 years, the mean flow depth and velocity increased by 67.9 and 49.5% compared to the return period of 5 years, respectively. The validation results of the HEC-RAS 2D model by using the inundation area extracted from Landsat-8 OLI satellite images for a 10-yr return period indicated that the fit percentage indicator was 86%, indicating a high agreement of flood modeling results based onthe TDX digital elevation model. Conclusion The results of the frequency analysis and estimation of flood peak discharges with a return period of 5 to 200 years in the Atrak River Basin showed that this basin with peak discharges between 487.8 m3/s (5-year flood) and 1605.6 m3/s (200-year flood) could be considered as one of the most dangerous basins in Iran, which could cause a lot of human and financial losses, especially for floods with a high return period. Although HEC-RAS 2D modeling based on the TDX digital elevation model with a resolution of 12 m indicated that this digital elevation model with an accuracy of 86% (14% error) was probably better than digital elevation models, such as SRTM, ASTER, and ALOS, with a resolution of 30 m , its validation for other flood-prone areas of Iran was necessary. References - Aerts, J. C. J. H. (2020). Integrating agent-based approaches with flood risk models: A review and perspective. Water Security, 11, 100076. - Arabameri, A., Rezaei, K., Cerdà, A., Conoscenti, C., & Kalantari, Z. (2019). A comparison of statistical methods and multi-criteria decision-making to map flood hazard susceptibility in Northern Iran. Science of the Total Environment, 660, 443-458. - Arcement, G. J., & Schneider, V. R. (1989). Guide for selecting Manning's roughness coefficients for natural channels and flood plains. Available from the US Geological Survey, Books, and Open-File Reports Section, Box 25425, Federal Center, Denver, CO 80225-0425, Water-Supply Paper, 2339, 1989, p. 38, Fig. 22, Tab 4, Ref. 23. - Bales, J. D., & Wagner, C. R. (2009). Sources of uncertainty in flood inundation maps. Journal of Flood Risk Management, 2(2), 139-147. - Brunner, G.W. (2016). HEC-RAS River Analysis System, 2D Modeling User’s Manual, Version 5.0. Davis, CA. - Bui, D. T., Panahi, M., Shahabi, H., Singh, V. P., Shirzadi, A., Chapi, K., & Ahmad, B. B. (2018). Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific Reports, 8(1), 1-14. - Costabile, P., Costanzo, C., Ferraro, D., Macchione, F., & Petaccia, G. (2020). Performances of the new HEC-RAS,Version 5 for 2-D hydrodynamic-based rainfall-runoff simulations at basin scale: Comparison with a state-of-the art model. Water, 12(9), 2326. - Dong, Y., Zhao, J., Floricioiu, D., Krieger, L., Fritz, T., & Eineder, M. (2021). High-resolution topography of the Antarctic Peninsula combining the TanDEM-X DEM and Reference Elevation Model of Antarctica (REMA) mosaic. The Cryosphere, 15(9), 4421-4443. - Falter, D., Vorogushyn, S., Lhomme, J., Apel, H., Gouldby, B., & Merz, B. (2013). Hydraulic model evaluation for large-scale flood risk assessments. Hydrological Processes, 27(9), 1331-1340. - Golshan, M., Jahanshahi, A., & Afzali, A. (2016). Flood hazard zoning using HEC-RAS in GIS environment and impact of manning roughness coefficient changes on flood zones in Semi-arid climate. Desert, 21(1), 24-34. - Grohmann, C. H. (2018). Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM, and ALOS AW3D30. Remote Sensing of Environment, 212, 121-133. - Gu, X., Zhang, Q., Singh, V. P., & Shi, P. (2017). Changes in magnitude and frequency of heavy precipitation across China and its potential links to summer temperature. Journal of Hydrology, 547, 718-731. - Hamed, K. H. (2008). Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis. Journal of hydrology, 349(3-4), 350-363. - Jahangir, M. H., Reineh, S. M. M., & Abolghasemi, M. (2019). Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather and Climate Extremes, 25, 100215. - Janizadeh, S., Pal, S. C., Saha, A., Chowdhuri, I., Ahmadi, K., Mirzaei, S., & Tiefenbacher, J. P. (2021). Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management, 298, 113551. - Karamouz, M., & Mahani, F. F. (2021). DEM uncertainty based coastal flood inundation modeling considering water quality impacts. Water Resources Management, 35(10), 3083-3103. - Khojeh, S., Ataie-Ashtiani, B., & Hosseini, S. M. (2022). Effect of DEM resolution in flood modeling: Acase study of Gorganrood River, Northeastern Iran. Natural Hazards, 1-21. - Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., & Bui, D. T. (2018). A comparative assessment of decision tree algorithms for flash flood susceptibility modeling at Haraz Watershed, northern Iran. Science of the Total Environment, 627, 744-755. - Leitao, J. P., Boonya-Aroonnet, S., Prodanović, D., & Maksimović, Č. (2009). The influence of digital elevation model resolution on overland flow networks for modeling urban pluvial flooding. Water Science and Technology, 60(12), 3137-3149. - Li, J., Yang, X., Maffei, C., Tooth, S., & Yao, G. (2018). Applying independent component analysis on Sentinel-2 imagery to characterize geomorphological responses to an extreme flood event near the non-vegetated Río Colorado terminus, Salar de Uyuni, Bolivia. Remote Sensing, 10(5), 725. - Li, J., Zhao, Y., Bates, P., Neal, J., Tooth, S., Hawker, L., & Maffei, C. (2020). Digital Elevation Models for topographic characterization and flood flow modeling along low-gradient, terminal dryland rivers: A comparison of spaceborne datasets for the Río Colorado, Bolivia. Journal of Hydrology, 591, 125617. - Modarres, R., Sarhadi, A., & Burn, D. H. (2016). Changes of extreme drought and flood events in Iran. Global and Planetary Change, 144, 67-81. - Muench, R., Cherrington, E., Griffin, R., & Mamane, B. (2022). 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