University of Isfahan Geography and Environmental Planning 2008-5362 31 2 2020 06 21 Groundwater Potential Assessment Using Sentinel 1 Radar Data Processing and Multi-Criteria Decision Analysis (MCDA) Technique (Case Study: the Sirjan Catchment) Groundwater Potential Assessment Using Sentinel 1 Radar Data Processing and Multi-Criteria Decision Analysis (MCDA) Technique (Case Study: the Sirjan Catchment) 1 24 25053 10.22108/gep.2020.122611.1292 FA Ali Mehrabi Assist. Prof. Department of Geography and Urban Planning, Faculty of Lit. &amp; Humanities, Shahid Bahonar University of Kerman, Kerman, Iran Sadegh Karimi Assistant Professor, Department of Geography, Shahid Bahonar University of Kerman, Kerman, Iran Majid Safipour MA Student of Environmental Hazards, Department of Geography, Shahid Bahonar University of Kerman, Kerman, Iran Journal Article 2020 04 19 Groundwater resources are an important natural resource for domestic, agricultural, and industrial use. Today, due to the population growth as well as agricultural, and industrial development, the demand for groundwater use has increased dramatically. Climate changes, repeated droughts, and the risk of surface water pollution as a result of human and industrial activities are other important factors in the human interest in using groundwater resources. However, the unplanned use of groundwater disrupts the natural nutrient balance of aquifers. The accumulation and movement of groundwater in an area depend on various factors such as geology, tectonics, soil type, geomorphological characteristics of the region, drainage pattern, land use, and the relationship between such factors. The tectonic factor is one of the most important factors in the concentration of groundwater resources. Earth faults and fractures, known as tectonic faults, cause more and more surface water to penetrate into the earth's crust and feed the groundwater aquifers. Therefore, the identification of tectonic faults is one of the important cases in the study of groundwater resources. Kerman province, and especially its northern and northwestern cities, due to the increasing expansion of pistachio orchards, has faced an increase in the extraction of groundwater resources and water scarcity is one of the most important problems in the region. Sirjan city is also one of the areas that needs the identification of new groundwater resources. Therefore, in this study, an attempt has been made to process Sentinel 2 multispectral images and Fuzzy-AHP methods, which are among the multi-criteria decision analysis techniques. The maps of the various factors influencing the creation of groundwater aquifers have been prepared in the GIS environment. The maps were weighed and combined to identify suitable areas with good potential. The main difference between this method and the AHP method is the difference in the method of weighting criteria and options so that in this method, the weighting is done as fuzzy. Methodology The textural analysis consists of quantifying the different gray levels of the image in terms of roughness and their distribution. The contextual analysis technique makes it possible to highlight image dissimilarities or homogeneous zones. There are several methods for textural analysis including structural, statistical, study-based methods, and fractal methods. In this study, a statistical approach known as Grey Level Co-occurrence Matrix (GLCM) proposed by Haralick (1979) was adopted. It allows the identification and selection of the parameters that best define the elements from the measurement of the gray tone distributions. The factors are the mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation. These factors have many applications in geological and topographic studies. In this study, these factors were used in the analysis of the main components and then in the filtering operation to extract tectonic faults. Lineaments are related to fractures and lithological boundaries and in some cases to geomorphic relief. Thus, lineaments appear on the image with a tonal difference. The Fuzzy-AHP method was first proposed by Chang (1996, p. 649). Discussion The textural analysis was performed on the Sentinel 1 radar image of the study area. The result were 8 images of different co-occurrence indices. Figure 3 in the text shows the images mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation. In order to extract tectonic faults, the analysis of the principle components on eight co-occurrence factors was performed. Since the first principle component contains about 90% of the image information, it shows the major structural features of the image. In order to reduce the noise and increase structural information as much as possible, the first main component was multiplied. In order to extract tectonic lines, the oriented filter was applied to the conjugate image of the first main component at zero, 45, 90, and 135 degree angles. In this way, the north-south, northeast-southwest, east-west, and southeast-northwest directions were highlighted, respectively. Figure 6 in the text shows the effective factors in the potential assessment. In order to achieve the final map of the potential of groundwater resources in the fuzzy hierarchical method, each criterion must first be weighed and merged accordingly. Table 2 in the text shows the binary comparison matrix of criteria. Accordingly, the lithological criterion with a weight of 0.378 has the most effective factor and the vegetation criterion with a weight of 0.043 has the least effect on the concentration of groundwater resources. About 3 percent of the study area is in the very good category (228 square kilometers), 11 percent in the good category (836 square kilometers), 52 percent in the average category (4016 square kilometers), 29 percent in the low category (2252 square kilometers), and 5 percent in the very low category (396 square kilometers). Very good and good areas are mostly in the foothills to the highlands of the study area. Conclusion The results of the grey level co-occurrence matrix method show the capability of this method in extracting tectonic lines. In addition, the higher spatial resolution of the Sentinel 1 radar images than the available optical images makes the separation and detection of tectonic lines better. By combining the effective layers in the concentration of groundwater, the study area was potentialized in terms of the existence of groundwater reserves. The results show that about 14% of the study area has good potential (mostly located in foothills and within calcareous and alluvial rocks) in this area. Due to the declining quality of water resources in existing wells, the identified areas with a good and very good potential can be explored specifically for new water resources. Keywords: Potential Assessment, Groundwater Resources, Remote Sensing, Fuzzy-AHP, Sirjan Basin. References - Agarwal, E., Rajat, A., Garg, R. D., & Garg, P. K. (2013). Delineation of Groundwater Potential Zone: An AHP/ANP Approach. Journal of Earth System Science, 122(3), 887-898. - Andualem, T. G., & Demeke, G. G. (2019). Groundwater Potential Assessment Using GIS and Remote Sensing: A Case Study of Guna Tana Landscape, Upper Blue Nile Basin, Ethiopia. Journal of Hydrology: Regional Studies, 24,100610. - Azadikhah, A., Bouzari, S., Yassaghi, A., & Emami, M. H. (2015). Formation of Extensional Basin in Internal Part of the Zagros Orogeny in West of Sirjan, Iran. Open Journal of Geology, 5, 821-827. - Baharvand, S., Rahnamarad, J., & Soori, S. (2016). Delineation of Groundwater Recharge Potential Zones Using Weighted Linear Combination Method (Case Study: Kuhdasht Plain, Iran). Journal of Geotechnical Geology, 12(2), 119-125. - Celik, R. (2019). Evaluation of Groundwater Potential by GIS-Based Multicriteria Decision Making as a Spatial Prediction Tool: Case Study in the Tigris River Batman-Hasankeyf Sub-Basin, Turkey. Water Journal, 11, 2630. - Cesar, S., Hedwige, C., Guimolaire, D., Ernestine, M., Etouna, J., Njandjock, P., & Nyeck, B. (2018). Radarsat-1 Image Processing for Regional-Scale Geological Mapping with Mining Vocation under Dense Vegetation and Equatorial Climate Environment, Southwestern Cameroon. The Egyptian Journal of Remote Sensing and Space Sciences, 21, S43–S54. - Chang, D. Y. (1996). Applications of the Extent Analysis Method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. - Chang, J., Tian, J., Zhang, Z., Chen, X., Chen, Y., Chen, S., & Duan, Z. (2018). Changes of Grassland Rain Use Efficiency and NDVI in Northwestern China from 1982 to 2013 and Its Response to Climate Change. Water Journal, 10(11), 1-20. - Chaudhry, A. K., Kumar, K., & Alam, M. A. (2019). Mapping of Groundwater Potential Zones Using the Fuzzy Analytic Hierarchy Process and Geospatial Technique. Geocarto International Journal, 14(3), 117-127. - Das, S. (2017). Delineation of Groundwater Potential Zone in Hard Rock Terrain in Gangajalghati Block, Bankura District, India Using Remote Sensing and GIS Techniques. Journal of Modeling Earth Systems and Environment, 3(4), 1589-1599. - Domingos, P., Sangam, S., Mukand, B., & Sarawut, N. (2017). Delineation of Groundwater Potential Zones in the Comoro Watershed, Timor Leste Using GIS, Remote Sensing, and Analytic Hierarchy Process (AHP) Technique. Journal of Applied Water Science, 7(1), 503-519. - Haralick, R. M. (1979). Statistical and Structural Approaches to Texture. Proc. IEEE, 67(5), 786–804. - Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M., & Fernandez, L. (2000). Temporal Evolution of Groundwater Composition in an Alluvial Aquifer (Pisuerga River, Spain) by Principal Component Analysis. Journal of Water Resources, 34, 807–816. - Jain, A. K., & Tuceryan, M. (1992). Texture Analysis. Chapter 11 in the Handbook of pattern recognition and Computer Vision by C. H. Chen 1992, 315. - Javhar, A., Chen, X., Bao, A., Jamshed, A., Yunus, M., Jovid, A., & Latipa, T. (2019). Comparison of Multi-Resolution Optical Landsat-8, Sentinel-2, and Radar Sentinel-1 Data for Automatic Lineament Extraction: A Case Study of Alichur Area, SE Pamir. Remote Sensing Journal, 11, 778-789. - Jenifer, M. A., & Jha, M. K. (2017). Comparison of Analytic Hierarchy Process, Catastrophe and Entropy Techniques for Evaluating Groundwater Prospect of Hard-Rock Aquifer Systems. Journal of Hydrology, 548, 605-624. - Khosroshahizadeh, S., Pourkermani, M., Almasian, M., Arian, M., & Khakzad, A. (2016). Lineament Patterns and Mineralization Related to Alteration Zone by Using ASAR-ASTER Imagery in Hize Jan-Sharaf Abad Au-Ag Epithermal Mineralized Zone (East Azarbaijan—NW Iran). Open Journal of Geology, 6(4), 232-250. - Lee, S., & Lee, C. W. (2015). Application of Decision-Tree Model to Groundwater Productivity Potential Mapping. Journal of Sustainability, 7, 13416–13432. - Masoumi, F., Eslamkish, T., Abkar, A. A., Honarmand, M., & Harris, J. R. (2017). Integration of Spectral, Thermal, and Textural Features of ASTER Data Using Random Forests Classification for Lithological Mapping. Journal of African Earth Sciences 129, 445-457. - Oh, H., Kim, Y., Choi, J., Park, E., & Lee, S. (2011). GIS Mapping of Regional Probabilistic Groundwater Potential in the Area of Pohang City, Korea. Journal of Hydrology, 399(3-4) 158-172. - Pourtaghi, Z. S., & Pourghasemi, H. R. (2014). GIS-based Groundwater Spring Potential Assessment and Mapping in the Birjand Township, Southern Khorasan Province, Iran. Hydrogeology Journal, 22(3), 643-662. - Rajasekhar, M., Raju, G. S., Sreenivasulu, Y., & Raju, R. S. (2019). Delineation of Groundwater Potential Zones in Semi-arid Region of Jilledubanderu River Basin, Anantapur District, Andhra Pradesh, India using Fuzzy Logic, AHP, and Integrated Fuzzy-AHP Approaches. Hydro Research, 2, 97–108. - Sukumar, M., & Selva, M. S. (2015). Discriminating Lineaments from the Aster Image by Analyzing the Object Properties. International Journal of Advanced Technology Energy Sciences, 3(1), 2348–7550. - Tseng, M. L., Lin, Y. H., Chiu, A. S. F., & Chen, C. Y. (2008). Fuzzy AHP Approach to TQM Strategy Evaluation. IEMS Journal, 7(1), 34–43 Groundwater resources are an important natural resource for domestic, agricultural, and industrial use. Today, due to the population growth as well as agricultural, and industrial development, the demand for groundwater use has increased dramatically. Climate changes, repeated droughts, and the risk of surface water pollution as a result of human and industrial activities are other important factors in the human interest in using groundwater resources. However, the unplanned use of groundwater disrupts the natural nutrient balance of aquifers. The accumulation and movement of groundwater in an area depend on various factors such as geology, tectonics, soil type, geomorphological characteristics of the region, drainage pattern, land use, and the relationship between such factors. The tectonic factor is one of the most important factors in the concentration of groundwater resources. Earth faults and fractures, known as tectonic faults, cause more and more surface water to penetrate into the earth's crust and feed the groundwater aquifers. Therefore, the identification of tectonic faults is one of the important cases in the study of groundwater resources. Kerman province, and especially its northern and northwestern cities, due to the increasing expansion of pistachio orchards, has faced an increase in the extraction of groundwater resources and water scarcity is one of the most important problems in the region. Sirjan city is also one of the areas that needs the identification of new groundwater resources. Therefore, in this study, an attempt has been made to process Sentinel 2 multispectral images and Fuzzy-AHP methods, which are among the multi-criteria decision analysis techniques. The maps of the various factors influencing the creation of groundwater aquifers have been prepared in the GIS environment. The maps were weighed and combined to identify suitable areas with good potential. The main difference between this method and the AHP method is the difference in the method of weighting criteria and options so that in this method, the weighting is done as fuzzy. Methodology The textural analysis consists of quantifying the different gray levels of the image in terms of roughness and their distribution. The contextual analysis technique makes it possible to highlight image dissimilarities or homogeneous zones. There are several methods for textural analysis including structural, statistical, study-based methods, and fractal methods. In this study, a statistical approach known as Grey Level Co-occurrence Matrix (GLCM) proposed by Haralick (1979) was adopted. It allows the identification and selection of the parameters that best define the elements from the measurement of the gray tone distributions. The factors are the mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation. These factors have many applications in geological and topographic studies. In this study, these factors were used in the analysis of the main components and then in the filtering operation to extract tectonic faults. Lineaments are related to fractures and lithological boundaries and in some cases to geomorphic relief. Thus, lineaments appear on the image with a tonal difference. The Fuzzy-AHP method was first proposed by Chang (1996, p. 649). Discussion The textural analysis was performed on the Sentinel 1 radar image of the study area. The result were 8 images of different co-occurrence indices. Figure 3 in the text shows the images mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation. In order to extract tectonic faults, the analysis of the principle components on eight co-occurrence factors was performed. Since the first principle component contains about 90% of the image information, it shows the major structural features of the image. In order to reduce the noise and increase structural information as much as possible, the first main component was multiplied. In order to extract tectonic lines, the oriented filter was applied to the conjugate image of the first main component at zero, 45, 90, and 135 degree angles. In this way, the north-south, northeast-southwest, east-west, and southeast-northwest directions were highlighted, respectively. Figure 6 in the text shows the effective factors in the potential assessment. In order to achieve the final map of the potential of groundwater resources in the fuzzy hierarchical method, each criterion must first be weighed and merged accordingly. Table 2 in the text shows the binary comparison matrix of criteria. Accordingly, the lithological criterion with a weight of 0.378 has the most effective factor and the vegetation criterion with a weight of 0.043 has the least effect on the concentration of groundwater resources. About 3 percent of the study area is in the very good category (228 square kilometers), 11 percent in the good category (836 square kilometers), 52 percent in the average category (4016 square kilometers), 29 percent in the low category (2252 square kilometers), and 5 percent in the very low category (396 square kilometers). Very good and good areas are mostly in the foothills to the highlands of the study area. Conclusion The results of the grey level co-occurrence matrix method show the capability of this method in extracting tectonic lines. In addition, the higher spatial resolution of the Sentinel 1 radar images than the available optical images makes the separation and detection of tectonic lines better. By combining the effective layers in the concentration of groundwater, the study area was potentialized in terms of the existence of groundwater reserves. The results show that about 14% of the study area has good potential (mostly located in foothills and within calcareous and alluvial rocks) in this area. Due to the declining quality of water resources in existing wells, the identified areas with a good and very good potential can be explored specifically for new water resources. Keywords: Potential Assessment, Groundwater Resources, Remote Sensing, Fuzzy-AHP, Sirjan Basin. References - Agarwal, E., Rajat, A., Garg, R. D., & Garg, P. K. (2013). Delineation of Groundwater Potential Zone: An AHP/ANP Approach. Journal of Earth System Science, 122(3), 887-898. - Andualem, T. G., & Demeke, G. G. (2019). Groundwater Potential Assessment Using GIS and Remote Sensing: A Case Study of Guna Tana Landscape, Upper Blue Nile Basin, Ethiopia. Journal of Hydrology: Regional Studies, 24,100610. - Azadikhah, A., Bouzari, S., Yassaghi, A., & Emami, M. H. (2015). Formation of Extensional Basin in Internal Part of the Zagros Orogeny in West of Sirjan, Iran. Open Journal of Geology, 5, 821-827. - Baharvand, S., Rahnamarad, J., & Soori, S. (2016). Delineation of Groundwater Recharge Potential Zones Using Weighted Linear Combination Method (Case Study: Kuhdasht Plain, Iran). Journal of Geotechnical Geology, 12(2), 119-125. - Celik, R. (2019). Evaluation of Groundwater Potential by GIS-Based Multicriteria Decision Making as a Spatial Prediction Tool: Case Study in the Tigris River Batman-Hasankeyf Sub-Basin, Turkey. Water Journal, 11, 2630. - Cesar, S., Hedwige, C., Guimolaire, D., Ernestine, M., Etouna, J., Njandjock, P., & Nyeck, B. (2018). Radarsat-1 Image Processing for Regional-Scale Geological Mapping with Mining Vocation under Dense Vegetation and Equatorial Climate Environment, Southwestern Cameroon. The Egyptian Journal of Remote Sensing and Space Sciences, 21, S43–S54. - Chang, D. Y. (1996). Applications of the Extent Analysis Method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. - Chang, J., Tian, J., Zhang, Z., Chen, X., Chen, Y., Chen, S., & Duan, Z. (2018). Changes of Grassland Rain Use Efficiency and NDVI in Northwestern China from 1982 to 2013 and Its Response to Climate Change. Water Journal, 10(11), 1-20. - Chaudhry, A. K., Kumar, K., & Alam, M. A. (2019). Mapping of Groundwater Potential Zones Using the Fuzzy Analytic Hierarchy Process and Geospatial Technique. Geocarto International Journal, 14(3), 117-127. - Das, S. (2017). Delineation of Groundwater Potential Zone in Hard Rock Terrain in Gangajalghati Block, Bankura District, India Using Remote Sensing and GIS Techniques. Journal of Modeling Earth Systems and Environment, 3(4), 1589-1599. - Domingos, P., Sangam, S., Mukand, B., & Sarawut, N. (2017). Delineation of Groundwater Potential Zones in the Comoro Watershed, Timor Leste Using GIS, Remote Sensing, and Analytic Hierarchy Process (AHP) Technique. Journal of Applied Water Science, 7(1), 503-519. - Haralick, R. M. (1979). Statistical and Structural Approaches to Texture. Proc. IEEE, 67(5), 786–804. - Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M., & Fernandez, L. (2000). Temporal Evolution of Groundwater Composition in an Alluvial Aquifer (Pisuerga River, Spain) by Principal Component Analysis. Journal of Water Resources, 34, 807–816. - Jain, A. K., & Tuceryan, M. (1992). Texture Analysis. Chapter 11 in the Handbook of pattern recognition and Computer Vision by C. H. Chen 1992, 315. - Javhar, A., Chen, X., Bao, A., Jamshed, A., Yunus, M., Jovid, A., & Latipa, T. (2019). Comparison of Multi-Resolution Optical Landsat-8, Sentinel-2, and Radar Sentinel-1 Data for Automatic Lineament Extraction: A Case Study of Alichur Area, SE Pamir. Remote Sensing Journal, 11, 778-789. - Jenifer, M. A., & Jha, M. K. (2017). Comparison of Analytic Hierarchy Process, Catastrophe and Entropy Techniques for Evaluating Groundwater Prospect of Hard-Rock Aquifer Systems. Journal of Hydrology, 548, 605-624. - Khosroshahizadeh, S., Pourkermani, M., Almasian, M., Arian, M., & Khakzad, A. (2016). Lineament Patterns and Mineralization Related to Alteration Zone by Using ASAR-ASTER Imagery in Hize Jan-Sharaf Abad Au-Ag Epithermal Mineralized Zone (East Azarbaijan—NW Iran). Open Journal of Geology, 6(4), 232-250. - Lee, S., & Lee, C. W. (2015). Application of Decision-Tree Model to Groundwater Productivity Potential Mapping. Journal of Sustainability, 7, 13416–13432. - Masoumi, F., Eslamkish, T., Abkar, A. A., Honarmand, M., & Harris, J. R. (2017). Integration of Spectral, Thermal, and Textural Features of ASTER Data Using Random Forests Classification for Lithological Mapping. Journal of African Earth Sciences 129, 445-457. - Oh, H., Kim, Y., Choi, J., Park, E., & Lee, S. (2011). GIS Mapping of Regional Probabilistic Groundwater Potential in the Area of Pohang City, Korea. Journal of Hydrology, 399(3-4) 158-172. - Pourtaghi, Z. S., & Pourghasemi, H. R. (2014). GIS-based Groundwater Spring Potential Assessment and Mapping in the Birjand Township, Southern Khorasan Province, Iran. Hydrogeology Journal, 22(3), 643-662. - Rajasekhar, M., Raju, G. S., Sreenivasulu, Y., & Raju, R. S. (2019). Delineation of Groundwater Potential Zones in Semi-arid Region of Jilledubanderu River Basin, Anantapur District, Andhra Pradesh, India using Fuzzy Logic, AHP, and Integrated Fuzzy-AHP Approaches. Hydro Research, 2, 97–108. - Sukumar, M., & Selva, M. S. (2015). Discriminating Lineaments from the Aster Image by Analyzing the Object Properties. International Journal of Advanced Technology Energy Sciences, 3(1), 2348–7550. - Tseng, M. L., Lin, Y. H., Chiu, A. S. F., & Chen, C. Y. (2008). Fuzzy AHP Approach to TQM Strategy Evaluation. IEMS Journal, 7(1), 34–43

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University of Isfahan Geography and Environmental Planning 2008-5362 31 2 2020 06 21 Classification and Assessment of Land Use Changes in Zanjan City Using Object-Oriented Analysis and Google Earth Engine System Classification and Assessment of Land Use Changes in Zanjan City Using Object-Oriented Analysis and Google Earth Engine System 25 42 24956 10.22108/gep.2020.120666.1242 FA Alireza Mohammadi Associate Professor, Department of Urban Geography and Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran (Corresponding Author Email: a.mohammadi@uma.ac.ir) Behrooz Khodabandehlou Master of Remote Sensing and GIS, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran Journal Article 2019 12 22 To assess environmental changes, monitoring systems and remote sensing satellites provide powerful tools that make the assessment of environmental change trends easier by multi-temporal comparisons. In recent decades, remote sensing data and GIS techniques for various aspects of urban spatial expansion and urban dispersal such as mapping (for expansion pattern), control (for process pattern recognition), measurement and evaluation (for analysis), and modeling (for Expansion simulation) are used. The object-based analysis is one of the emerging advanced techniques in the classification of satellite images. The object-oriented classification uses a segmentation process and a learning algorithm to analyze the spectral, spatial, and textural properties of the pixels. Along with the object-oriented classification method, Google Earth Engine, with extensive support for free satellite data and images, enables the classification and processing of high-speed satellite imagery that can be used in the monitoring and mapping land use. Methodology: In the present study, the digital data of the Landsat satellite provided by GEE are used. The data do not require pre-processing and initial correction (geometric, radiometric, etc.) and are readily available for processing. Landsat image types (1 to 8) can be summons with any processing level in GEE. In this study, atmospheric correction images of the Surface Reflectance Tier1 are used. This dataset is modified for atmospheric errors and includes OLI / TIRS sensors for Landsat 8. With simple coding patterns in GEE, the images of 1999, 2009, and 2019 are corrected for the processing step. GEE has provided a modern set of pixel-based classification that can be used for monitoring and mapping. By analyzing the corrected image of 1999, 2009, and 2019 and capturing the training samples, the images are classified with the support vector machine algorithms, random forest, and minimum distance. To perform object-oriented analysis and classification, images are segmented using the multiresolution segmentation algorithm in specialized recognition software. Geometric properties of land use classes (including shape, size, texture) are used for segmentation. By analyzing the results of the segmentation of images with different scale parameters, the optimal values of scale, shape, and compression for the images used are obtained. In this study, based on spatial resolution and image quality, four land use and land cover classes were considered in Zanjan urban areas. These classes include built-up, irrigated and urban green areas, dry farming, and rangelands. By selecting the above classes, training samples for multi-temporal images (1999, 2009, and 2019) are prepared. The nearest neighbor algorithm is used to classify images based on the object-oriented method. In this process, the maximum difference index of mean and NDVI vegetation index are also applied for each of the classes to reduce class mixing and improve the classification accuracy of influential parameters such as normalized difference built-up index (ndbi), mean and standard deviation of each band, area, the ratio of length to width, compaction, and brightness. Statistical parameters of kappa coefficients and overall accuracy are used for the accurate assessment of the classified images. To understand the changes in the area, after producing the land use maps and assessing them, the classification methods are used to evaluate the land use changes that occurred in the period 1999 to 2019. Discussion: After the classification of Landsat 5 and 8 satellite images, land use maps of 1999, 2009, and 2019 are prepared using object-oriented and pixel-based methods. Since in this study the parameters and characteristics of mean and standard deviation of bands, NDBI, NDVI indices, etc. are used to improve the results of the algorithm nearest neighbor object-oriented method, the results of image classification accuracy assessment show that the object-oriented method is weaker in separating rangelands and built-up in 1999 and 2009 than the support vector machine classification method. However, the object-oriented classification results for 2019 show the best performance of all the utilized classification algorithms. Due to the better results of the support vector machine classification method for 1999 and 2009 and the object-oriented method for 2019, the results of these methods are used in the assessment of land use changes in the study area. According to the results, significant changes have occurred in the region from 1999 to 2019. During this period, the land (mainly Zanjan) showed an increase of 5036 hectares. Also, the results show that Zanjan has grown and expanded into rangelands and dry farming in the suburbs over the period 1999 to 2019. Conclusion: Comparing the results of classifier accuracy assessment, the nearest neighbor object-oriented classification algorithm for 2019 showed better performance in terms of kappa coefficient and overall accuracy than other algorithms. Also, by comparing the results of the assessment of the classification maps of 1999 and 2009, the support vector machine algorithm showed the best performance compared to other classification algorithms in the study area. The support vector machine was the basis for the assessment of changes. Based on the results of land use changes assessment, in recent years, significant land use changes have occurred around Zanjan city. The reason for the increased land area in Zanjan in 2019 (26.12%) is the increase of population and the development of new settlements in the suburbs, and consequently, the reduction of agricultural rangelands. Keywords: Google Earth Engine, Object-Oriented, Support Vector Machine, Zanjan City. References: - Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study of Simly Watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 251-259. - Campbell, J. B., & Wynne, R. H. (2011). Introduction to Remote Sensing. Guilford Press. - De Oliveira Silveira, E. M., De Menezes, M. D., Júnior, F. W. A., Terra, M. C. N. S., & De Mello, J. M. (2017). Assessment of Geostatistical Features for Object-Based Image Classification of Contrasted Landscape Vegetation Cover. Journal of Applied Remote Sensing, 11(3), 036004. - Dewan, A. M., & Yamaguchi, Y. (2009). Land Use and Land Cover Change in Greater Dhaka, Bangladesh: Using Remote Sensing to Promote Sustainable Urbanization. Journal of Applied Geography, 29(3), 390-401. - Dingle Robertson, L., & King, D. J. (2011). Comparison of Pixel-and Object-Based Classification in Land Cover Change Mapping. International Journal of Remote Sensing, 32(6), 1505-1529. - El-Asmar, H. M., Hereher, M. E., & El Kafrawy, S. B. (2013). Surface Area Change Detection of the Burullus Lagoon, North of the Nile Delta, Egypt, Using Water Indices: A Remote Sensing Approach. The Egyptian Journal of Remote Sensing and Space Science, 16(1), 119-123. - Esam, I., Abdalla, F., & Erich, N. (2012). Land Use and Land Cover Changes of West Tahta Region, Sohag Governorate, Upper Egypt. Journal of Geographic Information System, 4(06), 483. - Feizizadeh, B., Blaschke, T., Nazmfar, H., Akbari, E., & Kohbanani, H. R. (2013). Monitoring Land Surface Temperature Relationship to Land Use/Land Cover from Satellite Imagery in Maraqeh County, Iran. Journal of Environmental Planning and Management, 56(9), 1290-1315. - Ghebrezgabher, M. G., Yang, T., Yang, X., Wang, X., & Khan, M. (2016). Extracting and Analyzing Forest and Woodland Cover Change in Eritrea Based on Landsat Data Using Supervised Classification. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 37-47. - Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Journal of Remote Sensing of Environment, 202, 18-27. - Huo, L. Z., Boschetti, L., & Sparks, A. M. (2019). Object-Based Classification of Forest Disturbance Types in the Conterminous United States. Journal of Remote Sensing, 11(5), 477. - Im, J., Jensen, J. R., & Tullis, J. A. (2008). Object‐Based Change Detection Using Correlation Image Analysis and Image Segmentation. International Journal of Remote Sensing, 29(2), 399-423. - Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote Sensing and Image Interpretation. John Wiley & Sons. - Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A Review of Supervised Object-Based Land-Cover Image Classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277-293. - Mahmoudi, F. T., Samadzadegan, F., & Reinartz, P. (2014). Object Recognition Based on the Context Aware Decision-Level Fusion in Multiviews Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1), 12-22. - Rawat, J. S., & Kumar, M. (2015). Monitoring Land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case Study of Hawalbagh Block, District Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77-84. - Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017). Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Journal of Frontiers in Earth Science, 5, 17. - Weih, R. C., & Riggan, N. D. (2010). Object-Based Classification vs. Pixel-Based Classification: Comparative Importance of Multi-Resolution Imagery. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences, 38(4), C7. - Weng, Q. (2012). Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements, Methods, and Trends. Journal of Remote Sensing of Environment, 117, 34-49. - Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing Object-Based and Pixel-Based Classifications for Mapping Savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), 884-893. - Yan, G., Mas, J. F., Maathuis, B. H. P., Xiangmin, Z., & Van Dijk, P. M. (2006). Comparison of Pixel‐Based and Object‐Oriented Image Classification Approaches—a Case Study in a Coal Fire Area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27(18), 4039-4055. - Zha, Y., Gao, J., & Ni, S. (2003). The Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing, 24(3), 583-594. To assess environmental changes, monitoring systems and remote sensing satellites provide powerful tools that make the assessment of environmental change trends easier by multi-temporal comparisons. In recent decades, remote sensing data and GIS techniques for various aspects of urban spatial expansion and urban dispersal such as mapping (for expansion pattern), control (for process pattern recognition), measurement and evaluation (for analysis), and modeling (for Expansion simulation) are used. The object-based analysis is one of the emerging advanced techniques in the classification of satellite images. The object-oriented classification uses a segmentation process and a learning algorithm to analyze the spectral, spatial, and textural properties of the pixels. Along with the object-oriented classification method, Google Earth Engine, with extensive support for free satellite data and images, enables the classification and processing of high-speed satellite imagery that can be used in the monitoring and mapping land use. Methodology: In the present study, the digital data of the Landsat satellite provided by GEE are used. The data do not require pre-processing and initial correction (geometric, radiometric, etc.) and are readily available for processing. Landsat image types (1 to 8) can be summons with any processing level in GEE. In this study, atmospheric correction images of the Surface Reflectance Tier1 are used. This dataset is modified for atmospheric errors and includes OLI / TIRS sensors for Landsat 8. With simple coding patterns in GEE, the images of 1999, 2009, and 2019 are corrected for the processing step. GEE has provided a modern set of pixel-based classification that can be used for monitoring and mapping. By analyzing the corrected image of 1999, 2009, and 2019 and capturing the training samples, the images are classified with the support vector machine algorithms, random forest, and minimum distance. To perform object-oriented analysis and classification, images are segmented using the multiresolution segmentation algorithm in specialized recognition software. Geometric properties of land use classes (including shape, size, texture) are used for segmentation. By analyzing the results of the segmentation of images with different scale parameters, the optimal values of scale, shape, and compression for the images used are obtained. In this study, based on spatial resolution and image quality, four land use and land cover classes were considered in Zanjan urban areas. These classes include built-up, irrigated and urban green areas, dry farming, and rangelands. By selecting the above classes, training samples for multi-temporal images (1999, 2009, and 2019) are prepared. The nearest neighbor algorithm is used to classify images based on the object-oriented method. In this process, the maximum difference index of mean and NDVI vegetation index are also applied for each of the classes to reduce class mixing and improve the classification accuracy of influential parameters such as normalized difference built-up index (ndbi), mean and standard deviation of each band, area, the ratio of length to width, compaction, and brightness. Statistical parameters of kappa coefficients and overall accuracy are used for the accurate assessment of the classified images. To understand the changes in the area, after producing the land use maps and assessing them, the classification methods are used to evaluate the land use changes that occurred in the period 1999 to 2019. Discussion: After the classification of Landsat 5 and 8 satellite images, land use maps of 1999, 2009, and 2019 are prepared using object-oriented and pixel-based methods. Since in this study the parameters and characteristics of mean and standard deviation of bands, NDBI, NDVI indices, etc. are used to improve the results of the algorithm nearest neighbor object-oriented method, the results of image classification accuracy assessment show that the object-oriented method is weaker in separating rangelands and built-up in 1999 and 2009 than the support vector machine classification method. However, the object-oriented classification results for 2019 show the best performance of all the utilized classification algorithms. Due to the better results of the support vector machine classification method for 1999 and 2009 and the object-oriented method for 2019, the results of these methods are used in the assessment of land use changes in the study area. According to the results, significant changes have occurred in the region from 1999 to 2019. During this period, the land (mainly Zanjan) showed an increase of 5036 hectares. Also, the results show that Zanjan has grown and expanded into rangelands and dry farming in the suburbs over the period 1999 to 2019. Conclusion: Comparing the results of classifier accuracy assessment, the nearest neighbor object-oriented classification algorithm for 2019 showed better performance in terms of kappa coefficient and overall accuracy than other algorithms. Also, by comparing the results of the assessment of the classification maps of 1999 and 2009, the support vector machine algorithm showed the best performance compared to other classification algorithms in the study area. The support vector machine was the basis for the assessment of changes. Based on the results of land use changes assessment, in recent years, significant land use changes have occurred around Zanjan city. The reason for the increased land area in Zanjan in 2019 (26.12%) is the increase of population and the development of new settlements in the suburbs, and consequently, the reduction of agricultural rangelands. Keywords: Google Earth Engine, Object-Oriented, Support Vector Machine, Zanjan City. References: - Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study of Simly Watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 251-259. - Campbell, J. B., & Wynne, R. H. (2011). Introduction to Remote Sensing. Guilford Press. - De Oliveira Silveira, E. M., De Menezes, M. D., Júnior, F. W. A., Terra, M. C. N. S., & De Mello, J. M. (2017). Assessment of Geostatistical Features for Object-Based Image Classification of Contrasted Landscape Vegetation Cover. Journal of Applied Remote Sensing, 11(3), 036004. - Dewan, A. M., & Yamaguchi, Y. (2009). Land Use and Land Cover Change in Greater Dhaka, Bangladesh: Using Remote Sensing to Promote Sustainable Urbanization. Journal of Applied Geography, 29(3), 390-401. - Dingle Robertson, L., & King, D. J. (2011). Comparison of Pixel-and Object-Based Classification in Land Cover Change Mapping. International Journal of Remote Sensing, 32(6), 1505-1529. - El-Asmar, H. M., Hereher, M. E., & El Kafrawy, S. B. (2013). Surface Area Change Detection of the Burullus Lagoon, North of the Nile Delta, Egypt, Using Water Indices: A Remote Sensing Approach. The Egyptian Journal of Remote Sensing and Space Science, 16(1), 119-123. - Esam, I., Abdalla, F., & Erich, N. (2012). Land Use and Land Cover Changes of West Tahta Region, Sohag Governorate, Upper Egypt. Journal of Geographic Information System, 4(06), 483. - Feizizadeh, B., Blaschke, T., Nazmfar, H., Akbari, E., & Kohbanani, H. R. (2013). Monitoring Land Surface Temperature Relationship to Land Use/Land Cover from Satellite Imagery in Maraqeh County, Iran. Journal of Environmental Planning and Management, 56(9), 1290-1315. - Ghebrezgabher, M. G., Yang, T., Yang, X., Wang, X., & Khan, M. (2016). Extracting and Analyzing Forest and Woodland Cover Change in Eritrea Based on Landsat Data Using Supervised Classification. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 37-47. - Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Journal of Remote Sensing of Environment, 202, 18-27. - Huo, L. Z., Boschetti, L., & Sparks, A. M. (2019). Object-Based Classification of Forest Disturbance Types in the Conterminous United States. Journal of Remote Sensing, 11(5), 477. - Im, J., Jensen, J. R., & Tullis, J. A. (2008). Object‐Based Change Detection Using Correlation Image Analysis and Image Segmentation. International Journal of Remote Sensing, 29(2), 399-423. - Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote Sensing and Image Interpretation. John Wiley & Sons. - Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A Review of Supervised Object-Based Land-Cover Image Classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277-293. - Mahmoudi, F. T., Samadzadegan, F., & Reinartz, P. (2014). Object Recognition Based on the Context Aware Decision-Level Fusion in Multiviews Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1), 12-22. - Rawat, J. S., & Kumar, M. (2015). Monitoring Land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case Study of Hawalbagh Block, District Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77-84. - Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017). Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Journal of Frontiers in Earth Science, 5, 17. - Weih, R. C., & Riggan, N. D. (2010). Object-Based Classification vs. Pixel-Based Classification: Comparative Importance of Multi-Resolution Imagery. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences, 38(4), C7. - Weng, Q. (2012). Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements, Methods, and Trends. Journal of Remote Sensing of Environment, 117, 34-49. - Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing Object-Based and Pixel-Based Classifications for Mapping Savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), 884-893. - Yan, G., Mas, J. F., Maathuis, B. H. P., Xiangmin, Z., & Van Dijk, P. M. (2006). Comparison of Pixel‐Based and Object‐Oriented Image Classification Approaches—a Case Study in a Coal Fire Area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27(18), 4039-4055. - Zha, Y., Gao, J., & Ni, S. (2003). The Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing, 24(3), 583-594.

https://gep.ui.ac.ir/article_24956_09870a5d06689e082303ecd7f54c3b8e.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 31 2 2020 06 21 Analysis of Moisture Sources and Spatial-Temporal Patterns Affecting Spring Snowfall in Chaharmahal and Bakhtiari Province of Iran Analysis of Moisture Sources and Spatial-Temporal Patterns Affecting Spring Snowfall in Chaharmahal and Bakhtiari Province of Iran 43 56 25072 10.22108/gep.2020.123237.1310 FA Zahra Ghassabi 1Assistant Professor of Atmospheric Hazards Prognostic Research Group of Atmospheric Science & Meteorological Research Center (ASMERC), Tehran, Iran Maede Fathi ASMERC, Pajoohesh Blvd, Shahid Kharrazi highway, Shahid Hemmadt highway (west), Tehran, I.R. of Iran, Masoumeh Norouzi 3M.S of Meteorology, Chaharmahal & bakhtiyari Meteorological Administration, Shahrekord, Iran Parviz Rezazadeh 4M.S of Meteorology, Iran Meteorological Organization (IRIMO), Tehran, Iran Journal Article 2020 05 27 The climatic history of Chaharmahal and Bakhtiari province in the Central Zagros region shows a huge amount of snowfall in the cold season. In recent years, the tendency of precipitation from snow to rain has increased in autumns and winters and wintertime snowfall has decreased compared to the long-term average of the province, while sometimes springtime snowfall can be seen in the province. In spring, dynamic systems that stimulate atmospheric instability are still present in the region, and sometimes the combination of dynamic-thermodynamic conditions causes heavy rainfalls. In the present study, the dynamic and thermodynamic conditions for three springtime snowfalls were analyzed and the effective moisture sources in springtime snowfall were obtained. Methodology Precipitation and temperature values in synoptic stations of Chaharmahal and Bakhtiari province in the period of 2000 to 2018 were provided by the Meteorological Organization. To select abnormal springtime precipitation, the anomaly of temperature, precipitation, and geopotential height at the level of 500-hPa were analyzed. The ERA5 analysis of the data from ECMWF with a horizontal resolution of 0.25° was used to calculate moisture flux, perceptible water and vorticity advection, the monthly anomaly of precipitation, height and temperature of 500-hPa, and instability indices. To analyze the causes of snowfall in spring and also to investigate the sources of moisture, the monthly anomalies of rainfall, the temperature at a height of 2 meters above the ground and height, and temperature of 500-hPa level were compared to the 30-year average (1981-2010). In a dynamic study, vorticity advection at the level of 500-hPa was measured. To identify the trajectory of atmospheric moisture, moisture flux at the level of 850-hPa and perceptible water were calculated. To investigate the thermodynamic conditions and atmospheric instability, Skew-T diagram and atmospheric instability indices including KI, TT, PW, and CAPE were used at Shahrekord station at 00 UTC on the day of snowfall. Discussion Snowfall is a climatic feature of Chaharmahal and Bakhtiari province that also occurs in spring, but in recent years, due to warmer weather and reduced snowfall in winter, snowfall in spring seems somewhat unexpected. Precipitation is one of the quantities whose prediction of location and intensity is associated with uncertainty. Therefore, in this study, for more accurate prediction, the moisture sources, and the dynamic and thermodynamic conditions of spring snowfall in the province were investigated. To select unusual springtime precipitation, anomalies of temperature, precipitation, and geopotential height at 500-hPa were examined. As a result, snowfalls were selected in the spring of 2004, 2009, and 2016, which were different from normal compared to the long-term 30-year average. Examination of the dynamic conditions of the mentioned systems showed that at the level of 500-hPa with the formation of a deep trough in the eastern Mediterranean to the Red Sea, the location of the study area in the east of this trough has caused instability and upward movements. In addition, there is a positive vorticity at the level of 500-hPa. Given that these conditions have occurred for all three systems, it can be concluded that the occurrence of snowfall in spring is due to a dynamic process. It is noteworthy that in April 2016, when the amount of snow was more than the other two cases, the trough formed in the area was much deeper than the other two ones, and the vorticity advection was higher. Analysis of the quantities of moisture flux and perceptible water showed that these systems supplied their moisture from the Arabian Sea, the southern Red Sea, the Sea of Oman, and the northern Indian Ocean. The sources of moisture for precipitation in the region are mainly the Arabian Sea, the Red Sea, the Sea of Oman, and the North Indian Ocean are located more than 2000 km far from the southwest of Iran. Moisture flux continues from a few days before the operation of the system with south and the southwest winds from the Arabian Sea and south of the Red Sea to the southwest of Iran. In addition, the amount of perceptible water on the day of the event increases sharply. Temperature analysis showed that the decrease in temperature on the days of the phenomenon was more severe than the previous days and compared to the climatic average, and the coldness of the entire air column illustrates snowfall in spring. Examination of climatic conditions including anomaly analysis of precipitation showed that their values in all cases were much higher than the long-term and the normal average of the region, and is consistent with prominent temperature and height anomalies at the level of 500-hPa. 15 to 30 decameter drop in height and more than 1° drop in temperature were observed at this level compared to the long term. Cooling of the atmospheric column due to the process of evaporation or melting along the path and especially in the adjacent layers of the earth's surface has an important role in precipitation in snow form. Examination of the values of instability indices in Shahrekord station also showed that these indices were prominent in the hours before the event and intensified the activity and convective cooling of the system. Conclusion The results showed that the sources of moisture for precipitation in the region are mainly the Red Sea, Arabian Sea, Oman Sea, and the North Indian Ocean, which were associated with a positive vorticity advection. Examination of thermodynamic conditions also showed that the instability indices in the hours before the onset of precipitation were favorable and intensified the convective activity of the systems. Convection cooling along with a severe decrease in temperature has shifted rain to snow. A significant decrease in temperature compared to the long-term average and the atmospheric cold column justifies the snowfall occurrence in spring. Keywords: Springtime Snowfall, Moisture Sources, Dynamic Analysis, Precipitation Anomaly, Chaharmahal and Bakhtiyari Province. References - Banacos, P. C., & Schultz, D. M. (2005). The Use of Moisture Flux Convergence in Forecasting Convective Initiation: Historical and Operational Perspectives. Journal of Weather and Forecasting, 20, 351- 366. - Dayan, U., Nissen, K., & Ulbrich, U. (2015). Atmospheric Conditions Inducing Extreme Precipitation Over the Eastern and Western Mediterranean. Natural Hazards Earth System Sciences Journal, 15, 2525- 2544. - Dyer, J. L., & Mote, T. L. (2006). Spatial Variability and Trends in Observed Snow Depth Over North America. Journal of Geophysical Research Letters, 33, 16503. - Frei, A., & Robinson, D. A. (1999). Northern Hemisphere Snow Extent: Regional variability. International Journal of Climatology, 19, 1535- 1560. - Gutzler, D. S. (2000). Covariability of Spring Snowpack and Summer Rainfall Across the Southwest United States. Journal of Climate, 13, 4018- 4027. - Holton, J. R. (2004). An Introduction to Dynamic Meteorology. Fourth Edition, San Diego, California, USA: Elsevier Academic Press. - Panziera, L., & Hoskins, B. (2014). Weather Developments Leading to Heavy Snow in the South- Eastern Lpine Region. National Weather Science. - Perry, B., & Konrad, C. E. (2006). Synoptic Patterns Associated with the Record Snowfall of 1960 in the Southern Appalachians. 63rd Eastern Snow Conference, Newark, Delaware USA. - Quiring, S. M., & Kluver, D. B. (2009). Relationship Between Winter/Spring Snowfall and Summer Precipitation in the Northern Great Plains of North America. Journal of Hydrometeorology, 10, 1203- 1217. - Stander J. H., Dyson, L., & Engelbrecht, C. J. (2016). A Snow Forecasting Decision Tree for Significant Snowfall Over the Interior of South Africa, South African. Journal of Science, 112. - Vuille, M., & Ammann, C. (1997). Regional Snowfall Patterns in the High, Arid Andes. Journal of Climate Change, 36, 413-423. - Zhang, Y., Li, T., & Wang, B. (2004). Decadal Change of the Spring Snow Depth Over the Tibetan Plateau: The Associated Circulation and Influence on the East Asian Summer Monsoon. Journal of Climate, 17, 2780- 2793. The climatic history of Chaharmahal and Bakhtiari province in the Central Zagros region shows a huge amount of snowfall in the cold season. In recent years, the tendency of precipitation from snow to rain has increased in autumns and winters and wintertime snowfall has decreased compared to the long-term average of the province, while sometimes springtime snowfall can be seen in the province. In spring, dynamic systems that stimulate atmospheric instability are still present in the region, and sometimes the combination of dynamic-thermodynamic conditions causes heavy rainfalls. In the present study, the dynamic and thermodynamic conditions for three springtime snowfalls were analyzed and the effective moisture sources in springtime snowfall were obtained. Methodology Precipitation and temperature values in synoptic stations of Chaharmahal and Bakhtiari province in the period of 2000 to 2018 were provided by the Meteorological Organization. To select abnormal springtime precipitation, the anomaly of temperature, precipitation, and geopotential height at the level of 500-hPa were analyzed. The ERA5 analysis of the data from ECMWF with a horizontal resolution of 0.25° was used to calculate moisture flux, perceptible water and vorticity advection, the monthly anomaly of precipitation, height and temperature of 500-hPa, and instability indices. To analyze the causes of snowfall in spring and also to investigate the sources of moisture, the monthly anomalies of rainfall, the temperature at a height of 2 meters above the ground and height, and temperature of 500-hPa level were compared to the 30-year average (1981-2010). In a dynamic study, vorticity advection at the level of 500-hPa was measured. To identify the trajectory of atmospheric moisture, moisture flux at the level of 850-hPa and perceptible water were calculated. To investigate the thermodynamic conditions and atmospheric instability, Skew-T diagram and atmospheric instability indices including KI, TT, PW, and CAPE were used at Shahrekord station at 00 UTC on the day of snowfall. Discussion Snowfall is a climatic feature of Chaharmahal and Bakhtiari province that also occurs in spring, but in recent years, due to warmer weather and reduced snowfall in winter, snowfall in spring seems somewhat unexpected. Precipitation is one of the quantities whose prediction of location and intensity is associated with uncertainty. Therefore, in this study, for more accurate prediction, the moisture sources, and the dynamic and thermodynamic conditions of spring snowfall in the province were investigated. To select unusual springtime precipitation, anomalies of temperature, precipitation, and geopotential height at 500-hPa were examined. As a result, snowfalls were selected in the spring of 2004, 2009, and 2016, which were different from normal compared to the long-term 30-year average. Examination of the dynamic conditions of the mentioned systems showed that at the level of 500-hPa with the formation of a deep trough in the eastern Mediterranean to the Red Sea, the location of the study area in the east of this trough has caused instability and upward movements. In addition, there is a positive vorticity at the level of 500-hPa. Given that these conditions have occurred for all three systems, it can be concluded that the occurrence of snowfall in spring is due to a dynamic process. It is noteworthy that in April 2016, when the amount of snow was more than the other two cases, the trough formed in the area was much deeper than the other two ones, and the vorticity advection was higher. Analysis of the quantities of moisture flux and perceptible water showed that these systems supplied their moisture from the Arabian Sea, the southern Red Sea, the Sea of Oman, and the northern Indian Ocean. The sources of moisture for precipitation in the region are mainly the Arabian Sea, the Red Sea, the Sea of Oman, and the North Indian Ocean are located more than 2000 km far from the southwest of Iran. Moisture flux continues from a few days before the operation of the system with south and the southwest winds from the Arabian Sea and south of the Red Sea to the southwest of Iran. In addition, the amount of perceptible water on the day of the event increases sharply. Temperature analysis showed that the decrease in temperature on the days of the phenomenon was more severe than the previous days and compared to the climatic average, and the coldness of the entire air column illustrates snowfall in spring. Examination of climatic conditions including anomaly analysis of precipitation showed that their values in all cases were much higher than the long-term and the normal average of the region, and is consistent with prominent temperature and height anomalies at the level of 500-hPa. 15 to 30 decameter drop in height and more than 1° drop in temperature were observed at this level compared to the long term. Cooling of the atmospheric column due to the process of evaporation or melting along the path and especially in the adjacent layers of the earth's surface has an important role in precipitation in snow form. Examination of the values of instability indices in Shahrekord station also showed that these indices were prominent in the hours before the event and intensified the activity and convective cooling of the system. Conclusion The results showed that the sources of moisture for precipitation in the region are mainly the Red Sea, Arabian Sea, Oman Sea, and the North Indian Ocean, which were associated with a positive vorticity advection. Examination of thermodynamic conditions also showed that the instability indices in the hours before the onset of precipitation were favorable and intensified the convective activity of the systems. Convection cooling along with a severe decrease in temperature has shifted rain to snow. A significant decrease in temperature compared to the long-term average and the atmospheric cold column justifies the snowfall occurrence in spring. Keywords: Springtime Snowfall, Moisture Sources, Dynamic Analysis, Precipitation Anomaly, Chaharmahal and Bakhtiyari Province. References - Banacos, P. C., & Schultz, D. M. (2005). The Use of Moisture Flux Convergence in Forecasting Convective Initiation: Historical and Operational Perspectives. Journal of Weather and Forecasting, 20, 351- 366. - Dayan, U., Nissen, K., & Ulbrich, U. (2015). Atmospheric Conditions Inducing Extreme Precipitation Over the Eastern and Western Mediterranean. Natural Hazards Earth System Sciences Journal, 15, 2525- 2544. - Dyer, J. L., & Mote, T. L. (2006). Spatial Variability and Trends in Observed Snow Depth Over North America. Journal of Geophysical Research Letters, 33, 16503. - Frei, A., & Robinson, D. A. (1999). Northern Hemisphere Snow Extent: Regional variability. International Journal of Climatology, 19, 1535- 1560. - Gutzler, D. S. (2000). Covariability of Spring Snowpack and Summer Rainfall Across the Southwest United States. Journal of Climate, 13, 4018- 4027. - Holton, J. R. (2004). An Introduction to Dynamic Meteorology. Fourth Edition, San Diego, California, USA: Elsevier Academic Press. - Panziera, L., & Hoskins, B. (2014). Weather Developments Leading to Heavy Snow in the South- Eastern Lpine Region. National Weather Science. - Perry, B., & Konrad, C. E. (2006). Synoptic Patterns Associated with the Record Snowfall of 1960 in the Southern Appalachians. 63rd Eastern Snow Conference, Newark, Delaware USA. - Quiring, S. M., & Kluver, D. B. (2009). Relationship Between Winter/Spring Snowfall and Summer Precipitation in the Northern Great Plains of North America. Journal of Hydrometeorology, 10, 1203- 1217. - Stander J. H., Dyson, L., & Engelbrecht, C. J. (2016). A Snow Forecasting Decision Tree for Significant Snowfall Over the Interior of South Africa, South African. Journal of Science, 112. - Vuille, M., & Ammann, C. (1997). Regional Snowfall Patterns in the High, Arid Andes. Journal of Climate Change, 36, 413-423. - Zhang, Y., Li, T., & Wang, B. (2004). Decadal Change of the Spring Snow Depth Over the Tibetan Plateau: The Associated Circulation and Influence on the East Asian Summer Monsoon. Journal of Climate, 17, 2780- 2793.

https://gep.ui.ac.ir/article_25072_99f068dbea0de051304f88fb896e3000.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 31 2 2020 06 21 Karst and Non-Karst Systems, Symbols of Habitat Patterns Index (Case Study: Romeshkan Region) Karst and Non-Karst Systems, Symbols of Habitat Patterns Index (Case Study: Romeshkan Region) 57 70 25236 10.22108/gep.2020.123395.1314 FA Somayeh Sadat Shahzeidi Assistant Professor, Department of Geography, Geomorphology, Faculty of Literature and Humanities, University of Guilan , Rasht, Iran Ali Bazvand MA student Department of Geography ,Hydrogeomorphology, Faculty of Geographical Sciences and Planning , University of Isfahan, Isfahan, Iran Alibazvand8@gmail.com Journal Article 2020 06 06 One of the processes that has been able to create the largest unit of form in geomorphology is the process of karstification. In many cases, the processes are not able to create a landscape or other units, but the karstization process has formed in different scales and one of the frameworks in geomorphology is related to these landscapes (Nojavan, et al., 2017, p. 99). The karst areas provide good opportunities for researchers to study the relationships and evolution of human societies. The recognition of past climatic relations, cave sedimentology, and the possibility of pollen studies, etc. can determine this issue on past human societies and identities. In dealing with various geomorphic units, humans have adapted to the environment according to the capabilities of the environment. According to this issue, the impact of karst landscapes on the realm of human societies and their distribution can be expressed. Karst areas in the past and present have a great impact on improving the condition of water resources and creating a suitable and beautiful visual landscape in the areas. Among these areas, we can mention the karst spring of Gharbalbiz in Yazd. It is noteworthy that this region has an ancient civilization and culture. The importance of recognition of karst areas lies in the way they are exploited. Karst areas are very important to the development of the civilian core. Regarding the forms of civilization in the past, it has been argued that the distribution and settlement of the population on Earth depend on the levels that have created the right conditions for the creation of the civilian core. The isohyetal maps of karst areas in Iran show that most of them are located on the Zagros Mountains, the Azerbaijani plateau, the northern regions of Iran, the Alborz Mountains, northeastern Iran (Khorasan), and a limited number of other highlands. Currently, 25 percent of the world's population uses karst water sources for drinking (Gillison, 2003, p. 23). The development and expansion of human resource centers depend on the size of karst resources in a region. Karst waters are able to move more than 1,000 kilometers inland and can be used in other regions such as western Iran (Limestone caves in Kalhorud, Asadabad, etc.). Human distribution and urban development are mainly in the western patterns of Iran in the Zagros. The purpose of this study is to investigate the effect of karst and non-karst systems onhabitat patterns index in the Romeshkan region according to environmental conditions. Methodology: In this study, the method of synoptic analysis was used. The method of comparing karstic and non-karstic areas was used in terms of how the settlements are distributed. To achieve the objectives of the research, geological maps 1: 250,000, topographic maps 1: 50,000, DEM 15 meters, and Google Earth images were used and the maps were drawn in the Global Mapper and Arc Gis10.5 software environment. After collecting the data, different layers of information were prepared including topographic, geological, karst, scattered caves and karst springs, summer settlements, rural, and urban areas. Each of these layers was matched with the karst areas of the region. Then, descriptive information related to layer overlap was extracted from Arc Gis10.5 software. Discussion Romeshkan is one of the cities of Lorestan province. In terms of geomorphology, it is one of the intermountain plains of the Central Zagros, which is located in the southwest of Lorestan and the northwest of Ilam province. Analysis of Geomorphology and Civilization of the Romeshkan Plain: According to the studied sources and theories of analytical geomorphology of Iran, the Romeshkan plain is one of the inland areas. In this regard, it can be said that it is one of the holes of Iran in the cold and wet Quaternary period. Due to the dissolution in the northwestern parts, its water is drained and it is not possible to trap water at the moment (Safari, 2013, p. 56-57). Oberlander (1956) refers to waterway patterns along with a focal point as evidence of the existence of these lakes. The presence of ancient hills in the bed and margins of these holes is evidence of this claim. In most of these hills, evidence of pottery fragments and remnants of pottery kilns have been found. The presence of such artifacts shows that the shores of the lake were a place of civilization and that the quality of water was far more favorable than during the warmer periods. Investigating the History of Habitation (Index Habitat Patterns) in the Research Area The karst areas are great heritages of original nature, traditional settlements, and the cultural development of local communities over thousands of years. Rich water resources, plant, and animal diversity as effective factors in sedentary have led to the establishment of human settlements of caves, shepherds, and nomadic and rural communities to these days. The stone and water resources of the karst lands, especially in the high levels of karst, west of the Zagros, have created unique architectures and traditional settlements. These perspectives can be a historical representation of the environmental factors, human resources, history, and culture of an ethnic group or nation. Studies conducted over the past few decades by a number of Iranian and foreign archaeologists on some of Iran's caves show that the inhabitants of the Iranian plateau are accustomed to using the cave as a home. These studies show that the caves of Iran have been among the most important caves in the world for human study and the history of evolution and livelihood (Javanshad, 1999, p. 153). Therefore, these areas have a very important historical and cultural value, and efforts should be made to keep these valuable sites. In the present study, three habitat patterns index were seen in the studied area including cave-dwelling, semi-nomadic, and sedentary settlements. Geomorphological studies of caves and habitable shelters show that caves are located within the Asmari formations and Quaternary. Romeshkan, with many springs, has provided more opportunities for cave-dwelling patterns. Important historical caves in Romeshkan include Viznhar Cave, Shir Cave, and Baba Kamal Cave. In this region, semi-nomadic life is associated with the karst and non-karst system, but it is more widespread in karst areas. This way of life starts from the beginning of spring to the beginning of autumn. Due to the expansion and duration of migration, the dependence on the sedentary life covers approximately more than half of the year. In this way of life, the villages on out of the karsts are widely involved. In field surveys, 29 summer settlements in the area were identified, some of which are available. Therefore, the karst morphology of the region has had a great impact on the continuity of this way of life. As the weather warms, this way of life becomes more connected to the karst structure of the region. When the snow melts on the slopes and there is a lot of snow and rain inside the karstic pits, a lot of snow and rain remains and can be extracted and used for the farmers’ water sources (Moghimi et al., ‌‌2019‌‌: ‌103‌). The nomadic pattern in the area represents the ancient settlements of the nomads. In general, the study of identified summer settlements shows that most of them are scattered in karst structures. The Romeshkan region has been one of the important communication crossings between the southwestern regions of Iran and Mesopotamia and has special prosperity. There are ancient hills, castles, manuscripts, and caves of the prehistoric period. Chalcolithic-metal in this area indicates the long history of settlement in these places (Cultural Heritage, Handicrafts, and Tourism Organization of Lorestan Province). The out-of-the-karst areas have been populated by watery springs, and surveys show that the highest density of villages belongs to the margin of the karst areas including the Quaternary alluvial plains and non-karst formations. Conclusion Topographic and geomorphic features in the past, especially in the Quaternary era, have provided suitable environmental conditions for cave life. It can be said that Karst's morphology, compared to other regions, has had favorable and influential conditions on the civil and civilizational patterns around. The model of the cave and semi-nomadic settlements can be adapted to the karst territories. In contrast, the sedentary pattern is affected differently by karst structures. Contrary to the margins of karst and non-karst areas, the special topography of these roughnesses and the scattering of karstic springs in these areas have caused the scattering of settlements. Thus, about 11% of the villages in the region are formed within the karst territories and about 89% in the Quaternary alluvium. In these sediments, groundwater aquifers and access to water resources through springs, wells, and aqueducts have caused a high density of rural areas in this area. Keywords: Karst, Romeshkan, Sedentary Settlements, Cave-dweller, Semi-nomadic. Resources - Andrejchuk, V., (2005). Karst as a Settling Factor. Proceedings of the 14th International Congress of Speleology, 21-28 August 2005, Kalamos, Hellas. 1, 331-333. - Baba Jamali, F., Karimi, E., & Javanroui, F. )2016(. Study and Analysis of Ancient Civilizations Using GIS-RS (Case Study: Achaemenid Sites of Marvdasht). Journal of Urban Studies of Shahid Bahonar University of Kerman, 3(2), 7. - Ministry of Cultural Heritage (2017). Handicrafts and Tourism Organization of Lorestan Province. - Gams, I., Nicod, J., Julian, M., Anthony, E., & Sauro, U. (1993). Environmental Change and Human Impacts on the Mediterranean Karsts of France, Italy, and the Dinaric Region. Catena Supplement Journal, 25, 59-98. - Geographical Culture of the Country's Villages. (1996). Geographical Organization of the Armed Forces. - Geographical Organization of the Armed Forces of the Islamic Republic of Iran. (n.d). Topographic Map: 1: 50,000, Sheets (I-II-IV‌) 5456, (III-IV‌) 5566. - Geological Survey of Iran. (n.d). Geological Map 1: 250000, No. 20504, Ilam Koohdasht Sheet. - Ghorbani, M. S. (2015). Karst Landscape as an Indicator of Settlement in the Region Kamyaran.Journal of Geographical Research, 47(4), 517-531. - Gillison, D. (2004). Caves, Processes Development and Management. London: Edvard Arnold. - Javanshad, A., (1999). Cave and Cave Rolling. Tehran: Saheb Kowsar Publication. - Mojtahedi, A. (1991). Introduction to the Geography of Settlement in Iran. Journal of Geographical Research, (22), 449-478. - Nikandish, N., Abolhassani, B., Baghbani, Z., Hakiminejad, M. (2013). Ecogeomorphology, Civilization, and Climate Change in the Ancient Silk Hills. National Conference of the Iranian Society of Geomorphology, Faculty of Geography, Tehran. - Nojavan, M. R., Shah Zaidi, S. S., & Ramesht, M. H. (2017). Karst Geomorphology. Tehran: Samt Publication. - Oberlander, T. (1965). The Zagros Streams. University of California, Berkeley, 101-8. - Podobnikar, T., Schoner, M., Jansa, J., & Pfeifer, N. (2009). Spatial Analysis of Anthropogenic Impact on Karst Geomorphology (Slovenia). Environmental Geology, 58, 257–268. - Pulina, M. (1977). Karst Areas in Poland and Their Changes by Human Impact. Landform Analysis, 1, 55-71. - Ramesht, M. H., & Babajmali, F. (2019). Analytical Geomorphology of Iran. Tehran: Samt Publication. - Rezaei, M. (2013). A Study of the Distribution of Bronze Age Settlements in the Romeshkan Plain. Master Thesis, Faculty of Literature and Humanities, Islamic Azad University, Central Tehran Branch. - Saffari, ‌ A., Ramesht, M. H., Hatamieh, Fard, ‌ R. (2013). Explaining the Paleohydrogeogeomorphological Developments of Kuhdasht Region. Journal of Applied Research in Geographical Sciences, 14(33), 56-57. - Sajjadieh, A. (2003). Report of the First Chapter of Review, Identification, and Documentation of Koohdasht City. Lorestan: Lorestan Cultural Heritage Documentation Center. - Sharifi, M. (2005). Zagros Settlement Complexities in the Late Pleistocene and Early Holocene. Essay Journal, 38(39), 355-346. One of the processes that has been able to create the largest unit of form in geomorphology is the process of karstification. In many cases, the processes are not able to create a landscape or other units, but the karstization process has formed in different scales and one of the frameworks in geomorphology is related to these landscapes (Nojavan, et al., 2017, p. 99). The karst areas provide good opportunities for researchers to study the relationships and evolution of human societies. The recognition of past climatic relations, cave sedimentology, and the possibility of pollen studies, etc. can determine this issue on past human societies and identities. In dealing with various geomorphic units, humans have adapted to the environment according to the capabilities of the environment. According to this issue, the impact of karst landscapes on the realm of human societies and their distribution can be expressed. Karst areas in the past and present have a great impact on improving the condition of water resources and creating a suitable and beautiful visual landscape in the areas. Among these areas, we can mention the karst spring of Gharbalbiz in Yazd. It is noteworthy that this region has an ancient civilization and culture. The importance of recognition of karst areas lies in the way they are exploited. Karst areas are very important to the development of the civilian core. Regarding the forms of civilization in the past, it has been argued that the distribution and settlement of the population on Earth depend on the levels that have created the right conditions for the creation of the civilian core. The isohyetal maps of karst areas in Iran show that most of them are located on the Zagros Mountains, the Azerbaijani plateau, the northern regions of Iran, the Alborz Mountains, northeastern Iran (Khorasan), and a limited number of other highlands. Currently, 25 percent of the world's population uses karst water sources for drinking (Gillison, 2003, p. 23). The development and expansion of human resource centers depend on the size of karst resources in a region. Karst waters are able to move more than 1,000 kilometers inland and can be used in other regions such as western Iran (Limestone caves in Kalhorud, Asadabad, etc.). Human distribution and urban development are mainly in the western patterns of Iran in the Zagros. The purpose of this study is to investigate the effect of karst and non-karst systems onhabitat patterns index in the Romeshkan region according to environmental conditions. Methodology: In this study, the method of synoptic analysis was used. The method of comparing karstic and non-karstic areas was used in terms of how the settlements are distributed. To achieve the objectives of the research, geological maps 1: 250,000, topographic maps 1: 50,000, DEM 15 meters, and Google Earth images were used and the maps were drawn in the Global Mapper and Arc Gis10.5 software environment. After collecting the data, different layers of information were prepared including topographic, geological, karst, scattered caves and karst springs, summer settlements, rural, and urban areas. Each of these layers was matched with the karst areas of the region. Then, descriptive information related to layer overlap was extracted from Arc Gis10.5 software. Discussion Romeshkan is one of the cities of Lorestan province. In terms of geomorphology, it is one of the intermountain plains of the Central Zagros, which is located in the southwest of Lorestan and the northwest of Ilam province. Analysis of Geomorphology and Civilization of the Romeshkan Plain: According to the studied sources and theories of analytical geomorphology of Iran, the Romeshkan plain is one of the inland areas. In this regard, it can be said that it is one of the holes of Iran in the cold and wet Quaternary period. Due to the dissolution in the northwestern parts, its water is drained and it is not possible to trap water at the moment (Safari, 2013, p. 56-57). Oberlander (1956) refers to waterway patterns along with a focal point as evidence of the existence of these lakes. The presence of ancient hills in the bed and margins of these holes is evidence of this claim. In most of these hills, evidence of pottery fragments and remnants of pottery kilns have been found. The presence of such artifacts shows that the shores of the lake were a place of civilization and that the quality of water was far more favorable than during the warmer periods. Investigating the History of Habitation (Index Habitat Patterns) in the Research Area The karst areas are great heritages of original nature, traditional settlements, and the cultural development of local communities over thousands of years. Rich water resources, plant, and animal diversity as effective factors in sedentary have led to the establishment of human settlements of caves, shepherds, and nomadic and rural communities to these days. The stone and water resources of the karst lands, especially in the high levels of karst, west of the Zagros, have created unique architectures and traditional settlements. These perspectives can be a historical representation of the environmental factors, human resources, history, and culture of an ethnic group or nation. Studies conducted over the past few decades by a number of Iranian and foreign archaeologists on some of Iran's caves show that the inhabitants of the Iranian plateau are accustomed to using the cave as a home. These studies show that the caves of Iran have been among the most important caves in the world for human study and the history of evolution and livelihood (Javanshad, 1999, p. 153). Therefore, these areas have a very important historical and cultural value, and efforts should be made to keep these valuable sites. In the present study, three habitat patterns index were seen in the studied area including cave-dwelling, semi-nomadic, and sedentary settlements. Geomorphological studies of caves and habitable shelters show that caves are located within the Asmari formations and Quaternary. Romeshkan, with many springs, has provided more opportunities for cave-dwelling patterns. Important historical caves in Romeshkan include Viznhar Cave, Shir Cave, and Baba Kamal Cave. In this region, semi-nomadic life is associated with the karst and non-karst system, but it is more widespread in karst areas. This way of life starts from the beginning of spring to the beginning of autumn. Due to the expansion and duration of migration, the dependence on the sedentary life covers approximately more than half of the year. In this way of life, the villages on out of the karsts are widely involved. In field surveys, 29 summer settlements in the area were identified, some of which are available. Therefore, the karst morphology of the region has had a great impact on the continuity of this way of life. As the weather warms, this way of life becomes more connected to the karst structure of the region. When the snow melts on the slopes and there is a lot of snow and rain inside the karstic pits, a lot of snow and rain remains and can be extracted and used for the farmers’ water sources (Moghimi et al., ‌‌2019‌‌: ‌103‌). The nomadic pattern in the area represents the ancient settlements of the nomads. In general, the study of identified summer settlements shows that most of them are scattered in karst structures. The Romeshkan region has been one of the important communication crossings between the southwestern regions of Iran and Mesopotamia and has special prosperity. There are ancient hills, castles, manuscripts, and caves of the prehistoric period. Chalcolithic-metal in this area indicates the long history of settlement in these places (Cultural Heritage, Handicrafts, and Tourism Organization of Lorestan Province). The out-of-the-karst areas have been populated by watery springs, and surveys show that the highest density of villages belongs to the margin of the karst areas including the Quaternary alluvial plains and non-karst formations. Conclusion Topographic and geomorphic features in the past, especially in the Quaternary era, have provided suitable environmental conditions for cave life. It can be said that Karst's morphology, compared to other regions, has had favorable and influential conditions on the civil and civilizational patterns around. The model of the cave and semi-nomadic settlements can be adapted to the karst territories. In contrast, the sedentary pattern is affected differently by karst structures. Contrary to the margins of karst and non-karst areas, the special topography of these roughnesses and the scattering of karstic springs in these areas have caused the scattering of settlements. Thus, about 11% of the villages in the region are formed within the karst territories and about 89% in the Quaternary alluvium. In these sediments, groundwater aquifers and access to water resources through springs, wells, and aqueducts have caused a high density of rural areas in this area. Keywords: Karst, Romeshkan, Sedentary Settlements, Cave-dweller, Semi-nomadic. Resources - Andrejchuk, V., (2005). Karst as a Settling Factor. Proceedings of the 14th International Congress of Speleology, 21-28 August 2005, Kalamos, Hellas. 1, 331-333. - Baba Jamali, F., Karimi, E., & Javanroui, F. )2016(. Study and Analysis of Ancient Civilizations Using GIS-RS (Case Study: Achaemenid Sites of Marvdasht). Journal of Urban Studies of Shahid Bahonar University of Kerman, 3(2), 7. - Ministry of Cultural Heritage (2017). Handicrafts and Tourism Organization of Lorestan Province. - Gams, I., Nicod, J., Julian, M., Anthony, E., & Sauro, U. (1993). Environmental Change and Human Impacts on the Mediterranean Karsts of France, Italy, and the Dinaric Region. Catena Supplement Journal, 25, 59-98. - Geographical Culture of the Country's Villages. (1996). Geographical Organization of the Armed Forces. - Geographical Organization of the Armed Forces of the Islamic Republic of Iran. (n.d). Topographic Map: 1: 50,000, Sheets (I-II-IV‌) 5456, (III-IV‌) 5566. - Geological Survey of Iran. (n.d). Geological Map 1: 250000, No. 20504, Ilam Koohdasht Sheet. - Ghorbani, M. S. (2015). Karst Landscape as an Indicator of Settlement in the Region Kamyaran.Journal of Geographical Research, 47(4), 517-531. - Gillison, D. (2004). Caves, Processes Development and Management. London: Edvard Arnold. - Javanshad, A., (1999). Cave and Cave Rolling. Tehran: Saheb Kowsar Publication. - Mojtahedi, A. (1991). Introduction to the Geography of Settlement in Iran. Journal of Geographical Research, (22), 449-478. - Nikandish, N., Abolhassani, B., Baghbani, Z., Hakiminejad, M. (2013). Ecogeomorphology, Civilization, and Climate Change in the Ancient Silk Hills. National Conference of the Iranian Society of Geomorphology, Faculty of Geography, Tehran. - Nojavan, M. R., Shah Zaidi, S. S., & Ramesht, M. H. (2017). Karst Geomorphology. Tehran: Samt Publication. - Oberlander, T. (1965). The Zagros Streams. University of California, Berkeley, 101-8. - Podobnikar, T., Schoner, M., Jansa, J., & Pfeifer, N. (2009). Spatial Analysis of Anthropogenic Impact on Karst Geomorphology (Slovenia). Environmental Geology, 58, 257–268. - Pulina, M. (1977). Karst Areas in Poland and Their Changes by Human Impact. Landform Analysis, 1, 55-71. - Ramesht, M. H., & Babajmali, F. (2019). Analytical Geomorphology of Iran. Tehran: Samt Publication. - Rezaei, M. (2013). A Study of the Distribution of Bronze Age Settlements in the Romeshkan Plain. Master Thesis, Faculty of Literature and Humanities, Islamic Azad University, Central Tehran Branch. - Saffari, ‌ A., Ramesht, M. H., Hatamieh, Fard, ‌ R. (2013). Explaining the Paleohydrogeogeomorphological Developments of Kuhdasht Region. Journal of Applied Research in Geographical Sciences, 14(33), 56-57. - Sajjadieh, A. (2003). Report of the First Chapter of Review, Identification, and Documentation of Koohdasht City. Lorestan: Lorestan Cultural Heritage Documentation Center. - Sharifi, M. (2005). Zagros Settlement Complexities in the Late Pleistocene and Early Holocene. Essay Journal, 38(39), 355-346.

https://gep.ui.ac.ir/article_25236_ca518ec10a8daf93e4fd298c656e5dfb.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 31 2 2020 06 21 Explaining the Indicators affecting Policy-making for Rural Management (Case Study: Kerman Province) Explaining the Indicators affecting Policy-making for Rural Management (Case Study: Kerman Province) 71 88 25108 10.22108/gep.2020.124287.1336 FA Ali Afsharipour PhD Candidate, Faculty of Geography Sciences and Planning, University of Isfahan, Isfahan, Iran Hamid Barghi Associate Professor, Faculty of Geography Sciences and Planning, University of Isfahan, Isfahan, Iran (*Corresponding Author Email: h.barghi@geo.ui.ac.ir) Yousef Ghanbari Associate Professor, Faculty of Geography Sciences and Planning, University of Isfahan, Isfahan, Iran Journal Article 2020 08 03 The existence of numerous challenges and problems in rural areas has forced geographers to do their research to solve these challenges. It can be said that one of the effective pillars in achieving rural development is the way decisions and policies are made. Futures studies are used to write about the future and possible changes in national, regional, and organizational contexts to respond to change. This study sought to explain the effective indicators in policy-making for rural regions management of Kerman province to help make more accurate decisions for the future of villages and policy-making appropriate to these indicators. In this regard, the main question was: What are the most effective indicators in policy-making for the management of rural areas of Kerman province in the future? The research method in this study was descriptive-analytical. The study followed a systematic and structural view with the method of futures studies and the use of MICMAC software. The results showed that the current policy-making system for the management of rural areas in Kerman province cannot be considered stable and desirable. The most influential indicators in policy-making for the management of rural areas of Kerman province were the environment (score 986), rurality (score 918), economy (score 884), and culture of rural people (score 850). Also, the indicators of the environment (score 952), economy (score 952), the sustainability of policies (score 884) were the most dependent factors. For better policy-making and optimal management of rural areas of the province in the future, strategic variables should be considered including environmental variables as an effective factor and the resulting form of policies, rurality or environmental-social identity, the economic identity of the village, the impact, and economic goals of policies, respectively. It is also possible to develop and sustain the rural management system of the province by manipulating and improving the variables of the economy, the role of the government, and increasing the participation of the people. Introduction: The need for the development and the existence of various challenges such as migration, poverty, and economic problems in rural areas, especially in developing countries, has led researchers to work to solve these problems. In this regard, governments make decisions and implement programs called policy-making. The executors of these decisions in the rural environment are the village managers. Because rural areas are different, policy-making to manage them must be appropriate by these features. In fact, in this study, the authors sought to identify and explain the influential indicators in policy-making for the management of rural areas of Kerman province to predict the impact of each indicator, and how each indicator will function in the future. With the complexity of development programs, it seems that it is necessary to adopt futuristic approaches. Futures studies are more capable of analyzing the situation in the future than other methods, and decisions based on this method can be long-term. It can be said that in order to achieve the goals of development and progress, the most important features that are important for policy-making in rural areas must be recognized and the impact of these factors on each other must be determined. Since our goal was to plan for the future of villages and to manage them properly in the future, structural analysis methods and futures research software were used in this research. Methodology: The research method in this research was descriptive-analytical in nature and practical in terms of purpose. In this research, with a systematic and structural view, we studied theoretical literature in the field of rural policy-making and management. Using a questionnaire, the status of these indicators and the impact of each in the rural areas of Kerman province were scored by the technique of interplay (structural analysis method). This information was analyzed in MICMAC software and finally, the most important and influential factors for policy-making and rural management were identified. The study area of this research was rural areas of Kerman province. According to the 2016 census, Kerman province had a population of 3164718 people, of which 1302557 people lived in villages, which constituted 41% of the province's population. Discussion: The structural analysis method was used by the software to obtain the main effective indicators and the influential indicators for the future of policy-making in the study area. Based on the number of variables, the dimensions of the matrix were 13 x 13, and with the formation of this matrix, the impact of each index on another index was determined by weighing from 0 to 3. The variables of the role of culture in the policy-making, management of rural areas, and the historical course of the management of rural communities in the province were among the influential variables, the variables of economy, government, and participation for policy-making were among the two-sided variables. Also, the variables of policy features and management of rural communities, policy management, multiplicity, the inadequate status of the current policy method, and specification pattern characteristics were known as influential or dependent variables and urbanized policy variable as the independent variable. The most relevant and strongest variables were the environment, rurality, economy, government, and participation. These variables are in fact key and strategic variables in policy-making and any decision to manage rural areas of Kerman province in the future. The indicators of the environment, rurality, economy, and culture had the greatest direct impact and the indicators of the environment, economic, and disproportionate status of the current policy-making method for the management of rural areas in Kerman province had the highest direct dependence. Conclusion: The results showed that due to the scattering of variables on the coordinate axis, it cannot be said that the current policy-making system for the management of rural areas in Kerman province is stable. Therefore, for better policy-making for the management of rural areas in the province in the future, we must consider strategic variables. These variables are the environment as an effective factor and the resulting form of policies, rurality or socio-environmental identity, and economic identity of the village, the impact and economic goals of policies, the role of government, and attention to rural participation in policy-making and decision-making. In fact, our key player in the future is the environment as an influential factor and the basis of policy-making. The environment is a form and the result of policy-making. We can also help to the evolution, sustainability, and improvement of the province's rural management system by changing and improving the variables of the economy, the role of the government, and increasing people's participation in policy-making. Keywords: Rural Development, Policy-Making, Rural Management, Kerman Province. References: - Alejandra, A., Cecilia, A. S., Nestor, M., & Lorena, H. (2020). Linking Farmers' Management Decision, Demographic Characteristics and Perceptions of Ecosystem Services in the Southern Pampa of Argentina. Journal of Rural Studies. In press. - Anríquez, G., & Stloukal, L. (2008). Rural Population Change in Developing Countries: Lessons for Policy-making. European View, 7(2), 309-317. - Dufva, M., Könnölä, T., & Koivisto, R. (2015).Multi-layered Foresight: Lessons from Regional Foresight in Chile. Futures, 73, 100-111. - Dye, T. R., & Dye, T. R. (1992). Understanding Public Policy. Englewood Cliffs, NJ: Prentice Hall. - Goodwin, M. (1998). The Governance of Rural Areas: some Emerging Research Issues and Agendas. Journal of Rural Studies, 14(1), 5-12. - Henderson, F., Steiner, A., Farmer, J., & Whittam, G. (2020). Challenges of Community Engagement in a Rural Area: the Impact of Flood Protection and Policy. Journal of Rural Studies, 73, 225-233. - Holland, J., Burian, M., & Dixey, L. (2003). Tourism in Poor Rural Areas: Diversifying the Product and Expanding the Benefits in Rural Uganda and the Czech Republic. PPT Working Paper, 12. - Kerr, K. (2001). Repercussion de la reforme de securite sociale sur le monde ruralseminaries degulf, university degulf Publication, LONDON. - Korten, D. C. (1980). Community Organization and Rural Development: A Learning Process Approach. Public Administration Review, 480-511. - Lewis, J. (2003). Housing Construction in Earthquake Prone Places: Perspectives, Priorities, and Projections for Development. The Australian Journal of EmergencyManagement, 18(2), 35-44. - Little, J. (2001). New Rural Governance? Progress in Human Geography, 25(1), 97-102. - Liu, J., Zhang, X., Lin, J., & Li, Y. (2019). Beyond Government-led or Community-based: Exploring the Governance Structure and Operating Models for Reconstructing China's Hollowed Villages. Journal of Rural Studies. In Press. - Lovell, S. A., Gray, A., & Boucher, S. E. (2018). Economic Marginalization and Community Capacity: How Does Industry Closure in a Small Town Affect Perceptions of Place?. Journal of Rural Studies, 62, 107-115. - Murdoch, J., & Abram, S. (1998).Defining the Limits of Community Governance. Journal of Rural Studies, 14(1), 41-50. - Prager, K., & Freese, J. (2009). Stakeholder Involvement in Agri-environmental Policy-Making–Learning from a Local and a State-level Approach in Germany. Journal of Environmental Management, 90(2), 1154-1167. - Prové, C., de Krom, M. P., & Dessein, J. (2019). Politics of Scale in Urban Agriculture Governance: A Transatlantic Comparison of Food Policy Councils. Journal of Rural Studies, 68, 171-181. - Shucksmith, M. (2010). Disintegrated Rural Development? Neo‐Endogenous Rural Development, Planning, and Place‐shaping in Diffused Power Contexts. Sociologia Ruralis, 50(1), 1-14. - Stevenson, N., Airey, D., & Miller, G. (2008). Tourism Policy-making: The Policymakers’ Perspectives. Annals of Tourism Research, 35(3), 732-750. - Tikai, P., & Kama, A. (2010). A Study of Indigenous Knowledge and its Role to Sustainable Agriculture in Samoa. Ozean Journal of Social Sciences, 3(1), 65-79. - Todaro, M. P. (1985). Economic Development in the Third World. UK: Longman - Tuitjer, G., & Steinführer, A. (2019). The Scientific Construction of the Village: Framing and Practicing Rural Research in a Trend Study in Germany, 1952–2015. Journal of Rural Studies. In press. - Visser, O., & Spoor, M. (2011). Land Grabbing in Post-Soviet Eurasia: the World’s Largest Agricultural Land Reserves at Stake. The Journal of Peasant Studies, 38(2), 299-323. - Wenchang, W. (2008). Rural Management– The Way Out for Tibetan Rural Areas. Business and Public Administration Studies, 3(3), 75. - WWW.Dictionary.cambridge.org (2019). (Definition of “Policy-making” from the Cambridge Advanced Learner's Dictionary & Thesaurus © Cambridge University Press). The existence of numerous challenges and problems in rural areas has forced geographers to do their research to solve these challenges. It can be said that one of the effective pillars in achieving rural development is the way decisions and policies are made. Futures studies are used to write about the future and possible changes in national, regional, and organizational contexts to respond to change. This study sought to explain the effective indicators in policy-making for rural regions management of Kerman province to help make more accurate decisions for the future of villages and policy-making appropriate to these indicators. In this regard, the main question was: What are the most effective indicators in policy-making for the management of rural areas of Kerman province in the future? The research method in this study was descriptive-analytical. The study followed a systematic and structural view with the method of futures studies and the use of MICMAC software. The results showed that the current policy-making system for the management of rural areas in Kerman province cannot be considered stable and desirable. The most influential indicators in policy-making for the management of rural areas of Kerman province were the environment (score 986), rurality (score 918), economy (score 884), and culture of rural people (score 850). Also, the indicators of the environment (score 952), economy (score 952), the sustainability of policies (score 884) were the most dependent factors. For better policy-making and optimal management of rural areas of the province in the future, strategic variables should be considered including environmental variables as an effective factor and the resulting form of policies, rurality or environmental-social identity, the economic identity of the village, the impact, and economic goals of policies, respectively. It is also possible to develop and sustain the rural management system of the province by manipulating and improving the variables of the economy, the role of the government, and increasing the participation of the people. Introduction: The need for the development and the existence of various challenges such as migration, poverty, and economic problems in rural areas, especially in developing countries, has led researchers to work to solve these problems. In this regard, governments make decisions and implement programs called policy-making. The executors of these decisions in the rural environment are the village managers. Because rural areas are different, policy-making to manage them must be appropriate by these features. In fact, in this study, the authors sought to identify and explain the influential indicators in policy-making for the management of rural areas of Kerman province to predict the impact of each indicator, and how each indicator will function in the future. With the complexity of development programs, it seems that it is necessary to adopt futuristic approaches. Futures studies are more capable of analyzing the situation in the future than other methods, and decisions based on this method can be long-term. It can be said that in order to achieve the goals of development and progress, the most important features that are important for policy-making in rural areas must be recognized and the impact of these factors on each other must be determined. Since our goal was to plan for the future of villages and to manage them properly in the future, structural analysis methods and futures research software were used in this research. Methodology: The research method in this research was descriptive-analytical in nature and practical in terms of purpose. In this research, with a systematic and structural view, we studied theoretical literature in the field of rural policy-making and management. Using a questionnaire, the status of these indicators and the impact of each in the rural areas of Kerman province were scored by the technique of interplay (structural analysis method). This information was analyzed in MICMAC software and finally, the most important and influential factors for policy-making and rural management were identified. The study area of this research was rural areas of Kerman province. According to the 2016 census, Kerman province had a population of 3164718 people, of which 1302557 people lived in villages, which constituted 41% of the province's population. Discussion: The structural analysis method was used by the software to obtain the main effective indicators and the influential indicators for the future of policy-making in the study area. Based on the number of variables, the dimensions of the matrix were 13 x 13, and with the formation of this matrix, the impact of each index on another index was determined by weighing from 0 to 3. The variables of the role of culture in the policy-making, management of rural areas, and the historical course of the management of rural communities in the province were among the influential variables, the variables of economy, government, and participation for policy-making were among the two-sided variables. Also, the variables of policy features and management of rural communities, policy management, multiplicity, the inadequate status of the current policy method, and specification pattern characteristics were known as influential or dependent variables and urbanized policy variable as the independent variable. The most relevant and strongest variables were the environment, rurality, economy, government, and participation. These variables are in fact key and strategic variables in policy-making and any decision to manage rural areas of Kerman province in the future. The indicators of the environment, rurality, economy, and culture had the greatest direct impact and the indicators of the environment, economic, and disproportionate status of the current policy-making method for the management of rural areas in Kerman province had the highest direct dependence. Conclusion: The results showed that due to the scattering of variables on the coordinate axis, it cannot be said that the current policy-making system for the management of rural areas in Kerman province is stable. Therefore, for better policy-making for the management of rural areas in the province in the future, we must consider strategic variables. These variables are the environment as an effective factor and the resulting form of policies, rurality or socio-environmental identity, and economic identity of the village, the impact and economic goals of policies, the role of government, and attention to rural participation in policy-making and decision-making. In fact, our key player in the future is the environment as an influential factor and the basis of policy-making. The environment is a form and the result of policy-making. We can also help to the evolution, sustainability, and improvement of the province's rural management system by changing and improving the variables of the economy, the role of the government, and increasing people's participation in policy-making. Keywords: Rural Development, Policy-Making, Rural Management, Kerman Province. References: - Alejandra, A., Cecilia, A. S., Nestor, M., & Lorena, H. (2020). Linking Farmers' Management Decision, Demographic Characteristics and Perceptions of Ecosystem Services in the Southern Pampa of Argentina. Journal of Rural Studies. In press. - Anríquez, G., & Stloukal, L. (2008). Rural Population Change in Developing Countries: Lessons for Policy-making. European View, 7(2), 309-317. - Dufva, M., Könnölä, T., & Koivisto, R. (2015).Multi-layered Foresight: Lessons from Regional Foresight in Chile. Futures, 73, 100-111. - Dye, T. R., & Dye, T. R. (1992). Understanding Public Policy. Englewood Cliffs, NJ: Prentice Hall. - Goodwin, M. (1998). The Governance of Rural Areas: some Emerging Research Issues and Agendas. Journal of Rural Studies, 14(1), 5-12. - Henderson, F., Steiner, A., Farmer, J., & Whittam, G. (2020). Challenges of Community Engagement in a Rural Area: the Impact of Flood Protection and Policy. Journal of Rural Studies, 73, 225-233. - Holland, J., Burian, M., & Dixey, L. (2003). Tourism in Poor Rural Areas: Diversifying the Product and Expanding the Benefits in Rural Uganda and the Czech Republic. PPT Working Paper, 12. - Kerr, K. (2001). Repercussion de la reforme de securite sociale sur le monde ruralseminaries degulf, university degulf Publication, LONDON. - Korten, D. C. (1980). Community Organization and Rural Development: A Learning Process Approach. Public Administration Review, 480-511. - Lewis, J. (2003). Housing Construction in Earthquake Prone Places: Perspectives, Priorities, and Projections for Development. The Australian Journal of EmergencyManagement, 18(2), 35-44. - Little, J. (2001). New Rural Governance? Progress in Human Geography, 25(1), 97-102. - Liu, J., Zhang, X., Lin, J., & Li, Y. (2019). Beyond Government-led or Community-based: Exploring the Governance Structure and Operating Models for Reconstructing China's Hollowed Villages. Journal of Rural Studies. In Press. - Lovell, S. A., Gray, A., & Boucher, S. E. (2018). Economic Marginalization and Community Capacity: How Does Industry Closure in a Small Town Affect Perceptions of Place?. Journal of Rural Studies, 62, 107-115. - Murdoch, J., & Abram, S. (1998).Defining the Limits of Community Governance. Journal of Rural Studies, 14(1), 41-50. - Prager, K., & Freese, J. (2009). Stakeholder Involvement in Agri-environmental Policy-Making–Learning from a Local and a State-level Approach in Germany. Journal of Environmental Management, 90(2), 1154-1167. - Prové, C., de Krom, M. P., & Dessein, J. (2019). Politics of Scale in Urban Agriculture Governance: A Transatlantic Comparison of Food Policy Councils. Journal of Rural Studies, 68, 171-181. - Shucksmith, M. (2010). Disintegrated Rural Development? Neo‐Endogenous Rural Development, Planning, and Place‐shaping in Diffused Power Contexts. Sociologia Ruralis, 50(1), 1-14. - Stevenson, N., Airey, D., & Miller, G. (2008). Tourism Policy-making: The Policymakers’ Perspectives. Annals of Tourism Research, 35(3), 732-750. - Tikai, P., & Kama, A. (2010). A Study of Indigenous Knowledge and its Role to Sustainable Agriculture in Samoa. Ozean Journal of Social Sciences, 3(1), 65-79. - Todaro, M. P. (1985). Economic Development in the Third World. UK: Longman - Tuitjer, G., & Steinführer, A. (2019). The Scientific Construction of the Village: Framing and Practicing Rural Research in a Trend Study in Germany, 1952–2015. Journal of Rural Studies. In press. - Visser, O., & Spoor, M. (2011). Land Grabbing in Post-Soviet Eurasia: the World’s Largest Agricultural Land Reserves at Stake. The Journal of Peasant Studies, 38(2), 299-323. - Wenchang, W. (2008). Rural Management– The Way Out for Tibetan Rural Areas. Business and Public Administration Studies, 3(3), 75. - WWW.Dictionary.cambridge.org (2019). (Definition of “Policy-making” from the Cambridge Advanced Learner's Dictionary & Thesaurus © Cambridge University Press).

https://gep.ui.ac.ir/article_25108_b311ba2d18a79f2ed743b0348a2a5f32.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 31 2 2020 06 21 Assessment and Zoning of the Risk of Flash Flooding Based on Physiographic Factors and Morphometric Indices (Case Study of Qasr-e Shirin Basin) Assessment and Zoning of the Risk of Flash Flooding Based on Physiographic Factors and Morphometric Indices (Case Study of Qasr-e Shirin Basin) 89 104 25162 10.22108/gep.2020.119766.1221 FA Mansor Parvin Assistant Professor of Geography, Payame Noor Univerity, Kermanshah, Iran (*Corresponding Author Email: mansorparvin@pnu.ac.ir) 0000-0003-3925-5695 Journal Article 2019 10 22 Introduction Sudden flash floods are generated by severe storms with high peak discharge (Abraham, 1984, p. 163) and are generally due to complex interactions between topographical, geological, geomorphological, and hydrological conditions (Abu Zaydou et al., 2016, 56). The flash flood is a complex phenomenon, whose prediction is very difficult (Cao et al., 2016, p. 2). The flash flood results in severe material damage and even human casualties and extreme erosion (Farhan & Iid, 2017, p. 718). It is the result of the activity of two groups of different parameters. The first group has meteorological features that vary in space and time, and the second group includes constant parameters including geomorphological and geological conditions (Josef et al., 2011, p. 755). The morphometric characteristics of drainage basins are significantly correlated with hydrological parameters (Maysa 2006, p. 1238) and the possibility of estimating their hydrologic behavior. Physiographic factors such as gradient, soil texture, land use, and rock permeability have different hydrological responses to precipitation occurrences in different basins. This affects the formation and characteristics of a sudden flood (Tinco et al., 2018, 595). Qasr-e Shirin Basin, due to the outcrops of Marne and Chile formations, geomorphologically, is an eroded area with a drainage network that is relatively dense and is susceptible to flash flood events due to heavy rainfalls. So far, there has not been any study to assess the risk of flash flood events in this basin since the assessment and zoning of the flash flood event in this basin is necessary. The purpose of this study is to assess and categorize the risk of flash flooding based on the morphometric and physiographic characteristics of Qasr-e Shirin Basin. Materials and Methods In this study, two methods of standardization of morphometric parameters and the FFPI model have been used. In the first method, 11 morphometric parameters were used to calculate the degree of risk. These parameters are calculated according to Equations (1) and (2). Equation1 : HD = Equation2 : HD = The MFFPI model uses six physiographic parameters to capture the potential hazard of a sudden flood. Each of these parameters has its weight and is classified into five classes. The weight of each parameter is multiplied in each of the five sub-parameters and the final score of each layer is calculated (Tinco et al., 2018, p. 596). In the next step, the six-layer layers are assembled in the Raster Calculator and the final map of the potential flood event is calculated (Tinco et al., 2018, p. 507). The layers of the topographic slope (S), flow accumulation (Fa), and amplitude curvature (Pc) are extracted from a 10-meter DEM. Lithology layer (L) from Geological map 1: 250,000 Qasr-e Shirin sheet, Land Use layer (LU) from modified land-use plan of Kermanshah province with 1: 100000 scale, and soil texture layer from 1: 250000 map of Kermanshah province. Findings The studied basin has six sub-basins and the drainage network model in Qasr-e Shirin basin and sub-basins is dendritic. In the standardization method, the total sum of the degree values of the eleven morphometric parameters showed that Qasr-e Shirin Basin and sub-basins 1, 2, and 3 have a high potential hazard. Sub-basin 4 has a high potential hazard and sub-basins 5 and 6 have a potential low risk of flash floods. According to the final map, the Falling Flood Potential (FFPI) of Qasr-e Shirin Basin has the extremes of high, medium, low, and very low levels of flash floods. The highest and the lowest risky areas of flash floods have 33.63% and 9.86% of the basin area, respectively. Areas with very low and low risk of flood occurrences correspond to the highlands of the basin, high mountain ranges, and river valleys prevailing on the river bed. Areas with high potential risk and a large number of flash floods are in line with the erosion plain and hill. Conclusion Calculating the risk according to eleven parameters showed that 83.3% of the area of Qasr-e Shirin basin had a high risk, 9.5% had a potential hazard, 7.2% had a potentially hazardous risk. In fact, 93% of the area of the Qasr-e-Shirin Basin had a potential high and severe risk of flash floods. According to the second method, about 60% of the area of Qasr-e Shirin Basin had a high potential hazard, about 20% had had a moderate potential, and about 20% of the basin area had a potentially hazardous and very low potential. A review of the map from the MFFPI model showed that the high heterogeneity of this map was influenced by the heterogeneity of slope parameters, directional direction, and flow density. The comparison of the results of the two models suggested that most of the area of the Qasr-e Shirin basin had a potentially high risk of occurrence of a flash flood. The degree of risk method, which is based on the measurement of eleven linear, shape, and ergonomic morphometric parameters, presented the potential risk of a flash flood event for the entire basin. Since the drainage network is responsible for the discharge of the flood, the results had a high degree of accuracy in assessing the risk of a flash flood event in the whole basin. But the MFFPI model used the effective physiographic parameters for creating floods in flood risk zoning and it identified high-risk areas within the basin. Finally, it can be admitted that the results of the two methods, despite differences like the parameters used, are complementary to each other. Based on the results of these models, the Qasr-e Shirin Basin had a high potential hazard in the event of a sudden flood event and the city of Qasr-e Shirin is in a very high-risk zone. Therefore, the Qasr-e Shirin Basin requires the implementation of protective projects and flood control. Keywords: Flash Floods, Morphometric Parameters, MFFPI Method, Flooding Potential, Precipitation. References: - Abrahams, A. D. (1984). Channel Networks: A Geographical Perspective. Journal of Water Resources Research, 20, 161-168. - Abuzied, S. M., & Mansour, B. M. (2019). Geospatial Hazard Modeling for the Delineation of Flash Flood-Prone Zones in Wadi Dahab Basin, Egypt. Journal of Hydroinformatics, 21(1), 180-206. - Abuzied, S., Yuan, M., Ibrahim, S., Kaiser, M., & Saleem, T. (2016). Geospatial Risk Assessment of Flash Floods in Nuweiba Area, Egypt. Journal of Arid Environments, 133, 54-72. - Bajabaa, S., Masoud, M., & Al-Amri, N. (2014). Flash Flood Hazard Mapping based on Quantitative Hydrology, Geomorphology and GIS Techniques (Case Study of Wadi Al Lith, Saudi Arabia). Arabian Journal of Geosciences, 7(6), 2469-2481. - Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas. Journal of Sustainability, 8(9), 948. - Constantinescu, S. (2006). Observații Asupra Indicatorilor Morfometrici Determinați Pe Baza. Journal of Natural Hazards and Risk, 5, 321-332. - Douvinet, J. (2014). Flash Flood Hazard Assessment in Small Agricultural Basins Coupling GIS-Data and Cellular Automata Modelling: First Experimentations in Upper-Normandy (France). International Journal of Agricultural and Environmental Information Systems (IJAEIS), 5(1), 59-80.‏ - El Maghraby, M., Masoud, M., & Niyazi, B. (2014). Assessment of Surface Runoff in Arid, Data Scarce Regions; An Approach Applied in Wadi Al Hamd, Al Madinal Al Munawarah, Saudi Arabia. Life Science Journal, 11(4). - Eze, E. B., & Efiong, J. (2010). Morphometric Parameters of the Calabar River Basin: Implication for Hydrologic Processes. Journal of Geography and Geology, 2(1), 18. - Farhan, Y., & Ayed, A. (2017). Assessment of Flash-Flood Hazard in Arid Watersheds of Jordan. Journal of Geographic Information System, 9(06), 717. - Farhan, Y., Anaba, O., & Salim, A. (2017). Morphometric Analysis and Flash Floods Assessment for Drainage Basins of the Ras En Naqb Area, South Jordan using GIS. Applied Morphometry and Watershed Management Using RS, GIS and Multivariate Statistics (Case Studies), 413. - Gregory, K. J., & Walling, D. E. (1973). Drainage Basin Form Process. New York: Wiley. - Haggett, P. (1965). Locational Analysis in Human Geography. London: Edward Arnold Ltd. - Horton, R. E. (1932). Drainage Basin Characteristics. American Geophysics Union Transactions, 13, 350-361. - Horton, R. E. (1945). Erosional Development of Streams and Their Drainage Basins; Hydrophysical Approach to Quantitative Morphology. Journal of Geological Society of America Bulletin, 56(3), 275-370.‏ - Howard, A. D. (1990). Role of Hypsometry and Planform in Basin Hydrologic Response. Journal of Hydrol Process, 4(4), 373–385. - Hungr, O. (2000). Analysis of Debris Flow Surges Using the Theory of Uniformly Progressive Flow. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 25(5), 483-495. - Jain, V., & Sinha, R. (2003). Evaluation of Geomorphic Control on Flood Hazard through Geomorphic Instantaneous Unit Hydrograph. Journal of Current Science, 85(11), 1596-1600.‏ - Majure, J. J., & Soenksen, P. J. (1991). Using a Geographic Information System to Determine Physical Basin Characteristics for Use in Flood Frequency Equations. In: Balthrop BH, Terry JE (Eds.), U.S. Geological Survey National Computer Technology Meeting-Proceedings, Phoenix, Arizona, November 14–18, 1988: U.S. Geological Survey Water-Resources Investigations Report, 90–4162, 31–40. - Masoud, M. H. (2016). Geoinformatics Application for Assessing the Morphometric Characteristics’ Effect on Hydrological Response at Watershed (Case Study of Wadi Qanunah, Saudi Arabia). Arabian Journal of Geosciences, 9(4), 280. - Mesa, L. M. (2006). Morphometric Analysis of a Subtropical Andean Basin (Tucuman, Argentina). Journal of Environmental Geology, 50(8), 1235-1242. - Minea, G. (2013). Assessment of the Flash Flood Potential of Bâsca River Catchment (Romania) based on Physiographic Factors. Journal of Open Geosciences, 5(3), 344-353. - Pallard, B., Castellarin, A., & Montanari, A. (2009). A Look at the Links between Drainage Density and Flood Statistics. Journal of Hydrology and Earth System Sciences, 13(7), 1019-1029. - Pareta, K., & Pareta, U. (2011). Quantitative Morphometric Analysis of a Watershed of Yamuna Basin, India Using ASTER (DEM) Data and GIS. International Journal of Geomatics and Geosciences, 2(1), 248-269.‏ - Patton, P. C., & Baker, V. R. (1976). Morphometry and Floods in Small Drainage Basins Subject to Diverse Hydrogeomorphic Controls. Journal of Water Resources Research, 12(5), 941-952. - Perucca, L. P., & Angilieri, Y. E. (2011). Morphometric Characterization of Del Molle Basin Applied to the Evaluation of Flash Floods Hazard, Iglesia Department, San Juan, Argentina. Journal of Quaternary International, 233(1), 81-86.‏ - Schumm, S. A. (1956). Evolution of Drainage Systems and Slopes in Badlands at Perth Amboy, New Jersey. Journal of Geological Society of America Bulletin, 67(5), 597-646. - Strahler, A. (1952). Dynamic Basis of Geomorphology. Journal of Geological Society of America Bulletin, 63, 938. - Sujatha, E. R., Selvakumar, R., Rajasimman, U. A. B., & Victor, R. G. (2015). Morphometric Analysis of Sub-Watersheds in Part of Western Ghats, South India Using ASTER DEM. Geomatics, Natural Hazards and Risk, 6(4), 326-341. - Taha, M. M., Elbarbary, S. M., Naguib, D. M., & El-Shamy, I. Z. (2017). Flash Flood Hazard Zonation based on Basin Morphometry Using Remote Sensing and GIS Techniques: A Case Study of Wadi Qena Basin, Eastern Desert, Egypt. Remote Sensing Applications: Society and Environment, 8, 157-167. - Tincu, R., Lazar, G., & Lazar, I. (2018). Modified Flash Flood Potential Index in order to Estimate Areas with Predisposition to Water Accumulation. Journal of Open Geosciences, 10(1), 593-606. - Yousif, M., & Bubenzer, O. (2015). Geoinformatics Application for Assessing the Potential of Rainwater Harvesting in Arid Regions. Case study: El Daba’a area, Northwestern Coast of Egypt. Arabian Journal of Geosciences, 8(11), 9169-9191. - Youssef, A. M., Pradhan, B., & Hassan, A. M. (2011). Flash Flood Risk Estimation along the St. Katherine Road, Southern Sinai, Egypt Using GIS based on Morphometry and Satellite Imagery. Environmental Eart Introduction Sudden flash floods are generated by severe storms with high peak discharge (Abraham, 1984, p. 163) and are generally due to complex interactions between topographical, geological, geomorphological, and hydrological conditions (Abu Zaydou et al., 2016, 56). The flash flood is a complex phenomenon, whose prediction is very difficult (Cao et al., 2016, p. 2). The flash flood results in severe material damage and even human casualties and extreme erosion (Farhan & Iid, 2017, p. 718). It is the result of the activity of two groups of different parameters. The first group has meteorological features that vary in space and time, and the second group includes constant parameters including geomorphological and geological conditions (Josef et al., 2011, p. 755). The morphometric characteristics of drainage basins are significantly correlated with hydrological parameters (Maysa 2006, p. 1238) and the possibility of estimating their hydrologic behavior. Physiographic factors such as gradient, soil texture, land use, and rock permeability have different hydrological responses to precipitation occurrences in different basins. This affects the formation and characteristics of a sudden flood (Tinco et al., 2018, 595). Qasr-e Shirin Basin, due to the outcrops of Marne and Chile formations, geomorphologically, is an eroded area with a drainage network that is relatively dense and is susceptible to flash flood events due to heavy rainfalls. So far, there has not been any study to assess the risk of flash flood events in this basin since the assessment and zoning of the flash flood event in this basin is necessary. The purpose of this study is to assess and categorize the risk of flash flooding based on the morphometric and physiographic characteristics of Qasr-e Shirin Basin. Materials and Methods In this study, two methods of standardization of morphometric parameters and the FFPI model have been used. In the first method, 11 morphometric parameters were used to calculate the degree of risk. These parameters are calculated according to Equations (1) and (2). Equation1 : HD = Equation2 : HD = The MFFPI model uses six physiographic parameters to capture the potential hazard of a sudden flood. Each of these parameters has its weight and is classified into five classes. The weight of each parameter is multiplied in each of the five sub-parameters and the final score of each layer is calculated (Tinco et al., 2018, p. 596). In the next step, the six-layer layers are assembled in the Raster Calculator and the final map of the potential flood event is calculated (Tinco et al., 2018, p. 507). The layers of the topographic slope (S), flow accumulation (Fa), and amplitude curvature (Pc) are extracted from a 10-meter DEM. Lithology layer (L) from Geological map 1: 250,000 Qasr-e Shirin sheet, Land Use layer (LU) from modified land-use plan of Kermanshah province with 1: 100000 scale, and soil texture layer from 1: 250000 map of Kermanshah province. Findings The studied basin has six sub-basins and the drainage network model in Qasr-e Shirin basin and sub-basins is dendritic. In the standardization method, the total sum of the degree values of the eleven morphometric parameters showed that Qasr-e Shirin Basin and sub-basins 1, 2, and 3 have a high potential hazard. Sub-basin 4 has a high potential hazard and sub-basins 5 and 6 have a potential low risk of flash floods. According to the final map, the Falling Flood Potential (FFPI) of Qasr-e Shirin Basin has the extremes of high, medium, low, and very low levels of flash floods. The highest and the lowest risky areas of flash floods have 33.63% and 9.86% of the basin area, respectively. Areas with very low and low risk of flood occurrences correspond to the highlands of the basin, high mountain ranges, and river valleys prevailing on the river bed. Areas with high potential risk and a large number of flash floods are in line with the erosion plain and hill. Conclusion Calculating the risk according to eleven parameters showed that 83.3% of the area of Qasr-e Shirin basin had a high risk, 9.5% had a potential hazard, 7.2% had a potentially hazardous risk. In fact, 93% of the area of the Qasr-e-Shirin Basin had a potential high and severe risk of flash floods. According to the second method, about 60% of the area of Qasr-e Shirin Basin had a high potential hazard, about 20% had had a moderate potential, and about 20% of the basin area had a potentially hazardous and very low potential. A review of the map from the MFFPI model showed that the high heterogeneity of this map was influenced by the heterogeneity of slope parameters, directional direction, and flow density. The comparison of the results of the two models suggested that most of the area of the Qasr-e Shirin basin had a potentially high risk of occurrence of a flash flood. The degree of risk method, which is based on the measurement of eleven linear, shape, and ergonomic morphometric parameters, presented the potential risk of a flash flood event for the entire basin. Since the drainage network is responsible for the discharge of the flood, the results had a high degree of accuracy in assessing the risk of a flash flood event in the whole basin. But the MFFPI model used the effective physiographic parameters for creating floods in flood risk zoning and it identified high-risk areas within the basin. Finally, it can be admitted that the results of the two methods, despite differences like the parameters used, are complementary to each other. Based on the results of these models, the Qasr-e Shirin Basin had a high potential hazard in the event of a sudden flood event and the city of Qasr-e Shirin is in a very high-risk zone. Therefore, the Qasr-e Shirin Basin requires the implementation of protective projects and flood control. Keywords: Flash Floods, Morphometric Parameters, MFFPI Method, Flooding Potential, Precipitation. References: - Abrahams, A. D. (1984). Channel Networks: A Geographical Perspective. Journal of Water Resources Research, 20, 161-168. - Abuzied, S. M., & Mansour, B. M. (2019). Geospatial Hazard Modeling for the Delineation of Flash Flood-Prone Zones in Wadi Dahab Basin, Egypt. Journal of Hydroinformatics, 21(1), 180-206. - Abuzied, S., Yuan, M., Ibrahim, S., Kaiser, M., & Saleem, T. (2016). Geospatial Risk Assessment of Flash Floods in Nuweiba Area, Egypt. Journal of Arid Environments, 133, 54-72. - Bajabaa, S., Masoud, M., & Al-Amri, N. (2014). Flash Flood Hazard Mapping based on Quantitative Hydrology, Geomorphology and GIS Techniques (Case Study of Wadi Al Lith, Saudi Arabia). Arabian Journal of Geosciences, 7(6), 2469-2481. - Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas. Journal of Sustainability, 8(9), 948. - Constantinescu, S. (2006). Observații Asupra Indicatorilor Morfometrici Determinați Pe Baza. Journal of Natural Hazards and Risk, 5, 321-332. - Douvinet, J. (2014). Flash Flood Hazard Assessment in Small Agricultural Basins Coupling GIS-Data and Cellular Automata Modelling: First Experimentations in Upper-Normandy (France). International Journal of Agricultural and Environmental Information Systems (IJAEIS), 5(1), 59-80.‏ - El Maghraby, M., Masoud, M., & Niyazi, B. (2014). Assessment of Surface Runoff in Arid, Data Scarce Regions; An Approach Applied in Wadi Al Hamd, Al Madinal Al Munawarah, Saudi Arabia. Life Science Journal, 11(4). - Eze, E. B., & Efiong, J. (2010). Morphometric Parameters of the Calabar River Basin: Implication for Hydrologic Processes. Journal of Geography and Geology, 2(1), 18. - Farhan, Y., & Ayed, A. (2017). Assessment of Flash-Flood Hazard in Arid Watersheds of Jordan. Journal of Geographic Information System, 9(06), 717. - Farhan, Y., Anaba, O., & Salim, A. (2017). Morphometric Analysis and Flash Floods Assessment for Drainage Basins of the Ras En Naqb Area, South Jordan using GIS. Applied Morphometry and Watershed Management Using RS, GIS and Multivariate Statistics (Case Studies), 413. - Gregory, K. J., & Walling, D. E. (1973). Drainage Basin Form Process. New York: Wiley. - Haggett, P. (1965). Locational Analysis in Human Geography. London: Edward Arnold Ltd. - Horton, R. E. (1932). Drainage Basin Characteristics. American Geophysics Union Transactions, 13, 350-361. - Horton, R. E. (1945). Erosional Development of Streams and Their Drainage Basins; Hydrophysical Approach to Quantitative Morphology. Journal of Geological Society of America Bulletin, 56(3), 275-370.‏ - Howard, A. D. (1990). Role of Hypsometry and Planform in Basin Hydrologic Response. Journal of Hydrol Process, 4(4), 373–385. - Hungr, O. (2000). Analysis of Debris Flow Surges Using the Theory of Uniformly Progressive Flow. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 25(5), 483-495. - Jain, V., & Sinha, R. (2003). Evaluation of Geomorphic Control on Flood Hazard through Geomorphic Instantaneous Unit Hydrograph. Journal of Current Science, 85(11), 1596-1600.‏ - Majure, J. J., & Soenksen, P. J. (1991). Using a Geographic Information System to Determine Physical Basin Characteristics for Use in Flood Frequency Equations. In: Balthrop BH, Terry JE (Eds.), U.S. Geological Survey National Computer Technology Meeting-Proceedings, Phoenix, Arizona, November 14–18, 1988: U.S. Geological Survey Water-Resources Investigations Report, 90–4162, 31–40. - Masoud, M. H. (2016). Geoinformatics Application for Assessing the Morphometric Characteristics’ Effect on Hydrological Response at Watershed (Case Study of Wadi Qanunah, Saudi Arabia). Arabian Journal of Geosciences, 9(4), 280. - Mesa, L. M. (2006). Morphometric Analysis of a Subtropical Andean Basin (Tucuman, Argentina). Journal of Environmental Geology, 50(8), 1235-1242. - Minea, G. (2013). Assessment of the Flash Flood Potential of Bâsca River Catchment (Romania) based on Physiographic Factors. Journal of Open Geosciences, 5(3), 344-353. - Pallard, B., Castellarin, A., & Montanari, A. (2009). A Look at the Links between Drainage Density and Flood Statistics. Journal of Hydrology and Earth System Sciences, 13(7), 1019-1029. - Pareta, K., & Pareta, U. (2011). Quantitative Morphometric Analysis of a Watershed of Yamuna Basin, India Using ASTER (DEM) Data and GIS. International Journal of Geomatics and Geosciences, 2(1), 248-269.‏ - Patton, P. C., & Baker, V. R. (1976). Morphometry and Floods in Small Drainage Basins Subject to Diverse Hydrogeomorphic Controls. Journal of Water Resources Research, 12(5), 941-952. - Perucca, L. P., & Angilieri, Y. E. (2011). Morphometric Characterization of Del Molle Basin Applied to the Evaluation of Flash Floods Hazard, Iglesia Department, San Juan, Argentina. Journal of Quaternary International, 233(1), 81-86.‏ - Schumm, S. A. (1956). Evolution of Drainage Systems and Slopes in Badlands at Perth Amboy, New Jersey. Journal of Geological Society of America Bulletin, 67(5), 597-646. - Strahler, A. (1952). Dynamic Basis of Geomorphology. Journal of Geological Society of America Bulletin, 63, 938. - Sujatha, E. R., Selvakumar, R., Rajasimman, U. A. B., & Victor, R. G. (2015). Morphometric Analysis of Sub-Watersheds in Part of Western Ghats, South India Using ASTER DEM. Geomatics, Natural Hazards and Risk, 6(4), 326-341. - Taha, M. M., Elbarbary, S. M., Naguib, D. M., & El-Shamy, I. Z. (2017). Flash Flood Hazard Zonation based on Basin Morphometry Using Remote Sensing and GIS Techniques: A Case Study of Wadi Qena Basin, Eastern Desert, Egypt. Remote Sensing Applications: Society and Environment, 8, 157-167. - Tincu, R., Lazar, G., & Lazar, I. (2018). Modified Flash Flood Potential Index in order to Estimate Areas with Predisposition to Water Accumulation. Journal of Open Geosciences, 10(1), 593-606. - Yousif, M., & Bubenzer, O. (2015). Geoinformatics Application for Assessing the Potential of Rainwater Harvesting in Arid Regions. Case study: El Daba’a area, Northwestern Coast of Egypt. Arabian Journal of Geosciences, 8(11), 9169-9191. - Youssef, A. M., Pradhan, B., & Hassan, A. M. (2011). 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University of Isfahan Geography and Environmental Planning 2008-5362 31 2 2020 06 21 Investigating the Effect of Vegetation Restoration on Morphometric Components of Nebaka and Its Role in the Stabilization of Sand Dunes in Nimroz Area of Sistan Province Investigating the Effect of Vegetation Restoration on Morphometric Components of Nebaka and Its Role in the Stabilization of Sand Dunes in Nimroz Area of Sistan Province 105 120 24957 10.22108/gep.2020.119973.1225 FA Moien Jahantigh 1- P.h.D candidate of Watershed management science and engineering Mansour Jahantigh Department Soil Conservation and Water Management, Sistan Agriculture and edition Natural Resources Research Center, AREEO, Zabol, Iran Journal Article 2019 11 09 Extended Abstract: 1- Introduction Existing challenges in arid regions have caused climatic and environmental problems such as low rain, winds with high speed and intensity as well as lack of vegetation. These problems happen by destroying and transporting particles leading to the influx of sand flowing into agricultural lands and residential centers. This is one of the most concerns of residents in the arid and desert region of the country. Because, it causes a lot of life and financial losses, the move of sands, and the formation of sand dunes are influenced by interactions between wind flow, the site of deposition, and the morphology of the sedimentation site that gives rise to wind landforms. The vegetation cover plays an important role in determining the morphology and dynamicsby influencing transportation conditions and trapping the sand carried by the winds. This process takes the form of creating a wind vision during a natural reaction, with the creation of the Nebaka phenomenon. The phenomenon appears in desert areas to neutralize wind erosion stress. Accordingly, the presence of vegetation is a prerequisite for Nebakas and controlling the flow of sand flows in arid and desert areas due to the specific climatic conditions in these areas. Numerous studies have been conducted to investigate the effect of vegetation factors on Nebaka formation. The volume of Nebakas is influenced by the vegetative form. The volume of its constituents is different from each other. Vegetation factors have played an important role in the development of Nebakas. Studies have shown that the vegetation cover has the main role in the formation and development of Nebakas so that vegetation reduces sediment replacement and limits its source. Among the critical areas that are referred to as the main focus of wind erosion, the Sistan region has always been affected by wind erosion. The present study was conducted with the aim of investigating the effect of vegetation restoration on morphometric components of Nebaka and its effect on sand dunes stabilization in the Nimroz area of Sistan province. 2- Methodology To achieve the purpose of the present study, after floodwater spreading and forestry operations in the Nimroz area of Sistan during 2003, parameters of Nebaka including Nebaka high, Nebaka base diameter, Nebaka volume, vegetation cover, plant height, wind direction, and back to the wind in 45 Nebakas to the Tamarix species were measured at different time intervals in a 16-year period by restoration vegetation and installing 5 linear transects with a length of 50 m randomly in the area. Then, by measuring the morphometric properties of Nebakas, the correlation of morphometric components was investigated using correlation analysis and multivariate regression analysis. 3– Results and Discussions In the correlation analysis regarding the morphometric characteristics of Nebaka, the findings showed that a significant correlation (at the level of 0.99) of the plant characteristics such as vegetation cover and high plant parameters such as Nebaka high, Nebaka base diameter, Nebaka volume, wind direction, and back to the wind. The multiple regression analysis approved 92.9 percentage of the volume changes of the Nebaka with the vegetation cover. Investigating the amount of sediment stabilized in the Nebaka also showed that increasing vegetation wills increased the volume of Nebaka in such a way that with increasing vegetation, the volume of Nebakas on average increased from .0.53m3 in 2008 to 15.69m3 in 2018 with the amount of stabilized sediments increased from 184.97 ton to 879.79 ton. The statistical comparison of the measured data showed that there is a significant difference (at the level of 0.01) between the mean stabilized sediments in Nebakas during the research process. According to the results of the study, the restoration of vegetation in the study area shows a good background for the formation of Nebakas. As a result, a considerable amount of sands has stabilized in these Nebakas. 4– Conclusions In this study, the role of wind activity in the formation and development of Nebaka areas where wind power was low was confirmed. Based on the result of this research, a significant amount of wind sediments has been stabilized in Nebakas. As a result, the Sistan area is always affected by wind erosion and the problem of sand dunes. The method of restoring vegetation by doing flood and forestry plans in susceptible areas is effective to stabilize the sand with the creation of the Nebaka phenomenon in the study area. Re-vegetation in the study area has provided a good basis for the creation of Nebakas in the region. Keywords: Canopy Cover, Desertification, Forestry, Sediment, Stabilization. References - Ahmed, M., Al-Dousari, N., Al-Dousari, A. (2015). The Role of Dominant Perennial Native Plant Species in Controlling the Mobile Sand Encroachment and Fallen Dust Problem in Kuwait. Arabian Journal of Geosciences, 9(2), 134. - Al-Awadhi, J. M. (2014). The Effect of a Single Shrub on Wind Speed and Nabkhas Dune Development: A Case Study in Kuwait. International Journal of Geosciences, 5(1), 20. - Corrigan, B. M., Van Wyk, B. E., Geldenhuys, C. J., & Durand, J. F. (2008). Vegetation Cover Changes of the Sand Forest in the KwaNibela Peninsula, St Lucia from 1937–2002. South African Journal of Botany, 2(74), 364. - Dougill, A. J., & Thomas, A. D. (2002). Nebkha Dunes in the Molopo Basin, South Africa and Botswana: Formation Controls and Their Validity as Indicators of Soil Degradation. Journal of Arid Environments, 50(3), 413-428. - Du, J., Yan, P., & Dong, Y. (2010). The Progress and Prospects of Nebkhas in Arid Areas. Journal of Geographical Sciences, 20(5), 712-728. - Haney, A., Bowles, M., Apfelbaum, S., Lain, E., & Post, T. (2008). Gradient Analysis of an Eastern Sand Savanna's Woody Vegetation, and Its Long-Term Responses to Restored Fire Processes. Journal of Forest Ecology and Management, 256(8), 1560-1571. - Jun, R., Lin, T., (2003). A Numerical Taxonomy of the Genus Nitraria from Gansu Province, China. Journal of Acta Botanica Boreali-Occidentalia Sinica, 23(4), 572–576. - Karavas, N., Georghiou, K., Arianoutsou, M., & Dimopoulos, D. (2005). Vegetation and Sand Characteristics Influencing Nesting Activity of Caretta Caretta on Sekania Beach. Journal of Biological Conservation, 121(2), 177-188. - Lancaster, N., & Baas, A. (1998). Influence of Vegetation Cover on Sand Transport by Wind: Field Studies at Owens Lake, California. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Group, 23(1), 69-82. - Lang, L., Wang, X., Hasi, E., & Hua, T. (2013). Nebkha (Coppice Dune) Formation and Significance to Environmental Change Reconstructions in Arid and Semiarid Areas. Journal of Geographical Sciences, 23(2), 344-358. Extended Abstract: 1- Introduction Existing challenges in arid regions have caused climatic and environmental problems such as low rain, winds with high speed and intensity as well as lack of vegetation. These problems happen by destroying and transporting particles leading to the influx of sand flowing into agricultural lands and residential centers. This is one of the most concerns of residents in the arid and desert region of the country. Because, it causes a lot of life and financial losses, the move of sands, and the formation of sand dunes are influenced by interactions between wind flow, the site of deposition, and the morphology of the sedimentation site that gives rise to wind landforms. The vegetation cover plays an important role in determining the morphology and dynamicsby influencing transportation conditions and trapping the sand carried by the winds. This process takes the form of creating a wind vision during a natural reaction, with the creation of the Nebaka phenomenon. The phenomenon appears in desert areas to neutralize wind erosion stress. Accordingly, the presence of vegetation is a prerequisite for Nebakas and controlling the flow of sand flows in arid and desert areas due to the specific climatic conditions in these areas. Numerous studies have been conducted to investigate the effect of vegetation factors on Nebaka formation. The volume of Nebakas is influenced by the vegetative form. The volume of its constituents is different from each other. Vegetation factors have played an important role in the development of Nebakas. Studies have shown that the vegetation cover has the main role in the formation and development of Nebakas so that vegetation reduces sediment replacement and limits its source. Among the critical areas that are referred to as the main focus of wind erosion, the Sistan region has always been affected by wind erosion. The present study was conducted with the aim of investigating the effect of vegetation restoration on morphometric components of Nebaka and its effect on sand dunes stabilization in the Nimroz area of Sistan province. 2- Methodology To achieve the purpose of the present study, after floodwater spreading and forestry operations in the Nimroz area of Sistan during 2003, parameters of Nebaka including Nebaka high, Nebaka base diameter, Nebaka volume, vegetation cover, plant height, wind direction, and back to the wind in 45 Nebakas to the Tamarix species were measured at different time intervals in a 16-year period by restoration vegetation and installing 5 linear transects with a length of 50 m randomly in the area. Then, by measuring the morphometric properties of Nebakas, the correlation of morphometric components was investigated using correlation analysis and multivariate regression analysis. 3– Results and Discussions In the correlation analysis regarding the morphometric characteristics of Nebaka, the findings showed that a significant correlation (at the level of 0.99) of the plant characteristics such as vegetation cover and high plant parameters such as Nebaka high, Nebaka base diameter, Nebaka volume, wind direction, and back to the wind. The multiple regression analysis approved 92.9 percentage of the volume changes of the Nebaka with the vegetation cover. Investigating the amount of sediment stabilized in the Nebaka also showed that increasing vegetation wills increased the volume of Nebaka in such a way that with increasing vegetation, the volume of Nebakas on average increased from .0.53m3 in 2008 to 15.69m3 in 2018 with the amount of stabilized sediments increased from 184.97 ton to 879.79 ton. The statistical comparison of the measured data showed that there is a significant difference (at the level of 0.01) between the mean stabilized sediments in Nebakas during the research process. According to the results of the study, the restoration of vegetation in the study area shows a good background for the formation of Nebakas. As a result, a considerable amount of sands has stabilized in these Nebakas. 4– Conclusions In this study, the role of wind activity in the formation and development of Nebaka areas where wind power was low was confirmed. Based on the result of this research, a significant amount of wind sediments has been stabilized in Nebakas. As a result, the Sistan area is always affected by wind erosion and the problem of sand dunes. The method of restoring vegetation by doing flood and forestry plans in susceptible areas is effective to stabilize the sand with the creation of the Nebaka phenomenon in the study area. Re-vegetation in the study area has provided a good basis for the creation of Nebakas in the region. Keywords: Canopy Cover, Desertification, Forestry, Sediment, Stabilization. References - Ahmed, M., Al-Dousari, N., Al-Dousari, A. (2015). The Role of Dominant Perennial Native Plant Species in Controlling the Mobile Sand Encroachment and Fallen Dust Problem in Kuwait. Arabian Journal of Geosciences, 9(2), 134. - Al-Awadhi, J. M. (2014). The Effect of a Single Shrub on Wind Speed and Nabkhas Dune Development: A Case Study in Kuwait. International Journal of Geosciences, 5(1), 20. - Corrigan, B. M., Van Wyk, B. E., Geldenhuys, C. J., & Durand, J. F. (2008). Vegetation Cover Changes of the Sand Forest in the KwaNibela Peninsula, St Lucia from 1937–2002. South African Journal of Botany, 2(74), 364. - Dougill, A. J., & Thomas, A. D. (2002). Nebkha Dunes in the Molopo Basin, South Africa and Botswana: Formation Controls and Their Validity as Indicators of Soil Degradation. Journal of Arid Environments, 50(3), 413-428. - Du, J., Yan, P., & Dong, Y. (2010). The Progress and Prospects of Nebkhas in Arid Areas. Journal of Geographical Sciences, 20(5), 712-728. - Haney, A., Bowles, M., Apfelbaum, S., Lain, E., & Post, T. (2008). Gradient Analysis of an Eastern Sand Savanna's Woody Vegetation, and Its Long-Term Responses to Restored Fire Processes. Journal of Forest Ecology and Management, 256(8), 1560-1571. - Jun, R., Lin, T., (2003). A Numerical Taxonomy of the Genus Nitraria from Gansu Province, China. Journal of Acta Botanica Boreali-Occidentalia Sinica, 23(4), 572–576. - Karavas, N., Georghiou, K., Arianoutsou, M., & Dimopoulos, D. (2005). Vegetation and Sand Characteristics Influencing Nesting Activity of Caretta Caretta on Sekania Beach. Journal of Biological Conservation, 121(2), 177-188. - Lancaster, N., & Baas, A. (1998). Influence of Vegetation Cover on Sand Transport by Wind: Field Studies at Owens Lake, California. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Group, 23(1), 69-82. - Lang, L., Wang, X., Hasi, E., & Hua, T. (2013). Nebkha (Coppice Dune) Formation and Significance to Environmental Change Reconstructions in Arid and Semiarid Areas. Journal of Geographical Sciences, 23(2), 344-358.

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