Journal of Spatial Analysis

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 Quantitative assessment of landslide sedimentation in the ILAM dam Basin

Information on the accurate volume of landslides and sedimentation in landslides is a research necessity, with the assumption that the bulk of sediment accumulated in the ILAM Dam (located between , E and , N) is related to the surface landslides of the basin. Although the role of landslides in erosion, sediment transport and sedimentation of slippery basins is confirmed and different experts understand and determine the relationship between the fluctuation of slopes and the fluctuation system in many respects more important than other areas. Because according to the results they can assess the widespread environmental changes, but comprehensive research on the scale of catchment basins has done very little (Harvey 2002). So far, the study of wet landscapes in Iran has been more sensitive to the factors, their sensitivity and their hazards, and there has been no study on the sedimentation of landslides.

Data and Method
First, using a geomorphologic system methodology with topographic maps of 1: 50000, geological map of 1: 100000, aerial photography1: 20000, Landsat TM1988 ETM2002,2013 satellite imagery, and Google Earth in the GIS environment in the following sub-basins and landslide events at the following levels The basin was drawn. The discharge data of the water and sediment flow of three hydrometric stations GOLGOL,CHAVIZ and MALEKSHAHI Station were provided from the waters of the ILAM province. Two models of estimated MPSIAC and EPM models have been used to estimate soil erosion and subsoil sedimentation. The Moran spatial correlation model was used to introduce the spatial pattern of landslides, and the fuzzy logic model was used to determine the relationship between the dependent landslide to the independent variables and the potential risk of landslide hazard in the basin. In order to elucidate the quantitative results of landslide sedimentation, empirical models of estimation of sediment erosion, hydrological model of discharge curve and sediment, observational statistics of sediment during statistical period, landfall time occurrence in compliance with the hydrometric station sediment peak during the statistical period of computation Estimated a small amount of sedimentation of the landslides of the ILAM dam basin.
Result and Discussion
The spatial correlation model of Moran showed that the data have spatial correlation and cluster pattern. The average total sediment production in the MPSIAC model in the GOLGOL basin was estimated to be 13.3 tons per hectare per year under the CHAVIZ basin of 10.3 tons per hectare for one year and 4.00 tons per hectare in the sub-basin MALEKSHAHI. Using hydrological model of discharge-sediment curve, the mean sediment was calculated during the statistical period at the hydrometric station of the sub-basin of GOLGOL 18.8 ton per hectare, the station CHAVIZ 10.4 tons and the station MALEKSHAHI 0.9 tons of sediment per hectare per year was calculated. According to the results of the research methodology, the observation of the sediment in the two stations of GOLGOL and CHAVIZ compared to estimated sediment is related to the events occurring in these two sub-basins.
The data of 16 active landslides were recorded. Under the GOLGOL basin, 9 landslide events occurred, and in the CHAVIZ basin, 7 landslide events, the time of landfall occurrence recorded with sedimentary peaks, the length of the statistical period, the precipitate in the sub-basins was almost synchronized. Average relationship between suspended period of the statistical period - average of the peak delayed flight time of the statistical period coinciding with the occurrence of landslide = the amount of suspended load of landfall occurrence in the basin.
The amount of suspended land slip under the GOLGOL 75088.19 - 315.85=74772.34
Landing slope under the Chavez Basin 19907.30 - 20.24=19887
The area of the sub-basin is about 29,000 hectares and the active landslide area is about 100 hectares. According to the calculations, 77772.34 tons of suspended sediment is a sedimentary passage passing at the GOLGOL hydrometric station, which shows with a coefficient of 1.4 times the suspended sediment load of approximately 104681 tons of landslide sedimentation in this sub-basin, which shows the amount of sediment yield 100 hectares of landslide, so each landslide hectare had an average of 1046. 81 tons of sediment deposited at the GOLGOL hydrometric station. The area under the Chavez Basin is about 14000 hectares and the active landslide area of this sub-basin is about 65 hectares. According to the data of the discharge data, the sedimentation of the Chavez hydrometric station is 19887 tons of suspended sediment load, which shows a 1.4 equivalent of 27842 tons of landslide sedimentation in this sub-basin, equivalent to a slope of 428.33 tons per hectare.
Conclusion
According to the calculations, it is concluded that in the sub-basin of flowering GOLGOL, 37.35% is equivalent to 4.9 tons per hectare per year, the increase of sediment is related to landslide events. As a result, 28.2 tons of sediment per hectare were introduced in one year Dam reaches ILAM. The results showed that in the CHAVIZ sub basin, 38.2 percent is equivalent to 4.6 tons per hectare per year for the increase of sediment related to landslide events. As a result, an amount of 14.5 tons of sediment per hectare has entered ILAM dam in one year. In the sub-basin MALEKSHAHI, there was no increase in sediment during the period without active landslide occurrence. A total of 1237314 tons of landslide deposition have entered the ILAM Dam. To control this threat, the appropriate action by the executive office for sustainable development should be applied.

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Zoning the possibility of landslides downstream of Sanandaj Dam
1-Introduction
The purpose of this study is to select the best model and identify landslide risk areas in the downstream basins of Sanandaj Dam. Every year, mass movements in the region cause damage to roads, power lines, natural resources, farms and residential areas, and increase soil erosion. Kurdistan province, with its mostly mountainous topography, high tectonic activity, diverse geological and climatic conditions, has the most natural conditions for mass movements. According to the available statistics, this province is the third province in terms of landslides after Mazandaran and Golestan. (Naeri, &Karami, 2018). The Gheshlagh River Basin is a mountainous region with a north-south trend. In terms of construction land, it is located on the structural zone of Sanandaj-Sirjan. The study area with an area of 970.7 square kilometers is located downstream of Sanandaj Dam. The city of Sanandaj is being studied within the region. Due to the type of climate and morphological processes, effective parameters are provided for landslides in the geography of the region.
2-Methodology
In this study, 9 effective factors for landslides, including slope, slope direction, fault distance, road distance, waterway distance, lithology, land use and precipitation were used. Using Google Landsat 8 ETM satellite imagery, Google Earth software identified 237 slip points. Then, the coordinates of the slip points were transferred to the Arc GIS software and a map of the landslide distribution area in this environment was prepared. Also, in this study, 89 non-slip points were prepared for use in the training and testing stages of Persephone neural network inside slopes less than 5 degrees. Artificial neural networks are made up of a large number of interconnected processing elements called neurons that act to solve a coordinated problem and transmit information through synapses. Neural networks begin to learn using the pattern of data entered into them. Learning models, which is actually determining their internal parameters, is based on the law of error correction. In this method, by correcting the error regularly, the best weights that create the most correct output for the network are identified. The neurons are in the form of an input layer, an output layer, and an intermediate layer. ahp includes a weighting matrix based on pairwise comparisons between factors and determines the level of participation of each factor in the occurrence of landslides. In this model, a large number of factors can be involved and the weight of each factor can be obtained using expert opinion.
3-Results
According to the results of the high-risk class neural network model, which occupies 31% of the basin area, it is the widest risk zone in the region. The middle class also accounts for more than 29 percent of the area, followed by the low-risk class. The results of the AHP model show that the middle class, with 32% of the area, has the highest dispersion in the region, the low-risk class and then the high-class are in the next position. The AHP model was used to prioritize the parameters affecting the landslide. The parameters of slope, lithology and land use play the most important role in the occurrence of landslides, respectively, and have the least role for slope direction, distance from fault and height. The results of the models used are consistent with the reality of the region's wide-risk hazards, and high-risk areas based on the models used are mostly located in the west and southwest of the basin. These areas correspond to the mountain unit and the steep slope. Based on the results of AHP model, the impact of human factors in the occurrence of landslides is weaker than the natural factors of the region and human factors play a stimulating and aggravating role in primary factors. Five methods for error detection were used to evaluate the models used
4-Discussion and conclusion
 .Due to the sensitivity of unstable slopes in the region, any planning to change the use and construction that increases the weight of the load on unstable slopes should be done in terms of geomorphological and geological conditions of the region.
Keywords: hazard zoning, landslide, neural network, AHP. Sanandaj Gheshlagh Watershed
 

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 Determining Strategies for Improving Environmental Resilience in Gharehshiran Watershed in Ardabil using SOAR Analysis Technique
Ebrahim Asgari - PhD Student of Watershed Science & Engineering, Yazd University, Yazd, Iran. Email: ebrahim.asgari90@yahoo.com
Mahboobeh Noori - PhD Student of Geography & Urban Planning, Yazd University, Yazd, Iran. Email: mnori@stu.yazd.ac.ir
MohammadReza Rezaei - Associate Professor of Geography and Urban Planning, Yazd University, Yazd, Iran. Email: mrezaei@yazd.ac.ir
Raoof Mostafazadeh - Associate Professor Department of Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. Email: raoofmostafazadeh@uma.ac.ir (Corresponding author)

Extended Abstract
Introduction: New approaches of crisis management have changed from the concepts of vulnerability to resilience and emphasize on strengthening the system's ability to deal with the risks of natural disasters. Therfore, the aim of this study was identifying the watershed capabilities of Qarahshiran and crisis management planning with emphasis on environmental resilience.
Methodology: The SOAR analytical technique and expert opinions of 52 experts were used to formulate the strategy, determine the strengths, opportunities, ideals and measurable results. The results of SOAR technique and crisis management prevention and preparedness strategies were compared with the environmental resilience of the field.
Results: Based on the results, reducing direct and indirect flood damage with 51.9% and low amount of soil erosion and water loss with 42.3%, were the most important results of the SOAR model. Out of 15 components of environmental resilience, the performance of 5 components was accepted as significant (α<0.05 confidence level). The evaluation of environmental resilience using one-sample t-test showed that the environmental dimension of resilience (2.67) with a significant level (α=0.003) has a significant that indicates high vulnerability and low resilience.
Conclusion: Considering site selection of watershed management structures, creating more opportunities and using the private sector potentials, and local NGOs will be useful in crisis management. Analysis of watershed resilience components in achieving integrated watershed management, proper knowledge of watershed function, possibility of self-regulation and recovery of balance and acceptance of adaptation to natural hazards, co-design of watershed residents, preparedness and coping with crisis can be more effective over the study area.
Key words: SOAR Model, Strategic Planning, Prevention and Preparedness, Resilience, Gharehshiran Watershed

 

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Introduction
Soil erosion is the process by which soil particles and components are separated from their main bed by an erosive agent and transported to another location. In the soil erosion process, there are three distinct phases: 1- separation of soil particles, 2- particle transfer and 3- sedimentation of transported materials. In water erosion, the erosive factors are rainfall and runoff. Erosion and the consequent reduction of soil fertility are among the issues that make it difficult to achieve sustainable agricultural development and environmental protection. It is important to study the quantity and quality of erosion in the country's watersheds and to prevent the loss of one of the richest and most valuable natural resources of the country, namely soil, and to fight against this process. (Tabatabai, 1392). Therefore, to calculate the rate of erosion and sediment production in most watersheds of the country that lack statistics or lack of statistics, the use of experimental models to estimate erosion and sediment is required. According to what has been said, the present study was conducted based on the following two main objectives: 1- Estimation of erosion and sediment in Adineh Masjed watershed, which is one of the main sub-basins of Kamal Saleh Dam, using EPM and MPSIAC experimental models and 2- Investigation and comparing two models and choosing a better model for similar regions and climates.

Materials and methods
Adineh Masjid watershed is one of the sub-basins of Dez and the main sub-basin of Kamal Saleh dam. Temperature, isotherm, geology of the area, slope and available information were performed and finally, by interpreting the photos, types, land units, current land use were determined and updated with field control. For a more detailed study, first, according to the condition of the main waterway and changes in the appearance of the land and vegetation and new land material, the ridges separating the basin were divided into 15 sub-basins. In EPM model, four watershed erosion coefficient (Ψ), land use coefficient (Xa), rock and soil susceptibility coefficient to erosion (Y) and average basin slope (I) and in MPSIAC model, nine geological, soil, climate factor (Climate), runoff, slope, vegetation, land use, current erosion status and waterway erosion are examined. Each model was scored according to data analysis and digital images and then placed in the relevant formula. Finally, the amount of erosion and sediment in the basin was estimated and the sedimentation class of the area was determined.
Results
To determine the score of nine factors affecting soil erosion using MPSIAC method and the four factors of EPM model, each of the factors affecting erosion in units were analyzed. Finally, by weighting, the points of each factor in the models were calculated. The degree of R deposition from the sum of the nine factors of MPSIAC model and the degree of Z erosion was obtained by combining the four EPM factors. Then, the amount of sediment production and erosion in the field of relationships related to each model was calculated and compared and analyzed. In MPSIAC model, the amount of specific sediment (M3 / Km2 / year) was calculated as 112.713 and the specific erosion (M3 / Km2 / year) was calculated as 375.71. In the EPM model, the amount of specific sediment (M3 / Km2 / year) was calculated as 213.95 and Specific erosion (M3 / Km2 / year) was calculated to be 395.86.

Discussion and conclusion
The results of sediment and erosion estimation were estimated separately for each sub-area using two models and it was found that the two models are somewhat relatively compatible with each other. The results of MPSIAC model, have more accuracy and reliability, and therefore the results of the MPSIAC model can be used to estimate the amount of sediment entering the Kamal Saleh Dam. However, due to the small distance between the results of the two models, if we do not have access to MPSIAC model data in similar areas, the EPM model can be used with less data and more easily accessible. It was also observed that in the upper and entrance parts of the basin, where the slope is higher and the vegetation is less, the amount of sediment production and erosion is higher in these areas. So that the upper parts of the basin are in the medium erosion class and the rest of the basin is in the low erosion class.

Keywords: watershed, erosion and sediment, modeling