Journal of Spatial Analysis

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Natural disasters are investigated of various dimensions and consequences of natural hazards. As well, they can become as a repeatable phenomenon in the absence of mitigation systems, and could be caused devastating consequences. Resiliency approach as a basis for reducing the negative effects is taken into account to reduce the impact of natural disasters. Today, the two tourist areas of Cheshmekile (Tonkabon County) and Sardabrud (Kelardasht County) as typical feature of regional tourism planning have important potentials for development of tourism. But in recent years they have repeatedly been invaded by floods so that in some cases the impact of economic, environmental, socio-cultural and physical environment is followed. In economic dimension, flash flood destroyed agricultural fields and rural houses and in socio-cultural dimension it has increased insecurity. And finally, in terms of the physical and environmental aspect, it has created the most damage such as adverse changes in the appearance of the landscape, loss of trees, and destruction of public infrastructure (roads and bridges network). It is an approved hypothesis that rural settlements cannot be moved to the riverbank, but have created a situation that endangered abiding rural settlement. Various aspects such as socio-cultural, economic and administrative highly effect on resiliency. Among them, the role of infrastructures such networks, the location of health care facilities, police stations, fire stations and disaster management offices, communication networks (telephone, Internet) are more important to improve resiliency. This paper seeks to answer the key question that is the infrastructure in promoting resiliency after flooding in the two areas satisfactory?  The methodology of the study is objective and analytical analysis is based on the nature and method. The main variables are infrastructures and resiliency. Resiliency as the dependent variable consists of two main components of individual and community resiliency. Required information on the objectives, data integrity and availability has been developed in both library and field methods. In previous studies, library and documentation center is studied. Questions are sorted in the distance range, rated and ranked based on the needs and nature of the research and the knowledge and the education level of the local community. Questions are tested initially and after a measurement of the level of reliability (0.812), which is obtained using Cronbach's alpha. First, to determine the total sample size of villages located in flood risk areas in the two basins 9 villages (50%) were selected. Cochran formula is used to determine sample size. According to Cochran formula for the total population 296 households that included 129 head of households for Sardabrud basin and 167 head of households for Cheshmekileh basin. After the initial survey the collected information is encoded using a statistical software SPSS and then has been processed according to the assumptions formulated. Based on the results of the questionnaire analysis, some indicators, same as access to aid agencies (Crescent) and disaster management center, there were no significant differences between rural settlements such as the two basins distance to the city center is short. The nearest major communication route roads - Branch is located at a distance of 5 km from the city of Kelardasht, but in Cheshmekileh basin there are less than 5 kilometers distance to the main road of the Caspian Sea. That is why the average satisfaction of the local authorities in these areas is much higher than Sardabrood basin. Check out the highlights of each area residents is showed more satisfaction on facilities and services infrastructure in Cheshmekile. Result. To understand the relationship between resiliency and infrastructure used is the correlation coefficient between these two measures 003/0 there is level. This relationship of mutual relations, the improvement of infrastructure in the area with 99% probability of increasing population resiliency against natural disasters (floods) within it. The average calculated for the physical aspects - infrastructure represents the position of the component. Ring roads in northern cities, near airports such as Ramsar Branch, and there are several large medical centers, access to police stations in both basins are made ​​from the perspective of the respondents favored the status of this criterion is to be evaluated. However, among the subset of infrastructure, the roads are better than others. The reason can be attributed to the investment and construction of new networks of communication. In the case of energy network, although the topography of the area is caused that part of the basin, some of villages such as Gavpol, Letak, Drazlat in Cheshmekile basin and Lush, Krdychal and Roudbarak in Sardabrood basin was still stay deprived of the gas network but have favorable drink water and electricity network. However, keeping the population in the rural area is largely dependent on the infrastructure. Resiliency in relation to rural and infrastructural facilities, access to places of temporary accommodation is very important but in this particular field in any of the villages still planning has been done.

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Various community groups can play important role in disaster management. Countries with different segments of people directly participate in activities to reduce the risk. Therefore, regarding the role of women's participation in disaster management process and as a part of human society will have an important role in this process, identify and analyze the factors affecting women's presence is essential. However, the central role of women in families and communities remains unknown in most parts of the world specially in planning and managing the disaster. The purpose of this study is to identify and understand the different capabilities of women to participate actively in the cycle of disaster management and providing strategies for increasing women's participation in the prevention, preparedness, response and recovery of probable disasters.    This study is an original and practical research. According to the theoretical research, a questionnaire was designed in four parts and it was completed through sampling. The sample population is women living in 22 districts of Tehran. This study implies that there is the low participation rate of women in disaster management among citizens of Tehran. To complete the data, proportional sampling was used and data were analyzed using factor analysis. Using this method, the data and the variables were summarized and the most effective factors were set in the partnership. These factors include disaster management, cultural factors and gender, fatalism, a feeling of power and confidence that the results of the factor analysis was performed using four dimensions. Based on tradition of social research and the findings of previous empirical research on women's participation in disaster management and the factors influencing voluntary participation, contextual condition of social variables (including socio-economic condition, occupation, marital status, number of children and age), as well as religious and fatalistic attitude would studied and evaluated the factors influencing the motivation and willingness to participate as a volunteer in the field of disaster management.    The findings show that KMO value was equivalent to 0.74 in four factors of disaster management and the total values of the sector were defined 67.42% of total variance of  the variables. KMO value in the sense of power and confidence variables was 0.72 and 65.27% of this segment can be explained by four factors the variability of the variables. In fatalism variable the KMO value was 0.599 and 59.56% of the four factors could explain the variability of variables. Finally, the KMO of socio-cultural norms was 0.71 and 70.52% of the variability of the variables was explained by five factors in this sector. Women cooperation alongside men play a major role in the use and implementation of policies and programs related to accidents. Thus, participation as one of the arguments in crisis management requires people involved in all processes related to the crisis management cycle. Since public participation opportunities and fields are different in societies and in different groups, so, to attract the participation in each group, identifying effective components is essential.    Finally, after using factor analysis and extracting four factors, including knowledge of effective crisis management, cultural factors and gender, fatalism, a sense of power and self-confidence were classified. In general, most people do not do any activities in disaster management and their awareness and knowledge does not lead to disaster management needs. Thus, organizational barriers, structural, administrative and educational activities to promote social and cultural constraints are considering strategies promoting women's participation in disaster management cycle.

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Human communities are affected by hazards, disasters and catastrophic events throughout history, including natural disasters (such as: earthquakes, hurricanes, floods, tornadoes) man-made disasters (such as: nuclear accidents, explosions, socio or political crisis, economic disturbances). Therefore, catastrophic events can have human or natural causes. These conditions show that human communities not only ever been stable, but they are continuously unstable and are exposed to disarranging events. Godschalk knows resiliency an important goal for two reasons. “First, because the vulnerability of technological and social systems cannot be predicted completely, resilience –the ability to accommodate change gracefully and without catastrophic failure- is critical in times of disaster. If we knew exactly when, where, and how disasters would occur in the future, we could engineer our systems to resist them. Since hazard planners must cope with uncertainty, it is necessary to design communities that can cope effectively with contingencies. Second, people and property should fare better in resilient communities struck by disasters than in less flexible and adaptive places faced with uncommon stress. In resilient communities, fewer building should collapse. Fewer power outages should occur. Fewer households and business should be put at risk. Fewer deaths and injuries should occur. Fewer communications and coordination breakdowns should take placeStructural analysis is first of all a tool of structuring the ideas. It gives the possibility to describe a system with the help of a matrix connecting all its components. By studying these relations, the method gives the possibility to reveal the variables essential to the evolution of the system. It is possible to use it alone (as a helps for reflection and/or decision making), or as part of a more complex forecasting activity. This method has 3 phases. Phase 1: considering the variables: The first stage consists in considering all the variables characterizing the studied system (external as well as internal variables); it is good at this point to be the most comprehensive possible and not to exclude, a priori, any possible path of research. Phase 2: description of the relations between the variables: In a systemic vision, a variable doesn’t exist other than as part of the relational web with the other variables. Also, structural analysis allows to connect the variables in a two-entries table (direct relations). Phase 3: identification of the key variables: This last phase consists in identifying the key variables; first, by a direct classification (easy to realize), then by an indirect classification. Direct classification:  The total of the connections in a row indicates the importance of the influence of a variable on the whole system (level of direct motricity). The total in a column indicates the degree of dependence of a variable (level of direct dependence). Indirect classification: One detects the hidden variables thanks to a program of matrix multiplication applied to an indirect classification. The structural analysis method seeks to highlight key variables, hidden or not, in order to ask the right questions and encourage participants to think about counter-intuitive aspects or behavior within the system. The direct influences of each variable on the set of other variables are illustrated in matrix form. Each element of the matrix represents an influence (0 = no direct relationship of influence on the two variables considered; 1 = a direct relationship of influence). We also took into account the level of influence between two variables. The following convention was used: 1 = low relationship; 2 = average; 3 = strong; P = potential relationship.. P levels were also given 0-3 ratings. By reading the matrix, we can classify the variables by their -level of direct influence: importance of influence of a variable on the whole system, obtained through the total of links created per line; - level of direct dependence: degree of dependence of a variable, obtained by the total of links created per column. The direct and indirect influences of the variable represent the system the most realistically. Highlighted are the determining factors (main determinants) of the situation under investigation. The input variables and result or output variables help participants understand the organization and structuring of the system under the microscopeBased on the results of direct influence matrix, social, economic and institutional variables are effectiveness in comparison to others. They have a great impact on system but physical variable effectiveness is much less than its impact. Among of mentioned variables, institutional variable had a significant numerical difference. Indirect cross-impact matrix showed significant differences in the institutional and social variables compared to other variables in the effectiveness and affected. The results indicate the high impact of these two variables on the system. In other words, institutional and social variables were influential factors in their community resilience. According to the results of direct influence matrix, strategic and key factors are including participation, assistance and interactions from social variables, readiness from intuitional variable and in indirect influence matrix; these factors are including participation, social identity, assistance and interactions from social variables and readiness from intuitional variable. Distribution of factors in axis influences of direct and indirect suggests that this system is unstable.

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Analysis of Rural Settlements Resilience against Earthquake
Case Study: Marivan County
 
Iran is a seismic prone country located over the Himalayan-Alpine seismic belt. Striking earthquakes during the past years and decades are strong proofs for vulnerability of rural areas in this country; loss of lives, damage to buildings, even demolishing villages have been experienced in Iran rural areas. All these fatal effects are evidences to make villages more resilience and strengthen their structures because in the case of vulnerable structures, earthquake can be tremendously destructive. Therefore, losses of live and property can be avoided through making resilience rural social, economic and physical structure like construction of buildings that sway rather than break under the stress of an earthquake. Making villages resilience are directly related to saving rural residents lives and their property. Briefly, reaching or maintaining rural areas capacities to an acceptable level are the main purpose of this study by analyzing mentioned structures. This study conducted in Marivan rural settlements which exposed to earthquake.
According to Morgan Table, 310 samples responded to the questionnaires. The samples of this study were selected by chance from 6 districts and 18 villages. The main methods for analysis of collected data were Dimatel, ANP and Statictical analysis by SPSS. The results of ANP and Dimatel analyses led to the determination of relation among the factors. It should be noted we used Delfi method for this part. Moreover, for the final part ANOVA analysis is used by the authors. 
All around the world, countries have different approaches to deal with hazards in order to mitigate fatal affects. In fact, the goal of all management practices is to reduce hazard impacts. Iran faces a variety of hazards because of placing in a special geographical position; in this regard earthquake is the most important one. Resiliency approach can improve the flexibility of rural settlements through strengthen the capabilities of them and reduce their vulnerability. In the present study, analysis of rural settlements resilience against earthquake has been investigated. The results show that the resiliency is lower than the average in the studied villages. Also, there was a significant difference among the studied villages in terms of the resiliency against earthquake. The findings are consistent with the results of Nouri and Sepahvand in 2016 and Rezaei et al., in 2014.
Considering the analysis of data and ANP analysis of the internal and external factors in a general and separate way, the studied villages of Marivan city can be considered as non-resilience structures; in this regard, the most important reason is the inappropriate condition in the internal factors of rural settlements. The poor quality of construction and the inadequate structure of buildings must be considered, as well. Another obvious reason is the existence of eroded texture in this area. According to external factors, relief does not cover rural areas and led to reduce the resilience of rural settlements. Investigating the resilience of rural settlements based on external factors not only indicates the inappropriate situation of rural structure in this analysis, but also it proves a more favorable situation than internal factors. The findings show that structure and the amount of structure confinement in decrease the tissue texture of rural settlements play a profound role; changing these factors requires a long time and long-term planning. Regarding the post hoc test, variance analysis suggests the highest resiliency in Zarivar with an average of 2.99 and the lowest survival rate in KhavumirAbad rural district with an average of 1.87. Moreover, according to the one-sample T-Test, the socio-cultural dimension with a mean of 3.05 has the best situation in terms of resiliency against earthquake in the studied villages. For improving resiliency in the studied villages, authors’ suggests are including: managing and organizing preparation measures and response along with effective actions to reduce the risks of earthquake and providing a crisis management department; strengthen scientific and research studies to identify and reduce the risks; applying the rules to retrofit the buildings and increasing the safety factors in new construction; mapping the vulnerabilities in rural areas; increasing people participation and preparing them to deal with an emergency situation caused by an earthquake.
 
Keywords: Resiliency, Rural Settlements, Earthquake, Marivan County
 
 

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Investigation of Temperature and Precipitation Changes in the Seymarreh Basin by Using CMIP5 Series Climate Models
 
Abstract
Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R²) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.            
The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km². Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd.
The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours.
The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R²) were used to evaluate the performance of the models and compare the results.
To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall.
By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period.
The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data.
The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5.
The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.
 

• : Climate Change, Climate Scenarios, Uncertainty, LARS-WG, Seymareh.