Journal of Spatial Analysis

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Understanding the changes in extreme precipitation over a region is very important for adaptation strategies to climate change. One of the most important topics in this field is detection and attribution of climate change. Over the past two decades, there has been an increasing interest for scientists, engineers and policy makers to study about the effects of external forcing to the climatic variables and associated natural resources and human systems and whether such effects have surpassed the influence of the climate’s natural internal variability. The definitions used in the 5th assessment report were taken from the IPCC guidance paper on detection and attribution, and were stated as follows: “Detection of change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small.  Attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assignment of statistical confidence”. Detection and attribution of human-induced climate change provide a formal tool to decipher the complex causes of climate change. In this study the optimal fingerprinting detection and attribution have been attempted to investigate the changes in the annual maximum of daily precipitation and the annual maximum of 5-day consecutive precipitation amount over the southwest of Iran.

This is achieved through the use of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources Project(APHRODITE) dataset as observation, a climate model runs and the standard optimal fingerprint method. To evaluate the response of climate to external forcing and to estimate the internal variability of the climate system from pre-industrial runs, the Norwegian Climate Center’s Earth System Model- NorESM1-M was used. We used up scaling to remap both grid data of observations and simulations to a large pixel. This remapped pixel coverages the area of the southwest of Iran. The optimal finger printing method needs standardized values like probability index(PI) or anomalies as input data, since the magnitude of precipitation varied highly from one region to another. The General Extreme Value distribution (GEV) is used to convert time series of the Rx1day and Rx5day into corresponding time series of PI.  Then we calculated non-overlapping 5-year mean PI time series over the area study. In this research, we applied optimal fingerprinting method by using empirical orthogonal functions.  The implementation of optimal fingerprinting often involves projecting onto k leading EOFs in order to decrease the dimension of the data and improve the estimate of internal climate variability. A residual consistency test used to check if the estimated residuals in regression algorithm are consistent with the assumed internal climate variability. Indeed, as the covariance matrix of internal variability is assumed to be known in these statistical models, it is important to check whether the inferred residuals are consistent with it; such that they are a typical realization of such variability. If this test is passed, the overall statistical model can be considered suitable.

Results obtained for response to anthropogenic and natural forcing combined forcing (ALL) for Rx1day and Rx5day show that scaling factors are significantly greater than zero and consistent with unit. These results indicate that the simulated ALL response is consistent with Rx1day observed changes. Also, it is found that the changes in observed extreme precipitation during 1951-2005 lie outside the range that is expected from natural internal variability of climate alone and greenhouse gasses alone, based on NorESM1-M climate model. Such changes are consistent with those expected from anthropogenic forcing alone. The detection results are sensitive to EOFs. We estimate the anthropogenic and natural forcing combined attributable change in PI over 1951–2005 to be 1.64% [0.18%, 3.1%, >90% confidence interval] for RX1day and 2.5% [1%,4%] for RX5day.

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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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