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Showing 3 results for Khuzestan Province.
Analysis of performance indicators in terms of healthy city in Khuzestan province city by using data envelopment analysis (DEA)
Nafise Marsousi, Majid Akbari, Nazanin Hajipour, Vahid Boustan Ahmadi,
Volume 21, Issue 63 (12-2021)
Abstract

According the increasing population, especially the urban population in the world and increasing environmental pollution caused by it, The need for urban planning and management approaches based on indicators such as Healthy Cities approach seems inevitable. The purpose of this paper is to analyze the efficiency and ranking of healthy city indicators through 36 indicators (socioeconomic, health services, environmental and health care). research method applied research is descriptive, analytic and development. To analyze the data from the non-parametric linear programming technique of data envelopment analysis, cross ineffective, models and software Dea slover Shannon entropy is used. The geographic area of this study is Khuzestan province and its statistical population is 22 cities according to the census of 2016. The results of this research show that in terms of relative efficiency of Ahwaz city due to the centrality of the province and the availability of infrastructure and sanitary services with a relatively high distance with the highest performance and high level of performance was in the first rank. And the cities of Dezful, Shosh, Khorramshahr, Shoshtar, Abadan, Masjed Soleyman and Behbahan were selected as semi-efficient cities. Finally, it can be concluded that in terms of having the indicators of the healthy city, most of the cities of the province are Inefficient (64%).


Assess the role of climate change and teleconnection signals at maximum temperature, Case study: Khuzestan province
Dr Gholamabbas Fallah Ghalhari, Fahimeh Shakeri,
Volume 22, Issue 67 (12-2022)
Abstract
In this Research, the maximum temperature of selected stations in Khuzestan province and the numerical values of 8 extreme climatic indicators belonging to the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) were used in the statistical period of 1987-2017. To analyze the trend of extreme climatic indices, the Man-Kendall test was used and to estimate the slope of the trend line, the Sen’s estimator was used. In this study, given the importance of global warming that severely affected all aspects of life, the authors explore the relationship between climatic factors and maximum temperature in Khuzestan province until to rely on it, and ones can predict and forecast air temperature at this region. For this purpose, the temperature of selected stations in Khuzestan province and numerical values of  8 climate indicators in the period 1987-2014 have been used. To understand the relationship between climate indicators and maximum temperature at 1 to 12 months of delays, Pearson’s correlation coefficient was used. The results showed that most of the extreme climatic indicators in the study period had a significant trend. The TX10 and TN10 indices have had negative trend in most stations and the TX90, TN90, TXx, TXn, TNx and TNn indices have had positive trend. According to the results of correlation coefficients can be concluded that all studied signals have a significant effect on the province's maximum temperature. The correlation between maximum temperature and indices PNA, TSA, WHWP, WP and NAO, was more than the other climate indicators. Results also showed that the entire indices except NAO have significant positive correlation with maximum temperature of the province. PNA index with a delay of 10 months has the highest positive correlation with maximum temperature of study area.
Detection of the effect of cloud microphysical structure on precipitation in Khuzestan province using MODIS cloud products
Mr Danesh Nasiri, Dr Reza Borna, Dr Manijeh Zohourian Pordel,
Volume 24, Issue 72 (3-2024)
Abstract
Knowledge of supernatural microphysical properties and revealing its relationship with the spatial temporal distribution of precipitation can significantly increase the accuracy of precipitation predictions. The main purpose of this study is to reveal the relationship between the Cloud microphysical structure and the distribution of precipitation in Khuzestan province. In this regard, first 3 inclusive rainfall events in Khuzestan province were selected and their 24-hour cumulative rainfall values were obtained. The rainfall event of 17December2006, was selected as a sample of heavy rainfall, 25 March 2019, as a medium rainfall case, and finally 27 October 2018, as a light rainfall case. Microphysical factors of clouds producing these precipitations were obtained from MODIS (MOD06) cloud product. These factors included temperature, pressure, and cloud top height, optical thickness, and cloud fraction. Finally, by generating a matrix with 64000 information codes, and performing spatial correlation analysis at a confidence level of 0.95, the relationship between the Cloud microphysical structure and the spatial values and distribution of selected precipitates was revealed. The results showed that in the case study of heavy and medium rainfall, the spatial average of 24-hour cumulative rainfall in the province was 36 and 12 mm, respectively. A fully developed cloud structure with a cloud ratio of more than 75% and a vertical expansion of 6 to 9 thousand meters, with an optical thickness of 40 to 50, has led to the occurrence of these widespread and significant rainfall in the province. While in the case of light rain, a significant discontinuation was seen in the horizontal expansion of the cloud cover in the province and the cloud cover percentage was less than 10%. In addition, the factors related to the vertical expansion of the cloud were much lower, so that the height of the cloud peak in this rainfall was between 3 to 5 thousand meters. The results of this study showed that in heavy and medium rainfall cases, a significant spatial correlation was observed at a confidence level of 0.95 between MOD06 Cloud microphysical factors and recorded precipitation values, while no significant spatial correlation was observed in light rainfall case.
 
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