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Seddigheh Farhood, Asadollah Khoorani, Abbas Eftekharian,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Introduction
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
Bibi Zahra Hosseini Giv, Sara Kiani, Syed Morovat Eftekhari, Mahdi Saghafi, Siros Esmaeili,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Introduction
Today, in addition to exploiting environmental resources, humans must be able to recognize environmental hazards and try to reduce their damages. The location of Iran in the Alpine-Himalaya mountain belt has made Iran one of the high-risk seismic areas, and the east of Iran is no exception to this rule. The fault activities of eastern Iran, especially east and west of Lut, are a serious threat to the residents of eastern Iran. The activity of old and young faults and the emergence of new faults have provided the basis for the occurrence of destructive earthquakes in these areas. And it still continues and has been able to provide problems for the population living in the east of Iran.
The purpose of this research is to investigate the role of the Giv fault system in the occurrence of morphotectonic evidence and active tectonic analysis in the studied area, which has achieved favorable results according to the model used and the studied sources. The model used in this research, which is derived from similar examples in foreign sources, mostly deals with the destructive aspect of tectonics and has achieved the desired goals. Based on this, it should be seen if the morphotectonic evidence of the Giv fault system can be a sign of more activity and more threat in this part of the range (southern domain of Baghran mountains) or not? After studying various sources, the sources that answer the research questions were selected and further analyzed, and the conceptual model derived from these sources, which has a qualitative-analytical aspect, was used. Therefore, according to the main objectives of this research, which follows the destructive and instantaneous tectonic aspect, sources were selected that provided the most information to answer the research question, the sum of the information expressing the active tectonics in the studied area.
Research Methodology
The Giv fault system is a part of the Nehbandan-Kash fault in the east of the Lut block, and in the Giv plain, north-east of the Lut, with an almost west-east direction, it passes through the south of the Giv village and continues to Deh Mir and Karijgan in the west of the Giv plain. Giv village is located in Khosf County in South Khorasan province and in the center of Giv plain, south of Baghran Birjand Mountains and north of Shah Mountain.
The current research is of applied and developmental research type, and according to the history of seismicity of the region and historical data, it can be a step in the direction of knowing the seismic risk areas and also reminds the local residents to be more prepared. The conceptual model used in this research, which is derived from similar foreign examples, examines mostly the destructive aspect of tectonics.
In this research, the library work started by collecting and receiving a series of domestic and foreign sources, followed by the translation of foreign sources over a long period of time. Also, statistics and information were received from Geological Organization and Geophysics Institute, Birjand University, Birjand Seismological Center. Field studies, interviews, surveys and field measurements, using geological and topographic maps and satellite images, and using Google Earth and Arc GIS software, analysis and synthesis of information were carried out. Most of the data were used as qualitative data and to some extent quantitative data in the analysis.
Result and Discuction
The morphotectonic evidence in the studied area indicates a high risk of seismicity in the Giv fault pack, which is more dangerous than other parts of the Giv fault system.All the evidences such as uplift and cliffs in the south of Giv, significant change of the bridge river near the mouth and bed digging in this section show the uplift and activity of the South Giv fault and the travertine formation associated with the earthquake in the south of Giv, as well as the evidence of the growth of the Young Giv fold in 5 km. North of Giv village, such as the deviation of Pol and Minakhan rivers and excavation of the Minakhan river bed (Antecedence phenomenon), the presence of three generations of alluvial fans in the vicinity of the Young Giv fold, all indicate active tectonics and the rise of the Giv fold and the occurrence of destructive earthquakes. All the above-mentioned evidences are a serious alarm for the residents of Giv fault, especially Giv village, and require more study work, strengthening of villages, and proper planning for construction works so that the past tragic events of Giv village do not repeat in the future and this issue is taken into consideration in the discussion of land development.
Today, in addition to exploiting environmental resources, humans must be able to recognize environmental hazards and try to reduce their damages. The location of Iran in the Alpine-Himalaya mountain belt has made Iran one of the high-risk seismic areas, and the east of Iran is no exception to this rule. The fault activities of eastern Iran, especially east and west of Lut, are a serious threat to the residents of eastern Iran. The activity of old and young faults and the emergence of new faults have provided the basis for the occurrence of destructive earthquakes in these areas. And it still continues and has been able to provide problems for the population living in the east of Iran.
The purpose of this research is to investigate the role of the Giv fault system in the occurrence of morphotectonic evidence and active tectonic analysis in the studied area, which has achieved favorable results according to the model used and the studied sources. The model used in this research, which is derived from similar examples in foreign sources, mostly deals with the destructive aspect of tectonics and has achieved the desired goals. Based on this, it should be seen if the morphotectonic evidence of the Giv fault system can be a sign of more activity and more threat in this part of the range (southern domain of Baghran mountains) or not? After studying various sources, the sources that answer the research questions were selected and further analyzed, and the conceptual model derived from these sources, which has a qualitative-analytical aspect, was used. Therefore, according to the main objectives of this research, which follows the destructive and instantaneous tectonic aspect, sources were selected that provided the most information to answer the research question, the sum of the information expressing the active tectonics in the studied area.
Research Methodology
The Giv fault system is a part of the Nehbandan-Kash fault in the east of the Lut block, and in the Giv plain, north-east of the Lut, with an almost west-east direction, it passes through the south of the Giv village and continues to Deh Mir and Karijgan in the west of the Giv plain. Giv village is located in Khosf County in South Khorasan province and in the center of Giv plain, south of Baghran Birjand Mountains and north of Shah Mountain.
The current research is of applied and developmental research type, and according to the history of seismicity of the region and historical data, it can be a step in the direction of knowing the seismic risk areas and also reminds the local residents to be more prepared. The conceptual model used in this research, which is derived from similar foreign examples, examines mostly the destructive aspect of tectonics.
In this research, the library work started by collecting and receiving a series of domestic and foreign sources, followed by the translation of foreign sources over a long period of time. Also, statistics and information were received from Geological Organization and Geophysics Institute, Birjand University, Birjand Seismological Center. Field studies, interviews, surveys and field measurements, using geological and topographic maps and satellite images, and using Google Earth and Arc GIS software, analysis and synthesis of information were carried out. Most of the data were used as qualitative data and to some extent quantitative data in the analysis.
Result and Discuction
The morphotectonic evidence in the studied area indicates a high risk of seismicity in the Giv fault pack, which is more dangerous than other parts of the Giv fault system.All the evidences such as uplift and cliffs in the south of Giv, significant change of the bridge river near the mouth and bed digging in this section show the uplift and activity of the South Giv fault and the travertine formation associated with the earthquake in the south of Giv, as well as the evidence of the growth of the Young Giv fold in 5 km. North of Giv village, such as the deviation of Pol and Minakhan rivers and excavation of the Minakhan river bed (Antecedence phenomenon), the presence of three generations of alluvial fans in the vicinity of the Young Giv fold, all indicate active tectonics and the rise of the Giv fold and the occurrence of destructive earthquakes. All the above-mentioned evidences are a serious alarm for the residents of Giv fault, especially Giv village, and require more study work, strengthening of villages, and proper planning for construction works so that the past tragic events of Giv village do not repeat in the future and this issue is taken into consideration in the discussion of land development.
Fateme Emadoddin, Dr Ali Ahmadabadi, Seyed Morovat Eftekhari, Masumeh Asadi Gandomani,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Introduction: Land subsidence is one of the environmental hazards that threatens most countries today, including the majority of Iran's plains (Ranjabr and Jafari, 2010). Damages caused by subsidence can be direct or indirect. Infrastructural effects are direct and indirect effects of subsidence, but economic, social and environmental effects are indirect effects of subsidence (Bucx, et al., 2015). The environmental effects of subsidence are related to other effects of subsidence, including the infrastructural, economic and social effects of subsidence. The southwest plain of Tehran is considered one of the most important plains of Iran due to its large areas of residential, agricultural and industrial lands from various aspects, especially economic, political and social. The subsidence of the Tehran plain was first noticed by the measurements of the country's mapping organization in the 1370s. Since 2004, the responsibility of investigating this phenomenon in the plains of Tehran was entrusted to the Organization of Geology and Mineral Explorations of the country. Although several researches have been done in the field of subsidence factors, amount and zoning. In the field of estimation of subsidence and changes in water level, spatial correlation of subsidence with changes in water level and estimation of vulnerability due to subsidence according to the density of population, settlements and facilities in the southwestern plain of Tehran has not been done.
Methodology: In the current research, we will analyze and estimate the spatial regression of the subsidence phenomenon by InSAR technique with water level changes from 2005 to 2017, as well as the environmental effects of subsidence in the southwest plain of Tehran by using Quadratic analysis method (O’Sullivan and Unwin, 2010). The criteria map of the current research is overlapped using the ANP method (Ahmedabadi and Ghasemi, 2015) weighting and finally with the SAW method (Emaduddin et al., 2014) in the Arc GIS 10.8 software, and the vulnerability map due to land subsidence in the study area is prepared.
Results: The average subsidence in 12 years is about 9.9 cm per year. Average subsidence has occurred in four main zones. Maximum and minimum subsidence have been observed in B (near the Sabashahr) and D (in east of plain) zones respectively. The results of the interpolation of the depth of the underground water in the study area indicate that the general trend of increasing the depth from the south (10 meter) to the north (more than 90 meter) of the plain. The results of spatial correlation showed that there is a significant direct relationship between the spatial layer of the average subsidence rate of Tehran Plain and the spatial data of the underground water level, and the R value is equal to 0.61. The distribution map of the underground water depth of the study area in the form of Quadrat analysis shows that in the main part of the plain, the depth of underground water is at an average level. The general trend of changes in the level of underground water is decreasing from northwest to southeast and is in 5 levels. The distribution of the networks shows that the rivers have three linear trends from north and northwest to south; their dispersion is mostly in the center of the study area. The flood rate is higher in the central plain networks. In study area, there are important arterial roads such as Tehran-Qom highway, Tehran-Saveh highway and Tehran Azadegan highway. The southern and northeastern areas of the study area are urban settlements such as Islamshahr, the 18th and 19th districts of Tehran Municipality and other residential areas such as Sabashahr. The major part of the region has fertile soil and the occurrence of subsidence can have negative effects on the fertility and texture of the soil in the study area. The results of vulnerability analysis due to subsidence show that there are 5 vulnerability classes in the study area including very low, low, medium, high and very high.
Conclusions: All in all most of the study areas (central, northern and western networks) are in medium, high and very high vulnerability. About 14,600 hectares of the study area are in medium vulnerability. Which is continuous from the west to the east of the study area. Most of the urban infrastructures are moderately vulnerable to subsidence. About 17,000 hectares of the southwestern plain of Tehran are very vulnerable. That more than half of the area of this area is covered by settlements and urban infrastructures. Therefore, the phenomenon of subsidence causes irreparable damage to the settlements and infrastructures in the southwest plain.
Methodology: In the current research, we will analyze and estimate the spatial regression of the subsidence phenomenon by InSAR technique with water level changes from 2005 to 2017, as well as the environmental effects of subsidence in the southwest plain of Tehran by using Quadratic analysis method (O’Sullivan and Unwin, 2010). The criteria map of the current research is overlapped using the ANP method (Ahmedabadi and Ghasemi, 2015) weighting and finally with the SAW method (Emaduddin et al., 2014) in the Arc GIS 10.8 software, and the vulnerability map due to land subsidence in the study area is prepared.
Results: The average subsidence in 12 years is about 9.9 cm per year. Average subsidence has occurred in four main zones. Maximum and minimum subsidence have been observed in B (near the Sabashahr) and D (in east of plain) zones respectively. The results of the interpolation of the depth of the underground water in the study area indicate that the general trend of increasing the depth from the south (10 meter) to the north (more than 90 meter) of the plain. The results of spatial correlation showed that there is a significant direct relationship between the spatial layer of the average subsidence rate of Tehran Plain and the spatial data of the underground water level, and the R value is equal to 0.61. The distribution map of the underground water depth of the study area in the form of Quadrat analysis shows that in the main part of the plain, the depth of underground water is at an average level. The general trend of changes in the level of underground water is decreasing from northwest to southeast and is in 5 levels. The distribution of the networks shows that the rivers have three linear trends from north and northwest to south; their dispersion is mostly in the center of the study area. The flood rate is higher in the central plain networks. In study area, there are important arterial roads such as Tehran-Qom highway, Tehran-Saveh highway and Tehran Azadegan highway. The southern and northeastern areas of the study area are urban settlements such as Islamshahr, the 18th and 19th districts of Tehran Municipality and other residential areas such as Sabashahr. The major part of the region has fertile soil and the occurrence of subsidence can have negative effects on the fertility and texture of the soil in the study area. The results of vulnerability analysis due to subsidence show that there are 5 vulnerability classes in the study area including very low, low, medium, high and very high.
Conclusions: All in all most of the study areas (central, northern and western networks) are in medium, high and very high vulnerability. About 14,600 hectares of the study area are in medium vulnerability. Which is continuous from the west to the east of the study area. Most of the urban infrastructures are moderately vulnerable to subsidence. About 17,000 hectares of the southwestern plain of Tehran are very vulnerable. That more than half of the area of this area is covered by settlements and urban infrastructures. Therefore, the phenomenon of subsidence causes irreparable damage to the settlements and infrastructures in the southwest plain.
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