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Showing 26 results for Climate Change
Seyyed Mohammad Khademi Nosh Abadi, Dr Maryam Omidi Najaf Abadi, Dr Seyyed Mehdi Mirdamadi,
Volume 9, Issue 4 (3-2023)
Volume 9, Issue 4 (3-2023)
Abstract
Industrial and agricultural activities in the world have led to an increase in the concentration of greenhouse gases such as carbon dioxide, methane and nitrogen oxide and have caused the earth's climate to become warmer. This phenomenon has caused climate change and has changed the thermal and rainfall patterns. Climate change in Iran in recent years has caused a decrease in rainfall and an increase in temperature and continuous droughts. Agricultural production in Iran has been affected by climate change and has faced a decrease in the production of crops such as wheat. Therefore, according to the government's policy of self-sufficiency in wheat production and the establishment of sustainable food security in the country, it is necessary to use climate smart agricultural technologies to sustainably increase agricultural productivity, Adapting and resilience of agriculture to climate change and reduction greenhouse gases emission from agriculture. The purpose of this study was to design a behavioral model for the use of climate smart agricultural technologies with an emphasis on motivation. The research method was quantitative, in terms of practical purpose, and research data was collected through a cross-sectional survey.The conceptual model was designed using the theory of planned behavior and the theory of norm activation. Bayesian structural equation modeling was used to test the model and hypotheses. The statistical population of this research was 800 wheat farmers of Nazarabad city, Alborz province. The sample size was calculated using Cochran formula 260 people, and stratified random sampling method with proportional assignment was determined as the sampling method. A researcher-made questionnaire was used to collect research data. The validity of the questionnaire was confirmed through agricultural extension and education experts, and its reliability was also confirmed through the pre-test and calculation of Cronbach's alpha coefficient. The findings of the research show that subjective norms, personal norms and perceived behavioral control related to the use of climate smart agricultural technologies have a significant effect on the intention to use these technologies. While the attitude towards the use of climate smart agricultural technologies do not have a significant effect on the intention to use these technologies. The variable of intention to use climate smart agricultural technologies also has a significant effect on the behavior of using these technologies.
Dr Seyed Keramat Hashemi Ana,
Volume 10, Issue 1 (5-2023)
Volume 10, Issue 1 (5-2023)
Abstract
Abstract
Introduction and issue: In today's century when the effects of climate change on different sectors are undeniable, investigating and analyzing the behavior during dry spells is always of special importance and basic priority. On the other hand, the occurrence of extreme events such as precipitation can accelerate the occurrence of climate change. In Iran, rainfall is one of the basic variables for evaluating the potential availability of water resources, but its temporal and spatial distribution is very uneven. The change of dry Spells depending on precipitation always have different fluctuations in different seasons of the year. It seems that this is due to the inherent behavior of precipitation, which generally shows itself as an unstable and unruly variable. This feature causes changes and differences in the temporal and spatial distribution of precipitation in arid and semi-arid regions such as Iran. This inconsistency will face fundamental challenges to regularize dry spells on a seasonal and monthly scale. With a detailed understanding of the behavioral mechanism of dry spells, it is possible to know more precisely the climatic condition of different regions in order to plan in sectors such as; Water resources, agriculture, health, transportation and etc we able to do basic and preventive measures compatible with climate change. It is hoped that this research and related studies will be a positive step towards a more accurate understanding of the climate and its behavior in different seasons of the year.
Data and method: In order to investigate the seasonal behavior of the duration of dry spells, we used daily precipitation data for 44 synoptic stations of Iran and a 30-year statistical period (1988-2018). To reveal the behavior of dry spells, the precipitation data after validation and temporal integration were classified on a seasonal scale.
After the statistical integration of the data, dry spells related to precepitation were extracted and long-term periods lasting more than 20 days were the basis of the study. In the next step, to determine the seasonal weight of courses was used, the step-by-step evaluation method of Swara's fuzzy-numerical logic (SWARA). Thus, in the first step, the longest and most frequent periods are sorted based on relative importance. In the second step, the initial weights of the courses are determined, and in the third and fourth steps, the final and normalized weights of the courses in different seasons are determined, and unrealistic results are removed from the final analysis for proper explanation.
Findings and Results: The effectiveness and weight of each of the criteria with the Swara method in the fuzzy environment showed that in the western and northern regions of the country, winter and spring seasons and criteria such as reversibility and percentage of probability of occurrence have the most initial weight in explaining the periods. In the final explanation, these two season,s had a high weight. These two seasons explain more than 65% of the weight of courses in these regions. In the southern regions and parts of the center (Isfahan, East Fars and West Kerman), winter and autumn explain more than 71% of the weight of periods. Among the criteria explaining the weight of the courses, the reversibility criterion and the probability of occurrence have taken more than 55% of the weight. The northern and humid regions of the country vary in criteria from periods such as; Reversibility, continuity and probability of occurrence are more apparent and this indicates that the border of dry areas in the future of Iran's climate will move towards northern areas. It can be acknowledged that the behavior of long-term dry periods is more a function of two criteria of reversibility and probability of their occurrence. The weighting of the criteria affecting dry periods showed that the return period and the continuation of periods in the cold seasons of the year in dry areas have a more irregular behavior than in wet areas and have more weight in explaining the periods. By determining the weight of seasons in explaining dry periods, we can have better planning and management in related sectors such as water and agriculture.
Key words: dry spells, weighing, precipitation, climate, Swara method, Iran.
Mrs Halimeh Shahzaei, Dr Mohsen Hamidianpour, Dr Mahsa Farzaneh,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Spatial analysis of Iran's climate change from the point of view of sensible heat flux and latent heat flux by Bowen method
Halimeh Shahzaei; Ms.c student of Climatology, Departement of Physical Geography, University of Sistan and Baluchistan, Zahedan, Iran.
Mohsen Hamidianpour[1]; Associate Professor, Departement of Physical Geography, University of Sistan and Baluchistan, Zahedan, Iran.
Mahsa Farzaneh; Ph.D Graduated. Climatology.
Abstract
Sensible heat flux and latent heat flux are among the variables that are closely related to temperature and humidity and show heat transfer on a surface. So, their changes can be considered related to changes in temperature and humidity. In this regard, the current research aims to analyze and reveal the climatic changes of Iran by examining the course of changes in sensible heat flux and latent heat and the ratio between the two. For this purpose, NCEP/NCAR reanalysis data including sensible and latent heat flux during the period 1948-2020 was used in Iran. Bowen coefficient was calculated from the ratio of these two heat fluxes. Interpolation methods were used for their spatio-temporal analysis. In addition, by using the non-parametric methods of Mann-Kendall and Shibsen, spatial and temporal changes were also investigated. The first part of the results showed that, spatially, the Bowen coefficient is a function of latitude and roughness. And in terms of time, the lowest value corresponds to the month of January and the highest value corresponds to the month of July. The results of the second part show that the Bowen coefficient has a positive trend over time. Its upward trend indicates an increase in the dryness coefficient of the country. So that this situation can be seen in the positive trend and increase in temperature.
Keywords: climate change, Bowen coefficient, global warming, spatio-temporal analysis.
[1]. Autehr corespound:Email: mhamidianpour@gep.usb.ac.ir
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.
Dr Sara Kiani, Dr Morad Kavyani, Dr Amirali Tavasoli,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
The Namak Lake is situated between three provinces: Isfahan, Qom, and Semnan. However, the functioning of Namak Lake and its susceptibility to environmental, ecological, economic, and social influences not only affect the immediate surroundings but also impact other provinces. Naturally, a crisis in this lake can have negative effects on human communities and the residents of the surrounding areas in terms of environmental, economic, and social aspects. Therefore, the aim of this research is to identify the temporal-spatial changes in the salinity of Namak Lake and, subsequently, to investigate and analyze the effects of these changes on the environmental security of the surrounding regions. To achieve this goal, salt zones were identified using soil salinity indices, including the Normalized Difference Salinity Index (NDSI), Salinity Index 1 (SI1), Salinity Index 2 (SI2), and Brightness Index (BI), over a 30-year period (1992-2021) with five-year intervals. Then, using the maximum likelihood method, the salt zones were classified into four land cover types, including water zone, moist zone, salt zone, and other uses. The results of this study indicate that due to the reduction in water inflow into the lake as a result of dam construction in the upstream basin and the effects of climate change, the water zone, or seasonal lake, of Namak Lake has disappeared and the salt zone has expanded in this area. The most significant changes in the lake are related to the northwestern part of the lake, where major rivers such as Jajrood, Shur, Qarechai, and Qamaroud flow into this part of the lake, contributing to its drainage. Therefore, dam construction on these rivers has led to a downward trend in water flow into the lake. Furthermore, the results suggest that due to the absence of settlements and human communities near Namak Lake and the natural and climatic conditions of the region, it is not expected that environmental incidents that could have security and political implications will occur in the short term.
Sahar Afiati, Bohloul Alijani, Sayyed Mohammad Hosseini,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
Cold and frost are one of the climatic hazards that cause damage to various activities every year. Climate change, on the other hand, causes spatial and temporal changes in glaciation. The purpose of this study is to analyze the temporal-spatial changes and predict the future of glaciers in Hamadan province. CanESM2 model was used to predict the minimum daily temperature in the province. Data mining of general circulation models was Downscaling using LARS-WG model. The above parameters were simulated for a period of 30 years (2050-2021) under three scenarios RCP2.6, RCP4.5 and RCP8.5 for selected stations. The results of the monthly minimum temperature survey in the study stations of the province showed that the minimum temperature in the period (2050-2021) in all studied stations according to all three scenarios will increase in all months of the year compared to the base period. The average minimum temperature of the province is equal to 2.5 degrees Celsius, which in the coming decades based on the scenarios of RCP2.6, RCP4.5 and RCP8.5 will reach 6, 6.2 and 6.3 degrees Celsius, respectively, which is the highest The changes are related to Nojeh station and the lowest is related to Hamedan. The spatial distribution of the beginning and end of freezing in the future period indicates that freezing in the northeastern and northern parts of the province starts earlier and ends later than in other parts of the province, while in the southern parts of the province it starts later and ends earlier. The results of examining the changes in the onset of frost in the next decade compared to the base period showed that in all stations studied the onset of frost will decrease between 3 to 11 days.
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