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Showing 2 results for Seasonality
Zeinabe Sharifi, Mehdi Nooripour, Maryam Sharifzadeh,
Volume 4, Issue 2 (7-2017)
Volume 4, Issue 2 (7-2017)
Abstract
Sustainable livelihoods approach as one of the new sustainable rural development approaches is one way of thinking and attempting to achieve development which arose in the late 1980s with the aim of progress and poverty alleviation in rural communities (Sojasi Ghidari et al.,2016).
Five critical concepts to understand sustainable livelihoods framework include the concept of vulnerability, livelihood assets, transforming structures and processes, livelihood strategies and livelihood outcomes (Motiee Langroodi et al,2012). According to the sustainable livelihoods framework, vulnerability is one of the fundamental concepts based on the vulnerability context (Forouzani et al.,2017). The vulnerability context forms the people's external environment. It comprises shocks (such as human, livestock or crop health shocks; natural hazards, like floods or earthquakes; economic shocks; conflicts in form of national or international wars) trends (such as demographic trends; resource trends; trends in governance), and seasonality (such as seasonality of prices, products or employment opportunities) and represents the part of the framework that is outside stakeholder’s control (Kollmair and Gamper, .(2002
Various research explored the factors influencing vulnerability and its dimensions and less research investigated to assess the vulnerability of rural households. Therefore, the purpose of this study is to investigate rural households' vulnerability in the Central District of Dena County. Accordingly, this research is to answer the following questions:
Five critical concepts to understand sustainable livelihoods framework include the concept of vulnerability, livelihood assets, transforming structures and processes, livelihood strategies and livelihood outcomes (Motiee Langroodi et al,2012). According to the sustainable livelihoods framework, vulnerability is one of the fundamental concepts based on the vulnerability context (Forouzani et al.,2017). The vulnerability context forms the people's external environment. It comprises shocks (such as human, livestock or crop health shocks; natural hazards, like floods or earthquakes; economic shocks; conflicts in form of national or international wars) trends (such as demographic trends; resource trends; trends in governance), and seasonality (such as seasonality of prices, products or employment opportunities) and represents the part of the framework that is outside stakeholder’s control (Kollmair and Gamper, .(2002
Various research explored the factors influencing vulnerability and its dimensions and less research investigated to assess the vulnerability of rural households. Therefore, the purpose of this study is to investigate rural households' vulnerability in the Central District of Dena County. Accordingly, this research is to answer the following questions:
- What is the status of rural households' vulnerability to shocks?
- What is the status of rural households' vulnerability to trends?
- What is the status of rural households' vulnerability to seasonality?
The research method is applied in terms of purpose and non-experimental survey in terms of data collection. The statistical population of the study consisted of 2500 rural households in the Central District of Dena County, which according to Krejcie and Morgan table 300 households were selected using cluster random sampling.
The research tool for data collection was a structured and research-made questionnaire. Face validity was used in order to determine the validity of the questionnaire and the face validity of the research tool was confirmed by a panel of experts. A pre-test study was carried out in order to determine the reliability of the various sections of the questionnaire, Cronbach's alpha was calculated and reliability of the questionnaire was confirmed.
Vulnerability was measured using 20 questions and in three sections including shocks (8 items), trends (6 items) and seasonality (6 items) with a three-point Likert scale (low, medium and high) and SPSS software was used to analyze data.
The results of calculated vulnerability showed that the rural households had the most vulnerability to shocks including "causing damage to crops due to frost", "causing damage to crops due to drought" and "plant pests and diseases". In contrast, rural households had the lowest vulnerability to shocks including "family fights and ethnic conflict", "animal disease" and "illness of family members".
The respondents had the most vulnerability to the trends including "the rise in food prices and other life necessities" and "the rise in the price of energy carriers such as diesel, gasoline, etc.". In contrast, the respondents had the least vulnerability in trends including "gradual air pollution" and "increase in households' population".
The respondents had the most vulnerability to the seasonality including "lack of funds and capital in low working seasons" and "fluctuations in the prices of agricultural products". In contrast, the respondents had the least vulnerability to seasonality including "the impossibility of growing crops in different seasons" and "decrease and increase in the amount of agricultural production in different seasons".
The results showed that generally respondents’ vulnerability to shocks, trends and seasonality and the total vulnerability was at a medium level for the majority of the respondents (over 40%), at a high level for about 25 percent of the respondents, at a very high level for about 15 percent of the respondents and at a very low level for only about 10 percent of the respondents. Therefore, it could be concluded that more than half of the respondents' vulnerabilities was at a low and medium level.
Furthermore, in two groups with low and medium vulnerability, the average vulnerability to shocks, trends and seasonality are almost the same, whereas in two groups with high and very high vulnerability, the most vulnerability referred to seasonality, trends and shocks, respectively.
According to the research findings, the following suggestions are offered in order to reduce the vulnerability of rural households.
In order to reduce the vulnerability of rural households to shocks including "causing damage to crops due to frost", "causing damage to crops due to drought" and "plant pests and diseases", it is suggested that educational courses are held by the relevant organizations such as Agriculture Jihad in order to get familiar with ways to deal with damages caused by frost, drought and plant pests and disease. In addition, the use of heating system before the frost, the use of drought resistant varieties, the use of such techniques as land fallowing in order to reduce the need for water, the use of integrated pests management are offered as well in order to reduce the vulnerability of rural households.
Considering that the respondents had the most vulnerability to the trends including "the rise in food prices and other life necessities" and "the rise in the price of energy carriers such as diesel, gasoline, etc.", the rise in food prices as well as energy carriers in rural areas should be cautiously.
Founding loan fund in order to give loan and credit to households in low working seasons as well as determining a guaranteed price for agricultural products by the relevant authorities to reduce the volatility of agricultural prices are recommended.
The research tool for data collection was a structured and research-made questionnaire. Face validity was used in order to determine the validity of the questionnaire and the face validity of the research tool was confirmed by a panel of experts. A pre-test study was carried out in order to determine the reliability of the various sections of the questionnaire, Cronbach's alpha was calculated and reliability of the questionnaire was confirmed.
Vulnerability was measured using 20 questions and in three sections including shocks (8 items), trends (6 items) and seasonality (6 items) with a three-point Likert scale (low, medium and high) and SPSS software was used to analyze data.
The results of calculated vulnerability showed that the rural households had the most vulnerability to shocks including "causing damage to crops due to frost", "causing damage to crops due to drought" and "plant pests and diseases". In contrast, rural households had the lowest vulnerability to shocks including "family fights and ethnic conflict", "animal disease" and "illness of family members".
The respondents had the most vulnerability to the trends including "the rise in food prices and other life necessities" and "the rise in the price of energy carriers such as diesel, gasoline, etc.". In contrast, the respondents had the least vulnerability in trends including "gradual air pollution" and "increase in households' population".
The respondents had the most vulnerability to the seasonality including "lack of funds and capital in low working seasons" and "fluctuations in the prices of agricultural products". In contrast, the respondents had the least vulnerability to seasonality including "the impossibility of growing crops in different seasons" and "decrease and increase in the amount of agricultural production in different seasons".
The results showed that generally respondents’ vulnerability to shocks, trends and seasonality and the total vulnerability was at a medium level for the majority of the respondents (over 40%), at a high level for about 25 percent of the respondents, at a very high level for about 15 percent of the respondents and at a very low level for only about 10 percent of the respondents. Therefore, it could be concluded that more than half of the respondents' vulnerabilities was at a low and medium level.
Furthermore, in two groups with low and medium vulnerability, the average vulnerability to shocks, trends and seasonality are almost the same, whereas in two groups with high and very high vulnerability, the most vulnerability referred to seasonality, trends and shocks, respectively.
According to the research findings, the following suggestions are offered in order to reduce the vulnerability of rural households.
In order to reduce the vulnerability of rural households to shocks including "causing damage to crops due to frost", "causing damage to crops due to drought" and "plant pests and diseases", it is suggested that educational courses are held by the relevant organizations such as Agriculture Jihad in order to get familiar with ways to deal with damages caused by frost, drought and plant pests and disease. In addition, the use of heating system before the frost, the use of drought resistant varieties, the use of such techniques as land fallowing in order to reduce the need for water, the use of integrated pests management are offered as well in order to reduce the vulnerability of rural households.
Considering that the respondents had the most vulnerability to the trends including "the rise in food prices and other life necessities" and "the rise in the price of energy carriers such as diesel, gasoline, etc.", the rise in food prices as well as energy carriers in rural areas should be cautiously.
Founding loan fund in order to give loan and credit to households in low working seasons as well as determining a guaranteed price for agricultural products by the relevant authorities to reduce the volatility of agricultural prices are recommended.
Hamideh Roshani, Raoof Mostafazadeh, Abazar Esmali-Ouri, Mohsen Zabihi,
Volume 7, Issue 4 (2-2021)
Volume 7, Issue 4 (2-2021)
Abstract
Introduction and objective:
Temporal and spatial variability of rainfall is one of the determining factors for water resources management, agricultural production, drought risk, flood control and understanding the effect of climate change. The impact of spatiotemporal patterns of precipitation on flood/drought hazard and available water resources is an undeniable issue in water resources management. Precipitation concentration (PCI) and Seasonality (SI) indices are the important indicators to determine the distribution of precipitation in a region which can lead to identify and manage before occurring natural hazards including flood and drought and hydro-meteorological storms. Several methods available to study the spatial and temporal distribution of rainfall. Indicators of rainfall concentration and seasonality are among the methods of studying rainfall dispersion that depend on the distribution of rainfall patterns at different time scales. Accordingly, the study and understanding of temporal and spatial changes in rainfall can lead to sound management policies in the field of water and soil resources by planners and decision makers.
Methodology:
The precipitation concentration index is presented as a powerful indicator for determining the temporal distribution of precipitation to show the distribution of precipitation and rain erosion. The increase in the value of this indicator indicates a low dispersion and a higher concentration of rainfall, which is closely related to the intensity of rainfall. Seasonality index as one of the key factors in detecting seasonal variation in the variables of natural ecosystems, measures the time distribution of hydrological components at different times of the year and uses each hydrological variable to classify different hydrologic variable regimes. In this regard, the present research aimed to investigate the spatial and temporal distribution and trend analysis of PCI and SI for 41 rain gauge stations of Golestan province (38-year study period) in annual, seasonal and dry and wet time scales. The Mann-Kendall test was used to determine the trend of time changes in PCI and SI indices during the study period in all selected rain gauge stations in Golestan province. Mann-Kendall test is one of the non-parametric tests to determine the trend in hydroclimate time series. The advantages of this method include its suitability for use in time series without a specific statistical distribution, as well as the effectiveness of this method in data with extreme values in time series. In order to determine the spatial pattern of PCI and SI indices in different time scales (annual, seasonal, and dry and wet periods), the method of inverse distance weighting was employed in GIS environment. In this method, a weight has been assigned to each point that decreases with increasing distance from the known value point. On the other hand, the effectiveness of the known point in estimating the unknown point and calculating the mean also decreases. In this regard, the best results are obtained when the behavior of the mathematical function is similar to the behavior of the observed phenomenon. The study area in terms of extent, topographic diversity, type of land use has a high heterogeneity that affects the characteristics and temporal and spatial occurrence of dry and wet periods. The average annual rainfall varies from about 150 to 750 mm over the study area.
Results:
According to the results, the average of PCI for annual, spring, summer, autumn, winter, dry and wet periods in the research area were obtained 13.15, 11.96, 13.15, 10.72, 9.96, 14.72, and 1072, respectively. Also, Chat station with 0.79 (seasonal distribution with dry and wet seasons) and Shastkalateh station with 0.47 (mainly seasonal distribution with short dry season) had the maximum and minimum of SI in the Golestan province, respectively. In addition, 27 and 14 of studied stations had the increasing (Significant and no-significant) and decreasing (Significant and no-significant) trend for PCI and SI.
Conclusions:
Non-compliance of precipitation in Golestan province with a single temporal and spatial pattern is another achievement of the present study. The results of the current research can be used as a roadmap for water resources planning and policy making in the study area. It is noteworthy that the PCI and SI indices do not emphasize the cumulative values of precipitation and address the pattern of rainfall distribution, which can be a better criterion for assessing changes in precipitation patterns at different time scales. In this regard, determining the priority of areas for protection and management of water and soil resources, and spatial pattern of agricultural crops. The trend of changes in PCI and SI indicators and its relationship with important climatic components can be considered in assessing the changes in pattern of precipitation and climatic variables.
Temporal and spatial variability of rainfall is one of the determining factors for water resources management, agricultural production, drought risk, flood control and understanding the effect of climate change. The impact of spatiotemporal patterns of precipitation on flood/drought hazard and available water resources is an undeniable issue in water resources management. Precipitation concentration (PCI) and Seasonality (SI) indices are the important indicators to determine the distribution of precipitation in a region which can lead to identify and manage before occurring natural hazards including flood and drought and hydro-meteorological storms. Several methods available to study the spatial and temporal distribution of rainfall. Indicators of rainfall concentration and seasonality are among the methods of studying rainfall dispersion that depend on the distribution of rainfall patterns at different time scales. Accordingly, the study and understanding of temporal and spatial changes in rainfall can lead to sound management policies in the field of water and soil resources by planners and decision makers.
Methodology:
The precipitation concentration index is presented as a powerful indicator for determining the temporal distribution of precipitation to show the distribution of precipitation and rain erosion. The increase in the value of this indicator indicates a low dispersion and a higher concentration of rainfall, which is closely related to the intensity of rainfall. Seasonality index as one of the key factors in detecting seasonal variation in the variables of natural ecosystems, measures the time distribution of hydrological components at different times of the year and uses each hydrological variable to classify different hydrologic variable regimes. In this regard, the present research aimed to investigate the spatial and temporal distribution and trend analysis of PCI and SI for 41 rain gauge stations of Golestan province (38-year study period) in annual, seasonal and dry and wet time scales. The Mann-Kendall test was used to determine the trend of time changes in PCI and SI indices during the study period in all selected rain gauge stations in Golestan province. Mann-Kendall test is one of the non-parametric tests to determine the trend in hydroclimate time series. The advantages of this method include its suitability for use in time series without a specific statistical distribution, as well as the effectiveness of this method in data with extreme values in time series. In order to determine the spatial pattern of PCI and SI indices in different time scales (annual, seasonal, and dry and wet periods), the method of inverse distance weighting was employed in GIS environment. In this method, a weight has been assigned to each point that decreases with increasing distance from the known value point. On the other hand, the effectiveness of the known point in estimating the unknown point and calculating the mean also decreases. In this regard, the best results are obtained when the behavior of the mathematical function is similar to the behavior of the observed phenomenon. The study area in terms of extent, topographic diversity, type of land use has a high heterogeneity that affects the characteristics and temporal and spatial occurrence of dry and wet periods. The average annual rainfall varies from about 150 to 750 mm over the study area.
Results:
According to the results, the average of PCI for annual, spring, summer, autumn, winter, dry and wet periods in the research area were obtained 13.15, 11.96, 13.15, 10.72, 9.96, 14.72, and 1072, respectively. Also, Chat station with 0.79 (seasonal distribution with dry and wet seasons) and Shastkalateh station with 0.47 (mainly seasonal distribution with short dry season) had the maximum and minimum of SI in the Golestan province, respectively. In addition, 27 and 14 of studied stations had the increasing (Significant and no-significant) and decreasing (Significant and no-significant) trend for PCI and SI.
Conclusions:
Non-compliance of precipitation in Golestan province with a single temporal and spatial pattern is another achievement of the present study. The results of the current research can be used as a roadmap for water resources planning and policy making in the study area. It is noteworthy that the PCI and SI indices do not emphasize the cumulative values of precipitation and address the pattern of rainfall distribution, which can be a better criterion for assessing changes in precipitation patterns at different time scales. In this regard, determining the priority of areas for protection and management of water and soil resources, and spatial pattern of agricultural crops. The trend of changes in PCI and SI indicators and its relationship with important climatic components can be considered in assessing the changes in pattern of precipitation and climatic variables.
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