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Volume 1, Issue 3 (10-2014)
Global changes in extremes of the climatic variables that have been observed in recent decades can only be accounted anthropogenic, as well as natural changes. Factors are considered, and under enhanced greenhouse gas forcing the frequency of some of these extreme events is likely to change (IPCC, 2007 Alexander et al., 2007). Folland et al. (2001) showed that in some regions both temperature and precipitation extremes have already shown amplified responses to changes in mean values. Extreme climatic events, such as heat waves, floods and droughts, can have strong impact on society and ecosystems and are thus important to study (Moberg and Jones, 2005). Climate change is characterized by variations of climatic variables both in mean and extremes values, as well as in the shape of their statistical distribution (Toreti and Desiato, 2008) and knowledge of climate extremes is important for everyday life and plays a critical role in the development and in the management of emergency situations. Studying climate change using climate extremes is rather complex, and can be tackled using a set of suitable indices describing the extremes of the climatic variables. The Expert Team on climate change detection, monitoring and indices, sponsored by WMO (World Meteorological Organization) Commission for Climatology (CCL) and the Climate Variability and Predictability project (CLIVAR), an international research program started in 1995 in the framework of the World Climate Research Programme, has developed a set of indices (Peterson et al., 2001) that represents a common guideline for regional analysis of climate. It is widely conceived that with the increase of temperature, the water cycling process will be accelerated, which will possibly result in the increase of precipitation amount and intensity. Wang et al. (2008), show that many outputs from Global Climate Models (GCMs) indicate the possibility of substantial increases in the frequency and magnitude of extreme daily precipitation. eneral circulation models (GCMs) are three-dimensional mathematical models based on principles of fluid dynamics, thermodynamics and radiative heat transfer. These are easily capable of simulating or forecasting present-future values of various climatic parameters. Output of GCMs can be used to analyze Extreme climate. For this study high quality time series data of key climate variables (daily rainfall totals and Maximum and minimum temperature) of 27 Synoptic stations were used across Iran from a network of meteorological stations in the country. In order to get a downscaled time series using a weather generator (LARS-WG), the daily precipitation output of HadCM3 GCM, SRES A2 and A1B scenario for 2011-2040 are estimated. The Nine selected precipitation indices of ETCCDMI[1] core climate indices are used to assess changes in precipitation extremes and monitor their trends in Iran in the standard-normal period 1961–1990 and future (2011-2030). Due to the purpose of this study, at first changes in extreme precipitation indices in the standard-normal period is evaluated and its results show annual maximum 1-day precipitation increased in many regions in the East of Iran. Simple measure of daily rainfall intensity (SDII), annual maximum consecutive 5-day precipitation, annual count of days with daily precipitation greater than 10mm (R10mm), annual count of days when rainfall is equal to or greater than 20 mm (R20mm) have increased in the central areas, regions in the north , north east and southern parts of Iran. Similar results are obtained for the R25mm index. The consecutive dry days (CDD) index has generally increased across the west areas, southwest, north, northwest and southeast of Iran and indices of consecutive wet days (CWD) decreased in these areas. Trends of extreme precipitation indices simulated by HadCM3 SRES A2 showing increases RX1Day in North West expect west Azerbaijan Province, central, southwest, north east and coasts of Caspian Sea. Similar results are obtained for the R5mm index expects northeast. There are mixed changes in R10mm across Iran, increasing in west, southwest, coasts of Caspian Sea, Hormozgan and Ardebil provinces, East Azerbaijan, Zanjan and Qazvin provinces. Similar results are obtained for the R20, 25 mm index in northeast, south of Caspian Sea, and some parts in western and central areas. Same as HadCM3 SRES A2 pattern there are mixed changes in R10mm across the region. Positive trends are seen in part of the Isfahan, Markazi, Kuhkilue , Lorestan, Ilam, Chaharmahaland Khozestan provinces and some part of Hormozgan and Kerman and some areas in north west. Similar results are obtained for the R20mm and R25mm index and in west of Yazd to north of Khozestan provinces have increased. Consecutive wet days (CWD) have increased over most of the west of Iran, Khorasn Razavi and Southern Khorasn provinces, In contrast consecutive dry days (CDD) index has generally increased in many parts of the region.
[1]. Expert Team on Climate Change Detection and Monitoring Indices
Volume 4, Issue 2 (7-2017)
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 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.
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