نشریه پژوهشهای تغییرات آب و هوایی
پیاپی 15 (پاییز 1402)

Projection of summer monsoon rains in Southeast Iran based on Ensemble model the precipitation factor has a variable and random nature and has a different behavior in terms of space and time. Therefore, the prediction of precipitation has more uncertainty compared to other meteorological variables. In the current study, the data output of Cordex database and CMIP5 models were used using the neural network method to reduce the uncertainty and estimate precipitation properly. The results showed that due to the high correlation of temperature, humidity and air pressure with precipitation, the use of these variables is beneficial in reducing the uncertainty of precipitation forecast. In addition, the non-linear method of artificial neural networks can be used to bias the rainfall data of Cordex database and CMIP5 to forecast the rainfall in the southeast of the country. Another result of this research is the increasing trend of rainfall in the southeast of Iran, especially in the coastal areas. This can be considered as a result of the increase in the level under the influence of rains affected or simultaneous with the southwest monsoon of India. The increasing trend of precipitation in the southern coasts is also related to the increase in the storage capacity of moisture content. The interannual variability of India's monsoon rainfall also shows a steady positive trend under continued global warming. Since both the increase in the duration of monsoon rains and the increase in interannual variability in the future are seen in most models, we can be confident in these predicted trends. Indian summer monsoon rainfall is also predicted to be higher under global warming in the 2050s compared to the baseline.

Stability in garden products depends on economic, social and environmental factors. The recognition of the stability level of these factors can be effective in developing sustainable horticulture development strategies and its effect on sustainable livelihoods. Therefore, the purpose of this study is to analyze the stability of garden products and its role in the livelihood of household grid operator villages of Bandar-e-Gaz Central District of County. The information required is collected through a questionnaire whose validity is obtained based on the experts of the relevant experts and its reliability by calculating the Cronbach's alpha coefficient. The statistical population of this study consists of the garden operators of three, western Gaz and Val-afra, calculated by the Cochran formula of sample size 250. Random sampling method was used to select sample sample. In order to measure the stability of garden products from 45 indexes for economic, social and environmental dimensions and to measure sustainable livelihoods, the grid operator uses 53 indices to separate the dimensions of financial, social, human, environmental and environmental assets. According to the spearman test, the economic, social and environmental sustainability of the garden products on sustainable livelihoods is effective. The results of the Kruskal test show that the stable livelihoods of operators in the three villages have a significant difference to the confidence level of 99 percent. The results of the analysis of one - way independent variance analysis on the level of culture of three villages also stated that there is significant difference in the three factors in three villages by 99 percent level.

The most significant factor that results in uncertainty about future water resource scarcity is climate change. Due to global warming, concerns about increasing or decreasing precipitation exist, which complicates water resource planning and management. Therefore, studying the trend of precipitation is of great importance. The linear trend reported in climate assessments reflects changes in average annual precipitation. However, the average trend cannot capture variations in other distribution aspects, including extreme precipitation events (high or low precipitation amounts). In this study, the Quantile Regression (QR) method was used to determine the trend of heavy precipitation (rainfall exceeding the 98th percentile) for seasonal and annual periods at 44 synoptic stations in Iran for two recent climatological standard normal periods: 1981-2010 and 1991-2020. For this purpose, after data quality control and homogenization, the 98th percentiles of seasonal and annual precipitation were calculated for both periods and compared. Then, the trends of these percentiles were estimated using the Quantile Regression method and tested. The results showed that compared to the two climatological standard normal periods, spring heavy precipitation had changed significantly in the southern of Alborz Mountain, summer heavy precipitation on the shores of the Caspian Sea, autumn heavy precipitation in the northwest and northeast regions of Iran, and winter heavy rainfall in the Zagros range. Similarly, spring-heavy precipitation in the southern of Alborz Mountain is decreasing, while summer-heavy precipitation on the shores of the Caspian Sea and autumn-heavy precipitation in the northwest and northeast regions of Iran are increasing.

Dew point temperature is one of the most important atmospheric variables and its long-term changes can lead to changes in the climatic characteristics of an area. In order to spatially analyze the trend of dew point temperature changes in Iran, the annual and monthly average data of this variable in 109 synoptic meteorological stations during the statistical period of 1990-2022 have been used. In order to investigate the effect of changes in other atmospheric variables on dew point temperature, the data of average annual relative humidity, average minimum and average maximum temperature, and average total annual precipitation have been used. In order to investigate the change process using the Mann-Kendall method and to identify the patterns governing the spatial changes of the dew point temperature in Iran, the spatial autocorrelation models of Global Moran and Getis-Ord General G statistic have been used. The results of investigating the trend of dew point temperature changes show that this variable has an increasing trend in the areas around the north and South Seas and the northwest region, and a decreasing trend in the central, eastern and northeastern regions of Iran. Spatial analysis of areas with high dew point temperature shows a severe cluster pattern, which means that areas with high dew point temperature are mainly spread in parts of the coasts of the Caspian Sea and many parts of the coasts of the Persian Gulf and Oman Sea, parts of northern Azerbaijan and Ardabil. This shows that the temperature of the dew point increases as it approaches moisture sources. Also, the spatial analysis of the areas with low dew point temperature also shows that these types of temperatures are generally concentrated in a large part of the central areas of the south of Khorasan province and the east and southeast of Iran.

Soil moisture is one of the most important factors that are affected by drought. Soil Moisture Deficit Index (SMDI) is one of the most important indicators that monitors drought based on soil moisture. But due to lack of direct measurement of soil moisture in weather stations and lack of information, it has been used to a limited extent. In this research, the meteorological data of Zarghan synoptic station was used during the statistical period of 30 years (1992 to 2022) and with the help of ET0 and AquaCrop models, the amount of soil moisture was estimated at the depths of 5, 15, 45 and 95 cm. Then the percentage of soil moisture deficiency and SMDI index were calculated to monitor drought. Also, the results of this index were compared and analyzed with two common indices SPI and RDI which are based on precipitation and evapotranspiration data.The highest value of this index occurred in 1996 after a period of heavy rain in 1994 and 1995, and the lowest value occurred in 2011 after the dry years of 2008 to 2010. Considering that the highest and lowest amount of rainfall is in 2004 and 2021, SPI and RDI drought indices have shown the most severe drought in the mentioned years. Severe droughts occurred in 2010, 2011, 2016 and 2021, and all three indicators had similar results. In examining the relationship between meteorological parameters and indices, the results showed that the SMDI index has the lowest coefficient of explanation with evapotranspiration (0.25), RDI index (0.4) and then SPI and precipitation index (0.45). Also, the results showed that there is a strong correlation between SPI and RDI. Regarding moderate and very severe droughts, where the amount of precipitation is greatly reduced and evapotranspiration increases, the RDI index is more accurate and shows the severity of the drought with higher accuracy, and its sensitivity to weather conditions is higher than the SPI index. Therefore, in arid and semi-arid regions where the severity of drought reaches the severe and very severe threshold, it is recommended to use indicators that take into account other factors in addition to precipitation.

The growing need to know the temporal and spatial structure of meteorological parameters in the tropopause transition zone caused the temporal changes of the temperature and height of this layer to be investigated during the last two decades (2002-2022) using the reanalyzed data of the Atmospheric Infrared Sounder (Aqua, MODIS, ARIS). Also, the relationship between the changes in tropopause characteristics and climate change of temperature and precipitation in Iran was studied using daily precipitation data of GPCP (2000-2022) and minimum, maximum and average daily temperature data of MERRA-2 model (1980-2022). In this regard, Pearson's correlation tests and regression analysis were used to investigate the relationship between research variables, and Kendall's seasonal time series and Mann-Kendall's ordinal tests were used to analyze regional mean daily and monthly trends. The results showed that the variables of tropopause temperature and height (TTH) have a negative correlation of 0.93 with each other (R2=0.85). On the other hand, the variable of the regional mean of daily tropopause height (TH) has a significant positive correlation with the variables of the daily earth's surface temperature (with correlation coefficients exceeding 0.8). Also, R2 values higher than 0.6 indicate a completely significant correlation of total monthly precipitation data with changes in monthly mean of TTH, which makes it possible to predict the rainfall anomaly in Iran by monitoring the tropopause characteristics. Time series analysis of the research variables using Kendall's seasonal and ordinal tests showed that the TH variable and the surface temperature variables are respectively with statistical values (τ). 0.18, 0.22, 0.27 and 0.32 have shown significant increasing trends in the last few decades. Finally, by introducing the TH as an indicator of climate change in Iran, the necessity of conducting more research in this field is emphasized.