Climatology
Mojtaba Nassaji Zavareh; Hossein Hokmabadi; Alireza Asadolahi
Abstract
Late spring frost cause a lot of damage to the agricultural sector every year. Prediction of this phenomenon is needed to active protection of plants. In this research, using FAO experimental method, daily and hourly data of two meteorological stations were used to determine the coefficients of the experimental ...
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Late spring frost cause a lot of damage to the agricultural sector every year. Prediction of this phenomenon is needed to active protection of plants. In this research, using FAO experimental method, daily and hourly data of two meteorological stations were used to determine the coefficients of the experimental model for prediction radiation frost in Qazvin Plain. also, in order to investigate the climatic condition of spring frost, the daily minimum temperature data of Qazvin and Buinzahra stations were used. The analysis of sixty years data in Qazvin stations showed that the intensity of frost has decreased during these years, but frequency of frost in Ordibehesht month has increased. Air, dew-point at two hours after sunset and minimum temperature relate 25 events of radiation frost at Simorgh station were used for regional coefficient calculation based on two models. These models were evaluated using 14 events of radiation frost at Tat stations. The mean absolute error(MAE) for testing and evaluating of Model1 was 0.71℃ and 1.21℃ and for Model2 was 0.67℃ and 1.09℃. The findings also showed that both models have acceptable accuracy in estimating the minimum temperature of the next day. It is proposed that these two models can be used for prediction of radiation frost in other regions.
All other Geographic fields of studies , Interdisciplinary
Mostafa Karimi; Sousan Heidari; Morteza sharif
Abstract
IntroductionIncrease temperatures and decrease rainfall can lead to the drying up of wetlands, lakes and rivers, the formation of aerosol centers, which directly and indirectly change the structure of society and the ecological conditions of lakes around the world; As a result, it leads to changes in ...
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IntroductionIncrease temperatures and decrease rainfall can lead to the drying up of wetlands, lakes and rivers, the formation of aerosol centers, which directly and indirectly change the structure of society and the ecological conditions of lakes around the world; As a result, it leads to changes in the distribution of animal and plant species, ecological diversity, changes in the plant phonological cycle, factors, growth and organisms, and ecological metabolism. These changes also severely affect vegetation in arid and semi-arid climates. Finally, changes in surface conditions caused by human activities may also affect various hydrological processes. Thus, the twenty-first century is facing many environmental problems, one of the most important of which is the variability of environmental and climatic parameters. Lake Urmia is one of the most important water areas in Iran and one of the largest salt lakes on earth. The lake plays an important role in the climatic, environmental and economic situation and a national and international natural heritage in the northwest of Iran.variability of environmental and climatic parameters is one of the most important challenges for human specific in arid and semiarid environment such as Iran. The purpose of this study is to investigate the changes in environmental and climatic parameters in the catchment of Lake Urmia in the last two decades. The purpose of the above was to answer the question of how the changes in environmental and climatic parameters in the basin and the relationship between these changes in the current conditions of the basin Lake Urmia.Data and methodsResearch data includes six categories: 1) TOPEX and Jason 1 to 3 satellites data to study of changes in altitude level of Lake Urmia, 2) Landsat 7 satellite images of 2000 and Landsat 8 of 2019 for extract lake water area changes and 3) Precipitation data from GPM[1] satellite product (IMERG[2]) 4) Vegetation index products of Modis sensor (Mod13A3 v006) to identify vegetation changes, 5) LST Night and daytime of Modis sensor (MOD11A2 v006) and finally 6) gridded reanalysis data (ERA5) to detect of trend air temperature, were used.First, the changes in the water level of the lake were extracted using the data of TOPEX and Jason 1 to 3 satellites, and in the next step, the trend of changes in its was calculated. Landsat 7 images of 2000 and Landsat 8 of 2019 using the Normalized Differential Water Index (MNDWI) were used to achieve changes in the lake's water area. Then LST (day and night) of MOD11A2 v006 products were converted into monthly data using MATLAB software. Finally, the trend changes in precipitation data, 2 m air temperature, LST (day and night) and vegetation (NDVI) were investigated using Mann-Kendall test (Mann, 1945; Kendall, 1975).ResultsThe highest changes in water level in the last two decades are from 2000 to 2010. The decrease in level is evident from the year 2000, from that year to 2010, the water level of the lake decreased by 4 meters and the highest slope of the decrease in it observed in the same period. The change in the area obtained from the MNDWI index is 2740 km2, which has caused the lake to decrease from 5143km2 to 2400km2 in 2019. The decrease of the lake level in its southern and eastern part has been more than the western and northern part. The trend of monthly precipitation changes shows two different temporal and spatial patterns. It is important to note that there is a monthly decreasing trend every three months in January, August and December in the central and southern parts of the basin. In contrast, in May and July, a marked increasing trend is observed in the eastern and southern half of the basin. Spatial displacement of incremental changes in air temperature indicates a clockwise movement from north to east and then south and west from May to August. The trend of day of the LST changes indicates a spatial contrast between the Lake and around it. This behavioral contradiction is more pronounced with the increase of the lake surface temperature and the decreasing trend in the southern and western regions corresponding to the agricultural areas in August, September and October. Changes in LST at the basin level from November to February, in which scattered and small incremental zones are observed, can also be due to reduced vegetation in the cold period of the year. In contrast to the daytime LST, at night what is most noticeable is large zones of temperature rise, especially from June to September throughout the basin. NDVI in the period 2019-2000 has had an increasing trend in all months, but with varying intensity and extent. Three temporal patterns are understandable in the process of basin vegetation change. Increased from January to May, then start decreasing trend from June to August and again increasing trend that continued until December. The lowest increasing trend is observed during the summer months from June to August.DiscussionLake Urmia has experienced a continuous decrease in water level since 2000, so that during the last twenty years, the water level has decreased by more than seven meters. The results of the present study also showed that there was a significant increasing trend in the NDVI index at the basin, especially with the southern of the basin. However, at the basin level, the trend of rainfall changes in this period (2000-2000) is not generally significant and also due to the occurrence of numerous droughts in the basin, which has also had an increasing trend and the expansion of irrigated lands, Demand for groundwater has increased. Therefore, this issue indicates various reasons other than changes in climatic parameters, especially precipitation in reducing the water level of Lake Urmia. In addition to the above, daytime and nighttime LST have increased during the warm period of the year as well as the air temperature on the lake. This increase increment evaporation, especially during periods when recharge is reduced due to seasonal dry. Although precipitation has increased at the end of spring, but with increasing temperature, precipitation increases with increasing evapotranspiration and water requirement of plants is neutralized. Therefore, the simultaneous change of environmental and atmospheric parameters can be considered as aggravating the conditions of hazardous events in this basin.ConclusionBased on the evaluation done in this study, it can be concluded that the basin of Lake Urmia is vulnerable. Therefore, the three main and significant effects of environmental variability in these areas are increasing ground temperature, vegetation and reducing water resources. The result of these conditions on the one hand and the increase of water needs of plants on the other hand will increase the stress on water resources, especially groundwater. Decreasing the lake surface and increasing consumption and reducing water resources can lead to the spread of bare surfaces and the occurrence of dust.
Ali Reza Ildoromi; Hamid Zareabyaneh; Maryam Bayatvarkeshy
Volume 17, Issue 43 , October 2013, , Pages 21-40
Abstract
Rainfall due to its noise and random nature has structural changes at different times. Because of large uncertainty, fluctuations in the amount of rainfall forecast is created the prediction of which has been difficult. In this article, precipitation predictability was carried out rescaled by range analysis ...
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Rainfall due to its noise and random nature has structural changes at different times. Because of large uncertainty, fluctuations in the amount of rainfall forecast is created the prediction of which has been difficult. In this article, precipitation predictability was carried out rescaled by range analysis (R/S) technique in Shiraz, Mashhad and Kerman regions. SnapshotHurst (H) showed that rainfall parameter has the ability of predictability, because H was higher than 0.5 and much closer to the value 1. Minimum Hurst value was 0.8 in Mashhad and maximum Hurst value was 0.92 in Shiraz. In order to predict rainfall we used artificial neural network. Type of input parameters based on Pearson correlation test between data from non-rainfall, were a combination of temperature and humidity data. Number of input parameters, the number of middle layers, and other information related to artificial neural network randomly were selected. As a whole, rainfall estimation was calculated through Peresptron multi-layer neural network for comparing the performance of neural network. Results showed that the use of 3 and 4 meteorological parameters has the best rank estimator. Proposed layouts for the Shiraz station is 1-21-21-3, for Kerman 1-25-25-3 and for Mashhad 1-19-19-4 in which 1-25-25-3 of have correlation coefficients more than 91 percent. Validation rainfall models showed that network designed for rainfall parameters has best performance rainfall in Mashhad, Shiraz and Kerman stations with 4, 11 and 14 percent error respectively. As a whole, results showed that neural network method with considering the temperaturel and humidity data for describing the process and their combination in predicting good results were offered.
