NIVAR
Year:2021 | Volume: | Issue: |Papers Count:12

In this study, air density and water vapor pressure for 184 synoptic meteorological stations in the year2004, 2005 and 2015 were calculated based on the theoretical of computing these two parameters. The results indicate monthly, seasonal and annual averages of WVP and air density were maximum values over shores of the Caspian-Sea and the Persian-Gulf, while the values were minimum over the highlands of the Zagros and Alborz mountains. These results are compatible with theoretical fundamentals indicating the exponential reduction of density with height. Moreover, it was observed that the monthly, seasonal and annual averages of air density over shores of the Caspian-Sea were higher than the corresponding values over the Persian-Gulf coasts. These conditions occur due to changes in air pressure as a result of cold high pressure system extension in the cool season where its related dominating ridge affects most regions of Iran causing air pressure increase over the Southern-Caspian-Sea(SCS) proportional to the corresponding value over Iran’ s-Southern-Coasts(ISC); hence the average density during winter season over the SCS where affected by high pressure systems is more than corresponding value over the ISC. During the warm seasons a result of heat low pressure system predomination over southern Iran, air pressure decrease over these regions, while the SCS area are subjected to relatively high pressure system. Therefore air density average over the ISC will be lower than the corresponding one over the SCS in the warm season.

The present study aims to investigate the changes in atmospheric conditions causing a hazardous climatic phenomenon known as hailstorm. These changes are classified and related to thermal and dynamic changes. In order to study the dynamics and synoptic of hail phenomenon in Mashhad, daily hail rainfall data were used during the 1980-2010 period. Then, one heavy rainfall specimen was identified and selected during the statistical period. The results of the hail analysis indicate that the highest frequency of hail occurrences were from 9 to 13 (UTC) in Mashhad city. Moreover, the highest frequency was from March to May months. In atmospheric predictions, there are different indicators for assessing the instability of atmosphere which lead to hail occurrence. The parameters and indicators under investigation in this study for 28 July 2009 include SHOW, TT, K, BI, RI, JEFF, Boyden, DCI, KO and SOI. According to the results of this research, when hail occurred in Mashhad, instability indices increased meaningfully, In addition, all instability indicators showed severe instable conditions (thunderstorm) during hailstone occurrence. Analyzing the weather charts indicated the changes of climatic parameters on the days of hailstorm and the days before them. Charts showed that the temperature and air pressure decreased while the relative humidity and dew point temperature increased on the days of hail in comparison with the earlier day.

Snowfall follows different patterns in various regions. Severity of precipitation in populated areas such as Tehran due to its effect on urban infrastructure and disruption of transportation is important in the urban management. In this study, heavy snowfall event in Tehran province investigated in January 2018. An important feature of this system was that the maximum snowfall occurred in the southern regions in comparison to the mountainous areas of the province. Synoptic-dynamic analysis and atmospheric simulation using the WRF model was done in this event. The establishment of a stationary front in the south of the Alborz Mountains for two days resulted in maximum system activity in the southern parts of the area. The formation of cold pressures over Russia and its expansion to the Iranian plateau in contrast to the low-pressure from the Mediterranean and Sudan to the central parts of Iran has caused a severe temperature gradient. Concurrent with warm front activity in the plains south of the province, maximum potential vorticity in the front of the trough, negative (positive) vorticity advection in the south of the Caspian Sea (south-west, some parts of the west and center of Iran) observed. The maximum tropopause folding (250 decameter) occurred in Tehran and the maximum moisture flux convergence observed in the southwest of the Alborz Mountains. The results of the 24-hour cumulative precipitation simulation showed that the model had underestimate for the rainfall in the study area and the position of the maximum of the precipitation forecasted incorrectly.

Evapotranspiration is one of the most important components of the hydrological cycle, which has an important role in water resource management. In the present study, the accuracy of the Hargreaves method for estimating evapotranspiration with the help of Adjusted coefficient K was investigated using artificial neural network model and M5 decision tree model. The weather data used in this study during the period of 2013-2004 from Farakhshahr station and Shahrekord airport in Chaharmahal and Bakhtiari province included minimum temperature, maximum temperature and relative humidity with cold and arid climate. Data were divided into 75% for training and validation and 25% for testing. The results show that neural network and decision tree model have good correlation modeling. Before using the Adjusted coefficient for Farkhshahr station, the root mean square of the Hargreaves strain was RMSE = 0. 90 according to the Penman-Monteith-FAO method, which after applying the Adjusted coefficient using the neural network to RMSE = 0. 69 and using The Adjusted coefficient for the decision tree was RMSE = 0 72. In general, the results showed that the Hargreaves model improved after using the Adjusted coefficient. Neutbay showed that the performance of the artificial neural network is more accurate, but the tree model offers linear, easier, and more intelligible relationships.

Droughts are the most important natural disasters which despite the low human casualties, have large casualties in economics and social consequences. Droughts exist in 5 forms Climate, Hydrology, Agriculture, Economic and Social; which the first form has priorities among other forms of occurrence and termination. this presented research is considered the climate drought and evaluated the valid indicators of drought on the various stations in Fars province. among all existing stations, the stations which have the more perfect and valid statistics of aerology rather than others were selected. . For this purpose, twenty-five years of precipitation (1990-2014) of nine (9) stations were used with the condition of high jump and complete accuracy and proper geographical distribution at the provincial level. Standardized Precipitation Index (SPI) was calculated at 12, 24 and 48 months at selected stations; interpolation maps were prepared. Finally, in order to identify the pattern of drought and display it on the map, respectively, Moran's Spatial Autocorrelation and Hot Spots analysis were used.

Analysis and prediction of complex systems and nonlinear changes in climate parameters, especially rainfall using chaos, fractal and fuzzy theory are considered appropriate solutions for recognizing equilibrium trend and dynamic analysis in parameters of climate change. Therefore, in the present study, the dynamic fluctuations in rainfall climatic parameters were analyzed according to the rainfall months in Shiraz synoptic stations in a period of 58 years (1956-2013). The examination was conducted within a fractal approach. Having implemented the Mathematical Reference Parameter related to the rainfalls per month through applying trigonometric fractal structure on the data obtained, the results were analyzed to compare with classical geometry fractal. Findings revealed that monthly precipitation trend in seven months along with the rainfall in January, February, March, April, May, November and December of fractal structure did not follow the structure of geometry fractal. In other words, the logic of the structure rainfall parameters in seven months with rainfall moves from equilibrium to in equilibrium. Findings, in addition, suggested that if we want to fully get access to the dynamics of fluctuations of these parameters in the months with rainfall, it would be appropriate to apply the logics of fuzzy and chaos for the future studies.

In the last few decades, the increase in the temperature of earth caused the imbalance in the planet’ s climate and also caused great changes in most parts of the planet’ s earth which named climate change. Drought is a phenomenon which can be affected a lot by the climate change. Paying attention to the changes of drought and also predicting it, can be effective in planning toward controlling and reducing its effects. In this article, by using The General Circulation of Barley HadCM3 and Lars_WG Downscaling model, under the publication scenarios B1, A1B and A2, the changes in the rainfall and daily temperature in the synoptic center of Rafsanjan in the period of 2018 to 2042 was reviewed. With considering the Determination coefficient (R2), there was a great correlation between the data acquired by the simulation, using the Lars_WG model and there was some visual data which could show the ability of the model in the future data stimulation. In the next step, RDI and EDI index were calculated for the basic period of 1993_2018 and future period of 2018_2042. Based on the results, A2 publication scenario, predicts worse conditions for future droughts of this region. Also final results showed that 2018_2019, 2020_2021, 2028_2029 and 2039_2040 faced hydrological droughts in all scenarios and the abundance of drought has increased comparing to the basic period.

Temperature is one of the main climatic parameters and its changes have a great impact on many natural processes, on the other hand, climatic indices which are based on temperature are considered as the main indicators of climate change in related researches. In this study, daily temperature data of 30-year temperature of the five synoptic stations in Guilan province were used for assessing climatic changes of the temperature parameters as well as the climatic indexes of the temperature extreme events in the region. In analyzing the results, significance levels of 0. 1, 1, 5 and 10% were considered. Trend analysis was examined using the Man-Kendall statistical and graphical trend tests, single-variable regression, and extreme climate indexes. The results show that temperature parameters maintain an increasing trend in the region with meaningful level of confidence. The arithmetic mean of the increase in temperature at all of the stations was 0. 5 and, as examples for the average temperature, the city of Rasht had an increasing decadal trend of 0. 46 degrees Celsius, which consequently causes 1. 38 degrees Celsius increase in the whole study period. Other extreme temperature indicators were also showing significant increasing trends in the region, as the proofs of climate change occurrence. In seasonal term, Temperature indices have the highest seasonal dependency in summer and the lowest dependence in autumn.

Accurate estimate of reference evapotranspiration is very important not only in planning andcomputing irrigation frequency, but also in studies related to water balance modeling in eacharea. The purpose of this study is to compare and evaluate nine different methods of estimatingthe reference evapotranspiration based on three general categories of temperature, radiation andmass transfer at nine study stations in Lorestan province from 1999 to 2019. The results of thesemethods were compared with the results of the FAO Penman Monteith. The performance ofdifferent methods was evaluated using R2, RMSE and CRM statistics. The results showed that inmost of the study stations, among the temperature-based methods, the Hargreaves Samani, among the radiation based methods, the Makkink and among the mass transfer methods, theWMO showed the best performance with the highest R2 and lowest RMSE. Also using ArcGISsoftware, spatial zoning was performed and maps of each category was presented.

Phenology is concerned with the study of natural period events in the life of plants and animals. Changes in phenological events are one of the important reaction of Living creatures to climate change. In this research, the phenological stages and thermal requirements of black Mashhad sweet cherry cultivar were investigated in the Barzok region of Kashan during the years 1394-95. For this purpose, the monitoring site of the meteorological parameters and phenological stages was created and phenological observations were carried out simultaneously with meteorological observations in accordance with the BBCH scale. The results showed that in the studied area, the cherry tree in its annual biological cycle needs up to 2040 degree-day in terms of effective temperature and 2775 thermal units in terms of active temperature by the end of the period. The highest thermal unit requirement for cherry is related to the shoot development phase, which is equal to 658. 8 degree-day in terms of effective temperature and 845. 5 thermal units in terms of active temperature, and the lowest thermal unit is 91 degree-day in terms of effective temperature and 127 thermal units in terms of active temperature related to the leaf development stage.

This research was conducted to determine the need for green water space in District 4 of Tehran Municipality and to calculate the required water resources for the total green space of the region. To determine the water need of green space plants, the California method (wucols) was used and to calculate the evaporation and suspension of reference and the amount of more load, the climatic data of the North Tehran Meteorological Station in a period of 30 years (1986-2016) was used. The net area of green space in the study area was 5683 hectares, which includes 215. 20 hectares of urban Park, 5311. 73 forestry and 156. 3 hectares of other green space divisions. Irrigation efficiency in this area was estimated at an average of 35 percent. The results showed that the amount of water required annually for irrigation of green space in the area was estimated at about 48. 6 million cubic meters with an efficiency of 35 percent and about 47. 3 million cubic meters with an efficiency of 45 percent. With the scenario of increasing irrigation efficiency by only 10%, 1. 3 million cubic meters per year, only water resources were saved. According to the amount of water that can be extracted from regional water resources, which is equal to 23. 62 million cubic meters per year, the annual water shortage of the region is calculated and its amount is equal estimated to 24. 98 million cubic meters with an efficiency of 35% this requires careful management and immediate operational solutions.

Evapotranspiration determination is a key factor for irrigation scheduling, water balance, irrigation system design and management and crop yields simulation. Reference Evapotranspiration is a complex and multivariate phenomenon that depends on climatic factors and most accurate way to estimate it is lysimeter but using Lysimeter requires a lot of time and money, hence the Evapotranspiration estimation is done by meteorological parameters and applying empirical models. These models have the coefficients that each coefficient is representative of regional conditions that equation is calibrated in that area. Nowadays Artificial Neural Networks (ANN) are being applied in several problems of water engineering where there is no clear relationship between effective parameters on the estimation of phenomenon. The purpose of this study was to evaluate Artificial Neural Networks and Experimental models in the estimation of evapotranspiration for Salman Farsi Agro-Industry. based on daily meteorological data and 3-years (March 2016 to March 2019) data from Lysimeter of the station, Evapotranspiration was calculated to above methods. The results of calculations showed that the Artificial Neural Network has better performance than all the empirical models, it has a RMSE, MAE and R2 respectively is equal to 1. 25, 0. 24 and 0. 97 mm/day Also among the empirical models, the Penman-FAO-Monteith model with RMSE, MAE and R2 equal to 2. 07, 3. 09 and 0. 91 mm/day is a priority. Also, 10 scenarios were defined to evaluate the accuracy of the neural network model by reducing the climatic parameters.