h. yazdanpanah

Agricultural products frost in spring imposes heavy financial losses to agriculture particularly in northwest of Iran’s orchards. Not only temperature is one of the most important climate parameters but also it is a very crucial element in the agricultural sector. Untimely temperature fluctuations and rise and fall which are usually unexpected will cause shock and heavy damages. Therefore taking into consideration the agricultural products frost and offering an approach would be of great importance for reducing relevant damages. In studies carried out by Omidvar and Dehghan Banadoki (2012) and Hesari et al. (2015) characteristics and different types of frosts have been considered in relation to the agricultural products. Different models were introduced to predict flowering date in different investigated regions. In more studies, in addition to determining the best model for predicting the date of occurance of flowering stage, probable date of last frost has been estimated as well. Investigating long term temperature changes is a method which applied by Martínez-Lüscher (2017) and Vitasse et al. (2018) to find out about established changes in flowering date and also changes in the last frost date. Nasr Esfahani and Yazdanpanah (2019) realized that 48-hour early warning for frost occurrence can be performed with adequate precision. Despite all studies in the field of products frost particularly during flowering date, it seems a rapid frost warning system must be established and provided to make early warning for each orchard. In this essay, since our goal is to make such early warning three days before frosting, so we have to investigate accuracy and validity of 72-hour minimum temperature simulation using WRF model. On the other hand, we must know phonological stage of each product in each orchard to inform the farmer about frost hazards based on critical temperature, therefore the second goal of this research is to detect phonological stages through Landsat 7 and Landsat 8 images.

In order to achieve the aim of current study, 72-hour minimum temperature simulation through the Weather Research and Forecasting (WRF) model was investigated and values of vegetation index were derived for a 30 meters pixel at an experimental orchard in Kahriz, West Azerbaijan Province, in 2016-2107. Computational grid for 2 meters temperature simulation using WRF model contains of three nested grid with horizontal resolution of 27, 9 and 3 kilometers. Horizontal resolution of terrain height and land use data is equal to 30 second (about 1 km). The initial and 3-h boundary conditions with 0.5º horizontal resolution from the Global Forecast System (GFS) were obtained from National Centers for Environmental Information (NCEI). Based on the previous research KFMYJ physical scheme configuration for WRF model were used in this research. Model's hindcasts at 03:00 UTC hour for each of 51 synoptic weather stations of northwest of Iran in internal computational grid were interpolated by MATLAB software with interp 3 function using linear method, then the obtained values were compared to minimum temperature observed in the stations by using MAE, MSE, RMSE and MSSS indicators. Phenological statistics, the time of beginning and end of growth stages were obtained from Iran Meteorological Organization. Besides, 77 Landsat 7 satellite images of ETM+ sensor, and 41 Landsat 8 images of OLI sensor were downloaded from United States Geological Survey website from March to September 2007-2016 with a spatial resolution of 30 meters. In this research, atmospheric and radiometric correction were performed with the FLAASH method on the metadata file in the ENVI software environment and then vegetation index was calculated using NDVI index.

Examining the evaluation indicators of the WRF model, results revealed a significant correlation and regression model between 2 meters temperature variable from WRF model output and minimum temperature variable observed in the entire stations for 72-hour simulation. As a result WRF model can be applied in 72-hour temperature simulation in the area of study. Another finding of this research indicated that in comparison to the field-recorded data, NDVI values gained from Landsat images properly indicates changes of phenology stages in the relevant apple orchard. In this study, the indicators used to evaluate the model error showed model hindcasts are more accurate for 24-hour and then 48-hour simulations than for 72-hour simulation, but the 72-hour simulation accuracy is not much different from 24-hour and 48-hour simulations. In northwestern Iran, which is a mountainous region, it is very difficult to simulate airflow in areas with complex topography, therefore the total correlation coefficient of all stations in all three simulations is in the range of 0.5, and the error rates of MAE and RMSE, respectively reaches about 2.8 and 3.8 Celsius. According to the second finding of this research, the NDVI indicator obtained from Landsat 7 and Landsat 8 satellite images can show the progress and changes in the phenological stages of apple trees.

 This study showed the efficiency of the WRF model for 72-hour simulation of the minimum temperature as well as the potential of Landsat 7 and Landsat 8 images in detecting apple phenological stages in the study area. Therefore, by using the WRF model for 72-hour minimum temperature simulation and recognizing the phenological stages from Landsat images, if the temperature in any orchard reaches a critical level in the next 72 hours due to the phenological stage, frost warning can be announced and then frost mitigation should be done by the farmer.

Poor air quality has a lot of damage on the environment and humans. Awareness of the air quality situation reduces health effects of air pollution. Isfahan, one of the most populous cities in Iran, is facing air pollution due to motor vehicle emissions and industries. Therefore, this study aimed to evaluate the variations in Air Quality Index(AQI) during During the year 2016 and its spatial zoning with Surfer. This study was performed with the aim of the comparative investigation of Air Quality Index in critical days in major city Isfahan. This study was a descriptive–analytic one. First, the required data of five criteria pollutants were taken from Department of Environment in Isfahan. The air quality index was measured based on the instructions and classified into Good, Average, Unhealthy for sensitive groups, Unhealthy, So unhealthy and Dangerous degrees according to the air quality standard tables. Based on the results, the air quality of Isfahan was found to be critical in 12 days of the year. According to the zoning maps, AQI values were found to be in critical conditions in the core towards east of Isfahan. Other results of this research indicate that urban management can be used to reduce the traffic of vehicles and reduce the activity of polluting industries in relation to reducing air pollution in Isfahan.

Tourism makes up a large part of the global economy and often as a key to economic growth in both developing and developed countries is used. One of the factors that should be considered in tourism is climate. Because in many countries of weather and climate are considered a capital value for tourism. But until now the source of tourism، tourism has a major role in the educational literature. Since much of modern tourism based on the use of natural features - is based on physical، development of tourist sites was not dependent on one source; it involves a wide range of resources، especially natural resources. The climate is considered as a source of basic or supplemental. So that from that day climate comfort that tourists visit places to go It is very important for planners and planners to climatic data before، after and during the visit to places of need. The study characteristics and climatic differences and variations in time and space that govern their relationship with human activities، Ways in order to analyze the environmental conditions within the planning of tourism offers. Tourism is important to determine the index for more comfort. Comfort tourism climate index (TCI) is a combination of methods that were presented in 1985 by Mieczkowski. The TCI is a combination of factors affecting the comfort of tourists. The index، climatic comfort of the tourists the best travel time is determined. Also، the calculation for different regions or even the wider world in the remarkable contribution to the tourist destination of choice. East Azerbaijan province with many features and attractions for the tourism industry and attract tourism، But such is not worthy of the blessings that will benefit the industry، Using the climatic comfort of tourists can be accurately planned and managed to attract tourists to this lucrative industry.

In this study، the studies of climatic conditions in East Azerbaijan province، to develop tourism in this province have dealt with the use of TCI. Due to the nature component of the approach to study the situation «a descriptive-analytic”. The purpose of this research، applied research and in literature، documents and library data collection method is used. In addition to analyzing data from Excel and GIS software is used. In this case، the first climate data from meteorological synoptic station is not needed in East Azerbaijan province، for a period of 20 years (2010-1990) were collected And then using TCI in different stages of 1)-calculation of daily comfort index (CID)، variables that are used in this index، The maximum daily temperature and average minimum daily relative humidity is. 2) Boarding comfort index (CIA)، variables that are used in this index includes the average daily temperature and average daily relative humidity. 3) Roth calculating rainfall (R)، 4) - calculation Rtbh number of sunshine hours (S)، 5)-Rtbh calculation of air flow (the mean wind speed) (W))، the province''s tourism climate comfort for each of the stations in different seasons were calculated. The TCI index stations for each month of the year، the results were entered into GIS software environment. According to the results obtained point to be، Comfort conditions for climate zones across the province-level data point to be generalized. In order to generalize the results point to the entire surface of the image distance weighting interpolation method (IDW) have used and to the point of information stations will be converted to surface and TCI and maps for the entire province is obtained.

In East Azerbaijan province، as one of the main tourist poles of various natural attractions، historical، cultural and prevailing climatic conditions in different regions of the province، in different seasons، the weather can be a factor be made to attract tourists. So that the quantitative evaluation Ptansl and detailed information about them can be made better use of these capabilities. Tourism in the comfort of the climate in different regions of East Azerbaijan province، the calculation of climatic parameters in each of the TCI and Rtbh each of the stations of various climate parameters، The ratings obtained for each of the parameters for each of the stations Metals replaced in the index formula and for each station in different seasons of the numerical value obtained among the rank and 0-100 are located. Indicate that climatic conditions so intolerable situation is ideal. The results of the index، the number of zones in the province climatourism GIS environment was used And the proportion and number of classes in different seasons for each station have the comfort of the climate is classified as tourists.

In recent years the influence of climatic factors on tourists'' satisfaction، increased sensitivity and its importance in selecting a suitable place for tourists are staying. In this study، climate comfort for the tourists in the month of separation of East Azerbaijan province (TCI) used to have. The results were used to determine the climatic comfort of TCI in East Azerbaijan province، indicating that this index is a large variation in different regions of the province. So that the annual index of TCI in the months of May، June، July، August and September (May، June، July، August and September) with the best conditions of climatic comfort of the tourists are and January، February، November and March (December، January، February and March) due to the dominance of the high-pressure system، with the worst of these conditions are considered. Examine and compare the results of the TCI index for different regions of the province، and showed consistent results fit with the realities of the region''s climate.