NIVAR
Year:2022 | Volume:45 | Issue:114-115|Papers Count:12

Modeling of extreme values, i. e., the highest and lowest values of a data from natural phenomena, by statistical formulas, makes it possible to predict the behavior of them in the future. One of the methods of analyzing extreme values, is the exceedances method, in which, all the exceedances’ values over a high threshold are modeled using the generalized Pareto distribution. In this paper, a suitable statistical model has been fitted to each of the data of high values and low values of temperature in Tabriz meteorological station in the period of 1951 to 2020 by using this method. Then the parameters of the models are estimated and their goodness of fit are evaluated. Finally, some necessary predictions, such as the average return periods of the minimum and maximum temperature values of Tabriz for different periods, have been made. One of the results of this study is that with 95% confidence, a high limit for the maximum temperature of Tabriz is equal to 42. 56 ° C with an average return period of 20 years and a low limit for the minimum temperature of Tabriz is equal to-22. 37 ° C with an average return period of 20 years.

The aim of this study is to evaluate the yield of rainfed wheat in Iran affected by drought. Climatic information and yield of reinfed wheat in Iran from 1983 to 2014 were prepared from ءMAJ and IRIMO. Accordingly, the climate type of each region was determined based on the extended De-Martone method. SPEI calculated in 3, 6, 9, 12 and 18 months scales in each climate. Using the Umbrothermic method, the wet period of the year was selected as the effective period of drought on the growth of rainfed wheat. During these periods, the average SPEI was taken each year to represent the annual SPEI changes in the scales studied. Rainfed wheat yield was standardized annually. The relationship between SPEI and rainfed wheat was estimated by the Spear-man correlation method. The temporal and spatial trends of drought and yield in Iran were also examined. Based on the results, the highest correlation was observed in the southeast, west and northwest on a scale of 3 to 6 months and the lowest correlation was observed in the east. The trend of drought in Iran is increasing and the trend of wheat yeild is not significant and sometimes decreasing trend. The results of this study will be useful in managing and planning the development of rainfed wheat cultivation based on future climatic conditions.

In this study, the effect of horizontal and vertical resolution on the precipitation forecast has been investigated. For this purpose, in this study, heavy rainfall occurred in the western region of Iran using GFS data as boundary and initial conditions in the WRF mesoscale numerical forecasting model with two horizontal resolutions and four combinations of position and number of vertical levels has been simulated. The results of this study show that on days when the intensity of precipitation is significant, the results of precipitation simulation improve with increasing horizontal resolution. Also, increasing the resolution of the model in the vertical direction does not necessarily lead to an increase in precipitation forecasting skills and sometimes over-predicted rainfall. Skills of forecasting improved for all rainfall thresholds when the resolution above the melting level was increased. And this is because of better prediction microphysical processes. Also, increasing the resolution of the model in the lower layer of troposphere only in heavy rainfall leads to an increase in forecasting skills. Therefore, the effect of increasing the resolution of the model on forecasting skills is strongly related to the height of added levels and rainfall intensity. In addition, the factor separation methodology shows that with increasing the resolution of the model throughout the troposphere layer, the prediction skill generally decreases in the mean state, and this subject is due to the negative interaction between lower-tropospheric processes and microphysical processes above melting level.

In disaster risk management, recognizing, predicting and anticipating its occurrence plays an important role in the losses reduction. Therefore, identifying extreme events and examining changes in their occurrence is very important. For this purpose, according to the guidelines of the World Meteorological Organization (WMO) the extreme events that occurred in East Azerbaijan during in the last 10 and 5 years (1388-1397) were analyzed. The studied indicators are: heat waves, cold waves, extreme precipitation, dust and hail. During the study period, different regions of the province have been affected by climate extreme events. Heat indices show that during the last 5 years, the heat wave, its length and intensity has increased compared to the 10-year period, which indicates the impact of climate change in recent years. The cold wave and its intensity have increased in the last 5 years, but the cold wave length has decreased relatively. Heavy rainfall has increased in the northern half of the province over the past 5 years. The number of hail events does not show a noticeable trend during 10 years and its distribution is almost the same for 10 and 5 years`. The cities of Maragheh, Tabriz, Ahar, Jolfa, Varzeqan, Sarab, AjabShir and Shabestar have experienced the most extreme events during the last 10 years, respectively.

In this study to predict dust storms, hourly dust data and monthly data maximum, minimum, average temperature, maximum wind speed, and total precipitation in three synoptic stations of Abadan, Ahvaz, and Bostan with statistics period for 25 years (1990-2014) were collected. To investigate the impact of dust storms from climatic fluctuations, in addition to the mentioned variables, the Standardized Precipitation Evapotranspiration Index (SPEI) was also calculated in the seasonal time window. Predicting the number of days with seasonal dust storms using four artificial intelligence methods including MLP, ANFIS, RBF, and GRNN was performed. These were evaluated in the form of three experiments including the effect of adding auxiliary features on the prediction, the effect of the number of previous seasons on the prediction, and the best technique among the models used. The results showed that in all stations, the use of all features has improved dust prediction and the value of the Mean Absolute Error (MAE) for Abadan, Ahvaz, and Bostan stations is equal to 1. 15, 1. 66, and 0. 66, respectively were obtained, all of which were related to the autumn season. In conclusion, it can be said that in Bostan station, if all the features and data of the last four seasons are used, the ANFIS model as input causes the prediction error to be reduced and a better result to be obtained. In the Abadan station, using the MLP model gives such a result.

In this study, numerical simulation of two advection and radiation fog events at Ardabil Airport in January 2015 was performed using the Weather Research and Forecasting (WRF) model and SW99 visibility algorithm. Six Planetary Boundary Layer (PBL) schemes including YSU, MYJ, ACM2, MYNN2. 5, MYNN3 and QNSE were used to evaluate the sensitivity of fog simulation to the PBL schemes. The results show that the simulation of these two fog events is sensitive to PBL. Also, due to the importance of accurate prediction of 2-m relative humidity, temperature and dew point temperature and 10-m wind speed in fog forecasting, the sensitivity of simulation of these variables to PBL scheme was investigated. The results showed that the simulation of advection fog event was successfully performed using most of PBL schemes mentioned above. YSU, ACM2 and MYNN2. 5 schemes performed better in simulation of advection fog. The QNSE scheme was not successful in simulating the advection fog event. In radiant fog simulation, most PBL schemas were not able to simulate the moisture required to form fog at the time of fog occurrence. Most PBL schemes were not able to simulate the moisture required for formation of fog at the time of the radiation fog event. A few hours before the onset of the radiation fog event, the WRF model with most schemes simulated the visibility reduction due to the fog. Generally, QNSE and MYJ schemes performed worse than other schemes in simulating temperature, dew point temperature, relative humidity and wind speed.

Due to prolonged droughts in the recent decades. The importance of predicting the flow rate of surface water in rivers for water resources management increases. In this regard the flow rates in the natural water ways. Which is the most important supplement source for water in dam storages. are considered as the most vital factors in predicting surface water. To evaluate the accuracy of different models have been used runoff data of thirteen stations without trend in dez catchment. In this study, artificial neural network, support vector regression and adaptive neuro-fuzzy inference system methods with clustering and grid partitioning approaches were used to the simulation of run-off in dez catchment. Simulation results of runoff using the mentioned methods were compared using the statistical indicators of R, RMSE and NSE. Comparison between ANN, ANFIS and SVR showed that although the difference in the accuracy of the models is very small. It can be said that all three models have acceptable answers. The results also show that the ANN and ANFIS with clustering approach models with NSE=0. 66 and 0. 66 respectively during the test period have the ability to simulate two models SVR and ANFIS with grid partitioning approach.

Mangrove forests have faced serious challenges in the last two decades due to climate change. Therefore, continuous monitoring of these ecosystems at local and regional levels is necessary to prioritize management measures and provide the desired infrastructure to reduce and/or prevent these tensions. In this study, mangrove forest changes between 2020-2013 were evaluated using satellite images (Landsat 8), HYCOM Data, and meteorological data from meteorological ground stations in the Persian Gulf and the Oman Sea. The results of the study indicate the trend of increasing the Sea surface temperature of the Oman Sea by 0. 12 and the Persian Gulf by 0. 2 ° C. The Sea surface salinity of the Oman Sea is increasing at a faster rate compared to the Persian Gulf. The average salinity is higher in the Persian Gulf. On the other hand, according to the NDVI index, the rate of change of chlorophyll content of plants in mangrove ecosystems on the coasts of the Persian Gulf was higher than the Oman Sea. The correlation between NDVI index and air temperature, precipitation and Sea surface temperature showed that these factors had the highest influence on the process of mangrove forest changes in the Persian Gulf and the Oman Sea. However, this impact was different in different regions. Mangrove in Qeshm region had the greatest effect (with 0. 8 R2 for air temperature and 0. 3 R2 Sea surface temperature). Mangrove ecosystems of Chabahar coast have shown the lowest correlation with these parameters.

The coastline is considered as the boundary between water and land. coasts are one of the most important environmental effects that directly affect human life. Rising sea levels due to global warming have made coastal cities among the areas under threat. Therefore, management and planning to prevent coastal erosion is one of the important issues that should be considered. In this research, we have studied the changes of shoreline using sentinel-1 satellite images in mazandaran province, the coast between the cities of chalous and tonekabon. For this purpose, two images on 15/01/2019 and 01/09/2021 have been used and the desired images have been taken from the google earth engine system. In the pre-processing step of the images, we corrected the height error of the study area and adjusted the speckle noise. The edge of the shore was discovered with the otsu threshold in the images. Finally, Digital Shoreline analysis system (DSAS) was used to calculate the amount of sedimentation and erosion of the coast by EPR technique. In 87% of the coast of this transects, we have witnessed coastal erosion. The average amount of erosion is about 7 meters per year.

Chlorophyll concentrations in marine environments indicate phytoplankton levels and ultimately the production of marine ecosystems. Chlorophyll concentration is not constant and depends on quantities such as sea surface temperature and aerosols optical depth, it also has seasonal variations and the correlation between these factors is various at different depths. Investigating the long-term relationship (2003 to 2020) between sea level temperature changes and aerosol optical depth with chlorophyll concentration on the Persian Gulf and the Sea of Oman coasts using daily MODIS products shows a considerable correlation in coastal areas. This correlation is the highest in shallowest water depths. The trend of sea-level temperature and light depth of aerosols changes in the two regions of the Persian Gulf and the Sea of Oman is similar, and its highest values are observed in summer, while its lowest is observed in winter. Remarkably, this seasonal trend is reversed in chlorophyll concentrations, with the lowest values observed in summer and the highest in winter. Additionally, the study of dust occurrence on July 30, 2018, showed the possibility of time delay in the correlation between sea-level temperature and aerosol optical depth with chlorophyll-a concentration.

Due to the importance of this red tide phenomenon and its effect on the habitats of marine life, the study of this phenomenon in the Persian Gulf and the Sea of Oman is necessary. This research is written only with a physical view and the study of other aspects is the responsibility of experts in the relevant fields. There are various methods to study this phenomenon, including field studies, satellite studies and numerical modeling. In this research, one-dimensional PROBE software has been used for different stations in the Oman Sea, the Strait of Hormuz and the Persian Gulf. The input data of the model include meteorological synoptic data: air temperature, u and v components of wind speed, cloudiness and relative humidity of Qeshm station in February 2008. Studies show that the modeling results of temperature, salinity, phosphate and nitrate are almost consistent with the measurement data. In stations located in the Persian Gulf and the Sea of Oman, in places where the amount of temperature, salinity and nutrients is maximum, the concentration of plankton, which depends on these parameters, also reaches its maximum. As predicted, the amount of green-blue plankton concentration is higher than (only) nitrate and phosphate dependent plankton concentration.

Predicting changes in meteorological variables in the long run is of great importance in the study of climate change. The aim of this study is to provide a perspective of temperature and precipitation changes in the western and southwestern regions of Iran using climate-generating radiation induction scenarios LARS-WG and display the results in GIS environment so that in the coming decades macro-planners In order to adopt compatible methods and reduce the consequences of global warming. For this purpose, daily precipitation, maximum temperature, minimum temperature and sunny hours of 34 meteorological stations were studied. The climate of these stations was determined based on the Domartan classification method. LARS-WG statistical exponential model for analyzing historical data on daily rainfall, solar radiation, and daily maximum and minimum temperatures at the stations under study and to simulate future meteorological data were utilized with considering RCP4. 5 and RCP8 climate scenarios. The results showed that the model can simulate with high accuracy. The 1980-2018 statistical period was compared with the 2018-2038 statistical period under the RCP4. 5 and RCP8. 5 scenarios; the results for the stations under study showed a general process of increasing temperature and decreasing precipitation.