نشریه پژوهش های اقلیم شناسی
پیاپی 51 (پاییز 1401)

Dust storms are always known as one of the natural hazards that affect various sectors such as health, agriculture, transportation, etc. and have very wide consequences, especially reduced soil fertility, damage to crops, drying of cover. Natural plant causes disorders of communication systems, disorders of mechanical systems and an increased risk of respiratory diseases. In general, the main source of dust storms, or in other words, the main origin of dust storms is located in arid regions of the world such as East Asia, Middle East, Latin America, Australia, parts of Europe, East and South Africa, North America. In addition to the internal centers of the country, the main effective centers are the centers located in Iraq, Syria, the Arabian Peninsula and Afghanistan. In order to manage dust storms, forecasting and routing this phenomenon is of great importance. The lowest and highest range of dust suspended particles is from a few nanometers to 100 microns. Large particles usually move by rolling, medium particles by jumping, fine particles such as clay particles due to their lightness rise to a high height above the ground and remain suspended in the air for a long time and descend after a long distance. In suspension motion, very fine soil particles, after rising from the ground due to their extraordinary lightness and high specific surface area, remain suspended in the air for a long time and in the presence of favorable atmospheric currents, sometimes travel hundreds or thousands of kilometers and up to more than a few altitudes. They extend a thousand meters above the ground.

Jazmourian basin is the most important basin in the southeast of Iran, which is located in Kerman, Sistan and Baluchestan provinces with the latitude coordinates of 33 26 to 36 29 north and the longitude of 16 56 to 26 26 east and with an area of 69374 square kilometers. After statistical study of the phenomenon of "dust" and the factors affecting it in the Jazmourian basin, the severe and widespread occurrence of "dust" in the Jazmourian basin is investigated. First, in order to investigate the "dust" mass in the region, the true color image of the Madis sensor of the Tera and Aqua satellites and the optical depth values of the airships are examined. Then, to investigate the prevailing atmospheric currents in the region, the HYSPLIT model is implemented as a matrix and in a leading way. In implementing the HYSPLIT model, GDAS meteorological data with a horizontal separation of 0.5 degrees have been used. Using the output of this model, it is possible to investigate the transfer of "dust" particles from this area.

The True Color Composite and the light depth of the Aqua satellite sensor on October 7 and 8, 2018 show that the AOD values in the whole region are high every 2 days. Also, the True Color Composite of the MADIS sensor of the Aqua satellite shows the high values of "dust" concentration in the Jazmorian region. And they have gone to the Persian Gulf region. Particle optical depth values at UTC06 on October 8, 2018 show that AOD values on the North Sea of Oman have increased significantly and reached 1. Also, the amount of this quantity has reached 1 in the southern half of Jazmourian region, but it has reached 0.7 in a large part. Particle optical depth values at UTC12 on October 8, 2018 show that AOD values on the north of the Oman Sea have increased and reached 1.2. Also, the amount of this quantity has reached 1 in the southern half of Jazmorian region, but has reached 0.7 in a large part of Jazmorian region. Also, the values of the optical depth of the particles at UTC18 on October 8, 2018 show that the AOD values on a large part of the Oman Sea remain 1.2. But the amount of this quantity has decreased in the southern half of Jazmourian region and has reached 0.7 only in a small part of Jazmourian region.

Dust from these areas can directly affect the provinces of Kerman, Sistan and Baluchestan, as well as Hormozgan. Therefore, it is very important to study the path of particles as well as the dust collection centers. In this study, the beginning of the dust storm from October 7 to 8, 2018 was selected for simulation and navigation. The results showed that the southern parts of Jazmourian basin (wetland area and its surroundings) can be used as a critical dust center in these areas. Particles from these areas also move towards the Sea of Oman, which affects most of Hormozgan province and southern Sistan, and is also important for navigation and navigation systems.

The importance of precipitation as a vital component is clear, but sometimes it becomes a heavy precipitations and flood and causes very damages and sometimes irreparable the flood situated areas. The present research, synoptic investigats the flood water precipitations in Iran North in October 2018, which has caused very damage to life, financial and environment in these areas. The purpose of research is providing a comprehensive research from causes and mechanisms atmospheric of heavy precipitations caused to flood in Iran North in 5 to 7 October 2018.

For research purposes, two categories of data have been used, the first category is recorded data with earth stations, which includes precipitation data of last 24 hours from the date of 5 October to 8 October 2018 which was received from the National Weather Administration website, the next category is also data the atmosphere upper levels, and the reanalysis data includes sea level pressure, geopotential heights, relative humidity, specific humidity, zonal wind, merridional wind, and omega, which are available from the National Oceanic and Atmospheric Administration website, is being prepared by the National Center for Environmental Prediction. The research was carried with environmental to circulation approach that For the one day before of precipitations start (4 October) and three days of precipitation day (5,6,7 October), maps of the geopotential height, wind direction and velocity, omega and humidit flow in 1000 to 500 hPa levels, jet stream and wind velocity in 500 to 200 hPa levels, total spatial humidity from 1000 to 500 hPa levels, hafmuler diagram of relative humidity of 1000 to 500 hPa levels and sea level pressure have been mapping with the Grads software and analyzed synoptic.

On the day before the start of precipitations, the region located between the pressure difference between the high pressure system in western Black Sea and Central Europe. The tabs of this high pressure system reach the center of the Caspian Sea, several high pressure centers in central and western China and Central Asia, the low pressure system of northern Europe and low pressure and Sudanese low pressure contract. with start of precipitations, the high pressure system in western Black Sea moved eastwards, leads to the transfer of humidity from the north to the northeast from the humidity sources of the Black Sea and the Caspian Sea to the region, and on this day, the pressure gradient between this high pressure and Sudanese low pressure tab in addition to the transfer of humidity to the region, has created baroclinic and convulsive atmosphere in the region. On the next day, high pressure system, the eastern movement from the Caspian Sea and the Aral lake, entered the are the humidity flow, with finished precipitations dominant high pressure moved to Central Asia and there are no tabs from it to the region of study .At the atmosphere upper levels at the before day of precipitations, there was a wet, windy wind flowing north to northwest from the Mediterranean and Black Seas to the region, with the start of precipitations, the southeast of the system, like omega blocking in the region, leded to the transfer of wet air from The Mediterranean, Black and Caspian Seas, and the next day, by precipitations reducing, an air convergent flow in the Republic of Azarbaijan leded to humidity transfer of the Caspian and Black Seas, and in addition, the southwest shallow humidity flow also in this day from Mediterranean Sea has entered the region, with the finished of precipitations, the main stream of winds in the western region. In these days, jet streams in the Caspian Sea around have been with indirect effects that created atmosphere baroclinic and convulsive in the area for flood water, wind speeds in the area. In the before and after days of precipitations, the atmospheric spatial humidity total was between 5 to 40 g/kg and relative humidity from 20 to 80 percent, and during precipitations days the humidity content increased and the spatial humidity total from 20 to 45 g/kg and relative humidity varied from 30 to 90 percent. In the before and days of precipitations, the omega negative values in the region, that is representative upward movement and baroclinic atmosphere, but the next day, Negative omega's levels have been receding from the region. The humidity sources in atmosphere upper levels the before and start days of precipitations of the Mediterranean and Black Seas and the precipitation next day from Mediterranean, Black and Caspian Seas, that in day finished of precipitations, humidity flow is slightly observed from the Mediterranean and Caspian Sea to the area, in earth surface near levels, there was humidity flow from the Mediterranean, Black and Caspian Seas, the before and start days of precipitations in the area, and in the second day, the precipitations included the Black, Caspian Seas and the Aral Lake, that with finished of precipitations are not humidity flow to region in this levels.

The results indicate that pressure gradient in region humidity flux from high pressure system and humidity stream flow system is due to the movement of the high pressure system towards low pressure in earth surface, the influx of humid air in atmosphere upper levels, high relative humidity and total spatial humid, indirect effects of atmospheric jet streams in atmosphere upper levels, high wind velocity in upper and lower levels of the earth, with major humidity sources that in atmosphere upper levels, including the Mediterranean, Black and Caspian Seas, in earth surface near levels, includes the Black, Caspian, Mediterranean Seas, and the Aral lake, and in earth surface, including the Black and Caspian Seas and the Aral lake, Conditions necessary to climb of humidity air intranting to area (omega negative) provided and creating destructive and flood water precipitation and has been in the study area.Keywords:Heavy precipitation, Synoptic Analysis, North of Iran

Dust storm is One of the natural phenomena that has a great impact on human life and the environment. Every year, some countries in the world where dust sources are located are affected by soil storms. Also, many more countries that do not have sources of dust particles are affected by the transfer of dust particles. Dust storms damage human health and respiratory system, disrupt power lines, disrupt road and air transportation, and agricultural sector Severely affected. The most important sources of dust production are deserts. After that, dried lakes can be named as the second source of dust production in the world. Glaciers and altered agricultural lands are other sources of dust in the world The largest source of dust in the world is in the Africa that imports large amounts of dust particles into the Earth's atmosphere each year. The Sahara Desert is the largest desert in the world with an area of 9 million square kilometers and is located in 10 countries in North Africa. Kok et al showed that sub-Saharan Africa emits about half of the world's dust. Then, the dust sources in the Middle East and Central Asia are in the second place and 30% of the production sources of global dust storms are located in them. One of the most important dust regions is the East Asian deserts, which emit 11% of the world's dust particles. Therefore, the Middle East is one of the most important regions in the world where many soil sources are located. In the Middle East, most of the sources of dust are located in Iraq, Syria and Saudi Arabia, but some of these sources are located in Iran, although the sources of production in Iraq and Syria are less active, however, these springs affect different parts of Iran by producing dust storms. In addition to scattered domestic springs, the main springs producing dust in Iran include the dried parts of the lake and Hamoon wetland in the southeast, the dried parts of the Horalhvizeh wetland (Hur al-Azim) in the southwest and part Of dried Urmia Lake in northwestern Iran. Sistan and Baluchestan province, which is located in southeastern Iran, in addition to high temperatures and low rainfall, is affected by 120-day winds. Today, the occurrence of dust storms is one of the most important problems of many people in the world and every year it causes a lot of damage in various aspect of human life. Countries in the dust belt are most affected by this environmental problem. As a country in the Middle East, Iran has always been affected by dust storms, which mainly originated from dust sources in other countries. The purpose of this study is to investigate the dust phenomenon in southeastern Iran and the Hamoon region. For this purpose a severe dust case was examined on April 26-28 in the Hamoon Lake region. The investigation from April 26 to 28 showed that in some stations in the region, the horizontal visibility has reached less than 1 kilometer, and the true color images of the Modis sensor and the RGB of the MSG satellite shows well the dust mass in this area. Also, the corrected reflection image of the MODIS sensor has shown the dust mass more clearly. It seems that the optical depth of the particles by using the DT and DB algorithm with a resolution of 10 km overestimated the concentration of dust particles in the study area. Comparing the output of the WRF-Chem model with the MERRA2 model shows that both models show the intensity of dust concentration in the Hamoon area, although the output values of the WRF-Chem model are higher than the output surface concentration values of MERRA2 model. Comparing the output of surface dust concentration of these two models with the PM10 concentration measured in Zabol station, shows that the two models overestimated dust concentration in comparison with the reported station data. Also, in this case study, the mean square error (MSRT) of MERRA2 model in Zahedan station was low, which indicates the acceptable performance of this model in this station in this dust case. Furthermore, the MSE of WRF-Chem model in Zahedan station is high, which indicates the poor performance of this model in Zabol station.

Thunderstorms are one of the most destructive atmospheric phenomena that occur every year in different parts of the country, especially in the southwestern region of the country.The effects of these storms, which are manifested in the form of hail, torrential rains, strong winds, thunderstorms, in addition to providing water resources, lead to irreparable human and financial losses.Therefore, awareness and knowledge of thunderstorms, especially in the southwestern region of the country requires a correct understanding of the distribution of meteorological systems and physical mechanisms responsible for the occurrence of these storms. In this research, first to select the code of thunderstorm phenomenon and perform a cluster analysis is used of synoptic station data, high atmosphere data and to modeling selected days with WRF from FNL data with 1*1 degree separation with A 24-hour period .Using clustering method 3, Thunderstorm events were selected as representative and using WRF model, modeling was performed for selected days with different schemas. The modeling results showed that in the representative days, the status of CAPE, CIN, LCL, LFC indices in the stations where the thunderstorm occurred was appropriate. The model output precipitation maps show that precipitation occurred in the study area, but no thunderstorms occurred at all stations, and only a few thunderstorms were recorded. The modeling results showed that in the representative days, the status of CAPE, CIN, LCL, LFC indices in the stations where the thunderstorm occurred was appropriate. The model output precipitation maps show that precipitation occurred in the study area, but no thunderstorms occurred at all stations, and only a few thunderstorms were recorded. On a global scale, thunderstorms are a phenomenon that is recorded in almost all parts of the world and its occurrence in hot seasons is much higher than cold seasons. The diversity of climatic conditions and geographical features of Iran has caused the phenomenon of thunderstorms to be reported in different parts of it every year. Modeling is one of the methods of studying thunderstorms. The motivation for paying attention to numerical models of the atmosphere is the very poor resolution of planetary climate models in relation to local and regional climatic processes. Numerical atmospheric models have been developed to improve spatial detail and attention to regional and local variations.One of the most important models is the WRF numerical prediction model. The WRF Climate Prediction Model is a mid-scale numerical model that is widely used by researchers today for local-scale weather forecasting, air quality studies, and regional climate research.Statistical calculations and graphs show that the occurrence of this phenomenon in the region depends on the arrival of synoptic and out-of-region systems and convection. In proportion to the seasons, if large-scale and synoptic systems penetrate to low latitudes and affect the atmosphere of the region, the probability of hurricane occurrence also increases. In contrast, with the gradual warming of the air and stabilization of the atmosphere, the occurrence of this phenomenon is reduced. It reaches zero.The results of validation showed that configurations 8, 1 and 2 (Table 1) based on all three statistical tests have the lowest error rate to simulate precipitation, temperature and wind parameters, respectively. In fact, in order to simulate the mentioned parameters, cumulus, boundary layer, surface layer and ground (soil) schemas are the most important schemas that the most probable states of these schemas are considered for model implementation.The output of the WRF model for modeling three-day hurricanes shows that in these days the conditions of the studied indicators in some southwestern regions are quite suitable for the occurrence of thunderstorms and in accordance with rainfall maps and station observations in these parts rain and storm Thunder has occurred which can be a testament to the accuracy of the model results. Finally, it can be concluded that the results of modeling thunderstorms with the WRF model are satisfactory and can be used to model this phenomenon.The output of the WRF model for modeling three-day hurricanes shows that in these days the conditions of the studied indicators in some southwestern regions are quite suitable for the occurrence of thunderstorms and in accordance with rainfall maps and station observations in these parts rain and storm Thunder has occurred which can be a testament to the accuracy of the model results. Finally, it can be concluded that the results of modeling thunderstorms with the WRF model are satisfactory and can be used to model this phenomenon.

Drought is a complex phenomenon which is difficult to define. It is a creeping phenomenon that slowly sneaks up and impacts many sectors of the economy, and operates on many different time scales. Although low rainfall is the source of any drought however, delaying rainfall has more effects on water resources and causes a great damage. Therefore, forecasting drought, especially in long term drought, is very important in agricultural water management and planning.Although reduced rainfall is very important on drought occurrence, but increasing or decreasing temperature and evapotranspiration can result in a sever or moderate drought situation. Several indices have been developed based on rainfall and evapotranspiration for drought analysis, however different indices significantly have different results. Because, drought is a multivariate phenomenon and along with the precipitation, the evapotranspiration factor also must be included in drought analysis, especially in the arid and semi-arid regions. The sequence theory, stochastic models, and conceptual models are most popular approaches in forecasting drought events. However, deterministic prediction of drought situation has been more noticeable for researchers in the recent years.Data-driven methods could aspire to extract relations that may further informand augment the current physical understanding. Support Vector Machine (SVM) is one of the data-driven algorithms which has been successfully applied in classification, regression and forecasting in the field of hydrology .It has been emphasized the need of combining an understanding of physics with data mining, not only to avoid generating misleading insight but also to produce new results. There are a number of studies using SVM in drought forecasting. The advantage of SVR is that it could transfer a non-linear problemto a linear problem using the kernel function, and be effective in solving a high dimension problem.

This study investigates the effect of climatic variables (precipitation, maximum and minimum temperature, evapotranspiration) and large-scale climatic signals arising from temperature fluctuations in the Pacific and Atlantic levels (SST) on the SPEI variable. The present study has been conducted in different climatic types of Iran. Accordingly, using the extended De Martone classification climate method, climatic types were identified and sample synoptic stations were selected from 8 climatic types. We applied SVR machine learning algorithm for prediction the SPEI, based on several meteorological predictors during the 1966–2014. In order to learn the SVR model we considered 80% of dataset for training and 20% of the rest as the testing dataset. We also applied the Lasso regression approach to select the important variables affecting the SPEI. In this regard, three different kernel functions were used for training the support vector regression (SVR) including linear, radial and polynomial kernels and the results were evaluated using different criterions.

Lasso regression results showed that in all climates, the most effective drought variable is precipitation. Among the teleconnection signals in all climates in Iran (except semi-arid cold and extra-arid warm), the effect of SST in the North Atlantic and South Pacific is effective on agricultural drought. The results showed that in all climates, the radial function showed the best results (except in the semi-arid cold, extra-arid climates) where the linear function had the best performance. The agreement index between the predictive and predictable values in the best model is 0.912 to 0.731, which is very humid and arid-warm climate, respectively. Therefore, the highest accuracy is related to Ramsar station and the lowest accuracy is related to Bushehr station. To evaluate the performance of the SVR model in predicting drought in training phase, the variables of drought including severity, duration and peak and magnitude were examined. The model is generally underestimated in estimating the severity, magnitude, and peak of drought (except at Tabas and Urmia stations, where the linear function showed different behaviors in the extra-arid warm and semi-arid cold climates). The largest difference between the observed and predicted values of drought severity was observed in the 75th percentile (Bushehr station).

Finally, it can be said that SVR is very efficient in predicting SPEI for most climates. However, its performance in different climates seems to be different, which may indicate the different role of regressions used in training SVM model and different variables. Finally, the results show that SVR is an attractive machine learning tool for drought prediction and can provide useful information for water management in agriculture.

Determining the cloudiness and the spatial and temporal characteristics of clouds is essential in forecasting the weather as well as climate studies. Studies show that changes in cloud cover negatively affect daily temperatures. (Dai, et al. 1999; Karl, et al. 1993). Accurate information about the physical and radiative properties of clouds is essential to determine the role of clouds in the climate system (Forster, et al. 2007). Data and To this study, two sets of satellite and observational data were used. Observational data include 6-hour rainfall data during daylight hours (06 to 12 GMT) for 26 days in January, April, October and, November 2018 from 399 meteorological stations in Iran. Satellite data also includes 1.5 level data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. The SEVIRI has 12 channels for measuring electromagnetic radiation. The radiance of three channels at visible and very-near infrared wavelengths (VIS0.6, VIS0.8, and NIR1.6) converts to reflectance. The radiance of eight channels from near-infrared to thermal infrared wavelengths (IR3.9, WV6.2, WV7.3, IR8.7, IR9.7, IR10.8, IR12.0, and IR13.4) converts to brightness temperature. These channels have 3*3 km spatial resolution at nadir. All of these channels have a temporal resolution of 15 minutes. Since 15-minute satellite data and 6-hour rainfall data are available, the minimum, maximum, and average of reflectance values (for channels 1 to 3) and brightness temperatures (for channels 4 to 11) have been calculated during these 6 hours and their correlation with precipitation has been analyzed.Changes in the 6-hour mean values of reflections and brightness temperatures for 90% of the data were investigated for rain and no rain conditions, separately. The results show that the mean of reflectance in rain conditions is higher than no-rain conditions. And the mean of brightness temperature in rain conditions is less than no-rain conditions for each channel.The study of the correlation between channels and precipitation shows a high correlation between VIS0.6 and VIS0.8 channels. The NIR1.6 channel has very poor communication with other channels, but this channel is important for identifying cloud ice particles. Channel IR3.9 correlates relatively poorly with channels VIS0.6, VIS0.8, NIR1.6 and, WV6.2, but shows a good correlation with other channels. WV6.2 and WV7.3 channels, which show the amount of humidity at different levels of the atmosphere, have a very high correlation of 0.91%. The WV7.3 channel correlates better with other channels than the WV6.2 channel. IR channels indicate ground, sea, and cloud temperatures, while WV6.2 indicates air temperature near the clouds. Therefore, this poor correlation is acceptable. Channel 7 to 11 are highly correlated with each other.The reflectivity of VIS0.6 and VIS0.8 channels has a positive correlation with precipitation and consequently cloudiness. Increasing the cloudiness increases the reflectivity. Because the reflection in these channels indicates the optical thickness of the cloud and the amount of water in the cloud. Therefore, the thicker the cloud, the greater its reflectivity. The NIR1.6 does not show much correlation with precipitation and is close to zero. Areas of rain clouds with a high optical thickness (high reflectivity VIS0.6) and large effective particle radius (low reflectance NIR1.6), with higher rainfall, compared to cloud areas with a low optical thickness (low reflectivity VIS0.6) and particle radius Small effect (high reflectivity NIR1.6) are specified. Infrared and water vapor channels have a negative correlation with precipitation. So more cloudiness leads to lower brightness temperature. The mean brightness temperature in the IR3.9 channel is the best indicator in this channel to detect the presence of clouds. Because it has a high correlation with precipitation, and also its difference in rain and no-rain conditions is more significant. The correlation between precipitation and the average 6-hour brightness temperature in all 5 channels is better than the minimum and maximum of 6-hour brightness temperature. Negative correlation also emphasizes that precipitation is inversely related to brightness temperature. In all channels, the mean difference of these parameters in the 6-hour mean brightness temperature mode has the best distinction between rain and no-rain conditions.Among the minimum, maximum and, average 6-hour reflectance in VIS0.6 and VIS0.8 channels, the highest correlation with precipitation is related to the 6-hour average reflectance in both channels and is about 0.44. As a result, they are the best channels to show the cloudy effect. The NIR1.6 does not have a good correlation with precipitation and cannot distinguish between rain and no-rain conditions. Therefore, the use of this channel for cloud detection is not recommended. Since the IR3.9 channel shows the structure of the cloud top well and is sensitive to particle size. Therefore, the average brightness temperature in this channel is a good indicator for detecting the amount of cloudiness. Also, among infrared channels, the IR3.9 channel has the highest correlation of -0.33 with precipitation.Among the water vapor channels, the best indicator for detecting the amount of cloudiness is the minimum 6-hour brightness temperature of the WV7.3 channel. Channels IR8.7, IR9.7, IR10.8, IR12.0 and, IR13.4, which mainly represent the cloud top temperature, show relatively similar correlations with precipitation, while they are highly correlated with each other. The negative correlation in infrared channels means that with decreasing brightness temperature in these channels, cloudiness and precipitation increase and vice versa. In these channels, the average 6-hour brightness temperature is a better indicator of the amount of cloudiness.Since the major changes of VIS0.6 channel in rain conditions are in the range of 26.7-44.7% and in no-rain conditions are in the range of 53.3-69.3% and about VIS0.8 channel are 32.7-49.6% and 58.3-73.9%, separately. Therefore, rain and no-rain conditions in VIS0.6 and VIS0.8 have the least overlap, so separating them will be easier. Among the infrared channels, only the WV6.2 have overlap in rain and no-rain conditions. So that the range of changes in rain and no-rain condition is 226-234 and 229-237 degrees Kelvin, Respectively. Therefore, its separation will be difficult. The rest of the infrared channels have slightly overlap.

Horizontal vision is a simple observational indicator of air quality. In clean atmosphere, the field of view is between 145 and 225 km and in normal atmosphere between 10 to 100 km and in polluted areas it is less than this amount. Preliminary research shows that the accumulation of air vents in the atmospheric column severely reduces horizontal visibility Today, with the increase of human activities in recent years and the increase in the concentration of suspended particles in the atmosphere, including: air conditioners affecting the depth of light, has caused a decrease in horizontal vision. In Sistan and Baluchestan province, dust storms are in critical condition, so that on January 5, 2015, wind speeds reached 102 kilometers per hour in Zahedan and Nusratabad and dust concentration (〖PM〗_2.5)increased to 115 μg / m3. Research shows that during the peak of dust storms, the concentration of pollutant particles in Zabol station increases and the wind speed reaches 70 km / h and the horizontal visibility is drastically reduced to even less than 100 meters. Therefore, considering the importance of the visual quantity in the low thermal pressure region of Sistan, the study and forecast of its annual average until 2022 with the help of spatio-temporal regression was considered in this study.

In this study, in order to predict the annual horizontal visibility, the statistical method of spatio-temporal regression with the help of R software and using the package spdep, plotKML, RgoogleMaps, tseries and maptools has been used. For this purpose, the data autocorrelation, data Stationary were first examined to determine the type of regression, error normalization test, error non-correlation test and error variance homogeneity test. Then, the test of significance of the regression line equation and the variance inflation index and the coefficient of determination of the data were calculated. Then, in order to predict the average annual horizontal visibility in the low thermal pressure region of Sistan, the spatio-temporal regression model was defined as follows: z_(s,t)=β_0+β_1 z_(s_1,t-1)+β_2 z_(s,t-1)+β_3 x_s+β_4 y_s+ε_(s,t) Where z_(s,t) the horizontal is view at time t in position s, s_1Position of the station closest to the station with position s, y_s and x_s Latitude and Longitude and ε_(s,t) is a set of errors. Then the upper and lower limits of the average annual horizontal visibility with 95% confidence interval were calculated according to the following relationship:A≡(z ̂_(s_0,t_0 )-1.96σ_(s_0,t_0 ),z ̂_(s_0,t_0 )+1.96σ_(s_0,t_0 ) ) Where σ_(s_0,t_0 )is the standard deviation of z ̂_(s_0,t_0 )

The results of spatio-temporal regression coefficients of the data show that the P-value for all variables is less than 0.05 and is significant in the model. And the generalized spatio-temporal regression model can predict the horizontal visibility variable in the coming years in the low thermal pressure region of Sistan.Spatial-temporal analysis of the average annual horizontal visibility shows that Sistan region has the lowest horizontal visibility until 2022, which is 6 to 7 km, followed by Zahedan region between 8 to 9 km. Predicted horizontal visibility In Ghaenat area, it is between 9 and 10 km, which is in the third rank among the stations in the region. The average horizontal visibility in Birjand area is between 10 and 12 km. Nehbandan station has better conditions than Birjand station in the coming years. However, the highest annual horizontal visibility is related to Sarbisheh station, which is 12 to 14 km, which in this regard can be said to have better conditions in terms of clean and healthy air. The plot points in 2022 show that Sistan and its southern parts, due to its proximity to the Loot Desert and the low altitude of the region compared to the surrounding areas, increase the intensity of its 120-day winds, which is one of the main reasons for the region's critical Horizontal view is towards its northern regions.

The general situation of Sistan low pressure region during the statistical period shows that its horizontal visibility decreases so that in 1986 with an average of 14.8 km started in 2018 it reaches 9.5 km. numerical model shows that the lowest average annual horizontal visibility during 2019 to 2022 with the rates of 6.9, 0.7, 7.1 and 7.2 km is related to Zahak station and its forecast shows that by 2022, it will increase by almost 300 meters, after that, Zabol station is located at 0.7, 7.1, 7.3 and 7.4 km, respectively, which the forecast of this station indicates that the average annual horizontal visibility will increase during the coming years. Its incremental figure is approximately 400 meters and 100 meters more than Zahak station. Nusrat Abad station is also facing an increase in annual horizontal visibility which reaches from 9.9 to 0.9 km, in Zahedan station, the average horizontal visibility has a decreasing trend, so that its value in 2019 is equal to 8.7 km, which in 2022 will reach 8.6 km. Which adds to the criticality of this station. Therefore, considering the high risk of all stations in the study area, it should be said that stations located in Sistan and Baluchestan province will have more severe conditions. The results show that the annual horizontal visibility increases in Birjand and Ghaen stations, which shows in 2019 with 10.0 and 9.2 km, respectively. And in 2022 it will reach 10.5 and 9.8 km. Finally, the results show that the average annual horizontal visibility in the coming years in the low-pressure region of Sistan will decrease, which can challenge the economic and demographic changes in the region.

Prediction of climatic variables in Sistan and Baluchestan province under the conditions of RCP  scenarios Mahsa Farzaneh 1  Sharareh Malboosi 2  Mohsen Hamidianpour 31- PhD in Climatology2- Climatology Research Institute-Climatic Disasters Group-Mashhad-Iran3- Physical Geography - Climatology,  Department of physical geography, University of Sistan and Baluchestan, Zahedan, Iran IntroductionDrought is one of the climatic phenomena and is one of the events that cause a lot of damage every year. In fact, this phenomenon is one of the main and recurring features of different climates and its effects are not limited to arid and semi-arid areas. This phenomenon can occur in any geographical area and cause a shortage of water resources in different sectors. Sistan and Baluchestan province is one of the southeastern provinces of Iran that is strongly affected by drought ad fluctuations and changes in meteorological variables. Given the importance and necessity of the effect of climate change on drought and more specifically on climate parameters, this study intends to identify climate change behavior on drought and some changes in meteorological parameters in this region and answer the following question. What are the changes in meteorological parameters in the stations used in the future based on general circulation models (GCMs), and How does the trend of changes in meteorological variables affect the frequency and severity of droughts in the studied province?Data and methodology The study area in the present study is southeastern Iran, Sistan and Baluchestan province. The data of this study include two categories: The first category includes meteorological variables such as daily precipitation, minimum temperature, maximum temperature and sundial related to synoptic stations located in the region during the statistical period of 30 years (2020-1987). The mentioned data have been obtained from the Statistics and Information Center of the Meteorological Organization of the country. The second category of data includes the output of GCMs. In this study, HadCM model data is used. In order to use the output of GCMs, the use of downscaling tools is required. One of the downscaling methods used is the LARS-WG model. The SPI Drought Index is based on the fact that rainfall deficits have different effects on groundwater, resource storage, soil moisture, snow cover, and stream flow.ConclusionDuring the study period, the precipitation regime will shift to the end of the season, ie winter and spring. In this regard, officials should pay more attention to the impact of spring rains on onshore stations in the occurrence of flash floods on a local scale. According to the distribution of precipitation in the province, it is determined that precipitation patterns are different in different regions of the southeastern part of the country. Some areas, such as Sistan, have the lowest rainfall in the province, while others are well distributed. Therefore, the distribution of rainfall in the province, especially heavy rainfall, is affected by local factors and synoptic conditions. The heaviest rainfall in the province occurs in the southern part of the province and is affected by sea moisture. These points are considered as dry points; But if the conditions for rainfall are provided, under the influence of sea moisture, they can receive the heaviest rainfall in the province. Heavy rains have also been recorded in the central heights of the province. Drought is one of the climates that is followed by rainfall, warming and rising temperatures and is one of the dangers that Iran has faced severe problems in recent decades. The results of the study showed that the severity of drought in 2006 and 2016 was more severe.In addition, the results of the modeling section showed that there is a great agreement between the simulated values of the base period and the modeled period.It means that the results of the LARS-WG model have adequate confidence. The general results of the surveys for the mentioned period show that the percentage of minimum temperature changes in the province has increased by 16.02%, the percentage of maximum temperature changes has increased by 8.49% and the percentage of precipitation has decreased by 9.85%. The number of dry days increases in the studied stations. The highest frequency of the severity of droughts has been related to very severe and severe droughts. Zoning maps show that the eastern and central parts of the study area are more affected by drought than the western parts..

Thunderstorms are local, medium-scale weather systems of 20 to 50 km that depend on the height of boiling clouds. They are associated with thunderstorms and indicate the high level of development of convective conditions in the humid air. The occurrence of thunderstorms is a part of the nature of Iran's climate, which causes a lot of human and financial losses in different regions every year, including the damage caused by thunderstorms in Khuzestan province during the study period 2001-2006. West and northwest of Iran are among the areas where the occurrence of severe thunderstorms with heavy rainfall has a high frequency. This study tries to identify the dynamic and atmospheric factors affecting thunderstorms in the west and northwest to provide appropriate information about the situation of this phenomenon for forecasting and prognosis, damage reduction, crisis management, and increasing the level of productivity of provided water resources.

To achieve the effect of atmospheric circulation on the pervasive occurrence of thunderstorms in western and northwestern Iran, the current weather codes that indicated the occurrence of thunderstorms in the region were selected, which include codes 29, 95, 96, 97, and 99, Data related to these codes were obtained from the Meteorological Organization. Also, to determine the prevailing atmospheric circulation conditions during thunderstorm days in the study area, sea level pressure (SLP), geopotential height (hPa), and air temperature data were extracted for 850 and 500 hPa levels on a daily scale. These data were Prepared from the network databases of the National Center for Environmental Prediction / Atmospheric Science (NCEP-NCAR) with a spatial resolution of 2.5 * 2.5 degrees and the European Medium-Term Forecast Center (ECMWF). With 1 * 1 degree spatial separation. The hierarchical method was used to determine the prevailing atmospheric circulation patterns on days with thunderstorms, which were clustered based on geopotential elevation data at the level of 500 hPa. After identifying the atmospheric patterns that caused the storm, the average maps of the patterns at the desired levels were drawn using Surfer software. The composite temperature map was drawn at two levels of 850 and 500 hPa at the time of the phenomenon and all maps were compared with the anomaly maps because this shows the difference between the prevailing values at the time of the phenomenon and the average values. Then, the Nova HySplit model was used to identify the air mass affecting the occurrence of this phenomenon. The input of this model is meteorological data of sea level pressure, geopotential altitude, and air temperature, which were obtained from two databases of the National Center for Atmospheric Research and data of the European Center for Medium-Range Forecasting.Discussion and

In the 29-year statistical period (1986-2014), a total of 27 days of widespread thunderstorms were identified. Examination of the monthly, seasonal and annual cycle of thunderstorms showed that the maximum occurrence of thunderstorms is in May and spring, and also the years 2005 and 2010 with 4 events had the highest frequency of thunderstorms. In terms of location, the provinces located in the west of the study area, including the provinces of West Azerbaijan and Kurdistan, experienced the most thunderstorms with a total of 49 cases, and Lorestan province, which is located in the south of this region, had the least thunderstorms with 8 cases. Maps of average sea level pressure during the days of thunderstorms showed that the occurrence of thunderstorms in the western and northwestern regions of Iran is associated with a low-pressure system throughout the region with values between 1007 and 1011 hPa. That is, the occurrence of thunderstorms in the region is possible under the negative anomalies of sea level pressure. In addition, average temperature maps at the 850 hp level during thunderstorm events show that the temperature has decreased to the northwest, and the map of temperature anomalies at the 850 hPa level shows that throughout the study area, the temperature decreases relative to Have experienced the average. Similar conditions were prevalent in the Middle Troposphere (500 hPa). The results of hierarchical clustering showed that the occurrence of thunderstorms is affected by three atmospheric patterns of blocking systems, cut-off-low, and shortwave, among which, cut-of-low with 14 cases, and the main pattern in the occurrence of thunderstorms in western and northwestern Iran. Back trajectory paths selected for thunderstorm days each pattern for 48-hour infiltration of air masses showed that the thunderstorm originated from different systems. The Mediterranean system is the main source of air mass affecting the western and northwestern regions during a thunderstorm, followed by the Sudanese system and the integration system in second and third place.

Thunderstorms are atmospheric phenomena that sometimes cause human and financial losses to humans. The results of this study showed that despite the local and regional nature of thunderstorms and their rarity, it is possible to study the atmospheric circulation conditions and the temperature of the lower and middle troposphere during the days of this phenomenon. The results of the statistical study showed that spring and April and May had the highest frequency of seasonal and monthly occurrence in the study area. Also, in the synoptic study, it was found that the occurrence of thunderstorms in the west and northwest of Iran, not only with sea surface pressure anomalies and 500 hPa but also with negative temperature anomalies in both levels. In the occurrence of thunderstorm atmospheric phenomena in the western and northwestern regions of Iran, atmospheric circulation patterns including blocking systems, cut-off-low, and shortwaves play a role in the middle level of the atmosphere. At the same time, Mediterranean, Sudanese and fusion pressures are forming on the Earth's surface. This adaptation of low surface pressure to high divergence enhances surface convergence and high divergence. This leads to increased vertical transmission and air ascent and instability, eventually leading to hurricanes.

Present study estimated the large-scale actual evapotranspiration in multiple land use landscape (e.g., Irrigated cultivation, rainfed and pastures) by employing the remote sensing approaches. Measurement of actual evapotranspiration is a difficult process which was calculated as a function of the various climatic and topographical factors for the studied area. Thus, using of models which can estimate the amount of actual evapotranspiration owing to a large and high reliability factor is one of applicable solutions. In recent decades, some methods have been proposed by researchers to determine and measure the actual evapotranspiration. These methods had some limitations and they mainly require a large number of ground-based measurements. Employing the methods based on meteorological stations data from the past has been used as a reference for the estimation of actual evapotranspiration. There are a number of disadvantages of traditional measurement operation such as the lack of sufficient number of measurement points, incomplete meteorological data in many areas, high cost and time consuming of collecting terrestrial information. On the other hand, these measurements are usually based on the weather stations (point based). So, the techniques based on remote sensing data have been developed to tackle these problems. Remote sensing technology has the ability to collect data in large spatial ranges and in short time ranges and the best method to estimate the actual evapotranspiration for areas with difficulty data access or lack of data.

In this study, actual evapotranspiration values were measured by using Surface Energy Balance Algorithm for Land (SEBAL) and Remote Sensing Technique in Fariman area of Khorasan Razavi province. The ground data were used in this study which are included as wind speed, dry temperature, relative humidity, minimum temperature, maximum temperature, sun hours, radiation and evaporation. These parameters were extracted from the automatic and synoptic stations of Fariman. Furthermore, Landsat 8 satellite images were employed to estimate the actual evapotranspiration, that were downloaded from the USGS website in geotiff format. In order to calculate the required parameters, eight days of landsat-8 satellite images (in 2015 and 2016) were used to enhance the proper estimation. In the SEBAL algorithm, the actual evapotranspiration is calculated as the latent heat flux which were determined by using the energy balance equation at the time of the satellite overpass. Because of the large area of study and the impossibility of using accurate evapotranspiration instruments such as lysimeter, eddy covariance and etc., for validation of the results of SEBAL algorithm, the FAO Penman-Monteith standard method was used as the reference. Comparisons among results were considered based on some indexes like R2 and RMSE.

The values of different parameters of the SEBAL algorithm were calculated parametrically. These parameters included as the normalized different vegetation index (NDVI), net radiation flux, land surface temperature, difference between air and land surface temperature, soil heat flux, sensible heat flux and finally actual instant and daily evapotranspiration. Statistical comparison of the SEBAL outcomes with FAO Penman-Monteith method shows that the coefficient of determination is 0.96 and the mean squared error is 0.5 mm per day. These results indicate the high accuracy of SEBAL algorithm in estimating actual evapotranspiration in semi-arid climates with multiple land use landscapes.

In this study, the actual evapotranspiration was estimated for part of the Fariman region with irrigated cultivation, rainfed and pasture lands by using the SEBAL algorithm. This model is a remote sensing method based on physical equations. The developed model which was based on the remote sensing technique estimates the amount of evapotranspiration owing to large - scale areas and areas where meteorological data are not available. The most important limitation of this study was the absence of measured actual evapotranspiration owing to which it was finally tried and examined by the best possible method to validating the estimated values . Therefore, the results obtained from the SEBAL algorithm were compared with the results of the FAO-Penman-Monteith method. Results show that the drift of the values obtained from the SEBAL algorithm was slightly higher than the reference method in some days, and some days it was lower. In total, the accuracy of the final results for the entire study area can be deduced from the accuracy of the results obtained in the considered part of the images and the integrity of the parameters used throughout the images. Finally, by considering the obtained results, it can be concluded that the SEBAL algorithm has reliable outputs for different land use areas and can be used.

Nowadays, high energy consumption has been one of the major global concerns in recent decades. Since one of the axis of achieving sustainable development is the optimization of energy consumption, providing solutions in this field, especially in the construction and housing sector, is of great importance. Currently, the use of sunshades is one of the common methods to optimize the energy consumption of the building and create environmental comfort conditions. Basically, in order to achieve thermal comfort inside the building, the operation of the open and closed parts of the wall must be carefully coordinated, and when there is a need for shade, the temperature of the indoor air must be increased. Therefore, the main purpose of designing sunshades will be to adapt them to the need for shade and sun in each area, so that when the need for shade prevented the direct penetration of sunlight into the room and in the need for the sun to allow this to enter. Give the rays into the room. Creating shadows on windows or glass walls prevents direct sunlight on the glass surface, and as a result, the heat generated by the sun in the space behind the glass is greatly reduced. This amount of reduction depends on the location of the shadow created. Exterior sunshades can reduce up to 90% of indoor sunshades by only 20 to 25% of the thermal effect of sunlight in a room. The effect of shading and the use of related techniques, in addition to providing indoor thermal comfort, has a significant effect on creating favorable thermal conditions and heat intake of people in outdoor environments.

According to the purpose of the research, the research method is applied and in terms of strategy and nature, it is a simulation, modeling, and logical reasoning. In this study, at first, with the method of measuring climatic-environmental variables, in order to achieve the optimal shape of the sunshade for the opening of a Kashfi house in the city of Khorramabad, it was done as follows:1- The average temperature changes per hour during the day for twelve years in the city of Khorramabad was recorded in a table. 2- In this table, the characteristics of the moments when the air temperature reaches 20 degrees Celsius were determined and the location of those moments was marked with a dot in another chart that includes temperature variables and days of the month for one hour. 3- From the connection of the obtained points on the calendar chart, the need for shade and sun and according to the hours of sunrise and sunset, the range of need for shade during the day for one year was obtained. 4- By transferring the obtained points on the path of the sun, the area of need for shade and sun was marked on this diagram. Then, using Ecotect analysis software, the shadow mask for the desired opening was obtained and, by matching the area of need for shade and sun on the diagram of the sun's path with the desired opening shadow mask, the appropriate shape of the sunshade of the opening was obtained.

Based on twelve-year data analysis of Khorramabad city temperature and obtaining the calendar of the need for shade and sun in this city and transferring it on the diagram of the path of the sun, when the need for shade and sun for building openings in this city Obtained; By modeling the Kashfi house in the Ecotect analysis software environment and obtaining a calendar of the need for shade and sun for the examined openings and its adaptation to the diagram of the need for shade and sun, the range of need for shade and sun to be covered by the sunshade is determined.

By specifying the range obtained in the diagram of the need for shade and sun in the city of Khorramabad, the features and specifications of the sunshade that covers the desired area were obtained. Therefore, considering that the sidewalls of the yard act as vertical side sunshades for first-floor openings, the first-floor opening sunshades do not need vertical side sunshades. However, due to the lack of this possibility, the second-floor sunshade also has a vertical side sunshade

Thunderstorms are local, medium-scale weather systems of 20 to 50 km that depend on the height of boiling clouds. They are associated with thunderstorms and indicate the high level of development of convective conditions in the humid air. The occurrence of thunderstorms is a part of the nature of Iran's climate, which causes a lot of human and financial losses in different regions every year, including the damage caused by thunderstorms in Khuzestan province during the study period 2001-2006. West and northwest of Iran are among the areas where the occurrence of severe thunderstorms with heavy rainfall has a high frequency. This study tries to identify the dynamic and atmospheric factors affecting thunderstorms in the west and northwest to provide appropriate information about the situation of this phenomenon for forecasting and prognosis, damage reduction, crisis management, and increasing the level of productivity of provided water resources.

To achieve the effect of atmospheric circulation on the pervasive occurrence of thunderstorms in western and northwestern Iran, the current weather codes that indicated the occurrence of thunderstorms in the region were selected, which include codes 29, 95, 96, 97, and 99, Data related to these codes were obtained from the Meteorological Organization. Also, to determine the prevailing atmospheric circulation conditions during thunderstorm days in the study area, sea level pressure (SLP), geopotential height (hPa), and air temperature data were extracted for 850 and 500 hPa levels on a daily scale. These data were Prepared from the network databases of the National Center for Environmental Prediction / Atmospheric Science (NCEP-NCAR) with a spatial resolution of 2.5 * 2.5 degrees and the European Medium-Term Forecast Center (ECMWF). With 1 * 1 degree spatial separation. The hierarchical method was used to determine the prevailing atmospheric circulation patterns on days with thunderstorms, which were clustered based on geopotential elevation data at the level of 500 hPa. After identifying the atmospheric patterns that caused the storm, the average maps of the patterns at the desired levels were drawn using Surfer software. The composite temperature map was drawn at two levels of 850 and 500 hPa at the time of the phenomenon and all maps were compared with the anomaly maps because this shows the difference between the prevailing values at the time of the phenomenon and the average values. Then, the Nova HySplit model was used to identify the air mass affecting the occurrence of this phenomenon. The input of this model is meteorological data of sea level pressure, geopotential altitude, and air temperature, which were obtained from two databases of the National Center for Atmospheric Research and data of the European Center for Medium-Range Forecasting.

In the 29-year statistical period (1986-2014), a total of 27 days of widespread thunderstorms were identified. Examination of the monthly, seasonal and annual cycle of thunderstorms showed that the maximum occurrence of thunderstorms is in May and spring, and also the years 2005 and 2010 with 4 events had the highest frequency of thunderstorms. In terms of location, the provinces located in the west of the study area, including the provinces of West Azerbaijan and Kurdistan, experienced the most thunderstorms with a total of 49 cases, and Lorestan province, which is located in the south of this region, had the least thunderstorms with 8 cases. Maps of average sea level pressure during the days of thunderstorms showed that the occurrence of thunderstorms in the western and northwestern regions of Iran is associated with a low-pressure system throughout the region with values between 1007 and 1011 hPa. That is, the occurrence of thunderstorms in the region is possible under the negative anomalies of sea level pressure. In addition, average temperature maps at the 850 hp level during thunderstorm events show that the temperature has decreased to the northwest, and the map of temperature anomalies at the 850 hPa level shows that throughout the study area, the temperature decreases relative to Have experienced the average. Similar conditions were prevalent in the Middle Troposphere (500 hPa). The results of hierarchical clustering showed that the occurrence of thunderstorms is affected by three atmospheric patterns of blocking systems, cut-off-low, and shortwave, among which, cut-of-low with 14 cases, and the main pattern in the occurrence of thunderstorms in western and northwestern Iran. Back trajectory paths selected for thunderstorm days each pattern for 48-hour infiltration of air masses showed that the thunderstorm originated from different systems. The Mediterranean system is the main source of air mass affecting the western and northwestern regions during a thunderstorm, followed by the Sudanese system and the integration system in second and third place.

Thunderstorms are atmospheric phenomena that sometimes cause human and financial losses to humans. The results of this study showed that despite the local and regional nature of thunderstorms and their rarity, it is possible to study the atmospheric circulation conditions and the temperature of the lower and middle troposphere during the days of this phenomenon. The results of the statistical study showed that spring and April and May had the highest frequency of seasonal and monthly occurrence in the study area. Also, in the synoptic study, it was found that the occurrence of thunderstorms in the west and northwest of Iran, not only with sea surface pressure anomalies and 500 hPa but also with negative temperature anomalies in both levels. In the occurrence of thunderstorm atmospheric phenomena in the western and northwestern regions of Iran, atmospheric circulation patterns including blocking systems, cut-off-low, and shortwaves play a role in the middle level of the atmosphere. At the same time, Mediterranean, Sudanese and fusion pressures are forming on the Earth's surface. This adaptation of low surface pressure to high divergence enhances surface convergence and high divergence. This leads to increased vertical transmission and air ascent and instability, eventually leading to hurricanes.

Many parameters affect the length of rice growth period, including air temperature, evapotranspiration, effective rainfall, soil temperature, soil moisture, amount of water available, fertilizer intake, crop management. Among the above factors, the effect of air temperature on the length of rice growth period is more significant, so in this study, using air temperature, the heat requirement (degree - day of growth) required to complete the length of rice growth period was investigated. The degree-day of growth is one of the most important indicators in agricultural management and knowledge of the extent and probability of its occurrence in the region will improve the agricultural calendar.

The purpose of this study was to investigate the effect of changing the planting date on the growth period of Hashemi cultivar rice based on degree-day of growth (GDD) in Guilan province. For this purpose, the average daily temperature data of 13 stations of Astara, Anzali, Talesh, Jirandeh, Deilman, Airport, Rudbar, Rudsar, Keshavarzi, Kiashahr, Lahijan, Masouleh and Manjil were used over an 11-year period (2008-2018). In this research, the degree-day of growth of Hashemi rice cultivar was 1450 units with physiological zero of 11 degrees Celsius for 8 planting dates with 10-day intervals from 10 April to 20 June. Harvest dates were calculated with 25, 50 and 75% probabilities using Pearson type III distribution function and were interpolated for plain areas of Guilan province with the help of GIDS algorithm.

Since it is necessary to consider the length of the rice growing period in low and rainy years (drought and wet years), we decided to examine the zoning at three levels of probability. Let's. It is worth mentioning that in this research, the first day of Farvardin is considered as the first day of Julius. First, harvest dates in cities with meteorological stations were calculated and compared, which showed that in all cultivation dates of Astara, Talesh, Rudsar and Lahijan, respectively, the longest growth period and Rudbar city, respectively. They also have the shortest growth period.In order to study the length of the growth period in other areas of the province that do not have a meteorological station, using the GIDS algorithm and with a spatial resolution of 900 meters (0.01 degrees Celsius) for all plain areas of Gilan province, the length of the growth period was internalized and the results Zoning was presented at three levels of probability. In order to show the error rate of the interpolation method, the root mean square error (RMSE) statistic was used. For example, according to the zoning done at the level of 25% probability, the areas of Shaft, Fooman and Soomehsara have shorter growth periods than other parts of the province. However, it should be borne in mind that the longer the planting date to summer, the shorter the growth period. For example, for the city of Fooman, if the planting date is on the 10th of Farvardin (July 10th), the harvest date will be approximately on the 6th of August (130th of July), ie the growth period is 120 days. Now, if we move the planting date in the same city to 10 June (72nd day of July), the harvest date will be 5 September (160th day of July), ie the length of the growth period is 88 days. This 32-day difference during the growing season can save on input consumption (fertilizer, water, time, planting costs.

Environmental changes affect the growth and development processes of plants and ultimately the production and yield of crops. Given this point, planning to select the appropriate planting date for each crop and reduce the negative effects of these environmental changes, has a significant impact on economic gain and prevent waste of resources. Therefore, zoning of areas based on GDD can be of special importance in carrying out timely and correct agricultural operations as well as better use of agricultural lands.In this study, the results showed that in all cultivation dates, the areas of Fooman, Shaft and Soomehsara had the shortest growth period and the longest growth period was related to Asalem, Talesh and Astara, Kiashahr and Rudbaneh regions. It was also found that the longer the planting date is from April and closer to June, the shorter the growing period will be due to the low temperature in April, when the rice plant will naturally receive less GDD. In all cities of the province, the most appropriate planting date in terms of shorter growth period was related to the dates of 10 and 20 June.