mahuod ahmadi

Drought as a long-term stage of water scarcity is a challenging issue in water resources management and a very widespread natural disaster. Being aware of the drought situation can significantly reduce the risk of losses caused by this phenomenon through predicting and zoning the severity of the drought. One method of determining drought is the Standardized Precipitation Index (SPI), which was proposed by (McKay et al., 1993), for drought monitoring in the Colorado area. The SPI index is one of the appropriate indices to be used due to its advantages in the regional analysis of drought and the temporal relationship between events.

First, TWSI data were downloaded from GRACE-CSR satellite. The TWSI data obtained from GRACE satellite were received using coding in Google Earth engine in EXCEL format and were provided for the entire province of Khuzestan. Since the TSDI index provides a comprehensive picture of drought, TSDI values had to be calculated after reviewing the TWSA data from Google Earth Engine. To calculate this index, TSD and cumulative TSD values were calculated first. Then, the total water shortage was calculated cumulatively. In addition, the 15-year SPI index (2002-2006) was used to study drought in Khuzestan province in this study. To do this, from the stations that had better conditions in terms of data, 11 stations were selected and SPI-6-12-24 was obtained through DIP software for each of the selected stations on a monthly and annual basis.

Drought study of SPI-12-24 in Khuzestan province showed that the onset of drought in this province started in 2008 or 2009 and continued until 2013 or 2016. Among all the stations, 2009 and 2012 were the most severe years in terms of drought and in most of the stations in SPI-12-24 these two years were the driest years in the drought periods, and they were in drought conditions in all these stations during these two years. In terms of drought severity, Safi Abad, Omidiyeh, Ahvaz and Abadan stations were all ranked first to fourth with very severe drought. With regard to time, November and January in SPI-12 with frequency of 4 and May, July, August and September in SPI-24 were the most affected by standard precipitation drought. The TWSA values for Khuzestan province from 2002 to 2016 showed that according to this figure, the value of TWSA in this area found a negative trend from April 2008 to December 2016. in Khuzestan province from 2008 to 2016, three dry periods were observed, which are from April 2008 to January 2010, April 2010 to January 2014 and May 2014 to December 2016. The lowest TWSI values in each period were -11.27, -13.03, and -10.58 mm.

In this study, spatial-temporal changes of TSDI water deficit index in Khuzestan province were investigated. To do this, first the monthly index SPI-12-24 was calculated using the monthly rainfall values of 11 meteorological stations for the whole region in the period 2002 to 2016 using the DIP software. Then, to calculate the TSDI index, the data of total water storage anomalies obtained from GRACE-CSR satellite were used. Drought survey of SPI-12-24 in Khuzestan province showed that drought in this province started in 2008 or 2009 and continued until 2013 or 2016. Among all the stations, 2009 and 2012 were the most severe years in terms of drought, and in most stations in SPI-12-24 were the driest years in the drought periods. In terms of severe drought, Safiabad, Omidieh, Ahvaz and Abadan stations all ranked first to fourth with very severe drought. November and January in SPI-12 and August and September in SPI-24 were mostly affected by standard rainfall drought, with 2% of the area in normal condition, 27% in moderate drought condition, 68% in severe drought condition and 3% in a very severe drought situation, meaning that most of Khuzestan province was covered by severe and very severe drought. The study of water shortage in Khuzestan province showed that in Khuzestan province, August, January, and April were the most affected by water shortage and August with -6/89, the driest month in the whole statistical period was studied, which according to the classification The TSDI index is in a very strong category. In terms of seasonality in winter, due to the fact that the amount of groundwater was strengthened in this season, its amount changes sinusoidally and sometimes it was in a moderate position and sometimes in a very severe category. Among the seasons, autumn had the least changes compared to the other seasons and was located in the middle to upper class.

Land surface temperature (LST) plays an important role in surface energy balance. A set of environmental parameters, such as temporal and geographical changes, thermal properties, biophysical properties, climatic parameters and subsurface conditions can cause heterogeneous spatio-temporal distribution of LST and its anomalies to be. LULCC-induced surface temperature anomalies have important implications for understanding the physical mechanisms associated with the surface to changes in various biophysical factors, including albido and surface roughness (also known as aerodynamic resistance). The purpose of this study is to evaluate the seasonal changes and abnormalities of daytime and nighttime land surface temperature in Iran based on LST derived from satellite data.

In this study, the following steps were performed:A study area:The whole country of Iran was wanted. To better reveal the behavior of surface temperature anomalies in Iran, the data has been converted to a seasonal scale and also for the first time in the country, surface temperature anomalies have been studied separately for night and day.B) DataB-1) Moderate Resolution Imaging Spectroradiometer(MODIS)To investigate the anomaly of land surface temperature, the MODIS sensor data of Terra satellite MOD_LSTAD and MOD_LSTAN products were used for day and night data with a horizontal separation of 10 km and the statistical period of 2001-2018, respectively.C) Calculate trend and trend slope using non-parametric Mann-Kendall and Sen’s testsIn order to evaluate the abnormal trend of land surface temperature in Iran, non-parametric Mann-Kendall (M-K) test was used. The non-parametric Sen's method was used to estimate the slope of the process in the time series of land surface temperature anomalies and day and night in Iran.

The results showed that the mean anomaly of daytime land surface temperature in Iran (LSTAD) in the three seasons of winter, spring and autumn is negative and in summer is positive. Also, the long-term mean anomaly of night surface temperature (LSTAN) is negative in cold seasons (winter and autumn) and positive in warm seasons. The positive maximum of LSTAD in Iran was 0.172 in summer and its negative maximum was -0.672 in autumn. The same statistical quantity was obtained for LSTAN positive anomaly in summer 0.266 and in autumn 0.244. The minimum LSTAD was calculated between -1.942 to -3.097 and the maximum was calculated between 1.047 to 2.865. For night, it showed a minimum between -0.748 to -1.296 and a maximum between 1.597 to 2.189. The average statistical trend of Iran LSTAD and LSTAN in all seasons except autumn is increasing. This amount, despite being incremental, is not significant. During the day, the maximum average trend of increasing abnormality is obtained in summer (0.744) and at night in spring (1.038). The minimum and maximum trends in both day and night in Iran are significant at the alpha level of 0.01 and in terms of trend intensity, the warm seasons are more intense. The highest computational Z-score of Mann-Kendall test was obtained at night with the value of 4.097 (spring). Also, the same maximum amount per day was calculated with the amount of 3.917 in summer.

In this study, we have evaluated the day and night land surface temperature anomaly of Iran using Terra satellite MODIS sensor data during a long-term statistical period (2001-2018). The non-parametric Mann-Kendall test was used to study the trend and the non-parametric Sen test was used to calculate the trend slope. The positive anomaly of Iran's land surface temperature is higher at night than during the day and this amount is also significant in the warm seasons of the year. The maximum positive anomaly was obtained during the day during the summer with a value of 0.172 degrees Celsius and for the night with a value of 0.266 degrees Celsius. The average anomaly trend of land surface temperature during the day and night in winter to summer is increasing and only in autumn this amount is decreasing. The minimum and maximum trend in each period of time is significant at the alpha level of 0.01 and the intensity of the trend is more at night than during the day. The main focus of negative anomalies is recognizable in low-lying dry areas, inland arid regions located in the east and southeast of Iran and inland holes of Iran. While the increasing anomaly in the highlands and high latitudes of Iran is significant. Also, the dominant upward trend can be seen in the highlands of Iran, except in autumn; In this regard (Fallah Ghalhari, Shakeri and Dadashi Roudbari,2019) who used three methods of microcirculation SDSM, MarkSimGCM and CORDEX simulated the minimum and maximum temperature of Iran under the models CanESM2, GFDL-ESM2M and MPI-ESM-LR up to 2100 ; It was concluded that the annual temperature anomalies of the selected models are at high latitudes and mountainous highlands, which is in line with the results obtained in this study. One of the most important roles of land surface temperature and its anomaly is changes in convective processes, mixture layer depth and wind speed. Therefore, increasing the anomaly of land surface temperature in Iran can increase convection on the one hand and change the regional wind speed. (Dadashi Roudbari,1399) in explaining the role of surface temperature and climate change has stated that the warm surface of convection increases and causes the mixing of surface air and high surface air. Since the velocities of horizontal winds at land level are zero and at higher levels, the vertical mixing of horizontal winds causes wind speeds close to the earth's land surface to increase and wind speeds at high levels to decrease. Variability in surface temperature also changes the air temperature near the surface. In addition to what has been said, land surface warming in the highlands of Alborz and Zagros also affects the carbon cycle; Because surface heating accelerates the melting of snow and ice in these areas, resulting in the release of excess carbon (Fili, Roir, Gotha, & Pregent, 2003). Therefore, it is worthwhile to pay more attention to policies related to carbon stabilization as well as programs related to water resources and dam construction based on what was addressed in this study.

Particulate matter (PM) or aerosols is a generic term used for a mixture of solid particles and liquid droplets in the atmosphere. Monitoring of natural (dust and volcanic ash) and anthropogenic particles (soot from biomass burning and industrial pollution) has attracted much attention in recent years. These particles can affect cloud properties, Earth's radiation budget, overall atmospheric circulation patterns, surface temperature, and precipitation. The emission of particulate matter and its associated stimuli comes from sources such as energy consumption and biomass burning in urban environments, and these two factors are commonly known as major contributors to PM2.5 concentrations in the atmosphere. However, surface PM2.5 concentrations are related to many factors such as meteorological conditions (eg temperature, wind speed, and relative humidity), land use type, population, and road networks, and so on. In recent years, many studies have been conducted using Aerosol Optical Depth (AOD) satellite measurements, AOD is a very important parameter for predicting Particulate matter (PM) at the Earth's surface in unmeasured locations or periods. AOD determines the amount of light absorbed or scattered by particulate matter. It is, therefore, an important parameter for predicting changes in PM although it may have deficiencies in this regard;The purpose of this study was to estimate the particulate matter in the atmosphere of Tabriz city using a high spatial resolution weighted regression model (0.1-degree arc, 10 km apart). For estimating PM2.5 in Tabriz during the period 1998 to 2016 will be used combined data from SeaWiFS, MISR, and MODIS data.
Research

As mentioned earlier, meteorological conditions can substantially affect the relation of AOD-PM2.5. Aerosol concentration variability can change particle extinction properties and thus affect visibility. Visibility is an indicator of urban air quality, and particular matter is adversely associated with visibility impairment.Geographically weighted regression (GWR) is a technique mainly intended to indicate where nonstationary is taking place on the map and that is where locally weighted regression coefficients move away from their global values. GWR is also a local form of linear regression used to model spatially varying relationships. Hourly data of particulate matter less than 2.5 µm (PM2.5) in air pollutants were obtained from Tabriz air quality control stations for 2016-2013. Aerosol Optical Depth (AOD) Data from Three Moderate Resolution Imaging Spectroradiometer (MODIS) of Terra and Aqua Satellites with Two Dark Target (DT) and Deep Blue (DB) Algorithms, Multi-angle Imaging SpectroRadiometer (MISR) and the GeoEye's OrbView-2 (AKA SeaStar) SeaWifs satellite sensor were used. The data were downloaded from the Ladsweb database of The National Aeronautics and Space Administration. Finally, non-parametric Mann-Kendall and Sens' slope estimator methods were used to investigate the trend and trend slope of the PM2.5 in Tabriz.

Statistical indices of R2 and RMSE for PM2.5 showed that satellite data have high accuracy in estimating PM2.5. The R2 of Long-term time series data was 0.878 and RMSE was 1.330. The annual mean distribution of PM2.5 in Tabriz showed that PM2.5 was higher in western parts of the city than in other areas. Therefore, this area was identified as a polluted area in Tabriz. The PM2.5 in the city of Tabriz from 11.29 to 16.86 µ/m3. The southern and northern regions of the city showed the smallest PM2.5. The western and northwestern regions of the city, especially Tabriz's 4th, 6th and 7th districts, are the main areas of heavy industry, high density of roads and accelerated urban sprawl. This geographic environment has a significant impact on the emissions of primary greenhouse gases and secondary mineral pollutants. Unfavorable weather conditions on the planetary boundary layer (PBL), continuous inversion, and poor wind speed in winter can cause more pollutants to accumulate in a shallow layer. The annual mean long-term PM2.5 of Tabriz city was calculated to be 14.04 µ/m3. During the period of the first study period (1998-2002), PM2.5 was lower than the long average but from the second period onwards the particles with a steep slope in Tabriz have increased as in the third period (2012- 2008) and the fourth period (2016-2013) of particulate matter exceeded the long-term average value.The highest Z-score of the Man-Kendall test was 2.69 in the western and northern parts of the city. Zones 1 and 2 also showed the lowest Z score of 1.75. The trend slope, which shows an increase in PM2.5 per head per year in Tabriz. According to the results, PM2.5 in Tabriz Variability between 0.250 to 0.25 µ/m3 (year-1). According to the results of Mann-Kendall test, Sense test also showed maximum gradient with 0.225 µ/m3 in western parts of Tabriz (4, 6 and 7 urban areas). The Z-score of the Man-Kendall test was 2.69 in the western and northern parts of the city. Zones 1 and 2 also showed the lowest Z score of 1.75. The trend slope, which shows an increase in PM2.5 per head per year in Tabriz.

In this study, Seawifs, MISR and MODIS satellite data were used to estimate PM2.5 using Geographic Weighted Regression (GWR). The results showed that the western and northwestern regions, along with significant parts of central Tabriz, have high PM2.5 values. In Tabriz, the lack of proper ventilation of wind speeds caused by urban buildings, along with the meteorological conditions of the local area, can be attributed to the high accumulation of particles. The least polluted areas were identified in the south and southeast of Tabriz. These areas are highly favorable for atmospheric dispersal due to low greenhouse gas emissions and meteorological conditions. The northern areas of Tabriz have a high PM2.5 due to the possible existence of farmland. However, economic and social factors such as industry, traffic, construction and burning of fossil fuels are direct sources of air pollution in Tabriz. But what is known is that socio-economic factors are less effective than natural factors in the city. Climatic conditions usually have a direct impact on PM2.5 in various aspects of wind-induced diffusion, precipitation of particulate matter, accumulation of particles in the air, and formation of secondary particles.

Aerosols are solid or liquid particles in the air with a typical radius of 0.001 to 100 μm, which have a significant and harmful effect on human health. Aerosols come from both natural and human sources, and in recent years, human activities associated with urbanization and industrialization have led to a steady increase in the amount of these particles in the airborne state. The study of the precise variability of the AI index in the long run can provide useful information on aggregates, their origin, spatial temporal variation, climate induction and its feedback in the climate system. This study the purpose of the seasonal evaluation of the Aerosol Absorption Index (AAI) was based on TOMS and OMI sensor data in Iran.

In this study, TOMS data from two Nimbus 7 satellites (1992-1979) and Earth Probe (1996-2005) and OMI (2015-2005) satellite EOS Aura The non-parametric Mann-Kendall test was used to identify the Aerosol Index trend.

Significant reduction in the trend The TOMS Satellite (EP) Earth Probe's Aerosol Index (AI) is far from waiting for dusty days and Aura satellite data. TOMS sensor data is not recommended for decomposition of the EP, because since 2001, due to the lack of proper calibration of this sensor, data from this sensor and the satellite provide irrational figures;The results have shown that TOMS sensor data is not suitable for the study of the EP, since since 2001, the data of this meter is not calibrated. The maximum incremental increase of the Aerosol index (AI) for OMI and the maximum decreasing trend of the Aerosol index (AI) for the winter (TOMS satellite satellite EP) was calculated in autumn (TOMS sensor of two Nimbus7 and EP satellites). In spring, the soil moisture content decreased and the activation of dust springs decreased relative to the winter season from areas with decreasing trend and increased areas with increasing trend. In summer, areas with an increasing trend based on Nimbus7 satellite (100%) and Aura (96.74%) of the total pixels are covered. Maximum incremental rate and also the maximum average value of the Aerosol index (AI) trend are obtained based on the OMI Satellite Aura sensor in the fall season. The increase in the Aerosol Index (AI) in Iran is due to environmental and climatic conditions. The summer Shamal wind, the dynamic and thermal patterns of West Asia, and the Indus Low Pressure, have the greatest role in increasing the hygiene of Iran.

The maximum trend in the general trend of the AI indicator in Iran in winter is the TOMS satellite Earth Probe satellite in the northeast and central parts, which, as said, is due to the lack of calibration of the sensor and the satellite. The maximum incremental trend of the Iranian Aerosol Index (AI) for the OMI and the maximum decrease of the Aerosol Index (AI) was calculated for the fall (Numbus7 Satellite TOMS Sensor). The average trend of Iran's AI index for winter is not significant for any of the two sensors and three satellites. In the spring, the intensity and percentage increase was increased relative to the winter season. The growing trend in most parts of Iran is associated with dusty events originating from dry and desert lands of southwestern Iran, especially in Iraq. In the summer, Nimbus7 and Aura satellite data showed more than 95% of the country's total traffic. Zones with increasing trend of TOMS sensor Nimbus7 satellites are seen in all metropolis of Iran with 500,000 to 1 million and more than one million people, and no negative trend was calculated in any cities. In the autumn, we see the maximum percentage and the intensity of the significant increase of the Aerosol Index (AI based on the OMI satellite satellite Aura in the country. This trend can be correlated with decreasing precipitation and the inhibition of the following particles in the airborne atmosphere due to reduced moisture content. The increase in the trend of Huawei index in Iran is due to environmental conditions such as land use, soil moisture, drought and ... The role of HomoSperm regional circulation systems such as Caspian Sea–Hindu Kush Index (CasHKI), negative phase of Pacific Decadal Oscillation (PDO)) with fluctuations, Shamal winds, short-term phenomena such as frontal systems, low-level jets (LLJs), and low pressure of the document on the transit of these particles are significant