فهرست مطالب seyed mohammad jafar nazemosadat

Based on the research and assessment carried out during the Climate Change Enabling Activity Project under United Nations Framework Convention on Climate Change (UNFCCC) and using the scenarios proposed by IPCC, it is estimated that if the CO2 concentration doubles by the year 2100, the average temperature in Iran will increase by 1.5-4.5°C which will cause significant changes in water resources, energy demand, agricultural products and coastal zones. The present study is aimed to investigate the characteristics of climate change in Iran and some parts of the neighboring countries. Identifying the spatio-temporal changes in three atmospheric variables comprising perceptible water (PW), specific humidity (SH) and vector wind (VW, U and V components) over 1960-2017 was the main themes of the study.

Monthly values of these variables during wintertime (January to March) were extracted from the CDC/ Reanalysis 2/ NOAA in 2.5 * 2.5 grids for the period of 1960-2017. The study area locates between 20o to 45o N and 30o to 70o E. After averaging monthly data into seasonal series, as first step, significant changes in the considered series were investigated between two equal periods having 29 years of data (1960-1988 and 1989-2017). In the second step, the 58 years of the study period were divided into five successive decades (1960-2009) and a period with eight years (2010-2017). The Kolmogorov-Smirnov (K-S) field significant test was used for assessing the spatio-temporal difference between the obtained maps associated with various decades.

According to Figures 1 and 2, for both of the 29-year time-scales (1960-1988 and 1989-2017), PW was maximum (12 to 17 kg/m2) alongside the northern coasts of the Persian Gulf and the Oman Sea. After this, PW had the highest values over the southern coasts of the Caspian Sea (10 to 12 kg/m2). Oppose to these coastal areas, minimum values of this variable with about 6 to 10 kg/m2 were associated with the Zagros mountains. In general, PW exhibited an inverse relationship with elevation. In contrast to PW, SH  maximized (4.2 to 5 g/kg) over the Zagros ranges and its relationship with elevation was generally positive. The lowest value of the SH data was about 3.5 g/kg suggesting relatively low variation in the SH data within the country. Compared to the 1960-1988 period, a significant decline was observed in the values of PW and SH in 1989-2017. Although this decline was obvious over all parts of the country, it was slightly significant for the southwestern (northwestern) districts. Compared to the first half of the study period (1960-1989), PW (or SH) decreased by about 2.5 kg/m2 (or 0.6 g/kg) in southwestern and 0.3 kg/m2 (or 0.15 g/kg) in northwestern parts of Iran for the recent half (1989-2017). Differences between wind data during these two time-periods were mostly either northerly or easterly suggesting a significant decrease in the rain-bearing southerly or westerly circulation over 1989-2017. Anomalies of the near-surface wintertime winds were mostly found to be southerly or westerly during 1960-1988 implicating the possibility of moisture transport from the Persian Gulf, the Oman Sea, the Mediterranean Sea, and the Red Sea into the most parts of Iran. Conversely, the anomalies were either northerly or easterly in1989-2017 suggesting less moisture transport into Iran for this recent period.  In the decadal time-scale, maximum values of PW, SH, as well as southerly or westerly circulations, were observed during 1960-1969. The given results suggest that the enhanced (or suppressed) values of PW and SH are generally harmonized with the strengthened southerly and westerly (or northerly and easterly) wind anomalies. For this period, prevailing of southeasterly winds over the Caspian Sea enhanced or suppressed the measure of PW, SH over the western or eastern coasts of the Sea, respectively. Even though the mentioned atmospheric circulation patterns were generally similar for the 1960-1969 and 1970-1979 decades, positive anomalies of PW and SH, as well as the westerly and southerly airflows, were slightly suppressed for the second decade. The anomalies of westerly and southerly winds decreased by about one-fifth for 1980-1989 as compared with that in 1960-1969 resulting in a significant decrease in the PW and SH data for this decade. Although these anomalies were slightly positive over most parts of Iran, their weakness did not allow significant improvement in the PW and SH values. The period of 2000-2009 was evaluated as the driest decade of the study period for which the negative anomalies of PW and SH, as well as westerly and southerly circulations, were maximized (in absolute values). In spite of the fact that these undesirable conditions have recovered during the period of 2010-2017, PW and SH were still very low for this recent period. With the exception of the 1990-1999 decade, PW and SH have continuously decreased for the decades after 1970. The rain-bearing southerly and westerly winds have been gradually replaced with dry northerly or easterly wind during the recent periods.

The findings showed that the PW and SH distribution patterns are close together in the 29-year periods, the measures were, however, significantly smaller in the second period than in the first. The wind anomalies, which were mostly southerly and westerly in 1960-1988, have been changed to northerly and easterly in 1989-2017. Since the southerly and westerly winds play an influential role in moisture transfer to Iran, their reduction in the second period is consistent with the observed decrease in PW and SH. Among the ten-year periods, the highest positive PW and SH abnormalities are associated with the 1960 and 1969 decade. This positive anomaly decreased over the time. Since a positive trend is observed for 2010-2017, it can be concluded that 2000-2010 is the driest decade of the study period. The positive anomalies of westerlies (easterlies) and southerlies (northerlies) increased (decreased) the magnitudes of PW and SH.

Land cover and soil moisture changes have a significant impact on land surface temperature (LST). Therefore, LST can be used to study land cover and desertification changes. Arsanjan County, which is located in the northeast of Fars province, has a relatively good forest and rangeland. Unfortunately, excessive harvesting of the groundwater resources and also reduced precipitation in this area caused to decrease water levels and dried up many wells in this area during recent years. So the area of the farmland and Bakhtegan Lake has decreased in this region during the last decades. However, so far, the condition of the LST and its relationship with land cover changes has not been assessed in Arsanjan County. In this study, spatial-temporal changes of LST and its relationship with vegetation and the water area of Bakhtegan Lake have been studied.

The eleven images related to Level-1 data of Landsat satellite was taken from 2003 to 2018. Since the vegetation situation in the study area is in the best vegetation and water area condition in April and May, so the images related to these months were selected to check the fluctuation of vegetation cover and water level of Bakhtegan Lake. The data pre-processing was performed in three sections: geometric, radiometric and atmospheric correction by ENVI software. The FLAASH algorithm, which is one of the best methods of atmospheric correction, was applied for atmospheric correction. In this study, NDVI was used to estimate the amount of vegetation. The Planck algorithm method was applied to calculate the LST. The change detection process was done using the index differencing method. To classify the LST map and the temporal-spatial changes, the LST difference map was normalized. Then, the normalized image was categorized using the standard deviation parameter in five temperature classes.

In the present study, 11 Landsat images were examined to investigate the spatial-temporal changes in land coverage and LST and the relationship between these two parameters from 2003 to 2018. The NDVI mean value was 0.25 in 2003, which decreased to 0.18 in 2018. On the other hand, the LST mean value had an upward trend as it increased from 29℃ in 2003 to 41.7℃ in 2018. The NDVI mean value was 0.66 in the farmland in 2003, however, its value reached to 0.33 in 2018. In contrast, LST mean value increased in the farmland from 20.9℃ in 2003 to 39.5.5℃ in 2018. Also, the LST mean value in the lake area increased from 20.1℃ in 2003 to 36.5 in 2018. Based on the results, the NDVI mean value in the rangeland and farmland decreased by 0.07 and 0.33, respectively, in 2018. However, due to the positive relationship between NDVI and LST in water-covered areas, the NDVI mean value increased by 0.39 in Bakhtegan Lake area in 2018. In contrast, the LST mean value in the rangeland, farmland and Bakhtegan Lake increased by 12.7℃, 18.6℃ and 16.4℃, respectively, in 2018 compared to 2003. The results indicated a negative relationship between NDVI and LST (R2= 0.862). The LST value decreases by increasing NDVI value in the vegetated area. In contrast, there was a positive correlation between NDVI and LST in salt-marshes and barren areas. According to the results, the highest negative correlation was obtained for the farmland, which was  -0.94. The reason for this high correlation can be related to the high density of vegetation cover in agricultural areas. The low negative correlation between NDVI and LST in the rangeland indicates the low vegetation density in rangeland and forest area. In order to study the area of decrease or increase of LST in the farmland, rangeland and water classes, the LST difference map was classified to five categories including very low temperature, low temperature, medium temperature, high temperature and very high temperature. According to the result of LST classification, the highest area was related to the moderate temperature class in all land covers, so that the highest area of this temperature class was associated with the rangeland by 86733 hectares. Since the vegetation density, especially in the farmland, had a significant decrease in 2018 compared to 2003, the area of high and very high-temperature classes increased in 2018, so that their area reached to 4625 ha and 7192 ha, respectively, in the farmland. Also, since the water area of the lake decreased in 2018 compared to 2003, the area of high and very high-temperature classes in these classes reached to 1824 ha and 3919 ha, respectively.

According to the results, the NDVI mean value in 2018 decreased in the farmland and rangeland and increased in the Bakhtegan Lake area. In contrast, the LST increased in the mentioned areas. The results of the LST classification showed that the highest amount of LST change is related to the moderate temperature class. Since the vegetation density, especially in the agricultural area, had a significant decrease in 2018 compared to 2003, the results showed that the area of high and very high temperatures had a higher increase than low and very low temperatures. Also, since the lake's water level decreased in 2018 compared to 2003, the area of high and very high temperatures in these classes increased. The findings show that there is a negative correlation between vegetation and land surface temperatures.

Drought is one of the most complex and destructive climatic phenomena, which can be perceived as the least understood natural disaster (Kao and Govindaraju, 2010). The most important and challenging characteristics of drought are frequency and return period (Bazrafshan et al., 2014; and Zhang et al. 2015). This complexity is derived from the interdependence of drought characteristics that make the univariate frequency analysis inefficient (Mirakbari et al., 2012). Therefore, instead of using traditional univariate analysis, a better approach is to derive the joint distribution of drought variables (Mishra and Singh 2010). Furthermore, insufficient data at the stations and the existence of ungagged areas necessitate regional analysis. Regional frequency analysis, on the one hand, provides the possibility of analysis for ungagged regions, and, on the other hand, provides better and more comprehensive information for meteorological stations using a combination of points and regional data. The main objective of this research is regional bivariate drought analysis in the semi-arid climate of Fars Province, Iran. In this regard, the index variable based on linear moments is one of the most advanced methods ( Núñez ez et al., 2011).

In recent years, due to excessive consumption and unbalance between water consumption and water resources, Iran has led to a severe shortage of renewable water sources. Therefore, in this situation, analyzing the status and presenting appropriate strategies is the main priority for policymakers. For this purpose, by designing a dynamic stochastic general equilibrium model, status of consumption of water resources has been studied in the household, agriculture and industry sectors. The existence and definite status of the stable situation, both parametric and numerically, has been investigated according to estimated values for Iran's economic parameters. According to the results of the research, it should be noted that at present, per capita water consumption is 1000 cubic meters per year. In other words, with current domestic consumption per capita, water per capita is less than that on the optimal Path. It is expected to achieve sustainable equilibrium in a long-term horizons by reforming consumption and providing policy solutions. Also, based on the simulation, from different policies, Establishing a 8-year limit on agricultural products with high water consumption is the most important policy for achieving the above equilibrium.

The ultimate goal of this study is using remote sensing technology and using MODIS data to calculate the amount of dust in the Persian Gulf and evaluation of linear correlation model with ground measurements of atmospheric dust. The method of this research was that initially deposition mask and clouds mask and atmospheric correction was applied to images. In the following, extract of dust values in the form of AOD parameter was performed by using cod of MATLAB software. Finally, evaluation of results was performed by using the AERONET station data and statistical indicators RMSE and RMSD. Initial results of algorithm was reflects the model ability to simulation of dust particles. The results showed that existing algorithms simulate the spectral reflectance data in the effective radius different for dust concentration in the LUT tables. Amount of correlation calculated form the 13 September 2009 in the band of 1.243 is 0.94 and for the band of 1.643 is 0.97 respectively. RMSE and RMSD indexes were calculated in these day for bands of 1.243 equal to 0.02 and 0.02, and day for bands of 1.643 equal to 0.1 and 0.01 respectively. The evaluation results showed that AOD values obtained from satellite images is significantly correlated with AOD measured in AERONET stations.

Dust prediction such as prediction of wind and rain needs to synoptic information to the earth's surface, upper layers of the atmosphere, the prediction maps of land surface and upper levels as well as using radar and satellites. The purpose of this study, use of remote sensing technology and MODIS images to estimate dust optical depth on the Persian Gulf surface and estimating the linear correlation relationship between the dust measurements in ground and atmospheric. The dust optical depth calculated using the code developed in MATLAB software. Evaluation of extracted data conducted using Pearson correlation coefficient, RMSE and RMSD index. In this study, optical depth obtained from image processing compared with the optical depths obtained from AERONET network. The evaluation results showed a high and significant correlation between the obtained optical depth and optical depths obtained from AERONET network (R2=0.99). The best and most suitable mode demonstrated for 1.243 and 1.643 bonds. At all stations, AOD value obtained from satellite image is bigger than AOD amount corresponding to the AERONET station and the algorithm used has overestimated. The cause of this more estimate can be use of limited particle's effective radius, because the scope of this effective radius is limited at the distribution of particle size in log-normal. Error resources at the retrieving particulate matter was defined such as sensor calibration error, pollution on the radiation angle, or poor predictor of water reflection.

The aim of this study evaluates the use of satellite images to change detection in coastlines and evaluate the use of optimum index and the modified normalized difference water index (MNDWI) in examining changes coastline in Dayyer city. As well as for the evaluation of coastline change in the study area, 8 points were taken on April 24th, 2015 along the coastline city of Dayyer with regard to access to the beach, coinciding with the passage of the Landsat satellite, OIF index with a standard deviation and correlation, the correlation between bands is determined. MNDWI index, a combination of green and mid-infrared bands is to determine the boundaries between land and water is suitable. The results show, given that coastal study area and satellite imagery, the highest of OIF for OLI sensor is obtained RGB 156 composition. Also, the algorithm MNDWI is ideal for discriminating between land and water. The average distance from the baseline to the border extracted from the optimum index is 340 meters and the average distance from the baseline to the border extracted from the MNDWI index is 64.28 m is obtained. The results showed to check coastline changes in Dayyer city, MNDWI index is more accurate than the optimal index.

Land surface temperature (LST) is a key parameter in estimating energy balance that has determinate role in climate change studies. Various scientists have studied monitoring of LST in recent decades. The land surface temperature, which is measured by means of thermometers for certain points, for large scale basin is not cost effective. Using of satellite images for estimating LST make the estimates easier and more economical than ground measurement. In this study, MODIS land surface temperature (LST) was evaluated. In addition, due to use of correction factors which may not always be available for Iran, land surface temperature estimated by Landsat 5 image,which its spatial resolution is much higher than MODIS, was also evaluated.
For this study, two groups of data were used: satellite data and in-situ data. Ground measurements were collected from 261 points of a wheat farm in Marvdasht plain located in Fars province. Temperature was measured in four height of wheat including: canopy cover, middle, 10 centimeter from floor and soil surface. After statistical tests, acceptable data were selected for the comparison. In this study, twenty eight satellite images were implemented; including 26 MODIS images (MOD02 & MOD11 product) and 2 level-1G Landsat 5 images. Land surface temperature was estimated from thermal band’s of Landsat 5 images by applying the necessary corrections. After providing land surface temperature (LST) maps, land surface temperature was extracted from LST map (Landsat5 & MODIS) based on the measurement points. Afterward, the equation between the observed data and estimated surface temperatures from Landsat 5 (MODIS images) were obtained. Relationship between estimated and in-situ data was analyzed on four different heights of the wheat. Land surface temperatures were also estimated by three different split–window algorithms from Becker and Li (1990), Price (1984) and Ultivertal (1994) and the coefficients were calibrated. Finally, Fisher test was used to determine significant differences between the observed and the estimated data.
It was found that the estimated temperature by satellite has the best correlations with the plant canopy temperature. Estimated data were evaluated against the in-situ data. Results showed that Landsat and MODIS images overestimated the LST by RMSE of 4.4 oC and 7.1 oC respectively. Error of Estimating LST with split–window algorithms was within the range of 3.5–3.7 degree centigrade. Among the three studied algorithms, Becker and Li (1990) approach showed the best performance (the least error). The significant differences between in-situ data and the satellite estimates were examined by Fisher Test. No significant differences were observed in any of the pairs of data.
For meso-scale and large-scale studies, using satellite images is efficient and economic than the point surface measurements. The choice of satellite images (Landsat or MODIS) is depend on the accuracy which is expected from the study.

Atmospheric aerosol plays a significant role in the Earth's radiation budget through radiative forcing and chemical perturbations. Aerosols are intricately linked to the climate system and the hydrologic cycle. The net effect of aerosols is to cool the climate system by reflecting sunlight. Quantifying the net effect requires accurate information on the global distribution of aerosol properties that have to be estimated from satellite observations. Estimating aerosol properties is also one of the first steps in generating high-level land surface products from satellite observations. Effective aerosol retrieval information is also essential to satellite imagery atmospheric correction. Satellite remote sensing has been employed to supplement the prediction of ground-level dust concentration. Satellites are able to cover vast spaces at a relatively low cost. For aerosol studies, the launch of the Moderate Resolution Imaging Spectro-radiometer (MODIS) has enabled the retrieval of aerosol optical depth (AOD) data globally from the satellite's spectral observation. MODIS AOD is a measure of light transmittal by aerosols in an atmospheric column during the satellite overpass. With the evolution of the retrieval algorithm, MODIS AOD has become increasingly important in the role of producing more accurate estimation for the aerosol. Estimation of aerosol loadings is of great importance to the studies on global climate changes. Meteorological numerical models and ground stations are not able to tracking and detection of dust storms and in many cases have significant errors. This issue show necessitates use of reconstruction ways dust according to remote sensing techniques. The purpose of this study, use of remote sensing technology and MODIS images to estimate dust concentration on the Persian Gulf surface and estimating the linear correlation relationship between the dust measurements in ground and atmospheric. In this study, we develop a new algorithm for estimating the aerosol optical depths using MODIS data over Persian Gulf surfaces. This algorithm is validated using AERONET measurements.
In the current study we analyze atmospheric aerosol optical properties over Persian Gulf. Annually, Dust storms are imported into Persian Gulf from the West and North West and South West. Data corresponding to the station was extracted for channels of 0.644, 0.855, 0.466, 0.553, 1.243 and 1.643 µm of satellite image. The ocean algorithm was designed to retrieve only over Dark Ocean, (i.e. away from glint). There is a special case when we retrieve over glint, and that is described below. The algorithm calculates the glint angle, which denotes the angle of reflection, compared with the specular reflection angle. The glint angle is defined as:Θ_glint=〖cos〗^(-1) ((cosθ_s cosθ_v )(sinθ_s sinθ_v cosϕ)) (1)
Where θ_s, θ_v and ϕ are the solar zenith, the satellite zenith and the relative azimuth angles (between the sun and satellite), respectively. The retrieval requires a single fine mode and a single coarse mode. The trick, however, is to determine which of the (4 x 5) twenty combinations of fine and coarse modes and their relative optical contributions that best mimics the MODIS-observed spectral reflectance. The reflectance from each mode is combined using η as the weighting parameter:ρ_λ^LUT (τ_0.55^tot )=ηρ_λ^f (τ_0.55^tot )(1-η)ρ_λ^c (τ_0.55^tot) (2)
Where ρ_λ^LUT(τ_0.55^tot) is a weighted average reflectance of an atmosphere with a pure fine mode 'f' and optical thickness τ_0.55^tot and the reflectance of an atmosphere with a pure coarse mode 'c' also with the same τ_0.55^tot. Before the final results are output, additional consistency checks are employed. Our primary means of true ‘validation’ is comparison with ocean-based sunphotometer measurements, specifically, those of AERONET. The AERONET measured AOD is easily interpolated to the exact MODIS wavelengths (for example 0.55 µm) by quadratic interpolation in log reflectance/log AOD space. The AERONET ‘sun-measured’ definition of FW differs from either of the MODIS (land or ocean) definitions, but should be correlated with either. AOD images were compared with values obtained in AERONET stations, in finally AOD values were evaluated using statistical indexes of Average, standard deviation, correlation, Mean Squared Error (RMSE) and Mean Difference Square Error (RMSD). In the following validation, we use AERONET Level 1.5 data of the Dalma, Bahrain, Abu Al Bukhoosh, Sir Bu Nuair, Umm Al Quwain, MAARCO and Mussafa station when available.
Discussion of Results & The evaluation results showed that good correlation exists between the AOD simulation and AERONET data, with the correlation coefficient exceeding 0.90. The best and most suitable mode demonstrated for 1.243 and 1.643 bonds with the correlation coefficient equal to 0.94 and 0.99 and RMSE and RMSD index equal to 0. 2 and 0.02 for band of 1.243 and 0.1 and 0.01 for band of 1.643, respectively. We conclude that significant limitations exist for aerosol retrieval using marine AERONET stations. The number of matching points between the two datasets may become sufficient toattempt the reduction of the current uncertainties. Given the identified uncertainties, the results of this study do not contradict these previous validation efforts. Future research may reduce these uncertainties and require modifications to the retrieval algorithm. We used AOT data from a comprehensive set of Persian Gulf AERONET stations to evaluate the dust retrieval algorithm. Data evaluation was performed by using the Pearson correlation coefficient, root mean square error index (RMSE) and root mean square deviation index(RMSD). The evaluation results showed that good correlation exists between the AOD simulation and AERONET data, with the correlation coefficient exceeding 0.90. The regression analysis of AOD data revealed similar limitations. We found that the AOD simulation are on average 5%–25% more than the corresponding AERONET values, depending on the regression weighting assumptions for the comparison dataset. To further evaluate the performance of the algorithm in comparison with the AERONET measurements, we used the RGB image from MODIS. Overall, the comparison with the AERONET data has revealed similar performance of the two satellite datasets with a tendency of the simulation AOTs to underestimate and the MODIS over-ocean AOTs to overestimate the AERONET values. The range of these discrepancies is comparable to the uncertainties associated with the limited number of ocean stations and natural aerosol variability. The comparison of AOTs in the Persian Gulf AERONET stations showed that AOD values in AERONET stations are, on average, 5%–25% lower than the corresponding simulation ones. While the biases in ocean retrieval algorithms and cloud screening procedures may not be excluded, these results indicate that aerosol loading in marine locations may differ significantly from that in adjacent land areas, thereby limiting the achievable validation accuracy. We conclude that significant limitations exist for aerosol retrieval using marine AERONET stations. These limitations can be linked to the extreme sparsity of the marine AERONET data, uncertainties associatedwith local conditions at the marine stations, and regression accuracy limits imposed by natural aerosol variability. The number of matching points between the two datasets may become sufficient toattempt the reduction of the current uncertainties. Given the identified uncertainties, the results of this study do not contradict these previous validation efforts. Future research may reduce these uncertainties and require modifications to the retrieval algorithm.