majid vazifedoust

Due to the upcoming climate threats, challenge of water shortage and its impact on the food security of the growing population in Iran, the optimal use of soil and water resources is very important. With the development of remote sensing technologies, free access to a variety of field data has become widely available, which can be used to reduce the uncertainty of simulation models. The aim of this study was to simulate actual evapotranspiration (ETa) and rice crop coefficients (Kc) during its growth stages using the SWAP model updated with satellite data and evaluate the accuracy of the results with/without updating. This research was conducted at the National Rice Research Institute of Iran in Rasht in the year of 2017. Based on the obtained results, total ETa measured by lysimeter and simulated by SWAP model with and without updating were 395.4, 373.2 and 363.6 mm, respectively. The average crop coefficients during the growth stages of vegetative, reproductive and ripening were estimated as 1.13, 1.49, 1.21, respectively. The crop coefficients for the proposed stages estimated by SWAP model without using satellite data were 1.02, 1.39, 1.04, respectively. After updating with satellite data, the crop coefficients were modified as 1.05, 1.43 and 1.07, respectively. Finally, the statistical analysis indicated that the SWAP model has a reasonable performance in estimation of ETa (RMSE=0.89; EF=0.98; R2=0.74) and rice crop coefficients (RMSE=0.53; EF=0.96; R2=0.63). The results indicate that the SWAP model combined with satellite data improved the accuracy of ETa estimation (RMSE=0.75; EF=0.99; R2=0.86) and rice crop coefficient (RMSE=0.40; EF=0.99; R2=0.74) at field scale.

In recent decades, several models have been developed to simulate farm water management. The main focus of most researchers is obtaining more products per unit of water consumed. Water productivity defined as crop yield per unit of water consumption. Due to limitation of water resources and its optimal consumption in order to save water and increase its productivity, this study was conducted to simulate water productivity indices of rice (Hashemi caltiver) using SWAP model in paddy soils at field scale. For this purpose, two closed-ended lysimeters were used to measure the actual evapotranspiration. The quantity of evapotranspiration was measured daily and water productivity based on irrigation water (WPir) and evapotranspiration (WPET) was calculated and compared with the simulated values afterward. The obtained results indicated that the SWAP model had a high accuracy for estimating amount of yield (R2=0.90 and RMSE=648.73) and the amount of water consumed in the evapotranspiration process (R2=0.89 and RMSE=164.07). Using the calibrated SWAP model, the water productivity indices from division of yield on evapotranspiration (WPET) and yield on irrigation water (WPir) in the studied farm was estimated amount of 0.553 and 0.876 kg/m3, respectively. Also, results showed that with increasing irrigation efficiency, elimination of deep percolation and reduction of evaporation, water use efficiency increases by 30%. In general, for optimal management of paddy soils in field scale and considering the optimal yield at harvest time, serious attention must be paid to water efficiency to come up with most effective ways to deal with water crisis and to increase the quantity and quality of rice production.

Nowadays, the use of simulation models is of great importance for optimal management of the production process and yield on farm scale. Due to the fact that combination of remote sensing with a model increases the accuracy of simulation, this study was conducted to evaluate the updated SWAP model combined with Sentinel 2 satellite images and AquaCrop model to simulate Hashemi rice yield in one of the research farms at the National Rice Research Institute of Iran. The measured parameters that were compared with two models included the quantity of dry matter of plant components during growth period. Evaluation of simulated and measured values indicated that the root mean square error of paddy and rice yield simulated with SWAP model updated with Sentinel 2 satellite images in two stages of reproductive and ripening were 6.28 and 142.40 kg/ha, respectively. Also, the RMSE values obtained for AquaCrop model were 9.55 and 338.46 kg/ha, respectively. Further evaluation of obtained statistics revealed that both SWAP updated with satellite images and AquaCrop models, can predict the yield and biomass of Hashemi cultivar with reasonably high accuracy. However, the AquaCrop model with a slight difference (R2=0.96), was less accurate in simulating the quantity of paddy than the SWAP model updated with satellite images. Furtheremore, when SWAP’s inputs were updated by data obtained from satellite images, at different stages, provided a higher accuracy to simulate the yield of paddy. Thus, it can be fairly used to simulate the rice yield.

Predicting crop yields before harvest using simulation models and remote sensing technology is very important in sustainable agricultural management. The objective of this study was to use Sentinel 2 satellite images to improve the accuracy of SWAP model to simulate Hashemi rice yield in one of the research farms at the Rice Research Institute of Iran. For this purpose, LAI, NDVI and SAVI indices were derived from satellite images for growth period of plant. Also, leaf area index was measured directly and weekly at the field level and compared with indices derived from satellite images to provide the best equation for estimating the amount of dry matter. Then, using multivariate linear regression equation, the amount of dry matter was estimated from satellite images and given as the input parameter in the SWAP model. Finally, the measured yield was compared with the simulated values by the SWAP model with and without using satellite imagery. The results showed, when measured dry matter was as the model input, the error of model was 3.93% equal to 396.88 kg/ha and the model accuracy was acceptable (R2=0.95). but when the estimated dry matter from the satellite imagery was modeled as input, the model simulated the yield value of paddy with R2 equal to 0.99 and an error of 2.04% equal to 241.20 kg/ha. Finally, based on the obtained results, updating the SWAP model using satellite imagery improved the simulation accuracy and the model was able to simulate rice yield with higher accuracy.

Given the importance of soil and water resources in the development of sustainable agriculture, increasing world population and the growing need for crop production, predicting crop yields using plant simulation models and remote sensing technology is very crucial. The aim of this study was to estimate the yield of rice components including straw, paddy and biomass of Hashemi cultivar during different growth stages with SWAP model and to provide regression equations by extracting NDVI and SAVI plant indices from Sentinel-2 and Landsat-7 and 8 satellite images. It was done in the National Rice Research Institute. Comparison of statistical variables indicated that the mean values of coefficient of determination (R2) and model efficiency factor (EF) in estimating the yield of rice components in different stages of growth with SWAP model were more than 0.70 and 0.90, respectively, and with an error of 1.93 to 6.54% was equivalent to 134.21 to 470.43 kg/ha. The slight difference between the measured and simulated values showed that the SWAP model estimates the rice yield in the study area with appropriate accuracy. The results also showed that the extracted NDVI and SAVI indices with very good accuracy estimate the yield of rice components at different stages of growth. However, the highest amount of correlation was related to the reproductive development stage. Finally, R2 for NDVI at different growth stages as well as  the entire growth period for straw, paddy, and biomass were higher than the SAVI index, revealing more accuracy of NDVI than SAVI.

The need for good quality water resources, in line with population growth and the diversity and multiplicity of pollutants and contaminants dictate the quantitative and qualitative management of water resources. In this regard, water resources monitoring and the real time availability of spatial and temporal information can play an important role in water resources management. Real time remote sensing technology using satellite images with proper accuracy is able to determine some of the qualitative parameters of water affecting the temperature or spectrum of light from the surface of the water. The aim of this study was to investigate the accuracy of water quality monitoring of Anzali wetland using remote sensing. For this purpose, after field study of Anzali wetland and rivers leading to it, suitable sites for sampling were selected. After selection of suitable points for sampling, in order to increase the accuracy of the regression models, satellite images were used in different seasons of the year (November, 2002, February, 2002, May 2003 and August 2003). During the satellite (landsat 7 and 8) passing above the area, samplings were done and the quality parameters including nitrate, ammonium, soluble phosphorus, total dissolved solids, suspended solids, conductivity and acidity were measured. After extracting satellite images and their reflections, the effective bands on water quality values were determine and regression models of the images were obtained. The results showed that the use of remote sensing technique in Anzali wetland could be able to estimate the acidity, total suspended solids and temperature well (with root mean percentage of normalized error less than 10%). Other qualitative parameters including nitrate, salinity, total soluble solids and ammonium (with root mean of normalized error less than 30%) and orthophosphate (with root mean percentage of normalized error greater than 30%) were estimated fairly and poorly, respectively. Therefore, the remote sensing technique is able to estimate most of the qualitative parameters with acceptable accuracy. Surely increasing the number of sampling points and frequencies, the accuracy of regression models and consequently the accuracy of estimation of qualitative parameters will increase.

Evapotranspiration is one of the most important processes in water and radiative transfer in hydrological cycle, and the required energy for this process is provided by solar radiation. Therefore, the accuracy of evapotranspiration estimation is strongly depends on the accuracy of solar radiation estimation. This study was conducted to evaluate the different surface solar radiation models such as empirical models (Angstrom and Hargreaves-Samani), physically-based models (NCEP and GLDAS) and a satellite observation model (CM-SAF). The results showed that the calibrated Angstrom model with R2=0.9 and SEE=2.58 was the most efficient model. However, the accuracy of this model is strongly depends on the calibration procedure and the existence of sunshine data. The GLDAS model with R2= 0.87 and SEE=3.5 was the second most efficient model after calibrated Angstrom model. The GLDAS model, in spite of 10.2% overestimation of surface solar radiation, can be the most efficient model in areas with the lack of meteorological data.

Precipitation is a major component of the hydrological cycle and is a crucial forcing data for hydrological and agricultural models. Great efforts have been made to generate gridded precipitation products, thereby leading to the increased availability of precipitation datasets at different spatial and temporal resolutions over the globe. This study provides an intercomparison of daily precipitation of four high spatial resolution gridded precipitation products (PERSIANN-CCS, CHIRPS, CFSR and AgMERRA) and that simulated by RegCM4 model and their evaluation against observational data over eight spring seasons (2003-2010) in the Qazvin Plain. Statistical evaluation of three convective precipitation schemes (Ku, Emanuel, and Grell) coupled in RegCm4 showed that Ku is the best scheme for spring precipitation modeling in the region. Based on continuous statistical indices, AgMERRA shows relative superiority to other products and RegCM. Categorical statisticsal indices indicated that CFSR performs better in detecting rainy days, but AgMERRA in detecting the non-rainy days and determining rainy and non-rainy days. Results showed that compared to other precipitation products, AgMERRA, in general, is closer to the observed daily precipitation during the spring over the Ghazvin Plain.

Droughts are long-term phenomena that affect vast areas, causing significant economic damages andlosses in human lives. Droughts are the most costly natural disaster in the world, and affect more people than any other natural disaster. Therefore, it is important to develop early warning systems to mitigate the effects of drought. The easiest way to monitor drought is to use drought indices that calculate drought severity, duration and actual range for each drought type. Several drought indices have been developed based on different variables and parametersto assess drought types. Soil moisture is a significant hydrological variable related to flood and drought and plays an important role in the process of converting precipitation into runoff andstorage of groundwater. Due to the difficulty, cost and time required for the field measurements of soil moisture, this parameter has not been widely used in drought indexes. Recent developments of global databases, based on satellite estimates, as well as rapid progress in hardware and software for modeling complex processes governing the water balance at the ground surface, have led to many efforts to deploy this new tool to reduce the limitations in this field. In this research, a new drought index based on soil moisture, derived from the land surface models of Global Land Data Assimilation System (GLDAS-SMDI) has been provided to monitor the evolution of drought severity.Thisindex is based on the fact that soil moisture is a determinant factor in most of complex environmental processes and has an important role in the occurrence of drought. The central Iran is located between 27N-37N latitudes and 48E-61E longitudes with an area of about 837,184 km2. There are 50 synoptic stations within the area. In the present study, soil moisture derived from Global Land Data Assimilation System using the GLDAS-SMDI index was used to prepare the spatial distribution map of drought in central Iran over the period of 2001-2004. The accuracy of the GLDAS-SMDI index based on satellite data was carried out using the evaluation criteria of R and RMSE compared with drought spatial distribution map derived from the SPI index based on monthly precipitationdata of 50 synoptic stations. In this study, the drought spatial distribution index of Soil Moisture based on the Global Land Data Assimilation System (GLDAS-SMDI) and SPI was obtained based on the monthly precipitation data from 50 synoptic stations over the period of 2001-2004. The results of the statistical criteria of the moisture drought spatial distribution mapcompatibility assessment based on GLDAS data with corresponding pixels on the drought spatial distribution map based on the precipitation data of thesynoptic stations showed that the drought severity map has had a high precision and good conformity with the land data (R=0.65, RMSE=0.22) based on GLDAS data.The highest correlation coefficient (0.74) was in 2004 and the lowest (0.45) in 2003.
The lowest and the highest mean errors in 2004 and 2001were 0.19 and 0.26, respectively,.The highest droughtseverity based on the GLDAS-SMDI index occurred in the Central Iran region at Iranshahr, Kahnuj, Bam, Baft and Birjandstationsduring the studied period. Droughts are hydro-meteorological anomalies characterized by prolonged shortage in regional water supply and can cause temporary difficulties (even failures) in water reservoirs. Today, most of the severe droughts are breaking out in terms of frequency, magnitude and duration due to constantly increasing water consumption, causing serious social, economic and environmental problems worldwide. Therefore, in order to deal with frequent droughts, great efforts have been made to estimate a more accurate assessment for better decision-making in order to prevent and mitigate drought losses. The most successful efforts among these methods might be the development and the use of various objective indices. In this research, the monthlymoisture data of the Global Land Data Assimilation System was evaluated to estimate the drought severity index based on soil moisture. The evaluation was performed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). This analysis has demonstrated that the GLDAS products have very good compatibility with the land data over the selected area of Central Iran on monthly timescales and a 0.25° spatial scale. As a result, it can be said that the GLDAS data has a good potential for useful application of hydrological simulation and the calculation of water balance sheet, in the regions with low observations and low quality station. Therefore, it can be concluded that the soil moisture output of Global Land Data Assimilation System can be used for rapid and low cost estimation of drought severity based on soil moisture, which is a major factor in many complex environmental processes and has an important role in the occurrence ofdrought. In order to increase the spatial accuracy of drought intensity maps, it is recommended that the satellite data be combined with the values ​​of ground stations.

This paper describes the behavior of dust storm occurred in Tehran, on April 24, 2017 by assessing the mesoscale and micro scale patterns, as well as meteorological parameters such as temperature, humidity, pressure, dew point, wind field, and visibility in the study area leaded to the occurrence of dust. This dust storm typically occurs at high wind speed, temperature drop, rise in dew point temperature and pressure, resulting reduction of visibility. The dust storm is also analyzed and tracked by satellite imagery. The RGB satellite images of the EUMETSAT sensor on April 24, reveals the motion of dust to the western, central Iran and the signs of dust genesis in Tehran province as well as the movement of Cirrus clouds to north and northwest of Iran as a signature of cold front. Cirrus clouds have been advanced to the northwest and center of Iran at 12:00 UTC. The concentrations of PM10 and PM2.5 particles can be considered as a suitable measure of the concentration of natural mobilized dust particles. The increase of PM10 and PM2.5 values is in coincidence with visibility reduction as well.

The algorithm of Sufrace Energy BALance (SEBAL) is one of the most widely used methods for determining actual evapotranspiration through remote sensing. However, assuming a linear relationship between temperature difference (dT) and land surface temperature (LST) and the visual identification of hot and cold pixels are the weak points of the method. Modified SEBAL (M-SEBAL) on the other hand, replaces two new concepts as hot and cold edges, with a significant impact on increasing the accuracy and automation of the execution of the algorithm and removing the need for skilled user. Although, determining the warm edge requires high computing and thus increase in computing time and possible errors. In this study, a simplified form of the M-SEBAL (SM-SEBAL) is developed and the results of executing of the three method on MODIS images and comparing the results with the observed data are shown. Results show that SEBAL overstimates the ET values and M-SEBAL and SM-SEBAL underestimate the same values. The highest sensible heat flux calculation error comes from M-SEBAL (33.87%) and SEBAL shows the lowest error rate (11.23%). The highest error in the calculation of the latent heat (LE) retrives from M-SEBAL (11.66%) and the lowest error rate retrives from SEBAL (5.05%). However, the maximum daily evapotranspiration observed error was for SEBAL (3.56%) and the lowest error was retrived from SM-SEBAL (1.81%) and then M-SEBAL (2.47%). In addition, the time required to perform SEBAL algorithm is up to four times more than the time needed to run other two algorithms.

Nowadays, evapotranspiration, which is an important component of water balance calculations, can be estimated using the technology of remote sensing. MOD16 actual evapotranspiration data are produced using the MODIS sensor imageries and have a spatial resolution of 1 Km. In the present study, MOD16 actual evapotranspiration data are compared with the actual evapotranspiration estimated using SWAP model. Using the measured soil moisture content data during the growing season in two 38.9 and 45.6 ha corn fields located in Qazvin Province, SWAP model was calibrated. The average RMSE values in the two fields under study were, respectively, 0.026 and 0.025. Considering the actual evapotranspiration estimated by the SWAP model as the evaluation basis, the MOD16 data were assessed. In this assessment, the RMSE values in the two fields under study were obtained, respectively, 1.46 and 1.94 mm/day. Also, the r2 values were calculated 0.86 and 0.87. The results of the present study suggest that MOD16 product can be effectively used when the measured data of evapotranspiration are not available.

Concentration and type of atmospheric aerosols are among of the most important factors affecting the macro and micro structure of clouds and their precipitation.
In this article, the case study has been carried out to investigate the relationship between atmospheric air pollution, microphysics of cloud and their precipitation over Tehran city. The case study in this paper (2012/12/06) was performed before the the rainfall that increased AQI index for particulate matter which made Tehran to be closed for two days. This event was compared with two similar rainfalls shortly before or after it and it was found out that in the two similar rainfalls the AQI index had been healthy during the days before precipitation. Ion concentrations in the rainwater were measured by ion-chromatography and microphysics of clouds were studied by MODIS on board Aqua and Terra satellite.
Findings: The results showed that ions concentration, especially anthropogenic ions (nitrate, nitrite, chloride and fluoride), in rainwater samples in polluted air was higher than in clear air. Also particulate pollution reduced the effective radius droplet of the mixed-phase clouds before the rain, and increased the cloud optical thickness, cloud water path and cloud top pressure and temperature during the precipitation.
Discussion and The ions in rain water and the cloud microphysics characteristics related to the concentration of aerosols in the atmosphere. Also, local particle pollutions changed regional microphysics of cloud

  Precipitation data have been widely used in many earth science applications ranging from crop yield estimates, tropical infectious diseases, drought and flood monitoring. However, in many tropical regions and parts of the mid-latitudes, rainfall estimates still remain a major challenge due to sparse rain gauges. To better develop applications for these regions, it is necessary to have rainfall data with adequate spatial and temporal resolutions. Precipitation data plays the key role in drought monitoring. Rain gauges are the mainly measuring methods for precipitation but they are concentrated in developed countries and are spare in developing countries and remote areas in the world. Researchers have shown that remote sensing techniques using space-borne sensors provide an excellent complement to continuous monitoring of rain events both spatially and temporally. Microwave and Visible/Infrared are the main forms of remote sensing technologies; both have varied advantages in terms of imaging accuracy and spatial-temporal resolutions. So, the fine spatial-temporal precipitation products need the coalescence of both. Tropical Precipitation Measuring Mission (TRMM) carrying sensors on precipitation provides the opportunity for fine spatial-temporal precipitation products. In this research for Central Iran, the precipitation data of TRMM satellite was evaluated and used to estimate the severity of a drought based on precipitation. Central Iran is located between 27N-37N latitudes and 48E-61E longitudes and has an area of about 837,184 km2. There are 50 synoptic stations within the area. The data set used includes monthly precipitation depth from both synoptic stations and TRMM data (3B43 V.7, in ASCII format). A five year (2001–2005) period were chosen for the analysis. The accuracy of precipitation data that are used from synoptic stations and TRMM satellite are provided by the source provider. Firstly, the evaluation of TRMM satellite data was measured using coefficient of determination (R2), mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) in 95% confidence levels. Then, remote sensing data of TRMM are used to provide the required data of precipitation drought index in central Iran for mapping the spatial distribution of drought. Finally, accuracy of the PDI drought index based on satellite data carried out using the evaluation criteria compared with drought spatial distribution map of the PDI based on ground-based precipitation measurements data and soil moisture values of 50 synoptic stations. In this study for the first time, a comparison between monthly rainfall values estimated by satellite products and rain gauge observations was implemented over the Central Iran. The validation of TRMM 3B43 data were performed at monthly, season and annual scales. The average of monthly rainfall, seasonal and annual for all selected synoptic stations and TRMM data were compared during the period 2001-2005. TRMM data at all time steps except August, estimates the average of monthly rainfall more than observed data. The correlation coefficient between the average of monthly rainfall, seasonal and annual rain gauge and TRMM has shown that this ratio is variable between 0.45 to 0.94 for all time steps, the average of this ratio is equal to 0.76. Highest and lowest values of R2 at monthly time step obtained for April (0.92) and June (0.45) respectively. In this time step, lowest and Highest values of statistical error criteria obtained for June and January, respectively. The seasonal time step, the highest and lowest correlation is related to the spring and summer with determination coefficient (R2) of 0.94 and 0.64, respectively. In this time step, lowest and Highest values of statistical error criteria obtained for summer and winter, respectively. Generally, TRMM data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. The correlation coefficient for the annual time step is equal to 0.83. The results of statistical criteria showed that TRMM rainfall data in all time steps overestimated for all months except of August. The lowest to the highest of statistical error criteria obtained for monthly, Seasonal and annually rainfall data, respectively. In the next step, spatial distribution of drought based on measured data from ground stations and TRMM data over the period 2001-2005 obtained from using Precipitation Drought Index (PDI) method in study area. The results of the statistical criteria of conformity assessment PDI spatial distribution map based on TRMM data with corresponding pixels spatial distribution map based on the synoptic stations precipitation data showed that the drought severity map based on TRMM data had a high precision and good conformity with ground data (R2=0.89, ME=0.08, MAE=0.14, RMSE=0.19). Also, the results of the evaluation criteria showed that PDI index in accordance with soil moisture values had the significant correlation (0.71) and the lowest estimation error (2.33). In this research, for estimation of drought severity index based on precipitation, the monthly precipitation data of TRMM satellite (3B43) was evaluated. The evaluation was measured using coefficient of determination (R2), mean error (ME), mean absolute error (MAE) and root mean square error (RMSE). This analysis has demonstrated that the TRMM rainfall products show very good agreement with gauge data over the selected area of Central Iran on monthly timescales and 0.25° space scales. In conclusion, it can be said that the satellite-based rainfall, e.g. TRMM data, have good potential for useful application to hydrological simulation and water balance calculations at monthly or seasonal time steps, which is a useful merit for regions where rain gauge observations are sparse or of bad quality. However, several shortcomings, such as the TRMM overestimates the rainfall in some years and areas and underestimates in other years and areas, and failed to detect the extreme rainfall, reduced the accuracy of stream flow simulation at short time steps and other applications including drought monitoring and flood forecasting. The above mentioned conclusions indicate that it is necessary to further develop algorithms of satellite-based rainfall estimation in terms of both the accuracy and spatiotemporal resolutions of rainfall estimates

Irrigation management, water use efficiency determination and provision of efficient solutions for increasing of limited water resources productivity are based on accurate estimation of crop yield and actual water consumption which its exact measurment is very difficult without proper tools. In this regard, Doroudzan irrigation and drainage network of Fars Province in Iran was divided into 86 simulation units using GIS. Water balance components and crop yield were determined in each spatial unit during a 10-year period (2000-2010) using distributed SWAP model and through MATLAB software programming. There was good correlation between the observed and simulated wheat yield amounts in each simulation unit. After validating of the model, the water balance components were simulated by calibrated model and the average water productivity in the Doroudzan network was estimated. The results showed despite decrement in water use in the network, water productivity has been increased means that higher yield can be produced by water managenment. Estimating of water balance components and yield production in the network, especially in crisis situations and water deficit condition can be used for agricultural water management evaluation and finding ways to increase water use efficiency.

Precipitation is one of the most efficient mechanisms for washing the atmospheric pollutants, specifically particulate ones out.The study chemistry of rain water was shown pollutions in atmosphere.
Accordingly, to do that 16 rainwater samples from two Mehrabad and Aghdasieh synoptic stations collected during the autumn and winter of 2012 and spring of 2013 (the city rainy seasons). Concentrations of major inorganic ions () and pH in bulk precipitation samples collected in the Tehran city of I.R. of Iran were analyzed. In this article, Enrichment Factors, Correlation Analysis and Principal Component Analysis had been used in order to identify the sources of ions found in sampled rain waters.
Findings: Results show that concentrations of the found substances were higher over the Mehrabad station. The mean concentration of ions indicated that,, and were the main anions, while ,, were the main cations. anion had important role in acidity of the rain water. The Bicarbonate anion had the highest concentration, among all other ions, over both stations and plays an important role in reduction of the acidity of the rain water. Study upon the potential sources of the ions over Tehran by means of Correlation Analysis, Enrichment Factor and Principle Component Analysis indicate that existence of Qom salt lake and desert in the south of the city were the main source of the and. There are anthropogenic sources for. The other ions were from anthropogenic and dust origin.
Discussion and The pollutants in rainwater over Tehran were derived from long range and local (industry and traffic) sources.

Landsat 7 ETM data is widely used in studies of the spatial distribution Kc and vegetation cover parameters in regional and global scales but SLC failure has greatly reduces its usefulness. Additionally, the failure is permanent and has failed subsequent attempts to recover the SLC, so required and practical way to address this problem is filling the pixels of missed data in the SLC-off images. Although, there are several proposed methods to fill the gap, but still have filled images quality in heterogeneous area is not satisfactory for more applications. This study was conducted to compare the geostatistics and MODIS auxiliary data methods to fill the pixels of missed data in the SLC-off images. The results showed that the IDW method with NRMSE 6.09% was the best method. The fusion with auxiliary images (MODIS) and ordinary Kriging methods resulted in NRMSE 14.75 and 16.9, respectively. The method of fusion with classified auxiliary images (MODIS) presented the lowest accuracy in estimating missed data.

Water resources development, requires frequency water recognition, temporal and spatial water distribution, also detailed assessment of its performance. Yusop et al (2007) used HEC-HMS model for rainfall-runoff modeling in a small watershed in Malaysia and reported the Satisfactory results of both calibration and verification periods (20). Planning in ungauged areas is required providing appropriate data. Thus using satellite data, is one of the methods solving this problem. For this purpose, large-scale models of the earth's surface such as GLDAS, have been updated based on satellite observations, are important tools for providing hydrological parameters. The global coverage of GLDAS, using data of the model have been considered in rainfall-run off studies. GLDAS model to evaluate the components of the water balance and energy changes in the Earth's surface, attempted to produce meteorological and hydrological parameters of high quality in the period 1948-2015. The purpose of this study, presented integrated model to estimated discharge in ungauged data.
In this study, the GLDAS model integrated with hydrological model, WMS/HEC-HMS. Integrated model was used in rainfall-runoff studies in Polroud area in Guilan province. In this case, the Tol-lat station observation data was used during 3 years (2003-2005) for calibration and 1 year 2006 for verification through two SMA and SCS methods. In this regard, for the SCS method, model calibrated parameters such as curve number, lag time and initial abstraction. For the SMA method, model calibrated soil storage, soil percolation rate, groundwater 1 storage coefficient, groundwater 2 storage coefficient and other parameters. After evaluating GLDAS results, analyzed the precipitation, surface runoff, subsurface runoff and temperature data in the pixels corresponding to the Tol-lat gauging station along the 10 (2004-2013) years. Then, using WMS/HEC-HMS, simulated runoff in the watershed, using two losses method SCS and SMA, 2004-2009 for coefficient calibration and 2010-2013 for verification. The results indicated that SMA and SCS method offer better results through integrated model. Evaluation based on criteria coefficient of determination (R2), Nash coefficient (E), the standard error of Bias, a root mean square error (RMSE) and Error showed that integrated HEC-HMS and GLDAS models is a useful tool for estimating run off in ungauged watershed.
The results show that integrated HEC-HMS and GLDAS models in SMA losses in 2013 (verification period), with 0.8 coefficient of determination, 0.77 and Nash coefficient, 1.5 RMSE and 6.1 Bias error. The integrated model with SCS method in 2005 (calibration period), with 0.9 coefficient of determination, 0.86 and Nash coefficient, 0.78 RMSE and 2.5 Bias error have highest efficient. Also the model in estimating the flood peak moment is better than the non-flood values. Although in both cases, the model results were acceptable.
The integrated model HEC-HMS and GLDAS presented in this study is an acceptable tool for predicting runoff in inaccessible and ungauged watershed.

The assessment of Guilan’s meteorological radar data (PPI and SRI) during autumn 2013 and winter 2014 shows the presence of a large number of pixels, mostly located in Guilan mountainous areas, involving incorrect reflectivities. For example, the daily average of incorrect reflectivity on the 26th of December 2014 calculated by PPI (plan position indicator) products of Guilan’s meteorological radar resulted in 47.014 dBZ in Lahijan synoptic station which would be equal to 31.6 mm/hr rain rate through Marshall-Palmer Z-R relationship. The importance of these results could be appreciated when we note that Lahijan synoptic station had a daily gauge rainfall height equal to zero on the same day. Thus, according to the position of incorrect reflectivities of Guilan’s meteorological radar, this study aims to evaluate the ground clutter influence on incorrect reflectivity appearance. The method used is to study the overlapping of the beam blocking map and the daily average of incorrect reflectivity map by changing the elevation angle of the radar beam at a clear air day. The results show that the ground clutter is the main factor responsible for incorrect reflectivities in %75.34 pixels of the image. Using comparison of the mean distance of the blocked pixels and the pixels containing the ground clutter to the radar antenna leads us to conclude that the real amount of the elevation angle is equal to -0.20°. The method adopted by assuming 5 km donut-shape regions around the radar antenna and using it to determine the radar elevation angle is one of the innovations of this study. Furthermore, to ensure the working of the actions undertaken for the clutter contamination mitigation, one requires a numerical threshold to determine the effectiveness of the actions. For this purpose, therefore, a method is developed based on the correlation between the radar’s 24-hr rainfall heights and their respective gauge data. The method provides a maximum threshold in dBZ unit for the clutter contamination while Z = 200 R1.6 is applied. The numerical thresholds resulted from the application of the method for the meteorological stations of Rasht (the Airport), Rasht (the Agriculture Faculty), Anzali, Talesh, Lahijan, Jirandeh, Masouleh, Deylaman and Manjil are equal to -3.748, -1.638, -6.074, 14.952, -3.611, -1.482, 12.466, 4.872 and 6.852 in dBZ, respectively. Also, a value of 9.536 dBZ is obtained for all the stations considered together. The latter value can be extended to all pixels of the radar image. As the conclusion, in spite of the fact that on the 26th of December 2014 as a non-rainy day, the average clutter was considerable, a close correlation between the radar’s rainfall heights and their respective gauge data is observed during the 15 days studied. The Pearson correlation coefficients (Pcc) in the meteorological stations of Rasht (the Airport), Rasht (the Agriculture Faculty), Anzali, Lahijan, Jirandeh, Deylaman and Manjil are significant at 1% statistical significance level, and also Pcc values in the meteorological stations of Talesh and Masouleh are larger than that can be excluded. Therefore, the clutter contamination of Guilan’s meteorological radar data cannot be considered an impediment to its application in practical purposes. However, for further progress, a higher degree of accuracy would be required.

Not only have clouds and precipitation (snow, rain and hail) made up an important part of the subsystem atmospheric phenomena of terrestrial climate, but are also considered as the main components of the water cycle. Aerosols play an important role in cloudiness and precipitation. In warm clouds with the same liquid water path, increase in aerosols concentration results in the reduction of effective radius of cloud droplets and increase of cloud albedo, which is referred to as “first indirect effect” or “Twomey effects” (Twomey, 1977;Twomey et al., 1984). Variation in concentration of aerosols causes variation in the thickness, life period and precipitation rate of the clouds, referred to as “second indirect effect” or “Albrecht effect”, (Albrecht, 1989; Hansen et al. 1997; Ackerman et al. 2000).
The city of Tehran, the capital of I. R. of Iran, is a mega-polis with a population of more than 10 million. The city is located in the central part of the Alborz Mountain Range and characterized by its complex topography and diverse climate in an area of about 700 Km2. The city's long term annual average precipitation is 300mm. The growth of the city during the last 30 years has turned the city to one of the most polluted cities in the country, particularly during autumn and winter. The geographical conditions of the city are the other most important factors affecting the city's environmental condition and the nature of the particulate matters that may be found in its atmosphere.
In this paper, the influence of aerosols on microphysics of clouds over Tehran during the years 2003 to 2012 has been investigated.
Material and In this paper, the influence of aerosols on microphysics of clouds over Tehran throughout the years 2003 to 2012 was studied. Correlations between microphysics of clouds and aerosols from the Moderate Resolution Imaging Spectroradiometer (MODIS) were examined over Tehran. These are both from the collection 5.1 level 3 daily data, at 10×10 resolution. Daily AOD, CER, COT, CWP, CTT and CTP observed by MODIS on board the Aqua satellite from 2003 to 2012 on 10×10 grids are used in this work. In this study, Aerosol Index (AI) and Aerosol Optical Depth (AOD) were used as a proxy for the cloud condensation nuclei (CCN). To reduce errors, water phase cloud is defined as having a cloud top pressure greater than 800hPa.
Pearson and Spearman correlation coefficients were calculated and written for all cloud microphysical quantities with AOD as well as AI by software SPSS-V13. For the study, scatter plot diagrams between CER, CWP, CF, CTT, CTP and COT with AI and AOD were drawn. Furthermore, scatter plot diagrams between cloud microphysics quantities were drawn. We used the AI and AOD as our aerosol products, as AI includes size dependence and has been shown to correlate better with CCN. Pearson and Spearman correlation coefficients and scatter plot diagrams showed that there are positive correlation coefficients between the aerosol index and cloud top temperature and cloud top pressure. However, there are negative correlation coefficients between aerosol index and cloud fraction, cloud optical thickness and cloud water path. There are no significant negative correlation coefficients between cloud effective radius and AI over Tehran. These correlations suggest the increase of aerosols in Tehran in the last 10 years, because of over seeding. In these 10 years, there was reduced cloudiness over Tehran. The correlations between quantities of cloud microphysics fully match with theoretical studies.
At first step the scatter plots of CER, CWP, CF (cloud fraction), CTT, CTP and COT versus AI and AOD, which were considered as the representative of aerosols in our study, were created to provide an early picture of the possible interrelationship between those cloud microphysics and aerosols characteristics. The same diagrams were plotted to provide a picture of the way microphysical characteristics of clouds vary with each other. Study on the relationship between the AOD and AI with CCN showed that AI is a better representative of CCN .Then, correlations between clouds microphysical properties and both AOD and AI were examined by means of Pearson and Spearman correlation coefficients which were calculated usingSPSS-V13. The results showed that there are positive correlation between the AI and CTT and CTP. However, there are negative correlation between aerosol index and CF, COT and CWP. Negative but insignificant correlation was observed between CER and AI over Tehran. The correlations suggest the increase in aerosols concentration over Tehran during the duration of study might be regarded as one of the main reasons for the reduced cloudiness and precipitation over Tehran. The correlations between quantities of cloud microphysics fully match the theoretical studies.

Despite the increase in population and the acute water shortage in recent years, reuse of agricultural waste has been considered as a solution. Due to the presence of contaminants in agricultural waste, attention to environmental issues and their impact on product quality is important. Phosphorus is one of the pollutants from agricultural waste that its unauthorized causes degrading aquatic ecosystems, reducing the quality of water resources and utrification. In the present study, trending of phosphorus removal by limestone and effecting of some factors such as particle size, temperature, pH on phosphorus removal and effecting of hydraulic e of flow on the adsorption isotherm was studied. In this regard, the absorption kinetics experiments on limestone mining Langmuir and Freundlich adsorption isotherm equation based on laboratory scale and physical model of drain channel was performed to investigate the effect of hydraulic flow drainage on the adsorption isotherm. Kinetic experiments showed that the rate of phosphorus removal by limestone was fixed after 30 hours. Two sizes of limestone, there was no significant difference in the percentage of phosphorus removal. Changes lead to a decrease in pH from 2 to 6 percent phosphorus removal was increased from 6 to 11 showed increased removal rate. Optimal pH for phosphorus removal was obtained for alkaline limestone. Increasing the temperature from 22 to 30 ° C was followed by a downward trend of phosphorus removal. Check isotherm, the Freundlich equation by limestone showed good agreement. Statistical comparison of adsorption isotherm and the physical model, the experimental results showed a statistically significant and did not stop.

Monitoring and evolution of drought is the first step in any drought management system. In this study, evaluation of a new indexa new method is provided to monitor the severity of drought with Remote Sensing Combined Drought Index (RCDI). The index is based on the fact that drought is a natural phenomenon caused by a combination of various factors such as a shortage in the amount of precipitation, less than the average long-term rainfall, temperature higher than normal and the properties of the soil moisture. The new index is a statistical index comparing the present hydrometeorological conditions with the long-term average characteristics in the same interest period within the year. Three data sources used in the RCDI index includes rainfall, temperature and vegetation data. In the present study, remote sensing data of TRMM and MODIS are used to provide the required data of RCDI index in central Iran for mapping the spatial distribution of drought over the period 2001-2004. Accuracy of the RCDI index based on satellite data carried out using the evaluation criteria of R and RMSE compared with soil moisture values based on monthly data of 50 synoptic stations in 95% confidence levels. The results of the evaluation criteria showed that drought severity index calculated by the RCDI index in accordance with soil moisture values had the significant correlation (0.61) and the lowest estimation error (1.98). Thus, a RCDI index could well use in drought early warning systems.

Variation of snow cover area (SCA) in small to large scale catchment can be studied using MODIS snow products on daily to montly time step since the year 2000. However، one of the major problems in applying the MODIS snow products is cloud obscuration which limits the utilization of these products. In the current study، variation of SCA was investigated in Karoun basin، western part of Iran، using MODIS 8-day snow cover product (MOD10A2). More over in order to overcome the cloud barrier in application of snow cover products، a simultaneous employment of the images from both MODIS optical sensor and AMSR-E microwave sensor was recommended. Meeting our target، the combination of MODIS and AMSR-E daily images was exercised to accomplish snow cover area in daily interval and afterwards، a comparison was made between the result and those which had been obtained by the sole utilization of either of them while the weather had been either cloudy and not been overcast. Validation of snow cover gained by combined images was additionally compared with the discharge of one of the catchments existing in Karoun basin. The results demonstrate that regardless of the fact that microwave data، featuring a coarse spatial resolution، can penetrate the cloud cover، on average، AMSR-E images approximately show 16% more snow cover in comparison to MODIS images. The results also illustrate that the correlation existing between snow cover rate of AMSR-E and MODIS images during cloudless days، the difference of average snow cover area decreases from 16% to 5%. Moreover، the upshot of validation by the exercise of daily discharge data indicates that by possessing a correlation coefficient of 0. 66، the correlation of snow cover and discharge in combined images features a higher accuracy in comparison to MODIS images with a correlation coefficient of 0. 55.

Updating the satellite data in crop growth models is a useful technique to estimate the crop yield on some large scale farms. Throughout the present study، the improvement of SWAP model simulation was investigated using this technique for crop yield estimation. The study was conducted during 2012 growing season at some three center- pivot farms، covered by fodder maize and sugar beet as the main crops in Qazvin Irrigation Network. SWAP model was run by two methods، namely through either non-updated or updated satellite Leaf Area Index (LAI) data. Results indicated that sugar beet and fodder maize yields estimated through updated SWAP model were improved by about 13. 7 and 14. 5 (%) in absolute percent error while they amounted to 3. 321 and 1. 621 (ton/ha) in RMSE. The obtained results indicated that the LAI assimilation technique (using satellite data) can greatly reduce errors related to model input parameters. It can reduce the uncertainty as well as estimate fairly accurately the crop yield in a large area and on any individual farm.