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Introduction Accurate quantification of environmental trends must consider variation at different temporal scales when ignoring variation at one scale could lead to incorrect conclusions about variation at another scale. Many environmental monitoring programs collect temporally resolved but irregular time series data to quantify trends for regulatory, management, or research purposes. Conducting a study to understand the trends and predict future conditions in hydrological aspects such as river water quality is essential. During the last decades, river water quality monitoring has increased by measuring several water quality parameters. Therefore, the analysis of water quality trends is important in providing information about changes or variations in water quality through time series data .Furthermore, determining the quality status of water resources is necessary to adopt proper policies to prevent and enhance the reduction of water quality. Additionally, based on this information, it is possible to identify the quality of river water and implement protective measures to improve and manage rivers and drainage basins in a more integrated way. In recent years, the water quality of the Karun River has been affected by various pollutants, including agricultural runoff and industrial wastewater; Therefore, it seems necessary to monitor the quality of the river and the process of its changes over time and place to know the current situation and provide the necessary measures in the future. Therefore, this research analyzed the Karun River's water quality trend over 20 years at four water quality monitoring stations. Materials and methods To check the quality of river water in hydrometric stations, the obtained data were assessed from physical and chemical parameters, including Total Dissolved Solids (TDS), Electrical Conductivity (EC), Sodium adsorption ratio (SAR), Na, and Cl in 20 years from 1998 to 2017 in four hydrometric stations including Gotvand, Shushtar, Mollasani, and Ahvaz of Karun river in the wet season (first six months of the water year) and dry season (the second half of the water year). The process of river water quality and inspecting the changes were conducted using the Mann-Kendall test and a geographic information system, respectively. Wilcox's classification was used to check the water quality from an agricultural point of view, as there are relevant standards. By putting the sodium absorption ratio against salinity, Wilcox presents a chart for the water quality assessment for agricultural purposes and can classify water into different classes based on EC and SAR values. Results and Discussion According to the results, the river water salinity in the wet season in three hydrometric stations significantly increased. The increment was at the level of 10% at the Shushtar and Mollasani stations. However, at the Ahvaz station, it rose to the level of five percent. Due to the different annual rainfall amounts during the study period, the river water’s electrical conductivity had relatively large fluctuations in all the investigated stations. The range of electrical conductivity (EC) alterations in the wet season was between 490 and 2800 µS/cm and in the dry season between 397 and 2806 µS/cm. TDS increased in the wet and dry seasons. Moreover, the p-value showed that the value of this statistic was significant at the level of 10% in the Shushtar and Mollasani stations and at the level of five percent in the Ahvaz station. The range of changes in the wet period was between 250 and 1750 mg/liter and in the dry period between 220 and 1700 mg/liter. The alterations in total dissolved solids were more in the wet than in the dry season and did not have a uniform trend. In fact, the decrease or increase in the amount of precipitation affected the intensity and weakness of the TDS amount during the year. Conclusion The results of the Mann-Kendall test showed that the parameters of TDS, SAR, Na, Cl, and EC increased during the last twenty years, indicating the expansion of the entry of sewage and industrial and agricultural effluents. According to the Wilcox index, water quality for agricultural purposes was in the average category in all the studied stations. Meanwhile, Na, Cl, and TDS parameters were in the average and inappropriate range in some years, being an alarm regarding the low water quality. Additionally, there is a risk of water quality decline in the investigated stations. In general, the watershed of the Karun River is noteworthy due to the presence of a large population, cities, and centers. Specifically, the city of Ahvaz and the heavy steel industries located in this watershed are important fundamental in terms of water consumption and producing pollutants affecting the quality of water resources, which faces many quantitative and qualitative challenges in water. The study of the changes in the water quality parameters of the stations located in the Karun River during the study period demonstrated that the amount of dissolved salts in these rivers increased and caused the reduction of water quality due to incorrect utilization and failure to comply with the principles of river exploitation. [ABSTRACT FROM AUTHOR]

Introduction and objective: Soybean (Glycine max L.) is an annual, self-pollinating, diploid plant belonging to the Leguminosae pea family and is one of the most important oil plants in the world. The amount of yield of soybean oil constitutes its economic part, which is dependent on the grain yield and its related traits. Knowing the relationships between these traits and the interaction between them is very important for reformers. Different reports of relationships between traits and seed yield in soybean have been presented; Therefore, in this research, to investigate the reaction of different soybean cultivars and lines, in terms of yield and yield components in three levels of drought stress and normal, in order to select traits that can be considered as selection criteria for selecting cultivars and lines. paid. Materials and Methods: In order to investigate the effect of drought stress on the yield and yield components and physiological characteristics of different soybean cultivars and lines and to analyze the relationships between traits in order to select resistant cultivars adapted to the dry or low water climatic conditions of Iran, an experiment was conducted in the form of a split. The plot was based on a randomized complete block design. In this investigation, the irrigation levels included: 1- no stress, 2- stress after flowering, 3- stress after podding as the main factor and soybean cultivars and lines were considered as secondary factors in four replications. Results: between the main factor (drought stress) in terms of plant height, leaf area, length of plant, number of seeds per plant, dry weight of plant, weight of seeds per plant, weight of 1000 seeds, biological yield, economic yield and plant yield. In the level unit and between the levels of the sub-factor (numbers and lines) in terms of all the assessed traits, there was a significant difference at the probability levels of 5 and 1 percent. In terms of the interaction between the main and secondary factors, only in terms of plant height, stem length, number of seeds per plant, number of seeds per seed, seed weight per seed and biological yield, a significant difference was observed at 5 and 1% probability levels. With the increase of stress from the flowering stage to the pod bearing area, the yield and yield components decreased and showed a decrease compared to the non-stressed condition. The non-stress treatment (normal) had the highest economic yield with an average of 3134.40 kg and compared to the stress treatment after podding which produced an average of 2765.85 kg, it produced 11.75% more yield, in other words, The increase in stress intensity has decreased grain yield. Among the cultivars studied, in terms of yield and yield components, Arian line and DPX genotype were identified as superior cultivars, and Kausar, Parsa and Saba lines were identified as weak cultivars. Conclusion: In general, the results obtained from this research showed that with the increase of stress from the flowering stage to the pod bearing area, the yield and yield components decreased and showed a decrease compared to the non-stressed condition. The non-stressed (normal) treatment with an average of 3134.40 kg had the highest economic yield and compared to the stress treatment after podding with an average of 2765.85 kg, it produced 11.75% more yield, in other words, with increasing stress, yield The grain has decreased. Among the studied cultivars, in terms of yield and yield components, Arian line and DPX genotype were identified as superior cultivars, and Kausar, Parsa, and Saba lines were identified as weak cultivars. [ABSTRACT FROM AUTHOR]

Nowadays, the air of most of Iran's cities especially high population metropolises hasn’t an optimal quality. This adverse quality is due to various pollutant resources such as automobiles, industries, heating devices, construction, and commercial activities during recent decades and there are more concerns about it. Therefore, monitoring air pollutants and studying their seasonal and spatial variations are specifically important. The present study aimed to evaluate air quality and seasonal and spatial variations of particulate matter (PM2.5 and PM10) in Tehran city. In this research, the AQI index has been used to determine the air quality of Tehran and to introduce the responsible pollutant. To investigate the variations of particulate matter (PM10 and PM2.5) in seasonal and spatial scales, the data of air pollution monitoring stations (18 stations) of Air Quality Control Company was used in 2018 and 2019. Data were analyzed using Excel and SPSS software and results of statistical analysis of pollutants distribution in Temporal – spatial scales are provided and they are drawn using Arc GIS software and analytical function of inverse Distance Weighting interpolation (IDW) as maps, tables, and graphs. Based on the results, favorable and unfavorable air quality were respectively observed in 83.8 and 16.2% of days in 2018, as well as 76.4 and 23.6% of days in 2019, which can be related to the changes in rainfall rate and wind speed in the years. The maximum seasonal concentration of particulate matter (PM10 and PM2.5) relates to summer and winter respectively and the minimum seasonal concentration of both pollutants relates to spring. Results of inverse Distance Weighting interpolation (IDW) also showed that the west, south, and central regions of Tehran got involved with particulate matter more than other regions.

Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstly, the most optimized member of wavelet families was chosen. Then, by analyzing the numerical models, the most accurate linking function and fitness function were selected for the GEP model. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WGEP models were introduced. The GEP models were trained, tested and validated in 37, 20- and 10-years periods, respectively. Also, using sensitivity analysis, the superior model and the most effective lags for estimating long-term rainfall were identified. The superior model estimated the target function with high accuracy. For instance, correlation coefficient and scatter index for this model were 0.946 and 0.310, respectively. Additionally, lags 1, 2, 4 and 12 were proposed as the most effective lags for simulating rainfall using hybrid model. Furthermore, results of the superior hybrid model were compared with GEP model that the hybrid model had more accuracy. [ABSTRACT FROM AUTHOR]

Rainfall is one of the most important elements to determine the climate. Therefore, it is important to estimate its value accurately. The main purpose of this study is the evaluation of the TRMM (Tropical Rain Measurement Mission) 3B42 rainfall estimates, an exponential model and conceptual cloud model in Lake Urmia Basin. Therefore, this study focuses on the comparison of these methods to identify and select the most appropriate model for rainfall estimation in Lake Urmia Basin. The comparison are performed during the period 2007 to 2011 and the hourly rainfall, temperature, barometric pressure and dew point temperature, the three-hourly rainfall rate of TRMM 3B42-V6 at 0.25° resolution and thermal infrared images (TIR) of Meteosat 7 at six-hour intervals are used. The results indicated acceptable match of estimated rainfall with rain-gauge data. Comparison of three methods of rainfall estimation shows that exponential model has the determination coefficient (equal to 0.61). In addition to the high correlation, due to low levels of RMSE and MAE (respectively 1.58 and 1.01), has a good performance to estimate rainfall in this basin. Therefore, this model can introduced as the most appropriate model for estimating rainfall in Lake Urmia basin.

Dams are studied not only for the benefits of construction but also for their environmental impacts. Providing drinking and agricultural water, flood control and power plant construction are some benefits of dams. Due to large evaporation area behind dams, their environmental impacts on local climatic parameters or regional ecosystem, affects the benefits of their construction. In this study, the most important methods of non-parametric statistical analysis, including common Mann-Kendall test and the Sen's slope estimator due to their appropriateness for the time series of meteorological observational data were used. The effects of the Gotvand Dam construction on three climatic parameters i.e. humidity, temperature and precipitation, have been investigated and evaluated by using data from five meteorological stations in the Dam area from 2001 to 2017, by time-series trend analysis on monthly and annual scales using non-parametric method of Sen's slope estimator and Mann- Kendall test. Evaluating the data of three stations near the Dam location and comparing them with the data of two control stations away from the Dam, showed that after Dam impoundment i.e. in August 2011, relative humidity and average temperature factors have increased and the annual precipitation has decreased in the area. A combination of significant increasing and decreasing trends at 95% confidence level was observed at various stations. In July, the average monthly temperature showed an increasing trend in Masjed Soleiman and Shushtar stations, but in other months or during the annual period at the stations under study, at a 95% confidence level, a significant trend for the average temperature was not observed. The average relative humidity trend analysis showed that Dezful Airport (in September and November), Shushtar station (in September) and Lali station (in June) had a significant increasing trend and Gotvand station (in July) showed a significant decreasing trend at a 95% confidence level. However, in other months or during the annual period at the stations under study, at this level of confidence, there was no significant trend for relative humidity. Only Dezful Airport station showed a significant decreasing trend at 95% confidence level for total rainfall on annual scale. In other months or during the annual period in other stations, no significant trend was recorded for total rainfall at 95% confidence level. Further studies are needed to determine exactly whether these changes are absolutely due to the Dam or caused by global climate change contributes. [ABSTRACT FROM AUTHOR]

Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios. Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010) of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN) and Time Seri (TS) methods were compared with the result of the Emission Scenarios (A2 & B1). HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029). The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2) in LARS-WG. Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1) was suitable for Tabriz and Khoy stations .In the Urmia station MA(1) was the best performance. Multiple Layer Perceptron with a 10 neurons in hidden layer and the output layer consists of five neurons had the lowest MSE and the highest correlation coefficient in modeling the values of annual precipitation. So MLP was determined as the best structure of neural network for rainfall prediction. According to results, precipitation predicted by the ANN model was very close to the results of A2 and B1 scenario, whereas TS has a significant difference with these scenarios. Average rainfall predicted by two A2 and B1 scenarios in Urmia station has more difference than other stations. Based on the B1 scenario, precipitation will increase 11 percent over the next two decades. It will decrease 10.7 percent according to A2 emissions scenario. According to ANN models and two A2 and B1 scenarios, the rates of rainfall will increase in Tabriz and Khoy stations. However, according to TS model, rainfall will decline 5.94 and 3.63 percent for these two stations, respectively. Conclusion: Global warming and climate change should have adverse effects on groundwater and surface water resources. Different models are used for simulating of thes effects. But, conformity of these models with the results of climate scenarios is an issue that has not been addressed. In the present research coincidence of TS model, ANN model and climate change scenarios was investigated. Results show under emissions scenarios, during the next two decades in Tabriz and Khoy stations, precipitation will increase. In Urmia station B1 and A2 scenario percent increase by 11 percent and 10.5 percent decline predicted, respectively. The results of Roshan and et al (4) and Golmohammad and et al, (7) investigations show increasing trend in the rainfall rate and confirming the results of this study According to results, the performance of ANN model is better than TS model for rainfall prediction and its result is similar to climate change scenarios. Similar results have been reported by Wang et al (29) and the Norani et al (20). Due to the significant difference between the TS and climate scenarios used in the study area, is not recommended, though it can be used as a plausible climate scenario to predict the precipitation of stations in the future studied. At the end, it is suggested that the similar studies carried out in a larger number of stations in the country with respect to global warming and climate change, to determine the validity of the methods used to the predicted rainfall.