مجید عظیم محسنی

White morning glory (Ipomoea lacunose L.) is one of the weeds that have recently spread in the fields of Golestan province and has become a serious challenge for farmers. In this study, the effect of different environmental conditions, including constant temperature, alternating temperature, water stress, salinity, high temperature, and burial depth on the germination and emergence of three seed populations of this plant collected in 2018, 2019, and 2020 (at the time of the experiment, these seeds were after-ripped 15, 3, and 0 months, respectively) were investigated. All experiments were conducted based on a completely randomized design with three replications. The results showed that the response of germination to the studied environmental factors was different in seed populations. Germination percentage started at 10°C and increased with increasing temperature. Although the germination percentage was significant at 40°C, it was completely stopped at 45°C. Alternating temperature compared to constant temperature increased the germination in the range of low temperatures; however, there was no difference with the constant temperature in the high-temperature range. The water potential required to reduce 50% of germination in the seeds of 2018, 2019, and 2020 was -0.83, -0.82, and -0.43 MPa, respectively. The tolerance of this species to salt stress was much higher than to water stress, and the increase of salinity up to the potential of -1.1 to -1.4 MPa reduced its maximum germination by 50%. Increasing the high temperature to 80 and 100 °C stimulated the germination, and no seeds survived at 120°C. In the populations of 2018 and 2020, the emergence percentage of seeds increased with the increase of burial depth; so that it reached its maximum value at a depth of 3 cm. By increasing the burial depth, the percentage of seedlings emergence of these populations decreased and stopped at a depth of 9 cm. The seedlings emergence of 2019 population also happened with the same process (but in smaller numbers), but no seedlings emergence occurred at a depth of 5 cm. In general, the results of this study showed that the reaction of white morning glory germination to environmental factors is different. Knowing the germination response of this species against the mentioned factors can help predict the germination and emergence of this plant to design suitable management strategies.

In seed research, germination percentage data is the result of counting and has a binomial distribution. Therefore, seed researchers use data transformation, especially square root transformation, to stabilize the variance and normalize the data before performing analysis of variance and comparison of treatments. Despite the use of data transformation, this method has fundamental issues in the structure that misleads the test results. Therefore, it is important to introduce and replace a method that preserves the research assumptions and provides acceptable results for researchers without using data transformation. The use of generalized linear model is an alternative method for analyzing germination data with binomial distribution. In this research, the generalized linear model will be introduced first. Then, the efficiency of this method will be illustrated using simulated and actual germination data.

In this research, first the simulated data was generated by the Monte Carlo method. Based on the simulated data, the significance level and the power of test of generalized linear model were computed. Then the actual data related to three experiments including the effect of acidity on germination of wheat varieties, the effect of water stress and salinity on germination of yellow sweet clover seeds, and the effect of alternating temperatures on germination of three lavender populations were used and the results of the generalized linear model were compared with the square root transformation method based on the data of three experiments.

The simulation results showed that the generalized linear model has a high efficiency to preserve the predetermined significance level and a high power in detecting significant differences in germination of the treatments. Moreover, the results of the comparison of the generalized linear model with the square root transformation method illustrated that the generalized linear model had a higher capability to detect significant differences between various treatments, especially in the treatments with unequal seeds in the Petri dish, and in the treatments in which the square root transformation method resulted in no significant difference among treatments, the generalized linear method showed a significant difference.

Generally, the results of this research demonstrated that the generalized linear model can be used as an alternative method to square root transformation in studies on the germination percentage of seeds with binomial distribution, without having the problems of the square root transformation method. Moreover, this model outperforms the square root transformation in detecting significant differences in germination of treatments with fixed and different seeds.

The analysis of spatio-temporal series is crucial but a challenge in different sciences. Accurate analyses of spatio-temporal series depend on how to measure their spatial and temporal relation simultaneously. In this article, one-sided dynamic principal components (ODPC) for spatio-temporal series are introduced and used to model the common structure of their relation. These principal components can be used in the data set, including many spatio-temporal series. In addition to spatial relations, trends, and seasonal trends, the dynamic principal components reflect other common temporal and spatial factors in spatio-temporal series. In order to evaluate the capability of one-sided dynamic principal components, they are used for clustering and forecasting in spatio-temporal series. Based on the precipitation time series in different stations of Golestan province, the efficiency of the principal components in the clustering of hydrometric stations is investigated. Moreover, forecasting for the SPI index, an essential indicator for detecting drought, is conducted based on the one-sided principal components.

In Iran, the climatic conditions are such that even in the rainiest areas of the country, there is a need for groundwater resources, and this demand is increasing every year. Since, groundwater is one of the most valuable water resources in Iran, it is very necessary to predict its changes in order to use it optimally with the aim of sustainable development. One of the most complex hydrological processes in nature is the rainfall-groundwater level process, which is affected by various physical and hydrological parameters. Although, various models have been presented to predict the changes in the groundwater level using the rainfall patterns, but less attention has been paid to the transfer function model. Hence, the main objective of this research is to introduce and use the transfer function (TF) model to predict the monthly groundwater level using rainfall data and to compare its results with ANFIS and Artificial Neural Network (ANN) models.In the present study, 30-year data (1992-2021) of meteorological stations and observation wells in 3 watersheds of Galikesh, Ramian and Mohamadabad were used to model the rainfall and groundwater level. of the Gorganroud river basin.Then, considering that the years closer to the present time have more accurate information about the situation of this time, the years were considered as a forward process in artificial neural networks. The model fitting and prediction of the groundwater level values using rainfall data for the next 12 months was performed with applying three models: Artificial Neural Network (ANN), Adaptive neuro fuzzy inference system (ANFIS), and transfer function (TF). For this purpose, MINITAB SAS, SPSS, and R software were used. Next step, the validation of the values predicted by the models was evaluated using three indices Mean Absolute Distance (MAD), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).The results of the autocorrelation plots of the groundwater level of the wells revealed that all time series have a seasonal trend with a period of 12 months. Based on the cross-correlation plots, it was also found that rainfall has direct effect on the groundwater level in the two watersheds of Galikesh and Mohamadabad with lag time of three months and in the Ramian watershed with a delay of one month. The validation results of the models using three indexes MAD, RMSE and MAPE revealed that the artificial neural network model for predicting the groundwater level using monthly rainfall data in all three investigated watersheds had the most appropriate performance (RMSE=0.0778, 0.0243, 0.0532m) and the ANFIS model is ranked second (RMSE=0.1841, 0.0832, 0.1012m). Although the transfer function model was less accurate than the other two methods (RMSE=0.5711, 0.5023, 0.3234m), but this model has performed well in fitting the monthly groundwater level values. This model is very effective in identifying the delay in the impact between the input and output variables, as well as expressing the model based on which the impact of rainfall can be expressed as a model.The results of this research show that all three models of artificial neural network, ANFIS and transfer function can be used to predict the groundwater level using monthly rainfall values. Consecutive overestimation and underestimation, which increases the error and decreases the performance of the models, was not observed for the three used models. Also, all three models perform well in detecting trends and data changes. However, the artificial neural network model is more accurate than the other models. In addition, when forward process is used in artificial neural network modeling, compared to the case where the complete series of data is used, the efficiency of the model is significantly improved.

Groundwater is one of the most valuable water resource in Iran, which is mainly influenced by rainfall patterns in a region. In the present study, 30-years rainfall and groundwater level data were used in the three watersheds of Galikesh, Ramian and Mohammadabad to model the rainfall and groundwater level. The prediction of the groundwater level using the rainfall data for the next 12 months was done using the Transfer Function (TF) and using SAS and MINITAB software. Validation of predicted values was done using MAD, RMSE and MAPE indices. The results showed that according to the autocorrelation diagrams, all time series have seasonal trend with a period of 12 months. The results of the autocorrelation diagrams showed that in Galikesh and Mohammadabad watersheds, rainfall has a direct effect on the groundwater level with a delay of three months, but in the Ramian watershed, this delay was one month. It was also found that the transfer function model had a good performance in fitting the monthly groundwater level in all 3 studied watersheds.

Seed dormancy removal by cold stratification is accompanied by the development of gluconeogenic competence. Although hydrogen cyanide can stimulate the germination of many herbaceous dormant seeds and increase gluconeogenesis in long term, its short-term effects on sugar metabolism require further investigation. Accordingly, an experiment in the form of complete randomized design was carried out to compare the effects of cold stratification and hydrogen cyanide on walnut (Juglans regia L.) kernel dormancy release and sugar metabolism. Imbibed walnut kernels were either cold stratified or pre-treated with hydrogen cyanide and changes in lipids, total soluble sugars, non-reducing sugars, starch as well as the activity of aldolase were assessed at two and 10 days after imbibition and compared to those of warm (27 °C) incubated kernels at the corresponding days. Walnut kernels pre-treated with hydrogen cyanide showed germination of about 88%, whereas the figure for warm incubated kernels was 52%. The cyanide-treated seeds before 10 days had lower levels of soluble sugars and starch but on the 10th day, had the greatest starch and highest aldolase activity compared to the cold-stratified and warm-incubated kernels. Lipid mobilization occurred in both warm incubated and cyanide pre-treated seeds but it was undetectable in cold stratified kernels. It was assumed that cyanide stimulates sugar glycolysis in the short term as evidenced by greater aldolase activity and decreased carbohydrate levels and in the next step through the activation of lipid gluconeogenesis leads to starch biosynthesis and the greater germination potential of walnut kernels.

Nowadays, demand for water is increasing especially in arid and semi-arid regions (e.g., Iran) due to population growth, economic development, higher standard of living, and changes in consumption patterns. Hence, optimal management of water resources in these areas is essential. Furthermore, climate change and increasingly extreme weather events have caused a surge in natural disasters (e.g., floods) over the past 50 years in arid and semi-arid regions. Thus, forecasting and modeling of runoff data is extremely necessary for planning and managing of water resources. Water flow forecasting plays a key role in flood reduction, reservoir optimization, and reservoir management. These models are mostly developed and applied for simulation and prediction. Therefore, different types of forecasting methods have been proposed over the decades including: Box and Jenkins (SARIMA), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Genetic Algorithm (GA) models. Forecasting hydrological reactions invariably involves uncertainty. So far, numerous studies have been performed to improve the reliability and accuracy of hydrological forecasts, resulting in reduced risk error. Therefore, the main objective of current research was to use artificial intelligence methods consisting of ANN, ANFIS, GA, and SARIMA models to predict monthly runoff data and also select the best model for the efficient management of water resources in the Gharasou River basin.

Gharasou river basin with an area of 1624 square kilometers is located in the west of Golestan province and has an important role in providing water resources required in this province. In this research, to model and forecast the runoff process, the monthly runoff time series of 4 hydrometric stations of Pol-Tuskestan, Naharkhoran, Ghazmahale, and Siah-ab over Gharasou River basin were used for a period of 36 years (1982-2018). The time series homogeneity was examined using the Chow`s method. Runoff data are time dependent, so initially these data were arranged in time series. After sorting the data, four models consisting of Box and Jenkins (SARIMA), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Genetic Algorithm (GA) models were used to forecast monthly runoff. To increase the prediction accuracy of other methods, the far time series of monthly runoff were first ignored based on to neural network method and then the number of effective years for modeling was determined. Later, the monthly runoff was forecasted for the next 12 months using four models consisting of SARIMA, ANN, ANFIS, and GA. Lastly, based on the forecasted values and using MAD, RMSE and MAPE indices, the accuracy and precision of SARIMA, ANN, ANFIS and GA models were compared. Modeling and forecasting were done using Minitab, R and SPSS software packages.

Based on the type of distribution of monthly runoff and the presence of zero data, log(1+Yt) conversion was used in the models to stabilize the variance. The results according to the autocorrelation diagrams revealed that the time series in all stations have seasonal trend with a period of 12 months. Then, the monthly runoff of the next 12 months was forecasted using four models including SARIMA, ANN, ANFIS and GA. Model validation results using three indicators of MAD, RMSE and MAPE revealed that the ANN model in the three hydrometric stations of Naharkhoran, Pol-Tuskestan and Siah-ab had the best performance. In these three hydrometric stations, after ANN model, the ANFIS model has been selected as the most suitable model. However, the performance of these two models has been very similar. In the Ghazmahaleh hydrometric station, two models of ANFIS and ANN had the best performance, respectively. In this study, it was also found that in four selected hydrometric stations, the GA model had a good performance after the two models of ANN and ANFIS. Although SARIMA model performed very well in identifying the trend of monthly runoff changes, it had the weakest performance among the methods. The forecast data using SARIMA model were overestimated compared to the actual data for March and April, but in other months, the forecast data using this model were relatively appropriate.

In this research, to model and predict the monthly runoff process, four models including SARIMA, ANN, ANFIS, and GA models were used for four selected hydrometric stations in Gharasou River basin. The results of model validation using three indicators of MAD, RMSE and MAPE showed that the ANN and ANFIS models had the best performance among the four models used. It was also found that time series of runoff data in hydrometric stations have undergone structural changes due to the physical and climatic alterations in the upstream of rivers. Therefore, the distant past of time series may cause deviations in modeling and forecasting results. To overcome this problem, making use of an algorithm to select the number of effective years in modeling and forecasting can be useful. Artificial neural network provides a suitable criterion for selecting the number of effective years due to its high accuracy in modeling and forecasting. Based on the effective years identified by this model, other models can be modified and provide more appropriate input data for forecasting from other models.

Nowadays, forecasting and modeling the rainfall-runoff process is essential for planning and managing water resources. Rainfall-Runoff hydrologic models provide simplified characterizations of the real-world system. A wide range of rainfall-runoff models is currently used by researchers and experts. These models are mainly developed and applied for simulation and prediction. They allow decision-makers to make the most effective decision for planning and operation. Rainfall-runoff modeling is essential in flood routing, flood prediction, flood frequency estimation, real-time flood forecasting, warning climate changes, and other cases. Time series analysis includes methods for analyzing and modeling time-series data to extract forecasts and other characteristics of the time-series data. Time series forecasting is using a model to predict future values based on previously observed values. The transfer function is an advanced and multivariate time series model that enables us to utilize other time series to produce response time-series forecasts. Although the rainfall-runoff process has been modeled by various methods so far, less attention has been paid to the transfer function model. The primary purpose of this study is to introduce the transfer function to model the rainfall-runoff process and compare its results to the common time series model (SAIRMA) in Ramian and Galikesh watersheds.

In this research, to model the rainfall-runoff process, the monthly averages of rainfall and discharge time series of Ramian and Galikesh watersheds were used for a period of 36 years (1981-2017). These two watersheds are branches of the Gorganroud River which has an essential role in providing water resources required in Golestan province. The time series homogeneity was examined using Chow`s method. The existence of trends in time series was investigated based on the moving average time series plot, and the existence of seasonal trends was explored using autocorrelation charts. The cross-correlation diagram was used to investigate the relationship between rainfall and runoff time series. The SARIMA and transfer function models were used to predict monthly runoff. Without considering the rainfall time series, the runoff time series was modeled by a SARIMA model. Also, considering the precipitation time series as an input time series, a transfer function is used to model runoff time series as a response time series. The transfer function modeling was performed in three steps, pre-whitening, selecting the appropriate parameters of the transfer function model, and finally fitting a SARIMA model to the residual values. For the SARIMA model, the goodness of fit test was evaluated based on the Box Pierce statistic. For the transfer function model, two indices were computed. The first index investigated the relationship between runoff and rainfall, and the second index performed the goodness of fit test for residual time series. Then, based on the forecasted and fitted values and using MAD, RMSE, MAPE, and E indices, the accuracy, and precision of SARIMA and transfer function models were compared.

 Results :

According to the autocorrelation diagrams, the results showed that the all-time series has a seasonal trend over 12 months. Also, according to the moving average time series, there was no significant shift in rainfall time series, but there was a decreasing trend in the runoff time series. The transformation log(1+Yt)   was used to the monthly average discharges time series. Then, the transfer function and SARIMA models were used to forecast the monthly average discharges for the next 12 months by using Minitab and SAS software. In the next step, the validation of the predicted data and the fitted data of monthly average discharges of two models were evaluated using MAD, RMSE, MAPE, and E indices. The results showed that the transfer function model in both Galikesh and Ramian watersheds has higher precision from the two perspectives of forecasting and model fitting than the Box and Jenkins (SARIMA) model.

Discussion & Conclusions :

In this research, to model the rainfall-runoff process, the monthly average of rainfall and runoff were used in Ramian and Galikesh watersheds. Then SARIMA and transfer function models were used to predict monthly runoff. The results showed that the transfer function model in both Galikesh and Ramian watersheds has higher precision than the SARIMA model from the two forecasting and model fitting perspectives. Changes in rainfall, directly and indirectly, cause changes in runoff, and this effect might be happened either at the same time or with a time lag. The transfer function model can consider the time lags of both time series in forecasting runoff time series. The SARIMA models lack the information of other environmental parameters and apply just the runoff in the present and past times to predict future values. Since the pattern of these parameters changes annually, not considering these changes leads to unreliable forecasts from SARIMA models. However, in transfer function models, it is possible to investigate the effect of more than one environmental parameter on the runoff changes, increasing the accuracy of model fitting and forecasting.

From 2011 to 2013, several major earthquakes occurred in the Makran Seismotectonic zone (Iran and Pakistan). The low event interval of these earthquakes in a tectonic zone may indicate their interaction. In this paper, the interactions between 2011 Dalbandin earthquake of Pakistan, 2013 Saravan and Goharan earthquakes of Makran seismotectonic zone in SE Iran was investigated by calculating the Coulomb stress changes. Maps and cross sections of coseismic stress changes show that Saravan and Goharan earthquakes have occurred in the stress enhanced areas indicating the positive effect of Dalbandin earthquake on trigerring the mentioned events. In order to quantify the seismicity level changes, statistical parameters including b-value, seismicity rate and temporal changes of local standard deviation of gradient (lsd) were calculated over a 10 year period (5 years before and after the Dalbandin earthquake).The results show that the seismicity rate of Makran seismotectonic zone has significantly increased after this event. The temporal variation graph of lsd parameter also decreases at the time of the Dalbandin earthquake event, indicating an increase in seismicity rate in the region. In overall, the results indicate that the Dalbandin earthquake is the beginning of an active seismic period in the Makran subduction zone on which the peak of activity occurred in 2013 with two years delay.

SummaryThe Module Index (MI = Al2O3/SiO2) is a criterion for determining the ore quality in bauxite deposits. Despite of wide use of the MI, it can not provide reliable criteria for determining of the ore quality due to less statistical attention to outlier data. In this paper, we have introduced the Bauxite Quality Index (BQI) as a new criterion for determining of the bauxite ore quality. We used geochemical data of the Jajarm bauxite deposit to evaluate the ability of the mentioned index. The BQI could be able to classify the ore zones based on the ore quality and economic importance. The BQI would be control the outlier data and reveal relationship between the variables, which are the most important characteristics of this index.IntroductionBauxite is the most important source of aluminum. The quality of bauxite ore depends on mineral composition of the ore which has been calculated by the Module index (Al2O3/SiO2 ). This paper introduced more comprehensive bauxite quality index (BQI) than the MI, using multivariate statistical analysis. We apply geochemical data of the Jajarm bauxite deposit to evaluate the ability of the BQI. Methodology and ApproachesThe BQI is determined based on factor analysis of geochemical data of the Jajarm bauxite deposit. We investigate the relationships between ore quality and alumina-silica contents using the MI and BQI. Then, the economic potential of the ore zones is evaluated by either MI and BQI. Finally, the correlation coefficients of the different parameters are calculated, and the all obtained results are compared and discussed. Results and ConclusionsThe Bauxite Quality Index (BQI) is a new suggested criterion for determination of bauxite ore quality. This criterion reveals hidden relationships of Al2O3 and SiO2 with other variables and outlier data. Comparison of the MI and the BQI indicates that the BQI is more reliable than the MI in recognition of the high grade ore bodies. x.

The variable and complex nature of the sediment load of rivers has led that the estimation of sediment entering the reservoirs and the production of long term sediment, for determining the lifetime of the structures encounter with the problem. Application of sediment rating curves is one of the most common methods for estimating the suspended sediment load of rivers. Regardless of the accuracy of sediment measurement in sampling and measuring the concentration of samples and also measuring the corresponding discharge and with due attention to the spatial and temporal changes of suspended sediment in river management and reform operations, the providing of sediment rating curves is one of the most important resources of error for estimating sediment. Therefore, providing time backup curves for sediment rating curves is necessary for evaluation of accuracy and precision of sediment rating curves. So, in this research are presented time backup curves for sediment rating curves for investigating temporal variations of the sediment rating curve and are applied for considering slope changes of curve and annual changes of it too. Also, in this research compared the results of the sediment rating curve method with the results of some statistical methods for estimating the suspended sediment load in three hydrometric stations in Gamasiab watershed. The results of validations showed that transfer function model due to take into account time parameter with SSE 0.00003 ton/day, 3.24 ton/day, 5.92 ton/day, respectively for stations: Sang-Surakh, Vasaj and Marvil has high precision in the forecasting and moreover it presents confidently unbiased forecasts for the future.

Seismicity pattern studies are one of the effective tools in the interpretation of variation in seismic sequence. The study of variations of seismicity parameters as a function of time indicates that the temporal distribution of events is not uniform, and these parameters can give quantitative information about the seismic patterns of different regions. In this research, to investigate temporal variations of seismicity pattern in the Zagros fold and thrust belt, the Schreider algorithm is applied. This algorithm that introduced by Schreider (1990) to detect seismic quiescence has been used in different parts of the world. For this purpose, four earthquakes with Mw≥6 that have recently been occurred in Zagros have been studied. At first, a complete catalogue from the period of 2000 to 2017 within a circular area has been selected. Then, the catalogues are homogenized to ML and the Minimum magnitude of completeness are computed (Mc=3.4). To perform Schreider algorithm, the time between consecutive earthquakes ( ) should be calculated. The smoothness procedure is used to evaluate a convolution function of . The smoothness of this function is done by Gaussian function. In the R radius, smoothness parameter (s) has controlled the extent of surrounding earthquakes to detect smooth values. The kth seismic event is related with the temporal convolution T(k) that decrease and increase indicate seismic activity or low seismic activity, respectively. The numbers of earthquakes that are located in the nearest distance to main shock determine the l parameter. The value of l is determined when the function f(n,s) is approximately zero. Therefore, the function T(k) depends on the s and l parameters. To investigate temporal variations of seismicity, in addition to the temporal convolution (T(k)) plot the magnitude-time, number-time and space-time plots have drawn. The results show that before the 2006 Silakhor and 2014 Mormori earthquakes, both of which occurred in the north part of Zagros, the precursory doughnut pattern is seen. Several years before the 2013 Dashti and 2005 Qeshm earthquakes, both of which occurred in the south part of Zagros, the seismic quiescence is seen for which ended with the sudden occurrence of these earthquakes. The result of sensitivity analysis showed that smoothing parameters of Schreider algorithm have a significant influence on the algorithm outcomes, and these parameters should be selected with more accuracy. The results of this paper show that Schreider algorithm can demonstrate precursory seismic quiescence before the occurrence of large earthquakes due to the intelligent usage of t parameter.

Soil erosion and its issues are among the most important environmental challenges. In many areas, soil erosion affected valuable natural resources and soil fertility. The recognition of the factors affecting runoff and soil erosion, and the determination of their issues are essential for soil and water management conducive to sustainable development. The assessment and analysis of the changes associated with the soil characteristics; runoff and sediment lead to proper planning and management.
THEORETICAL FRAMEWORK: Review of former studies shows that the land use change is one of the main effective factors regarding the erosion intensity whose effect is sometimes more than the rainfall intensity and slope. For quantifying the effects of land use change and slope on runoff, sediment and soil nutrients, the present study was carried out with the aim of comparing the chemical and physical properties of soil in different land uses and slope classes, and of investigating their effects on runoff and sediment.
This research was accomplished in Kechik watershed on loess formation located in Maravetape township, Golestan province.Rainfall simulations were done in 4-square-meter plots on five land use including forest (Natural forest and Reforestation), rangeland, farmland (Watermelon and Harvested Wheat) and four-slope classes including 3-12%, 12-18%, 18-25% and 25-40%. Based on the three-nested design, 96 simulation experiments and samplings were done. Output runoff and sediment and soil samples were collected. Nutrient amount (Phosphorus, Nitrogen, and Organic matter), Cation Exchange Capacity, Antecedent moisture content and Soil density were measured at the laboratory.
The result showed a change in the type of land use (natural forest, forestation, rangeland, watermelon farmland and harvested wheat) due to the increase in runoff from 12.8, 40, 25.2, and 40.7 to 54.9 lit., respectively. The average sediment changed from 57.5, 230.9, 119.1, and 1369.6 to 1190.1 regarding the land uses, respectively. The reduction of phosphorus was found from 13.4, 8.8, 10.8, and 6.3 to 4.4 in different land uses. The organic matter reduced from 6.4, 4, 3.2, and 1.6 to 1.1 in different land uses, respectively. The cation exchangeable capacity reduced from 16.8, 10.3, 9.1, and 6.2 to 7.6 c mol kg-1 in different land uses, respectively. The soil moisture changed from 22.2, 6.4, 12.8, and 6.8 to 6.7 in different land uses, respectively. The soil density in different land uses increased from 1.05, 1.16, 1.25, and 1.63 to 1.57 gr cm3, respectively.
According to the results, organic matter is the only variable that has significant difference affected by slope changing in all of the land uses. Also, the investigation of the relation between measured variables of runoff and sediment showed that the organic matter, phosphorus, cation exchange capacities have the highest effects on runoff and sediment, respectively. The increase in runoff in farmland comparing to the forest shows the importance of forests in reducing runoff, flood management, and ground water recharge. On the other hand, sediment production in farmlands 20 times more than in forests shows the important of forest in preventing sediment production, filling the reservoir of dams, and losing valuable soil.

Recognition of effective factors on erosion and determines their negative consequences are essential to proper management of soil and water resources and achieve to sustainable development. Previous studies indicate that land use type and slope are important in runoff volume, soil erosion and nutrient loss.
In most researches due to difficulty operation of simulating rainfall in large-scale plots led to use of small plots. In some cases, the slope changes at this level havent proper representative of natural conditions.
The objective of this study is assessing the effect of different vegetation cover type, slope and rainfall intensity on changes in runoff, soil and nutrient losses in relative large-scale plots.
The study area (Kechik watershed) is located in 25 Km east of Maravetapeh city in Golestan province at 55° 57´ 52" to 55° 57´ 10" longitude and 37° 42´ 15" to 37° 46´ 25" latitude. This watershed has a uniform petrology and loess formation almost covered entire watershed. Rainfall simulation sites were selected by three nested design. Five Land use include of forest (Natural forest and Reforestation), rangeland, farmland (Watermelon and Harvested Wheat) and four slope classes include of 3-12, 12-18, 18-25 and 25-40 were considered. In land uses 4, 2, 4, 4 and 2 slope class was selected, relatively. Rainfall simulation using two rainfall intensity of return period 50 and 100 year (90 and 105 mm hr-1 relatively) were accomplished. Under considering of 3 repeat for each plot, 96 plots were selected. Output runoff and sediment of 1*4 meter plots throughout simulation were collected. Sediment concentration, runoff coefficient, nutrient amount (phosphorus, nitrogen, organic matter) and Cation Exchange Capacity were measured at the laboratory. Results using SAS, Minitab and SPSS were analyzed.
Analyzing plots outputs showed, in forest (Natural forest and Reforestation), rangeland and farmlands (Watermelon and Harvested wheat) mean of organic matter (OM) changes 9.1, 6.7, 3.6, 2.2 and 1.8%, mean of N 0.60, 0.30, 0.28, 0.17 and 0.15 %,mean of CEC 15.8, 11.2, 9.6, 6.4 and 7.7 Cmol kg-1, mean of P 16.4, 11.31, 15.4, 8.6 and 5.9 mg kg-1, and runoff coefficient 11.1, 26.6, 16.7, 27,1 and 36.5% and sediment 20, 57.7, 29.7, 342.3 and 297.5 gr m2, respectively. According to results, slope factor had different effects on measured variables. The most and fewest slope effect was observed on sediment of farmland (watermelon) with 9.1 times difference and nitrogen of rangeland with 1.04 times difference. Measured variable didnt show any trend with rainfall intensities. The highest significant positive correlation was found between runoff and sediment (r=0.93**).
Measuring of physical and chemical variables in different land uses and slope classes showed when land use type gradually changes from forest to rangeland and farmlands and also slope increase, significant decrease in chemical variables and significant increase in physical observed. According to results, proper management of steep farmlands can have an important role to decrease the runoff coefficient, soil and nutrient loss.

Since information about temporal variation of suspended sediment establishes a basis for watershed and river management, it is crucial to utilize a method for modeling and forecasting suspended sediment that takes all influential factors into account. In this research, the transfer function model is used to study temporal variation of suspended sediment on seven selected stations in Gorganrood river. In order to evaluate the performance of the model in forecasting suspended sediment compared to the sediment rating curve, only flow discharge is considered as a highly effective factor in variation of suspended sediment. Although this comparison reveals that these two variables are mainly related at the same time but to develop into a more precise and accurate set of forecasts, the values of suspended sediment and flow discharge at past time should be taken in the model. Based on the validation criteria, the transfer function model was accurately fitted in both stations. Finally, comparing two models from forecasting point of view revealed that the transfer function model with model efficiency 98 and 97 for Hajighoshan and Voshmgir stations, respectively and with the bias less than 1 for both stations presents more accurate and precise forecasts compared to sediment rating curve.

The amount of sediment load significantly changes along a river due to environmental effects. This variability should be considered to estimate the amount of sediment load. In this research، spatial and seasonal variation of sediment load along of Gorganroud river were evaluated based on the information of 7 hydrometry stations sited in main branch of the river namely، Tamar، Hajighoshn، Gonbad، Voshmgir، Aghghala and Basirabad. Since the amount of sediment load is directly correlated with water discharge، for assuming the effect of water discharge، linear form of sediment rating curve is used where the linear form is produced by the logarithm transformation. Based on this linear relationship with possibly different slopes، the generalized analysis of covariance with logarithm of discharge as its covariate was used to compare the logarithm of sediment load as its response. In order to precisely estimate the amount of sediment load and adequately modify the sediment rating curve، the spatial comparison was drawn separately at low، middle and high rate of water discharge. Spatial evaluation demonstrated an increase in sediment load from Tamer station to Basirabad but there are some distinctive areas between mentioned stations with noticeably decreasing (80% in average) in their sediment load mainly due to reservoir building. For seasonal evaluation of sediment load، the percentage of sediment yield at different seasons was compared. This evaluation revealed that spring with at least 50% and at most 70% of total suspended sediment discharge has the highest amount of suspended sediment yield in this river.

Grade control and ore quality determination has always been considered a challenging task for geologists and mining engineers. Ore quality is dependent on various factors such as size and geometry of a deposit, geological disturbances, and texture and ore mineralogical composition. Determination of factors which control ore grade and prediction of a suitable area are very important in terms of economics and productivity of mining operations. This study has tried to develop spatial and statistical analyses for evaluating ore quality and recognition of ore grade influencing factors at Jajarm Bauxite Mine. Through incorporating topographic, geologic and geochemical data, the effects of faults, elevation and depth on the ore grade and module (wt% Al2O3/Wt% SiO2 ratio) has been assessed. The results showed that the amounts of Al2O3 and module increased as we get closer to the faults. Furthermore, there was not any effect of faults type and strike on grade and module. A negative correlation between both elevation and depth with the amounts of Al2O3 and module indicates ore quality improvement in lowlands and shallow depths. Thus, the combination of geographic information system (GIS) methods and multivariate statistical analyses might be more efficient in geo-spatial assessments.

Physalis alkekengi (Solanaceae) is a rich source of different antioxidants, which has been advised for the treatment of various diseases such as inflammation and rheumatism. In this paper, antioxidant properties of different parts of P. alkekengi including leave, calyx, green and orange fruits randomly collected from around the Tonekabon-Iran were studied. Antioxidant activity of extract was measured with DPPH radical scavenging activity, inhibition of fatty acid oxidation and ferric-reducing antioxidant power. Also, the total phenol, flavonoid, quercetin, luteolin, anthocyanin and carotenoid contents of samples were determined. The tests were conducted in a completely randomized design with 4 replications. The leaves of P. alkekengi contained the most content of the total phenol, flavonoid and ferric-reducing antioxidant power. The maximum of DPPH radical scavenging activity (1.81±0.214 g/gFW), peroxidation fatty acid inhibition (75.53±5.35 %), and total carotenoids content (10.05±1.45 mg/gFw) were observed in orange fruit of P. alkekengi. The highest value of luteolin was measured in the leaves of green fruit stage, and it was 0.198±0.001 mg/gDW. Querccetin was found only in the leaves of vegetative and flowering stages, 9.2±0.05 and 9±0.53 μg/gDw respectively.

The Argash antimony ore deposit is located at south of Neyshabour and the eastern part of the Sabzevar zone. This deposit occurs within a sequence of Eocene-Oligocene volcanic-intrusion rocks. The mineralization occurs as open space filling, taking place as irregular veins, veinlets and hydrothermal breccias. The mineralogy of the veins is comparatively simple. The primary antimony-bearing mineral is stibnite. Other sulfides have been found in association with stibnite are pyrite, chalcopyrite and sphalerite. Quartz, illite, adularia, chlorite, epidote and kaolinite are the main hydrothermalg gangue minerals occurring around the veins. Hydrothermal alteration is zoned around the veins and consists of three major types including silicification within the immediate veins, argillic alteration enveloping the veins, and propylitic alteration distal to the veins. The intensity of silicification and argillic alteration decreases with depth and away from the veins. The evaluation of the geochemical data set based on multivariate statistical analyses indicates positive correlation between Zn, Ag, As and Sb and the same distribution pattern of these elements in the boreholes. The mineralogic, alteration and geochemical characteristics of the studied area and comparison with epithermal ore deposits indicate that the Arghash quartz-antimony veins represent an epithermal system of the low-sulfidation type. This data suggests that boiling and cooling were the main ore deposition processes in the area.

This research was carried out for predicting probability of forest type's presence using topographic attributes in the Educational and Research Forest of Doctor Bahramnia, district 1, with 1714 ha area. Field sampling was performed, based on cluster random sampling and systematic random sampling and 249 plots were sampled with 0.1 hectare area (without plantation area). Diameter of trees larger than 12.5 cm, species information and geographic position were registered in each plot with GPS. Since, thick trees are dominated in the canopy cover, 10 thick trees in each plot were selected to determine the forest type. Using computing of frequency percent of species, forest type in each plot was determined and four types of Fagus-Carpinus, Quercus-Carpinus, Carpinus-Parrotia and Parrotia-Acer (mixed with Carpinus betulus) were determined in the study area. The topographic attribute maps were derived from a digital terrain model and these attributes were extracted in location of plots. Logistic regression was implemented to analysis of forest type’s correlation with attributes and to construct a predictive model. The analysis was performed using 70% of the plots and each forest type map was resulted by extrapolation of model in GIS. Validation of results was performed by 30% of the remained plots and total accuracy computed for each model. Validation result of accuracy showed that spatial prediction models of forest types for Fagus-Carpinus and Quercus-Carpinus have narrow amplitude than Carpinus-Parrotia and Parrotia-Acer (mixed with Carpinus betulus) types that spread in large extent region. This result showed that spatial predictive models of forest types, which have narrow amplitude, are more acceptable. The results also showed that consideration of altitude, solar radiation potential and aspect in the model were the main factors which are controlling forest types in the study area.