نشریه آب و خاک
سال سی و یکم شماره 3 (پیاپی 53، امرداد و شهریور 1396)

Due to the increase in water consumption resulting from climate change and rapid population growth, overexploitation of groundwater resources take place particularly in arid regions. This increased consumption and reduced groundwater quality is a major problem especially in arid areas of concern among water resources managers and planners. The use of modern simulation tools to evaluate the performance of an aquifer could help the managers and planners to decide. In this research, finite difference method was used to simulate the behavior of the quality and quantity of groundwater.
Increasing the concentration of salts in the groundwater aquifers intensifies with severe water withdrawing and causes the uplift of salt water in aquifers. This is much more severe in adjacent aquifers of saline aquifers in deserts and coastal areas. Front influx of saltwater into freshwater aquifers causes interference and disturbance in water quality and complex hydro-chemical reactions occurs in the joint border area including the process of cation, groundwater flow, the reduction of sulfate, the reaction of Carbonatic and changes in the dolomitic calcite. Sarayan Aquifer has a negative balance and the annual groundwater table drawdown of 62 cm.
In this study, Total Dissolved Solids (TDS) as a groundwater quality factor was simulated to investigate the effect of the overexploitation on the saline interface of desert aquifer using MT3D module of GMS model for a period of 5 years with time steps of 6 months. One of the most important steps of the simulation of groundwater quality is to use qualitative model to predict the groundwater level which in this study were performed by quantitative models in two steady and unsteady flow states with time steps of 6 months The four basic steps of a proper modeling of the groundwater quality are sensitivity analysis of the input parameters, calibration of the sensitive parameters of the model, validation of the time step and groundwater quality forecast for the future periods. These modeling steps were carried out for steady and unsteady states by GMS software.
Aquifer hydraulic conductivity and the specific yield of aquifers were selected as two critical parameters of quantitative model in steady and unsteady states. The model was calibrated based on these two parameters and then using pest method, the value of these parameters was finalized. In order to evaluate the response of the aquifer to different periods of droughts, the verification of the model was conducted during the ten periods. The results show that observed water level has suitable correlation with simulated water level. In the same period, the simulation of water quality for TDS parameter carried out using the results of the quantitative model. After identification of sensitive parameters in the model, calibration of the model was carried out taking into account the factor of 0.5 for the ratio of horizontal to vertical distribution, vertical diffusion length of 0.2, 1 meter for effective molecular diffusion coefficient, and 20 for longitudinal diffusion.
In the total area of the aquifer, the water demand of all sectors are supplied using groundwater resources. This water withdrawal trend exacerbated the decline in groundwater levels and reduced water quality. Also in the southern strip of the aquifer, there is a desert saline groundwater aquifer, which causes the intrusion of salt water to the aquifer and negative effects on its quality. The factors influencing the salinity of groundwater in the Sarayan Aquifer are geological formations, supplying the aquifer from salty formations in the region, evaporation from the shallow part of the aquifer especially in the southern strip that leaves salt and reducing the volume of water, existence of fine soil in the media of groundwater flow. Front influx is from saltwater desert aquifer to the Sarayan Aquifer. Due to the osmotic pressure of the soil layers in the aquifer, the pollutants transferred from the higher concentration to lower concentration and an influx of salt water into the aquifer will occur from outside of the aquifer. Since the direction of groundwater flow is from the north to the south of the aquifer and salt water intrusion is from the south to the north, the velocity of saltwater intrusion dropped so quickly water. However, overexploitation of groundwater and negative aquifer balance caused uplift of the salt water in aquifer.
Review of the result of forecasted TDS concentration in Sarayan Aquifer, shows an increase in TDS concentration. This increase indicates that there is no potential for more water withdrawing in the southern parts of the aquifer by urban and agricultureal sectors. The variaty of TDS changes between 712 mg/lit in the northern strip of the aquifer to 8500 mg/lit in the southern strip shows that due to the increased concentration of TDS, the border area of water users will be changed. The forecasting of the future status of aquifer water quality showed that continuing withdrawing of water intensifies salt water interference from the desert and concentration of TDS will increase during the next 5 years. To manage aquifer quality and quantity, three scenarios of water withdraw reduction were used. The results are shown restoration of the aquifer quality and quantity using these scenarios.
Therefore the result of this research shows that the management of groundwater is necessary to improve the quality of desert aquifers and prevent salt water interference from desert considering recent droughts.

Solute transport modeling in water bodies and especially rives, provides a useful tools for decision-makers and consumers, to take a timely decisions in order to prevent or avoid any catastrophic consequences. Many factors such as velocity, cross-section area, bedform and etc. affect the transport of any solute particle in the media. The aforementioned factors vary both in width and depth, which cause some of the particles to deviate from the main channel to areas in which the particle stays for a longer time. Those particles are released to the main channel gradually causing a heavy-tailed breakthrough curve in downstream of the injection point. The areas where solute spends more time than the main channel is called deadzones. Any area in the channel that reduce the velocity field to relatively zero could be categorized as deadzones, such as areas behind a boulder, in pocket beside the river, behind or inside a vegetation cluster. In streams containing deadzones the classical adevection-dispersion equation (ADE) cannot meet the requirements. Scholars proposed many different approaches to capture the heavy-tailed behavior. One of the renowned and novel approaches to quantify the role of deadzones in natural streams is using Fractional Calculus. This field of mathematics helps to change the fundamental assumption of ADE, which is laid upon “central limit theorem” and subsequently generate a new equation based on fractional partial derivatives, The Fractional Advection –Dispersion Equation (FADE). The FADE provides a better description of solute movement in natural streams, because of its general form and non-locality.
In this research, FADE for a non-uniform but steady condition in natural streams has been investigated. FADE is a product of central limit theorem generalization and hence can capture the anomalous behavior of solute transport in streams containing deadzones. One of the important points that can be inferred from tracer study in such streams is that the concentration of a point is influenced by the point in far upstream; that is either the solute particle comes faster than the main cloud or slower and defy the basic assumption. Since classic ADE just considers the points in the closest proximity of the study point to calculate the bearkthrough curve (BTC), it cannot be used in cases that the deadzones are involved; which calls for a more comprehensive consideration of the stream. Interestingly, factional derivatives are non-local; fractional derivatives consider the state of the function in its whole domain. Hence, FADE can accurately describe and calculate the BTC in streams affected by deadzones. The big difference between FADE and classic ADE is that, the former is based on Levy motion while the later developed and shows Brownian motion. Also, the order of spatial derivative in FADE is a value , unlike the classic ADE in which the order is fixed to . To investigate the FADE in depth standard Grunwald definition of fractional derivatives were employed for discretization, and an algorithm is proposed. The more the order of the fractional partial derivative decreases, the more would be the effect of the deadzones, this means that while the order is equal to , the streamflow do not experience any deadzones in its way. In order to fit the data, two variables in the FADE needs to be calibrated; the unknown parameters were estimated using Particle Swarm Optimization (PSO) technique.
The model was validated using a set of data from a small stream. The comparison shows that the model can accurately recreate the BTC and perform dramatically better than classic ADE which even in some stations is incapable of predicting the peak concentration and the arrival time of the particle to the station. Quantitatively, the difference between the FADE and observation represented by RMSE in average is 0.236 while for classic ADE is around 4 times higher. The difference in the first two stations is negligible for the stream does not contain any deadzones. This shows that not only the FADE is capable of considering deadzones, but also if needed it can easily reduce to classic ADE. This flexibility offers vast opportunity for application. In addition, the sensitivity of the model to the calibration parameter was analyzed, the analysis shows that both variables (order of the derivative and skewness parameter) should be treated with great care, since a little variation in their value drastically alter the shape of the BTC, and decrease the accuracy of the prediction. In this research, the capability of FADE were investigated and proved that this model performing far better in predicting solute transport in natural streams in which the variability of the morphology may hinder the solutes movement.

Rainfed agriculture plays an important role in food production. In Iran, 6 million hectares of cultivated landsare rainfed. Moreover, about10% of raw agricultural products are being produced by rainfed agriculture. Yields of rainfed fields are decreased due to drought in recent years in Iran. Supplementary irrigation is a suitable management to improve and enhance the yield of rainfed agriculture. Determination of appropriate time of supplementary irrigation is necessary in each region. But water allocation for this practice is the main challenge, because water resources are restricted. Therefore, water allocation management between irrigated and rainfedfields could be a viable strategy. Water resources for supplementary irrigation in rainfed fields are saved through deficit irrigation in irrigated lands or from rivers. The purpose of this study is optimum water allocation for supplementary irrigation in wheat and barley farms from rivers to around rainfed fields in Kamyaran region. In this study, supplementary irrigation is considered in three management methods of autumn irrigation, spring irrigation and both of them.
Kamyaran is located in Kurdistan province in west of Iran. The area of rainfed field is very vast in this region. Usually, rainfed fields are located in high slop lands and far from water resources in Kamyaran region. Supplementary irrigation is possible in rainfed fields around to water resources and with slope of less than 8%. The area of sub-basins with appropriate situations in Kamyaran region was calculated by geographic information system (GIS). Ratio of wheat to barley in rainfed fields is 3 to 1. Rivers in each sub-basin is the only water resources for supplementary irrigation in Kamyarn region. In this study, the objective function is maximizing net benefit. Also, constraints are total available water volumes in rivers at supplementary irrigations times and rainfed fields with appropriate situation for supplementary irrigation. Decision variable is rainfed area with different irrigation managements (autumn supplementary irrigation, spring supplementary irrigation, autumn늚 supplementary irrigations and rainfed managements). The total costs and income of agricultural production are found in statistical books of agriculture jihad in 2008-2009 growing season.
The lands around of rivers with suitable slope are about 30% of rainfed land of Kamyaran. The appropriate rainfed fields in sub-basins of A, B, C, D, E, F and INT were 125.39, 15.52, 18.11, 1111.26, 96.51, 48.13 and 49.55 Km2, respectively. The results of Optimization model showed the supplementary irrigation managements are different in each sub-basin because of different discharge of river in each sub-basin in different months. The optimal supplementary irrigation management for barley rainfed fields is autumnsupplementary irrigation. The yields of barley rainfed fields increase about 90% by autumn supplementary irrigation. The optimal supplementary irrigation managements for wheat are different in each sub-basin, but autumn늚 supplementary irrigations is best managed if water resources will be enough in each sub-basin. Due to restriction of water in rivers at supplementary irrigation time, some of wheat and barley fields remain rainfed in Aߠ and D sub-basin. The results showed minimum and maximum increase of wheat production in D and INT sub-basins are 29 and 134%, respectively. Also production increasing are 87, 112 and 126% in Aߠ, E and F, respectively. Increasing of barley production in the sub-basins of E, F and INT, are 61, 96 and 96%, respectively. Other sub-basins of Aߠ and D remained in rainfed farming. Net benefit increase about 65 and 275% for wheat and barley fields respectively, in 2014. Water productivity in all sub-basins for both wheat and barley is 74.8 and 44.5%, respectively.
This study showed supplementary irrigation management increased the yield and net benefit in rainfed fields of Kamyaran sub-basins. Resultsshowed about 30% of rainfed land of Kamyaran, are suitable for supplementary irrigation. The results of optimization models showed total increase of wheat production in Aߠ,E, F, D and INT sub-basins are 87, 112, 126, 29, 134%, respectively. Also increase of barley production in the sub-basins of E, F and INT, are 61, 96 and 96%, respectively. The result showed production increase about double in Kamayaran region. Also, net benefit increase about 65 and 275% in wheat and barley fields respectively.It has been suggested in A, B, C sub-basin, autumn supplementary irrigation of wheat, in E, F and INT sub-basins, autumn and spring supplementary irrigation for wheat and autumn supplementary irrigation for barley and in D sub-basin, autumn and spring supplementary irrigation for wheat.

In recent decades, the most important issue for agricultural activities is maximizing the productions. Today, wheat is grown on more lands than any other commercial crops and continues to be the most important food grain source for humans. Sustainable agriculture is a scientific activity based on ecological principles with focus on achieving sustainable production. It requires a full understanding of the relationships between crop production with soil and land characteristics. Furthermore, one of the objectives of sustainable agriculture is enhancing the agricultural production efficiency through applying proper management, which requires a deep understanding of relationships between production rate, soil and environment characteristics. Hence, the first step in this process is finding appropriate methods which are able to determine the correct relationships between measured characteristics of soil and environment with performance rate. The aim of this study was evaluating the performance of neuro-genetic hybrid model in predicting wheat yield by using land characteristics in the west of Herris City.
The study area was located in the northwest of east Azarbaijan province, Heris region. In this study, 80 soil profiles were surveyed in irrigated wheat farms and soil samples were taken from each genetic horizon for physical and chemical analyses. In this region, soil moisture and temperature regimes are Aridic border to Xeric and Mesic, respectively. The soils were classified as Entisols and Aridisols. We used 1×1 m woody square plots in each profile to determine the amounts of yield. Because of nonlinear trend of yield, a nonlinear algorithm hybrid technique (neural-genetics) was used for modeling. At first step, the average weight of soil characteristics (from depth of 100 cm) and landscape parameters of selected profiles were measured for modeling according to the annual growing season of wheat. Then, land components and wheat yield were considered as inputs and output of model, respectively. For this reason, genetic algorithm was investigated to train neural network. Finally, estimated wheat yield was obtained using input data. Root mean square error (RMSE) and Coefficient of determination (r2), Nash-Sutcliffe Coefficient (NES) indices were used for assessing the method performance.
The sensitivity analysis of model showed that soil and land parameters such as total nitrogen, available phosphorus, slope percentage, content of gravel, soil reaction and organic matter percentage played an important role in determining wheat yield in the studied area. The soil organic matter and total nitrogen had the highest and lowest correlation with wheat yield quantity and quality, respectively, indicating the total nitrogen was the most important soil property for determination of wheat yield in our studied area. We found that network learning process based on genetic algorithms in the learning process had lower error. The findings showed that beside of confirming the desired results in the case of using sigmoid activation function in the hidden layer and linear activation function in the output layer of all neural networks, it is demonstrated that the proposed hybrid technique had much better results. These findings also confirm better prediction ability of neural network based on error back propagation algorithm or Levenberg-Marquardt training algorithm compared to other types of neural network confirms.
Using nonlinear techniques in modeling and forecasting wheat yield due to its nonlinear trend and influencing variables is inevitable. Recently, genetic algorithms and neural network techniques is considered as the most important tools to model nonlinear and complex processes. Despite the advantages of these techniques there are a lot of weaknesses. Imposing specific conditioned form by researchers in the techniques of genetic algorithms and stopping neural network learning at the optimal points are the main weaknesses of these techniques, while searching for global optimal point and not imposing a specific functional forms are the robustness of genetic algorithm techniques and neural networks, respectively. Results of this study indicated that the proposed hybrid technique had much better results. Correlation coefficient (0.87) and average deviation square error (473.5) were high and low, respectively. It can be concluded that the surveyed soil properties have very strong relationship with the yield. Implementation of appropriate land management practices is thus necessary for improving soil and land characteristics to maintain high yield, preventing land degradation and preserving it for future generations required for sustainable development.

Water scarcity is the most important limiting factor in the production of crops in arid and semi-arid regions. Thus, actions for increasing the efficiency and productivity of farm water is inevitable. A large proportion of the water, used in irrigation, evaporates, so an effective solution for conserving water is to control the evaporation on arable lands. Nowadays using mulch or plastic mulch is common and it makes efficient use of water in furrow irrigation possible by conserving and storing soil moisture. Mulch does not let dry air contact topsoil and it also prevents topsoil from solar irradiance and reduces evaporation and maintain soil moisture. Recent research in order to economize on water use and irrigation efficiency and water use efficiency has led. Thus, regarding the problem of water scarcity, the objective of this research is to investigate the effects of evaporation suppressing monolayers on the efficiency of water consumption and growth indices of seed corn single cross SC 704 in an arid and semi-arid region.
This research was conducted in Shahrekord University during 2015. The experimental design was randomized complete block design with 6 treatments and 3 replications. The treatments include control treatment (uncovering) and transparent plastic wrap, black plastic, cotton gunny and white and blue pp woven fabric. Planting and growing operations were conducted due to agronomic principles. Changes in soil moisture within the root-zone during the season were measured by using thetaprobe and all operations by measuring the amount of irrigation water used in all experimental plots of each treatment were applied separately using flow measurement and the amount and time of each irrigation was determined and applied based on MAD=50 by supplying required water.
The measurement results showed that variance analysis of relative water content (RWC) and water efficiency under the impact of different coverings had a significance difference with p-value of 0.01. Also the amount of the dry matter and harvest index of corn showed significance with p-value of 0.05. Results showed that mulch at all stages of measuring the impact of increasing the leaf relative water content it could originate from growing trend of air temperature during the period. Under these treatments the plants are expected to experience more desirable conditions regarding maintaining and distributing of soil moisture in comparison with other treatments and the indicator. The highest amount of dry matter calculated is for the blue pp woven fabric treatment that shows the ideal growth conditions and appropriate performance of the plant under the impact of this covering and the lowest amount is for the cotton gunny treatment. Leaf area index (LAI) is one of the important growth indices. In flowering (anthesis) stage, the maximum amount of LAI is 5.08 for the blue pp woven fabric treatment. The minimum amount of LAI is 2.5 for the cotton gunny treatment and it is because of There macroporous coating that weed growth has been hindering plant growth. On the basis of the hundred seed weight, the heaviest weight is 18.18 for the white plastic treatment and the lowest weight is 13.46 for the indicator treatment. The highest amount of harvesting index (HI) is 53.97 for the transparent plastic treatment and the lowest amount is 41.12 for the black plastic treatment.The corresponding amount is an increase of 32 percent compared to control treatment. The reason of reduction of HI is the reduction of seed performance than biological performance in water scarcity. One of the indices for evaluating irrigation management is water efficiency. The highest amount of water efficiency is 2.6 and 2.7 kg/m3for the blue pp woven fabric and white pp woven fabric covering and it reduces water wastage in form of evaporation and causes water conservation. And it protects the top soil from solar irradiance.
This research was conducted at Shahrekord University to investigate the effects of various coverings on water efficiency and corn seed performance. Using covering causes temperature growth in the soil under the covering and it also causes further and fast plant growth. It reduces evaporation from topsoil. As a result, it causes soil moisture to be invariable and because of lack of light under the coverings, photosynthesis is impossible, thus, weeds could not grow. Blue pp woven fabric of mulch to mulch increased 42% dry matter was cotton sack. Mulches effect of the corn harvest index showed a clear plastic mulch to increase 32 percent harvest index compared to the control. Mulches blue pp woven fabric, white pp woven fabric, cotton gunny, black plastic and transparent plastic, respectively, increases of 92, 85, 28, 14 and 78 percent of water use efficiency were compared to control.Therefore, plants under the impact of blue pp woven fabric and white pp woven fabric coverings access more water and nutrients than the indicator treatment, so water efficiency increases. Using coverings has conserved moisture more in the top layers of soil by reducing evaporation form topsoil.

AquaCrop model was developed to simulate crop response to water consumption and irrigation management. The model is easy to use, works with limited input, and has acceptable accuracy. In this study, the data of an alfalfa field (as a perennial fodder plant) in the Iranian city of Ardestan was used to calibarate and validate the performance of AquaCrop model to simulate the crop productivity in relation to water supply and irrigation management.
The data of Fajr-e Esfahan Company farms of Ardestan County were used for calibration and validation of the AquaCrop model, simulating the alfalfa performance in different harvests and over different years. The farms are 1004 m above sea level and located in 33°2' to 33°30' North and 55°20' to 55°22' East. The farm under investigation included ten plots of alfalfa field, with an area of 280 hectares. The data of two plots were used for calibration and, two others used for validation.
Considering that alfalfa is a perennial plant, the data regarding the first harvest was defined as sowing, and transplanting was used to refer to the next harvests. Considering the physiological changes of plants over a year and during different harvests, the numerical value of different parameters, including primary vegetation, maximum vegetation, the depth of primary root development, the maximum depth of primary root development, crop coefficient, germination date, flowering, vegetation senescence, and physiological maturity, were defined for the model. The CRM, NRMSE, R2, and EF indices were used for verification of the calibration results. The CRM index determines the overestimation or underestimation of the model. The EF index is variable between 1 and 0, where 1 indicates optimal performance of the model. If all estimated and measured values were equal, the value of CRM and NRMSE would be zero, and EF would be one.
After calibration, validation was performed to examine the performance of the model. Hence, the actual performance rate for different harvests and the results of simulations were compared. Lower NRMSE value is indicative of high accuracy of the model in estimation of the performance. The value of CRM was mostly positive, showing the underestimation of the model in most of the simulations. The maximum performance happened during the first harvest year. The annual harvest decreased with an average rate of 1.2, compared to former years. The evaporation and transpiration rate was calculated by the model and the results were compared with potential evapotranspiration (FAO Penman-Monteith) and National Document of Irrigation (NET WAT). The reference crop evapotranspiration (ET0) had the highest value, and was calculated through FAO Penman-Monteith equation. The numerical value of potential crop evapotranspiration (ETc), which is the result of multiplication of crop coefficient by reference crop evapotranspiration (ET0), was greater than the results of the model, i.e. the estimated actual evapotranspiration. The discrepancy between them is the result of stress coefficient (ET0×Kc×Ks), which the model takes into account in estimation of actual plant water requirement. Evapotranspiration refers to two factors, namely the water lost by transpiration from plants and by evaporation from the soil. The plant transpiration and green cover are considered to be the generating part; AquaCrop is able to examine and improve transpiration efficiency through managerial statements. The values of transpiration from plants and evaporation from the soil for alfalfa were differentiated from the values estimated by the model. The productivity of evaporation, transpiration, and evapotranspiration were calculated by the model. The difference in the productivity values of the plots during different years was the result of difference in chemical composition, harvest index, and transpiration rate.
The AquaCrop model performed well in simulation of crop performance compared to actual annual, and even monthly, performance, and its results were very close to the actual performance. The model is sensitive to temperature changes, and it is suggested to use the Growing Degree Days (GDD) instead of Calendar Days section. . The Version 5 of AquaCrop model can, in addition to moisture stress, include salinity stress in calculations; this is evident in the variation of actual evaporation and transpiration values estimated by the model. In this study, the annual evaporation and transpiration rate was predicted by the model. The higher rate of evaporation can lead to a 27 to 44 percent decrease in the efficiency of evapotranspiration (Y ET-1), compared to transpiration efficiency (Y T-1).

The proper management of water resources in a watershed requires precise understanding and modelling of the hydrological processes. HSPF model uses an infiltration excess mechanism to simulate streamflow and requires the hourly precipitation data as input. Despite the high accuracy of the HSPF model, the lack of rainfall data at short time scales (hour and less than hour) restricts implementation of the model especially for long time simulations. Some studies have applied simple division for daily rainfall disaggregation into the hourly values to provide data required by the HSPF model. In simple division, each rainfall event is divided into 24 pulse stochastically and the peak flows may not be simulated correctly due to the lower rainfall intensities.
In this study, Random Parameter Bartlet-Lewis Rectangular Pluse (BLRPM) model was used for daily rainfall disaggregation into the hourly values to provide data needed by the HSPF model. The model parameters were calibrated using the 1, 24 and 48 hour rainfall data time series of the rain gauge stations inside (Jovestan and Zidasht) and outside (Kalk Chal) the watershed for the period of 2006-2009. To cluster the wet days, the BLRPM model was run several times and a generated sequence which had the best match with the original one in terms of daily totals was selected. Then, the synthetic sequence of hourly rainfall depths was modified based on a proportional adjusting procedure to add up exactly to the given daily depths. The calibrated model was then implemented to disaggregate the daily rainfall data of the watershed for the period of 1995-2005. The resultant hourly rainfall data were then fed into the HSPF hydrologic model to simulate the daily runoff. Parameterization of the BLRPM and HSPF models was also done while keeping the Kalk Chal station out of the calibration.
Sum of weighted squared error was calculated to be 1.03 when the data recorded in Kalk Chal station was also considered for parameter estimation of the BLRPM model. Maximum weighted square error was equal to 0.7 for lag-1 auto covariance of daily rainfall data. Keeping the Kalk Chal station out of the BLRPM model parameterization resulted in improved performance of the model. Sum of the weighted error decreased to 0.36 by removing the Kalk Chal station data. The results indicated that the weighted square error values decreased for all of the BLRPM model parameters when Kalk Chal station was not considered for calibration. The lag-1 auto covariance of daily rainfall data had the greatest reduction in weighted square error from 0.7 to 0.07 with and without including the Kalk Chal data set, respectively. The BLRPM model parameters also varied when data of the Kalk Chal station were removed from the calibration process. The k parameter value increased and the values of λ,  and v decreased due to removal of the Kalk chal station data. The highest variation was observed for v decreased from 2.74 to 0.33 by removing the Kalk Chal station. The calibrated BLRPM model, with and without taking into account the Kalk Chal station data set, was employed to disaggregate daily rainfall data into the hourly values. The HSPF model was calibrated using the daily observed streamflow data recorded in Galinak station to simulate daily streamflow in reach 27. The daily streamflow simulations in reach 27 were conducted by implementing the hourly generated rainfall data sets. The results showed that inclusion of the hourly rainfall data recorded in Kalk chal station for parameterization of the BLRPM model caused the reproduction of high-intensity rainfall data in disaggregation process and consequently led to the overestimation of peak flows by HSPF model. Exclusion of the Kalk Chal station for BLRPM model parameterization improved the daily streamflow simulation with Nash-Sutcliff efficiency = 0.76, coefficient of determination = 0.79 and RMSE = 7.11 m3.s-1. These results demonstrated the sensitivity of HSPF model to the weather station selection and rainfall intensities.
The Kalk Chal station located outside of the studied region, with high intensity-short duration rainfall pattern caused heterogeneity of the input hourly rainfall data for parameter estimation of BLRPM model. Parameter estimation of the BLRPM model with inclusion of the hourly rainfall data of Kalk Chal station resulted in generation of greater intensities in disaggregation process. Despite the same values of daily rainfall data in streamflow simulations, the high rainfall intensities caused by the data set of Kalk Chal station led to the overestimation of peak flows. The results indicated the high sensitivity of HSPF model to the rainfall intensities.

The heavy metal concentration in agricultural lands, due to the toxicity, persistence and their accumulation in the environment has become a major concern. Ophiolitic formations extend in southern part of central Iran and parallel to folds of the Zagros Mountains, is located in the north of Neyriz town and in the west of Bakhtegan Lake. Rock weathering of these complexes forms sediments and soils with a large amount of Mn, Ni, Cr, Co, Mg and Fe. Laboratory analysis of Neyriz ophiolitic rocks indicates that they are a source of heavy metals as well, and may cause problems for the environment. However, there is no investigation in Neyriz area regarding contamination of the soils. The present study was conducted to assess soils pollution in Ghal-e Bahman area, 20 km from Neyriz which derived from ophiolitic formations of this area.
The study area located in the Ghal-e Bahman region, eastern part of Bakhtegan Lake. The soils of this region are affected from Neyriz ophiolite. In this region, three physiographic units including a hill, an alluvial fan and a lowland (playa) were separated. In each unit, some pedons were dug and classified according to American Soil Taxonomy. Soil samples were obtained from each genetic horizon and rock samples were also taken from ophiolitic formation. Then, chemical and physical properties were determined. Heavy metals were also extracted by nitric acid and amount of Cr, Ni, Co and Fe were calculated. Enrichment Factor (EF) and Geo-accumulation indices (Igeo) were also calculated and soils were classified according to their pollution level.
In general, soils on different landforms had different horizon properties and different classification. They are varied from a shallow, thin layer on hills to relative deep layer on lowland. These soils were classified in three different subgroups according to American Soil Taxonomy. Soils on ophiolitic hills classified as Lithic Torriothents because of a thin surface layer on a weathered bedrock. Soils developed on alluvial fan landform, with several alluvial subsurface horizons with different rock fragments percentage and size, was classified as Typic Torrifluvents; and the soils on lowland (Bakhtegan playa (was Gypsic Aquisalids because of salt and gypsum concentration in all layers and had redox color (chroma of less than 2) affected by high level of groundwater in the soil surface and subsurface layers.
The results showed that the amount of chromium with the average of 2200 mgkg-1, was 10 to 40 times higher than the Iran and Europe threshold levels (100 and 150 mgkg-1, respectively). The amount of nickel, with the average of 300 mgkg-1,were 10 fold higher than the threshold level and cobalt (19 mgkg-1) was lower than criteria defined by soils standards of Iran and Europe (40 mgkg-1).The amount of studied metals were the highest in ophiolitic hills, and playa soils were in second place in this respect. The amount of metals had a significant decrease in alluvial fan but didn’t drop under threshold level. The lowest amount of heavy metals in alluvial fan was probably because of the high percentage of sand, higher permeability and low soil water retention in all horizons. The negative significant correlation between the elements and sand also confirms this hypothesis. In addition, increasing elements at the depth of 70 cm of the soil in alluvial fan showed that land type (orchards) and long period of irrigation may cause leaching heavy metals from topsoil to the soil depth. However, no significance correlation was observed between the elements and soil organic carbon. The correlation coefficients between three elements revealed that all of them had the similar geologic origin and thus their spatial occurrence in soils can be attributed to the weathering of similar parent material.
Igeo showed an almost constant trend from ophiolitic hill (7.7-7.8) to alluvial fan (7.2-7.7) and a significant decrease in playa (3.9-6.2) for all metals. The variation of EF for nickel had an almost constant trend from ophiolitic hill (with the average of 0.6) to alluvial fan (with the average of 0.7) and a significant decrease in playa (with the average of 0.1). Also, a decreasing trend was observed from ophiolite hill (0.9 and 0.6 for chromium and cobalt, respectively) to alluvial fan (0.5 for both) and playa (0.3 and 0.1 for chromium and cobalt, respectively). A decreasing trend observed for indices can be due to the reduction of sediment transport processes and dilution effect of elements from hill to playa during the deposition and their formation .It seems that the EF index and the Igeo provide more useful information about hydrologic processes during formation of landform and development of soils than absolute values of heavy metals.
The present study showed that the amounts of chromium and nickel were higher than the threshold in studied soil. The soils derived from ophiolitic formation showed the highest values and the soils over alluvial fans had the lowest levels of heavy metals. Useful information was obtained from EF index and Igeo about the prominent geomorphic processes during landforms formation
Future studies should be focused on possible transfer of these elements into the groundwater and also trees of the orchards in Ghal-e Bahman region.

Water scarcity is an important challenge worldwide, especially in arid and semi-arid regions. Water-scarce countries will have to rely more on the use of non-conventional water resources to partly alleviate water scarcity. The reuse of wastewater for irrigation is considered to be beneficial for crop production, and due to its nitrogen and phosphorus content, it can help to reduce the requirements for commercial fertilizers. However, under certain conditions, this type of water if not well managed, can have negative impacts on cultivated crops and soils, particularly on soil salinity and sodicity, and may pollute groundwater, as a result of high nitrogen concentration of most treated wastewater. Besides nitrogen (N) contamination of surface and ground waters has become a serious and global environmental problem. The risk of groundwater contamination by N depends largely on the N input to agricultural fields in the form of inorganic fertilizers and on its effective use of agricultural crops. Improvement of irrigation and nitrogen application management during the growing period can be achieved using mathematical models. The goal of this study was to assess the effects of irrigation with raw and treated wastewater by using the HYDRUS-1D model for simulation of water and nitrate transport in a maize field.
The experimental station of the College of Agriculture and Natural Resources, University of Tehran, was considered as a case study. The information of maize growing season in 2010, as well as raw and treated wastewater of Ekbatan housing complex was considered as a source of irrigation water for simulation of water and nutrient movements in the soil by HYDRUS-1D software package. HYDRUS-1D numerically solved the Richards equation for describing the variably-saturated water flow in a radially symmetric domain and the convection-dispersion equation for solute transport. The soil hydraulic properties were described using the van Genuchten-Mualem model. Since the direct measurement of soil hydraulic parameters in the field or laboratory is time consuming and costly, they were estimated using the ROSETTA model, using particle size and bulk density data determined on soil samples taken from depths of 0-20, 20-40, 40-60 cm.
The results showed that water contents increased after any irrigation event, and then decreased gradually during the following hours and days, until the next irrigation took place. Deeper depths showed smaller water content variations since root water uptake and soil evaporation were more pronounced at shallower depths. Simulated plant water uptake was estimated to be 80% of the water application, indicating the high irrigation efficiency of the system. Cumulative deep percolation (DP) values increased rapidly at around 43 days after planting. This is obtained due to higher irrigation water depth applied at irrigation events after this time because of rapid growth of maize crop that is occurring due to increase air temperature at this time. Simulated deep percolation reached 6.98 cm which is 13% of the total amount of water applied during the growing season. Simulation results showed that N leaching at 60 cm depth for about 7.61 and 2.64 kg N ha-1 for raw and treated wastewater, respectively. Nitrogen concentration for raw and treated wastewater decreased due to root nutrient uptake. The results also showed that the crop N uptake was 76.2% and 81.9% of total N input (TNI) during the growing season, while 19.4% and 14.5% of TNI was retained in the soil at the end of the season for raw and treated wastewater, respectively.
The HYDRUS-1D model was used to simulate the transport of N-NO3- under the raw and treated wastewater application in the soil. Simulation results provided detailed moisture and N regime, as well as bottom boundary flux for percolation and N leaching estimation. N leaching is closely correlated with vertical water flow. The N leaching distributions at the bottom of the soil profile (60 cm) are similar to the corresponding water flux distributions. The results also showed that the crop N uptake was 130 and 60 kg N ha-1 during the growing season for raw and treated wastewater, respectively. As the results showed wastewater can use as a source of N for crops and it can help to reduce the requirements for commercial fertilizers, and decrease their negative environmental impacts. It is suggested that the model parameters can be measured practically, in order to be used for model calibration and validation. Besides, the simulation can be done for a longer period of time to evaluate the effect of rainfall and different cultivations on solute transport.

Soil erodibility (K factor) is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. This factor can be directly determined as the mean rate of soil loss from standard plots divided by erosivity factor. Since measuring the erodibility factor in the field especially watershed scale is time-consuming and costly, this factor is commonly estimated by pedotransfer functions (PTFs) using readily available soil properties. Wischmeier and Smith (1978) developed an equation using multiple linear regressions (MLR) to estimate erodibility factor of the USA using some readily available soil properties. This equation has been used to estimate K based on soil properties in many studies. As using PTFs in large sales is limited due to cost and time of collecting samples, recently soil spectroscopy technique has been widely used to predict certain soil properties using Point SpectroTransfer Functions (PSTFs). PSTFs use the correlation between soil spectra in Vis-NIR (350-2500 nm) and certain soil properties. The objective of this study was to develop PSTFs and PTFs for soil erodibility factor prediction in the Simakan watershed Fars, Iran.
The Semikan watershed, which mainly has calcareous soil with more than 40% lime (total carbonates), is located in the central of Fars province, between 30°06'-30°18'N and 53°05'-53°18'E (WGS′ 1984, zone 39°N) with an area of about 350 km2. For this study, 40 standard plots, which are 22.1×1.83 m with a uniform ploughed slope of 9% in the upslope/downslope direction, were installed in the slopes of 8-10% and the deposit of each plot was collected after rainfall. From each plot three samples were sampled and some physicochemical properties including soil texture, organic matter, water aggregate stability, soil permeability, pH, EC were analyzed Spectra of the air-dried and sieved soil samples were recorded in the Vis-NIR-SWIR (350 to 2500 nm) range at 1.4- to 2-nm sampling intervals in a standard and controlled dark laboratory environment using a portable spectroradiometer apparatus (FieldSpec 3, Analytical Spectral Device, ASD Inc.). Some bands which had the highest correlation with K factor were chosen as input parameter for developing PSTFs. A stepwise multiple linear regression method was used for developing PTFs and SPTFs. R2, RMSE and ME were used for comparing PTFs and SPTFs.
The K values varied from 0.005 to 0.023 t h MJ−1 mm−1 with an average standard deviation of 0.014 and of 0.003 t h MJ−1 mm−1, respectively. The K estimated by Wischmeier and Smith (1978) equation varied from 0.015 to 0.045 t h MJ−1 mm−1 with an average of 0.030 t h MJ−1 mm−1. There was a significant difference (p

Cadmium is one of the toxic heavy metals which is highly problematic in today's industrial world. It is essential to study the techniques for removing or reducing its availability, toxicity and consequently its hazardous effects in environment. Biochar is an amendment reported to be efficient in fixing heavy metals. Pyrolysis temperature is among the most important factors affecting biochar's characteristics, such as pH, CEC and specific surface area and generally it's potential to sorb heavy metals. On the other hand, soil moisture regime could affect pH and EC and consequently the Cd availability. Iran is the second producer of pistachio in the world and consequently a large volume of pistachio waste byproducts would be created annually. Converting this byproduct to biochar may be an efficient tool to prevent its accumulation. On the other hand, the produced biochar could be used as a soil amendment. The present study was conducted to evaluate biochar produced from pistachio nutshell under different temperatures for reducing Cd availability under different moisture regimes.
The soil texture in the present study was sandy-loam. Raw pistachio nutshell (RPN) was used to produce biochar under different temperatures. RPN was rapped in aluminum foils and heated for 2 h in a muffle furnace under 200, 400 and 600 °C. The pH, EC and concentrations of P, K, Fe, Mn, Zn and Cu of RPN and produced biochars were determined. A completely randomized experimental design with factorial arrangement including nine biochar treatments (control (no amendment), RPN and biochars produced under 200, 400 and 600 °C at 2% and 4% rates), and two moisture regims (20% w/w and waterlogging) was carried out with two replications. The samples were spiked with 25 and 50 mg Cd kg-1 and incubated for 90 days under laboratory temperature. Available Cd extracted by DTPA-TEA on 15, 30, 60 and 90 days after incubation. Cadmium concentration determined by Atomic Absorption Spectrometry (Mark and Model: HITACHI- ZCAST 2300). Analysis of variance and compare of means used to evaluate the effects of various treatments on DTPA-Cd.
The nutrient concentrations of biochar were increased with increasing the production temperature. The RPN and biochar of 200 ºC had the least nutrient concentrations while the biochar of 600 ºC showed the highest nutrient concentrations. The increases of pH and EC occurred with increasing the biochar production temperature. The pH ranged from 6.36 to 9.36 and EC range was 13.5-31.9 dS m-1. The analysis of variance showed that biochar, moisture regime and their interaction significantly affected DTPA-Cd on all of the studied times (P Generally, regarding the decrease of DTPA-Cd by biochars, especially biochar of 600 °C, it can be concluded that biochar of pistachio nut shell particularly under 600 °C might be considered as an inexpensive and green environmental sorbent for Cd, however its potential to reduce Cd uptake by plants and Cd movement in environment requires further studies. Furthermore, the knowledge of the mechanisms that are responsible for Cd retention on biochar and desorption kinetic of sorbed Cd need further investigation.

Atmospheric dust is an important source of heavy metals, particularly in urban environments. Heavy metals can easily attach to dust particles and be distributed in large areas. Therefore, assessing the extent of heavy metals pollution present in nuisance dust is important for establishing pollution control strategies and evaluating the results of previous measurements. Heavy metals contamination in atmospheric dust of Kermanshah provine has not been previously investigated. The main objective of this initial study was to determine the concentrations of heavy metals in atmospheric dust samples that were collected from Kermanshah province and to assess their contamination level. The results can provide a baseline for use in future environmental impact assessments and to guide pollution mitigation targets.
Dust samples were collected from 49 sites across the province, during the summer 2013. Dust sampling sites were selected in different urban (35 site) and suburban (14 site) locations in Kermanshah, Songhor, Gilangharb, Ghasre-Shirin, Sahneh, Sarpolzahab, Kangavar, Paveh and Javanrood cities. Dust collectors were installed on the roof of buildings about 3–4 m above the ground level. Each collection tray consisted of a circular plastic surface (320 mm in diameter, 120 mm depth) that was fixed on holders with 33 cm height and covered with a 2 mm PVC mesh on top to form a rough area for trapping saltant particles. The dust samples were analyzed for their Zn, Cu, Ni, Cr, Mn and Fe concentrations using an Atomic Absorption Spectrophotometer. In the present study, geo-accumulation index (Igeo), enrichment factor (EF), pollution index (PI) and integrated pollution index (IPI) were calculated to assess the heavy metal contamination level in the atmospheric dust.
The results showed that except for Fe and Mn, all heavy metal concentrations of atmospheric dust in Kermanshah provine were higher than in the background soils of world, showing that these heavy metals are likely from anthropogenic sources. The order of mean Igeo values was Ni> Zn> Cu> Cr> Mn> Fe, similar to the order of their EFs and PIs, which can also be seen as the decreasing order of their overall contamination degrees in atmospheric dust of Kermanshah province. The mean Igeo for Ni points to moderately to strongly pollution. 59% of calculated Igeo for Ni falls into class 2 (moderately polluted) and 37% into class 3 (moderately to strongly polluted), while according to the Igeo values for Mn (98%) and Fe (100%), they were practically unpolluted (class 0). The maximum EFs of Zn, Cu and Ni were higher than 10, which show that Zn, Cu and Ni in atmospheric dusts mainly originate from anthropogenic sources. It seems that EFs can also be an effective tool to differentiate the natural origins from anthropogenic sources. The mean EF (11.2) and 94% of Ni EFs were in the range of 5–20 indicating that Ni was a main contaminant in studied samples. Mn had 41% EFs less than 2 and 59% EFs in the range of 2–5, with mean EF less than 2, indicating minimal enrichment. The analytical results of heavy metals Igeo are same as the analytical results of EFs. The PIs of Zn, Cu and Ni were in the ranges of 2.1 to 11.3, 1.7 to 18.3 and 3.3 to 13.6, with an average of 3.8, 3.3 and 6.9, respectively. These data indicate that Zn, Cu and Ni may cause serious pollution in atmospheric dust of Kermanshah. The IPIs of atmospheric dust samples vary from 1.9 to 6.2 with mean value of 2.9, indicating that all studied samples were polluted by heavy metals.
The concentrations of heavy metals that were investigated in this study were compared with the reported data of other cities and with the background values of elements in the world soils. The concentrations of Zn, Cu, Ni and Cr in urban dust samples, and Fe and Mn in suburban dust samples were higher than their respective values in the world soils. The results indicate that atmospheric dusts in Kermanshah provin have elevated metal concentrations in general. The calculated values of Igeo and EF of heavy metals revealed the order of Igeo and EF as Ni> Zn> Cu> Cr> Mn> Fe. The high Igeo and EF for Ni, Zn and Cu in atmospheric dusts indicated that there was a considerable Ni, Zn and Cu pollution (Especially nickel), which possibly originate from traffic and industrial activities. The Igeo and EF of Mn and Fe were low. The results of PI also supported Zn, Cu and Ni serious pollution in atmospheric dust. Similarly, IPI results confirmed atmospheric dust samples pollution by heavy metals. These findings indicated that more attention should be paid to heavy metal contamination of atmospheric dusts in Kermanshah, especially in case of Ni.

Evaluation of the nutrient status in soil is important from the nutritional, environmental, and economical aspects. Potassium is an essential element for plant growth and is a dynamic ion in the soil system. Soil testing is a useful tool that can help to ensure the efficient use of applied plant nutrients. Soil tests measure the quantity of a nutrient element that is extracted from a soil by a particular chemi¬cal extracting solution. The measured quantity of extractable nutrient in soil is then used to predict the crop yield response to application of the nutri¬ent as fertilizer, manure, or other amendments. Over the years, many different soil testing pro¬cedures and extracting solutions were evaluated in an effort to identify a method that provides the most reliable prediction of crop yield response to nutrient application. It was determined that some soil testing procedures are best suited for particular soil types and climatic regions, whereas other soil testing procedures are better suited for different soil types and climates. Olive is a strategic and economic product in Iran. Fars province is the second largest olive producer in Iran. There is no general information about K status in the soils and olive trees of the Fars province as well as no introduced appropriate K extractants for theses soils. Therefore, the objectives of this study were: i) evaluating potassium status of some soils of olive orchards of Fars province and ii) introducing appropriate k extractants for extracting available K in these soils.
Fars province, with an area of 122000 km2 is located in southern Iran. The elevation varies from 500 m to 4400 m above mean sea level. Based on the information regarding olive orchards of Fars province, 13 typical olive orchards were selected. 26 surface (0-30 cm) and subsurface (30-60 cm) soil samples were taken. Physiochemical properties of the soil samples were determined based on standard methods. Soil reaction, texture, electrical conductivity, calcium carbonate, organic carbon, and cation exchange capacity were identified. The 12 K extracting solutions were 1M NaCl, 2M NaCl, 0.01M CaCl2, Morgan, AB-DTPA, 1M NH4OAC, 0.25M NH4OAC, 1M NaOAC, 2M HCl, 0.1M HNO3, 1M HNO3, and 0.025M H2SO4. The K contents of leaf samples were determined in 1g of each sample. The samples were dried and then ashed in 450°C for 4 h. 2M HCl was used to digest the samples. Potassium in all the filtrated extracts were then analyzed using flame photometer.
The all soils were calcareous (average of 48.7 and 50.2% calcium carbonate equivalent in surface and subsurface, respectively) with pH in range of 7.05-7.8. The textural classes were loam, clay loam, and sandy loam. The results showed that the concentrations of K extracted varied widely with the used method, because each extractant remove different portions of K. Among the 12 tested methods, boiling 1 mol/L HNO3 extracted highest amount of K (mean 696.1 mg/kg and range of 203.3-1893 mg/kg) which extracted soluble, exchangeable, and non-exchangeable potassium forms due to its high concentration of H and 2mol/L HCl removed the lowest amount of K (mean 32.7 mg/kg and range of 2.6-148.5 mg/kg). Correlation coefficients between K extracted by 12 extractants were de¬termined. The correlation between potassium extracted by all the chemical methods was positive and significant except for boiling 1M HNO3. Between all tested extractants, 0.25M NH4OAC and 1M NH4OAC had the highest correlation (p≤0.01, r= 0.999). The relationship between soil potassium and potassium concentration in olive leaves were evaluated. Maximum correlation between leaf K and extracted soil K were noticed in 2M NaCl, 0.25M NH4OAC, 1M NaOAC, and AB-DTPA (r=0.721, 0.718, 0.717, and 0.714, respectively) and the minimum correlation was noticed in 1M HNO3 (r= 0.661).
The concentrations of K extracted varied widely with applied method, because of desorbing different portions of K by each method, different concentrations of each extracting solution, and the different times of equilibration. On average, the quantity of extracted K by 12 methods were in the following order: boiling 1M HNO3> 1M NH4OAC> 0.25M NH4OAC> AB-DTPA> morgan> 0.1 HNO3> 0.025M H2SO4> 0.01M CaCl2> 1M NaCl> 1M NaOAC> 2M NaCl> 2M HCl. This study showed that 2M NaCl, 0.25M NH4OAC and 1M NaOAC would be suitable as soil testing methods for determining available K for olive in the soils of Fars province. These extractants were the best because of high correla¬tion with plants potassium. In addition, advantages of these extractants are low cost and simplicity. As a recommendation, using of K fertilizers in most olive orchards of the province will improve quantity and quality of the yield.

Elemental sulfur is byproduct of natural gas refining which during this process, H2S is removed from sour gas and after changes to solid sulfur, it is stored in large block forms. Continuous precipitation of sulfur and its oxidation causes soil acidification and as a result, nutrient cations such as Ca2, Mg2, Na and K will leach from the soil profile. Also, sulfate accumulation led to soil acidification and accelerates the silicates weathering in upper layer of the soil profile. Accumulation of water soluble sulfate in the soil and increase the nutrient cations leaching from the soil depend on sulfate resistance rate. Also, addition of sulfur to the soil for a long time can cause calcium sulfate formation that will cause problems such as increase in soil salinity. Shahid Hashemi Nezhad gas refinery is located about 35 km south of Sarakhs city and about165 km east north of Mashhad. In addition to exploiting, refining and producing 50 × 106 m3.day-1 natural gas, recovered sulfur with %99/9 purity and 2000 tons per day production capacity is one of the byproducts of this gas complex.
22 soil samples were collected from surface soil in Shahid Hashemi Nezhad gas refinery (3 samples) and nearby areas (19 samples) (Fig.1). Soil extracts pH was measured in equilibrium with pure water and with KCl 1M solution in 1:2.5 soil solution ratio. EC of the soil samples was measured in different soil water ratios to obtain the EC 1:1 (Fig.2). Total sulfate content was measured by gravimetry method at geochemistry laboratory of Faculty of Sciences at Ferdowsi University of Mashhad. To get the digestion extract, a mixture of 2 ml concentrated HF, 5 ml HCl and 8 ml HNO3was added to 0.5 gr soil in a teflon vessel, then heated for 60 min at 170° C. After cooling, the solution was evaporated at 130 °C to dry it. Then, the dried salt was dissolved in a mixture of 2 ml HNO3 and 2 ml HCl and diluted with deionized water up to 25 ml. Ca2 and Mg2犉筬쇪 measured through titration of the soil extract with EDTA 0.01 N and in EBT reagent at the first stage, and titration of the soil extract by EDTA 0.01 N and in Moroxide reagent at the second stage. Na and K contents were determined using AAS method at geochemistry laboratory at Ferdowsi university of Mashhad after extraction with CaCl2 0.01 M.
Based on EC values, 77% of the soil samples were non-saline (EC Salinity is the major factor affecting decrease of the samples quality for agriculture. Exposed formations in the area with highly soluble rocks causes to increase the soluble salts in the soil. The second factor is high temperature and low precipitation that led to increase the evaporation from the soil surface and accumulation of salts on the soil. Recovered sulfur from natural gas processing can reduce the soil pH and increases the soluble salts to some extent, especially in the inside refinery samples, and then decreases the soil quality for agricultural purposes. Except for one, all studied samples were classified as non-saline and non-sodic soils. Furthermore, the samples were classified in two classes of flocculated soils and potentially dispersive soils based on SAR and EC. ESP index indicates that there is no serious problem regarding sodium concentration in the soils. The pH values indicate that the samples were almost alkaline soils except for the samples inside the site, which are slightly acidic. Acidity of those few samples are attributed to the sulfur released from gas refinery process and its effect on the soil pH.

Peat is an organic soil which is formed by the accumulation of decayed vegetative matter that have formed in areas of poor water drainage. The mineral components of peat are derived from inorganic matter contained in sediments and by adsorption from groundwater. The inorganic (mineral) fraction of peat usually includes only 2–10 Percent of its dry weight, but for highly decomposed peats can increase to about 60 percent of dry weight. Thin sections of peat reveal detailed information of composition, structure, fabric and particularly pore properties which influence water retention and movement. Peat is a concentrated form of soil organic matter which has environmental, industrial, agricultural and medical uses that range from sustaining the productive capacity of agricultural land. This study has been focused on micromorphological and mineralogical properties of Suteh peat swamp forest (PSF) in Golestan province, north of Iran. Golestan province is the third largest cereal producer in Iran but scarcity of water and salinity are most important major problems in this area. This area has been covered by almost 400,000 hectares of forests. Suteh PSF has been chosen as a swamp that contains organic and inorganic matters. As the inorganic composition of peat varies considerably from region to region, study of mineralogical and micromorphological of Suteh PSF can be useful in order to identification of Golestan province peat swamps. Since the early 1990s, micromorphological studies have become increasingly popular in the analysis of lakeside settlements. The evaluation of soils considers thin-section observations, macromorphological features, and laboratory data. Micromorphological analyses allow the characterization of natural and anthropogenic sediments, which in turn enables the determination of sedimentary processes and depositional environment.
This study was carried out in April 2014. The samples were collected from zero to 40 cm depth of swamp areas, within a 10 cm radius. At each sampling station, peat samples were collected with a trowel. The area included the north side of the Alborz Mountains and extended northward to the township of Gorgan. The altitude was approximately 950–2000 m a.s.l. According to the Gorgan Natural Resources Bureau report, Suteh is temperate to semi-arid on the Emberger climate diagram. To achieve the purpose, samples were dried and prepared based on standard methods. These studies were carried out using polarized microscope on thin sections and polished section at the Mineralogy Laboratory of the Amirkabir University of Technology.To prepare thin sections for microscopy studies, samples with polyester, cobalt oxide and hardener have been combined. Polyester formed the matrix of the section and hardener (HCl H2O2) has been used to reduce a hard time getting. Cobalt oxide has been used as a catalyst between them. The samples have been kept tight in special containers. Due to the presence of organic matter, much time was needed to harden them. The samples were dried and tightened for 20 days. Then, the samples were polished by various polishers (No. 400, 600, 800, 1000 and 2000). After that, they were polished for 20 minutes by the suspension of alumina (Al2O3 H2O).
The coarse material that formed groundmass were composed of quartz, muscovite, orthoclase, calcite, opacity pyroxene biotite and opaque minerals. Some flakes of muscovite, pyroxene and biotite showed weathering. Fe–Mn components were most common in opaque minerals. Quartz crystals were seen in abundance in most sections. Weathered surface of orthoclase was seen in some sections. The large biotite crystals were seen at different sections with pleochroism light brown to dark brown. Root and other organ residues in varieties states of decomposition were observed in some sections. Fragments of organ and tissue residues were rather few and found mostly in the surface of Suteh PSF. For detailed assessment of opaque minerals, one of the grains was selected and analyzed. The weathering of minerals showed the normal stability trend, i.e. quartz >muscovite>biotite. Biotite loses its pleochroism and alters first to a mica-vermiculite interstratified clay mineral. Polished sections study showed Fe components were the major and dominate in the sections.
Thin sections results showed the samples contained quartz, orthoclase, muscovite, biotite, calcite, opacity pyroxene and opaque minerals. Polished sections results revealed that Fe components were most common in opaque minerals in the sections. Micromorphological study showed root and other organ residues in Suteh PSF that this showed this soil composed of a mixture of organ residues and organic material.

All living organisms, such as plants, animals and humans depends on the water and life may exist in a place where water is available. Groundwater is the main source of drinking water for more than 5.1 billion people around the world, especially in arid and semi- arid regions such as Iran. Currently, groundwater provided about 60 percent of the worlds drinking water and 77.8 percent of the Iran's drinking water. In recent years, it has been found that groundwater quality is also important as much as its quantity. Nowadays, pollution of groundwater resources from pollutants, especially heavy metals reduces the quality of these resources. Heavy metals are one of the most important environmental pollutant that its entering into the water is raised by agricultural activities, industrial and urban development. Among the heavy metals, arsenic is a toxic and carcinogenic metalloids which are widely distributed in the environment and it has a twentieth abundance of elements in the Earth's crust with an average of 1.8 mg kg-1. Arsenic has been classified in the first group of cancer-causing compounds. It has different effects such as horny skin, liver, skin and bladder cancer, mental disorders, damage to neurons, blood pressure, lower IQ and reducing white blood cells and red blood. The Maximum permissible arsenic in drinking water is 10 micrograms per liter which has been identified by the World Health Organization and America Environmental Protection Agency. According to national standards of Iran, limitation of arsenic in drinking water is 10 micrograms per liter. So far, numerous studies were done to evaluate the environmental contamination of heavy metals, especially arsenic using geostatistical methods. The aim of this study was to evaluate the quality of groundwater in terms of Arsenic pollution.
study area is Hamedan - Bahar aquifer with an area of 800 square kilometers that is located on the northern slopes of Alvand Mountains. The central part of Hamadan city, Lalejin, Saleh Abad and Bahar city is located in the study area. To conduct this study, concentrations of arsenic was investigated in 94 groundwater points. To determine the spatial distribution of arsenic, different geostatistical methods were used. Then the results of this methods were compared using cross validation technique and MAE & ¬MBE index and the most suitable method was chosen for this purpose. Eventually RBF method by multiquadric model was used. Moreover Contamination probability map was developed using indicator kriging models.
Arsenic concentrations were in the range between 5 – 79.5 micrograms per liter. Also The average concentration was 12.4 micrograms per liter. While the threshold for arsenic in water defined 10 micrograms per liter by the World Health Organization (WHO). So an average of arsenic in ground water is higher than limits of international standard. The spatial correlation analysis showed that the concentrations of arsenic in groundwater have no strong spatial dependency. So, for zoning this variable, between the nonparametric methods, radial basis function (RBF) by Multiquadric model was used. This method had lowest MAE and MBE index for arsenic in groundwater. The highest concentration of arsenic was in the industrial zone in the north of Hamadan (Hamedan, Tehran road). In general Excessive concentrations of arsenic are visible in the three areas : The first area is between Hamedan and Tehran Road Industrial Estate, that the high rate of abnormalities was found in this area (79.5 μg/L). Also the suburbs of Saleh-Abad and the Bahar city has high arsenic concentration. In these areas, groundwater levels were high and pollutants can penetrate more easily. The results of the contamination map using an indicator kriging method showed that 21.18% of aquifer moderately contaminated and about 10.9% of the aquifer area have a high contamination possibility. Polluted groundwater is matched with agricultural land especially the potato fields.
The results showed that the average concentration of arsenic in groundwater of Hamedan-Bahar basin is more than WHO and Iran department of environmental guidelines. The highest concentration of arsenic in agricultural lands and consequently in groundwater resources is due to the existence of polluting industries, the geological structure of the area where arsenic concentration naturally is high, cultivation of potatoes and other crops in the region and indiscriminate use of pesticides and chemical fertilizers in agriculture.

Potato production has fourth rank in the world after rice, wheat, and maize with the production of 321 million tons from 19.6 million hectares. In Iran this important crop has third rank after wheat and tomatoes with the production of 4.6 million tons. Potato is a temperate crop, growing and yielding well in cool and humid climates or seasons, but it is also cultivated in tropical to sub-polar climatic regions, and represents a major food crop in many countries. Potato is sensitive to nutrients deficiency especially phosphorus and zinc. At least one-third of the cultivated soils globally are estimated to contain too low amounts of bioavailable zinc for optimal crop production. In Iran more than 70 percent of irrigated soils suffer from zinc deficiency. Many reasons have role in mentioned deficiency such as calcareous and alkaline soils, lower organic carbon and higher application of phosphorus fertilizer. So, evaluation of zinc fertilizers efficiency is essential under different soil phosphorus conditions.
This project was carried out in order to investigate the effect of zinc sulfate levels on yield, nutrients concentration and zinc recovery and agronomic efficiency under different phosphorus conditions in potato (Solanum tuberosum L.) in Hamedan province (Tajarak station). The current research was done as a randomized complete block design with 9 treatments, three replications and three locations (with different soil phosphorus levels). The phosphorus locations were involved two locations with 10-15 mg available P per kg of soil (without or with phosphorus application) and a locations with 20-25 mg available P per kg of soil. Zinc treatments were consisted of soil application of 0, 20, 40, 60, 80, 100 and120 kg of zinc sulfate (ZnSO4.7H2O) per hectare and foliar spray of zinc sulfate at the rate of 5 grams per liter at one week before and one week after flowering. After harvesting, the tuber and shoot yield, tubers and shoot zinc uptake, nutrients concentration were measured in different parts of potato plant, and recovery and agronomic efficiency of applied zinc fertilizer were calculated.
The results showed that the zinc treatments significantly affected the tuber yield of potato. The application of 40 kg.ha-1 zinc sulfate and foliar spray of Zn one week after flowering evidenced the highest and the lowest yield, respectively and the difference between these treatments were 17 percent. The differences between without Zn application and foliar spray of Zn one week after flowering were not significant on yield which showed that the time of fertilizer foliar application is very important and by delaying of foliar spray the yield could not increase. The zinc treatments affected significantly tuber zinc uptake and the foliar spray of Zn one week after flowering by 80 percent increase comparing with control, had the highest tubers zinc uptake. The tuber and shoot zinc concentration were significantly affected by the zinc sulfate levels. The highest and lowest concentration of zinc in shoot and tubers were observed in the foliar spray of Zn one week after flowering and control. This treatment caused 160 and 24 percent increasing in shoot and tubers zinc concentration in comparison with control. In spite of considered increase in zinc content by foliar application of zinc one week after flowering, the potato yield did not increase considerably. The tuber and shoot yield were affected significantly by different phosphorus locations (p

Potassium (K) is one of the major essential macronutrients for biological growth and development. The ability of some bacteria to release potassium from unavailable forms is an important feature for increasing plant yields of high-K-demand crops. Application of soil microorganisms is one approach to enhance crop growth. Some bacteria are efficient in releasing K from mineral sources and in recent years in order to produce and make of potassium biofertilizers, attention to the potassium releasing bacteria has been increased. Production of organic acids and acidic polysaccharides by the microorganisms are the main mechanisms by which K is released. Microorganisms play a central role in the natural P and K cycles. Many microorganisms in the soil are able to solubilize ‘unavailable’ forms of K-bearing minerals, such as micas, illite and orthoclases, by excreting organic acids which either directly dissolves rock K or chelate silicon ions to bring the K into solution. Recently, attention to the release of potassium from bacteria has been increased because some of efficient bacteria can be used as potassium biofertilizers to meet plant K needs. Hence, the objectives of this study were to in-vitro assessment of potassium releasing of some isolates belonged to Pseudomonas genus.
A laboratory dissolution study was carried out using a completely randomized design with three replicates. The factorial experiment contained two factors; 1-bacteria (including five bacterial treatments and un-inoclated treatment) and 2- mica minerals (including biotite and muscovite). Micas flakes were powdered and passed through a 0.5 mm sieve. Available forms of K were removed by washing with 0.1 M HCl and then distilled water, before adding the minerals to Aleksandrov medium For this reason, a microbial incubation study in the Aleksandrov liquid medium containing mica and tricalcium phosphate was designed for a period of one month and 5 strains of potassium releasing bacteria belonged to the genus Pseudomonas (S6-6, S10-3, S14-3, S19-1 and S21-1) along with the un-inoculated treatment (control) were applied. In this experiment, the release of potassium and phosphorus in liquid Aleksandrov medium were measured at intervals of 5 days in incubation period of 30 days. Nutrient Broth was used to prepare an overnight culture of bacteria to inoculate Aleksandrov medium. It should be mentioned that Aleksandrov medium was used to determine the amount of released P from tricalcium phosphate (TCP) while muscovite was added to the medium as a sole source of potassium. Concentration of P was determined spectrophotometrically by ammonium-vanadate-molybdate method and K was determined by flame photometry.
The results showed that dissolved potassium and phosphorus in the inoculated medium were significantly increased and the amount of potassium released by the isolates was between 2.17 and 3.23 mg g-1 and the highest potassium release was achieved with isolate S14-3 (3.23 mg g-1), which that compared to the non-bacterial control showed an increase of 48.85 %, and significant difference was found with other isolates. Bacterial incubation experiment indicated the ability of isolates to release potassium from K-containing minerals such as biotite and muscovite and the XRD analysis revealed an alter in chemical structure of clay minerals. Especially, presence of 19.5Å peak in muscovite (saturated with magnesium) treated with isolate S14-3 showed the released space of K from the interlayer is filled or associated with a number of bacterial metabolites. It seems that the same mechanisms could be effective in releasing K from micas and P from TCP, in other words there is a co-solubilizing mechanism for mica and TCP.
Discussion and It appears tha depletion of potassium from minerals has occurred but further tests will confirm this topic. The enhanced releasing of mineral K might be attributed to the release of organic acids from the bacteria, a mechanism which plays a pivotal role in solubilizing phosphate from inorganic source of phosphate. The mechanism of potassium release from minerals is still not clear. Productions of acids or chelates are main mechanisms to release K from potassium containing minerals. Among the bacterial strains under study, Pseudomonas sp. S14-3 was the most efficient strain in K release from micas and phosphate solubilization from TCP. However, more experiments need to be done especially in pot and field experiments to study the role of these strains in K nutrition of crops.

Salinity is one of the problems of arid and semi-arid soils. Identification and classification of saline/alkaline soils is necessity for dealing with difficult situations and correct management. Considering the nature of salinity data and selection of befitting methods to process data before use artificial neural network, can result in better simulations. The aim of this study was to investigate the optimal method for data processing to enhance the accuracy of surface soil salinity simulation and improve the efficiency of decision tree algorithm.
The study area was 88940.4 hectares of Marvast plain located in central Iran (54° 5´to 54° 18´ east longitude and 30° 10´to 30° 35´north latitude). This region faces with problems of soil and water resources salinity. In this study, the effect of data processing on increasing accuracy of simulation of soil surface salinity was assessed in Marvast region using decision tree algorithm. For this purpose, the decision tree algorithm was applied and simulation was performed using three approaches i.e. original data, logarithmic data and standardized data. Finally, five statistics including R¡ Rmse¡ %Rmse¡ MAE and Bias were calculated to evaluate the performance of used simulation methods.
In this study, when the logarithmic data was used, the composition of band 7 – elevation was identified as the most appropriate condition. The created tree can estimate the soil salinity by five laws:If elevation is less than 1519, then the average of surface soil salinity will be 147.9 ds/m.
If elevation is between 1519 to 1569.9, then the average of surface soil salinity will be 43.6 ds/m.
If elevation is between 1569.9 to 1609.8, then the average of surface soil salinity will be 17.5 ds/m.
If elevation is more or equal to 1609.8 and pixel value of band 7 (ETM sensor) in selected point is less than 0.295, then the average of surface soil salinity will be 4.7 ds/m.
If elevation is higher or equal to 1609.8 and pixel value of band 7 (ETM sensor) in selected point is more than or equal to 0.295, then the average of surface soil salinity will be 1.4 ds/m.
For the approach of using the logarithmic data, decision tree algorithm used two parameters out of 46 independent variables introduced into the model. R¡ Rmse¡ %Rmse¡ MAE and Bias for this method was computed to be 0.76, 0.49, 38.57, 0.37 and -0.14, respectively. The application of logarithmic data was recognized as the best method considering the lower calculated error and its less input requirement. Using Easy fit software, the distribution of salinity data was found to be Log Pearson 3. Thus, the use of logarithmic data improved model performance. Our findings were in agreement with those of Afkhami et al (2015) who increased the simulation accuracy of suspended sediment with artificial intelligence methods (Artificial neural networks and ANFIS) using logarithmic data.
As effective factors for soil salinity simulation vary in different regions, application of a unique method and indicator to estimate soil salinity in deferent region may not be possible.. The application of semi intelligent algorithm which limits user intervention and selects effective parameters for simulation would increase the simulation accuracy. Furthermore, considering the nature of salinity data and selection of befitting methods to process before using decision tree algorithm can effectively improve model performance. The current study was conducted to select an appropriate approach to enhance the simulation accuracy of surface soil salinity. The results demonstrate that the performance of decision tree algorithm as one of the artificial intelligence models can be affected by input data. In this study, Log-Pearson3 distribution was defined as the distribution of salinity data. Moreover, despite existence of significant correlation coefficients for three simulation methods, the error was lower when logarithmic data was used. Since the probability distribution of salinity data in the studied area was logarithmic (Log-Pearson 3), the reduction in error rate can be attributed to the probability distribution of salinity data.

Soil salinization is increasing across developing world countries and agricultural production is decreasing as a result of this stress. Climate change could adversely affect soil salinization trend through the decrease in rainfall and increased evapotranspiration in arid regions. Policy and decision makers require continuous and quantitative monitoring of soil salinity to adapt with the adverse effects of climate change and increasing need for food. Indices derived from near surface or satellite based sensors are increasingly applied for monitoring of soil salinity so a considerable number of these indices are introduced already for soil salinity monitoring. Different regression methods have been already used for modeling and verification of developed models amongst them multiple linear regression (including stepwise, forward selection and backward elimination) and partial least square regression are the most important methods.
To evaluate different approaches for modeling soil salinity against remotely sensed data, an area of about 50000 ha was selected in Sabzevar- Davarzan plain during 2013 and 2014 years. The locations of sampling points were determined using Latin Hypercube Sampling (LHS) strategy. Sampling density was 97 points for 2013 and 25 points for 2014. All points were sampled down to 90 cm depth in 30 cm increments. Totally 366 soil samples were analyzed in the laboratory for electrical conductivity of saturated extract. Electromagnetic induction device (EM38) was also used to measure bulk soil electrical conductivity for the sampling points at the first year and sampling points and 8 points around it at the second year. Totally 97 and 225 EM measurements were also recorded for first and second years respectively. Mean measured soil EC data were calibrated against the EM measurements. Finding the fair correlations, the EM and EC data could be converted to each other. 23 spectral indices derived from Landsat 8 images in the sampling dates along with DEM were used as independent variables. Multiple Linear Regression (MLR) and Partial Least Square Regression (PLSR) methods were evaluated for their fitness in predicting soil salinity from independent variables in different calibration and verification datasets.
Different multiple linear regression approaches using the first year data for training and second year data for testing the models and vice versa were evaluated which produced determination coefficients of about 22 to 88 percent in the training dataset but this regression did not reach to 29 percent in the test dataset. Due to the multiple co-linearity amongst the independent variables the multiple linear regression methods were not applicable to all variables. Excluding the co-linear variables, log- transforming and randomizing them into train and test datasets improved the determination coefficient of model and its validation at an acceptable level. Application of partial least square regression using the original and log- transformed data of first and second years as train and test datasets and vice versa introduced determination coefficients of about 39 to 85 percent in the training dataset but were not able to predict in the test dataset. Random dividing of all data into train and test datasets considerably increased the determination coefficient in the verification dataset. Repeating the randomization showed that the approach has the required consistency for predicting the coefficients of variables.
Wide range of independent variable could be used for predicting soil salinity from remotely sensed data and indices. On the other hand the independent variables generally show multi-colinearity amongst themselves. Correlation matrix, variance inflation factor and tolerance indices could be used to identify multi-colinearity. Removing or scaling the variable with high colinearity could improve the regression. Different data transformation methods including log- transformation could also significantly improve the strength of regression. In this research EM data showed more significant correlations with spectral indices in comparison with laboratorial measured EC data. As the EM38 device measures the reflectance in special range of spectrum this higher correlation could be expected. Such models should be calibrated and verified against ground truth data. Generally a part of data set is used for calibrating (making the model) and the remained for verifying (testing the model). Random dividing of the total data of 2 years into calibration (2/3 of data) and verification (1/3 of data) could significantly improve the regression in the verification data set. This procedure increases the range of variability for data used for calibration and verification and prevents outlier predictions.

Human activities such as intensive cultivation and land use changes alter nutrients fluxes (mainly iron) and mineralogy in soil and terrestrial ecosystems. Iron is an essential element for plants and microorganisms and its solubility is controlled by stable hydroxides, oxyhydroxides, and oxides. In general, parent material, climate, and landscape position are the major factors that accelerate the weathering of the minerals and rocks containing Fe in the regional scale. However, long-term cultivation and intensified agriculture may be the dominant attributes of modifications in soil properties like Fe compounds mainly in arid and semiarid regions, where the irrigational and agricultural practice is current over long-term periods. Although substantial data is documented on Vertisols properties, few studies are available to assess the effects of long-term continuous cultivation on the characteristic and distribution of iron oxides and their mineralogy, mainly in calcareous environments.
This study was conducted in the Piranshahr - Pasvah area (36° 46 to 36° 50 N and 45° 09 to 45° 50 E, 1500 m above sea level), West Azarbaijan Province, northwest of Iran. Six soil profiles belonging to three subgroups of Vertisols order (Chromic Calcixererts, Typic Haploxererts, and Typic Calcixererts) were described and sampled from the cultivated soils and similar soils from the nearby uncultivated region as grassland. Soil samples were air-dried and passed through a 2-mm mesh sieve before the analysis. Soil analysis included particle-size distribution, pH and electrical conductivity (EC), soil organic carbon (SOC), calcium carbonate equivalent (CCE), cation exchange capacity (CEC), the determination of iron oxides forms and mineralogical composition. Free or pedogenic Fe oxides (Fed) including crystalline, poorly crystalline, and organically bound Fe were extracted by dithionite–citrate–bicarbonate (DCB) method. Poorly crystalline and organically bound Fe (Feo) were extracted using 0.2 M ammonium oxalate (AO). Organic complex of Fe (Fep) was extracted by 0.1 M Na-pyrophosphate at pH 10. All Fe oxide forms were determined using atomic absorption spectrometry. The difference between DCB-Fe and AO-Fe was considered as an estimation of crystalline Fe oxides form.
The results showed that long-term cropping caused a considerable drop in organic carbon and calcium carbonates along with a noticeable rise in the values of clay and cation exchange capacity as a result of accelerated alteration by farming activities and interactions between the used irrigation water and soils receiving it. Long-term cultivation improved the amount of Fed and Fecry (crystalline Fe) from 1 to 64% and 44 to 90%, respectively, than those of uncultivated soils which can be explained in some pathways: (1) accelerated weathering of Fe-bearing minerals (such as biotite, chlorite, feldspars, amphibole, and pyroxene) in the cultivated soils and (2) the higher temperature condition and the more number of wetting–drying cycles in the cultivated soils compared to the uncultivated soils. Despite the fact that long-term cultivation caused a significant decrease in organic matter, a pronounced increase in organic complex of Fe with the range of 19 to 61% was recorded with farming practices. Such pattern can be contributed to the chemistry of organic matter and the presence of more stable fraction (passive fraction) of soil organic matter in the cultivated soils. The XRD patterns of primary Fe-bearing minerals (such as amphibole, pyroxene, and feldspar) had less intense in the cultivated soils compared to those of the adjacent uncultivated soils, indicating that probably cultivation promoted the instability and weatherability of Fe-bearing minerals as well as the loss of Fe from the minerals. In contrast, the X-ray reflections of secondary Fe-oxide minerals such goethite appeared to be higher, sharper and intense by long-term cropping, suggesting that agricultural practices also promoted the crystallization of the soil Fe oxides. Compared to the uncultivated soils, long-term agricultural practices caused some changes in X-ray reflections of chlorite, illite, and smectite.
The results showed that the weathering of Fe-bearing minerals and layer silicates, as well as the production of Fe oxide forms were promoted under long-term continuous cropping. Under cultivation, a pronounced increase in Fe-oxide forms, particularly Fed and Fecry, was recorded for the most of the examined soils which can be associated with the combined effects of increased soil temperature and moisture content from irrigation and farming practices. As emphasized, the combined effects of increased compounds from agricultural input (such as chemical and organic fertilizers, the compound of irrigation water, and moldboard tillage) as well as increased precipitation from irrigation interacted to create conditions for: (1) more intense the weathering of Fe-bearing minerals and (2) the more production of iron oxides forms in the cultivated soils.

Gravel, cobbles and boulders as erodible parameters play significant role to control wind erosion. Therefore, our understandings of gravel, cobbles and boulders percentage variations help to analyze events in the landscape. Close-range photogrammetry as an accurate measurement tool based on photos analysis has been extraordinary improved in recent years and its usage is rapidly growing in environmental analyses. It seems that close range photogrammetry in mapping and measuring the shapes and surfaces have a great potential. Currently close range photogrammetry is mostly used for preparation of Digital Elevation Model (DEM) and Digital Terrain Model (DTM). Now, high resolution DEMS only can be created using 3D Laser Scanner and close range photogrammetry. Despite having a considerable potential, close range photogrammetry has been rarely used in quantitative natural resource studies. In the current assessment, we examined the ability of close range photogrammetry for a quantitative parameter (i.e. percentage of gravel, cobbles and boulder).
In this study, we tried to used the close range photogrammetry and assess its performance to estimate the percentage of gravel, cobbles and boulders. For this purpose, a specific quadrat was designed for close range photogrammetry and the required photography tools and techniques were determined. In order to prepare the mapping of gravel, cobbles and boulders percentage, a sampling plan using OLI data was designed for the plain of Tehran-Karaj and photography was performed accordingly. Photos were processed using the PhotoScan software and Orthophotos and Digital Terrain Models were then created. The photos were classified by two 1- Decision Tree Analysis using Digital Terrain Models that it was done using the ERDAS IMAGINE 2015 software; 2- Object-based Classification using Orthophotos and Digital Terrain Models that the eCognition Developer 9 software was used. Gravel, cobbles and boulders percentage of each quadrat was estimated based on more accurate method and used as the dependent variable for modeling process. To model gravel, cobbles and boulders percentage, OLI data was firstly preprocessed to extract reflectance of the bands and then spectral indices were used. Geometric correction and radiometric correction using ATCOR3 were carried out in preprocessing phase and spectral indices of soil characterize were used to enhance the image. Finally, the reflectance of the bands and the spectral indices were used to create a multiple regression model using IBM SPSS Statistics 22 software.
The results showed that the Close Range Photogrammetry software (PhotoScan) is able to fix the distortion in photos well. One-dimensional relief displacement error was removed by PhotoScan. Interior and exterior orientation was done very well using the software and measurements which were calibrated by it. High quality Ortho-Photos and high resolution Digital Terrain Models were created using PhotoScan.
Classification by Decision Tree Analysis using Digital Terrain Models was done by the ERDAS IMAGINE 2015 software. First-order and Second-order polynomial interpolation was applied to Digital Terrain Models and the uniform surfaces were created. Two surfaces (original one created by PhotoScan and Interpolated Surface) were then compared and the gravel, cobbles and boulders parts were separated using some thresholds. The results indicated that this method can create the gravel, cobbles and boulders map rapidly but the accuracy is moderate.
Comparing with Decision Tree Analysis, Object-based Classification by the eCognition Developer 9 software which uses Orthophotos and Digital Terrain Models was more accurate. However, the latter was time-consuming as it is needed to be done manually in many different steps and there were many options to be created for final layer.
Automatic linear modeling in IBM SPSS Statistics 22 software was used to create multiple regression model and Iron Oxide and Inferred indices and reflectance of the bands 1, 2, 3 and 7 of OLI Sensor were selected by the software. The coefficient of determination of the model was more than 0.9 showing the good potential of the close-range photogrammetry. This model was used to create maps of percentage and the final map was in full compliance with the field observations.
Our results showed that the Close Range Photogrammetry has a vast potential and it can be an important tool in the environmental studies in the future.

Southeast watersheds of Iran including Great Karoon, Karkheh, Jarrahi and Zohreh have the most significant contribution in the water supply of the agriculture, industry, drinking water and hydroelectric power plants over Iran. 25 percent of the country’s electricity is produced from hydroelectric power plants located in this region. The existence of a monthly relatively high resolution gridded precipitation dataset is of the most important needs of water resources management for such as deciding on the suitable time of dewatering and discharge of dams, calibration of dynamical monthly forecasting models and drought early warning. Even considering all observation stations governed by Meteorological Administration and Ministry of Power, the density of stations is not so enough to use them for calibration of hydro-climate model outputs. To overcome this deficiency, one way to fill the gap is using bias corrected global gridded precipitation dataset such as APHRODITE, CMORPH, PRESIANN and other newly generated data.
Material and Watershed of Karkheh, great Karoon, Jarrahi and Zohreh are the area of study which covers southwest provinces of Khuzestan, Kermanshah, Ilam, Chaharmohal-Bakhtiari, Kohkiluyeh and Buyerahmad, Isfahan, Hamadan, Fars and Lorestan, which is shown in figure 2. There are 135 observation station in the area of study which governs by Iran Meteorological Organization and Ministry of Power. Area of study covers by 75 grids of 0.5×0.5 degree latitude and longitude. For each grid there is an APHRODITE precipitation data. In the 34% of grids, there is no observation station. The main goal of this study is to attribute a reliable monthly precipitation data to all grids without any observation station. Period of APHRODITE data set is 1987-2007, which is same to observation period. Firstly regional bias of APHRODITE data set has been computed by comparing observed precipitation with APHRODITE one. Then bias corrected APHRODITE precipitation (Composite APHRODITE Observation dataset) has been placed in non-observation grids. Efficiency of composite precipitation data has been determined by statistical parameters of bias, correlation and Nash-Sutcliff indices.
In this research the results have been evaluated at monthly and seasonal time scales. In the case of seasonal time scale, we found that the minimum APHRODITE’s bias of 1.2 mm has been occurring in summer, while the maximum bias has been occurring in winter by 40.9mm. It means that the bias is high in the rainy season. Seasonal correlations were statistically acceptable in 0.05 significant levels, showing same seasonal fluctuations in APHRODITE and rain gage data. To provide seasonal composite APHRODITE-Observed precipitation gridded data set, mean seasonal bias of APHRODITE has been removed, while preserving seasonal fluctuation. The highest spatial correlation of 0.8 was detected in autumn, while it was about 0.7 for spring and winter. The minimum seasonal correlation was in summer by 0.5. There were also a good agreement between area averaged observation and APHRODITE data, when considering statistical indices of bias, Nash-Sutcliff and relative percentage errors. Results show the cumulative distribution function of APRODITE data is behind of the observed cumulative distribution function data, meaning that APHRODITE reaches its maximum earlier than observation data. This implies that APHRODITE cannot capture well the extreme monthly precipitation. Monthly correlations are approximately greater than 0.9, but the only exception is September with a correlation coefficient of 0.52. All correlations are significant in 0.05 levels. The highest spatial correlation was occurred in Novembers. Monthly Nash-Sutcliff was 0.96 in monthly time series. The categorical percentage score was 94.1%. These results strongly confirm that APHRODITE precipitation data is a good option for replacement in grid cells without observations. The number of observation stations per cell is varied from 1 to 7. We found that the maximum monthly correlations occur in grid cells of 0.5×0.5 degree latitude and longitude which having at least 3 observation stations. The three-station bias has been applied to APHRODITE data, then bias-removed data has been replaced with grid cells without observations. Spatial patterns of new composite APHRODITE-observation data set has good agreement with observation in the areas having intense observation stations. They also can capture well the spatial precipitation distribution of rainy areas located in the center of basin and low rainfall areas located in the southwest of the region. The results of this research can be used in calibration of dynamical seasonal forecasting outputs, drought early warning and rain-runoff simulation.

Climate change in the current century is an important environmental challenge facing the world. Increase in atmospheric concentration of greenhouse gases such as CO2 as a result of human activities has caused a change in a number of hydroclimatic parameters. Climate change and global warming are the most important issues that have attracted many attentions in recent years. Climatic changes have interpreted as significant changes in average weather over a long period (Salari and ghandomkar, 2012). Global warming may cause drastic fluctuations in various processes and also it can significantly affect mean and variance of relative humidity, precipitation, solar radiation and etc. Global warming phenomena can change the components of the hydrological cycle and re-distribute the world's water resources in time and space. This may exacerbate desertification in arid and semi-arid countries such as Iran (Ahmadi and Radmanesh, 2014). Therefore, a large part of hydroclimatic researches has focused on temperature trend analysis at different spatial and temporal scales,
In the present study, the long-term temperature data from 24 climatological stations uniformly distributed over the West Azarbayjan province during 1981-2013 were used for investigating the temperature trends. The aim of trend test is to specify whether an increasing or decreasing trend exists in time series. Since parametric tests have some assumptions such as normality, stability, and independence of variables which may not be valid for most hydrologic series, the nonparametric methods are more preferred in meteorological and hydrological studies. In addition, the nonparametric trend analysis methods are less sensitive to extreme values compared to parametric trend tests. Nonparametric tests can also be applied regardless of linearity or nonlinearity of time series trend (Khalili et al. 2015). One of the most well-known nonparametric tests is the Mann–Kendall test (Mann 1945; Kendall 1975). Existence of more than one significant autocorrelation among data is long-term persistence (LTP). The presence of LTP in time series results in the underestimation of serial correlation and overestimation of the significance of the Mann-Kendall test (Koutsoyiannis 2003). In addition, Koutsoyiannis and Montanari (2007) pointed out that the Hurst phenomenon (Hurst 1951) is one of the most major sources of uncertainty in hydrometeorological trend analysis. Hamed (2008) studied the impact of LTP and Hurst phenomenon on the Mann–Kendall test, and Kumar et al. (2009) named it as the MK4. Since the MK3 test (Mann-Kendall method after the removal of the effect of all significant auto-correlation coefficients) is a generalized version of the MK2 (Mann-Kendall method after removing the effect of significant lag-1 auto-correlation), the MK3 and MK4 tests were used in this study and explained briefly in the following sections according to Kumar et al. (2009) and Dinpashoh et al. (2014). In the current study, the MK4 test was employed.
In this study, the mean monthly and annual air temperature trends were investigated using non-parametric Mann-Kendall test by considering the Hurst coefficient (MK4) for West Azarbayjan province. The Sen's slope estimator was also used for estimation of the slope of the trend line. Results indicate that 71% of selected stations (17 stations out of 24 considered stations) experienced a significant positive trend and only 7 stations (%29 of studied stations) did not show a significant upward trend in annual temperature time series. The highest increasing temperature rate (0.12 °C/Year) in annual timescale was found in Chehriq station. On monthly time scale, the numbers of months with increasing trends were 6 times greater than those with negative trends. Most of the stations had significant positive trends in mean temperature in February and March, Moreover, according to calculated Sen's slope, the mean air temperature of West Azarbayjan province increased by 0.05 °C/Year (1.65 °C during the study period).
The results show that the temperature of West Azarbayjan province substantially increased. The temperature increment can cause more drought occurrence and crop yield loss. As most of people’s income in this province depends on agricultural activates, temperature rise seems to have led to many social and economic problems in our studied area. Further, drying up of Urmia Lake and decreasing water input to the Urmia Lake basin can intensify the environmental problems.