نشریه آب و خاک
سال سی و دوم شماره 6 (پیاپی 62، بهمن و اسفند 1397)

Horizontal subsurface flow constructed wetlands have long been applied to improve water or wastewater quality. Previous studies on wetland systems have focused on trying to comprehend the processes leading to the removal of pollutants. Comparatively, there have been fewer studies dedicated to the assessment of flow distribution on hydraulic behavior through the wetland. Researchers declared the aspect ratio (length to width ratio), inlet-outlet configuration, the size of the porous media and the loading rate of constructed wetland could influence hydraulic retention time (HRT). Su et al. (2009) have stated that in free water surface constructed wetlands, when the aspect ratio is greater than 5, the hydraulic efficiency will reach 0.9, or even higher. If the project site or field area cannot meet the theoretical standard, the recommended aspect ratio is higher than 1.88 to ensure some hydraulic efficiency greater than 0.7. The present study was an attempt to analyse, with the aid of 3D numerical simulation and tracer study, how flow distribution affected hydraulic behavior by using 3 different input flow layouts. The treatment system consisted of a horizontal subsurface flow in a constructed wetland having an aspect ratio of 6.5 and the bed slope of one percent. The geometry of this system, which was 4 m wide × 26 m long × 1 m deep, was planted with Phragmites australis. Inlet configurations were selected as a variable parameter. Three different inlet flow configurations including midpoint-midpoint (A), corner- midpoint (B) and uniform-midpoint (C), with the same fixed outlet configurations, were studied. The average flow discharge in each configuration was 6.58, 6.52 and 6.4 m3/day, respectively. Dye tracer was used to draw retention time distribution curves in each configuration for assessing the internal dispersion, short-circuiting and hydraulic parameters such as effective volume rate which is derived by division of mean retention time per nominal retention time. The 3D model presented, which was built on the Comsol Multiphysics platform, was implemented for fluid flow to show internal hydraulic patterns in the system. Hence, the hydraulic model used the Darcy equation to simulate a stationary water flow through the bed. The simulations were verified by using real data obtained from the existing constructed wetland. It was mostly used to show pressure throughout the system for each configuration of the inlet and the outlet. The mean retention time for each configuration was found to be 4.53, 3.24 and 4.65 days, respectively. A marked reduction of the mean hydraulic retention time signified leaving tracer concentration from the outlet rapidly, high short-circuiting and dead volume and finally defective treatment process influenced by changing the inlet to the corner. According to tracer breakthrough curve, the effective volumes for configurations A and C were 87.5%, as compared to 62.1% for the configuration B. The two-day difference of tpeak between configurations 2 and 1, and 3 was probably due to the establishment of preferential streamlines resulting in short-circuiting and areas of dead volume in the system. The value of tpeakis related to dispersion, in the sense that a retention time distribution curve with a small peak time generally contains low dispersion. Simulation results showed the pressure difference from the inlet to the outlet ranged from 12-14, 14-15 and 10-13 cm H2O for A, B and C layouts, respectively. It was shown that the maximum pressure gradient occurred at the outset of the influent wastewater at the inlet, and it was gradually reduced to the lowest values at the outlet ports. Consequence of surface pressure demonstrated uniform pressure from inlet toward outlet at configuration C. Simulated streamlines approved this result, while range of high and low pressure area at configuration B was the most. There was a strong association between tracer experiments and simulation works. One of the major findings of this study was the significant shorter hydraulic mean retention time of the corner inlet setup. There are many that may cause these effects, although short-circuiting may be the primary one. A large low-pressure zone appeared at the opposite corner that was neither inlet nor outlet in this configuration. This paper investigated the hydraulic performance and short-circuiting effects on water flow due to three different inlet patterns (i.e. midpoint, corner, and uniform) in horizontal subsurface flow wetlands based on dye tracer measurements and numerical modeling. The results showed that the uniform inlet could provide the highest hydraulic efficiency (i.e. longest hydraulic retention time, HRT), in comparison to other two setups. The performance of the three different layouts was also investigated in terms of hydraulic parameters. Short-circuiting was influenced by lower hydraulic retention time, leading to inadequate treatment. Uniform-midpoint and midpoint-midpoint yielded the best effective volume as compared to the corner-midpoint. It was demonstrated that these two cases increased dispersion and used the whole capacity of the constructed wetland for the treatment process. The most important result of this paper was the evaluation of internal hydraulic pattern thorough the wetland, something not investigated in previous research works. Based on the simulation results, the spatial pressure distribution in wetland cells was depicted. Finally, it can be concluded that the best configuration of inlet-outlet layout based on both numerical simulations and physical experiments is uniform-midpoint. Meanwhile, midpoint-midpoint is preferable to corner-corner by all performance criteria.

During the last decades, important research efforts were conducted to identify and quantify the contribution of different sources delivering suspended sediment to the rivers. This knowledge also proved to be essential to provide estimations of catchment sediment budgets. The type of sources (i.e. soil types, rock types, and land uses) to discriminate depends on the local catchment context. Generally, the targeting of sediment management strategies is a key requirement in developing countries because of the limited resources available. Proper implementation of the soil conservation plans and sediment control programs should be done to inform of the relative importance of contribution the sediment resources as well as identification of crisis centers in the watersheds. During the last decades, this approach has been increasingly applied to identify and ‘trace’ several distinctive characteristics of the source material that can be compared to the same characteristics measured on river suspended sediment samples. Todays, fingerprinting techniques, provide an appropriate method for rapid and low cost information on main sources of sediment. in this study, the mentioned technique in the contribution of sediment resources, identify the critical units using the seven geochemical tracers' properties in the Dare Anar basin of Baghmalek in the Khuzestan province. The focus of this paper is upon quantifying the sources of suspended sediment transported on the Bakhmalek River in order to help guide future surface water sediment reduction efforts for turbidity-impaired streams. The statistical methods were used by the comparison of means and discriminant analysis, to select the optimal combination of tracers and contribution sediment sources. The geochemical tracers tested for their ability to distinguish between sediment sources with the Kruskal–Wallis one-way analysis of variance H test, which is able to test for the independence of more than two variables without presuming either normal or non-normal distributions. Tracers proving significance (p<0.05) between sources were retained. Tracers passing the Kruskal–Wallis H test that were non-conservative (suspended sediment tracer values that were not bracketed by sediment source tracer values) and removed before the performance of the mixing analysis. Tracers passing the first stage of statistical analysis were entered into a stepwise Discriminant Function Analysis (DFA) intended to optimize the number used in the mixing model. This analysis results in the smallest combination of tracers that are capable of correctly distinguishing 100% of the sources through the minimization of Wilks’ Lambda (Collins et al.1998). The analysis was run separately for each drainage basin using IBM SPSS Statistics v. 20.0. From the seven measurement fingerprinting properties, three of them were selected for geology formations and land use by statistics method such as discriminate analysis and compare means tests. Then, a portion of each source determinate by mixed models. Outputs from the discriminant function analysis show the discriminatory power of the final composite of tracers to be 100% successful in the sources classification for Catchment. Finally, among the seven selected tracer included the Lead, Zinc, Copper, Iron, Manganese, Nickel, and Chromium, have identified sediment sources by three elements included the Copper, Manganese, and Iron the amount 54.7, 31 and 14.3 percent respectively. Quaternary and Gachsaran formations, having the highest share in the sedimentary; the aspect ratio was 1.4 and 1.38 respectively. The poor pasture and forest land uses were responsible the highest and the lowest values of the basin sediment with 71.5 and 0.3 percent, respectively. The mitigation of nonpoint-source pollutants, such as sediment, in larger basins is rarely a straightforward procedure due to the number of sources and erosional processes contributing to their concentration in waterways. Therefore, the fingerprinting techniques with the relative efficiency 98.2 percent, having the high accuracy and precision in determinate appropriate method to sediment sources basin and separated of the sediment active units. Low relative error and high model efficiency coefficient confirm the results. Also the field observation is the same as model results. The results were indicating the environmental management strategies must be comprehensive for the study area, that need to reduce surface erosion and hill-slope/channel connectivity and the control gullies development by the commercial cultivation and the range reclamation. Sediment fingerprinting revealed that stream bank erosion in general, and of legacy sediments in particular, from Quaternary and Gachsaran formations to Baghmalek River is at the root of the regional sediment loading problem.

Change and decrease in atmospheric precipitation in recent years as well as increase in population and further demand for agriculture in the arid and semi-arid regions (such as Naghan) has led to a significant decrease in surface and groundwater resources. Therefore, achieving optimal utilization of water in agriculture, new irrigation systems has been considered to gain the most crop yield with less amount of water consumption. Also cultivated area can be expanded by these systems, containing lands with irregular topography, due to the high water distribution uniformity. Besides developing irrigation system, irrigation management is an important tool for increasing crop productivity. Researchers have shown that applying deficit irrigation (DI) under drip system, has led to improve the quality of grape yield, decrease water consumption and increase water efficiency. The aim of this study is to investigate the effect of irrigation system and water stress on water consumption, yield and physiological indices of grapes. The study field was located in Naghan, Chaharmahal & Bakhtiari Province, Iran. Experiences were done during summer 2016, in a completely randomized block design, with four replications in a grapevine garden The treatments included: CTRL, Furrow irrigation as common method in the area (control), surface irrigation with 100% water requirement (SI100), surface irrigation with 60% water requirement(SI60), drip irrigation with 100% water requirement(DI100) and drip irrigation with 60% water requirement (DI100).At the beginning of the experiences, 20 vine trees were selected with average of 60 years old. The field was divided into blocks, and the treatments were applied, randomly. Then the blocks were set up for the surface and drip irrigation. As the next step, required water was collected in a reservoir to obtain constant and reliable amount of water. In the control treatment, irrigation schedule of the gardeners (custom of the region) were considered in which irrigation event was at the beginning of the season. Also, drip and surface irrigation treatments were according to the soil water deficit. At the end of the experiment, water use efficiency, product performance, RWC, number of cubes per cluster, the weight of the cube in the cluster, cluster length, the number of main branches of the cluster and also qualitative properties such as soluble solids (Brix), total acid and pH of grape juice were measured. According to the results of qualitative traits, the amount of applied water significantly affected the grapes pH in the level of 5%. The lowest grapes pH was due to the control treatment and the highest to the surface irrigation 60%. Also, measuring total soluble solids (TSS) in grape indicated significant difference in 1% level which revealed that deficit and drip irrigation increased sugar in grapes and therefore quality of the crop. The results of quantitative traits showed the number of cubes in treatments had a significant difference at a probability level of 1%. Number of cubes in surface irrigation treatment 100% (SI100) had the highest value, while the quality of the crop was lower. The treatments differed significantly in weight of 100 cubes and the drip irrigation treatment 100% (DI100) did not have a significant difference with control treatment, while deficit irrigation resulted in reducing the crop weight. Relative water content of leaves (RWC) had the highest amount in the control treatment, while low water stress reduced this index. Wet and dry yields were highest in the control treatments (CTRL); while, the lowest amount was due to the low irrigation treatments of DI60 and SI60 with 19% and 34% reduction, respectively for the wet and dry yield. Drip irrigation with 100% water requirement (DI00) was not significantly different from the control treatment in most of the quality parameters, cluster and yield characteristics but had less water consumption and higher water use efficiency. Regarding the conditions of the region and the reduction of water resources, an accurate and efficient plan for irrigation is needed. So, the common method of irrigating in the region was assessed, as well as new methods of applying drip system and deficit irrigation. The results of this study indicate that drip irrigation system with 100% water requirement has no significant difference with the conventional irrigation method in the region, on quality and quantity of the gape yield. However, applying the drip system reduced the water consumption by 40%, and increased efficiency. Hence, drip irrigation system is suggested to be replaced by the traditional system.

Groundwater is the only major source of water for drinking, agricultural and industrial purposes in the Marand city, and its vital importance makes sure that its quality is seriously considered. With qualitative zoning, the process of underground water quality changes is determined at any time, place, and condition. It is possible to save time and cost by removing the stations with similar quality status and install new stations at times that are different or critical. In this paper, using the observational data of wells in Marand watershed, the spatial distribution of some groundwater quality parameters has been studied and analyzed using land-based methods. Geostatistical methods for estimating the unknown are remarkably effective. In order to predict the spatial distribution of groundwater quality, data was collected from 48 water wells, semi-deep wells, springs, and others from the Water Resources Management Company. In this research, the spatial variation process of five qualitative parameters of water include EC, electrical conductivity, chlorine and sulfate (SO42-) anions, and Sodium Rate Absorption (SAR) and soluble solids (TDS): Total Dissolved Salts) were studied. After reviewing, some of them were omitted due to statistical deficiencies. Common time base was selected for studying the Blue Years 2003-2005, and the years 1388-88 and 1391-1391. Data homogeneity was evaluated for the statistical period between 1384-1384 by the sequencing test method. According to the mentioned method, there was no heterogeneity in the data. Statistical deficits were determined according to the correlation coefficient of a variable. Data were normalized using SPSS 18.0 software using logarithmic transformation method and their elongation and bending values were obtained in the range -2 and 2. In this study, for estimation of groundwater quality parameters including EC, TDS, Cl-, SAR and SO42-, piezometric wells data were used during the years 84, 88 and 91. Statistical analysis methods consisted of conventional Kriging method in Spherical, Gaussian and exponential modes and Weighted Inverse Distance (IDW) methods with power from 1 to 3 were studied. Cross-validation, G statistics (GetisOrd General G) and Morans Index were used to select the best and most suitable interpolation method. The values of all three evaluation methods were calculated and analyzed using Arc / GIS 10.3 software. Based on the cross-evaluation method, the Kriging method is less effective than RMSE and ME in comparison with the Inverse Distance Weighting method. The zonation map of anion SO42- in year 2012 with G statistics and Moran index was 21.41 and 0.99 %, had the highest interaction in spatial structure and EC zonation map in year 2005 with Moran index and G statistic was 0.16 and 45 respectively has the least interaction of spatial structure. Charts of Changes in Quality Parameters showed that, water quality in latitude and longitude, values which were Cl, EC, SAR, and TDS and SO42 anions between the years 2005-2009 in the western-eastern part have been intangible and have been steeply sloping in the year 91. But in the North-South direction of 84 to 91 increased and then decreased in the middle of basin. Finally, by disconnecting the map of land use and geology of the watershed with the zoning maps of each of the parameters, it is concluded that due to the distribution of villages, residential areas and agricultural lands around them in the center and east of the watershed, the trend of groundwater quality parameters had been changed. The underground waters of Marand country watershed were influenced by human activities. Also, some geological formations and gypsum and dolomite minerals in the area in groundwater quality have led to an increase in TDS values and sulfidation of water resources in the eastern parts of the basin. Groundwater quality is always influenced by various factors such as flow direction, groundwater level, climatic factors (precipitation, evapotranspiration, etc.), type and composition of geological formations of the region and human factors (land use, extraction of groundwater resources, Entry of household wastewater and agriculture into groundwater resources, etc.). Therefore, due to the importance of the use of groundwater resources and the limitations of its use, it is suggested that continuous monitoring of groundwater quality changes should be carried out using ground-based methods and in order to evaluate the effective factors of water quality parameters spatial distribution maps was prepared and analyzed. In the present study, based on the previous studies, two geology formation and land use types were selected to prepare map of water quality parameters and it turned out that both of these factors are the most important factors affecting the groundwater quality in the Marand country watershed.

Estimating the design flood of the basin for the design of hydraulic structures, stabilization of river banks, watersheds and flood zoning projects are the most important in hydraulic and hydrological issues and projects. The flood used to design structures and influenced by hydrological events is called design flood which depends on structure safety, cost, life expectancy, and possible damage. Intensity- duration- frequency (IDF) curves of rainfall is a hydrological tool for the estimation of the design flood and design of hydraulic structures. These curves are constructed for a region from rainfall data which are recorded at various continuations. Usually, in some countries such as Iran which has a large extent, there is not enough rain-gage station; or the length of the statistical period is low, so it is impossible to calculate the IDF curves. But since it is not usually possible to access daily rainfall data, the fractal theory can be used to estimate the precipitation data in different consistency and the IDF curve with a very good accuracy. Korramabad river basin, one of sub-basin of Karkhe basin, often has been exposed to destructive floods and damages caused by it. In this research, the intensity-duration-frequency curves of the catchment area are estimated using fractal theory at first, and then the design precipitations are obtained in different return periods. In the next step we calibrated HEC-HMS rainfall- runoff model and finally the design floods are estimated in different return periods. The HEC-HMS model is an extension of the HEC-1 model under Windows, with all its capabilities. Hydrographs calculated by this model are used directly or in combination with other software for various purposes such as water supply, urban drainage, flood and flow forecasting, land use change, flood control studies and exploitation of reservoir systems. In this research, SCS curve number method and recession method are used to calculate the losses and base flow. Also for estimation of runoff, SCS curve number, Snyder unit hydrograph and Clark unit hydrograph are used in three methods and after comparing the results of the three methods in the calibration stage of the model, the Clarke unit hydrograph method is identified as the best method for estimating runoff. Also the Maskingham method has also been used for flood routing. The data needed for this study include rain gage data, hydrometric data, physiographic data of the basin, and also amounts of CN or curve number of the basin. Rain gage and hydrometric station used in this research are Chamanjir station. In this research, due to the importance of peak discharge for designing hydraulic structures, the optimization of the parameters has been done using the peak-weighted RMS Error criterion. In the calibration step, Comparison between hydrographs shows that there is a good agreement between computational and observational hydrographs, in such a way that the difference between the simulated and the actual peak discharge are 0.6, 0.2 percent for the selected floods. After calibration of the HEC-HMS rainfall runoff model for the studied area, this model is used to estimate the design flood. In the process of conversion precipitation to runoff, it is necessary to determine the pattern for temporal distribution of rainfall at the stations and in the area. To do so, the non-dimensional rainfall data are plotted for some storms with different time durations. For making data for each storm non-dimensional, the accumulated depth of precipitation to the desired time step was divided by the total depth of storm’s rainfall. The same procedure was carried out to make the time axis non-dimensional. By analyzing the precipitation data of the recorder station at the basin, it was found that in the majority of the precipitations, 20% of rainfall occurs in the first quarter, 20% in the second quarter, 40% in the third quarter and 20% in the fourth quarter. The results of this research show that:Daily precipitation data have a fractal characteristic in the ranging from 1 to 8 days, and during this time interval, rainfall data can be converted from a continuity to another continuity.
Due to the lack of recorded rainfall statistics in different continuations, using fractal method can be a useful way to prepare IDF curves in this basin and these curves are obtained based on the daily rainfall data available.
There is high efficiency of fractal model and HEC-HMS model in this catchment and the Gambel probabilistic distribution is appropriated for the maximum daily rainfall data of this basin.

Water is an important element of all living things. Availability of fresh water in any region is very important. Therefore, understanding the rainfall characteristics is so crucial in water resources management. One of the main tools in analyzing storms is Huff curve. Many investigators used this method for rainfall analysis with different duration. The main aim of this study is plotting and analyzing storms characteristics in the five stations namely Ajabshir, Azarshahr, Bonab, Bostan-Abad and Ligvan. In this study, using the 517 storms in the selected stations (located in the East of Urmia Lake), the Huff curves were extracted. The time period used is from 2001 to 2015. Quality of data was checked carefully prior to analysis. In the first step, the total selected storms were classified into the four distinct classes according to their rainfall duration including i) 0-2, ii) 2-6, iii) 6-12 and more than 12 hours. Then the Huff curves of each category were plotted for different probabilities of 10 percent, 20 percent, … and 90 percent. Analysis conducted for each of the classes, separately. Moreover, the Huff curves were plotted using the information of all events (i.e. without classification). In this study, some commonly used statistical distributions in hydrology were utilized. The three newly defined indices namely S, I, and Qwere defined and used in the present study. The design storm hyetographs for the selected stations and all the events (without classification) prepared for 50 percent Huff curves. The mathematical model of Huff curves were extracted as the Logistic model. The model parameters were estimated using the Curve Expert software. According to the 50 percent probability for Huff curves, the following results were obtained. For the short- time (0-2 hours) storms, the most proportion of rain received in the first and second quartiles. In the first quartile, between 28 to 44 percent of the total rainfall depth received in the selected stations. In the other words, short storms initiated with high intensity and followed by mild intensity. In the case of 2-6 hours storms class, in the two stations, a large portion of the rain (about 34 up to 39 percent) received in the first quartile. However, in the other two stations about 31 up to 34 percent of total rain received in the second quartile. In the station namely Ligvan (about 28percent of total precipitation depth) received in the third quartile. In some of the stations, and in the case of rainfall duration class of 2-6 hours storms starts with high intensity. However, in some of the other sites rain begin with mild intensity. In addition, for the storms with 6-12 hours duration, three stations can be included in the second quartile, because about 31percent of total precipitation received in this time quartile. However, in the two stations, (about 29 up to 33percent of the precipitation depth) received in the third quartile. In the class of duration 6 to 12 hours, storms begins with mild intensity and the intensity of rain increases as time advances then, finally the intensity of rain decreases till rain ceases. In addition, it can be concluded that for the storms with duration of more than 12 hours, for the station namely Azarshahr a large portion of precipitation (about 35percent of precipitation depth) received in the first quartile. Furthermore, in the two stations about 30 percent of total precipitation received in the second quartile. However, in a station namely Ligvan about 32 percent of total precipitation depth received in the third quartile. In other words, storms with duration of more than 12 hours, different stations had different temporal patterns. Based on 90 percent probability Huff curve, it was found that in the case of short- time storm class, almost in all of the stations, rainfall begins with mild intensity. Then the intensity increases gradually to reach peak in the end of the third quartile. In the 25 percent of remaining time (i.e. the last quartile) the intensity decreased again until the rain terminated. For the rainfall classes of duration more than 2 hours, precipitation reaches to the peak in the last quartile. In the other words, the precipitation begins with low intensity and gradually increases its intensity till the end of rain. In this study, three new indices that represent the ratio of precipitation at 50 to 90 percent probabilities were introduced and the values of these indices were calculated for the selected stations. It can be concluded that the most portion of rainfall received in first quartile and or second quartile for storms having duration less than 6 hours. Whereas for storms with duration more than 6 hours, rainfall started with low intensity and then the intensity increased through the rainfall duration. The results indicated that at all of the stations and for each of the duration time classes, the order of changing the values of S, I and Q indices was as S>I>Q. The modeling of the cumulative percent of precipitation as a function of cumulative percent of rainfall duration time performed using the Logistic model for each of the time classes and then its parameters were calculated which are presented in the Table 4. Based on the results, it was found that the Logistic model is able to fit the mentioned curve very well for all of the selected stations. The correlation coefficients estimated between the observed and modeled values were found to be between 0.978 and 0.998 for the sites. The results of this study anticipated to be useful in design of urban drainage structures and rainfall- runoff modeling.

Curve number (CN) is a hydrologic parameter used to predict the direct runoff depth or the excessive rainfall that infiltrates into the soil. This parameter, which indicates surface water retention, is very important in the processes relating to flooding. Vegetation of the region is a major factor affecting peak flow and flood volume. The peak flow is highly influenced by the land surface characteristics, for example at the time that vegetation coverage is naturally low or while vegetated areas are decreasing, the peak discharges increase as well. In this study, the flood hydrograph of Kareh-Bas Basin was simulated using the HEC-HMS model. The simulation was used to estimate the values of the annual curve number in the basin of interest. Model data requirements for this study were temperature, precipitation, and evapotranspiration and discharge time series. The model was calibrated for the period 2000-2010. Then, the model was implemented independently for simulating of rainfall-runoff for each year without any change in the optimized parameters. The model was calibrated only by changing curve number. The average curve number of the basin for each year was computed using the weighted mean method. The MODIS leaf area index raster maps were downloaded from the Modis site. The maps were converted into ASCII format for spatial statistics and calculating the monthly spatial average. The correlation between the curve number and leaf area index was investigated by a nonlinear curve fitting. This lead to the development of a curve number as a function of the vegetation cover for each year. Finally, the accuracy of the developed relationship was investigated using the Nash-Sutcliffe efficiency coefficient by comparing the curve number obtained from the HEC-HMS model and the simulated values from the new relationship. The obtained Nash-Sutcliff coefficient of 0.58 showed that the HEC-HMS model was capable to simulate the flood hydrograph with relatively good accuracy. The sub-basin spatial mean showed that the sub-basins 1 and 2 take the highest curve number values. This indicates that surface water retention in these sub-basins is less than the other sub-basins, which may lead to a sharper hydrological response or flood. In sub-basins 3 and 4, where vegetation density is higher thus land use acts as a predominant factor in hydrologicalbehavior of these sub-basins, the curve number was lower. The study shows the hydrological response depends on the temporal variation of the land cover, for instance in 2010, when the leaf area index increased by a factor of 1.4, the curve number has decreased to 47. As it is predictable with decreasing vegetation the peak discharge and flood volume was increasing. We found a direct nonlinear relationship between basin scale Leaf Area Index and Curve Number with a correlation coefficient of 0.7, indicating that the variation of the curve number is a function of the leaf area index. The developed model allows calculating curve number values based on the remotely sensed leaf area index. This relationship can be used as an auxiliary function for capturing the vegetation changes and dynamics. The accuracy of the derived equation was evaluated in terms of Nash-Sutcliffe's efficiency coefficient. A value of Nash-Sutcliff coefficient of 0.72 showed that this relationship is good enough for calculating basin or sub-basin curve number values capturing the dynamics of leaf area index. The obtained Nash-Sutcliff efficiency coefficient from HEC-HMS showed that the model was able to simulate the flood hydrograph of Kareh-bas basin with relatively good accuracy. However, the visual interpretation shows there is a weakness in the simulation of the falling limb of the simulated hydrographs. This may be an indication that the drainage of stored water at the basin was not well-simulated by the model. In general, it can be said that peak discharge and flood volume were under-estimated. By increasing the curve number, the peak discharge values also were increasing. The pair data for spatially weighted values for curve number and averaged annual leaf area index showed that an increase in leaf area index leads to a lower value in obtained curve number. This may result in lower peak discharge and volume of the flood. Such relationships may be taken as a measure for flood control. Meanwhile remotely sensed leaf area index products may be considered as an opportunity to capture the dynamics of the land cover.

Predicting and mapping soil organic carbon (SOC) contents and stocks are important for C sequestration, greenhouse gas emissions and national carbon balance inventories. The SOC plays a vital role in sustaining agricultural productions in arid ecosystems. It shows very quick and direct changes with atmosphere through the photosynthesis and the SOC decomposition. The depletion of C storage not only exacerbates the risk of soil erosion but also reduces agricultural production. An accurate knowledge of regional SOC contents and stocks and their spatial distribution are essential to optimize the soil management and land-use policy for SOC sequestration. Today, digital soil mapping methods such as geostatistics and artificial neural network (ANN) have focused more on SOC contents and stocks mapping. Geostatistics is a robust tool widely applied to model and quantify soil variation and analyze the spatial variability of SOC in large scale. The ANN as a nonlinear technique has been received much less attention for modeling SOC contents and stocks. Therefore, in this study, we aimed to develop and compare the performance of ordinary Kriging, co-kriging, inverse distance weighting (IDW) and artificial neural network models in predicting and mapping the SOC contents and stocks in East and Southeast of the Kohgiluyeh and Boyer-Ahmad province, southern Iran. The composite soil samples were collected randomly from the 0-15 cm soil depths at 204 sampling sites at different land uses in east and southeast of the Kohgiluyeh and Boyer-Ahmad province. The collected soil samples were air-dried, ground, and sieved to pass through a 2 mm mesh. Soil properties such as organic carbon contents and stocks, pH, electrical conductivity (EC), bulk density (BD) and soil texture were determined according to the standard analysis protocols. The normality tests were done according to the Kolmogrov–Smirnov method, and the variability of SOC contents and stocks were analyzed by the classical statistics (mean, maximum, minimum, standard deviation, skewness, and coefficient of variations). The digital elevation model (DEM), slope gradient, precipitation and temperature and Normalized Difference Vegetation Index (NDVI) were used as co-variables (auxiliary data). The NDVI was obtained by the remotely sensed data of LANDSAT 8. The geostatistical parameters were calculated for each soil property as a result of corresponding semivariogram analysis. The spatial prediction maps of soil properties were generated by ordinary kriging (OK), cokriging (Co-K) inverse distance weighting (IDW) with powers of 1, 2, 3, 4 and 5 as well as the Artificial Neural Network (Multilayer Perception model, MLP) methods. The mentioned interpolation methods were used to prepare the SOC spatial distribution maps by using the 80 % of data as the training datasets. The prediction results were then evaluated by the validation data set (20 % of all data). The differences between the observation and prediction values were evaluated by Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (R2) and Concordance Correlation Coefficient (CCC). The spatial distribution maps of the SOC contents and stocks in the study area were finally developed by ArcGIS 10 software. The SOC content for all samples largely varied from 0.20 to 3.96 % .The high coefficient of variation of 53.38 % demonstrates the strong spatial variation of SOC content in the study area. The SOC stocks had also a relatively high variability compared with other soil properties. Such strong variation could be attributed to the diverse soil types, land covers and other environmental conditions across the study area. The average SOC content for forest land use was significantly higher than the other land uses. The intensive tillage in cropland soils appears to have induced the acceleration of organic carbon oxidations leading to the lowest SOC contents and stocks. By increasing the mean precipitation within our study area (in eastern and northeastern regions), the SOC contents and stocks increased significantly. The inverse trend was, however, observed for temperature implying the fact that the higher the temperature, the lower the SOC. Gaussian model was found to be the best model for parameters such as SOC contents and stocks due to the lowest RSS and R2.Overall, the results denoted the higher ability of ANN compared to geostatistical techniques (cokriging, kriging and IDW methods) in estimating both soil organic carbon contents and stocks. According to the results, ANN (MLP) method with one hidden layers with 50 neurons performed better in estimating soil organic carbon contents and stocks atunsampled points, whereas the largest errors were obtained for IDW method. The good performance of ANN method can be attributed to the division of the study area and the capability of ANN to capture the nonlinear relationships between SOC and environmental factors i.e. slope, DEM, precipitation, temperature and NDVI. The results suggest that the proposed structural method for ANN can play a vital role in improving the prediction accuracy of SOC spatial variability in large scale.

The Fe forms diversity is related to parent materials, climate, soil process, biocycles, water table fluctuation, redox, organic matter and etc. in soil. The main Fe forms are Fed (extracted by dithionite citrate bicarbonate), Feo (extracted by oxalate ammonium) and Fe crystals. Feo/ Fed ratio also shows active Fe forms. Magnetic susceptibility (MS) increases when ferri-magnetite is formed due to soil processes. This characteristic (MS) changes with parent material, climate, relief, and organism. Therefore, this study was undertaken to evaluate different Fe forms and MS with soil forming factors in some gypsic soils of Khuzestan province. The study area was located in Ramhormoz and Haft-Kel regions in Khuzestan province. Soil moisture and temperature regimes were ustic and hypertermic, respectively. Soil parent material consisted of the eluvial deposit of Gachsaran and Aghajari geological formations. The soil profiles location was selected according to topography map, ETM+ Landsat satellite images, and then 14 soil pedons were dug and described according to the standard methods. All horizons or layers were sampled and 5 pedons were selected for the analysis of different Fe forms. Fed and Feowere, respectively, extracted by citrate-bicarbonate-dithionite (CBD) and oxalate ammonium, and Fe cocentration was then determined by atomic absorption spectrometry. Furthermore, MS was determined by MS2 meter Barlington Dual frequency in low (0.46 kHz) and high (4.6 kHz) frequencies. All MS were calculated for carbonates, gypsum, and OM free. These calculations were also done for Fe forms in these samples. The statistical analysis was carried out with SPSS and Pierson methods between Fe forms and MS. The Duncan’s test was used to compare the mean values. Pedons were classified as Entisols, Inceptisols, and Aridisols soil orders. The range of clay content, pHe, ECe, CEC, OM, CCE and gypsum was 15-59%, 7.1-8.5, 0.6-58.1 dS/m, 4.2-22.4 cmol(c)/kg, 0.3-2.4%, 21.2-39.7%, and 0-78.7%, respectively. All epipedons were classified to be ochric and developed soils had cambic diagnostic horizon (Bw) in subsurface. Feo content was maximum in young soil under poor drainage, and minimum Feo content was observed for developed pedons with good drainage class. The sepedons have not been cultivated yet. Feo was maximum at surface soils in all pedons, and decreased with increasing depth. A decreasing trend was observed from surface to subsurface for Fe content in cultivated soils. This negative trend was not, however, detected in poor drainage class or pedons with lithologic discontinuity. This trend can be ascribedto more organic matter content in surface soil in comparison with subsurface soil. Organic matters increase soil acidity and therefore, Feo can not be converted to other Fe forms under this circumstance. Maximum Feo was determined under poor drainage class in low lands. In addition, Fed displayed no trend from the surface to depth at most pedons. Maximum Fed was foundin old plain and the hill slope summit. This Fed was positively strongly correlated with soil development trend. Fed had a positive association with clay content (r=0.463), and negative correlation with sand content (r= -0.411), salinity (r= -0.533), and total carbonate, gypsum and OM (r= - 0.389). Feom (Feo menerogic) was maximum in Byz (4.04 gr/kg soil) and minimum content for Feomwas found in Byb (0.29 gr/kg soil). Maximum andminimum Fedmwas measured in Cy (9.21 g/kg) and Bg2 (1.54 g/kg), respectively. The Feo/ Fed ratio was largerin young soil and decreased with time. These values decreased from the surface to depth with the range from 0.07 to 0.8. The greatest and lowest Feo/ Fedwere, respectively, observed inthe hills and the low lands. There was no significant difference in Feo/ Fed between hill and plain. MS changed from 5 to 25.5. Maximum and minimum MS was detected in the hills and the low lands. MS decreased with depth in almost all horizons. The highest and lowestMS were, respectively, found in pedon 3 (Byb horizon) and pedon 12 in the Bw3 horizon. The MS minerogenicwas statistically significantly associated to sand content (r=0.56**) and significantly negatively correlated with total carbonates, gypsum, OM (r=-0.667**), silt content (r= -0.506) and clay content (r= -0.456). The positive relationship between sand content and MS can be explained by the effect of magnetic materials inherited from the parent materials. Fed and Feo- Fed showed a close correlation with soil development. Feo/Fed ratio increased with decreasing soil age. Feo content had a positive correlation with total carbonate, OM, salinity. MS was more in older soils such as hill physiographic unit but it was low in younger soils or soils with weak drainage. MS was greatly affected by sand material size which seems to be linked to parent materials. MS showed no trend with soil development but land use, drainage and parent material largely impacted MS and different Fe forms in these gypsiferous soils.

Trees pruning wastes by turning into compost and adding to soil improves the physical, chemical and biological properties of the soil. Soil biological indices are important aspects of soil quality, so soil quality is measured using different biological properties. The organic compounds are regularly released from plants into the rhizosphere, which increase the activity of the soil microbial community and improve the health of the soil. The organic matter such as compost, stimulates microbial activity like the enzymatic activity and microbial biomass in the soil. Another method to improve soil quality is the use of the microorganisms potential. The arbuscular mycorrhizal fungi (AMF) in soil can stimulate and increase soil microbial activity and also improve the activity of enzymes and microbial biomass in soil. The application of microorganisms and the addition of the organic matter to the rhizosphere can change the microbial communication composition of the rhizosphere. The Limiting roots to investigate the biological and chemical changes and the extent of these properties in the rhizosphere are challenges that have been less addressed. The rhizobox is one of the used tools to study the rhizosphere changes. The main objective of the present study was to investigate the effects of the compost prepared from pruning wastes of apples and grapes trees and also pruning wastes of apples and grapes trees on soil quality, in the presence of arbuscular mycorrhizal fungi, in rhizosphere of the wheat under the rhizobox conditions.  The present study was carried out in a completely randomized factorial design with three replications in rhizobox under greenhouse condition. The factors included the organic matter (compost of trees pruning wastes, trees pruning wastes and control) and soil (the rhizosphere and non-rhizosphere soil) in mycorrhizal inoculation conditions. The soil sample with light texture and low available phosphorus was prepared. The pruning wastes of apple and grape trees were collected from urmia orchards. Also, the compost of trees pruning wastes was prepared from the research greenhouse of Urmia University. The compost and pruning wastes were ground and crushed and then passed through a 0.5 mm sieve for the greenhouse experiment. The plants were planted in the rhizobox with the dimensions of 20 × 15 × 20 cm (length × width × height). The compost and pruning wastes were added to the boxes based on 1.5% pure organic carbon (each box contained 5.799 kg of soil). Glomus fasciulatum as mycorrhizal inoculation was used. The control treatments contained sterile soil with mycorrhizal inoculation and without organic matter. The wheat seeds (Triticumae stivum L.) of Pishtaz cultivar were grown in rhizoboxes. At the end of the growth period, organic carbon (OC) by Walkley-Black method, microbial biomass carbon (MBC) and microbial biomass phosphorus (MBP) by fumigation extraction method, metabolic quotient index (qCO2) (microbial respiration per unit of biomass), microbial quotient index (microbial biomass carbon per unit of organic carbon), carbon availability index (CAI) (substrate-induced respiration/microbial biomass ratio), colonization Percentage of arbuscular mycorrhizal fungi, and acid (ACP) and alkaline (ALP) phosphomonoesterase enzymes activities by spectrophotometry method, were determined. The results showed that the application of compost significantly increased organic carbon, microbial biomass carbon, microbial biomass phosphorus and decreased MBC/MBP compared with the control treatment. Furthermore, compost increased the organic carbon, microbial biomass carbon and microbial biomass phosphorus in the rhizosphere soil by 8.08, 45.79 and 37.18 % compared with the non-rhizosphere soil, respectively. The pruning wastes increased 1.45, 1.26 and 1.30 fold metabolic quotient, carbon availability and acid phosphomonoesterase activity in the rhizosphere compared with non-rhizosphere soil, respectively.The highest activity of the alkaline phosphomonoesterase enzyme and the percentage of mycorrhizal root colonization were also related to pruning waste treatments in rhizosphere soils. Different characteristics of the organic matter and the microbial inoculation led to an increase in the biological indices in the rhizosphere zone compared with non-rhizosphere soils. The application of organic matter in the soil, along with microbial inoculation, will accelerate the biological activity of the soil and thus contributes to a better cycle of nutrients in the soil. Following the application of organic matter, microorganisms rapidly grew and led to an increase in biological activity, such as increase activity of phosphomonoesterase enzymes, carbon and phosphorus of microbial biomass in the rhizosphere. It could be argued that increased activity of phosphomonoesterases and the microbial biomass and decreased metabolic quotient in the soil were influenced by the application of the organic materials and mycorrhizal inoculation. The findings of this study have a number of important implications for future practice. Therefore, the use of the organic materials and biological potential of the microorganisms are one of the most important tools to maintain organic carbon balance of the soil, contributing to the stimulation of soil microbiological activities.

Land use and Land cover (LULC) information has been identified as one of the crucial data components for a range of applications including global change studies, urban planning, agricultural crop characterization, and forest ecosystem classification. The derivation of such information increasingly relies on remote sensing technology due to its ability to acquire valuable spatiotemporal information on LULC. One of the major approaches to deriving LULC information from remotely sensed images is classification. Numerous image classification algorithms exist. Among the most popular are the maximum likelihood classifier (MLC), artificial neural network (ANN) classifiers and decision tree (DT) classifiers. Conventional parametric method like MLC is based on statistical theory and assumes a multivariate normal distribution for each class. In case of data that has non-normal distribution (which is common with LULC data), the parametric classifiers may fail since the inability to resolve interclass confusion. This inability is the major limitation of parametric classifiers. Nonparametric classifiers like ANNs and DTs, which do not rely on any assumptions for the class distributions of data, could overcome the aforementioned limitations of parametric classifiers. The support vector machines (SVMs), a nonparametric classifier, that has recently been used in numerous applications in image processing, represents a group of theoretically superior machine learning algorithms. The SVM employs optimization algorithms to locate the optimal boundaries between classes. It was found competitive with the best available classification methods, including ANN and DT classifiers. The classification accuracy of SVMs is based upon the choice of the classification strategy and kernel function. The objective of this study was to investigate the sensitivity of SVM architecture including classification strategy and kernel types to identify LULC information from Landsat Enhanced Thematic Mapper (ETM) remote sensing data in Gavshan dam watershed in west of Iran. SVMs were used to classify orthocorrected Landsat ETM images of May, 2016. Image pre-processing such as atmospheric correction were conducted before utilization. Three classification strategies (One versus one, one versus all and ordinal) and three types of kernels (linear, polynomial and radial basis function) were used for the SVM classification. A total of 18 different models were developed and implemented for sensitivity analysis of SVM architecture. A two-layer feed-forward Perceptron network classifier with sigmoid hidden and softmax output neurons was also used for comparison. The network was trained using scaled conjugate gradient backpropagation algorithm. A total of 1320 ground control points were collected to train, validate and test the SVM and ANN models. Ground truth locations on each image were identified using the GPS coordinates for extracting spectral reflectance data of seven bands (Bands 1-7) of Landsat ETM images. The LULC class of each point was identified using land survey or Google earth images. The identified LULC classes were agriculture, buffer forests, orchard, ranges brush, range grasses, urban areas, roads and water. The results suggest that the choice of classification strategy and kernel types play an important role on SVMs classification accuracy. Statistical evaluation of the SVM models against the ground control points showed that the one versus one classification strategy had the highest accuracy than the two other ones for any kernel function type and the polynomial kernel function had the highest accuracy than the two other kernels for any classification strategy. The SVM model with polynomial (n=3) kernel and one versus one classification strategy outperformed all SVMs models and gave the highest overall classification accuracy of 78.5 and Kappa coefficient of 68.5. The McNemar’s test clearly showed significant improvement of the best SVM model in comparison to the ANN model (P<0.001). Also, the user accuracy and producer accuracy achieved by best SVM model were higher than ANN model for all LULC classes. In both approaches water and agriculture categories have high accuracy while roads have low accuracy. The resulting LULC map indicated that most parts of the studied area (52.8%) have been assigned to the agriculture. The ranges brush and range grasses categories cover 12.5% and 26.8% of the watershed, respectively. Only about 2.7% of the watershed have been covered with trees. This study suggests that the SVMs approach based on Landsat ETM bands may provide reliable and accurate LULC information even better that best ANN approaches. However, choice of classification strategy and kernel types play an important role on SVMs classification accuracy. Best model of polynomial kernel and one versus one classification strategy outperformed all SVMs and ANN models and gave the highest classification accuracy.

Fe is the first identified micronutrient for crops and required in higher amount than other micronutrients. Fe plays important roles in enzyme metabolism, protein metabolism, chlorophyll construction, chloroplast evolution, photosynthesis, respiration and reduction-oxidation reaction as well as organic acids metabolism. Iron, as an essential micronutrient, has great contribution in important antioxidant enzymes activity and through which affects plant tolerance against environmental stresses. Plant enzymes including superoxide dismutase, catalase and glutathione peroxidase are among the most important enzymes scavenging the hydrogen peroxide have iron in their structure, so they affected by iron deficiency. In this study, the effect of soil, foliar and fertigation application of humic acid on iron availability, chlorophyll types and superoxide dismutase, catalase and glutathione peroxidase enzymes in canola (Hyola 308) were evaluated. Results showed that highest total iron content in plant leaves was obtained in 0.4 percent foliar application and the lowest was belonged to control treatment. Highest iron content in plant stem and active iron was obtained in humic acid application through irrigation at 2000 mg L-1 by 85 and 44.86 mg kg-1, respectively, and lowest amounts were obtained in control by 54.62 and 20.40 mg kg-1. Also, greatest concentration of chlorophyll a, chlorophyll b and total chlorophyll were recorded under0.4 percent humic acid foliar application by 3.58, 1.79 and 5.37 and the lowest chlorophyll contents were associated to control. Highest activities for plant enzymes superoxide dismutase and glutathione peroxidase were obtained under0.1 percent foliar application of humic acid by 4.20 and 1.95 (Iu/gr. FW) and the highest activity for catalase enzyme by 4.46 Iu/gr FW in 1000 mg L-1 humic acid through was irrigation and the lowest enzyme activity obtained in control treatment. Findings showed that application of various levels of humic acid increased plant enzyme activity compared to control in all of three application method (soil, foliar and application through irrigation water). Increasing humic acid concentration decreased enzyme activities. Also, there was negative correlation between activity of plant enzymes and concentation of chlorophyll types and active iron. Active iron and antioxidant enzymes represent iron status within cell cytoplasm. Based on the results of this study, active iron concentration and activity of antioxidant enzymes are appropriate indices for evaluating plant tolerance to iron deficiency compared to assessing total iron content in leaves.

Soil texture is the average size of soil particles which depends on the relative proportion of sand, silt and clay contents. Soil texture is one of the most important features used by soil and environmental scientists to describe soils. Soil texture directly affects the soil porosity, which in turn, determines water-retention and flow characteristics, nutrient-holding capacity, internal drainage, sorption characteristics and long-term soil fertility. High-resolution soil maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection. Given the soil texture roles in controlling the soil functions, it is necessary to understand the spatial distribution of this feature in regional scale. As soil texture is a staticproperty, regional scale soil texture maps can thus help environmental scientists to predict different soil-related processes. The objective of this study was to develop a soil textural class map using Terra satellite MODIS sensor images. To achieve this goal, the digital elevation model SRTM radar of the studied area for soil samples from different altitudes and slopes was prepared in foursen consecutive 30 meters time frame. The nearest neighbor method with an error of less than 0.5 pixels was used and the elevation layers were mosaicked and transmitted to the UTM ZON-38 coordinate system and GIS Ready Became. The normalized difference vegetation index of bands 1 and 2 of the matrix was obtained to isolate the reflection of the electromagnetic spectrum of vegetation and soil. This final mosaicked digital elevation model was then divided into different altitudes to accurately evaluate the surface texture. The 60 spatial points were selected to estimate the texture of surface soil in thestudied area with systematic randomized sampling. In the current study, soil texture was determined forthe air-dried samplesusing hydrometer. The SWIR bands of MODIS with resolution of 500 meters were selected for sampling dates. After corrections, DN values of the bands for sampling points were extracted. The Pearson correlation coefficient and step wise regression techniques were used to establish proper relationships between the DN values of the SWIR bands and the soil particles. Kriging and cokriging methods were also employed to create a spatially distributed map of the soil textural classes. The results showed that there is a close correlation between the SWIR bands of the terra satellite and the MODIS sensor with band 3, and using this auxiliary variable significantly reduces the estimation error. The best model for fitting semivariogram for clay, silt and sand contents were spherical, spherical and exponential models and the best fitting Cross-semivariogram for clay, silt and sand contents were spherical, exponential and exponential models, respectively. The highest and lowest error estimation was, respectively, related to sand and clay content. The maximum and minimum decrease of estimation error by the auxiliary variables was found for sand and clay content, respectively. The nugget/sill ratio of the kriging semivariograms was greater than 25%for sand and claycontentand lower than 25%for sand and silt content. This indicates that sand and silt contents had a strong spatial dependency, and clay content hada moderate spatial dependency. These ratios for cokriging cross-semivariograms of sand, silt and clay contentsware less than 25%. The interpolation of estimated soil texture was also determined using the cokriging method with 70% of the soil texture measured in the laboratory. Our results indicated thatcokriging method estimated the soil particles more accurately as compared with linear multi-variable stepwise regression and kriging methods. Application of cokriging method also reduces the number of sampling points and the estimation error of soil texture zoning. Therefore, cokriging method seems to be better suited in impact assessments for data-scarceregions such as Iran.

Due to the negative effects on human being health, the decrease of cadmium bioavailability in waters and soils is necessary. The main origins of cadmium ions in environment consist of batteries, phosphate fertilizers, mining, pigments, stabilizers, and alloys. Many methods such as ion exchange, chemical precipitation, flotation, ultrafiltration, nanofiltration membranes, reverse osmosis, and electrocoagulation have been used for the removal of cadmium. Notably, adsorption is proven the most practical technique for heavy metal ions removal of pollutants from wastewater and contaminated soils. Among the various adsorbents, chitosan has introduced to be an efficient one, due to its unique characteristics such as antimicrobial activity, biocompatibility, non-toxicity, and being low-cost bio-adsorbent. Chitosan is a derivative of N-deacetylated of chitin, a naturally occurring polysaccharide taken from crustaceans i.e. shrimps and crabs, and fungal biomass. The presence of amine and hydroxyl groups in the backbone of chitosan gives the polymer its high binding capacity in adsorption processes. Chitosan can decrease the metal ion concentration to near zero. This work evaluates the modified chitosan’s potential as a bio-adsorbent in the water system and also its potential as a soil amendment in the soil system in terms of the adsorption and desorption of Cd2+. It is also worth noting that there is no report on the removal of cadmium ions by ionically crosslinked chitosan/κ-carrageenan materials, especially in soil systems. The chitosan-based magnetic bio-adsorbent was prepared through in situ co-precipitation of iron ions in the presence of chitosan with high molecular weight. The surface (0-30cm) soil samples were collected from a field in University of Maragheh in the North East of Iran. Some physio- chemical properties of the soil used in this study were determined. Adsorption of cadmium on the bio-adsorbent was investigated using batch experiments. After adsorption, the adsorbent loaded with cadmium ions was washed with distilled water before treating it with 90 ml of 0.1M ethylenediaminetetraacetic acid (EDTA) for the determination of the metal desorption. The experimental data of Cd2+ adsorption and desorption isotherm were fitted by Freundlich and Longmuir models. The crystalline nature and phase analysis for pure chitosan and magnetic chitosan bio-adsorbent was confirmed by XRD analysis. The diffractogram of chitosan consisted of two typical crystalline peaks at 2θ= 10.8A° and 20.42A°, corresponding to the partial crystalline structure of chitosan and the hydrated crystals of the remained α-chitin chains in pure chitosan, respectively. The characteristic peaks of chitosan in the XRD pattern of the magnetic bio-adsorbent disappeared, indicating of the amorphous structure of chitosan. It suggests that the addition of magnetite nanoparticles obviously affects the crystallinity of chitosan. On analyzing the values of r2 and RMSE obtained using Freundlich and Langmuir models, it was observed that Freundlich model provided the best fit for the experimental adsorption and desorption data at the ranged of the Cd2+ concentration studied in the soil and water systems. To evaluate the efficiency of the modified chitosan as an efficient bio-adsorbent in water and soil system, the difference between adsorption and desorption amounts, Δq, was calculated. The less amounts of Δq, the more efficient adsorbent in a water system. This means that the adsorbent can be reused several times. In contrast, in a soil system, a positive relationship was found between the amounts of Δq and the efficiency of the adsorbent. This means that the adsorbent can immobilize the adsorbatesand therefore, may be used as a metal immobilizing amendment in soil. As the initial concentrations raised, the amounts of Δq increased in the water system; therefore, it seems that the bio-adsorbent may not efficient at high initial concentrations. In the soil system, the more amounts of Δq decreases, the more efficiency of the adsorbent as a cadmium immobilization increases. Therefore, the bio-adsorbent used can be relatively efficient as a soil modifier. The results revealed the magnetic bio-adsorbent based on chitosan can be sorb Cd2+ from water and soil systems. The maximum adsorption capacity (b) of cadmium onto the adsorbent appeared to increase from the water system to the soil system, from 750.2 to 992.7 µmol/g, respectively. On analyzing the values of r2 and RMSE obtained using Freundlich and Langmuir models, it found that Freundlich model provided the best fit for the experimental adsorption and desorption data at the ranged of the Cd2+ concentration studied in both water and soil systems. By comparing the amounts of Δq, the difference between adsorption and desorption amounts, the bio-adsorbent is not economically feasible at high initial concentrations in the water system. But, the more decrease amounts of Δq in the soil system, the more increase efficiency of the adsorbent as a cadmium immobilization. So that, the bio-adsorbent used can be relatively economic as a soil modifier.