نشریه پژوهش های حفاظت آب و خاک
سال بیست و چهارم شماره 4 (مهر و آبان 1396)

Riverine wetlands are waterbodies that are located at lowlands alongside rivers and are inundated during floods or recharged by aquifers. In order to identify and locate suitable sites for wetlands, the hydrological properties including watershed physiography, river discharge and its temporal (seasonal) variation as well as groundwater table fluctuation should be studied. Continued population growth and urgent need to conservation, rehabilitation and expansion of natural and valuable environments such as wetlands which support environmental services required by human should be strongly considered for sustainable development. The aim of this study is to assess utilization of biological capacity of rivers to restore the riverine wetlands in order to improve the biological and ecological conditions of the country’s wetlands.
In this research, the potential of Tajan River within the Sarakhs county, was identified considering presence of the streamside tree cover, specific ecotourism capacity, specific bird watching capacity, appropriate morphology of the river and inundation characteristics of floodplains. Creation of riverine wetlands alongside the Tajan River can play an important role in flood mitigation through storing of precipitation induced surface runoff. To this end, all factors affecting on delineation of proposed wetlands boundaries including geomorphological and geological, hydrologic, hydrodynamic, economic, social and developmental characteristics of the Tajan watershed were investigated. During field survey from the study area, on the basis of predominant condition from biodiversity, species, ecological, hydrologic and hydraulic point of view, four sites were determined as suitable for riverine wetlands within the watershed. Then, the cross-section of each of these sites have been surveyed and measured in detail in order to assess their morphological and hydraulic characteristics.
On the basis of flow Hydraulics and river morphology studies, two out of the four selected sites which are located within the Sarakhs’ Jahanbani Forest, due to low velocity of flow and high width of river in these river reaches were assessed as suitable for implementation of vegetation and wetlands restoration measures. Analysis of monthly discharge data of Polekhatoon- Kashafrud hygrometry station in recent years, indicates that the Tajan river flow is nil or negligible during warm months. Therefore, to supply water for the suggested wetlands, it should be relied upon floods and annual discharges from their upstream sub watersheds.
The annual discharge of the Tajan river at one of the suggested sites for the riverine wetlands within the Sarakhs’ Jahanbani Forest was estimated about125.9 MCM. This site due to its high potential for tourism and high diversity of birds can be exploited from various perspectives if tourism activities are developed in the region.

Coastal aquifers are of the most important freshwater resources in many countries, especially in arid and semi arid zones. Due to the proximity and contact with the sea, and thus the threat of contamination because of the seawater intrusion, management and protection of these freshwater resources are quite necessary. Therefore, the main goal of the present study is to develop a new numerical model for simulation of the contaminant transport in coastal aquifers (seawater intrusion into coastal aquifers) using discontinuous Galerkin method.
In this study, Discontinuous Galerkin methods which have been less developed in engineering problems were applied for discretization of the coupled nonlinear system of flow and solute transport equations in a saturated porous medium and a fully implicit backward Euler scheme was applied for temporal discretization. The primal DGs have been developed successfully for density dependent flows by applying initial and boundary conditions to the coupled equations. Then, to linearize the resulting nonlinear systems, Picard iterative technique was applied and Chavent Jaffre slope limiter was used to eliminate the nonphysical oscillations appeared in the solution. Note that the formulation which was used is based on the equivalent freshwater head and normalized mass fraction as dependent variables.
Five benchmark problems including standard Henry problem together with its two modified versions, Elder problem and Goswami Clement experimental problem in three distinct phases were simulated for validation and verification of the numerical code. For all the benchmark problems, the results were compared against other solutions in order to assess the model accuracy. The solution convergence was proved for the standard henry problem. Applying the Chavent Jaffre slope limiter to the experimental test showed a satisfactory results obtained from the simulations. In comparison with other numerical solutions, the present model revealed a good accuracy for all the problems.
The DG model were verified and evaluated using the above-mentioned problems. The results from simulations showed a good accuracy for DG method. In portions of the domain where the velocity is high, it was indicated that the DG methods in comparison with other numerical methods e.g. finite difference, do not emerge non-physical oscillations. Also, the results show a less numerical dispersion in comparison with other numerical methods such as finite volume methods. In addition, simulating the experimental problem with the current model shows the practical aspects of the developed model based on discontinuous Galerkin.

Due to absolute dependence of agriculture on water, determine of drought condition in each region is very useful in planning the food sourcing. Unfortunately, there is no same definition about the “drought condition”, so there are some indexes to determine it. Standardized Precipitation Index (SPI) is one the meteorological indexes, which widely used to determine agricultural drought conditions. Evapotranspiration Deficit Index (ETDI) was also designed for this purpose. This index is used to determine agricultural drought conditions in arid and semi-arid region. Although there were most research about other drought indexes such as SPI, but there is few studies in oversea countries about use of ETDI index. Thus in this study tried to determine drought by ETDI and SPI indexes in Neyshabur plain by used of climate change models
This research was conducted to determine drought condition in Neyshabur plain located at longitude between 58˚ 13’-59˚ 30’ N and latitude between 35˚ 40’-36˚ 39’ E, Iran. Evapotranspiration Deficit Index (ETDI) was developed based on weekly evapotranspiration deficit to determine drought condition in this region. In order to comparison of the ETDI results to other drought indices, we used Standardized Precipitation Index (SPI) as one the most common drought index. The data were collected from Neyshabur meteorological station for irrigated farms (wheat in Soleymani and Faroub farms, barley and corn) and rain-fed farms (rain-fed wheat) during 1992-2011. In order to estimate weather data for each index in the irrigated farms during two future periods (2020-2039 and 2080-2099), HADCM3, ECHOAM and CGCM3 T47 models were used based on A2, B1 and A1B scenarios and the climate model that has been used in rain-fed farm is the HADCM3 based on A2 and B1 scenarios. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were used to comparison of the ETDI and SPI results.
Results showed that average ETDI were in initial wet condition for Faroub farm during base period (1992-2011) while it will be in drought condition during future periods (2020-2039 and 2080-2099). ETDI index was in normal condition for Soleymani farm during base period. Average ETDI indexes for these farms were in normal and initial dry condition during 2020-2039 and 2080-2099 periods, respectively. For barley and corn, ETDI indexes were in normal and initial dry condition during base period, respectively. This index was in normal statute for both of them during future periods. The ETDI value for rain-fed wheat was less compared to irrigated wheat during base period, although, this index will be increased during future periods. In most of scenarios, ETDI indexes showed negative values. It means that high drought condition will be happened during future periods due to deficit evapotranspiration. Results according to SPI index revealed that this region was in moderately drought condition and this situation will not change.
High differences were obtained between ETDI and SPI results. Since agricultural drought depends on evapotranspiration deficits, ETDI is better index compared to SPI. The value of RMSE revealed poor adaptation between two indexes during future periods. In addition, ETDI were not correlated with SPI for all the scenarios in all scenarios. These differences are reasonable because SPI index only uses precipitation data and ETDI uses evapotranspiration. According to the results, it seems that SPI cannot be suggested as a good index in agricultural studies.

Soil physical, chemical and biological properties can be considered as the most important features of soil quality indices related to land cover and also climate changes. Afforestation with native and non-native species were employed for rehabilitation of natural degraded lands. Usually due to the study of soil quality, some properties are regarded that are more sensitive to environmental changes. Soil biological and biochemical properties are the features that response to environmental changes and ecosystem management in the short term. Soil as the site bed is affected by selected species type. Regarding to species type and its effect on soil quality properties is as management algorithm that is due to quantity preserve and long term stability of afforested areas.
To aim studying the effect of different land covers (Robinia pseudoacacia, Fraxinus excelsior, Cupressus arizonica and Pinus elderica) on soil physical-chemical, microbiological and CO2 emission characters, the present research was regraded in the Chitgar park. Sampling was done from the organic (litter) and mineral (0-20cm) layers using of the randomly systematic method. Litter quality properties (carbon and nitrogen), soil bulk density, texture, water content, pH, organic carbon, total nitrogen, nitrate, ammonium, carbon microbial biomass, nitrogen microbial biomass, CO2 emission (and their fluxes in different temperature and moisture regime) measured in the laboratory.
Results is indicating the afforested stands had significantly different effects on the whole of the physical-chemical properties of soil and litter (except carbon content). According to our findings, the highest amount of CO2 emission (1 mg CO2 m-2 d-1), microbial biomass of carbon (510.31mg kg-1) and nitrogen (0.13 mg kg-1) were belong to Robinia pseudoacacia stand. In addition, the maximum of CO2 emission was occurred in field capacity moisture regime and higher temperature.
The change of degraded land covers, afforestation with needle leaved and broad leaved species, improved the fertility indices, especially biological indices of soil quality in the study area. The highest amount of microbial respiration, microbial biomass of carbon and nitrogen were found under afforested site with broadleaved species and the least amount were detected in bare land. The results are indicated that the land use change and destroying of ecosystem plant covers is due to a severe reduction of mentioned indices in Chitgar region. So, land use change is a treat for soil health and quality in study area that must be considered in management of degraded natural ecosystem

Soil and biological factors can be extremely variable in place and time. Therefore, differences in soil water infiltration both in place and time is expected. Due to temporal variability of infiltration parameter, its direct estimation requires lots of time and sampling. Hence, empirical and physical equations exist for quantification of infiltration process in surface irrigation. Plenty methods are available for estimating infiltration equations parameters, which are different, depending on estimated data in the field. Selecting proper method depends on the available field data for estimating infiltration parameters. Some random parameters occur in different parts of the field that cause variations in the soil water infiltration. Considering infiltration spatial variations in irrigation increases accuracy in estimating infiltrated water volume and distribution uniformity. Therefore, in the current study it is attempted to survey variations of soil water infiltration parameters in furrow irrigation during three farming seasons.
Current study was carried out using collected data during the farming seasons 2011, 2012 and 2013 from a field located in Meshkindasht area of Karaj. Type of the crop was maize and the field had a loamy soil. At the each of the farming seasons, 8 separate experiments were implemented that each one was comprised of 4 furrows with the length of 120 meters and width of 75 centimeters. At the each of the farming seasons, 15 irrigation events were done. 8 experiments were implemented at relatively identical sites during three farming seasons. At the each of the irrigation events, type 3 WSC flumes were used for measuring the inflow and outflow of the furrows. In this study, Kostiakov-Lewis infiltration equation was used. For each irrigation, a and k parameters of Kostiakov-Lewis equation were calculated using volume-balance method in Excel software. In this method using furrow inflow and outflow hydrographs, basic infiltration rate was calculated. Means of spatial and temporal variations of the Kostiakov-Lewis equation parameters in three farming seasons were compared using Duncan’s Multiple Range Test using SPSS software.
Data were subjected to composite analysis of variance using General Linear Model by SPSS software. Effects of experiment number (spatial variations) and irrigation event (temporal variations) on variations of k infiltration parameter were significant at %1 level; while, effect of farming season was significant at %5 level. Difference between the 8 experiments during three farming seasons was significant at %5 level. Effects of the three aforementioned sources of variation on alterations of a infiltration parameter were significant at %1 level. Effect of experiment number on the average of temporal variations of a infiltration parameter in three farming seasons, showed significant difference at %5 level among the 8 experiments. Effect of farming season on the average of spatial and temporal variations of a infiltration parameter indicated its significant superiority (0.6108) in the farming season 2011 compared to the rest of the seasons at %5 level. While, in the farming seasons 2012 and 2013 there were no significant differences between the averages of the mentioned variations.
In general, there were significant differences in three farming seasons in regard to the average variations of each of the a and k infiltration parameters and also among the 8 experiments regarding the mean variations of each of the mentioned parameters. Finally, taking significant differences of the average spatial and temporal variations of a and k infiltration parameter into consideration, seasonal variations of soil water infiltration parameters in the three farming seasons were not negligible. Based on the results of the current investigation, a and k infiltration parameters of the Kostiakov-Lewis equation, showed erratic and unpredictable variations, spatially and temporally in different farming seasons.

Assessment of suspended sediment load is very important. Water quality and environmental is under impression of sediment load. As well as the design of hydraulic structures and other water supply facilities, watershed management, soil conservation programs and another major problem caused by sediment yield is dependent on the accurate estimation of sediment load. As a direct estimation of sediment load is very difficult and time consuming, so this led the researchers to estimate sediment load as indirect that it is possible to resort to various methods. One easy way to indirectly estimate the sediment load is sediment rating curve. It can only represent invariable amount of sediment in flow and due to various factors in nature may be there is several sediment load for a known flow rate. On the basis of this study quantile regression and random forest methods was used that can estimate sediment load for a given flow rate in the various probability. The use of these two methods can be analyzed sediment load in great flood and special events.
In this study, sediment rating curve models, quantile regression and random forest was used to estimate sediment load in four stations Gorganrood River Jangaldeh, Nodeh, Arazkoose and Ghazaghli in Golestan province. For this purpose, flow and sediment data was collected at four studied stations and separated into two parts, 75% for training and 25% for testing. The rating curve was obtained using fitted power equation between discharge and sediment load. Quantile regression and random forest algorithms were implemented using R statistical software. The optimal values of the variable parameters of the two methods were determined using trial and error method. By running the model, the amounts of sediment associated with specified flow were calculated in different probability level (1% to 99%).
Using these two methods, sediment load was estimated in quantiles 2.5, 50 and 97.5%, respectively and range of uncertainty was determined in each station. In Jangaldeh and Nodeh stations random forest were selected as best method with RMSE criterion 96 and 210 tons per day and quantile regression were selected as best method with RMSE criterion 6453 and 24886 tons per day in Arazkoose and Ghazaghli stations. Classic rating curve method estimate sediment load in Jangaldeh, Nodeh, Arazkoose and Ghazaghli stations with RMSE 199, 288, 7505 and 25811 tons per day respectively.
The results showed that classic sediment rating curve not only unable to estimate the sediment load in the range of uncertainties in specified flow rate but also estimates sediment load with more error. Quantile regression and random forest methods can be estimate sediment load in various probabilities for a specified flow and this has contributed greatly to accurate and comprehensive planning for the construction of hydraulic structures and in this way, the dangers of the destruction of the facility reduction due to the great flood.

Infiltered or runoff waters from uplands especially after raining can cause groundwater and water pollution in lowlands which affect soil quality. Monitoring and assesmnet of suface waters in dry lands that alive the wetlands is very important. The objectives of this study were to assess the quality of suface water-inflows to the Meyghan Lake and their consequences on the chemical characteristics of Meyghan wetland soils.
Manicipal wastewater of Arak, Shahrab River and wastewater of processing plant of sodium sulfate were sampled in May and November, 2014. Electrical conductivity, differnt solids, major cations and anions, nitrogen, phosphorus and organic carbon were measured and tested in the waters. Two layers (0-30 cm and 30-60 cm) of soils were sampled in the release sites of municipal wastewater, Shahrab River, Farahan River and sodium sulfate plant wastewater in May and November, 2104. Soil chemical properties were measured and statisticaly analized for these 4 sampling sites.
The study showed that manicipal wastewater of Arak has significantly lowere salinity, sodium adsorption ratio, cations and anions concentrations and solids as compared to other sampled waters. But organic carbon, nitrogen and phosphorus concentrations were higher in this water that significantly decreased the salinity and increase organic carbon, phosphorous and nitrogen contents of the soil sampled from this site. The improvement of soil properties can increase aquatic plant growth, which will be the source of water pollution with plant residues and organic materials. In conterast the wastewater of sodium sulfate processing plant has markedly high concentrations of soluble salts, sodium adsorption ratio and particularly suspension solids. It is not suitable for plant growth and can change soil physical properties in this site. Chemical properties of water of Shahrab River were close to the chemical properties of surface and bottom layers of soil in this place. It shows that the the harmony and stability of soil and water environment in this sampling site. But study of surface and bottem layers of wetland soil in the release site of Farah River shows that the surface soil has higher organic carbon, nitrogen and phosphorus contents and lower salinity compared to those measured in the soil sampled from Shahrab River site. It may be related to anthropgenic effect in agriculral uplands, creating a good environment for plants growing in this salty lake, increasing its pollution by plant residues and organic matters.
The high quality of urban wastewater of Arak may improve soil chemical properties for growing of plants. In contrast the low quality of wastewater of sodium sulfate processing plant worsened soil chemical properties. So combining of two waste waters and mixing them before releasinge into the Meighan Lake may be a good suggestion. However this proposal needs a special research.

In addition to the limitations of conventional for investigation of rainfall trend tests (OLR, Mann-Kendall, Sen), these are only provided to estimate the conditional mean or median and do not consider different quantiles. Investigation of trend of changes of empirical probability distribution function (EPDF) and empirical cumulative distribution function (ECDF) can lead to more detailed information in comparison with conventional methods. The objectives of this study are application of quantile regression for estimation of changes in EPDF and ECDF of five annual rainfall series in northern Iran and interpreting their results.
The linear regression lines were estimated using quantile regression considering quantiles (0.05-0.95 with 0.01 steps). Then slope-quantiles curve were extracted based on slopes of quantile regression lines and considering 0.05 statistical significance level. The graphs of trends of different annual rainfall series were presented considering selected quantiles 0.9, 0.7, 0.5, 0.3 and 0.1 and the results were analyzed. The curves of EPDF and ECDF belonging to first and last years of data recorded periods estimated with concept of quantile regression and their curves were plotted and comparison between first and last year curves were done.
The numbers of lines with positive slopes were considerably more than negative slopes considering 0.05 statistical significance level. Investigation of quantile regression lines showed decreasing and increasing trend lines for upper and lower quantiles respectively for Mashhad series but increasing and decreasing trend lines for upper and lower quantiles of other series. In addition, the magnitude of slope lines for lower quantiles were more than those belonging to upper quantiles for Gorgan, Bandar-Anzali, Tabriz and Urmia series but there were inverse pattern for Mashhad series. Comparative assessment of ECDF curves belonging to first and last recoded years revealed apparent difference in lower level of probability for Tabriz, Gorgan, Urmia and (to some extent) Bandar-Anzali but a limited difference for Mashhad series. Assessment of changes pattern of EPDF showed relatively constant location parameters but changes in scale parameters that resulted in peakness for last year EPDF of Mashahad but flatness for Gorgan, Bandar-Anzali, Tabriz and Urmia series. In addition, most of the changes related to end part of EPDF for Mashhad and Bandar-Anzali series while related to beginning for Gorgan, Tabriz and Urmia series.
Thequantile regression can be used without affecting the limitations of conventional methods for trend analysis to access a wider range of applied analysis. There were apparent differences between signs of quantile regression lines belonging to upper and lower levels in all of five investigated series that showed Conventional methods could not be a useful tool for trend assessment of extreme events. The results of this study showed quantile regression is a suitable method to analyses changes in EPDF and ECDF of time series and this type of analysis present more useful results with comparison to conventional trend analysis methods.

Quantitative prediction of river discharge one of the most important elements in the management of surface water resources, especially take suitable decisions in occurrence of floods and drought events. Various approaches introduced in hydrology to predict river discharge which intelligence models are the most important ones.In this study, recorded data sets in kashkan watershed area located in lorestan were used to investigate the precision of different river discharge prediction models. The support vector machine model as a gene expression programming model and Bayesian network models selected for modeling of daily river discharge and the results were compared to examine the accuracy of studied models. In some studies, the expressed models used for daily river discharge prediction but the main objectives of this study are application of these models to predict daily discharge for a watershed.
In this study kashkan river basin was selected as the study area and observed daily river flow of this basin in the poldokhtar station were applied for calibration and validation of models. For this purpose, first 80 percent of daily river flow data (2004-2011) were selected to calibrate models and 20 percent of data (2012-2014) were used to validate models. Gene expression programming solution is a technique that is automatically programmed using the PC programming and evolutionary algorithm is a member of the family. Support vector machine is also an efficient learning system is based on the theory of constrained optimization. Bayesian networks, display meaningful relationships between parameters in the process is unclear and non-cyclic directed graph of nodes to display random variables for representing probabilistic relationships between variables considered magmatic arc. Criteria of correlation coefficient, root mean square error and coefficient, mean absolute error and performance of models were used to evaluation models.
The results showed that all three models, Bayesian networks, support vector machine and gene expression programming, in a structure consisting of 1 to 5 delay gives better results than any other structure. Also of results according to the evaluation criterion was that the models used support vector machine model, most accurate R=0.880 and the lowest Root Mean Square Error RMSE=0.002m3/s and the lowest average absolute error MAE=0.001m3/s the validation phase is capable. Also, the estimates of minimum, maximum and median has shown good performance.
support vector machine model outperformed the Bayesian network modeling and gene expression programming. So, support vactor machine model can be effective in forecasting the daily stream flow and in turn facilitate the development and implementation of surface water management strategies will be useful. And a step in making management decisions to improve the quantity of surface water create.

Heavy metals such as cadmium causing numerous adverse effects on the aqueous solutions. So the presence of heavy metals in surface water and ground water has become a major inorganic contamination problem. There are several method to remove the heavy metals from aqueous solutions that adsorption is one of the best method. The aim of this study was to evaluate the efficiency of Silicon dioxide nanoparticles modified by 2-aminopyridin and EDTA as adsorbent for the removal of Cd2 ions from aqueous solutions.
Material and Silicon dioxide nanoparticles with toluene put in ultrasonic bath. Then mixture of 2-aminopyridin and EDTA with ratio of 1:1 added to primary solution. After 24 hours reflux on oil bath and 10 minutes centrifuging, sample rinsed by chloroform, ethanol and deionized water. In the end, the product prepared after drying at 100 o C for 24 hours. Experiments were conducted to study the effect of solution pH, temperature, contact time, absorbent amount and initial metal concentration. Experiment conducted in bath system with three replicate. The test solutions of various concentrations were prepared from the stock solution. The solution pH was adjust using 0.1 M HNO3 and 0.1 M NaOH at the beginning of the experiment and not controlled afterwards. Solution containing adsorbate and adsorbent was taken in 250 mL capacity conical flask and agitated at 120 rpm in a shaker at predetermined time intervals. Samples putted on the centrifuge with 4000 rpm for 5 minutes .Lead ions were determined spectrophotometrically by atomic absorption spectrophotometer. Finally for determination of adsorption mechanism, adsorption isotherm models and kinetic models were studied .Results was analyzed by Excel software.
The Results showed that, the highest uptake and optimum conditions was observed in pH=4, contact time 35 minutes and temperature 25℃. The Analysis of Langmuir isotherm (R2=0.93) and Freundlich isotherm (R2=0.94) show that, experimental data were fitting match by both isotherm but it is more fitting match with Freundlich isotherm. Kinetic studies showed that, kinetic of adsorption according to pseudo second order equation (R2=0.99).
Base on the results it can conclude that, modified silicon dioxide nanoparticles could be used as a best adsorbent for the removal of heavy metals from aqueous solutions especially for the industrial wastewaters because of the cost, easy to use, renewable, suitable and environmental friendly. Comparing with other similar studies these were found to be the excellent adsorbents and can be successfully used by Industries for heavy metal removal.

Since ground water and dwindling water resources is important for the operation of research and modeling is important. Assessment and prediction of groundwater level to help predict groundwater resources. The use of artificial intelligence methods based on the theory of data mining is used to predict the water table fluctuation. The support vector machine in artificial intelligence methods and the methods of geostatistical Kriging method has considerable precision in order to predict the time and location of the water table is level. In this study, the combination of support vector machine and Kriging model as a new way to predict when and where water table fluctuation in the plain area Silakhor is used. In the first phase, modeling when using support vector machine model data 11 piezometric wells in the region were carried out using support vector machine and secondly to predict the location of monthly data output of the first stage as an input earth model Statistics were used.
Data of 11 observation wells in the Silakhor plain data collected in the course of the past ten years in both normal and abnormal SVM model were used as input. Using the software Matlab function algorithm support vector machine was configured as an input at each stage of a well is this model. In addition, the maximum error in the calculation of the wells with 0.2 Keyvareh is due to lack of observation data in interval or lack of access to the region is to read water level. Absolute estimation interpolation and location estimate is a major feature. This means that estimates the quantity of sampling points is equal to the measured value and variance estimate is zero. In fact this model to estimate the amount of variance minimizes unknowns. Thus, the curves are drawn based routing and thus go beyond the boundary drawn. This feature makes the model the spatial distribution of data which are dependent on terrain, Kriging in calculations of high accuracy. Locate underground water level role in reducing the cost of drilling a well in the region. This way you can reach the height of water to be achieved in the region or even decline or rise of water table revealed
The ability of Support Vector Machine, and of course, a relatively new as a useful tool in water resource management to predict fluctuations in groundwater levels were evaluated in Silakhor plain. According to the accuracy of this method in predicting groundwater level can be comprehensive and appropriate program management discussion groundwater resources to be expected. In this study, using geostatistical methods in the area of water resources in plain Silakhor predicted with high accuracy are discussed. The most important issue in the analysis of spatial-temporal data to determine the dependence structure of the data. Whatever the choice of models and model are more accurate, the prediction will be more accurate. In terms of predicting when it should be noted that the time for the distance away from the last viewing time predicted prediction accuracy will be reduced. According to the study area in the last ten years has been well observed that in the best case scenarios predicted by the model, the highest coefficient of determination for wells Chughadun (DC = 0/96), sugar (DC = 0 / 94) and Valian (DC = 0/93) was calculated, which represents a hybrid model to predict groundwater level is the wells could be decided.

Today, conversion of industrial wastes using methods with the least environmental detrimental effects and profitable application is supposed as a fundamental solution to manage huge quantity of waste material produced in different industries. In this regard, conversion and reuse of hugely and intensive extension of produced dairy factory waste are possible solutions in order to achieve sustainable development. However, the direct or applied application of industrial wastes for soil conservation has not been reported yet. Accordingly, the present study was planned to assess the feasibility of industrial wastes produced biochar in soil erosion control under laboratorial conditions.
In order to conduct the present study, some industrial wastes of the Kaleh Amol Dairy Factory were examined for chemical properties. It was then converted to biochar under temperature of 300 to 350 °C and applied for soil conservation in three levels of 400, 800 and 1200 g m-2 on small experimental plots filled by erosion prone soil of Marzanabad Region in Mazandaran Province, northern Iran. The plots were subjected to rainfall simulation with intensities of 50 and 90 mm h-1 after a span time of 35 days on biochar application in the Rainfall and Erosion Laboratory of Tarbiat Modares University. Study rain were simulated based on intensity-duration-frequency relationships developed for the station at the vicinity of the soil origin. Ultimately, sediment concentrations were measured during simulation and just after commencement of runoff and the soil erosion was consequently calculated.
The results of the present experiments under laboratorial conditions showed that the maximum soil erosion occurred in control plots. So that, the sediment concentrations for treated plots with 400, 800 and 1200 g m-2 of biochar were 47, 52 and 49 % for rain intensity of 50 mm h-1, and 36, 51 and 54 % for rain intensity of 90 mm h-1 of those recorded for control plots at confidence level of 99 %, respectively. The soil erosion rates for the same treatments were also significantly (P The results of this study suggested a positive effect of application of biochar produced from industrial wastes of dairy factory on reduction of sediment concentration and soil erosion. The conversion of aforesaid industrial wastes to biochar is therefore recommended for soil conservation.

Kerosene is colorless liquid and slightly heavier than gasoline that specific odor removes after evaporation. The majority fraction of kerosene contains hydrocarbons between 11 to 15 carbon molecules. Farmland ecosystems may be polluted with petroleum hydrocarbons via different ways such as transportation and spill of crude oil from resource of petroleum storage. These pollutants have some effect on the texture of the soil and microbial community. The aim of this research is understands the effect of kerosene pollution on two different soils.
Material and two different soils samples were collected from desert and farmland soils. Six microcosms were designed. Indeed each soil has three microcosms such as: unpolluted microcosm, polluted microcosm, and polluted microcosm with nutrient (Nitrogen and Phosphor). Some factors were assayed in each microcosm during 120 day of experiment. These factors include: total heterotrophic bacteria, total Kerosene degrading bacteria, dehydrogenase enzyme and Kerosene biodegradation. For enumeration of heterotrophic bacteria nutrient agar medium was used. In this method serial dilutions were done from each soil and spread on nutrient agar medium then different colonies were counted. For enumeration of degrading bacteria Bushnel-Hass (BH) medium were used.
The results of this study show that the highest quantity of heterotrophic bacteria related to farmland soil (1 × 1010). The quantities of degradative bacteria significantly were lower than heterotrophic bacteria in all soil microcosms. By passing the time if treatment from zero to 120 days two different patterns were seen in soils. In desert soil from the beginning of experiment until day 30 the increment pattern were recorded but in day 60 the remarkable decrease were observed in the quantity of bacteria. The quantity of degradative bacteria have decrement pattern until 60th day of experiment but after this day these bacteria have increment pattern. In desert soil increase in the quantity of degrading bacteria in all microcosms were recorded in day 30 of experiment. After this time the decrement pattern were observed. In farmlands soil increase in the quantity of bacteria were started from day 90 and continue until the end of experiment. The highest quantity of degrading bacteria related to farmland soil (2 × 10 6) and the lowest quantity related to desert soil ( 3 × 104). The best deydrogenase activity between different microcosms related to pollute microcosm with nutrient. The highest biodegradation of Kerosene in all studied soil belong to farmland microcosm (95 %). Statistical analysis of the results shows that there is a significant correlation between MPN quantity of heterotrophic bacteria and other assayed factors.
The results of this study confirmed that the selection of best bioremediation strategies belong to type of soil and in this research it was confirmed that the type of soil have significant in the percentage of degradation.

Pollution is one of the most important factors that lead to the wasting of water resources.This research was an experimental study in a batch system in a lab environment pollution College of Agriculture and Natural Resources, University of Birjand. All chemicals used in the study with high purity of 99% were obtained from Merck, Germany. The adsorbent (zeolite Semnan) was prepared. Zeolite particles with crushing and screening assay with an average diameter of 360 micrometer sieve (particles with a diameter of 315 to 425 micro-meters remaining balance of between only in particle size) was selected (this was repeated twice). After being washed with distilled water, incubated for 24 hours at a temperature of 40 ° C was placed inside to dry. Stoke-soluble chromium (1000 ppm) using potassium dichromate solution powder was prepared in distilled water twice. Results and Discussion The effect of pH as an important parameter in the absorption of heavy metal ions is clearly visible. Absorption of chromium ions increased with increasing pH and at pH 5 is the highest value. reason for this is that hexavalent chromium and absorbent surface charge are highly dependent on pH. The balance of absorption and adsorption efficiency as well as economic characteristics, the most important for the development of water treatment technology based on natural attraction. With increasing contact time from 20 to 120 minutes, with initial concentration of hexavalent chromium removal efficiency of 50 milligrams per liter of 79.3 to 81.9 percent percent increase in contact time of 120 minutes and after this time removal of garlic during the monotony. Another important parameter in the process of absorbing the contact time can be noted. Increase the rate of absorption from 0.3 to 3 g led to increase the absorption of chromium ions. This increase is due to the availability of larger and more is absorbed sites, is expected. The results clearly showed that the absorption efficiency is increased to an optimum value and then the optimal value, the increase is very small and negligible. Removal efficiency decreases with increasing initial concentration of hexavalent chromium. So that by increasing the concentration of 25 ppm to 200 ppm, removal from 95.6 to 88.7 percent percent. The optimum concentration of 50 ppm as the amount was used in the following experiments. Increasing the temperature from 22 ° C to 50 ° C, removal from 17/99 percent to 94.8 percent. This can be due to increased pore size and activate attributed absorbent surface. Further increasing the temperature increases mobility and reduces metal ions. The results showed the acidity of an aqueous solution Myknd.balatryn play a role in the absorption of hexavalent chromium absorption by absorbent 5pH = happens. The optimal time to absorb Chrome for clinoptilolite zeolite is about 120 minutes. The efficiency of removal sorbent concentration and initial concentration of soluble Cr (VI) to Cr (VI) ions in the solution depends on the capacity of balance by increasing the initial chromium concentration decreases because the absorber is saturated with increasing initial concentration of chromium. With the increasing amount of sorbent due to the increased surface area increases adsorbents removal of hexavalent chromium from wastewater dummy. The optimum temperature in order to attract Chrome for clinoptilolite zeolite powder at about 22 ° C. Conversion data with the Langmuir isotherm model, with a correlation coefficient higher than the Freundlich isotherm models. In kinetic studies, the pseudo-second-order kinetic model to attract hexavalent chromium knows index.

Soil is one of the main non-renewable natural resources that today its destruction is considered one of the most severe problems all over the world. In recent decades, rapid and unsustainable changes and land use due to the urban development activities and increasing population created a great deal of modifications in land cover and land use and has been increasing the environmental degradation including soil degradation. Therefore, reviewing these changes through satellite images, evaluating and forecasting their potentialities via modeling can help managers and planners to make more effective decisions. The aim of this study was to assess the changes in land use during the period 1992 to 2015 via using satellite images to calculate the rate of land use changes to each other and predict possible changes in land use in the years 2020, 2025, 2030 and 2035, using cellular automata - Markov model (CA-Markov) in Joupar plain, Kerman province.
In order to prepare land use plans the three periods of Landsat satellite images including Landsat 5 satellite TM (1992), Landsat 7 ETM (2000) and Landsat 8 satellite OLI (2015) were used in this study. To prepare land use maps through satellite images, initially the mentioned images were exposed to primary pre-processing such as geometric and atmospheric corrections. In addition, via providing training samples the satellite images were classified and their accuracy were evaluated using Idrisi imagery software through maximum probability algorithm. The developed land use maps of different periods were transited to CA-Markov model in order to produce transition probability matrix. Ultimately, the transition probability matrix was produced that shows the likelihood of transition of one land use to others. Then the chain analysis of cellular automata – Markov on the basis of land use plans and transition probability matrix in CA-Markov model with an emphasis on land use changes were expected in 2020, 2025, 2030 and 2035 were implemented in Idrisi software with various numbers of repetitions and steps. Based on the survey results, changes in land use and the level of current land use changes calculated, compared and evaluated and the future land use changes were predicted.
Results and The results of the detection of changes in the first period (1992-2000) revealed the highest increase in land area which was attributed to the use of pasture, grassland, irrigated agriculture and orchard and the highest decrease in land area was related to bed stream. In the second period (2000-2015) the greatest increase in land area was associated to the use of irrigated agriculture, orchards and bed stream and the greatest reduction was in pasture and grassland use. The results obtained from the prediction of future user changes of the region based on CA-Markov showed decreasing levels of land use attributed to orchard and irrigated agriculture and increasing levels of land use associated to pasture, grassland and bed stream comparing to 2015. Also the results obtained from the prediction of the findings regarding the years 2020, 2025, 2030 and 2035 revealed a reduction in land use related to bed stream, pasture and grassland due to the lack of rainfall and temperature rise and this will lead in the destruction of vegetation cover as wellas the more soil degradation. Also, due to the lack of rainfall, the recent droughts and previous studies we can conclude that the approach of CA-Markov model is more compatible with the conditions of the region.

Rainfall estimation in regions facing lack meteorological stations is critical in hydrology and meteorology studies. The problem in Iran that lacks dense weather station network, especially in the highlands, is more important. Aphrodite is a dense network that provides daily precipitation data in a resolution of 0.25*0.25 degree. But before operating network, its accuracy and uncertainty should be evaluated. About this database, few studies have been conducted in Iran and the world. This study aimed to evaluate the accuracy of Aphrodite in Golestan province at daily, monthly and yearly scales. Also its accuracy compared with two conventional interpolation techniques and for more data operation, the network was modified.
In this study, 19 rain gauges and 5 synoptic stations were used to evaluate the Aphrodite network. Grid points network were selected based on the nearest point to meteorological station and data analysis was performed in daily, monthly and yearly scales. After accuracy evaluation of network, the correction factor of means ratio was employed for modification of monthly and annual rainfall network data. As well modified network data were compared with interpolation methods of Ordinary Kriging (OK) and Inverse Distance Weighting (IDW). Cross-validation technique was used for evaluation and error indices was calculated for comparison of different methods.
The results of MBE in all studied scales showed that in spite of good correlation of Aphrodite and meteorological data, Aphrodite data has large underestimation error that this is higher in high rainfall areas. Obtained modification factors of the monthly and annual data vary from 1.2 to 1.8 in cold and warm months, respectively. In addition to removal underestimation error from modified network, the average of error is reduced remarkably. Also RMSE of annual rainfall data, is declined from 247 to 194 mm. considering that, network modification needs to be multiplied in a constant (correction factor) so the correlation coefficients remained unchanged. Comparison of modified data network with interpolation methods showed that modified network is more efficient than both interpolation methods. Correlation coefficients of actual and estimated data showed that the correlation of modified network with actual data is positive in all the months but in case of IDW and OK, correlation coefficients was negative in some months that represent modified network in addition to fewer error also in the detection of low and high rainfall points is more efficient than the interpolation methods.
This study showed that high resolution Aphrodite network has good accuracy in rainfall estimation especially in high elevation regions. Also this network is more accurate than interpolation methods of OK and IDW. But because of its underestimation error, before than operating Aphrodite data, the network must be modified.

Desertification is a major concern of many countries occurs as a result of climatic and human factors. Wind erosion is one of the important factors of soil degradation in arid and semi-arid areas. According to evaluation of United Nation Conference of Desertification, desertification phenomenon threats the future of 785 million people who lived in arid areas which consists 17.7 percent of world's population. Arid and semi-arid areas have occupied most parts of Iran. The aim of this study was quantitatively assessment the environmentally sensitivity area to desertification based on ESAs (Environment Sensitive Areas to Desertification) model in the Muksokhteh and Rotok region of Saravan as one of the critical areas prone to erosion in the Iran.
In this research, at the first, the land units map was established including: silty- sandy land with halophytes plants, active hills and sandy zones, silty-sandy land with palm use, old terraces, waterways and canals, salt marsh land, Talkhab and Rotok river sand bed with plant cover, clay pans with sand dunes, fan-shaped alluvial with low gravel surface and high salinity, and crop lands. It was found that in this model four criteria including soil quality, vegetation cover quality, land use management quality and climate quality are desertification factors. These factors were calculated via scoring sub-criteria in each area. Then, the mapping of areas sensitivity to desertification was drawn by GIS software.
The findings of this research showed that the climate quality, management quality and vegetation quality criteria have played the most important role in sensitivity of area to desertification especially in active hills and sandy zones and clay pans with sand dunes. It was found that the study area has critical conditions. It includes high critical (C3) and moderate critical (C2) classes of desertification which cover 99.7%, and 0.3% of the region, respectively
Base on the results, desertification severity classes showed the study area has high sensitivity to wind erosion and desertification. By noticing the evaluated factors, it is understandable that the climate factor has the intensive effect on desertification throughout the study region that they are out of control by human being. The obtained results showed that the study area is susceptible to desertification base on ESAs model, therefore it requires management of the organizations working on combat desertification. It can be concluded that the assessment of desertification sensitivity has priority than plan combating actions in this region.