Irrigation
Sh. Nourinezhad; M.M. Rajabi; T. Fathi
Abstract
Introduction Simulation of quantity and quality of surface runoff in mountainous watersheds is one of the most challenging topics in modeling due to its unique features, such as unusual topography and complex hydrological processes. One of the lesser-known aspects of modeling such catchments is ...
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Introduction Simulation of quantity and quality of surface runoff in mountainous watersheds is one of the most challenging topics in modeling due to its unique features, such as unusual topography and complex hydrological processes. One of the lesser-known aspects of modeling such catchments is the uncertainty analysis of water quality predictions, especially about the vital phosphorus parameter. Phosphorus is one of the important quality variables in water, and its increase in water resources can cause eutrophication phenomena in streams and reservoirs of dams. Due to the importance of the phosphorus parameter and the fact that water quality modeling has not been employed in the Karaj catchment area so far, in this research, total phosphorus has been modeled as a water quality parameter along with the flow and sediment discharge. This study aims to identify the most sensitive parameters of the model to flow, sediment, and total phosphorus discharge and calibrate, validate and analyze the parametric uncertainty of the SWAT model in predicting these three variables in a mountainous catchment. The case study was the catchment area of the Karaj River upstream of Bileqan pond, which is one of the mountainous watersheds in Iran. There are two critical water structures along the Karaj River, namely Amirkabir dam and Bilqan pond. Amirkabir dam (Karaj) is a multi-purpose project that is constructed to supply drinking water to Tehran and regulate water for irrigation and agriculture in the suburbs of Karaj. The Bileqan pond is also the essential point of supply and transfer of drinking water in Tehran. Given the importance of this region in supplying water for different uses, providing a calibrated model for quantitative and qualitative variables of water can be the basis for decisions to apply future management scenarios in this basin.Materials and Methods The case study was the Karaj River catchment area upstream of Bilqan Basin, which with an average height of 2880 meters, is one of the mountainous areas located in the Alborz Mountains. This basin with an area of 1076 square kilometers in the north, includes parts of Mazandaran province. In the east and south of the catchment area includes parts of Tehran province and most of it is located in Alborz province. The average annual temperature and rainfall in this basin are 12.1 °C and 480 mm, respectively, and the average of 117 glacial days during the year is observed in this area. The long-term daily data of synoptic stations adjacent to the study area from the beginning of 1998 to the end of 2018 (21 years in total) was introduced to the model. Also, daily data of relative humidity, rainfall, minimum and maximum temperature, solar radiation hours, and wind speed as meteorological parameters measured at stations in the study area were introduced to the model. It should be noted that there was a lot of missing data in meteorological information, especially for daily temperature data. In addition to the above information, daily flow data discharged from Amirkabir dam and technical specifications of the dam were introduced to the model. In addition, orchard management information, including irrigation periods and information related to phosphate fertilizers used in regional orchards, were presented to the model. The global sensitivity analysis method was used to determine the sensitive parameters of the model. Furthermore, the SUFI2 algorithm was used in SWAT_CUP software to calibrate and analyze the parametric uncertainty of the SWAT model. This algorithm quantifies the output uncertainty by 95% prediction uncertainty boundaries.Results and Discussion According to the results of sensitivity analysis, the parameters Baseflow alpha-factor (ALPHA_BF), Manning’s “n” value for overland flow (OV_N), and Precipitation Laps rate (PLAPS) were the most sensitive parameters to flow, sediment, and total phosphorus, respectively. The best Nash-Sutcliffe (NS) coefficients for runoff, sediment, and total phosphorus simulation obtained in all stations and after full calibration and validation periods were equal to 0.76, 0.56, and 0.92, respectively. Simulation of the peak points of the diagram of all three quantities was also associated with increased uncertainty and decreased model prediction accuracy, but due to the placement of more than 70% of the measured runoff and sediment values and nearly 60% of the measured total phosphorus values in the prediction uncertainty boundaries generated by SUFI2 algorithm the final value of the parameters used in the calibration process can be appropriate for simulating future scenarios in similar mountain catchments. The main weakness of the model is simulating the peak points of flow and sediment discharge. In the case of flow and sediment discharge, the liability of modeling can be generalized due to the lack of accurate prediction of the snowmelt inflow to the river in spring, which begins to increase in February and reaches the peak point in May. A considerable number of missing data in meteorological stations can effectively reflect the lack of accurate model prediction at the peak points. In this region, missing daily temperature data compared to other meteorological parameters has been significant. The dependency of the SWAT model on many experimental and quasi-experimental models such as SCS-CN and MUSLE can be another factor affecting the weakness in predicting the peak points of the sediment discharge, as well.Conclusion According to the uncertainty analysis results, most of observed flow, sediment and total phosphorus discharge values were within the uncertainty prediction boundaries generated by the SUFI2 algorithm. The NS coefficient for all three variables has met the satisfactory modeling threshold. Therefore, it seems that the sensitive parameters identified and used in the calibration process in this study and their final values can be appropriate for modeling future scenarios for this study area and similar mountain catchments. One of the limitations of the present study was a large number of missing data in meteorological stations, especially for the temperature variable. Thus, providing required measured meteorological data to the model may emhance the simulation, especially at peak points.
B. Sarcheshmeh; J. Behmanesh; vahid Rezaverdinejad
Abstract
Introduction: Drying Urmia Lake, located in northwest of Iran, is mainly related to the reduction in rivers flowing into the lake and hydrological parameters changes. Considering the importance and critical ecological conditions of Urmia Lake, the purpose of this research is to accommodate the environmental ...
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Introduction: Drying Urmia Lake, located in northwest of Iran, is mainly related to the reduction in rivers flowing into the lake and hydrological parameters changes. Considering the importance and critical ecological conditions of Urmia Lake, the purpose of this research is to accommodate the environmental water requirement in managing rivers leading to the lake, including Zarrinehrood as the largest river to the lake. Moreover, water scarcity was assessed by QQE approach in this basin.
Materials and Methods: Tennant method is easy, rapid, inexpensive, and is based on empirical relationships between the recommended percent of the MAF. The ecological conditions of the river have been determined for use in this method. In this study, different levels of EFR were calculated to protect the relevant levels of habitat quality defined in the Tennant method. Also the fraction of Blue Water Resources (BWR) required to protect a “good” level of habitat quality was considered as the suitable EFR. If it is less than the lower limit, the habitat quality will be in degraded status.
,
SQQE is a complete index to demonstrate water scarcity by considering water quantity and quality and EFR indicator.
, ,
The Smakhtin method provided an indicator for assessing the water scarcity.
WSI =
Where WSI is the index of water scarcity, MAR is the mean annual flow and EWR is the environmental water requirement of river. If the water scarcity index is more than one, the river would suffer from water shortage and not be able to meet the environmental water requirement. When the water scarcity index is between 0.6 and 1, the river would be under stress, and if it is between 0.3 and 0.6 Harvesting conditions from the river is moderate, and if it is less than 0.3 the river is ecologically safe and has no shortage.
Results and Discussion: According to the Smakhtin method, can be noticed that the calculations of this method are the same quantitative index of the other method used in this research. Only the quantitative conditions are evaluated in the Smakhtin method. However, in addition to the quantity (blue water footprint), environmental requirement and water quality are also included in the other method used in this research. Figure 1 shows the mean annual flow (MAF) and environmental flow requirement (EFR). As shown in figure 1, the majority river flow has been conducted from January to June and the rest from July to December. The annual BWR in the Nezamabad station was equal to 1208 × 106 (m3/year). To protect the habitat health of Zarrinehrood river at a good level, 400×106 (m3) of water must be left in the river per year. Therefore EFR was equivalent to 33.11% of the annual BWR. It is about one-third of total BWR.
In this station, EFR ranged from 60×106 (m3/year) as severely degraded to 2400×106 (m3/year) as maximum habitat health situation by using the Tennant table (Fig 2).
Figure 1- Environmental flow requirement (EFR) and mean annual flow (MAF) for the (Nezamabad station) Zarrinehrood river basin
Figure 2- Different levels of total environmental flow requirement (EFR) in the (Nezamabad station) Zarrinehrood river. Habitat quality levels with the flows shown in table 3 (Tennant) have be matched
The BWF and the BWA for the studied station were calculated 830×106 and 808×106 (m3/year), respectively. The BWF is 1.02 times the BWA. Therefore, the WSI Smakhtin and S Quantity will be 1.02.
The total GWF in this station was 1.08 times the BWR. Thus, the S Quality will be 1.08.
P is a demonstrator that shows the percentage of EFR in total BWR. It is related with the EFR to protect the habitat quality in a “good” level.
As you know, the number in the bracket shows that 33.11% of the total BWR of the basin is required as EFR, for maintaining the ecological habitat condition at the ‘good’ level. Other percentages of EFR are used to represent other ecological levels of habitat condition.
The S Quantity and S Quality for the Nezamabad station in Zarrinehrood river basin were obtained 1.02 and 1.08, respectively. Both indices are above the threshold (1.0), and the basin suffer from both qualitative and quantitative deficiencies. Thus, the final water scarcity indicator, SQQE, is 1.02 (33.11%) |1.08.
Conclusion: The EFR for protecting the good ecological level is not enough in some months during a year. Water scarcity was evaluated by simultaneously considering water quantity, water quality and EFR in the Zarrinehrood river basin in Iran. Compared with the Smakhtin method as another method of water scarcity assessment, the Smakhtin Index is only quantitatively, but the SQQE Index provides a comprehensive assessment of the water scarcity. The results imply that the studied region is suffering from both water quantity, water quality problems. The water pollution has a big role in causing the water scarcity in the river basin. This shows that only aiming on reducing water consumption cannot help impressive reduce the water scarcity. It is necessary to pay attention to reduce water pollution and water conservation. Even in the areas that the hydrological and ecological data are rare, the QQE approach as a holistic method could be used.
Omid Nasiri-Gheidari; Safar Marofi
Abstract
Introduction: Due to the rapid rate of population growth, water resource topics wasmainly affected by the economic and social components, however, the importance of environmental issues in such projects has gained more attention. As pollution loads are increasing, it has become more essential to incorporate ...
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Introduction: Due to the rapid rate of population growth, water resource topics wasmainly affected by the economic and social components, however, the importance of environmental issues in such projects has gained more attention. As pollution loads are increasing, it has become more essential to incorporate water quality in water resource management issues. Under this condition, optimal water allocation by considering multiple objectives of water quality and quantity issues can lead to sustainable and optimal benefit of stakeholders. This study was done in order to balance environmental and economic concerns in water resource allocation.
Materials and Methods: Based on game theory concepts and fuzzy programming procedure, two new methodologies were developed for sustainable water resource allocation in river systems. The proposed methods which include a multi-objective bargaining and fuzzy programming approaches were utilized to analysis strategies of interaction between environmental protection and economical income. Two groups of players, consists of player 1 for environmental and player 2 for economic issues were considered in order to apply the developed models. As players will not be satisfied with the outcome of each other, they will begin the bargaining process. Throughout the bargaining rounds, players will reduce their expectations. After several negotiations, the interval between the reset goal values and outcomes will be decreased. The bargaining process will be finished if final solutions reach to the determined goals. In the study, the Total Dissolved Solids (TDS) were considered as water quality indicators of environmental objective function, since salinity is the important problem of the study area. Using crop production function in economic income objective function makes it possible to incorporate deficit irrigation in different crop growth stages. Since allocation problems include many decision variables, conventional (non-linear) crop production function will have high computational costs and linear form of it can reduce the complexity of the optimization model. Therefore,additive (linear) form of crop production function was taken into consideration instead of multiplicative form. Total pollution load discharged into the river (ton per year) and economical income of the system (thousand dollars per year) wasconsidered as environmental and economic values, respectively. During the fuzzy programming procedure, the purpose is to achieve a compromise solution. In this approach, the individual maximum and minimum values of objectives is used to define the membership function. This procedure will maximize the satisfaction degree of the constructed membership functions of the objectives. The presented methodology was illustrated in a part of Karoon-Dez river system between Gotvand dam, Dez dam and Ahvaz city, as a case study. The area of Karoon-Dez river basin is about 67000 square kilometers and it is located in the southwestern part of Iran. The selected area includes 8 agro-industrial and 3 traditional agricultural sub-sectors.
Results and Discussion: Using a linear form of crop production function for calculating the total benefit of the system leads to significant reduction in run-time of the optimization model and make irrigation programming possible by regarding crop growth stages and the available water amount. The results of this study showed that Nash equilibrium, which provides a base for decision makers to choose a strategy, was reached at the fourth round of bargaining process. Moreover, balance between economic and environmental objectives is available by reducing economical expectation and environmental concerns from 553636 to 496216 thousand dollars per year and from 68264 to 87251 tons per year, respectively. In these cases, the annual allocated water to environmental and economical player will be 6123 MCM (5318 to agro-industrial sub-sectors and 805 to agricultural sub-sectors) and 6453 MCM (5730 to agro-industrial sub-sectors and 723 to agricultural sub-sectors) respectively. The results of the fuzzy programming approach demonstrated that at optimal condition, environmental and economic objective function was 85999 tons per year 500422 thousand dollars per year, respectively and allocated water to water users are 6354 MCM per year (agricultural and agro-industrial sub-sectors of the system will be (763 and 5591 MCM per year). Agro-Industrial sub-sector 3 will take the maximum allocated annual water (1789 MCM per year) and Agro-Industrial sub-sector 5 will receive the minimum annual allocated water (151 MCM per year). Comparison of two investigated approaches showed that their results are in agreement with each other.
Conclusions: Results of applying the developed methodology to the Karoon-Dez river system demonstrated that it is effective and applicable to determine sustainable water allocation policies. Finding of this study reveals that the proposed framework can facilitate decision-making process and optimize allocated water to different water users under conflicting objectives. Therefore, the developed procedure can be used as a managerial tool for optimal water allocation strategies, which is in accordance with sustainable development approach. It is easy to apply the presented methodology to other river systems with high pollution loads of agricultural return flows.
jalil javadi orte cheshme; mahmood kashefipoor
Abstract
Introduction: Nowadays, contamination of water is one of the problems that are more considered. Fecal Coliform (FC) is one of the most common indicator organisms for monitoring the quality of water. The problem that complicates the modeling of indicator organisms such as Fecal Coliform is determining ...
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Introduction: Nowadays, contamination of water is one of the problems that are more considered. Fecal Coliform (FC) is one of the most common indicator organisms for monitoring the quality of water. The problem that complicates the modeling of indicator organisms such as Fecal Coliform is determining the appropriate amount and an optimum rate of decay. It has been reported by many scientists that the decay coefficient or mortality rate is significantly affected by environmental elements. In this study, the effect of environmental parameters such as temperature, turbidity, radiation and suspended sediment concentration on the coliform decay coefficient hasbeen verified to have a dynamic and variable decay coefficient for better and reliable estimations of fecal coliform concentartion values.
Materials and Methods: Karun River is the longest and largest river in Iran. In this study, due to the accumulation of pollutants from industrial and agricultural wastes near Ahvaz city and for existence of quality measurement stations along the river, the Mollasani station to Farsiat station was selected to simulate and evaluate the hydrodynamic and quality of the river. The FASTER model has been used for modeling of the flow, sediment and water pollution. In this study, the dynamic roughness Manning coefficient has been used for more accurate simulate the flow, that had been added to the model by Mohammadi and Kashefipour. In Coliform bacteria and sediment modeling, some other dynamic parameters such as longitudinal dispersion coefficient are important and increasing or decreasing of these parameters are very significant and the accuracy of the Advection-Dispersion Equation (ADE) depends on the choice of the theoretical and/or experimental relations of these parameters. It was previously found that the Fisher equation performs the best for Karun river in modeling coliform, and this equation was therefore used in this study to calculate the dispersion coefficient. In order to investigate the effect of suspended sediment concentration on coliform decay rates, first this parameter must be modeled. In this research, the von Rijn method was used for modeling the suspended sediment load. In order to modeling the caliform, all dates of measuring were firstly determined in Zargan station; for each date the model was run for several times. For each run the decay coefficient was selected accordingly, until the predicted concentration by the model has the least difference inthe corresponding measured values. After that, the measured amount of environmental parameters such as Temperature, TUrbidity, RAdiation and also, the modeled values of suspended Sediment concentration wasdetermined for the same dates. Then, using a statistical software a relationship was developed to describe the decay coefficient as follows:
(1)
Results and Discussion: Using a statistical software, an equationfor decay coefficient was derived as follow:
(2)
Where K is decay coefficient (hr-1), T temperature (°C), TU turbidity (NTU), RA radiation(mmH2o-Vaporizeable) and Se suspended sedimentconcentration (kg/m3). Equation (2) was then added to the FASTER model, so the model was able to calculate the decay coefficient using the calculated suspended sediment at any time of simulation and this equation (dynamic decay coefficient). To be able to compare the dynamic decay coefficient and constant decay coefficient, the model was performed repeatedly for the whole calibration period and each time one constant K was given to the model. The best constant decay coefficient for the period of calibration and validation patterns was obtained to be K= 0.05 hr-1.Tables (1) and (2) show the amount of accuracy in predicting the suspended sediment concentration and coliform in both calibration and verification patterns, respectively. Table (1) shows that the FASTER model was able to estimate the suspended sediment concentration relatively accurate. Table (2) compares the effect of a constant decay coefficient versus the dynamic decay coefficient inaccurate estimation of fecal coliform concentrations.
Table 1- Comparison of the estimated error and correlation of suspended sediment
Pattern R2 a %E RMSE
Calibration 0.85 0.95 29.81 0.039
Verification 0.87 1.3 30.52 0.059
Table 2- Statistical parameters for coliform concentrations predicted and measured
Perioud k R2 a %E RMSE
Calibration Relation (2) 0.97 1.2 19 1906
0.05 0.92 2 50 4341
Verification Relation (2) 0.94 1.4 20 3860
0.05 0.77 1.5 44 7384
Conclusions: Comparison of the predicted fecal coliform concentrations with the corresponding measured values in the calibration and verification periodsshowed that the error estimate improved respectively about 31% and 24% when the dynamic decay coefficient was used instead of a constant value (the best constant value was obtained 0.05hr-1). The concentration of coliform bacteria in Zargan station during the total time of studying is more than 1000 CFU/100ml. Due to coliform bacteria concentrations and compared them with the levels allowed by the Standards, Karun river water is not suitable for human's drinking, confined livestock drink, food industry, oyster farming, irrigation products that are consumed raw and recreational uses (contact with water) like swimming.
S.M. Hosseini-Moghari; K. Ebrahimi
Abstract
Introduction: Groundwater resources are the main source of fresh water in many parts of Iran. Groundwater resources are limited in quantity and recently due to increase of withdrawal, these resources are facing great stress. Considering groundwater resources scarcity, maintaining the quality of them ...
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Introduction: Groundwater resources are the main source of fresh water in many parts of Iran. Groundwater resources are limited in quantity and recently due to increase of withdrawal, these resources are facing great stress. Considering groundwater resources scarcity, maintaining the quality of them are vital. Traditional methods to evaluate water quality insist on determining water quality parameter and comparison between them and available standards. The decisions in these methods rely on just specific parameters, in order to overcome this issue, water quality indices (WQIs) are developed. Water quality indexes include a range of water quality parameters and using mathematical operation represent an index to classify water quality. Applying the classic WQI will cause deterministic and inflexible classifications associated with uncertainties and inaccuracies in knowledge and data. To overcome this shortcoming, using the fuzzy logic in water resources problems under uncertainty is highly recommended. In this paper, two approaches are adopted to assess the water quality status of the groundwater resources of a case study. The first approach determined the classification of water samples, whilst the second one focused on uncertainty of classification analysis with the aid of fuzzy logic. In this regard, the paper emphasizes on possibility of water quality assessment by developing a fuzzy-based quality index even if required parameters are inadequate.
Materials and Methods: The case study is located in the northwest of Markazi province, Saveh Plain covers an area of 3245 km2 and lies between 34º45′-35º03′N latitude and 50º08′-50º50′E longitudes. The average height of the study area is 1108 meter above mean sea level. The average precipitation amount is 213 mm while the mean annual temperature is 18.2oC. To provide a composite influence from individual water quality parameters on total water quality, WQI is employed. In other words, WQI is a weighting average of multiple parameters. The present research used nine water quality parameters (Table 2). In this paper Fuzzy Water Quality Indices (FWQIs) have been developed, involving fuzzy inference system (FIS), based on Mamdani Implication. Firstly, five linguistic scales, namely: Excellent, Good, Poor, Very poor, and Uselessness were taken into account, and then, with respect to ‘If→then’ rules the FWQIs were developed. Later, the seven developed FIS-based indexes were compared with a deterministic water quality index. Indeed seven FWQIs based on different water quality available parameters have been developed. Then developed indices were used to evaluate the water quality of 17 wells of Saveh Plain, Iran.
Results and Discussion: The present study analysed groundwater quality status of 17 wells of Saveh Plain using FWQI and WQI. Based on the driven results from WQI and its developed fuzzy index, similar performance was observed in most of the cases. Both of them indicated that the water quality in six wells including NO.1, 2, 6, 12, 13, and 17 were suitable for drinking. Due to the fact that the values of both indexes were under 100, the mentioned wells could be considered as drinking water supplies. The indexes illustrated the very poor quality of wells NO.7, 9, 10, 11, 14, and 16. As a result, according to FWQI1 along with WQI, nearly 35% of wells have proper drinking water quality, while approximately 30% and 35% of them suffered from poor and very poor quality, respectively. The overall picture of water quality within the study area was not satisfying, hence, an accurate site selection for discovering water recourses with appropriate quality for drinking purpose must be responsible authorities’ priority. Analysis of FWQI2, FWQI3 and FWQI4 revealed that elimination of the parameters slightly changed the result of FWQI2; however, FWQI3 and FWQI4 did not vary considerably. Thus, Cl influenced the water quality slightly, but Ca and K did not affect the water quality of the plain. The results showed that inexistence of one of the mentioned parameters would not affect the computational process adversely. A glance at FWQI5, FWQI6 and FWQI7 revealed the improper performance of FWQI5 to show wells’ water quality status. Throughout the FWQI5 evaluation process, all the wells’ water quality stood in Excellent category. Due to the considerable values of TDS in the Plain, elimination of this parameter in FWQI5 caused inappropriate evaluation. Hence, whenever a case study deals with a high value of a specific quality parameter, elimination of that parameter would negatively demote validation of the analysis. Figures (3)-(6) represented the results of WQI along with seven FWQIs for 17 utilized wells’ water quality assessment in the study area during the proposed periods.
Conclusion: Throughout the present study, the capability of seven FIS-based indexing procedures in modelling the water quality analysis of 17 wells of Save Plain was discussed. The proposed FWQIs were developed on the basis of Mamdani approach by applying triangular and trapezoidal membership functions to determine the groundwater quality of the case study according to the nine parameters. The results revealed that FWQI1-4 outperformed others. On the other hand, FWQI5-7 which eliminated three out of the nine parameters, did not made a valid contribution to the computational context. This might be related to omitting the effective water quality parameters from the inputs of the model. The results also illustrated that, only six out of 17 wells of the region could be considered as suitable sources for the drinking purpose. The water quality status in five wells was not satisfying, and six wells were plagued by very poor quality of water.
S. Khazaei; H. Ansari; B. Ghahraman; A.N. Ziaee
Abstract
With increasing population and scarcity of fresh water,one of possible solutions is, using marginal waters (saline and sodic water). Using marginal waters should be taken into consideration and special studies. Since most processes related to soil and water, take place in unsaturated field condition, ...
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With increasing population and scarcity of fresh water,one of possible solutions is, using marginal waters (saline and sodic water). Using marginal waters should be taken into consideration and special studies. Since most processes related to soil and water, take place in unsaturated field condition, The purpose of this research is evaluation of saline and sodic water effect on diffusivity and unsaturated hydraulic conductivity.for this purpose, two soil types include loamy and sandy, two levels of SAR, 5 and 20, two levels of EC, 4 and 12 ds/m and distilled water were used. NaCl, CaCl2 and MgCl2 salts at Ca:Mg=2:1 were used to prepare treatments. Diffusivity was measured by one step out flow method at the suction of 15 bar. Unsaturated hydraulic conductivity calculated by using the diffusivity and the slope of the soil moisture charactristic curve. At both soils with increasing SAR and decreasing EC, diffusivity and unsaturated hydraulic conductivity decreased and this reduction was more at low moistures. Sandy soil was affected less than loamy soil. In comparison of treatments that cause the least and the most dispersion, diffusivity and hydraulic conductivity for loamy soil, decreased 100% and for sandy soil at low moistures, diffusivity and hydraulic conductivity decreased about 91% and 99%, respectively.
