JWSS - Journal of Water and Soil Science

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Physiographic characteristics of Atrak Watershed described by a number of parameters were used in regression models to estimate maximum daily discharges. These parameters were sub-watershed area, main waterway length, mean waterway slope, mean watershed elevation and mean watershed slope. Based on the results of correlation between the above parameters and their suitability for discharge estimation, three regression models were developed for further analysis. Model 1 applied area as the independent variable to estimate maximum daily discharge. In model 2 area and mean watershed elevation were the independent variables. Model 3 used area and mean waterway slope as the independent variables. Even though the results of testing did identified all three models as appropriate for application, further testing selected model 1 as the most appropriate. Recommendations were made for model application to similar watersheds lacking the necessary data.

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In this paper, a numerical solution is presented for one-dimensional unsaturated flows in the subsurface. Water flow in the subsurface, however, is highly nonlinear and in most cases, exact analytical solutions are impossible. The method of reference-operators has been used to formulate a discrete model of the continuum physical system. Many of the standard finite difference methods and also the finite volume method are special cases of the method of reference-operators. Unlike elementary finite difference methods, the method of reference-operators may by used to construct finite difference schemes on grids of arbitrary structure. A one-dimensional model was developed to predict the soil-water suction (negative pressure head) and water content in a vertical column of a layered soil. The model was verified against some available analytical solutions and experimental results and, in all cases, it showed good agreement.

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Flood is one of the catastrophic events that has attracted the hydrologists’ attention. In this research one of the important flood indices, i.e. maximum-daily mean-discharge, was determined for several western Iran watersheds, namely, in the catchments of Gamasiab, Qarasou, Saimare, Kashkan, Sezar and Abshineh. Daily data were prepared from stream-gauging stations and a 30-year concurrent period was selected.

 Flood frequency analysis was performed using HYFA and TR computer programs and optimum distributions were chosen by goodness of fit tests. Extreme flow values having different return periods of 2, 5, 10, 25, 50, 100, 500 and 1000 years were calculated. Modeling was done with regional analysis using multiple regression technique between maximum-daily mean-discharge and physiographic characteristics of the basins. The most important parameter for the selection of the model was the adjusted coefficient of determination while significant level, standard error and observed discharger vs. computed discharge plot acted as controlling parameters. Finally, different models with different parameters were selected from power, exponential, linear and logarithmic forms. The results showed the power model to be the best among the four types. The main channel length, drainage density and time of concentration were the most effective parameters on flow. After analyzing the errors, it appeared that increasing the return period would cause an increase in the model error. At 1000-year return period, the error reached 32.2%.

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Sensitivity of developmental stages of three sunflower cultivars to day length and temperature changes under field conditions were evaluated, and their development rates during various growth stages were modeled in a field experiment conducted in 1996 at the Agricultural Research Station, Isfahan University of Technology. Five dates of planting (April 27, May 12 and 27, and June 12 and 29) and three open pollinated sunflower cultivars (Record, Vnimik 8931 and Armavirec) were evaluated using a randomized complete block design with split-plot layout in three replications. Date of planting was considered to be the main plot and cultivars were randomized in sub-plots.

Number of days from planting (P) to head visible (HV) and P to first anther (FA) were significantly reduced with delay in planting as the result of increase in temperature during these periods. Number of days from P to physiological maturity (PM) was also significantly reduced with delay in P. This response, however, could not be explained by changes in temperature variables or day length. Number of days from HV to FA, in harmony with the partial stability of maximum and minimum temperatures during this period, was not affected by date of planting. Duration from FA to PM of the last planting date was significantly shorter than the other planting dates. This response was related to the persistence of the effect of high and stable maximum temperatures prevailing during HV to FA period of the last planting date. Armavirec was significantly earlier than Record and Vnimic 8931 for number of days from P to HV and from P to FA Cultivars showed significantly large differences for the FA to PM and P to PM durations. Armavirec was the earliest and Record was the latest cultivar. Based on the results obtained, it may be concluded that the cultivars under study were non-sensitive to photoperiod. Development rate (DR) of Armavirec responded linearly and DR of Record and Vnimic 8931 responded non-linearly to increases in temperature variables during P to HV and P to FA Development of Vnimic 8931 was faster than Record at high temperatures. DR of the cultivars decreased linearly during P to PM as day length increased. The relationship between DR and photoperiod could be used as a practical model for estimating P to PM duration of these sunflower cultivars.

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The objective of this research was to evaluate bromide leaching in a field under corn, wheat and alfalfa. Potassium bromide (300 kg/ha) was uniformly applied and 15 mm of water was sprinkled over the plots in the first and second years. Plots were leached 8 times during the first year and 9 times in the second year (each time with 100 mm of water). Soil samples were collected at 0-30, 30-60, 60-90 and 90-120 cm depths two days after each leaching practice. Bromide concentration in soil samples was measured using an ion selective electrode. Moisture content in each plot was measured using a neutron meter to a depth of 120 cm and after calculation of evaporation from soil surface, the net water applied was determined. CXTFIT software and Regional Stochastic Model (RSM) were used to simulate leaching under field conditions. The results showed that flow velocity and dispersivity of treatmens were not significantly different from the control in the first year, indicating that treatments had no effect on preferential flow. Control treatments were not significantly different in the first and second years. In the second year, flow velocity in wheat, corn and alfalfa treatments were 1.54, 1.86 and 2.21 times higher than flow velocity in the control, respectively. Dispersivity in alfalfa and corn treatments were 4.30 and 5.30 times higher as compared to the control. The increase in flow velocity and dispersivity is caused by an increase of preferential flow in the second year. The root channels remaining in soil at the end of the first year may also have increased preferential flow. After adding 25 cm of water, 30% of bromide leached from the top 50 cm soil in all plots in the first year and control plots in the second year but the values in the second year were 47, 67 and 70% of bromide leaching from the top 50 cm soil in wheat, corn and alfalfa plots, respectively.

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A field experiment was conducted in 2000 at the Agricultural Research Station, Isfahan University of Technology, to model the response of four safflower genotypes to day length and temperature changes under field conditions. Five planting dates (March 12, April 12, May 10, June 8, and July 12) and four safflower genotypes (Arak 2811, local variety Koseh, Nebraska 10 and Varamin 295) were evaluated using a randomized complete block design with split-plot layout in three replications. Date of planting was considered as the main plot and cultivars were randomized in the sub-plots. Number of days from planting (P) to emergence (E), stem elongation (SE) to head visible (HV), and HV to flowering initiation (FI) significantly reduced with delay in planting as the result of increase in temperature during these periods. Number of days from P to SE, duration of flowering (DF) and termination of flowering (TF) to physiological maturity (PM) were significantly affected by planting date and reduced as day length increased. The same was observed in the case of number of days from P to 50% flowering (MF) and to PM. Large co-variation of day length with temperature may explain a portion of day length contribution to the variation in the above periods. Varamin 295 was later than other genotypes with respect to the duration from P to HV, and specially, for rosette duration. In addition and for unknown reasons, the rate of development (RD) of Varamin 295 at all developmental periods could not be explained by day length and/or temperature variables. Among other genotypes, Koseh with 125 days, and Nebrska 10 with 118 days from P to PM were the latest and the earliest genotypes, respectively. The response of Koseh to planting dates, as measured by the duration of various developmental stages, differed from Arak 2811 and Nebraska 10. This was attributed to the probable response of Koseh to day length. RD of Koseh, Arak 2811, and Nebraska 10 during P to MF was explained by a linear regression and RD of Koseh during P to PM by a polynomial regression with day length by mean temperature as an independent variable. RD of Arak 2811 and Nebraska 10 during P to PM was explained by minimum temperature. It seems that partial sensitivity of Koseh to day length has a considerable significance in its adaptation to environmental conditions prevailing in the summer under Isfahan climatic conditions.

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Heavy metals including cadmium (Cd) and lead (Pb) are entering agricultural soils from different routes and mainly due to human activities. Accumulated Cd and Pb in the soil would eventually enter the human and animal food chains and pose threat to their health. Therefore, evaluating heavy metal accumulation is necessary to prevent soil and environmental pollutions and should be considered by researchers as well as policy makers. This study was conducted to model the accumulation rates of Cd and Pb in the agro-ecosystems of Isfahan, Mobarakeh, Lenjan, Borkhar, Najafabad, Khomeinishahr and Felavarjan. Cadmium and lead accumulation rates in the agro-ecosystems were computed using a stochastic mass balance model which uses Latin Hypercube sampling in combination with Monte-Carlo simulation procedure. Agricultural information including crop types, crop area and yield, the type and the number of livestock, application rate of mineral fertilizers, compost and sewage sludge and also metal concentration in plant and amendments were used to quantify Cd and Pb accumulation rates. Modeling Cd and Pb accumulation rates indicated that the metals are accumulating in the agricultural lands in the studied townships. The largest Cd (18 g ha^-1 yr^-1) and Pb (260 g ha^-1 yr^-1) accumulation rates were found in the township of Isfahan but the minimum accumulation rates were found in township of Lenjan for Cd (3 g ha^-1 yr^-1) and Mobarakeh for Pb (10 g ha^-1 yr^-1). The major input route to agricultural soils is phosphate fertilizers for Cd but for Pb is manure on the regional scale. High application rates of sewage sludge and compost in agricultural lands in the township of Isfahan could result in considerable amounts of Cd and Pb entering the soils of this region.

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The potential of artificial neural network models for simulating the hydrologic behaviour of catchments is presented in this paper. The main purpose is the modeling of river flow in a multi-gauging station catchment and real time prediction of peak flow downstream. The study area covers the Upper Derwent River catchment located in River Trent basin. The river flow has been predicted (at Whatstandwell gauging station) using upstream measured data. Three types of ANN were used for this application: Multi-layer perceptron, Recurrent and Time lag recurrent neural networks. Data with different lengths (1 month, 6 months and 3 years) have been used, and flow with 3, 6, 9 and 12 hours lead-time has been predicted. In general, although ANN shows a good capability to model river flow and predict downstream discharge by using only upstream flow data, however, the type of ANN as well as the characteristics of the training data was found as very important factors affecting the efficiency of the results.

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  Phytoremediation is a new technology that employs plants to remediate contaminated soils. This method compared to those that involve the use of large scale energy consuming equipments is an inexpensive method. Phytoremediation models are useful tools to further understanding the governing processes and also to manage the contaminated soils. A thorough literature review indicates that very few models have been developed for phytoremediation due to the complexity of the phenomena. The objective of this study was to develop a simple model for phytoremediation of lead and cadmium. A new formulation of phytoremediation was established based on soil and plant responses to heavy metal pollution. A large quantity of a sandy loam soil was thoroughly mixed to ensure homogeneous different concentration levels by lead and cadmium. These contaminated soils were transferred to some plastic pots. Land Cress (Barbarea verna) and Spinach (Spinacia oleracea L.) seeds were germinated in pots containing 8 kg of contaminated soil. Plants were harvested at five time intervals. The concentrations of Pb and Cd in the plant and soil samples were digested by wet oxidation and 4 M Nitric acid digestion methods, respectively, and were determined by flame and graphite furnace atomic absorption spectrometry methods. Proposed models then were calibrated using the collected data and validated quantitatively. The results indicated that the soil adsorption isotherms followed a linear form for both Pb and Cd concentrations. The results also indicated that the phytoremediation rate of Pb by Land Cress and Spinach are first-order function of Pb concentration in soil. In contrast, a zero-order function of soil Cd contaminations was obtained. Combining these two results of soil and plant responses to Pb and Cd pollution, a simple model with reasonable performance was derived to predict the time needed for remediation of soil Pb (R² > 0.98). However, in the case of Cd, the derived models appeared to be useful to make only some overall estimations of the remediation (R²≈^�0.70).

 

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Drought is an unavoidable natural disaster in dry and arid regions of the world. Studies indicate that Iran with its geographical and climatic characteristics is poor in water resources. Moreover, it is estimated that in 2025, Iran should increase the water resources by 112% in order to maintain status quo. Therefore, the occurrence of drought is more prevalent in the future. This implies a real challenge for researchers to study how to manage farms during the onset of drought. Inappropriate management strategies result in loss of resources, which in turn leads to more destructive impacts of drought, consequently leading to more droughts in future. The purpose of this survey study was to identify factors influencing drought management among farmers in Fars province, Iran. A multistage stratified random sampling technique was used to select a representative sample of farmers who has experienced drought in recent years. A total of 258 farmers were interviewed using a questionnaire. A panel of experts verified face validity. A pilot study was used to assess the reliability of the measuring instrument. Cluster analysis and Structural Equation Modeling were used for data analysis. Results indicated that farmers with different social, economical, and technical characteristics chose different management strategies when coping with drought conditions. Despite farmers' efforts to manage drought, they face harsh consequences. These consequences include economic losses and degradation of farming environment. It is therefore recommended that policy makers who are involved in drought management programs learn more about farmers' drought mitigation techniques and make further attempts to increase the efficiency and effectiveness of techniques used by farmers.

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Soil erosion is an important economical, social and environmental problem requiring intensive watershed management for its control. In recent years, modeling has become a useful approach for assessing the impact of various erosion-reduction approaches. ِDue to limited hydrologic data in mountainous watersheds, watershed modeling is, however, subject to large uncertainties. In this study, SWAT2000 was applied to simulate runoff and sediment discharge in Beheshtabad watershed, a sub-basin of Northern Karun catchment in central Iran, with an area of 3860 km2. Model calibration and uncertainty analysis were performed with SUFI-2. Four indices were used to assess the goodness of calibration, viz., P-factor, d-factor, R2 and Nash-Sutcliffe (NS). Runoff data (1996-2004) of six hydrometery stations were used for calibration and validation of this watershed. The results of monthly calibration p-factor, d-factor, R2 and NS values for runoff at the watershed outlet were 0.61, 0.48, 0.85 and 0.75, respectively, and for the validation, these statistics were 0.53, 0.38, 0.85 and 0.57, respectively. The values for calibration of sediment concentration at the watershed outlet were 0.55, 0.41, 0.55 and 0.52, respectively, and for the validation, these statistics were 0.69, 0.29, 0.60 and 0.27, respectively. In general, SWAT simulated runoff much better than sediment. Weak simulation of runoff at some months of the year might be due to under-prediction of snowmelt in this mountainous watershed, model’s assumptions in frozen and saturated soil layers, and lack of sufficient data. Improper simulation of sediment load could be attributed to weak simulation of runoff, insufficient data and periodicity of sediment data.

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Phytoremediation is a new, in-situ and emerging remediation technology for contaminated soils. This technology, compared to other methods, is a sustainable, natural, relatively cheap and applicable to large scale area. Modeling phytoremediation provides quantitative insight for the governing process as well as for managers to assess the remediated sites. The objective of this study was to introduce a macroscopic phytoremediation model for Ni and Cd- polluted soils. The proposed model assumes that relative transpiration reduction function can resemble total soilNi and Cd concentrations. Combining the related functions of soil and plant responses to soil Ni and Cd concentrations, the phytoremediation rate of Ni and Cd was predicted. In order to test the proposed model, large quantities of soil were thoroughly polluted with Ni and Cd. Upland Cress (Lepidum sativum) and Ornamental Kale (Brassica olerace var. Viridis) seeds were then germinated in the contaminated soils. The experimental pots were irrigated with fresh water to reach field capacity. Upland Cress and Ornamental Kale were harvested three and four times, respectively. At each harvest, relative transpiration, Ni and Cd contents of soil samples and plants were measured. Comparison of the maximum error, root mean square error, coefficient of determination, modeling efficiency and coefficient of residual mass indicated that the non-threshold non-linear model provide high efficiency to predict relative transpiration for Upland Cress and Ornamental Kale, respectively. The results also indicated that the proposed macroscopic model can well predict the phytoemediation rate of the Ni and Cd by Upland Cress (R2>0.83) and Ni by Ornamental Kale (R2=0.78).

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Soil bulk density measurements are often required as an input parameter for models that predict soil processes. Nonparametric approaches are being used in various fields to estimate continuous variables. One type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm was introduced and tested to estimate soil bulk density from other soil properties, including soil textural fractions, EC, pH, SP, OC and TNV. As many as eight nearest neighbors, based on cross validation technique were selected to perform bulk density prediction from the attributes of 136 soil samples. The nonparametric k-NN technique mostly performed equally well using Pearson correlation coefficient (r=0.86), root-mean-squared errors (RMSE=2.5) maximum error (ME=0.15), coefficient of determination (CD=1.3), modeling efficiency (EF=0.75) and coefficient of residual mass (CRM=0.001) statistics. It can be concluded that the k-NN technique is an alternative to other techniques such as pedotransfer functions (PTFs).

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Due to the important role of climatic parameters such as radiation, temperature, precipitation and evaporation rate in water resources management, this study employed time series modeling to forecast climatic parameters. After normality test of the parameters, nonparametric Mann-Kendall test was used in order to do trend analysis of data at P-value<0.05. Relative humidity and evaporation (with significant trend, -0.348 and -0.42 cm, respectively), as well as air temperature, wind speed, and sunshine were selected for time series modeling. Considering the Autocorrelation function (ACF) and Partial Autocorrelation function (PACF) and trend of data, appropriate models were fitted. The significance of the parameters of the selected models was examined by SE and t statistics, and both stationarity and invertibility conditions of Autoregressive (AR) and Moving average (MA) were also tested. Then, model calibration was carried out using Kolmogorov-Smirnov, Anderson- Darling and Rayan-Joiner. The selected ARIMA models are ARIMA(0,0,11)*(0,0,1), ARIMA(2,0,4)*(1,1,0), ARIMA(4,0,0)*(0,1,1), ARIMA (1,0,1)*(0,1,1), ARIMA (1,0,0)*(0,1,1) for relative humidity, evaporation, air temperature, wind speed and sunshine, respectively. The fitted models were then used to forecast the parameters. Finally, trend analysis of forecasted data was done in order to investigate the climate change. This study emphasizes efficiency of time series modeling in water resources studies in order to forecast climatic parameters.

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The task of modern agriculture is to safeguard the production of high quality food, in a sustainable natural environment under the precondition of pollution not exceeding accepted norms. The sustainability of current land use in agro-ecosystems can be assessed with respect to heavy metal accumulation in soils by balancing the input/ output fluxes. The objectives of this study were to model accumulation rate and the associated uncertainty of Zn in the agro-ecosystems of 3 arid and semi-arid provinces (Fars, Isfahan and Qom). Zinc accumulation rates in the agro-ecosystems were computed using a stochastic mass flux assessment (MFA) model with using Latin Hypercube sampling in combination with Monte-Carlo simulation procedures. Agricultural information including crop types, crop area and yield, kind and number of livestock, application rates of mineral fertilizers, compost and sewage sludge and also metal concentration in plants and soil amendments were used to quantify Zn fluxes and Zn accumulation rates. The results indicated that Zn accumulates considerably in agricultural lands of the studied townships especially in Najafabad (3009 g ha-1yr-1). The major Zn input routes to the agricultural soils (and due to agricultural activities) were manure and mineral fertilizers and the major part of the uncertainty in the Zn accumulation rate resulted from manure source.

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The objective of this study was to investigate the effect of potassium zeolite on ammonium ion sorption and retention in a saturated sandy loam soil in laboratory conditions with four treatments of 0, 2, 4 and 8 g zeolite per kg soil. The study was conducted as a completely randomized block design. Simulation of ammonium ion leaching was performed using Hydrus-1D model in the soil columns. Ammonium nitrate fertilizer with a concentration of 10g per liter was added to soil columns and then leaching was performed. Results of the study showed that adding potassium zeolite to soil causes reduction in the mobility of ammonium ion and increase in the retention of ammonium in soil. Also, the results of the Convection- Dispersion (CDE) and Mobile- Immobile (MIM) models investigation indicated that the ammonium ion sorption by soil followed the Freundlich isotherm model. Absorption isotherms and diffusion and dispersion coefficients were determined using the inverse modeling technique. Based on the results obtained, optimized values of Freundlich isotherm of model were much less than the observed amounts. This shows that the Hydrus-1D model is not able to predict the ammonium ion mobility in soil macropores, and as a result, reduces greatly the amount of absorption parameters. Because the soil was disturbed, CDE model estimation was closer to the observed values in all four treatments

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Zinc (Zn) is an essential trace element for plants as well as for animals and humans. There is a significant relationship between soils, plants and humans Zn status in a certain agro-ecosystem. The objectives of this study were to assess Zn status of soils in 3 arid and semiarid provinces of Iran and to model the relationship between wheat grain Zn and agro-ecosystem parameters. About 137 soil and wheat samples were collected randomly from the agricultural soils of Fars, Isfahan and Qom and were analysed in laboratory. Modeling the relationship between wheat grain Zn and agro-ecosystem parameters was done using least square based and robust methods. The results indicated that total Zn concentration of soils (range, 21-149 mg kg-1 mean, 75.2 mg kg-1) was in normal ranges. The DTPA-extractable Zn concentrations were below the critical level (0.8 mg kg-1) in 16% of the surveyed fields. The Zn concentration in 80% of wheat grains was sufficient (more than 24 mg kg-1) with respect to plant nutrition (range, 11.7-64 mg kg-1 mean, 31.6 mg kg-1). However, Zn bioavailability for consumers was generally low in more than 75% of the samples. This is because of high phytic acid to Zn molar ratio (more than 15). Soil DTPA-extractable Zn and available P were entered in to most of regression models significantly. Regression analysis showed that most of models fitted to wheat grain Zn concentration and soil Zn and influenced by agro-ecosystem parameters had a weak prediction power, despite their high determination coefficient. This means that factors other than those considered here have a strong influence on the uptake of Zn by wheat in these soils.

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Shahrekord aquifer is depleted by almost 800 deep and semi-deep wells, the majority of which are agricultural wells and some have urban usage. In southern parts of the plain, the water table has fallen strongly because of immoderate discharge and decreased the quality of water by urban wastewater. The main objective of this study is investigation of subsurface dam construction and its effects on water table in consumption locations, reduction of deliveries costs and interception of contaminant transport. Therefore, the Shahrekord aquifer model was simulated with hydrodynamic coefficients calibration by PMWIN5.3 Software. The southern outlet of plain (near Bahram-Abad village) was selected to study subsurface dam construction, then a horizontal-flow barrier in this place was set with mean hydraulic conductivity equal to 0.5 m/day. Water table situation and nitrate concentration were analyzed using ArcGIS9.2 software before and after dam construction. The results showed that the subsurface dam rises groundwater level in 4 kilometers distance of upstream areas. Also, the available volume of water increased about 1.5 Mm3. Nitrate concentration didn't show to be considerably different from the initial state. But, it is likely that contamination in the storage resource will rise because it is located near Shahrekord water treatment plant and also due to the discharge of wastewater wells.

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The main scope of this research is evaluation of Soil Conservation Service Procedure in derivation of initial abstraction of precipitation in watershed scale. For this purpose Dalaki watershed which is located in south east of Iran was selected then by using hec-hms and GIS models and a number of observed rainfall runoff events some parameters like CN of watershed ,K and X of Muskingam method and initial abstraction of precipitation were calibrated through two different search algorithm of univariate and Nelder & Mead methods. The early results of this research indicated the superiority of Univariate search algorithm over the Nelder&Mead method both in calibration and also validation processes. Then using calibrated CN and Initial abstraction parameters which were derived through Univariate search algorithm, the factor between initial abstraction and potential retention of surface runoff (S) in each of sub basins were estimated. 0.13, 0.43 and 0.19 were derived as the above mentioned factor respectively for Minimum, Maximum and mean of the above mentioned factor in this step of the research which showed an acceptable compatibility to the offered factor of 0.2 by SCS. Then in rainfall runoff modeling process of this watershed SCS offers a reliable method of initial abstraction estimation.

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The geographical location of Isfahan province has led the province to be at risk of drought. One of the ways to mitigate drought is evaluation and monitoring of drought based on indices that can determine its intensity and permanence in each region. In this research, for drought and trend analysis standard precipitation index and Mann-Kendall test were used, respectively. Also, monthly precipitation time series of Isfahan province was applied to forecast drought from 1970 to 2009. For this purpose, Box and Jenkins modeling approach (1976) was used which has three main steps, namely model identification, parameter estimation, goodness of fit test or time independency and normal test of residual. The results showed that most of the stations in Isfahan province were faced with severe drought in the year 2000 and this situation was repeated one more time in 2008. Also, the results brought forth multiplicative models in all the stations. ARIMA (1,0,0) (0,1,1) showed the highest correlations between control and forecast data in Isfahan, Meime and Ardestan stations, and the model ARIMA (0,0,1) (0,1,1) displayed the highest correlation between control and forecasted data in Naein, Freydoonshahr, Khansar and Natanz. These models were selected as the best models through which the amount of precipitation was predicted till 2015. The trend of forecast data across Isfahan province showed that in most months the trend is not significant.