JWSS - Journal of Water and Soil Science

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An extensive data collection on precipitation and runoff is required for development and implementation of soil and water projects. The unit hydrograph (UH) is an appropriate base for deriving flood hydrographs and therefore provides comprehensive information for planners and managers. However, UH derivation is not easy job for whole watersheds. The development of UH by using easily accessible rainfall data is then necessary. Besides that, the validity evaluation of different statistical modeling methods in hydrology and UH development has been rarely taken into account. Towards the attempt, the present study was planned to compare the efficiency of different modeling procedures in hydrograph and 2-h representative UH relationship in Kasilian watershed with concentration time of some 10h. The study took place by using 23 storm events occurred during four seasons within 33 years and applying two and multivariable regression models and 36 variables. According to the results, the median of estimated errors in estimation of 2-h UH dependent variables for verification stage varied from 37 to 88%. The results verified the better performance of cubic and linear bivariate models and logarithm-transformed data in multivariable model as well. The efficiency of multivariable models decreased when they were subjected to principle component analysis. The performance of backward method was frequently proved for estimation of dependent variables based on evaluation criteria, whereas the forward was found to be more efficient for time-dependent factors estimation.

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Temporal and spatial distribution of water components in watersheds, estimation of water quality, and uncertainties

associated with these estimations are important issues in freshwater studies. In this study, Soil and Water Assessment

Tool (SWAT) model was used to estimate components of freshwater availability: blue water (surface runoff plus deep

aquifer recharge), green water flow (actual evapotranspiration) and green water storage (soil water), in Hamadan-Bahar

watershed. Also, the Sequential Uncertainty Fitting program (SUFI2) was used to calibrate and validate the SWAT

model and do the uncertainty analysis. Degree of uncertainty is calculated by R-factor and P-factor parameters. In this

paper, results of calibration and validation are given for the river monthly discharge. In most stations, especially in

outlet of the watershed (Koshkabad station), simulation of river discharge was satisfactory. Values of R-factor in

calibration of monthly runoff were 0.4-0.8. These small values show good calibration of runoff in this watershed.

Values of P-factor were 20-60%. These small values show high uncertainty in estimations. For most stations of the

watershed, lack of data on river-water withdrawal caused poor simulation of base-flow and therefore the P-factor values

were low. Nash-Sutcliff (NS) coefficient was 0.3-0.8 after calibration, which shows good model calibration of outlet.

This study provided good information on the components of freshwater availability at spatial (sub-basin) and temporal

(monthly) scales with 95% prediction uncertainty ranges. The results of uncertainty analysis of components of

freshwater availability show that uncertainty ranges of average monthly blue water are larger than the other

components, because of its sensitivity to more parameters.

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In order to classify and determine the plant communities in Karkas mountainsides, central Iran, quantitative data of 46 environmental characteristics including climate (12 characteristics), soil (26 characteristics), geology and physiology (8 characteristics), were analyzed based on partial Euclidean distance indicator using minimum variation method. The results showed the clustered separation of studied sites at 68% similarity level with three different growth places. Of these 46 factors, 24 factors (8 climate characteristics and 16 soil characteristics) were effective in this classification at 95% certainty level. Only 24 of these 46 factors were effective in this classification at 95% certainty level. These 24 effective factors were divided into 8 climate and 16 soil characteristics. In another classification, combinational percentage index of 17 plant species were investigated as an effective factor in separating growth place and determining the plant communities. Result of cluster classification indicated that the studied sites cold be classified to 3 habitats.

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The purpose of this study was to compare the physical and chemical characteristics of soils covered with vegetation and soils without vegetation in Dagh-e- Sorkh Ardestan area.To achieve the goal, first the vegetation was classified using physiognomic method, and for each vegetation type, the distinctive area was specified for soil and vegetation sampling. Vegetation sampling was done by stratified random sampling. Alongside pursuing the case, twenty two soil physical and chemical factors were investigated also for each growth type and area without vegetation. In the next step, to investigate the similarities and dissimilarities of the soils of desert areas by means of PC-ORD software, the cluster analysis was performe. After simplifying the one-way ANOVA, the most important soil factors which were effective in causing differences in the area’s soils were identified. Results show that the soils of area covered with vegetation differed much from the soils without vegetation physically in such a way that, the soil texture became heavier and gravel percentage became less in the areas without vegetation. Regarding the chemical characteristics, the frequencies of sodium, magnesium, calcium and chlorines and electrical conductivity were highly different. Because of topographic condition of land without vegetation, runoff is directed to this place and deposits salts there. Also, high groundwater level and capillary flowing salts are the important reasons for the salinity of this place. These are the limiting factors for the vegetation establishment in the desert areas of Ardestan.

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The temporal and spatial vegetation dynamics is highly dependent on many different environmental and biophysical factors. Among these, climate is one of the most important factors that influence the growth and condition of vegetation. Of the abiotic factors affecting the geographic distribution of vegetation type, climate is probably the most important. Ecological research has traditionally aimed to generalize vegetation types that are assumed to be homogenous. Most of climatic classifications related to bioclimate are focused on limited climatic factors such as temperatue, precipitation and combination of them. As climate is a compound phenomena using limited factors cannot show the climate of a region, and as a result most climatic factors must be considered in bioclimatic classification. Therefore, a climatic study using various climatic factors could reveal the effective factors in distribution of vegetation. In order to determine bioclimatic zones in Chahar-Mahal & Bakhtiari province using multivariate statistical method, 71 climatic variables, which were more important in plant ecological conditions, were selected and evaluated by the factor analysis. The factor analysis revealed that the first three factors which explain %91.8 of total variance among the selected variables were temperature, precipitation, and radiation. According to results and using hierarchical cluster analysis in Ward’s method, bioclimatic classification in Chahar-Mahal province was carried out and 5 bioclimatic zones were found. In addition, Chahar-Mahal province was classified by 4 traditional climatic classification methods (Koppen, Gaussen, Emberger and De Martonne) and those classes were compared to climatic classes obtained by multivariate statistical method. The latter comparison was suggestive of the fact that multivariate statistical method provides a more appropriate classification in comparison to the traditional methods, specially because more dominant vegetation species could be defined for each of the newly described climatic classes. Furthermore, dominant species were determined for each climatic region.

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Floodwater Spreading (FS) plays an effective role in improving soil fertility, ground water recharge, vegetation cover, and desertification control. The soil fertility might increase as a result of a suitable suspended sediment material transferred to the downstream by flood events. To define a relevant FS method which increases the efficiency of the FS projects, it is necessary to study the quality and quantity of transported sediment material, spatially and temporarily. In this research, this subject was investigated by taking soil samples throughout 13 FS stations for physical and chemical analysis over 5 years. Within each of the 13 selected stations in the three first flooded dikes, soil sampling was carried out using random-systematic method. The total Nitrogen, absorbed Phosphorous and Potassium, and Organic Carbon of each sample were analyzed. Because of the abnormality of data, nonparametric test was adopted to compare means. All stations were classified into three groups using cluster analysis method. Based on the results, the variations of fertility factors are irregular between the dikes and amongst years. This could have been affected by several factors such as the quality and quantity of diverted flood, the characteristic of FS sites, and irregularity of sediment material deposited on the sites. Despite the low quality of soil fertility prior to the construction of these stations, in general, FS has a considerable role in improving the soil fertility. However, desirable objectives may be achieved in long term through occurrence of diverse flood events and suitable maintenance of the stations.

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Estimation of flood hydrograph is of necessities in hydrological studies such as flood mitigation projects. This estimation in un-gauged watersheds is usually taken place using geomorphological characteristics of watersheds. The objective of this research is to estimate synthetic unit hydrograph using regional flood frequency analysis and geomorphological parameters of watersheds. 1-hour and 2-hour hydrographs of two watersheds, Kanisavaran and Maranj Watersheds, were generated using maximum discharge data based on regional flood frequency analysis. Estimated hydrographs were compared with observed data and the efficiency of the model was evaluated using Nash-Sutcliffe coefficient, absolute and bias errors. The results showed that multiple regression models give more acceptable results among others for the computation of synthetic unit hydrograph (higher coefficient of determination). The Nash-Sutcliffe coefficient was 0.98 for 1-hour hydrograph while it was 0.93 for the 2-hour hydrograph. The absolute error in 1-hour hydrograph and 2-hour hydrograph was 0.13 and 1.2, respectively. The bias error was close to zero for both hydrographs, indicating that the proposed model is efficient. The model may be used for estimation of synthetic unit hydrograph in similar un-gauged watersheds.

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This study performs trend analysis of hydroclimatic varibles and their possible effects on the water resources variability. Nonparametric Mann-Kendall and spearman tests were used to investigate trend analysis of mean annual and 24-hr maximum rainfall, flood and low flow parameters of 23 hydrometery and 18 synoptic stations in Sefid-Roud basin. The results showed that mean annual and 24-hr rainfall parameters are decreasing in few stations while most of stations representing negative trend for low flow and flood time series. Applying Sequential Mann-Kendall test revelad that this negative trend is started from 1965 to 1970 for rainfall parameters and from 1970 to1980 for flow (low flow and flood) parameters. Results show that climate change has probability affected variability of climatic variables, while changing of land use may have aslo affeteced extreme flow trends during recent decads. Therefor it can be noted that combination of climate chanege effects and human activities on water recources have affected the negative trend of hydroclimatics variables.

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In this study, in order to determe low flow conditions in Karkhe watershed, 5 indices of Q7,10, Q7,20, Q30,10, Q4,3 and Q95 were used for analyzing 12 hydrometric station data in the years of 1345-46 to 1380-81. Discharge data homogeneity was performed by Run Test. The Q95 index was determined by flow duration curve (FDC) and other indices were determined using 4, 7 and 30-day low flow frequency analysis. After calculating the indices, periods of low flows were determined. The indices were regionalized by Kriging method. The results showed that for the most stations, low stream flows happened in the years of 1345-46, 1377-78, 1378-79, 1379-80 and 1380-81 and the percentages of stations having low flows in these years were 68, 92, 84, 75 and 59, respectively. According to the regional maps of low flows in Karkhe watershed, maximum low flows are located in central and southern areas and all of the mentioned indices decrease from south to the north of this watershed.

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Studies on the soils of western Golestan province show that regardless of increased rain and presence of clay minerals with high cation exchange capacity, potassium extractable with ammonium acetate is low. In order to find the reason for this low amount of available K, clay minerals and micromorphology of the soil porosity were studied. Twenty disturbed and undisturbed samples from each horizon were taken for physicochemical properties, mineralogy and micromorphological studies. Four selected profiles included Gypsic Aquisalids, Typic Endoaquepts, Typic Calcixerolls and Typic Hapludalfs. The results showed that in addition to the clay content and type of clay minerals in soils that can affect soil available K (Kava.), it seems soil porosity can also affect Kava. mainly through their effects on extension of roots, water and nutrients transmission. Favorable content of clay and dominance of smectites in Mollisols and also higher porosity and dominance of channel porosities caused the presence of higher Kava, in these soils. presence of HIS. poor soil drainage, reduction of Fe3+ in smectite crystal lattice in Inceptisols, and also less amount of porosities caused the presence of higher potassium fixation and reduction of Kava. in these soils.

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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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Models are helpful tools to predict runoff, sediment and soil erosion in watershed conservation practices. The objectives of this research were to investigate sensitivity analysis, calibration and validation of EUROSEM model in estimation of runoff in Tangh-e-Ravagh sub-basin of Karoun watershed. The model was tested in a one hectare experimental test site. The area was divided into nine elements according to EUROSEM user's manual. A triangular weir was installed at the outlet of the area to collect runoff in specified time periods for six rainfall events. Sensitivity analysis of the model was performed by a ±10% change in the dynamic parameters of the model and examining the outputs for a rainstorm. Sensitivity analysis showed that total runoff was sensitive to saturated hydraulic conductivity and insensitive to soil cohesion. Sensitivity analysis indicated that the model sensitivity depends on evaluation conditions and it is site-specific in nature. Calibration and validation of the model was performed on input parameters. Calibration of hydrographs was performed by decreasing saturated hydraulic conductivity and capillary drive and increasing initial soil moisture. Validation results showed that EUROSEM model simulated well the total runoff and peak of runoff discharge, but it could not simulate well the time of runoff, time to peak discharge

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In order to monitor the changing water table in the field, determination of the main sampling points is very important to reduce sites and save time and cost. Principal Component Analysis (PCA) is one of the data reduction techniques used to extract the important components that explain the variance of a system. In this paper, the PCA was used to identify the effective wells of Qheidar Aqufer, Zanjan, to determine the groundwater level and remove the less important ones. From the study region which an area of about 920 km2, 48 wells (sites) were investigated. Using PCA, the relative importance of each well was calculated between 0 (for completely ineffective well) to 1 (for the very effective wells). The study showed the elimination of wells whose relative importance was less than 0.5 (i.e. half the total number of wells), coefficient of variation of groundwater level relative to the use of all wells did not greatly increase, and the error to determine the level of groundwater was less than 13 percent.

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Landslide as a global disaster causes great human and financial damages. Identification of landslide causes and zonation assist in instability control and construction projects siting. This study aimed to identify landslide causes and instability zonation in Ardal county, Chaharmahal va Bakhtiari province, Iran, using Analytical Hierarchy Process (AHP). Current landslides were delineated through field survey and interpretation of Earth Google images and geologic maps. By using Digital Elevation Model (DEM), slope, aspect, geologic, soil, distance to faults, distance to roads, distance to rivers and landuse/landcover maps and expert knowledge, the pairwise comparison matrix was designed. The weights for all the involved thematic maps were calculated and susceptible zones were mapped. The hazard map indicated more than 77% of current landslides are located in the severe and very severe hazard classes. Comparing landslide hazard map and trigger maps revealed the most influential factors in landslides are distance to roads and slope maps while distance to faults and aspect show the lowest impacts on landslides.

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Over-parameterization is a well-known and often described problem in hydrological models, especially in distributed models. Therefore, using special methods to reduce the number of parameters via sensitivity analysis is important to achieve efficiency. This paper describes a sensitivity analysis strategy that graphically assigns for each parameter a relative sensitivity index and relationship of the parameter and the outputs of the model. The method is illustrated with an application of SWAT model in the Chehelchai catchment, Golestan province. In this study, total water yield, along with four major parts of water budget including surface runoff, lateral flow, groundwater and evapotranspiration was selected as objective function. SWAT is a river basin model that can be used to predict the impact of land management practices on water, sediment and agricultural chemical yield in watersheds. A relative sensitivity index was used for ranking the sensitivity of parameters. The results showed that soil evaporation compensation facto (ESCO), CN, soil available water capacity (SOL-AWC), deep aquifer percolation fraction (RCHRG-DP) and soil bulk density (SOL-BD) have the most influence on river flow. These parameters are generally stated as the most sensitive parameters of SWAT model in most of the same researches worldwide

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Local scour around piers is the major cause of their foundation failure in the river bends that endangers the stability of the structure and its efficiency. Riprap is commonly placed around the bridges piers for local scour protection. The aim of this study was to present an equation for estimating stable riprap diameter around a cylindrical bridge pier in river bends. In this study, using an experimental model with a 180 degree bend stability, four different riprap diameters under different flow conditions and clear water flow were studied. Empirical relationships based on dimensional analysis for stable riprap design around the bridge foundation was presented. The experimental results were compared with equations provided by other researchers, including Lauchlan (1999), Parola (1995) and Chiew (1995). Results showed that the presented equation in this paper has a good precision. The simple equation presented in this study included all factors important to the instability of the riprap, and recommends designing ripraps around the bridge pier in river bends.

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Soft computing techniques have been extensively studied and applied in the last three decades for scientific research and engineering computing. The purpose of this study was to investigate the abilities of multilayer perceptron neural network (MLP) and neuro-fuzzy (NF) techniques to estimate the soil-water retention curve (SWRC) from Khozestan sugarcane Agro-Industries data. Sensitivity analysis was used for determining the model inputs and appropriate data subset. Also, in this paper, the van Genuchten and Fredlund and xing models were used to predict SWRC. Measured soil variables included particle size distribution, organic matter, bulk density, calcium carbonate, sodium adsorption ratio, electrical conductivity, acidity, mean weight diameter, plastic and liquid limit, resistance of soil penetration, water saturation percentage and water content for matric potentials -33, -100, -500 and -1500 kPa. The results of this study in terms of various statistical indices indicated that both MLP and NF provide good predictions but the neural network provides better predictions than neuro-fuzzy model. For example, using MLP and NF models values of NMSE at prediction θs, θr, α, n and m in Fredlund and Xing equation corresponded to (0.059, 0.065), (0.154, 0.162), (0.109, 0.117), (0.129, 0.135) and (0.129, 0.145), respectively. Furthermore, α and n parameters at the first depth, and θr and α parameters at the second depth in Fredlund and Xing equation were estimated with higher accuracy compared with equivalent parameters in van Genuchten equation

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The aim of this study was to assess the drinking quality of Shahrekord aquifer based on a GWQI (groundwater quality index) within a GIS framework. To do this, samples from 97 wells were analyzed for pH, Electrical Conductance (EC), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Turbidity, Ca2+, Mg2+, Na+, K+, Cl-, HCO3- and SO42-, and total hardness was also calculated. These water quality parameters were geostatistically mapped. Maps showed that maximum quality of water occurs in the northwest while the lowest quality occurs in the south of aquifer. To calculate GWQI index, each map was difference-normalized and converted to a rank map. Assuming the mean value of each rank map to be the weight of corresponding parameter, a GWQI map was created with values varying from 0 (lowest) to 99 (highest quality). Mean GWQI of 84 indicates a relatively good drinking quality of water in the aquifer. However, based on the GWQI map the quality of water declines from very good (GWQI=87) in northwest to a lower quality (GWQI= 80) in southern part of the aquifer. The lower quality of water in the southern part may have been caused by industrial activities, intensive animal husbandry, presence of wastewater plant, irrigation with treated municipal effluent and also by the inward hydraulic gradient. Map removal sensitivity analysis indicated that TSS and to some extent Na+ were important water parameters in this aquifer, which must be monitored with greater accuracy and frequency.

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Advance velocity is an important factor in surface irrigation system design and simulation. Volume balance is a simple model based on continuity equation used in surface irrigation design and management. In the past volume balance models, it is generally assumed that the upstream depth of surface water is constant and equal to normal depth. This initial assumption may cause significant errors in computing advance flow. In this paper, a modified volume balance (MVB) model is developed to predict the advance curve in furrow irrigation. In the suggested method the upstream surface, water depth is actual depth and variable in time. Predicted advance distance of VB, VB-ZI and MVB was compared to the observed data obtained for the three furrow lengths of 60, 80 and 90m. Evaluation indexes indicated that the modified volume balance equation is more accurate than the previous equations by RMSE 9.26, 7.37 and 6.76 respectively. Sensitivity analysis showed that the inlet discharge has the greatest effect on the model and the model is more sensitive to decreasing the discharge amount than to increasing it

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The aim of the study was to investigate the effects of land use on soil quality parameters using multivariate statistical analysis. Soil samples (0-25 and 25-50 cm depths) were taken from three land uses in forest area of Marivan including forest, rangeland, and cultivated land. Soil characteristics of pH, EC, sand, silt, clay and CaCO3 content, water-stable aggregates and their organic carbon content were measured. Principal component, cluster and discriminant analyses were used to evaluate the soil quality. Principal component analysis classified soil properties into five factors. The most important factors were soil aggregates organic carbon content and aggregate stability indices. Schematic distribution of factors and also cluster analysis showed the same pattern. Soil aggregates organic carbon content, water-stable aggregates and aggregate stability indices were the most sensitive factors to land use changes. These soil properties and factors had the same pattern in forest and rangeland, but significantly reduced in the cultivated land use. Land use change from forest to cultivated land resulted in significant decrease of aggregates organic carbon content, water-stable aggregates and also an increase of pH. The results showed the usefulness of multivariate statistical methods for integration of the soil properties and determination of different soil quality indices.