JWSS - Journal of Water and Soil Science

In this study, in order to analyze the trends of annual precipitation, the information from 21 synoptic meteorological stations located in the Urmia Lake basin in a 30-year time period (1986-2015) was used. For this purpose, the Sequential Mann-Kendall test was used. The date of sudden change (if exist) in the precipitation time series of each station was identified. Significance of the trend in each of the time series and its direction (decrease or increase) in each of the stations were tested at 0.05 level. The results showed that 10 out of the 21 stations had a significant decreasing trend. Three stations (Sarab, Bostanabad and Sardasht) had significant increasing trends. Precipitation trends of eight stations were insignificant. Also, the study of sudden breaking points in the annual rainfall time series of the selected stations revealed that about 57.143 percent of the stations (12 stations) showed a significant sudden change in their annual rainfall series. In other words, more than half of the selected stations exhibited a   sudden change in their time series. The date of the sudden change of precipitation in eight stations (namely, Bonab, Sarab, Urmia, Oshnavieh, Kahrizi, Miyandoab, Bokan and Saghez) belonged to the middle part of the time series (i.e. 1996-2005). The sudden change date  of t hree stations (namely, Sardasht, Nagade and Tekab) belonged to the first decade of time series (i.e. 1986-1995) and only the sudden change date of  one station (namely, Maragheh) belonged to the last decade of time series (i.e. 2006-2015).

Groundwater quality evaluation is very necessary to provide drinking water. Groundwater excessive consumption can cause subsidence and penetration of saline groundwater into freshwater aquifers in Ajabshir Plain, on the Urmia lake margin. The main goal of the current project was to evaluate the groundwater quality by employing the qualitative indices of groundwater and GIS. Ten parameters of 15 wells including EC, TDS, total hardness as well as the concentration of Ca⁺⁺, Na⁺, Mg⁺⁺, K⁺, SO₄^--, HCO₃^- and Cl^- were analyzed. At first, the maps of parameters concentration were prepared by the kiriging method. Then based on WHO drinking water standards, the maps were standardized and ranked for drawing the maps of quality indices. The results showed that quality index changes were in the range of moderate (61) to acceptable (81). Removing the single map method of sensitivity analysis detected the quality index was more sensitive to the K⁺ parameter. Finally, the quality index from the eastern north to the western south of Ajabshir Plain and the other areas was ranked in the acceptable and moderate classes, respectively.

Regionalization is one of the useful tools for carrying out effective analyses in regions lacking data or with having only incomplete data. One of the regionalization methods widely used in the hydrological studies is the clustering approach. Moreover, another effective factor on clustering is the degree of importance and participation level for each of these attributes. In this study, it was tried to use a broad range of attributes to compare their performance in regionalization. Then, according to the importance and role of each attribute in regionalization, the appropriate weight for each of the attributes in each category was determined using the principal component analysis (PCA) method, and the effect of this weighting in forming the homogenous regions was investigated by the Ward's clustering method. In this regard, the maximum 24-hour rainfall data of 63 meteorological stations located in Urmia Lake Basin (ULB) was used in this study during a time period of 30 years (1979-2008). Furthermore, seven categories of attributes were defined in order to regionalize the rainfall. The results showed that by considering different attributes and combining them with each other, a different clustering is obtained in each category in terms of the number of clusters and stations. Among seven categories of attributes, it was found that the geographical and climatic-geographical categories of attributes showed a more appropriate clustering over the ULB. Additionally, the weighting of attributes could have more effect on improving homogeneity and forming the independent clusters in most cases in terms of the scattering of station and how to locate over the basin.

Evaluating the possible relationships between vegetation and environmental characteristics can assist managers to identify effective factors influencing plants establishment and to characterize various vegetation communities. This study was aimed to evaluate the effects of long term grazing exclusion ( more than 33 years) and the controlled grazing system (resting – rotation grazing system) on the vegetation distribution and some soil properties in the Hamzavi research station in Hanna area-Semirom, Isfahan. Six transects (three parallel transects and three transects perpendicular to the general slope of the area) were established in each area and 10 square plots with the size of 2m² were placed along each transect; then, the cover percentage, production and list of all plant species were recorded. In each area, eighteen plots were collected randomly and in each plot, five soil samples were collected from 0-30 cm of the soil and then the samples were mixed and one sample of the compound was selected as an evidence plot. Soil properties such as pH, EC, CaCO₃, organic carbon, absorbable phosphor, total nitrogen, K, Ca, Mg, soil saturated percentage, cation exchange capacity, soil clay, silt, sand and fine sand contents were measured in the soil laboratory. The independent t test was used to compare the vegetation characteristics in two areas. Cation exchange capacity, CaCO₃, gravel percentage, soil phosphor content and grazing management were identified as the most discriminative factors in separating vegetation communities based on Canonical correspondence analysis (CCA) and cluster analysis. Controlled grazing management significantly modified some soil characteristics and increased the production (352 versus 184.2 kg/ha) and vegetation cover percentage (25.46 versus 18.37), as compared to the exclusion area (α= 5%). The vegetation density was increased significantly in the exclusion rather than controlled grazing area (3.03 versus 2.02 plant/m²). This study, therefore, revealed that controlled grazing management was more effective on improving some soil quality and vegetation characteristics rather than p long term grazing exclusion in the semi-arid ecosystems. So, avoiding long term grazing exclusion in semi-arid rangelands is suggested.

Regional flood frequency studies are initialized by the delineation of the homogeneous catchments. This study was based on "Region of Influence" concept, aiming to find the similar catchments in the south of Caspian Sea. The methodology utilized the Particle Swarm Optimization Algorithm, PSO, to optimize the fuzzy system over a dataset of catchment properties. The main catchment variables in relation to flood were determined by the principle component analysis method and employed as the inputs in the fuzzy system. Catchments grouping was performed over these fuzzy input variables by the iterative process. The optimum similar groups were obtained by PSO, and the heterogeneous L-moment index was used as the termination criterion for the optimization process. A total of 61 hydrometric stations located in the study area were selected and their relevant catchments' physical, climatic and hydrologic properties in relation to flood were studied. Principle Component Analysis by Variomax Rotation Factor over the catchments datasets tended to four out of 16 physical variables, including area, mean elevation, Gravelious Factor and Form Factor, as the main parameters in terms of homogeneity with 84 percent of accumulative variance. These variables, as well as mean annual rainfall, were used as the input data to define the fuzzy system. PSO algorithm was then employed to optimize the developed fuzzy system. The developed algorithm tended to yield the best result in the 9th iteration with 26 and 22 for the minimum average and the optimum values of cost function, respectively. The topology of the resulting algorithm included inertia weight, local and acceleration rates, the number of generations and population size, with the values of 0.7298, 1.4962, 1.4962, 10 and 5, respectively. This study tended to a total of 61 regions of influence, proportional to the relevant 61 sites. According to the geographical location of the catchments in the region, it could be concluded that the geographical proximity doesn't necessarily involve homogeneity. The obtained results indicated the efficient potential of PSO-FES in the delineation of the homogenous catchments in the study area.

Changes in Climate parameters have been accelerated in the coming age, which can affect agricultural activities directly and indirectly. Temperature and precipitation are the most complex climatic factors. Spectral analysis is a scientific and efficient technique used to recognize and detect the hidden behaviors of these variables. In this research, in order to study and analyze the temperature and precipitation return periods using spectral analysis, the statistics of climate parameters (precipitation, mean, maximum and minimum temperature) for a period of 27 years (1989-2015) were used for the sustainable land management. For this purpose, the climatic data of temperature and precipitation entered the MATLAB software environment and Periodogram of each of the climatic parameters was drawn in a separate way. The results of each Periodogram study showed that the absolute minimum of temperature had significant cycles with the return periods of 3.8 and 2.4 years; the absolute maximum of temperature had a significant cycle with a return period of 2.1 years and the mean temperature was significant with a return period of 2.7 years. Also, the review of the Periodogram related to precipitation showed a significant cycle with a return period of 3.4 years. The Results from studying cycles indicated the existence of short-term return periods for climate variables in the region. Given this issue and the need to protect agricultural products, especially garden products, it should be done by applying water and soil resources management methods, including creating terraces and increasing soil roughness; Also, cultivation of appropriate plant species for the suitable regional climatic conditions, Drought resistant and low water requirement, the most optimal conditions could be created for the cultivation of horticultural and agricultural products.

Exploring the homogenous regions for site specific management is important, especially in the areas under different anthropogenic activities. This was investigated using multi-way analysis including Factor Analysis, Hierarchical Clustering Analysis and k means in the areas under long-term wastewater irrigation over a period of more than 40 years, in Shahre Rey, south of Tehran. By using Factor Analysis model, eight factors as eight geochemical groups were extracted to explain approximately 60% of the total variance related to 37 soil physicochemical properties. The most important groups included the nutrient elements (OM, OC and N), micronutrients (Mn and B), soil water adsorption capacity (Clay, Silt, Sand and CEC), salinity and osmotic pressure (EC, OP and TDS) and sodification (SAR and Na). The maximum values of Cophenet and Silhouette coefficients were equal to 0.77 and 0.83, respectively, dictating the selection of the average linkage approach in Hierarchical Clustering Analysis and three clusters in the k-average method with 19, 24 and 34 mapping units. The Thiessen Polygons method in GIS was applied to separate the geochemical groups in the form of mapping units. This output, which was, in fact, the combination of multi-way models and its visual representation in GIS under separated mapping units of study area, could present suitable management activities for the areas under each cluster.

Determination of water quality is an essential issue in water resources management and its monitoring and zoning should be considered as an important principle in planning. In this study, in order to investigate the quality of groundwater resources (springs, wells and qanats) in Semnan watershed, first, the water quality index for drinking and agricultural purposes was obtained by means of measuring SO₄, Cl, Na, Mg, PH, EC, SAR, TDS in 55 groundwater sources. For calculating the parameters weight in WQI, the fuzzy hierarchy analysis process was used with the Chang's development analysis. Due to the lack of sampling points for zoning of the entire area, regarding the existence of EC data for the majority of groundwater resources used in this catchment (354 sources), as well as the high correlation (Adjusted R²=0.99) between WQI with EC, the mentioned indexes of other resources were estimated based on the regression relationship with EC. To analyze the spatial distribution and monitor the zoning of the groundwater quality, the ArcGIS version 10.3 and Geostatistical method such as simple Kriging and ordinary Kriging were used; additionally certain methods including Inverse distance weighting and Radial Basis Function were utilized. The performance criteria for evaluating the used methods including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), %RMSE and R² were used to select the appropriate method. Our results showed that the ordinary Kriging and Radial Basis Function were the best methods to estimate the groundwater quality.

Shortage of irrigation water is a major problem constraining in agricultural production in arid and semi-arid regions. Deficit irrigation is one way to cope with water scarcity and increase water use efficiency. Determining the optimum applied water based on economic analysis is a major key to the deficit irrigation strategy. In this study, the required equations were derived to determine the optimum applied water for sugar beet when crop price is a function of the applied water. The results showed that the optimum applied water under land limiting conditions (144.98 cm) resulted in the maximum net benefit per unit area (2089741 Rials ha^-1). Applying the optimum water depth under land limiting resulted in 17.48% decrease in the applied water and 15.05% increase in the total net benefit, in comparison with the maximum yield condition. In water limiting conditions (land is not limiting), the total net benefit was maximized by applying the saved water to put larger areas of land under irrigation. Applying the optimum water depth under water limit condition resulted in 31.2% decrease in applied water and 45 and 52.36% increase in the planting area and the total net benefit, in comparison with the maximum yield condition, respectively. Sugar beet planting can be, therefore, profitable if the applied water depth is greater than 67.53 cm in this study area.

Rivers are one of the most important source of water supply for drinking and farming purposes. Zard River is one of the surface water resources of Khuzestan province. The purpose of this study is to evaluate the quality of the river water and to observe the trend of changes in the water quality of this river in the Mashin station during the period of 1997-2015 by using the Man-Kendal, Spearman, variance analysis statistical methods and the least significant difference (LSD) and cluster analysis. LSD test shows SAR, Na, Cl, pH parameters up to 2010 (before Jare dam construction) were significant at 95% confidence level compared to 2015 (year of control). No changes were made after dam construction. According to Mann-Kendal non-parametric test, pH, Ca and SO₄ have a significant upward trend to the 0.037, 0.393 and 0.376 respectively, the variables Cl, SAR, Na and temperature have a significant decreasing trend to the -0.387, -0.417, -0.386 and -0.1 respectively. Also Spearman test shows that the dam improved the quality of river water regarding to salinity. Variance analysis show that pH, SAR, Na, Cl, Ca and SO₄ have significant difference. Cluster analysis classified the qualitative data before the construction of the dam in three clusters and after the construction of the dam were divided into two clusters where TDS variable was less distant than other variables. As a result, the quality of the irrigation water is changed downward and the TDS is more similar to the other variables compared.

Use of the curve gradient of the Soil Water Retention Curves (SWRC) in the inflection point (S Index) is one of the main indices for assessing the soil quality for management objectives in agricultural and garden lands. In this study Anneling Simulated – artificial neural network (SA-ANN) hybrid algorithm was used to identify the most effective soil features on estimation of S Index in Jiroft plain. For this purpose, 350 disturbed and undisturbed soils samples were collected from the agricultural and garden lands and then some physical and chemical soil properties including Sand, Silt, Clay percent, Electrical Conductivity at saturation, Bulk Density, total porosity, Organic Mater, and percent of equal Calcium Carbonate were measured. Moreover, the soil moisture amount was determined within the suctions of 0, 10, 30, 50, 100, 300, 500, 1000, 1500 KP using pressure plate. Then, the determinant features influencing the modeling of S Index were derived using SA-ANN hybrid algorithm. The results indicated that modeling precision increased by reducing the input variables. According to the sensitivity analysis, the Bulk Density had the highest sensitivity coefficient (sensitivity coefficient=0.5) and was identified as the determinant feature for modeling the S Index. So, since increasing the number of features does not necessarily increase the accuracy of modeling, reducing input features is due to cost reduction and time-consuming research.

Rivers are one of the most important source of water supply for drinking and farming purposes. Zard River is one of the surface water resources of Khuzestan province. The purpose of this study is to evaluate the quality of the river water and to observe the trend of changes in the water quality of this river in the Mashin station during the period of 1997-2015 by using the Man-Kendal, Spearman, variance analysis statistical methods and the least significant difference (LSD) and cluster analysis. LSD test shows SAR, Na, Cl, pH parameters up to 2010 (before Jare dam construction) were significant at 95% confidence level compared to 2015 (year of control). No changes were made after dam construction. According to Mann-Kendal non-parametric test, pH, Ca and SO₄ have a significant upward trend to the 0.037, 0.393 and 0.376 respectively, the variables Cl, SAR, Na and temperature have a significant decreasing trend to the -0.387, -0.417, -0.386 and -0.1 respectively. Also Spearman test shows that the dam improved the quality of river water regarding to salinity. Variance analysis show that pH, SAR, Na, Cl, Ca and SO₄ have significant difference. Cluster analysis classified the qualitative data before the construction of the dam in three clusters and after the construction of the dam were divided into two clusters where TDS variable was less distant than other variables. As a result, the quality of the irrigation water is changed downward and the TDS is more similar to the other variables compared.

Soil is one of the most important natural resources of countries in which erosion occurs. In this research, the effect of soil characteristics on the amount of erosion at the suborder level was studied. For this purpose, 77 soil samples (0-30 cm) were prepared and the parameters were determined in the laboratory. The semi-variograms of soil parameters and their spatial distribution maps were prepared with GS⁺ and GIS, respectively. The study area was divided into work units by combining land use and geology maps and water erosion was estimated at each unit by the EPM method. By drilling profiles in different parts of study area, soil suborders were determined by Soil Taxonomy and the average values of parameters in each suborder was estimated. The principle components analysis (PCA) was then used for data analysis. The results showed that three parameters of silt, organic carbon and electrical conductivity could account for 30.384% as the first main component; clay, sand and vegetation could explain 11.189% as the second main component; and slope and height covered 15.330% as the third main component; in total, 63.805% percent of erosion variation could be justified by three main components. The lowest and highest amounts of erosion (69.12 and 343.57 m³/km², respectively) were estimated in Xeralfs and Fluvents suborders. The erosion class of suborders at the study area was determined to be “few” and “medium”.

In this study, the groundwater level (GWL) of the Sarab Qanbar region located in the south of Kermanshah, Iran, was estimated using the Wavelet- Self- Adaptive Extreme Learning Machine (WA- SAELM) model. An artificial intelligence method called “Self- Adaptive Extreme Learning Machine” and the “Wavelet transform” method were implemented for developing the numerical model. First, by using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and the effective lags in estimating GWL, eight distinctive SAELM and WA- SAELM models were developed. Later, the values of the observational well were normalized for estimating GWL. Next, the most optimized mother wavelet was chosen for the modeling. By evaluating the results of SAELM and WA- SAELM, it was concluded that the WA- SAELM models could estimate the values of the objective function with higher accuracy. Then, the superior model was introduced, showing that it could be very accurate in forecasting the GWL. In the test mode, for example, the values of R (correlation coefficient), Main absolute error (MAE) and the NSC- Sutcliffe efficiency coefficient (NSC) for the superior model were calculated to be 0.995, 0.988 and 0.990, respectively. Furthermore, an uncertainty analysis was conducted for the numerical models, proving that the superior model had an underestimated performance.

The estimation of reference crop evapotranspiration (ETo) is one the important factors in hydrological studies, irrigation planning, and water resources management. This study attempts to explore the possibility of predicting this key component using three different methods in the Sistan plain: Generalized Linear Models (GLM), Random Forest (RF) and Gradient Boosting Trees (GBT). The maximum and minimum temperature, mean temperature, maximum and minimum humidity, mean humidity, rainfall, sunshine hours, wind speed, and pan evaporation data were applied for years between 2009 to 2018. Using various networks, the ETo as output parameter was estimated for different scenarios including the combination of daily scale meteorological parameters. In order to evaluate the capabilities of different models, results were compared with the ETo calculated by FAO Penman-Monteith as the standard method. Among studied scenarios, M1 covering the maximum number of input parameters (10 parameters) showed the highest accuracy for GBT model, with the lowest RMSE (0.633) and MAE (0.451) and the maximum coefficient of regression (R = 0.993). Air temperature was found as the most sensitive parameters during sensitivity analysis of studied models. It indicated that accuracy and precision of temperature data can improve the results. Application of the GBT model could decrease the time consumed to run the model by 70%. Therefore, the GBT model is recommended for estimation of ETo in the Sistan plain.

Given the fact that the DRASTIC index is ineffective in addressing the saltwater uprising issue in coastal plains, in the present study, three factors including land use, distance to shoreline, and differences between groundwater and sea level were added to the DRASTIC index. The proposed modification to DRASTIC was validated using the measured electrical conductivity (EC) data gathered from groundwater monitoring wells throughout the Talesh Plain. The results showed that the coefficient of correlation between the map of EC over the region and the modified DRASTIC was 0.52, while for the original DRASTIC, the coefficient was 0.45, thereby implying a stronger relationship between EC and the modified DRASTIC in the Talesh Plain. Sensitivity analysis also showed that DRASTIC and the modified DRASTIC were the most sensitive to, respectively, depth to groundwater (D) and land use (Lu). According to the single-parameter sensitivity analysis results, depth to water table and net recharge were the most effective parameters in DRASTIC,  whereas the modified DRASTIC was the most sensitive to land use and depth to groundwater. It could be concluded that modifying the DRASTIC index would result in decreasing the area of very high and high vulnerable classes, and the area classified as low and moderate vulnerable could be increased.

The interaction of population growth, technological improvement and climate change have impacted severely on agricultural and environmental sustainability. In Iran, conventional tillage practice has resulted in soil erosion and loss of soil organic matter. In this regard, Conservation Agriculture (CA) forms part of this alternative paradigm to agricultural production systems approaches and can be regarded as a means to enhancing food productivity, reducing poverty, and mitigating the consequences of climate change in rural households. The objectives of this study were to examine the determinants and impacts of CA technology on wheat yield, poverty gap and water use. To this end, an endogenous switching regression (ESR) model was employed to estimate the impacts of CA technology on continuous variables such as wheat yield, poverty gap and water use. A sample of 260 farmers from Zarghan district was selected for interview collection of necessary farm level data. The results indicated that in the select equation of ESR model, ten coefficients (out of 12) are significant at the 5% level or higher. Knowledge of soil quality, access to credit, access to information, education, farm size, ownership of machinery, participation in agricultural extension activities and farmer’ perception have positive and significant effects on the probability of adopting CA. In contrast, variables such as the distance to shopping center and number of land parcels have negative and significant influence on adoption. Also, the results of ESR model and counterfactual analysis showed that wheat yield would increase by 1.05 tons and poverty gap and water use would decrease by 20% and 910 cubic meters per hectare respectively if farmers adopt CA technology.

Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DR_ave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments.

Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstly, the most optimized member of wavelet families was chosen. Then, by analyzing the numerical models, the most accurate linking function and fitness function were selected for the GEP model. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WGEP models were introduced. The GEP models were trained, tested and validated in 37, 20- and 10-years periods, respectively. Also, using sensitivity analysis, the superior model and the most effective lags for estimating long-term rainfall were identified. The superior model estimated the target function with high accuracy. For instance, correlation coefficient and scatter index for this model were 0.946 and 0.310, respectively. Additionally, lags 1, 2, 4 and 12 were proposed as the most effective lags for simulating rainfall using hybrid model. Furthermore, results of the superior hybrid model were compared with GEP model that the hybrid model had more accuracy.