JWSS - Journal of Water and Soil Science

Today, supplying water to meet the sustainable development goals is one of the most important concerns and challenges in most countries. Therefore, identification of the areas with groundwater potential is an important tool for conservation, management and exploitation of water resources. The purpose of this research was to prepare the potential groundwater map in Nahavand, Hamedan Province, using the weight of evidence model and combining it with logistic regression. For this purpose,  the information layers of slope angle, slope aspect, slope length, altitude, plan curvature, profile curvature, TWI, SPI, distance from fault, fault density, distance from river, drainage density, lithology and land use were identified as the  factors affecting groundwater potential and digitized in the ArcGIS software. After designing the groundwater potential map with these three methods, ROCs were used to evaluate the results. Of 273 springs identified in this study, 191 (70%) were used to prepare the groundwater potential map and 82 springs (30%) were used to evaluate the model. The area under curve (AUC) obtained from the ROC curve showed an accuracy of 80.4% for the weight of evidence model and 82.5% for the weight of the evidence- regression combined model

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.

Water resources management depends on the precise assessment of water storage and access in each region, as well as environmental interactions of these resources. The man objective of this study was to delineate the potential zones of groundwater storage using FAHP. Mapping and assessment of it required maps of geomorphology, drainage, density, lineament density, slope and vegetation, which were initially prepared as the input layers in FAHP; the appropriate weights were attributed to them based on FAHP. Potential zones of ground water were classified into five classes of poor, average, good, very good and excellent. The number and density of available wells and springs in the study area dealt with the potential of the region for groundwater storage. So, ROC was used to assess the validation of results, considering spring points as signs of water resources. According to the results, classes of very good, good, average, weak, and very weak were ranked as the first to the last in terms of privilege order with an area of 37.7, 55, 40, 107, and 98.4 square kilometers, respectively.

Selection of the appropriate distribution function and estimation of its parameters are two fundamental steps in the accurate estimation of flood magnitude. This study relied on the concept of optimization by meta heuristic algorithms to improve the results obtained from the conventional methods of parameter estimation, such as maximum likelihood (ML), moments (MOM) and probability weighted moments (PWM) methods. More specifically, this study aimed to improve flood frequency analysis using the Artificial Bee Colony algorithm (ABC). The overall performance of this algorithm was compared to the conventional methods by employing goodness of fit statistics, correlation coefficient (CC), coefficient of efficiency (CE) and root mean square error (RMSE). The study area, Babolrood catchment located in southern bank of Caspian Sea, has been subjected to annual flooding events. A total of 6 hydrometry stations in the study area were delineated and their data were used in the analysis of 6 distribution functions of Normal, Gumbel, Gamma, Pearson Type 3, General Extreme Value and General Logistic. This analysis indicated that Gamma and Pearson Type 3 were the most appropriate distribution functions for flood appraisal in the study area, according to the ABC and conventional methods, respectively. Also, the results showed that ABC outperformed ML, MOM and PWM; so, Gamma could be recommended as the most reliable distribution function for flood frequency analysis in the study area.

Land subsidence as a hydrogeomorphology event is currently occurring dangerously in many plains of the country due to uncontrolled groundwater extraction from water bearing layers, and accordingly monitoring and studying this phenomenon seems to be necessary. In this study, land subsidence rate of the Najafabad aquifer was determined through the Differential Radar Interferometry (DInSAR) processing of the ASAR and PALSAR radar data and the results were validated by comparying with the differential leveling and groundwater level drowdown data. Processing of the ASAR sensor data estimates the land subsidence in the Najafabad plain at an average annual subsidence rate of 6.7 cm and a total of 41 cm during 6 years period and processing of the PALSAR data suggests an annual rate of 7.7 cm and total subsidence of 30 cm during 4 years period. Most of the occured displacements are related to the Tiranchi, Koushk, Ghahderijan, Goldasht and Falavarjan cities. The simultaneous groundwater level data with acquisition date of the radar satellite images between 2002 and 2014 shows a drawdown ranging from 0.5 to 46.5 meters in the south and east to north of the Najafabad city consistent with the estimated land subsidence areas.The DInSAR processing of the PALSAR data has led to a more accurate results with higher spatial resolution. Results of the radar data processing can be employed for the hazard zonation directly utilized for management and planning of control and preventive measures.

Delineation of gully erosion susceptible areas by using statistical models, as well as optimum usage of existing data and information with the least time and cost and more precision, is important. The main objective of this study is to determine the areas accuracy to gully erosion and susceptibility mapping by using data mining of the bivariate Dempster-Shafer, linear multivariate statistical methods and their integration in Semirom watershed, southern Isfahan province. First, the geographical location of a total of 156 randomly gullies were mapped using preliminary reports, satellite imagery interpretation and field survey. In the next step, 14 conditioning parameters of the gullies in the study area were selected including the topographic, geomorphometric, environmental, and hydrologic parameters using the regional environmental characteristics and the multicollinearity test for modeling. Then, the Dempster-Shafer statistical, linear regression, and ensembled methods were developed using 70% of the identified gullies and 14 effective parameters as dependent and independent variables, respectively. The remaining 30% of the gully distribution dataset were used for validation. The results of the multivariate regression model showed that land use, slope and distance to drainage network parameters have the most significant relation to gully occurrence. The gully erosion susceptibility maps were prepared by individual and ensemble methods and they were divided to 5 classes of very low to very high rate. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate gully erosion susceptibly maps. The verification results showed that the AUC of ensemble method (0.948) is higher than Dempster-Shafer (0.924) and Multivariate regression (0.864) methods. Also, the the seed core area index (SCAI) value of the ensembled model from very low to very high susceptible classes have a decreasing trend that indicating a proper separation of susceptible classes by this model.

In sustainable agriculture, cow manure is used for greater productivity, a rich source of E-Coli pathogenic bacteria. The objective of this research was to investigate the simultaneous effect of the fractionation size of cattle manure and irrigation water salinity on the retention of E-Coli bacteria in the depths of the sand column with a height of 10 cm under saturated flow. Four different particle fractions of cow manure (1-2, 0.5-1, 0.25-0.5, and smaller than 0.25 mm) were added to the surface of the sand column at the scale of 30 tons per hectare, then leaching was done with different salinities (0, 0.5, 2.5, 5, and 10 dS/m) up to 10 pore volumes, then samples were taken from the depths of 0, 3, 6, and 12 cm. The number of bacteria in each sample was determined by the live counting method. The results showed that the effect of all sources of change and their interaction effects on the retention of bacteria in the soil is significant at the level of 5%. Salinity had a negative effect on the retention of bacteria, and the highest and lowest values of the relative concentration of bacteria (the result of dividing the number of bacteria in each soil depth by the initial number of bacteria in the desired manure treatment) were in 0 dS/m and 10 dS/m salinity of leaching water, respectively. By decreasing the size of cow manure particles due to the increase in hydrophobicity and blocking of preferential pores, the retention of bacteria decreased in all investigated soil depths. The highest and lowest retention of bacteria in the soil were investigated in the largest cow manure particle size (1-2 mm) and the smallest cow manure particle size (less than 0.25 mm), respectively. In addition, the highest relative concentration of bacteria in the soil was seen in the depth of 0-3 cm, and no significant difference was seen in other soil depths.