JWSS - Journal of Water and Soil Science

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.

Studying the trends of water table in any region especially in arid and semi-arid regions is an important issue. This study focuses on assessing groundwater table changes in Tabriz plain. For this purpose, non-parametric Mann-Kendall test is used. In studing groundwater level the information of 14 pizometric wells in the period of 1991-2013 was used. Significant levels of 1, 5 and 10% were used for the trend test. Slope of trend lines is estimated using the sen's estimator method. The homogeneity of trends were tested using the Van Belle and Hughes method. The results showed that groundwater level in the most of pizometric wells have decreasing trend, That was significante in 1% sifnificance level. According to the research, trend of groundwater level was negative in all of the stations in April and maximum negative trend was belong to Dizaj Leily Khany station (Z= -6/47) that was significante in 1% sifnificance level. Also the minimum negative trend was belong to Ana Khaton station (Z= -0/322). The minimum groundwater level was -1.45 in Said-Abad station.

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.

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.

One of the ways to increase water productivity in agriculture is the use of new irrigation systems; for the precise design of these systems, water quality assessment is needed. The purpose of this study was to study the groundwater quality of Khorramabad plain for the implementation of drip irrigation systems. The qualitative indices of EC, SAR, TDS, TH, Na and pH were related to the statistical years 2006-2012. In this research, the data were normalized first and it was determined that the data were abnormal; so, the logarithmic method was used for normalization. To evaluate the groundwater quality of the area, land use methods were used. Among different methods, the ordinary kriging interpolation method with the least root mean square error for all parameters was used. Quality zoning maps showed that in the north and southwest, EC and SAR concentrations were in poor condition in terms of qualitative classification. TDS had a concentration of more than 4000 milligramrels, and Na had a concentration of more than 15 milligrams / ltr. In these areas, TH with the concentration of more than 730 mg / l had the highest contamination; in the central area of the plain, there was a higher risk of carbonate sediments. LSI rates in the western regions were more than one, which included about 12% of the plain; there were restrictions on the implementation of droplet systems in these areas. The best quality for implementing these systems was located in the south-east of the plain, covering 19% of the plain. Finally, the integrated map of qualitative characteristics showed that the maximum concentration of qualitative characteristics was located in the northern, central and southern regions, which included 62.29% of the plain area.

Groundwater supplies a major portion of two basic human needs: drinking and agricultural water. Forecasting, monitoring, evaluating the performance and planning of this vital resource require modelling. The lag time of the groundwater level fluctuations against the rainfall is one of the essential data of the models. The purpose of the present study was to evaluate the piezometers behaviour by using the Pearson cross-correlation method between SPI and GRI indices in the Shiraz alluvial plain in order to determine the mentioned lag time. The results showed a similar behaviour for 86.2% of the piezometers. In 79.3% of the piezometers, groundwater level was declined one month after the rainfall event. The best correlation coefficient between the aforementioned indices was observed along the southwestern to the northeastern axis of the plain. The northern alluvial plain has a better correlation, as compared to the southern section because of the northern-southern slope of the plain. The central area of the plain had the highest correlation coefficient. The maximum correlation coefficients occurred at a time scale of 48 months. Also, since 2004, due to the decline in the atmospheric precipitation in the Shiraz plain, the SPI index has surpassed the drought level, although the trend has not been significant. However, the GRI does not follow this trend, showing a significant hydrological drought. The reason can be the disproportionate water extraction to recharge ratio in the alluvial aquifer of the plain.

Understanding the quality of groundwater resources, which are the largest available freshwater reservoir, is one of the needs in planning and developing water resources. The purpose of this research was to study the quality changes of groundwater resources in the upstream aquifers of Zayandehrood Dam (1995-2016) and to evaluate water quality in terms of drinking and agricultural consumption and evaluation of IRWQIGC. For this purpose, EC, TDS, SAR, PH, TH, Cl, CO₃, Ca, Mg, Na, K, HCO₃ and NO₃ parameters and heavy elements including zinc, copper, lead, cadmium and arsenic were investigated from laboratory samples. In the upstream aquifers of the Zayandehrood Dam, the water classification was mainly agricultural in the C2-S1 range, and it was generally acceptable in the drinking classes. The amount of heavy elements was allowed. The average amount of nitrate in the Chehelkhaneh, Damanehdaran, Boein-Miandasht and Chadegan aquifers was calculated to be 43.77, 48.08, 35.53 and 26.36 mg / l, respectively, and the maximum nitrate levels in these areas, however, were often exceeded. Nitrate zoning and IRWQIGC were performed by the kriging method. The lowest index values, which fell into relatively poor classes, were in the south and southwestern parts of Boein-Miandasht and south and south-west of the Chehelkhaneh, and in the central parts of Damanehdaran, and the south of Chadegan.

One of the most important and complex processes in the watersheds is the identification and prediction of surface water changes. The main processes associated with surface water include precipitation, percolation, evapotranspiration and runoff. In this research, the semi-distributed model, SWAT, was used to simulate ground water and surface water in Semnan catchment in a monthly scale. A sensitivity analysis was perfomed to evaluate and demonstrate the influence of the model parameters on the four major components of water budget including surface runoff, lateral flow, groundwater and evapotranspiration. River discharge data from 2004 to 2014 were used for the calibration and those of 2014 to 2016 were applied for the validation. The results of sensitivity analysis showed that the most sensitive parameters were: SoL_K(Saturated hydraulic conductivity), CH_K2 (Effective hydraulic conductivity in main channel), RCHRG_DP(Deep aquifer percolation fraction and CN2 (Moisture condition II curve number). The simulation accuracy using Nash-Sutcliffe and coefficient of determination for Shahrmirzad, Darjazin, and Haji Abad hydrometric stations was about 0.60 to 0.80 and 0.80 to 0.90 for the calibration and validation period, respectively, showing a good performance in the simulation of river flow. According to the water balance results, about 87.6% of the total inflow into the watershed was actual evapotranspiration, 3% was surface run off, 3% was percolation, and the rest was related to the soil moisture storage.

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 construction of irrigation network and the water transfer from Karkheh Dam to Dashte-Abbas, due to neglecting the groundwater resources has increased groundwater level and waterlogging of the agricultural land in the recent years. The aim of this study was, therefore, to optimize the conjunctive use of surface and groundwater resources in Dashte-Abbas to minimize waterlogging problems and achieve the maximum net income. For this purpose, the behavior of groundwater was simulated using the system dynamics (SD) approach. The conjunctive use of surface and groundwater resources was then optimized using the Vensim multi-criteria optimization method with the objective function of maximizing the net income of the plain. The SD model calibration was done using climatic, hydrological, agricultural, and environmental data from the 2001-2009 time period; then it was validated based on the information from the 2009-2016 period. Evaluation of the developed SD model showed that the model had high accuracy in simulating key variables such as groundwater levels (ME=60cm, R²=97%, RMSE=47cm) and groundwater salinity (RMSE=100μS/cm, R²=74%, and ME=123μS/cm). Furthermore, the results of the optimization model showed that the optimum use of surface and groundwater resources for the agricultural demand was 65% and 35%, respectively. To sum up, it could be concluded that with the optimization of the conjunctive use of surface and groundwater resource, s about 10 MCM of water consumption could be annually saved to irrigate almost 800 ha of the new lands.

The Khanmirza plain is one of Iran’s fertile plains that is located in Chaharmahal Bakhtiari province. Agriculture in the area is very prosperous, but the lack of rain and over-harvesting from consumption wells has led to a reduction in groundwater levels, even causing land subsidence. Moreover, the high usage of chemical manures, especially nitrate manures, has increased the number of solutes and chemical materials in the groundwater. Thus, for this plain, making artificial ponds is important to modify the storage of the aquifer. In this study, to define the optimum locations of the artificial ponds, the effect of 12 factors was considered. The analytic hierarchy process (AHP) method was used to introduce the weight of each parameter in comparison to other factors. Afterward, the spatial priority of all factors was derived using the Geographic Information System (GIS) technique. The produced GIS layers were laid on each other and the optimum locations were obtained. Agricultural drainage was an effective index for recharge purposes. The results of the study demonstrated that groundwater level decline got the maximum weight (40%), while the land slope had the minimum weight, since the vicinity to available floodways was considered as an independent criterion. The results also showed that regions with a total area of 18 km² in north and north-west of the Khanmirza plain could be the optimum and most suitable places for artificial ponds construction.

Reducing surface water resources and successive droughts and consequently excessive use of groundwater resources, especially for agricultural purposes, have caused irreparable damage to the natural resources of the country. In the meantime, knowing the status of the water balance of the plain can help to effective management of water resources in the region. Samalqan plain is located in a semi-arid climate in North Khorasan Province. Since the surface water resources for water supply are not very reliable, so, the main source of water supply in the region is well. Due to the existence of rivers in the plain, the low thickness of the alluvium, groundwater level fluctuations, and the high uncertainty in the calculation of hydrodynamic coefficients, the need for careful hydrogeological studies and determining the role of each parameter affecting groundwater is necessary. This study was conducted to simulate the Samalqan aquifer and analysis of water balance for the years 2003 to 2013 using the MODFLOW model. To identify the groundwater recharge rate, this component was estimated by the SWAT model. Calibration and validation of the model with an error of 1.1% and 1.2%, respectively, indicated that an appropriate estimation between the simulated and observed heads. Assessment of the groundwater hydrograph in the observation wells showed that the groundwater level in most places has many monthly and seasonal fluctuations. After drawing the potential lines of the plain, the inputs and outputs were identified, and using the reserve volume changes, the water balance was determined. The results showed that the water balance of the plain was negative and the reservoir deficit was estimated at 9.14 million cubic meters. Therefore, this model can be used to predict the future situation of aquifer and the management of water resources in the region.

An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.

The impact of different management options on the region and the existing conditions can be evaluated with minimal cost and time to select the most practical case using various tools including mathematical models. In this study, the SWAT hydrological model was performed from 2009 to 2019 using climatic, hydrological, and hydrometric data in the Malayer catchment, and the final model was validated by SWAT-CUP. To reduce the amount of uncertainty in the input parameters to the MODFLOW model, using the values of surface recharge from the implementation of the SWAT hydrological model, quantitative modeling of Malayer aquifer was performed more reliably in GMS software by using MODFLOW model. After modeling the study area in the 2009-2018 period and calibrating the model in the years from 2018 to 2019, the mean values of absolute error (MAE) were 0.35-0.65 m, and root means square error (RMSE) was 0.62-0.94 m, which seems acceptable considering computational and observational heads equal to 1650 m. Results of water level changes in observation wells located in the Malayer region indicate that the groundwater level in the aquifer has decreased by an average value of 9.7 m in the 10-year study period.