JWSS - Journal of Water and Soil Science

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.

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.

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.

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.

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.