Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R2) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial–proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001–2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.

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Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China

A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation

Prediction of groundwater level in a basin is of immense importance for the management of groundwater resources, especially in coastal regions where the water table fluctuations are to be limited to avoid sea water intrusion. Lack of strong predictive tools, or perhaps the lack of experienced users of those tools, may contribute to problems in data interpretation and failure to reach consensus about the need for key water management actions. Therefore, it is extremely important to comprehend the spatiotemporal variations of the water level for the management of groundwater in the coastal areas. In this article, a hybrid, artificial neural network-geostatistics methodology is presented for spatiotemporal prediction of groundwater levels. The proposed model contains two separated stages. At the first stage, an artificial neural network is trained for each piezometer for time-series modeling of the water level, so that the model can predict the groundwater level the next month. At the second stage, ...

Spatiotemporal Groundwater Level Forecasting in Coastal Aquifers by Hybrid Artificial Neural Network-Geostatistics Model: A Case Study

In most arid zones, groundwater (GW) is the major source of domestic, agriculture, drinking, and industrial water. Accordingly, the monitoring of its quality by different techniques and tools is a vital issue. The purpose of this paper is the evaluation of the combination of principal components analysis (PCA) and geostatistics as a technique for (1) identifying the processes affecting the groundwater chemistry of the detrital unconfined Middle Miocene Aquifer (MMA) of the Hajeb elyoun Jelma (HJ) aquifer (Tunisia) and (2) mapping the controlling variables for groundwater quality. This work is based on a limited database recorded in 22 wells tapping the aquifer and unequally distributed in the field. The proposed approach is carried out in two steps. In the first step, the application of PCA revealed that rock–water interaction, agriculture irrigation and domestic effluents could explain 85 % of the observed variability of the chemical GW quality of the MMA. As a result, two new variables are defined: V1 (rock–water interaction influence) and V2 (irrigation and domestic effluent influence). In the second step, the spatial variability of these variables over the extent of the MMA is mapped by applying a kriging interpolation technique. The results of this study suggest that, while both natural and anthropogenic processes contribute to the GW quality of the MMA, natural impacts can be considered as the most important.

Implementation and evaluation of multivariate analysis for groundwater hydrochemistry assessment in arid environments: a case study of Hajeb Elyoun–Jelma, Central Tunisia

An evaluation of ~250 samples of groundwater in the Amman Zarqa Basin for selenium along with other major and trace elements showed that concentrations of Se ranged between 0.09 and 742 μg/L, with an average value of about 24 μg/L. Selenium concentrations exceeded the recommended threshold for drinking water of the World Health Organization (WHO; 10 μ/L of Se) in 114 samples, with greater than 50 μg/L (quantity equivalent to the Jordanian standard of the allowed concentration of the element in water) of Se in nine cases. The average concentrations of Se in the lower, middle, and upper aquifers of the basin were 3.41, 32.99, and 9.19 μg/L, respectively. Based on the correlation with geologic formations and the statistical analysis of major/minor constituents and Piper tri-linear diagrams, we suggest that carbonate/phosphate dissolution, oxidation–reduction processes, and fertilizers/irrigation return flow are, together, the primary factors affecting the chemistry of the groundwater. Factor analysis helped to define the relative role of limestone-dolomitic dissolution in the aquifers (calcium, magnesium, and bicarbonate), agricultural activities (sulfate, nitrates, phosphorus, and potassium), oxidation–reduction factor (Eh, Fe, Mn, Cu, Zn, and Se), and anthropogenic (industrial) factor (EC, Fe, Cr, Co, Zn, and As). The high variability in Se concentrations might be related to the possibility of a multi-source origin of Se in the area.

Groundwater vulnerability to selenium in semi-arid environments: Amman Zarqa Basin, Jordan

The main objective of this study was to assess the spatial and temporal variability of groundwater level fluctuations in the Amman–Zarqa basin, during the period 2001–2005. In the year 2003, as a consequence of war, there was a sudden increase in the population in this basin. Knowing that the basin is already heavily populated and witnesses most of the human and industrial activities in Jordan, this study was prompted to help make wise water resources management decisions to cope with the new situation. Data from 31 fairly distributed wells in the upper aquifer of the basin were subjected to geostatistical treatment. Kriging interpolation techniques have indicated that the groundwater flow directions remained almost constant over the years. The two main directions are SW–NE and E–W. Kriging mapped fluctuations have also showed that drop and rise events are localized in the basin. Forecasting possibilities for management purposes were tackled using autocorrelation analysis. The constructed autocorrelograms indicated, in general, the temporal dependence of seasonal water level fluctuations, and that forecasting can be carried out within a period of 3–21 months. Several suggestions were made to mitigate the drop and rise hazards in the detected sites.

Rakad A. Ta′any

Papers

Geostatistical analysis of spatiotemporal variability of groundwater level fluctuations in Amman–Zarqa basin, Jordan: a case study