Groundwater vulnerability assessment is critical for the effective groundwater management, particularly in areas with significant anthropogenic activities, such as agriculture. In this study, seven different methods, namely, DRASTIC, Pesticide DRASTIC, SINTACS, Nitrate SINTACS, GOD, AVI, and SI, were implemented using Geographical Information System techniques in Nea Moudania aquifer, Chalkidiki, Greece, to evaluate and identify groundwater vulnerability zones. The study area was classified into five categories: very low, low, moderate, high, and very high vulnerability. The southern and south-western parts of the study area had the highest pollution potential; the corresponding potential is lower in the northern part. Furthermore, single-parameter sensitivity analysis has revealed that the vadose zone and the topography were the most influential parameters of the vulnerability indexes, while the hydraulic conductivity exhibited the lowest effective weight. Finally, nitrate concentrations, taken from 23 observation wells, were employed for the validation of the aforementioned seven methods, using the coefficient of determination (R²). Results showed that Pesticide DRASTIC and Nitrate SINTACS were the most accurate and efficient methods for the present study area, which is characterized by intense agricultural activities.

Groundwater vulnerability assessment is of pivotal importance for the sustainable management of groundwater resources, particularly in regions with intense agricultural activity. This research primarily aims to assess and delineate groundwater vulnerability zones using a comparative approach of three different GIS-based modified models, namely Pesticide DRASTIC-LU, Nitrate SINTACS-LU and Nitrate NV index. For this reason, eight hydrogeological parameters were employed to analyze the spatial distribution of groundwater vulnerability in the Nea Moudania aquifer, Chalkidiki, Greece. This multi-model methodology was implemented to ascertain the most reliable method for the study area. Results indicated that the southern and southwestern parts of the study area exhibited the highest vulnerability potential, whilst the northern part displayed the lowest. Moreover, single-parameter sensitivity analysis has revealed that land use and topography were the most critical parameters of the vulnerability indexes, whereas hydraulic conductivity was the least influential. Finally, the three vulnerability models were validated with nitrate concentrations of groundwater samples. Results revealed that the Nitrate NV index was the most accurate method, trailed by the Pesticide DRASTIC-LU and the Nitrate SINTACS-LU.

In this paper, we discuss the accuracy of aquifer transmissivity (T) and storativity (S) values, obtained through the processing of hourly and 5-min groundwater level data, regularly and accurately recorded by automated stations. In particular, we discuss the role of the selection of (a) the initial undisturbed hydraulic head level, which might be influenced by prior pumping cycles, and (b) the exact time of start or shutdown of the pump, which might not be exactly recorded. Furthermore, the accuracy of T and S values based on sparse measurements is also examined. The Cooper–Jacob method and the recovery test method have been applied to obtain both T and S, and T values, respectively. Groundwater level measurements at Moudania aquifer, Chalkidiki, Greece, are used as an illustrative example. Our main conclusions are (a) assuming that pumping starts earlier than it actually does, leads to the underestimation of T and the overestimation of S. (b) Transmissivity might be overestimated if the residual drawdown, due to previous pumping cycles, is substantial. (c) In recovery tests, the deviation of the straight line that fits the experimental points from the point (1,0) is an indication of residual drawdown, and (d) sparse measurements can offer reasonable estimates.

In this paper, we examine the accuracy of estimating the hydrogeological parameters, transmissivity (T) and storativity (S), in a confined aquifer, when there are not enough available data for pumping flow rate values. While the most popular methods, used to estimate aquifer characteristics, assume that the pumping flow rate is constant during pumping, this is practically infeasible. Violation of this assumption results in errors, which are examined in this paper using field drawdown measurements. To find the aquifer characteristics, we use two methods, testing various pumping flow rates. Firstly, we employ the Cooper -Jacob equations to calculate (T) and (S) values. Afterwards, we use these values to create hypothetical drawdowns using Theis equation and finally we estimate the Root Mean Square Error (RMSE) between the actual and the hypothetical drawdowns. Then, we repeat the same process, replacing the Cooper -Jacob equations with Genetic Algorithms and Theis equation to find the aquifer characteristics by minimizing the RMSE between the actual and the hypothetical drawdowns. Although the process is applied only in three datasets, the results indicate that regardless of the method used, the obtained values of aquifer characteristics (T, S) are not considerably affected by inaccurate pumping flow rate estimations.

Groundwater vulnerability assessment is an essential step for the efficient management of groundwater resources, especially in areas with intensive anthropogenic activities and groundwater pollution. In the present study, the DRASTIC method was applied using geographic information system (GIS) to evaluate groundwater vulnerability zones in Erbil Dumpsite area, Central Erbil Basin, North Iraq. Results showed that the area was classified into the following vulnerability classes: very low (16.97%), low (27.67%), moderate (36.55%) and high (18.81%). The southern, south-eastern and northern part of the study area had the highest vulnerability potential, whereas the central-northern, northern and north-western portion of the study area revealed the lowest vulnerability potential. Moreover, results of the single-parameter sensitivity analysis showed that amongst the seven DRASTIC parameters the unsaturated zone and the aquifer media were the most influencing parameters. Finally, the correlation of 25 nitrate concentration values with the final vulnerability map, using the Pearson correlation coefficient, gave a satisfactory result equal to R = 0.72.