Groundwater is a crucial natural resource for regular socio-economic function. The occurrence of potable groundwater is widespread, and, for that reason, it is an essential source of water supply worldwide. Groundwater provides more than 40% of the world’s irrigation water and around 50% of municipal water extractions [1
The estimated worldwide groundwater abstraction is around 982 km3
/year, corresponding to around a quarter of the total water abstraction. Of this groundwater volume, 70% is for irrigation, 21% is for domestic use, and the remainder is mostly for industrial use [2
In several countries, the deficient access of populations to appropriate water supplies causes severe socio-economic vulnerability, and consequently, poverty. However, it is possible to promote water poverty to water prosperity through the sustainable exploitation of groundwater from high-quality, relatively low-cost, drought-resilient aquifers [3
During the last decades, an increasing global awareness regarding the key ecological, hydrological, and economic roles of groundwater bodies has driven both national and international authorities to create instruments intended to protect aquifer systems from pollution and assure the normal provision of ecosystem services. A good example of such practices are fundamental norms included in European river basin management plans, as required by the Water Framework Directive and by the Groundwater Directive of the European Union [4
The assessment of groundwater vulnerability to pollution is a major hydrogeological topic that faces a number of constraints, namely, the great complexity of groundwater systems resulting from the occurrence of three-dimensional groundwater flow and pollutant transport through heterogeneous and anisotropic geologic materials, often coupled with the poorly known nature and spatial distribution of pollution sources [8
Van Duijvenbooden and Waegeningh [10
] define groundwater vulnerability to pollution as the sensitivity of groundwater quality to an imposed pollutant load, which is determined by the intrinsic characteristics of the aquifer. The intrinsic groundwater vulnerability, which cannot be clearly quantified by means of a standardized method, encompasses the properties of hydrogeological systems that control the subsurface water circulation through the unsaturated zone and the aquifer, and therefore, also determine the risk of a pollutant to reaching groundwater [8
On the other hand, specific vulnerability also considers the properties of a contaminant, namely, its load, time of impact, as well as the spatial features of the pollution source [11
In general terms, groundwater vulnerability to pollution can be assessed considering three types of methods [14
]: (i) Geographical Information System (GIS)-based qualitative methods; (ii) process-based methods; and (iii) statistical methods (which encompass artificial intelligence methods). Intrinsic vulnerability can be assessed by using GIS-based qualitative methods and statistical methods.
GIS-based qualitative methods based on the subjective rating of hydrogeological factors are commonly used to evaluate groundwater vulnerability, because these methods are relatively simple to employ, inexpensive, less time-demanding, and are often suitable for application in situations of data scarcity [14
]. Furthermore, the application of such methods is useful as a decision-making instrument for sustainable management of water resources, as well as for land-use planning. However, a number of downsides are pointed out for GIS-based qualitative methods, such as the subjective nature of vulnerability evaluation, the possibility of relying on simplistic hydrogeological characterizations, the lack of a physical basis, and the difficulty of evaluating the accuracy of results, and in many cases, of delimiting areas with different levels of vulnerability to pollution [8
]. Some of the most common methods are: DRASTIC [16
], GOD [17
], System for Early Evaluation of Pollution potential from Agricultural Groundwater Environments (SEEPAGE) [18
], Aquifer Vulnerability Index (AVI) [19
], SINTACS [20
], EPIK [22
], ISIS [23
], KARSTIC [24
], and Susceptibility Index (SI) [25
Given the fundamental role of safe water supply and the technical difficulties and high costs of aquifer remediation operations, pollution prevention is the most adequate strategy in sustainable management of groundwater resources [26
]. For that purpose, the application of groundwater vulnerability indices coupled with GIS analysis is a valuable and cost-efficient approach to identify and help protect the most vulnerable areas at various scales [27
In fact, GIS allows one to generate, manipulate, analyze, and visualize geographic information in a flexible and integrated manner. This is achieved by using several layers of information and large data sets, thus meeting the requirements of complex hydrogeological conceptual models, which often require a holistic and transdisciplinary perspective in order to obtain an accurate representation of the spatial variability of groundwater vulnerability. GIS is also useful in overcoming other issues in the assessment of groundwater vulnerability, including: (i) the need to compare the results obtained by applying different indices, highlighting the advantages and disadvantages of each method [29
]; (ii) the combination of different indices in order to obtain more realistic results representing separately, for instance, the vulnerability conditions in the topsoil, the unsaturated zone, and the aquifer [31
GIS-based tools for the assessment of groundwater vulnerability are scarce and usually do not address fundamental issues, specifically: (i) to provide a user-friendly software that overcomes the high-cost and low flexibility of proprietary software, without compromising the code’s reliability; (ii) to allow adaptations to groundwater vulnerability indices, namely, by changing the factor’s weights and ratings; and (iii) to allow detailed vulnerability map comparisons. In most of the cases described in the literature, the assessment of intrinsic groundwater vulnerability is carried out using GIS proprietary software to produce vulnerability index maps [32
]. For instance, Oroji [30
] used proprietary software to assess groundwater vulnerability through DRASTIC and GOD indices in the Asadabad plain. Yousefi et al. [33
] also used proprietary software to estimate DRASTIC and SINTACS methods in the Khorramabad-Lorestan Plain, western Iran.
However, open source and free applications have significant benefits, namely, the free distribution and the public availability of the code, which can be modified by any user [37
]. QGIS is one of the most popular GIS open source software programs, with the important advantage of making the development of plugins using Python language easy and quick [38
]. QGIS integrates external algorithms from other software, which complements itself.
Some GIS open source applications for QGIS involving groundwater vulnerability methods have been developed so far; for instance, the Groundwater Vulnerability Mapping
plugin, hosted in the QGIS official repository [39
], and the PaPRIKa
QGIS plugin [40
]. Rossetto et al. [41
] also developed the FREE and open source software tools for WATer resource management (FREEWAT) platform, a free and open source software integrated in QGIS for planning and management of water resources, with specific attention to groundwater. Duarte et al. [42
] developed a GIS open source application under QGIS open source software, named DRASTIC Model
, which allows automatic creation of groundwater vulnerability maps within a friendly graphic interface.
The objective of this work was to present a new GIS open source application for the assessment of groundwater vulnerability to pollution—the Groundwater Vulnerability Tool (GVTool). This application incorporates features from DRASTIC Model and adds three more GIS-based qualitative methods: GOD, SINTACS, and SI. Also, new functionalities were included: (i) Comparative Analysis, allowing one to perform more detailed comparisons between different groundwater vulnerability maps; (ii) Symbology to reclassify the symbology of the maps; and (iii) Map Statistics to estimate the statistics of the resulting maps. Therefore, the user can evaluate the weaknesses and strengths of each method and decide which is the most suitable method to represent groundwater vulnerability in a specific study area.
3. Implementation of GVTool
is accessed through a button that opens a window comprising nine menus (Figure 1
). The first menu allows one to open vector or raster files to the canvas. The next four menus correspond to the vulnerability index menus. The fifth menu (Comparative Analysis
) allows one to perform detailed comparisons between the vulnerability maps generated in previous menus. The Map Statistics
menu allows one to compute the statistics of a vulnerability map. The Symbology
menu allows one to reclassify index values and to apply a specific symbology, defined by the user, to a specific map or sets of maps. The last menu includes the help
related to the application.
In each vulnerability index menu, there are a certain number of buttons according to the methodology (Figure 1
). Each button opens a window composed of a set of parameters to be defined. Some of the parameters are similar in all windows, such as inputs, cell size, extent, attributes, and outputs. The ratings can be defined in a table with the default descriptions of the method. However, the user can import the existing characteristics in the attribute table (always according to the attribute chosen) and then manually assign the ratings. In this respect, since GVTool
shares some implementation features with DRASTIC Model
, a more detailed explanation may be found in Duarte et al. [42
]. The GVTool
structure diagram is presented in Figure 2
Some vulnerability factors, namely, depth to groundwater, net recharge, and topography rely on the creation of raster files. For this purpose, GVTool
, similar to DRASTIC Model
, computes pixel values through one of the following spatial interpolation methods: (i) inverse distance weighting [50
]; (ii) Kriging [51
]; (iii) cubic spline approximation [52
]; or (iv) spatial approximation using spline with tension [53
3.1. Depth to Groundwater
The Depth to Groundwater factor is used in all four methods. It is defined as D in DRASTIC and GOD indices, G in SI, and S in SINTACS method. The code developed to implement the four indices was similar. However, in SINTACS there are not classes to modify (see Section 3.7
). This factor represents the depth to the water table. The interface includes two methods: base and improvement. The base method is used when enough information regarding the depth to groundwater is available (usually water table depth values measured in wells). In this case, depth values are interpolated to create the corresponding raster. If this information is unavailable, the improvement method is applied, considering a Digital Elevation Model (DEM) and a simple hydrogeological conceptual model, which relies on the user knowledge on the local relation between depth to groundwater and landforms (as described in Duarte et al. [42
]). The alternative method generates a surface through drainage network segments (rivers or streams) with values ranging from 0 m to a maximum depth value, which can be modified by the user. A distance raster is created from drainage network segment data. More information is available in Duarte et al. [42
]. The depth to groundwater surface is created from drainage network segments, which are generated from DEM. In the D and G parameters, from DRASTIC and GOD, respectively, the same ratings are assigned. In the remaining parameters from GOD and SINTACS, the ratings are different.
3.2. Net Recharge
The Net Recharge factor is used in DRASTIC and SI as R, and in SINTACS as I. The implementation of this method was also similar in the first three cases, differing in SINTACS in the ratings and the implementation (see Section 3.7
). The implemented methodology includes three methods, as described by Duarte et al. [42
]: (i) in the base method the Net Recharge raster is obtained from a simplified water budget. which estimates recharge from precipitation, overland flow, and evapotranspiration; (ii) the second method relies on recharge rates available in regional hydrogeological studies and assumes that the spatial variability of this process is minor; and (iii) the third method may be applied in areas where the spatial variability of precipitation is mainly controlled by altitude and requires a regional recharge rate, a DEM, and a regression model expressing precipitation as a function of altitude.
3.3. Factors Related to Soil, to The Unsaturated Zone, and to Aquifer Features
The Aquifer factor is defined as Aquifer Media (A) in DRASTIC, Aquifer Lithology (A) in SI, and Acquifero (A) in SINTACS. The implemented methodology is similar in all cases. The user defines the ratings to each geological unit represented in the input shapefile, according to the geological map, in order to create a raster file. Each vulnerability index assigns its own ratings.
The Soil factor is defined as Soil Media (S) in DRASTIC and Tipologia della copertura (T) in SINTACS. The implementation is also similar in both methods, only differing in the ratings assigned. The functionality assigns the ratings to the input shapefile, according to the soil map, and a raster is created based on that information.
The LU graphic interface from SI method allows the possibility of importing the attribute table and to assign ratings according to the land cover classes represented in the input shapefile.
The Topography factor is used in DRASTIC and SI as Topography (T) and as Superficie Topografica
(S) in SINTACS. The implemented methodology was similar in all methods, except in the ratings attribution in SINTACS, as explained in Section 3.7
. The methodology implemented allows one to create a DEM from quoted points or to insert the DEM as an input. The Geographic Resources Analysis Support System (GRASS) algorithm r.slope.aspect
was used to compute the slope map and the reclassification of the values was implemented through reclassifyvalues
from System for Automated Geoscientific Analyses (SAGA). The assigned ratings are defined according to the method.
3.5. Hydraulic Conductivity
The Hydraulic Conductivity factor is used in DRASTIC as C and SINTACS as Conducibilita
(C). The Hydraulic conductivity values may be available from previous regional studies and are inserted by the user in the respective attribute table for each geological unit. Alternatively, the user may apply typical values according to the geological materials occurring in the aquifers. Section 3.7
explains the implementation of C ratings in SINTACS.
3.6. SINTACS Soggicenza, Infiltrazione, Conducibilita, and Superficie Topografica Factors
The assignment of ratings in S, I, C, and S factors is different from the corresponding procedures adopted in the remaining vulnerability indices. The ratings are assigned based on curves defined for SINTACS [20
]. The automatic assignment of ratings was carried out in several steps. First, the (x,y) coordinates of the points that compose the curve were obtained using the Engauge Digitizer
open source software (http://markummitchell.github.io/engauge-digitizer/
). Subsequently, a text file of the curve (x,y) coordinates was created and added to the application folder. The application can automatically read the input values and reclassifies the raster accordingly. The raster of each factor is then created with the assigned ratings. This process is the same for each factor, where the ratings are defined by a curve [20
3.7. Final Maps
The final maps resulting from the application of groundwater vulnerability assessment methods are obtained from the direct application of Equations (1) to (4) presented in Section 2
. The graphic interface is normally composed of the input fields, according to the number of parameters, and by one output field linked to the final vulnerability map. In the case of SINTACS, the last functionality opens a window (Figure 3
) with six options according to Civita [20
In this window Figure 3
b the user must choose the adequate impact according to the knowledge of the study area. Then, a new window shows up with the predefined weights and the input and output fields (Figure 3
). The weights can be also modified by the user.
3.8. Comparative Analysis Menu Implementation
The Comparative Analysis menu was created to carry out an enhanced side-by-side vulnerability map comparison by subtracting pixel values from two raster maps with normalized index values. It is composed of two input fields, and four spin boxes with predefined values corresponding to the minimum and maximum values of raster and the minimum and maximum values of the normalization interval, and one output field. Since each final map has different index scales and intervals, the functionality was automatized with a process of normalization of the predefined set of values in each map. A spin box was created to perform this possibility of selection. The normalization was implemented through r.rescale algorithm from Geographic Resources Analysis Support System (GRASS). The comparative map provides the differences between two indices. The greater the absolute value for every pixel of the comparative map, the greater the contrast of groundwater vulnerability. To perform the differences, the r.mapcalc.simple algorithm from GRASS was implemented in the functionality.
3.9. Map Statistics
The Map Statistics menu was also incorporated in the GVTool, with a functionality through which the user can define the input vulnerability map and the output file with the computed statistics (minimum, maximum, mean, standard deviation, and range of index values). This implementation was based on rasterlayerstatistics algorithm from QGIS. The output file of this algorithm is an html file. However, this functionality has an option to create the statistic variables in a word pad file through a check box option.
3.10. Apply Symbology
Through the Apply Symbology
menu, the user may define new vulnerability classes (by defining new index intervals) and apply a specific symbology to a certain vulnerability map or group of maps. This procedure is useful to compare different vulnerability maps using harmonized classes (Figure 4
). To implement this functionality, a table was created, with predefined classes for each index, using QTableWidget
The first group of this functionality is composed of a table including the four implemented methods and the respective predefined vulnerability classes. The user can change these values or keep the default ones. In the second line of the table, the user finds a combo box, which allows one to choose the raster file (which must be open in QGIS canvas). The check boxes are intended for selecting the maps to which the new index classes and symbology will be applied. The output field allows one to save a .qml file with the style saved.
4. GVTool Illustrative Application
In this section, we illustrate the GVTool application and performance in a study area in order to highlight the applications’ ability to assess groundwater vulnerability to pollution. A possible workflow is presented; however, GVTool could be employed in many different ways, depending on the hydrogeological features of each study area, as well as on the user requirements.
The study area is a sector from Serra da Estrela mountain (Central Portugal) ,which was chosen to illustrate the application of GVTool given its demanding groundwater vulnerability assessment conditions, particularly the complexity of the respective hydrogeological system and the data scarcity concerning some vulnerability factors, as explained in the following sections.
4.1. Hydrogeological Framework
was tested in a sector of Serra da Estrela mountain (40° 15–38′ N; 7° 18–47′ W), Central Portugal, with an area of 116.9 km2
, and an altitude ranging from 875 m in Manteigas town to 1993 m at the Torre summit, which is the highest point on the Portuguese mainland (Figure 5
Serra da Estrela is located in the Central-Iberian Zone of the Iberian Massif and the most important tectonic structure is the NNE-SSW Bragança-Vilariça-Manteigas fault zone. The main geological units are Variscan granitic rocks, Pre-Cambrian metasedimentary rocks and Cenozoic Sedimentary Cover, alluvia, and glacial deposits.
The Serra da Estrela climate is typically Mediterranean, but, to the northwest (NW), an Atlantic influence prevails [55
]. According to Iberian Climate Atlas (AEMET-IM) [57
], these climatic features are also expressed by the Köppen-Geiger classification, since the south-eastern (SE) part of the mountain is mainly of the climate subtype Csa (warm temperate, with dry and hot summers), while in the north-western area the climatic subtype is Csb (warm temperate, with dry and warm summers).
The mean annual precipitation is around 2500 mm in the highest areas of Serra da Estrela. On the western side of the mountain the total precipitation is lower than that on the eastern side, and it increases with elevation [58
]. The data concerning snow precipitation is scarce. The mean annual air temperature is less than 7 °C in the upper part of the mountain and is less than 4 °C at the vicinity of the Torre summit [59
According to Espinha Marques et al. [60
], the dominant soils at the studied sector are: (i) Humic, Leptic, and Skeletic Umbrisols; (ii) Lithic and Umbric Leptosols; (iii) Umbric Fluvisols; and (iv) Rock outcrops. These soils are coarse textured, acidic, with high humified organic matter content, and the typical soil profile is ACR (A: A horizon; C: C horizon; R: hard bedrock) with an umbric A horizon.
The conditions of the local hydrogeological cycle conditions are mainly determined by the geological, tectonic, pedological, and climatic features, typical of a Mediterranean mountain region. As for groundwater circulation, fractured media exist in poorly weathered granitic and metasedimentary rocks and porous media are dominant in alluvia and quaternary glacial deposits, as well as in the most weathered granites and metasedimentary rocks [61
Urban and agricultural pollution sources are limited to Manteigas town and its vicinity. However, other pollution sources are more widespread throughout the study region: (i) groundwater and surface water pollution resulting from road de-icing operations, namely, due to the use of NaCl and CaCl2
as de-icing agents in the roads that cross the mountain and reach the Torre summit [62
] and; (ii) the impact of wildfire on water bodies, consisting, mainly, of the pollution of aquifers, streams, and ponds caused by pyrogenic polycyclic aromatic hydrocarbons resulting from vegetation combustion [63
4.2. Application of GVTool to The Region of Serra da Estrela
Serra da Estrela mountain corresponds to a protected area where some types of data are scarce or inexistent, such as information regarding depth to water table. The most relevant available data are presented in Table 2
Firstly, the GVTool
was applied to the study area (the Manteigas-Torre sector, Figure 5
) through an approach that consisted of selecting the most suitable ranges and ratings of each groundwater vulnerability factor for each groundwater vulnerability assessment method (Table 3
). This selection was based on hydrogeological research carried out in recent years in Serra da Estrela. For this purpose, the hydrogeological conceptual model presented by Espinha Marques et al. [61
] was particularly relevant. Then, a map for each factor was generated and the final output consisted of four groundwater vulnerability maps, expressing the results according to the DRASTIC, GOD, SINTACS, and SI methods (Figure 6
The maps obtained following this procedure applied index intervals in accordance with previous studies, namely, Aller et al. [16
] for DRASTIC, Foster et al. [17
] for GOD, Al Kuisi et al. [47
] for SINTACS, and Stigter et al. [25
] for SI. These maps (Figure 6
) are more suitable to represent the overall spatial distribution of groundwater vulnerability in the case of DRASTIC, SINTACS, and SI, and are less appropriate in the case of GOD. In fact, given the hydrogeological conditions of the Manteigas-Torre sector (e.g., Espinha Marques et al. [61
]) the GOD index map oversimplifies the spatial distribution of groundwater vulnerability, since it represents homogeneous areas encompassing very different conditions (for example, valley bottoms and slopes), which is not realistic.
presents fundamental statistics for each vulnerability index using the GVTool Map Statistics
In the second stage, in order to make vulnerability maps more comparable, a uniform vulnerability classification was defined according to a procedure presented by Lobo Ferreira and Oliveira [66
]. These authors highlighted the difficulty of comparing vulnerability maps representing values from different vulnerability indices (each one with its own vulnerability classes). The accuracy of this procedure is greatly improved by defining the same set of vulnerability classes for all methods. For this purpose, four vulnerability classes were defined (Table 5
), using the Apply Symbology
window: (i) Low, (iii) Moderate, (iv) High, and (v) Very High.
From the maps presented in Figure 6
and Figure 7
, we can conclude that: (i) generally, DRASTIC, SINTACS, and SI present more realistic results than GOD; (ii) DRASTIC, SINTACS, and SI are more sensitive than GOD to the spatial impacts of geology, topography, and depth to water table in groundwater vulnerability; and (iii) the application of SI for creating the maps from Figure 6
d and Figure 7
d represents an important improvement over the maps from DRASTIC, GOD, and SINTACS because, due to the inclusion of the LU factor, this method is able to represent the influence on groundwater vulnerability of agricultural and urban land use classes, along with classes corresponding to protected areas from Serra da Estrela Natural Park.
Since DRASTIC, SINTACS, and SI assess groundwater vulnerability through a larger variety of factors, the respective maps are more capable of representing subtler spatial variations than the ones obtained using GOD index. This effect is particularly evident in the second set of vulnerability maps (Figure 7
), obtained with similar vulnerability index intervals. Moreover, these vulnerability maps express more clearly a shift of SINTACS results towards higher vulnerability values when compared to DRASTIC and SI.
Given the results of the earlier stages of the application of GVTool
to the Manteigas-Torre sector, the functionalities of the Comparative Analysis
menu were applied in order to compare different pairs of vulnerability maps where the value of each pixel was calculated from the subtraction of normalized index values, from 0 to 100 (Figure 8
The (DRASTIC-SINTACS) comparative map (Figure 8
) points out that the results are, in general terms, quite analogous, as should be expected from methods based on similar sets of vulnerability factors. Indeed, both methods tend to deliver higher vulnerability values in areas close to streams and valley bottoms (as illustrated by Figure 7
a,b), where the water table is shallower, and slopes are less steep. However, Figure 8
shows that DRASTIC values are generally higher than SINTACS, especially in steeper slopes and in convex landforms, such as summits and crests. On the contrary, SINTACS values tend to be higher than DRASTIC along the main valley bottom and some of its tributaries.
In most of the study area, the DRASTIC-SI comparative map (Figure 8
) shows that DRASTIC computes higher vulnerability than SI, especially in steeper slopes and convex landforms. However, the effect of the LU factor from SI index is quite noticeable in the northeast (NE) part of the map, in the Manteigas town area, where urban and agricultural land use prevail, and a greater balance between DRASTIC and SI index values is represented.
The application of GVTool to the Manteigas-Torre sector has shown that some methods are more adequate for assessing and mapping groundwater vulnerability in this region than others. Several types of map comparisons were carried out, starting with side-by-side visual map comparisons. In order to reduce the subjectivity of this procedure, other comparisons were made in subsequent stages using the functionalities Apply Symbology and Comparative Analysis. These comparisons revealed that DRASTIC, SINTACS, and SI perform better than GOD when applied to this study area.
The overall vulnerability values tend to be lower in the SI map, intermediate in the DRASTIC map, and higher in the SINTACS map. In order to strictly represent the intrinsic groundwater vulnerability, DRASTIC is the most balanced choice. However, the representation of the influence of specific urban and agricultural land use classes is only achieved by means of the SI map.
The sustainable management of water resources at different scales requires mapping tools, namely, GIS-based tools, which are crucial for improving the decision-making processes involving water users, private companies, public institutions, and authorities. Groundwater vulnerability maps are fundamental for spatial planning, since these instruments may be used to identify, and if necessary, to protect the most sensitive areas, for instance, by removing or preventing the installation of potentially pollutant economic activities. GVTool is a new GIS-based groundwater vulnerability assessment tool developed under QGIS, a free and open source application, with the purpose of providing a friendly and accurate application for creating and comparing groundwater vulnerability maps using four well-established methods: DRASTIC, GOD, SINTACS, and SI. GVTool can be improved, modified by any user, or even adapted to different hydrogeological contexts in different regions. This possibility enhances the usability of GVTool in the open source software context, since it is friendly and intuitive, and it is available to any user of any scientific area, familiarized or not with GIS software. The application of different vulnerability assessment methods to a certain study area will often produce contrasting results, and consequently, the comparison of different vulnerability maps is a central issue in the application of GVTool. Using different groundwater vulnerability assessment methods may result in contrasting maps, expressing different spatial distributions of vulnerability index values and classes. GVTool has the capability of helping to identify the most appropriate methods for a particular study area, and even to decide if the best choice is to create a composite vulnerability map.
The GVTool workflow may be easily adapted to the hydrogeological features of each study region. With the purpose of illustrating the GVTool functionalities and capabilities, a groundwater vulnerability assessment was carried out in the Manteigas-Torre sector. Side-by-side map comparisons were performed, which highlighted the way specific vulnerability factors influence the computed index values. Additionally, GVTool helped perceive if each method tended to shift index values towards higher or lower vulnerability classes. The application may also be very useful in integrated assessments, allowing multidisciplinary and interdisciplinary approaches and helping researchers to choose the best method or combination of methods to express groundwater vulnerability to pollution.
In order to address some limitations of the present GVTool version, the application will be improved with further functionalities, such as: (i) the addition of groundwater vulnerability assessment methods more suitable for fractured and karstic environments; (ii) to increase GVTool flexibility by providing the user the possibility of modifying the implemented indices, and even to create their own index, i.e., the user can compose the index with the parameters, weights, and ratings better adjusted to the study area characteristics; (iii) the addition of a functionality dedicated to combining two or more ordinary vulnerability maps into a composite vulnerability map; and (iv) to couple GVTool with a new GIS-based tool, dedicated to delineating wellhead protection areas (this tool will be implemented by the authors in the near future).