ABSTRACT
The study demonstrates the utilization of the Quantum Geographic Information System (QGIS) package, an open-source geographic information system (GIS) tool for evaluating the quantity and quality of groundwater resources. This research employed data from public sources pertaining to several sampling stations in the Indian city of Dehradun as its case study and converted this information into a digitized format. The use of QGIS allows for processing and analysis of huge data, leading to the creation of informative maps that depict the spatial distribution of groundwater characteristics. By employing spatial analysis techniques, the study aimed to gain insights into the state of groundwater resources, including their availability, variations in level, and suitability for drinking and irrigation purposes. By visualizing and analyzing the groundwater data using QGIS, the study contributes to the broader goal of managing water resources efficiently and helps policymakers, water resource managers, and other stakeholders make informed decisions regarding groundwater management, allocation, and conservation strategies.
HIGHLIGHTS
Easily monitor groundwater resources using open-source GIS software, QGIS.
Improved data visualization for efficient water resource management.
Easy identification of locations requiring water harvesting to increase levels.
Determines drinking and irrigation appropriateness by geographical analysis.
Research enhances groundwater management and conservation.
INTRODUCTION
Fresh water is an essential component for the preservation of all forms of life; the issue of inadequate access to this resource is one that is felt in every part of the world (Fredriksson 2016; Singha & Singha 2023). As a result of the ever-increasing demand for freshwater that is caused by the rise in population, the focus has shifted to the protection and management of groundwater (Baalousha & Lowry 2022; Polemio & Voudouris 2022). To effectively manage water resources, it is necessary to handle a substantial quantity of data, and the instrument that is currently the most effective and efficient for doing so is geographic information systems (GIS) (Dadhich et al. 2016; Stevović & Nestorović 2016; Kumar & Singh 2018; Habeeb & Weli 2021; Kpiebaya et al. 2022; Radulović et al. 2022; Hosseininia & Hassanzadeh 2023; Noori & Singh 2023; Rukshar et al. 2023). Management of water resources is one of the necessary steps towards effective planning, development, and distribution of water resources, as well as for the most efficient use of water resources (Loucks & van Beek 2017; Criollo et al. 2019). Water resources management, which involves planning, developing, distributing, and managing the optimum use of water resources, needs to adapt well to the current and future need of allocation of water (Criollo et al. 2019). In order to manage groundwater resources, a wide range of physical and chemical parameters must be monitored and evaluated on a regular basis (Kumar et al. 2017). In addition to geology and isotopes, these characteristics, which are used to conceive the behaviour of the environmental system, are commonly stored in various scales and forms, such as maps, spreadsheets, or databases (Criollo et al. 2019). Collecting, preserving, analyzing, and visualizing data are all made easier with the assistance of GIS. In the scientific community as well as the public and corporate sectors GIS software is used widely (Kumar et al. 2022). Platforms based on GIS can be utilized to conduct assessments of local and regional water quality, water supply, zone mapping, and risk (Criollo et al. 2019). Depending on the local water table depth, groundwater quality varies greatly. It is primarily controlled by the amount and content of dissolved solids, although it also changes with the seasons. Managing and protecting groundwater resources depends critically on the groundwater quality assessment made possible by GIS, particularly given their sensitivity to contamination from both natural and human causes (Ram et al. 2021). Groundwater models produced with appropriate groundwater quality data transformed into GIS databases provide an accurate evaluation of the geographical distribution of groundwater quality and how it varies over time and with changes in land use. Information regarding the risk of groundwater contamination may assist in the selection of suitable locations for specific activities, thereby minimizing the adverse effects on groundwater and facilitating the more effective management of groundwater resources (Loucks & van Beek 2017). Researchers utilize several technologies such as ArcGIS (Mahboob et al. 2017; Krishan et al. 2023), QGIS (De Filippis et al. 2019; Azam et al. 2022; Kpiebaya et al. 2022), and Modular Finite-Difference Flow Model (MODFLOW) (De Filippis et al. 2019) for groundwater quality evaluation. ArcGIS is a widely utilized platform for geospatial analysis of groundwater; however, it operates on a subscription model. In contrast, QGIS serves as an open-source alternative, providing a cost-effective solution for geospatial analysis. MODFLOW is used for modeling groundwater flow, sometimes in combination with GIS technologies; yet it necessitates substantial data and is complex. A comparison of several GIS systems for groundwater quality research, together with their unique features, advantages, and limitations, is presented in Table 1. The QGIS programme was chosen for this investigation because it outperformed other GIS systems, even in the most demanding computer environments (Chen et al. 2010). This study uses spatial interpolation techniques to examine the geographical distribution of groundwater quality measurements to identify pollution hotspots, understand contamination sources, and develop enhanced management strategies in the Dehradun district using QGIS, an open-source GIS software. Dehradun relies heavily on its groundwater sources for drinking water supply (National Institute of Hydrology 1997). The contour tool of the QGIS software was employed in conjunction with the inverse distance weighted (IDW) (Ilayaraja & Ambica 2015; Raghav & Singh 2021; Shukla et al. 2021) interpolation technique to enhance the spatial representation of water quality parameters on thematic maps. IDW interpolation is favoured over other geostatistical methods such as Kriging because of its rapid visualizations. simplicity, speed, and computational efficiency, especially for smooth spatial patterns and the ability to capture general trends by emphasizing closeness rather than intricate spatial correlations (Munyati & Sinthumule 2021). There is a scarcity of studies evaluating the quantity and quality of groundwater resources in the Dehradun district using QGIS software. This study enhances the effective monitoring of groundwater levels and quality, hence supporting informed decision-making and superior management in more locations.
Comparison of various GIS technologies for groundwater quality studies
Technology . | Features . | Advantages and disadvantages . | References . |
---|---|---|---|
Remote sensing and GIS |
|
| Arulbalaji et al. (2019) |
Spatial interpolation technique |
|
| Ahmad et al. (2021), Raghav & Singh (2021), Shukla et al. (2021) |
Geostatistical techniques |
|
| Ilayaraja & Ambica (2015), Mahboob et al. (2017), Polemio & Voudouris (2022), Karim et al. (2024) |
Water quality index (WQI) technique |
|
| Ram et al. (2021), Tefera et al. (2021), Karim et al. (2024) |
Multicriteria decision technique (MCDT) |
|
| Kpiebaya et al. (2022), Mandal et al. (2023) |
Technology . | Features . | Advantages and disadvantages . | References . |
---|---|---|---|
Remote sensing and GIS |
|
| Arulbalaji et al. (2019) |
Spatial interpolation technique |
|
| Ahmad et al. (2021), Raghav & Singh (2021), Shukla et al. (2021) |
Geostatistical techniques |
|
| Ilayaraja & Ambica (2015), Mahboob et al. (2017), Polemio & Voudouris (2022), Karim et al. (2024) |
Water quality index (WQI) technique |
|
| Ram et al. (2021), Tefera et al. (2021), Karim et al. (2024) |
Multicriteria decision technique (MCDT) |
|
| Kpiebaya et al. (2022), Mandal et al. (2023) |
STUDY AREA
An image of the research area's map, displaying Dehradun's groundwater stations (own work).
An image of the research area's map, displaying Dehradun's groundwater stations (own work).
METHODOLOGY
In the analysis of geographical groundwater data using QGIS, 15 sampling stations were selected for study. The data used for analysis is sourced from India's Central Ground Water Board (CGWB), Ministry of Jal Shakti, Department of Water Resources, River Development, and Ganga Rejuvenation, Government of India. The relevant information extracted from the CGWB site is organized and presented in Tables 2 and 3. To incorporate the geographical data into the analysis, the shape file of the Dehradun map is georeferenced using the georeferencing tool in QGIS. This process aligns the map to its correct spatial location, allowing for an accurate overlay of the groundwater station data onto the base map of Dehradun. The geographical data of the groundwater stations, obtained from the CGWB WRIS, are digitized and superimposed onto the base map of Dehradun. This integration enables a comprehensive investigation of both the level and quality of groundwater in the region. The use of QGIS in this process provides a powerful tool for visualizing and analyzing geographical groundwater data, contributing to a better understanding of the groundwater situation in Dehradun.
Groundwater levels at various sampling stations in Dehradun
S. No. . | Ground water stations . | Latitude . | Longitude . | Basin . | Level (m) . |
---|---|---|---|---|---|
1 | Lal tapar | 14.640 | 30.120 | Ganga | 16.95 |
2 | Kanwali | 12.800 | 30.315 | Ganga | 11.48 |
3 | Chhorba | 21.010 | 30.419 | Ganga | 21.50 |
4 | Kuanwala | 5.730 | 30.245 | Ganga | 5.15 |
5 | Ramgarh | 6.440 | 30.331 | Ganga | 6.02 |
6 | Motichur | 11.030 | 30.013 | Ganga | 11.03 |
7 | Rishikesh | 6.490 | 30.105 | Ganga | 5.32 |
8 | Jhajra | 13.110 | 30.340 | Ganga | 7.91 |
9 | Nanda ki Chauki | 10.620 | 30.347 | Ganga | 13.68 |
10 | Redapur | 7.520 | 30.404 | Ganga | 6.43 |
11 | Rampura | 5.260 | 30.370 | Ganga | 9.34 |
12 | Selakui | 15.740 | 30.354 | Ganga | 14.19 |
13 | Majra | 4.430 | 30.292 | Ganga | 24.20 |
14 | Sabhawala | 7.730 | 30.369 | Ganga | 10.14 |
15 | Singhniwala | 9.090 | 30.331 | Ganga | 7.53 |
S. No. . | Ground water stations . | Latitude . | Longitude . | Basin . | Level (m) . |
---|---|---|---|---|---|
1 | Lal tapar | 14.640 | 30.120 | Ganga | 16.95 |
2 | Kanwali | 12.800 | 30.315 | Ganga | 11.48 |
3 | Chhorba | 21.010 | 30.419 | Ganga | 21.50 |
4 | Kuanwala | 5.730 | 30.245 | Ganga | 5.15 |
5 | Ramgarh | 6.440 | 30.331 | Ganga | 6.02 |
6 | Motichur | 11.030 | 30.013 | Ganga | 11.03 |
7 | Rishikesh | 6.490 | 30.105 | Ganga | 5.32 |
8 | Jhajra | 13.110 | 30.340 | Ganga | 7.91 |
9 | Nanda ki Chauki | 10.620 | 30.347 | Ganga | 13.68 |
10 | Redapur | 7.520 | 30.404 | Ganga | 6.43 |
11 | Rampura | 5.260 | 30.370 | Ganga | 9.34 |
12 | Selakui | 15.740 | 30.354 | Ganga | 14.19 |
13 | Majra | 4.430 | 30.292 | Ganga | 24.20 |
14 | Sabhawala | 7.730 | 30.369 | Ganga | 10.14 |
15 | Singhniwala | 9.090 | 30.331 | Ganga | 7.53 |
Qualitative data at various sampling stations in Dehradun
S. No. . | Ground water stations . | Ca (mg/L) . | Cl (mg/L) . | F (mg/L) . | K (mg/L) . | Mg (mg/L) . | Na (mg/L) . | EC at 25 °C (μmhos/cm) . | pH . | Total hardness (mg/L) . |
---|---|---|---|---|---|---|---|---|---|---|
1 | Lal tapar | 4 | 3.5 | 0.06 | 1 | 38 | 7 | 391 | 8 | 170 |
2 | Kanwali | 24 | 14 | – | 1 | 36 | 74 | 713 | 8 | 210 |
3 | Chhorba | 32 | 11 | – | 0.4 | 26 | 18 | 430 | 8 | 190 |
4 | Kuanwala | 24 | 7.1 | 0.08 | 1.5 | 12 | 6.9 | 270 | 8.03 | 110 |
5 | Ramgarh | 56 | 7 | – | 2 | 26 | 9 | 482 | 8 | 250 |
6 | Motichur | 40 | 3.5 | 0.2 | 2 | 7.2 | 23 | 334 | 8 | 130 |
7 | Rishikesh | 28 | 11 | 0.1 | 1 | 14 | 7 | 275 | 8 | 130 |
8 | Jhajra | 28 | 7 | 0.1 | 1 | 14 | 9 | 274 | 8 | 130 |
9 | Nanda ki Chauki | 28 | 14 | 0.1 | 1 | 15 | 15 | 330 | 7 | 130 |
10 | Redapur | 8 | 3.5 | – | 1 | 2.4 | 14 | 112 | 8 | 30 |
11 | Rampura | 32 | 11 | – | 1 | 2.4 | 10 | 210 | 8 | 90 |
12 | Selakui | 20 | 3.5 | 0.01 | 1 | 14 | 14 | 204 | 8 | 80 |
13 | Majra | 76 | 7 | 0.09 | 1 | 14 | 14 | 541 | 8 | 250 |
14 | Sabhawala | 2 | 7 | – | 1 | 47 | 11 | 403 | 8 | 200 |
S. No. . | Ground water stations . | Ca (mg/L) . | Cl (mg/L) . | F (mg/L) . | K (mg/L) . | Mg (mg/L) . | Na (mg/L) . | EC at 25 °C (μmhos/cm) . | pH . | Total hardness (mg/L) . |
---|---|---|---|---|---|---|---|---|---|---|
1 | Lal tapar | 4 | 3.5 | 0.06 | 1 | 38 | 7 | 391 | 8 | 170 |
2 | Kanwali | 24 | 14 | – | 1 | 36 | 74 | 713 | 8 | 210 |
3 | Chhorba | 32 | 11 | – | 0.4 | 26 | 18 | 430 | 8 | 190 |
4 | Kuanwala | 24 | 7.1 | 0.08 | 1.5 | 12 | 6.9 | 270 | 8.03 | 110 |
5 | Ramgarh | 56 | 7 | – | 2 | 26 | 9 | 482 | 8 | 250 |
6 | Motichur | 40 | 3.5 | 0.2 | 2 | 7.2 | 23 | 334 | 8 | 130 |
7 | Rishikesh | 28 | 11 | 0.1 | 1 | 14 | 7 | 275 | 8 | 130 |
8 | Jhajra | 28 | 7 | 0.1 | 1 | 14 | 9 | 274 | 8 | 130 |
9 | Nanda ki Chauki | 28 | 14 | 0.1 | 1 | 15 | 15 | 330 | 7 | 130 |
10 | Redapur | 8 | 3.5 | – | 1 | 2.4 | 14 | 112 | 8 | 30 |
11 | Rampura | 32 | 11 | – | 1 | 2.4 | 10 | 210 | 8 | 90 |
12 | Selakui | 20 | 3.5 | 0.01 | 1 | 14 | 14 | 204 | 8 | 80 |
13 | Majra | 76 | 7 | 0.09 | 1 | 14 | 14 | 541 | 8 | 250 |
14 | Sabhawala | 2 | 7 | – | 1 | 47 | 11 | 403 | 8 | 200 |
GUIDELINES FOR INTERPRETING HEAT MAPS
To visually understand the level and quality of groundwater, heat maps are generated. Heat maps use colour gradients to represent the severity of the problem, indicating areas with higher or lower groundwater levels and highlighting variations in groundwater quality across the study area. Valuable insights can be gained regarding the level and quality of groundwater in the Dehradun region by creating heat maps and analyzing the spatial distribution of groundwater data. This analysis helps in identifying areas with critical groundwater issues, supporting effective decision-making and management of groundwater resources. The validation procedure for heat maps and spatial interpolation methodologies is of paramount importance for accurate analysis. The validation process for a heat map entails assessing whether the visual representation faithfully corresponds to the underlying data. This includes ensuring that significant patterns and trends are appropriately emphasized, as well as confirming that the colour gradients, legend nomenclature, and contextual data are suitable for interpretation (Vacanti 2019; Sibrel et al. 2020). This validation is performed by means of comparisons with data obtained from the CGWB, the Ministry of Jal Shakti, the Department of Water Resources, River Development, and Ganga Rejuvenation, Government of India. Adhering to the guidelines outlined below facilitates a deeper understanding of heat maps and enables the extraction of insights from patterns, trends, and anomalies within the data.
Identification of the data
Identify the variable and the kind of data being represented, specifying whether it is numerical or categorical. Numerical data are quantitative, but categorical data are qualitative in character. Ascertain the objective of the visualization, whether it seeks to illustrate correlation, distribution, density, or another metric.
Comprehend the data context
Comprehend the context of the data, regardless of whether it is performance-related, spatial, or time-series data. A heat map's interpretation can be altered by the context of the data. If the heat map displays time series data, it is important to observe time-related trends, including seasonality, surges, or periodic patterns.
Examine the axes
Identify the representations of the x and y axes, which may include time, categories, spatial locations, or continuous variables. Familiarization with each axis facilitates a better interpretation of the correlations or comparisons depicted by the heat map.
Examine the legend and its colour
The heat map employs a colour gradient to denote values. Darker or more intense legend's colours often signify greater values, whereas lighter colours denote lower values. Custom colour schemes may even be employed for particular data categories. Examining the legend's colour is crucial for comprehending accurate mapping.
Identify clusters and patterns
Focus on regions with comparable colour intensity. Similar-coloured clusters can be used to identify patterns or groupings within the data. Symmetric patterns or colour repetitions on the map might draw attention to recurring patterns or relationships in the data.
Observe any annotations
Annotations are intended to draw attention to critical points, clarify unusual values, or direct readers to areas of interest. Locate any text, numerals, or symbols that are superimposed on the thermal map.
Detecting anomalies
It is possible to identify substantial deviations from the norm by examining the anomalies, which may be crucial for decision-making or additional research. Outliers frequently manifest as isolated colour blocks that exhibit a stark contrast to their surroundings. These may be anomalies, rare occurrences, or data errors.
Spatial interpolation method
The contour tool of the QGIS software was employed in conjunction with the IDW interpolation technique to enhance the spatial representation of water quality parameters on thematic maps. IDW interpolation is favoured over other geostatistical methods due its rapid visualizations. Simplicity, speed, and computational efficiency, especially for smooth spatial patterns and the ability to capture general trends by emphasizing closeness.
RESULTS AND DISCUSSION
Current level of groundwater
Quality of groundwater
In assessing groundwater quality, parameters, such as pH, calcium, sodium, absorption ratio, total dissolved solids (TDS), electrical conductivity (EC) at 25 °C, and nitrate concentration (NO3), are crucial as they provide valuable insights into the water's overall chemistry, often reflecting the geological formations it passes through and potential issues related to water hardness, salinity, and its suitability for drinking and agricultural use, particularly in specific regions (Singh & Hussian 2016; Kumar et al. 2022). It becomes possible to assess the quality of the groundwater, identify potential contamination sources, monitor changes over time, and make informed decisions regarding water resource management and treatment processes by digitizing and analyzing groundwater wells using these parameters. Industrial waste, sewage, fertilizers, and pesticides from urbanization and agriculture pollute groundwater. Groundwater management is needed to provide drinking water and safeguard ecosystems. Inadequate urban waste management and excessive agricultural pesticide usage can severely harm groundwater quality (Pradhan et al. 2023). The analysis results can help determine the suitability of groundwater for various purposes, such as drinking water, irrigation, and industrial uses, and guide actions to ensure the protection and conservation of this vital resource.
Calcium
Electrical conductivity
Chlorides
Magnesium
Fluoride
Sodium
pH
Total hardness
CONCLUSION
The research indicates that the water table is in a state of decline, with most of the Dehradun district experiencing water levels that exceed 15 mbgl. The spatial distribution of groundwater levels before and after the rainy season indicates that there has been an increase in water levels in numerous locations because of rainfall. This assists in the identification of areas that require special consideration for water harvesting to enhance the water level. The spatial distribution of qualitative parameters including pH, EC, total hardness, and calcium, chlorides, magnesium, fluorides, and sodium major ion concentrations, indicates that the most of area except a few patches in Doiwala and Raipur areas of Dehradun, the water is suitable for human consumption and irrigation purposes. This study highlights the importance of regular groundwater monitoring to ensure sustainable water resource management. The study's accuracy increases as the number of datasets increases. QGIS aids in visualizing and analyzing the groundwater data and contributes to the broader goal of managing water resources efficiently and helps policymakers, water resource managers, and other stakeholders in making informed decisions for sustainable groundwater resource management, pollution control, and land use planning in the region.
FUNDING
No funding was received to assist with the preparation of this manuscript.
AUTHOR CONTRIBUTION
A.M. conceptualized and investigated the study, manuscript preparation, reviewed and edited the article; N.A.S. conceptualized and reviewed the process; P.M. reviewed and edited the article; G.P. reviewed the work; N.S.M. reviewed and editing. The final manuscript was read and approved by all authors.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.