Identification of sustainable groundwater recharge plan using AHP-, RS-, and GIS-based MCDA: a case study of Kawardha District of Chhattisgarh, India

Groundwater is a key source of fresh water and essential for the health and well-being of all living beings. It plays a vital role in supporting economic development and meeting human needs. However, the over-exploitation of groundwater has become a significant concern. To address this issue and prevent excessive extraction, it is important for authorities to consider implementing legislative measures. Major reasons for groundwater depletion are population growth, urbanization, industrialization, deforestation, and climate change, as well as mis-management of the existing water resources (Nassery et al. 2017; Baghel et al. 2023). The need for high-quality water has greatly expanded in recent years due to a growth in agricultural, industrial and domestic activities. Groundwater is the most widely used fresh water entity due to purity and easily availability (Dinka et al. 2015; Maghrebi et al. 2021). Poor management of fresh water resources could lead to significant problems in the future, especially if groundwater is prioritized for use by households, agriculture, and industrial sectors (Kuylenstierna et al. 1997). Global food security and economic growth highly depends on groundwater resources towards qualitative aspects and sustainable resources (Tortajada & González-Gómez 2022). In India, many regions are experiencing a decline in groundwater levels at a rate of approximately 1–2 m per year (Singh & Singh 2002). India is now the largest groundwater user in the world. About 70% of groundwater withdrawn globally is used in the agriculture sector and irrigation in the arid/semi-arid counties (Maroufpoor & Shiri 2020; Mukherjee et al. 2022). The significance of groundwater resources in India is underscored by the fact that approximately 87% is used for agriculture, while the remaining 11 and 2% are allocated to domestic and industrial purposes, respectively (Mukherjee 2018). India has vast geographical features and climatic variability along with uncertainty, proper management of the water resources is quite difficult due to interstate disputes. India is blessed with a sufficient amount of rainfall as well as snowmelt fresh water from the river system (Prasad & Roy 2005). Due to inadequate management of water resources in India, much of the runoff flows from upstream to downstream areas and is excessively discharged into the ocean (Jain et al. 2007). Adverse climatic conditions like drought also occurred in various places in India. Hence, groundwater is the only source of water for survival as well as food security (Shah & Mishra 2020).

Climatic variability, particularly in terms of rainfall intensity, plays a major role in determining infiltration rates and runoff generation from a catchment area. The rainfall intensity is high as compared to the infiltration rate, maximum probability of peak runoff in shorter time span and vice versa (Hou et al. 2019). The catchment's curve number is relatively high, which could contribute to increased runoff due to insufficient infiltration rates or groundwater recharge (Sahu et al. 2010). Further, various researchers have suggested the groundwater recharge plans have benefitted from the above-mentioned problems (Rajasekhar et al. 2019; Dar et al. 2021). Historical data highlight that India has experienced over exploitation of groundwater without conservation measures. In this context various cases of groundwater depletion were observed in water shortage regions in India. Henceforth, India will be on the edge of a major upcoming water calamity (Singh et al. 2018). The status of groundwater not only depends on single parameters, it is linked with number of spatio-temporal parameters, i.e., precipitation, snow-melt water, topography, land use and land cover patterns, physio-chemical properties of soil, existing water resources, climatic variability, geographical features of the region, etc. However, more or less most important parameter of groundwater contribution is precipitation and existing water resources of the catchment (Shah 2008). In sustainable planning of groundwater resources development, excess runoff management is the major criteria for storage structure inside the catchment area (Chenini et al. 2010). In recent past, most of the conventional methods are based on historical data sets which are adopted for groundwater recharge plans by making the small check dams, ponds, artificial lakes, farm ponds, etc. (Abraham & Mohan 2019). Hydrogeological, geological, and geophysical techniques have been used in a variety of field-based conventional methods to delineate GPZs; however, these methods are often point-based, costly, laborious and time consuming in spatiotemporal information (Doke et al. 2021). Presently, GIS- and RS-based techniques are most widely adopted for groundwater exploration and recharge planning and mitigation (Jat et al. 2009; Beden et al. 2023). The frequency ratio is among the improved methodological strategies for groundwater exploration that various scientists have been working on in recent years (Amponsah et al. 2023). The decision tree model (Stumpp et al. 2016), the maximum entropy model (Rahmati et al. 2016), the fuzzy logic model (Cameron & Peloso 2001), the Dempster-Shafer model (Roy & Datta 2019), the weights of evidence model (Masetti et al. 2007; Tahmassebipoor et al. 2016), the artificial neural network model (Mohanty et al. 2010; Chitsazan et al. 2015), and the logistic regression model have all been effectively utilized towards groundwater studies on a global level.

The AHP is one of the most widely used and respected decision-making methods among various MCDA techniques. Its popularity stems from its structural simplicity, effectiveness, minimal bias, clarity, efficiency, and its ability to consider multiple factors that influence a watershed (Srdjevic & Medeiros 2008; Jhariya et al. 2016; Akinlalu et al. 2021). Saaty created the AHP technique for decision-making in the early 1980s (Saaty 1980). It has a number of benefits and uses quantitative and qualitative techniques to address issues (Chandio et al. 2013). By maintaining natural surface water flow, GPZs can be used for artificial groundwater recharge, thereby enhancing groundwater storage. In regions where aquifers have been depleted due to excessive development, artificial recharge techniques are often employed to boost sustainable productivity (Gnanachandrasamy et al. 2018). The process of exploring groundwater is essentially a geophysical and hydrogeological operation that relies on accurate interpretation of hydrological indicators and evidence (Antony Ravindran 2012). If the necessary steps are not taken promptly, India may face a severe water crisis, with many states already experiencing significant water scarcity. Although the country receives an average of 1,200 mm of precipitation annually, which is more than many comparably sized countries, the distribution of this rainfall is uneven. Consequently, different regions can experience drought conditions despite the overall high levels of precipitation (Pathak & Dodamani 2019; Biswas et al. 2023). In many areas worldwide, groundwater levels have rapidly declined as a result of the monsoon's repeated failure and overuse. Because of the general decline in the groundwater table, there is also less water per person accessible. But significant precipitation is lost because of surface drainage into the ocean (Joshi & Tyagi 1994; Briscoe 2005; Kumar et al. 2005). Areas with higher drainage density promote increased surface runoff compared to those with lower drainage density. Surface water bodies, such as rivers and ponds, can act as recharge zones, enhancing the potential for nearby groundwater replenishment. Therefore, recognizing and measuring these features is critical in developing a groundwater potential model for a specific region. In India, about 42% of its area has been covered with rocky formation. Therefore, it is considered a crucial zone, and managerial measures are very difficult in these areas. Understanding the groundwater conditions completely requires a detailed hydrogeological examination (Saraf & Choudhury 1998).

Groundwater recharge structures have become an important aspect of water management practices in current times due to the increasing scarcity of water resources. The Kawardha district in Chhattisgarh is an area that has been grappling with significant water shortages, largely due to the over-exploitation of groundwater and inadequate water management practices. In this regard, the current study aims to suggest suitable groundwater recharge structures for the Kawardha district of Chhattisgarh. The study will focus on identifying the best possible locations for the implementation of recharge structures, considering the geological, hydrological and topographical characteristics of the region.

The study will explore the implementation of recharging wells, percolation tanks, check dams, and other effective structures to enhance groundwater recharge in the region. It will also recommend management practices to ensure the proper maintenance and long-term sustainability of these recharge structures. These practices will focus on preserving the integrity of the recharge systems and mitigating damage from both natural and human-induced factors. In summary, the study aims to provide valuable insights into the groundwater recharge situation in the Kawardha district of Chhattisgarh and recommend appropriate recharge structures for the area. This will not only address water scarcity issues but also support the sustainable management of groundwater resources in the region.

Slope, geology, rainfall, drainage network, drainage density, soil, geomorphology, lineament, land use land cover (LULC), and relief were analysed to establish a GPZ for the study area. For the purpose, Shuttle Radar Topography Mission (SRTM) DEM was used for the delineation of a drainage network of the study area and validation of drainage pattern, survey of India toposheets have been utilized. Soil data have been acquired from the food and agriculture organization (FAO) site, lineament density map downloaded from the Bhuvan (ISRO), India website. Rainfall fine resolution (0.25° × 0.25°) gridded data have been downloaded from India Meteorological Department (IMD), Pune. Additionally, rainfall data were extracted using Python code and then converted into area-weighted average rainfall with MATLAB and ArcGIS software. The geomorphological map of the study area was obtained from the Bhukosh website, which is maintained by the Geological Survey of India (GSI). Hydrogeological data were manually collected from the Office of the Senior Geohydrologist in Raipur, Chhattisgarh. Furthermore, the LULC map was created using 30-meter resolution imagery from LANDSAT 8-9 OLI/TIRS C2 L1 for the year 2022, which was downloaded from the USGS Earth Explorer website managed by NASA. Observation well discharge data have been collected from the Central Ground Water Board (CGWB) website report. SRTM DEM data were also downloaded from the USGS earth explorer website. Detailed information on data acquisition is shown in Table 1. Processing of entire GIS data sets was done by ArcGIS 10.5 and ERDAS 2016.

To develop a sustainable groundwater recharge plan by the identification of groundwater potential zones, different thematic layers were prepared using remote sensing data, topographic maps, geological maps reports and data from different sources. The satellite data, Landsat 8-9 OLI/TIRS C2 L1 of 30 m resolution geocoded imagery pertaining to year 2022 was downloaded from USGS Earth Explorer for preparation of land use/land cover map. It was generated from the Landsat 8 data in ERDAS IMAGINE software using supervised classification techniques. The different layers such as geology, geomorphology, lineament, drainage density soil, slope, rainfall, and groundwater fluctuation maps were digitized using ERDAS IMAGINE and ArcGIS software SRTM DEM has been used to prepare the slope map of the study area. The pre-monsoon and post-monsoon groundwater level maps were prepared from the data taken from the State Groundwater Department of the bore wells and the inventory wells using interpolation techniques with the help of ArcGIS software. Rainfall maps were also prepared with the help of ArcGIS software using interpolation techniques.

The regular grid-based computation method, with the aid of GIS software, is considered a powerful way to store, process and analyze urban structure, and spatial growth patterns (Yeh & Li 2002). The basic concept of such a method is to divide the whole space into continuous square grids after settling on a predefined cell size (henceforth grid cell); however, an appropriate selection has to be justified enough, which is always a critical step for initializing the geoprocessing environmental settings. A common argument suggests that cell size should be sufficiently large to ensure accurate computational results and interpretation. To determine the optimal cell size, the coverage area of the largest physical features was considered. In Dhaka city, only a small number of buildings (8 out of 270,392) had a coverage area exceeding 10,000 m². Therefore, a grid cell size of 100 × 100 m was chosen, which aligns with common practices in the literature (Yeh & Li 2002; Larondelle et al. 2014) and is based on Dhaka city's building feature. The spatial variability of ECD has to be localized as per one of the basic requirements in spatial analysis, so it was assumed that man-made activities can be considered almost constant within a grid cell size of (100 × 100) sq.m ground area.

The analytical hierarchy process (AHP) was developed by Saaty in 1980 for pairwise comparison for understanding the priority from all the alternatives. This method indicates the relative importance between each other towards output result. This method solves the decision-making process, because of its simplicity and accuracy. This method is adopted for tangible and non-tangible criteria. The AHP process is based on three basic principles: decomposition, prioritization, and synthesis (Saaty 1980).

The decomposition is a complex problem to be decomposed I to different levels which can be manageable with few elements. It is a top to bottom process. The level of decomposing is simplifying the complex problem. A lower level of hierarchy defines detailed elements of the previous one level. The higher one is denoted as global objective.

In the prioritization, the AHP conducts comparisons of the decision elements in a systematic way to determine their priority scales. While paired comparisons are a straightforward concept (crucial for involving non-technically trained experts), their application in the AHP is innovative and unique. Only homogeneous elements are to be compared. The pairwise comparison judgments are made using the AHP's fundamental scale, which consists of absolute values from 1 to 9 and their reciprocals, as shown in Table 2.

MCDA is a highly effective tool that is widely used to solve difficult decision-making issues. The AHP involves structuring a complex decision into a hierarchical model, starting with defining the main goal and breaking it down into criteria and sub-criteria. Decision-makers then use pairwise comparisons to evaluate the relative importance of each criterion and alternative, constructing a matrix to derive weights. These weights are used to rank alternatives, with a consistency check ensuring the judgments are logical. The final step aggregates these results to identify the best alternative, and optionally, sensitivity analysis can be performed to test the robustness of the decision against variations in the inputs (Machiwal et al. 2011; Neshat et al. 2014; Roozbahani et al. 2018; Dey et al. 2021). The step by step concise outline involved in AHP is as follows:

Construct pairwise comparison matrices: For each level of the hierarchy (e.g., criteria, sub-criteria), construct a matrix where each element represents the relative importance of criterion i compared to the criterion j.

Calculate the consistency ratio (CR):

• Consistency Index (CI) is computed and is provided in Equation (3) to evaluate the consistency of relative evaluations of all the criteria and sub-criteria.

  

  (3)
• Compute the CR by dividing CI by the Random Consistency Index (RI):

  

  (4)

  where RI is the average consistency index for random matrices of size n (provided in AHP Table 3). The analysis can proceed with a CR of 0.10 or less, according to Saaty. The judgment must be evaluated if the consistency value is more than 0.10 in order to determine the underlying causes of the inconsistency and make the required modifications (Saaty 1980). If the CR value is 0, which suggests the pairwise comparison has perfect consistency. Because the threshold value is not greater than 0.1, the judgements matrix is comparatively consistent (Bera et al. 2019).

Aggregate of the results:

These equations facilitate a systematic approach to decision-making in AHP, ensuring that decisions are based on structured comparisons and consistent judgments (Malczewski 1999; Saaty 2004; Achu et al. 2020).

For identification of appropriate area a groundwater recharge zone map is prepared using the MCDA technique. In the groundwater potential zones maps areas showing low discharge will be given first preference for groundwater recharge planning (Koohbanani et al. 2018). In general post monsoon the groundwater level map is observed for identifying the appropriate area for groundwater recharge; the areas showing high depth of water in the post monsoon season will be selected for recharge planning.

Assessment of subsurface storage is done on the basis of average depth to water level records. For determining the amount of storage space that is available, contour maps that have been created using average post-monsoon water level data and acceptable contour intervals can be employed. The overall area in which the inter-contour areas between subsequent contours are determined and the amount of subsurface storage space that is open for recharging is determined by multiplying the volume of water below a particular cut-off level by the specific yield of the aquifer material. The cut-off water level is chosen in this manner to prevent water logging in the area after recharging. For our study, 2 m is chosen as the cutoff level.

It is necessary to evaluate the real source water requirement in order to determine the subsurface storage area. It is necessary to specify the average recharge efficiency of each individual structure based on the knowledge gathered from field tests. The amount of water needed for artificial recharge is determined by multiplying the volume of subsurface storage capacity by the reciprocal of the planned structure's proposed recharge efficiency. This results in the total volume of actual source water needed at the surface. In the present study, site selection criteria adopted for check dam, percolation tank and farm pond are illustrated in Table 4.

Geomorphology, which depicts a region's topography and landforms, is one of the crucial factors usually utilized to define groundwater potential zones. Additionally, it provides information on processes including temperature variations, geochemical reactions, and the distribution of different landform features (Kumar et al. 2016a, 2016b; Thapa et al. 2017). In terms of landforms, the area can be divided into two separate regions: (1) the hilly western part and (2) the flat eastern region. The Maikal and Mangata ranges are located in the western region of the district. The district slopes generally in the direction of the east. Landforms such as hills, valleys, plains, plateaus brought about by erosion, and floodplains created by filled river beds may be found in the district's center and southern regions. There are plateaus, hills, and high-level plateaus of volcanic origin in the district's northern portion. The western region of the district is a part of the Banjara sub-basin, while the majority of it is a part of the Seonath sub-basin. The surface drainage system is formed by the tributaries of the Seonath and Banjara rivers. Except for the southwest portion of the Banjara sub-basin, where the gradient is towards the north, the area's overall gradient is toward the southeast. The Maikal mountain ranges of the Satpura border the district's northern and western regions. The southeast has a minimum elevation of 300 m above sea level, while the tallest mountain, Kesmarda, is situated in the northwest at an elevation of 931 m above sea level. In the present study, 12 classes are involved in the geomorphological map of the study area. A detailed description of each class is shown in Table 6. A geomorphology map of the study area is shown in Figure 3(b).

LULC provides groundwater requirements as well as critical data on infiltration, soil moisture, surface water, groundwater, etc. In this study, the LULC map is prepared using supervised classification from Landsat 8 imageries of the year 2022. Six classes of LULC, viz. water bodies, forest, barren land, settlement, agricultural and fallow land were identified and demarcated as shown in Table 7 and Figure 3(c).

Drainage density is primarily derived from the demarcation of streams and stream order shown in Figure 3(d). Drainage density has a significant impact on both groundwater availability and contamination (Ganapuram et al. 2009). The drainage system depends on the lithology and is a significant measure of infiltration rate. Permeability and drainage density have the opposite relationships. Therefore, it is essential in determining the groundwater potential zone. The drainage density of a drainage basin is determined by dividing the total length of its rivers by the drainage basin's area. The area's potential for groundwater is less favourable due to low infiltration caused by high drainage density. The potential of the groundwater is increased more by excessive infiltration caused by low drainage density. The drainage density was prepared and reclassified in five classes, viz. 0–0.706 km/sq.km, 0.707–1.2 km/sq.km, 1.21–1.71 km/sq.km, 1.72–2.28 km/sq.km and 2.29–3.4 km/sq.km shown in Figure 3(e).

Rainfall is the main source of water in the hydrologic cycle and the main determinant of a region's groundwater (Nouaceur & Murărescu 2016). For the current study IMD gridded rainfall data were used for the year 2022 for rainfall map preparation. The spatial distribution of rainfall was mapped using the IDW interpolation method (Earls & Dixon 2007). The rainfall map is categorizing into five classes (1,200–1,400 mm), (1,400–1,500 mm), (1,500–1,600 mm), (1,600–1,700 mm) and (1,700–1,800 mm) shown in Figure 3(f).

A crucial topographical component is the slope, which describes how steep the ground surface is. Slope gives important information on the types of geologic and geodynamic processes occurring at the level of the region (Riley et al. 1999). Overall, the topography in the district varies between 273 and 1,016 m (743 m elevation difference) above mean sea level. Slope of the surface primarily influences surface run-off and infiltration rate (Singh et al. 2013). Larger slopes create less recharge because the water from precipitation does not have enough time to dwell in the saturated zone and rushes fast down a steep hill when it rains (ref). The slope map was prepared using SRTM 30 m DEM and reclassified into five classes (0–2%), (2–4%), (4–6%), (6–12%) and (12–25%) shown in Figure 3(g).

Lineaments are structurally regulated linear or curved characteristics. It can be recognized by their comparatively linear alignments in the satellite image (Nampak et al. 2014; Nag & Kundu 2016). Lines represent the faulting and fracture zones leading to increased secondary porosity and permeability. By adding a Web Mapping Service (WMS) layer to ArcMap 10.5, extracting the research region's lineaments, and using the line density tool to create a lineament density map, lineaments of the study area were taken from Bhuvan NRSC data. The lineament density is reclassified in five classes 0–0.065 km/Sq.km, 0.065–0.195 km/Sq.km, 0.195–0.344 km/Sq.km, 0.344–0.532 km/Sq.km and 0.532–0.924 km/Sq.km. The ranks for lineament density are determined by how closely spaced the lineaments are to one another. The strength of the groundwater potential is shown to diminish as the distance from the lineaments rises, shown in Figure 3(h).

The type of soil has a big impact on how much water may seep into underground formations, which also has an impact on groundwater recharge (Das 2017). The hydraulic characteristics of the soil and its texture are the main factors considered for determining the rate of infiltration. In our study the soil map was prepared by using FAO soil data and a total of four types of soil are found under the study area: clay, sandy loam, loam and cay loam, as shown in Figure 3(k). The elevation difference of the study area is about 743 m, which is estimated by using the DEM shown in Figure 3(l).

Random consistency index = 1.41 (value taken from Table 2)

According to Saaty (1984), it is adequate to continue the analysis with a CR of 0.10 or below. The judgment must be evaluated if the consistency value is more than 0.10 in order to determine the underlying causes of the inconsistency and make the required modifications. If the CR value is 0, this suggests the pairwise comparison has perfect consistency. Because the threshold value is no higher than 0.1, the judgements matrix is essentially consistent. Furthermore, influencing percentage is decided by the normalized principal eigenvector, which is shown in Table 10.

Groundwater is a resource that can be restored, but in the previous four to five decades, diverse anthropogenic activities and unbalanced development have drastically reduced the recharge of this important life-sustaining resource. A thorough understanding of groundwater potential is essential for effective planning and sustainable development of an area. The planning and building of the remedial structures that will improve the processes of groundwater recharge require such information. The eight parameters listed above were taken into consideration while determining groundwater potential zones. Based upon the pairwise comparison matrix, weights were assigned in accordance with these characteristics when using the AHP technique. The values, 0.11 and 0.074, respectively, were found to be the CR and CI for high GPZs. The Kawardha district's groundwater potential zones map, which was created using ArcGIS, is a useful resource for learning how groundwater potential is distributed over various places. According to the analysis, 60.46% of the district's total area, or the bulk of the district, has moderate groundwater potential. This suggests a moderate capacity for groundwater availability in these areas. Following the moderate zone, the high potential zone covers 20.91% of the district, encompassing parts of Bodla, Pandariya, and Kawardha blocks, where groundwater availability is generally favorable. On the other side, 17.60% of the district is made up of low potential areas, including the Bodla region and a few blocks in Pandariya that have lower groundwater potential. Smaller portions of the district are represented by the extremely low and high potential zones, which make up 0.50 and 0.53% of the district's total area, respectively. While the very low potential areas indicate regions with highly constrained groundwater supply, the very high potential areas emphasize the zones with the district's greatest groundwater potential shown in Figure 4(a). The distribution of groundwater potential zones according to area and percentage is shown in Table 11.

The examination of eight contributing elements was added into the groundwater potential zones map produced by ArcGIS. Discharge data from the research region were used to verify the accuracy of the map that was generated. In the present study, 19 observation wells data (2021–22) were utilized for validation of groundwater potential zones. These observation well locations were overlaid onto the groundwater potential map, allowing for a comparative analysis between the predicted potential zones and the observed discharge values. By placing the positions of the observation wells across the groundwater potential map, a comprehensive assessment of the map's accuracy and reliability was conducted (Maghrebi et al. 2023). The efficacy of the model and the validity of the identified potential zones can both be determined by comparing the projected potential zones with the actual groundwater discharge data. This validation procedure (Table 12) aids in ensuring that the generated groundwater potential zones map corresponds to the actual groundwater dynamics that have been observed (Yeganeh et al. 2024). Following is the accuracy assessment table.

On the basis of Table 12, the accuracy assessment was performed by the following calculations (6):

Total agree points = 15

The present study provides a comprehensive approach to evaluating and implementing sustainable groundwater recharge strategies. It is an in-depth discussion considering specific conditions: High Slope: The mountainous region of Kawardha, with its varied topography, poses significant challenges for groundwater recharge. High slopes typically result in rapid runoff and less water infiltration, which reduces groundwater recharge potential. Rainfall characteristics: Heavy rainfall (1,200–1,800 mm): The region experiences substantial annual rainfall, which, while beneficial for recharge, also poses risks of erosion and runoff if not managed properly. Rainfall intensity: High-intensity rainfall increases surface runoff and erosion, reducing the effectiveness of traditional recharge methods and increasing the need for robust and adaptive solutions. Erratic distribution: Irregular rainfall distribution can create periods of drought followed by intense rainfall events, complicating recharge efforts and leading to variable groundwater levels (Das et al. 2019a, 2019b; Mirboluki et al. 2024). Lack of conservation measures: The absence of existing conservation measures exacerbates issues related to runoff and erosion, leading to reduced groundwater recharge and potentially impacting local water availability and agricultural productivity.

Based on the characteristics and challenges outlined, the study suggests implementing various low-cost conservation measures. Following are the measures that can address the specific issues.

Farm ponds: Design criteria: Suitable for areas with slopes less than 5% and a maximum water level of 2–2.5 m. Advantages: Farm ponds can capture and store runoff during heavy rainfall, helping to reduce soil erosion and increase infiltration rates. They are especially useful for small-scale, localized water storage and can be integrated into agricultural practices to improve water availability during dry periods. Percolation ponds: Design criteria: Designed for slopes less than 10% and with a maximum water level of 6–7 m. Advantages: These ponds facilitate groundwater recharge by allowing water to percolate into the soil over a larger area. They are effective in regions with moderate slopes and can help enhance groundwater recharge by storing water for longer periods. Check dams: Design criteria: Effective on slopes less than 15% with a maximum water level of 4–5 m. Advantages: Check dams can slow down runoff, reduce soil erosion, and promote water infiltration. They are particularly useful in mountainous regions to manage high runoff and facilitate groundwater recharge in areas with steeper slopes.

AHP can be used to prioritize various recharge strategies based on factors such as cost, effectiveness, and ease of implementation. This multi-criteria decision analysis method helps in evaluating and selecting the most appropriate conservation measures for specific sites. RS technologies can provide detailed topographic, hydrological, and land use data. This data is crucial for identifying suitable locations for farm ponds, percolation ponds, and check dams. It also helps in monitoring changes in land cover and assessing the impact of conservation measures. GIS integrates and analyzes spatial data to identify optimal locations for implementing recharge measures. It can help visualize slope gradients, rainfall patterns, and land use, facilitating better planning and management of groundwater recharge projects.

Use GIS to map out suitable locations for each type of conservation measure based on slope, rainfall intensity, and soil characteristics. Incorporate AHP to evaluate and prioritize sites based on various factors like cost, feasibility, and potential impact.

Engage local communities in the planning and implementation phases to ensure the measures align with local needs and practices. Community involvement can also enhance maintenance and sustainability. Monitoring and Evaluation: Implement a robust monitoring system using RS and GIS to track the effectiveness of conservation measures over time. Regularly assess water levels, infiltration rates, and soil erosion to adjust strategies as needed. Capacity building: Train local stakeholders in the construction and maintenance of farm ponds, percolation ponds, and check dams. Providing education on water conservation practices and the benefits of these measures can foster long-term sustainability.

In summary, addressing groundwater recharge in Kawardha requires a multifaceted approach that considers the unique topographical and climatic challenges. By integrating AHP, RS, and GIS with practical, low-cost conservation measures, it's possible to develop a sustainable groundwater management strategy that enhances water availability and mitigates the impacts of erratic rainfall and high runoff. As for the limitation of the study, initial cost is slightly higher then the agronomical measures.

The study employs a robust methodology integrating the AHP, RS, and GIS to assess and identify optimal groundwater recharge strategies. The use of these tools enables a detailed analysis of various parameters, including geology, geomorphology, LULC, soil, lineament, drainage density, slope, and rainfall. The key findings involved the groundwater potential zones, water deficit zones and recharge structures (farm pond, percolation pond and check dam) and their locations. The study's recharge plan estimates a vadose zone of 150 MCM suitable for artificial recharge, requiring 199.5 Mm³ of water. The plan considers various factors such as post-monsoon water levels, subsurface storage, surface water needs, and runoff estimation. The thematic maps and proposed structures are validated against observed data, demonstrating an overall map accuracy of approximately 79%. From the analysis and findings, targeted recharge measures, local community engagement, ongoing monitoring (groundwater levels) and holistic approach (integrate groundwater recharge plan) are recommended for sustainable groundwater recharge planning and management. This study provides a systematic approach for sustainable groundwater management in Kawardha District by identifying appropriate recharge zones and structures, thereby addressing the critical issues of water scarcity and poor management. The proposed plan, supported by detailed analyses and practical recommendations, lays the groundwork for improving groundwater recharge and ensuring more effective water resource management in the region.

In conclusion, the proposed groundwater recharge plan involves identifying suitable areas, estimating subsurface storage, determining surface water needs, calculating runoff, and recommending appropriate recharge structures. Effective implementation, ongoing monitoring, and community involvement are essential for sustainable groundwater management in Kawardha. The study provides valuable insights into regional water resource planning and offers a systematic approach to evaluating groundwater potential.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.