Identification of Groundwater Potential Zones in Lohit District, Arunachal Pradesh, Using Remote Sensing and Geographical Information System

Identification of Groundwater Potential Zones in Lohit District, Arunachal Pradesh, Using Remote Sensing and Geographical Information System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Groundwater Potential Zones in Lohit District, Arunachal Pradesh, Using Remote Sensing and Geographical Information System Roshni Rai, Dr. Suchitra S Pardeshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8597500/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study delineates groundwater potential zones in Lohit District, Arunachal Pradesh, using an integrated Remote Sensing (RS), Geographic Information System (GIS), and weighted overlay approach. Multiple thematic layers namely geology, soil, drainage density, slope, lineament density, rainfall, and land use/land cover were generated and analysed to represent the controlling factors of groundwater occurrence and recharge. Geological information was obtained from the Geological Survey of India, soil data from the FAO Digital Soil Map, drainage from Survey of India topographic sheets, and terrain and land-cover parameters from SRTM-GDEM and satellite imagery. The Analytic Hierarchy Process (AHP) was applied to assign scientifically consistent weights to each thematic layer based on their relative influence on groundwater potential. These weighted layers were integrated in a GIS environment to produce a comprehensive groundwater potential zonation map. The district was classified into five groundwater potential categories: poor (41.89 km²), fair (922.22 km²), moderate (1,014.62 km²), high (1,631.33 km²), and very high (405.78 km²). The results indicate that a substantial portion of the district falls within moderate to high potential zones, while areas categorized as poor and fair require focused groundwater development and recharge interventions to ensure sustainable water availability. Geographic Information Systems Geomorphology Hydrology Geology Groundwater potential zones RS&GIS Land use/ Land cover AHP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Groundwater constitutes one of the most important freshwater resources on Earth, supplying nearly 60% of the world’s drinking water and approximately 40% of global irrigation demands, while accounting for less than 1% of total global water reserves (EPA, 2009; UNESCO, 2018). Despite its limited proportion, groundwater plays a disproportionately large role in sustaining human societies, agriculture, and ecosystems. In India, this dependence is even more pronounced, as groundwater has become the backbone of domestic water supply, irrigation, and industrial growth. India is currently the world’s largest extractor of groundwater, accounting for nearly 25% of global groundwater withdrawal, with about 70% of this abstraction used for agricultural irrigation (IDR, 2019; FAO, 2020). Rapid population growth, agricultural intensification, and urban expansion have placed enormous pressure on aquifer systems, leading to widespread groundwater depletion, declining water tables, and deteriorating water quality (CGWB, 2020; Gleeson et al., 2012). Traditional groundwater exploration techniques such as well inventory, pumping tests, and hydrogeological field surveys provide valuable localized information but are expensive, time-consuming, and difficult to implement over large and inaccessible terrains (Jha et al., 2013; Todd & Mays, 2005). These limitations are particularly significant in mountainous and remote regions where logistics, accessibility, and data scarcity constrain conventional hydrogeological investigations. Consequently, there has been a growing shift toward the use of geospatial technologies for groundwater resource assessment and management. The integration of Remote Sensing (RS) and Geographic Information Systems (GIS) has revolutionized groundwater studies by enabling rapid, cost-effective, and spatially explicit analysis of hydrogeological conditions (Sabins, 2007; Gupta et al., 2014). Remote sensing provides synoptic and repetitive coverage of large areas, allowing the extraction of critical information related to land use and land cover, geomorphology, drainage patterns, lineaments, soil moisture, and vegetation—factors that strongly influence groundwater recharge and storage. GIS, on the other hand, offers powerful tools for storing, analyzing, and integrating these spatial datasets, enabling the development of spatial decision-support systems for groundwater potential mapping (Burrough & McDonnell, 1998; Machiwal et al., 2011). Among GIS-based techniques, weighted overlay analysis is widely used for delineating groundwater potential zones. In this method, multiple thematic layers are assigned weights according to their relative influence on groundwater occurrence and recharge and are then integrated to generate a composite groundwater potential index (Malczewski, 2006; Magesh et al., 2012). However, the reliability of this approach largely depends on the rational and consistent assignment of weights, which can be subjective if not supported by a structured decision-making framework. To overcome this limitation, the Analytic Hierarchy Process (AHP) has been increasingly adopted in groundwater studies. AHP is a multi-criteria decision-making technique that uses pairwise comparisons to derive objective and mathematically consistent weights for different criteria (Saaty, 2008). When integrated with GIS, AHP enhances the robustness and transparency of weighted overlay analysis, making it highly suitable for groundwater potential assessment and artificial recharge site selection (Rahmati et al., 2015; Kaliraj et al., 2017). This RS–GIS–AHP framework has been successfully applied in diverse hydrogeological settings worldwide to identify potential groundwater zones and guide sustainable water management (Jasrotia et al., 2013; Fenta et al., 2015). Groundwater occurrence is controlled by several physical and environmental factors, including geology, geomorphology, slope, soil type, land use and land cover, drainage density, lineament density, and rainfall distribution (Freeze & Cherry, 1979; Todd & Mays, 2005). In tectonically active and mountainous regions, structural features such as faults and fractures play a particularly important role in groundwater movement and storage (Singh & Prakash, 2002). Therefore, an integrated multi-criteria geospatial approach is essential for accurately mapping groundwater potential in such complex terrains. Despite the growing use of RS–GIS techniques in northeastern India, systematic groundwater potential studies remain limited in spatial resolution and regional coverage. Lohit District of Arunachal Pradesh—a region characterized by rugged topography, complex tectonics, heavy rainfall, and fragile hydrogeological conditions—has received very little scientific attention. The increasing dependence on groundwater for domestic and agricultural needs, combined with climate variability and infrastructural development, has heightened the risk of unsustainable groundwater use in the district. The present study addresses this critical research gap by integrating multi-thematic geospatial datasets with an AHP-based weighted overlay approach within a GIS framework to delineate groundwater potential zones in Lohit District. The objective is to generate a scientifically robust and spatially explicit groundwater potential map that can support sustainable groundwater management, recharge planning, and water-resource decision-making in this ecologically sensitive and data-scarce region of northeastern India. Study Area Lohit District is in the eastern part of Arunachal Pradesh, India, between 27°33′–29°22′ N latitudes and 95°15′–97°24′ E longitudes, covering about 5,212 km² (Census of India, 2011). It is bounded by Lower Dibang Valley in the west, Anjaw in the north, Changlang in the south, and Tinsukia District of Assam in the southwest (Figure 1). The district is named after the Lohit River, a major tributary of the Brahmaputra, which originates in eastern Tibet and flows through the region before entering Assam (Bora & Patgiri, 2017). Physiographically, Lohit District exhibits strong topographic variation, ranging from low-lying alluvial plains in the south to steep mountainous terrain in the central and northern parts. The southern plains form part of the Upper Assam basin and are influenced by rivers such as the Lohit, Dibang, Kamlang, and Noa-Dihing, which deposit fertile alluvium but also cause seasonal flooding (Singh, 2010). These plains abruptly transition into the Lesser Himalayan foothills and further into the Higher Himalayas, where elevations exceed 5,000 m and terrain is structurally complex (Borah, 2019; Rai & Sharma, 2016). The district has a dense dendritic drainage network dominated by the Lohit River and its tributaries. Fluvial and glacial sediments deposited by these rivers strongly influence aquifer properties and groundwater storage. Climatically, Lohit experiences a humid subtropical to alpine climate with high monsoonal rainfall, which contributes to floods, landslides, and soil erosion, affecting groundwater recharge. The region is mainly inhabited by tribal communities such as the Mishmi, Khamti, and Singpho. Traditional land-use practices, including shifting cultivation (jhum), combined with steep terrain and heavy rainfall, significantly influence vegetation cover, soil conditions, and groundwater availability across the district. Data used The datasets utilized in the present study were acquired from authenticated and widely recognized global and national agencies. These datasets were selected based on their spatial resolution, thematic relevance, and proven reliability for geospatial and environmental research. A brief description of each data source and its purpose in the study is provided below. Table 1: Data Sources and Their Specifications Data Type Source / Organization Resolution / Scale Format Purpose in Study Precipitation Data WorldClim (www.worldclim.org) ~1 km spatial resolution Raster Climatic analysis, rainfall distribution studies Soil Data FAO Digital Soil Map of the World (DSMW) 1:5,000,000 scale Vector Soil classification and suitability assessment Elevation Data SRTM DEM 30 m spatial resolution Raster Terrain analysis (slope, aspect, drainage) Orthoimage IRS Cartosat-1 2.5 m spatial resolution Raster High-resolution land use/land cover mapping Satellite Imagery IRS Resourcesat-2 LISS-III 23.5 m spatial resolution Raster Vegetation analysis, supplementary LULC classification Precipitation datasets were obtained from the WorldClim database, which provides globally consistent, high-resolution climate layers suitable for environmental modeling. Soil characteristics for the study area were derived from the FAO Digital Soil Map of the World (FAO, 2012), one of the most comprehensive global soil inventories. Topographic information was extracted from the Shuttle Radar Topography Mission (SRTM) DEM at 30-meter resolution, enabling detailed derivation of geomorphological parameters such as slope and elevation. For detailed spatial mapping and interpretation, high-resolution IRS Cartosat-1 Orthoimagery (2.5 m) was used to support land use and land cover (LULC) mapping. Additionally, IRS Resourcesat-2 LISS-III multispectral imagery (23.5 m) was utilized for vegetation assessment and to enhance classification accuracy across heterogeneous landscapes. Methodology The groundwater potential zonation of Lohit District was carried out using an integrated Remote Sensing (RS), GIS, and Analytic Hierarchy Process (AHP) based weighted overlay approach, as summarized in Fig. 2 (Flow chart of the methodology). The flow chart illustrates the sequential workflow starting from data acquisition, thematic layer generation, multi-criteria evaluation, and final groundwater potential mapping. As shown in Fig. 2 , the first step involves the collection of multiple thematic layers that control groundwater occurrence, including geomorphology, geology, lineament density, land use/land cover, soil type, slope, and rainfall. These factors are widely recognized as key determinants of groundwater storage, movement, and recharge (Todd & Mays, 2005 ; Jha et al., 2010 ; Kaliraj et al., 2017 ). Remote sensing data such as satellite imagery and Digital Elevation Models (DEM) were used to extract landforms, slope, drainage, and land-cover information, while GIS was used to store, process, and spatially integrate all datasets within a common georeferenced framework (Sabins, 2007 ; Jensen, 2016 ). The second stage in the flow chart corresponds to the preparation and classification of individual thematic layers. Each layer was categorized into hydrologically meaningful classes based on its influence on groundwater potential. For example, alluvial plains and valley fills were ranked higher than steep hill slopes due to their greater infiltration and storage capacity, while areas with high lineament density were assigned higher ranks because fractures and faults enhance groundwater movement (Krishnamurthy et al., 1996 ; Raghunath, 2006 ). In the third step (Fig. 2 ), the Analytic Hierarchy Process (AHP) was applied to determine the relative importance of each thematic layer. Pairwise comparisons were performed using Saaty’s 1–9 scale, and consistency ratios were checked to ensure logical reliability of the weighting scheme (Saaty, 2008 ; Malczewski, 2006 ). The derived weights were then applied in a GIS-based weighted overlay analysis, where all raster layers were combined to generate a composite groundwater potential index. Finally, as shown in the last step of the flow chart, the groundwater potential index was classified into five zones: very high, high, moderate, low, and very low groundwater potential. This final groundwater potential map provides a spatial representation of groundwater availability across Lohit District and serves as a scientific basis for groundwater exploration, artificial recharge planning, and sustainable water resource management (Jha et al., 2010 ; Kaliraj et al., 2017 ). Thematic Layers Rainfall Rainfall is the primary source of groundwater recharge, as it governs the amount of water that infiltrates into aquifers (Foster et al., 2004 ). Lohit District receives high and well-distributed monsoonal rainfall, creating favourable conditions for groundwater replenishment. Gridded rainfall data from the Climate Research Unit (CRU) and the NCAS dataset (30′ × 30′ resolution) were interpolated using the Inverse Distance Weighting (IDW) method in ArcGIS to generate an annual rainfall map (Harris et al., 2014 ). This rainfall layer was used as an important thematic input for groundwater potential mapping. Elevation strongly influences rainfall distribution, with precipitation generally decreasing at higher altitudes (Dunne & Leopold, 1978 ). Consequently, the lower-lying areas of Lohit receive greater rainfall and are more favourable for groundwater recharge, while higher mountainous regions experience lower rainfall and increased runoff, limiting infiltration (Aymonier et al., 2010 ). Geology The geology of Lohit District (Fig. 4 ) reflects a complex tectonic and sedimentary history that strongly controls groundwater occurrence and movement. The low-lying valleys are mainly composed of Quaternary fluvial and glacial sediments deposited by active river systems, providing favourable conditions for groundwater storage and recharge (Rao & Dutta, 2014 ). The Dihing Group (Pliocene–Pleistocene) represents younger sedimentary formations associated with Neogene tectonic uplift (Kumar & Singh, 2016 ). Older basement rocks are represented by the Paleoproterozoic Tilung Formation and the Bomdila Group, which consist of metasedimentary and metamorphic rocks interlayered with the Pari Mountain Gneiss, reflecting a complex metamorphic history (Bhattacharyya & Pal, 2011 ; Ranjit & Mehta, 2019 ). At higher elevations, the Dibang Group (Cretaceous age) includes marine, continental, and volcanic sequences that record Mesozoic tectonic activity (Srivastava & Prasad, 2017). The Lohit Granitoid Complex, emplaced during the Late Cretaceous to Paleocene, marks a major magmatic phase linked to the collision between the Indian and Eurasian plates (Ghosh & Mishra, 2020 ). This diverse geological framework, illustrated in Fig. 4 , ranges from highly permeable alluvial deposits to low-permeability crystalline rocks and therefore exerts a strong control on groundwater storage, flow paths, and recharge potential across Lohit District (Saha & Verma, 2018 ; Yadav & Patnaik, 2014 ). Drainage density Drainage density was derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) using ArcGIS to evaluate the influence of surface hydrology on groundwater recharge potential. Drainage density reflects the degree of channel development within a basin and is controlled by factors such as climate, lithology, relief, infiltration capacity, and vegetation cover (Strahler, 1952 ; Nag, 1998 ). According to Nag ( 1998 ), areas characterized by permeable subsurface materials, gentle relief, and dense vegetation tend to exhibit low drainage density, as higher infiltration reduces surface runoff and limits channel formation. In contrast, regions with impermeable rocks, steep slopes, and sparse vegetation display high drainage density due to increased runoff and rapid surface flow. The drainage density map of Lohit District (Fig. 5 ) shows that high drainage density occurs mainly in mountainous and structurally resistant terrains, where steep slopes and low infiltration promote rapid runoff. Conversely, low drainage density is observed in valley floors and areas underlain by permeable sediments and dense vegetation, indicating favourable conditions for groundwater infiltration and recharge. These spatial patterns are consistent with the conceptual models proposed by Strahler ( 1952 ) and Nag ( 1998 ). Thus, drainage density serves as an important indicator for groundwater potential assessment, as areas with lower drainage density are more suitable for groundwater recharge and storage, while high drainage density zones are less favourable due to rapid runoff and limited infiltration. Land use/ Land Cover The Land Use and Land Cover (LULC) pattern of Lohit District reflects a diverse and ecologically significant landscape (Fig. 6 ). Forests dominate the district, covering about 67.30% of the total area, indicating a largely natural and well-vegetated environment that plays a vital role in biodiversity conservation, soil protection, and hydrological regulation (Singh & Saha, 2015 ). Agricultural land constitutes 14.37%, highlighting the importance of farming as a primary livelihood in the region. Wastelands account for 10.51%, representing degraded or less productive areas that are vulnerable to erosion and require careful management. Grassland and grazing land occupy 2.87%, supporting livestock and traditional pastoral activities. Water bodies cover 1.88%, while snow and glacial areas contribute 1.76%, mainly in the high-altitude northern parts of the district. Built-up areas occupy 1.31%, indicating limited but growing human settlements, and shifting cultivation accounts for a very small proportion (0.01%), reflecting localized traditional land-use practices. Table 2 summarizes the percentage distribution of LULC classes in Lohit District. Sr No Classes Area in use (%) 1 Forest 67.29628404 2 Agricultural land 14.37164169 3 Wastelands 10.50634541 4 Grassland & Grazing land 2.869747127 5 Water bodies 1.879265181 6 Snow / Glacial 1.759458081 7 Built up 1.308384107 8 Shifting cultivation 0.008874364 Total 100 The findings from the Land Use and Land Cover (LULC) analysis show that forests are the predominant land cover type in the Lohit district, followed by agricultural land. This diverse land-use composition underscores the balanced interaction between natural ecosystems and human-altered spaces, which is critical for sustainable development and environmental management (Ghosh et al., 2020 ). Lineaments Lineaments are linear or curvilinear geological features, such as faults and fractures, that significantly Lineaments are linear or curvilinear geological features such as faults, fractures, and joints that exert a strong control on groundwater movement, storage, and aquifer behavior (Keller & Green, 2015 ). These structures increase secondary porosity and permeability of rocks, thereby creating preferential pathways for groundwater flow and enhancing subsurface storage capacity (Chorowicz, 2005 ). Fractures and fault zones within lineaments facilitate the downward percolation of rainwater and allow groundwater to move more easily through otherwise impermeable rock formations. Mapping lineaments is therefore an important step in groundwater potential assessment, as zones with high lineament density generally exhibit higher groundwater prospects. Wells located close to major lineaments or their intersections are more likely to yield higher discharge because they tap fractured and more permeable zones (Sivapalan & Goh, 2007). Moreover, dense lineament networks influence aquifer connectivity and can lead to complex groundwater flow patterns, especially in structurally controlled terrains (Zhang et al., 2014 ). In Lohit District, the spatial distribution of lineaments (Fig. 7 ) highlights structurally weak zones that are favorable for groundwater accumulation and movement. These zones are therefore assigned higher weights in the groundwater potential analysis. Overall, lineaments provide critical insight into subsurface hydrological conditions and are essential for effective groundwater exploration and sustainable water resource management (Singh et al., 2017 ). Soil Soil characteristics play a crucial role in groundwater recharge, as they control infiltration, percolation, and water retention within the vadose zone. The soil data used in this study were obtained from the FAO Digital Soil Map of the World (DSMW) through the FAO Soil Portal (FAO, 2020 ). These data provide spatial information on soil texture, depth, and hydrological properties across Lohit District. The soil in the valley and low-lying areas is predominantly sandy loamy to loamy sand, which is coarse-textured and moderately acidic. These soils exhibit high permeability and infiltration capacity, making them favorable for groundwater recharge. In contrast, the higher-altitude and hill regions are dominated by clay and clay loam soils, which have finer particles, lower permeability, and higher water-holding capacity. Such soils tend to restrict vertical percolation and promote surface runoff, thereby limiting groundwater recharge. Shallow soil depth in many parts of the district, often underlain by bedrock within a few ten centimeters, further influences groundwater occurrence by reducing storage in the soil zone and increasing dependence on fractures and weathered rock layers for groundwater accumulation. Therefore, areas with sandy and loamy soil were assigned higher weights in the groundwater potential analysis, while clay-dominated zones were given lower weights due to their limited infiltration capacity. Slope Slope is one of the most important topographic parameters controlling surface runoff, infiltration, and groundwater recharge. In this study, slope was derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) using ArcGIS to generate a spatially distributed slope map of Lohit District (Fig. 9 ). Areas with steep slopes tend to promote rapid surface runoff, reducing the time available for water to infiltrate into the subsurface, whereas gentle slopes allow greater infiltration and percolation, enhancing groundwater recharge (Chorowicz, 2005 ; Melesse et al., 2013 ). The slope pattern of Lohit District shows that low-lying plains and valley floors have gentle slopes, providing favourable conditions for groundwater infiltration and storage. In contrast, the north-western, north-eastern, and central hilly regions exhibit steep slopes, which are associated with high runoff, soil erosion, and limited groundwater recharge (Singh et al., 2017 ). These steep terrains mainly support rapid drainage rather than groundwater accumulation. Consequently, gentle and moderately sloping areas were assigned higher weights in the groundwater potential analysis, while steep and very steep slope zones were given lower weights due to their poor infiltration capacity. Weighted Overlay Method Groundwater potential zones (GWPZs) in Lohit District were delineated using a Weighted Overlay Analysis integrated with the Analytic Hierarchy Process (AHP) in ArcGIS 10.8.2. AHP, developed by Saaty and implemented using K.D. Goepel’s AHP tool (Version 15.09.2018), was used to objectively determine the relative importance of each thematic layer influencing groundwater occurrence and recharge. Seven thematic layers were selected based on hydrogeological significance: rainfall, geology, slope, drainage density, land use/land cover (LULC), lineament density, and soil. These layers were converted into raster format and reclassified into suitability classes ranging from very low to very high. Ranking of individual classes was based on their contribution to groundwater infiltration, storage, and movement, following established hydrogeological principles and previous studies (Krishnamurthy et al., 1996 ; Saraf & Chowdhary, 1998 ). Using AHP, each thematic layer was compared pairwise against the others on a scale of 1–7 to express their relative importance for groundwater potential. A normalized comparison matrix was generated, and weights were calculated after consistency verification. The final weights indicate that rainfall (38%) and geology (24.7%) exert the strongest control on groundwater potential, as rainfall governs recharge and geology controls aquifer storage and permeability. Slope (13.1%) plays a secondary role by regulating runoff and infiltration, while drainage density (8.9%) and LULC (6.6%) reflect surface hydrological and land-surface conditions. Lineament density (5%) and soil (3.7%) have localized but important influences on subsurface flow and infiltration capacity. The weighted thematic layers were then integrated in ArcGIS using the weighted overlay function according to: $$\:\text{GWPZ}=\sum\:({W}_{i}\times\:{R}_{i})$$ where \(\:{W}_{i}\) is the AHP-derived weight and \(\:{R}_{i}\) is the reclassified rank of each thematic layer. The final composite groundwater potential map was classified into high, moderate, and low groundwater potential zones. This AHP-based weighted overlay framework provides a transparent, reproducible, and scientifically robust method for groundwater potential mapping and supports sustainable groundwater development and land-use planning in Lohit District. Table 3 AHP-derived weights for groundwater potential parameters Criteria A or B Scale (1–7) Weightage (%) A B Rainfall Geology A 3 38 Slope A 3 Drainage Density A 5 LULC A 5 Lineament Density A 5 Soil A 7 Geology Slope A 3 24.7 Drainage Density A 3 LULC A 5 Lineament Density A 5 Soil A 5 Slope Drainage Density A 1 13.1 LULC A 3 Lineament Density A 3 Soil A 5 Drainage Density LULC A 1 8.9 Lineament Density A 2 Soil A 3 LULC Lineament Density A 1 6.6 Soil A 3 Lineament Density Soil A 1 5 Soil A 1 3.7 Table 3 presents a systematic multi-criteria framework for evaluating groundwater potential in Lohit District, Arunachal Pradesh, by incorporating the major controlling factors of groundwater recharge and storage. The criteria include rainfall, geology, slope, drainage density, land use/land cover (LULC), lineament density, and soil characteristics, each of which plays a distinct role in governing infiltration, subsurface flow, and aquifer development. These parameters were evaluated using a standardized suitability scale ranging from 1 to 7, where higher values indicate more favourable conditions for groundwater occurrence and recharge. As shown in Table 3 , rainfall (38%) and geology (24.7%) received the highest weights because rainfall is the primary source of groundwater recharge, while geological formations control porosity, permeability, and aquifer thickness. Slope (13.1%) and drainage density (8.9%) were assigned moderate weights, as they influence surface runoff and infiltration potential—gentler slopes and lower drainage density generally favour groundwater recharge. LULC (6.6%) reflects the impact of vegetation cover, agriculture, and built-up areas on infiltration and evapotranspiration, while lineament density (5%) represents subsurface fractures and faults that enhance groundwater movement and storage. Soil characteristics (3.7%), though assigned a lower weight, are still important for controlling infiltration and water-holding capacity at the surface. The division of parameters into primary and secondary factors further highlights their relative influence on groundwater potential. Primary factors such as rainfall and geology exert dominant control over recharge and storage, whereas secondary factors like slope, LULC, drainage density, lineaments, and soil modify groundwater behaviour at local and sub-regional scales. This weighted and integrated approach, summarized in Table 2 , provides a robust quantitative basis for groundwater potential zonation and supports informed decision-making for sustainable groundwater management, land-use planning, and resource conservation in Lohit District. Results and Discussion This study provides a comprehensive hydrogeological evaluation of groundwater potential in Lohit District, Arunachal Pradesh, using an integrated Remote Sensing–GIS–AHP framework. The spatial distribution of groundwater potential is strongly governed by the combined influence of rainfall, lithology, geomorphology, slope, drainage density, lineament density, soil characteristics, and land use/land cover, consistent with established hydrogeological principles (Todd & Mays, 2005 ; Krishnamurthy et al., 1996 ; Jha et al., 2010 ). The results indicate that the southern and central parts of the district exhibit relatively high groundwater potential due to their low relief, moderate to high rainfall, and favorable geological and soil conditions. These areas are characterized by gently sloping terrain, which reduces surface runoff and promotes infiltration, thereby enhancing recharge (Freeze & Cherry, 1979 ; Melesse et al., 2013 ). In contrast, the northern and northeastern mountainous regions display lower groundwater potential, primarily due to steep slopes, high drainage density, and compact lithological units, which result in rapid runoff and limited infiltration (Nag, 1998 ; Strahler, 1952 ). Geological and structural controls play a crucial role in groundwater occurrence in Lohit District. Zones with moderate to high lineament density exhibit improved secondary porosity and permeability due to the presence of fractures, faults, and joints, which act as conduits for groundwater flow and storage (Chorowicz, 2005 ; Keller & Pinter, 2002 ). Similarly, areas with low drainage density indicate higher infiltration capacity and subsurface storage, whereas high drainage density reflects impermeable lithology and high runoff conditions that are unfavorable for groundwater recharge (Nag, 1998 ). Soil and land-use patterns further influence recharge processes. Permeable valley soils and agricultural lands facilitate infiltration, while forest cover reduces erosion and enhances soil moisture retention, thereby supporting recharge (Mitsch & Gosselink, 2007 ; Ghosh et al., 2020 ). Conversely, clay-rich soils and barren or rocky surfaces impede infiltration and reduce groundwater potential. The final groundwater potential map (Fig. 10 ), generated using the AHP-based weighted overlay technique, classifies Lohit District into five groundwater potential zones: poor, fair, moderate, high, and very high. The areal distribution of these zones is presented in Table 3 . Using the Weighted Overlay Method, the district was classified into five groundwater potential zones: poor, fair, moderate, high, and very high. The classification is as follows: Table 4 Groundwater potential zones of study area Sr No Ground Water Potential Zone Area 1 Poor Potential Zone 41.89 km² 2 Fair Potential Zone 922.22 km² 3 Moderate Potential Zone 1,014.62 km² 4 High Potential Zone 1,631.33 km² 5 Very High Potential Zone 405.78 km² The high and very high groundwater potential zones together cover approximately 2,037 km², representing the most promising areas for groundwater development. These zones coincide with regions of low slope, favorable lithology, moderate lineament density, and adequate rainfall, which together create optimal conditions for recharge and storage (Saraf & Choudhury, 1998; Krishnamurthy et al., 1996 ). In contrast, the poor potential zones, occupying only 41.89 km², correspond to steep, structurally compact, and high-relief areas where groundwater occurrence is limited due to rapid surface runoff and minimal infiltration. Overall, the results confirm that groundwater availability in Lohit District is primarily controlled by geomorphological and geological factors interacting with climatic and land-use conditions. The integration of AHP with GIS-based weighted overlay analysis provides a robust and transparent framework for groundwater potential zonation, supporting sustainable groundwater management, well-siting, and long-term water-resource planning in this geologically complex and environmentally sensitive Himalayan region. Conclusion In conclusion, this study highlights the effectiveness of remote sensing and GIS techniques in delineating diverse groundwater zones within different geological contexts. The results obtained contribute to the creation of a more accurate groundwater potential map for the studied area. Such precise mapping is invaluable for the strategic planning and effective management of groundwater development programs, providing essential insights for sustainable water resource utilization in the region. The integration of advanced technologies like remote sensing and GIS enhances our understanding of groundwater dynamics and facilitates informed decision-making for the optimal utilization of this vital resource. This study used map overlay techniques to conduct qualitative analysis, resulting in a map with three distinct categories. High groundwater potential areas were mainly found in flat regions with extensively fractured amygdaloidal basaltic rock formations. These regions had low drainage density and moderate to low lineament density, despite basalt not typically being considered a good aquifer. However, due to significant jointing and weathering, it has become a reasonably effective aquifer. Geomorphological investigations revealed that the northern part of the basin is undeveloped and functions as a runoff zone, while the drainage area is mature and represents a saturated zone with good infiltration capacity. Thick weathering zones within the region were identified, highlighting groundwater potential in this aquifer. The study highlights the use of remote sensing and GIS techniques to delineate groundwater zones in diverse geological contexts, providing a more accurate groundwater potential map for effective planning and management of groundwater development programs. The future scope of this research involves refining analytical approaches, such as the Analytic Hierarchy Process (AHP), and exploring advanced remote sensing and GIS methodologies for groundwater mapping. Long-term monitoring of NDVI and land cover dynamics will continue to assess trends, with a focus on understanding drivers and implications for biodiversity. Ongoing demographic studies will track population changes, and sustainable land management practices will be implemented to counteract potential over-exploitation. Integrated conservation and development strategies, community-based initiatives, and climate change adaptation will be explored. Policy recommendations will be formulated for sustainable development, advocating for effective governance. Collaborative research and knowledge-sharing initiatives will deepen the understanding of complex relationships, contributing to broader scientific discourse. References Aymonier, C., Renaud, J., & Lamon, J. (2010). Orographic effects on precipitation and groundwater recharge in mountainous regions. Hydrological Processes, 24 (18), 2522–2535. Bhattacharyya, C. C., & Pal, T. (2011). Metamorphic evolution of the Bomdila Group and Pari Mountain Gneiss in Arunachal Pradesh, NE India. Journal of Asian Earth Sciences, 42 (3), 345–358. Bora, D. K., & Patgiri, G. (2017). River systems and geomorphology of Arunachal Pradesh. Indian Journal of Geomorphology, 22 (1), 15–29. Borah, D. (2019). Himalayan tectonics and geomorphology of Arunachal Pradesh. Journal of the Geological Society of India, 93 (2), 157–169. Burrough, P. A., & McDonnell, R. A. (1998). Principles of Geographical Information Systems . Oxford University Press. Central Ground Water Board (CGWB). (2020). Dynamic Ground Water Resources of India . Ministry of Jal Shakti, Government of India. Chorowicz, J. (2005). Lineament analysis for groundwater studies. Hydrogeology Journal, 13 (2), 253–267. Dunne, T., & Leopold, L. B. (1978). Water in Environmental Planning . W.H. Freeman. Environmental Protection Agency (EPA). (2009). Ground Water and Drinking Water . U.S. Environmental Protection Agency. FAO. (2012). Harmonized World Soil Database (Version 1.2) . Food and Agriculture Organization of the United Nations. FAO. (2020). FAO Aquastat Database . Food and Agriculture Organization of the United Nations. Fenta, A. A., Kifle, A., Gebreyohannes, T., & Hailu, G. (2015). Groundwater potential mapping using GIS and AHP. Journal of Hydrology: Regional Studies, 3 , 164–181. Freeze, R. A., & Cherry, J. A. (1979). Groundwater . Prentice Hall. Foster, S., Hirata, R., Gomes, D., D’Elia, M., & Paris, M. (2004). Groundwater Quality Protection . World Bank. Ghosh, S., & Mishra, S. (2020). Petrogenesis of the Lohit granitoids. Journal of Asian Earth Sciences, 195 , 104342. Ghosh, S., Dutta, D., & Debnath, R. (2020). Land use change and hydrological response in northeastern India. Environmental Earth Sciences, 79 , 255. Gleeson, T., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (2012). Water balance of global aquifers. Nature, 488 , 197–200. Gupta, M., Srivastava, P., & Islam, T. (2014). GIS-based groundwater potential mapping. Hydrogeology Journal, 22 (7), 1483–1497. Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high-resolution grids of monthly climatic observations. International Journal of Climatology, 34 (3), 623–642. India Development Review (IDR). (2019). India’s Groundwater Crisis . IDR. Jasrotia, A. S., Kumar, R., & Saraf, A. K. (2013). GIS-based groundwater recharge assessment. Environmental Earth Sciences, 68 , 1285–1296. Jensen, J. R. (2016). Introductory Digital Image Processing (4th ed.). Pearson. Jha, M. K., Chowdary, V. M., & Chowdhury, A. (2010). Groundwater potential mapping. Hydrological Sciences Journal, 55 (4), 531–548. Jha, M. K., Chowdhary, V. M., & Chowdhury, A. (2013). Groundwater assessment using RS and GIS. Hydrogeology Journal, 21 , 803–816. Kaliraj, S., Chandrasekar, N., & Magesh, N. S. (2017). GIS–AHP based groundwater potential zone mapping. Journal of Hydrology, 547 , 461–475. Keller, E. A., & Pinter, N. (2002). Active Tectonics . Prentice Hall. Keller, E. A., & Green, R. (2015). Geological controls on groundwater flow. Hydrogeology Journal, 23 , 1561–1573. Krishnamurthy, J., Mani, A., Jayaraman, V., & Manivel, M. (1996). Groundwater resources development. International Journal of Remote Sensing, 17 , 1867–1884. Kumar, A., & Singh, S. (2016). Neogene tectonics of the Dihing basin. Journal of Asian Earth Sciences, 118 , 35–49. Machiwal, D., Jha, M. K., & Mal, B. C. (2011). GIS-based groundwater potential mapping. Environmental Earth Sciences, 63 , 119–131. Magesh, N. S., Chandrasekar, N., & Soundranayagam, J. P. (2012). GIS-based groundwater potential zones. Environmental Earth Sciences, 65 , 1373–1385. Malczewski, J. (2006). GIS-based multicriteria decision analysis. International Journal of Geographical Information Science, 20 (7), 703–726. Melesse, A. M., Weng, Q., Thenkabail, P. S., & Senay, G. B. (2013). Remote sensing of hydrology. Hydrological Processes, 27 , 302–315. Mitsch, W. J., & Gosselink, J. G. (2007). Wetlands (4th ed.). Wiley. Nag, S. K. (1998). Morphometric analysis using remote sensing. Journal of the Indian Society of Remote Sensing, 26 (4), 169–178. Raghunath, H. M. (2006). Hydrogeology . New Age International. Rahmati, O., Nazari Samani, A., Mahdavi, M., Pourghasemi, H. R., & Zeinivand, H. (2015). Groundwater potential mapping. Arabian Journal of Geosciences, 8 , 9319–9330. Rai, S., & Sharma, R. (2016). Himalayan tectonics of Arunachal Pradesh. Geological Journal of India, 88 , 421–435. Ranjit, S., & Mehta, P. (2019). Proterozoic lithostratigraphy of Arunachal Pradesh. Journal of Earth System Science, 128 , 33. Rao, V. V., & Dutta, S. (2014). Quaternary sedimentation in NE India. Quaternary International, 325 , 79–90. Sabins, F. F. (2007). Remote Sensing: Principles and Interpretation (3rd ed.). W.H. Freeman. Saaty, T. L. (2008). Decision making with AHP. International Journal of Services Sciences, 1 (1), 83–98. Saraf, A. K., & Chowdhary, V. M. (1998). GIS-based groundwater assessment. International Journal of Remote Sensing, 19 (18), 3521–3540. Saha, D., & Verma, S. (2018). Hydrogeology of NE India. Journal of the Geological Society of India, 91 , 585–598. Singh, A. K., & Prakash, S. R. (2002). Tectonic control on groundwater. Journal of Earth System Science, 111 (4), 497–508. Singh, S. (2010). Floodplain geomorphology of Assam. Geographical Review of India, 72 , 1–12. Singh, R., & Saha, S. (2015). Forest ecology of Arunachal Pradesh. Indian Journal of Forestry, 38 (3), 245–254. Singh, S., Yadav, P., & Kumar, S. (2017). Structural control on groundwater. Journal of Hydrology, 552 , 234–245. Strahler, A. N. (1952). Hypsometric analysis of erosional topography. Geological Society of America Bulletin, 63 , 1117–1142. Todd, D. K., & Mays, L. W. (2005). Groundwater Hydrology (3rd ed.). Wiley. UNESCO. (2018). World Water Development Report . United Nations. Yadav, P., & Patnaik, S. (2014). Geological evolution of Arunachal Himalaya. Journal of Earth Sciences, 25 (3), 487–502. Zhang, Y., Li, X., & Zhang, W. (2014). Structural control on groundwater flow. Hydrogeology Journal, 22 , 1365–1379. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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background.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/595c8c6953a113d1b7f9d95f.jpeg"},{"id":100419221,"identity":"db3a48c3-7060-445a-8a08-8ed2f6a45dce","added_by":"auto","created_at":"2026-01-16 13:26:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":380432,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart illustrating the integrated RS–GIS–AHP methodology adopted for delineating groundwater potential zones in Lohit District, Arunachal Pradesh.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/84c2c9d0f10922cbc3804a6c.png"},{"id":100419504,"identity":"c3798e7f-c751-4b0b-b984-d25497a8619b","added_by":"auto","created_at":"2026-01-16 13:26:58","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115778,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of annual rainfall in Lohit District, Arunachal Pradesh, derived from CRU and NCAS gridded rainfall data.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/6ac454029a12b0a245f55646.jpeg"},{"id":100419708,"identity":"25e25336-dc9f-44ae-b4a5-f4d88403cd0d","added_by":"auto","created_at":"2026-01-16 13:27:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":148196,"visible":true,"origin":"","legend":"\u003cp\u003eGeological map of Lohit District showing the distribution of Quaternary alluvium, sedimentary, metamorphic, and igneous formations that control aquifer characteristics and groundwater occurrence.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/33d56351c0fc8789764f31b7.jpeg"},{"id":100419161,"identity":"59ccc1a3-ee67-40fd-bf74-ae99940a8cee","added_by":"auto","created_at":"2026-01-16 13:26:42","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":153556,"visible":true,"origin":"","legend":"\u003cp\u003eDrainage density map of Lohit District depicting the spatial variation of stream network intensity, highlighting areas of high runoff and low infiltration versus zones of greater groundwater recharge potential.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/4f404adb873d987285d871f6.jpeg"},{"id":100419825,"identity":"4e43f6f5-ea46-4156-96af-0555b7392391","added_by":"auto","created_at":"2026-01-16 13:27:19","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":195561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLand use/land cover (LULC) map of Lohit District showing the spatial distribution of forests, agricultural land, wastelands, grasslands, water bodies, built-up areas, and snow/glacial zones.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/5990f3724d5fbe3868f4bee2.jpeg"},{"id":100422074,"identity":"a11bc30a-cf59-4faf-8fdb-7d3d5efad5ef","added_by":"auto","created_at":"2026-01-16 14:05:34","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":120498,"visible":true,"origin":"","legend":"\u003cp\u003eLineament map of Lohit District showing the distribution of faults and fractures controlling groundwater movement.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/97b9a0263e0bd9f6329d9405.jpeg"},{"id":100419874,"identity":"022e9d95-a118-4263-8e57-85ac6480b6fb","added_by":"auto","created_at":"2026-01-16 13:27:22","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":94159,"visible":true,"origin":"","legend":"\u003cp\u003eSoil map of Lohit District showing the spatial distribution of soil types influencing groundwater recharge.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/d697cce7a73f693f8f0014b0.jpeg"},{"id":100421887,"identity":"b5e9edd2-e9bd-4c38-8a98-3eb51949f19a","added_by":"auto","created_at":"2026-01-16 14:00:37","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":174634,"visible":true,"origin":"","legend":"\u003cp\u003eSlope map of Lohit District\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/f7b7d672845e37a6dde645a1.jpeg"},{"id":100421948,"identity":"cfa863d0-20f3-4277-9eb2-8b07c1c4e6cf","added_by":"auto","created_at":"2026-01-16 14:03:25","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":153081,"visible":true,"origin":"","legend":"\u003cp\u003eGroundwater Potential Map of Lohit District\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/6a22987d58730cb7ab42d071.jpeg"},{"id":100423808,"identity":"75563f13-7cf9-419c-8612-40f32daedfd9","added_by":"auto","created_at":"2026-01-16 14:14:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2595301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8597500/v1/6ed3afc8-2e0c-45b7-baad-743369b18aa2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIdentification of Groundwater Potential Zones in Lohit District, Arunachal Pradesh, Using Remote Sensing and Geographical Information System\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGroundwater constitutes one of the most important freshwater resources on Earth, supplying nearly 60% of the world\u0026rsquo;s drinking water and approximately 40% of global irrigation demands, while accounting for less than 1% of total global water reserves (EPA, 2009; UNESCO, 2018). Despite its limited proportion, groundwater plays a disproportionately large role in sustaining human societies, agriculture, and ecosystems. In India, this dependence is even more pronounced, as groundwater has become the backbone of domestic water supply, irrigation, and industrial growth. India is currently the world\u0026rsquo;s largest extractor of groundwater, accounting for nearly 25% of global groundwater withdrawal, with about 70% of this abstraction used for agricultural irrigation (IDR, 2019; FAO, 2020). Rapid population growth, agricultural intensification, and urban expansion have placed enormous pressure on aquifer systems, leading to widespread groundwater depletion, declining water tables, and deteriorating water quality (CGWB, 2020; Gleeson et al., 2012).\u003c/p\u003e\n\u003cp\u003eTraditional groundwater exploration techniques such as well inventory, pumping tests, and hydrogeological field surveys provide valuable localized information but are expensive, time-consuming, and difficult to implement over large and inaccessible terrains (Jha et al., 2013; Todd \u0026amp; Mays, 2005). These limitations are particularly significant in mountainous and remote regions where logistics, accessibility, and data scarcity constrain conventional hydrogeological investigations. Consequently, there has been a growing shift toward the use of geospatial technologies for groundwater resource assessment and management.\u003c/p\u003e\n\u003cp\u003eThe integration of Remote Sensing (RS) and Geographic Information Systems (GIS) has revolutionized groundwater studies by enabling rapid, cost-effective, and spatially explicit analysis of hydrogeological conditions (Sabins, 2007; Gupta et al., 2014). Remote sensing provides synoptic and repetitive coverage of large areas, allowing the extraction of critical information related to land use and land cover, geomorphology, drainage patterns, lineaments, soil moisture, and vegetation\u0026mdash;factors that strongly influence groundwater recharge and storage. GIS, on the other hand, offers powerful tools for storing, analyzing, and integrating these spatial datasets, enabling the development of spatial decision-support systems for groundwater potential mapping (Burrough \u0026amp; McDonnell, 1998; Machiwal et al., 2011).\u003c/p\u003e\n\u003cp\u003eAmong GIS-based techniques, weighted overlay analysis is widely used for delineating groundwater potential zones. In this method, multiple thematic layers are assigned weights according to their relative influence on groundwater occurrence and recharge and are then integrated to generate a composite groundwater potential index (Malczewski, 2006; Magesh et al., 2012). However, the reliability of this approach largely depends on the rational and consistent assignment of weights, which can be subjective if not supported by a structured decision-making framework.\u003c/p\u003e\n\u003cp\u003eTo overcome this limitation, the Analytic Hierarchy Process (AHP) has been increasingly adopted in groundwater studies. AHP is a multi-criteria decision-making technique that uses pairwise comparisons to derive objective and mathematically consistent weights for different criteria (Saaty, 2008). When integrated with GIS, AHP enhances the robustness and transparency of weighted overlay analysis, making it highly suitable for groundwater potential assessment and artificial recharge site selection (Rahmati et al., 2015; Kaliraj et al., 2017). This RS\u0026ndash;GIS\u0026ndash;AHP framework has been successfully applied in diverse hydrogeological settings worldwide to identify potential groundwater zones and guide sustainable water management (Jasrotia et al., 2013; Fenta et al., 2015).\u003c/p\u003e\n\u003cp\u003eGroundwater occurrence is controlled by several physical and environmental factors, including geology, geomorphology, slope, soil type, land use and land cover, drainage density, lineament density, and rainfall distribution (Freeze \u0026amp; Cherry, 1979; Todd \u0026amp; Mays, 2005). In tectonically active and mountainous regions, structural features such as faults and fractures play a particularly important role in groundwater movement and storage (Singh \u0026amp; Prakash, 2002). Therefore, an integrated multi-criteria geospatial approach is essential for accurately mapping groundwater potential in such complex terrains.\u003c/p\u003e\n\u003cp\u003eDespite the growing use of RS\u0026ndash;GIS techniques in northeastern India, systematic groundwater potential studies remain limited in spatial resolution and regional coverage. Lohit District of Arunachal Pradesh\u0026mdash;a region characterized by rugged topography, complex tectonics, heavy rainfall, and fragile hydrogeological conditions\u0026mdash;has received very little scientific attention. The increasing dependence on groundwater for domestic and agricultural needs, combined with climate variability and infrastructural development, has heightened the risk of unsustainable groundwater use in the district.\u003c/p\u003e\n\u003cp\u003eThe present study addresses this critical research gap by integrating multi-thematic geospatial datasets with an AHP-based weighted overlay approach within a GIS framework to delineate groundwater potential zones in Lohit District. The objective is to generate a scientifically robust and spatially explicit groundwater potential map that can support sustainable groundwater management, recharge planning, and water-resource decision-making in this ecologically sensitive and data-scarce region of northeastern India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLohit District is in the eastern part of Arunachal Pradesh, India, between 27\u0026deg;33\u0026prime;\u0026ndash;29\u0026deg;22\u0026prime; N latitudes and 95\u0026deg;15\u0026prime;\u0026ndash;97\u0026deg;24\u0026prime; E longitudes, covering about 5,212 km\u0026sup2; (Census of India, 2011). It is bounded by Lower Dibang Valley in the west, Anjaw in the north, Changlang in the south, and Tinsukia District of Assam in the southwest (Figure 1). The district is named after the Lohit River, a major tributary of the Brahmaputra, which originates in eastern Tibet and flows through the region before entering Assam (Bora \u0026amp; Patgiri, 2017).\u003c/p\u003e\n\u003cp\u003ePhysiographically, Lohit District exhibits strong topographic variation, ranging from low-lying alluvial plains in the south to steep mountainous terrain in the central and northern parts. The southern plains form part of the Upper Assam basin and are influenced by rivers such as the Lohit, Dibang, Kamlang, and Noa-Dihing, which deposit fertile alluvium but also cause seasonal flooding (Singh, 2010). These plains abruptly transition into the Lesser Himalayan foothills and further into the Higher Himalayas, where elevations exceed 5,000 m and terrain is structurally complex (Borah, 2019; Rai \u0026amp; Sharma, 2016).\u003c/p\u003e\n\u003cp\u003eThe district has a dense dendritic drainage network dominated by the Lohit River and its tributaries. Fluvial and glacial sediments deposited by these rivers strongly influence aquifer properties and groundwater storage. Climatically, Lohit experiences a humid subtropical to alpine climate with high monsoonal rainfall, which contributes to floods, landslides, and soil erosion, affecting groundwater recharge.\u003c/p\u003e\n\u003cp\u003eThe region is mainly inhabited by tribal communities such as the Mishmi, Khamti, and Singpho. Traditional land-use practices, including shifting cultivation (jhum), combined with steep terrain and heavy rainfall, significantly influence vegetation cover, soil conditions, and groundwater availability across the district.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData used\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in the present study were acquired from authenticated and widely recognized global and national agencies. These datasets were selected based on their spatial resolution, thematic relevance, and proven reliability for geospatial and environmental research. A brief description of each data source and its purpose in the study is provided below.\u003c/p\u003e\n\u003cp\u003eTable 1: Data Sources and Their Specifications\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource / Organization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResolution / Scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFormat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePurpose in Study\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePrecipitation Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eWorldClim (www.worldclim.org)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e~1 km spatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eClimatic analysis, rainfall distribution studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSoil Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eFAO Digital Soil Map of the World (DSMW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1:5,000,000 scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eVector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eSoil classification and suitability assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eElevation Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eSRTM DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e30 m spatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eTerrain analysis (slope, aspect, drainage)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eOrthoimage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eIRS Cartosat-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.5 m spatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eHigh-resolution land use/land cover mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSatellite Imagery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eIRS Resourcesat-2 LISS-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e23.5 m spatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eRaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eVegetation analysis, supplementary LULC classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePrecipitation datasets were obtained from the WorldClim database, which provides globally consistent, high-resolution climate layers suitable for environmental modeling. Soil characteristics for the study area were derived from the FAO Digital Soil Map of the World (FAO, 2012), one of the most comprehensive global soil inventories.\u003c/p\u003e\n\u003cp\u003eTopographic information was extracted from the Shuttle Radar Topography Mission (SRTM) DEM at 30-meter resolution, enabling detailed derivation of geomorphological parameters such as slope and elevation. For detailed spatial mapping and interpretation, high-resolution IRS Cartosat-1 Orthoimagery (2.5 m) was used to support land use and land cover (LULC) mapping. Additionally, IRS Resourcesat-2 LISS-III multispectral imagery (23.5 m) was utilized for vegetation assessment and to enhance classification accuracy across heterogeneous landscapes.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe groundwater potential zonation of Lohit District was carried out using an integrated Remote Sensing (RS), GIS, and Analytic Hierarchy Process (AHP) based weighted overlay approach, as summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Flow chart of the methodology). The flow chart illustrates the sequential workflow starting from data acquisition, thematic layer generation, multi-criteria evaluation, and final groundwater potential mapping.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the first step involves the collection of multiple thematic layers that control groundwater occurrence, including geomorphology, geology, lineament density, land use/land cover, soil type, slope, and rainfall. These factors are widely recognized as key determinants of groundwater storage, movement, and recharge (Todd \u0026amp; Mays, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jha et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kaliraj et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Remote sensing data such as satellite imagery and Digital Elevation Models (DEM) were used to extract landforms, slope, drainage, and land-cover information, while GIS was used to store, process, and spatially integrate all datasets within a common georeferenced framework (Sabins, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jensen, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second stage in the flow chart corresponds to the preparation and classification of individual thematic layers. Each layer was categorized into hydrologically meaningful classes based on its influence on groundwater potential. For example, alluvial plains and valley fills were ranked higher than steep hill slopes due to their greater infiltration and storage capacity, while areas with high lineament density were assigned higher ranks because fractures and faults enhance groundwater movement (Krishnamurthy et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Raghunath, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the third step (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the Analytic Hierarchy Process (AHP) was applied to determine the relative importance of each thematic layer. Pairwise comparisons were performed using Saaty\u0026rsquo;s 1\u0026ndash;9 scale, and consistency ratios were checked to ensure logical reliability of the weighting scheme (Saaty, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Malczewski, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The derived weights were then applied in a GIS-based weighted overlay analysis, where all raster layers were combined to generate a composite groundwater potential index.\u003c/p\u003e \u003cp\u003eFinally, as shown in the last step of the flow chart, the groundwater potential index was classified into five zones: very high, high, moderate, low, and very low groundwater potential. This final groundwater potential map provides a spatial representation of groundwater availability across Lohit District and serves as a scientific basis for groundwater exploration, artificial recharge planning, and sustainable water resource management (Jha et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kaliraj et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eThematic Layers\u003c/h3\u003e\n\u003cp\u003eRainfall\u003c/p\u003e \u003cp\u003eRainfall is the primary source of groundwater recharge, as it governs the amount of water that infiltrates into aquifers (Foster et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Lohit District receives high and well-distributed monsoonal rainfall, creating favourable conditions for groundwater replenishment. Gridded rainfall data from the Climate Research Unit (CRU) and the NCAS dataset (30\u0026prime; \u0026times; 30\u0026prime; resolution) were interpolated using the Inverse Distance Weighting (IDW) method in ArcGIS to generate an annual rainfall map (Harris et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This rainfall layer was used as an important thematic input for groundwater potential mapping.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eElevation strongly influences rainfall distribution, with precipitation generally decreasing at higher altitudes (Dunne \u0026amp; Leopold, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Consequently, the lower-lying areas of Lohit receive greater rainfall and are more favourable for groundwater recharge, while higher mountainous regions experience lower rainfall and increased runoff, limiting infiltration (Aymonier et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGeology\u003c/h3\u003e\n\u003cp\u003eThe geology of Lohit District (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) reflects a complex tectonic and sedimentary history that strongly controls groundwater occurrence and movement. The low-lying valleys are mainly composed of Quaternary fluvial and glacial sediments deposited by active river systems, providing favourable conditions for groundwater storage and recharge (Rao \u0026amp; Dutta, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The Dihing Group (Pliocene\u0026ndash;Pleistocene) represents younger sedimentary formations associated with Neogene tectonic uplift (Kumar \u0026amp; Singh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOlder basement rocks are represented by the Paleoproterozoic Tilung Formation and the Bomdila Group, which consist of metasedimentary and metamorphic rocks interlayered with the Pari Mountain Gneiss, reflecting a complex metamorphic history (Bhattacharyya \u0026amp; Pal, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ranjit \u0026amp; Mehta, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At higher elevations, the Dibang Group (Cretaceous age) includes marine, continental, and volcanic sequences that record Mesozoic tectonic activity (Srivastava \u0026amp; Prasad, 2017). The Lohit Granitoid Complex, emplaced during the Late Cretaceous to Paleocene, marks a major magmatic phase linked to the collision between the Indian and Eurasian plates (Ghosh \u0026amp; Mishra, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis diverse geological framework, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, ranges from highly permeable alluvial deposits to low-permeability crystalline rocks and therefore exerts a strong control on groundwater storage, flow paths, and recharge potential across Lohit District (Saha \u0026amp; Verma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yadav \u0026amp; Patnaik, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDrainage density\u003c/h3\u003e\n\u003cp\u003eDrainage density was derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) using ArcGIS to evaluate the influence of surface hydrology on groundwater recharge potential. Drainage density reflects the degree of channel development within a basin and is controlled by factors such as climate, lithology, relief, infiltration capacity, and vegetation cover (Strahler, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1952\u003c/span\u003e; Nag, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Nag (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), areas characterized by permeable subsurface materials, gentle relief, and dense vegetation tend to exhibit low drainage density, as higher infiltration reduces surface runoff and limits channel formation. In contrast, regions with impermeable rocks, steep slopes, and sparse vegetation display high drainage density due to increased runoff and rapid surface flow.\u003c/p\u003e \u003cp\u003eThe drainage density map of Lohit District (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) shows that high drainage density occurs mainly in mountainous and structurally resistant terrains, where steep slopes and low infiltration promote rapid runoff. Conversely, low drainage density is observed in valley floors and areas underlain by permeable sediments and dense vegetation, indicating favourable conditions for groundwater infiltration and recharge. These spatial patterns are consistent with the conceptual models proposed by Strahler (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1952\u003c/span\u003e) and Nag (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, drainage density serves as an important indicator for groundwater potential assessment, as areas with lower drainage density are more suitable for groundwater recharge and storage, while high drainage density zones are less favourable due to rapid runoff and limited infiltration.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLand use/ Land Cover\u003c/h2\u003e \u003cp\u003eThe Land Use and Land Cover (LULC) pattern of Lohit District reflects a diverse and ecologically significant landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Forests dominate the district, covering about 67.30% of the total area, indicating a largely natural and well-vegetated environment that plays a vital role in biodiversity conservation, soil protection, and hydrological regulation (Singh \u0026amp; Saha, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Agricultural land constitutes 14.37%, highlighting the importance of farming as a primary livelihood in the region.\u003c/p\u003e \u003cp\u003eWastelands account for 10.51%, representing degraded or less productive areas that are vulnerable to erosion and require careful management. Grassland and grazing land occupy 2.87%, supporting livestock and traditional pastoral activities. Water bodies cover 1.88%, while snow and glacial areas contribute 1.76%, mainly in the high-altitude northern parts of the district. Built-up areas occupy 1.31%, indicating limited but growing human settlements, and shifting cultivation accounts for a very small proportion (0.01%), reflecting localized traditional land-use practices.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esummarizes the percentage distribution of LULC classes in Lohit District.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClasses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea in use (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.29628404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.37164169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWastelands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.50634541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrassland \u0026amp; Grazing land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.869747127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.879265181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSnow / Glacial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.759458081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.308384107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShifting cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008874364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings from the Land Use and Land Cover (LULC) analysis show that forests are the predominant land cover type in the Lohit district, followed by agricultural land. This diverse land-use composition underscores the balanced interaction between natural ecosystems and human-altered spaces, which is critical for sustainable development and environmental management (Ghosh et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLineaments\u003c/h3\u003e\n\u003cp\u003eLineaments are linear or curvilinear geological features, such as faults and fractures, that significantly Lineaments are linear or curvilinear geological features such as faults, fractures, and joints that exert a strong control on groundwater movement, storage, and aquifer behavior (Keller \u0026amp; Green, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These structures increase secondary porosity and permeability of rocks, thereby creating preferential pathways for groundwater flow and enhancing subsurface storage capacity (Chorowicz, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Fractures and fault zones within lineaments facilitate the downward percolation of rainwater and allow groundwater to move more easily through otherwise impermeable rock formations.\u003c/p\u003e \u003cp\u003eMapping lineaments is therefore an important step in groundwater potential assessment, as zones with high lineament density generally exhibit higher groundwater prospects. Wells located close to major lineaments or their intersections are more likely to yield higher discharge because they tap fractured and more permeable zones (Sivapalan \u0026amp; Goh, 2007). Moreover, dense lineament networks influence aquifer connectivity and can lead to complex groundwater flow patterns, especially in structurally controlled terrains (Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Lohit District, the spatial distribution of lineaments (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) highlights structurally weak zones that are favorable for groundwater accumulation and movement. These zones are therefore assigned higher weights in the groundwater potential analysis. Overall, lineaments provide critical insight into subsurface hydrological conditions and are essential for effective groundwater exploration and sustainable water resource management (Singh et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSoil\u003c/h3\u003e\n\u003cp\u003eSoil characteristics play a crucial role in groundwater recharge, as they control infiltration, percolation, and water retention within the vadose zone. The soil data used in this study were obtained from the FAO Digital Soil Map of the World (DSMW) through the FAO Soil Portal (FAO, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These data provide spatial information on soil texture, depth, and hydrological properties across Lohit District.\u003c/p\u003e \u003cp\u003eThe soil in the valley and low-lying areas is predominantly sandy loamy to loamy sand, which is coarse-textured and moderately acidic. These soils exhibit high permeability and infiltration capacity, making them favorable for groundwater recharge. In contrast, the higher-altitude and hill regions are dominated by clay and clay loam soils, which have finer particles, lower permeability, and higher water-holding capacity. Such soils tend to restrict vertical percolation and promote surface runoff, thereby limiting groundwater recharge.\u003c/p\u003e \u003cp\u003eShallow soil depth in many parts of the district, often underlain by bedrock within a few ten centimeters, further influences groundwater occurrence by reducing storage in the soil zone and increasing dependence on fractures and weathered rock layers for groundwater accumulation. Therefore, areas with sandy and loamy soil were assigned higher weights in the groundwater potential analysis, while clay-dominated zones were given lower weights due to their limited infiltration capacity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSlope\u003c/h2\u003e \u003cp\u003eSlope is one of the most important topographic parameters controlling surface runoff, infiltration, and groundwater recharge. In this study, slope was derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) using ArcGIS to generate a spatially distributed slope map of Lohit District (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Areas with steep slopes tend to promote rapid surface runoff, reducing the time available for water to infiltrate into the subsurface, whereas gentle slopes allow greater infiltration and percolation, enhancing groundwater recharge (Chorowicz, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Melesse et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe slope pattern of Lohit District shows that low-lying plains and valley floors have gentle slopes, providing favourable conditions for groundwater infiltration and storage. In contrast, the north-western, north-eastern, and central hilly regions exhibit steep slopes, which are associated with high runoff, soil erosion, and limited groundwater recharge (Singh et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These steep terrains mainly support rapid drainage rather than groundwater accumulation.\u003c/p\u003e \u003cp\u003eConsequently, gentle and moderately sloping areas were assigned higher weights in the groundwater potential analysis, while steep and very steep slope zones were given lower weights due to their poor infiltration capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWeighted Overlay Method\u003c/h2\u003e \u003cp\u003eGroundwater potential zones (GWPZs) in Lohit District were delineated using a Weighted Overlay Analysis integrated with the Analytic Hierarchy Process (AHP) in ArcGIS 10.8.2. AHP, developed by Saaty and implemented using K.D. Goepel\u0026rsquo;s AHP tool (Version 15.09.2018), was used to objectively determine the relative importance of each thematic layer influencing groundwater occurrence and recharge.\u003c/p\u003e \u003cp\u003eSeven thematic layers were selected based on hydrogeological significance: rainfall, geology, slope, drainage density, land use/land cover (LULC), lineament density, and soil. These layers were converted into raster format and reclassified into suitability classes ranging from very low to very high. Ranking of individual classes was based on their contribution to groundwater infiltration, storage, and movement, following established hydrogeological principles and previous studies (Krishnamurthy et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Saraf \u0026amp; Chowdhary, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing AHP, each thematic layer was compared pairwise against the others on a scale of 1\u0026ndash;7 to express their relative importance for groundwater potential. A normalized comparison matrix was generated, and weights were calculated after consistency verification. The final weights indicate that rainfall (38%) and geology (24.7%) exert the strongest control on groundwater potential, as rainfall governs recharge and geology controls aquifer storage and permeability. Slope (13.1%) plays a secondary role by regulating runoff and infiltration, while drainage density (8.9%) and LULC (6.6%) reflect surface hydrological and land-surface conditions. Lineament density (5%) and soil (3.7%) have localized but important influences on subsurface flow and infiltration capacity.\u003c/p\u003e \u003cp\u003eThe weighted thematic layers were then integrated in ArcGIS using the weighted overlay function according to:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{GWPZ}=\\sum\\:({W}_{i}\\times\\:{R}_{i})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i}\\)\u003c/span\u003e\u003c/span\u003eis the AHP-derived weight and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{i}\\)\u003c/span\u003e\u003c/span\u003eis the reclassified rank of each thematic layer. The final composite groundwater potential map was classified into high, moderate, and low groundwater potential zones.\u003c/p\u003e \u003cp\u003eThis AHP-based weighted overlay framework provides a transparent, reproducible, and scientifically robust method for groundwater potential mapping and supports sustainable groundwater development and land-use planning in Lohit District.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAHP-derived weights for groundwater potential parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eA or B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScale (1\u0026ndash;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWeightage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLineament Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGeology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLineament Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLineament Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLineament Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLineament Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLineament Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a systematic multi-criteria framework for evaluating groundwater potential in Lohit District, Arunachal Pradesh, by incorporating the major controlling factors of groundwater recharge and storage. The criteria include rainfall, geology, slope, drainage density, land use/land cover (LULC), lineament density, and soil characteristics, each of which plays a distinct role in governing infiltration, subsurface flow, and aquifer development. These parameters were evaluated using a standardized suitability scale ranging from 1 to 7, where higher values indicate more favourable conditions for groundwater occurrence and recharge.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, rainfall (38%) and geology (24.7%) received the highest weights because rainfall is the primary source of groundwater recharge, while geological formations control porosity, permeability, and aquifer thickness. Slope (13.1%) and drainage density (8.9%) were assigned moderate weights, as they influence surface runoff and infiltration potential\u0026mdash;gentler slopes and lower drainage density generally favour groundwater recharge. LULC (6.6%) reflects the impact of vegetation cover, agriculture, and built-up areas on infiltration and evapotranspiration, while lineament density (5%) represents subsurface fractures and faults that enhance groundwater movement and storage. Soil characteristics (3.7%), though assigned a lower weight, are still important for controlling infiltration and water-holding capacity at the surface.\u003c/p\u003e \u003cp\u003eThe division of parameters into primary and secondary factors further highlights their relative influence on groundwater potential. Primary factors such as rainfall and geology exert dominant control over recharge and storage, whereas secondary factors like slope, LULC, drainage density, lineaments, and soil modify groundwater behaviour at local and sub-regional scales. This weighted and integrated approach, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, provides a robust quantitative basis for groundwater potential zonation and supports informed decision-making for sustainable groundwater management, land-use planning, and resource conservation in Lohit District.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThis study provides a comprehensive hydrogeological evaluation of groundwater potential in Lohit District, Arunachal Pradesh, using an integrated Remote Sensing\u0026ndash;GIS\u0026ndash;AHP framework. The spatial distribution of groundwater potential is strongly governed by the combined influence of rainfall, lithology, geomorphology, slope, drainage density, lineament density, soil characteristics, and land use/land cover, consistent with established hydrogeological principles (Todd \u0026amp; Mays, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Krishnamurthy et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Jha et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results indicate that the southern and central parts of the district exhibit relatively high groundwater potential due to their low relief, moderate to high rainfall, and favorable geological and soil conditions. These areas are characterized by gently sloping terrain, which reduces surface runoff and promotes infiltration, thereby enhancing recharge (Freeze \u0026amp; Cherry, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Melesse et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, the northern and northeastern mountainous regions display lower groundwater potential, primarily due to steep slopes, high drainage density, and compact lithological units, which result in rapid runoff and limited infiltration (Nag, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Strahler, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1952\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGeological and structural controls play a crucial role in groundwater occurrence in Lohit District. Zones with moderate to high lineament density exhibit improved secondary porosity and permeability due to the presence of fractures, faults, and joints, which act as conduits for groundwater flow and storage (Chorowicz, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Keller \u0026amp; Pinter, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Similarly, areas with low drainage density indicate higher infiltration capacity and subsurface storage, whereas high drainage density reflects impermeable lithology and high runoff conditions that are unfavorable for groundwater recharge (Nag, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil and land-use patterns further influence recharge processes. Permeable valley soils and agricultural lands facilitate infiltration, while forest cover reduces erosion and enhances soil moisture retention, thereby supporting recharge (Mitsch \u0026amp; Gosselink, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ghosh et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, clay-rich soils and barren or rocky surfaces impede infiltration and reduce groundwater potential.\u003c/p\u003e \u003cp\u003eThe final groundwater potential map (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), generated using the AHP-based weighted overlay technique, classifies Lohit District into five groundwater potential zones: poor, fair, moderate, high, and very high. The areal distribution of these zones is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the Weighted Overlay Method, the district was classified into five groundwater potential zones: poor, fair, moderate, high, and very high. The classification is as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroundwater potential zones of study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGround Water Potential Zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor Potential Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.89 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair Potential Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e922.22 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate Potential Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,014.62 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Potential Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,631.33 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery High Potential Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405.78 km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe high and very high groundwater potential zones together cover approximately 2,037 km\u0026sup2;, representing the most promising areas for groundwater development. These zones coincide with regions of low slope, favorable lithology, moderate lineament density, and adequate rainfall, which together create optimal conditions for recharge and storage (Saraf \u0026amp; Choudhury, 1998; Krishnamurthy et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In contrast, the poor potential zones, occupying only 41.89 km\u0026sup2;, correspond to steep, structurally compact, and high-relief areas where groundwater occurrence is limited due to rapid surface runoff and minimal infiltration.\u003c/p\u003e \u003cp\u003eOverall, the results confirm that groundwater availability in Lohit District is primarily controlled by geomorphological and geological factors interacting with climatic and land-use conditions. The integration of AHP with GIS-based weighted overlay analysis provides a robust and transparent framework for groundwater potential zonation, supporting sustainable groundwater management, well-siting, and long-term water-resource planning in this geologically complex and environmentally sensitive Himalayan region.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study highlights the effectiveness of remote sensing and GIS techniques in delineating diverse groundwater zones within different geological contexts. The results obtained contribute to the creation of a more accurate groundwater potential map for the studied area. Such precise mapping is invaluable for the strategic planning and effective management of groundwater development programs, providing essential insights for sustainable water resource utilization in the region. The integration of advanced technologies like remote sensing and GIS enhances our understanding of groundwater dynamics and facilitates informed decision-making for the optimal utilization of this vital resource.\u003c/p\u003e \u003cp\u003eThis study used map overlay techniques to conduct qualitative analysis, resulting in a map with three distinct categories. High groundwater potential areas were mainly found in flat regions with extensively fractured amygdaloidal basaltic rock formations. These regions had low drainage density and moderate to low lineament density, despite basalt not typically being considered a good aquifer. However, due to significant jointing and weathering, it has become a reasonably effective aquifer. Geomorphological investigations revealed that the northern part of the basin is undeveloped and functions as a runoff zone, while the drainage area is mature and represents a saturated zone with good infiltration capacity. Thick weathering zones within the region were identified, highlighting groundwater potential in this aquifer. The study highlights the use of remote sensing and GIS techniques to delineate groundwater zones in diverse geological contexts, providing a more accurate groundwater potential map for effective planning and management of groundwater development programs.\u003c/p\u003e \u003cp\u003eThe future scope of this research involves refining analytical approaches, such as the Analytic Hierarchy Process (AHP), and exploring advanced remote sensing and GIS methodologies for groundwater mapping. Long-term monitoring of NDVI and land cover dynamics will continue to assess trends, with a focus on understanding drivers and implications for biodiversity. Ongoing demographic studies will track population changes, and sustainable land management practices will be implemented to counteract potential over-exploitation. Integrated conservation and development strategies, community-based initiatives, and climate change adaptation will be explored. Policy recommendations will be formulated for sustainable development, advocating for effective governance. Collaborative research and knowledge-sharing initiatives will deepen the understanding of complex relationships, contributing to broader scientific discourse.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAymonier, C., Renaud, J., \u0026amp; Lamon, J. (2010). Orographic effects on precipitation and groundwater recharge in mountainous regions. \u003cem\u003eHydrological Processes, 24\u003c/em\u003e(18), 2522\u0026ndash;2535.\u003c/li\u003e\n \u003cli\u003eBhattacharyya, C. C., \u0026amp; Pal, T. (2011). Metamorphic evolution of the Bomdila Group and Pari Mountain Gneiss in Arunachal Pradesh, NE India. \u003cem\u003eJournal of Asian Earth Sciences, 42\u003c/em\u003e(3), 345\u0026ndash;358.\u003c/li\u003e\n \u003cli\u003eBora, D. K., \u0026amp; Patgiri, G. (2017). River systems and geomorphology of Arunachal Pradesh. \u003cem\u003eIndian Journal of Geomorphology, 22\u003c/em\u003e(1), 15\u0026ndash;29.\u003c/li\u003e\n \u003cli\u003eBorah, D. (2019). Himalayan tectonics and geomorphology of Arunachal Pradesh. \u003cem\u003eJournal of the Geological Society of India, 93\u003c/em\u003e(2), 157\u0026ndash;169.\u003c/li\u003e\n \u003cli\u003eBurrough, P. A., \u0026amp; McDonnell, R. A. (1998). \u003cem\u003ePrinciples of Geographical Information Systems\u003c/em\u003e. Oxford University Press.\u003c/li\u003e\n \u003cli\u003eCentral Ground Water Board (CGWB). (2020). \u003cem\u003eDynamic Ground Water Resources of India\u003c/em\u003e. Ministry of Jal Shakti, Government of India.\u003c/li\u003e\n \u003cli\u003eChorowicz, J. (2005). Lineament analysis for groundwater studies. \u003cem\u003eHydrogeology Journal, 13\u003c/em\u003e(2), 253\u0026ndash;267.\u003c/li\u003e\n \u003cli\u003eDunne, T., \u0026amp; Leopold, L. B. (1978). \u003cem\u003eWater in Environmental Planning\u003c/em\u003e. W.H. Freeman.\u003c/li\u003e\n \u003cli\u003eEnvironmental Protection Agency (EPA). (2009). \u003cem\u003eGround Water and Drinking Water\u003c/em\u003e. U.S. Environmental Protection Agency.\u003c/li\u003e\n \u003cli\u003eFAO. (2012). \u003cem\u003eHarmonized World Soil Database (Version 1.2)\u003c/em\u003e. Food and Agriculture Organization of the United Nations.\u003c/li\u003e\n \u003cli\u003eFAO. (2020). \u003cem\u003eFAO Aquastat Database\u003c/em\u003e. Food and Agriculture Organization of the United Nations.\u003c/li\u003e\n \u003cli\u003eFenta, A. A., Kifle, A., Gebreyohannes, T., \u0026amp; Hailu, G. (2015). Groundwater potential mapping using GIS and AHP. \u003cem\u003eJournal of Hydrology: Regional Studies, 3\u003c/em\u003e, 164\u0026ndash;181.\u003c/li\u003e\n \u003cli\u003eFreeze, R. A., \u0026amp; Cherry, J. A. (1979). \u003cem\u003eGroundwater\u003c/em\u003e. Prentice Hall.\u003c/li\u003e\n \u003cli\u003eFoster, S., Hirata, R., Gomes, D., D\u0026rsquo;Elia, M., \u0026amp; Paris, M. (2004). \u003cem\u003eGroundwater Quality Protection\u003c/em\u003e. World Bank.\u003c/li\u003e\n \u003cli\u003eGhosh, S., \u0026amp; Mishra, S. (2020). Petrogenesis of the Lohit granitoids. \u003cem\u003eJournal of Asian Earth Sciences, 195\u003c/em\u003e, 104342.\u003c/li\u003e\n \u003cli\u003eGhosh, S., Dutta, D., \u0026amp; Debnath, R. (2020). Land use change and hydrological response in northeastern India. \u003cem\u003eEnvironmental Earth Sciences, 79\u003c/em\u003e, 255.\u003c/li\u003e\n \u003cli\u003eGleeson, T., Wada, Y., Bierkens, M. F. P., \u0026amp; van Beek, L. P. H. (2012). Water balance of global aquifers. \u003cem\u003eNature, 488\u003c/em\u003e, 197\u0026ndash;200.\u003c/li\u003e\n \u003cli\u003eGupta, M., Srivastava, P., \u0026amp; Islam, T. (2014). GIS-based groundwater potential mapping. \u003cem\u003eHydrogeology Journal, 22\u003c/em\u003e(7), 1483\u0026ndash;1497.\u003c/li\u003e\n \u003cli\u003eHarris, I., Jones, P. D., Osborn, T. J., \u0026amp; Lister, D. H. (2014). Updated high-resolution grids of monthly climatic observations. \u003cem\u003eInternational Journal of Climatology, 34\u003c/em\u003e(3), 623\u0026ndash;642.\u003c/li\u003e\n \u003cli\u003eIndia Development Review (IDR). (2019). \u003cem\u003eIndia\u0026rsquo;s Groundwater Crisis\u003c/em\u003e. IDR.\u003c/li\u003e\n \u003cli\u003eJasrotia, A. S., Kumar, R., \u0026amp; Saraf, A. K. (2013). GIS-based groundwater recharge assessment. \u003cem\u003eEnvironmental Earth Sciences, 68\u003c/em\u003e, 1285\u0026ndash;1296.\u003c/li\u003e\n \u003cli\u003eJensen, J. R. (2016). \u003cem\u003eIntroductory Digital Image Processing\u003c/em\u003e (4th ed.). Pearson.\u003c/li\u003e\n \u003cli\u003eJha, M. K., Chowdary, V. M., \u0026amp; Chowdhury, A. (2010). Groundwater potential mapping. \u003cem\u003eHydrological Sciences Journal, 55\u003c/em\u003e(4), 531\u0026ndash;548.\u003c/li\u003e\n \u003cli\u003eJha, M. K., Chowdhary, V. M., \u0026amp; Chowdhury, A. (2013). Groundwater assessment using RS and GIS. \u003cem\u003eHydrogeology Journal, 21\u003c/em\u003e, 803\u0026ndash;816.\u003c/li\u003e\n \u003cli\u003eKaliraj, S., Chandrasekar, N., \u0026amp; Magesh, N. S. (2017). GIS\u0026ndash;AHP based groundwater potential zone mapping. \u003cem\u003eJournal of Hydrology, 547\u003c/em\u003e, 461\u0026ndash;475.\u003c/li\u003e\n \u003cli\u003eKeller, E. A., \u0026amp; Pinter, N. (2002). \u003cem\u003eActive Tectonics\u003c/em\u003e. Prentice Hall.\u003c/li\u003e\n \u003cli\u003eKeller, E. A., \u0026amp; Green, R. (2015). Geological controls on groundwater flow. \u003cem\u003eHydrogeology Journal, 23\u003c/em\u003e, 1561\u0026ndash;1573.\u003c/li\u003e\n \u003cli\u003eKrishnamurthy, J., Mani, A., Jayaraman, V., \u0026amp; Manivel, M. (1996). Groundwater resources development. \u003cem\u003eInternational Journal of Remote Sensing, 17\u003c/em\u003e, 1867\u0026ndash;1884.\u003c/li\u003e\n \u003cli\u003eKumar, A., \u0026amp; Singh, S. (2016). Neogene tectonics of the Dihing basin. \u003cem\u003eJournal of Asian Earth Sciences, 118\u003c/em\u003e, 35\u0026ndash;49.\u003c/li\u003e\n \u003cli\u003eMachiwal, D., Jha, M. K., \u0026amp; Mal, B. C. (2011). GIS-based groundwater potential mapping. \u003cem\u003eEnvironmental Earth Sciences, 63\u003c/em\u003e, 119\u0026ndash;131.\u003c/li\u003e\n \u003cli\u003eMagesh, N. S., Chandrasekar, N., \u0026amp; Soundranayagam, J. P. (2012). GIS-based groundwater potential zones. \u003cem\u003eEnvironmental Earth Sciences, 65\u003c/em\u003e, 1373\u0026ndash;1385.\u003c/li\u003e\n \u003cli\u003eMalczewski, J. (2006). GIS-based multicriteria decision analysis. \u003cem\u003eInternational Journal of Geographical Information Science, 20\u003c/em\u003e(7), 703\u0026ndash;726.\u003c/li\u003e\n \u003cli\u003eMelesse, A. M., Weng, Q., Thenkabail, P. S., \u0026amp; Senay, G. B. (2013). Remote sensing of hydrology. \u003cem\u003eHydrological Processes, 27\u003c/em\u003e, 302\u0026ndash;315.\u003c/li\u003e\n \u003cli\u003eMitsch, W. J., \u0026amp; Gosselink, J. G. (2007). \u003cem\u003eWetlands\u003c/em\u003e (4th ed.). Wiley.\u003c/li\u003e\n \u003cli\u003eNag, S. K. (1998). Morphometric analysis using remote sensing. \u003cem\u003eJournal of the Indian Society of Remote Sensing, 26\u003c/em\u003e(4), 169\u0026ndash;178.\u003c/li\u003e\n \u003cli\u003eRaghunath, H. M. (2006). \u003cem\u003eHydrogeology\u003c/em\u003e. New Age International.\u003c/li\u003e\n \u003cli\u003eRahmati, O., Nazari Samani, A., Mahdavi, M., Pourghasemi, H. R., \u0026amp; Zeinivand, H. (2015). Groundwater potential mapping. \u003cem\u003eArabian Journal of Geosciences, 8\u003c/em\u003e, 9319\u0026ndash;9330.\u003c/li\u003e\n \u003cli\u003eRai, S., \u0026amp; Sharma, R. (2016). Himalayan tectonics of Arunachal Pradesh. \u003cem\u003eGeological Journal of India, 88\u003c/em\u003e, 421\u0026ndash;435.\u003c/li\u003e\n \u003cli\u003eRanjit, S., \u0026amp; Mehta, P. (2019). Proterozoic lithostratigraphy of Arunachal Pradesh. \u003cem\u003eJournal of Earth System Science, 128\u003c/em\u003e, 33.\u003c/li\u003e\n \u003cli\u003eRao, V. V., \u0026amp; Dutta, S. (2014). Quaternary sedimentation in NE India. \u003cem\u003eQuaternary International, 325\u003c/em\u003e, 79\u0026ndash;90.\u003c/li\u003e\n \u003cli\u003eSabins, F. F. (2007). \u003cem\u003eRemote Sensing: Principles and Interpretation\u003c/em\u003e (3rd ed.). W.H. Freeman.\u003c/li\u003e\n \u003cli\u003eSaaty, T. L. (2008). Decision making with AHP. \u003cem\u003eInternational Journal of Services Sciences, 1\u003c/em\u003e(1), 83\u0026ndash;98.\u003c/li\u003e\n \u003cli\u003eSaraf, A. K., \u0026amp; Chowdhary, V. M. (1998). GIS-based groundwater assessment. \u003cem\u003eInternational Journal of Remote Sensing, 19\u003c/em\u003e(18), 3521\u0026ndash;3540.\u003c/li\u003e\n \u003cli\u003eSaha, D., \u0026amp; Verma, S. (2018). Hydrogeology of NE India. \u003cem\u003eJournal of the Geological Society of India, 91\u003c/em\u003e, 585\u0026ndash;598.\u003c/li\u003e\n \u003cli\u003eSingh, A. K., \u0026amp; Prakash, S. R. (2002). Tectonic control on groundwater. \u003cem\u003eJournal of Earth System Science, 111\u003c/em\u003e(4), 497\u0026ndash;508.\u003c/li\u003e\n \u003cli\u003eSingh, S. (2010). Floodplain geomorphology of Assam. \u003cem\u003eGeographical Review of India, 72\u003c/em\u003e, 1\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eSingh, R., \u0026amp; Saha, S. (2015). Forest ecology of Arunachal Pradesh. \u003cem\u003eIndian Journal of Forestry, 38\u003c/em\u003e(3), 245\u0026ndash;254.\u003c/li\u003e\n \u003cli\u003eSingh, S., Yadav, P., \u0026amp; Kumar, S. (2017). Structural control on groundwater. \u003cem\u003eJournal of Hydrology, 552\u003c/em\u003e, 234\u0026ndash;245.\u003c/li\u003e\n \u003cli\u003eStrahler, A. N. (1952). Hypsometric analysis of erosional topography. \u003cem\u003eGeological Society of America Bulletin, 63\u003c/em\u003e, 1117\u0026ndash;1142.\u003c/li\u003e\n \u003cli\u003eTodd, D. K., \u0026amp; Mays, L. W. (2005). \u003cem\u003eGroundwater Hydrology\u003c/em\u003e (3rd ed.). Wiley.\u003c/li\u003e\n \u003cli\u003eUNESCO. (2018). \u003cem\u003eWorld Water Development Report\u003c/em\u003e. United Nations.\u003c/li\u003e\n \u003cli\u003eYadav, P., \u0026amp; Patnaik, S. (2014). Geological evolution of Arunachal Himalaya. \u003cem\u003eJournal of Earth Sciences, 25\u003c/em\u003e(3), 487\u0026ndash;502.\u003c/li\u003e\n \u003cli\u003eZhang, Y., Li, X., \u0026amp; Zhang, W. (2014). Structural control on groundwater flow. \u003cem\u003eHydrogeology Journal, 22\u003c/em\u003e, 1365\u0026ndash;1379.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Prof Ramkrishna More A.C.S. College, Pune","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Groundwater potential zones, RS\u0026GIS, Land use/ Land cover, AHP","lastPublishedDoi":"10.21203/rs.3.rs-8597500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8597500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study delineates groundwater potential zones in Lohit District, Arunachal Pradesh, using an integrated Remote Sensing (RS), Geographic Information System (GIS), and weighted overlay approach. Multiple thematic layers namely geology, soil, drainage density, slope, lineament density, rainfall, and land use/land cover were generated and analysed to represent the controlling factors of groundwater occurrence and recharge. Geological information was obtained from the Geological Survey of India, soil data from the FAO Digital Soil Map, drainage from Survey of India topographic sheets, and terrain and land-cover parameters from SRTM-GDEM and satellite imagery. The Analytic Hierarchy Process (AHP) was applied to assign scientifically consistent weights to each thematic layer based on their relative influence on groundwater potential. These weighted layers were integrated in a GIS environment to produce a comprehensive groundwater potential zonation map. The district was classified into five groundwater potential categories: poor (41.89 km\u0026sup2;), fair (922.22 km\u0026sup2;), moderate (1,014.62 km\u0026sup2;), high (1,631.33 km\u0026sup2;), and very high (405.78 km\u0026sup2;). The results indicate that a substantial portion of the district falls within moderate to high potential zones, while areas categorized as poor and fair require focused groundwater development and recharge interventions to ensure sustainable water availability.\u003c/p\u003e","manuscriptTitle":"Identification of Groundwater Potential Zones in Lohit District, Arunachal Pradesh, Using Remote Sensing and Geographical Information System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 13:11:17","doi":"10.21203/rs.3.rs-8597500/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e6ddbb3-2889-4136-8735-d7f4685f588d","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61106647,"name":"Geographic Information Systems"},{"id":61106648,"name":"Geomorphology"},{"id":61106649,"name":"Hydrology"},{"id":61106650,"name":"Geology"}],"tags":[],"updatedAt":"2026-01-16T13:11:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 13:11:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8597500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8597500","identity":"rs-8597500","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}