Changes in groundwater storage in the Southern plain of Western Nepal: Insights from remote sensing, modeling, and field studies

Changes in groundwater storage in the Southern plain of Western Nepal: Insights from remote sensing, modeling, and field studies | 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 Changes in groundwater storage in the Southern plain of Western Nepal: Insights from remote sensing, modeling, and field studies Prajwal Aryal, Vishnu Prasad Pandey, Bhesh Raj Thapa, Nabin Tiwari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7235632/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Groundwater (GW) levels in many parts of the world are depleting, including the western Terai region of Nepal, yet the extent of depletion and availability of the resource remain unknown. This study intends to characterize the state of GW potential and depletion in the southern plain of Western Nepal by applying field-based, model-based, and remote-sensing-based approaches. Borehole lithologs were used to evaluate storage potential. Observed GW level data, along with GRACE satellite data, were used to characterize the depletion. Primary data were collected to assess annual GW abstraction. The Static storage potential of a deep confined aquifer is estimated in a range of 8–800Mm³, and that of an unconfined shallow aquifer is 7,500–15,000Mm³. Regional terrestrial water storage (2004–2022) is trending downward. Locally, depletion (2008–2023) across municipalities ranges from negligible (0.1 m, Tikapur Municipality) to high (3.3 m, Gauriganga Municipality). Average annual GW abstraction for agriculture and domestic needs is estimated as 378.39Mm³ and 44.38Mm³, respectively. Based on insights from field and model-based studies, as well as techniques documented in literature, it is recommended to consider non-structural strategies for the conservation of GW. The findings of this study are constructive for better planning and future development of GW resources in the area. Groundwater depletion Indo-Gangetic plain Nepal recharge Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Water demands are increasing due to factors like population growth, irrigation requirements for food security, and economic development. It has led to a state of water stress in different parts of the world (Döll et al., 2014). Both groundwater (GW) and surface water supply water to meet different demands. GW accounts for about 30% of the world’s freshwater and approximately 99% of liquid freshwater on the Earth (Gupta et al., 2018; United Nations, 2022). The availability and withdrawal of GW differ globally based on aquifer condition (Famiglietti, 2014). With ever-increasing withdrawal, GW is depleting globally, with an estimated increase in depletion from 126 (± 32) km³/yr. in 1960 to 283 (± 40) km³/yr. in 2000 (Wada et al., 2010). Ingo Gangetic Plan (IGB) in South Asia is one of the regions experiencing water stress and simultaneous depletion of GW (Hanasaki et al., 2008). The IGB, spanning about 2.25 million Km² area (Taneja et al., 2014), has a GW availability of about 1750.92 billion cubic meters (BCM) (Gayen, 2022), which is being utilized by about a 1.5 billion population (Pal et al., 2009; Mukherjee et al., 2015). Over-abstraction (about 250 km³/yr.) and subsequent depletion (about 0.1–0.75 m/yr.) of GW is found in most parts of the IGB, including the western Terai of Nepal (A. MacDonald et al., 2013; Sinha et al., 2018). The Terai region of Nepal, mainly made up of fresh alluvial deposits, features multiple layers of good aquifers at different depths that are relatively connected (Gupta et al., 2018). It is believed that aquifers in Terai are highly productive with available safe yield of about 9 BCM/year (Shrestha et al., 2018; Kansakar, 2005); however, the GW is depleting at a faster rate (Shrestha et al., 2018; A. MacDonald et al., 2016). Therefore, it is necessary to deploy a comprehensive study to characterize the state of GW potential and depletion to aid in designing strategies for effective management of the Terai’s GW system (Masih et al., 2014; Mekonnen & Hoekstra, 2016). With the advancement of remote sensing and machine learning techniques and tools, the GW study is promising to reach unprecedented heights (Deoli & Kumar, 2020). Satellite data such as Gravity Recovery and Climate Experiment (GRACE), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) provide an alternative approach to study different aspects of GW around the World (Hu et al., 2017; Li et al., 2019; Döll et al., 2014). Numerical models like Modular Finite-Difference Groundwater Flow Model (MODFLOW) (McDonald & Harbaugh, 1988), Finite Element Flow (FEFLOW) (Diersch, 2013), and Global Land Data Assimilation System-Catchment Land Surface Model (GLDAS-CLSM) (Liu et al., 2019) are also some powerful GW modelling tools that offer flexibility and robustness for various applications (Hu et al., 2021). However, for a data-scarce region like Nepal’s Terai, the accuracy of the study using satellite data along with modeling tools only is considered less appropriate, as it requires a comprehensive data set (hydrogeological, hydrological, topographical) with better spatial-temporal resolution (Li et al., 2019). Furthermore, various integrated approaches, including Geographical Information System (GIS)-based method (Longley et al., 2015), Multi-Criteria Decision Analysis (MCDA) (Triantaphyllou & Triantaphyllou, 2000), Analytic Hierarchy Process (AHP) (Saaty, 1980), are promising tools for GW study. Nonetheless, the applicability and accuracy of these techniques largely depend upon the number of parameters – such as slope, rainfall, soil type, land use/cover (LULC), and drainage density, among others – and weights assigned to them, in succession could be complex and time consuming (Adiat et al., 2012; Kumar et al., 2014). Field-based methods, including hydrogeological survey, geophysical method, and GW level monitoring method, provide a first-hand and convenient data set to analyze the spatial-temporal availability and variation of GW in an area (Hu et al., 2021). Many GW studies around the world have deployed field-based methods (e.g., Olawepo et al., 2013; Agbalagba et al., 2019; Musa & Mohammed, 2015; Jerbi et al., 2018). A study of GW using in-situ data (lithology, monitoring well) in IGB has identified the north-western part as being mostly affected by the GW depletion problem (MacDonald AM et al., 2016). Also, in Terai and inner Terai region of Nepal, recent studies (e.g., Roshani et al., 2020; Shrestha et al., 2018; Pathak, 2017) conducted using lithological data and observed GW level data has assessed the status of GW availability and depletion in both confined and unconfined aquifers with acceptable accuracy, but with coarser resolution and without in-depth analysis. Spatially well-distributed, lithological data and GW level data could provide ease to the study and represent the existing status of GW with satisfactory accuracy. The field-based method, along with the use of GIS, is found to have been producing acceptable results for GW studies (e.g., Nouayti et al., 2019; Memon et al., 2020; Saadi et al., 2021). With some constraints of time and resources, these field-based methods integrated with GIS can be considered an appropriate approach for a comprehensive study of GW in the Western Terai region of Nepal. In this context, of having high GW potentiality, decreasing GW water level, and lack of GW-related comprehensive studies in Western Terai of Nepal; this research, with a case of Kailali district, aims to focus on the following specific objectives: i) to assess the static GW storage potential; ii) to characterize the status of GW depletion; iii) to evaluate the average annual GW abstraction; and iv) to assess prospects of GW recharge. 2. Study Area and Methods Kailali, located in the Southern plain of Western Nepal in Far Western Province (Fig. 1 ), is one of Nepal's 77 districts. As of 2021, the Center Bureau of Statistics (CBS), the district has a little more than 0.9 million residents. Kailali district covers 3,235 km 2 of area and rises from 135 to 1967 meters above mean sea level (masl). Siwalik Hills, Bhabar Zone, Middle Terai, and Lower Terai are the four main geographic regions that make up the district (N. B. Thapa & Thapa, 1969). Approximately 71% of the district's total land area is forest, and 24.9% of it is agricultural land (CBS, 2013). It is made up of a thick alluvial deposit and a recent sedimentation belt with ongoing river deposition (Lewison et al., 2020). There are eleven meteorological and hydrological stations spread out across the district. Approximately 1,840 mm of precipitation and 700 mm of evapotranspiration take place per year (L. Thapa et al., 2015). The area below 300 masl (Terai region) has been considered as an aquifer boundary. Moreover, the Siwalik region, over 1000 masl, has not been included in this study. For the Siwalik zone's alternating strata of mudstone, conglomerate, and sandstone, which are associated with incredibly low groundwater potential (Suryabhagavan, 2017). The areal extent along with related features of the study area is shown in Fig. 1 . 2.1. Data preparation Selecting the study area, figuring out whether data are available, acquiring available data and pre-processing, and setting up a GIS database are the first steps. Lithological data (29 locations) and GW level monitoring data (19 locations) were taken from the Groundwater Resources Development Board (GWRDB). A key informant survey and a primary household (HH) survey were implemented to collect data on various aspects of GW at the local level. A survey was conducted with 89 households and a few key informants during a field visit. Climatic data, including precipitation, at nine stations were acquired from the Department of Hydrology and Meteorology (DHM). In addition, literature reviews and expert suggestions were used to collect secondary data. Table 1 shows the data that were acquired along with their sources. The overall methodological framework for achieving all four objectives is depicted in Fig. 2 . Table 1 Data and Sources S.N. Sources Data Temporal Resolution Spatial Resolution 1 GWRDB Dhangadhi Borehole Lithology, Monitoring Well Data 2008–2023 Across the Study Area 2 Primary Survey Field Survey Data on the Status of GW. 2023 Across the Study Area 3 Literatures, DHM, NARC, FAO Sy, S, Climate Data, Soil Data, Crop Data, Monthly Climate Data Across the Study Area 4 Google Earth Engine, DoI GRACE data, DEM, Command area maps 2004–2022 0.5°x0.5°, 30mx30m DEM, Whole Kailali 2.2. Assessment of aquifer storage potential The static GW storage potential of the study area was assessed in a GIS platform. Accounting for both the driller’s log and the resistivity log (resistivity value of underlying materials). Six different lithological units have been identified from 29 lithological data sets. Further, three separate hydrogeological units have been designated as a result of lithological classification. A GIS database has been prepared along with the boundary of the GW basin. The GW basin area, just as multiplied by the measured aquifer thickness in GIS, yields the aquifer volume. Finally, the aquifer volume was multiplied by the storage coefficient to produce the static GW storage potential in the aquifers beneath the research area. Based on the three alternatives of constant values of the storage coefficients (specific yield = 0.1,0.15,0.2 and storativity = 0.005, 0.0005, and 0.00005) (Saha & Dwivedi, 2018; Sahu et al., 2018) acquired from literature, three separate storage potential values were estimated. Figure 1 shows the spatial dispersion of borehole locations, and Table 2 displays the resistivity range used for lithology categorization (Woobaidullah et al., 2019) and three hydrogeological units that have been classified underneath the research area. Table 2 Reclassification of Lithologs S.N. Apparent Resistivity (ohm-m) Lithological type Hydrogeological units 1 75 Gravel 2.3. Characterization of GW depletion Both regional and local depletion of GW are evaluated in this study. Data from the Gravity Recovery and Climate Experiment (GRACE) are used for evaluating the regional depletion. The resulting data from GRACE is presented as anomalies, specifically as liquid water equivalent (LWE) anomalies relative to the mean between 2004 and 2009. To analyze the data in the study area, the GRACE data grids were examined and compared to the mean value for anomaly calculation. Furthermore, the Mann-Kendall test was run on the anomalies in order to identify and understand any recurrent patterns in GW storage variation. Simultaneously, monitoring GW level data at 19 wells, scattered across the study area, is utilized to characterize local depletion. Here, depth to water level data (monthly data) were provided by GWRDB, Dhangadhi, from 2008 to 2023. A GIS database was established, and the contour map of the water table for the entire year was generated. The water table contour maps show fluctuations in the water table depth across the seasons at different spatial locations. Additionally, six different sites were spotted to exhibit higher annual fluctuations in their depth to water. In those identified spots, an Excel spreadsheet was used to perform a temporal trend analysis of the GW level again using observed GW level data. The spatial distribution of monitoring wells in the study area is depicted in Fig. 6 . 2.4. Assessment of GW abstraction The average annual GW abstraction within the study area was estimated as a sum of irrigational abstraction and HH abstraction. Irrigational abstraction was estimated based on the Crop Water and Irrigation Requirement tool (CROPWAT) (Allen et al., 1998), and HH abstraction was estimated based on HH survey data. The study area was divided into 11 clusters for the HH survey, with the understanding that each cluster represents a single local level. Sample size was determined based on the total number of HHs in a cluster. A total of 89 households were surveyed in this research. Finally, the annual average GW abstraction per HH was calculated and multiplied by the total number of HHs in a specific cluster to obtain the overall GW abstraction in that cluster. Moreover, by aggregating the abstraction volumes in each cluster, the average yearly GW abstraction was determined. For irrigational abstraction, climate and precipitation data at nine stations in the study area were acquired from DHM, soil data from the National Soil Science Research Center (NARC) soil map and the Soil-Plant-Air-Water (SPAW) tool, and crop statistics from literature, key informant surveys, and default data from the Food and Agriculture Organization (FAO). For this study, five key crops –paddy, wheat, mustard, potatoes, and maize – were taken into consideration. The command area for potential GW irrigation was approximated, using the map of surface irrigation command area from the Department of Water Resources and Irrigation (DWRI) and the Land Use/Cover (LULC) map from the International Center for Integrated Mountain Development (ICIMOD). Eventually, the estimated total annual irrigation water depth in CROPWAT was multiplied by the GW irrigation command area to obtain a total annual GW abstraction for irrigation. Figure 3 shows the locations of the sample HHs that were surveyed during the field study, the command area for surface irrigation systems, and the total amount of cultivable land in the study region. 2.5. Identifying potential solutions for enhancing groundwater recharge. Potential techniques for GW recharge augmentation in the study area were discussed based on recommendations from experts, a review of the literature, and interpretation of data from the primary field survey. During field study, ideas were obtained from a range of local activists, engineers, agriculture and environmental experts, and other pertinent staff members. They made recommendations on the topic, which are found to be promising in the context of the research area. Further, engineers and environmentalists from the water supply office, Tikapur Municipality, Godawari Municipality, GWRDB, and Dhangadhi Sub-Metropolitan were interviewed on the topic. A range of lakes, ponds, streams, and woodlands was visited for reconnaissance and fieldwork to gather visual data on the status of potential recharge sites. Additionally, topographic maps, geographic maps, and LULC maps were studied to gather information on the slope, soil type, geographic conditions, and other features present in the study area. A thorough examination and cataloging of existing research on groundwater recharge augmentation in local and regional settings was carried out. Lastly, based mostly on information gathered from the user's group, key informants, socioeconomic situation, and geographic location of the area, certain strategies to enhance GW recharge were formulated and discussed. 3. Results and Discussion 3.1. Spatial distribution of aquifer thickness and GW storage potential There are two aquifers beneath the study area, namely, shallow Aquifer (i.e., Aquifer-1), unconfined in nature, and deep Aquifer (i.e., Aquifer-2), naturally confined. An aquitard layer (4–17 m thick) atop the deep aquifer has confined Aquifer-2. Artesian wells are also found throughout the majority of the study area. Aquifer-1 is regarded as naturally unconfined since no limiting layer was found in the top soil region. Aquifer-1 appears to be thicker in the central part than in other parts, with a thickness range of 20 to 60 meters. The Aquifer-2 is observed to be thicker in the southeast section of the study area, with thicknesses ranging from 44 to 128 meters. The static GW storage potential of Aquifer-1 is estimated as 7,500 Mm³, 11,000 Mm³, and 15,000 Mm³, and that of Aquifer-2 as 800 Mm³, 80 Mm³, and 8 Mm³, respectively, for three different values of specific yields or storage coefficients (Table 3 ). They vary spatially within the study area, in both the aquifers, as shown in Fig. 4 . About 4.3 Mm³/km² of GW availability has been assessed in the region, which is little greater than the average GW availability of about 3.5 Mm³/km² in the alluvial area of the IGB (Rajmohan & Prathapar, 2013; Gayen, 2022). The south-eastern region offers high potential for deep aquifers, whereas the central region's shallow aquifer is estimated as more productive. In the study location, an unconfined shallow aquifer is found to be quite productive; however, it is not possible to make firm conclusions regarding the deep aquifer's storage capacity since it has not been fully accounted for (accounted for only a portion up to 220 m). The shallow aquifer in the area has been found to have the static GW storage potential of 6m³/m², this result aligns with the static GW storage potential assessed in the shallow aquifer of Terai region of West Bengal (11.6 m³/m²) (Haque, 2001) and in the Kathmandu Valley (6.25m³/m²) (Pandey et al., 2010). Table 3 GW storage potential for various specific yields (Sy) and storage coefficients (S) Aquifer-1 Storage (MCM) Sy = 0.1 Sy = 0.15 Sy = 0.2 7500 11000 15000 Aquifer-2 Storage (MCM) S = 0.005 S = 0.0005 S = 0.00005 800 80 8 3.2. Groundwater depletion characteristics GW depletion can be attributed to various drivers, for example, excessive abstraction, deforestation, unplanned development activities, and climate change, among others. Based on observed data from locally accessible monitoring wells and remote sensing data products (i.e., GRACE), the overall status of GW depletion in the research area was evaluated. The GRACE satellite data outputs unambiguously showed the study area's regional Terrestrial Water Storage (TWS) depletion. A Mann-Kendall analysis was used to further quantify the temporal trends in water storage depletion. Figure 5 displays findings of the Mann-Kendall trend analysis for an average regional trend (anomalies relative to the 2004–2009 average) for the years 2004–2022. The TWS is certainly trending downward, as indicated by the Z value of -6.367. In addition, the analysis's computed Z-value significantly surpasses the 1.96 critical value at the 95% level of significance, enabling us to rule out the null hypothesis that there is no trend. Furthermore, the observed trend's statistical significance is further supported by the p-value of 0.000. A strong decreasing trend is shown by the Mann-Kendall test's computed tau (τ) value of -0.27. Although, the exact figure of water lost from ground has not been assessed in this study, similar studies (Tiwari et al., 2009; Bhanja et al., 2018) using GRACE data in conjugate with numerical model shows, the northern part of IGB probably witnessed the largest rate of loss of GW in the World (at the rate of 54 ± 9 km³/yr.) between 2002 and 2008, and GW depletion of about 12.56 ± 1.37 km³/yr. Respectively, indicating the overall loss in GW resources. For analyzing temporal and spatial variation in depth-to-GW level, the kriging interpolation technique was used in a GIS platform to develop a raster surface of the entire study area for all 12 months. Results of spatial variation in GW levels are shown in Fig. 6 . The monitoring wells in Gauriganga have the greatest depth-to-GW level, with Lamki-chuha and Ghodaghodi following closely behind. The maximum and minimum depth-to-GW levels are found as 4.8 meters in April and 1.95 meters in August, respectively. It has been discovered that the depth-to-GW level varies only seasonally in many places within the research area. Trend studies from 2008 to 2023 show that, whereas wells in Godawari and Lamki-chuha have falling trends and Tikapur has no trends, places like Dhangadhi, Gauriganga, and Ghodaghodi exhibit increasing trends in depth-to-GW-level (see Fig. 6 for spatial distribution of GW level as well as locations of monitoring wells). There are signs of depletion even though there isn't a constant depletion every month or in every well. This data reflects people's knowledge, which is demonstrated by the submersible pump being lowered as a result of the GW level declining. Study shows that throughout time, major urban areas witnessed a decline in the water level in shallow aquifers (maximum of about 0.22m decline in GW level per year), which closely aligns with the average depletion in the Northern IGB (0.1 to 0.75m/yr.) (Sinha et al., 2018). Due to excessive GW resource exploitation for residential needs, including industrial and commercial needs, the water level in urban areas like Dhangadhi, Ghodaghodi, and Gauriganga is reported to be declining. Figure 7 shows the variation trend of depth-to-water-level in six distinct identified wells. 3.3. Annual groundwater abstraction The average annual GW abstraction in the area was assessed as a sum of irrigational and household (HH) abstractions. Household abstraction of GW to meet drinking and livestock demand is estimated as 44.38 MCM/yr. The most populous city in the area, Dhangadhi, extracts about 12.17 MCM/yr, whereas Joshipur Gaupalika makes the least abstraction of 1.04 MCM/yr. Figure 8 shows how HH abstracts GW at every local unit to meet their domestic needs. Furthermore, about 237m³ of GW is abstracted per year by an average household in the study area, only to fulfill their domestic demand. On the other hand, approximately 90% of the total GW abstraction in the region is attributed to irrigation. Regarding the irrigational abstraction, a total of 378.39 MCM of GW is needed annually to sustain agriculture in 556.46 km² of cultivable land (out of the total 1030.83km² of cultivable land), which does not have access to surface irrigation facilities present in the district (about 23 facilities are present, ranging in size from modest to large). For the current cropping pattern, an estimated total annual irrigational water depth of 0.68m is required from GW sources, where rice alone requires 0.34m (Table 4 ), whereas rain solely suffices for the maize. Likewise, in most of the parts of the IGB, shallow tube wells (about 20-50m deep) are used extensively in the study area to draw GW from the shallow aquifer to fulfill the majority of GW demands. (see Table 4 for irrigation water-depth requirements for different crops). Table 4 Irrigation Water-Depth Requirement for Different Crops Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Depth(m) Rice 0 0 0 0 0.3 0.04 0 0 0 0 0 0 0.34 Wheat 0.008 0.04 0.07 0.009 0 0 0 0 0 0 0 0.004 0.13 Maize 0 0 0 0 0 0 0 0 0 0 0 0 0 Potato 0.02 0.02 0 0 0 0 0 0 0 0. 0.046 0.047 0.13 Mustard 0.013 0.041 0.019 0 0 0 0 0 0 0 0 0.009 0.08 Total Annual Irrigation Water-Depth Requirement 0.68 3.4. Potential solutions for enhancing groundwater recharge Recharge, either natural or artificial, is are only source to the GW reservoirs. Natural GW recharge consists of several methods, including deep percolation of precipitation, percolation of water through streams and water impoundment beds, flow through adjacent subsurface, and percolation of irrigation water (Healy, 2010). Percolation of rainfall and seepage of stream into the ground are the major contributors of high GW recharge (> 300mm/yr) in most of the northern part of the IGB, including the study area (Mukherjee & Bhanja, 2018). However, if natural methods of GW recharge are insufficient to supply depleting GW, due to over-abstraction and climate variability in the area, we could turn towards various artificial methods. Artificial GW recharge can be defined as the practice of increasing the water reaching the underground reservoirs (Walton, 1965; Ineson, 1957). Artificial methods involve direct surface techniques (flooding, ditch and furrow system, recharge basin, over-irrigation), direct sub-surface techniques (natural openings, wells), and indirect techniques (aquifer modification) (Asano, 2016). The Terai region, including Kailali, has a large area covered by forest, streams, ponds, and large irrigational land. The area has favorable settings for high natural recharge. However, without the conservation of such natural settings, natural recharge is found to be at risk. Therefore, inferred from the field survey, maps study, geographical condition, and literature review, this study highly recommends safeguarding and enriching the available natural resources (forest, ponds, streams, wetlands, etc.) to best enhance the GW recharge in the study area. Nevertheless, the natural GW recharge is hindered in the study area due to anthropogenic factors (unmanaged urbanization, deforestation, lack of knowledge regarding the importance of GW recharge) and natural factors (climate variability). In this context, the study area requires implementation of some artificial techniques (construction of percolation ponds, check dams), prioritizing the non-structural ones (e.g., public awareness, and recharge encouraging policies tired up with issuing house construction permits, among others), to enhance the recharge phenomenon. Out of the many artificial-structural techniques, the construction of check dams and percolation ponds, especially in the Bhabar zone, could boost the GW recharge rate and also control soil erosion. The Bhabar zone, a narrow strip 8–16 Km wide (Hagen, 1969) in the foothills of the Siwalik hills, consists of a thick permeable bed of cobbles, pebbles, coarser sand, and minor clay bands, marking the promising prospects of GW recharge (Mehta & Adyalkar, 1962). Previous studies show that about 34% of annual rainfall on the Bhabar zone and about 23% of rainfall on the Terai zone would percolate naturally to the aquifers (Bhandari & Pathak, 2016). Therefore, interventions like constructions of check dams and percolation ponds in the Bhabar zone would certainly enhance the GW recharge. Such check dams also act as a delay action dams in small streams, which would contribute to GW recharge as practiced in some parts of Pakistan (contributing about 332Mm³ water storage for recharge) (Aftab et al., 2018). Additionally, large flat land is required for the construction of a percolation pond in the area does not seem to be a constraint, as the study area consists of large inhabited land in the Bhabar zone and in the Terai zone as well. Also, the materials for the construction of check dams are available in the local environment, which in turn makes the construction economic, utilizes the local workforce, and is environmentally friendly. In this context, other techniques such as the construction of a recharge well, aquifer modification, and other structured recharge techniques would prove to be costly and demanding. However, the implementation of the mentioned, cheaper artificial recharge techniques is also bound to some challenges. In the developing region with high population density like the IGB, despite the policy-level agreement, socio-political disputes, technical failure, hydrological, and institutional context are the major challenges for the implementation of managed aquifer recharge (Richard-Ferroudji et al., 2018). Ever-growing population, their needs, and disorganized urbanization pose a great threat to preserving existing natural resources and the stillness of natural settings for the natural recharge of GW in the study area. Non-structural techniques such as extensive public awareness, GW-related policies, and enhancement of the role of GWRDB in Kailali with its total presence to oversee, carry out, and run the region's numerous groundwater project, would require a substantial amount of time and consistent effort to produce an expected outcome. Furthermore, the abstraction of construction materials for check dams and percolation ponds from the local resource sites would raise concerns of local resource depletion and ecological consequences. Even though this research has highlighted some insightful strategies to enhance GW recharge in the study area, putting these findings into practice would require significant efforts. Although, majority of stakeholders now concur on a single point that recharge activities in the study area are in danger, conflicting interests of stakeholders may hinder the implementation of recharge enhancement programs. However, to overcome this problem, a separate authority could be established for licensing GW abstraction and to oversee managed GW recharge (Brunner et al., 2014). In this context, local user groups, local governments, the GWRDB, and other interested parties are found to have a promising role in this regard. 4. Conclusions With an application of remote sensing, modeling, and field-study approaches, this research characterized the status of GW availability and depletion, estimated GW abstraction for various uses, and discussed potential strategies for addressing the issue of depletion. There are two major aquifers, namely, the unconfined aquifer on top (Aquifer-1) and the confined aquifer on the bottom (Aquifer-2). The static GW potential of Aquifer-1 is estimated as 11,000 MCM (with Sy = 0.15) and that of Aquifer-2 as 80 MCM (with S = 0.0005). The estimate of confined aquifer, however, is based on accounting of aquifer depth up to 220m only. The storage potential has spatial variation, with high potential in the south-eastern region of the confined aquifer and the central part of the unconfined aquifer. Regional terrestrial water storage (2004–2022) is trending downward. Locally, depletion (2008–2023) of GW level across municipalities ranges from negligible (0.1m) in Tikapur to high (3.3m) in Gauriganga. Average annual GW abstraction for agriculture and domestic needs is estimated as 378.39Mm³ and 44.38Mm³, respectively. The majority of GW abstraction (about 90%) is attributed to irrigation. Given the current situation, expanding groundwater irrigation—which includes surface irrigation schemes as a conjunctive use—can be an appropriate course of action, especially in the central part of the study area, which has the least surface irrigational facilities and higher GW storage potential. Though GW levels are decreasing over time at a number of sites, the area's GW availability and recharge condition are not at a crisis point and do not require expensive or urgent solutions. However, extensive public awareness on the state of GW resources, encouraging water use efficiency, conservation of potential recharge areas, recharge-encouraging policies such as mandatory provision of recharge ponds/pits to get approval for constructing a house/building, and simple artificial recharge techniques appear to be satisfactory at this stage. The policy makers, the local government, and the federal government may find the findings of this study useful for the future towards the sustainable GW development and conjunctive use of water in the southern plain of Western Nepal. Declarations Funding This research was supported by a thesis grant from the Center for Water Resources Studies (CWRS), Institute of Engineering, Tribhuvan University, in collaboration with Kathmandu Valley Water Supply Management Board (KVWSMB), under the project ‘Collaborative Research and Capacity Strengthening for Enhancing Water Security’. Author Contribution Prajwol Aryal: Methodology, Field Study, Software, Data Collection and Processing, Investigation, Writing-Original Draft manuscript.Vishnu Prasad Pandey: Supervision, Methodology, Data Collection, Writing, Receiving, and Editing manuscript.Bhesh Raj Thapa: Conceptualization, Methodology, Supervision, Investigation, Reviewing and Editing manuscript.Nabin Tiwari: Conceptualization, Methodology, Supervision, Reviewing and Editing manuscript. Acknowledgement The author would like to acknowledge Tribhuvan University, Institute of Engineering, Center for Water Resources Studies (CWRS), and Kathmandu Valley Water Supply Management Board (KVWSDB). The author is also grateful to DHM, GWRDB Kailali, Private Drilling Companies, and Dhangadhi Sub-Metro for their support and guidance during this research. Data Availability Data will be provided upon request. References Adiat, K. A. N., Nawawi, M. N. M., & Abdullah, K. (2012). 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Estimation of groundwater potential using GIS modeling in Kohistan region Jamshoro district, Southern Indus basin, Sindh, Pakistan (a case study). Acta Geophysica , 68 (1), 155–165. Mukherjee, A., & Bhanja, S. N. (2018). Estimating Present-Day Groundwater Recharge Rates in India. Groundwater of South Asia , 37–47. Mukherjee, A., Saha, D., Harvey, C. F., Taylor, R. G., Ahmed, K. M., & Bhanja, S. N. (2015). Groundwater systems of the Indian Sub-Continent. Journal of Hydrology: Regional Studies , 4 , 1–14. https://doi.org/10.1016/j.ejrh.2015.03.005 Musa, A., & Mohammed, I. U. (2015). Geophysical investigations of lithology and groundwater potentials of recharge zones. International Journal of Multidisciplinary Research and Development , 2 (3), 437–446. Nouayti, A., Khattach, D., Hilali, M., & Nouayti, N. (2019). Mapping potential areas for groundwater storage in the High Guir Basin (Morocco): contribution of remote sensing and geographic information system. Olawepo, A. O., Fatoyinbo, A. A., Ali, I., & Lawal, T. O. (2013). Evaluation of groundwater potential and subsurface lithologies in Unilorin quarters using resistivity method. The African Review of Physics , 8 . Pal, D. K., Bhattacharyya, T., Srivastava, P., Chandran, P., & Ray, S. K. (2009). Soils of the Indo-Gangetic Plains: Their historical perspective and management. Current Science , 96 (9), 1193–1202. Pandey, V. P., Chapagain, S. K., & Kazama, F. (2010). Evaluation of groundwater environment of Kathmandu Valley. Environmental Earth Sciences , 60 , 1329–1342. Pathak, D. (2017). Delineation of Groundwater Potential Zone in the Indo-Gangetic Plain Through Gis Analysis. Journal of Institute of Science and Technology , 22 (1), 104–109. https://doi.org/10.3126/jist.v22i1.17760 Rajmohan, N., & Prathapar, S. A. (2013). Hydrogeology of the Eastern Ganges Basin: an overview . Richard-Ferroudji, A., Raghunath, T. P., & Venkatasubramanian, G. (2018). Managed aquifer recharge in India: Consensual policy but controversial implementation. Water Alternatives , 11 (3), 749–769. Roshani, B. C., Pathak, D., & Gautam, R. (2020). Hydrogeological study in and around Birendranagar Municipality, Surkhet Valley, Mid-Western Nepal. Bulletin of the Department of Geology , 22 , 41–48. Saadi, O., Nouayti, N., Nouayti, A., Dimane, F., & Elhairechi, K. (2021). Application of remote sensing data and geographic information system for identifying potential areas of groundwater storage in middle Moulouya Basin of Morocco. Groundwater for Sustainable Development , 14 , 100639. Saaty, T. L. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society , 41 (11), 1073–1076. Saha, D., & Dwivedi, S. N. (2018). Groundwater Availability of Northern and Southern Bank Aquifers of the Middle Ganga Plain, India . Springer Singapore. https://doi.org/10.1007/978-981-10-3889-1_7 Sahu, S., Saha, D., & Shukla, R. R. (2018). Sone megafan: a non-Himalayan megafan of craton origin, forming a potential groundwater reservoir in marginal parts of Ganga Basin, India. Hydrogeology Journal , 26 (8), 2891–2917. https://doi.org/10.1007/s10040-018-1829-8 Shrestha, S. ., Tripathi, G. N., & Laudari, D. (2018a). Groundwater Resources of Nepal: An Overview . Springer Singapore. https://doi.org/10.1007/978-981-10-3889-1_11 Shrestha, S. R., Tripathi, G. N., & Laudari, D. (2018b). Groundwater resources of Nepal: An overview. Groundwater of South Asia , 169–193. Sinha, R., Gupta, S., & Nepal, S. (2018). Groundwater dynamics in North Bihar plains. Current Science , 2482–2493. Suryabhagavan, K. . (2017). Application of remote sensing and GIS for groundwater potential zones identification in Bata river basin, Himachal Pradesh, India . Journal of Geomatics , 11 (May), 66−76. Taneja, G., Pal, B. D., Joshi, P. K., Aggarwal, P. K., & Tyagi, N. K. (2014). Farmers preferences for climate-smart agriculture: An assessment in the Indo-Gangetic Plain . Intl Food Policy Res Inst. https://books.google.com.np/books?id=_45iAwAAQBAJ Thapa, L., Thapa, H., & Magar, B. (2015). Perception, trends and impacts of climate change in Kailali District, Far West Nepal. International Journal of Environment , 4 , 62. https://doi.org/10.3126/ije.v4i4.14099 Thapa, N. B., & Thapa, D. P. (1969). Geography of Nepal: Physical, Economic, Cultural \& Regional . Orient Longmans. https://books.google.com.np/books?id=AldXAAAAMAAJ Tiwari, V. M., Wahr, J., & Swenson, S. (2009). Dwindling groundwater resources in northern India, from satellite gravity observations. Geophysical Research Letters , 36 (18). Triantaphyllou, E., & Triantaphyllou, E. (2000). Multi-criteria decision making methods . Springer. United Nations. (2022). Water Development Report 2022: Groundwater: Making the Invisible Visible . Wada, Y., Van Beek, L. P. H., Van Kempen, C. M., Reckman, J. W. T. M., Vasak, S., & Bierkens, M. F. P. (2010). Global depletion of groundwater resources. Geophysical Research Letters , 37 (20), 1–5. https://doi.org/10.1029/2010GL044571 Walton, W. C. (1965). Ground-water recharge and runoff in Illinois. Illinois State Water Survey. Report of Investigation; No. 48 . Woobaidullah, A. S. M., Shahnewaz, S. M., Islam, M. M., Islam, M. K., & Hossain, M. Z. (2019). Electrical resistivity survey in the investigation of hydrogeological condition of Sylhet-Sunamganj Haor area, Bangladesh. Journal of Nepal Geological Society , 58 , 21–28. https://doi.org/10.3126/jngs.v58i0.24570 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 02 Aug, 2025 Submission checks completed at journal 02 Aug, 2025 First submitted to journal 28 Jul, 2025 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7235632","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511853930,"identity":"94755fcd-a4e3-4e56-81d0-4afd98266479","order_by":0,"name":"Prajwal Aryal","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Prajwal","middleName":"","lastName":"Aryal","suffix":""},{"id":511853931,"identity":"798ff81f-81fe-4a6b-8ad8-9f12193cad2a","order_by":1,"name":"Vishnu Prasad 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storage (TWS) observed from GRACE data\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7235632/v1/6f5a5d859572cee876f5a796.jpg"},{"id":90907367,"identity":"6c11c60d-fc43-4588-a4ce-d744c2770238","added_by":"auto","created_at":"2025-09-09 13:22:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1385110,"visible":true,"origin":"","legend":"\u003cp\u003eGroundwater Level Contour Map of all 12 Months\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7235632/v1/9725841a90900a8884f400b3.png"},{"id":90905664,"identity":"fd00adb3-0911-4cb1-a5c6-1eb45d47b738","added_by":"auto","created_at":"2025-09-09 13:06:21","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":104990,"visible":true,"origin":"","legend":"\u003cp\u003eGroundwater Level Variation Trends in Six Identified Wells\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7235632/v1/40e17e3ea7c74c306281b5bd.jpg"},{"id":90905660,"identity":"f0e65286-19a1-49e3-a573-92ed67fde937","added_by":"auto","created_at":"2025-09-09 13:06:21","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":148469,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Household GW Abstraction of Different Local Units\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7235632/v1/a4ff08b229c730566c71293c.jpg"},{"id":91148626,"identity":"8fae9838-7c5a-47f6-81e5-f398b1b455fc","added_by":"auto","created_at":"2025-09-12 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Introduction","content":"\u003cp\u003eWater demands are increasing due to factors like population growth, irrigation requirements for food security, and economic development. It has led to a state of water stress in different parts of the world (D\u0026ouml;ll et al., 2014). Both groundwater (GW) and surface water supply water to meet different demands. GW accounts for about 30% of the world\u0026rsquo;s freshwater and approximately 99% of liquid freshwater on the Earth (Gupta et al., 2018; United Nations, 2022). The availability and withdrawal of GW differ globally based on aquifer condition (Famiglietti, 2014). With ever-increasing withdrawal, GW is depleting globally, with an estimated increase in depletion from 126 (\u0026plusmn;\u0026thinsp;32) km\u0026sup3;/yr. in 1960 to 283 (\u0026plusmn;\u0026thinsp;40) km\u0026sup3;/yr. in 2000 (Wada et al., 2010). Ingo Gangetic Plan (IGB) in South Asia is one of the regions experiencing water stress and simultaneous depletion of GW (Hanasaki et al., 2008). The IGB, spanning about 2.25\u0026nbsp;million Km\u0026sup2; area (Taneja et al., 2014), has a GW availability of about 1750.92\u0026nbsp;billion cubic meters (BCM) (Gayen, 2022), which is being utilized by about a 1.5\u0026nbsp;billion population (Pal et al., 2009; Mukherjee et al., 2015). Over-abstraction (about 250 km\u0026sup3;/yr.) and subsequent depletion (about 0.1\u0026ndash;0.75 m/yr.) of GW is found in most parts of the IGB, including the western Terai of Nepal (A. MacDonald et al., 2013; Sinha et al., 2018). The Terai region of Nepal, mainly made up of fresh alluvial deposits, features multiple layers of good aquifers at different depths that are relatively connected (Gupta et al., 2018). It is believed that aquifers in Terai are highly productive with available safe yield of about 9 BCM/year (Shrestha et al., 2018; Kansakar, 2005); however, the GW is depleting at a faster rate (Shrestha et al., 2018; A. MacDonald et al., 2016). Therefore, it is necessary to deploy a comprehensive study to characterize the state of GW potential and depletion to aid in designing strategies for effective management of the Terai\u0026rsquo;s GW system (Masih et al., 2014; Mekonnen \u0026amp; Hoekstra, 2016).\u003c/p\u003e\u003cp\u003eWith the advancement of remote sensing and machine learning techniques and tools, the GW study is promising to reach unprecedented heights (Deoli \u0026amp; Kumar, 2020). Satellite data such as Gravity Recovery and Climate Experiment (GRACE), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) provide an alternative approach to study different aspects of GW around the World (Hu et al., 2017; Li et al., 2019; D\u0026ouml;ll et al., 2014). Numerical models like Modular Finite-Difference Groundwater Flow Model (MODFLOW) (McDonald \u0026amp; Harbaugh, 1988), Finite Element Flow (FEFLOW) (Diersch, 2013), and Global Land Data Assimilation System-Catchment Land Surface Model (GLDAS-CLSM) (Liu et al., 2019) are also some powerful GW modelling tools that offer flexibility and robustness for various applications (Hu et al., 2021). However, for a data-scarce region like Nepal\u0026rsquo;s Terai, the accuracy of the study using satellite data along with modeling tools only is considered less appropriate, as it requires a comprehensive data set (hydrogeological, hydrological, topographical) with better spatial-temporal resolution (Li et al., 2019). Furthermore, various integrated approaches, including Geographical Information System (GIS)-based method (Longley et al., 2015), Multi-Criteria Decision Analysis (MCDA) (Triantaphyllou \u0026amp; Triantaphyllou, 2000), Analytic Hierarchy Process (AHP) (Saaty, 1980), are promising tools for GW study. Nonetheless, the applicability and accuracy of these techniques largely depend upon the number of parameters \u0026ndash; such as slope, rainfall, soil type, land use/cover (LULC), and drainage density, among others \u0026ndash; and weights assigned to them, in succession could be complex and time consuming (Adiat et al., 2012; Kumar et al., 2014). Field-based methods, including hydrogeological survey, geophysical method, and GW level monitoring method, provide a first-hand and convenient data set to analyze the spatial-temporal availability and variation of GW in an area (Hu et al., 2021). Many GW studies around the world have deployed field-based methods (e.g., Olawepo et al., 2013; Agbalagba et al., 2019; Musa \u0026amp; Mohammed, 2015; Jerbi et al., 2018). A study of GW using in-situ data (lithology, monitoring well) in IGB has identified the north-western part as being mostly affected by the GW depletion problem (MacDonald AM et al., 2016). Also, in Terai and inner Terai region of Nepal, recent studies (e.g., Roshani et al., 2020; Shrestha et al., 2018; Pathak, 2017) conducted using lithological data and observed GW level data has assessed the status of GW availability and depletion in both confined and unconfined aquifers with acceptable accuracy, but with coarser resolution and without in-depth analysis.\u003c/p\u003e\u003cp\u003eSpatially well-distributed, lithological data and GW level data could provide ease to the study and represent the existing status of GW with satisfactory accuracy. The field-based method, along with the use of GIS, is found to have been producing acceptable results for GW studies (e.g., Nouayti et al., 2019; Memon et al., 2020; Saadi et al., 2021). With some constraints of time and resources, these field-based methods integrated with GIS can be considered an appropriate approach for a comprehensive study of GW in the Western Terai region of Nepal. In this context, of having high GW potentiality, decreasing GW water level, and lack of GW-related comprehensive studies in Western Terai of Nepal; this research, with a case of Kailali district, aims to focus on the following specific objectives: i) to assess the static GW storage potential; ii) to characterize the status of GW depletion; iii) to evaluate the average annual GW abstraction; and iv) to assess prospects of GW recharge.\u003c/p\u003e"},{"header":"2. Study Area and Methods","content":"\u003cp\u003eKailali, located in the Southern plain of Western Nepal in Far Western Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), is one of Nepal's 77 districts. As of 2021, the Center Bureau of Statistics (CBS), the district has a little more than 0.9\u0026nbsp;million residents. Kailali district covers 3,235 km\u003csup\u003e2\u003c/sup\u003e of area and rises from 135 to 1967 meters above mean sea level (masl). Siwalik Hills, Bhabar Zone, Middle Terai, and Lower Terai are the four main geographic regions that make up the district (N. B. Thapa \u0026amp; Thapa, 1969). Approximately 71% of the district's total land area is forest, and 24.9% of it is agricultural land (CBS, 2013). It is made up of a thick alluvial deposit and a recent sedimentation belt with ongoing river deposition (Lewison et al., 2020). There are eleven meteorological and hydrological stations spread out across the district. Approximately 1,840 mm of precipitation and 700 mm of evapotranspiration take place per year (L. Thapa et al., 2015).\u003c/p\u003e\u003cp\u003eThe area below 300 masl (Terai region) has been considered as an aquifer boundary. Moreover, the Siwalik region, over 1000 masl, has not been included in this study. For the Siwalik zone's alternating strata of mudstone, conglomerate, and sandstone, which are associated with incredibly low groundwater potential (Suryabhagavan, 2017). The areal extent along with related features of the study area is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data preparation\u003c/h2\u003e\u003cp\u003eSelecting the study area, figuring out whether data are available, acquiring available data and pre-processing, and setting up a GIS database are the first steps. Lithological data (29 locations) and GW level monitoring data (19 locations) were taken from the Groundwater Resources Development Board (GWRDB). A key informant survey and a primary household (HH) survey were implemented to collect data on various aspects of GW at the local level. A survey was conducted with 89 households and a few key informants during a field visit. Climatic data, including precipitation, at nine stations were acquired from the Department of Hydrology and Meteorology (DHM). In addition, literature reviews and expert suggestions were used to collect secondary data. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the data that were acquired along with their sources. The overall methodological framework for achieving all four objectives is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData and Sources\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\" colname=\"c1\"\u003e\u003cp\u003eS.N.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSources\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTemporal Resolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpatial Resolution\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\u003eGWRDB Dhangadhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBorehole Lithology, Monitoring Well Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2008\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAcross the Study Area\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\u003ePrimary Survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eField Survey Data on the Status of GW.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAcross the Study Area\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\u003eLiteratures, DHM, NARC, FAO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSy, S, Climate Data, Soil Data, Crop Data,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMonthly Climate Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAcross the Study Area\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\u003eGoogle Earth Engine, DoI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGRACE data, DEM, Command area maps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2004\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u0026deg;x0.5\u0026deg;, 30mx30m DEM, Whole Kailali\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\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Assessment of aquifer storage potential\u003c/h2\u003e\u003cp\u003eThe static GW storage potential of the study area was assessed in a GIS platform. Accounting for both the driller\u0026rsquo;s log and the resistivity log (resistivity value of underlying materials). Six different lithological units have been identified from 29 lithological data sets. Further, three separate hydrogeological units have been designated as a result of lithological classification. A GIS database has been prepared along with the boundary of the GW basin. The GW basin area, just as multiplied by the measured aquifer thickness in GIS, yields the aquifer volume. Finally, the aquifer volume was multiplied by the storage coefficient to produce the static GW storage potential in the aquifers beneath the research area. Based on the three alternatives of constant values of the storage coefficients (specific yield\u0026thinsp;=\u0026thinsp;0.1,0.15,0.2 and storativity\u0026thinsp;=\u0026thinsp;0.005, 0.0005, and 0.00005) (Saha \u0026amp; Dwivedi, 2018; Sahu et al., 2018) acquired from literature, three separate storage potential values were estimated. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the spatial dispersion of borehole locations, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the resistivity range used for lithology categorization (Woobaidullah et al., 2019) and three hydrogeological units that have been classified underneath the research area.\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\u003eReclassification of Lithologs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS.N.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApparent Resistivity (ohm-m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLithological type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHydrogeological units\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\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSticky clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTop Soil\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\u003e5\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSilty clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAquitard\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\u003e15\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSandy clay\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\u003e25\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClayey gravel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAquifers\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\u003e40\u0026ndash;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium to coarse sand\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\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGravel\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Characterization of GW depletion\u003c/h2\u003e\u003cp\u003eBoth regional and local depletion of GW are evaluated in this study. Data from the Gravity Recovery and Climate Experiment (GRACE) are used for evaluating the regional depletion. The resulting data from GRACE is presented as anomalies, specifically as liquid water equivalent (LWE) anomalies relative to the mean between 2004 and 2009. To analyze the data in the study area, the GRACE data grids were examined and compared to the mean value for anomaly calculation. Furthermore, the Mann-Kendall test was run on the anomalies in order to identify and understand any recurrent patterns in GW storage variation. Simultaneously, monitoring GW level data at 19 wells, scattered across the study area, is utilized to characterize local depletion. Here, depth to water level data (monthly data) were provided by GWRDB, Dhangadhi, from 2008 to 2023. A GIS database was established, and the contour map of the water table for the entire year was generated. The water table contour maps show fluctuations in the water table depth across the seasons at different spatial locations. Additionally, six different sites were spotted to exhibit higher annual fluctuations in their depth to water. In those identified spots, an Excel spreadsheet was used to perform a temporal trend analysis of the GW level again using observed GW level data. The spatial distribution of monitoring wells in the study area is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Assessment of GW abstraction\u003c/h2\u003e\u003cp\u003eThe average annual GW abstraction within the study area was estimated as a sum of irrigational abstraction and HH abstraction. Irrigational abstraction was estimated based on the Crop Water and Irrigation Requirement tool (CROPWAT) (Allen et al., 1998), and HH abstraction was estimated based on HH survey data. The study area was divided into 11 clusters for the HH survey, with the understanding that each cluster represents a single local level. Sample size was determined based on the total number of HHs in a cluster. A total of 89 households were surveyed in this research. Finally, the annual average GW abstraction per HH was calculated and multiplied by the total number of HHs in a specific cluster to obtain the overall GW abstraction in that cluster. Moreover, by aggregating the abstraction volumes in each cluster, the average yearly GW abstraction was determined. For irrigational abstraction, climate and precipitation data at nine stations in the study area were acquired from DHM, soil data from the National Soil Science Research Center (NARC) soil map and the Soil-Plant-Air-Water (SPAW) tool, and crop statistics from literature, key informant surveys, and default data from the Food and Agriculture Organization (FAO). For this study, five key crops \u0026ndash;paddy, wheat, mustard, potatoes, and maize \u0026ndash; were taken into consideration. The command area for potential GW irrigation was approximated, using the map of surface irrigation command area from the Department of Water Resources and Irrigation (DWRI) and the Land Use/Cover (LULC) map from the International Center for Integrated Mountain Development (ICIMOD). Eventually, the estimated total annual irrigation water depth in CROPWAT was multiplied by the GW irrigation command area to obtain a total annual GW abstraction for irrigation. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the locations of the sample HHs that were surveyed during the field study, the command area for surface irrigation systems, and the total amount of cultivable land in the study region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Identifying potential solutions for enhancing groundwater recharge.\u003c/h2\u003e\u003cp\u003ePotential techniques for GW recharge augmentation in the study area were discussed based on recommendations from experts, a review of the literature, and interpretation of data from the primary field survey. During field study, ideas were obtained from a range of local activists, engineers, agriculture and environmental experts, and other pertinent staff members. They made recommendations on the topic, which are found to be promising in the context of the research area. Further, engineers and environmentalists from the water supply office, Tikapur Municipality, Godawari Municipality, GWRDB, and Dhangadhi Sub-Metropolitan were interviewed on the topic. A range of lakes, ponds, streams, and woodlands was visited for reconnaissance and fieldwork to gather visual data on the status of potential recharge sites. Additionally, topographic maps, geographic maps, and LULC maps were studied to gather information on the slope, soil type, geographic conditions, and other features present in the study area. A thorough examination and cataloging of existing research on groundwater recharge augmentation in local and regional settings was carried out. Lastly, based mostly on information gathered from the user's group, key informants, socioeconomic situation, and geographic location of the area, certain strategies to enhance GW recharge were formulated and discussed.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Spatial distribution of aquifer thickness and GW storage potential\u003c/h2\u003e\u003cp\u003eThere are two aquifers beneath the study area, namely, shallow Aquifer (i.e., Aquifer-1), unconfined in nature, and deep Aquifer (i.e., Aquifer-2), naturally confined. An aquitard layer (4\u0026ndash;17 m thick) atop the deep aquifer has confined Aquifer-2. Artesian wells are also found throughout the majority of the study area. Aquifer-1 is regarded as naturally unconfined since no limiting layer was found in the top soil region. Aquifer-1 appears to be thicker in the central part than in other parts, with a thickness range of 20 to 60 meters. The Aquifer-2 is observed to be thicker in the southeast section of the study area, with thicknesses ranging from 44 to 128 meters. The static GW storage potential of Aquifer-1 is estimated as 7,500 Mm\u0026sup3;, 11,000 Mm\u0026sup3;, and 15,000 Mm\u0026sup3;, and that of Aquifer-2 as 800 Mm\u0026sup3;, 80 Mm\u0026sup3;, and 8 Mm\u0026sup3;, respectively, for three different values of specific yields or storage coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). They vary spatially within the study area, in both the aquifers, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. About 4.3 Mm\u0026sup3;/km\u0026sup2; of GW availability has been assessed in the region, which is little greater than the average GW availability of about 3.5 Mm\u0026sup3;/km\u0026sup2; in the alluvial area of the IGB (Rajmohan \u0026amp; Prathapar, 2013; Gayen, 2022). The south-eastern region offers high potential for deep aquifers, whereas the central region's shallow aquifer is estimated as more productive. In the study location, an unconfined shallow aquifer is found to be quite productive; however, it is not possible to make firm conclusions regarding the deep aquifer's storage capacity since it has not been fully accounted for (accounted for only a portion up to 220 m). The shallow aquifer in the area has been found to have the static GW storage potential of 6m\u0026sup3;/m\u0026sup2;, this result aligns with the static GW storage potential assessed in the shallow aquifer of Terai region of West Bengal (11.6 m\u0026sup3;/m\u0026sup2;) (Haque, 2001) and in the Kathmandu Valley (6.25m\u0026sup3;/m\u0026sup2;) (Pandey et al., 2010).\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\u003eGW storage potential for various specific yields (Sy) and storage coefficients (S)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAquifer-1 Storage (MCM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSy\u0026thinsp;=\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSy\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSy\u0026thinsp;=\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAquifer-2 Storage (MCM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u0026thinsp;=\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u0026thinsp;=\u0026thinsp;0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u0026thinsp;=\u0026thinsp;0.00005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\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\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Groundwater depletion characteristics\u003c/h2\u003e\u003cp\u003eGW depletion can be attributed to various drivers, for example, excessive abstraction, deforestation, unplanned development activities, and climate change, among others. Based on observed data from locally accessible monitoring wells and remote sensing data products (i.e., GRACE), the overall status of GW depletion in the research area was evaluated. The GRACE satellite data outputs unambiguously showed the study area's regional Terrestrial Water Storage (TWS) depletion. A Mann-Kendall analysis was used to further quantify the temporal trends in water storage depletion. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays findings of the Mann-Kendall trend analysis for an average regional trend (anomalies relative to the 2004\u0026ndash;2009 average) for the years 2004\u0026ndash;2022. The TWS is certainly trending downward, as indicated by the Z value of -6.367. In addition, the analysis's computed Z-value significantly surpasses the 1.96 critical value at the 95% level of significance, enabling us to rule out the null hypothesis that there is no trend. Furthermore, the observed trend's statistical significance is further supported by the p-value of 0.000. A strong decreasing trend is shown by the Mann-Kendall test's computed tau (τ) value of -0.27. Although, the exact figure of water lost from ground has not been assessed in this study, similar studies (Tiwari et al., 2009; Bhanja et al., 2018) using GRACE data in conjugate with numerical model shows, the northern part of IGB probably witnessed the largest rate of loss of GW in the World (at the rate of 54\u0026thinsp;\u0026plusmn;\u0026thinsp;9 km\u0026sup3;/yr.) between 2002 and 2008, and GW depletion of about 12.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37 km\u0026sup3;/yr. Respectively, indicating the overall loss in GW resources.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor analyzing temporal and spatial variation in depth-to-GW level, the kriging interpolation technique was used in a GIS platform to develop a raster surface of the entire study area for all 12 months. Results of spatial variation in GW levels are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The monitoring wells in Gauriganga have the greatest depth-to-GW level, with Lamki-chuha and Ghodaghodi following closely behind. The maximum and minimum depth-to-GW levels are found as 4.8 meters in April and 1.95 meters in August, respectively. It has been discovered that the depth-to-GW level varies only seasonally in many places within the research area. Trend studies from 2008 to 2023 show that, whereas wells in Godawari and Lamki-chuha have falling trends and Tikapur has no trends, places like Dhangadhi, Gauriganga, and Ghodaghodi exhibit increasing trends in depth-to-GW-level (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e for spatial distribution of GW level as well as locations of monitoring wells). There are signs of depletion even though there isn't a constant depletion every month or in every well. This data reflects people's knowledge, which is demonstrated by the submersible pump being lowered as a result of the GW level declining. Study shows that throughout time, major urban areas witnessed a decline in the water level in shallow aquifers (maximum of about 0.22m decline in GW level per year), which closely aligns with the average depletion in the Northern IGB (0.1 to 0.75m/yr.) (Sinha et al., 2018). Due to excessive GW resource exploitation for residential needs, including industrial and commercial needs, the water level in urban areas like Dhangadhi, Ghodaghodi, and Gauriganga is reported to be declining. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the variation trend of depth-to-water-level in six distinct identified wells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Annual groundwater abstraction\u003c/h2\u003e\u003cp\u003eThe average annual GW abstraction in the area was assessed as a sum of irrigational and household (HH) abstractions. Household abstraction of GW to meet drinking and livestock demand is estimated as 44.38 MCM/yr. The most populous city in the area, Dhangadhi, extracts about 12.17 MCM/yr, whereas Joshipur Gaupalika makes the least abstraction of 1.04 MCM/yr. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows how HH abstracts GW at every local unit to meet their domestic needs. Furthermore, about 237m\u0026sup3; of GW is abstracted per year by an average household in the study area, only to fulfill their domestic demand. On the other hand, approximately 90% of the total GW abstraction in the region is attributed to irrigation. Regarding the irrigational abstraction, a total of 378.39 MCM of GW is needed annually to sustain agriculture in 556.46 km\u0026sup2; of cultivable land (out of the total 1030.83km\u0026sup2; of cultivable land), which does not have access to surface irrigation facilities present in the district (about 23 facilities are present, ranging in size from modest to large). For the current cropping pattern, an estimated total annual irrigational water depth of 0.68m is required from GW sources, where rice alone requires 0.34m (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), whereas rain solely suffices for the maize. Likewise, in most of the parts of the IGB, shallow tube wells (about 20-50m deep) are used extensively in the study area to draw GW from the shallow aquifer to fulfill the majority of GW demands. (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for irrigation water-depth requirements for different crops).\u003c/p\u003e\u003cp\u003e\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\u003eIrrigation Water-Depth Requirement for Different Crops\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFeb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMar\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eApr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJun\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eJul\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eOct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eNov\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eDec\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003eDepth(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWheat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e\u003cb\u003e0.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotato\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e\u003cb\u003e0.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMustard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Annual Irrigation Water-Depth Requirement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Potential solutions for enhancing groundwater recharge\u003c/h2\u003e\u003cp\u003eRecharge, either natural or artificial, is are only source to the GW reservoirs. Natural GW recharge consists of several methods, including deep percolation of precipitation, percolation of water through streams and water impoundment beds, flow through adjacent subsurface, and percolation of irrigation water (Healy, 2010). Percolation of rainfall and seepage of stream into the ground are the major contributors of high GW recharge (\u0026gt;\u0026thinsp;300mm/yr) in most of the northern part of the IGB, including the study area (Mukherjee \u0026amp; Bhanja, 2018). However, if natural methods of GW recharge are insufficient to supply depleting GW, due to over-abstraction and climate variability in the area, we could turn towards various artificial methods. Artificial GW recharge can be defined as the practice of increasing the water reaching the underground reservoirs (Walton, 1965; Ineson, 1957). Artificial methods involve direct surface techniques (flooding, ditch and furrow system, recharge basin, over-irrigation), direct sub-surface techniques (natural openings, wells), and indirect techniques (aquifer modification) (Asano, 2016).\u003c/p\u003e\u003cp\u003eThe Terai region, including Kailali, has a large area covered by forest, streams, ponds, and large irrigational land. The area has favorable settings for high natural recharge. However, without the conservation of such natural settings, natural recharge is found to be at risk. Therefore, inferred from the field survey, maps study, geographical condition, and literature review, this study highly recommends safeguarding and enriching the available natural resources (forest, ponds, streams, wetlands, etc.) to best enhance the GW recharge in the study area. Nevertheless, the natural GW recharge is hindered in the study area due to anthropogenic factors (unmanaged urbanization, deforestation, lack of knowledge regarding the importance of GW recharge) and natural factors (climate variability). In this context, the study area requires implementation of some artificial techniques (construction of percolation ponds, check dams), prioritizing the non-structural ones (e.g., public awareness, and recharge encouraging policies tired up with issuing house construction permits, among others), to enhance the recharge phenomenon. Out of the many artificial-structural techniques, the construction of check dams and percolation ponds, especially in the Bhabar zone, could boost the GW recharge rate and also control soil erosion. The Bhabar zone, a narrow strip 8\u0026ndash;16 Km wide (Hagen, 1969) in the foothills of the Siwalik hills, consists of a thick permeable bed of cobbles, pebbles, coarser sand, and minor clay bands, marking the promising prospects of GW recharge (Mehta \u0026amp; Adyalkar, 1962). Previous studies show that about 34% of annual rainfall on the Bhabar zone and about 23% of rainfall on the Terai zone would percolate naturally to the aquifers (Bhandari \u0026amp; Pathak, 2016). Therefore, interventions like constructions of check dams and percolation ponds in the Bhabar zone would certainly enhance the GW recharge. Such check dams also act as a delay action dams in small streams, which would contribute to GW recharge as practiced in some parts of Pakistan (contributing about 332Mm\u0026sup3; water storage for recharge) (Aftab et al., 2018). Additionally, large flat land is required for the construction of a percolation pond in the area does not seem to be a constraint, as the study area consists of large inhabited land in the Bhabar zone and in the Terai zone as well. Also, the materials for the construction of check dams are available in the local environment, which in turn makes the construction economic, utilizes the local workforce, and is environmentally friendly. In this context, other techniques such as the construction of a recharge well, aquifer modification, and other structured recharge techniques would prove to be costly and demanding.\u003c/p\u003e\u003cp\u003eHowever, the implementation of the mentioned, cheaper artificial recharge techniques is also bound to some challenges. In the developing region with high population density like the IGB, despite the policy-level agreement, socio-political disputes, technical failure, hydrological, and institutional context are the major challenges for the implementation of managed aquifer recharge (Richard-Ferroudji et al., 2018). Ever-growing population, their needs, and disorganized urbanization pose a great threat to preserving existing natural resources and the stillness of natural settings for the natural recharge of GW in the study area. Non-structural techniques such as extensive public awareness, GW-related policies, and enhancement of the role of GWRDB in Kailali with its total presence to oversee, carry out, and run the region's numerous groundwater project, would require a substantial amount of time and consistent effort to produce an expected outcome. Furthermore, the abstraction of construction materials for check dams and percolation ponds from the local resource sites would raise concerns of local resource depletion and ecological consequences. Even though this research has highlighted some insightful strategies to enhance GW recharge in the study area, putting these findings into practice would require significant efforts. Although, majority of stakeholders now concur on a single point that recharge activities in the study area are in danger, conflicting interests of stakeholders may hinder the implementation of recharge enhancement programs. However, to overcome this problem, a separate authority could be established for licensing GW abstraction and to oversee managed GW recharge (Brunner et al., 2014). In this context, local user groups, local governments, the GWRDB, and other interested parties are found to have a promising role in this regard.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eWith an application of remote sensing, modeling, and field-study approaches, this research characterized the status of GW availability and depletion, estimated GW abstraction for various uses, and discussed potential strategies for addressing the issue of depletion. There are two major aquifers, namely, the unconfined aquifer on top (Aquifer-1) and the confined aquifer on the bottom (Aquifer-2). The static GW potential of Aquifer-1 is estimated as 11,000 MCM (with Sy\u0026thinsp;=\u0026thinsp;0.15) and that of Aquifer-2 as 80 MCM (with S\u0026thinsp;=\u0026thinsp;0.0005). The estimate of confined aquifer, however, is based on accounting of aquifer depth up to 220m only. The storage potential has spatial variation, with high potential in the south-eastern region of the confined aquifer and the central part of the unconfined aquifer. Regional terrestrial water storage (2004\u0026ndash;2022) is trending downward. Locally, depletion (2008\u0026ndash;2023) of GW level across municipalities ranges from negligible (0.1m) in Tikapur to high (3.3m) in Gauriganga. Average annual GW abstraction for agriculture and domestic needs is estimated as 378.39Mm\u0026sup3; and 44.38Mm\u0026sup3;, respectively. The majority of GW abstraction (about 90%) is attributed to irrigation. Given the current situation, expanding groundwater irrigation\u0026mdash;which includes surface irrigation schemes as a conjunctive use\u0026mdash;can be an appropriate course of action, especially in the central part of the study area, which has the least surface irrigational facilities and higher GW storage potential. Though GW levels are decreasing over time at a number of sites, the area's GW availability and recharge condition are not at a crisis point and do not require expensive or urgent solutions. However, extensive public awareness on the state of GW resources, encouraging water use efficiency, conservation of potential recharge areas, recharge-encouraging policies such as mandatory provision of recharge ponds/pits to get approval for constructing a house/building, and simple artificial recharge techniques appear to be satisfactory at this stage. The policy makers, the local government, and the federal government may find the findings of this study useful for the future towards the sustainable GW development and conjunctive use of water in the southern plain of Western Nepal.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by a thesis grant from the Center for Water Resources Studies (CWRS), Institute of Engineering, Tribhuvan University, in collaboration with Kathmandu Valley Water Supply Management Board (KVWSMB), under the project \u0026lsquo;Collaborative Research and Capacity Strengthening for Enhancing Water Security\u0026rsquo;.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePrajwol Aryal: Methodology, Field Study, Software, Data Collection and Processing, Investigation, Writing-Original Draft manuscript.Vishnu Prasad Pandey: Supervision, Methodology, Data Collection, Writing, Receiving, and Editing manuscript.Bhesh Raj Thapa: Conceptualization, Methodology, Supervision, Investigation, Reviewing and Editing manuscript.Nabin Tiwari: Conceptualization, Methodology, Supervision, Reviewing and Editing manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author would like to acknowledge Tribhuvan University, Institute of Engineering, Center for Water Resources Studies (CWRS), and Kathmandu Valley Water Supply Management Board (KVWSDB). The author is also grateful to DHM, GWRDB Kailali, Private Drilling Companies, and Dhangadhi Sub-Metro for their support and guidance during this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be provided upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdiat, K. A. N., Nawawi, M. N. M., \u0026amp; Abdullah, K. (2012). Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool\u0026ndash;a case of predicting potential zones of sustainable groundwater resources. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e440\u003c/em\u003e, 75\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003eAftab, S. M., Siddiqui, R. H., \u0026amp; Farooqui, M. A. (2018). 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P., \u0026amp; Venkatasubramanian, G. (2018). Managed aquifer recharge in India: Consensual policy but controversial implementation. \u003cem\u003eWater Alternatives\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 749\u0026ndash;769.\u003c/li\u003e\n\u003cli\u003eRoshani, B. C., Pathak, D., \u0026amp; Gautam, R. (2020). Hydrogeological study in and around Birendranagar Municipality, Surkhet Valley, Mid-Western Nepal. \u003cem\u003eBulletin of the Department of Geology\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 41\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eSaadi, O., Nouayti, N., Nouayti, A., Dimane, F., \u0026amp; Elhairechi, K. (2021). Application of remote sensing data and geographic information system for identifying potential areas of groundwater storage in middle Moulouya Basin of Morocco. \u003cem\u003eGroundwater for Sustainable Development\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 100639.\u003c/li\u003e\n\u003cli\u003eSaaty, T. L. (1980). 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Report of Investigation; No. 48\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWoobaidullah, A. S. M., Shahnewaz, S. M., Islam, M. M., Islam, M. K., \u0026amp; Hossain, M. Z. (2019). Electrical resistivity survey in the investigation of hydrogeological condition of Sylhet-Sunamganj Haor area, Bangladesh. \u003cem\u003eJournal of Nepal Geological Society\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e, 21\u0026ndash;28. https://doi.org/10.3126/jngs.v58i0.24570\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Groundwater depletion, Indo-Gangetic plain, Nepal, recharge","lastPublishedDoi":"10.21203/rs.3.rs-7235632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7235632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater (GW) levels in many parts of the world are depleting, including the western Terai region of Nepal, yet the extent of depletion and availability of the resource remain unknown. This study intends to characterize the state of GW potential and depletion in the southern plain of Western Nepal by applying field-based, model-based, and remote-sensing-based approaches. Borehole lithologs were used to evaluate storage potential. Observed GW level data, along with GRACE satellite data, were used to characterize the depletion. Primary data were collected to assess annual GW abstraction. The Static storage potential of a deep confined aquifer is estimated in a range of 8\u0026ndash;800Mm\u0026sup3;, and that of an unconfined shallow aquifer is 7,500\u0026ndash;15,000Mm\u0026sup3;. Regional terrestrial water storage (2004\u0026ndash;2022) is trending downward. Locally, depletion (2008\u0026ndash;2023) across municipalities ranges from negligible (0.1 m, Tikapur Municipality) to high (3.3 m, Gauriganga Municipality). Average annual GW abstraction for agriculture and domestic needs is estimated as 378.39Mm\u0026sup3; and 44.38Mm\u0026sup3;, respectively. Based on insights from field and model-based studies, as well as techniques documented in literature, it is recommended to consider non-structural strategies for the conservation of GW. The findings of this study are constructive for better planning and future development of GW resources in the area.\u003c/p\u003e","manuscriptTitle":"Changes in groundwater storage in the Southern plain of Western Nepal: Insights from remote sensing, modeling, and field studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 13:06:16","doi":"10.21203/rs.3.rs-7235632/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-15T01:54:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T18:12:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191636518794056028308578424089029464320","date":"2025-09-08T11:05:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132596954706170870415844238044959437717","date":"2025-09-03T14:13:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-03T06:40:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-02T12:03:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-02T12:02:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-07-28T15:40:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"552bfe75-f308-4b95-8ce3-29fd4299f4fb","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T07:09:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 13:06:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7235632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7235632","identity":"rs-7235632","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}