Assessment of Seasonal Groundwater Variability Using Time-Lapse Electrical Resistivity Tomography in the Co To Island Area, Gulf of Tonkin, Vietnam

Assessment of Seasonal Groundwater Variability Using Time-Lapse Electrical Resistivity Tomography in the Co To Island Area, Gulf of Tonkin, Vietnam | 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 Assessment of Seasonal Groundwater Variability Using Time-Lapse Electrical Resistivity Tomography in the Co To Island Area, Gulf of Tonkin, Vietnam Trung Nguyen Nhu, Phong Dang Xuan, Thu Trinh Hoai, Nam Bui Van, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6849979/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Marine Geophysical Research → Version 1 posted 9 You are reading this latest preprint version Abstract This research utilized time-lapse Electrical Resistivity Tomography (ERT) to assess the seasonal variability of groundwater resources on Co To Island, Gulf of Tonkin, Vietnam. Repeated ERT surveys were conducted during the dry (April 2024) and rainy (September 2024) seasons to monitor subsurface resistivity changes related to groundwater salinity and saturation. Physicochemical parameters were also measured in 15 wells to understand seasonal hydrogeological variations. The study demonstrated the effectiveness of time-lapse ERT in assessing seasonal groundwater variability on Co To Island, revealing a general increase in subsurface water volume during the rainy season. Notably, the research highlighted localized complexities, including a paradoxical expansion of a saline zone in a paleo-lagoon area during the rainy season (Line T2), contrasting with the typical dilution of seawater intrusion observed elsewhere (Line T23). The research also indicated aquifer resilience in an area with minimal extraction (Line T22). This work provides a valuable baseline for future monitoring and sustainable water resource management in this sensitive coastal environment. Time-lapse ERT saltwater intrusion Gulf of Tonkin groundwater resource Co To Island Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Electrical Resistivity Tomography (ERT) has become a widely used and effective method for mapping the distribution of saline and fresh groundwater boundaries in coastal areas (Benkabbour et al. 2004 ; Nguyen et al. 2005, 2007, 2008, 2025; Nguyen and Trinh 2013; El Yaouti et al. 2009 ; Naidu et al. 2013 ; Fadili et al. 2015 ; Werner et al. 2013 ; Kuksz et al. 2024). The bulk electrical resistivity of saturated porous media is sensitive to the conductivity of the pore fluid, which is directly related to its salinity. Time-lapse ERT, involving repeated measurements over time, allows for the monitoring of changes in subsurface resistivity distributions, providing valuable information on the dynamics of groundwater systems in response to various hydrological and environmental factors, such as seasonal variations and tidal cycles (Cordell et al. 2015 ; Dietrich et al. 2018 ; Bighash and Murgulet, 2015 ; Chang et al. 2022 ). In coastal areas, time-lapse ERT is particularly useful for examining the dynamics of the freshwater-saltwater interface due to season and tidal influences (Dimova et al. 2012 ; Cheng et al. 2023; Dimova et al. 2012 ; Zhang et al. 2021 ). The difference in electrical resistivity between fresh and saline water allows ERT to delineate these zones in the subsurface (Dimova et al. 2012 ). Time-lapse ERT can also effectively monitor changes in groundwater conditions that occur over longer seasonal timescales (Chang et al. 2022 ; Lin et al. 2024 ; Ganiyu et al. 2021). Seasonal variations in rainfall and evapotranspiration lead to changes in the water content of the vadose zone, which can be tracked by time-lapse ERT (Chang et al. 2022 ). Higher water content generally corresponds to lower resistivity values (Chang et al. 2022 ). Archie's law can be used to relate these resistivity changes to variations in water content (Archie 1942 ; Chang et al. 2022 ). Changes in resistivity patterns over different seasons can indicate the rise and fall of the groundwater table (Chang et al. 2022 ; Lin et al. 2024 ). This is important for assessing the impact of climate change and water management practices on groundwater resources (Chang et al. 2022 ). By monitoring water content changes in response to groundwater level fluctuations through time-lapse ERT, it is possible to estimate the specific yield of unconfined aquifers (Dietrich et al. 2018 ; Chang et al. 2022 ; Lin et al. 2024 ). Specific yield is a key parameter for assessing the water storage capacity of groundwater reservoirs (Lin et al. 2024 ; Chang et al. 2022 ). Empirical formulas like Van Genuchten (VG) and Brooks-Corey (BC) models, incorporated with Soil-Water Characteristic Curves (SWCC), can be used to estimate specific yield and groundwater level from inverted resistivity data (Lin et al. 2024 ). Time-lapse ERT can be used to assess the seasonal variation of saltwater intrusion, often showing that intrusion is more pronounced during the dry season due to decreased groundwater levels from low precipitation and potentially increased groundwater extraction (Ganiyu et al. 2021; Abdelkader and Brahim, 2016). Increases in groundwater electrical conductivity, which corresponds to lower resistivity, can indicate the extent of saline water intrusion (Abdelkader and Brahim, 2016). Freshwater resources on offshore islands like Co To are vital for their economic and social well-being. However, these resources are frequently threatened by seawater intrusion, which degrades water quality and reduces the availability of fresh groundwater. Understanding the spatial and temporal variability of this intrusion under the influence of seasonal changes in recharge and tidal fluctuations is essential for effective water resource management. This study focuses on the Co To Island area in Gulf of Tonkin, Quang Ninh province, Vietnam, which experiences a significant seasonal climate variation. We utilize time-lapse ERT data acquired at different times to assess the variability of groundwater resources in response to these factors. The analysis of these data aims to provide a detailed understanding of seawater intrusion mechanisms and the dynamics of the freshwater-seawater interface in the island's aquifers. 2. Geological and Hydrogeological Overview 2.1 Geological Overview Geological surveys by Hoang Van Khon (1999) indicate that the bedrock of Coto Island comprises interlayered coarse and fine-grained clastic rocks of the Coto Formation, aged late Ordovician - early Silurian (O 3 -S 1 ct). They outcrop in the high topography area (Fig. 2 ). These poorly sorted rocks show alternating beds of coarse sandstones (sometimes with acidic volcanic fragments) and banded siltstones, with variable thickness. The Coto Formation has three sub-formations. The upper unit features medium to small-grained multi-mineral sandstone, siltstone, and claystone with banded textures, occasionally interbedded with tuff and coarse sandstones, and sometimes containing shale and conglomerate lenses. The middle sub-formation comprises two members: Member 1 is mainly coarse multi-mineral sandstone and thick gray gravel with some fine sandstone and silty clay (90–135 m). Member 2 is characterized by siltstone-claystone and sericite-chlorite schists with sparse fine-grained sandstone (60–110 m). The lower sub-formation is poorly sorted coarse multi-mineral sandstone alternating with banded siltstone and claystone, with rare tuff sandstone, tuff conglomerate lenses, and multi-mineral conglomerate; shale may occur. Major faults trend NE-SW, N-S, and NW–SE, with steep dips common in NW–SE and N-S faults, creating fracture zones sometimes with 10–15 m hydrothermal quartz veins (Hoang Van Khon, 1999). Overlying these are unconsolidated Quaternary sediments (eluvi-proluvi, marine-aeolian, lagoonal), a few to tens of meters thick, and distributed about 50% area of the study area (Fig. 2 ). Marine sediments (mv \(\:{Q}_{2}^{2-3}\) ) are medium-coarse sand with shells (> 5 m). Marine-aeolian (mv \(\:{Q}_{2}^{2-3}\) ) is mainly sand with clay. Lagoonal (m \(\:{bQ}_{2}^{2-3}\) ) is mixed pebbles, sand, mud, and clay (2–4 m). Alluvial (dp \(\:{Q}_{2}^{2-3}\) ) is pebbles, sand, and clay (2–3 m). 2.2 Hydrogeological overview According to Hoang Van Khon's 1999 study, Coto Island contains two primary aquifers: the Quaternary porous aquifer and the fractured O 3 -S 1 ct aquifer. The Quaternary porous aquifer, an unconfined system covering approximately 9 km² across the island's north, central, and south, is mainly recharged by direct rainfall. Near the foothills, it also receives recharge from the underlying O 3 -S 1 ct fissure aquifer. Groundwater flow in this aquifer is strongly influenced by hydrometeorological conditions, leading to significant water table fluctuations between dry (near-empty wells) and rainy (full or overflowing wells) seasons, with levels ranging from 0.7 to 1.5 m below the surface depending on topography. Despite varying sedimentary origins, this aquifer exhibits water-bearing capacity and relatively good storage, with permeability ranging from 1 to 13 m/day (average 8.42 m/day). Water chemistry is predominantly sodium chloride or sodium chloride-bicarbonate, generally fresh and suitable for domestic use, although localized areas experience poor water quality. The fractured O 3 -S 1 ct aquifer, found within the Upper Ordovician-Lower Silurian Coto Formation, underlies much of the island, either directly beneath the Quaternary aquifer or exposed at elevations from 5 m to over 100 m. Its water level varies with terrain (0.7–6.3 m), and it shares a water level with the overlying Quaternary aquifer where they are in direct contact. Lithological logs from drilled wells (40–70 m deep) indicate primarily multi-mineral sandstone with sericite shale interbeds, yielding 0.4 to 1.5 l/s. Fault systems significantly enhance this aquifer's productivity, as demonstrated by the high yield of well LK2 despite a small recharge area due to extensive fracturing. Total dissolved solids (TDS) in these wells range from 86 to 208 mg/l, and VES surveys at four wells indicate a resistivity of approximately 27.6–32.6 Ωm (Hoang Van Khon, 1999). 3. Methodology and Data Acquisition 3.1 ERT Data Acquisition The ERT acquisition To investigate the seasonal variation of groundwater in the Coto Island area, we conducted time-lapse electrical resistivity tomography (ERT) surveys on five survey lines at the end of the dry season (April 2024) and the end of the rainy season (September 2024). The locations and measurement parameters of the survey lines are shown in Fig. 1 and Table 1 . Due to the relatively flat terrain of the survey lines, topographic variations along the lines were disregarded in this study. The average elevations of survey lines T2, T21, T22, and T23 are 6, 8, 10, and 11 meters, respectively (Table 1 ). All survey lines are oriented Northeast–Southwest. The length of the survey lines ranges from 350–355 meters (Table 1 ). The electrical resistivity tomography surveys were performed using an IRIS Syscal Pro, 72-electrode system, manufactured by IRIS Instruments, France. The Wenner-Schlumberger electrode configuration was employed, with an electrode spacing of 5 meters. The root mean square error of ERT-acquired data is observed to be 0.7% by repeated measurement. Table 1 Parameters of Time-lapse ERT lines acquired at Coto Island. Number Name of survey line Type of Array Number of electrodes Separation factor “n” Number of data points Electrode spreading direction Length of line (m) Average elevation of lines (m) 1 Line 2 W-S 72 12 696 NE-SW 355 4,2 2 Line 21 W-S 72 20 1056 NE-SW 355 16.5 3 Line 22 W-S 72 20 1056 NE-SW 355 10.1 4 Line 23 W-S 71 20 980 NE-SW 350 5,4 The physicochemical acquisition The physicochemical parameters of water samples from 15 existing boreholes and dug wells in the study area, located near or coincident with the survey lines, were also measured repeatedly during the dry season (April 2024) and the rainy season (September 2024). The dug wells (A1, A2, A3, etc.) are characterized by their limited depth, spanning from several meters up to 8 meters. They are predominantly composed of fine sand and silt-mixed sand. Water tables in these wells vary between 1.1 and 3 meters during the dry season, and between 0.4 and 1.5 meters during the rainy season. The borehole depths, namely A4, A12, A14, and A16, range from 19 to 45 m (Table 2 ). The geological materials retrieved from these boreholes consist mainly of sandstone and weathered shale. The physicochemical properties of the water samples were determined using a German-made WTW 3430 instrument. The measured parameters encompassed electrical conductivity (EC), total dissolved solids (TDS), temperature (T), and pH. All EC data in the dry and rainy seasons are corrected to 25 0 C. The seasonal variations in these parameters are presented in Table 2 . Table 2 Changes in the well water table and TDS between the dry and rainy seasons No Well name Well eleva. Well depth Dry season Rainy season Water table (m) EC (µS/cm) TDS (mg/l) pH Water table (m) EC (µS/cm) TDS (mg/l) pH 1 A1 6.4 3.3 1.7 358.4 231.8 5.99 0.75 171.7 111.0 6.97 2 A2 6.7 2.5 1.7 534.7 345.9 7.58 0.50 269.1 174.1 6.61 3 A3 9.4 2.4 1.85 466.7 301.9 6.1 0.76 234.6 151.8 5.96 4 A5 8.8 2.7 2 463.3 299.7 7.3 1.9 440.1 284.7 5.76 5 A7 7.9 4.1 2.35 340.3 220.1 6.95 0.50 374.8 242.4 6.3 6 A8 7.9 4.2 3.1 192.0 124.2 5.52 1.93 206.0 133.3 4.53 7 A9 3.7 2.7 2.1 267.9 173.3 7.37 0.92 294.2 190.3 6.06 8 A10 8.5 3.7 2.8 169.1 109.4 6.8 1.30 238.7 154.4 6.1 9 A11 8.2 6.5 1.63 337.8 218.5 4.93 0.56 353.8 228.8 6.36 10 A13 8.5 4.7 3.44 334.8 216.5 5.1 0.58 306.9 198.5 6.17 11 A15 7 1.5 1.1 140.1 90.6 10.2 0.2 203.1 131.4 8.8 12 A4 14.9 45 4 479.5 310.1 7.64 0.4 258.8 167.4 6.89 13 A12 9.4 28 - 194.1 125.6 6.4 - 238.1 154.0 6.91 14 A14 4.9 42 - 587.0 379.7 6.3 - 108.6 70.3 8.47 15 A16 3.7 19 9 6647.0 4299.5 8.86 1.85 6405.7 4143.4 6.48 3.2 The Inversion and Analysis of Time-Lapse ERT Data The acquired apparent resistivity data were processed and inverted to obtain 2D resistivity models of the subsurface. The inversion process typically uses specialized software, e.g., Res2Dinv (Loke 2001 ) and algorithms that aim to find a resistivity distribution that best fits the error function (Ln norm) between the measured and calculated apparent resistivity values (Loke and Barker 1996 ; Loke et al. 2010 ; Menke 2018 ). The root mean square (RMS) error is usually used to assess the quality of the inversion. The parameters of the Res2Dinv software for inversion interpretation are referred to the Table 3 . Table 3 Initial damping factor 0.15 Minimum damping factor 0.02 Damping factor optimized at each iteration Yes Higher damping factor (HDF) for the first layer No Apply HDF for blocks at side of model to reduce the edge effect Yes Adjust DF for changes in distances between blocks in the model Yes Horizon mesh size 4 nodes Vertical mesh size Finer mesh Type of forward modeling method Finite element Automatically grid size Enable Line search local optimize Yes Error change convergence limit 1.0 % Percentage RMS error for convergence 1.50% Number of iterations (Maximum) 15 Check model resistivity for extreme values Yes The ratio of the first layer thickness to the unit electrode space 0.375 Time-lapse inversion constrain (0 = None,1&2 = Smooth,3 = Robust) Type of time-lapse inversion method (0 = Simultaneous,1 = Sequential) The rate at which the layer thickness increates with depth 1 0 1.1 Allow the number of model parameters to exceed the number of data point Yes Factor to increase the model depth range 1.05 Reduce the effect of the side blocks Yes, slight All blocks have equal widths Yes The width of the model cell 0.5 unit electrode spacing Type of data inversion constraint Robust data constraint Type of model inversion constraint Robust model constraint Standard Gauss–Newton optimization method Yes Smoothness constraint only used on model resistivity values Yes Average apparent resistivity used for reference model Yes Type of initial model Homogeneous half-space The logarithm of apparent resistivity used for inversion Yes To assess seasonal groundwater variability, the inverted resistivity models from the dry and rainy seasons for lines T2, T21, T22, and T23 were compared. Differences in resistivity distribution between the two seasons can indicate changes in water content and salinity within the aquifers. Lower resistivity values generally suggest higher water content or increased salinity (or both), while higher resistivity values may indicate lower saturation or fresher water. The reduction in water saturation (desaturation) might have been analyzed by comparing the resistivity models. 4. Results and Discussion 4.1 Hydrogeological investigation The physicochemical parameters of water samples were measured seasonally (dry and rainy seasons) at 15 existing boreholes and dug wells in the study area. These included 11 dug wells (exploiting the porous aquifer) with well depths ranging from 1.5 to 6.5 m and wellhead elevations at sea level from 5 to 9.4 m. Four boreholes (A4, A12, A14, and A16), tapping the fractured aquifer, had borehole depths from 19 to 45 meters and wellhead elevations from 3.7 to 14.9 m. The data consistently show a lower water table during the dry season compared to the rainy season. Dug well measurements (Fig. 3 a) illustrate this, with dry season water table averaging 2.0 m (range: 1.1–3.1 m) and elevations averaging 4.7 m above sea level (range: 2.9–7.5 m). In contrast, rainy season measurements in dug wells averaged 0.8 m (range: 0.2–1.9 m) and 5.9 m above sea level (range: 4.0-8.6 m), resulting in an average seasonal water table fluctuation of 1.2 m. Similarly, borehole data from A4 and A16 support this trend. Dry season water levels were recorded at 4 m (10.9 m elevation) in A4 and 9 m (-5.3 m elevation) in A16, while rainy season levels rose to 0.4 m (14.5 m elevation) and 1.85 m (1.85 m elevation), respectively. Therefore, the evidence strongly suggests a decline in groundwater levels during the dry season. The seasonal pH trend was mixed across the studied wells. Specifically, dug wells A1, A11, A13, and boreholes A12 and A14 showed an increase in pH during the dry season. Conversely, the remaining wells exhibited a decrease in pH during the drier period, suggesting a shift towards more acidic conditions in those locations. The overall pH in dug wells ranged from 4.9 to 10.2 (average 6.1) in the dry season and from 4.5 to 8.8 (average 5.8) in the rainy season. Similarly, borehole pH values ranged from 6.3 to 8.8 (average 7.3) in the dry season and from 6.5 to 8.5 (average 7.1) in the rainy season. Notably, dug well A5 experienced the largest pH decrease (-1.4) from the dry to the rainy season, while dug well A11 showed the highest increase (1.4). Generally, pH fluctuations were more pronounced in boreholes compared to dug wells, with the maximum decrease reaching − 2.4 and the maximum increase reaching 2.2. The TDS values in the dug wells varied from 104.6 mg/l at well A10 to a maximum of 4359.7 mg/l A16 in the dry season. During the rainy season, TDS values showed rather complex fluctuations compared to the dry season: some wells exhibit a decrease in TDS, and some other wells show an increase. The wells with the most significant decrease in TDS during the rainy season are wells A1, A2, A3, and A4. For instance, TDS drops from 216.5 mg/l to 198.5 mg/l at well A13 near survey lines T23 or from 310.1 mg/l to 167.4 mg/l at well A4 near line 22. Conversely, the wells that experience an increase in TDS during the rainy season include well A8, A9, and A10 near survey line T2. For instance, TDS rises from 109 mg/l to 154 mg/l at well A10. Table 2 indicates that there is no consistent trend in the changes of both TDS and pH between the rainy and dry seasons across all the surveyed wells. This variation could reflect differences in hydrogeological conditions and the chemical interaction processes occurring at each specific well location. Fractured aquifers have much larger seasonal water level fluctuations than porous aquifers (more or less than 5 times). 4.2 Geophysical investigation Line T2 ( Fig. 4 ) : The resistivity cross-section in the dry season (Model 1) displays resistivity values that range from below 7.2 Ωm to above 400 Ωm. The resistivity cross-section of Line 2 (Fig. 8) shows a vertical boundary dividing the cross-section into two distinct parts: The beginning part of the line from electrode 0 to 80 m has a low resistivity of 7.2–20 Ωm. The second part, from electrode 80 m to the end, has a high resistivity of 54–151 Ωm. The very thin layer near the surface generally shows lower resistivity values of 20 Ωm (except for some small spots). The lower part of the section shows a distinct separation into two portions: the first portion from electrode 0 m to 160 m has the lowest resistivity, within the range of 7.2 to 75 Ωm. Wherein, the 0–80 m segment has the lowest resistivity values (7.2–20 Ωm), and the segment from 75 to 160, the resistivity gradually increases from 20 to 75 Ωm. From electrode 160 m to the end of the line, the cross-section shows the highest resistivity values, within the range of 100 to 400 Ωm. In which the upper section from electrode 160 to 220 m (with a thickness of roughly 10 m) has even higher resistivity values (150–400 Ωm). The resistivity cross-section in the rainy season (Model 2) also displays the same resistivity scale. In contrast to the dry season (Model 1), the rainy season shows a more widespread distribution of lower resistivity values, suggesting a general decrease in resistivity across the surface compared to the dry season. There are still some patches of higher resistivity (yellow and orange), but they appear less extensive than in Model 1. The zones of very low resistivity (blue and purple) at depth appear to be more continuous and potentially more widespread in Model 2, particularly in the first segment from 0 m to 160 m. The localized higher resistivity zones at depth on the right side, while still present, show a slight decrease in resistivity in Model 2 compared to Model 1. Specifically, the resistivity in the last segment, from electrode 240 m to the end, drops from 150 Ωm in the dry season to 75 Ωm in the rainy season. Line T21 ( Fig. 5 ) : The resistivity section in the dry season displays a range of resistivity values from 20 Ωm to above 1500 Ωm. In this resistivity cross-section, three distinct layers are observable: the top layer with a thickness of some meters to 15 m, which registers the highest resistivity, ranging from 75-1500 Ωm. The top layer exhibits a heterogeneous high-resistivity distribution. In which the central section of the line exhibits low resistivity values ranging from 75–150 Ωm, while the remaining sections show high resistivity values between 150–1500 Ωm. The middle layer, which is marked by low resistivity (75–150 Ωm) The middle layer shows quite a constant resistivity, around 150 Ωm, and only a tiny region close to electrode 220 m records a 75 Ωm resistivity. The bottom layer exhibits a uniform resistivity value of approximately 400 Ωm. The rainy season resistivity Section (Model 2) uses the same resistivity scale as Model 1 (from 20 Ωm to 1500 Ωm). In contrast to the dry season, the rainy season cross-section shows a more widespread distribution of lower resistivity values near the surface. The presence of blue and green colors (indicating resistivity below 75 Ωm) is more prevalent across the surface in Model 2 compared to Model 1. This suggests a general decrease in resistivity across the surface due to increased moisture content. While patches of higher resistivity (yellow and orange) are still present, they appear to be less extensive than in Model 1, particularly in the last portion from electrode 180 m to the end, the high resistivity zones (orange, yellow) are segmented into smaller blocks by low resistivity zones (blue/green). The zones of lower resistivity (green and purple) at depth appear to be more continuous and potentially more widespread in Model 2, particularly in the segment from electrode 160 m to 240 m. Line T22 ( Fig. 6 ) : The dry resistivity section (Model 1) displays the resistivity simple distribution ranging from 75 Ωm to above 400 Ωm. However, the resistivity cross-section shows generally a more widespread distribution of high resistivity values of 400 Ωm. There are some low-resistivity portions in both the right and left sides at depth. The lower part of the section on the left side, from electrode 0 to 115 m, exhibits a heterogeneous resistivity distribution of 75–150 Ωm, and from electrode 160 to 310 m, the resistivity value is 150 Ωm. The rainy season resistivity cross-section (Model 2) nearly matches the dry season resistivity cross-section, displaying no structural variations and only a slight change in resistivity values. - Line T23 ( Fig. 7 ) : The dry resistivity section (Model 1) displays the resistivity distribution ranging from 10 Ωm to above 4600 Ωm. Notably, the resistivity cross-section is divided into two distinct parts at electrode 120 m. The first part of the line from electrodes 0 to 120 m exhibits a low resistivity of 10–20 Ωm (except for the top thin layer, which is 2000–4600 Ωm). Two low resistivity blocks (10 Ωm) appear at electrodes 55–75 m and 85–110 m, extending from a few meters down to a depth of 25 m. The latter part of the line shows a high resistivity value of 20–400 Ωm. In this area, the resistivity cross-section is characterized by high resistivity zones. The top part of the section (from electrode 120 to 200 m) displays a low resistivity layer, roughly between 20–75 Ωm, and around 7–14 meters in thickness. After that is a 150–400 Ωm high resistivity layer. Next is a high resistivity part with a value of 150–400 Ωm in the section between electrode 200 and 350 m of the line. In contrast to the dry season, the rainy season cross-section shows a more widespread distribution of higher resistivity values near the surface of the beginning part of the cross-section (20–75 Ωm). The low resistivity blocks (10–15 Ωm) in dry season resistivity cross-section have reduced and some have disappeared from the rainy season resistivity cross section. The lower part of the rainy season resistivity cross-section, from electrode 120 to 250 m, shows a more widespread distribution of lower resistivity values (75–150 Ohm) compared to the dry season resistivity cross-section. The last part of the rainy season resistivity cross-section (from electrode 240 m to 350 m) is almost unchanged compared to the dry season resistivity cross-section. 4.3 Discussion - (Line T2): The resistivity cross-section illustrates the distribution between freshwater and saltwater zones based on resistivity values (Nguyen et al. 2025). Accordingly, in the study area, regions with resistivity values below 20 Ωm are identified as saltwater zones, and conversely, regions with resistivity values above 20 Ωm are classified as freshwater zones (Nguyen et al. 2025). On the Model 1 cross-section Line T2 (Fig. 4 ) recorded during the dry season, the low resistivity zone (< 10 Ωm) at the beginning of the survey line (0-120 m) is identified as a saline intrusion area (Nguyen et al. 2025). Paradoxically, the rainy season resistivity cross-section shows an expansion of this saline intrusion area compared to the dry season, despite the recharge of freshwater into the aquifers. The phenomenon of salinity re-infection during the rainy season in this area is demonstrated by the TDS analysis results in some dug wells, A8, A9, A10, around this paleo-lagoon system. The rainwater recharge is evident along the survey line T2 from the 120 m electrode mark to the end of the line, where low resistivity zones (20–75 Ωm) are more extensive during the rainy season. The phenomenon of saline intrusion at the end of line T2 in the rainy season is more widespread than in the dry season (low resistivity area is more widespread) can be explained by the presence of a paleo-lagoon in the study area. The T2 survey line area was previously a mangrove swamp, which has been converted into a residential area in recent decades. The remaining evidence of this paleo-lagoon is a system of ditches extending from Xuan Truong Bay to the area of survey line T23. This paleo-lagoon is now disconnected from the sea due to the construction of a freshwater reservoir that separated it from Xuan Truong Bay, leading to the entrapment of saltwater within the aquifers. During the rainy season, the rainwater recharging the aquifers has pushed and concentrated the residual saltwater from the surrounding areas towards the end of this paleo-lagoon where the T2 line is located, causing the observed expansion of the saline interface. On the contrary, Line T23 has a mass of salt water at the beginning of the line in the dry season, and in the rainy season, the rainwater recharged into the aquifer dilutes and pushes this mass of salt water out to sea. This proves that the saltwater intrusion area in this line section is due to seawater intrusion from the sea. This mechanism is entirely different from the saltwater intrusion mechanism in Line T2 due to saltwater stagnating in the paleo-lagoon, causing salinity. We observe the TDS data in borehole A14 (exploiting the fissured water layer) in this paleo-lagoon area, we see that the TDS content in the rainy season is lower than in the dry season (reduce from 4299.5 mg/l to 4143.4 mg/l), which proves that the rainwater replenished into the fissured water layer has diluted the salt water. This water aquifer may not be affected by the phenomenon of re-salinization in the rainy season in this ancient lagoon area, and the phenomenon of re-salinization only occurs in the upper porous water layer. In contrast to Line T2, Line T23 is located far away from the paleo-lagoon. The widespread higher resistivity at the beginning of the rainy season, in contrast to the dry season, suggests that the aquifer has been recharged with freshwater from rainwater. This influx of freshwater likely diluted the saltwater that had become more concentrated during the dry season. Conversely, the spatial extent of low resistivity (visualized in green, representing values below 75–150 Ωm) is greater in the middle section (from electrode 120 to 250 m) of the rainy season resistivity cross-section compared to the dry season resistivity cross-section. This implies a general lowering of resistivity attributed to increased water saturation within the fractured rock mass during the rainy season. - (Line 21): The primary difference between the dry season (Model 1) and rainy season (Model 2) resistivity cross-sections lies in the overall distribution of resistivity values, particularly in the top and middle layers. The rainy season (Model 2) exhibits a general decrease in near-surface resistivity compared to the dry season (Model 1). This is likely due to the infiltration of rainwater, which increases the electrical conductivity of the subsurface materials. Lower resistivity zones are more widespread near the surface in Model 2, while higher resistivity zones are less extensive. At depth, the zones of low resistivity appear more continuous and potentially larger in the rainy season, suggesting increased saturation. The higher resistivity zones at depth on the right side might show a slight decrease in resistivity during the rainy season, indicating some degree of moisture influence at deeper levels as well. Despite these changes, the overall structural patterns of resistivity (e.g., the general trend of lower resistivity on the left side and higher resistivity on the right side) appear to be maintained between the two models. This suggests that the underlying geological formations are the primary control on the resistivity distribution, with seasonal moisture variations causing changes in the magnitude and spatial extent of these resistivity zones. The low resistivity zones extending from the surface, cutting through the high resistivity layer down to the middle layer, form expanded low resistivity areas during the rainy season. These areas are identified as the primary recharge zones for rainwater into the aquifer. Field observation revealed a fault at an outcrop near the electrode 210 m, reinforcing the interpretation that these low resistivity zones may correspond to faults. Considering borehole LK2 (See location in Fig. 2 ), which has an elevation of 15.8 m and a water table of 6.3 m (measured in the dry season, Hoang Van Khon,1999), and borehole A4, with an elevation of 14.5 m and a water table of 4 m (also in the dry season), it becomes clear that the high resistivity zone (400–2000 Ωm) in the upper section of the profile is largely situated above the water table during the dry period. During the rainy season, when the water table increases (to 0.4 m at borehole A4; LK2 could not be measured due to access limitations), this high resistivity mass is submerged below the water table. However, its resistivity remains elevated (400–2000 Ωm, refer to Model 2), with only a limited number of low resistivity zones emerging. This observation suggests that the high resistivity zone represents a solid rock formation, with fracturing limited to fault zones (represented by the low resistivity zones in Model 2). - (Line T22): The low resistivity layer (30–150 Ωm) on the cross-section T22 can likely be interpreted as a fractured aquifer. The lack of significant changes in structure and resistivity values between the cross-sections measured in the dry and rainy seasons suggests that this aquifer has probably reached a stable saturated level. Although there are no direct boreholes along this line, information from the nearby borehole A4 supports this hypothesis. At borehole A4, with the wellhead elevation of 14.9 m, the water table shows fluctuations from a depth of 4 meters in the dry season to 0.4 meters in the rainy season (Table 2 ). Comparing the average elevation of the survey line (10.1 meters) with the wellhead elevation of the borehole, we observe that survey line T22 is 4.8 meters lower than the wellhead of borehole A4. Thus, during the dry season, the elevation of the survey line is approximately equal to the water table. It can be concluded that the entire survey line T22 lies below the observed water table at borehole A4 in both seasons. Furthermore, due to the local community on the island prioritizing the use of a centralized water supply system from lakes, the groundwater resources in this area experience minimal extraction, thus being well-preserved. 5. Conclusion The application of time-lapse ERT in Co To Island effectively demonstrated its utility in assessing the seasonal variability of groundwater resources, revealing a general increase in subsurface water volume during the rainy season across the surveyed areas. The study successfully highlighted the temporal dynamics of groundwater systems in response to seasonal hydrological variations. Notably, the research identified localized complexities, including the influence of a paleo-lagoon on salinity distribution (Line T2) and the resilience of the aquifer near Line T22 due to minimal extraction and consistent saturation. Conversely, Line T23 showed a typical pattern of seawater intrusion being pushed back by freshwater recharge in the rainy season. Overall, this study underscores the efficacy of time-lapse ERT as a sophisticated tool for continuously monitoring groundwater resources in sensitive coastal island environments facing challenges from both natural seasonal changes and potential anthropogenic impacts. The detailed resistivity information obtained provides a valuable baseline for future investigations and the development of informed and sustainable water resource management strategies for Co To Island. Declarations Author Contribution Nguyen Nhu Trung, Dang Xuan Phong, Trinh Hoai Thu wrote the main manuscript text. Nguyen Nhu Trung, Trinh Hoai Thu, Bui Van Nam interpreted data. Bui Van Nam and Nguyen Van Diep collected the data and prepared figures.All authors reviewed the manuscript Acknowledgements: This research is funded by the Basic Science Development Program in the felds of Chemistry, Life Sciences, Earth Sciences and Marine Sciences for the period 2017–2025 of The Vietnam Academy of Science and Technology (VAST) under Grant Number KHCBBI.01/24–25 and Grand Number NCVCC10.02/25–25. The authors kindly thank the funding organization. References Abdelkader T, Ahmed and Brahim Askri (2016) Seawater Intrusion Impacts on the Water Quality of the Groundwater on the Northwest Coast of Oman. Water Environment Research 88(8):732-740, DOI:10.2175/106143016X14609975747045 Archie GE (1942) The electrical resistivity log as an aid in determining some reservoir characteristics. Pet. Trans. AIME 1942, 146, 54–62. Benkabbour B, Toto EA, Fakir (2004) Using DC resistivity method to characterize the geometry and the salinity of the plioquaternary consolidated coastal aquifer of the Mamora plain, Morocco. Environ Geol 45:518–526 Bighash P, Murgulet D (2015) Application of factor analysis and electrical resistivity to understand groundwater contributions to coastal embayments in semi-arid and hypersaline coastal settings. Sci. Total Environ. 532, 688–701. Chang PY, Puntu JM, Lin DJ, Yao HJ, Chang LC, Chen KH, Lu WJ, Lai TH, Doyoro YG (2022) Using Time-Lapse Resistivity Imaging Methods to Quantitatively Evaluate the Potential of Groundwater Reservoirs. Water, 14, 420. Cheng Xing, Yufeng Zhang, Xiujun Guo, Jitong Sun (2023) Time series investigation of electrical resistivity tomography reveals the key drivers of tide and storm on groundwater discharge. Estuarine, Coastal and Shelf Science 282 (2023) 108225. https://doi.org/10.1016/j.ecss.2023.108225 Cordell D Johnson, Peter W Swarzenski, Christina M Richardson, Chris G Smith, Kevin D Kroeger, Priya M Ganguli (2015) Ground-truthing Electrical Resistivity Methods in Support of Submarine Groundwater Discharge Studies: Examples from Hawaii, Washington, and California. Journal of Environmental and Engineering Geophysics, 20(1):81-87. https://doi.org/10.2113/jeeg20.1.81 Dietrich S, Carrera J, Weinzettel P, Sierra L (2018) Estimation of specific yield and its variability by electrical resistivity tomography. Water Resour. Res. 54, 8653–8673 Dimova, NT, Swarzenski PW, Dulaiova H, Glenn CR (2012) Utilizing multichannel electrical resistivity methods to examine the dynamics of the fresh water–seawater interface in two Hawaiian groundwater systems, J. Geophys. Res., 117, C02012, doi:10.1029/2011JC007509. El Yaouti F, El Mandour A, Khattach D, Benavente J, Kaufmann O (2009) Salinization processes in the unconfned aquifer of BouAreg (NE Morocco): a geostatistical, geochemical, and tomographic study. Appl Geochem 24:16–31. Fadili A, Mehdi K, Riss J, Najib S, Makan A, Boutayab K (2015) Evaluation of groundwater mineralization processes and seawater intrusion extension in the coastal aquifer of Oualidia, Morocco: hydrochemical and geophysical approach. Arab J Geosci. https:// doi.org/10.1007/s12517-015-1808-5 Hoang Van Khon (chief editor) (1999) Report on hydrogeological investigation—engineering geology and search for water sources on Co To island, Quang Ninh. Archived at the Northern Union of Water Resources Planning and Investigation, p 120. Ganiyu Olabode Badmus, Olukayode Dewunmi Akinyemi, Adewole Michael Gbadebo, John Adebayo Oyedepo, Gbolahan Muyiwa Folarin (2021) Assessment of seasonal variation of saltwater intrusion using integrated geophysical and hydrochemical methods in some selected parts of Ogun Waterside, Southwest, Nigeria. Environmental Earth Sciences 80:99, https://doi.org/10.1007/s12665-021-09379-y. Lin DJ, Chang PY, Puntu JM, Doyoro YG, Amania HH, Chang LC (2024) Estimating the Specific Yield and Groundwater Level of an Unconfined Aquifer Using Time-Lapse Electrical Resistivity Imaging in the Pingtung Plain, Taiwan. Water, 15, 1184. https://doi.org/10.3390/w15061184 Loke MH (2001) Tutorial: 2-D and 3-D electrical imaging surveys, 128 p. Geotomo Software, Malaysia. Loke MH, Barker RD (1996) Rapid least-squares inversion of apparent resistivity pseudosections by a quasi-Newton method. Geophys Prospect 44(1):131–152. Loke MH, Wilkinson PB, Chambers JE (2010) Fast computation of optimized electrode arrays for 2D resistivity surveys. Comput Geosci 36(11):1414–1426. Łukasz Kaczmarek, Grzegorz Sinicyn, Krzysztof Kochanek,, Bartosz Bednarz, Mateusz Grygoruk, Maria Grodzka-Łukaszewska (2024) Electrical resistivity imaging data for hydrogeological and geological investigations of Szuszalewo peatland (North-East Poland). Data in Brief, 2352-3409, https://doi.org/10.1016/j.dib.2024.110626. Menke W (2018) Geophysical data analysis: discrete inverse theory. Academic Press, Amsterdam. Naidu LS, Rao G, VVS, Mahesh J, Padalu G, Sarma VS, Prasad PR, Rao SM (2013) An integrated approach to investigate saline water intrusion and to identify the salinity sources in the Central Godavari delta, Andhra Pradesh. India Arab J Geosci 6(10):3709–3724 Nguyen Nhu Trung, Dang Xuan Phong, Trinh Hoai Thu, Bui Vn Nam, Nguyen Van Diep (2025) Mapping freshwater and seawater intrusion using electrical resistivity tomography and physicochemical data: an application in Coto Island, Gulf of Tonkin. Marine Geophysical Research , 46 (1). Nguyen Nhu Trung, Trinh Hoai Thu (2013) Investigation of the saltwater intrusion in the Pleistocene aquifer in the coastal zone of Red River Delta. In: Proceedings of the 11th SEGJ international symposium, Yokomaha, 2013. https://doi.org/10.1190/segj112013-062. Nguyen Nhu Trung, Nguyen Ba Minh, Nguyen Van Nghia (2005) Using electrical resistivity and hydrogeological modeling for investigating saltwater intrusion in Haiphong coastal plain. In: Proceeding of the international workshop, Hanoi geoengineering, 25 No v. 2005, pp 171–178 Nguyen Nhu Trung, Nguyen Van Nghia, Nguyen Ba Minh(2007) Forecasting the saltwater intrusion in Haiphong area by the electrical resistivity and hydrogeology modeling methods. J Earth Sci 29(3):277–283 Nguyen Nhu Trung, Trinh Hoai Thu, Nguyen Van Nghia (2008) Application of electrical resistivity and hydrogeology modeling methods to map and forecast the saltwater intrusion in Thai Binh province. J Geol Ser B. Werner AD, Bakker M, Post VEA, Vandenbohede A, Chunhui Lu, Ataie-Ashtiani B, Simmons CT, Barry DA (2013) Seawater intrusion processes, investigation and management: recent advances and future challenges. Adv Water Resources 51:3 26. https://doi.org/10.1016/j.advwatres.2012.03.004 Yeh TC, Liu JS, Glass RJ, Baker K, Brainard JR, Alumbaugh DL, LaBrecque D (2002) A geostatistically based inverse model for electrical resistivity surveys and its applications to vadose zone hydrology, Water Resour. Res., 38(12), 1278, doi:10.1029/2001WR001204. Zhang YF, Wu, Zhang JX, Guo KK, Xing XJ, Li CN, Wu HF (2021) Analysis of seasonal differences in tidally influenced groundwater discharge processes in sandy tidal flats: a case study of Shilaoren Beach, Qingdao, China. J. Hydrol. 603 (C), 127128. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Marine Geophysical Research → Version 1 posted Editorial decision: Revision requested 10 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviewers agreed at journal 14 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 09 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 08 Jun, 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. 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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-6849979","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469203739,"identity":"2872a70b-dcb8-4eb2-9efa-a6ab9b451f31","order_by":0,"name":"Trung Nguyen Nhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBADOTYSFDODSWOYFgmitSQ2EK3FXLr/2GfeHbXpfeztD5h5aurqGCRyD+DVYjnnMPNs3jPHc9t4zhgw8xw7LMEgkZeAV4vBjWRmZt62Y7ltEjkMjDPYDkgwAPUSpSWdTf75A8YZ/+qI1lKTwCbBYMDwsY1ZgoG9h6AWY8a5bQcM23hyDA587Dss2UZYS+JjhrdtdfLy7ccfPkj4VsfPz8yDXwsUHAaTB0AEscmgjkh1o2AUjIJRMCIBAA77PIM5hF6hAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Earth Sciences, VAST","correspondingAuthor":true,"prefix":"","firstName":"Trung","middleName":"Nguyen","lastName":"Nhu","suffix":""},{"id":469203740,"identity":"d57da066-e159-41f2-8b09-da499afccb71","order_by":1,"name":"Phong Dang Xuan","email":"","orcid":"","institution":"Institute of Earth Sciences, VAST","correspondingAuthor":false,"prefix":"","firstName":"Phong","middleName":"Dang","lastName":"Xuan","suffix":""},{"id":469203741,"identity":"1c370b10-8ddc-42c0-9fca-168cb79e204d","order_by":2,"name":"Thu Trinh Hoai","email":"","orcid":"","institution":"Institute of Earth Sciences, VAST","correspondingAuthor":false,"prefix":"","firstName":"Thu","middleName":"Trinh","lastName":"Hoai","suffix":""},{"id":469203742,"identity":"15309101-0409-45a8-b145-442eab05cfce","order_by":3,"name":"Nam Bui Van","email":"","orcid":"","institution":"Institute of Earth Sciences, VAST","correspondingAuthor":false,"prefix":"","firstName":"Nam","middleName":"Bui","lastName":"Van","suffix":""},{"id":469203743,"identity":"35e0e216-50af-4cfe-9727-66fa5dc827f9","order_by":4,"name":"Diep Nguyen Van","email":"","orcid":"","institution":"Institute of Earth Sciences, VAST","correspondingAuthor":false,"prefix":"","firstName":"Diep","middleName":"Nguyen","lastName":"Van","suffix":""}],"badges":[],"createdAt":"2025-06-09 02:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6849979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6849979/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11001-025-09599-y","type":"published","date":"2025-11-19T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84461519,"identity":"fba2f7a4-adb3-4d04-8f4c-76020b4cebcb","added_by":"auto","created_at":"2025-06-12 08:59:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":292500,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram showing the location of the study area in Dong Tien Commune, Coto Island, Gulf of Tonkin, Vietnam and the time-lapse ERT survey lines. Lines T2, T21, T22, and T23 are the repeated survey lines conducted in the dry season (April 2024) and the rainy season (September 2024).\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6849979/v1/e3b37e7a4d0dedd7cbaffc52.jpeg"},{"id":84460748,"identity":"4be8a3c5-9024-486b-a9db-521b71b4d2be","added_by":"auto","created_at":"2025-06-12 08:51:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":343271,"visible":true,"origin":"","legend":"\u003cp\u003eGeological schematic of the study area in Coto Island and the stratigraphic column of well LK2 (Modified from Hoang et al. 1999 and Nguyen et al. 2025)\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6849979/v1/ea359c98ee72e36b1a21d9c8.jpeg"},{"id":84460745,"identity":"e73011e9-1273-4451-9537-da3e22fe9720","added_by":"auto","created_at":"2025-06-12 08:51:18","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38446,"visible":true,"origin":"","legend":"\u003cp\u003eStratigraphic columns and water tables in dry and rainy seasons of dug wells (a) and boreholes (b) in the study area. Water table drops in dry season comparing to rainy season.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6849979/v1/ca931a1bb3d85612ae85fb6d.jpeg"},{"id":84460747,"identity":"8e73affe-3a9f-46b5-ab38-c2f9f3aa2dda","added_by":"auto","created_at":"2025-06-12 08:51:18","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":241058,"visible":true,"origin":"","legend":"\u003cp\u003eInverted 2-D resistivity tomography sections for line T2: a) Model 1 resistivity cross-section obtained in the dry season (April 2024); b) Model 2 resistivity cross-section obtained in the wet season (September 2024.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6849979/v1/2e9c400b7d6bf4ac82154a63.jpeg"},{"id":84461528,"identity":"3ecd0331-ee44-4268-9cce-31feee52b3d9","added_by":"auto","created_at":"2025-06-12 08:59:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":223218,"visible":true,"origin":"","legend":"\u003cp\u003eInverted 2-D resistivity tomography sections for line T21: a) Model 1 resistivity cross-section obtained in the dry season (April 2024); b) Model 2 resistivity cross-section obtained in the rainy season (September 2024).\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6849979/v1/d26e3baeb2c0279bd32caa32.jpeg"},{"id":84460753,"identity":"b8728215-3362-4bc1-9acb-017557d8cc1f","added_by":"auto","created_at":"2025-06-12 08:51:18","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":245925,"visible":true,"origin":"","legend":"\u003cp\u003eInverted 2-D resistivity tomography sections for line T22: a) Model 1 resistivity cross-section obtained in the dry season (April 2024); b) Model 2 resistivity cross-section obtained in the wet season (September 2024). The rainy season resistivity cross-section (Model 2) nearly matches the dry season resistivity cross-section, displaying no structural variations and only a slight change in resistivity values.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6849979/v1/61405d2e2ec30194543726e3.jpeg"},{"id":84460761,"identity":"8b5666be-82dd-4f24-8c4b-987075455dce","added_by":"auto","created_at":"2025-06-12 08:51:18","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":219514,"visible":true,"origin":"","legend":"\u003cp\u003eInverted 2-D resistivity tomography sections for line T23: a) Model 1 resistivity cross-section obtained in the dry season (April 2024); b) Model 2 resistivity cross-section obtained in the wet season (September 2024).\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6849979/v1/548056945041200a46f41832.jpeg"},{"id":96650971,"identity":"3e7b5e32-2394-42ac-803f-93e861a95f58","added_by":"auto","created_at":"2025-11-24 16:13:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2504228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6849979/v1/3bb0fddd-302f-4f77-a55d-fb4a5228d720.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Seasonal Groundwater Variability Using Time-Lapse Electrical Resistivity Tomography in the Co To Island Area, Gulf of Tonkin, Vietnam","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eElectrical Resistivity Tomography (ERT) has become a widely used and effective method for mapping the distribution of saline and fresh groundwater boundaries in coastal areas (Benkabbour et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Nguyen et al. 2005, 2007, 2008, 2025; Nguyen and Trinh 2013; El Yaouti et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Naidu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fadili et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Werner et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kuksz et al. 2024). The bulk electrical resistivity of saturated porous media is sensitive to the conductivity of the pore fluid, which is directly related to its salinity. Time-lapse ERT, involving repeated measurements over time, allows for the monitoring of changes in subsurface resistivity distributions, providing valuable information on the dynamics of groundwater systems in response to various hydrological and environmental factors, such as seasonal variations and tidal cycles (Cordell et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dietrich et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bighash and Murgulet, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In coastal areas, time-lapse ERT is particularly useful for examining the dynamics of the freshwater-saltwater interface due to season and tidal influences (Dimova et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cheng et al. 2023; Dimova et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The difference in electrical resistivity between fresh and saline water allows ERT to delineate these zones in the subsurface (Dimova et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Time-lapse ERT can also effectively monitor changes in groundwater conditions that occur over longer seasonal timescales (Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ganiyu et al. 2021). Seasonal variations in rainfall and evapotranspiration lead to changes in the water content of the vadose zone, which can be tracked by time-lapse ERT (Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Higher water content generally corresponds to lower resistivity values (Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Archie's law can be used to relate these resistivity changes to variations in water content (Archie \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1942\u003c/span\u003e; Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Changes in resistivity patterns over different seasons can indicate the rise and fall of the groundwater table (Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is important for assessing the impact of climate change and water management practices on groundwater resources (Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By monitoring water content changes in response to groundwater level fluctuations through time-lapse ERT, it is possible to estimate the specific yield of unconfined aquifers (Dietrich et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specific yield is a key parameter for assessing the water storage capacity of groundwater reservoirs (Lin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Empirical formulas like Van Genuchten (VG) and Brooks-Corey (BC) models, incorporated with Soil-Water Characteristic Curves (SWCC), can be used to estimate specific yield and groundwater level from inverted resistivity data (Lin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Time-lapse ERT can be used to assess the seasonal variation of saltwater intrusion, often showing that intrusion is more pronounced during the dry season due to decreased groundwater levels from low precipitation and potentially increased groundwater extraction (Ganiyu et al. 2021; Abdelkader and Brahim, 2016). Increases in groundwater electrical conductivity, which corresponds to lower resistivity, can indicate the extent of saline water intrusion (Abdelkader and Brahim, 2016).\u003c/p\u003e \u003cp\u003eFreshwater resources on offshore islands like Co To are vital for their economic and social well-being. However, these resources are frequently threatened by seawater intrusion, which degrades water quality and reduces the availability of fresh groundwater. Understanding the spatial and temporal variability of this intrusion under the influence of seasonal changes in recharge and tidal fluctuations is essential for effective water resource management. This study focuses on the Co To Island area in Gulf of Tonkin, Quang Ninh province, Vietnam, which experiences a significant seasonal climate variation. We utilize time-lapse ERT data acquired at different times to assess the variability of groundwater resources in response to these factors. The analysis of these data aims to provide a detailed understanding of seawater intrusion mechanisms and the dynamics of the freshwater-seawater interface in the island's aquifers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Geological and Hydrogeological Overview","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Geological Overview\u003c/h2\u003e \u003cp\u003eGeological surveys by Hoang Van Khon (1999) indicate that the bedrock of Coto Island comprises interlayered coarse and fine-grained clastic rocks of the Coto Formation, aged late Ordovician - early Silurian (O\u003csub\u003e3\u003c/sub\u003e-S\u003csub\u003e1\u003c/sub\u003ect). They outcrop in the high topography area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These poorly sorted rocks show alternating beds of coarse sandstones (sometimes with acidic volcanic fragments) and banded siltstones, with variable thickness. The Coto Formation has three sub-formations. The upper unit features medium to small-grained multi-mineral sandstone, siltstone, and claystone with banded textures, occasionally interbedded with tuff and coarse sandstones, and sometimes containing shale and conglomerate lenses. The middle sub-formation comprises two members: Member 1 is mainly coarse multi-mineral sandstone and thick gray gravel with some fine sandstone and silty clay (90\u0026ndash;135 m). Member 2 is characterized by siltstone-claystone and sericite-chlorite schists with sparse fine-grained sandstone (60\u0026ndash;110 m). The lower sub-formation is poorly sorted coarse multi-mineral sandstone alternating with banded siltstone and claystone, with rare tuff sandstone, tuff conglomerate lenses, and multi-mineral conglomerate; shale may occur. Major faults trend NE-SW, N-S, and NW\u0026ndash;SE, with steep dips common in NW\u0026ndash;SE and N-S faults, creating fracture zones sometimes with 10\u0026ndash;15 m hydrothermal quartz veins (Hoang Van Khon, 1999). Overlying these are unconsolidated Quaternary sediments (eluvi-proluvi, marine-aeolian, lagoonal), a few to tens of meters thick, and distributed about 50% area of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Marine sediments (mv\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{2}^{2-3}\\)\u003c/span\u003e\u003c/span\u003e) are medium-coarse sand with shells (\u0026gt;\u0026thinsp;5 m). Marine-aeolian (mv\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{2}^{2-3}\\)\u003c/span\u003e\u003c/span\u003e) is mainly sand with clay. Lagoonal (m\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{bQ}_{2}^{2-3}\\)\u003c/span\u003e\u003c/span\u003e) is mixed pebbles, sand, mud, and clay (2\u0026ndash;4 m). Alluvial (dp\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{2}^{2-3}\\)\u003c/span\u003e\u003c/span\u003e) is pebbles, sand, and clay (2\u0026ndash;3 m).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Hydrogeological overview\u003c/h2\u003e \u003cp\u003eAccording to Hoang Van Khon's 1999 study, Coto Island contains two primary aquifers: the Quaternary porous aquifer and the fractured O\u003csub\u003e3\u003c/sub\u003e-S\u003csub\u003e1\u003c/sub\u003ect aquifer.\u003c/p\u003e \u003cp\u003eThe Quaternary porous aquifer, an unconfined system covering approximately 9 km\u0026sup2; across the island's north, central, and south, is mainly recharged by direct rainfall. Near the foothills, it also receives recharge from the underlying O\u003csub\u003e3\u003c/sub\u003e-S\u003csub\u003e1\u003c/sub\u003ect fissure aquifer. Groundwater flow in this aquifer is strongly influenced by hydrometeorological conditions, leading to significant water table fluctuations between dry (near-empty wells) and rainy (full or overflowing wells) seasons, with levels ranging from 0.7 to 1.5 m below the surface depending on topography. Despite varying sedimentary origins, this aquifer exhibits water-bearing capacity and relatively good storage, with permeability ranging from 1 to 13 m/day (average 8.42 m/day). Water chemistry is predominantly sodium chloride or sodium chloride-bicarbonate, generally fresh and suitable for domestic use, although localized areas experience poor water quality.\u003c/p\u003e \u003cp\u003eThe fractured O\u003csub\u003e3\u003c/sub\u003e-S\u003csub\u003e1\u003c/sub\u003ect aquifer, found within the Upper Ordovician-Lower Silurian Coto Formation, underlies much of the island, either directly beneath the Quaternary aquifer or exposed at elevations from 5 m to over 100 m. Its water level varies with terrain (0.7\u0026ndash;6.3 m), and it shares a water level with the overlying Quaternary aquifer where they are in direct contact. Lithological logs from drilled wells (40\u0026ndash;70 m deep) indicate primarily multi-mineral sandstone with sericite shale interbeds, yielding 0.4 to 1.5 l/s. Fault systems significantly enhance this aquifer's productivity, as demonstrated by the high yield of well LK2 despite a small recharge area due to extensive fracturing. Total dissolved solids (TDS) in these wells range from 86 to 208 mg/l, and VES surveys at four wells indicate a resistivity of approximately 27.6\u0026ndash;32.6 Ωm (Hoang Van Khon, 1999).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology and Data Acquisition","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 ERT Data Acquisition\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eThe ERT acquisition\u003c/strong\u003e \u003cp\u003eTo investigate the seasonal variation of groundwater in the Coto Island area, we conducted time-lapse electrical resistivity tomography (ERT) surveys on five survey lines at the end of the dry season (April 2024) and the end of the rainy season (September 2024). The locations and measurement parameters of the survey lines are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Due to the relatively flat terrain of the survey lines, topographic variations along the lines were disregarded in this study. The average elevations of survey lines T2, T21, T22, and T23 are 6, 8, 10, and 11 meters, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All survey lines are oriented Northeast\u0026ndash;Southwest. The length of the survey lines ranges from 350\u0026ndash;355 meters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The electrical resistivity tomography surveys were performed using an IRIS Syscal Pro, 72-electrode system, manufactured by IRIS Instruments, France. The Wenner-Schlumberger electrode configuration was employed, with an electrode spacing of 5 meters. The root mean square error of ERT-acquired data is observed to be 0.7% by repeated measurement.\u003c/p\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\u003eParameters of Time-lapse ERT lines acquired at Coto Island.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName of survey line\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType of Array\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of electrodes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeparation factor \u0026ldquo;n\u0026rdquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of data points\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eElectrode spreading direction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLength of line (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAverage elevation of lines (m)\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\u003eLine 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNE-SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,2\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\u003eLine 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNE-SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.5\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\u003eLine 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNE-SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.1\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\u003eLine 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNE-SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5,4\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 \u003cstrong\u003eThe physicochemical acquisition\u003c/strong\u003e \u003cp\u003eThe physicochemical parameters of water samples from 15 existing boreholes and dug wells in the study area, located near or coincident with the survey lines, were also measured repeatedly during the dry season (April 2024) and the rainy season (September 2024). The dug wells (A1, A2, A3, etc.) are characterized by their limited depth, spanning from several meters up to 8 meters. They are predominantly composed of fine sand and silt-mixed sand. Water tables in these wells vary between 1.1 and 3 meters during the dry season, and between 0.4 and 1.5 meters during the rainy season. The borehole depths, namely A4, A12, A14, and A16, range from 19 to 45 m (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The geological materials retrieved from these boreholes consist mainly of sandstone and weathered shale. The physicochemical properties of the water samples were determined using a German-made WTW 3430 instrument. The measured parameters encompassed electrical conductivity (EC), total dissolved solids (TDS), temperature (T), and pH. All EC data in the dry and rainy seasons are corrected to 25\u003csup\u003e0\u003c/sup\u003eC. The seasonal variations in these parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \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\u003eChanges in the well water table and TDS between the dry and rainy seasons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWell name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWell eleva.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWell depth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eDry season\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003eRainy season\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater table (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEC (\u0026micro;S/cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTDS (mg/l)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWater table (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEC (\u0026micro;S/cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTDS (mg/l)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003epH\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\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e358.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e231.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e171.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e111.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.97\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\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e534.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e345.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e269.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e174.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.61\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\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e466.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e301.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e234.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e151.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.96\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\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e463.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e299.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e440.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e284.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.76\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\u003eA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e340.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e220.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e374.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e242.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.3\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\u003eA8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e192.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e206.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e133.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e267.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e173.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e294.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e190.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e109.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e238.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e154.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e337.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e218.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e353.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e228.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e334.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e216.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e306.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e198.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e140.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e203.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e131.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e479.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e310.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e258.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e167.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e194.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e125.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e238.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e154.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e587.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e379.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e108.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6647.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4299.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6405.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4143.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.48\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=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The Inversion and Analysis of Time-Lapse ERT Data\u003c/h2\u003e \u003cp\u003eThe acquired apparent resistivity data were processed and inverted to obtain 2D resistivity models of the subsurface. The inversion process typically uses specialized software, e.g., Res2Dinv (Loke \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and algorithms that aim to find a resistivity distribution that best fits the error function (Ln norm) between the measured and calculated apparent resistivity values (Loke and Barker \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Loke et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Menke \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The root mean square (RMS) error is usually used to assess the quality of the inversion. The parameters of the Res2Dinv software for inversion interpretation are referred to the Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\" width=\"100%\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial damping factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum damping factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDamping factor optimized at each iteration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher damping factor (HDF) for the first layer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApply HDF for blocks at side of model to reduce the edge effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjust DF for changes in distances between blocks in the model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorizon mesh size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 nodes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVertical mesh size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFiner mesh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of forward modeling method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinite element\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutomatically grid size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine search local optimize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError change convergence limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u0026nbsp;\u0026nbsp; %\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage RMS error for convergence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of iterations (Maximum)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCheck model resistivity for extreme values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe ratio of the first layer thickness to the unit electrode space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime-lapse inversion constrain (0\u0026thinsp;=\u0026thinsp;None,1\u0026amp;2\u0026thinsp;=\u0026thinsp;Smooth,3\u0026thinsp;=\u0026thinsp;Robust)\u003c/p\u003e \u003cp\u003eType of time-lapse inversion method (0\u0026thinsp;=\u0026thinsp;Simultaneous,1\u0026thinsp;=\u0026thinsp;Sequential)\u003c/p\u003e \u003cp\u003eThe rate at which the layer thickness increates with depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllow the number of model parameters to exceed the number of data point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor to increase the model depth range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduce the effect of the side blocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, slight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll blocks have equal widths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe width of the model cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 unit electrode spacing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of data inversion constraint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobust data constraint\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of model inversion constraint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobust model constraint\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Gauss\u0026ndash;Newton optimization method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoothness constraint only used on model resistivity values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage apparent resistivity used for reference model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of initial model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomogeneous half-space\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe logarithm of apparent resistivity used for inversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\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\u003eTo assess seasonal groundwater variability, the inverted resistivity models from the dry and rainy seasons for lines T2, T21, T22, and T23 were compared. Differences in resistivity distribution between the two seasons can indicate changes in water content and salinity within the aquifers. Lower resistivity values generally suggest higher water content or increased salinity (or both), while higher resistivity values may indicate lower saturation or fresher water. The reduction in water saturation (desaturation) might have been analyzed by comparing the resistivity models.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Hydrogeological investigation\u003c/h2\u003e \u003cp\u003eThe physicochemical parameters of water samples were measured seasonally (dry and rainy seasons) at 15 existing boreholes and dug wells in the study area. These included 11 dug wells (exploiting the porous aquifer) with well depths ranging from 1.5 to 6.5 m and wellhead elevations at sea level from 5 to 9.4 m. Four boreholes (A4, A12, A14, and A16), tapping the fractured aquifer, had borehole depths from 19 to 45 meters and wellhead elevations from 3.7 to 14.9 m.\u003c/p\u003e \u003cp\u003eThe data consistently show a lower water table during the dry season compared to the rainy season. Dug well measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) illustrate this, with dry season water table averaging 2.0 m (range: 1.1\u0026ndash;3.1 m) and elevations averaging 4.7 m above sea level (range: 2.9\u0026ndash;7.5 m). In contrast, rainy season measurements in dug wells averaged 0.8 m (range: 0.2\u0026ndash;1.9 m) and 5.9 m above sea level (range: 4.0-8.6 m), resulting in an average seasonal water table fluctuation of 1.2 m. Similarly, borehole data from A4 and A16 support this trend. Dry season water levels were recorded at 4 m (10.9 m elevation) in A4 and 9 m (-5.3 m elevation) in A16, while rainy season levels rose to 0.4 m (14.5 m elevation) and 1.85 m (1.85 m elevation), respectively. Therefore, the evidence strongly suggests a decline in groundwater levels during the dry season.\u003c/p\u003e \u003cp\u003eThe seasonal pH trend was mixed across the studied wells. Specifically, dug wells A1, A11, A13, and boreholes A12 and A14 showed an increase in pH during the dry season. Conversely, the remaining wells exhibited a decrease in pH during the drier period, suggesting a shift towards more acidic conditions in those locations. The overall pH in dug wells ranged from 4.9 to 10.2 (average 6.1) in the dry season and from 4.5 to 8.8 (average 5.8) in the rainy season. Similarly, borehole pH values ranged from 6.3 to 8.8 (average 7.3) in the dry season and from 6.5 to 8.5 (average 7.1) in the rainy season. Notably, dug well A5 experienced the largest pH decrease (-1.4) from the dry to the rainy season, while dug well A11 showed the highest increase (1.4). Generally, pH fluctuations were more pronounced in boreholes compared to dug wells, with the maximum decrease reaching \u0026minus;\u0026thinsp;2.4 and the maximum increase reaching 2.2.\u003c/p\u003e \u003cp\u003eThe TDS values in the dug wells varied from 104.6 mg/l at well A10 to a maximum of 4359.7 mg/l A16 in the dry season. During the rainy season, TDS values showed rather complex fluctuations compared to the dry season: some wells exhibit a decrease in TDS, and some other wells show an increase. The wells with the most significant decrease in TDS during the rainy season are wells A1, A2, A3, and A4. For instance, TDS drops from 216.5 mg/l to 198.5 mg/l at well A13 near survey lines T23 or from 310.1 mg/l to 167.4 mg/l at well A4 near line 22. Conversely, the wells that experience an increase in TDS during the rainy season include well A8, A9, and A10 near survey line T2. For instance, TDS rises from 109 mg/l to 154 mg/l at well A10.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates that there is no consistent trend in the changes of both TDS and pH between the rainy and dry seasons across all the surveyed wells. This variation could reflect differences in hydrogeological conditions and the chemical interaction processes occurring at each specific well location. Fractured aquifers have much larger seasonal water level fluctuations than porous aquifers (more or less than 5 times).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Geophysical investigation\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLine T2 (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe resistivity cross-section in the dry season (Model 1) displays resistivity values that range from below 7.2 Ωm to above 400 Ωm. The resistivity cross-section of Line 2 (Fig.\u0026nbsp;8) shows a vertical boundary dividing the cross-section into two distinct parts: The beginning part of the line from electrode 0 to 80 m has a low resistivity of 7.2\u0026ndash;20 Ωm. The second part, from electrode 80 m to the end, has a high resistivity of 54\u0026ndash;151 Ωm. The very thin layer near the surface generally shows lower resistivity values of 20 Ωm (except for some small spots). The lower part of the section shows a distinct separation into two portions: the first portion from electrode 0 m to 160 m has the lowest resistivity, within the range of 7.2 to 75 Ωm. Wherein, the 0\u0026ndash;80 m segment has the lowest resistivity values (7.2\u0026ndash;20 Ωm), and the segment from 75 to 160, the resistivity gradually increases from 20 to 75 Ωm. From electrode 160 m to the end of the line, the cross-section shows the highest resistivity values, within the range of 100 to 400 Ωm. In which the upper section from electrode 160 to 220 m (with a thickness of roughly 10 m) has even higher resistivity values (150\u0026ndash;400 Ωm).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe resistivity cross-section in the rainy season (Model 2) also displays the same resistivity scale. In contrast to the dry season (Model 1), the rainy season shows a more widespread distribution of lower resistivity values, suggesting a general decrease in resistivity across the surface compared to the dry season. There are still some patches of higher resistivity (yellow and orange), but they appear less extensive than in Model 1. The zones of very low resistivity (blue and purple) at depth appear to be more continuous and potentially more widespread in Model 2, particularly in the first segment from 0 m to 160 m. The localized higher resistivity zones at depth on the right side, while still present, show a slight decrease in resistivity in Model 2 compared to Model 1. Specifically, the resistivity in the last segment, from electrode 240 m to the end, drops from 150 Ωm in the dry season to 75 Ωm in the rainy season.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLine T21 (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe resistivity section in the dry season displays a range of resistivity values from 20 Ωm to above 1500 Ωm. In this resistivity cross-section, three distinct layers are observable: the top layer with a thickness of some meters to 15 m, which registers the highest resistivity, ranging from 75-1500 Ωm. The top layer exhibits a heterogeneous high-resistivity distribution. In which the central section of the line exhibits low resistivity values ranging from 75\u0026ndash;150 Ωm, while the remaining sections show high resistivity values between 150\u0026ndash;1500 Ωm. The middle layer, which is marked by low resistivity (75\u0026ndash;150 Ωm) The middle layer shows quite a constant resistivity, around 150 Ωm, and only a tiny region close to electrode 220 m records a 75 Ωm resistivity. The bottom layer exhibits a uniform resistivity value of approximately 400 Ωm.\u003c/p\u003e \u003cp\u003eThe rainy season resistivity Section (Model 2) uses the same resistivity scale as Model 1 (from 20 Ωm to 1500 Ωm). In contrast to the dry season, the rainy season cross-section shows a more widespread distribution of lower resistivity values near the surface. The presence of blue and green colors (indicating resistivity below 75 Ωm) is more prevalent across the surface in Model 2 compared to Model 1. This suggests a general decrease in resistivity across the surface due to increased moisture content. While patches of higher resistivity (yellow and orange) are still present, they appear to be less extensive than in Model 1, particularly in the last portion from electrode 180 m to the end, the high resistivity zones (orange, yellow) are segmented into smaller blocks by low resistivity zones (blue/green). The zones of lower resistivity (green and purple) at depth appear to be more continuous and potentially more widespread in Model 2, particularly in the segment from electrode 160 m to 240 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLine T22 (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe dry resistivity section (Model 1) displays the resistivity simple distribution ranging from 75 Ωm to above 400 Ωm. However, the resistivity cross-section shows generally a more widespread distribution of high resistivity values of 400 Ωm. There are some low-resistivity portions in both the right and left sides at depth. The lower part of the section on the left side, from electrode 0 to 115 m, exhibits a heterogeneous resistivity distribution of 75\u0026ndash;150 Ωm, and from electrode 160 to 310 m, the resistivity value is 150 Ωm.\u003c/p\u003e \u003cp\u003eThe rainy season resistivity cross-section (Model 2) nearly matches the dry season resistivity cross-section, displaying no structural variations and only a slight change in resistivity values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e- \u003cb\u003eLine T23 (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe dry resistivity section (Model 1) displays the resistivity distribution ranging from 10 Ωm to above 4600 Ωm. Notably, the resistivity cross-section is divided into two distinct parts at electrode 120 m. The first part of the line from electrodes 0 to 120 m exhibits a low resistivity of 10\u0026ndash;20 Ωm (except for the top thin layer, which is 2000\u0026ndash;4600 Ωm). Two low resistivity blocks (10 Ωm) appear at electrodes 55\u0026ndash;75 m and 85\u0026ndash;110 m, extending from a few meters down to a depth of 25 m. The latter part of the line shows a high resistivity value of 20\u0026ndash;400 Ωm. In this area, the resistivity cross-section is characterized by high resistivity zones. The top part of the section (from electrode 120 to 200 m) displays a low resistivity layer, roughly between 20\u0026ndash;75 Ωm, and around 7\u0026ndash;14 meters in thickness. After that is a 150\u0026ndash;400 Ωm high resistivity layer. Next is a high resistivity part with a value of 150\u0026ndash;400 Ωm in the section between electrode 200 and 350 m of the line.\u003c/p\u003e \u003cp\u003eIn contrast to the dry season, the rainy season cross-section shows a more widespread distribution of higher resistivity values near the surface of the beginning part of the cross-section (20\u0026ndash;75 Ωm). The low resistivity blocks (10\u0026ndash;15 Ωm) in dry season resistivity cross-section have reduced and some have disappeared from the rainy season resistivity cross section. The lower part of the rainy season resistivity cross-section, from electrode 120 to 250 m, shows a more widespread distribution of lower resistivity values (75\u0026ndash;150 Ohm) compared to the dry season resistivity cross-section. The last part of the rainy season resistivity cross-section (from electrode 240 m to 350 m) is almost unchanged compared to the dry season resistivity cross-section.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Discussion\u003c/h2\u003e \u003cp\u003e- (Line T2): The resistivity cross-section illustrates the distribution between freshwater and saltwater zones based on resistivity values (Nguyen et al. 2025). Accordingly, in the study area, regions with resistivity values below 20 Ωm are identified as saltwater zones, and conversely, regions with resistivity values above 20 Ωm are classified as freshwater zones (Nguyen et al. 2025). On the Model 1 cross-section Line T2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) recorded during the dry season, the low resistivity zone (\u0026lt;\u0026thinsp;10 Ωm) at the beginning of the survey line (0-120 m) is identified as a saline intrusion area (Nguyen et al. 2025). Paradoxically, the rainy season resistivity cross-section shows an expansion of this saline intrusion area compared to the dry season, despite the recharge of freshwater into the aquifers. The phenomenon of salinity re-infection during the rainy season in this area is demonstrated by the TDS analysis results in some dug wells, A8, A9, A10, around this paleo-lagoon system. The rainwater recharge is evident along the survey line T2 from the 120 m electrode mark to the end of the line, where low resistivity zones (20\u0026ndash;75 Ωm) are more extensive during the rainy season. The phenomenon of saline intrusion at the end of line T2 in the rainy season is more widespread than in the dry season (low resistivity area is more widespread) can be explained by the presence of a paleo-lagoon in the study area. The T2 survey line area was previously a mangrove swamp, which has been converted into a residential area in recent decades. The remaining evidence of this paleo-lagoon is a system of ditches extending from Xuan Truong Bay to the area of survey line T23. This paleo-lagoon is now disconnected from the sea due to the construction of a freshwater reservoir that separated it from Xuan Truong Bay, leading to the entrapment of saltwater within the aquifers. During the rainy season, the rainwater recharging the aquifers has pushed and concentrated the residual saltwater from the surrounding areas towards the end of this paleo-lagoon where the T2 line is located, causing the observed expansion of the saline interface. On the contrary, Line T23 has a mass of salt water at the beginning of the line in the dry season, and in the rainy season, the rainwater recharged into the aquifer dilutes and pushes this mass of salt water out to sea. This proves that the saltwater intrusion area in this line section is due to seawater intrusion from the sea. This mechanism is entirely different from the saltwater intrusion mechanism in Line T2 due to saltwater stagnating in the paleo-lagoon, causing salinity.\u003c/p\u003e \u003cp\u003eWe observe the TDS data in borehole A14 (exploiting the fissured water layer) in this paleo-lagoon area, we see that the TDS content in the rainy season is lower than in the dry season (reduce from 4299.5 mg/l to 4143.4 mg/l), which proves that the rainwater replenished into the fissured water layer has diluted the salt water. This water aquifer may not be affected by the phenomenon of re-salinization in the rainy season in this ancient lagoon area, and the phenomenon of re-salinization only occurs in the upper porous water layer.\u003c/p\u003e \u003cp\u003eIn contrast to Line T2, Line T23 is located far away from the paleo-lagoon. The widespread higher resistivity at the beginning of the rainy season, in contrast to the dry season, suggests that the aquifer has been recharged with freshwater from rainwater. This influx of freshwater likely diluted the saltwater that had become more concentrated during the dry season. Conversely, the spatial extent of low resistivity (visualized in green, representing values below 75\u0026ndash;150 Ωm) is greater in the middle section (from electrode 120 to 250 m) of the rainy season resistivity cross-section compared to the dry season resistivity cross-section. This implies a general lowering of resistivity attributed to increased water saturation within the fractured rock mass during the rainy season.\u003c/p\u003e \u003cp\u003e- (Line 21): The primary difference between the dry season (Model 1) and rainy season (Model 2) resistivity cross-sections lies in the overall distribution of resistivity values, particularly in the top and middle layers. The rainy season (Model 2) exhibits a general decrease in near-surface resistivity compared to the dry season (Model 1). This is likely due to the infiltration of rainwater, which increases the electrical conductivity of the subsurface materials. Lower resistivity zones are more widespread near the surface in Model 2, while higher resistivity zones are less extensive. At depth, the zones of low resistivity appear more continuous and potentially larger in the rainy season, suggesting increased saturation. The higher resistivity zones at depth on the right side might show a slight decrease in resistivity during the rainy season, indicating some degree of moisture influence at deeper levels as well. Despite these changes, the overall structural patterns of resistivity (e.g., the general trend of lower resistivity on the left side and higher resistivity on the right side) appear to be maintained between the two models. This suggests that the underlying geological formations are the primary control on the resistivity distribution, with seasonal moisture variations causing changes in the magnitude and spatial extent of these resistivity zones. The low resistivity zones extending from the surface, cutting through the high resistivity layer down to the middle layer, form expanded low resistivity areas during the rainy season. These areas are identified as the primary recharge zones for rainwater into the aquifer. Field observation revealed a fault at an outcrop near the electrode 210 m, reinforcing the interpretation that these low resistivity zones may correspond to faults. Considering borehole LK2 (See location in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which has an elevation of 15.8 m and a water table of 6.3 m (measured in the dry season, Hoang Van Khon,1999), and borehole A4, with an elevation of 14.5 m and a water table of 4 m (also in the dry season), it becomes clear that the high resistivity zone (400\u0026ndash;2000 Ωm) in the upper section of the profile is largely situated above the water table during the dry period. During the rainy season, when the water table increases (to 0.4 m at borehole A4; LK2 could not be measured due to access limitations), this high resistivity mass is submerged below the water table. However, its resistivity remains elevated (400\u0026ndash;2000 Ωm, refer to Model 2), with only a limited number of low resistivity zones emerging. This observation suggests that the high resistivity zone represents a solid rock formation, with fracturing limited to fault zones (represented by the low resistivity zones in Model 2).\u003c/p\u003e \u003cp\u003e- (Line T22): The low resistivity layer (30\u0026ndash;150 Ωm) on the cross-section T22 can likely be interpreted as a fractured aquifer. The lack of significant changes in structure and resistivity values between the cross-sections measured in the dry and rainy seasons suggests that this aquifer has probably reached a stable saturated level. Although there are no direct boreholes along this line, information from the nearby borehole A4 supports this hypothesis. At borehole A4, with the wellhead elevation of 14.9 m, the water table shows fluctuations from a depth of 4 meters in the dry season to 0.4 meters in the rainy season (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Comparing the average elevation of the survey line (10.1 meters) with the wellhead elevation of the borehole, we observe that survey line T22 is 4.8 meters lower than the wellhead of borehole A4. Thus, during the dry season, the elevation of the survey line is \u003cem\u003eapproximately\u003c/em\u003e equal to the water table. It can be concluded that the entire survey line T22 lies below the observed water table at borehole A4 in both seasons. Furthermore, due to the local community on the island prioritizing the use of a centralized water supply system from lakes, the groundwater resources in this area experience minimal extraction, thus being well-preserved.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe application of time-lapse ERT in Co To Island effectively demonstrated its utility in assessing the seasonal variability of groundwater resources, revealing a general increase in subsurface water volume during the rainy season across the surveyed areas. The study successfully highlighted the temporal dynamics of groundwater systems in response to seasonal hydrological variations. Notably, the research identified localized complexities, including the influence of a paleo-lagoon on salinity distribution (Line T2) and the resilience of the aquifer near Line T22 due to minimal extraction and consistent saturation. Conversely, Line T23 showed a typical pattern of seawater intrusion being pushed back by freshwater recharge in the rainy season. Overall, this study underscores the efficacy of time-lapse ERT as a sophisticated tool for continuously monitoring groundwater resources in sensitive coastal island environments facing challenges from both natural seasonal changes and potential anthropogenic impacts. The detailed resistivity information obtained provides a valuable baseline for future investigations and the development of informed and sustainable water resource management strategies for Co To Island.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNguyen Nhu Trung, Dang Xuan Phong, Trinh Hoai Thu wrote the main manuscript text. Nguyen Nhu Trung, Trinh Hoai Thu, Bui Van Nam interpreted data. Bui Van Nam and Nguyen Van Diep collected the data and prepared figures.All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThis research is funded by the Basic Science Development Program in the felds of Chemistry, Life Sciences, Earth Sciences and Marine Sciences for the period 2017\u0026ndash;2025 of The Vietnam Academy of Science and Technology (VAST) under Grant Number KHCBBI.01/24\u0026ndash;25 and Grand Number NCVCC10.02/25\u0026ndash;25. The authors kindly thank the funding organization.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelkader T, Ahmed and Brahim Askri (2016) Seawater Intrusion Impacts on the Water Quality of the Groundwater on the Northwest Coast of Oman. Water Environment Research 88(8):732-740, DOI:10.2175/106143016X14609975747045\u003c/li\u003e\n\u003cli\u003eArchie GE (1942) The electrical resistivity log as an aid in determining some reservoir characteristics. Pet. 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Comput Geosci 36(11):1414\u0026ndash;1426.\u003c/li\u003e\n\u003cli\u003eŁukasz Kaczmarek, Grzegorz Sinicyn, Krzysztof Kochanek,, Bartosz Bednarz, Mateusz Grygoruk, Maria Grodzka-Łukaszewska (2024) Electrical resistivity imaging data for hydrogeological and geological investigations of Szuszalewo peatland (North-East Poland). Data in Brief, 2352-3409, https://doi.org/10.1016/j.dib.2024.110626.\u003c/li\u003e\n\u003cli\u003eMenke W (2018) Geophysical data analysis: discrete inverse theory. Academic Press, Amsterdam.\u003c/li\u003e\n\u003cli\u003eNaidu LS, Rao G, VVS, Mahesh J, Padalu G, Sarma VS, Prasad PR, Rao SM (2013) An integrated approach to investigate saline water intrusion and to identify the salinity sources in the Central Godavari delta, Andhra Pradesh. India Arab J Geosci 6(10):3709\u0026ndash;3724\u003c/li\u003e\n\u003cli\u003eNguyen Nhu Trung, Dang Xuan Phong, Trinh Hoai Thu, Bui Vn Nam, Nguyen Van Diep (2025) Mapping freshwater and seawater intrusion using electrical resistivity tomography and physicochemical data: an application in Coto Island, Gulf of Tonkin. \u003cem\u003eMarine Geophysical Research\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eNguyen Nhu Trung, Trinh Hoai Thu (2013) Investigation of the saltwater intrusion in the Pleistocene aquifer in the coastal zone of Red River Delta. In: Proceedings of the 11th SEGJ international symposium, Yokomaha, 2013. https://doi.org/10.1190/segj112013-062.\u003c/li\u003e\n\u003cli\u003eNguyen Nhu Trung, Nguyen Ba Minh, Nguyen Van Nghia (2005) Using electrical resistivity and hydrogeological modeling for investigating saltwater intrusion in Haiphong coastal plain. In: Proceeding of the international workshop, Hanoi geoengineering, 25 No v. 2005, pp 171\u0026ndash;178 \u003c/li\u003e\n\u003cli\u003eNguyen Nhu Trung, Nguyen Van Nghia, Nguyen Ba Minh(2007) Forecasting the saltwater intrusion in Haiphong area by the electrical resistivity and hydrogeology modeling methods. J Earth Sci 29(3):277\u0026ndash;283 \u003c/li\u003e\n\u003cli\u003eNguyen Nhu Trung, Trinh Hoai Thu, Nguyen Van Nghia (2008) Application of electrical resistivity and hydrogeology modeling methods to map and forecast the saltwater intrusion in Thai Binh province. J Geol Ser B.\u003c/li\u003e\n\u003cli\u003eWerner AD, Bakker M, Post VEA, Vandenbohede A, Chunhui Lu, Ataie-Ashtiani B, Simmons CT, Barry DA (2013) Seawater intrusion processes, investigation and management: recent advances and future challenges. Adv Water Resources 51:3 26.\u003cbr\u003e https://doi.org/10.1016/j.advwatres.2012.03.004\u003c/li\u003e\n\u003cli\u003eYeh TC, Liu JS, Glass RJ, Baker K, Brainard JR, Alumbaugh DL, LaBrecque D (2002) A geostatistically based inverse model for electrical resistivity surveys and its applications to vadose zone hydrology, Water Resour. Res., 38(12), 1278, doi:10.1029/2001WR001204.\u003c/li\u003e\n\u003cli\u003eZhang YF, Wu, Zhang JX, Guo KK, Xing XJ, Li CN, Wu HF (2021) Analysis of seasonal differences in tidally influenced groundwater discharge processes in sandy\u003cbr\u003e tidal flats: a case study of Shilaoren Beach, Qingdao, China. J. Hydrol. 603 (C),\u003cbr\u003e 127128.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"marine-geophysical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mari","sideBox":"Learn more about [Marine Geophysical Research](http://link.springer.com/journal/11001)","snPcode":"11001","submissionUrl":"https://submission.nature.com/new-submission/11001/3","title":"Marine Geophysical Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Time-lapse ERT, saltwater intrusion, Gulf of Tonkin, groundwater resource, Co To Island","lastPublishedDoi":"10.21203/rs.3.rs-6849979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6849979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research utilized time-lapse Electrical Resistivity Tomography (ERT) to assess the seasonal variability of groundwater resources on Co To Island, Gulf of Tonkin, Vietnam. Repeated ERT surveys were conducted during the dry (April 2024) and rainy (September 2024) seasons to monitor subsurface resistivity changes related to groundwater salinity and saturation. Physicochemical parameters were also measured in 15 wells to understand seasonal hydrogeological variations. The study demonstrated the effectiveness of time-lapse ERT in assessing seasonal groundwater variability on Co To Island, revealing a general increase in subsurface water volume during the rainy season. Notably, the research highlighted localized complexities, including a paradoxical expansion of a saline zone in a paleo-lagoon area during the rainy season (Line T2), contrasting with the typical dilution of seawater intrusion observed elsewhere (Line T23). The research also indicated aquifer resilience in an area with minimal extraction (Line T22). This work provides a valuable baseline for future monitoring and sustainable water resource management in this sensitive coastal environment.\u003c/p\u003e","manuscriptTitle":"Assessment of Seasonal Groundwater Variability Using Time-Lapse Electrical Resistivity Tomography in the Co To Island Area, Gulf of Tonkin, Vietnam","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 08:51:13","doi":"10.21203/rs.3.rs-6849979/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-10T07:25:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-08T17:20:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-08T08:51:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120753298987271929670770999272099012110","date":"2025-06-14T14:54:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319279895280150663797365595398697990715","date":"2025-06-11T09:13:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T07:44:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-09T13:10:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-09T13:10:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Marine Geophysical Research","date":"2025-06-09T02:30:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"marine-geophysical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mari","sideBox":"Learn more about [Marine Geophysical Research](http://link.springer.com/journal/11001)","snPcode":"11001","submissionUrl":"https://submission.nature.com/new-submission/11001/3","title":"Marine Geophysical Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7e7266e8-de9f-4e94-8e06-a94cac34c6b7","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:10:18+00:00","versionOfRecord":{"articleIdentity":"rs-6849979","link":"https://doi.org/10.1007/s11001-025-09599-y","journal":{"identity":"marine-geophysical-research","isVorOnly":false,"title":"Marine Geophysical Research"},"publishedOn":"2025-11-19 15:57:41","publishedOnDateReadable":"November 19th, 2025"},"versionCreatedAt":"2025-06-12 08:51:13","video":"","vorDoi":"10.1007/s11001-025-09599-y","vorDoiUrl":"https://doi.org/10.1007/s11001-025-09599-y","workflowStages":[]},"version":"v1","identity":"rs-6849979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6849979","identity":"rs-6849979","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}