Comparative AComparative Analysis of Underground Water Table Levels from the Surface Terrain in Part of Ile-Ife, Osun State, Nigerianalysis of Underground Water Table Levels from the Surface Terrain in Part of Ile-Ife, Osun State, Nigeria

Abstract

Understanding the spatial relationship between surface terrain and groundwater depth is critical for informed borehole siting, especially in crystalline basement environments like Ile-Ife, Nigeria, where aquifers are highly discontinuous. This study integrates Shuttle Radar Topographic Mission (SRTM) elevation data, borehole depth records, and resistivity-based geophysical sounding (ADMT-200S) to examine how topographic configuration influences underground water table levels in Ife Central and Ife East Local Government Areas. Kriging interpolation was applied to model groundwater depth across 20 borehole sites, while profile curves and Pearson correlation analysis were used to assess terrain-groundwater interactions. The findings reveal that groundwater tends to occur at shallower depths in low-lying regions and becomes progressively deeper in elevated terrains. A moderate inverse correlation (R = –0.21) was observed between elevation and groundwater depth, supporting the hypothesis that topography exerts a measurable—though not exclusive—influence on aquifer depth. Integration of resistivity data further confirmed the presence of fractured zones and weathered basement units, which contribute significantly to subsurface water storage. This study fills a critical methodological gap by offering a reproducible, terrain-informed framework for groundwater modeling in basement complex terrains. The results hold practical value for sustainable groundwater development, particularly in regions facing water scarcity and limited hydrogeological infrastructure.

Keywords

Introduction

Fig. 1 Study area maps Ife Central and Ife East Local Government Areas (LGAs)

Figure 3: ADMT-200S survey map showing apparent resistivity variation and inferred subsurface anomalies (Source: Field EM survey processed with AIDU-APP).

Figure 4: Kriged interpolation map of groundwater table depths across the study area. Deeper water tables are concentrated in elevated zones, while shallow tables dominate low-lying terrain.

Figure 5: SRTM-based elevation map showing surface terrain variation. Elevation decreases from east to central zones, suggesting potential for groundwater accumulation in lower-lying regions.

Figure 6: Profile curves comparing surface elevation and interpolated water table levels across selected transects. Groundwater levels tend to follow terrain trends, though localized exceptions exist.

Figure 7: Scatterplot showing Pearson correlation between surface elevation and groundwater depth. The inverse trend supports the hypothesis that topography influences aquifer depth.

This combined visual-statistical analysis provided strong evidence for the hypothesis that surface terrain configuration significantly impacts underground water table levels, although the relationship is not perfectly linear due to subsurface heterogeneity.

3. DISCUSSION

This study provides detailed and localized insight into the relationship between surface terrain configuration and underground water table depth within the Precambrian Basement Complex of Ile-Ife, southwestern Nigeria. By integrating borehole depth data, SRTM-derived digital elevation models, and audio-frequency domain electromagnetic resistivity data from the ADMT-200S system, the research moves beyond conventional surface mapping to offer robust empirical evidence of topographic control on groundwater distribution. The Kriging-interpolated groundwater table model demonstrated marked spatial heterogeneity in depth-to-water levels. Shallower water tables were observed in central low-lying regions of Ife Central, while deeper aquifers occurred in topographically elevated zones, particularly in the eastern and northeastern parts of the study area. This distribution aligns with established principles of topographically driven recharge and discharge zones, wherein depressions facilitate infiltration and accumulation of subsurface water. This spatial pattern supports earlier findings by [7], who reported similar recharge behavior in the crystalline terrains of southwestern Nigeria. The inverse relationship between surface elevation and groundwater depth is further confirmed by the overlay of interpolated water table surfaces and SRTM DEM data (Figure 5). Longitudinal profile curves extracted along key transects (Figure 6) show that surface highs correspond to deeper water tables, while depressions align with shallower depths. This inverse trend is statistically validated through Pearson correlation analysis (Figure 7), which yielded an R² value of –0.52. Although moderate, this correlation reinforces the hypothesis that topographic configuration significantly influences aquifer geometry. Comparable patterns have been reported by [15] in GIS-based assessments of terrain-groundwater dynamics, although their study focused on sedimentary environments, making this finding in a crystalline setting especially valuable. The use of ADMT-200S electromagnetic data enriched the study’s interpretative depth by identifying subsurface resistivity anomalies indicative of water-saturated zones. These anomalies were spatially consistent with areas of low elevation and shallow groundwater, thereby providing geophysical validation for terrain-based predictions. Previous studies by [8,14] have affirmed the reliability of EM methods in hard-rock aquifer mapping, but few have combined them with DEM analysis. This study advances those efforts by demonstrating a systematic and spatial correlation between resistivity signatures and topographic depressions—effectively triangulating geological, geophysical, and remote sensing datasets for improved accuracy. However, localized deviations from the general inverse trend were also observed. In some topographic depressions, deeper groundwater was recorded. These anomalies are likely due to non-topographic factors such as overburden heterogeneity, the absence of fractures or weathered zones, or over-extraction from existing boreholes. Similar deviations were highlighted by [13], who warned against overgeneralization in terrain-groundwater models and emphasized the need to integrate lithological and anthropogenic data for more nuanced interpretations. This study directly addresses a critical methodological gap in regional hydrogeological research: the lack of integrated, location-specific modeling that combines DEMs, EM surveys, and borehole data in Nigeria’s crystalline terrains. While many prior studies have focused on sedimentary basins [18, 19], few have operationalized such a multifaceted approach within hard-rock environments. The current study contributes a replicable, resource-efficient methodology that is especially valuable in data-scarce settings. In summary, the findings affirm the central hypothesis that surface terrain significantly influences groundwater table variability in Ile-Ife. The integration of DEM, EM, and borehole datasets not only strengthened predictive modeling but also revealed the spatial limits of topographic control. These results offer practical value for borehole siting, groundwater exploration, and sustainable water resource planning in similar basement complex regions across sub-Saharan Africa.

4. LIMITATIONS OF THE STUDY

Despite its integrative and methodologically rigorous approach, this study acknowledges several limitations that could influence the generalizability and applicability of its findings. First, the number of borehole control points (n = 20) was relatively limited in relation to the spatial extent of the study area. Although Kriging interpolation was applied to estimate groundwater depths in unsampled areas, the precision of such models could be improved with denser spatial sampling, particularly in geologically heterogeneous zones. Second, while the SRTM DEM provides reliable elevation data at 30-meter resolution, finer-resolution terrain models (e.g., LiDAR) could better capture micro-topographic variations, especially in complex urban or agricultural landscapes where small-scale depressions and ridges may affect local recharge dynamics. Third, the electromagnetic data from the ADMT-200S system, though valuable, primarily identifies resistivity contrasts without directly quantifying hydrogeological parameters such as transmissivity or porosity. This restricts its interpretation to qualitative inferences unless supplemented with aquifer testing or lithological logs. Fourth, the study assumes that elevation is the dominant control on water table depth, whereas factors such as lithological variation, subsurface fractures, vegetation cover, land use changes, and anthropogenic withdrawals may exert significant local influence. These were not independently quantified in the present analysis. Finally, temporal dynamics were not explored. All datasets reflect static or single-time representations of the hydrogeological system. Incorporating time-series water level data or seasonal DEM updates would enable dynamic modeling of recharge-discharge cycles and better capture human or climate-induced fluctuations. Future research could address these limitations by expanding sampling density, incorporating multi-temporal datasets, and integrating additional environmental variables through machine learning or multivariate geostatistical frameworks. The Kriging-generated groundwater table model revealed spatial heterogeneity in depth-to-water levels, with shallower groundwater concentrated in central low-lying regions of Ife Central and deeper levels associated with ridges and uplands, particularly in eastern and northeastern zones. These findings are consistent with the principles of topographically driven recharge and groundwater accumulation, where depressions act as collection basins facilitating infiltration, as observed in [7] in a similar terrain in southwestern Nigeria. When overlaid with the SRTM DEM (Figure 5), the interpolated groundwater surface shows a reverse trend to the terrain gradient, reinforcing the notion of an inverse elevation-to-depth relationship. This is further confirmed through longitudinal profile curves (Figure 6), which demonstrate that topographic highs often correspond to deeper groundwater tables, and vice versa. This topography-groundwater trend is statistically supported by the Pearson correlation (R² = -0.52), reflecting a moderate but meaningful inverse relationship between surface elevation and water table depth. This numerical correlation is comparable to that reported by [15] in their GIS-based terrain-groundwater analysis across alluvial zones, highlighting the consistency of terrain control even in crystalline settings. The inclusion of audio-frequency domain electromagnetic data from the ADMT-200S system added an interpretative depth by identifying resistivity anomalies linked to subsurface saturation. These geophysical results matched spatially with areas of shallow water table, adding geophysical validation to terrain-based predictions. Prior studies such as [14, 8] underscore the reliability of EM surveys in hard-rock aquifer delineation, though few studies have triangulated such data with DEM models. This integrated approach differentiates our study from theirs by explicitly linking resistivity anomalies with topographic depressions and shallow aquifer zones. Nevertheless, the study also exposed localized exceptions to the general inverse trend, particularly where deeper groundwater occurred in topographic lows. Such deviations are attributed to non-topographic factors like the variability in overburden composition, absence of weathered zones, or human-induced abstraction pressures. This complexity reflects the findings of [13], who warned against simplistic assumptions of linearity in terrain-groundwater modeling and called for the incorporation of geologic and land use attributes to improve accuracy. A significant gap addressed by this study is the lack of empirical, location-specific evidence for topography-groundwater interaction in crystalline terrains such as Ile-Ife. While many prior studies focused on sedimentary basins or large-scale hydrological units e.g., [18,19], few have offered a fine-scale, spatially grounded comparison using DEMs, borehole data, and EM surveys together. By filling this gap, the present research provides a replicable, resource-efficient methodology for groundwater planning in other basement terrains with limited drilling history. In summary, this study confirms the central hypothesis that terrain configuration significantly influences groundwater table distribution in Ile-Ife. The integration of DEM, EM, and borehole data provided strong empirical backing for the topographic control model while also identifying the limits of that model. These insights offer a practical framework for siting boreholes, refining hydrological zoning, and guiding land-use decisions in hydrogeologically similar regions of Nigeria and beyond.

5. CONCLUSION

This study has comprehensively investigated the influence of surface terrain configuration on underground water table depth within the crystalline basement complex environment of Ile-Ife, Osun State. Through the integration of multiple geospatial and geophysical datasets—including borehole observations, digital elevation models derived from the Shuttle Radar Topographic Mission (SRTM), and resistivity data collected using the ADMT-200S audio-frequency electromagnetic technique—the research demonstrated a consistent spatial trend: groundwater tables are shallower in topographic depressions and deeper in elevated terrains. These findings were supported by Kriging interpolation, profile curve analysis, and Pearson correlation, which confirmed a moderate but meaningful inverse relationship between surface elevation and groundwater depth. The addition of electromagnetic data allowed for deeper insights into subsurface structure and validated the zones predicted as water-bearing by topographic and borehole analyses. This methodological triangulation reinforces the robustness of the conclusions and addresses a critical gap in regional groundwater modeling by introducing a high-resolution, localized assessment method applicable in hard rock environments. More importantly, this research contributes to the academic and practical discourse on groundwater management by offering a reproducible workflow for identifying high-potential groundwater zones in areas lacking detailed hydrological infrastructure. It advances previous work by not only comparing elevation and groundwater depth but also validating those findings with geophysical field data. This integrated approach surpasses traditional models that rely solely on topography or sparse borehole records, offering a more nuanced understanding of hydrogeological behavior in terrain-driven recharge zones. Furthermore, the study emphasizes the role of geomorphological setting in guiding groundwater recharge and informs the placement of boreholes and planning of water infrastructure. The insights gained here can assist government agencies, hydrologists, and urban planners in optimizing resource allocation, mitigating drilling risks, and promoting sustainable groundwater development. Looking ahead, the framework established in this study can be expanded by incorporating temporal datasets to model seasonal fluctuations, integrating lithological logs for improved stratigraphic characterization, and applying machine learning algorithms to further refine spatial predictions. In conclusion, this research provides both theoretical enrichment and practical utility, paving the way for more informed and resilient groundwater management strategies across the urbanizing basement complex regions of Nigeria and beyond.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used GEMINI 2.0 Flash Service in order to improve the readability of the manuscript.  After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Author Contribution:

Caleb Olutayo Oluwadare: Conceptualization, Writing- Review & Editing, Supervision. 

Segun Isaac Olonade: Writing- Original draft preparation, Visualization.

John Adeyemi Eyinade: Methodology, Data curation, Formatting.

Joshua Ayodeji Oluwadare: Project Administration, Data Interpretation

Conflicts of Interest: The authors declare no conflicts of interest.

Funding: The research received no external funding.

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