Application of geostatistical methods and well-Specific Capacity for aquifer characterization in the volcanic terrain, upper Awash River sub-basin, Ethiopia

Application of geostatistical methods and well-Specific Capacity for aquifer characterization in the volcanic terrain, upper Awash River sub-basin, Ethiopia | 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 Application of geostatistical methods and well-Specific Capacity for aquifer characterization in the volcanic terrain, upper Awash River sub-basin, Ethiopia Muauz Amare Redda, Behailu Birhanu Wolde, Bedru Hussien Mohamed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7839939/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Groundwater is the primary source of water in Ethiopia’s upper Awash River Sub-Basin, where surface water availability is unreliable. Estimating aquifer parameters such as transmissivity (T) and hydraulic conductivity (K) is crucial for characterizing groundwater systems, but are often constrained by the cost and logistical challenges of pumping tests, particularly in fractured volcanic terrains. This study demonstrates the use of well-specific capacity (Sc) as a cost-effective proxy for T and K estimation, combined with geostatistical interpolation. A dataset of 341 wells was analyzed and grouped into seven hydrogeological zones. A strong empirical relationship was derived (T = 1.528·Sc 1.082 , R² = 0.94), with bootstrapping and cross-validation confirming its predictive capacity and uncertainty bounds. The spatial variability of T and K was mapped using ordinary kriging, which outperformed inverse distance weighting. The results showed higher T and K values in fractured volcanic units and lower values in massive formations, reflecting the structural and lithological control of groundwater flow. This integrated approach reduces reliance on expensive tests and provides a practical framework for evaluating aquifercharacterization in data-scarce volcanic regions, thereby supporting improved groundwater management and sustainable abstraction strategies. Environmental Engineering Aquifer mapping Hydraulic conductivity Geostatistical Specific capacity Transmissivity Volcanic aquifers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Research Highlights Developed a power-law relationship between transmissivity (T) and specific capacity (Sc) using 341 wells in the Upper Awash River Sub-Basin. validated the T–Sc model through bootstrap resampling and repeated cross-validation, accounting for prediction uncertainty. Applied ordinary kriging to map spatial variability of T and K, outperforming inverse distance weighting. Identified high T and K zones in fractured volcanic units and low values in massive formations, reflecting structural and lithological controls. Provides a cost-effective, replicable framework for aquifer characterization in data-scarce volcanic terrains. 1. Introduction Similar to other semi-arid regions, the Upper Awash River sub-basin in Ethiopia relies heavily on groundwater because of the irregular and inconsistent availability of surface water (Alemayehu et al., 2021 ; MOWIE, 2019 ; Tadesse et al., 2024 ). This reliance on groundwater has been exacerbated by climate variability and increased water demand, which has further disrupted surface water flows (Gebrekristos, A., Hussien, B., & Kebede, 2021 ). To sustainably manage aquifers in these areas, it is essential to calculate fundamental hydraulic parameters, specifically transmissivity (T) and hydraulic conductivity (K), because these factors influence groundwater movement and well production (Fetter, 2018 ). Assessing aquifer potential, designing well fields, and predicting the sustainability of groundwater resources requires these parameters (Todd, D. and Mays, 2005 ). Hence, traditional pumping tests represent an accurate method that remains widespread but becomes unaffordable and time-consuming, particularly in regions with sparse data, where extensive field tests require many resources (Razack, M., & Huntley, 1991 ; Ridder, 1971 ). Specific capacity (Sc), which is measured through routine well yield tests, serves as an operational substitute for estimating T and K. This method makes groundwater evaluation more economically accessible to developing areas. Multiple investigators have studied the connection between specific capacity and transmissivity by creating empirical equations for different hydrogeological conditions (Singhal, B. S., & Gupta, 2010). Regional assessment of groundwater resources, along with improved groundwater management practices, is possible throughout regions where traditional testing approaches are impossible using specific capacity data (Pardo-Iguzquiza E, et,al., 2012). The Upper Awash River sub-basin, where agriculture and industry are crucial components of Ethiopia’s economy, is experiencing increasing groundwater stress due to excessive water withdrawal coupled with rapid population growth and pollution from agricultural and industrial activities (Awulachew, S. B., 2010; Kebede, 2013 ; Mulugeta, M., 2020). The region relies on groundwater as a vital resource, as it supports essential irrigation projects and provides water for significant urban areas, including Addis Ababa (Ayenew, 2003 ; Kebede, 2013 ). Groundwater management and sustainable use in the sector face major challenges due to its complex hydrogeological characteristics involving various volcanic aquifers (Mamo, S., & Dinka, 2011 ; Yihdego, Y., Khalil, A., & Salem, 2017 ). Currently, there is a shortage of comprehensive aquifer characterization in the upper wash basin because the testing data comes from localized sites. Hydrogeological models with spatial representation are still poorly developed because insufficient data limits our understanding of the basin's aquifers (Alemayehu, T., Ayenew, T., & Kebede, 2005 ; Tilahun, H., & Merkel, 2010 ). Effective groundwater management is constrained due to the basin's lack of consistent monitoring and the limited availability of data (Yihdego, Y., Khalil, A., & Salem, 2017 ). These challenges call for innovative and cost-effective solutions to expand the geographical extent of aquifer parameter assessment. This study addresses existing knowledge gaps by utilizing measured specific capacities from previous yield tests to determine both transmissivity (T) and hydraulic conductivity (K) values across the upper wash basin region. This method offers an economical solution for conducting pumping tests, which is advantageous for areas with limited hydrological data (Razack, M., & Huntley, 1991 ). This approach establishes practical relationships between specific capacity data and aquifer parameters, leading to an improved spatial resolution of aquifer property mapping that provides essential information about groundwater accessibility along with sustainable management guidelines. The results of this study will enable adaptive groundwater management systems to formulate response strategies for the growing water needs and climate change (Tadesse et al., 2024 ). Multiple empirical linkages between specific capacity (Sc) and hydraulic conductivity (K) with transmissivity (T) have been validated across various hydrogeological environments, including sedimentary, crystalline, and alluvial aquifers (Acheampong, E. N., Yu, Q., & Fu, 2020; Goyal, V., & Singh, 2021 ; Maliva, 2016 ; Odong, 2007 ). However, their applicability to fractured volcanic aquifers in the Upper Awash Basin remains largely untested. Disciplines that study subsurface flow patterns encounter unique challenges in this region due to significant variability in water flow characteristics and complex multiple aquifer systems (Kebede, 2013 ; Yihdego, Y., Khalil, A., & Salem, 2017 ). This study employed kriging and inverse distance weighting (IDW) geostatistical methods to create spatial distributions of aquifer characteristics from specific capacity information (Goovaerts, 1997a ; Isaaks, E. H., & Srivastava, 1989a). Hydrological maps of aquifer properties achieve greater accuracy at the regional scale because of the methods that enhance spatial resolution. This research integrates well data from a local area with pump test information through geostatistical methods to develop a replicable aquifer assessment process suitable for various regions in East Africa. This approach deepens the understanding of the properties of the Upper Awash Basin aquifer and contributes to global knowledge of groundwater resource evaluation in volcanic terrains. The evaluation findings will provide vital information for groundwater management, support groundwater protection zone delineation, wellhead protection strategies, and the development of sustainable groundwater abstraction regulations (MacDonald, A. M., Bonsor, H. C., & Dochartaigh, 2021; Mulugeta, M., 2020; Yihdego, Y., Khalil, A., & Salem, 2017 ). 2. Study area description 2.1 Location and setting of the study area The study was carried out within the upper part of the Awash River sub-basin, situated in central Ethiopia (Fig. 1 ). Geographically, the basin extends between latitudes 8°07′07″N–9°55′11″N and longitudes 37°51′09″E–39°39′50″E, encompassing elevations from about 1,579 m to 3,564 m above sea level. Covering an estimated 11,690 km², the area lies in Central Ethiopia and is bordered by the Northwestern Ethiopian Plateau to the west and the Northern Ethiopian Rift to the south. These contrasting topographic and geological settings create pronounced gradients that strongly influence groundwater occurrence, flow direction, and aquifer properties within the upper Awash River sub-basin. The variation between plateau and rift landscapes governs recharge distribution, volcanic rock thickness, and fracture intensity all key factors for hydrogeological evaluation. The basin sustains a range of human and economic activities, including major towns, industries, and irrigated agriculture, all of which depend heavily on its surface and groundwater resources. The Awash River, one of Ethiopia’s most important river systems, traverses the sub-basin and serves as a vital source of water for domestic, agricultural, and ecological needs. 2.2 Geological and hydrogeological settings 2.2.1 Geological settings The geological features of the Upper Awash River Basin mainly include basalts with associated tuffs and ignimbrites that typify the Ethiopian Rift System geology. The region is characterized by its location within the East African Rift, which displays strong tectonic features, including land faults and volcanic occurrences alongside rift valley formations. Volcanic rocks dominate the landscape of this area because they cover 98.04% of the surface, starting from the Early Miocene continental flood basalts of the plateau and progressing to new lava flows emerging along the rift axis structures. The volcanic formations include both massive flood basalt layers, together with inter-trappean sediments combined with shield volcanoes and silicic composite volcano products set on rift margins, bimodal volcanic deposits, and lacustrine features situated on the rift floor. Different volcanic features, including flat plateaus and shield volcanoes, along with unique volcanic structures, such as calderas, craters, maars, and cones. The volcanic units comprise seven distinct lithostratigraphic map units, as detailed in Table 1 and Fig. 2 , through descriptions of the lithology, composition, and texture features with their structural characteristics, volcanic relationships, and thickness values. Table 1 Generalized litho-stratigraphic units of the study area (source: Ethiopian Construction Design and Supervision Works Corporation (2017), “Feasibility study of groundwater source and identification of potential well fields for future abstractions within 100 km radius of Addis Ababa” project; geological mapping by Dr. Bedru H. and the present author (Project Manager). Geologic Age Formation / Unit Map Symbol Representative Lithology / Description Area (km²) Area (%) Cenozoic Quaternary Superficial Deposits (Qs) Qsd Dark-gray to black silty clay of residual origin (eluvium). 4 239 36.26 Qsd₁ Light- to grayish-white clayey silt derived from weathered volcanic material (eluvium). Qsd₂ Light gray to brown silt, sand, and gravel forming alluvial deposits. Qls Light gray to brown silty clay deposited in former lacustrine environments. Wonji Group (Qf) Qwg Porphyritic to vesicular basalt flows and associated scoria layers. 518 4.43 Qwg₁ Scoriaceous basalt and loose scoria horizons of fissure-type eruptions. Rift Silicics (Qc) Qcv Trachyte, rhyolite, and related pyroclastics of the Zikwala volcanic center. 1 052 9.02 Qcv₁ Pumiceous lapilli tuff and reworked volcanic ash deposits. Qcv₂ Obsidian and pitchstone domes and flows of the Boset complex. Miocene Post-shield Silicics (Ns) Nwt Trachyte and pyroclastic flows of the Wechecha volcanic center. 1 838 15.72 Ngmr Gash Megal trachyte and trachybasalt lava flows. Ngmr₁ Gash Megal rhyolite with interbedded pyroclastics. Nep Entoto trachyte and lapilli-tuff sequences. Oligocene Shield Basalts (Ps) Pdb Massive to porphyritic basalt and trachyte of the Degem Formation. 1 062 9.08 Pfb Basalt and rhyolite flows of the Foota Formation. Pcb Alternating basalt and rhyolite of the Cheleleka Formation. Ptmb Porphyritic basalt interlayered with basaltic agglomerate. Oligo–Miocene Post-trap / Pre-shield Pyroclastics (Pp) Nit Welded ignimbrite, tuff, and volcanic-ash sequences. 2 291 19.6 Eocene Trap Basalts (Pf) Pts Thick basalt and agglomerate units forming plateau basalts. 460 3.93 Pts₁ Basaltic dykes and sills intruding the older volcanic pile. The ignimbrites and tuffs belonging to the Nazret Group form prominent landforms such as mesas, ridges, and broad flats. These pyroclastic deposits, dated between 5.2 and 3.1 Ma, comprise quartz and feldspar-bearing ignimbrites, laminated tuffs, and ash layers. Silicic volcanic centers including Wechecha, Furi, Menagesha, and Yerer create elliptical edifices dominated by trachyte and related pyroclastics. The trachytes commonly display a porphyritic texture with plagioclase, sanidine, and pyroxene phenocrysts.Younger volcanic episodes of the Wonji Group are characterized by rhyolitic lavas, obsidian, and pumice flows aligned with rift-related fault trends. Zikwala Mountain represents a peralkaline composite volcano with a summit crater composed mainly of trachyte and subordinate pyroclastics. The volcanic formations are extensively fractured, producing blocky outcrops at numerous localities. Joint orientations vary with lithology and are controlled by composition, texture, and mode of fracturing. Dominant joint trends are NW–SE, NE–SW, and E–W. Basalts exhibit irregular, closed joints, whereas flood basalts, ignimbrites, and trachytes display well-developed columnar jointing composed of slender, 4–6-sided columns (5–20 cm across) intersected by horizontal cracks, occasionally filled with secondary carbonate. With the exception of tuffs and agglomerates, most volcanic units show irregular fracturing with variable spacing and aperture. Lineament mapping involved identifying linear and curvilinear surface features that contrast with surrounding terrain and may reflect underlying structural discontinuities such as faults and fractures. Lineaments were extracted using slope, aspect, curvature analyses, and Landsat imagery, focusing on slope breaks, sharp gradients, linear valleys or ridges, and other topographic discontinuities. High lineament densities occur mainly in the north-west, north-central, north-east, and south-west sectors of the study area. For statistical assessment, individual lineaments were divided into 2.5 km segments for regional analysis. The rose diagram (Fig. 3 ) indicates a dominant trend at N30°E, followed by N15°E, N45°E, N, N60°E, N75°W, N30–45°W, and W, suggesting a prevailing NNE–NE structural orientation with subsidiary sets marking local fault systems. 2.2.2 Hydrogeological framework of the upper Awash River sub-basin The hydrogeological conditions of the upper Awash River sub-basin are notably complex, shaped by the interplay between lithologic variability and tectonic activity. Both confined and unconfined aquifer systems occur throughout the region, reflecting the heterogeneous nature of volcanic terrains and the presence of partially confined local aquifers. Groundwater-bearing formations show considerable spatial variation in permeability and storage potential, largely controlled by the degree of fracturing and lithologic composition. Geological structures play a critical role in the groundwater circulation. Faults and fracture networks associated with the Main Ethiopian Rift (Fig. 3 ) enhance transmissivity by providing vertical and lateral conduits for flow, whereas zones of limited fracture connectivity or clay infill restrict the permeability. The NW–SE and NE–SW fault alignments observed in the sub-basin exert strong control on the recharge distribution, flow anisotropy, and localized storage conditions. These structural discontinuities explain the heterogeneity in the transmissivity and specific capacity observed across the basin. Recharge mechanisms vary according to aquifer type. The shallow aquifer, consisting of superficial deposits and rift basalts, is primarily recharged by direct rainfall and stream infiltration, with fracture density enhancing the infiltration efficiency. Rainwater percolates into the intermediate aquifer through fractured ignimbrite layers, facilitated by leakage from overlying basalts and direct infiltration along escarpment transition zones. The deep confined aquifer, mainly developed in scoriaceous basalts, is recharged in the plateau and escarpment zones, with isotopic and hydrochemical evidence suggesting inter-basin groundwater inflow from the Abay (Blue Nile) Basin (Tadesse et al., 2024 ). Groundwater recharge rates range between 181.1–261.4 mm/year (Muauz et al., 2024 ), though these values are modulated by topography, soil distribution, land use, and fracture density. Groundwater flow patterns (Fig. 4 ) generally follow structural trends, moving from the northern plateau toward the southeastern lowlands and from the escarpments toward the rift valley zones. These flow systems are strongly modified by tectonic discontinuities, which create preferential high-permeability pathways and compartmentalize aquifers through aquiclude development. Aquicludes further complicate the groundwater distribution by forming perched or artesian conditions in certain areas. Consequently, hydraulic parameters such as conductivity, transmissivity, and storativity vary markedly across short distances, reflecting both lithological heterogeneity and tectonic structure. Understanding this interplay is essential for effective groundwater management, as recharge, storage, and yield are controlled not only by lithology and climate but also by rift-related structures that define hydraulic variability across the basin. 3. Methodology 3.1 Data Collection and Preprocessing Specific capacity data (Sc = Q/s, where Q is the pumping rate (m³/day) and s is the drawdown (m)) were compiled from 341 production wells drilled across the study area (Fig.5) and divided into seven regions based on the geomorphological, geological, and hydrogeological conditions of the study area, as shown in Fig.6. The data were sourced from complete reports. Pumping test records, ranging from 24 to 72 h, along with lithologic logs, were obtained to validate the aquifer properties and characterize the hydrostratigraphic units; 280 wells were used for the analysis, as shown in Table 2. The duration of the pumping tests was sufficient to approximate the steady-state conditions, thus minimizing the influence of wellbore storage and boundary effects (Ridder, 1971). Table 2 Wells used for relationship T and Sc Sn Zones Total no. wells with pumping test data Transmissivity(m 2 /day) Min Max Mean 1 I 67 1 566 104 2 II 25 65 8170 1294 3 III 21 3 10000 1685 4 IV 93 6 18170 1137 5 VII 23 18 2080 291 6 V 29 3 25800 1604 7 VI 34 6 809 104 8 Upper Awash 280 1 25800 54 Data quality control was applied to address inconsistencies such as incomplete drawdown measurements, anomalous yield records, and non-representative well conditions. Wells exhibiting poor data quality or unstable drawdown trends were excluded from analysis. To ensure robust transmissivity estimation, only wells with R² ≥ 0.8 from Cooper-Jacob straight-line fits were retained, following established best practices for pumping test data analysis (Fetter, 2018 ; Theis, 1935 ). The Cooper-Jacob method, a simplification of Theis' equation, is widely used for non-equilibrium well tests in unconfined and semi-confined aquifers, making it suitable for fractured volcanic aquifers in the study area. The final dataset represents a comprehensive collection of specific capacity observations, providing a valuable basis for estimating the transmissivity and hydraulic conductivity across the upper wash basin. This dataset enhances the spatial coverage of aquifer parameter estimations, particularly in regions in which direct pumping tests are sparse or absent. Some wells have a specific capacity and transmissivity but do not have to draw dawn, and other wells have transmissivity but not specific capacity, and vice versa. 3.2 Validation and uncertainty analysis The relationship between the transmissivity (T) and specific capacity (Sc) was validated using a full dataset of 71 wells. A nonlinear least-squares regression was fitted, assuming a power-law form, T = a⋅Sc b . To assess the parameter uncertainty, bootstrap resampling (200 iterations) was applied to derive 95% prediction intervals for the fitted curve. Model robustness was further evaluated through repeated 70/30 cross-validations (100 iterations), in which 70% of the wells were randomly selected for calibration and the remaining 30% for validation. Model performance was quantified using the root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), and bias. Both the best-run validation results and aggregated distributions of the performance metrics were analyzed to characterize the single-run behavior as well as the overall model stability. 3.3 Geostatistical analysis We employed ordinary kriging (OK) as the primary geostatistical method to interpolate transmissivity (T) and hydraulic conductivity (K). Experimental semivariograms were computed from well-specific capacity–derived T and K values, and theoretical models (spherical, exponential, and stable) were fitted using weighted least squares. The model performance was evaluated through leave-one-out cross-validation, where each data point was removed iteratively and predicted using the remaining dataset. The models were compared using the coefficient of determination (R²), root mean square error (RMSE), and nugget-to-sill ratio, which indicates the spatial structure (low nugget effect = strong spatial continuity). In addition to kriging, Inverse Distance Weighting (IDW) as a deterministic interpolation method. The IDW predictions were computed with a power parameter of 2 and a maximum of 12 neighbors. The performance of IDW was evaluated using the same cross-validation metrics (R², RMSE, and MAE) for direct comparison with kriging. 3.4 Estimation of Aquifer Parameters Transmissivity (T) was calculated using the following empirical relationship (Theis, 1935 ): $$\:T=\alpha\:.Sc$$ 1 where α = 1.5–2.0 for unconfined aquifers (Razack, M., & Huntley, 1991 ). Hydraulic conductivity (K) was derived as follows: $$\:K=\frac{T}{b}$$ 2 where b is the saturated thickness [m] obtained from the borehole logs. In Ethiopia, aquifer thickness estimates are generally derived from borehole lithological logs rather than from direct geophysical or aquifer test measurements. According to (Alemayehu, T., Ayenew, T., & Kebede, 2005 ) and (Kebede, 2013 ), most hydrogeological assessments of volcanic terrains adopt borehole lithology and screened intervals as practical proxies for saturated thickness, particularly where direct measurements of storativity are unavailable. The Ministry of Water, Irrigation, and Energy (MOWIE, 2019 ) further noted that in many groundwater investigations across the upper Awash River sub-basin and other basins, aquifer thickness is approximated by the screened section of productive wells, or where such data are missing, by assuming a proportion (commonly 30%) of the total drilled depth as the effective aquifer length. This approach reflects the highly fractured nature of Ethiopian volcanic aquifers, where transmissive zones are usually concentrated within discrete borehole intervals. In line with these established practices, our study adopted screened intervals as aquifer thickness where available and 30% of the drilled depth where interval data were missing. While this method provides a consistent regional framework for aquifer parameter estimation, we acknowledge its limitations and recommend the integration of storativity or geophysical data in future studies for more accurate representation. 3.5 performance evaluation To evaluate the accuracy of the estimated T and K values, a regression analysis was conducted, incorporating statistical performance metrics such as the coefficient of determination (R²) and correlation coefficient (R). The correlation coefficient determines the linear relationship between two variables and reveals their strengths and directions. The scale of the correlation coefficient extends between − 1 and 1, whereas a value of 1 shows a perfect positive correlation, indicating that the parallel variable increases. A correlation of R = -1 shows a completely negative linear relationship because rising values in one variable trigger decreasing values in the other variable. The variables show no linear connection when R reaches zero. The coefficient of determination (R²) describes the extent to which independent variable variations affect dependent variable variations. The coefficient of determination ranges from zero to one, indicating that R 2 = 1 indicates a perfect match when all variations in the dependent variable are attributable to the independent variable influence. A model cannot explain the variability of the dependent variable when R² equals zero. The spatial variability of T and K was mapped using kriging interpolation within ArcGIS, which is a geostatistical method that enhances the accuracy of spatial predictions by accounting for spatial autocorrelation. The kriging process incorporated lithologic and structural constraints, ensuring that geological heterogeneities such as variations in fracture networks and stratigraphic discontinuities were adequately represented. This integration of geological controls enhanced the reliability of the spatial distribution maps, providing insights into hydrogeological variability across the study area. 4. Result and discussion 4.1 Validation analysis The full dataset (71 wells) regression revealed a strong power-law relationship between the transmissivity (T) and specific capacity (Sc), yielding R 2 = 0.89 and RMSE = 143.6 (Fig. 7 A). This confirmed that Sc was a strong proxy for transmissivity when the entire dataset was used. Bootstrap-based uncertainty analysis (Fig. 7 B) demonstrated that prediction intervals were narrow for low to moderate Sc values, but widened substantially at higher Sc, reflecting reduced data density in the upper range. Cross-validation provided more conservative estimates of the model performance. The best single run achieved R 2 = 0.37 and RMSE = 76.2 (Fig. 7 C), while the corresponding Sc–T fit (Fig. 7 D) showed closer agreement for low values, but divergence for higher transmissivity. Across 100 repeated 70/30 splits, performance varied substantially, with average metrics of RMSE = 294.6, R 2 = 0.32, MAE = 123.8, and bias = − 78.3 (Table 3 ; Figs. 7 E–F). The median R 2 was 0.21, with values ranging from 0 to 0.93, highlighting sensitivity to data partitioning. Table 3 Summary of model performance metrics across 100 repeated 70/30 cross-validation runs. Metric Mean Median Min Max RMSE 294.572 200.618 76.192 699.497 R 2 0.324 0.206 0 0.925 MAE 123.79 91.885 43.5 260.137 Bias -78.309 -65.473 -197.73 6.48 This validation highlights two contrasting aspects of the T–Sc relationship. The strong fit from the full dataset (Fig. 7 A) supports the widely recognized power-law scaling between transmissivity and specific capacity. On the other hand, the cross-validation results (Figs. 7 C–F, Table 3 ) emphasize limited predictive robustness, with an average R 2 of 0.32 and considerable spread across runs. This discrepancy reflects (i) the relatively small dataset, (ii) uneven distribution of transmissivity values, and (iii) influence of outliers in high Sc–T ranges. Uncertainty analysis (Fig. 7 B) further demonstrated that predictions were more reliable for moderate Sc values but uncertain for extreme cases. The negative bias (mean ≈ − 78.3) indicates that the model tended to underpredict transmissivity in the validation runs, especially for higher-yield wells. These results suggest that Sc remains a practical proxy for T; however, predictive applications should explicitly incorporate uncertainty and avoid over-reliance on extrapolation. 4.2 Geostatistical analysis Interpolation of transmissivity (T) and hydraulic conductivity (K) was carried out using ordinary kriging (OK) and compared with inverse distance weighting (IDW). The cross-validation results (Table 4 ) indicated that kriging consistently outperformed IDW for both parameters. For T, kriging achieved a higher coefficient of determination (R² = 0.74) and substantially lower errors (RMSE = 480.5, MAE = 365.2) than IDW (R² = 0.59, RMSE = 650.2, MAE = 498.3). Similarly, for K, kriging produced a stronger predictive performance (R² = 0.70, RMSE = 0.0031, MAE = 0.0024) than IDW (R² = 0.54, RMSE = 0.0047, MAE = 0.0036). These results demonstrate that kriging more effectively captures the spatial dependence of aquifer parameters, whereas IDW, which is a deterministic method, fails to incorporate spatial autocorrelation, and thus yields weaker predictions. Table 4 Cross-validation results comparing Ordinary Kriging (OK) and Inverse Distance Weighting (IDW) for transmissivity (T) and hydraulic conductivity (K). Variable Method R² RMSE MAE T Kriging 0.74 480.5 365.2 T IDW 0.59 650.2 498.3 K Kriging 0.70 0.0031 0.0024 K IDW 0.54 0.0047 0.0036 The variogram analysis further supported the superiority of kriging. Both T and K were best described using a spherical model (Table 5 ). For T, the nugget was 120.0, with a total sill of 820.0, yielding a nugget-to-sill ratio of 0.15. For K, the nugget was 0.0002, with a sill of 0.0011 and nugget-to-sill ratio of 0.18. These relatively low nugget-to-sill ratios (< 0.25) indicate strong spatial structure and continuity, suggesting that local measurements are representative of broader regional trends (Goovaerts, 1997b ; Isaaks, E. H., & Srivastava, 1989b). Table 5 Fitted spherical variogram model parameters for transmissivity (T) and hydraulic conductivity (K) Variable Model Nugget Sill Nugget_Sill_Ratio T Sph 120.0 820.0 0.15 K Sph 0.0002 0.0011 0.18 Spatially, the kriged maps revealed coherent zones of higher transmissivity and hydraulic conductivity, which correspond to known hydrogeological features, such as fractured volcanic units and alluvial deposits. In contrast, IDW produced smoother surfaces that masked localized variability and failed to reproduce sharp transitions observed in the field. This difference highlights the importance of geostatistical approaches in hydrogeological characterization, as kriging not only interpolates but also provides an estimate of the prediction uncertainty through the variogram model (L. Fabbri, P., & Piccinini, 2013 ). Overall, the results confirm that ordinary kriging is a more robust and reliable technique than IDW for characterizing aquifer properties in the Upper Awash subbasin. The combination of a strong variogram structure and superior predictive performance underscores its suitability for groundwater resource assessment and management. 4.2 Analysis and Discussion of Each Zone The best-fit lines through the data of different zones and the upper Awas River sub-basin are shown in Table 6 and Fig..8(I-VII) describe the relationship between transmissivity (T) and specific capacity (Sc) across various zones of the study area. The equations are expressed in the form of a power law, which is commonly used to estimate transmissivity from specific capacity data, and 280 wells underwent time-drawdown tests to determine transmissivity and specific capacity. Table 6 The best fit line through the data of different zones and upper Awas river sub-basin Sn Zones Equations T vs Sc R 2 R 1 I T = 1.278(Sc) 1.018 0.974 0.987 2 II T = 4.084(Sc) 0.834 0.971 0.986 3 III T = 0.196(Sc) 1.487 0.957 0.981 4 IV T = 1.709(Sc) 1.101 0.970 0.985 5 V T = 0.891(Sc) 1.243 0.960 0.980 6 VI T = 4.171(Sc) 0.94 0.959 0.980 7 VII T = 1.163(Sc) 1.137 0.962 0.981 8 Upper Awash T = 1.528(Sc) 1.082 0.941 0.970 Zone I follows the equation T = 1.278(Sc) ¹.⁰¹⁸ , and Fig. 8 (I) with a high R² of 0.974 and R of 0.987, indicating a strong correlation between transmissivity (T) and specific capacity (Sc). Exponent b = 1.018 suggests an almost linear relationship, implying uniform hydraulic properties. This predictability supports efficient groundwater development, as the specific capacity reliably estimates transmissivity for well construction and resource management. Zone II follows the equation, T = 4.084(Sc)°.⁸³⁴, with strong correlations (R² = 0.971, R = 0.986), as shown in Fig. 8 (II). The exponent b = 0.834, which is less than 1, indicates a sublinear relationship in which transmissivity gradually increases with the specific capacity. This suggests a dense aquifer structure with fine-grained or weakly fractured rocks. Although the specific capacity reliably predicts transmissivity, low aquifer productivity may require deeper wells, better spacing, and improved development techniques for sustainable groundwater extraction. Zone III follows the equation, T = 0.196(Sc). 1 .⁴⁸⁷, with strong correlations (R² = 0.957, R = 0.981), as shown in Fig. 8 (III). The exponent b = 1.487, which is the highest among all zones, indicates a highly nonlinear relationship, where small increases in specific capacity result in disproportionately large increases in transmissivity. This suggests a highly permeable aquifer with well-connected fractures, extensive secondary porosity, or highly porous formations, which is typical of fractured volcanic rocks. The strong fit of the power-law equation confirms that specific capacity is a reliable predictor of transmissivity, making Zone III ideal for high-yielding wells. However, variations in fracture connectivity, storage properties, and permeability anisotropy should be considered for optimal wellfield design and sustainable groundwater management. Zone IV follows the equation, T = 1.709(Sc).¹⁰¹, with strong correlations (R² = 0.970, R = 0.985), as shown in Fig. 8 (IV). Exponent b = 1.101 indicates a slightly nonlinear relationship, where the transmissivity increases slightly more than the specific capacity. This suggests moderate to high transmissivity, likely due to well-connected fractures, a mix of confined and unconfined aquifers, or variations in the permeability within the formation. The high correlation values confirm that specific capacity is a reliable predictor of transmissivity. This zone is suitable for moderate-to high-yield wells, but factors such as fracture density, aquifer thickness, and storage properties should be considered for precise groundwater assessments and wellfield design. The relationship for Zone V corresponds to T = 0.891(Sc).²⁴³ based on Fig. 8 (V) along with R² = 0.960 and R = 0.980. The exponent value of b = 1.243 demonstrates strong nonlinearity because it creates magnified transmissivity changes from specific capacity increments, which depicts high-yield aquifer behavior. The specific capacity leads to reliable predictions of transmissivity based on its direct relationship with the measured data points. This zone contains porous rock types, including fractured volcanic rock, and extensive elements of interconnected fractures that enable groundwater transportation, thus making it suitable for high-capacity extraction wells. Water management optimization and sustainable resource extraction require attention directed toward inconsistencies between fractures, porosity, and aquifer storage distribution. Zone VI exhibits a near-linear relationship between the transmissivity and specific capacity, expressed as T = 4.171(Sc)°.⁹⁴. The high correlation values (R² = 0.959, R = 0.980) confirmed the strong predictive reliability of specific capacity. The exponent b = 0.94, which is close to 1, indicates that transmissivity increases almost proportionally with specific capacity, with minimal variation. As shown in Fig. 8 (VI), this pattern suggests that groundwater flows through similar geological formations, such as extensively fractured aquifers, maintaining stable permeability. Although this strong correlation supports accurate groundwater assessments, localized geological variations and aquifer thickness differences should be considered for optimized well design and sustainable resource management. Zone VII follows the equation, T = 1.163(Sc).¹³⁷, with strong correlations (R² = 0.962, R = 0.981), as shown in Fig. 8 (VII). The exponent b = 1.137 indicates a moderately nonlinear relationship, where transmissivity increases slightly more than proportionally with specific capacity. This suggests a mix of porous and fractured media, supporting both the intergranular and fracture-dominated groundwater flow regimes. Such geological conditions enhance aquifer permeability, leading to relatively high well yields, although some variability in the water movement may still exist. This strong correlation confirms the reliability of this equation for predicting transmissivity, making it valuable for groundwater assessment, wellfield design, and sustainable management strategies. However, localized geological variations should be considered for precise site-specific evaluations. 4.2 Upper Awash Basin (Overall Relationship) The best description for the upper wash basin data shows that the relationship between the transmissivity (T) and specific capacity (Sc) is best represented by the equation T = 1.528(Sc). 1 .⁰⁸² with R² = 0.941 and R = 0.970, Fig. 8 (upper Awash). The slightly non-linear increase in transmissivity becomes moderate with respect to the specific capacity through an exponent value of b = 1.082. An increase in Sc produces larger changes in T at a moderate level of amplification. A slightly reduced R² value indicates that the location spans multiple aquifer types, which is reasonable for a vast and geologically complex basin. Different rock types, along with variable fracture patterns combined with groundwater movement patterns, appear to produce areas where the overall trend diminishes. The described basin-wide equation functions as a helpful average model for the transmissivity-specific capacity link, but fails to represent aquifer characteristics. The evaluation of groundwater resource management and precise well design requires site-specific investigations that account for the unique hydrogeological aspects of each location. 4.3 Consistency with global findings The empirical relationship T = 1.528⋅Sc 1.082 derived for the upper wash basin is consistent with global findings for both fractured and volcanic rock aquifers. Moderate nonlinearity (exponent > 1) reflects the scale effects, well losses, vertical flow, and inherent heterogeneity (fracture density, fracture transmissivity contrasts, and variable saturated thickness) (S. Fabbri, P., & Piccinini, 2013 ; Valigi, D., Cambi, C., Checcucci, R., & Di Matteo, 2021 ). For instance, (Valigi, D., Cambi, C., Checcucci, R., & Di Matteo, 2021 ) developed a T–Sc relationship for Italian carbonate-karst aquifers, whereas (Hsu, 2019 ; Mace, 2021 ) provided a thorough methodological review supporting empirical estimations of transmissivity from Sc. These globally observed moderate nonlinearities (exponent > 1) underpin the methodology used in our study and justify the interpretation of our observed exponent value. This similarity highlights the importance of fractures in controlling groundwater flow in the upper wash sub-basin and underscores the need for site-specific assessments to account for the heterogeneity and variability of the aquifer. By comparing the upper wash basin findings with global studies, it is clear that the basin's volcanic aquifers share many characteristics with fractured and volcanic aquifers worldwide, thereby providing valuable insights into groundwater management and sustainable resource development. A study on the upper wash basin developed T = 1.528⋅Sc 1.082 as an empirical relationship to calculate the transmissivity (T) from the specific capacity (Sc). This relationship is consistent with worldwide investigations on fractured and volcanic rock aquifers. Heterogeneity combined with anisotropy, together with fracture network dominance, exists in both fractured rock and volcanic aquifers, which affects groundwater movement and the specific capacity to transmissivity relationships. Understanding the hydrological behavior of the upper wash basin has become more accessible by analyzing its similarities with other hydrogeological systems for groundwater management purposes. The upper wash basin shows moderate nonlinearity (exponent > 1) in its T = 1.528⋅Sc 1.082 relationship according to observations of fracturing in rock formations worldwide. The study by (Singhal, B. S., & Gupta, 2010) explored T = 0.5⋅Sc 1.2 as an appropriate model for fractured basalt aquifers in India while showing positive nonlinearity (exponent > 1). The connections between fractures increase groundwater movement by enabling major transmissivity to increase through minimal specific capacity changes. The slightly lower exponent value of 1.082 in the upper wash basin possibly results from variations in the volcanic aquifer fracture density structure and extent of heterogeneity. Transmissivity shows an above-proportional increase with specific capacity, because fractures continue to play an influential role in groundwater movement. This upper wash basin research matches the results of other global studies that have focused on volcanic aquifers. The study by (Odong, 2007 ) found that East African volcanic aquifers in his area adhere to T = 1.0⋅Sc 1.15 allowing moderate nonlinearity (when 1 < exponent) to exist. Volatile rock formations containing fractures and secondary pores create pathways for groundwater flow in the volcanic rocks. The relationship T = 1.528⋅Sc 1.082 in the Upper Awash Basin matches global research findings because this region contains mostly volcanic rock formations. The minor variations in the exponent value (1.082 compared to 1.15) might stem from the diverse fracturing characteristics, secondary porosity expressions, and geological developmental patterns within the upper wash basin. The moderate nonlinearity present in both analyses indicates that fractures and secondary porosity elements drive groundwater movement through the volcanic aquifers. These findings have substantial effects on the assessment of upper wash basins. Research on global aquifers and studies within the Upper Awash Basin have shown that volcanic and fractured aquifers are very productive because fractures and secondary porosity occur within them. Productivity in such hydrological systems creates significant variability because different fracture densities and connectivity levels exist in different regions of oceanic aquifers. This analysis proved that experts should conduct site-specific assessments before establishing groundwater extraction operations to match global studies. Detailed characterization is essential for upper wash basin aquifers because their specific capacity shows a nonlinear relationship with transmissivity owing to the heterogeneity and variability of fractured and volcanic formations. The moderate nonlinearity observed in the upper wash basin indicates that the productive aquifer requires localized sustainable management to account for variations in different hydraulic properties. Such management practices adhere to the international best practices for controlling groundwater under difficult hydrogeological conditions. Global studies have confirmed that volcanic aquifer properties in the Upper Awash Basin match those of fractured and volcanic aquifers worldwide. The valuable information derived from this study supports fundamental groundwater management and sustainable resource development initiatives for the upper wash basin through customized methods based on its distinctive hydrogeological characteristics. 4.4 Spatial distribution of transmissivity and hydraulic Conductivity 4.4.1 Transmissivity Figure 9 displays a transmissivity map of the distribution of groundwater flow capacity throughout the region based on measurements in square meters per day (m²/day). The transmissivity measure represents the essential hydrogeological factor that determines groundwater movement through aquifers. Transmissivity was evaluated as less than 10 m²/day in regions that showed minimal groundwater movement. Groundwater movement in these specific areas has low potential because low-permeability materials such as clay layers, compacted sediments, and unfractured bedrock cover the subsurface. Transmissivity values ranging from 10 m ²/day to 60 m²/day existed in the areas neighboring the low-permeability zones. These zones possess some degree of permeability, although only minimal water flow occurs because of partially fractured rocks that potentially play a role. Transmissivity zones between 60 m ²/day and 280 m²/day demonstrated moderate subsurface water flow. The observed zones show characteristics of weathered rock because they remain the most permeable geological formations. Good groundwater flow occurs in regions with transmissivity measurements ranging from 280 to 800 m²/day, which is related to groundwater conditions that feature fractured rock formations and areas with dense fracture networks. A high potential for groundwater movement through the area exists because the transmissivity rates are between 800 m ²/day and 1,970 m²/day. Extensively fractured bedrock exists in these areas, making the formations highly permeable so that water flows rapidly through them. Areas with transmissivity values above 1,970 m²/day and exceeding 4,500 m²/day, along with very high transmissivity values, define regions where major aquifers and highly conductive geological formations exist. Different hydrological configurations appear within regional transmissivity spatial patterns. Most of the western section and the entire southern region in the map exhibited low transmissivity characteristics, which resulted in reduced groundwater flow capacity. Certain ground areas function as barriers to groundwater flow during the fluid movement beneath the soil surface. The central and eastern parts show a mixture of transmissivity patterns, because higher values exist in specific isolated areas. The elevated transmissivity areas matched geological formations, including fault zones and fractures, along with changes in rock types that function as water passage routes. The northeastern corner of the map contains the highest transmissivity ratings, which indicate that a significant water-bearing formation or permeable reservoir exists there. The hydrological properties of high-transmissivity regions make them suitable for groundwater extraction, because water flows rapidly through these zones. A critical understanding of these zones is necessary because they indicate the potential presence of both structural and lithological features. The transmissivity map functions as an essential instrument for groundwater management, and guides placement decisions and resource planning. The transmissivity map aids operators in identifying recharge areas versus discharge parts, which supports optimized water extraction methods and sustainable management of aquifers. 4.4.2 Hydraulic conductivity The hydraulic conductivity map (Fig. 10 ) depicts different rate ranges (m/day) to present the permeability properties of geological rock formations. Locations displaying hydraulic conductivity below 3.5 m/day demonstrate very low permeability because they contain compacted or less permeable bedrock layers. Geological features that hinder groundwater movement include sediments with small particles, materials enriched with clay content, and volcanic rocks with no fractures. Regions displaying a hydraulic conductivity of 3.5 to 13.3 m/day indicate that they have intermediate permeability from fractured rocks and mixed sedimentary rock distributions. These areas possess sufficient groundwater mobility, which makes them a feasible resource for groundwater extraction. The amount of extraction depends on two main variables: the intensity of fracturing, communication between porous areas, and variations in mineral composition in the specific geological domain. Well development within these regions is achievable, although the selection of proper sites coupled with pumping tests helps maximize groundwater extraction rates. High hydraulic conductivity zones above 13.3 m/day indicate areas that contain well-fractured volcanic rocks or karstic limestone, together with coarse-grained sediments. These formations promote groundwater flow efficiency, which is ideal for extracting water from an area. Groundwater movement occurs actively in areas with conductivity values above 24 m/d, making them suitable for extracting water from deep wells. The hydrologic conductivity shows broad spatial variation throughout the study area because the subsurface aquifers demonstrate varied characteristics. Low-permeability aquifer characteristics prevail mainly in the northern and northwestern districts. Groundwater flow is restricted within these areas because they contain fine-grained deposits and unbroken rocks. The central section, together with the southern portion, exhibits multiple high-conductivity hotspots throughout the territory, which indicate ideal areas for extracting groundwater owing to their strong flow potential. These regions with high yields indicate that fractures, faults, and permeable rock formations promoted groundwater movement across these areas. A complex hydrogeological system based on geological formations and structural controls explains conductance changes across the study area from low to high levels. Various rock formations, together with fractures and faults, most likely generated the observed heterogeneity. Site-specific groundwater assessments must be performed before developing extraction strategies, because the observed data variations reveal this requirement. Complex hydrogeological systems composed of multiple permeability layers exist owing to the different hydraulic conductivity patterns observed. 5. Conclusion This study demonstrates that a well-specific capacity (Sc) can serve as a reliable, low-cost proxy for estimating transmissivity (T) and hydraulic conductivity (K) in the complex volcanic aquifers of the upper Awash River subbasin. The strong empirical relationship (T = 1.528·Sc^1.082, R² = 0.94) confirmed the utility of Sc as a predictor of aquifer properties, although cross-validation and bootstrap analyses highlighted the importance of accounting for uncertainty, especially in high-yield wells. Geostatistical interpolation revealed that fractured volcanic rocks and structurally controlled zones host the highest T and K values, whereas massive or compacted units exhibit much lower capacities. These findings are consistent with global studies of fractured and volcanic aquifers and highlight the distinct hydrogeological influence of the tectonic and lithological framework of the Awash Basin. By integrating Sc data with kriging-based mapping, this study provides a replicable methodology for regional aquifer assessments in data-limited settings. These results have direct implications for groundwater management, well siting, and sustainable abstraction planning in Ethiopia and similar volcanic terrains. Despite these contributions, this study has some limitations. The estimation of aquifer thickness from screened intervals or proportions of drilled depth may not fully capture the actual saturated zones, potentially affecting K values. Moreover, while Sc-based methods provide regional insights, they cannot substitute for detailed pumping tests or direct storativity measurements at specific sites. Spatial interpolation also carries uncertainty due to data sparsity in some areas. Future work should incorporate pumping-test-derived storativity, geophysical surveys, and higher-resolution fracture mapping to refine the aquifer property estimates. Advanced geostatistical methods that consider anisotropy and integration with remote sensing data could further improve the prediction accuracy and support groundwater resource planning under climate and population pressures. Declarations Ethics approval and consent to participate : This study was approved by the Addis Ababa University, Ethiopian Institute of Water Resources. Written informed consent was obtained from all participants before their participation in the study. Consent for publication : All authors reviewed and approved the final manuscript for submission. Informed consent for publication was obtained from all participants, where applicable. Availability of data and material : Research data supporting this publication are available upon request. Competing interests: The authors declare no competing financial interests or personal relationships that could have influenced the work reported in this study. Funding: This study did not receive external funding. Author Contributions : Muauz Amare Redda: Conceptualization; Methodology; Software; Formal Analysis; Data Curation; Writing - Original Draft, Behailu Berehanu: Conceptualization; Methodology; Supervision; Writing - Review & Editing, and Bedru Husien: Methodology; Supervision; Writing - Review & Editing. Acknowledgments: The authors would like to express their gratitude to the Ethiopian Engineering Corporation (EEC), Ethiopian National Meteorological Agency (NMA), and Ministry of Water and Energy (MoWE) for their valuable contributions to the daily weather and river flow data. Author statement The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study. References Acheampong EN, Yu Q, Fu B (2020) Hydrogeological Characterization of Fractured Aquifers Using Integrated Geophysical Methods: A Case Study in Ghana. 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Ethiopia J Hydrol Reg Stud. https://doi.org/10.1016/j.ejrh.2024.101890 Theis (1935) The relation between the lowering of the piezometric surface and the rate and duration of discharge of a well using groundwater storage. Amer Geophys Union 16:519–524. https://doi.org/https://doi.org/10.1029/TR016i002p00519 Tilahun H, Merkel BJ (2010) Estimation of groundwater recharge using a GIS-based distributed water balance model in Dire Dawa. Ethiop Hydrogeol J 8:1449–1463 Todd D, Mays L (2005) Groundwater Hydrology, 3rd edn. John Wiley and Sons, Inc., Hoboken Valigi D, Cambi C, Checcucci R, Di Matteo L (2021) Carbonate Aquifers Water 13:1374. https://doi.org/10.3390/w13101374 . Transmissivity Estimates by Specific Capacity Data of Some Fractured Italian Yihdego Y, Khalil A, Salem HS (2017) Nile River’s Basin Dispute: Perspectives of the Grand Ethiopian Renaissance Dam (GERD). Glob J Human-Social Sci Res 17:1–20 Additional Declarations The authors declare no competing interests. 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08:17:15","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9464,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/c066a9a55c6c065c489d5c90.png"},{"id":93470931,"identity":"4cf39a84-4fe0-4f54-9808-29d1774c133a","added_by":"auto","created_at":"2025-10-14 08:17:15","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128386,"visible":true,"origin":"","legend":"","description":"","filename":"rs78399390structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/bbb902cc1f3612930ebf741d.xml"},{"id":93470925,"identity":"8ca42337-4374-4920-a4e4-42382942c3fc","added_by":"auto","created_at":"2025-10-14 08:17:15","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136002,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/ed536e547ddac9fcf6fa1a99.html"},{"id":93472357,"identity":"c0f9bbd0-446f-4701-97e2-5dc247dfe8cc","added_by":"auto","created_at":"2025-10-14 08:25:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1217105,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/06b85eb715d0aae13aedf13e.png"},{"id":93470900,"identity":"31a62283-b35e-4e0b-8247-6ac995bfc8a9","added_by":"auto","created_at":"2025-10-14 08:17:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":804612,"visible":true,"origin":"","legend":"\u003cp\u003eGeological map of the Upper Awash River sub-basin showing the spatial distribution of volcanic formations, major faults, and structural trends used to delineate hydrostratigraphic units.(source: Ethiopian Construction Design and Supervision Works Corporation (2017), “Feasibility study of groundwater source and identification of potential well fields for future abstractions within 100 km radius of Addis Ababa” project; geological mapping by Dr. Bedru H. and the present author (Project Manager)).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/fa39a24315c240cd61472066.png"},{"id":93472358,"identity":"53fc0e13-4cd8-4df8-b0aa-0b1c63fb7c76","added_by":"auto","created_at":"2025-10-14 08:25:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223526,"visible":true,"origin":"","legend":"\u003cp\u003eRose diagram illustrating the dominant orientations of lineaments and structural features in the study area. The primary trend is N30°E, with secondary sets aligned N15°E, N45°E, and W–NW, indicating a prevailing NNE–NE structural pattern controlling groundwater flow.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/ca7d347c0a2d56152f5e2966.png"},{"id":93470904,"identity":"5048acc4-2d47-432b-abff-72daf66ab31e","added_by":"auto","created_at":"2025-10-14 08:17:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1129825,"visible":true,"origin":"","legend":"\u003cp\u003eHydrogeological map of the study area\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/d25befa84425037ffa1480f4.png"},{"id":93472359,"identity":"f5cc62ae-4747-4006-bc5e-43ae8b13ae1d","added_by":"auto","created_at":"2025-10-14 08:25:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":557548,"visible":true,"origin":"","legend":"\u003cp\u003ea spatial distribution map of the wells in the study area\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/6c9f717b6dd0a8a023364a7e.png"},{"id":93470907,"identity":"6c991ab9-34b0-411e-972e-c85dfed2c5c6","added_by":"auto","created_at":"2025-10-14 08:17:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":507086,"visible":true,"origin":"","legend":"\u003cp\u003eZone classification of the study area\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/d3166433d14a975e18f17806.png"},{"id":93470958,"identity":"b62069da-cf70-4b65-9aa9-8e530fe36dd0","added_by":"auto","created_at":"2025-10-14 08:17:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":262790,"visible":true,"origin":"","legend":"\u003cp\u003eA-F. Validation of the power-law relationship between transmissivity (T) and specific capacity (Sc).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/7307c36b30878e79699d8c92.png"},{"id":93470912,"identity":"41d6cb96-5849-4fa8-9781-50c129a6826b","added_by":"auto","created_at":"2025-10-14 08:17:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":160700,"visible":true,"origin":"","legend":"\u003cp\u003eI-VII \u0026amp; Upper Awash. Transmissivity versus well specific capacity in different zones and the whole study area (upper Awash River sub-basin\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/b68b87d9e7609b603308b032.png"},{"id":93473565,"identity":"1a296e53-2673-43cf-a9fa-25647d1206e0","added_by":"auto","created_at":"2025-10-14 08:41:15","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":490593,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of transmissivity map of the study area\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/afe1dda1f9e344e7044c829b.png"},{"id":93472363,"identity":"f0a8421f-9d1e-42a4-bd9c-22ebd283baeb","added_by":"auto","created_at":"2025-10-14 08:25:15","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":475978,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of hydraulic conductivity map of the study area using Kriging interpolation\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/8536b42a564892fb15793502.png"},{"id":93473760,"identity":"fc552e5c-72f3-4b68-acda-70dcbbe7632b","added_by":"auto","created_at":"2025-10-14 08:49:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6574313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7839939/v1/ec2f6761-af4d-4bc2-95f6-df330d52aa41.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eApplication of geostatistical methods and well-Specific Capacity for aquifer characterization in the volcanic terrain, upper Awash River sub-basin, Ethiopia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Research Highlights","content":"\u003cul\u003e\n \u003cli\u003eDeveloped a power-law relationship between transmissivity (T) and specific capacity (Sc) using 341 wells in the Upper Awash River Sub-Basin.\u003c/li\u003e\n \u003cli\u003evalidated the T\u0026ndash;Sc model through bootstrap resampling and repeated cross-validation, accounting for prediction uncertainty.\u003c/li\u003e\n \u003cli\u003eApplied ordinary kriging to map spatial variability of T and K, outperforming inverse distance weighting.\u003c/li\u003e\n \u003cli\u003eIdentified high T and K zones in fractured volcanic units and low values in massive formations, reflecting structural and lithological controls.\u003c/li\u003e\n \u003cli\u003eProvides a cost-effective, replicable framework for aquifer characterization in data-scarce volcanic terrains.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eSimilar to other semi-arid regions, the Upper Awash River sub-basin in Ethiopia relies heavily on groundwater because of the irregular and inconsistent availability of surface water (Alemayehu et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; MOWIE, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tadesse et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This reliance on groundwater has been exacerbated by climate variability and increased water demand, which has further disrupted surface water flows (Gebrekristos, A., Hussien, B., \u0026amp; Kebede, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To sustainably manage aquifers in these areas, it is essential to calculate fundamental hydraulic parameters, specifically transmissivity (T) and hydraulic conductivity (K), because these factors influence groundwater movement and well production (Fetter, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Assessing aquifer potential, designing well fields, and predicting the sustainability of groundwater resources requires these parameters (Todd, D. and Mays, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHence, traditional pumping tests represent an accurate method that remains widespread but becomes unaffordable and time-consuming, particularly in regions with sparse data, where extensive field tests require many resources (Razack, M., \u0026amp; Huntley, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Ridder, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). Specific capacity (Sc), which is measured through routine well yield tests, serves as an operational substitute for estimating T and K. This method makes groundwater evaluation more economically accessible to developing areas. Multiple investigators have studied the connection between specific capacity and transmissivity by creating empirical equations for different hydrogeological conditions (Singhal, B. S., \u0026amp; Gupta, 2010). Regional assessment of groundwater resources, along with improved groundwater management practices, is possible throughout regions where traditional testing approaches are impossible using specific capacity data (Pardo-Iguzquiza E, et,al., 2012).\u003c/p\u003e\u003cp\u003eThe Upper Awash River sub-basin, where agriculture and industry are crucial components of Ethiopia\u0026rsquo;s economy, is experiencing increasing groundwater stress due to excessive water withdrawal coupled with rapid population growth and pollution from agricultural and industrial activities (Awulachew, S. B., 2010; Kebede, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mulugeta, M., 2020). The region relies on groundwater as a vital resource, as it supports essential irrigation projects and provides water for significant urban areas, including Addis Ababa (Ayenew, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Kebede, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Groundwater management and sustainable use in the sector face major challenges due to its complex hydrogeological characteristics involving various volcanic aquifers (Mamo, S., \u0026amp; Dinka, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yihdego, Y., Khalil, A., \u0026amp; Salem, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCurrently, there is a shortage of comprehensive aquifer characterization in the upper wash basin because the testing data comes from localized sites. Hydrogeological models with spatial representation are still poorly developed because insufficient data limits our understanding of the basin's aquifers (Alemayehu, T., Ayenew, T., \u0026amp; Kebede, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tilahun, H., \u0026amp; Merkel, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Effective groundwater management is constrained due to the basin's lack of consistent monitoring and the limited availability of data (Yihdego, Y., Khalil, A., \u0026amp; Salem, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These challenges call for innovative and cost-effective solutions to expand the geographical extent of aquifer parameter assessment.\u003c/p\u003e\u003cp\u003eThis study addresses existing knowledge gaps by utilizing measured specific capacities from previous yield tests to determine both transmissivity (T) and hydraulic conductivity (K) values across the upper wash basin region. This method offers an economical solution for conducting pumping tests, which is advantageous for areas with limited hydrological data (Razack, M., \u0026amp; Huntley, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). This approach establishes practical relationships between specific capacity data and aquifer parameters, leading to an improved spatial resolution of aquifer property mapping that provides essential information about groundwater accessibility along with sustainable management guidelines. The results of this study will enable adaptive groundwater management systems to formulate response strategies for the growing water needs and climate change (Tadesse et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultiple empirical linkages between specific capacity (Sc) and hydraulic conductivity (K) with transmissivity (T) have been validated across various hydrogeological environments, including sedimentary, crystalline, and alluvial aquifers (Acheampong, E. N., Yu, Q., \u0026amp; Fu, 2020; Goyal, V., \u0026amp; Singh, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maliva, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Odong, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, their applicability to fractured volcanic aquifers in the Upper Awash Basin remains largely untested. Disciplines that study subsurface flow patterns encounter unique challenges in this region due to significant variability in water flow characteristics and complex multiple aquifer systems (Kebede, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yihdego, Y., Khalil, A., \u0026amp; Salem, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This study employed kriging and inverse distance weighting (IDW) geostatistical methods to create spatial distributions of aquifer characteristics from specific capacity information (Goovaerts, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1997a\u003c/span\u003e; Isaaks, E. H., \u0026amp; Srivastava, 1989a). Hydrological maps of aquifer properties achieve greater accuracy at the regional scale because of the methods that enhance spatial resolution.\u003c/p\u003e\u003cp\u003eThis research integrates well data from a local area with pump test information through geostatistical methods to develop a replicable aquifer assessment process suitable for various regions in East Africa. This approach deepens the understanding of the properties of the Upper Awash Basin aquifer and contributes to global knowledge of groundwater resource evaluation in volcanic terrains. The evaluation findings will provide vital information for groundwater management, support groundwater protection zone delineation, wellhead protection strategies, and the development of sustainable groundwater abstraction regulations (MacDonald, A. M., Bonsor, H. C., \u0026amp; Dochartaigh, 2021; Mulugeta, M., 2020; Yihdego, Y., Khalil, A., \u0026amp; Salem, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Study area description","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Location and setting of the study area\u003c/h2\u003e\u003cp\u003eThe study was carried out within the upper part of the Awash River sub-basin, situated in central Ethiopia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Geographically, the basin extends between latitudes 8\u0026deg;07\u0026prime;07\u0026Prime;N\u0026ndash;9\u0026deg;55\u0026prime;11\u0026Prime;N and longitudes 37\u0026deg;51\u0026prime;09\u0026Prime;E\u0026ndash;39\u0026deg;39\u0026prime;50\u0026Prime;E, encompassing elevations from about 1,579 m to 3,564 m above sea level. Covering an estimated 11,690 km\u0026sup2;, the area lies in Central Ethiopia and is bordered by the Northwestern Ethiopian Plateau to the west and the Northern Ethiopian Rift to the south. These contrasting topographic and geological settings create pronounced gradients that strongly influence groundwater occurrence, flow direction, and aquifer properties within the upper Awash River sub-basin. The variation between plateau and rift landscapes governs recharge distribution, volcanic rock thickness, and fracture intensity all key factors for hydrogeological evaluation. The basin sustains a range of human and economic activities, including major towns, industries, and irrigated agriculture, all of which depend heavily on its surface and groundwater resources. The Awash River, one of Ethiopia\u0026rsquo;s most important river systems, traverses the sub-basin and serves as a vital source of water for domestic, agricultural, and ecological needs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Geological and hydrogeological settings\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Geological settings\u003c/h2\u003e\u003cp\u003eThe geological features of the Upper Awash River Basin mainly include basalts with associated tuffs and ignimbrites that typify the Ethiopian Rift System geology. The region is characterized by its location within the East African Rift, which displays strong tectonic features, including land faults and volcanic occurrences alongside rift valley formations. Volcanic rocks dominate the landscape of this area because they cover 98.04% of the surface, starting from the Early Miocene continental flood basalts of the plateau and progressing to new lava flows emerging along the rift axis structures. The volcanic formations include both massive flood basalt layers, together with inter-trappean sediments combined with shield volcanoes and silicic composite volcano products set on rift margins, bimodal volcanic deposits, and lacustrine features situated on the rift floor. Different volcanic features, including flat plateaus and shield volcanoes, along with unique volcanic structures, such as calderas, craters, maars, and cones. The volcanic units comprise seven distinct lithostratigraphic map units, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, through descriptions of the lithology, composition, and texture features with their structural characteristics, volcanic relationships, and thickness values.\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\u003eGeneralized litho-stratigraphic units of the study area\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e(source: Ethiopian Construction Design and Supervision Works Corporation (2017), \u0026ldquo;Feasibility study of groundwater source and identification of potential well fields for future abstractions within 100 km radius of Addis Ababa\u0026rdquo; project; geological mapping by Dr. Bedru H. and the present author (Project Manager).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGeologic Age\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFormation / Unit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMap Symbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRepresentative Lithology / Description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eArea (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eArea (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"19\" rowspan=\"20\"\u003e\u003cp\u003e\u003cb\u003eCenozoic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003e\u003cb\u003eQuaternary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSuperficial Deposits (Qs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQsd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDark-gray to black silty clay of residual origin (eluvium).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e4 239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e36.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQsd₁\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLight- to grayish-white clayey silt derived from weathered volcanic material (eluvium).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQsd₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLight gray to brown silt, sand, and gravel forming alluvial deposits.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLight gray to brown silty clay deposited in former lacustrine environments.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWonji Group (Qf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQwg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePorphyritic to vesicular basalt flows and associated scoria layers.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQwg₁\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eScoriaceous basalt and loose scoria horizons of fissure-type eruptions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRift Silicics (Qc)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQcv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTrachyte, rhyolite, and related pyroclastics of the Zikwala volcanic center.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1 052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e9.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQcv₁\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePumiceous lapilli tuff and reworked volcanic ash deposits.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQcv₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eObsidian and pitchstone domes and flows of the Boset complex.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eMiocene\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePost-shield Silicics (Ns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNwt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTrachyte and pyroclastic flows of the Wechecha volcanic center.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1 838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e15.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNgmr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGash Megal trachyte and trachybasalt lava flows.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNgmr₁\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGash Megal rhyolite with interbedded pyroclastics.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEntoto trachyte and lapilli-tuff sequences.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eOligocene\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eShield Basalts (Ps)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePdb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMassive to porphyritic basalt and trachyte of the Degem Formation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1 062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e9.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePfb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBasalt and rhyolite flows of the Foota Formation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePcb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlternating basalt and rhyolite of the Cheleleka Formation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePtmb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePorphyritic basalt interlayered with basaltic agglomerate.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOligo\u0026ndash;Miocene\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-trap / Pre-shield Pyroclastics (Pp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWelded ignimbrite, tuff, and volcanic-ash sequences.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eEocene\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTrap Basalts (Pf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eThick basalt and agglomerate units forming plateau basalts.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePts₁\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBasaltic dykes and sills intruding the older volcanic pile.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe ignimbrites and tuffs belonging to the Nazret Group form prominent landforms such as mesas, ridges, and broad flats. These pyroclastic deposits, dated between 5.2 and 3.1 Ma, comprise quartz and feldspar-bearing ignimbrites, laminated tuffs, and ash layers. Silicic volcanic centers including Wechecha, Furi, Menagesha, and Yerer create elliptical edifices dominated by trachyte and related pyroclastics. The trachytes commonly display a porphyritic texture with plagioclase, sanidine, and pyroxene phenocrysts.Younger volcanic episodes of the Wonji Group are characterized by rhyolitic lavas, obsidian, and pumice flows aligned with rift-related fault trends. Zikwala Mountain represents a peralkaline composite volcano with a summit crater composed mainly of trachyte and subordinate pyroclastics.\u003c/p\u003e\u003cp\u003eThe volcanic formations are extensively fractured, producing blocky outcrops at numerous localities. Joint orientations vary with lithology and are controlled by composition, texture, and mode of fracturing. Dominant joint trends are NW\u0026ndash;SE, NE\u0026ndash;SW, and E\u0026ndash;W. Basalts exhibit irregular, closed joints, whereas flood basalts, ignimbrites, and trachytes display well-developed columnar jointing composed of slender, 4\u0026ndash;6-sided columns (5\u0026ndash;20 cm across) intersected by horizontal cracks, occasionally filled with secondary carbonate. With the exception of tuffs and agglomerates, most volcanic units show irregular fracturing with variable spacing and aperture. Lineament mapping involved identifying linear and curvilinear surface features that contrast with surrounding terrain and may reflect underlying structural discontinuities such as faults and fractures. Lineaments were extracted using slope, aspect, curvature analyses, and Landsat imagery, focusing on slope breaks, sharp gradients, linear valleys or ridges, and other topographic discontinuities. High lineament densities occur mainly in the north-west, north-central, north-east, and south-west sectors of the study area. For statistical assessment, individual lineaments were divided into 2.5 km segments for regional analysis. The rose diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicates a dominant trend at N30\u0026deg;E, followed by N15\u0026deg;E, N45\u0026deg;E, N, N60\u0026deg;E, N75\u0026deg;W, N30\u0026ndash;45\u0026deg;W, and W, suggesting a prevailing NNE\u0026ndash;NE structural orientation with subsidiary sets marking local fault systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Hydrogeological framework of the upper Awash River sub-basin\u003c/h2\u003e\u003cp\u003eThe hydrogeological conditions of the upper Awash River sub-basin are notably complex, shaped by the interplay between lithologic variability and tectonic activity. Both confined and unconfined aquifer systems occur throughout the region, reflecting the heterogeneous nature of volcanic terrains and the presence of partially confined local aquifers. Groundwater-bearing formations show considerable spatial variation in permeability and storage potential, largely controlled by the degree of fracturing and lithologic composition.\u003c/p\u003e\u003cp\u003eGeological structures play a critical role in the groundwater circulation. Faults and fracture networks associated with the Main Ethiopian Rift (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) enhance transmissivity by providing vertical and lateral conduits for flow, whereas zones of limited fracture connectivity or clay infill restrict the permeability. The NW\u0026ndash;SE and NE\u0026ndash;SW fault alignments observed in the sub-basin exert strong control on the recharge distribution, flow anisotropy, and localized storage conditions. These structural discontinuities explain the heterogeneity in the transmissivity and specific capacity observed across the basin.\u003c/p\u003e\u003cp\u003eRecharge mechanisms vary according to aquifer type. The shallow aquifer, consisting of superficial deposits and rift basalts, is primarily recharged by direct rainfall and stream infiltration, with fracture density enhancing the infiltration efficiency. Rainwater percolates into the intermediate aquifer through fractured ignimbrite layers, facilitated by leakage from overlying basalts and direct infiltration along escarpment transition zones. The deep confined aquifer, mainly developed in scoriaceous basalts, is recharged in the plateau and escarpment zones, with isotopic and hydrochemical evidence suggesting inter-basin groundwater inflow from the Abay (Blue Nile) Basin (Tadesse et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGroundwater recharge rates range between 181.1\u0026ndash;261.4 mm/year (Muauz et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), though these values are modulated by topography, soil distribution, land use, and fracture density. Groundwater flow patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e) generally follow structural trends, moving from the northern plateau toward the southeastern lowlands and from the escarpments toward the rift valley zones. These flow systems are strongly modified by tectonic discontinuities, which create preferential high-permeability pathways and compartmentalize aquifers through aquiclude development.\u003c/p\u003e\u003cp\u003eAquicludes further complicate the groundwater distribution by forming perched or artesian conditions in certain areas. Consequently, hydraulic parameters such as conductivity, transmissivity, and storativity vary markedly across short distances, reflecting both lithological heterogeneity and tectonic structure. Understanding this interplay is essential for effective groundwater management, as recharge, storage, and yield are controlled not only by lithology and climate but also by rift-related structures that define hydraulic variability across the basin.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data Collection and Preprocessing\u003c/h2\u003e\u003cp\u003eSpecific capacity data (Sc = Q/s, where Q is the pumping rate (m\u0026sup3;/day) and s is the drawdown (m)) were compiled from 341 production wells drilled across the study area (Fig.5) and divided into seven regions based on the geomorphological, geological, and hydrogeological conditions of the study area, as shown in Fig.6. The data were sourced from complete reports. Pumping test records, ranging from 24 to 72 h, along with lithologic logs, were obtained to validate the aquifer properties and characterize the hydrostratigraphic units; 280 wells were used for the analysis, as shown in Table 2. The duration of the pumping tests was sufficient to approximate the steady-state conditions, thus minimizing the influence of wellbore storage and boundary effects (Ridder, 1971).\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\u003eWells used for relationship T and Sc\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSn\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eZones\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal no. wells with pumping test data\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eTransmissivity(m\u003csup\u003e2\u003c/sup\u003e/day)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\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\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e104\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\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1294\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\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1685\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\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1137\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\u003eVII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e291\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\u003eV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1604\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\u003eVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e104\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\u003eUpper Awash\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54\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\u003eData quality control was applied to address inconsistencies such as incomplete drawdown measurements, anomalous yield records, and non-representative well conditions. Wells exhibiting poor data quality or unstable drawdown trends were excluded from analysis. To ensure robust transmissivity estimation, only wells with R\u0026sup2; \u0026ge; 0.8 from Cooper-Jacob straight-line fits were retained, following established best practices for pumping test data analysis (Fetter, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Theis, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1935\u003c/span\u003e). The Cooper-Jacob method, a simplification of Theis' equation, is widely used for non-equilibrium well tests in unconfined and semi-confined aquifers, making it suitable for fractured volcanic aquifers in the study area.\u003c/p\u003e\u003cp\u003eThe final dataset represents a comprehensive collection of specific capacity observations, providing a valuable basis for estimating the transmissivity and hydraulic conductivity across the upper wash basin. This dataset enhances the spatial coverage of aquifer parameter estimations, particularly in regions in which direct pumping tests are sparse or absent. Some wells have a specific capacity and transmissivity but do not have to draw dawn, and other wells have transmissivity but not specific capacity, and vice versa.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Validation and uncertainty analysis\u003c/h2\u003e\u003cp\u003eThe relationship between the transmissivity (T) and specific capacity (Sc) was validated using a full dataset of 71 wells. A nonlinear least-squares regression was fitted, assuming a power-law form, T\u0026thinsp;=\u0026thinsp;a\u0026sdot;Sc\u003csup\u003eb\u003c/sup\u003e. To assess the parameter uncertainty, bootstrap resampling (200 iterations) was applied to derive 95% prediction intervals for the fitted curve. Model robustness was further evaluated through repeated 70/30 cross-validations (100 iterations), in which 70% of the wells were randomly selected for calibration and the remaining 30% for validation. Model performance was quantified using the root mean square error (RMSE), coefficient of determination (R\u0026sup2;), mean absolute error (MAE), and bias. Both the best-run validation results and aggregated distributions of the performance metrics were analyzed to characterize the single-run behavior as well as the overall model stability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Geostatistical analysis\u003c/h2\u003e\u003cp\u003eWe employed ordinary kriging (OK) as the primary geostatistical method to interpolate transmissivity (T) and hydraulic conductivity (K). Experimental semivariograms were computed from well-specific capacity\u0026ndash;derived T and K values, and theoretical models (spherical, exponential, and stable) were fitted using weighted least squares. The model performance was evaluated through leave-one-out cross-validation, where each data point was removed iteratively and predicted using the remaining dataset. The models were compared using the coefficient of determination (R\u0026sup2;), root mean square error (RMSE), and nugget-to-sill ratio, which indicates the spatial structure (low nugget effect\u0026thinsp;=\u0026thinsp;strong spatial continuity). In addition to kriging, Inverse Distance Weighting (IDW) as a deterministic interpolation method. The IDW predictions were computed with a power parameter of 2 and a maximum of 12 neighbors. The performance of IDW was evaluated using the same cross-validation metrics (R\u0026sup2;, RMSE, and MAE) for direct comparison with kriging.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Estimation of Aquifer Parameters\u003c/h2\u003e\u003cp\u003eTransmissivity (T) was calculated using the following empirical relationship (Theis, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1935\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:T=\\alpha\\:.Sc$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere α\u0026thinsp;=\u0026thinsp;1.5\u0026ndash;2.0 for unconfined aquifers (Razack, M., \u0026amp; Huntley, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Hydraulic conductivity (K) was derived as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:K=\\frac{T}{b}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere b is the saturated thickness [m] obtained from the borehole logs.\u003c/p\u003e\u003cp\u003eIn Ethiopia, aquifer thickness estimates are generally derived from borehole lithological logs rather than from direct geophysical or aquifer test measurements. According to (Alemayehu, T., Ayenew, T., \u0026amp; Kebede, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and (Kebede, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), most hydrogeological assessments of volcanic terrains adopt borehole lithology and screened intervals as practical proxies for saturated thickness, particularly where direct measurements of storativity are unavailable. The Ministry of Water, Irrigation, and Energy (MOWIE, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) further noted that in many groundwater investigations across the upper Awash River sub-basin and other basins, aquifer thickness is approximated by the screened section of productive wells, or where such data are missing, by assuming a proportion (commonly 30%) of the total drilled depth as the effective aquifer length. This approach reflects the highly fractured nature of Ethiopian volcanic aquifers, where transmissive zones are usually concentrated within discrete borehole intervals. In line with these established practices, our study adopted screened intervals as aquifer thickness where available and 30% of the drilled depth where interval data were missing. While this method provides a consistent regional framework for aquifer parameter estimation, we acknowledge its limitations and recommend the integration of storativity or geophysical data in future studies for more accurate representation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 performance evaluation\u003c/h2\u003e\u003cp\u003eTo evaluate the accuracy of the estimated T and K values, a regression analysis was conducted, incorporating statistical performance metrics such as the coefficient of determination (R\u0026sup2;) and correlation coefficient (R). The correlation coefficient determines the linear relationship between two variables and reveals their strengths and directions. The scale of the correlation coefficient extends between \u0026minus;\u0026thinsp;1 and 1, whereas a value of 1 shows a perfect positive correlation, indicating that the parallel variable increases. A correlation of R = -1 shows a completely negative linear relationship because rising values in one variable trigger decreasing values in the other variable. The variables show no linear connection when R reaches zero. The coefficient of determination (R\u0026sup2;) describes the extent to which independent variable variations affect dependent variable variations. The coefficient of determination ranges from zero to one, indicating that R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1 indicates a perfect match when all variations in the dependent variable are attributable to the independent variable influence. A model cannot explain the variability of the dependent variable when R\u0026sup2; equals zero. The spatial variability of T and K was mapped using kriging interpolation within ArcGIS, which is a geostatistical method that enhances the accuracy of spatial predictions by accounting for spatial autocorrelation. The kriging process incorporated lithologic and structural constraints, ensuring that geological heterogeneities such as variations in fracture networks and stratigraphic discontinuities were adequately represented. This integration of geological controls enhanced the reliability of the spatial distribution maps, providing insights into hydrogeological variability across the study area.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Result and discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Validation analysis\u003c/h2\u003e\u003cp\u003eThe full dataset (71 wells) regression revealed a strong power-law relationship between the transmissivity (T) and specific capacity (Sc), yielding R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.89 and RMSE\u0026thinsp;=\u0026thinsp;143.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). This confirmed that Sc was a strong proxy for transmissivity when the entire dataset was used. Bootstrap-based uncertainty analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) demonstrated that prediction intervals were narrow for low to moderate Sc values, but widened substantially at higher Sc, reflecting reduced data density in the upper range.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCross-validation provided more conservative estimates of the model performance. The best single run achieved R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.37 and RMSE\u0026thinsp;=\u0026thinsp;76.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), while the corresponding Sc\u0026ndash;T fit (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eD) showed closer agreement for low values, but divergence for higher transmissivity. Across 100 repeated 70/30 splits, performance varied substantially, with average metrics of RMSE\u0026thinsp;=\u0026thinsp;294.6, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.32, MAE\u0026thinsp;=\u0026thinsp;123.8, and bias = \u0026minus;\u0026thinsp;78.3 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eE\u0026ndash;F). The median R\u003csup\u003e2\u003c/sup\u003e was 0.21, with values ranging from 0 to 0.93, highlighting sensitivity to data partitioning.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of model performance metrics across 100 repeated 70/30 cross-validation runs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRMSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e294.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e200.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e699.497\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMAE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e123.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e260.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBias\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-78.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-65.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-197.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003cp\u003eThis validation highlights two contrasting aspects of the T\u0026ndash;Sc relationship. The strong fit from the full dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) supports the widely recognized power-law scaling between transmissivity and specific capacity. On the other hand, the cross-validation results (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u0026ndash;F, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) emphasize limited predictive robustness, with an average R\u003csup\u003e2\u003c/sup\u003e of 0.32 and considerable spread across runs. This discrepancy reflects (i) the relatively small dataset, (ii) uneven distribution of transmissivity values, and (iii) influence of outliers in high Sc\u0026ndash;T ranges.\u003c/p\u003e\u003cp\u003eUncertainty analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) further demonstrated that predictions were more reliable for moderate Sc values but uncertain for extreme cases. The negative bias (mean \u0026asymp; \u0026minus;\u0026thinsp;78.3) indicates that the model tended to underpredict transmissivity in the validation runs, especially for higher-yield wells. These results suggest that Sc remains a practical proxy for T; however, predictive applications should explicitly incorporate uncertainty and avoid over-reliance on extrapolation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Geostatistical analysis\u003c/h2\u003e\u003cp\u003eInterpolation of transmissivity (T) and hydraulic conductivity (K) was carried out using ordinary kriging (OK) and compared with inverse distance weighting (IDW). The cross-validation results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) indicated that kriging consistently outperformed IDW for both parameters. For T, kriging achieved a higher coefficient of determination (R\u0026sup2; = 0.74) and substantially lower errors (RMSE\u0026thinsp;=\u0026thinsp;480.5, MAE\u0026thinsp;=\u0026thinsp;365.2) than IDW (R\u0026sup2; = 0.59, RMSE\u0026thinsp;=\u0026thinsp;650.2, MAE\u0026thinsp;=\u0026thinsp;498.3). Similarly, for K, kriging produced a stronger predictive performance (R\u0026sup2; = 0.70, RMSE\u0026thinsp;=\u0026thinsp;0.0031, MAE\u0026thinsp;=\u0026thinsp;0.0024) than IDW (R\u0026sup2; = 0.54, RMSE\u0026thinsp;=\u0026thinsp;0.0047, MAE\u0026thinsp;=\u0026thinsp;0.0036). These results demonstrate that kriging more effectively captures the spatial dependence of aquifer parameters, whereas IDW, which is a deterministic method, fails to incorporate spatial autocorrelation, and thus yields weaker predictions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCross-validation results comparing Ordinary Kriging (OK) and Inverse Distance Weighting (IDW) for transmissivity (T) and hydraulic conductivity (K).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKriging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e480.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e365.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e650.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e498.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKriging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe variogram analysis further supported the superiority of kriging. Both T and K were best described using a spherical model (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For T, the nugget was 120.0, with a total sill of 820.0, yielding a nugget-to-sill ratio of 0.15. For K, the nugget was 0.0002, with a sill of 0.0011 and nugget-to-sill ratio of 0.18. These relatively low nugget-to-sill ratios (\u0026lt;\u0026thinsp;0.25) indicate strong spatial structure and continuity, suggesting that local measurements are representative of broader regional trends (Goovaerts, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997b\u003c/span\u003e; Isaaks, E. H., \u0026amp; Srivastava, 1989b).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFitted spherical variogram model parameters for transmissivity (T) and hydraulic conductivity (K)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNugget\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSill\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNugget_Sill_Ratio\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSph\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e820.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSph\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\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\u003eSpatially, the kriged maps revealed coherent zones of higher transmissivity and hydraulic conductivity, which correspond to known hydrogeological features, such as fractured volcanic units and alluvial deposits. In contrast, IDW produced smoother surfaces that masked localized variability and failed to reproduce sharp transitions observed in the field. This difference highlights the importance of geostatistical approaches in hydrogeological characterization, as kriging not only interpolates but also provides an estimate of the prediction uncertainty through the variogram model (L. Fabbri, P., \u0026amp; Piccinini, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, the results confirm that ordinary kriging is a more robust and reliable technique than IDW for characterizing aquifer properties in the Upper Awash subbasin. The combination of a strong variogram structure and superior predictive performance underscores its suitability for groundwater resource assessment and management.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Analysis and Discussion of Each Zone\u003c/h2\u003e\u003cp\u003eThe best-fit lines through the data of different zones and the upper Awas River sub-basin are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig..8(I-VII) describe the relationship between transmissivity (T) and specific capacity (Sc) across various zones of the study area. The equations are expressed in the form of a power law, which is commonly used to estimate transmissivity from specific capacity data, and 280 wells underwent time-drawdown tests to determine transmissivity and specific capacity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe best fit line through the data of different zones and upper Awas river sub-basin\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSn\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZones\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEquations T vs Sc\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR\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\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;1.278(Sc)\u003csup\u003e\u003cb\u003e1.018\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.987\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\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;4.084(Sc)\u003csup\u003e\u003cb\u003e0.834\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.986\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\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;0.196(Sc)\u003csup\u003e\u003cb\u003e1.487\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.981\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\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;1.709(Sc)\u003csup\u003e\u003cb\u003e1.101\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.985\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\u003eV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;0.891(Sc)\u003csup\u003e\u003cb\u003e1.243\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.980\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\u003eVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;4.171(Sc)\u003csup\u003e\u003cb\u003e0.94\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.980\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\u003eVII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;1.163(Sc)\u003csup\u003e\u003cb\u003e1.137\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.981\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\u003eUpper Awash\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;1.528(Sc)\u003csup\u003e\u003cb\u003e1.082\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.970\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\u003eZone I follows the equation T\u0026thinsp;=\u0026thinsp;1.278(Sc)\u003csup\u003e\u0026sup1;.⁰\u0026sup1;⁸\u003c/sup\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e(I) with a high R\u0026sup2; of 0.974 and R of 0.987, indicating a strong correlation between transmissivity (T) and specific capacity (Sc). Exponent b\u0026thinsp;=\u0026thinsp;1.018 suggests an almost linear relationship, implying uniform hydraulic properties. This predictability supports efficient groundwater development, as the specific capacity reliably estimates transmissivity for well construction and resource management.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eZone II follows the equation, T\u0026thinsp;=\u0026thinsp;4.084(Sc)\u0026deg;.⁸\u0026sup3;⁴, with strong correlations (R\u0026sup2; = 0.971, R\u0026thinsp;=\u0026thinsp;0.986), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e (II). The exponent b\u0026thinsp;=\u0026thinsp;0.834, which is less than 1, indicates a sublinear relationship in which transmissivity gradually increases with the specific capacity. This suggests a dense aquifer structure with fine-grained or weakly fractured rocks. Although the specific capacity reliably predicts transmissivity, low aquifer productivity may require deeper wells, better spacing, and improved development techniques for sustainable groundwater extraction.\u003c/p\u003e\u003cp\u003eZone III follows the equation, T\u0026thinsp;=\u0026thinsp;0.196(Sc).\u003csup\u003e1\u003c/sup\u003e.⁴⁸⁷, with strong correlations (R\u0026sup2; = 0.957, R\u0026thinsp;=\u0026thinsp;0.981), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e (III). The exponent b\u0026thinsp;=\u0026thinsp;1.487, which is the highest among all zones, indicates a highly nonlinear relationship, where small increases in specific capacity result in disproportionately large increases in transmissivity. This suggests a highly permeable aquifer with well-connected fractures, extensive secondary porosity, or highly porous formations, which is typical of fractured volcanic rocks. The strong fit of the power-law equation confirms that specific capacity is a reliable predictor of transmissivity, making Zone III ideal for high-yielding wells. However, variations in fracture connectivity, storage properties, and permeability anisotropy should be considered for optimal wellfield design and sustainable groundwater management.\u003c/p\u003e\u003cp\u003eZone IV follows the equation, T\u0026thinsp;=\u0026thinsp;1.709(Sc).\u0026sup1;⁰\u0026sup1;, with strong correlations (R\u0026sup2; = 0.970, R\u0026thinsp;=\u0026thinsp;0.985), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e (IV). Exponent b\u0026thinsp;=\u0026thinsp;1.101 indicates a slightly nonlinear relationship, where the transmissivity increases slightly more than the specific capacity. This suggests moderate to high transmissivity, likely due to well-connected fractures, a mix of confined and unconfined aquifers, or variations in the permeability within the formation. The high correlation values confirm that specific capacity is a reliable predictor of transmissivity. This zone is suitable for moderate-to high-yield wells, but factors such as fracture density, aquifer thickness, and storage properties should be considered for precise groundwater assessments and wellfield design.\u003c/p\u003e\u003cp\u003eThe relationship for Zone V corresponds to T\u0026thinsp;=\u0026thinsp;0.891(Sc).\u0026sup2;⁴\u0026sup3; based on Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e (V) along with R\u0026sup2; = 0.960 and R\u0026thinsp;=\u0026thinsp;0.980. The exponent value of b\u0026thinsp;=\u0026thinsp;1.243 demonstrates strong nonlinearity because it creates magnified transmissivity changes from specific capacity increments, which depicts high-yield aquifer behavior. The specific capacity leads to reliable predictions of transmissivity based on its direct relationship with the measured data points. This zone contains porous rock types, including fractured volcanic rock, and extensive elements of interconnected fractures that enable groundwater transportation, thus making it suitable for high-capacity extraction wells. Water management optimization and sustainable resource extraction require attention directed toward inconsistencies between fractures, porosity, and aquifer storage distribution.\u003c/p\u003e\u003cp\u003eZone VI exhibits a near-linear relationship between the transmissivity and specific capacity, expressed as T\u0026thinsp;=\u0026thinsp;4.171(Sc)\u0026deg;.⁹⁴. The high correlation values (R\u0026sup2; = 0.959, R\u0026thinsp;=\u0026thinsp;0.980) confirmed the strong predictive reliability of specific capacity. The exponent b\u0026thinsp;=\u0026thinsp;0.94, which is close to 1, indicates that transmissivity increases almost proportionally with specific capacity, with minimal variation. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e (VI), this pattern suggests that groundwater flows through similar geological formations, such as extensively fractured aquifers, maintaining stable permeability. Although this strong correlation supports accurate groundwater assessments, localized geological variations and aquifer thickness differences should be considered for optimized well design and sustainable resource management.\u003c/p\u003e\u003cp\u003eZone VII follows the equation, T\u0026thinsp;=\u0026thinsp;1.163(Sc).\u0026sup1;\u0026sup3;⁷, with strong correlations (R\u0026sup2; = 0.962, R\u0026thinsp;=\u0026thinsp;0.981), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e (VII). The exponent b\u0026thinsp;=\u0026thinsp;1.137 indicates a moderately nonlinear relationship, where transmissivity increases slightly more than proportionally with specific capacity. This suggests a mix of porous and fractured media, supporting both the intergranular and fracture-dominated groundwater flow regimes. Such geological conditions enhance aquifer permeability, leading to relatively high well yields, although some variability in the water movement may still exist. This strong correlation confirms the reliability of this equation for predicting transmissivity, making it valuable for groundwater assessment, wellfield design, and sustainable management strategies. However, localized geological variations should be considered for precise site-specific evaluations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Upper Awash Basin (Overall Relationship)\u003c/h2\u003e\u003cp\u003eThe best description for the upper wash basin data shows that the relationship between the transmissivity (T) and specific capacity (Sc) is best represented by the equation T\u0026thinsp;=\u0026thinsp;1.528(Sc).\u003csup\u003e1\u003c/sup\u003e.⁰⁸\u0026sup2; with R\u0026sup2; = 0.941 and R\u0026thinsp;=\u0026thinsp;0.970, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e (upper Awash). The slightly non-linear increase in transmissivity becomes moderate with respect to the specific capacity through an exponent value of b\u0026thinsp;=\u0026thinsp;1.082. An increase in Sc produces larger changes in T at a moderate level of amplification.\u003c/p\u003e\u003cp\u003eA slightly reduced R\u0026sup2; value indicates that the location spans multiple aquifer types, which is reasonable for a vast and geologically complex basin. Different rock types, along with variable fracture patterns combined with groundwater movement patterns, appear to produce areas where the overall trend diminishes.\u003c/p\u003e\u003cp\u003eThe described basin-wide equation functions as a helpful average model for the transmissivity-specific capacity link, but fails to represent aquifer characteristics. The evaluation of groundwater resource management and precise well design requires site-specific investigations that account for the unique hydrogeological aspects of each location.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Consistency with global findings\u003c/h2\u003e\u003cp\u003eThe empirical relationship T\u0026thinsp;=\u0026thinsp;1.528\u0026sdot;Sc\u003csup\u003e1.082\u003c/sup\u003e derived for the upper wash basin is consistent with global findings for both fractured and volcanic rock aquifers. Moderate nonlinearity (exponent\u0026thinsp;\u0026gt;\u0026thinsp;1) reflects the scale effects, well losses, vertical flow, and inherent heterogeneity (fracture density, fracture transmissivity contrasts, and variable saturated thickness) (S. Fabbri, P., \u0026amp; Piccinini, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Valigi, D., Cambi, C., Checcucci, R., \u0026amp; Di Matteo, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, (Valigi, D., Cambi, C., Checcucci, R., \u0026amp; Di Matteo, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) developed a T\u0026ndash;Sc relationship for Italian carbonate-karst aquifers, whereas (Hsu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mace, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) provided a thorough methodological review supporting empirical estimations of transmissivity from Sc. These globally observed moderate nonlinearities (exponent\u0026thinsp;\u0026gt;\u0026thinsp;1) underpin the methodology used in our study and justify the interpretation of our observed exponent value.\u003c/p\u003e\u003cp\u003eThis similarity highlights the importance of fractures in controlling groundwater flow in the upper wash sub-basin and underscores the need for site-specific assessments to account for the heterogeneity and variability of the aquifer. By comparing the upper wash basin findings with global studies, it is clear that the basin's volcanic aquifers share many characteristics with fractured and volcanic aquifers worldwide, thereby providing valuable insights into groundwater management and sustainable resource development.\u003c/p\u003e\u003cp\u003eA study on the upper wash basin developed T\u0026thinsp;=\u0026thinsp;1.528\u0026sdot;Sc\u003csup\u003e1.082\u003c/sup\u003e as an empirical relationship to calculate the transmissivity (T) from the specific capacity (Sc). This relationship is consistent with worldwide investigations on fractured and volcanic rock aquifers. Heterogeneity combined with anisotropy, together with fracture network dominance, exists in both fractured rock and volcanic aquifers, which affects groundwater movement and the specific capacity to transmissivity relationships. Understanding the hydrological behavior of the upper wash basin has become more accessible by analyzing its similarities with other hydrogeological systems for groundwater management purposes.\u003c/p\u003e\u003cp\u003eThe upper wash basin shows moderate nonlinearity (exponent\u0026thinsp;\u0026gt;\u0026thinsp;1) in its T\u0026thinsp;=\u0026thinsp;1.528\u0026sdot;Sc\u003csup\u003e1.082\u003c/sup\u003e relationship according to observations of fracturing in rock formations worldwide. The study by (Singhal, B. S., \u0026amp; Gupta, 2010) explored T\u0026thinsp;=\u0026thinsp;0.5\u0026sdot;Sc\u003csup\u003e1.2\u003c/sup\u003e as an appropriate model for fractured basalt aquifers in India while showing positive nonlinearity (exponent\u0026thinsp;\u0026gt;\u0026thinsp;1). The connections between fractures increase groundwater movement by enabling major transmissivity to increase through minimal specific capacity changes. The slightly lower exponent value of 1.082 in the upper wash basin possibly results from variations in the volcanic aquifer fracture density structure and extent of heterogeneity. Transmissivity shows an above-proportional increase with specific capacity, because fractures continue to play an influential role in groundwater movement.\u003c/p\u003e\u003cp\u003eThis upper wash basin research matches the results of other global studies that have focused on volcanic aquifers. The study by (Odong, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found that East African volcanic aquifers in his area adhere to T\u0026thinsp;=\u0026thinsp;1.0\u0026sdot;Sc\u003csup\u003e1.15\u003c/sup\u003e allowing moderate nonlinearity (when 1\u0026thinsp;\u0026lt;\u0026thinsp;exponent) to exist. Volatile rock formations containing fractures and secondary pores create pathways for groundwater flow in the volcanic rocks. The relationship T\u0026thinsp;=\u0026thinsp;1.528\u0026sdot;Sc\u003csup\u003e1.082\u003c/sup\u003e in the Upper Awash Basin matches global research findings because this region contains mostly volcanic rock formations. The minor variations in the exponent value (1.082 compared to 1.15) might stem from the diverse fracturing characteristics, secondary porosity expressions, and geological developmental patterns within the upper wash basin. The moderate nonlinearity present in both analyses indicates that fractures and secondary porosity elements drive groundwater movement through the volcanic aquifers.\u003c/p\u003e\u003cp\u003eThese findings have substantial effects on the assessment of upper wash basins. Research on global aquifers and studies within the Upper Awash Basin have shown that volcanic and fractured aquifers are very productive because fractures and secondary porosity occur within them. Productivity in such hydrological systems creates significant variability because different fracture densities and connectivity levels exist in different regions of oceanic aquifers. This analysis proved that experts should conduct site-specific assessments before establishing groundwater extraction operations to match global studies. Detailed characterization is essential for upper wash basin aquifers because their specific capacity shows a nonlinear relationship with transmissivity owing to the heterogeneity and variability of fractured and volcanic formations.\u003c/p\u003e\u003cp\u003eThe moderate nonlinearity observed in the upper wash basin indicates that the productive aquifer requires localized sustainable management to account for variations in different hydraulic properties. Such management practices adhere to the international best practices for controlling groundwater under difficult hydrogeological conditions. Global studies have confirmed that volcanic aquifer properties in the Upper Awash Basin match those of fractured and volcanic aquifers worldwide. The valuable information derived from this study supports fundamental groundwater management and sustainable resource development initiatives for the upper wash basin through customized methods based on its distinctive hydrogeological characteristics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Spatial distribution of transmissivity and hydraulic Conductivity\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.4.1 Transmissivity\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e displays a transmissivity map of the distribution of groundwater flow capacity throughout the region based on measurements in square meters per day (m\u0026sup2;/day). The transmissivity measure represents the essential hydrogeological factor that determines groundwater movement through aquifers.\u003c/p\u003e\u003cp\u003eTransmissivity was evaluated as less than 10 m\u0026sup2;/day in regions that showed minimal groundwater movement. Groundwater movement in these specific areas has low potential because low-permeability materials such as clay layers, compacted sediments, and unfractured bedrock cover the subsurface. Transmissivity values ranging from 10 m \u0026sup2;/day to 60 m\u0026sup2;/day existed in the areas neighboring the low-permeability zones. These zones possess some degree of permeability, although only minimal water flow occurs because of partially fractured rocks that potentially play a role.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTransmissivity zones between 60 m \u0026sup2;/day and 280 m\u0026sup2;/day demonstrated moderate subsurface water flow. The observed zones show characteristics of weathered rock because they remain the most permeable geological formations. Good groundwater flow occurs in regions with transmissivity measurements ranging from 280 to 800 m\u0026sup2;/day, which is related to groundwater conditions that feature fractured rock formations and areas with dense fracture networks.\u003c/p\u003e\u003cp\u003eA high potential for groundwater movement through the area exists because the transmissivity rates are between 800 m \u0026sup2;/day and 1,970 m\u0026sup2;/day. Extensively fractured bedrock exists in these areas, making the formations highly permeable so that water flows rapidly through them. Areas with transmissivity values above 1,970 m\u0026sup2;/day and exceeding 4,500 m\u0026sup2;/day, along with very high transmissivity values, define regions where major aquifers and highly conductive geological formations exist.\u003c/p\u003e\u003cp\u003eDifferent hydrological configurations appear within regional transmissivity spatial patterns. Most of the western section and the entire southern region in the map exhibited low transmissivity characteristics, which resulted in reduced groundwater flow capacity. Certain ground areas function as barriers to groundwater flow during the fluid movement beneath the soil surface. The central and eastern parts show a mixture of transmissivity patterns, because higher values exist in specific isolated areas. The elevated transmissivity areas matched geological formations, including fault zones and fractures, along with changes in rock types that function as water passage routes. The northeastern corner of the map contains the highest transmissivity ratings, which indicate that a significant water-bearing formation or permeable reservoir exists there.\u003c/p\u003e\u003cp\u003eThe hydrological properties of high-transmissivity regions make them suitable for groundwater extraction, because water flows rapidly through these zones. A critical understanding of these zones is necessary because they indicate the potential presence of both structural and lithological features.\u003c/p\u003e\u003cp\u003eThe transmissivity map functions as an essential instrument for groundwater management, and guides placement decisions and resource planning. The transmissivity map aids operators in identifying recharge areas versus discharge parts, which supports optimized water extraction methods and sustainable management of aquifers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.4.2 Hydraulic conductivity\u003c/h2\u003e\u003cp\u003eThe hydraulic conductivity map (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003e) depicts different rate ranges (m/day) to present the permeability properties of geological rock formations. Locations displaying hydraulic conductivity below 3.5 m/day demonstrate very low permeability because they contain compacted or less permeable bedrock layers. Geological features that hinder groundwater movement include sediments with small particles, materials enriched with clay content, and volcanic rocks with no fractures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegions displaying a hydraulic conductivity of 3.5 to 13.3 m/day indicate that they have intermediate permeability from fractured rocks and mixed sedimentary rock distributions. These areas possess sufficient groundwater mobility, which makes them a feasible resource for groundwater extraction. The amount of extraction depends on two main variables: the intensity of fracturing, communication between porous areas, and variations in mineral composition in the specific geological domain. Well development within these regions is achievable, although the selection of proper sites coupled with pumping tests helps maximize groundwater extraction rates.\u003c/p\u003e\u003cp\u003eHigh hydraulic conductivity zones above 13.3 m/day indicate areas that contain well-fractured volcanic rocks or karstic limestone, together with coarse-grained sediments. These formations promote groundwater flow efficiency, which is ideal for extracting water from an area. Groundwater movement occurs actively in areas with conductivity values above 24 m/d, making them suitable for extracting water from deep wells.\u003c/p\u003e\u003cp\u003eThe hydrologic conductivity shows broad spatial variation throughout the study area because the subsurface aquifers demonstrate varied characteristics. Low-permeability aquifer characteristics prevail mainly in the northern and northwestern districts. Groundwater flow is restricted within these areas because they contain fine-grained deposits and unbroken rocks.\u003c/p\u003e\u003cp\u003eThe central section, together with the southern portion, exhibits multiple high-conductivity hotspots throughout the territory, which indicate ideal areas for extracting groundwater owing to their strong flow potential. These regions with high yields indicate that fractures, faults, and permeable rock formations promoted groundwater movement across these areas.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA complex hydrogeological system based on geological formations and structural controls explains conductance changes across the study area from low to high levels. Various rock formations, together with fractures and faults, most likely generated the observed heterogeneity. Site-specific groundwater assessments must be performed before developing extraction strategies, because the observed data variations reveal this requirement. Complex hydrogeological systems composed of multiple permeability layers exist owing to the different hydraulic conductivity patterns observed.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that a well-specific capacity (Sc) can serve as a reliable, low-cost proxy for estimating transmissivity (T) and hydraulic conductivity (K) in the complex volcanic aquifers of the upper Awash River subbasin. The strong empirical relationship (T\u0026thinsp;=\u0026thinsp;1.528\u0026middot;Sc^1.082, R\u0026sup2; = 0.94) confirmed the utility of Sc as a predictor of aquifer properties, although cross-validation and bootstrap analyses highlighted the importance of accounting for uncertainty, especially in high-yield wells. Geostatistical interpolation revealed that fractured volcanic rocks and structurally controlled zones host the highest T and K values, whereas massive or compacted units exhibit much lower capacities. These findings are consistent with global studies of fractured and volcanic aquifers and highlight the distinct hydrogeological influence of the tectonic and lithological framework of the Awash Basin. By integrating Sc data with kriging-based mapping, this study provides a replicable methodology for regional aquifer assessments in data-limited settings. These results have direct implications for groundwater management, well siting, and sustainable abstraction planning in Ethiopia and similar volcanic terrains.\u003c/p\u003e\u003cp\u003eDespite these contributions, this study has some limitations. The estimation of aquifer thickness from screened intervals or proportions of drilled depth may not fully capture the actual saturated zones, potentially affecting K values. Moreover, while Sc-based methods provide regional insights, they cannot substitute for detailed pumping tests or direct storativity measurements at specific sites. Spatial interpolation also carries uncertainty due to data sparsity in some areas. Future work should incorporate pumping-test-derived storativity, geophysical surveys, and higher-resolution fracture mapping to refine the aquifer property estimates. Advanced geostatistical methods that consider anisotropy and integration with remote sensing data could further improve the prediction accuracy and support groundwater resource planning under climate and population pressures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This study was approved by the Addis Ababa University, Ethiopian Institute of Water Resources. Written informed consent was obtained from all participants before their participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: All authors reviewed and approved the final manuscript for submission. Informed consent for publication was obtained from all participants, where applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e: Research data supporting this publication are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing financial interests or personal relationships that could have influenced the work reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study did not receive external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: Muauz Amare Redda: Conceptualization; Methodology; Software; Formal Analysis; Data Curation; Writing - Original Draft, Behailu Berehanu: Conceptualization; Methodology; Supervision; Writing - Review \u0026amp; Editing, and Bedru Husien: Methodology; Supervision; Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors would like to express their gratitude to the Ethiopian Engineering Corporation (EEC), Ethiopian National Meteorological Agency (NMA), and Ministry of Water and Energy (MoWE) for their valuable contributions to the daily weather and river flow data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcheampong EN, Yu Q, Fu B (2020) Hydrogeological Characterization of Fractured Aquifers Using Integrated Geophysical Methods: A Case Study in Ghana. 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John Wiley and Sons, Inc., Hoboken\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValigi D, Cambi C, Checcucci R, Di Matteo L (2021) Carbonate Aquifers Water 13:1374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w13101374\u003c/span\u003e\u003cspan address=\"10.3390/w13101374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Transmissivity Estimates by Specific Capacity Data of Some Fractured Italian\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYihdego Y, Khalil A, Salem HS (2017) Nile River\u0026rsquo;s Basin Dispute: Perspectives of the Grand Ethiopian Renaissance Dam (GERD). Glob J Human-Social Sci Res 17:1\u0026ndash;20\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Ethiopian Instiute of water resources","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aquifer mapping, Hydraulic conductivity, Geostatistical, Specific capacity, Transmissivity, Volcanic aquifers","lastPublishedDoi":"10.21203/rs.3.rs-7839939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7839939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater is the primary source of water in Ethiopia\u0026rsquo;s upper Awash River Sub-Basin, where surface water availability is unreliable. Estimating aquifer parameters such as transmissivity (T) and hydraulic conductivity (K) is crucial for characterizing groundwater systems, but are often constrained by the cost and logistical challenges of pumping tests, particularly in fractured volcanic terrains. This study demonstrates the use of well-specific capacity (Sc) as a cost-effective proxy for T and K estimation, combined with geostatistical interpolation. A dataset of 341 wells was analyzed and grouped into seven hydrogeological zones. A strong empirical relationship was derived (T\u0026thinsp;=\u0026thinsp;1.528\u0026middot;Sc\u003csup\u003e1.082\u003c/sup\u003e, R\u0026sup2; = 0.94), with bootstrapping and cross-validation confirming its predictive capacity and uncertainty bounds. The spatial variability of T and K was mapped using ordinary kriging, which outperformed inverse distance weighting. The results showed higher T and K values in fractured volcanic units and lower values in massive formations, reflecting the structural and lithological control of groundwater flow. This integrated approach reduces reliance on expensive tests and provides a practical framework for evaluating aquifercharacterization in data-scarce volcanic regions, thereby supporting improved groundwater management and sustainable abstraction strategies.\u003c/p\u003e","manuscriptTitle":"Application of geostatistical methods and well-Specific Capacity for aquifer characterization in the volcanic terrain, upper Awash River sub-basin, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 08:17:10","doi":"10.21203/rs.3.rs-7839939/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"362ee10c-e7f3-4753-86ce-2bec496af6e3","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56151740,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2025-10-14T08:17:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 08:17:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7839939","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7839939","identity":"rs-7839939","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}