Correlation of Geoelectric Data with Aquifer Parameters to Delineate the Groundwater Potential of Hard rock Terrain in Central Uganda

Abstract

Knowledge of aquifer parameters is essential for management of groundwater resources. Conventionally, these parameters are estimated through pumping tests carried out on water wells. This paper presents a study that was conducted in three villages (Tumba, Kabazi, and Ndaiga) of Nakasongola District, central Uganda to investigate the hydrogeological characteristics of the basement aquifers. Our objective was to correlate surface resistivity data with aquifer properties in order to reveal the groundwater potential in the district. Existing electrical resistivity and borehole data from 20 villages in Nakasongola District were used to correlate the aquifer apparent resistivity (q e ) with its hydraulic conductivity (K e ), and aquifer transverse resistance (TR) with its transmissivity (T e ). K e was found to be related to q e by; Log ðK e Þ ¼ À0:002q e þ 2:692. Similarly, TR was found to be related to T by; TR ¼ À0:07T e þ 2260. Using these expressions, aquifer parameters (T c and K c ) were extrapolated from measurements obtained from surface resistivity surveys. Our results show very low resistivities for the presumed water-bearing aquifer zones, possibly because of deteriorating quality of the groundwater and their packing and grain size. Drilling at the preferred VES spots was conducted before the pumping tests to reveal the aquifer characteristics. Aquifer parameters (T o and K o ) as obtained from pumping tests gave values (29,424.7 m 2 /day, 374.3 m/day), (9,801.1 m 2 /day, 437.0 m/day), (31,852.4 m 2 /day, 392.9 m/day). The estimated aquifer parameter (T c and K c ) when extrapolated from surface geoelectrical data gave (7,142.9 m 2 /day, 381.9 m/ day), (28,200.0 m 2 /day, 463.4 m/day), (19,428.6 m 2 /day, 459.2 m/ day) for Tumba, Kabazi, and Ndaiga villages, respectively. Interestingly, the similarity between the K c and K o pairs was not significantly different. We observed no significant relationships between the T c and T o pairs . The root mean square errors were estimated to be 18,159 m 2 /day and 41.4 m/day.

Evaluation of groundwater potential in Kabirwala area, Pakistan: A case study by using geophysical, geochemical and pump data

“…; Batte et al . ; Majumdar and Das ; Niwas and Celik ; Sikandar and Christen ; Akhter and Hasan ; Hasan et al . ).…”

Deciphering transmissivity and hydraulic conductivity of the aquifer by vertical electrical sounding (VES) experiments in Northwest Bangladesh

“…In past three decades, several investigators have studied the relations between aquifer parameters and geoelectric properties (Ponzini et al 1984;Kelly and Frohlich 1985;Onuoha and Mbazi 1988;Mbonu et al 1991;Kalinski et al 1993;Frohlich et al 1996;Dasargues 1997;Singhal et al 1998;Niwas and De Lima 2006;Shevnin et al 2006;Batte et al 2010;Egbai 2011;Ezeh 2011;Majumdar and Das 2011;Sikandar and Christen 2012;Asfahani 2012;Niwas and Celik 2012;Nwosu et al 2013;Ugada et al 2013). Batte et al (2010) correlated geoelectric data with aquifer parameters to delineate the groundwater potential of hard rock terrain in central Uganda. Egbai (2011) used information from vertical electrical sounding (VES) for the determination of the transmissivity of aquifers in Anwai, Delta State of Nigeria.…”

Geoelectrohydraulic parameters of shallow sandy aquifer in Itu, Akwa Ibom State (Nigeria) using geoelectric and hydrogeological measurements

“…This observation then opened up the research space for direct estimation of aquifer parameters from geophysical data like electrical resistivity since both electrical and hydraulic properties convey valuable information on pore space geometry and aquifer heterogeneity (Mazac et al, 1985;Börner et al, 1996;Christensen and Sorensen, 1998;Aristodemou and Thomas-Betts, 2000;Slater, 2007;Soupios et al, 2007;Börner, 2009;Batte et al, 2010;Gemail et al, 2011;Niwas et al, 2011). Thus, by correlating borehole derived aquifer parameter distribution information with similar information generated from the analyses of surface resistivity data, a more realistic hydraulic conductivity and other aquifer property relationship (Yadav and Abolfazli, 1998) can be generated.…”

“…Thus, by correlating borehole derived aquifer parameter distribution information with similar information generated from the analyses of surface resistivity data, a more realistic hydraulic conductivity and other aquifer property relationship (Yadav and Abolfazli, 1998) can be generated. Such relationships can be used to optimally estimate aquifer parameters in other locations in the vicinity (see Robinson and Metternicht, 2006;Soupios et al, 2007;Batte et al, 2010).…”

“…In spite of all the well documented advantages, values of aquifer parameters estimated entirely from surface geophysical data have been found to be fraught with errors even when they are acquired under very favourable conditions. The sources of such errors include inversion errors like those arising from smoothness constraints and other parameter resolution problems, the fidelity level of the field data and the extent of validity of assumptions used in the petrophysical relations that connect the measured variables with the geoelectrohydraulic parameters (Batte et al, 2010). These drawbacks have prompted the development of hybrid methodologies.…”