Investigating the spatio-temporal variability in groundwater and surface water interactions: a multi-technique approach

M S Andersen,I Cartwright,N P Unland,G C Rau,H Hofmann,J Reed,B S Gilfedder,A P Atkinson

doi:10.5194/hess-17-3437-2013

Abstract

Abstract. The interaction between groundwater and surface water along the Tambo and Nicholson rivers, southeast Australia, was investigated using 222Rn, Cl, differential flow gauging, head gradients, electrical conductivity (EC) and temperature profiles. Head gradients, temperature profiles, Cl concentrations and 222Rn activities all indicate higher groundwater fluxes to the Tambo River in areas of increased topographic variation where the potential to form large groundwater–surface water gradients is greater. Groundwater discharge to the Tambo River calculated by Cl mass balance was significantly lower (1.48 × 104 to 1.41 × 103 m3 day−1) than discharge estimated by 222Rn mass balance (5.35 × 105 to 9.56 × 103 m3 day−1) and differential flow gauging (5.41 × 105 to 6.30 × 103 m3 day−1) due to bank return waters. While groundwater sampling from the bank of the Tambo River was intended to account for changes in groundwater chemistry associated with bank infiltration, variations in bank infiltration between sample sites remain unaccounted for, limiting the use of Cl as an effective tracer. Groundwater discharge to both the Tambo and Nicholson rivers was the highest under high-flow conditions in the days to weeks following significant rainfall, indicating that the rivers are well connected to a groundwater system that is responsive to rainfall. Groundwater constituted the lowest proportion of river discharge during times of increased rainfall that followed dry periods, while groundwater constituted the highest proportion of river discharge under baseflow conditions (21.4% of the Tambo in April 2010 and 18.9% of the Nicholson in September 2010).

Highlights

Constraining the interaction between groundwater and rivers is important for calculating water balances and sustainable levels of water extraction (Tsur and Graham-Tomasi, 1991), maintaining healthy river ecology (Boulton, 1993; Krause et al, 2007; Lambs, 2004), understanding biogeochemical reactions at the groundwater–surface water interface (Peyrard et al, 2011; Sophocleous, 2002; Woessner, 2000) and determining the source and fluxes of nutrients and solutes carried by rivers
Average 222Rn activities were highest for the Tambo River during August 2011 (380 ± 62 Bq m−3) and lowest during April 2011 (160 ± 50 Bq m−3). 222Rn activities were generally higher at 28.5, 16.2 and 13.8 km compared to other locations with average activities of 302 ± 51, 288 ± 51 and 326 ± 37 Bq m−3, respectively. 222Rn activities were generally lower at 20.0 and 1.8 km, with average 222Rn activities of 184 ± 52 Bq m−3 and 105 ± 37 Bq m−3, respectively. 222Rn activities in the Nicholson River were generally lower than those in the Tambo River, with 16 of the 27 samples yielding activities below 120 Bq m−3
By combining the use of chemical and physical tracer methods on the Tambo River, increased groundwater influxes were identified near areas of increased topographic variation, where the potential for higher groundwater–surface water gradient formation is increased

Summary

Introduction

Constraining the interaction between groundwater and rivers is important for calculating water balances and sustainable levels of water extraction (Tsur and Graham-Tomasi, 1991), maintaining healthy river ecology (Boulton, 1993; Krause et al, 2007; Lambs, 2004), understanding biogeochemical reactions at the groundwater–surface water interface (Peyrard et al, 2011; Sophocleous, 2002; Woessner, 2000) and determining the source and fluxes of nutrients and solutes carried by rivers. In order to estimate groundwater discharge to rivers and to define gaining and losing reaches, a number of physical, chemical and numerical methods have been developed (Kalbus et al, 2006). Differential flow gauging uses the difference in river discharge at two points along a reach in order to calculate net gains or losses along that stretch (Cey et al, 1998; Harte and Kiah, 2009; McCallum et al, 2012; Ruehl et al, 2006). While gauging stations are usually spaced far apart and often overlook variations at smaller spatial scales, long time series of measurements are commonly available, allowing for analysis of temporal trends and comparison with other methods (McCallum et al, 2013).

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Discussion

Conclusion