Journal of Hydrology 505 (2013) 299–311 Contents lists available at ScienceDirect Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol Dynamic analysis of stream flow and water chemistry to infer subsurface water and nitrate fluxes in a lowland dairying catchment ⇑ Simon J.R. Woodward a, , Roland Stenger a, Vincent J. Bidwell b,1 a Lincoln Agritech Limited, Private Bag 3062, Hamilton 3240, New Zealand b Lincoln Agritech Limited, PO Box 69133, Lincoln 7640, New Zealand article info summary Article history: The use of process-based, dynamic and spatially-explicit models to describe water and nitrogen fluxes at Received 8 October 2012 the catchment-scale is often hampered by a shortage of detailed land use, hydrological and biogeochem- Received in revised form 30 April 2013 ical information. Accordingly, such complex models tend to be restricted to a small number of well inves- Accepted 28 July 2013 tigated catchments, often associated with research projects. On the other hand, stream flow and stream Available online 12 October 2013 water chemistry time series data are available for a much larger number of catchments, e.g. for many This manuscript was handled by Laurent Charlet, Editor-in-Chief, with the assistance catchments that are routinely monitored by government agencies for state-of-the-environment report- of M. Todd Walter, Associate Editor ing. It was the main aim of this study to provide a spatially lumped model that allows meaningful anal- ysis of catchment-scale water and nitrate fluxes based on such data sets. Keywords: Based on stream flow time series data, catchment hydrodynamics are often analysed using approaches Shallow groundwater derived from the linearised Boussinesq equation, which has analytical solutions for dynamic groundwa- Denitrification ter discharge expressed in terms of eigenvalues and eigenfunctions (eigenmodel approach). Calibrated Groundwater discharge Boussinesq models generally yield a good reproduction of stream flow dynamics, and stable estimates Lumped catchment model for aquifer parameters such as hydraulic conductivity and mean aquifer depth. By linking a soil water bal- Boussinesq equation ance model with two Boussinesq groundwater eigenmodels linked in series, and assuming constant sol- ute concentrations discharging from each source, a dynamic catchment model predicting stream flow and water chemistry at the catchment outlet (‘‘StreamGEM’’) was developed. Compared with previous approaches, inclusion of water chemistry in this model both aided hydrological understanding, and allowed assessment of catchmentscale nitrate fluxes. Simultaneous calibration of the model to stream flow and nitrate concentration data from a small lowland dairying catchment yielded good predictions to both variables (Nash–Sutcliffe Model Efficiency of 0.90 and 0.84), and the fitted parameters were able to be used to estimate annual flow and nitrate fluxes through near- surface, shallow groundwater, and deeper groundwater reservoirs conceptually present in the catchment. The calibration was cross-validated using an independent time series from the same catchment. The results support the hypothesis, based on groundwater observations, that stream flow in the catchment is the result of mixed discharge from a shallower, rapidly draining zone of oxidised groundwater carrying rel- atively high loads of agricultural nitrate, with a relatively deeper and slower draining zone of reduced groundwater that is essentially nitrate free. The proportions of stream flow discharging from the near-sur- face, shallow groundwater, and deeper groundwater reservoirs were estimated to be 5%, 80% and 15%, respectively. In spite of its small contribution to total stream flow, the deeper groundwater reservoir sus- tained stream flow during summer and dominated stream water chemistry 61% of the time. By combining the flow and nitrate concentration estimates derived from model calibration, it was esti- mated that discharge of shallow groundwater was responsible for 91% of the nitrate load entering the stream. However, the predicted nitrate concentration in this reservoir was significantly lower than the predicted nitrate concentration of near-surface flow and root zone leachate concentrations estimated using a nutrient budgeting model. This indicates that denitrification occurs within this reservoir. On the basis of the calibrated model, it was estimated that 36% of the nitrate recharged from the vadose zone gets denitrified within the shallow groundwater reservoir, and up to 9% in the deeper groundwater reservoir. Ó 2013 Elsevier B.V. All rights reserved. ⇑ Corresponding author. Tel.: +64 7 858 4840. E-mail addresses: [email protected] (S.J.R. Woodward), 1. Introduction [email protected] (R. Stenger), [email protected] (V.J. Bidwell). 1 Present address: Vincent Bidwell Consulting, 17 Brookside Road, Rolleston 7614, Diffuse nutrient losses from agriculture pose a globally recogni- New Zealand. sed threat to the quality of the world’s freshwater resources 0022-1694/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jhydrol.2013.07.044 300 S.J.R. Woodward et al. / Journal of Hydrology 505 (2013) 299–311 À (Cherry et al., 2008; Heathwaite, 2010). Losses of nitrate ðNO3 Þ are dynamics and catchment baseflow (Pauwels et al., 2002). Numeri- particularly difficult to manage, as the anion is highly mobile and cal solutions of the full non-linear equation (Rupp and Selker, leaches rapidly to contaminate the underlying groundwater that 2006), and analytical solutions of the linearised equation (Pauwels subsequently discharges to surface waters (Pärn et al., 2012). This and Troch, 2010), can both be used to simulate water table and dis- means that nitrate losses cannot be effectively managed by ripar- charge dynamics, and by calibration to stream flow or groundwater ian exclusion zones or vegetation strips that are more effective in level time series, to estimate aquifer parameters such as saturated reducing the transfer of contaminants that are predominantly hydraulic conductivity and mean aquifer thickness. transported in surface runoff (e.g. sediment, phosphorus and mi- The Boussinesq model can be readily extended to include addi- crobes). Furthermore, the large volume and slow flow rate of many tional flow paths, such as overland/near-surface (NS) flow (Bidwell groundwater systems means that large quantities of leached ni- et al., 2008) or connection with regional groundwater systems trate can be stored and may continue to be discharged to surface (Broda et al., 2012). In an earlier study, Bidwell et al. (2008) cou- waters long after leaching losses have been reduced (Wriedt and pled an eigenvalue–eigenfunction solution of the linearised hori- Rode, 2006; Basu et al., 2010). zontal-aquifer Boussinesq equation (eigenmodel approach) with A mitigating factor is that in some situations nitrate leached a soil water balance and vadose zone model to estimate the rela- into groundwater may be denitrified, predominantly to harmless tive proportions contributed by near-surface drainage and ground- dinitrogen gas (N2). For denitrification to occur, oxygen-depleted water discharge to flow in the Pukemanga Stream, which drains a conditions, suitable electron donors (e.g. carbon, pyrite) and a small (3.0 ha), steep, farmed hill catchment in the southern Hakari- microbial community with the metabolic capacity for denitrifica- mata Range, west of Whatawhata in the Waikato region of New tion are required (Rivett et al., 2008). As nitrate is not necessarily Zealand. Results over 7 years indicated that 78–93% of stream flow conserved in the groundwater system, it is essential to understand was generated from groundwater discharge, the remainder coming not only the leaching losses, but also the flow paths and any atten- from surface runoff and interflow near the soil surface. uation processes possibly occurring between the bottom of the Temporal changes in the relative contributions of overland/ root zone and groundwater discharge into surface waters. The spa- near-surface flow and groundwater discharge may also be reflected tial and temporal variability of these conditions poses a significant in the chemistry of the stream water. Stewart et al. (2007), for challenge to identifying, measuring and quantifying denitrification example, showed how weekly samples from Pukemanga Stream, at a catchment scale (Wriedt and Rode, 2006; Groffman et al., analysed for oxygen-18, silica, tritium and sulphur hexafluoride, 2009; Hesser et al., 2010), and to establishing defensible cause–ef- provided additional evidence for the dominance of groundwater fect relationships, which are needed for improved resource flow. The longer transit times associated with water flowing along management. deeper flow paths was reflected in lower concentrations of tritium Most catchment scale nitrate modelling is done using spatially- and reactive ions, and higher concentrations of silica, relative to explicit forward models such as SWAT (Conan et al., 2003; Ekana- overland/near-surface flow water. This suggests that, by encapsu- yake and Davie, 2005; Glavan et al., 2011; Lam et al., 2012)or lating temporal changes in discharge from multiple flowpaths with MODFLOW-RT3D (Wriedt and Rode, 2006), which require detailed different biogeochemical characteristics, stream chemistry mea- land use and physical data that are often unavailable outside re- surements could provide a useful adjunct to stream flow recording. search projects. At the same time,