Source protection zone delineation has evolved over the past decades from fixed radius or analytical and numerical methods which do not consider uncertainty, to more complex stochastic numerical approaches. In this paper we explore options for delineating a source protection zone, while considering the inherent uncertainty involved in characterizing hydraulic conductivity. We consider a representative pumping well in an unconfined alluvial aquifer under steady-state flow conditions, with the hydraulic conductivity distribution inferred from borehole lithology data in the West Melton area near Christchurch, New Zealand. Lithologies are categorized according to their inferred hydraulic flow and transport properties, using two to four hydrofacies groupings. Probabilistic source protection zones are determined for alternative lithology categorization scheme and hydrofacies conductivity parameterization methods. Results show that the choice of calibration method significantly impacts the delineated source protection zone. In heterogeneous aquifers, the degree of protection offered by deeper pumping wells may be overstated, and forward particle tracking proved more comprehensive than backward tracking due to the complexity of flow paths near the well screen. Simple models, such as homogeneous models, require upscaled parameters to effectively represent aquifer heterogeneity, providing insights into how simplified source protection zone delineation could be made more robust in highly heterogeneous contexts.

Rapid transmission of contaminants in groundwater can occur in alluvial gravel aquifers that are permeated by highly conductive small-scale open framework gravels (OFGs). This open framework gravel structure and the associated distribution of hydraulic properties is complex, and so assessments of contamination risks in these aquifers are highly uncertain. Geostatistical models, based on lithological data, can be used to quantitatively characterize this structure. These models can then be used to support analyses of the risks of contamination in groundwater systems. However, these geostatistical models are themselves accompanied by significant uncertainty. This is seldom considered when assessing risks to groundwater systems. Geostatistical model uncertainty can be reduced by assimilating information from hydraulic system response data, but this process can be computationally challenging. We developed a sequential conditioning method designed to address these challenges. This method is demonstrated on a transition probability based geostatistical simulation model (TP), which has been shown to be superior for representing the connectivity of high permeability pathways, such as OFGs. The results demonstrate that the common modelling practice of adopting a single geostatistical model may result in realistic predictions being overlooked, and significantly underestimate the uncertainties of groundwater transport predictions. This has important repercussions for uncertainty quantification in general. It also has repercussions if using ensemble-based methods for history matching, since it also relies on geostatistical models to generate prior parameter distributions. This work highlights the need to explore the uncertainty of geostatistical models in the context of the predictions being made.

A groundwater monitoring network surrounding a pumping well (such as a public water supply) allows for early contaminant detection and mitigation where possible contaminant source locations are often unknown. This numerical study investigates how the contaminant detection probability of a hypothetical sentinel-well monitoring network consisting of one to four monitoring wells is affected by aquifer spatial heterogeneity and dispersion characteristics, where the contaminant source location is randomized. This is achieved through a stochastic framework using a Monte Carlo approach. A single production well is considered that results in converging non-uniform flow close to the well. Optimal network arrangements are obtained by maximizing a weighted risk function that considers true and false positive detection rates, sampling frequency, early detection, and contaminant travel time uncertainty. Aquifer dispersivity is found to be the dominant parameter for the quantification of network performance. For the range of parameters considered, a single monitoring well screening the full aquifer thickness is expected to correctly and timely identify at least 12% of all incidents resulting in contaminants reaching the production well. This proportion increases to a global maximum of 96% for a network consisting of four wells and very dispersive transport conditions. Irrespective of network size and sampling frequency, more dispersive transport conditions result in higher detection rates. Increasing aquifer heterogeneity and decreasing aquifer spatial continuity also lead to higher detection rates, though these effects are diminished for networks of 3 or more wells. Statistical anisotropy has no effect on the network performance. Earlier detection, which is critical for remedial action and supply safety, comes with a significant cost in terms of detection rate, and should be carefully considered when a monitoring network is being designed.

Monte Carlo uncertainty analysis, model calibration and optimization applications in hydrology, usually involve a very large number of forward transient model solutions, often resulting in computational bottlenecks. Parallel processing can significantly reduce overall simulation time, benefiting from the architecture of modern computers. This work investigates system performance using two realistic flow and transport modeling scenarios, applied to various modeling hardware, to provide information on the expected performance of parallel simulations and inform investment decisions. We investigate how performance, measured in terms of speedup and efficiency, changes with increasing number of parallel processes. We conclude that the maximum performance achieved by parallelization can range from 40% to 100% of the theoretical limit, with the lower increases associated with multi‐CPU servers. The number of parallel processes required to maximize performance is application dependent, and in contrast to common practice, often needs to be significantly larger than the total number of system CPU cores. Further testing is required to better understand how the physical problem being simulated affects the optimal number of parallel processes needed. Finally, when laptops are considered for modeling applications, careful consideration should be given not only to the specifications but also to the intended use designated by the manufacturer.

Diffuse nitrate leaching from agricultural areas is a major environmental problem in many parts of the world. Understanding where in a catchment nitrate is removed is key for designing effective land use management strategies that protect water quality, while minimizing the impact on economic development. In this study we assess the effects of spatially targeted nitrate leaching regulation in a basin with limited knowledge of the complexity of chemical heterogeneity. Three alternative nitrate reactivity spatial parameterizations were incorporated in a catchment-scale flow and transport model and used to evaluate the effectiveness of four possible spatially targeted regulation options. Our findings confirm that denitrification parameterization cannot be numerically determined based on model inversion alone. Detailed field based characterization using physical and geochemical methods should be considered and incorporated in the numerical inversion scheme. We also demonstrate that there are potential benefits of implementing spatially targeted regulation compared to spatially uniform regulation. Focusing regulation in areas where nitrate residence time is short, such as riparian zones or areas with low natural N-reduction, results in greater reduction of N-discharges through groundwater. Significantly improved efficiencies can be expected when delineation of management zones considers the chemical heterogeneity and groundwater flow paths. These improved efficiencies are achieved by adopting management rules that regulate land use in discharge sensitive areas, where leaching changes contribute the most to the catchment nitrate discharges. In our case study, regulation in discharge sensitive zones was twice as efficient compared to other management options.

River recharge inputs constitute a significant proportion of the water balance in many New Zealand aquifers. There is a large uncertainty regarding estimates of river-groundwater fluxes based on hydraulic measurements alone, particularly for large braided river systems that present a number of technical challenges for accurate flow measurement. Hydrochemistry information, such as radon gas activity, provides an additional secondary means for measuring river - groundwater exchange at the riparian margins of a fluvial system. In this study we examined the usefulness of Radon-222 for determining the rates at which the Waimakariri River recharges the Canterbury Plains aquifer at Halkett and Crossbank. Radon concentrations measured in the Waimakariri River were very low and concentrations increased in shallow groundwater with increasing distance from the river, which was consistent with radon ingrowth processes and prior knowledge about the site hydrology. The data were modelled using the ingrowth equation for radon to determine values for the equilibrium radon value and groundwater velocity near the river, using simplified assumptions about transport flowpaths. The estimated groundwater seepage velocities of 350 m/day and 390 m/day at the Halkett and Crossbank sites, respectively, are relatively high but feasible near a large braided river. There was no significant variation in radon concentrations (differences were within analytical error) in the shallow groundwater with flow in the Waimakariri River, which ranged from about 50 to 250 m3/s. Translating groundwater velocities to effective recharge fluxes requires simplifying assumptions concerning the dimensions of the effective recharging area, whether recharge is constant along a particular reach, and on the estimated effective porosity of the groundwater system. None of these properties are reliably known at the study site and this precluded any recharge flux estimations from the radon data. Despite these limitations, radon can be used to infer useful knowledge about riparian aquifer systems. For example, continuous measurement of radon at a single observation well would provide information about how recharge at a particular location varies with time and river flow. Estimation of groundwater velocities using radon measurement from wells located at regular intervals down a river could give information on the likely variation in recharge amounts, and intensive monitoring of radon over a small spatial region could provide detail on preferential flow paths in a riparian zone.