A numerical investigation of the effects of model parameterization on the delineation of source protection zones under uncertainty.

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.

Reducing Uncertainty of Groundwater Redox Condition Predictions at National Scale, for Decision Making and Policy.

Understanding hydrogeochemical heterogeneity, associated with natural nitrate attenuation, is an integral part of implementing integrated land and water management on a regional or national scale. Redox conditions are a key indicator of naturally occurring denitrification in the groundwater environment, and often used to inform spatial planning and targeted regulation. This work describes the development of a statistical redox condition model for the groundwater environment at a national scale, using spatially variable physiochemical descriptors as predictors.

Aquifer heterogeneity controls to quality monitoring network performance for the protection of groundwater production wells.

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.

Parallel Simulation in Subsurface Hydrology: Evaluating the Performance of Modeling Computers.

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.

Achieving unbiased predictions of national-scale groundwater redox conditions via data oversampling and statistical learning.

An important policy consideration for integrated land and water management is to understand the spatial distribution of nitrate attenuation in the groundwater system, for which redox condition is the key indicator. This paper proposes a methodology to accommodate the computational demands of large datasets, and presents national-scale predictions of groundwater redox class for New Zealand. Our approach applies statistical learning methods to relate the redox class determined on groundwater samples to spatially varying attributes.

The effects of denitrification parameterization and potential benefits of spatially targeted regulation for the reduction of N-discharges from agriculture.

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.