Using Expert Participation to Evaluate the Accuracy of Hand-Drawn Water-Table Maps.

Water-table maps are fundamental to hydrogeological studies and a manual, hand-drawn method is still commonly used to produce them. Despite this, the accuracy and variability of such maps have received little attention in international literature. In a unique experiment, 63 groundwater professionals drew water-table equipotential contours based on the same dataset of point measurements and were asked to infer flow directions and predict groundwater elevations at predefined locations. The root mean squared error (RMSE) for the average map calibration data was 10.5 m, which is accuracy comparable to numerical groundwater models. This study confirmed that to produce hand-drawn water-table maps, practitioners seek to not only fit the spatial data, but also to conform to their own cognitive model of hydrogeological concepts and processes. The calibration accuracy increased with experience; from a RMSE of 13.3 m for practitioners with 0-3 years of experience to a RMSE of 9.2 m for those with four or more years. Despite considerable variability in the style of the hand-drawn water-table maps, the maps were consistent in their representation of the dominant regional groundwater flow directions. There was less consensus, however, in predicting the direction of surface water-groundwater interaction for a stream reach. Hand-drawn water-table mapping remains useful and valid, especially as a starting point for hydrogeological conceptualization, yet further work is required to resolve issues around transparency, repeatability, and reproducibility.

A generic environmental risk assessment framework for deep-sea tailings placement.

Deep-sea tailings placement (DSTP) involves the oceanic discharge of tailings at depth (usually >100 m), with the intent of ultimate deposition of tailings solids on the deep-sea bed (>1000 m), well below the euphotic zone. DSTP discharges consist of a slurry of mine tailings solids (finely crushed rock) and residual process liquor containing low concentrations of metals, metalloids, flotation agents and flocculants. This slurry can potentially affect both pelagic and benthic biota inhabiting coastal waters, the continental slope and the deep-sea bed. Building on a conceptual model of DSTP exposure pathways and receptors, we developed a stressor-driven environmental risk assessment (ERA) framework using causal pathways/causal networks for each of eight pelagic and benthic impact zones. For the risk characterisation, each link in each causal pathway in each zone was scored using four levels of likelihood (not possible, possible, likely and certain) and two levels of consequence (not material, material) to give final risk rankings of low, potential, high or very high risk. Of the 246 individual causal pathways scored, 11 and 18 pathways were considered to be of very high risk and high risk respectively. These were confined to the benthic zones in the mixing zone (continental slope) and the primary and secondary deposition zones. The new risk framework was then tested using a case study of the Batu Hijau copper mine in Indonesia, the largest DSTP operation globally. The major risk of DSTP is smothering of benthic biota, even outside the predicted deposition zones. Timescales for recovery are slow and may lead to different communities than those that existed prior to tailings deposition. We make several recommendations for monitoring programs for existing, proposed and legacy DSTP operations and illustrate how georeferenced causal networks are valuable tools for ERA in DSTP.

Visualization of Aqueous Geochemical Data Using Python and WQChartPy.

Graphical methods have been widely used for visualization, classification, and interpretation of aqueous geochemical data to obtain a better understanding of surface and subsurface hydrologic systems. This method note presents WQChartPy, an open-source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data. WQChartPy can handle various data formats including Microsoft Excel, comma-separated values (CSV), and general delimited text. The 12 diagrams include eight traditional diagrams (trilinear Piper, Durov, Stiff, Chernoff face, Schoeller, Gibbs, Chadha, and Gaillardet) and four recently proposed diagrams (rectangle Piper, color-coded Piper, contour-filled Piper, and HFE-D) that have not been implemented in existing graphing software. The diagrams generated by WQChartPy can be saved as portable network graphics (PNG), scalable vector graphics (SVG), or portable document format (PDF) files for scientific publications. Jupyter and Google Colab notebooks are available online to illustrate how to use WQChartPy with example datasets. The geochemical diagrams can be generated with several lines of Python codes. Source codes of WQChartPy are publicly available at GitHub (https://github.com/jyangfsu/WQChartPy) and PyPI (https://pypi.org/project/wqchartpy/).

A spatial causal network approach for multi-stressor risk analysis and mapping for environmental impact assessments.

Environmental impact assessment (EIA) relies on rigorous scientific assessment of all potential causal pathways by which large-scale developments may impact on valued assets in a region. Despite their importance to informed decision-making, many EIAs are flawed by incomplete analysis of causal pathways, limited spatial assessment and a lack of transparency about how risks have been evaluated across the region. To address these, we describe an EIA methodology based on network analysis of potential causal pathways in a given region. This network approach is coupled with a systematic evaluation of the likelihood, consequence and mitigation options for each causal pathway from one or more human activities to multiple valued assets. The method includes analysis of the confidence in these evaluations, recognizing where knowledge gaps constrain assessments of risks to particular assets. The causal network approach is complemented by a spatially explicit analysis of the region that allows residual risk (i.e. risk remaining after all feasible mitigations) to be mapped for all valued assets. This identifies which activities could lead to potential impacts of varying concern (rated from 'very low' to 'very high'), their likely pathways, which valued assets are at risk and where these residual risks are greatest. The output maps reveal 'risk hotspots' where more detailed local-scale assessments and monitoring should focus. The method is demonstrated by application to potential impacts on 8 valued assets (aquifers, ecosystems and protected species) due to unconventional gas resource development in the Cooper Basin, central Australia. Results show which activities and causal pathways are of potential concern to different valued assets and where residual risk is greatest for particular species and ecosystems. This spatial causal network provides a systematic, consistent and transparent assessment of potential impacts, improving the quality of decision-making about planned developments and their environmental risks.

A background color scheme for piper plots to spatially visualize hydrochemical patterns.

The combination of ternary diagrams of cations and anions with a central diamond graph make the Piper plot very useful in visualizing groundwater chemistry datasets. One of the major drawbacks is that it is hard to link spatial attributes of the dataset to the plot. In this study, we propose a background color scheme of the Piper plot so that spatial representations of these data can be colored according to their location in the Piper plot. The color scheme is chosen to have maximum resolution while still being perceptually uniform. The linking between Piper plot and maps through this color scheme allows the interpretation of the trends and processes deduced from the Piper plot in terms of the location in the aquifer, the geology, and the groundwater flow dynamics. The colored Piper plot is applied to a groundwater quality dataset of the Condamine Alluvium in Queensland, Australia.