A modelling approach to assess the impact of land mining on marine biodiversity: Assessment in coastal catchments experiencing catastrophic events (SW Brazil)
Graphical abstract
Introduction
Mining has generated environmental damage worldwide (Fernández-Caliani et al., 2009; Johnson and Hallberg, 2005; Li et al., 2014). The activity has boomed over the second half of the 20th century (Schaffartzik et al., 2016) and there is compelling evidence that the magnitude of associated threats are increasing globally (Rosenau-Tornow et al., 2009). In emerging economies such as Brazil (Meira-Neto and Neri, 2017), China (Li et al., 2014), and India (Muduli et al., 2013), for example, the push for economic development can undermine both industry regulations and management interventions. Besides directly affecting local terrestrial ecosystems (Sonter et al., 2014), mining also affects downstream systems with contaminated water from waste deposits (Leppänen et al., 2017; Rodriguez-Iruretagoiena et al., 2016), and spillages resulting from tailing dam failures (Kossoff et al., 2014; Segura et al., 2016). This last factor can be especially environmentally relevant for downstream marine environments. Sudden collapse of tailing dams can transport large quantities of trace elements and sediment particles over long distances and short time, which then disperse and accumulate in coastal waters. Furthermore, remobilization of sediments after strong storms or wind-driven resuspension could result in an intermittent increase of the total concentration of pollutants even if spill pollution stopped (Hatje et al., 2017). The focus of this paper is on catastrophic events associated with substantial increases in the extent and volume of sediments entering the coastal zone, which can cause ecosystem degradation throughout a region.
Understanding the effects of mining dam collapse on downstream marine systems requires modelling the flow of particles from land to the sea. To date, a substantial number of approaches to modelling runoff and sediment transport has focused on the ongoing pollutant loads resulting from long-term land use changes and non-point pollution sources, such as agricultural runoff and forestry (Álvarez-Romero et al., 2014; Haynes et al., 2007; Jiang et al., 2015; Kroon et al., 2012). Only a few approaches have accounted for extreme disturbances associated with the release of large quantities of pollutants from mining (e.g. Galván et al., 2016). Even less attention has been paid to combining hydrological and oceanographic modelling techniques for predicting the distribution of contaminants in the sea (e.g. Chérubin et al., 2008; Paris and Chérubin, 2008). Oceanographic models are advantageous over remote sensing techniques because of their ability to detect the dispersal patterns of pollutants (e.g. Marta-Almeida et al., 2016), disentangling the extent to which sediment plumes are associated with river discharge (Osadchiev and Zavialov, 2013).
Despite the occurrence of these large-scale incidents over a period of several decades (Kossoff et al., 2014), accidental release of contaminants often occur in places where we have no baseline or pre-disturbance data on ecosystem conditions (e.g. Gomes et al., 2017). The paucity of ecological knowledge of the pre-disturbed state means that we are unable to competently assess the extent of impacts. Furthermore, most studies to date focus on single ecosystems to quantify the impact of land-based disturbances on biodiversity (e.g. Magris et al., 2018; Makino et al., 2013; Saunders et al., 2017), when in reality multiple ecosystems exist in close proximity (e.g., coral reefs, seagrass beds, algal beds, rocky reefs). Existing efforts using a suite of marine ecosystems assumed they respond equally to sediment pollutants (i.e. uniform sensitivity) (e.g. Álvarez-Romero et al., 2013) or predicted only aggregated patterns of exposure to multiple land-based drivers at a relatively coarse scale (Halpern et al., 2015).
Our study proposes a modelling approach to assess the long-term impact of land-based mining catastrophes (i.e. tailing dam failure) on marine ecosystems. We applied a hydrological model that estimates sediment transport following a tailing dam spill event, and used those results as inputs for a coastal-circulation model to simulate the dispersal of pollutants in the sea. We focused on tracking sediments because they have severe environmental impact in their own right (Brodie et al., 2012), and are closely associated with other stressors such as contaminants (e.g. iron, aluminium) (Restrepo et al., 2016). Given the data-limited context associated with catastrophes, we estimated sensitivities of key marine ecosystems (coral reefs, rocky reefs, seagrass meadows, and rhodolith beds) through a meta-analysis. This approach allows us to identify regions most impacted by land-based mining, and at particular risk of ongoing impact. Finally, to illustrate the utility of our modelling approach for conservation, we assessed the extent of impacts on areas of special interest to conservation, such as those under protection (e.g. marine protected areas - MPAs). MPAs intend to eliminate, minimise, or reduce human pressures on protected ecosystems, making them vulnerable to anthropogenic disasters such as tailing dam failure.
Section snippets
Case study
We applied our approach to study the catastrophic tailing dam collapse of the Doce river (Southeastern Brazil, Fig. 1). In November 2015 about 39 million of cubic meters of metal-contaminated slurry polluted riverine and coastal waters when the tailing dam collapsed (Hatje et al., 2017). Instrumental sediment-quality records at the estuary showed that over a week after the arrival of the contaminated plume, the concentrations of some heavy metals (e.g. Cu and Zn) were >200 times higher than
Prediction performance of the SWAT model
Comparisons for observed and simulated monthly discharges across all gauges (Figs. S6–S9) showed that SWAT model achieved “very good” or “good” performance for the calibration time period, whereas it achieved “satisfactory” performance for the validation time period, according to criteria suggested by Moriasi et al., 2007, Moriasi et al., 2015. Following the same criteria, the model's performance could be considered “satisfactory” in simulating monthly sediment load for both time periods (see
Discussion
The ongoing increase of sediment delivered to the ocean (Milliman and Farnsworth, 2013) has several detrimental consequences for a range of ecological processes that might affect whole marine ecosystems. Impacts are especially severe when human activities deliver high sediment loads over short timescales such as the case of intensive and extensive sediment resulting from land-based mining operations. For the Doce river off the southeastern Brazil, our results demonstrate a tractable modelling
Conclusion
In conclusion, this study represents the first preliminary assessment of the hydrological consequences of the recent tailing dam collapse and future conditions on the marine area influenced by land-based mining activity within the Doce river catchment. Analyses of hydrological modelling combined with oceanographic dispersion were conducted through the quantification of exposure to pollution, sensitivity of ecosystems, and their occurrence in coastal marine area adjacent to the river mouth. Our
Acknowledgements
RAM is thankful to CNPQ/Brazil and MITAC/Canada (Mitacs Globalink Early Career Fellowship program) for financial support. RAM also acknowledges ICMBio/MMA and UVic for support in developing this project. NCB received financial support from NSERC. JAFM was funded by the German Research Foundation (DFG project TI 824/3-1).
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