Impact of climate and anthropogenic stressors on groundwater inputs to inland coastal waters

点击翻译按钮获取中文摘要

Inland coastal waters are of enormous recreational, economic and ecological value to Canada, yet due to degrading water quality this value is being rapidly diminished. For example, it is estimated that restoring the Great Lakes from their current impaired state will lead to direct economic benefits of USD $6.5-11.8 billion from recreation and tourism alone. Unless effective policy and programs are implemented, water quality will continue to degrade as inland coastal areas are exposed to increasing human impacts, and the climate changes – this could lead to irreversible ecological and economic consequences. To restore and protect inland coastal water quality, policy makers and regulators are urgently requesting guidance on the contribution of various pollution sources including groundwater discharge. While studies have shown that groundwater can be a major pathway for delivering pollutants to coastal waters, its contribution to degrading water quality is poorly understood. For instance, groundwater represents as much as 50% of the water entering surface waters in the Great Lakes Basin, and yet groundwater inputs remain one of the largest uncertainties for long term water quality predictions and in the development of effective policy and programs. Managing pollutant inputs from groundwater is extremely challenging because there is often a large time delay (up to decades) between when land use changes or best management practices are implemented, and when changes in coastal water quality are observed. This is because pollutants often travel very slowly in aquifers. Because of this delay, policy and programs targeted at managing groundwater pollutant inputs will be too late to prevent water quality crises (e.g., frequent massive algal blooms in Lake Erie and Lake Winnipeg, and subsequent shutdown of coastal ecosystems) if they are not proactively implemented based on strong scientific understanding and long term predictions.