Human populations are increasingly impacted by climate change, especially in the Arctic, where Polar Amplification is accelerating climate impacts to more than four times the global average. The complex, interconnected risks that result from climate change need to be measured, tracked and prioritized, to characterize highly impacted areas. We reviewed a cross section of published vulnerability indices developed to characterize change in the Alaskan Arctic, including ecosystem dynamics like permafrost thaw, water availability, animal habitat and migration, and anthropogenic risks from marine & aviation infrastructure, and oil spill impacts. Further, we evaluated the spatiotemporal relationship of vulnerability indices within the context of industrial development in the Arctic, including shipping, mining, and energy extraction. While this cross section offers a diversity of analysis, the accessibility of the indices to Arctic communities needs improvement. Increasing accessibility of these tools will allow further evaluation of these interconnected vulnerabilities, and understanding feasibility and limitations.

Hypoxic zones and associated nitrate pollution from farms, cities, and industrial facilities is driving declines in water quality that affect ecosystems, economies and human health in major rivers and coastal areas worldwide. In the Mississippi River, the United States Environmental Protection Agency set a goal of reducing nitrogen loading 20% by 2025 but estimating progress toward this goal is difficult because data from in-stream gauges and laboratory samples are too sparse. Satellites have the potential to provide sufficient data across the Mississippi River, if a key methodological challenge can be overcome. Satellites provide data from visible light, but nitrates are only observable with ultraviolet light. We address this methodological challenge by using a two-step surrogate modeling procedure to link optical data and nitrates in the Lower Mississippi River. First, we correlate in-situ nitrate measurements to common water quality parameters, particularly turbidity and chlorophyll, using data from water sensors installed at Baton Rouge, Louisiana, USA, and a long-term data set from Louisiana State University. Second, we correlate these water quality data to satellite estimates of water quality parameters. We found a correlation between these water quality parameters and nitrate concentrations, as indicated by a coefficient of determination, when the relationship was viewed in non-linear parameter space. The spatial extent of the correlation was tested with an upstream nitrate sensor 140 km north of the estimation location. These results provide proof-of-concept that we can develop models that use satellite data to provide large scale monitoring of nitrates across the Mississippi River Basin and other impaired rivers, globally.