Stormwater green infrastructure (SGI), including rain gardens, detention ponds, bioswales, and green roofs, is being implemented in cities across the globe to reduce flooding, combined sewer overflows, and pollutant transport to streams and rivers. Despite the increasing use of urban SGI, few studies have quantified the cumulative effects of multiple SGI projects on hydrology and water quality at the watershed scale. To assess the effects of SGI, Washington, DC, Montgomery County, MD, and Baltimore County, MD, were selected based on the availability of data on SGI, water quality, and stream flow. The cumulative impact of SGI was evaluated over space and time by comparing watersheds with and without SGI, and by assessing how long-term changes in SGI impact hydrologic and water quality metrics over time. Most Mid-Atlantic municipalities have a goal of achieving 10-20% of the landscape drain runoff through SGI by 2030. Of these areas, Washington, DC currently has the greatest amount of SGI (12.7% of the landscape drained through SGI), while Baltimore County has the lowest (7.9%). When controlling for watersheds size and percent impervious surface cover, watersheds with greater amounts of SGI have less flashy hydrology, with 44% lower peak runoff, 26% less frequent runoff events, and 26% less variable runoff. Watersheds with more SGI also show 44% less NO3(-) and 48% less total nitrogen exports compared to watersheds with minimal SGI. There was no significant reduction in phosphorus exports or combined sewer overflows in watersheds with greater SGI. When comparing individual watersheds over time, increases in SGI corresponded to non-significant reductions in hydrologic flashiness compared to watersheds with no change in SGI. While the implementation of SGI is somewhat in its infancy in some regions, cities are beginning to have a scale of SGI where there are statistically significant differences in hydrologic patterns and water quality.

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Spatial prioritization is a critical step in conservation planning, a process designed to ensure that limited resources are applied in ways that deliver the highest possible returns for biodiversity and human wellbeing. In practice, many spatial prioritizations fall short of their potential by focusing on places rather than actions, and by using data of snapshots of assets or threats rather than estimated impacts. We introduce spatial action mapping as an approach that overcomes these shortfalls. This approach produces a spatially explicit view of where and how much a given conservation action is likely to contribute to achieving stated conservation goals. Through seven case examples, we demonstrate simple to complex versions of how this method can be applied across local to global scales to inform decisions about a wide range of conservation actions and benefits. Spatial action mapping can support major improvements in efficient use of conservation resources and will reach its full potential as the quality of environmental, social, and economic datasets converge and conservation impact evaluations improve.