A landscape measure of urban stormwater runoff effects is a better predictor of stream condition than a suite of hydrologic factors
Abstract
Restoration and protection of urban stream ecosystems require knowledge of the primary causes of their degradation. Conventional stormwater drainage has been identified as a primary source of stress to streams, but it remains unclear if the proximal stressor to stream biota can be represented by flow regime alone or requires a metric integrating the range of stressors associated with stormwater runoff or with urban land use more generally. We used the information-theoretic approach to assess whether various hydrologic indicators better predicted SIGNAL (a biotic index using macroinvertebrate families) than did attenuated imperviousness (AI; a landscape measure of connected imperviousness that inversely weights impervious areas by their distance from the nearest stormwater drain or stream) or total imperviousness (TI). The best models using hydrologic indicators were much less plausible than the overall best model, which used only AI. Predictors in the best hydrologic models characterized the magnitude of low-flow antecedent events, overall flow variability, and antecedent flow flashiness. TI was a poorer predictor than AI, but similarly plausible as some hydrologic models. The results suggest that although there are components of the flow regime that degrade stream ecosystems, AI is a better predictor because it integrates hydrologic and other stormwater-driven stressors, such as changes to water quality. Management of stream condition in our study area should focus on addressing conventional stormwater drainage and its associated alterations to hydrology and water quality. The identification of a single metric provides useful insights for others trying to identify simple predictors of complex phenomena.
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