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Adapting urban best management practices for resilience to long-term environmental changes.

ABSTRACT: Stormwater best management practices (BMPs) help mitigate the adverse effects of urban development on stream hydrology and water quality, and are widely specified in development requirements and watershed management plans. However, design of stormwater BMPs largely relies on experience with historic climate, which may not be a reliable guide to the future. To inform BMP design that is robust to future conditions, it is important to examine how potential changes in precipitation, temperature, and potential evapotranspiration will affect the performance of BMPs. We use continuous simulation modeling to examine BMP performance under current and potential future climatic conditions and determine the changes needed in site configuration to address future impacts. We perform modeling for five development types in five different regions of the United States and explore both conventional ("gray") and green infrastructure (GI) stormwater management approaches. If stormwater designs are adapted to address potential future climate conditions, this study suggests that the most cost-effective approaches may use both gray and green BMPs. If the magnitude of extreme weather events increases dramatically, then gray practices that provide detention storage may have better cost-effectiveness. Incorporating risk of future climate impacts into stormwater design may help communities become more resilient. PRACTITIONER POINTS: There is a risk that projected changes in meteorological forcing will negatively affect stormwater BMP performance. Under projected future climate conditions, this study suggests the most cost-effective approaches may use both gray and green BMPs. If the magnitude of extreme weather events increases dramatically, gray practices that provide detention storage may have better cost-effectiveness. Flexibility is beneficial in adaptation and resilience planning due to uncertainty in projected precipitation volume and intensity changes.

SUBMITTER: Job SC

PROVIDER: S-EPMC8018564 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Liming has been used extensively in Scandinavia and elsewhere since the 1970s to counteract the negative effects of acidification. Communities in limed lakes usually return to acidified conditions once liming is discontinued, suggesting that liming is unlikely to shift acidified lakes to a state equivalent to pre-acidification conditions that requires no further management intervention. While this suggests a low resilience of limed lakes, attributes that confer resilience have not been assessed, limiting our understanding of the efficiency of costly management programs. In this study, we assessed community metrics (diversity, richness, evenness, biovolume), multivariate community structure and the relative resilience of phytoplankton in limed, acidified and circum-neutral lakes from 1997 to 2009, using multivariate time series modeling. We identified dominant temporal frequencies in the data, allowing us to track community change at distinct temporal scales. We assessed two attributes of relative resilience (cross-scale and within-scale structure) of the phytoplankton communities, based on the fluctuation frequency patterns identified. We also assessed species with stochastic temporal dynamics. Liming increased phytoplankton diversity and richness; however, multivariate community structure differed in limed relative to acidified and circum-neutral lakes. Cross-scale and within-scale attributes of resilience were similar across all lakes studied but the contribution of those species exhibiting stochastic dynamics was higher in the acidified and limed compared to circum-neutral lakes. From a resilience perspective, our results suggest that limed lakes comprise a particular condition of an acidified lake state. This explains why liming does not move acidified lakes out of a "degraded" basin of attraction. In addition, our study demonstrates the potential of time series modeling to assess the efficiency of restoration and management outcomes through quantification of the attributes contributing to resilience in ecosystems.

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Adapting urban best management practices for resilience to long-term environmental changes.

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