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Exploring the effects of green infrastructure placement on neighborhood-level flooding via spatially explicit simulations

机译:通过空间显式模拟探索绿色基础设施布置对邻里级洪水的影响

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State and local governments are increasingly considering the adoption of legislation to promote green infrastructure (e.g., bioswales, green roofs) for stormwater management. This interest emerges from higher frequencies of combined sewer outflows, floods and exposure of residents and habitat to polluted water resulting from growing urbanization and related pressure on stormwater management facilities. While this approach is promising, there are many unknowns about the effects of specific implementation aspects (e.g., scale, layout), particularly as urban settlements and climate conditions change over time. If green infrastructure is to be required by law, these aspects need to be better understood. We developed a spatially-explicit process-based model (the Landscape Green Infrastructure Design model, L-GriD) developed to understand how the design of green infrastructure may affect performance at a neighborhood scale, taking into consideration the magnitude of stormevents, and the spatial layout of different kinds of land cover. We inform the mechanisms in our model with established hydrological models. In contrast with watershed data-intensive models in one extreme and site level cost-savings calculators in the other, our model allows us to generalize principles for green infrastructure design and implementation at a neighborhood scale, to inform policy-making. Simulation results show that with as little as 10% surface coverage, green infrastructure can greatly contribute to runoff capture in small storms, but that the amount would need to be doubled or tripled to deal with larger storms in a similar way. When placement options are limited, layouts in which green infrastructure is dispersed across the landscape particularly vegetated curb cuts are more effective in reducing flooding in all storm types than clustered arrangements. As opportunities for green infrastructure placement increase and as precipitation increases, however, patterns that follow the flow-path and accumulation of water become more effective, which can be built on an underlying curb-cut layout. If space constraints prevented any of these layouts, random placement would still provide benefits over clustered layouts. (C) 2016 Elsevier Ltd. All rights reserved.
机译:州和地方政府越来越多地考虑通过立法来促进用于雨水管理的绿色基础设施(例如生物通道,绿色屋顶)。这种兴趣来自下水道,下水道的洪灾以及居民和生境暴露于污染的频率更高,这些污染是由于城市化进程的不断发展以及雨水管理设施的相关压力所致。尽管这种方法很有希望,但对于具体实施方面(例如规模,布局)的影响,还有许多未知数,尤其是随着城市居住区和气候条件的变化而变化。如果法律要求绿色基础设施,则需要更好地理解这些方面。我们开发了一种基于空间的,基于过程的模型(景观绿色基础设施设计模型,L-GriD),旨在了解绿色基础设施的设计如何影响邻域尺度的性能,同时考虑到暴风雨的规模和空间各种土地覆被的布局。我们通过建立的水文模型来告知模型中的机制。与一个极端中的分水岭数据密集型模型和另一个极端中的站点级成本节省计算器相比,我们的模型使我们能够推广邻里规模的绿色基础设施设计和实施原则,以为决策提供依据。仿真结果表明,只有低至10%的地面覆盖率,绿色基础设施才能在小暴风雨中捕获径流,但是要以类似的方式应对更大的暴风雨,需要将其数量增加一倍或三倍。当布置选项受到限制时,绿色基础设施分散在整个景观中的布局(尤其是植被遏制的砍伐)比集群布置更有效地减少了所有风暴类型的洪水。但是,随着绿色基础设施布局机会的增加和降水量的增加,遵循流动路径和水积聚的模式变得更加有效,这可以建立在底层路边砍伐的布局上。如果空间限制阻止了这些布局中的任何布局,则随机放置仍将提供优于集群布局的好处。 (C)2016 Elsevier Ltd.保留所有权利。

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