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Rapid expert tool for different professions based on estimated ecosystem variables for retrofitting of drainage systems

机译:基于估算的生态系统变量,为排水系统改造提供不同专业的快速专家工具

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摘要

There is a need for a geospatial decision support tool for different professions such as drainage engineers and urban planners, which is useful for a quick assessment of the potential of ecosystem services when retrofitting sustainable drainage systems (SuDS) in urban areas. Therefore, the aim of this paper is to develop an innovative rapid decision support tool based on ecosystem service variables for retrofitting of key SuDS techniques by different professionals such as drainage engineers, developers, ecologists, planners and social scientists. This unique and transparent spreadsheet-based tool proposes the retrofitting of a SuDS technique that obtained the highest ecosystem service score for an urban site. This approach is based on a novel ecosystem service philosophy adapted to SuDS rather than on traditional engineering judgement associated with variables based on quick community and environment assessments. For the Greater Manchester example case study area, a comparison with the traditional approach of determining community and environment variables indicates that infiltration trenches, soakaways and belowground storage systems are usually less preferred than permeable pavement systems regardless of the professional perspective. However, ponds also received relatively high scores, because of their great potential impact in terms of water quality improvement and flood control. The estimation of variables was undertaken with high confidence and manageable error.
机译:需要针对诸如排水工程师和城市规划师之类的不同专业的地理空间决策支持工具,该工具对于在城市地区改造可持续排水系统(SuDS)时快速评估生态系统服务的潜力很有用。因此,本文的目的是开发一种基于生态系统服务变量的创新快速决策支持工具,以供排水工程师,开发商,生态学家,规划师和社会科学家等不同专业人士改造关键的SuDS技术。这个独特且透明的基于电子表格的工具建议对SuDS技术进行改造,该技术在城市地区获得了最高的生态系统服务评分。这种方法基于适用于SuDS的新型生态系统服务理念,而不是基于基于快速社区和环境评估与变量相关联的传统工程判断。对于大曼彻斯特案例研究区,与确定社区和环境变量的传统方法的比较表明,无论从专业角度来看,渗透沟,索道和地下存储系统通常不如渗透性路面系统那么可取。但是,由于池塘在改善水质和防洪方面具有巨大的潜在影响,因此池塘也获得了相对较高的分数。变量的估计具有很高的置信度和可管理的误差。

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