School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia E-mail:
Swiss Federal Institute of Aquatic Science & Technology (EAWAG), Überlandstrasse 133, 8600 Dübendorf, Switzerland.
Water Sci Technol. 2022 Feb;85(4):961-969. doi: 10.2166/wst.2022.046.
Planning for future urban development and water infrastructure is uncertain due to changing human activities and climate. To quantify these changes, we need adaptable and fast models that can reliably explore scenarios without requiring extensive data and inputs. While such models have been recently considered for urban development, they are lacking for stormwater pollution assessment. This work proposes a novel Future Urban Stormwater Simulation (FUSS) model, utilizing a previously developed urban planning algorithm (UrbanBEATS) to dynamically assess pollution changes in urban catchments. By using minimal input data and adding stochastic point-source pollution to the build-up/wash-off approach, this study highlights calibration and sensitivity analysis of flow and pollution modules, across the range of common stormwater pollutants. The results highlight excellent fit to measured values in a continuous rainfall simulation for the flow model, with one significant calibration parameter. The pollution model was more variable, with TSS, TP and Pb showing high model efficiency, while TN was predicted well only across event-based assessment. The work further explores the framework for the model application in future pollution assessment, and points to the future work aiming to developing land-use dependent model parameter sets, to achieve flexibility for model application across varied urban catchments.
由于人类活动和气候的变化，未来城市发展和水基础设施的规划具有不确定性。为了量化这些变化，我们需要适应性强、速度快的模型，这些模型能够在不依赖大量数据和输入的情况下可靠地探索各种情景。虽然这些模型最近已经被用于城市发展的研究，但在城市暴雨污染评估方面仍存在空白。本研究提出了一种新颖的未来城市雨水模拟（FUSS）模型，利用先前开发的城市规划算法（UrbanBEATS），动态评估城市流域中的污染变化。该研究通过使用最少的输入数据，并在累积/冲刷方法中添加随机点源污染，突出了流量和污染模块在常见雨水污染物范围内的校准和敏感性分析。研究结果表明，在连续降雨模拟中，流量模型与实测值拟合良好，仅需一个重要的校准参数。污染模型的变异性更大，TSS、TP 和 Pb 的模型效率较高，而 TN 仅在基于事件的评估中预测效果较好。本研究进一步探讨了该模型在未来污染评估中的应用框架，并指出未来的工作旨在开发基于土地利用的模型参数集，以实现模型在不同城市流域中的灵活应用。
