Advancing Stormwater Modelling and Management in Urban Watersheds

加拿大每年冬天在道路和停车场上使用500多万吨道路盐。城市雨水径流经常携带大量的物理化学污染物，其中包括道路盐、石油碳氢化合物、重金属、橡胶碎屑、沉积物和磷。尽管我们已经并将继续在加拿大和世界各地的城市雨水管理基础设施方面进行大量投资，但我们对控制这些系统内常见污染物的去向和传输的复杂物理、化学和生物过程以及对我们宝贵的饮用水地下水资源和城市溪流中敏感的水生生物的不利影响知之甚少。这项为期五年的研究计划将在开发具有高度原创性和创新性的方法方面取得突破性进展，以便更准确地划定污染源，以及更好地保护源水免受进一步退化的命运和迁移特征，并将开发改善受损水域质量的新技术。这项研究将综合运用理论、实验室实验、实地试验、基于物理的数值模型和先进的机器学习建模技术，(1)增进对冬季风暴事件严重性的认识，以便更有效地利用现有的遥感和地面实时天气预报数据，以优化已确定的盐分脆弱地区内的道路盐用量；(2)增进关于设计新一代公路强化排水原位径流处理系统的知识，以保护环境敏感地区；(3)发展更准确的方法，模拟暴雨前长时间炎热的夏季干燥天气期间，雨水管理池塘内的温度密度分层；因此，透过研究冷水流内复杂的三维温度分布，加深我们对富热池塘污水的混合和扩散过程对接收溪流内水生生物的影响的认识；(4)增进对控制低氧/缺氧区的频率、持续时间和程度的机制的科学知识，以及从池塘底部沉积物中释放重金属和营养物质的微生物群落的丰富程度，并开发更准确的模型来预测不同维护清理计划下雨水管理池塘的污染物去除效率；以及(5)增进对控制城市流域水系沉积物和磷负荷的基本过程的了解，以进一步深入了解可用于缓解城市溪流中劣质水质的机会，以保护今世后代的人类健康和水生生物。

Canada uses more than 5 million tonnes of road salt every winter on roads and parking lots. Urban stormwater runoff frequently carries within it a substantial physicochemical pollutant burden containing road salt, petroleum hydrocarbons, heavy metals, rubber crumbs, sediment, and phosphorus. Although significant investments have been and will continue to be made in stormwater management infrastructure in our cities across Canada and around the world, we know very little about the complex physical, chemical, and biological processes that govern the fate and transport of common pollutants within these systems and the adverse effects on our precious drinking groundwater sources and the sensitive aquatic life in urban streams. This five-year research program will lead to groundbreaking advances in the development of highly original and innovative methods for more accurate contaminant source delineation and on the fate and transport characterization for better protection of source waters from further degradation and will develop novel technologies for improvement of the quality of impaired waters. This research will apply a combination of theoretical, laboratory experiments, field-scale pilot tests, physically-based numerical models as well as advanced machine learning modeling techniques, (1) to advance knowledge on the severity of winter storm events for a more efficient use of the available, both remotely-sensed and ground-based, real-time weather forecast data to optimize road salt application rates within the identified salt-vulnerable areas; (2) to advance knowledge on the design of a new generation of highway enhanced drainage in-situ runoff treatment system for protection of the environmentally sensitive areas; (3) to develop more accurate methods for modelling the thermal density stratification that occurs in stormwater management ponds during extended hot summer dry-weather periods prior to a storm event; hence, advancing our knowledge of the mixing and dispersion processes of thermally enriched pond effluent impact on the aquatic life within the receiving streams through investigation of the complex 3D temperature profiles within a cold-water stream; (4) to advance scientific knowledge on the mechanisms that govern the frequency, duration, and extent of hypoxic/anoxic zones and the abundance of microbial communities that release the heavy metals and nutrients from the pond bottom sediments and develop more accurate models for prediction of stormwater management pond pollutant removal efficiency under different maintenance cleanout schedule; and (5) to advance knowledge of the fundamental processes that govern stream sediment and phosphorus loads in urban watersheds to provide further insight on opportunities that can be used to mitigate poor water quality in urban streams to protect human health and the aquatic life for the present and the future generations.