Low-Impact Development (LID) in Coastal Watersheds: Infiltration Swale Pollutant Transfer in Transitional Tropical/Subtropical Climates

The control of runoff pollution is one of the advantages of low-impact development (LID) or sustainable drainage systems (SUDs), such as infiltration swales. Coastal areas may have characteristics that make the implementation of drainage systems difficult, such as sandy soils, shallow aquifers and flat terrains. The presence of contaminants was investigated through sampling and analysis of runoff, soil, and groundwater from a coastal region served by an infiltration swale located in southern Brazil. The swale proved to be very efficient in controlling the site’s urban drainage volumes even under intense tropical rainfall. Contaminants of Cd, Cu, Pb, Zn, Cr, Fe, Mn and Ni were identified at concentrations above the Brazilian regulatory limit (BRL) in both runoff and groundwater. Soil concentrations were low and within the regulatory limits, except for Cd. The soil was predominantly sandy, with neutral pH and low ionic exchange capacity, characteristic of coastal regions and not very suitable for contaminant retention. Thus, this kind of structure requires improvements for its use in similar environments, such as the use of adsorbents in soil swale to increase its retention capacity.

Probabilistic Modelling of Sustainable Urban Drainage Systems

Sustainable Urban Drainage Systems (SuDS) gatherer effective strategies and control systems for stormwater management especially in highly urbanized areas characterized by large impervious surfaces that increase runoff peak flow and volume. The main goal is to restore the natural water balance by increasing infiltration, evapotranspiration and promoting rainwater reuse. This paper proposes an analytical probabilistic approach for the modelling SuDS applicable to different structures and goals. Developed equations allow to estimate the probability of overflow and the probability of pre-filling at the end of dry periods, to evaluate the efficiency of the storage in rainwater management and its ability to empty between consecutive events. A great advantage of the proposed method is that it allows to consider a chain of rainfall events; this aspect is particularly important for control systems SuDS characterized by low outflow rates which storage capacity is often not completely available at the end of a dry period because pre-filled by previous events. Suggested formulas were tested to two cases studies in Milan and Genoa, Italy.

Urban Flood Risk and Economic Viability Analyses of a Smart Sustainable Drainage System

Urban drainage systems are in transition from functioning simply as a transport system to becoming an important element of urban flood protection measures providing considerable influence on urban infrastructure sustainability. Rapid urbanization combined with the implications of climate change is one of the major emerging challenges. The increased concerns with water security and the ageing of existing drainage infrastructure are new challenges in improving urban water management. This study carried out in the Seixal area in Portugal examines flood risk analyses and mitigation techniques performed by computational modelling using MIKE SHE from the Danish Hydraulic Institute (DHI). Several scenarios were compared regarding flood risk and sustainable urban drainage systems (SuDS) efficiency. To obtain a more accurate analysis, the economic viability of each technique was analyzed as well through (i) life cost analysis and (ii) taking into account the damages caused by a certain type of flood. The results present that the best scenario is the one that will minimize the effects of great urbanization and consequently the flood risk, which combines two different measures: permeable pavement and detention basin. This alternative allows us to fully explore the mitigation capacity of each viable technique, demonstrating a very important improvement in the flood mitigation system in Seixal.

Machine Learning and Urban Drainage Systems: State-of-the-Art Review

In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies.