The importance of considering the compound effects of multiple hazards has increased in recent years due to their catastrophic impacts on human lives and property. Compound effects correspond to events with multiple concurrent or consecutive drivers, e.g., heavy storms, coastal flooding, high tides, and sea level rise (SLR). There is a recent evidence on inundation caused by SLR-driven groundwater rise, and there is a distinct knowledge gap in understanding the compound inundation effects of this phenomenon considering the important hydrologic and hydraulic considerations under compound events. To fill this knowledge gap, we developed a novel analytical framework to understand the movements of the surface flow under typical precipitation events considering their interaction with uprising groundwater and SLR in a coastal watershed located in Oakland Flatlands, CA, USA, home to several disadvantaged communities. This modelling approach simulates the dynamics of compound flooding in two dimensions of the earth surface in a fine resolution, which is critical for devising proper flood management strategies. The reason to focus on disadvantaged coastal communities is that such communities typically encounter disproportionate environmental injustices due to the lack of sufficient drainage capacity in their infrastructure. Our results show that by considering the compound effect of SLR, groundwater inundation and precipitation flooding, the drainage capacity of infrastructure will be substantially exceeded, such that over 700 acres of the built infrastructure could be flooded. This is a considerable increase compared to scenarios that do not consider compound effect, or scenarios that consider inappropriate combinations of driving factors. In sum, our results highlight the significance of considering compound effects in the coastal inundation analyses, with a particular emphasis on the role of groundwater rise.

The goal of this research is identifying major sources of uncertainty of an environmentally-sustainable urban drainage infrastructure design, based on hydrologic analysis and life cycle assessment (LCA). The uncertainty analysis intends to characterize and compare relative roles of unreliability, incompleteness, technological difference, and spatial and temporal variation in life cycle impact assessment (LCIA) data, as well as natural variability in hydrologic data. Uncertainties are analyzed using a robust Monte Carlo simulation approach, performed by High Throughput Computing (HTC) and interpreted by Morse-Scale regression models. The uncertainty analysis platform is applied to a watershed-scale LCA of rainwater harvesting systems (RWH) to control combined sewer overflows (CSOs). To consider the watershed-scale implications, RWH is simulated to serve for both the water supply and CSO control in an urban watershed in Toledo, Ohio, USA. Results suggest that, among the studied parameters, rainfall depth (as a hydrologic parameter) caused more than 86% of the uncertainty, while only 7% of the uncertainty was caused by LCIA parameters. Such an emphasis on the necessity of robust hydrologic data and associated analyses increase the reliability of LCA-based urban water infrastructure design. In addition, results suggest that such a topology-inspired model is capable of rendering an optimal RWH system capacity as a function of annual rainfall depth. Specifically, if the system could capture 1/40th of annual rainfall depth in each event from rooftops, the RWH system would be optimal and, thus, lead to minimized life cycle impacts in terms of global warming potential (GWP) and aquatic eco-toxicity (ETW). This capture depth would be around 2.1 cm for Toledo (given an 85 cm/year rainfall and 200 m2 typical roof area), which could be achieved through an RWH system with 4.25 m3 capacity per household, assuming a uniform plan for the entire studied watershed. Capacities smaller than this suggested optimal value would likely result in loss of RWH potable water treatment savings and CSO control benefits, while capacities larger than the optimal would likely incur an excessive wastewater treatment burden and construction phase impacts of RWH systems.

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