Dynamics of extreme risk and resilience for distributed stormwater infrastructure: Insights from 8,000 large culverts in Northeastern USA

Código

I-EBHE0046

Autores

Omar Wani, Omid Emamjomehzadeh

Tema

WG 1.10: Hydrologic Design - Solutions & Communication

Resumo

During extreme rainfall events, floodwaters inundate many road networks - disrupting mobility, jeopardizing traffic, and impacting roads? structural integrity. It is estimated that approximately 75% of flood-related fatalities occur when individuals drive into or attempt to walk through floodwaters. Furthermore, anticipated changes in the loading brought on by climate and land-use change may cause additional capacity deficits in stormwater infrastructure, potentially leading to enormous financial losses. Within this context, the economic impact of culvert failures is significant. For example, the average cost of replacing a culvert in the USA, according to one study, could amount to approximately $ 800,000, with the highest recorded cost being $ 4.2 million. Therefore, it is highly desirable to have a unified, uncertainty-aware framework that produces insights into the risk and resilience of the distributed culvert infrastructure - utilizing high-resolution DEM and other readily available geospatial features. Decision makers are additionally interested in knowing how the culvert failure risks change (a) with the location and size of the catchment, i.e., the dynamics of risk in space, and (b) with land use change and shifts in precipitation patterns due to climate change - i.e., dynamics in time. Here we propose such a framework. We aim to present results from the application of this framework on 8,000 large culverts in the Northeastern United States. Our framework builds on previous work that calculates the hydraulic capacity of culverts to find the return period of peak storm discharge they can handle in Hudson Valley, New York State. However, we additionally leverage (1) Monte Carlo simulations to treat uncertainties, (2) a higher-resolution DEM that is able to resolve small streams and geolocate culverts, (3) large geospatial datasets of catchment characteristics influencing discharge generation, and (4) past and projected rainfall fields. Within this framework, we run extensive simulations to understand the interconnected and compound risks and generate insights on the corresponding resilience of the infrastructure. We also analyze dependencies between resilience and (a) different types of road networks, (b) watershed characteristics like average slope and the order of the stream, (c) land use characteristics, and (d) the rarity of the extreme rainfall event. We finally comment on the scalability of this framework in understanding spatial and temporal dynamics of risks to distributed stormwater infrastructure serving connected road networks over large areas.