Infrastructure & Investment

Abstract

Pluvial (rain-driven) flooding poses a significant threat to urban areas worldwide, necessitating accurate flood prediction for effective flood risk adaptation and damage mitigation. This research explores the impact of incorporating below ground city textures, such as basements, garages, and tunnels, into urban flood models on the extent and propagation of surface and subsurface flooding and damages. A high-resolution 1&2D Rain On Mesh (ROM) hydrodynamic flood model for the city of Cambridge was developed. The model integrated various layers of geospatial data and the city's DEM. An extended version of the model, the Basement Model, applies an approach to incorporate below ground city textures within a surface flooding model and was developed for the MIT campus neighborhood, approximately 10% of the case study area. The Campus has a unique feature that 33 of the buildings of the main campus are connected via their basements. A recent crowd-sources activity revealed that there are over 1000 potential points where water can enter the buildings.

Our simulation results showed significant basement flooding that altered the spatial-temporal pattern of surface flooding in the study area as compared to the surface model. The basement model identified significant water inflow to the basement reducing the volume of surface flood waters and reducing much of the surface flooding observed in the surface model. Moreover, this model detected flooding within buildings that lacked directed surface flooding due to flow between the basements.

These models were compared in a flood risk study. Using the basement model the expected damages from the 100-year flood were 33% of the expected damages using state of the art USACE surface flood damage methods. Additionally, there was significant differenced in which buildings were at risk, the total amount of flooding volume, the spatial-temporal propagation, and the damage assessments within structures.

The incorporation of basements and below ground city textures into flood modeling proves to be invaluable in accurately predicting flood extent and propagation. These findings contributed to MIT’s Climate Resilience Pathway to develop of a resilient flood management plan incorporating climate change into campus design processes.

Plain-language Summary

Incorporating the flood storage capacities of basement in urban flood modeling can have a significant impact on the potential flood damage in at building and neighborhood scale. A case study of the MIT Campus shows expected damages can be over 70% less if basements are modeled.

Abstract

This research investigates the critical factors governing the extent and propagation of urban flooding, and their risks on urban systems. A drainage-catchment-based -2D (Catch) and a 1&2D Rain on Mesh (ROM) FE hydrodynamic flood models were constructed, validated, and compared for case studies of Cambridge and Cleveland. The Catch model directs the volume of run-off in a catchment directly into the pipe system bypassing any localized surface flooding in the catchment. Flooding would occur only when a pipe surcharges and that volume is routed over the surface. In contrast the ROM model distributes the rainfall directly on a 2D mesh, calculates the infiltration at each cell and utilizes the city terrain, road, green-zones and building textures geospatial data to route the surface flow via the shallow flow equations to manholes. The ROM model has coupled 1D pipe inflow/outflow with the surface 2D flow and is able to account for the interaction of surcharge flows as well as surface rainfall flows.

Spatial results of the ROM simulations, see Figure 1, revealed for a range of design storms flood extents 7-9.5 times greater and depths 1.2-7 times deeper across the city during peak flooding, as compared to the Catch model. Additionally, the ROM model effectively identified surface flooding in areas where drainage-based model predicted no flooding. The temporal flood propagation was vastly different between the two models, with the ROM model showing peak flooding that more than 5% of the city remained flooded at the conclusion as compared to the 0% for the drainage-based model. The underestimation of duration and extent of flooding greatly impacts the expected damages on urban infrastructure and current drainage-based models dramatically underestimate this simulation output. Our findings demonstrate that ROM mechanisms, when introduced in pluvial flooding simulations, along with the granular details of city texture incorporated within the model, are critical determinants of the extent and propagation of highly dynamic urban flooding. The results highlight the significance of incorporating ROM approaches and granular city information for more informed and refined pluvial and fluvial regional flood modeling, offering valuable insights into urban flooding impacts.

Plain-language Summary

Catchment-based surface runoff models underestimate the duration and extent of flooding as compared to models that incorporate of rain-on-mesh surface runoff modeling in urban drainage analysis. This underestimation of flood extent and duration leads to underestimation of the expected damages and impacts on lifespan of urban infrastructure. Case studies of Cambridge, Massachusetts and Cleveland, Ohio are presented.

Authors' Summary: This Compendium Volume presents a series of guidance notes and more detailed complementary technical notes that offer practical insights in support of enhancing the climate resilience of infrastructure investment projects in Sub-Saharan Africa. This first introductory chapter starts with an overview of the investment conditions and climatic context in the region, followed by a description of the scope of this Compendium Volume and individual notes, target audiences, and a roadmap for users of the contents covered in this Volume.

A computational tool developed by researchers at the MIT Joint Program on the Science and Policy of Global Change pinpoints specific counties within the United States that are particularly vulnerable to economic distress resulting from a transition from fossil fuels to low-carbon energy sources. By combining county-level data on employment in fossil fuel (oil, natural gas, coal) industries with data on populations below the poverty level, the tool identifies locations with high risks for transition-driven economic hardship.

Pages

Subscribe to Infrastructure & Investment