Assessing Flooding from Changes in Extreme Rainfall: Using the Design Rainfall Approach in Hydrologic Modeling

Area of Science:

• Climate science
• Hydrology
• Environmental engineering

Background:

• Eastern North Carolina (ENC) faces frequent catastrophic floods from extreme rainfall, often linked to tropical cyclones.
• Accurate quantification of future extreme events and flooding is crucial for effective stormwater management.

Purpose of the Study:

• To visualize potential future flooding extents in ENC using downscaled global climate model data.
• To assess projected changes in extreme precipitation and their impact on riverine flooding under future climate scenarios.

Main Methods:

• Utilized dynamically and statistically downscaled (DD and SD) global climate model rainfall data for two scenarios.
• Computed future precipitation intensity-duration-frequency (PIDF) curves using DD data.
• Applied PIDF curves to Hurricane Matthew rainfall to create "Matthew 2100" projections.
• Simulated 2100 discharges and flood extents in the Neuse River Basin using HEC-HMS and HEC-RAS models.

Main Results:

• Dynamically downscaled (DD) datasets demonstrated superior representation of historical extreme rainfall changes compared to statistically downscaled (SD) datasets.
• Projected rainfall increases in ENC (up to 112%) surpass U.S. published values but remain within historical ranges.
• Matthew 2100 simulations indicate potential increases of 23-69% in peak discharges, 0.4-3 m in water surface elevation, and 8-57% in flooded area.

Conclusions:

• Projected increases in extreme precipitation and subsequent flooding pose significant threats to ENC's population, ecosystems, agriculture, infrastructure, and economy.
• The novel approach using downscaled climate data provides critical insights for future flood risk assessment and adaptation strategies in coastal regions.