Assessing Climate Change-Induced Flood Risk in the Conasauga River Watershed: An Application of Ensemble Hydrodynamic Inundation Modeling

Abstract

Abstract. This study evaluates the impact of potential future climate change on flood regimes, floodplain protection, and electricity infrastructures across the Conasauga River Watershed in the southeastern United States through ensemble hydrodynamic inundation modeling. The ensemble streamflow scenarios were simulated by the Distributed Hydrology Soil Vegetation Model (DHSVM) driven by (1) 1981–2012 Daymet meteorological observations, and (2) eleven sets of downscaled global climate models (GCMs) during the 1966–2005 historical and 2011–2050 future periods. Surface inundation was simulated using a GPU-accelerated Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) hydrodynamic model. Nine out of the eleven GCMs exhibit an increase in the mean ensemble flood inundation areas. Moreover, at the 1 % annual exceedance probability level, the flood inundation frequency curves indicate a ~ 16 km2 increase in floodplain area. The assessment also shows that even after flood-proofing, four of the substations could still be affected in the projected future period. The increase in floodplain area and substation vulnerability highlights the need to account for climate change in floodplain management. Overall, this study provides a proof-of-concept demonstration of how the computationally intensive hydrodynamic inundation modeling can be used to enhance flood frequency maps and vulnerability assessment under the changing climatic conditions.

Performance Execution Plan 2021 ORNL/AF R&D Collaboration

“…In addition to the research tasks, we have also started to set up some modeling tools on Miller, the AF HPC11 system at ORNL, which will eventually be used to conduct the operational flood and inundation forecasting. Three peer-reviewed journal papers were accepted/published in the past project year (Morales-Hernández et al [2021], Dullo et al [2021b], Dullo et al [2021a]). In addition, Sharif et al [2020] received the Best Paper Award in the 2020 Platform for Advanced Scientific Computing Conference (PASC20).…”