Quantifying the impacts of land cover change on the hydrologic response to Hurricanes Katrina and Ida in the Gulf Coast
Abstract
Hurricanes are among the deadliest and costliest natural disasters. Katrina and Ida hit Louisiana's Gulf Coast as major hurricanes in 2005 and 2021, respectively. They caused significant damages, including immediate flooding and subsequent surplus runoff along the Mississippi River and its contributing rivers. Unfortunately, these damages are often exacerbated by increases in crop land areas and decreases in vegetation areas. Between year 1850 (pre-industrial era) and year 2015 (post-industrial era), crop area has increased by 15% on average while broadleaf deciduous tree area has decreased by 20% over the Lower Mississippi River Basin. To quantify the impact of land use land cover change on the hydrologic response to hurricanes Katrina and Ida in the Gulf Coast, we conduct a twin hydrologic experiment using the ParFlow hydrologic model coupled to the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using two land cover scenarios of the pre- and post-industrial periods. We employed the hydrologic model, ParFlow, which is a massively parallel, three-dimensional hydrologic model simulating coupled surface and subsurface flows for spatial resolutions from meters to kilometers. ParFlow will provide realistic hydrologic dynamics to ELM to better capture the surface energy partition and fluxes in ELM and allow more detailed analysis of the hydrologic response to land cover change during hurricane events.
Results of the twin simulation suggests that changes in land cover led to higher flood magnitude and earlier flood peak for both hurricane Katrina and Ida. This study offers insight into how land cover change influences hurricane-induced flood discharges change in term of volume and timing. While our results focus on the Gulf Coast, these simulations may have relevance to other coastal areas affected by hurricanes.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMNH32C0485T