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Abstract Hydrologic connectivity controls the lateral exchange of water, solids, and solutes between rivers and floodplains, and is critical to ecosystem function, water treatment, flood attenuation, and geomorphic processes. This connectivity has been well‐studied, typically through the lens of fluvial flooding. In regions prone to heavy rainfall, the timing and magnitude of lateral exchange may be altered by pluvial flooding on the floodplain. We collected measurements of flow depth and velocity in the Trinity River floodplain in coastal Texas (USA) during Tropical Storm Imelda (2019), which produced up to 75 cm of rainfall locally. We developed a two‐dimensional hydrodynamic model at high resolution for a section of the Trinity River floodplain inspired by the compound flooding of Imelda. We then employed Lagrangian particle routing to quantify how residence times and particle velocities changed as flooding shifted from rainfall‐driven to river‐driven. Results show that heavy rainfall initiated lateral exchange before river discharge reached flood levels. The presence of rainwater also reduced floodplain storage, causing river water to be confined to a narrow corridor on the floodplain, while rainwater residence times were increased from the effect of high river flow. Finally, we analyzed the role of floodplain channels in facilitating surface‐water connectivity by varying model resolution in the floodplain. While the resolution of floodplain channels was important locally, it did not affect as much the overall floodplain behavior. This study demonstrates the complexity of floodplain hydrodynamics under conditions of heavy rainfall, with implications for sediment deposition and nutrient removal during floods.
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Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María
Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.
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- PAR ID:
- 10293619
- Date Published:
- Journal Name:
- Hydrology
- Volume:
- 8
- Issue:
- 3
- ISSN:
- 2306-5338
- Page Range / eLocation ID:
- 104
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/hydrology8030104
