- NSF-PAR ID:
- Date Published:
- Journal Name:
- Frontiers in Climate
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)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.more » « less
Compound flooding, characterized by the co‐occurrence of multiple flood mechanisms, is a major threat to coastlines across the globe. Tropical cyclones (TCs) are responsible for many compound floods due to their storm surge and intense rainfall. Previous efforts to quantify compound flood hazard have typically adopted statistical approaches that may be unable to fully capture spatio‐temporal dynamics between rainfall‐runoff and storm surge, which ultimately impact total water levels. In contrast, we pose a physics‐driven approach that utilizes a large set of realistic TC events and a simplified physics‐based rainfall model and simulates each event within a hydrodynamic model framework. We apply our approach to investigate TC flooding in the Cape Fear River, NC. We find TC approach angle, forward speed, and intensity are relevant for compound flood potential, but rainfall rate and time lag between the centroid of rainfall and peak storm tide are the strongest predictors of compounding magnitude. Neglecting rainfall underestimates 100‐year flood depths across 28% of the floodplain, and taking the maximum of each hazard modeled separately still underestimates 16% of the floodplain. We find the main stem of the river is surge‐dominated, upstream portions of small streams and pluvial areas are rainfall dominated, but midstream portions of streams are compounding zones, and areas close to the coastline are surge dominated for lower return periods but compounding zones for high return periods (100 years). Our method links joint rainfall‐surge occurrence to actual flood impacts and demonstrates how compound flooding is distributed across coastal catchments.
Tropical cyclone (TC) events are major drivers of compound flooding due to the interaction of wind‐driven storm surge and TC rainfall. Traditionally, coastal flood risk models have only taken into account surge flooding, even though it is known that the role of rainfall‐runoff is critical. There is limited understanding about the types of TC events that are capable of producing significant compounding and how site conditions at the coast affect the extent to which storm surge and rainfall‐runoff interact. This study investigates a suite of historical TCs making landfall near the Cape Fear River Estuary, NC, through a loosely coupled physical modeling methodology in order to draw conclusions about the spatial and temporal patterns of storm surge and rainfall that are able to induce significant compound impacts. Results indicate that intense outer rain bands falling over inland portions of the study area can be a driver of river‐surge compounding (increasing river levels by up to 0.36 m), while intense eyewall rainfall along the coast can result in localized compound impacts to coastal streams and tributaries if peak rainfall occurs near the time of peak storm tide. These localized compound impacts can result in defined interaction zones, where neither storm tide alone nor rainfall‐runoff alone can fully explain the observed maximum water levels. These results provide insight about the relative timing and spatial patterns of rainfall and storm surge that are capable of inducing compound flooding during TC events.
Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabine Lake, TX. We employ several trivariate statistical models, including vine‐copulas and a conditional extreme value model, to examine the sensitivity of results to the choice of data pre‐processing steps, statistical model setup, and outliers. We define a response function that represents water levels resulting from the interaction between discharge and surge processes inside Sabine Lake and explore how it is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. Under data constraints, this highlights the need for testing various statistical modelling approaches, while the choice of an optimal approach remains subjective.
Abstract. We investigate here the effects of geometric properties (channel depth andcross-sectional convergence length), storm surge characteristics, friction,and river flow on the spatial and temporal variability of compound floodingalong an idealized, meso-tidal coastal-plain estuary. An analytical model isdeveloped that includes exponentially convergent geometry, tidal forcing,constant river flow, and a representation of storm surge as a combination oftwo sinusoidal waves. Nonlinear bed friction is treated using Chebyshevpolynomials and trigonometric functions, and a multi-segment approach isused to increase accuracy. Model results show that river discharge increasesthe damping of surge amplitudes in an estuary, while increasing channeldepth has the opposite effect. Sensitivity studies indicate that the impactof river flow on peak water level decreases as channel depth increases,while the influence of tide and surge increases in the landward portion ofan estuary. Moreover, model results show less surge damping in deeperconfigurations and even amplification in some cases, while increasedconvergence length scale increases damping of surge waves with periods of 12–72 h. For every modeled scenario, there is a point where river dischargeeffects on water level outweigh tide/surge effects. As a channel isdeepened, this cross-over point moves progressively upstream. Thus, channeldeepening may alter flood risk spatially along an estuary and reduce thelength of a river estuary, within which fluvial flooding is dominant.more » « less