Harnessing Data and Models to Characterize Hurricane Hazard, Enhance Flash Flood Modeling and Conduct Place-based Assessment of Socio-Economic Vulnerability
Abstract
Hurricane induced flooding is one of the most catastrophic natural disasters imposing extensive damage and disruption to societies. Flooding is an on-going global-scale socio-economic risk that is likely to increase in the future under climate change and human development. The massive socioeconomic impacts provoked by extreme floods is clear motivation for improved understanding of flood drivers, including these drivers in coupled hydrologic and hydrodynamic modeling, and also characterizing the confluence of socio-economic vulnerability with flood characteristics. This presentation is multifold to report various developments we have made recently. This includes presenting an approach that accounts for multiple components with their likelihood of coincidence for appropriate characterization of hurricane hazard knowing that current operational scaling is lacking the consideration of potential drivers of a hazardous situation such as terrestrial and coastal flooding. We show that the multihazard indexing approach better characterizes the hurricane hazard and is more appropriate for risk-informed decision-making. This is followed by a high-resolution modeling based on the Weather Research and Forecasting Hydrological model to improve the skill of hydrometeorological forecasts through simulating prognostic (e.g., soil moisture) and diagnostic (e.g., energy fluxes) variables. However, land surface models most often do not provide accurate and reliable estimates of fluxes and storages and are subject to large uncertainties stemming from hydrometeorological forcing, model parameters, boundary or initial condition and model structure. Here, we present a state-of-the art data assimilation methodology to explore the benefit of jointly assimilating our recent machine-learning based downscaled satellite SMAP soil moisture product at 1-km resolution and the USGS streamflow observations to significantly improve the estimation of storages and fluxes, hence better forecasting skill during extreme events including Hurricane Harvey as our case study. Finally, building resilience to flash floods requires understanding of the socio-economic characteristics of the societies and their vulnerability to these extreme events. We present a framework for assessment of socio-economic vulnerability (SEV) to flash floods and show how coincidence of SEV and flash flood hazard can identify the hotspots. Results indicate the resemblance and heterogeneity of flash flood spatial clustering and vulnerability of the regions and show how identifying these spatial patterns will assist stakeholders and decision makers reach informed and effective decisions under uncertainty for planning and allocating resources.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH226...05M
- Keywords:
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- 1805 Computational hydrology;
- HYDROLOGY;
- 1817 Extreme events;
- HYDROLOGY;
- 1821 Floods;
- HYDROLOGY;
- 1847 Modeling;
- HYDROLOGY