Abstract
Heavy precipitation and storm surges often co-occur and compound together to form sudden and severe flooding events. However, we lack comprehensive observational tools with high temporal and spatial resolution to capture these fast-evolving hazards. Remotely sensed images provide extensive spatial coverage, but they may be limited by adverse weather conditions or platform revisiting schedule. River gauges could provide frequent water height measurement but they are sparsely distributed. Riverine flood and storm surge models, depending on input data quality and calibration process, have various uncertainties. These lead to inevitable temporal and spatial gaps in monitoring inundation dynamics. To fill in the observation gaps, this paper proposes a probabilistic method to estimate daily inundation probability by combining the information from multiple sources, including satellite remote sensing, riverine flood depth, storm surge height, and land cover. Each data source is regarded as a spatial evidence layer, and the weight of evidence is calculated by assessing the association between the evidence presence and inundation occurrence. Within a Bayesian model, the fusion results are daily inundation probability whenever at least one data source is available. The proposed method is applied to estimate daily inundation in Harris, Texas, impacted by Hurricane Harvey. The results agree with the reference water extent, high water mark, and extracted tweet locations. This method could help to further understand flooding as an evolving time-space process and support response and mitigation decisions.
Highlights
In the past 50 years, over 650 Atlantic cyclones have lead to life loss, property damage, and psychological consequences [1,2]
During Hurricane Harvey, about half of the casualties were found outside the FEMA 500-year floodplain [4]
To fill the gaps of lacking consistent flood maps, this study proposes a probabilistic method to combine multi-source data, including remote sensing data, estimation from riverine flooding and storm surge models, and underlying surface features, into a daily inundation risk map
Summary
In the past 50 years, over 650 Atlantic cyclones have lead to life loss, property damage, and psychological consequences [1,2]. In the U.S, two to three tropical cyclones cause about 50 deaths per year. 90% of fatalities are water-related, such as those caused by drowning, among which the storm surge is responsible for roughly half of the total deaths. The deadliest storm from 1963 to 2012 was Katrina, costing nearly 40% of the fatalities [3]. In 2017, the hurricane season in the Atlantic got the most attention due to its abnormal intensity and enormous damage, notably from Harvey, Irma, and Maria. During Hurricane Harvey, about half of the casualties were found outside the FEMA 500-year floodplain [4]. To facilitate early warning and prevent damage, risk maps need to be well prepared and communicated with emergency agencies and the public as frequently as possible
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