Search Results (3)

Tropical cyclones (TCs) are natural hazards causing extensive damage to society, infrastructure, and the natural environment. Due to the multi-hazardous nature of TCs, comprehensive risk assessments are essential to understanding how to better prepare for potential impacts. This study develops an integrated methodology for TC multi-hazard risk assessment that utilises the following individual assessments of key TC risk components: a variable enhanced bathtub model (VeBTM) for storm surge-driven hazards, a random forest (RF) machine learning model for rainfall-induced flooding, and indicator-based indices for exposure and vulnerability assessments. To evaluate the methodology, the regions affected by TC Debbie (2017) for Queensland and TC Winston (2016) for Fiji’s main island of Viti Levu were used as proof-of-concept case studies. The results showed that areas with the highest risk of TC impacts were close to waterbodies, such as at the coastline and along riverine areas. For the Queensland study region, coastal populated areas showed levels of “high”, “very high”, and “extreme” risk, specifically in Bowen and East Mackay, driven by the social and infrastructural domains of TC risk components. For Viti Levu, areas classified with an “extreme” risk to TCs are primarily areas that experienced coastal inundation, with Lautoka and Vuda found to be especially at risk to TCs. Additionally, the Fiji case study was validated using post-disaster damage data, and a statistically significant correlation of 0.40 between TC Winston-attributed damage and each tikina’s overall risk was identified. Ultimately, this study serves as a prospective framework for assessing TC risk, capable of producing results that can assist decision-makers in developing targeted TC risk management and resilience strategies for disaster risk reduction. Full article

One of the hazards associated with tropical cyclones (TCs) is a storm surge, which leads to coastal inundation and often results in loss of life and damage to infrastructure. In this study, we used GIS-based bathtub models and tide-gauge-derived water levels to assess coastal inundation scenarios for the landfall region of TC Debbie. The three scenarios modelled what could have happened if the TC’s maximum storm surge had coincided with the maximum storm tide for that day, month, or TC season, where the water levels were determined through analysis of tide gauge data, using a new method called the variable enhanced Bathtub Model. Additionally, this study analysed the impact of excluding the correction of water levels with the Australian Height Datum. Our study found that between the least and most severe scenarios, with the input water-level difference for the model along the coastline being 0.43 m, the observed inundation depth of the analysed populated region increased from 0.25 m to 1 m. Ultimately, it was found that in the worst-case scenario, the study region could have experienced coastal inundation 0.63 m higher than it did, inundating 72.53 km² of the coast. The results of this study support the consensus that coastal inundation is highly dependent on the characteristics of the terrain, and that coastal inundation modelling, such as that completed in this study, needs to be performed to better inform decision makers and communities of the potential impacts of TC-induced storm surges. Full article

Tropical Storm Debby brought severe flooding to portions of southwestern Florida during the summer of 2012. Remotely-sensed images were collected to document the flooding and test the results of Hydrologic and Hydraulic (H & H) storm water models constructed by the Southwest Florida Water Management District (SWFWMD). One image, a satellite, multi-band SPOT image was provided to the SWFWMD by the Federal Emergency Management Agency (FEMA). This image was collected within 48 h of the storm event. The SWFWMD also contracted for a very high resolution (60 cm Ground Sample Distance (GSD)) fCIR image to be captured for selected watersheds in Citrus, Hernando and Pasco counties, the areas most impacted by the flooding. Modeled floodplain results were compared to remotely-sensed images that were georeferenced and analyzed using remote sensing techniques. The higher resolution fCIR images more clearly identified flooding for better comparison with modeled results. Although the fCIR images, which were collected three to four days after the storm event, under predicted the overall extent of the modeled floodplain, as the images could not confirm the presence of flooding in areas obscured by dense vegetation, they did consistently confirm both the location and shape of flooding simulated by the model. By using image analysis methods on the Near-Infrared (NIR) band of the fCIR image in conjunction with the Digital Elevation Model (DEM), however, it was possible to identify the extent of flooding in those obscured areas. Field surveys of high water elevations indicated that many locations had receded within hours of the storm event, limiting the ability of the fCIR image from capturing peak flood level in all areas. Overall, these remotely-sensed images provided a good validation of predicted flood levels for a design storm of the magnitude of Tropical Storm Debby. Full article