Hurricane Flood Hazard Assessment for the Archipelago of San Andres, Providencia and Santa Catalina, Colombia

Abstract

Despite the low occurrence of tropical cyclones at the archipelago of San Andres, Providencia, and Santa Catalina (Colombia), Hurricane Iota in 2020 made evident the area vulnerability to tropical cyclones as major hazards by obliterating 56.4 % of housing, partially destroying the remaining houses in Providencia. We investigated the hurricane storm surge inundation in the archipelago by forcing hydrodynamic models with synthetic tropical cyclones and hypothetical hurricanes. The storm surge from synthetic events allowed identifying the strongest surges using the probability distribution, enabling the generation of hurricane storm surge flood maps for 100 and 500 year return periods. This analysis suggested that the east of San Andres and Providencia are the more likely areas to be flooded from hurricanes storm surges. The hypothetical events were used to force the hydrodynamic model to create worst-case flood scenario maps, useful for contingency and development planning. Additionally, Hurricane Iota flood levels were calculated using 2D and 1D models. The 2D model included storm surge (SS), SS with astronomical tides (AT), and SS with AT and wave setup (WS), resulting in a total flooded area (percentage related to Providencia’s total area) of 67.05 ha (3.25 %), 65.23 ha (3.16 %), and 76.68 ha (3.68%), respectively. While Hurricane Iota occurred during low tide, the WS contributed 14.93 % (11.45 ha) of the total flooded area in Providencia. The 1D approximation showed that during the storm peak in the eastern of the island, the contribution of AT, SS, and wave runup to the maximum sea water level was −3.01%, 46.36%, and 56.55 %, respectively. This finding provides evidence of the water level underestimation in insular environments when modeling SS without wave contributions. The maximum SS derived from Iota was 1.25 m at the east of Providencia, which according to this study has an associated return period of 3,234 years. The methodology proposed in this study can be applied to other coastal zones and may include the effect of climate change on hurricane storm surges and sea-level rise. Results from this study are useful for emergency managers, government, coastal communities, and policymakers as civil protection measures.

Spatio-temporal morphological variability of a tropical barrier island derived from the Landsat collection

“…Throughout the last decade, the 40 km long Marambaia barrier island has been investigated for its sedimentary dynamics and geological evolution (e.g., Borges and Nittrouer, 2016;Gomes et al, 2019;Reis et al, 2020;Dadalto et al, 2022), with fewer studies quantifying the shoreline dynamics (Oliveira et al, 2008;Bahiense et al, 2014;Santos et al, 2019;. Given the geomorphological importance of this barrier island and the emergency to understand the morphological behavior of coastal features due to scenarios of sea level rise (IPCC, 2022) and increased storminess (Young and Ribal, 2019;Rey et al, 2021), this work presents a contribution to the diagnostic of its morphological trends over 36 years , supported by Landsat imagery analysis. In contrast to the previous works, this study expands the time scale of the observation, and more images were processed.…”

Estudo das mudanças na linha de costa e vulnerabilidade costeira associada no litoral sul do Rio de Janeiro (SE Brasil)

“…As zonas costeiras, ambientes de interface entre continente e oceano, são áreas densamente ocupadas (Masselink & Hughes, 2003), em que com o aumento de eventos extremos (Reguero et al, 2019;Rey et al, 2021;Young & Ribal, 2019), a crescente subida do nível do mar (Losada et al, 2013) e o aumento da erosão costeira (Mentaschi et al, 2018;Payo et al, 2018) demandam um melhor conhecimento do comportamento morfodinâmico destes ambientes. Recentemente, Vousdoukas et al (2020) verificaram que uma proporção substancial da costa arenosa do mundo está sofrendo erosão, uma situação que pode ser acentuada pelas mudanças climáticas.…”

Evidence of Sea Level Rise at the Peruvian Coast (1942-2019)

“…Moreover, according to Woodworth et al (2019), the fluctuation in sea level change is due to various physical forces (such as atmospheric pressure, wind stress, precipitation, river runoff, ice melting, ocean current or steric component), many of which that are difficult to predict accurately, but accurate prediction are essential to managing risks of coastal flooding (Rey et al, 2018;Rey et al, 2021) and coastal erosion (Aboitiz et al, 2008;Payo et al, 2008;Wahl et al, 2014). This is the reason why estimating the amplitude changes in the seasonal sea level cycle is so crucial and useful.…”