Tropical Cyclone Impact and Forest Resilience in the Southwestern Pacific

Abstract

Tropical cyclones (TCs) can have profound effects on the dynamics of forest vegetation that need to be better understood. Here, we analysed changes in forest vegetation induced by TCs using the normalized difference vegetation index (NDVI). We used an accurate historical database of TC tracks and intensities, together with the Willoughby cyclone model to reconstruct the 2D surface wind speed structure of TCs and analyse how TCs affect forest vegetation. We used segmented linear models to identify significant breakpoints in the relationship between the reconstructed maximum sustained wind speed (Wmax) and the observed changes in NDVI. We tested the hypothesis that the rate of change in damage caused by TCs to forest and recovery time would increase according to Wmax thresholds as defined in the widely used Saffir–Simpson hurricane wind scale (SSHWS). We showed that the most significant breakpoint was located at 50 m/s. This breakpoint corresponds to the transition between categories 2 and 3 TCs in the SSHWS. Below this breakpoint, damages caused to forest vegetation and the time needed to recover from these damages were negligable. We found a second breakpoint, with a sharp increase in damages for winds >75 m/s. This suggested that extremely intense tropical cyclones, which might be more frequent in the future, can cause extreme damages to forest vegetation. Nevertheless, we found high variation in the observed damages and time needed to recover for a given Wmax. Further studies are needed to integrate other factors that might affect the exposure and resistance to TCs as well as forests’ capacity to recover from these disturbances.

Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics

“…Based on our models, these proportions can reach ~45%–90% for MSW of 86 m s −1 (the proposed ‘Category 6’ tropical cyclone threshold value suggested by Wehner & Kossin, 2024). Our model indicates a sharp increase in damage when wind speeds reach Category 3 intensity or higher (≥50 m s −1 ); similar patterns were found using a remote sensing vegetation index in the southwest Pacific (Delaporte et al., 2022). This nonlinear increase in tree damage with greater wind speed is due to the geometric scaling of wind effects on trees, which is proportional to the square of the horizontal wind speed (Ancelin et al., 2004; Gardiner et al., 2000; Mayhead, 1973).…”

“…Predictions were made for an 'average Study Identity' (i.e., we ignored the varying Study Identity intercept to generate predictions), a mean DBH of 20 cm and a mean wood density of 0.60 g cm −3 . (Delaporte et al, 2022). This nonlinear increase in tree damage with greater wind speed is due to the geometric scaling of wind effects on trees, which is proportional to the square of the horizontal wind speed (Ancelin et al, 2004;Gardiner et al, 2000;Mayhead, 1973).…”

Global vulnerability and resilience of coastal ecosystems to tropical cyclones in a warming climate

“…We define the vulnerability to tropical cyclones for each ecoregion for the three intensity categories (low, middle, and high) as being either vulnerable, resilient, or dependent [56] (c.f., Fig. 1) based on the panarchy principles [52] and ecosystems dynamics model assumptions [31].…”

Combining field and remote sensing data to estimate forest canopy damage and recovery following tropical cyclones across tropical regions

“…the normalized difference vegetation index-NDVI) derived from optical (350-2500 nm) remotely sensed data can observe vegetation dynamics and ecosystem productivity as they capture the state of an ecosystem through time and space (Chambers et al 2007a, Ostertag et al 2008, without limitations of field sampling. Previous studies have used remote sensing vegetation greenness metrics, such as NDVI (Delaporte et al 2022), leaf area index (LAI) (Wang et al 2010), enhanced vegetation index (EVI) (Rogan et al 2011), and non-photosynthetic vegetation (NPV) (Negrón-Juárez et al 2014, Feng et al 2020 to measure the impact of cyclones on forests, since these vegetative indices (VIs) are able to capture changes to forest state and health. For instance, Lee et al (2008) used remote sensing to calculate NDVI as a proxy for canopy damage from typhoon Herb in a tropical forest in Taiwan.…”