Reduction of tsunami inundation by coastal forests in Yogyakarta, Indonesia: a numerical study

• Main Authors: W. Ohira, K. Honda, K. Harada
• Format: Article
• Language: English
• Published: Copernicus Publications 2012-01-01
• Series: Natural Hazards and Earth System Sciences
• Online Access: http://www.nat-hazards-earth-syst-sci.net/12/85/2012/nhess-12-85-2012.pdf

Coastal forests are known to protect coastal areas from environmental degradation. In this paper, we examined another important role of coastal forests – to mitigate tsunami devastations to coastal areas. Using a two-dimensional numerical model (Harada and Imamura model, 2005), we evaluated the damping effects of a coastal forest to resist tsunami inundation in Yogyakarta, Indonesia. In the simulations, we set up a two-km long control forest with a representative topography of the study site and experimented its damping performance sensitivity under various width configurations, e.g. 20, 40, 60, 80, 100 and 200 m. The initial tsunami wave was set such that the inundation depth at the front edge of the forest would not exceed 4 m (tree fragility limit). The forest variables such as species, density, DBH, height and canopy size were determined from a typical forest of the site (<i>Casuarina</i> plantation, 4 trees/100 m<sup>2</sup>, Diameter at Breast Height = 0.20 m). The results showed that coastal forest with 100 m width reduced inundation flux, depth and area by 17.6, 7.0 and 5.7%, respectively. Exponential models were found to describe the relationships between forest width and tsunami inundation transmission. An additional experiment was performed using actual topography and a forest plantation plan with 100 m width for 2.46 km<sup>2</sup>. In this experiment, the results showed that the plan would reduce inundation flux by 10.1%, while the exponential model estimated it to be 10.6%, close to the numerical model results. It suggests that statistical models of forest width and damping effects are useful tools for plantation planning, as it allows for quicker evaluation of the impact of coastal forest without simulation modeling that requires a lot of data, time and computing power.