Evaluation of the wave attenuation function of a coastal black pine Pinus thunbergii forest using the individual-based dynamic vegetation model SEIB-DGVM

Abstract

The wave attenuation function of a Japanese black pine forest was evaluated based on its growth at different initial planting densities (P_ini) using the spatially explicit, individual-based, dynamic global vegetation model. The forest dynamics were simulated for 150 years utilizing datasets for tree density and stem diameter at different stand ages obtained in the field. To elucidate the ability of the forest to reduce the wave height (m), a long linear wave that propagates on dry ground was assumed. The attenuation of (m) was expressed as follows: , where , x, and k_i are the initial wave height (m), the distance (m), and the wave attenuation coefficient (m⁻¹), respectively. The tree destruction caused by the waves was considered in order to estimate k_i. The model suggested that there was a peak age that maximized k_i and was dependent on , and that the maximum k_i attained decreased with increasing . When P_ini was varied widely from 0.5 to 4 m⁻², the maximum k_i for a relatively low wave height (≤3 m) changed dramatically. For example, when the maximum k_i ranged from 0.008 to 0.031 m⁻¹, depending on P_ini. Thus, utilizing a relatively low P_ini would be an efficient way of quickly creating a forest capable of sufficient wave attenuation in areas where a relatively high wave height (≥4 m) is expected. It was concluded that regular harvesting and planting would be required to realize the full potential of the coastal forests to attenuate waves, and that tailoring P_ini is one of the management options that could be used to establish a wave prevention forest.

Keywords:

• Density effect
• Disaster prevention
• Ecological modeling
• Forest dynamics
• Tsunami

Acknowledgments

The author is grateful to Profs. A. Hagihara, H. Sekiguchi, and T. Asano as well as Drs. R. Tabuchi, S. Fujii, and T. Sakamoto for their helpful suggestions. This study was partly supported by Exploratory Collaborative Research H20–02 from the Disaster Prevention Research Institute, Kyoto University.