Systems-based landslide susceptibility model as a tool in disaster risk management - Abucay, E. R. (Compendium of Human Ecology Researches in the Philippines, 2003)
A number of initiatives from provision of early warning systems, hazard maps, upgrading of weather equipment among others have been done, mainly to reduce the impacts of disaster events. However, the complexities of disasters such as rainfall induced landslides require a systems approach to better understand their nature and dynamics. Simulation results showed that all sampling sites are susceptible (MS3) to highly susceptible (MS1, MS2 and MS4) to landslide occurrence. As rainfall approaches the 24-hr total rainfall of 150mm, landslide susceptibility index (LSI) values tend to decrease. The potential landslide site (MS1) for example had the lowest LSI value when rainfall was equal to or more than 150mm. On the other hand, MS3 site, which had higher slope material strength rating than the other three sites, was classified only as susceptible. Scenario-based simulations results showed that the forest land use (various tree ages) had high susceptibilities. Charcoal-making activities in a forest land use resulted to significant reduction in the LSI. On the other hand, a forest land use with trees >20 years were found to be susceptible to landslide. However, disturbance of man (charcoal making) resulted to a decline in the LSI. Meanwhile, conversion of the forest to agricultural land use (annual) crops such as rice and corn resulted to higher susceptibilities. On the other hand, practicing agroforestry system rather than traditional farming yielded significant reduction in landslide susceptibilities from highly susceptible to susceptible. The effect of climate mainly the variability in daily rainfall resulted to decline in LSI values at each rainfall threshold levels. Moreover, the scenario modeling simulation integrated the different agriculture activities from annual cropping, perennial crops to agroforestry system. If some of the model variables are held constant, the type of vegetation in the study area was found to be the most influential variable in the LSI model of this study.