Affected by climate change owing to global warming, the frequency of extreme rainfall events has gradually increased in recent years. Many studies have analyzed the impacts of climate change in various fields. However, uncertainty about the scenarios they used is still an important issue. This study used two and four multi-scenarios at the base period (1979–2003) and the end of the 21st century (2075–2099) to collect the top-ranking typhoons and analyze the rainfall conditions of these typhoons in two catchments in northern Taiwan. The landslide-area characteristics caused by these typhoons were estimated using empirical relationships, with rainfall conditions established by a previous study. In addition to counting landslide-area characteristics caused by the typhoons of each single scenario, we also used the ensemble method to combine all scenarios to calculate landslide-area characteristic statistics. Comparing the statistical results of each single scenario and the ensembles, we found that the ensemble method minimized the uncertainty and identified the possible most severe case from the simulation. We further separated typhoons into the top 5%, 5%–10%, and 10%–15% to confirm possible changes in landslide-area characteristics under climate change. We noticed that the uncertainty of the base period and the end of the 21st century almost overlapped if only a single scenario was used. In contrast, the ensemble approach successfully distinguished the differences in both the average values of landslide-area characteristics and the 95% confidence intervals. The ensemble results indicated that the landslide magnitude triggered by medium- and high-level typhoons (top 5%–15%) will increase by 24%–29% and 125%–200% under climate change in the Shihmen Reservoir catchment and the Xindian River catchment, respectively, while landslides triggered by extreme-level typhoons (top 5%) will increase by 8% and 77%, respectively. Still, the uncertainty of landslide-area characteristics caused by extreme typhoon events is slightly high, indicating that we need to include more possible scenarios in future work.
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