Abstract:
Climate change is widely recognized as a global
phenomenon that has far-reaching consequences, including
an increase in the frequency and severity of landslides. In
response to this global concern, this study narrows its focus to
predict landslide susceptibility in the Kegalle District in Sri
Lanka, specifically, emphasizing the hydrological aspects
influenced by climate change within this region. Potential
changes in future rainfall regimes were projected using the
HadGEM3-GC31-LL model from the Coupled Model
Intercomparison Project Phase 6 (CMIP6). Coarse-resolution
rainfall from this model was statistically downscaled to the
meteorological station level using the Long Ashton Research
Station Weather Generator, known as LARS-WG. In addition
to the rainfall influence, the effects of watershed slope,
elevation, soil type, land use and land cover type, and the
distance from the river were also considered in the Analytical
Hierarchy Process (AHP). By analyzing historical landslide
occurrences, results demonstrated a concordance of 50%-
70% between observed landslide occurrences and rainfall
effects. The incorporation of other landslide-triggering
factors improved the accuracy by up to 86%. Subsequently,
future landslide maps were generated. The output will aid the
decision-makers in prioritizing mitigation efforts and
disaster-resilient urban planning.
Citation:
L. Gunasinghe, L. Gunawardhana and L. Rajapakse, "Predictive Analysis of Landslide Susceptibility under Hydrological Aspects of Climate Change in Kegalle District, Sri Lanka," 2023 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2023, pp. 119-124, doi: 10.1109/MERCon60487.2023.10355394.