Parsimonious high-resolution landslide susceptibility modeling at continental scales
Citation
Mirus, B. B., Belair, G. M., Wood, N. J., Jones, J., Martinez, S. N. (2024). Parsimonious high-resolution landslide susceptibility modeling at continental scales. AGU Advances, 5(5): e2024AV001214. Link to paper
Abstract
Landslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental-scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under-represent landslide potential in moderate and gently sloping terrain. This study leverages an extensive landslide database (N = 613,724), a high-resolution digital elevation model (10 m), and high-performance computing resources to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. The authors calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split-sample calibration. They then down-sample results to a 90 m grid to account for uncertainty in digital elevation models and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories (N = 172,367) reinforces model selection but highlights spatially variable performance. Therefore, the authors propose a susceptibility classification approach using the concentration of landslide-prone terrain within each down-sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. The resulting map characterizes landslide susceptibility more consistently than prior models, and the transparent classification approach provides flexibility for different risk-reduction tolerances.