The influence of spatial patterns in rainfall on shallow landslides

Abstract

Understanding how rainfall events influence the pattern and magnitude of landslide response is an important research focus from geomorphological and hazard planning perspectives. Few studies quantitatively relate spatial patterns in rainfall and landslides, largely due to difficulties in acquiring landslide inventories and data on rainfall patterns for individual storm events. However, the increasing availability of frequent, high-resolution satellite imagery and weather radar is overcoming these impediments. Here, we aim to a) identify which factors most influence susceptibility to shallow landslides at the event scale and b) assess how the spatial density of landslides varies in relation to rainfall. We combine landslide inventories spanning study areas located across the upper North Island of New Zealand with rainfall estimates from weather radar to assess the influence of different explanatory variables using a logistic regression model. We found land cover and slope exert the largest influence on landslide susceptibility ahead of intra-event rainfall intensities and pre-event rainfall accumulations. Of the rainfall variables, maximum 12-h rainfall normalised by the 10-y recurrence interval intensity and the 10-d pre-event accumulation normalised by mean annual rainfall had the most influence on susceptibility. Forest cover reduced the sensitivity of landslide spatial density to variations in slope, rainfall, and rock type, in contrast to pasture. We observed a 3.5-fold increase in mean landslide density once the maximum 12-h intensity was ≥25 % above the 10-y recurrence interval intensity for pastoral land on weak sedimentary rocks. This threshold is consistent with the increase in 12-h rainfall by late century under the highest levels of projected warming in New Zealand, which suggests that the landslide response to storm rainfall could be significantly amplified by climate change. Our study demonstrates the insights gained from combining event inventories with spatial rainfall data to better understand factors influencing landslide susceptibility.