Investigating the influences of precipitation, snowmelt, and freeze-thaw on rockfall in Glenwood Canyon, Colorado using terrestrial laser scanning

Abstract

Understanding the triggering factors leading to rockfall is essential in managing their risk to transportation infrastructure. Precipitation and freeze-thaw (FT) are widely studied rockfall triggers, but developing reliable, quantitative methods to forecast rockfall in response to weather events remains challenging. Terrestrial laser scanning (TLS) is a powerful tool for high-accuracy modeling of rock slopes, but the frequency of scanning is often too low to correlate rockfall behavior with weather events or seasonal trends. We conducted a TLS campaign between 2017 and 2022 in Glenwood Canyon, Colorado, to investigate the seasonal variability in rockfall triggering and conditioning mechanisms. A total of 44 scans were collected over 5 years and were processed to allow for consistent detection of rockfalls larger than 0.0036 m³ in volume. Meteorological variables relating to precipitation, snowpack, and temperature were modeled using the National Weather Service SNODAS product and were used to complete an exploratory analysis of the correlation of various weather indices with rockfall rate over time. It was found that the short-term sum of liquid precipitation and snowmelt (averaged over the scanning interval or the max single-day total) was a strong predictor of rockfall volume rate between 2018 and 2020, especially in the spring and summer months; a linear model of max daily liquid was able to explain 65% of the variance (R²_adj) in rockfall volume rate in March through August. This implicates springtime snowmelt and rain-on-snow events as strong predictors of rockfall at the study site. We interpret these observations to indicate that snowmelt and rainfall are acting to trigger blocks that have been conditioned (destabilized) over the preceding winter.