Statistical correlation between meteorological and rockfall databases

Abstract. Rockfalls are a major and essentially unpredictable sources of danger, particularly along transportation routes (roads and railways). Thus, the assessment of their probability of occurrence is a major challenge for risk management. From a qualitative perspective, it is known that rockfalls occur mainly during periods of rain, snowmelt, or freeze–thaw. Nevertheless, from a quantitative perspective, these generally assumed correlations between rockfalls and their possible meteorological triggering events are often difficult to identify because (i) rockfalls are too rare for the use of classical statistical analysis techniques and (ii) not all intensities of triggering factors have the same probability. In this study, we propose a new approach for investigating the correlation of rockfalls with rain, freezing periods, and strong temperature variations. This approach is tested on three French rockfall databases, the first of which exhibits a high frequency of rockfalls (approximately 950 events over 11 years), whereas the other two databases are more typical (approximately 140 events over 11 years). These databases come from (1) national highway RN1 on Réunion, (2) a railway in Burgundy, and (3) a railway in Auvergne. Whereas a basic correlation analysis is only able to highlight an already obvious correlation in the case of the "rich" database, the newly suggested method appears to detect correlations even in the "poor" databases. Indeed, the use of this method confirms the positive correlation between rainfall and rockfalls in the Réunion database. This method highlights a correlation between cumulative rainfall and rockfalls in Burgundy, and it detects a correlation between the daily minimum temperature and rockfalls in the Auvergne database. This new approach is easy to use and also serves to determine the conditional probability of rockfall according to a given meteorological factor. The approach will help to optimize risk management in the studied areas based on their meteorological conditions.