Probabilistic landslide ensemble prediction systems: Lessons to be learned from hydrology

Abstract

Landslide early warning has a long tradition in landslide research. Early warning can be defined as the provision of timely and effective information that allows individuals exposed to a hazard to take action to avoid or reduce their risk and prepare for effective response. In the last decade, hydrological forecasting started operational mode of so called ensemble prediction systems (EPS) following on the success of the use of ensembles for weather forecasting. Those probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty at larger scales and explicitly introduce them into the model results. Now that convective-scale numerical weather predictions and high-performance computing are getting more common, landslide early warning should attempt to learn from past experiences made in the hydrological forecasting community. This paper reviews and summarizes concepts of ensemble prediction in hydrology and how ties to landslide research could improve landslide forecasting. Three future research directions were identified: (1) evaluation of how and to what degree probabilistic landslide forecasting improves predictive skill; (2) adaptation and development of methods for validating and calibrating probabilistic landslide models; (3) application of data assimilation methods to increase the quality of physical parametrization and increased forecasting accuracy.