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PROBABILISTIC LANDSLIDE ENSEMBLE PREDICTION SYSTEMS: LESSONS TO BE LEARNED FROM HYDROLOGY (2018)
Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslide triggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.
Nat. Hazards Earth Syst. Sci., 18, 2183–2202, 2018
Department of Geography and Regional Research, University of Vienna, Universitätsstraße 7, 1010 Vienna, Austria