Suchergebnisse
Filter
4 Ergebnisse
Sortierung:
Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology
In: Natural hazards and earth system sciences: NHESS, Band 18, Heft 8, S. 2183-2202
ISSN: 1684-9981
Abstract. 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.
Die Flutkatastrophe im Juli 2021 in Deutschland: ein Jahr danach: die Aufarbeitung und erste Lehren für die Zukunft
In: Schriftenreihe des DKKV 62
Institutional Risk and Crisis Communication on Natural Disaster Risks in Yangon, Myanmar
In: IJDRR-D-23-03033
SSRN