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., landslidetriggering 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.

Crucial to most landslide early warning system (EWS) is the precise prediction of rainfall in space and time. Researchers are aware of the importance of the spatial variability of rainfall in landslide studies. Commonly, however, it is neglected by implementing simplified approaches (e.g. representative rain gauges for an entire area). With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on deterministic and geostatistical methods. With kriging usually being a labour-intensive, manual task, a simplified variogram modelling routine was applied for the automated processing of up-to-date point information data. Validation showed quite satisfactory results, yet it also revealed the drawbacks that are associated with univariate geostatistical interpolation techniques which solely rely on rain gauges (e.g. smoothing of data, difficulties in resolving small-scale, highly intermittent rainfall). In the perspective, the potential use of citizen scientific data is highlighted for the improvement of studies on landslide EWS.

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 10 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 15 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. 20

Shallow landslide processes in geologically prone areas are recognised to pose threat to both human life and property. As precipitation is one of the main triggers for landslides, hydro-meteorological interrelationships and related future changes regarding frequency and magnitude of landslide processes in particular are of major interest. Long-term monitoring investigations of active landslide sites can provide a better understanding of the kinematic behaviour and triggering conditions of slope instabilities induced by hydrometeorological patterns. In this study, we present the installation and first results of a long-term monitoring setup in the Flysch Zone of Lower Austria equipped with a large variety of combined hydrological and geotechnical measuring techniques. The geological unit of the Flysch Zone, characterised by high contents of clay and the corresponding weathering products, is exceptionally prone to earth and debris slides which are mostly triggered by heavy precipitation events or snow melting. The landslide under investigation is situated in the heterogeneous lithology of Flysch deposits, surrounded by private property and without any agricultural usage. There is evidence of landslide activity since the 1950s. As it is showing only moderate displacement velocities (max. 20 cm in 2009), it represents a predestined study site for a long-term monitoring and the testing of new monitoring techniques. One of the main aims of this study is to characterise the internal structure, assess the current landslide dynamics and to analyse recent process activity by means of surface and subsurface monitoring installations. Surface methods currently include terrestrial laser scanning, GNSS and total station measurements. With these, surface movement rates of approx. 12 cm/6 months have been detected in the most active part of the landslide. Inclinometer measurements together with results from core drillings and penetrations tests suggest a complex, rotational landslide system with different shear zones, consisting of a more active part in the upper 3 m underlain by a less active part down to 9-m depth. As this monitoring site is designed to be operated for at least 10 years, information about its structure and high-resolution, multi-temporal data about its dynamics can be correlated with hydrological cause variables in the future. These insights and the exemplary nature of the study site regarding shallow landslide processes in Flysch deposits will be useful for the development of novel analysis methods for both Lower Austria as well as study sites with similar initial conditions.

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