Threshold or Limit? Precipitation Dependency of Austrian Landslides, an Ongoing Challenge for Hazard Mapping under Climate Change

Abstract

Climate change is set to increase landslide frequency around the globe, thus increasing the potential exposure of people and material assets to these disturbances. Landslide hazard is commonly modelled from terrain and precipitation parameters, assuming that shorter, more intense rain events require less precipitation volume to trigger a slide. Given the extent of non-catastrophic slides, an operable vulnerability mapping requires high spatial resolution. We combined heterogeneous regional slide inventories with long-term meteorological records and small-scale spatial information for hazard modelling. Slope, its (protective) interaction with forest cover, and altitude were the most influential terrain parameters. A widely used exponential threshold to estimate critical precipitation was found to incorrectly predict meteorological hazard to a substantial degree and, qualitatively, delineate the upper boundary of natural conditions rather than a critical threshold. Scaling rainfall parameters from absolute values into local probabilities (per km²) however revealed a consistent pattern across datasets, with the transition from normal to critical rain volumes and durations being gradual rather than abrupt thresholds. Scaled values could be reverted into site-specific nomograms for easy appraisal of critical rain conditions by local stakeholders. An overlay of terrain-related hazard with infrastructure yielded local vulnerability maps, which were verified with actual slide occurrence. Multiple potential for observation bias in ground-based slide reporting underlined the value of complementary earth observation data for slide mapping and early warning.

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

“…However, there is currently no consensus on how to select the most appropriate rain gauges for landslides [11,23]. In a methodology similar to that employed in previous studies [5,47], we computed the rainfall data from the nearest rain gauges. The rainfall parameters for each landslide were determined through the distance-weighted mean of precipitation data collected from the five nearest rain gauges (see Equation ( 1)).…”

“…Weather forecasts can predict potential rainfall values several days in advance for areas that are susceptible to landslides, which can indicate the possibility of landslide activity. As a result, rainfall thresholds have become one of the most cost-effective tools in landslide warning systems and susceptibility analysis, which have been widely implemented in many regions [4][5][6][7]. The establishment of rainfall thresholds is intricately linked to the sustainable development of regional geo-hazard warning and prevention.…”

“…The accuracy of both the I-D and E-D models depends on multiple factors, each carrying its own weight in terms of influencing the outcomes, such as the resolution of rainfall and geo-hazard data [29], the relative locations of nearby rain gauges and landslides [30], and the criterion used to define the lower bound of the threshold. To enhance the prediction accuracy of the rainfall thresholds with regard to landslide occurrence, more advanced monitoring techniques and more complex statistical models were developed in the last decade, including the utilization of higher-resolution rainfall and geospatial data [5,31], the integration of landslide-related hydrologic models [32,33], the incorporation of detailed geologic and geomorphological information on the study area [34,35], the utilization of black box models for predictive analytics [36,37], and the use of non-triggering rainfall data in the analysis [38]. These methods expanded the possibilities in the field of landslide prediction, providing a more comprehensive understanding of the landslide dynamics.…”

Analysis of landslide Susceptibility and Tree Felling Due to an Extreme Event at Mid-Latitudes: Case Study of Storm Vaia, Italy

“…During storm Vaia, nine landslide movements were triggered, and these were identified, photographed, and characterised [27]. They were then used to assess landslide susceptibility in the event of an extreme precipitation event of the same magnitude as storm Vaia.…”

Insights on the climate of Bhutan from a new daily 1 km gridded data set for temperature and precipitation

“…For more detailed studies about the local climate, it is essential to have reliable data in high temporal and spatial resolution. The data are either used as input for various models, which often need daily data (e.g., Feigl et al, 2021; Offenthaler et al, 2020) or for bias correction of climate models (e.g., Casanueva et al, 2020; Chimani et al, 2019; Pastén‐Zapata et al, 2020). Especially in mountainous terrain, the spatial resolution should be as high as possible to resolve the differences between valleys and ridges.…”