The growing availability of multi-temporal landslide inventories, for example from multi-epoch LiDAR, InSAR, and monitoring, has precipitated a shift from static landslide susceptibility evaluations to a better understanding of both spatial and temporal variations in landslide activity. In parallel, the development of regional to global hydroclimatic models, re-analysis products, next generation remote sensing products, and compilations of in-situ observations (such as ERA5, SMAP-L4, and GSDR) is allowing researchers to obtain a broader understanding of the hydro-meteorological conditions that affect landslide activity: for example soil moisture, snow melt, precipitation, and meso and synoptic scale weather systems. Currently, researchers and practitioners are exploring how linkages between historical landslide activity and hydro-meteorological drivers can be integrated to improve data driven models for landslide situational awareness and early warning systems. This session seeks to bring together a wide range of perspectives from geomorphology, hydrology, meteorology, remote sensing, data science and beyond to share experiences and to spur future research advances and operational application development.
Subtopics may include:
• Constructing multi-temporal landslide activity data sets utilizing remote sensing data and/or point source terrestrial data
• Linking regional landslide activity trends and variability to hydro-meteorological, geological, morphological, or other conditions.
• Evaluating the suitability of different hydroclimatic models, re-analysis datasets, remote sensing products, and in-situ observations to different landslide and terrain types or research objectives
• Approaches to quantifying linkages between hydro-meteorological drivers and landslide activity
• Development and testing of new algorithms and infrastructure, including machine and deep learning approaches, to support weather-related landslide situational awareness and warning
Subtopics may include:
• Constructing multi-temporal landslide activity data sets utilizing remote sensing data and/or point source terrestrial data
• Linking regional landslide activity trends and variability to hydro-meteorological, geological, morphological, or other conditions.
• Evaluating the suitability of different hydroclimatic models, re-analysis datasets, remote sensing products, and in-situ observations to different landslide and terrain types or research objectives
• Approaches to quantifying linkages between hydro-meteorological drivers and landslide activity
• Development and testing of new algorithms and infrastructure, including machine and deep learning approaches, to support weather-related landslide situational awareness and warning