Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

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.</p

Beyond ESPREssO - Integrative risk assessment 2025 synergies and gaps in climate change adaptation and disaster risk reduction

Climate change including the more frequent occurrence and increased intensity of extreme climate events are important drivers of disaster events. This causality is accompanied by the fact that long-term impacts of climate change are connected with a high-level of uncertainty: complex interactions, feedback loops and underlying nonlinear effects that describe the consequences in this dynamic context. Special modelling approaches are required to increase understanding of these connections with climate change and related global issues, like environmental, social, economic and political matters. Resilience is a concept that can be used when tackling climate change impacts and decrease vulnerabilities. The holistic concept goes parallel with the understanding of “managing risks instead of managing disasters”! This contribution elaborates now this line of thought and characterizes a risk-oriented modelling and design-oriented perspective. We present overviews on climate change adaptation (CCA) and disaster risk reduction (DRR), respectively, and the related frameworks and methods. Finally, we consider the links between the ESPREssO project with the PLACARD experience as coordination action. Similarities and differences are characterized in detail. Based on this specific comparison, we propose a solution-oriented approach which might overcome the distinctions regarding the different approaches of the projects towards a transformational resilience management perspective, summarizing synergies and gaps as an example for integrative risk assessment beyond ESPREssO. We conclude with a comprehensive framework based on the 5 priority areas (referred as “mission”, terminology introduced in the Horizon Europe Framework) included in the final document of ESPREssO, which could be seen as an example for an integrative risk management combining quantitative and qualitative approaches

Sentinel-1 and Ground-Based Sensors for Continuous Monitoring of the Corvara Landslide (South Tyrol, Italy)

The Copernicus Sentinel-1 mission provides synthetic aperture radar (SAR) acquisitions over large areas with high temporal and spatial resolution. This new generation of satellites providing open-data products has enhanced the capabilities for continuously studying Earth surface changes. Over the past two decades, several studies have demonstrated the potential of differential synthetic aperture radar interferometry (DInSAR) for detecting and quantifying land surface deformation. DInSAR limitations and challenges are linked to the SAR properties and the field conditions (especially in mountainous environments) leading to spatial and temporal decorrelation of the SAR signal. High temporal decorrelation can be caused by changes in vegetation (particularly in nonurban areas), atmospheric conditions, or high ground surface velocity. In this study, the kinematics of the complex and vegetated Corvara landslide, situated in Val Badia (South Tyrol, Italy), are monitored by a network of three permanent and 13 monthly measured benchmark points measured with the differential global navigation satellite system (DGNSS) technique. The slope displacement rates are found to be highly unsteady and reach several meters a year. This paper focuses firstly on evaluating the performance of DInSAR changing unwrapping and coherence parameters with Sentinel-1 imagery, and secondly, on applying DInSAR with DGNSS measurements to monitor an active and complex landslide. To this end, 41 particular SAR images, coherence thresholds, and 2D and 3D unwrapping processes give various results in terms of reliability and accuracy, supporting the understanding of the landslide velocity field. Evolutions of phase changes are analysed according to the coherence, the changing field conditions, and the monitored ground-based displacements

Ballungsraumnahe Waldoekosysteme Abschlussbericht

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