early warning systems; landslides; soil wetness; precursors; electrical resistivity tomography; Time Domain Reflectometry
Stähli Manfred, Wicki Adrian (2021),
Understanding and Reducing Landslide Disaster RiskVolume 3 Monitoring and Early Warning, Springer International Publishing, Cham.
Wicki Adrian, Jansson Per-Erik, Lehmann Peter, Hauck Christian, Stähli Manfred (2021), Simulated or measured soil moisture: which one is adding more value to regional landslide early warning?, in
Hydrology and Earth System Sciences, 25(8), 4585-4610.
Wicki Adrian, Lehmann Peter, Hauck Christian, Seneviratne Sonia I., Waldner Peter, Stähli Manfred (2020), Assessing the potential of soil moisture measurements for regional landslide early warning, in
Landslides.
WickiAdrian, HauckChristian, Monitoring critically saturated conditions for shallow landslide occurrence using electrical resistivity tomography, in
Vadose Zone Journal.
Napf ERT monitoring data
| Author |
Wicki, Adrian |
| Publication date |
08.04.2022 |
| Persistent Identifier (PID) |
doi: 10.16904/envidat.307 |
| Repository |
envidat.ch
|
| Abstract |
The dataset contains the electrical resistivity tomography (ERT) monitoring data from the publication Wicki and Hauck (2022). It contains the unprocessed monitoring data and the filtered monitoring data prior to the inversion process.The data is organized in two zip-files: Napf_Raw_BIN.zip: Raw monitoring data in bin-format Napf_Filtered_DAT.zip: Filtered monitoring data in dat-format including topography of the monitoring lineThe zip files contain the apparent resistivity measurements (ohm m) of the individual measurements. The naming convention of the files is according to following convention: site_profile_configuration_date_time.format
In mountainous areas, landslides triggered by heavy rain present a serious risk to people and infrastructure. Recent major events in central Switzerland have demonstrated the numerousness, abruptness and seemingly unpredictability of landslides based on weather information only. As a consequence of these events, a dedicated research effort has been initiated in Switzerland (and worldwide) to advance fundamentals and develop tools for the early warning of landslides at the regional scale. While most studies focused on the use of precipitation information, e.g. intensities and accumulations, to assess thresholds for the initiation of landslides, less work has been put into the utilization of soil wetness information to anticipate the imminent occurrence of landslides. In this respect, most attempts were made to estimate (spatial or local) soil saturation with numerical hydrological models to assess the criticality of the antecedent soil wetness in terms of slope stability. Such numerical models, however, have limitations with regard to the representation of true soil conditions, and it is very challenging to run them in real-time. Here, in this proposed SNF project, the overarching objective is instead to assess the value of in-situ soil wetness measurements in advertising imminent occurrence of landslides for potential use in future Landslide Early Warning Systems (LEWS). To this end, a comprehensive analysis of available soil wetness data from several pre-alpine and alpine sites in Switzerland will be conducted with the aim to assess statistical values of such time series anteceding observed landslide triggering events that can separate landslide events from non-events. In a second step, a unique dataset of a recent field experiment (artificial irrigation of a hillslope until failure) will be revisited and analyzed in this respect. Then, a comparison of four different soil wetness measurement techniques at one common location in a landslide-prone region (Emmental) will be made with the aim of identifying advantages or disadvantages, respectively, of certain soil-wetness information. And finally, a state-of-the-art numerical model for the spatial simulation of landslide triggering will be applied to serve as a benchmark for the soil-wetness-measurement derived indicators.The project will pursue particular innovations in the analysis of soil-wetness time series by identifying characteristic patterns of soil moisture behavior (e.g. variability-mean relationships) that could be used as precursors of landslides. To this end, the large and complementary dataset of longterm and event-based soil wetness measurements, which is immediately available for this project, is a unique treasure trove to answer the overarching question.As an expected outcome of this project, decision-makers and experts responsible for the warning of natural hazards will receive a better knowledge base for the design of a national soil moisture observatory and to issue regional to national warnings regarding imminent landslide hazard.