Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam.

The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years

Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting?

Predicting rainfall-induced landslides hinges on the quality of the rainfall product. Satellite rainfall estimates or rainfall reanalyses aid in studying landslide occurrences especially in ungauged areas, or in the absence of ground-based rainfall radars. Quality of these rainfall estimates is critical; hence, they are commonly crosschecked with their ground-based counterparts. Beyond their temporal precision compared to ground-based observations, we investigate whether these rainfall estimates are adequate for hindcasting landslides, which particularly requires accurate representation of spatial variability of rainfall. We developed a logistic regression model to hindcast rainfall-induced landslides in two sites in Japan. The model contains only a few topographic and geologic predictors to leave room for different rainfall products to improve the model as additional predictors. By changing the input rainfall product, we compared GPM IMERG and ERA5 rainfall estimates with ground radar–based rainfall data. Our findings emphasize that there is a lot of room for improvement of spatiotemporal prediction of landslides, as shown by a strong performance increase of the models with the benchmark radar data attaining 95% diagnostic performance accuracy. Yet, this improvement is not met by global rainfall products which still face challenges in reliably capturing spatiotemporal patterns of precipitation events.Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347Disaster Prevention Research Institute, Kyoto University http://dx.doi.org/10.13039/501100006086Deutscher Akademischer Austauschdienst http://dx.doi.org/10.13039/50110000165

Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting?

<jats:title>Abstract</jats:title><jats:p>Predicting rainfall-induced landslides hinges on the quality of the rainfall product. Satellite rainfall estimates or rainfall reanalyses aid in studying landslide occurrences especially in ungauged areas, or in the absence of ground-based rainfall radars. Quality of these rainfall estimates is critical; hence, they are commonly crosschecked with their ground-based counterparts. Beyond their temporal precision compared to ground-based observations, we investigate whether these rainfall estimates are adequate for hindcasting landslides, which particularly requires accurate representation of spatial variability of rainfall. We developed a logistic regression model to hindcast rainfall-induced landslides in two sites in Japan. The model contains only a few topographic and geologic predictors to leave room for different rainfall products to improve the model as additional predictors. By changing the input rainfall product, we compared GPM IMERG and ERA5 rainfall estimates with ground radar–based rainfall data. Our findings emphasize that there is a lot of room for improvement of spatiotemporal prediction of landslides, as shown by a strong performance increase of the models with the benchmark radar data attaining 95% diagnostic performance accuracy. Yet, this improvement is not met by global rainfall products which still face challenges in reliably capturing spatiotemporal patterns of precipitation events.</jats:p&gt

Rainfall-Induced Landslides: Slope Stability Analysis Through Field Monitoring

Rainfall-induced shallow landslides are hazardous phenomena that could cause several damages to infrastructures and people. To understand the hydrological and mechanical triggering conditions of shallow landslides in an area of Oltrepo Pavese (Northern Apennines, Italy) a field monitoring was conducted. In this work the results of 16 months monitoring are shown, focusing on the hydrological behaviour of the studied materials as function of rainfall and its effect on slope stability. Keywords Slope monitoring Shallow landslides Stability analysis Introduction Rainfall-induce

Ultra-trace analysis of ³⁶Cl by accelerator mass spectrometry: an interlaboratory study

A first international <sup>36</sup>Cl interlaboratory comparison has been initiated. Evaluation of the final results of the eight participating accelerator mass spectrometry (AMS) laboratories on three synthetic AgCl samples with <sup>36</sup>Cl/Cl ratios at the 10<sup>−11</sup>, 10<sup>−12</sup>, and 10<sup>−13</sup> level shows no difference in the sense of simple statistical significance. However, more detailed statistical analyses demonstrate certain interlaboratory bias and underestimation of uncertainties by some laboratories. Following subsequent remeasurement and reanalysis of the data from some AMS facilities, the round-robin data indicate that <sup>36</sup>Cl/Cl data from two individual AMS laboratories can differ by up to 17%. Thus, the demand for further work on harmonising the <sup>36</sup>Cl-system on a worldwide scale and enlarging the improvement of measurements is obvious