Regional-scale back-analysis using TRIGRS: an approach to advance landslide hazard modeling and prediction in sparse data regions
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Landslides in Kerala, India, have been shown to be preceded not only by critical rainfall over a short period but also a much longer period of elevated pore pressure. Such rainfall-triggered landslides are difficult to monitor due to a lack of adequate data on the locations of failures and precipitation. Here, a method is presented using Transient Rainfall Infiltration and Grid-based Regional Slope stability (TRIGRS) as a tool to model the relationship between critical rainfall and antecedent pore pressure as they relate to slope stability, which can be useful for hazard assessment in sparse data regions. This is demonstrated by parameterizing the model with a combination of regional data sources, remote sensing, and temporal back-analysis based on two known failure events (June 2004 and July 2007). Ranges of possible geotechnical and hydraulic parameters were obtained from various local and regional sources, and soil thickness was modeled as a function of slope angle. Rainfall was estimated using satellite microwave radiometry data. For back-analysis, combinations of cohesion, friction angle, and water table depth were then tested in TRIGRS using trial and error until the predicted and observed failure times coincided for the two failure events. While the spatial prediction accuracy of the model is low and multiple solution sets are expected to exist, the results confirm that information regarding the critical pre-failure conditions and stability changes over time can be derived despite data-poor circumstances. Future studies can be undertaken extending this method to characterize many parameter combinations and incorporate more failure cases to develop probabilistic early-warning thresholds.
Regional-scale back-analysis using TRIGRS: an approach to advance landslide hazard modeling and prediction in sparse data regions.
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