Application of Sensitivity Analysis for Process Model Calibration of Natural Hazards
AbstractSensitivity analysis (SA) describes how varying inputs to a model subsequently varies its outputs. Its inclusion can support the systematic calibration of numerical models to back-calculate intensity properties of past torrent events that would otherwise be difficult or impossible to collect during their occurrence. Sensitivity analysis for model calibration is assessed with the back-calculation of a known torrent event. In particular, FLO-2D, a cell-based numerical model, is used to simulate the 2005 debris flow event that occurred in Brienz, Switzerland. Under 4000 simulations were completed with ranges of physically reasonable parameter values. Model results were compared in 3-dimensions with both sediment deposition extents (x, y) and estimated deposition heights (z) from available post-event images. The comparisons highlighted that more accurate input and validation data, namely the flow behavior of hazardous processes and post-event deposition heights, are required to produce stronger agreements between simulated and observed results. Furthermore, the application of SA for model calibration supports systematic exploration of large parameter spaces characteristic of complex phenomena like natural hazard events. These findings demonstrated how important model input factors can be identified, which provide guidance for future data collection efforts to capture both the rheology and the spatial distribution of hazards more accurately. View Full-Text
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Chow, C.; Ramirez, J.; Keiler, M. Application of Sensitivity Analysis for Process Model Calibration of Natural Hazards. Geosciences 2018, 8, 218.
Chow C, Ramirez J, Keiler M. Application of Sensitivity Analysis for Process Model Calibration of Natural Hazards. Geosciences. 2018; 8(6):218.Chicago/Turabian Style
Chow, Candace; Ramirez, Jorge; Keiler, Margreth. 2018. "Application of Sensitivity Analysis for Process Model Calibration of Natural Hazards." Geosciences 8, no. 6: 218.
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