Modeling debris-flow runout patterns on two alpine fans with different dynamic simulation models

Schraml, Klaus; Thomschitz, B.; McArdell, Brian W.; Graf, Christoph; Kaitna, Roland

doi:10.5194/nhess-15-1483-2015

Cited by 71 publications

(65 citation statements)

References 61 publications

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“…Another example is based on a parameter study from [52]. They used, amongst other, RAMMS-DF (developed at the WSL Institute for Snow and Avalanche Research SLF and the Swiss Federal Institute for Forest, Snow and Landscape Research WSL) to conduct debris-flow runout simulations.…”

Section: Proposed Evaluation Conceptmentioning

confidence: 99%

“…Here, the objective was to find those set of basal and internal friction parameters θ * = [μ * , ξ * ] where the error for the deposition distribution d D gets a minimum; respectively, the evaluation benchmark T a maximum. As a first step, [52] performed six simulations with an initially fixed parameter μ, varying only ξ from 120 to 1300. Based on these simulations, they kept the best fit parameter ξ * = 300 constant and conducted seven more simulations with μ ranging from to 0.20.…”

Section: Proposed Evaluation Conceptmentioning

confidence: 99%

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Evaluation concepts to compare observed and simulated deposition areas of mass movements

2017

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The simulation of geophysical mass flows, including debris flows, rock and snow avalanches, has become an important tool in engineering hazard assessment. Especially the runout and deposition behaviour of observed and expected mass flows are of interest. When being confronted with the evaluation of model performance and sensitivity, there are no standard, objective approaches. In this contribution, we review methods that have been used in literature and outline a new approach to quantitatively compare 2D simulations of observed and simulated deposition pattern. Our proposed method is based on the comparison of normalized partial areas which can be plotted in a ternary diagram to visualize the degree of over-and under-estimation. Results can be summed up by a single metric between -1 (no fit) and 1 (perfect fit). This study shall help developers and end-users of simulation models to better understand model behaviour and provides a possibility for comparison of model results, independent of simulation platform and type of mass flow.

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Section: Proposed Evaluation Conceptmentioning

confidence: 99%

Section: Proposed Evaluation Conceptmentioning

confidence: 99%

Evaluation concepts to compare observed and simulated deposition areas of mass movements

2017

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“…Peres and Cancelliere, 2014;Bordoni et al, 2015) and on the physical modelling of the propagation phase (e.g. Schraml et al, 2015), susceptibility models (Brabb, 1984), suitable to depict prediction images of the sites where these phenomena are more likely to activate on a catchment/regional scale, are required as well. Combining the two approaches allows optimization of the use of early warning systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy)

Cama

Lombardo

Conoscenti

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios.The study area is the small catchment of the Itala torrent (10 km 2 ), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chronovalidation, based on the time partition of the landslide in- Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chronovalidated models. These are interpreted as effects of the nonlinearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.

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“…However, while a more complex model that includes entrainment can better replicate debris flow heights near the point of initiation [57], this additional process introduces another source of uncertainty into the model with the introduction of additional parameters (e.g., erosion rate), which require values that may not be known. Event and site-specific parameters for debris flow models are estimated through calibration, and efforts usually focus on flow resistance [48] and rheological parameters [58]. These parameters are associated with wide ranges of plausible values in literature and may be difficult to determine which are most representative for a given event.…”

Section: Introductionmentioning

confidence: 99%

“…Following Mazzorana et al [12], process modeling is the first three of five steps to accurately assessing the physical vulnerability of the built environment. A range of recognized methods have been applied to different process models including empirical [7,[18][19][20][21][22][23], empirical-statistical combined with simple flow equations [24], topographic gradient-based [25], numerical-based with the integration of shallow water equations [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41], and smoothed particle hydrodynamics (SPH) or Lagrangian [42][43][44][45][46] (see References [47,48] for review). Of the numerical models, 1-(e.g., DAN-W [28]; DFEM-1D [49]) or 2-(e.g., FLO-2D [27]; RAMMS-DF [35,36]; TopRunDF [7]; MassMov2D [34]) dimensional runout modeling approaches can be adapted.…”

Section: Introductionmentioning

confidence: 99%

Application of Sensitivity Analysis for Process Model Calibration of Natural Hazards

2018

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Sensitivity 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.

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Modeling debris-flow runout patterns on two alpine fans with different dynamic simulation models

Cited by 71 publications

References 61 publications

Evaluation concepts to compare observed and simulated deposition areas of mass movements

Evaluation concepts to compare observed and simulated deposition areas of mass movements

Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy)

Application of Sensitivity Analysis for Process Model Calibration of Natural Hazards

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