GIS-based cell model for simulating debris flow runout on a fan

Gregoretti, Carlo; Degetto, Massimo; Boreggio, Mauro

doi:10.1016/j.jhydrol.2015.12.054

Cited by 68 publications

(47 citation statements)

References 74 publications

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“…The employed GIS-based debris flows cell routing model is able to simulate the routing and the entrainment-deposition processes of solid-liquid mixtures having a grain-collision dominated rheology (Gregoretti et al, 2018a), also known as stony debris flows (Takahashi, 2007). It represents the fully bi-phase version of the one proposed by Gregoretti et al (2016a), and it allows a better simulation of the entrainment process.…”

Section: Gis-based Routing Cell Modelmentioning

confidence: 99%

“…Gregoretti et al (2016a), K is an empirical constant ranging between 0 and 1, U MAX is the velocity corresponding to the steepest of the eight possible flow directions k MAX , α MAX is equal to ϑk MAX in the case of gravity-driven flow, otherwise α MAX is equal to (ϑ K + k ) MAX being k the angle that the horizontal forms with the line joining the flow surface of the reference cell with that of the cell where the flow is directed, ϑ LIM and U LIM are limit values for ϑ and U, respectively. The parameters ϑ LIM and U LIM assume different values for erosion (U LIM-E and ϑ LIM-E ) and deposition (U LIM-D and ϑ LIM-D ), and they should be determined by field measurements or numerical backanalysis.…”

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confidence: 99%

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Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

2018

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Debris flows are among the most hazardous phenomena in mountain areas. To cope with debris flow hazard, it is common to delineate the risk-prone areas through routing models. The most important input to debris flow routing models are the topographic data, usually in the form of Digital Elevation Models (DEMs). The quality of DEMs depends on the accuracy, density, and spatial distribution of the sampled points; on the characteristics of the surface; and on the applied gridding methodology. Therefore, the choice of the interpolation method affects the realistic representation of the channel and fan morphology, and thus potentially the debris flow routing modeling outcomes. In this paper, we initially investigate the performance of common interpolation methods (i.e., linear triangulation, natural neighbor, nearest neighbor, Inverse Distance to a Power, ANUDEM, Radial Basis Functions, and ordinary kriging) in building DEMs with the complex topography of a debris flow channel located in the Venetian Dolomites (North-eastern Italian Alps), by using small footprint fullwaveform Light Detection And Ranging (LiDAR) data. The investigation is carried out through a combination of statistical analysis of vertical accuracy, algorithm robustness, and spatial clustering of vertical errors, and multi-criteria shape reliability assessment. After that, we examine the influence of the tested interpolation algorithms on the performance of a Geographic Information System (GIS)-based cell model for simulating stony debris flows routing. In detail, we investigate both the correlation between the DEMs heights uncertainty resulting from the gridding procedure and that on the corresponding simulated erosion/deposition depths, both the effect of interpolation algorithms on simulated areas, erosion and deposition volumes, solid-liquid discharges, and channel morphology after the event. The comparison among the tested interpolation methods highlights that the ANUDEM and ordinary kriging algorithms are not suitable for building DEMs with complex topography. Conversely, the linear triangulation, the natural neighbor algorithm, and the thin-plate spline plus tension Boreggio et al.Interpolation Influence on Routing Modeling and completely regularized spline functions ensure the best trade-off among accuracy and shape reliability. Anyway, the evaluation of the effects of gridding techniques on debris flow routing modeling reveals that the choice of the interpolation algorithm does not significantly affect the model outcomes.

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Section: Gis-based Routing Cell Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

2018

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“…Debris flow is rapid, gravity-induced mass movement consisting of a mixture of water, sediment, wood and anthropogenic debris that propagate along channels incised on mountain slopes and onto debris fans (Gregoretti et al, 2016). It has been reported in over 70 countries and often causes severe economic losses and human casualties, seriously retarding social and economic development (Imaizumi et al, 2006;Tecca and Genevois, 2009;Dahal et al, 2009;Liu et al, 2010;Cui et al, 2011;McCoy et al, 2012;Degetto et al, 2015;Tiranti and Deangeli, 2015;Hu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Rainfall threshold calculation for debris flow early warning in areas with scarcity of data

Pan

Jiang

Wang

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Debris flows are natural disasters that frequently occur in mountainous areas, usually accompanied by serious loss of lives and properties. One of the most commonly used approaches to mitigate the risk associated with debris flows is the implementation of early warning systems based on well-calibrated rainfall thresholds. However, many mountainous areas have little data regarding rainfall and hazards, especially in debris-flow-forming regions. Therefore, the traditional statistical analysis method that determines the empirical relationship between rainstorms and debris flow events cannot be effectively used to calculate reliable rainfall thresholds in these areas. After the severe Wenchuan earthquake, there were plenty of deposits deposited in the gullies, which resulted in several debris flow events. The triggering rainfall threshold has decreased obviously. To get a reliable and accurate rainfall threshold and improve the accuracy of debris flow early warning, this paper developed a quantitative method, which is suitable for debris flow triggering mechanisms in meizoseismal areas, to identify rainfall threshold for debris flow early warning in areas with a scarcity of data based on the initiation mechanism of hydraulic-driven debris flow. First, we studied the characteristics of the study area, including meteorology, hydrology, topography and physical characteristics of the loose solid materials. Then, the rainfall threshold was calculated by the initiation mechanism of the hydraulic debris flow. The comparison with other models and with alternate configurations demonstrates that the proposed rainfall threshold curve is a function of the antecedent precipitation index (API) and 1 h rainfall. To test the proposed method, we selected the Guojuanyan gully, a typical debris flow valley that during the 2008-2013 period experienced several debris flow events, located in the meizoseismal areas of the Wenchuan earthquake, as a case study. The comparison with other threshold models and configurations shows that the selected approach is the most promising starting point for further studies on debris flow early warning systems in areas with a scarcity of data.

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“…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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GIS-based cell model for simulating debris flow runout on a fan

Cited by 68 publications

References 74 publications

Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

Rainfall threshold calculation for debris flow early warning in areas with scarcity of data

Application of Sensitivity Analysis for Process Model Calibration of Natural Hazards

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