Sensitivity and identifiability of rheological  parameters in debris flow modeling

Abstract. Over the past decades, several numerical models have been developed to understand, simulate and predict debris flow events. Typically, these models simplify the complex interactions between water and solids using a single-phase approach and different rheological models to represent flow resistance. In this study, we perform a sensitivity analysis on the parameters of a debris flow numerical model (FLO-2D) for a suite of relevant variables (i.e., maximum flood area, maximum flow velocity, maximum height and deposit volume). Our aims are to (i) examine the degree of model overparameterization and (ii) assess the effectiveness of observational constraints to improve parameter identifiability. We use the Distributed Evaluation of Local Sensitivity Analysis (DELSA) method, which is a hybrid local–global technique. Specifically, we analyze two creeks in northern Chile (∼29∘ S, 70∘ W) that were affected by debris flows on 25 March 2015. Our results show that SD (surface detention) and β1 (a parameter related to viscosity) provide the largest sensitivities. Further, our results demonstrate that equifinality is present in FLO-2D and that the final deposited volume and maximum flood area contain considerable information to identify model parameters.

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Sensitivity and identifiability of rheological parameters in debris flow modeling

10.5194/nhess-2020-37 ◽

2020 ◽

Author(s):

Gerardo Zegers ◽

Pablo A. Mendoza ◽

Alex Garces ◽

Santiago Montserrat

Keyword(s):

Sensitivity Analysis ◽

Debris Flow ◽

Flow Velocity ◽

Numerical Models ◽

Maximum Flow ◽

Rheological Parameters ◽

Model Parameters ◽

Relevant Variables ◽

Distributed Evaluation ◽

Maximum Flow Velocity

Abstract. Over the past decades, several numerical models have been developed to understand, simulate and predict debris flow events. Typically, these models simplify the complex interactions between water and solids using a single-phase approach and different rheological models to represent flow resistance. In this study, we perform a sensitivity analysis on the parameters of a debris flow numerical model (FLO-2D) for a suite of relevant variables (i.e., maximum flood area, maximum flow velocity, maximum flow velocity, deposit volume). Our aims are to (i) examine the degree of model overparameterization, and (ii) assess the effectiveness of observational constraints to improve parameter identifiability. We use the Distributed Evaluation of Local Sensitivity Analysis (DELSA) method, which is a hybrid local-global technique. Specifically, we analyze two creeks in northern Chile that were affected by debris flows on March 25, 2015. Our results show that SD and β1 – a parameter related to viscosity – provide the largest sensitivities. Further, our results demonstrate that equifinality is present in FLO-2D, and that final deposited volume and maximum flood area contain considerable information to identify model parameters.

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Impact of a Random Sequence of Debris Flows on Torrential Fan Formation

Geosciences ◽

10.3390/geosciences9020064 ◽

2019 ◽

Vol 9 (2) ◽

pp. 64 ◽

Cited By ~ 4

Author(s):

Nejc Bezak ◽

Jošt Sodnik ◽

Matjaž Mikoš

Keyword(s):

Debris Flow ◽

Numerical Simulations ◽

Rheological Properties ◽

Debris Flows ◽

Random Sequence ◽

Rheological Parameters ◽

Model Parameters ◽

Maximum Height ◽

Wide Range ◽

The Impact

Debris flows with different magnitudes can have a large impact on debris fan characteristics such as height or slope. Moreover, knowledge about the impact of random sequences of debris flows of different magnitudes on debris fan properties is sparse in the literature and can be improved using numerical simulations of debris fan formation. Therefore, in this paper we present the results of numerical simulations wherein we investigated the impact of a random sequence of debris flows on torrential fan formation, where the total volume of transported debris was kept constant, but different rheological properties were used. Overall, 62 debris flow events with different magnitudes from 100 m3 to 20,000 m3 were selected, and the total volume was approximately 225,000 m3. The sequence of these debris flows was randomly generated, and selected debris fan characteristics after the 62 events were compared. For modeling purposes, we applied the Rapid Mass Movement Simulations (RAMMS) software and its debris flow module (RAMMS-DF). The modeling was carried out using (a) real fan topography from an alpine environment (i.e., an actual debris fan in north-west (NW) Slovenia formed by the Suhelj torrent) and (b) an artificial surface with a constant slope. Several RAMMS model parameters were tested. The simulation results confirm that the random sequence of debris flow events has only some minor effects on the fan formation (e.g., slope, maximum height), even when changing debris flow rheological properties in a wide range. After the 62 events, independent of the selected sequence of debris flows, the final fan characteristics were not significantly different from each other. Mann–Whitney (MW) tests and t-tests were used for this purpose, and the selected significance level was 0.05. Moreover, this conclusion applies for artificial and real terrain and for a wide range of tested RAMMS model rheological parameters. Further testing of the RAMMS-DF model in real situations is proposed in order to better understand its applicability and limitations under real conditions for debris flow hazard assessment or the planning of mitigation measures.

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Uncertainty quantification and global sensitivity analysis with dependent inputs: Application to the 2D hydraulic model of the Loire River

10.5194/egusphere-egu2020-18939 ◽

2020 ◽

Author(s):

Lucie Pheulpin ◽

Vito Bacchi

Keyword(s):

Sensitivity Analysis ◽

Uncertainty Quantification ◽

Large Scale ◽

Global Sensitivity Analysis ◽

Numerical Models ◽

Hydraulic Model ◽

Model Parameters ◽

Traditional Methods ◽

Global Sensitivity ◽

Loire River

Hydraulic models are increasingly used to assess the flooding hazard. However, all numerical models are affected by uncertainties, related to model parameters, which can be quantified through Uncertainty Quantification (UQ) and Global Sensitivity Analysis (GSA). In traditional methods of UQ and GSA, the input parameters of the numerical models are considered to be independent which is actually rarely the case. The objective of this work is to proceed with UQ and GSA methods considering dependent inputs and comparing different methodologies. At our knowledge, there is no such application in the field of 2D hydraulic modelling.At first the uncertain parameters of the hydraulic model are classified in groups of dependent parameters. Within this aim, it is then necessary to define the copulas that better represent these groups. Finally UQ and GSA based on copulas are performed. The proposed methodology is applied to the large scale 2D hydraulic model of the Loire River. However, as the model computation is high time-consuming, we used a meta-model instead of the initial model. We compared the results coming from the traditional methods of UQ and GSA (i.e. without taking into account the dependencies between inputs) and the ones coming from the new methods based on copulas. The results show that the dependence between inputs should not always be neglected in UQ and GSA.

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Simplified simulation of rock avalanches and subsequent debris flows with a single thin-layer model. Application to the Prêcheur river (Martinique, Lesser Antilles)

10.5194/egusphere-egu21-2752 ◽

2021 ◽

Author(s):

Marc Peruzzetto ◽

Clara Levy ◽

Yannick Thiery ◽

Gilles Grandjean ◽

Anne Mangeney ◽

...

Keyword(s):

Debris Flow ◽

Thin Layer ◽

Debris Flows ◽

Initial Conditions ◽

Rock Avalanche ◽

Numerical Code ◽

Lesser Antilles ◽

Rheological Parameters ◽

Model Parameters ◽

High Discharge

This work focuses on the use of thin-layer models for simulating fast gravitational flows for hazard assessment. Such simulations are sometimes difficult to carry out because of the uncertainty on initial conditions and on simulation parameters. In this study, we aggregate various field data to constrain realistic initial conditions and to calibrate the model parameters. By using the SHALTOP numerical code, we choose a simple and empirical rheology to model the flow (no more than two parameters), but we model more finely the geometrical interactions between the flow and the topography. We can thus model both a rock avalanche, and the subsequent remobilization of the deposits as a high discharge debris flow.Using the Pr&#234;cheur river catchment (Martinique, Lesser Antilles) as a case study, we focus on extreme events with a high potential to impact populations and infrastructures. We use geological and geomorphological data, topographic surveys, seismic recordings and granulometric analysis to define realistic simulation scenarios and determine the main characteristics of documented events. The latter are then reproduced to calibrate rheological parameters. With a single rheological parameter and the Coulomb rheology, we thus model the emplacement and main dynamic characteristics of a recent rock avalanche, as well as the travel duration and flooded area of a documented high discharge debris flow. Then, in a forward prediction simulation, we model a possible 1.9x106 m3 rock avalanche, and the instantaneous remobilization of the resulting deposits as a high-discharge debris flow. We show that successive collapses allow to better reproduce the dynamics of the rock avalanche, but do not change the geometry of the final deposits, and thus do not influence the initial conditions of the subsequent debris flow simulation. A progressive remobilization of the materials slows down the debris flow and limits overflow, in comparison to instantaneous release. However, we show that high discharge debris flows, such as the one considered for model calibration, are better reproduced with an instantaneous initiation. The range of travel times measured for other significant debris flows in the Pr\^echeur river is consistent with our simulation results, with various rheological parameters and the Coulomb or Voellmy rheology.

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Field observations of the 17 July 2006 Tsunami in Java

Natural Hazards and Earth System Science ◽

10.5194/nhess-7-177-2007 ◽

2007 ◽

Vol 7 (1) ◽

pp. 177-183 ◽

Cited By ~ 29

Author(s):

F. Lavigne ◽

C. Gomez ◽

M. Giffo ◽

P. Wassmer ◽

C. Hoebreck ◽

...

Keyword(s):

Numerical Models ◽

Maximum Flow ◽

Maximum Height ◽

Flow Depth ◽

Central Java ◽

Inundation Depth ◽

Richter Scale ◽

Tsunami Earthquake ◽

Run Up ◽

The One

Abstract. The 17 July 2006, a tsunami struck the southern coast of Java, Indonesia, causing over 730 casualties. The triggering earthquake located 225 km off the coast of Pangandaran (9.222° S, 107.320° E), occurred at 15:19 LT (UTC +7) with a 7.7 magnitude on the Richter scale (Harward Center and CEA/DAM). In order to calibrate numerical models and understand the phenomenon, we conducted a 6-weeks field survey in July and August 2006 from Cimerak district in West Java to Gunung Kidul district in Central Java. Data collection involved measurements of wave height before its breaking, flow depth, run-up height, inundation depth, flow directions and a detailed chronology of the tsunami. Eyewitnesses accounted for three main waves. The maximum height of the second wave ranged from 4.2 to 8.6 m before its breaking. Maximum flow depth after the wave's breaking reached 5 m, and maximum runup heights reached 15.7 m. Our run-up values are about 1.5 higher than those obtained by the other field surveys carried out until present. They are also higher than the values computed through preliminary models. The 17 July 2006 tsunami has been generated by a "tsunami earthquake", i.e. an earthquake of low or medium scale that triggers a tsunami of high magnitude. The run-up heights progressively decreased eastwards, which is consistent with a tsunami triggered by fault dislocation, as the one that hit the Nicaragua's coast with similar run-up heights on the 2 September 1992. An earthquake with associated landslides could also have generated the 17 July 2006 tsunami, as ever observed in Papua-New-Guinea in 1998.

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Application of distributed sensitivity analysis to a model of turbulent open channel flow in a natural river channel

Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences ◽

10.1098/rspa.1994.0128 ◽

1994 ◽

Vol 447 (1929) ◽

pp. 49-63 ◽

Cited By ~ 18

Keyword(s):

Sensitivity Analysis ◽

River Flow ◽

Numerical Models ◽

Input Parameter ◽

River Channel ◽

Distributed Model ◽

Model Parameters ◽

Model Assessment ◽

Spatially Distributed ◽

Traditional Approaches

Sensitivity analysis is a widely applied tool used to investigate the predictions of numerical models of environmental processes. This paper illustrates the importance of undertaking a sensitivity analysis that considers the spatially distributed nature of model predictions, rather than simply assessing the sensitivity of one or more model parameters that are assumed to represent the distributed behaviour of the system. A spatially distributed sensitivity analysis is applied to the output from a distributed model of turbulent river flow, used to simulate the flow processes in a natural river channel bifurcation. Examples are provided for an input parameter which illustrates the importance of sensitivity analysis with respect to model assessment and error analysis. The distributed nature of the analysis suggests the importance of spatial feedback in environmental systems that more traditional approaches to sensitivity analysis cannot reveal.

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A Sensitivity Analysis of the SPACSYS Model

Agriculture ◽

10.3390/agriculture11070624 ◽

2021 ◽

Vol 11 (7) ◽

pp. 624

Author(s):

Yan Shan ◽

Mingbin Huang ◽

Paul Harris ◽

Lianhai Wu

Keyword(s):

Sensitivity Analysis ◽

Wheat Yield ◽

Climatic Conditions ◽

Water Dynamics ◽

Ecological Modelling ◽

Soil Conditions ◽

Model Parameters ◽

Nutrient Inputs ◽

Nutrient Pool ◽

Wheat Field

A sensitivity analysis is critical for determining the relative importance of model parameters to their influence on the simulated outputs from a process-based model. In this study, a sensitivity analysis for the SPACSYS model, first published in Ecological Modelling (Wu, et al., 2007), was conducted with respect to changes in 61 input parameters and their influence on 27 output variables. Parameter sensitivity was conducted in a ‘one at a time’ manner and objectively assessed through a single statistical diagnostic (normalized root mean square deviation) which ranked parameters according to their influence of each output variable in turn. A winter wheat field experiment provided the case study data. Two sets of weather elements to represent different climatic conditions and four different soil types were specified, where results indicated little influence on these specifications for the identification of the most sensitive parameters. Soil conditions and management were found to affect the ranking of parameter sensitivities more strongly than weather conditions for the selected outputs. Parameters related to drainage were strongly influential for simulations of soil water dynamics, yield and biomass of wheat, runoff, and leaching from soil during individual and consecutive growing years. Wheat yield and biomass simulations were sensitive to the ‘ammonium immobilised fraction’ parameter that related to soil mineralization and immobilisation. Simulations of CO2 release from the soil and soil nutrient pool changes were most sensitive to external nutrient inputs and the process of denitrification, mineralization, and decomposition. This study provides important evidence of which SPACSYS parameters require the most care in their specification. Moving forward, this evidence can help direct efficient sampling and lab analyses for increased accuracy of such parameters. Results provide a useful reference for model users on which parameters are most influential for different simulation goals, which in turn provides better informed decision making for farmers and government policy alike.

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Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling

Journal of Elasticity ◽

10.1007/s10659-021-09833-9 ◽

2021 ◽

Author(s):

Sebastian Brandstaeter ◽

Sebastian L. Fuchs ◽

Jonas Biehler ◽

Roland C. Aydin ◽

Wolfgang A. Wall ◽

...

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Model Parameters ◽

Computational Studies ◽

Growth And Remodeling ◽

Global Sensitivity ◽

Output Variability ◽

Constrained Mixture ◽

The One ◽

Influential Parameters

AbstractGrowth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.

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Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽

10.3390/en13174290 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4290

Author(s):

Dongmei Zhang ◽

Yuyang Zhang ◽

Bohou Jiang ◽

Xinwei Jiang ◽

Zhijiang Kang

Keyword(s):

Sensitivity Analysis ◽

Gaussian Processes ◽

Latent Variable ◽

History Matching ◽

Latent Variable Models ◽

High Dimensional ◽

Model Parameters ◽

Variable Model ◽

Assisted History Matching ◽

Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

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Role of the Rheological Parameters in Debris Flow Modelling

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1092/1/012041 ◽

2021 ◽

Vol 1092 (1) ◽

pp. 012041

Author(s):

M A Khan ◽

Z Mustaffa ◽

A L B Balogun ◽

M A M Al-Bared ◽

A Ahmad

Keyword(s):

Debris Flow ◽

Rheological Parameters ◽

Flow Modelling

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