Spatial Global Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood modelling

This paper presents a spatial Global Sensitivity Analysis (GSA) approach in a 2D shallow water equations based High Resolution (HR) flood model. The aim of a spatial GSA is to produce sensitivity maps which are based on Sobol index estimations. Such an approach allows to rank the effects of uncertain HR topographic data input parameters on flood model output. The influence of the three following parameters has been studied: the measurement error, the level of details of above-ground elements representation and the spatial discretization resolution. To introduce uncertainty, a Probability Density Function and discrete spatial approach have been applied to generate 2,000 DEMs. Based on a 2D urban flood river event modelling, the produced sensitivity maps highlight the major influence of modeller choices compared to HR measurement errors when HR topographic data are used. The spatial variability of the ranking is enhnaced. Sobol index maps produced by a spatial GSA rank in space the weight of each uncertain parameter on the variability of output parameter of interest.The weight of the modeller choices, with respect to the influence of HR dataset accuracy is enhanced.Added value is for modeller to better understand limits of his model.Requirements and limits for this approach are related to subjectivity of choices and to computational cost.

[1]  Philippe Gourbesville,et al.  Use of 3D classified topographic data with FullSWOF for high resolution simulation of a river flood event over a dense urban area , 2016, ArXiv.

[2]  Peter A. Vanrolleghem,et al.  Uncertainty in the environmental modelling process - A framework and guidance , 2007, Environ. Model. Softw..

[3]  Josiane Zerubia,et al.  Structural Approach for Building Reconstruction from a Single DSM , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  F. Bouchut.  Nonlinear Stability of Finite Volume Methods for Hyperbolic Conservation Laws: and Well-Balanced Schemes for Sources , 2005 .

[5]  Peter F. Fisher,et al.  Causes and consequences of error in digital elevation models , 2006 .

[6]  V. Merwade,et al.  Estimation of uncertainty propagation in flood inundation mapping using a 1‐D hydraulic model , 2015 .

[7]  C. Sampson,et al.  Benchmarking urban flood models of varying complexity and scale using high resolution terrestrial LiDAR data , 2011 .

[8]  Jon C. Helton,et al.  Survey of sampling-based methods for uncertainty and sensitivity analysis , 2006, Reliab. Eng. Syst. Saf..

[9]  Jing Li,et al.  Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas , 2009 .

[10]  S. Djordjević,et al.  Potential and limitations of 1D modelling of urban flooding , 2004 .

[11]  Nathalie Saint-Geours,et al.  Sensitivity analysis of spatial models: application to cost-benefit analysis of flood risk management plans , 2012 .

[12]  Olivier Delestre,et al.  A limitation of the hydrostatic reconstruction technique for Shallow Water equations , 2012 .

[13]  Stefano Tarantola,et al.  Sensitivity analysis of spatial models , 2009, Int. J. Geogr. Inf. Sci..

[14]  Stefano Tarantola,et al.  Sensitivity Analysis as an Ingredient of Modeling , 2000 .

[15]  V. Guinot,et al.  Sensitivity equations for hyperbolic conservation law-based flow models , 2007 .

[16]  Daniel G. Aliaga,et al.  A Survey of Urban Reconstruction , 2013, Comput. Graph. Forum.

[17]  Fabio Remondino,et al.  UAV PHOTOGRAMMETRY FOR MAPPING AND 3D MODELING - CURRENT STATUS AND FUTURE PERSPECTIVES - , 2012 .

[18]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[19]  S. Wechsler.  Uncertainties associated with digital elevation models for hydrologic applications: a review , 2006 .

[20]  A. Saltelli,et al.  Sensitivity Anaysis as an Ingredient of Modeling , 2000 .

[21]  W. Walker,et al.  Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support , 2003 .

[22]  Laura Uusitalo,et al.  An overview of methods to evaluate uncertainty of deterministic models in decision support , 2015, Environ. Model. Softw..

[23]  Hélène Coullon,et al.  FullSWOF_Paral: Comparison of two parallelization strategies (MPI and SKELGIS) on a software designed for hydrology applications , 2013, ArXiv.

[24]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[25]  B. Iooss,et al.  A Review on Global Sensitivity Analysis Methods , 2014, 1404.2405.

[26]  Philippe Gourbesville,et al.  Performance assessment of modelling tools for high resolution runoff simulation over an industrial site , 2013 .

[27]  Florent Lafarge,et al.  Building large urban environments from unstructured point data , 2011, 2011 International Conference on Computer Vision.

[28]  Paul D. Bates,et al.  Distributed Sensitivity Analysis of Flood Inundation Model Calibration , 2005 .

[29]  Michel Lang,et al.  Étude du risque d'inondation d'un site industriel par des crues extrêmes : de l'évaluation des valeurs extrêmes aux incertitudes hydrologiques et hydrauliques , 2015 .

[30]  Xavier Briottet,et al.  Very high resolution land cover extraction in urban areas , 2013 .

[31]  F. Nex,et al.  UAV for 3D mapping applications: a review , 2014 .

[32]  Jean A. Cunge.  What Do We Model? What Results Do We Get? An Anatomy of Modelling Systems Foundations , 2014 .

[33]  Emmanuel Audusse,et al.  A Fast and Stable Well-Balanced Scheme with Hydrostatic Reconstruction for Shallow Water Flows , 2004, SIAM J. Sci. Comput..

[34]  Sara Grobljar.  Flood Modelling in Urban Areas , 2018, Acta hydrotechnica.

[35]  M. Pirotton,et al.  Detailed Inundation Modelling Using High Resolution DEMs , 2010 .

[36]  Stefano Tarantola,et al.  A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study , 2014, Environ. Model. Softw..

[37]  Yoshihisa Kawahara,et al.  The Uncertainty of Local Flow Parameters During Inundation Flow Over Complex Topographies with Elevation Errors , 2013 .

[38]  Olivier Delestre,et al.  FullSWOF: A free software package for the simulation of shallow water flows , 2014, 1401.4125.

[39]  F. Dottori,et al.  Detailed data is welcome, but with a pinch of salt: Accuracy, precision, and uncertainty in flood inundation modeling , 2013 .

[40]  V. Guinot,et al.  Uncertainty analysis of river flooding and dam failure risks using local sensitivity computations , 2012, Reliab. Eng. Syst. Saf..

[41]  Yen-Chang Chen,et al.  Retardance coefficient of vegetated channels estimated by the Froude number , 2008 .

[42]  Laurence Smith,et al.  The role of expert opinion in environmental modelling , 2012, Environ. Model. Softw..

[43]  Stéphane Cordier,et al.  FullSWOF: A software for overland flow simulation / FullSWOF : un logiciel pour la simulation du ruissellement , 2012, ArXiv.

[44]  Philippe Gourbesville,et al.  Hydroinformatics and Its Role in Flood Management , 2014 .

[45]  Thomas D. M. Willis,et al.  Systematic analysis of uncertainty in flood inundation modelling , 2014 .

[46]  Bertrand Iooss,et al.  Global sensitivity analysis for models with spatially dependent outputs , 2009, 0911.1189.

[47]  Frédéric Grelot,et al.  Multi-scale spatial sensitivity analysis of a model for economic appraisal of flood risk management policies , 2014, Environ. Model. Softw..

[48]  Jim W. Hall,et al.  Applied Uncertainty Analysis for Flood Risk Management , 2014 .

[49]  Wilfried Linder,et al.  Digital Photogrammetry , 2003 .

[50]  Philippe Gourbesville,et al.  Uncertainty related to high resolution topographic data use for flood event modeling over urban areas: toward a sensitivity analysis approach , 2015 .

[51]  A. Mynett,et al.  Urban flood modelling combining top-view LiDAR data with ground-view SfM observations , 2015 .

[52]  John W. Fisher,et al.  Automatic registration of LIDAR and optical images of urban scenes , 2009, CVPR.

[53]  Carlo Meloni,et al.  Uncertainty Management in Simulation-Optimization of Complex Systems : Algorithms and Applications , 2015 .

[54]  P. Bates,et al.  Benchmarking 2D hydraulic models for urban flooding , 2008 .

[55]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[56]  Florian Pappenberger,et al.  Multi-method global sensitivity analysis of flood inundation models. , 2008 .

[57]  Philippe Gourbesville,et al.  Calibration of physically based models: back to basics? , 2003 .

[58]  P. Bates,et al.  Effects of spatial resolution on a raster based model of flood flow , 2001 .

[59]  A. Saltelli,et al.  A quantitative model-independent method for global sensitivity analysis of model output , 1999 .

[60]  Bertrand Iooss.  Revue sur l’analyse de sensibilité globale de modèles numériques , 2011 .

[61]  Yann Richet,et al.  Propagation des incertitudes dans les modèles hydrauliques 1D , 2015 .

[62]  Jean-Stéphane Bailly,et al.  Analyse de sensibilité globale d'un modèle spatialisé pour l'évaluation économique du risque d'inondation , 2011 .

[63]  X. Briottet,et al.  VERY HIGH RESOLUTION LAND COVER EXTRACTION IN URBAN AREAS : VERY HIGH RESOLUTION URBAN LAND COVER EXTRACTION USING AIRBORNE HYPERSPECTRAL IMAGES , 2013 .