Characterization of process-oriented hydrologic model behavior with temporal sensitivity analysis for flash floods in Mediterranean catchments

Abstract. This paper presents a detailed analysis of 10 flash flood events in the Mediterranean region using the distributed hydrological model MARINE. Characterizing catchment response during flash flood events may provide new and valuable insight into the dynamics involved for extreme catchment response and their dependency on physiographic properties and flood severity. The main objective of this study is to analyze flash-flood-dedicated hydrologic model sensitivity with a new approach in hydrology, allowing model outputs variance decomposition for temporal patterns of parameter sensitivity analysis. Such approaches enable ranking of uncertainty sources for nonlinear and nonmonotonic mappings with a low computational cost. Hydrologic model and sensitivity analysis are used as learning tools on a large flash flood dataset. With Nash performances above 0.73 on average for this extended set of 10 validation events, the five sensitive parameters of MARINE process-oriented distributed model are analyzed. This contribution shows that soil depth explains more than 80% of model output variance when most hydrographs are peaking. Moreover, the lateral subsurface transfer is responsible for 80% of model variance for some catchment-flood events' hydrographs during slow-declining limbs. The unexplained variance of model output representing interactions between parameters reveals to be very low during modeled flood peaks and informs that model-parsimonious parameterization is appropriate to tackle the problem of flash floods. Interactions observed after model initialization or rainfall intensity peaks incite to improve water partition representation between flow components and initialization itself. This paper gives a practical framework for application of this method to other models, landscapes and climatic conditions, potentially helping to improve processes understanding and representation.

[1]  P. Tarolli,et al.  Analysis of flash flood regimes in the North-Western and South-Eastern Mediterranean regions , 2012 .

[2]  Jacques Lavabre,et al.  Impact of imperfect rainfall knowledge on the efficiency and the parameters of watershed models , 2001 .

[3]  C. Fortuin,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory , 1973 .

[4]  A. Saltelli,et al.  Making best use of model evaluations to compute sensitivity indices , 2002 .

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

[6]  Peter C. Young,et al.  Uncertainty , sensitivity analysis and the role of data based mechanistic modeling in hydrology , 2006 .

[7]  H. Andrieu,et al.  The Catastrophic Flash-Flood Event of 8–9 September 2002 in the Gard Region, France: A First Case Study for the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory , 2005 .

[8]  S. Uhlenbrook,et al.  Sensitivity analyses of a distributed catchment model to verify the model structure , 2005 .

[9]  Sandrine Anquetin,et al.  The use of distributed hydrological models for the Gard 2002 flash flood event: analysis of associated hydrological processes. , 2010 .

[10]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[11]  S. Grunwald,et al.  A global sensitivity analysis tool for the parameters of multivariable catchment models , 2006 .

[12]  V. Ducrocq,et al.  Benefit of coupling the ISBA land surface model with a TOPMODEL hydrological model version dedicated to Mediterranean flash-floods , 2010 .

[13]  Willy Bauwens,et al.  Sobol' sensitivity analysis of a complex environmental model , 2011, Environ. Model. Softw..

[14]  George Kuczera,et al.  A limited‐memory acceleration strategy for MCMC sampling in hierarchical Bayesian calibration of hydrological models , 2010 .

[15]  R. Srinivasan,et al.  A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .

[16]  D. H. Pilgrim,et al.  Problems of rainfall-runoff modelling in arid and semiarid regions , 1988 .

[17]  Erwin Zehe,et al.  Temporal dynamics of model parameter sensitivity for computationally expensive models with the Fourier amplitude sensitivity test , 2011 .

[18]  W. Rawls,et al.  Prediction of soil water properties for hydrologic modeling , 1985 .

[19]  Paola Annoni,et al.  Sixth International Conference on Sensitivity Analysis of Model Output How to avoid a perfunctory sensitivity analysis , 2010 .

[20]  Stefano Tarantola,et al.  Random balance designs for the estimation of first order global sensitivity indices , 2006, Reliab. Eng. Syst. Saf..

[21]  P. Reed,et al.  Characterization of watershed model behavior across a hydroclimatic gradient , 2008 .

[22]  Stefano Tarantola,et al.  Calculating first-order sensitivity measures: A benchmark of some recent methodologies , 2009, Reliab. Eng. Syst. Saf..

[23]  Nanée Chahinian,et al.  Distributed hydrological modelling of a Mediterranean mountainous catchment – Model construction and multi-site validation , 2007 .

[24]  Keith Beven,et al.  Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations , 2006 .

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

[26]  Patrick M. Reed,et al.  Advancing the identification and evaluation of distributed rainfall‐runoff models using global sensitivity analysis , 2007 .

[27]  Keith Beven,et al.  Changing ideas in hydrology — The case of physically-based models , 1989 .

[28]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[29]  Tyler Smith,et al.  Bayesian methods in hydrologic modeling: A study of recent advancements in Markov chain Monte Carlo techniques , 2008 .

[30]  G. Saulnier,et al.  Sensitivity of flash‐flood simulations on the volume, the intensity, and the localization of rainfall in the Cévennes‐Vivarais region (France) , 2009 .

[31]  George Kuczera,et al.  Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory , 2006 .

[32]  Eric Gaume,et al.  Surveying flash floods: gauging the ungauged extremes , 2008 .

[33]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[34]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[35]  E. Gaume,et al.  A modeling approach to assess the hydrological response of small mediterranean catchments to the variability of soil characteristics in a context of extreme events , 2008 .

[36]  A. O'Hagan,et al.  Probabilistic sensitivity analysis of complex models: a Bayesian approach , 2004 .

[37]  A. Saltelli,et al.  An alternative way to compute Fourier amplitude sensitivity test (FAST) , 1998 .

[38]  Jing Yang,et al.  Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis , 2011, Environ. Model. Softw..

[39]  Peter C. Young,et al.  State Dependent Parameter metamodelling and sensitivity analysis , 2007, Comput. Phys. Commun..

[40]  Thibault Mathevet,et al.  A downward structural sensitivity analysis of hydrological models to improve low-flow simulation , 2011 .

[41]  Olivier P. Le Maître,et al.  Polynomial chaos expansion for sensitivity analysis , 2009, Reliab. Eng. Syst. Saf..

[42]  Erwin Zehe,et al.  Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity , 2011 .

[43]  F. L. Dimet,et al.  Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods , 2009 .

[44]  Emad Habib,et al.  Sensitivity of Conceptual and Physically Based Hydrologic Models to Temporal and Spatial Rainfall Sampling , 2009 .

[45]  Stefano Tarantola,et al.  Sensitivity analysis practices: Strategies for model-based inference , 2006, Reliab. Eng. Syst. Saf..

[46]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[47]  Neil McIntyre,et al.  Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis , 2003 .

[48]  Joseph A. C. Delaney Sensitivity analysis , 2018, The African Continental Free Trade Area: Economic and Distributional Effects.

[49]  Vijay P. Singh,et al.  Evaluation of seasonal and spatial variations of lumped water balance model sensitivity to precipitation data errors , 2006 .

[50]  Cindy Lebeaupin,et al.  A numerical study of three catastrophic precipitating events over southern France. I: Numerical framework and synoptic ingredients , 2008 .

[51]  Silvio Davolio,et al.  Orographic triggering of long lived convection in three dimensions , 2009 .

[52]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[53]  I. Sobol Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[54]  Thierry Alex Mara,et al.  Extension of the RBD-FAST method to the computation of global sensitivity indices , 2009, Reliab. Eng. Syst. Saf..

[55]  Hélène Roux,et al.  A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments , 2011 .

[56]  Luca Brocca,et al.  Catchment scale soil moisture spatial–temporal variability , 2012 .

[57]  Alan B. Anderson,et al.  Improved generalized Fourier amplitude sensitivity test (FAST) for model assessment , 2003, Stat. Comput..

[58]  B. Efron,et al.  The Jackknife Estimate of Variance , 1981 .

[59]  F. L. Dimet,et al.  Sensitivity analysis and parameter estimation for the distributed modeling of infiltration excess overland flow , 2007 .

[60]  Y. Kerr,et al.  Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations , 2012 .

[61]  P. Reed,et al.  Hydrology and Earth System Sciences Discussions Comparing Sensitivity Analysis Methods to Advance Lumped Watershed Model Identification and Evaluation , 2022 .

[62]  G. Hornberger,et al.  Approach to the preliminary analysis of environmental systems , 1981 .

[63]  George Kuczera,et al.  Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .

[64]  Günter Blöschl,et al.  Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology , 2012 .

[65]  Saltelli Andrea,et al.  Sensitivity Analysis for Nonlinear Mathematical Models. Numerical ExperienceSensitivity Analysis for Nonlinear Mathematical Models. Numerical Experience , 1995 .

[66]  Cajo J. F. ter Braak,et al.  Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? , 2009 .

[67]  Carolina Massmann,et al.  Analysis of the behavior of a rainfall-runoff model using three global sensitivity analysis methods evaluated at different temporal scales , 2012 .

[68]  Soroosh Sorooshian,et al.  A framework for development and application of hydrological models , 2001, Hydrology and Earth System Sciences.

[69]  Etienne Leblois,et al.  The SAFRAN‐ISBA‐MODCOU hydrometeorological model applied over France , 2008 .

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

[71]  D. Labat,et al.  Characterization of catchment behaviour and rainfall selection for flash flood hydrological model calibration: catchments of the eastern Pyrenees , 2015 .

[72]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

[73]  Xin Li,et al.  Modelling irrigated maize with a combination of coupled-model simulation and uncertainty analysis, in the northwest of China , 2012 .

[74]  K. Beven,et al.  Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach , 1996 .

[75]  Jon C. Helton,et al.  Multiple predictor smoothing methods for sensitivity analysis: Description of techniques , 2008, Reliab. Eng. Syst. Saf..

[76]  Günter Blöschl,et al.  A compilation of data on European flash floods , 2009 .

[77]  Thibault Mathevet,et al.  Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models , 2006 .