Preprints
https://doi.org/10.5194/gmd-2021-425
https://doi.org/10.5194/gmd-2021-425
Submitted as: methods for assessment of models
22 Dec 2021
Submitted as: methods for assessment of models | 22 Dec 2021
Status: a revised version of this preprint is currently under review for the journal GMD.

How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model? Application to the PESHMELBA model

Emilie Rouzies1, Claire Lauvernet1, Bruno Sudret2, and Arthur Vidard3 Emilie Rouzies et al.
  • 1INRAE, RiverLy, Lyon-Villeurbanne, 69625 Villeurbanne Cedex, France
  • 2ETH Zurich, Chair of Risk, Safety and Uncertainty Quantification, Stefano-Franscini-Platz 5, CH-8093 Zurich, Switzerland
  • 3Univ. Grenoble-Alpes, Inria, CNRS, Grenoble-INP, LJK, 38000 Grenoble, France

Abstract. Pesticide transfers in agricultural catchments are responsible for diffuse but major risks to water quality. Spatialized pesticide transfer models are useful tools to assess the impact of the structure of the landscape on water quality. Before considering using these tools in operational contexts, quantifying their uncertainties is a preliminary necessary step. In this study, we explored how global sensitivity analysis can be applied to the recent PESHMELBA pesticide transfer model to quantify uncertainties on transfer simulations. We set up a virtual catchment based on a real one and we compared different approaches for sensitivity analysis that could handle the specificities of the model: high number of input parameters, limited size of sample due to computational cost and spatialized output. We compared Sobol' indices obtained from Polynomial Chaos Expansion, HSIC dependence measures and feature importance measures obtained from Random Forest surrogate model. Results showed the consistency of the different methods and they highlighted the relevance of Sobol' indices to capture interactions between parameters. Sensitivity indices were first computed for each landscape element (site sensitivity indices). Second, we proposed to aggregate them at the hillslope and the catchment scale in order to get a summary of the model sensitivity and a valuable insight into the model hydrodynamical behaviour. The methodology proposed in this paper may be extended to other modular and distributed hydrological models as there has been a growing interest in these methods in recent years.

Emilie Rouzies et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-425', Fanny Sarrazin, 04 Feb 2022
    • CC3: 'Reply on RC1', Emilie Rouzies, 11 Apr 2022
  • CEC1: 'Comment on gmd-2021-425', Juan Antonio Añel, 21 Feb 2022
    • CC1: 'Reply on CEC1', Emilie Rouzies, 08 Mar 2022
      • CEC2: 'Reply on CC1', Juan Antonio Añel, 08 Mar 2022
        • CC2: 'Reply on CEC2', Emilie Rouzies, 10 Mar 2022
  • RC2: 'Comment on gmd-2021-425', Heng Dai, 14 Apr 2022
  • AC1: 'Final response on gmd-2021-425', Emilie Rouzies, 12 May 2022

Emilie Rouzies et al.

Emilie Rouzies et al.

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Short summary
Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows selecting the most influential input parameters but it has to be adapted to spatial modelling. This study will (i) identify relevant methods that can be transposed to any hydrological and water quality models, (ii) improve the fate of pesticides knowledge.