Preprints
https://doi.org/10.5194/hess-2021-550
https://doi.org/10.5194/hess-2021-550
 
22 Nov 2021
22 Nov 2021
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Revisiting parameter sensitivities in the Variable Infiltration Capacity model

Ulises Sepúlveda1, Pablo A. Mendoza1,2, Naoki Mizukami3, and Andrew J. Newman3 Ulises Sepúlveda et al.
  • 1Department of Civil Engineering, Universidad de Chile, Santiago, Chile
  • 2Advanced Mining Technology Center, Universidad de Chile, 5 Santiago, Chile
  • 3National Center for Atmospheric Research, Boulder, CO, USA

Abstract. Despite the Variable Infiltration Capacity (VIC) model being used for decades in the hydrology community, there are still model parameters whose sensitivities remain unknown. Additionally, understanding the factors that control spatial variations in parameter sensitivities is crucial given the increasing interest to obtain spatially coherent parameter fields over large domains. In this study, we investigate the sensitivities of 43 soil, vegetation and snow parameters in the VIC model for 101 catchments spanning the diverse hydroclimates of continental Chile. We implement a hybrid local-global sensitivity analysis approach, using eight model evaluation metrics to quantify sensitivities, with four of them formulated from runoff time series; two characterizing snow processes, and the remaining two based on evaporation processes. Our results confirm an over-parameterization for the processes analysed here, with only 12 (i.e., 28 %) parameters found as sensitive, distributed among soil (7), vegetation (2) and snow (3) model components. Correlation analyses show that climate variables – in particular, mean annual precipitation and aridity index – are the main controls on parameter sensitivities. Additionally, our results highlight the influence of the leaf area index on simulated hydrologic processes – regardless on the dominant climate types – and the relevance of hard-coded snow parameters. Based on correlation results and the interpretation of spatial sensitivity patterns, we provide guidance on the most relevant parameters for model calibration according to the target processes and the prevailing climate type. Overall, the results presented here contribute to improved understanding of model behaviour across watersheds with diverse physical characteristics that encompass a wide hydroclimatic gradient from hyper-arid to humid systems.

Ulises Sepúlveda et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-550', Anonymous Referee #1, 14 Dec 2021
    • AC1: 'Reply on RC1', Pablo Mendoza, 07 Feb 2022
  • RC2: 'Comment on hess-2021-550', Anonymous Referee #2, 23 Dec 2021
    • AC2: 'Reply on RC2', Pablo Mendoza, 07 Feb 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-550', Anonymous Referee #1, 14 Dec 2021
    • AC1: 'Reply on RC1', Pablo Mendoza, 07 Feb 2022
  • RC2: 'Comment on hess-2021-550', Anonymous Referee #2, 23 Dec 2021
    • AC2: 'Reply on RC2', Pablo Mendoza, 07 Feb 2022

Ulises Sepúlveda et al.

Ulises Sepúlveda et al.

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Short summary
This paper characterizes parameter sensitivities across > 5,500 grid cells for a commonly used macro-scale hydrological model, including a suite of eight performance metrics and 43 soil, vegetation and snow parameters. The results show that the model is highly overparameterized and, more importantly, help to provide guidance on the most relevant parameters for specific target processes across diverse climatic types.