Preprints
https://doi.org/10.5194/hess-2020-6
https://doi.org/10.5194/hess-2020-6

  28 Jan 2020

28 Jan 2020

Review status: this preprint has been withdrawn by the authors.

The HOOPLA toolbox: a HydrOlOgical Prediction LAboratory to explore ensemble rainfall-runoff modeling

Antoine Thiboult1, Gregory Seiller2,a, Carine Poncelet3,a, and François Anctil1 Antoine Thiboult et al.
  • 1Dept. of Civil and Water Engineering, Université Laval, Québec, Canada
  • 2DHI, Nantes, France
  • 3Crédit Agricole Sud Rhône Alpes, Grenoble, France
  • aformerly at: Université Laval, Québec, Canada

Abstract. This technical report introduces the HydrOlOgical Prediction LAboratory (HOOPLA) developed at Université Lavalfor ensemble lumped hydrological modelling. HOOPLA includes functionalities to perform calibration, simulation, and forecast for multiple hydrological models and various time steps. It includes a range of hydrometeorological tools such as calibration algorithms, data assimilation techniques, potential evapotranspiration formulas and a snow accounting routine. HOOPLA is a flexible framework coded in MATLAB that allows easy integration of user-defined hydrometeorological tools. This report also illustrates HOOPLA's functionalities using a set of 31 Canadian catchments.

This preprint has been withdrawn.

Antoine Thiboult et al.

Interactive discussion

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Antoine Thiboult et al.

Model code and software

HOOPLA A. Thiboult, G. Seiller, and F. Anctil https://doi.org/10.5281/zenodo.2653969

Antoine Thiboult et al.

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This preprint has been withdrawn.

Short summary
HOOPLA, the HydrOlOgical Prediction LAboratory, is a toolbox that converts precipitation into river runoff. It relies on numerical models to compute snow accumulation and melting, water loss to the atmosphere, and the main on-land water cycle processes. HOOPLA includes several techniques to handle forecast uncertainty. In particular, it adopts a probabilistic approach to describe the model structure, the initial condition, and the meteorological uncertainties.