the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-model approach in a variable spatial framework for streamflow simulation
Abstract. Accounting for the variability of hydrological processes and climate conditions between catchments and within catchments remains a challenge in rainfall–runoff modelling. Among the many approaches developed over the past decades, multi-model approaches provide a way to take into account the uncertainty linked to the choice of model structure and its parameter estimates. Semi-distributed approaches make it possible to account explicitly for spatial variability while maintaining a limited level of complexity. However, these two approaches have rarely been used together. Such a combination would allow us to take advantage of both methods. The aim of this work is to answer the following question: What is the possible contribution of a multi-model approach within a variable spatial framework compared to lumped single models for streamflow simulation?
To this end, a set of 121 catchments with limited influence in France was assembled, with precipitation, potential evapotranspiration and streamflow data at the hourly time step over the period 1998–2018. The semi-distribution set-up was kept simple by considering a single downstream catchment defined by an outlet, and one or more upstream sub-catchments. The multi-model approach was implemented with 13 rainfall–runoff model structures, three calibration options and two spatial frameworks, for a total of 78 distinct modelling options. A simple average method was used to combine the various simulated streamflow at the outlet of the catchments and sub-catchments. The most efficient lumped model on a given catchment was taken as the benchmark for model evaluation.
Overall, the semi-distributed multi-model approach yields better performance than the different lumped models considered individually. The gain is mainly brought about by the multi-model set-up, with the spatial framework providing a benefit on a more occasional basis. These results, based on a large catchment set, evince the benefits of using a multi-model in a variable spatial framework to simulate streamflow.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(3413 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3413 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-569', Wouter Knoben, 02 May 2023
Dear authors,
Please find attached an annotated pdf with comments.
A main point of improvement I see is that the manuscript can (should?) account for sampling uncertainty in the KGE scores. For comparative analysis such as this (where differences in KGE scores between various options are used to decide which of the options is better), it is important to get some idea of the uncertainty associated with the scores themselves. An R tool exists that makes estimating these uncertainties for NSE and KGE straightforward from existing time series of observations and simulations (Clark et al., 2021) .
More generally, I think the mansucript can be clarified here and there (see comments in the pdf) and possibly streamlined a bit (though I'm unsure what to scrap - it just seems to have a large number of figures).
Kind regards,
Wouter Knoben
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., et al. (2021). The abuse of popular performance metrics in hydrologic modeling. Water Resources Research, 57, e2020WR029001. https://doi.org/10.1029/2020WR029001- AC1: 'Reply on RC1', Cyril Thébault, 02 Jun 2023
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RC2: 'Comment on egusphere-2023-569', Trine Jahr Hegdahl, 12 May 2023
Dear authors,
Please see the enclosed pdf for minor comments.
Kind regards
Trine J Hegdahl
- AC2: 'Reply on RC2', Cyril Thébault, 02 Jun 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-569', Wouter Knoben, 02 May 2023
Dear authors,
Please find attached an annotated pdf with comments.
A main point of improvement I see is that the manuscript can (should?) account for sampling uncertainty in the KGE scores. For comparative analysis such as this (where differences in KGE scores between various options are used to decide which of the options is better), it is important to get some idea of the uncertainty associated with the scores themselves. An R tool exists that makes estimating these uncertainties for NSE and KGE straightforward from existing time series of observations and simulations (Clark et al., 2021) .
More generally, I think the mansucript can be clarified here and there (see comments in the pdf) and possibly streamlined a bit (though I'm unsure what to scrap - it just seems to have a large number of figures).
Kind regards,
Wouter Knoben
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., et al. (2021). The abuse of popular performance metrics in hydrologic modeling. Water Resources Research, 57, e2020WR029001. https://doi.org/10.1029/2020WR029001- AC1: 'Reply on RC1', Cyril Thébault, 02 Jun 2023
-
RC2: 'Comment on egusphere-2023-569', Trine Jahr Hegdahl, 12 May 2023
Dear authors,
Please see the enclosed pdf for minor comments.
Kind regards
Trine J Hegdahl
- AC2: 'Reply on RC2', Cyril Thébault, 02 Jun 2023
Peer review completion
Journal article(s) based on this preprint
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Charles Perrin
Vazken Andréassian
Guillaume Thirel
Sébastien Legrand
Olivier Delaigue
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3413 KB) - Metadata XML