Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments

Abstract

Abstract. This work investigates the added value of ensembles constructed from seventeen lumped hydrological models against their simple average counterparts. It is thus hypothesized that there is more information provided by all the outputs of these models than by their single aggregated predictors. For all available 1061 catchments, results showed that the mean continuous ranked probability score of the ensemble simulations were better than the mean average error of the aggregated simulations, confirming the added value of retaining all the components of the model outputs. Reliability of the simulation ensembles is also achieved for about 30% of the catchments, as assessed by rank histograms and reliability plots. Nonetheless this imperfection, the ensemble simulations were shown to have better skills than the deterministic simulations at discriminating between events and non-events, as confirmed by relative operating characteristic scores especially for larger streamflows. From 7 to 10 models are deemed sufficient to construct ensembles with improved performance, based on a genetic algorithm search optimizing the continuous ranked probability score. In fact, many model subsets were found improving the performance of the reference ensemble. This is thus not essential to implement as much as seventeen lumped hydrological models. The gain in performance of the optimized subsets is accompanied by some improvement of the ensemble reliability in most cases. Nonetheless, a calibration of the predictive distribution is still needed for many catchments.

Transferring global uncertainty estimates from gauged to ungauged catchments

“…the review by Liu and Gupta, 2007), informal methods related to the GLUE (generalised likelihood uncertainty estimation) framework (Beven and Freer, 2001), multi-model approaches (Duan et al, 2007;Velazquez et al, 2010) and other global uncertainty quantification methods (Montanari and Brath, 2004;Solomatine and Shrestha, 2009;Weerts et al, 2011;Ewen and O'Donnell, 2012). A comprehensive review of the topic can be found in Matott et al (2009) and .…”

Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures

“…In some cases, when the sensitivity was considered small, their designers have fixed some of their parameters in order to favour the parsimony of the models, reducing computation time and equifinality issues. These models, or part of, were exploited by Velázquez et al (2010) for exploring multimodel ensemble forecasting and by Seiller et al (2012) for assessing the robustness of the ensemble under contrasted climate.…”

“…Several confidence intervals are thus plotted (from 0.1 to 0.9) with, for example, 0.5 corresponding to the quartiles spread (25 to 75 %) and 0.9 corresponding to the spread of the 5 to 95 % quantiles. Thus, for each of the 3360 simulations and each confidence interval, statement if observed discharge is included or not is verified, resulting in a reliability graph (Boucher et al, 2009;Velázquez et al, 2010).…”

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

“…This is because adding only temporal aspects of the spatial observations to the objective function is not sufficient for achieving significant improvements in simulated spatial patterns if model parameterisation is not flexible enough to physically adjust to the observed pattern. Besides, the model structure, parameterisations and calibration schemes have usually been designed for streamflow optimisations (Vazquez et al, 2011;Velázquez et al, 2010). In order to ensure compatibility between the spatial pattern calibration target and model parameterisation, the flexibility of the spatial model parameterisation needs to be reconsidered.…”