Benchmarking hydrological models for low-flow simulation and forecasting on French catchments

Nicolle, P; Pushpalatha, Raji; Perrin, Charles; Dagognet, François; Thiéry, Dominique; Mathevet, Thibault; Lay, Matthieu Le; Besson, François; Soubeyroux, Jean‐Michel; Viel, Christian; Regimbeau, Fabienne; Andréassian, Vazken; Maugis, Philippe; Augeard, Bénédicte; Morice, E.

doi:10.5194/hessd-10-13979-2013

Cited by 16 publications

(15 citation statements)

References 60 publications

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“…The findings presented here are in line with some other comparison studies, such as Reed et al (2004), Nicolle et al (2014), Orth et al (2015) and te Linde (2008), who all found that added complexity can but does not necessarily lead to improvements. However, in contrast to Orth et al (2015), we found that low flows are better represented by the complex models, whereas they found that low flows were best represented by a very simple model.…”

Section: Effect Of Sub-grid Heterogeneitysupporting

confidence: 92%

The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

Nijzink

Samaniego

Mai

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Heterogeneity of landscape features like terrain, soil, and vegetation properties affects the partitioning of water and energy. However, it remains unclear to what extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from subgrid topography heterogeneity was incorporated into the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expertknowledge-based model constraints reduces model uncertainty, and whether (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge-based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as an overall measure of model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 %, respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. In addition, it was shown that suitable semi-quantitative prior constraints in combination with the transfer-function-based regularization approach of mHM can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

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Section: Effect Of Sub-grid Heterogeneitysupporting

confidence: 92%

The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

Nijzink

Samaniego

Mai

et al. 2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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“…The multi-model approach has been proven to be more robust and provides better performance than individual models [47]. Simple multi-model approaches that combine the outputs of hydrological models improve simulation and forecasting efficiency [47].…”

Section: Discussionmentioning

confidence: 99%

Influence of Parameter Sensitivity and Uncertainty on Projected Runoff in the Upper Niger Basin under a Changing Climate

Oyerinde¹,

Diekkrüger²

2017

Preprint

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Hydro-climatic projections in West Africa are attributed with high uncertainties that are difficult to quantify. This study assesses the influence of the parameter sensitivities and uncertainties of three rainfall runoff models on simulated discharge in current and future times using meteorological data from 8 Global Climate Models. The IHACRES Catchment Moisture Deficit (IHACRES-CMD) model, the GR4J and the Sacramento model were chosen for this study. During model evaluation, 10,000 parameter sets have been generated for each model and used in a sensitivity and uncertainty analysis using the Generalized Likelihood Uncertainty Estimation (GLUE) method. Out of the three models, IHACRES-CMD recorded the highest Nash-Sutcliffe Efficiency (NSE) of 0.92 and 0.86 for the calibration (1997)(1998)(1999)(2000)(2001)(2002)(2003) and the validation (2004)(2005)(2006)(2007)(2008)(2009)(2010) period respectively. The Sacramento model was able to adequately predict low flow patterns on the catchment while the GR4J and IHACRES-CMD over and under estimate low flow respectively. The use of multiple hydrological models to reduce uncertainties caused by model approaches is recommended along with other methods of sustainable river basin managements.

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“…4, the performances of these models are diverse: some flow signatures are well modeled (R 2 above 0.8 for mean specific flow and the 95th quantile, above 0.7 for the 5th quantile, runoff ratio, skewness of daily flow, mean 30-day maximum), but some other models perform very poorly (R 2 below 0.2 for low flow frequency and variability of low flow duration). It is well recog-nized that modeling low flows can be difficult (e.g., Nicolle et al, 2014;Donnelly et al, 2016;Zhang et al, 2015) and the correlation matrices (see Supplement) showed that these two flow signatures were poorly correlated with catchment descriptors. This highlights the difficulties in understanding process and physical controls to predict low flows with the datasets currently available to us.…”

Section: Hydrological Interpretation Of Classes Using Cartmentioning

confidence: 99%

Understanding hydrologic variability across Europe through catchment classification

Kuentz

Arheimer

Hundecha

et al. 2017

Hydrol. Earth Syst. Sci.

129

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Abstract. This study contributes to better understanding the physical controls on spatial patterns of pan-European flow signatures -taking advantage of large open datasets for catchment classification and comparative hydrology. Similarities in 16 flow signatures and 35 catchment descriptors were explored for 35 215 catchments and 1366 river gauges across Europe. Correlation analyses and stepwise regressions were used to identify the best explanatory variables for each signature. Catchments were clustered and analyzed for similarities in flow signature values, physiography and the combination of the two. We found the following. (i) A 15 to 33 % (depending on the classification used) improvement in regression model skills when combined with catchment classification versus simply using all catchments at once. (ii) Twelve out of 16 flow signatures were mainly controlled by climatic characteristics, especially those related to average and high flows. For the baseflow index, geology was more important and topography was the main control for the flashiness of flow. For most of the flow signatures, the second most important descriptor is generally land cover (mean flow, high flows, runoff coefficient, ET, variability of reversals). (iii) Using a classification and regression tree (CART), we further show that Europe can be divided into 10 classes with both similar flow signatures and physiography. The most dominant separation found was between energy-limited and moisturelimited catchments. The CART analyses also separated different explanatory variables for the same class of catchments. For example, the damped peak response for one class was explained by the presence of large water bodies for some catchments, while large flatland areas explained it for other catchments in the same class. In conclusion, we find that this type of comparative hydrology is a helpful tool for understanding hydrological variability, but is constrained by unknown human impacts on the water cycle and by relatively crude explanatory variables.

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Benchmarking hydrological models for low-flow simulation and forecasting on French catchments

Cited by 16 publications

References 60 publications

The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

Influence of Parameter Sensitivity and Uncertainty on Projected Runoff in the Upper Niger Basin under a Changing Climate

Understanding hydrologic variability across Europe through catchment classification

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