An evaluation framework for distributed hydrological models was developed, based on hydrological signatures derived from precipitation, streamflow, and air temperature data. The goal is to characterize the dominant processes, identifying those that are well or poorly represented by the model.
A sensitivity analysis was conducted to confirm and qualify the relationships between the hydrological signatures and the parameters of the distributed J2000 model for the Ardèche watershed. This analysis allowed for a model diagnosis and provided recommendations for its improvement.

The evaluation of hydrological models is typically based on comparisons of observed and sim-ulated streamflow time series using performance metrics such as the Nash-Sutcli˙e Eÿciency. Although it provides relevant measures of the predictive performance of a model, this type of approach provides very little information on the reasons behind good or bad performance. Instead, Gupta et al. (2008) proposed to use hydrological signatures which are indicators that characterize catchment behaviors. Because they can be related to hydrological processes, using them when comparing observation with simulation enable the evaluation of the model while o˙ering diagnostics, i.e. indications on the hydrological processes that are well or badly repre-sented in the model.In this PhD thesis, we focus on the interpretations and diagnostic power of hydrological signatures and how they can be used to guide the improvement of a distributed model. We present the building of a set of hydrological signatures, using only widely available data – pre-cipitation, streamflow and air temperature – to characterize the hydrological functioning of 4 Ardèche sub-catchments (South East of France) and 10 snow dominated catchments of the Southern Sierra mountains (California, USA). Already existing and new hydrological signatures are selected and/or designed. Collectively, they can characterize catchment behavior in a wide variety of hydro-climatic contexts. We demonstrate the value of additional snow measurements to evaluate the information content of snow dedicated hydrological signatures. In the context of the Ardèche catchment, we set up the J2000 distributed model and use a sensitivity analysis to understand how the hydrological signatures are linked to the model parameters. This provides insights into how they are to be interpreted in the context of the J2000 Ardèche model and allows the assessment of their diagnostic power. Finally, combining the results of the sensi-tivity analysis with comparisons between observed and simulated hydrological signatures, we undertake an in-depth diagnostic of the model to provide and test recommendations for its improvement. Deficiencies of the model functioning are identified, mainly related to soil and groundwater storage and fluxes, highlighting issues in the spatial representation of soil and geological properties.

Funding

50% Compagnie Nationale du Rhône (CNR), 50% Agence de l’eau Rhône Méditerranée Corse.

For more information

• Horner, I, Branger, F, McMillan, H, Vannier, O, Braud, I. Information content of snow hydrological signatures based on streamflow, precipitation and air temperature. Hydrological Processes. 2020; 1– 17. https://doi.org/10.1002/hyp.13762
• Horner, I., Branger, F., Braud I., Vannier, O., Lauvernet, C., Assessment of hydrological signatures for the diagnosis and improvement of a process-based distributed hydrological model through a sensitivity analysis, Journal of Hydrology, in revision.

Cite the thesis

Ivan Horner. Construction et évaluation de signatures hydrologiques pour le diagnostic et l'amélioration d'un modèle hydrologique distribué. Hydrologie. Université Grenoble Alpes [2020-..], 2020. Français. ⟨NNT : 2020GRALU014⟩. ⟨tel-02928272⟩

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