[1] Given the contradictory results from recent studies, this paper compares classical regionalization schemes of catchment model parameters over the wide range of hydroclimates found in France. To ensure the generality of the conclusions, we used two lumped rainfall-runoff models applied to daily data over a large set of 913 French catchments. Three types of approaches were considered: regionalization using regression, regionalization based on spatial proximity and regionalization based on physical similarity. This comparison shows that in France, where a dense network of gauging stations is available, spatial proximity provides the best regionalization solution. The regression approach is the least satisfactory, with results very close to those obtained using one median parameter set for the whole country. The physical similarity approach is intermediary. However, the results obtained with these three methods lag far behind those obtained by full model calibration. Our results also show that some improvement could be made by combining spatial proximity and physical similarity, and that there is still considerable room for progress in the field of ungaged catchment modeling.

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1] This paper examines the possible solutions that may allow a rainfall-runoff model to cope with the existence of unknown intercatchment groundwater flows over a given catchment. On the basis of a large catchment set we compare four versions of the GR4J and the SMAR rainfall-runoff models that differ in the way they use one of their parameters to adjust catchment-scale water balance. We show that from both the hydrological likelihood and the modeling efficiency point of view it is preferable to explicitly represent intercatchment groundwater transfers. The surrogate corrective solutions tested in this paper (correcting or scaling factors applied to the climatic input data or to the catchment area) that are sometimes used in practice could be used on the sole grounds of streamflow simulation efficiency, but we show that they should be avoided since they may lead to obviously unrealistic corrections and consequently yield a similarly unrealistic distribution between evaporation streamflow and underground fluxes.Citation: Le Moine, N., V. Andréassian, C. Perrin, and C. Michel (2007), How can rainfall-runoff models handle intercatchment groundwater flows? Theoretical study based on 1040 French catchments, Water Resour. Res., 43, W06428,

Abstract. As all hydrological models are intrinsically limited hypotheses on the behaviour of catchments, models -which attempt to represent real-world behaviour -will always remain imperfect. To make progress on the long road towards improved models, we need demanding tests, i.e. true crash tests. Efficient testing requires large and varied data sets to develop and assess hydrological models, to ensure their generality, to diagnose their failures, and ultimately, help improving them.

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All that glitters is not gold is one of those universal truths that also applies to hydrology, and particularly to the issue of model calibration, where a glittering mathematical optimum is too often mistaken for a hydrological optimum. This commentary aims at underlining the fact that calibration difficulties have not disappeared with the advent of the latest search algorithms. While it is true that progress on the numerical front has allowed us to quasi-eradicate miscalibration issues, we still too often underestimate the remaining hydrological task: screening mathematical optima in order to identify those parameter sets which will also work sufficiently outside the calibration period.

[1] This article describes an alternative to the optimization strategies classically adopted to calibrate the parameters of rainfall-runoff models. This new method, called discrete parameterization, relies on the sole use of the prior information on parameters gained on other catchments. The optimum parameter set is simply searched within a collection (a library) of predefined optima. This library is composed of parameter sets representing a large number of actual catchments. The method was tested on a set of 900 catchments (from Australia, France, and the United States) using two daily lumped rainfall-runoff models and was compared to more classical calibration approaches. Results are very similar for both models. Although the discrete parameterization method is not as efficient as a classical global search calibration approach when long time series are available for calibration, it provides more robust parameter sets when flow time series available for calibration becomes shorter than 2 years. This makes the method particularly interesting in the cases of poorly gauged catchments where available flow records are short. In case of limited data, the advantage of the proposed approach over the classical calibration approaches was more significant for the more complex model. On our set of 900 catchments, the optimum parameter set is generally selected among parameter sets from the library corresponding to catchments spatially close to the studied catchment. However other criteria of physical similarity may be relevant to select the donor catchment in the library.