Log-transformed discharge is often used to calculate performance criteria to better focus on low flows. This prior transformation limits the heteroscedasticity of model residuals and was largely applied in criteria based on squared residuals, like the Nash–Sutcliffe efficiency (NSE). In the recent years, NSE has been shown to have mathematical limitations and the Kling–Gupta efficiency (KGE) was proposed as an alternative to provide more balance between the expected qualities of a model (namely representing the water balance, flow variability and correlation). As in the case of NSE, several authors used the KGE criterion (or its improved version KGE′) with a prior logarithmic transformation on flows. However, we show that the use of this transformation is not adapted to the case of the KGE (or KGE′) criterion and may lead to several numerical issues, potentially resulting in a biased evaluation of model performance. We present the theoretical underpinning aspects of these issues and concrete modelling examples, showing that KGE′ computed on log-transformed flows should be avoided. Alternatives are discussed.

Testing hydrological models under changing conditions is essential to evaluate their ability to cope with changing catchments and their suitability for impact studies. With this perspective in mind, a workshop dedicated to this issue was held at the 2013 General Assembly of the International Association of Hydrological Sciences (IAHS) in Göteborg, Sweden, in July 2013, during which the results of a common testing experiment were presented. Prior to the workshop, the participants had been invited to test their own models on a common set of basins showing varying conditions specifically set up for the workshop. All these basins experienced changes, either in physical characteristics (e.g. changes in land cover) or climate conditions (e.g. gradual temperature increase). This article presents the motivations and organization of this experiment—that is—the testing (calibration and evaluation) protocol and the common framework of statistical procedures and graphical tools used to assess the model performances. The basins datasets are also briefly introduced (a detailed description is provided in the associated Supplementary material).

The ability of hydrological models to deal with changing conditions should not be taken for granted: it is an unfortunate but well-known problem of hydrology that the model structure and/or parameters optimized for certain conditions may not be transferable in time. Consequently, it is essential that, for application under changing conditions (e.g. in climate change impact studies), models be thoroughly assessed for their extrapolation capacity using adequate protocols. This editorial provides an overview of the Special Issue of Hydrological Sciences Journal compiled after a workshop on this theme held during the General Assembly of the International Association of Hydrological Sciences (IAHS) in Gothenburg (Sweden) in 2013. The Workshop participants had been invited to apply a calibration and evaluation protocol to their own models on a given set of changing basins. The results show that this protocol is an appropriate and instructive way of assessing the suitability of hydrological models to be applied under changing conditions. This special issue also includes papers following alternative testing methodologies, as well as an opinion paper on the definition of non-stationarity.

Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases.

Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.