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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 di, Water Resources

The data are the hydrological model results of 12 models calibrated to the Ourthe catchment at Tabreux in the Belgian Ardennes. It provides hourly modeled streamflow, states and fluxes results for a selection of feasible parameter sets applied to 5 catchments (Ourthe at Tabreux, Ourthe Orientale at Mabompré, Ourthe Occidentale at Ortho, Semois at Membre-Pont, Lesse at Gendron). The models were calibrated by several institutes and universities working on the Meuse basin and gathering at the Meuse International Symposium in Liège. The data were used for the study Behind the scenes of streamflow performance by Bouaziz et al., 2021, HESS., The data are the hydrological model results of 12 models calibrated to the Ourthe catchment at Tabreux in the Belgian Ardennes. It provides hourly modeled streamflow, states and fluxes results for a selection of feasible parameter sets applied to 5 catchments (Ourthe at Tabreux, Ourthe Orientale at Mabompré, Ourthe Occidentale at Ortho, Semois at Membre-Pont, Lesse at Gendron). The models were calibrated by several institutes and universities working on the Meuse basin and gathering at the Meuse International Symposium in Liège. The data were used for the study Behind the scenes of streamflow performance by Bouaziz et al., 2021, HESS.

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 n

The root zone storage capacity (Sr) of vegetation is an important parameter in the hydrological behaviour of a catchment. Traditionally, Sr is derived from soil and vegetation data. However, more recently a new method has been developed that uses climate data to estimate Sr based on the assumption that vegetation adapts its root zone storage capacity to overcome dry periods. This method also enables one to take into account temporal variability of derived Sr values resulting from changes in climate or land cover. The current study applies this new method in 64 catchments in Finland to investigate the reasons for variability in Sr in boreal regions. Relations were assessed between climate-derived Sr values and climate variables (precipitation-potential evaporation rate, mean annual temperature, max snow water equivalent, snow-off date), detailed vegetation characteristics (leaf cover, tree length, root biomass), and vegetation types. The results show that in particular the phase difference between snow-off date and onset of potential evaporation has a large influence on the derived Sr values. Further to this it is found that (non-)coincidence of snow melt and potential evaporation could cause a division between catchments with a high and a low Sr value. It is concluded that the climate-derived root zone storage capacity leads to plausible Sr values in boreal areas and that, apart from climate variables, catchment vegetation characteristics can also be directly linked to the derived Sr values. As the climate-derived Sr enables incorporating climatic and vegetation conditions in a hydrological parameter, it could be beneficial to assess the effects of changing climate and environmental conditions in boreal regions., Water Resources

Why do equal precipitation events not lead to equal discharge events across space and time? The easy answer would be because catchments are different, which then leads to the second question: Why do hydrologists often use the same rainfall-runoff model for different catchments? Probably because specifying and distributing hydrological processes across catchments is not straightforward. It requires catchment data and proper tools to evaluate the details and spatial representation of the modelled processes. However, making a model more specific and distributed can improve the performance and predictive power of the hydrological model. Therefore, this thesis evaluates the added value of including spatial characteristics in rainfall-runoff models. Most model experiments in this thesis are carried out in the Ourthe catchment, a subcatchment of the Meuse basin. This catchment has a strong seasonal behaviour, responds quickly to precipitation and has a large influence on peak flows in the Meuse. It has a variety of landscapes, among which steep forested slopes and flat agricultural fields. This thesis proposes a new evaluation framework (Framework to Assess Realism of Model structures (FARM)), based on different characteristics of the hydrograph (hydrological signatures). Key element of this framework is that it evaluates both performance (good reproduction of signatures) and consistency (reproduction of multiple signatures with the same parameter set). This framework is used together with various other model evaluation tools to evaluate models at three levels: internal model behaviour, model performance and consistency, and predictive power. The root zone storage capacity (Sr) of vegetation is an important parameter in conceptual rainfall-runoff models. It largely determines the partitioning of precipitation into evaporation and discharge. Distribution of a climate derived Sr-value (i.e., based on precipitation and evaporation) was compared with Sr-values deri, Water Resources

International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although model comparison studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes. We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on a variety of specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of best-performing parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. the Nash–Sutcliffe efficiency), clear differences could be observed for specific events. We analysed the hydrographs of these specific events and conducted three types of statistical analyses on the entire time series: cumulative discharges, empirical extreme value distribution of the peak flows and flow duration curves for low flows. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the studied catchments. This intercomparison enhanced the understanding of the hydrological functioning of the catchment, in particular for low flows, and enabled to identify present knowledge gaps for other parts of the hydrograph. Above all, it helped to evaluate each model against a set of alternative models., Water Resources

International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although model comparison studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes. We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on a variety of specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of best-performing parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. the Nash–Sutcliffe efficiency), clear differences could be observed for specific events. We analysed the hydrographs of these specific events and conducted three types of statistical analyses on the entire time series: cumulative discharges, empirical extreme value distribution of the peak flows and flow duration curves for low flows. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the studied catchments. This intercomparison enhanced the understanding of the hydrological functioning of the catchment, in particular for low flows, and enabled to identify present knowledge gaps for other parts of the hydrograph. Above all, it helped to evaluate each model against a set of alternative models.

Root zone storage capacity ( S r ) is an important variable for hydrology and climate studies, as it strongly influences the hydrological functioning of a catchment and, via evaporation, the local climate. Despite its importance, it remains difficult to obtain a well-founded catchment representative estimate. This study tests the hypothesis that vegetation adapts its S r to create a buffer large enough to sustain the plant during drought conditions of a certain critical strength (with a certain probability of exceedance). Following this method, S r can be estimated from precipitation and evaporative demand data. The results of this ‘‘climate-based method’’ are compared with traditional estimates from soil data for 32 catchments in New Zealand. The results show that the differences between catchments in climate-derived catchment represen- tative S r values are larger than for soil-derived S r values. Using a model experiment, we show that the climate-derived S r can better reproduce hydrological regime signatures for humid catchments; for more arid catchments, the soil and climate methods perform similarly. This makes the climate-based S r a valuable addition for increasing hydrological understanding and reducing hydrological model uncertainty., Water Resources