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.

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.

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.

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 derived from soil samples in 32 New Zealand catchments. The comparison is based on spatial patterns and a model experiment. It is concluded that climate is a better estimator for Sr than soil, especially in wet catchments. Within the Meuse basin, climate derived Sr -values have been estimated as well; applying these newly derived storage estimates improved model results.
Two types of distribution have been tested for the Ourthe catchment: the distribution of meteorological forcing and the distribution of model structure. The distribution of forcing was based on spatially variable precipitation and potential evaporation. These were averaged at different levels within in the model, thereby creating four levels of model state distribution. The model structure was distributed by using two hydrological response units (HRUs), representing wetlands and hillslopes. Eventually, a lumped and a distributed model structure were compared, each with four levels of model state (forcing) distribution. From this, it is concluded that distribution of model structure is more important than distribution of forcing. However, if the model structure is distributed, the forcing should be distributed as well.
Knowing that distribution of model structure is relevant, more detailed process conceptualisations have been tested for the Ourthe Orientale, a subcatchment of the Ourthe. An additional agricultural HRU was introduced for which Hortonian overland flow and frost in the topsoil are assumed to be relevant. In addition, a degree-day based snow module has been added to all HRUs. Adding these process conceptualisations improved the performance and consistency of the model on an event basis. However, the implemented processes and the related signatures are sensitive to errors in forcing and model outliers and should therefore be implemented carefully.
This thesis finishes with two explorative comparisons; one comparing the newly developed model of the Ourthe Orientale catchment with other catchments; the second between the newly developed model and other models, including the HBV configuration currently used for operational forecasting in the Meuse basin. These comparisons were carried out based on visual inspections of parts of the hydrograph. The results show that the newly developed model can be applied in neighbouring catchments with similar performance. The comparison with other models demonstrates that a very quick overland flow component and a parallel configuration of fast and slow runoff generating reservoirs is important to reproduce the dynamics of the hydrograph related to different time scales. Both aspects are included in the newly developed model. As a results, the newly developed model is better able to reproduce most of the dynamics of the hydrograph than the operational HBV configuration, used at the moment of writing.
Distribution and detailed process conceptualisation are very beneficial for rainfall-runoff modelling of the Ourthe catchment. However, they should be applied with care. Conceptual models are a strong simplification of reality. When confronting them only with discharge data, there is a risk of misinterpreting other hydrological processes.
This thesis suggests two possible opportunities to further improve conceptual models. First, catchment understanding could be increased by adding more physical meaning to the models, such as the climate derived root zone storage capacity. And second, remote sensing and plot scale data could be combined to link hydrological processes at different scales. In this way conceptual models can probably be used to get more insight into scaling issues, which occur when moving from hillslope to catchment scale.

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.

Incorporating spatially variable information is a frequently discussed option to increase the performance of (semi- )distributed conceptual rainfall-runoff models. One of the methods to do this is by using this spatially variable information to delineate Hydrological Response Units (HRUs) within a catchment. In large parts of Europe the original forested land cover is replaced by an agricultural land cover. This change in land cover probably affects the dominant runoff processes in the area, for example by increasing the Hortonian overland flow component, especially on the flatter and higher elevated parts of the catchment. A change in runoff processes implies a change in HRUs as well. A previous version of our model distinguished wetlands (areas close to the stream) from the remainder of the catchment. However, this configuration was not able to reproduce all fast runoff processes, both in summer as in winter. Therefore, this study tests whether the reproduction of fast runoff processes can be improved by incorporating a HRU which explicitly accounts for the effect of agriculture. A case study is carried out in the Ourthe catchment in Belgium. For this case study the relevance of different process conceptualisations is tested stepwise. Among the conceptualisations are Hortonian overland flow in summer and winter, reduced infiltration capacity due to a partly frozen soil and the relative effect of rainfall and snow smelt in case of this frozen soil. The results show that the named processes can make a large difference on event basis, especially the Hortonian overland flow in summer and the combination of rainfall and snow melt on (partly) frozen soil in winter. However, differences diminish when the modelled period of several years is evaluated based on standard metrics like Nash- Sutcliffe Efficiency. These results emphasise on one hand the importance of incorporating the effects of agricultural in conceptual models and on the other hand the importance of more event based model evaluation.

The catchment representative root zone storage capacity (Sr), i.e. the plant available soil water, is an important parameter of hydrological systems. It does not only influence the runoff from catchments, by controlling the partitioning of water fluxes but it also influences the local climate, by providing the source for transpiration. Sr is difficult to observe at catchment scale, due to heterogeneities in vegetation and soils. Sr estimates are traditionally derived from soil characteristics and estimates of root depths. In contrast, a recently suggested method allows the determination of Sr based on climate data, i.e. precipitation and evaporation, alone (Gao et al., 2014). By doing so, the time-variable size of Sr, is explicitly accounted for, which is not the case for traditional soil based methods. The time-variable size of Sr reflects root growth and thus the vegetation’s adaption to medium-term fluctuations in the climate. Thus, we tested and compared Sr estimates from this ’climate based method’ with estimates from soil data for 32 catchments in New Zealand. The results show a larger range in climate derived Sr than in soil derived Sr. Using a model experiment, we show that a model using the climate derived Sr is more accurately able to reproduce a set of hydrological regime signatures, in particular for humid catchments. For more arid catchments, the two methods provide similar model results. This implies that, although soil database information has some predictive power for model soil storage capacity, climate has a similar or greater control on Sr, as climate affects the evolving hydrological functioning of the root zone at the time scale of hydrological interest. In addition, Sr represents the plant available water and thus root surface, volume and density, and is therefore a more complete descriptor of vegetation influence on water fluxes than mere root depth. On balance, the results indicate that climate has a higher explanatory power than soils for catchment representative root zone storage capacity.