Hydrological models frequently suffer from limited predictive power despite adequate calibration performances. This can indicate insufficient representations of the underlying processes. Thus, ways are sought to increase model consistency while satisfying the contrasting priorities of increased model complexity and limited equifinality. In this study, the value of a systematic use of hydrological signatures and expert knowledge for increasing model consistency was tested. It was found that a simple conceptual model, constrained by four calibration objective functions, was able to adequately reproduce the hydrograph in the calibration period. The model, however, could not reproduce a suite of hydrological signatures, indicating a lack of model consistency. Subsequently, testing 11 models, model complexity was increased in a stepwise way and counter-balanced by ''prior constraints,'' inferred from expert knowledge to ensure a model which behaves well with respect to the modeler's perception of the system. We showed that, in spite of unchanged calibration performance, the most complex model setup exhibited increased performance in the independent test period and skill to better reproduce all tested signatures, indicating a better system representation. The results suggest that a model may be inadequate despite good performance with respect to multiple calibration objectives and that increasing model complexity, if counter-balanced by prior constraints, can significantly increase predictive performance of a model and its skill to reproduce hydrological signatures. The results strongly illustrate the need to balance automated model calibration with a more expert-knowledge-driven strategy of constraining models.

Abstract. The use of flexible hydrological model structures for hypothesis testing requires an objective and diagnostic method to identify whether a rainfall-runoff model structure is suitable for a certain catchment. To determine if a model structure is realistic, i.e. if it captures the relevant runoff processes, both performance and consistency are important. We define performance as the ability of a model structure to mimic a specific part of the hydrological behaviour in a specific catchment. This can be assessed based on evaluation criteria, such as the goodness of fit of specific hydrological signatures obtained from hydrological data. Consistency is defined as the ability of a model structure to adequately reproduce several hydrological signatures simultaneously while using the same set of parameter values. In this paper we describe and demonstrate a new evaluation Framework for Assessing the Realism of Model structures (FARM). The evaluation framework tests for both performance and consistency using a principal component analysis on a range of evaluation criteria, all emphasizing different hydrological behaviour. The utility of this evaluation framework is demonstrated in a case study of two small headwater catchments (Maimai, New Zealand, and Wollefsbach, Luxembourg). Eight different hydrological signatures and eleven model structures have been used for this study. The results suggest that some model structures may reveal the same degree of performance for selected evaluation criteria while showing differences in consistency. The results also show that some model structures have a higher performance and consistency than others. The principal component analysis in combination with several hydrological signatures is shown to be useful to visualise the performance and consistency of a model structure for the study catchments. With this framework performance and consistency are evaluated to identify which model structure suits a catchment better compared to other model structures. Until now the framework has only been based on a qualitative analysis and not yet on a quantitative analysis.

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 representative 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.

Key points:• distribution of model structure increases model performance and consistency • distribution of model structure requires distributed model state distribution • a parallel configuration of model structures gives good resultsThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/hyp.10445This article is protected by copyright. All rights reserved.The effect of forcing and landscape distribution on performance and consistency of model structures Tanja Euser, Markus Hrachowitz, Hessel Winsemius, Hubert Savenije It is often challenging to determine the appropriate level of spatial model forcing and model distribution in conceptual rainfall-runoff modelling. This paper compares the value of incorporating both spatially distributed forcing data and spatially distributed model conceptualisations based on landscape heterogeneity, applied to the Ourthe catchment in Belgium. Distributed forcing data were used to create a spatial distribution of model states. Eight different configurations were tested: a lumped and distributed model structure, each with four levels of model state distribution. The results show that in the study catchment the distributed model structure can in general better reproduce the dynamics of the hydrograph, and furthermore, that the differences in performance and consistency between calibration and validation are smallest for the distributed model structure with distributed model states. For the Ourthe catchment, it can be concluded that the positive effect of incorporating a distributed model structure is larger than that of incorporating distributed model states. Distribution of model structure increases both model performance and consistency.

Abstract. The Bowen ratio surface energy balance method is a relatively simple method to determine the latent heat flux and the actual land surface evaporation. The Bowen ratio method is based on the measurement of air temperature and vapour pressure gradients. If these measurements are performed at only two heights, correctness of data becomes critical. In this paper we present the concept of a new measurement method to estimate the Bowen ratio based on vertical dry and wet bulb temperature profiles with high spatial resolution. A short field experiment with distributed temperature sensing (DTS) in a fibre optic cable with 13 measurement points in the vertical was undertaken. A dry and a wetted section of a fibre optic cable were suspended on a 6 m high tower installed over a sugar beet trial plot near Pietermaritzburg (South Africa). Using the DTS cable as a psychrometer, a near continuous observation of vapour pressure and air temperature at 0.20 m intervals was established. These data allowed the computation of the Bowen ratio with a high spatial and temporal precision. The daytime latent and sensible heat fluxes were estimated by combining the Bowen ratio values from the DTS-based system with independent measurements of net radiation and soil heat flux. The sensible heat flux, which is the relevant term to evaluate, derived from the DTS-based Bowen ratio (BR-DTS) was compared with that derived from co-located eddy covariance (R 2 = 0.91), surface layer scintillometer (R 2 = 0.81) and surface renewal (R 2 = 0.86) systems. By using multiple measurement points instead of two, more confidence in the derived Bowen ratio values is obtained.