{'fraction_non_alphanumeric': 0.013688212927756654, 'fraction_numerical': 0.003041825095057034, 'mean_word_length': 5.783505154639175, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The effect of forcing and landscape distribution on performance and consistency of model structures

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. Copyright © 2015 John Wiley & Sons, Ltd.