A framework to assess the realism of model structures using hydrological signatures

Abstract

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.

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

“…The runoff signatures are viewed in such a way that streamflow data can be broken up into several samples, each of them a manifestation of catchment functioning (Euser et al, 2013;Hrachowitz et al, 2014;Westerberg and McMillan, 2015). Five different signatures are used in this study and described in the following:…”

“…More specifically, we investigate the relative importance of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. This correspondence between model and "reality", often described as "working for the right reasons" (Kirchner, 2006;Kavetski and Fenicia, 2011;Euser et al, 2013), is essential if the model is to be used as a tool for improving the understanding of a hydrological system and/or used for prediction and extrapolation, such as simulating the impacts of land use change, variability in climatological forcing, etc.…”

HESS Opinions: The complementary merits of competing modelling philosophies in hydrology

“…This was in the past demonstrated by many studies (e.g. Jothityangkoon et al, 2001;Atkinson et al, 2002;Fenicia et al, 2008bFenicia et al, , 2014Euser et al, 2013;Coxon et al, 2014;Hrachowitz et al, 2014;Willems, 2014).…”

“…duration curves (e.g. Euser et al, 2013;Pfannerstill et al, 2014;Guse et al, 2016;Kelleher et al, 2017). In addition, model calibration and/or evaluation against observed spatial patterns remains currently still under-exploited.…”

“…catchment signatures, such as flow duration curves (e.g. Jothityangkoon et al, 2001;Eder et al, 2003;Yadav et al, 2007;Martinez and Gupta, 2011;Sawicz et al, 2011;Euser et al, 2013;Willems et al, 2014;Shafii and Tolson, 2015;Westerberg and McMillan, 2015) but also temporal dynamics and/or spatial patterns in state and flux variables the model may not have been calibrated to, such as snow cover (e.g. Parajka and Blöschl, 2008), groundwater (e.g.…”