Abstract
Abstract. Low flows are often poorly reproduced by commonly used hydrological models, which are traditionally designed to meet peak flow situations. Hence, there is a need to improve hydrological models for low flow prediction. This study assessed the impact of model structure on low flow simulations and recession behaviour using the Framework for Understanding Structural Errors (FUSE). FUSE identifies the set of subjective decisions made when building a hydrological model and provides multiple options for each modeling decision. Altogether 79 models were created and applied to simulate stream flows in the snow dominated headwater catchment Narsjø in Norway (119 km2). All models were calibrated using an automatic optimisation method. The results showed that simulations of summer low flows were poorer than simulations of winter low flows, reflecting the importance of different hydrological processes. The model structure influencing winter low flow simulations is the lower layer architecture, whereas various model structures were identified to influence model performance during summer.
Highlights
ObjectivesThis paper aims to contribute to the improvement of hydrological models for low flow prediction
It appears that the FlogNSE and the FMARE show a good model performance for the FlogNSE range from 1 to 0.8
Using specific model structure combinations of different conceptual models resulted in different model performances for summer and winter low flows
Summary
This paper aims to contribute to the improvement of hydrological models for low flow prediction
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