4.4 Article

When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species?

期刊

ECOHYDROLOGY
卷 13, 期 3, 页码 -

出版社

WILEY
DOI: 10.1002/eco.2193

关键词

ecological assessment; hydrological modelling; model optimization; species abundance; species preferences

资金

  1. Bundesministerium fur Bildung und Forschung [01LN1320A]
  2. Deutsche Forschungsgemeinschaft [1466/1-1]
  3. Horizon 2020 Framework Programme [642317]

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Riverine species have adapted to their environment, particularly to the hydrological regime. Hydrological models and the knowledge of species preferences are used to predict the impact of hydrological changes on species. Inevitably, hydrological model performance impacts how species are simulated. From the example of macroinvertebrates in a lowland and a mountainous catchment, we investigate the impact of hydrological model performance and the choice of the objective function based on a set of 36 performance metrics for predicting species occurrences. Besides species abundance, we use the simulated community structure for an ecological assessment as applied for the Water Framework Directive. We investigate when a hydrological model is sufficiently calibrated to depict species abundance. For this, we postulate that performance is not sufficient when ecological assessments based on the simulated hydrology are significantly different (analysis of variance, p < .05) from the ecological assessments based on observations. The investigated range of hydrological model performance leads to considerable variability in species abundance in the two catchments. In the mountainous catchment, links between objective functions and the ecological assessment reveal a stronger dependency of the species on the discharge regime. In the lowland catchment, multiple stressors seem to mask the dependence of the species on discharge. The most suitable objective functions to calibrate the model for species assessments are the ones that incorporate hydrological indicators used for the species prediction.

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