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Preprints
https://doi.org/10.5194/hess-2020-181
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-181
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 15 May 2020

Submitted as: research article | 15 May 2020

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This preprint is currently under review for the journal HESS.

Predicting probabilities of streamflow intermittency across a temperate mesoscale catchment

Nils H. Kaplan1, Theresa Blume2, and Markus Weiler1 Nils H. Kaplan et al.
  • 1Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, 79098 Freiburg, Germany
  • 2Hydrology, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany

Abstract. The fields of eco-hydrological modelling and extreme flow prediction and management demand for detailed information of streamflow intermittency and its corresponding landscape controls. Innovative sensing technology for monitoring of streamflow intermittency in perennial rivers and intermittent reaches improve data availability, but reliable maps of streamflow intermittency are still rare. We used a large dataset of streamflow intermittency observations and a set of spatial predictors to create logistic regression models to predict the probability of streamflow intermittency for a full year, and, wet and dry periods for the entire 247 km2 Attert catchment in Luxembourg. Similar climatic conditions across the catchment permit a direct comparison of the streamflow intermittency among different geological and pedological regions. We used spatial predictors describing land cover, track (road) density, terrain metrics, soil and geological properties as local as well as integral catchment information. The terrain metrics catchment area and profile curvature were the most important predictors for all models. However, the models which include the dry period of the year reveal the importance of soil hydraulic conductivity, bedrock permeability and in case of the annual model the presence of tracks (roads) during low flow conditions. A classification of spatially distributed streamflow intermittency probabilities into ephemeral, intermittent and perennial reaches allows the estimation of stream network extent under various conditions. This approach is a first step to provide detailed spatial information for hydrological modelling as well as management practice.

Nils H. Kaplan et al.

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Nils H. Kaplan et al.

Data sets

Monitoring ephemeral, intermittent and perennial streamflow: a dataset from 182 sites in the Attert catchment, Luxembourg N. H. Kaplan, E. Sohrt, T. Blume, and M. Weiler https://doi.org/10.5194/essd-11-1363-2019

Nils H. Kaplan et al.

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Short summary
In the recent decades the demand for detailed information of spatial and temporal dynamics of the stream network had grown in the fields of eco-hydrology and extreme flow prediction. We use temporal streamflow intermittency data obtained at various sites using innovative sensing technology as well as spatial predictors to predict and map probabilities of stream flow intermittency. This approach has the potential to provide intermittency maps for hydrological modelling and management practice.
In the recent decades the demand for detailed information of spatial and temporal dynamics of...
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