Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 21, issue 1 | Copyright
Hydrol. Earth Syst. Sci., 21, 549-570, 2017
https://doi.org/10.5194/hess-21-549-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Jan 2017

Research article | 27 Jan 2017

Spatially distributed characterization of soil-moisture dynamics using travel-time distributions

Falk Heße1, Matthias Zink1, Rohini Kumar1, Luis Samaniego1, and Sabine Attinger1,2 Falk Heße et al.
  • 1Department of Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
  • 2Institute of Geosciences, Faculty for Chemistry and Earth Sciences, Friedrich-Schiller-University Jena, Jena, Germany

Abstract. Travel-time distributions are a comprehensive tool for the characterization of hydrological system dynamics. Unlike the streamflow hydrograph, they describe the movement and storage of water within and throughout the hydrological system. Until recently, studies using such travel-time distributions have generally either been applied to lumped models or to real-world catchments using available time series, e.g., stable isotopes. Whereas the former are limited in their realism and lack information on the spatial arrangements of the relevant quantities, the latter are limited in their use of available data sets. In our study, we employ the spatially distributed mesoscale Hydrological Model (mHM) and apply it to a catchment in central Germany. Being able to draw on multiple large data sets for calibration and verification, we generate a large array of spatially distributed states and fluxes. These hydrological outputs are then used to compute the travel-time distributions for every grid cell in the modeling domain. A statistical analysis indicates the general soundness of the upscaling scheme employed in mHM and reveals precipitation, saturated soil moisture and potential evapotranspiration as important predictors for explaining the spatial heterogeneity of mean travel times. In addition, we demonstrate and discuss the high information content of mean travel times for characterization of internal hydrological processes.

Download & links
Publications Copernicus
Download
Short summary
Travel-time distributions are a comprehensive tool for the characterization of hydrological systems. In our study, we used data that were simulated by virtue of a well-established hydrological model. This gave us a very large yet realistic dataset, both in time and space, from which we could infer the relative impact of different factors on travel-time behavior. These were, in particular, meteorological (precipitation), land surface (land cover, leaf-area index) and subsurface (soil) properties.
Travel-time distributions are a comprehensive tool for the characterization of hydrological...
Citation
Share