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 9 | Copyright

Special issue: Observations and modeling of land surface water and energy...

Hydrol. Earth Syst. Sci., 21, 4323-4346, 2017
https://doi.org/10.5194/hess-21-4323-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Sep 2017

Research article | 01 Sep 2017

Toward seamless hydrologic predictions across spatial scales

Luis Samaniego1, Rohini Kumar1, Stephan Thober1, Oldrich Rakovec1, Matthias Zink1, Niko Wanders2,6, Stephanie Eisner3,a, Hannes Müller Schmied4,5, Edwin H. Sutanudjaja6, Kirsten Warrach-Sagi7, and Sabine Attinger1 Luis Samaniego et al.
  • 1Department of Computational Hydrosystems, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
  • 2Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
  • 3Center for Environmental Systems Research, University of Kassel, Kassel, Germany
  • 4Institute of Physical Geography, Goethe-University Frankfurt, Frankfurt, Germany
  • 5Senckenberg Biodiversity and Climate Research Centre (BiK-F), Frankfurt, Germany
  • 6Universiteit Utrecht, Department of Physical Geography, Utrecht, the Netherlands
  • 7Institute of Physics and Meteorology, University of Hohenheim, Stuttgart, Germany
  • anow at: Division for Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research, Ås, Norway

Abstract. Land surface and hydrologic models (LSMs/HMs) are used at diverse spatial resolutions ranging from catchment-scale (1–10km) to global-scale (over 50km) applications. Applying the same model structure at different spatial scales requires that the model estimates similar fluxes independent of the chosen resolution, i.e., fulfills a flux-matching condition across scales. An analysis of state-of-the-art LSMs and HMs reveals that most do not have consistent hydrologic parameter fields. Multiple experiments with the mHM, Noah-MP, PCR-GLOBWB, and WaterGAP models demonstrate the pitfalls of deficient parameterization practices currently used in most operational models, which are insufficient to satisfy the flux-matching condition. These examples demonstrate that J. Dooge's 1982 statement on the unsolved problem of parameterization in these models remains true. Based on a review of existing parameter regionalization techniques, we postulate that the multiscale parameter regionalization (MPR) technique offers a practical and robust method that provides consistent (seamless) parameter and flux fields across scales. Herein, we develop a general model protocol to describe how MPR can be applied to a particular model and present an example application using the PCR-GLOBWB model. Finally, we discuss potential advantages and limitations of MPR in obtaining the seamless prediction of hydrological fluxes and states across spatial scales.

Download & links
Publications Copernicus
Special issue
Download
Short summary
We inspect the state-of-the-art of several land surface (LSMs) and hydrologic models (HMs) and show that most do not have consistent and realistic parameter fields for land surface geophysical properties. We propose to use the multiscale parameter regionalization (MPR) technique to solve, at least partly, the scaling problem in LSMs/HMs. A general model protocol is presented to describe how MPR can be applied to a specific model.
We inspect the state-of-the-art of several land surface (LSMs) and hydrologic models (HMs) and...
Citation
Share