What can we learn from multi-data calibration of a process-based ecohydrological model?

Sylvain Kuppel, Doerthe Tetzlaff, Marco Maneta, Chris Soulsby

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)
30 Downloads (Pure)


We assessed whether a complex, process-based ecohydrological model can be appropriately parameterized to reproduce the key water flux and storage dynamics at a long-term research catchment in the Scottish Highlands. We used the fully-distributed ecohydrological model EcH2O, calibrated against long-term datasets that encompass hydrologic and energy exchanges, and ecological measurements. Applying diverse combinations of these constraints revealed that calibration against virtually all datasets enabled the model to reproduce streamflow reasonably well. However, parameterizing the model to adequately capture local flux and storage dynamics, such as soil moisture or transpiration, required calibration with specific observations. This indicates that the footprint of the information contained in observations varies for each type of dataset, and that a diverse database informing about the different compartments of the domain, is critical to identify consistent model parameterizations. These results foster confidence in using EcH2O to contribute to understanding current and future ecohydrological couplings in Northern catchments.
Original languageEnglish
Pages (from-to)301-316
Number of pages16
JournalEnvironmental Modelling and Software
Early online date12 Jan 2018
Publication statusPublished - Mar 2018

Bibliographical note

This work was funded by the European Research Council
(project GA 335910 VeWa). M. Maneta acknowledges support from
the U.S National Science Foundation (project GSS 1461576) and U.S
National Science Foundation EPSCoR Cooperative Agreement #EPS1101342.
All model runs were performed using the High Performance
Computing (HPC) cluster of the University of Aberdeen, and
the IT Service is thanked for its help in installing PCRaster and other
libraries necessary to run EcH2O and post-processing Python routines
on the HPC cluster. Finally, the authors are grateful to the
many people who have been involved in establishing and
continuing data collection at the Bruntland Burn, particularly
Christian Birkel, Maria Blumstock, Jon Dick, Josie Geris, Konrad
Piegat, Claire Tunaley, and Hailong Wang.


  • catchment hydrology
  • ecohydrology
  • process-based modelling
  • multi-objective calibration
  • information content
  • EcH2O


Dive into the research topics of 'What can we learn from multi-data calibration of a process-based ecohydrological model?'. Together they form a unique fingerprint.

Cite this