Kuppel, SylvainTetzlaff, DoertheManeta, MarcoSoulsby, Chris2019-01-122019-01-122018-03Kuppel, S, Tetzlaff, D, Maneta, M & Soulsby, C 2018, 'What can we learn from multi-data calibration of a process-based ecohydrological model?', Environmental Modelling and Software, vol. 101, pp. 301-316. https://doi.org/10.1016/j.envsoft.2018.01.0011364-8152http://hdl.handle.net/2164/11771This 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.163695400141457engcatchment hydrologyecohydrologyprocess-based modellingmulti-objective calibrationinformation contentEcH2OGE Environmental SciencesQE GeologyEuropean Research CouncilGA 335910 VeWaGEQEJournal article10.1016/j.envsoft.2018.01.001101