TY - JOUR
T1 - An automated calibration framework and open source tools for 3D lake hydrodynamic models
AU - Baracchini, Theo
AU - Hummel, Stef
AU - Verlaan, Martin
AU - Cimatoribus, Andrea
AU - Wüest, Alfred
AU - Bouffard, Damien
PY - 2020
Y1 - 2020
N2 - Understanding lake dynamics is crucial to provide scientifically credible information for ecosystem management. In this context, three-dimensional hydrodynamic models are a key information source to assess critical but often subtle changes in lake dynamics occurring at all spatio-temporal scales. However, those models require time-consuming calibrations, often carried out by trial-and-error. Through a new coupling of open source software, we present here a flexible and computationally inexpensive automated calibration framework. The method, tailored to the calibration data available to the user, aims at (i) reducing the time spent on calibration, and (ii) making three-dimensional lake modelling accessible to a broader range of users. It is demonstrated for two different lakes (Lake Geneva and Greifensee) with an extensive multi-variable observational dataset. Models mean absolute errors are reduced by up to ~50% over the baseline. Guidelines on heat and momentum transfer parameters are given with their dependence on the observational setup.
AB - Understanding lake dynamics is crucial to provide scientifically credible information for ecosystem management. In this context, three-dimensional hydrodynamic models are a key information source to assess critical but often subtle changes in lake dynamics occurring at all spatio-temporal scales. However, those models require time-consuming calibrations, often carried out by trial-and-error. Through a new coupling of open source software, we present here a flexible and computationally inexpensive automated calibration framework. The method, tailored to the calibration data available to the user, aims at (i) reducing the time spent on calibration, and (ii) making three-dimensional lake modelling accessible to a broader range of users. It is demonstrated for two different lakes (Lake Geneva and Greifensee) with an extensive multi-variable observational dataset. Models mean absolute errors are reduced by up to ~50% over the baseline. Guidelines on heat and momentum transfer parameters are given with their dependence on the observational setup.
KW - Auto-calibration
KW - Delft3D-FLOW
KW - Model performance evaluation
KW - Observational uncertainty
KW - OpenDA
KW - Parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=85090558406&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2020.104787
DO - 10.1016/j.envsoft.2020.104787
M3 - Article
AN - SCOPUS:85090558406
SN - 1364-8152
VL - 134
SP - 1
EP - 16
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 104787
ER -