How morphology shapes the parameter sensitivity of lake ecosystem models. (February 2021)

• Record Type: Journal Article
• Authors: Andersen, Tobias Kuhlmann; Bolding, Karsten; Nielsen, Anders; Bruggeman, Jorn; Jeppesen, Erik; Trolle, Dennis
• Is Part Of: Environmental modelling & software. Volume 136(2021)
• Publication Date: 2021-02
• Subjects: Global sensitivity analysis -- Lake ecosystem modelling -- Process-based modelling -- Moment-independent sensitivity method -- Variance-based sensitivity method -- FABM-PCLake; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104945
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A global sensitivity analysis of a lake ecosystem model (GOTM-FABM-PCLake) was undertaken to test the impacts of lake morphology on parameter sensitivity in three different lakes. The analysis was facilitated by the Parallel Sensitivity and Auto-Calibration tool (parsac) and included a screening step with the density-based Borgonovo's method followed by in-depth analysis with both Borgonovo's and the variance-based Sobol' methods. The Borgonovo's method proved efficient in ranking the most influential parameters and its results were corroborated by the Sobol' method. For total phosphorus and total nitrogen, parameters related to the benthic-pelagic coupling and phytoplankton, were particularly important for the shallower lakes, whereas the most important parameters for total nitrogen were related mainly to the benthic-pelagic coupling in the deepest lake. For chlorophyll a, phytoplankton and zooplankton parameters were most influential. We conclude that lake morphology shapes the parameter sensitivity of lake ecosystem models. Highlights: A global sensitivity analysis (SA) of a lake ecosystem model was carried out. parsac facilitated and eased the SA of the three calibrated lake models. Density- and variance-based SA methods gave similar ranking of influential parameters. Lake morphology influenced parameter sensitivity.