Sensitivity analysis for complex ecological models - A new approach

  • Authors:

  • Vardit Makler-Pick;Gideon Gal;Malka Gorfine;Matthew R. Hipsey;Yohay Carmel

  • Affiliations:

  • Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel;Y. Allon Kinneret Limnological Laboratory, IOLR, P.O. Box 447, Migdal 14950, Israel;William Davidson Faculty of Industrial Engineering and Management Technion, Israel Institute of Technology, Israel;School of Earth & Environment, University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia;Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2011

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Abstract

A strategy for global sensitivity analysis of a multi-parameter ecological model was developed and used for the hydrodynamic-ecological model (DYRESM-CAEDYM, DYnamic REservoir Simulation Model-Computational Aquatic Ecosystem Dynamics Model) applied to Lake Kinneret (Israel). Two different methods of sensitivity analysis, RPART (Recursive Partitioning And Regression Trees) and GLM (General Linear Model) were applied in order to screen a subset of significant parameters. All the parameters which were found significant by at least one of these methods were entered as input to a GBM (Generalized Boosted Modeling) analysis in order to provide a quantitative measure of the sensitivity of the model variables to these parameters. Although the GBM is a general and powerful machine learning algorithm, it has substantial computational costs in both storage requirements and CPU time. Employing the screening stage reduces this cost. The results of the analysis highlighted the role of particulate organic material in the lake ecosystem and its impact on the over all lake nutrient budget. The GBM analysis established, for example, that parameters such as particulate organic material diameter and density were particularly important to the model outcomes. The results were further explored by lumping together output variables that are associated with sub-components of the ecosystem. The variable lumping approach suggested that the phytoplankton group is most sensitive to parameters associated with the dominant phytoplankton group, dinoflagellates, and with nanoplankton (Chlorophyta), supporting the view of Lake Kinneret as a bottom-up system. The study demonstrates the effectiveness of such procedures for extracting useful information for model calibration and guiding further data collection.