TY - JOUR
T1 - Sensitivity analysis for complex ecological models - A new approach
AU - Makler-Pick, Vardit
AU - Gal, Gideon
AU - Gorfine, Malka
AU - Hipsey, Matthew R.
AU - Carmel, Yohay
N1 - Funding Information:
This research was supported by grants from the Ministry of Science and Technology Israel , the Federal Ministry of Education and Research , Germany (BMBF) and the Israel Water Authority . Support for V.M. was provided by the Yohay Ben-Nun Scholarship fund and The Australia-Israel Scientific Exchange Foundation . We thank the anonymous reviewers for their constructive comments and suggestions.
PY - 2011/2
Y1 - 2011/2
N2 - 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.
AB - 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.
KW - DYRESM-CAEDYM
KW - Ecosystem model
KW - Global sensitivity
KW - Lake Kinneret
KW - Sensitivity analysis
UR - http://www.scopus.com/inward/record.url?scp=78049294603&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2010.06.010
DO - 10.1016/j.envsoft.2010.06.010
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AN - SCOPUS:78049294603
SN - 1364-8152
VL - 26
SP - 124
EP - 134
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
IS - 2
ER -