Sensitivity analysis of a process-based ecosystem model: Pinpointing parameterization and structural issues

2013 ◽  
Vol 118 (2) ◽  
pp. 505-528 ◽  
Author(s):  
Christoforos Pappas ◽  
Simone Fatichi ◽  
Sebastian Leuzinger ◽  
Annett Wolf ◽  
Paolo Burlando
Keyword(s):  
Sensitivity Analysis ◽  
Ecosystem Model

A comparison of sensitivity analysis and error analysis based on a stream ecosystem model

1981 ◽  
Vol 12 (3) ◽  
pp. 173-190 ◽  
Author(s):  
R.H. Gardner ◽  
R.V. O'Neill ◽  
J.B. Mankin ◽  
J.H. Carney
Keyword(s):  
Sensitivity Analysis ◽  
Error Analysis ◽  
Ecosystem Model ◽  
Stream Ecosystem

scholarly journals EMPOWER-1.0: an Efficient Model of Planktonic ecOsystems WrittEn in R

2015 ◽  
Vol 8 (7) ◽  
pp. 2231-2262 ◽  
Author(s):  
T. R. Anderson ◽  
W. C. Gentleman ◽  
A. Yool
Keyword(s):  
Sensitivity Analysis ◽  
Marine Ecosystem ◽  
Light Attenuation ◽  
Parameter Tuning ◽  
Ecosystem Model ◽  
Marine Ecosystems ◽  
Model Structure ◽  
Structural Sensitivity ◽  
The North ◽  
Preliminary Model

Abstract. Modelling marine ecosystems requires insight and judgement when it comes to deciding upon appropriate model structure, equations and parameterisation. Many processes are relatively poorly understood and tough decisions must be made as to how to mathematically simplify the real world. Here, we present an efficient plankton modelling testbed, EMPOWER-1.0 (Efficient Model of Planktonic ecOsystems WrittEn in R), coded in the freely available language R. The testbed uses simple two-layer "slab" physics whereby a seasonally varying mixed layer which contains the planktonic marine ecosystem is positioned above a deep layer that contains only nutrient. As such, EMPOWER-1.0 provides a readily available and easy to use tool for evaluating model structure, formulations and parameterisation. The code is transparent and modular such that modifications and changes to model formulation are easily implemented allowing users to investigate and familiarise themselves with the inner workings of their models. It can be used either for preliminary model testing to set the stage for further work, e.g. coupling the ecosystem model to 1-D or 3-D physics, or for undertaking front line research in its own right. EMPOWER-1.0 also serves as an ideal teaching tool. In order to demonstrate the utility of EMPOWER-1.0, we implemented a simple nutrient–phytoplankton–zooplankton–detritus (NPZD) ecosystem model and carried out both a parameter tuning exercise and structural sensitivity analysis. Parameter tuning was demonstrated for four contrasting ocean sites, focusing on station BIOTRANS in the North Atlantic (47° N, 20° W), highlighting both the utility of undertaking a planned sensitivity analysis for this purpose, yet also the subjectivity which nevertheless surrounds the choice of which parameters to tune. Structural sensitivity tests were then performed comparing different equations for calculating daily depth-integrated photosynthesis, as well as mortality terms for both phytoplankton and zooplankton. Regarding the calculation of daily photosynthesis, for example, results indicated that the model was relatively insensitive to the choice of photosynthesis–irradiance curve, but markedly sensitive to the method of calculating light attenuation in the water column. The work highlights the utility of EMPOWER-1.0 as a means of comprehending, diagnosing and formulating equations for the dynamics of marine ecosystems.

scholarly journals Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 2: Sensitivity analysis and model calibration

2020 ◽  
Vol 13 (10) ◽  
pp. 4691-4712
Author(s):  
Chia-Te Chien ◽  
Markus Pahlow ◽  
Markus Schartau ◽  
Andreas Oschlies
Keyword(s):  
Sensitivity Analysis ◽  
Climate Model ◽  
Driving Forces ◽  
Ingestion Rate ◽  
Latin Hypercube Sampling ◽  
Ecosystem Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Model Solutions ◽  
Elemental Stoichiometry

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton–ecosystem model (OPEM), implemented in the University of Victoria Earth System Climate Model (UVic-ESCM), using a Latin hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3-, PO43-, O2, and surface chlorophyll a concentrations. The simulations closest to the data with respect to our metric exhibit very low rates of global N2 fixation and denitrification, indicating that in order to achieve rates consistent with independent estimates, additional constraints have to be applied in the calibration process. For identifying the reference parameter sets, we therefore also consider the model's ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3- inventory. Global O2 varies by a factor of 2 and NO3- by more than a factor of 6 among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (Q0,phyN) and zooplankton maximum specific ingestion rate. Q0,phyN is revealed as a major determinant of the oceanic NO3- pool. This indicates that unravelling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via Q0,phyN, is a prerequisite for understanding the marine nitrogen inventory.

scholarly journals Temporal Sensitivity Analysis of the MONICA Model: Application of Two Global Approaches to Analyze the Dynamics of Parameter Sensitivity

Agriculture ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 37 ◽  
Author(s):  
Xenia Specka ◽  
Claas Nendel ◽  
Ralf Wieland
Keyword(s):  
Sensitivity Analysis ◽  
Dynamic Parameter ◽  
Ecosystem Model ◽  
Parameter Sensitivity ◽  
Ecological Models ◽  
Fast Method ◽  
Morris Method ◽  
Sensitive Parameters ◽  
Original Approach ◽  
Simulation Time

Sensitivity analysis (SA) is often applied to evaluate the behavior of ecological models in which the integrated soil and crop processes often vary over time. In this study, the time dependence of the parameter sensitivity of a process-based agro-ecosystem model was analyzed for various sites and model outputs. We applied the Morris screening and extended FAST methods by calculating daily sensitivity measures. By analyzing the daily elementary effects using the Morris method, we were able to identify more sensitive parameters compared with the original approach. The temporal extension of the extended FAST method revealed changes in parameter sensitivity during the simulation time. In addition to the dynamic parameter sensitivity, we noticed different relationships between parameter sensitivity and simulation time. The temporal SA performed in this study improves our understanding of the investigated model’s behavior and demonstrates the importance of analyzing the sensitivity of ecological models over the entire simulation time.

scholarly journals Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

2020 ◽  
Author(s):  
Chia-Te Chien ◽  
Markus Pahlow ◽  
Markus Schartau ◽  
Andreas Oschlies
Keyword(s):  
Sensitivity Analysis ◽  
Climate Model ◽  
Driving Forces ◽  
Ingestion Rate ◽  
Latin Hypercube Sampling ◽  
Ecosystem Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Model Solutions ◽  
Elemental Stoichiometry

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton-ecosystem model (OPEM), implemented in the University of Victoria Earth-System Climate Model (UVic-ESCM), using a Latin-Hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3−, PO43−, O2, and surface chlorophyll a concentrations. According to our metric the optimal model solutions comprise low rates of global N2 fixation and denitrification. These two rate estimates turned out to be poorly constrained by the data. For identifying the “best” model solutions we therefore also consider the model’s ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3− inventory. Global O2 varies by a factor of two and NO3− by more than a factor of six among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (QN0,phy) and zooplankton maximum specific ingestion rate. QN0,phy is revealed as a major determinant of the oceanic NO3− pool. This indicates that unraveling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via QN0,phy, is a prerequisite for understanding the marine nitrogen inventory.

Calibration, validation and sensitivity analysis of an ecosystem model applied to artificial streams

2008 ◽  
Vol 42 (4-5) ◽  
pp. 1167-1181 ◽  
Author(s):  
Sandrine Sourisseau ◽  
Anne Bassères ◽  
Frédéric Périé ◽  
Thierry Caquet
Keyword(s):  
Sensitivity Analysis ◽  
Ecosystem Model ◽  
Artificial Streams
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