Final Activity Report Summary - SIMPLIC (Mathematical Modeling of Marine Ecosystems in a sustainable development perspective: model development, parameterization and simplification ...)
The main objective of the project was to conceptualise tractable and reliable marine ecosystem models that can be coupled with a 3D high resolution hydrodynamical model for long time simulation in a sustainable development perspective. These models were to be derived from complex validated ecosystem models. Other objectives included;
To implement data assimilation technique in ecosystem model in order to assess the improvement of model performances due to data assimilation.
To study the problem of carbon sequestration by marine ecosystems.
Study of the modifications that occurred during the last decades in the Black Sea ecosystem.
Work completed as part of the project covered:
A 1D complex ecosystem model of the Ligurian Sea representing carbon, nitrogen and silicium pathways, including seawater chemistry (pH-pCO2 dynamics), particle aggregation, the microbial loop, and the food web from phytoplankton to carnivore predators and validated against DYFAMED data series has been used as a starting point for study the problem of model simplification. Simplified aggregated models were derived from this complex model (e.g. in which either the unbalanced algal growth, the functional group diversity or the explicit description of the microbial loop was sacrificed). To overcome the problem of data availability with adequate spatial and temporal resolution, we used the output of the complex model as the baseline of perfect knowledge to calibrate the simplified models. Objective criteria were used to compare the performances of the different simplified models and the complex model and all the models were compared with the DYFAMED data available for the Ligurian Sea. The comparison of the outputs of the different simplified models will allow assessing the key biogeochemical processes that have to be thoroughly and explicitly represented in models in order to obtain a good representation of the food-web dynamics and biogeochemical fluxes. It will also allow understanding how to upscale small scales (in time and space) biogeochemical processes for their inclusion in 3D coupled models. We show that even the most simple (NPZD) model is able to represent the ecosystem global features described by the complex model (e.g. the primary and secondary productions, the particulate organic matter export flux, ..). However, a certain degree of sophistication in the formulation of some biogeochemical processes is required to produce realistic behaviours. In general, a 9 state variable model that has the functional group diversity removed, but which retains the bacterial loop and the unbalanced algal growth performs best.
Data assimilation techniques, particularly well adapted to non-linear systems such as marine ecosystems, have been implemented to optimize the state estimation. Two versions of the Kalman filter have been implemented and compared. We have analysed the improvement of model results induced by the use of these data assimilation techniques and we have identified key-data that deserved to be assimilated and for which we need adequate temporal and spatial coverage. Moreover, we have studied the utility of using data assimilation to compensate for model deficiencies. In particular, we have applied data assimilation with a poorly parameterized NAPZD ecosystem model.
We have coupled a carbon sub-model to the developed ecosystem models in order to estimate the exchanges of CO2 at the air-sea interface and to assess the potential role of the region as a sink for atmospheric carbon.
A complex ecosystem model of the Black Sea ecosystem has been developed in order to understand the changes in ecosystem functioning that have occurred in the area during the last years.
To implement data assimilation technique in ecosystem model in order to assess the improvement of model performances due to data assimilation.
To study the problem of carbon sequestration by marine ecosystems.
Study of the modifications that occurred during the last decades in the Black Sea ecosystem.
Work completed as part of the project covered:
A 1D complex ecosystem model of the Ligurian Sea representing carbon, nitrogen and silicium pathways, including seawater chemistry (pH-pCO2 dynamics), particle aggregation, the microbial loop, and the food web from phytoplankton to carnivore predators and validated against DYFAMED data series has been used as a starting point for study the problem of model simplification. Simplified aggregated models were derived from this complex model (e.g. in which either the unbalanced algal growth, the functional group diversity or the explicit description of the microbial loop was sacrificed). To overcome the problem of data availability with adequate spatial and temporal resolution, we used the output of the complex model as the baseline of perfect knowledge to calibrate the simplified models. Objective criteria were used to compare the performances of the different simplified models and the complex model and all the models were compared with the DYFAMED data available for the Ligurian Sea. The comparison of the outputs of the different simplified models will allow assessing the key biogeochemical processes that have to be thoroughly and explicitly represented in models in order to obtain a good representation of the food-web dynamics and biogeochemical fluxes. It will also allow understanding how to upscale small scales (in time and space) biogeochemical processes for their inclusion in 3D coupled models. We show that even the most simple (NPZD) model is able to represent the ecosystem global features described by the complex model (e.g. the primary and secondary productions, the particulate organic matter export flux, ..). However, a certain degree of sophistication in the formulation of some biogeochemical processes is required to produce realistic behaviours. In general, a 9 state variable model that has the functional group diversity removed, but which retains the bacterial loop and the unbalanced algal growth performs best.
Data assimilation techniques, particularly well adapted to non-linear systems such as marine ecosystems, have been implemented to optimize the state estimation. Two versions of the Kalman filter have been implemented and compared. We have analysed the improvement of model results induced by the use of these data assimilation techniques and we have identified key-data that deserved to be assimilated and for which we need adequate temporal and spatial coverage. Moreover, we have studied the utility of using data assimilation to compensate for model deficiencies. In particular, we have applied data assimilation with a poorly parameterized NAPZD ecosystem model.
We have coupled a carbon sub-model to the developed ecosystem models in order to estimate the exchanges of CO2 at the air-sea interface and to assess the potential role of the region as a sink for atmospheric carbon.
A complex ecosystem model of the Black Sea ecosystem has been developed in order to understand the changes in ecosystem functioning that have occurred in the area during the last years.