How grid computing allows for a better management of coastal resources.
The Aveiro Lagoon in Portugal is a national treasure. With a length of about 45km and separated from the Atlantic Ocean by a sandy dune barrier, this shallow lagoon is one of Europe’s last pristine coastal marshes and a haven for many bird species. The Ria de Aveiro, as it is known locally, is also an important source of revenue in the region, fuelling not only the tourism and aquaculture industries but also artisan fishing and the collection of fleur de sel, a prized variety of salt.
In the past few years the lagoon (technically a haff-delta) has been threatened by a decrease in water quality due to industrial, urban and agricultural effluents, but thanks to the Ria’s economic, ecologic and cultural importance, there is a strong push to preserve its ecosystem. The key to long-term sustainability is efficient management and to achieve that, decision-makers need to have a solid understanding of this environment.
Modelling the Aveiro Lagoon
Marta Rodrigues and Anabela Oliveira, together with colleagues from National Laboratory for Civil Engineering, Portugal (LNEC), applied a three-dimensional computational model called ECO-SELFE to the Aveiro Lagoon scenario. ECO-SELFE is a fully-coupled ecological-hydrodynamic model. This means that it has modules that determine physical variables (e.g. currents, water temperature or salinity) alongside others for biochemical processes (e.g. carbon, nitrogen cycles) and even ecological relationships at the base of the food chain (e.g. plankton mortality or availability of prey).
The idea was to determine how the different ecological input parameters are interconnected and which ones are the most likely to affect the model results and the health of the lake.
The output of the ECO-SELFE model is a complex but detailed simulation of the Aveiro Lagoon ecosystem. Unsurprisingly, the model is very demanding in terms of computing power and the team turned to the grid computing resources provided by INGRID, the Portuguese National Grid Initiative, for help. Grid computing allowed them to improve computational efficiency significantly, saving a lot of time: the analysis was complete in about a month.
The results, published in the Journal of Coastal Research, show that the variable most affected by playing around with the different inputs was the concentration of phytoplankton.
Phytoplankton is made up of microscopic plants at the base of the food chain in all aquatic environments. These tiny plants play crucial roles in the cycles of oxygen and carbon and due to that they are very sensitive to sudden environmental changes.
The study concludes that phytoplankton abundance depends on the delicate balance between a wide range of life-cycle parameters (e.g. mortality & growth rates or base temperature for growth), but the mortality of zooplankton is especially important. Since these microscopic animals feed mostly on phytoplankton – when zooplankton is abundant, phytoplankton mortality increases accordingly; if zooplankton is not dominant, the phytoplankton concentration is usually determined by the parameters that control its growth.
In practice, this means that researchers can use phytoplankton concentration as the ‘canary in the mine’ for the Aveiro Lagoon ecosystem - if there is a sudden or drastic change, action needs to be taken. The study also demonstrates the importance of measuring the input parameters for the ecological processes related to the phytoplankton growth and zooplankton mortality as accurately as possible, as those values can influence the overall model considerably. Ideally, plankton input parameters should be site-specific, not just a constant number applied across the board. This allows for the natural variation observed in nature to be reflected by the model and to improve its accuracy.
- M. Rodrigues, A. Oliveira, M. Costa, A.B. Fortunato and Y. Zhang. Sensitivity Analysis of an Ecological Model applied to the Ria de Aveiro. Journal of Coastal Research. (.pdf)
This case study was prepared with the help of Anabela Oliveira.