Please note that this article was previously published in H2O’s Water matters
Image courtesy: EU Science Hub
Summary
Everywhere along coasts harmful algae blooms, jellyfish blooms or pollution can occur. Locally applicable high-resolution models can predict the spread of such phenomena and help governments and other stakeholders to take action. Developing localized models, normally labour-intensive and costly, has recently become much more feasible. Within the COPERNICUS programme, a framework has been developed to set up localized models as ‘nested models’ within larger scale models. Important in this respect is the use of Remote Sensing data for validation and calibration, in addition to locally obtained data.
Introduction
Local problems such as harmful algal blooms can sometimes occur in many places along the coast. The consequences of industrial activities can also vary greatly from place to place. Governments and other stakeholders can benefit from localized sea and coastal models to help them take the right measures. By using Remote Sensing and smart modelling, development of such models becomes much less time-consuming and costly.
The development of localized models can often be a cumbersome and time-consuming task, particularly when developing the driving conditions for such models and locating sufficient information for calibration and validation procedures. However, Remote Sensing (RS) provides ever increasing coverage and resolution, both spatial and temporal, of key parameter data sets and is an increasingly attractive resource for the calibration, validation, and state-updating of models.
The European Space Agency’s (ESA) COPERNICUS program provides a large array of freely available RS data about the marine environment. These products are already integrated with the Copernicus Marine Environment Monitoring Service (CMEMS), and supply large scale global and regionalized models with data for hindcasting (running models on historic data to provide information on past events) and future projections.
These two sources combined provide the starting material for localized and/or problem specific models. Global and regional outputs are downscaled to rapidly set-up and calibrate localized models.
This approach is particularly beneficial in data poor regions. The large spatial and temporal coverage of the global models and RS products for seas and coasts, for what is traditionally an information sparse system depending on few and far between monitoring stations or infrequent and costly monitoring campaigns, is a boon to the field. The framework used to develop localized models, if automated, could enable municipalities and regional authorities with limited funding to conduct investigative studies on marine and coastal developments, construct localized observatories, as well as monitoring ecological conditions.
Method
In this study, three localized models for European waters were generated based on a combination of global and regional models and RS data; making use of new SENTINEL satellite data sets. Developing the models was done as part of the H2020 ECOPOTENIAL and H2020 ODYSSEA projects.
The localized models are ‘nested models’, i.e. models that operate within the boundary conditions of the underlying large scale models, but function independently. In all three cases below the coastal modelling system is based on the Delft3D modelling suite.
The accuracy of the remote sensing data is limited, but due to the application of error statistics and uncertainty analysis they can still be useful in calibrating and validating local models, especially hydrodynamic and biogeochemical models.
Figure 1: Conceptual Framework of the development of a localized model for the case of the Wadden Sea (ECOPOTENTIAL Project in which CMEMS and SENTINEL data services were used).
Broadly speaking the following three cases followed the framework that was used to set up a model for the Wadden Sea in the ECOPOTENTIAL project. Figure 1 shows that in that case Copernicus data provided additional calibration and validation points to complement existing Dutch monitoring provided by Rijkswaterstaat (RWS; Directorate-General for Public Works and Water Management) monitoring campaigns and stations. Optical satellites provided additional data on biological processes over the entire area which were impossible to obtain through conventional measurements. With the new localized model the primary production from algae within the Wadden Sea can be monitored much better and harmful algal blooms can be better predicted.
In a similar way, data from optical satellites on suspended sediments in the water column can aid in the calibration and validation of localized sediment transport models. This is critical for understanding the impact of sand mining and dredging activities on the local ecology as well as determining the magnitude and extent of impacts of such activities.
Valencia, Spain
In the case application of the Valencia Coast a hydrodynamic model was generated and validated utilizing CMEMS models. Data from the large scale model and Sentinel Satellite images across the sea surface were used for validation. In this case data assimilation is not yet integrated.
This localized model can now produce high resolution data sets which can inform shipping activities and port activities reliant on specific sea conditions and currents. If a jellyfish bloom Is detected somewhere, with a special ‘plug-in’ the model can be utilized to track the spread and impact zones of the jellyfish population. Such information is key for recreation sectors, sea-side communities, and also the fishing and mariculture industries. All these sectors can be negatively impacted by the effects of jellyfish blooms. The warning time that the localized model can provide, allows for actions to be taken in order to mitigate the negative impacts.
Aegean Sea
Another example is the Thracian Sea/North Aegean Observatory, which is located at the northern-most part of the Aegean Sea. Currently this observatory works with a localized model of sea currents and waves; integration of water quality is in progress. Every day the system retrieves CMEMS data of the past day for a 2 days forecast run. The localized model offers high resolution short-term forecasting of the regions’ water levels, currents, and wave heights. This coastal prediction system is run daily, so that timely prediction information is available for the end-users.
Results are validated against the new in-situ instruments deployed within the project, including unmanned gliders and sensor-equipped buoys on site. Early validation of the model with these instruments in 2019 and 2020 showed a first and promising confirmation of the accuracy and reliability of the predictions.
With this localized model an important source of information has become available for maritime activities in the region, such as shipping, off shore energy platforms and fishing, at a high spatial resolution. Validation of the water quality forecasting component will be done by mid-2020. A separate forecast map for water quality, disseminated through social media, is already used by local fishermen.
Figure 2: In the case of the localized Thracian Sea Model, downscaled and adapted CMEMS data are utilized to predict short term flow patterns with high resolution. This information is relevant for maritime activities in the region.
Gulf of Gokova
The third example is the Gulf of Gokova in Turkey, which covers a 100-km long and narrow Aegean Sea gulf, and where a Special Environmental Protection Area (SEPA) has been declared since 1988. Human activities in and around the gulf include agriculture, tourism, fisheries, and maritime activities. The main threats in this area are illegal fishing, tourism, pollution, coastal development and habitat destruction, organic and inorganic waste and invasive species due to climate change and the Suez Canal opening, which allows non-native species to migrate from the Red Sea and Indian Ocean.
In order to perform ecosystem impact assessment and support operation of the above-mentioned sectors, operational forecasts are needed at a better resolution than CMEMS products. Consequently, a high-resolution coastal modelling system was set up to provide operational information on currents, waves and biogeochemistry to the main end-users: cruise companies, ferries to Greece, fishing and sponge diving, recreational fishing, and tourism (hotels, yachts). Assimilation of CMEMS data products is automated to operationally provide temperature, salinity, water level, and current boundaries to this nested model. Copernicus products were complemented with NOAA-GFS meteorological information. Currently operational forecasts are published on social media daily, with more than 100 end-users regularly following the updates. By the end of 2020 dissemination will be done through the ODYSSEA platform for localized observatories along the Mediterranean coast.
Figure 3: Depth pattern for the Gokova Gulf based on Remote Sensing data (in meters).
Towards better and more cost efficient localized models
Continued work is being undertaken to automate the calibration process and to include state-updating of the model. Figure 1 depicts the envisioned final processing chain, which is sought to be achieved through this continued work. To this point, successful integration of Remote Sensing has been achieved in the calibration process. The application of data assimilation strategies is an on-going effort, in order to further refine the models and thus improve predictions. Once the chain has been automated, the cost of effort and time in developing localized models based on these COPERNICUS services will be greatly reduced. This removes the current barriers to the use of these services. Model performance can be further improved through the use of multiple COPERNICUS based and locally available data sets.
References
- https://www.ecopotential-project.eu/
- http://odysseaplatform.eu/
- https://earth.esa.int/web/sentinel/missions/sentinel-1/news/-/article/copernicus-sentinel-1-supports-detection-of-shoreline-positions
- https://sentinel.esa.int/web/sentinel/home/-/journal_content/56/247904/3869448
- https://www.copernicus.eu/sites/default/files/2018-10/copernicus4regions.pdf
