RioMAR first hydrodynamical biscay hindcast simulation.
The Regions Of Freshwater Influence (ROFIs) are defined as the area on the continental shelf in the vicinity of estuaries, where freshwater from the river significantly influence water properties and circulation. These regions are subject to a variety of dynamic forcings, including tides, winds and river runoffs, which exhibit considerable variability. The objective of this study is to identify the dominant time scales of variability of the three major ROFIs (Seine, Loire, and Gironde) of the French continental shelf and their evolution over the last two decades. In order to address these questions, we developed a high-resolution numerical simulation of the physical dynamics of the Bay of Biscay and the English Channel. The results demonstrate that the three ROFIs oscillate on the same two cycles: the semidiurnal cycle, caused by tides, and the annual cycle, linked to the seasonal variation in runoff. At the weekly and monthly time scales, two contrasting dynamics emerge. The Seine ROFI, situated in the eastern English Channel, is predominantly influenced by tides and exhibits a third oscillatory cycle, namely the fortnightly cycle. In the Bay of Biscay, the Loire and Gironde ROFIs are more influenced by wind events that shape their monthly variations. The interannual variations are primarily driven by runoff, with an observed reduction in ROFIs extension over the twenty years simulated correlated with a decrease in runoff. However, the combination of shorter timescale events impacts the interannual variability, leading to years with distinctive dynamics of the ROFIs.
Temporal scales of variability in Regions Of Freshwater Influence on the French continental shelf over two decades
FAIR Implementation Profile (FIP) for the RiOMar simulation data from the Bay of Biscay.
Case Study 2 - RiOMar Bay of Biscay Simulation Data FIP
First case study workshop for the Blue RiOMar case study.
Hack4RiOMar: First FAIR2Adapt Workshop for the RiOMar Case Study
The FAIR2Adapt community is dedicated to advancing climate change adaptation in Europe by promoting FAIR and open data sharing to enhance the accessibility, interoperability, and usability of environmental and socio-economic data in support of climate change adaptation efforts.
# RiOMar Case Study ### Coastal Water Quality Anticipation to manage coastal zone ecosystem responses for biodiversity conservation ## Vision & Ambitions The aim of this case study is to: 1) Assess coastal water quality and marine ecosystems, anticipate and prepare for emerging climate impacts by the end of the century; 2) Identify risks and vulnerabilities using RiOMar model outputs as a step forward for the definition of scientifically-based CCA strategies. ## Description ### Coastal water quality assessments (France) Monitoring water quality in river-dominated ocean margins and adopting relevant measures to support local businesses such as oyster farmers who are dealing with increasing threats to their business due to climate change. 1) Stakeholders cannot easily access the large amounts of high-resolution data generated by RiOMar; 2) uncertainty about which indicators to compute to assess the vulnerability of coastal zone ecosystems to climate change; 3) difficulties in finding relevant information in their native language (French). ## FAIR2Adapt Contribution FAIRification and data sharing by transforming RiOMar data into Analysis Ready Cloud Optimised data (ARCO), development of tools for enabling stakeholders to exploit the FDOs and easily develop risk and vulnerability maps to develop scientifically-based CCA strategies and plans. ## Lead Partner IFREMER ## External Stakeholders involved in CCA The French Biodiversity Office (OFB), Regional River Agencies, professional users of the coastal zone (e.g. aquaculture farming structures)
Fouilloux, Anne. "Biscayan RiOMar." ROHub. Mar 08 ,2025. https://w3id.org/ro-id/ea3ac578-ab0a-47c8-a10a-9d06678a9450.
Case study workshops wherestakeholders meet to define user requirements and/or co-develop solutions.
Image of the water color captured by the MERIS sensor on the European Envisat satellite on 06/04/2011 – Image credit: European Space Agency (ESA).
Economy, business and finance/Business information/Business finance/Shareholder
2) Identify risks and vulnerabilities using RiOMar model outputs as a step forward for the definition of scientifically-based CCA strategies.
1) Stakeholders cannot easily access the large amounts of high-resolution data generated by RiOMar; 2) uncertainty about which indicators to compute to assess the vulnerability of coastal zone ecosystems to climate change; 3) difficulties in finding relevant information in their native language (French)
### Coastal Water Quality Anticipation to manage coastal zone ecosystem responses for biodiversity conservation
Economy, business and finance/Economic sector/Agriculture/Aquaculture