Hack4RiOMar: First FAIR2Adapt Workshop for the RiOMar Case Study
# The French Project RIOMar (River-dominated Ocean Margins) The French project **RIOMar** (River-dominated Ocean Margins), a winner of the *"Ocean & Climate" Priority Research Program*, focuses on an in-depth study of the environment in five major coastal areas of mainland France influenced by rivers. Its objective is to provide scientifically grounded solutions to maintain or restore sustainable ecosystems in regions particularly affected by climate change and anthropogenic inputs transported and deposited by rivers. --- ## Scientifically Grounded Solutions to Mitigate Risks to Vulnerable Coastal Ecosystems Coastal areas are crucial for humanity, especially those dominated by rivers flowing into the ocean. Their future evolution poses a significant challenge, as it is estimated that **80% of the global population will live within 40 kilometers of the coast by 2030**. At the intersection of land and sea, **estuaries, lagoons, and deltas** experience daily interactions between freshwater and marine waters. The major estuaries of France (*Seine, Loire, and Gironde*) and the Rhône delta are just a few examples of these "transitional" environments along the French coastline. Currently, the **885 coastal municipalities of mainland France** account for over **10% of the population**. According to recent estimates (*source: Ministry of Ecological Transition*), an additional **4.5 million inhabitants** are expected to settle in these regions by 2040. In such conditions, the **coastal ocean environment** in these areas will become increasingly vulnerable to various combined human and climate-induced stressors: - Warming - Acidification - Eutrophication* - Hypoxia* - Contamination - Extreme events Simulating their evolution under the combined influence of **anthropogenic pressure and climate change** is a major challenge. The number of unknown and often unpredictable parameters is considerable. Addressing this requires a **comprehensive approach**, grounded in the **multidisciplinary analysis** of the numerous environmental factors affecting these coastal zones. The knowledge generated will enable the **co-construction of scientifically based solutions** with environmental managers for the **sustainable development** of coastal areas. --- ## An Original, Multidisciplinary, and Innovative Integrated Approach Spanning **six years (2022-2028)**, the RIOMar (*River-dominated Ocean Margins*) project aims to tackle all these challenges. Scientific studies within the project focus on five major coastal areas in mainland France influenced by large rivers: - The Seine - The Loire - The Gironde - The Rhône - The Charente Various scenarios for future evolution will be explored in collaboration with numerous stakeholders from these regions. **Relevant indicators** will be developed, in consultation with environmental managers, to propose **tailored and sustainable solutions** for public policies. --- ### Glossary - **Eutrophication**: The excessive enrichment of water with nutrients, leading to algal blooms and oxygen depletion. - **Hypoxia**: Low oxygen levels in water, threatening aquatic life.
This is the grid of the Croco model used to generate RiOMar data.
All the RiOMar datasets were generated on the HPC of IFREMER (Brest, France) and made accessible to everyone on the IFREMER DATAMOR.
Raw RiOMar dataset generated by the Croco RiOMar numerical model. It corresponds to one month of simulation (January 2006).
Small Zarr file generated from the original RiOMar data by selecting a few timestep to reduce the total size and make it easier to write Jupyter notebook examplifying the transformation from the original grid to DGGS Healpix grid.
A sample dataset in DGGS Level 12 Healpix
A sample dataset in DGGS Level 13 Healpix
A sample dataset in DGGS Level 14 Healpix
## Abstract Traditional map projections introduce distortions, especially for global data. Discrete Global Grid Systems (DGGS) offer an alternative by dividing the Earth into equal-area grid cells at different resolutions. This paper describes xdggs, a new Xarray extension that simplifies working with DGGS. Xdggs provides a unified API for various DGGS libraries and integrates seamlessly with the Pangeo ecosystem through extending the widely used Xarray library to use the DGGS-specific cell identifiers as an index. This development makes DGGS more accessible and will lead to facilitating data analysis on a planetary scale. Xdggs aims to provide a user-friendly API that hides the implementation complexities of different DGGS libraries. And because it integrates seamlessly with Xarray, a popular tool for geospatial data analysis, xdggs promotes FAIR data practices by simplifying data access and interoperability and can become a valuable tool for geospatial scientists and application developers working with global datasets. ## How to cite Kmoch, A., Bovy, B., Magin, J., Abernathey, R., Coca-Castro, A., Strobl, P., Fouilloux, A., Loos, D., Uuemaa, E., Chan, W. T., Delouis, J.-M., and Odaka, T.: XDGGS: A community-developed Xarray package to support planetary DGGS data cube computations, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W12-2024, 75–80, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-75-2024, 2024.
XDGGS: A community-developed Xarray package to support planetary DGGS data cube computations
This is a rendered Jupyter notebook showing how to create a RO-Crate (using the Python API rocrate) for archiving the RiOMar dataset into the NIRD archive. The metadata used for the creation of the RO-Crate correspond to what is needed when archiving in the NIRD archive (Norwegian Research Data Archive).
Rendered Jupyter Notebook to create RO-Crate from RiOMar dataset
This is the rendered Jupyter notebook showing how to regrid a sample of RiOMar data into DGGS (Healpix).
Rendered Jupyter Notebook for regridding RiOMar sample zarr dataset to DGGS Helix Level 12.
Read a netCDF original dataset from RiOMar and select a few timestep to save it in Zarr. This is the rendered Jupyter nobteook.
Rendered Jupyter Notebook to create a sample RiOMar dataset
This is the rendered Jupyter Notebook that uses Datatree (Xarray) to examplify how to create a multi-resolution dataset with DGGS (different resolution).
Rendered Jupyter Notebook for demonstrating the usage of Multi-resolution RiOMar data
This is the rendered Jupyter notebook that shows how to use Virtualizarr with Kerchunk to create a RiOMar datasets with all the necessary files required for a full RiOMar data analysis.
Rendered Jupyter Notebook using VirtualZarr with Kerchunk for RiOMar data
This is the rendered Jupyter notebook to Visualize RiOMAR dggs-transformed data with xddgs and lonboard
Rendered Jupyter Notebook to visualize RiOMAR dggs-transformed data with xddgs and lonboard
This image shows how a RiOMar dataset can be visualized with xdggs (that uses lonboard).
RiOMar dataset transformed into DGGS (Healpix)
Simula Research Laboratory, Oslo, Norway
Python requirements.txt for running the Jupyter Notebooks.
In this notebook, we will learn how to create a simple RO-Crate from the RiOMar data. We will then identify any missing metadata that needs to be added to the original dataset’s metadata.
Create RO-Crate from RiOMar dataset (Jupyter Notebook)
The goal is to regrid a sample of RiOMar data into DGGS (Healpix).
Regridding Sample RiOMar dataset to DGGS (Jupyter Notebook)
The goal is to create a smaller RiOMar dataset to test regridding to Healpix on Pangeo EOSC.
Create a small sample RiOMar dataset (Jupyter Notebook)
The use of multiscale Zarr in combination with the Discrete Global Grid System (DGGS) for Earth Observation aims to enhance the efficiency and scalability of storing, accessing, and analyzing large geospatial datasets. Multiscale Zarr allows for efficient compression and chunking of data, enabling rapid access to high-resolution imagery and other spatial datasets. When integrated with DGGS, which provides a hierarchical, cell-based representation of the Earth’s surface, this approach facilitates seamless multi-resolution analysis, improves interoperability, and supports advanced geospatial processing and visualization, ultimately enabling more effective monitoring and understanding of environmental changes.
Multi-resolution RiOMar data (Jupyter Notebook)
The goal is to create a virtualzarr for all RiOMar data using Kerchunk (since Icechunk does not work at the moment on Pangeo-EOSC or for data on datamor (https access).
VirtualZarr with Kerchunk for RiOMar data (Jupyter Notebook)
## Purpose The goal is to visualize the DGGS transformed RiOMar data with xdggs and lonboard. ## Description In this notebook, we will: - Open DGGS RiOMar dataset - Visualize it using a colorblind-friendly colormap
Visualize RiOMAR dggs-transformed data with xddgs and lonboard (Jupyter Notebook)
## RiOMar Case Study This [FAIR2Adapt](http://fair2adapt-eosc.eu) case study is led by IFREMER and builds on the outcomes of the RiOMar Project - Coastal Water Quality Anticipation to manage coastal zone ecosystem responses for biodiversity conservation. The RiOMar project’s data is high-resolution and complex to manipulate. To effectively support climate adaptation strategies and plans, it is crucial to maintain the high-resolution quality while enabling efficient data fusion with diverse datasets. ## Event The Hack4RiOMar workathon brought together six motivated participants in person, collaborating to advance the FAIR2Adapt RiOMar Case Study—the first workshop in our series of FAIR2Adapt case studies. We extend our gratitude 👏 to the external experts who participated at their own expense, bringing their expertise to tackle challenges and drive progress. ## Funding FAIR2Adapt project (101188256) is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Agency. Neither the European Union nor the granting authority can be held responsible for them.
Fouilloux, Anne, Jean-Marc Delouis, Tina Odaka, Even Moa Myklebust, Justus Magin, and Ola Formo Kihle. "Hack4RiOMar: First FAIR2Adapt Workshop for the RiOMar Case Study." ROHub. Jan 23 ,2025. https://w3id.org/ro-id/a0007726-0f5b-4ccb-a160-f350d669805e.
Agenda and minutes from the first workshop for the Biscayan RiOMar Case Study (Hack4RiOMar event).
meeting_agenda_and_minutes(1st workshop).pdf
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.
The Hack4RiOMar workathon brought together six motivated participants in person, collaborating to advance the FAIR2Adapt RiOMar Case Study—the first workshop in our series of FAIR2Adapt case studies.
Politics/International relations/International organisation
Hack4RiOMar: First FAIR2Adapt Workshop for the RiOMar Case Study.
This [FAIR2Adapt](http://fair2adapt-eosc.eu) case study is led by IFREMER and builds on the outcomes of the RiOMar Project - Coastal Water Quality Anticipation to manage coastal zone ecosystem responses for biodiversity conservation.