Mission
Approximately 300 million people rely on desalinated water daily, but high production costs hinder widespread adoption despite technological advancements. In northern Chile, a projected 273% increase in desalinated water production by 2027 reflects the growing pressures of water scarcity and mining activities in the region.
Desalination plants face challenges from extreme oceanographic events—both natural and anthropogenic—that negatively affect water quality and efficiency, including oil spills, high turbidity, and harmful algal blooms (HABs). Case studies indicate that red tides can increase production costs by up to 30% and lead to unscheduled plant shutdowns, significantly impacting the sustainability of human and industrial consumption in areas facing severe water pressure. The rapid proliferation of planktonic communities can contaminate water supplies by increasing levels of organic matter, total suspended solids, and turbidity.
This thesis seeks to enhance understanding of coastal circulation and phytoplankton dynamics in Antofagasta Bay, a densely populated and arid region in Chile, home to mining and desalination plants. The study area is situated within an upwelling system, characterized by nutrient-rich and oxygen-poor waters near the coast and influenced by poleward-propagating coastal waves.
The first step of the thesis will be to analyze satellite data to characterize coastal phenomena and phytoplankton dynamics in Antofagasta Bay, in particular parameters like chl-a, SST, turbidity, and organic matter concentration during time periods of HAB blooms. The S2 and S3 missions provide different spatial resolutions of 10 and 300 (1k) meters, respectively, making them well-suited for capturing fine-resolution variations in environments such as coastal bays. The upcoming TRISHNA mission, with its fine resolution of 57 meters, will further improve our understanding of thermal processes at very fine scales. Satellite data will be supplemented by in situ measurements from four short (1-2 days) oceanographic campaigns capturing turbidity, dissolved oxygen, and chlorophyll concentration down to 200m.
In recent years, machine learning models based on multiple satellite variables, combined with historical data and environmental conditions, have aimed to predict HAB occurrences without fully understanding the underlying dynamics. Research shows that combining satellite ocean color data with meteorological factors, such as sea SST, chlorophyll-a concentration, and wind speed, can improve HAB forecasts. To assess the impact of HABs on water quality and desalination processes, a statistical approach utilizing satellite data will be implemented. Integrating satellite observations with statistical models will help predict the occurrence and duration for the blooms impacting the plant functioning.
The second step of the thesis will be to implement a three-dimensional regional model (CROCO-PISCES) of high spatial resolution to study plankton dynamics. A fine-scale multigrid resolution system (AGRIF) will be employed to represent very small scales (<1 km) near the desalination plant. This approach will enable detailed analysis and enhance the model’s ability to simulate complex coastal dynamics. Since CROCO-PISCES does not simulate HABs, the statistical model based on satellite data will be used to hindcast HABs using model fields. The model will also be used to study the impact of highly saline water discharge from the plant on the bottom water conditions (e.g. oxygenation) in the bay area.
Our approach integrates hydrodynamic physical models with statistical methods to provide valuable insights into the triggering factors of HABs, such as nutrient levels and temperature variations. This innovative methodology will facilitate risk management for water quality, enhancing our ability to respond effectively to environmental changes. It will enhance understanding and management of marine ecosystems under increasing anthropogenic pressures, supporting sustainable water resource management in arid regions and aligning with Sustainable Development Goals for water access and sanitation.
Key Tasks:
Developing algorithms for real-time data processing from satellite sources.
Develop a statistical model based on machines learning methods to detect HABs.
Hindcast HABs using a high-resolution dynamical-biogeochemical coupled model.
Studying the impact of discharge of hypersaline waters on the marine environment.
Prerequisites:
Coastal Oceanography & Marine Biology: Understanding coastal ecosystems and phytoplankton dynamics.
Remote Sensing Techniques: Experience with satellite data from Sentinel-2, Sentinel-3, and TRISHNA.
Computer Skills & Programming: Proficiency in Python, R, or Shell for data processing and model development.
Numerical Modeling: Interest in developing mathematical and computational models for environmental simulations.
Biogeochemical Cycles: Basic knowledge of chemical and biological interactions
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For more Information about the topics and the co-financial partner (found by the lab !);
contact Directeur de thèse – vincent.echevin@locean.ipsl.fr
Then, prepare a resume, a recent transcript and a reference letter from your M2 supervisor/ engineering school director and you will be ready to apply online before March 14th, 2025 Midnight Paris time !
Profil
Engineer Degree (equivalent to MSc) in Remote Sensing with experience in numerical modeling
Infos pratiques
LOCEAN
Mot du recruteur
More details on CNES website : https://cnes.fr/fr/theses-post-doctorats
