Project description
Advancing snow science in forests
Snow water equivalent (SWE) monitoring has far-reaching implications for water management, climate research and environmental understanding. Accurate and reliable SWE monitoring is essential for sustainable resource planning and management. However, it remains an unsolved challenge in hydrology due to limitations in orbital sensors, the intricate snowpack variability, and errors in numerical models. Implementing snow data assimilation (SDA) in forested regions is particularly difficult. With the support of the Marie Skłodowska-Curie Actions programme, the SDA-For project will train researchers in advanced radiative transfer modelling and AI techniques, enabling the assimilation of remote sensing data to improve SWE estimation in forested areas. The project aims to establish the first-ever SDA-based SWE inference in forests and will foster the project fellow’s expertise in snow science.
Objective
Despite its importance, snow water equivalent (SWE) monitoring remains an unresolved issue in modern hydrology. This is a consequence of the limitations of orbital sensors, the large spatial variability of the snowpack and the combined errors of meteorological forcings and numerical models. Snow data assimilation (SDA) of remotely sensed data into numerical models is one way to advance the estimation of SWE distribution in remote regions. However, implement SDA initiatives in forested areas is challenging, limiting the development of SDA initiatives in more than 20% of the Northern Hemisphere. The aim of this project is to train the candidate in sophisticated radiative transfer modelling and AI, to implement snow-forest interactions in data assimilation pipelines for a better understanding of snow freshwater resources. This main objective will be developed through the creation of an international network of experts, to train the researcher in different related topics. This includes Dr Jessica Lundquis (Outgoing phase host) from the University of Washington, an authority on forest-snow interactions, and Dr Simon Gascoin (returning phase host) from CESBIO (Toulouse, France), with long experience in multidisciplinary snow remote sensing. The results will be used to enhance the current capabilities of the MuSA tool, an open source and highly scalable data assimilation system developed by the researcher. The project will be the first effort to infer SWE in forest through SDA of spacial imagery, and therefore is of great interest for many stakeholders and scientists. The project is designed with an obvious focus on training the researcher in new techniques but also in soft skills thanks to the mentoring programs of both the University of Washington and CESBIO. The ultimate goal of the project is to improve the career prospects of the researcher, making him an authority in snow science and providing the scientific community with a new and sophisticated SDA tool.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesearth and related environmental scienceshydrology
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
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Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinator
31062 Toulouse Cedex 9
France