PhD position at Univ. Bordeaux (in collab with BRGM) on Satellite-derived data assimilation for sandy coast evolution

139 views

Skip to first unread message

Déborah Idier

unread,

Apr 11, 2022, 8:47:34 AM4/11/22

to coastal_list

Dear coastal listers

The University of Bordeaux (EPOC lab.) and BRGM are offering a full-time PhD position on Satellite-derived data assimilation for sandy coast evolution.

Please find the details below.

-----------------------

Supervisor: Bruno Castelle (Senior CNRS Research Scientist, UMR EPOC)
Co-supervisor: Déborah Idier (HdR, BRGM)
Doctoral School: Ecole Doctorale #304 Sciences & Environnement (Université de Bordeaux)
Employer : Université de Bordeaux.
Salary (approximately): 24 k€ (gross) / year
Location: EPOC Laboratory, Univ. Bordeaux

-----------------------

ABSTRACT

Climate change, scarcity of sediment supply and increasing global population along the coasts will cause unprecedented socio-economic losses and environmental changes in the coming decades. Sustainable coastal zone planning and management requires a better understanding of past and future developments. Recently important progress has been made in the numerical modelling of shoreline change along sandy coasts building on the development of so-called “hybrid” or “reduced-complexity” approaches (e.g. Vitousek et al., 2017). These models, such as LX-Shore co-developed by EPOC-BRGM (Robinet et al., 2018), allowing, only in certain environments, to simulate shoreline change on large spatial ((10 km)) temporal ( (10 ans)) scales, and even to perform shoreline projection at the 2100 horizon in a context of climate change (D’Anna et al., 2021).

Free-of-charge publicly available optical satellite imagery can now be used to provide shoreline data on similar time and space scales using a variety of techniques [23]. (Luijendijk et al., 2018 ; Vos et al., 2019). However, until now, these satellite data have never been used to assess, calibrate or improve hybrid shoreline models described above. Contrary to many geophysical applications [26], data-assimilation applications to shoreline evolution are scarce (Vitousek et al., 2017 ; Ibaceta et al., 2020). These are the only data-assimilation applications to shoreline models, but they did not address an entire stretches of coast with satellite images on a multi-decadal time scale.

The objective of this PhD project is to explore how satellite data assimilation can improve our understanding and capacity to predict shoreline evolution with an hybrid model such as LX-Shore, in the past, and the in the future, on multi-decadal timescales. International application sites (Brazil, Australia, Tunisia, southwest France) will be determined in concertation with the international partners of Project SHORMOSAT (cf §funding). This work will address the following questions:

What is the optimal data-assimilation technique for reduced-complexity shoreline models?
Does data-assimilation improve pas shoreline evolution reconstruction and does it allow to identify the primary sources of model error?
Can satellite-data-assimilation near inlet and estuary mouths, which dynamics is not included in hybrid models, extend the scope of application of such shoreline models along coasts interrupted by these features?
What are the links between model free parameters and the variability in incident wave climate, and to what extent accounting this variability reduces the projection uncertainties?

A technical advance will be the development of an improved version of the LX-Shore model including a data-assimilation module. From a scientific perspective, expected progress is: (1) prediction of short- to medium-term shoreline evolution on a larger range of sandy coast environments, (2) high-frequency (1 day)) multi-decadal shoreline reconstruction, (3) better understanding and quantification of the respective contribution of the different forcing factors in past evolutions (e.g. sea level rise, wave climate variability, extreme events), (4) reduction of shoreline projection uncertainties.

References

D'Anna, M., Castelle, B., Idier, D., Rohmer, J., Le Cozannet, G., Thieblemont, R., Bricheno, L., 2021. Uncertainties in shoreline projections to 2100 at Truc Vert beach (France): Role of sea-level rise and equilibrium model assumptions. Journal of Geophysical Research: Earth Surface, 126, e2021JF006160, https://doi.org/10.1029/2021JF006160.

Ibaceta, R., Splinter, K.D., Harley, M.D., Turner, I.L., 2020. Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave Climates. Geophys. Res. Lett. 47. https://doi.org/10.1029/2020GL090724.

Luijendijk, A., Hagenaars, G., Ranasinghe, R., Baart, F., Donchyts, G., Aarninkhof, S., 2018. The State of the World’s Beaches. Scientific Reports, 8(1), https://doi.org/10.1038/s41598-018-24630-6.

Robinet A., Idier D., Castelle B., Marieu V. (2018) A reduced-complexity shoreline change model combining longshore and cross-shore processes: The LX-Shore model, Environmental Modelling & Software, 109, 1-16, https://doi.org/10.1016/j.envsoft.2018.08.010.

Vos, K., Splinter, K.D., Harley, M.D., Simmons, J.A., Turner, I.L., 2019b. CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling & Software, 122, 104528, https://doi.org/10.1016/j.envsoft.2019.104528.

Vitousek, S., Barnard, P.L., Limber, P., Erikson, L., Cole, B., 2017b. A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change. Journal of Geophysical Research Earth Surface 122 (4), 782–806, https://doi.org/10.1002/2016JF004065.

Keywords

Shoreline, satellite imagery, data- assimilation, reconstruction, projection.

Funding

This work will be funded by project SHORMOSAT (ANR 2022-2026) involving EPOC, BRGM and CEREGE, as well as many international collaborations (e.g. Plymouth University). This project will also fund the functioning of the PhD work (computing facilities, workshop and conferences, publication fees, …). The student will be employed Université de Bordeaux.

CANDIDATE PROFILE

We seek highly motivated candidates with:

MSc degree in, Physical Oceanography, Statistics and Signal Processing, Remote Sensing or a closely related discipline;
Good knowledge of nearshore and/or morphodynamic processes;
Strong background in numerical modelling and scientific programming (e.g. : Fortran, Python, Matlab).
Proficient in English and excellent scientific writing skills.
Strong motivation for academic research.

The selected candidate should, if possible, have also the following qualities: Autonomy, scientific curiosity, open-mindness. He should have if possible excellent teamwork, writing and communication skills. Skills in French reading, writing and speaking will also be appreciated.

APPLICATION PROCEDURE

The PhD contract is for a period of 3 years, with a starting date in October 2022. Applicants should send by email to Bruno Castelle (bruno.c...@u-bordeaux.fr) and Déborah Idier (d.i...@brgm.fr) the documents listed below before the 1st of May 2022.