Improvement of (Ultra-) High-Energy Hadronic Interaction Modeling
Rami Oueslati (IFPA)

Cette thèse se concentre sur l’amélioration de la modélisation des interactions hadroniques à haute et très haute énergie, avec une attention particulière aux collisions proton-proton (pp) et proton-antiproton (p¯p). Elle explore l’unitarité de la matrice S et compare deux schémas d’unitarisation : l’éikonal et la matrice U. L’analyse montre que le schéma U-matrice s’avère particulièrement efficace pour intégrer les corrélations entre pomérons, ce qui influence les sections efficaces totales, élastiques, diffractives et les distributions de multiplicité. De plus, cette recherche a fourni une explication de la nature des échanges multiples de pomérons dans les deux schémas, en mettant en lumière leurs impacts respectifs sur les phénomènes observés . Des modèles multi-canaux et phénoménologiques ont également été développés, offrant une description des sections efficaces et de la production de multiparticules sur une large gamme d’énergies . Les résultats soulignent que le schéma U-matrice est mieux adapté aux théories avec des sections efficaces croissantes, telles que la QCD à haute énergie, et ouvre des perspectives pour comprendre les interactions complexes à très haute énergie, notamment dans le contexte des rayons cosmiques.

Assessing the success of the Montreal Protocol: trends of halogenated gases from ground-based, satellite, and model data
Irene Pardo Cantos (GIRPAS)

Robust post-processing algorithms for near-infrared high­-contrast imaging of
protoplanetary disks
Sandrine Juillard (PSILab)

Exoplanet atmospheric characterization using amortized simulation based inference
Malavika Vasist (PSILab)

Simulating and analyzing climate change impacts on crop yields in Morocco
Iliass Loudiyi (UMCCB)

Cereal production is a cornerstone of Morocco’s agriculture and food security, covering more than half of the country’s farmland. Yet this vital sector faces growing challenges due to climate change, with more frequent droughts, rising temperatures, and increasingly erratic rainfall already destabilizing yields and increasing reliance on imports.

This research examines how Morocco’s cereal sector can adapt to these changing conditions while maintaining long-term productivity. The study combines three complementary approaches: advanced crop growth simulations using the CARAIB dynamic vegetation model, high-resolution regional climate projections, and machine learning techniques to identify the most critical drivers of yield variability.

Findings indicate that, under a high-emission (RCP 8.5) scenario, cereal yields could decline by 10% to 35% by the 2080s. The atmospheric CO₂ fertilization effect may help sustain production until mid-century, but its benefit diminishes as drought, water stress, and temperature extremes intensify. However, adaptation strategies show strong potential. In particular, evaluating different sowing windows indicates that sowing in December consistently delivers higher and more stable yields, whereas sowing in early September or late January can reduce production by up to one-third. Growth cycles are also projected to shorten significantly by the end of the century, reducing the duration of grain development and further constraining productivity. Machine learning analysis highlights precipitation timing, water stress, and temperature extremes during the flowering and grain-filling stages as key drivers of yield variability.

In conclusion, this research bridges the gap between climate science and agricultural policy, offering robust, quantitative recommendations to guide both immediate and long-term adaptation strategies. The methods are transferable to other semi-arid regions confronting comparable threats to food security. By strengthening evidence-based decision-making, the study supports Morocco’s national development priorities while advancing the Sustainable Development Goals on climate adaptation and food security.

Assessment and future projection of climate change impacts on terrestrial ecosystem productivity and carbon balance using satellite/model approach
Arpita Verma (UMCCB)

Forests and grasslands are central to Europe’s carbon balance, biodiversity, and climate resilience. Yet both ecosystems are increasingly affected by climate change and land-use transitions, which determine how much carbon they can store, how efficiently plants use water, and how well they can withstand droughts. In Wallonia, Belgium, where forests cover more than half the territory, understanding these dynamics is essential for sustainable land and forest management.

This research developed a high-resolution simulation framework that integrates satellite-based land cover maps, species trait information, and regional climate projections within the CARAIB Dynamic Vegetation Model. The framework reconstructed ecosystem functioning from 1980 to 2020 and explored trajectories until 2070 under contrasting climate (RCP2.6 and RCP8.5), land-use, and management scenarios.

The results show that forests have generally accumulated carbon over recent decades, while grasslands, though highly productive, are more vulnerable to heat and drought stress. Conservation and afforestation scenarios enhanced carbon storage and ecosystem resilience, but these gains diminished under severe climate change. Adaptive forest management strategies, particularly thinning and regeneration, improved water-use efficiency and drought resilience for most species. Species responses diverged markedly: alder (Alnus glutinosa) maintained strong resilience, while poplar (Populus nigra) and Scots pine (Pinus sylvestris) proved highly sensitive in unmanaged, high-emission futures.

By introducing species-level resilience indicators and combining land-use and management pathways with climate projections, this thesis provides new insights into carbon dynamics, water-use efficiency, and drought adaptation. Its findings offer a robust scientific basis for land-use and forest policies that aim to sustain carbon storage, biodiversity, and resilience in temperate regions under accelerating climate change.

Towards a better understanding of the meteoroid complex using forward scatter radio observations of the BRAMS network
Joachim Balis (COMETA)

Every day, millions of meteoroids enter Earth’s atmosphere. Studying them provides valuable insights into the distribution and properties of dust and small bodies in the Solar System, the dynamics of the upper atmosphere, and the influx of extraterrestrial material to Earth. Their very high speeds also pose a significant threat to spacecraft.
This thesis explores how forward scatter continuous wave radio observations can be used to reconstruct meteoroid trajectories and speeds. The work is based on data from the Belgian RAdio Meteor Stations (BRAMS) network. BRAMS offers several advantages over traditional radars, including wider detection coverage, higher sensitivity, and lower cost.
Results demonstrate that BRAMS can effectively contribute to the systematic retrieval of meteoroid trajectories and speeds. This work paves the way for characterizing smaller and higher-altitude objects inaccessible to optical systems, enhancing our understanding of the meteoroid complex.

Analysis of the variability of sea surface temperature and chlorophyll-a in the South China Sea using satellite and reanalysis data
Thi Hong Ngu Huynh (GHER)

In this study, the variability of sea surface temperature (SST) and chlorophyll-a (Chl-a) in the South China Sea (SCS) is investigated by using high-resolution satellite-reconstructed data.

The most disadvantage of satellite-derived data is a large percentage of missing data mainly due to cloud coverage or rain that becomes extremely serious in the SCS because it is located in the tropics. Therefore, long-term and high-resolution datasets, SST and Chl-a, were reconstructed for the SCS by using the DINEOF (Data INterpolated Empirical Orthogonal Functions) tool. Statistical methods, such as EOF and combined EOF analyses, partial least squares regression, and cross-correlation analysis were then applied to analyse these datasets in order to explore the seasonal and inter-annual variability of SST and Chl-a. Furthermore, the satellite-reconstructed data were also used in association with reanalysis datasets of surface wind, temperature, and salinity to easily interpret the variability of SST and Chl-a.

The variability of SCS SST is strongly affected by monsoons. The first mode of the SST variability presents the winter cooling under the effect of the northeast monsoon. The second mode shows the summer warming associated with the southwest monsoon. The third mode captures the mid-summer cooling in the western-central SCS regulated by the atmospheric cyclone (anticyclone) that strongly develops in the monsoon transition periods. The variability of SCS Chl-a is also influenced by the monsoons. The first mode of the Chl-a variability shows the out-of-phase variability between the coastal and open-sea regions, often peaking when the northeast monsoon sets in. The second mode presents the increase in Chl-a when the northeast monsoon prevails. The third mode exhibits the increase in Chl-a in summer, also regulated by the atmospheric cyclone (anticyclone). In general, the co-variability of SST and Chl-a is more affected by the northeasterly than southwesterly winds. Spatially, the co-variability of SST and Chl-a is regulated by the surface wind in the western and northwestern SCS more than the other regions. In addition, the variability of SCS SST and Chl-a is affected by both the mixed-layer depth (MLD) and the barrier-layer thickness (BLT). However, the role of MLD dominates that of BLT on the variability of SST and Chl-a in most parts of the SCS. The cross-correlation analysis between SST/Chl-a and Niño3.4 indicates that the anomalous variability of the seasonal SST and Chl-a in the SCS is influenced by El Niño-Southern Oscillation with different time lags.

Study of Marine Heatwaves in a semi-enclosed coastal area: their detection, formation, drivers and impacts, using a combination of satellite and in situ data
Cécile Pujol (GHER)

Marine heatwaves (MHWs) and marine cold spells (MCSs) are discrete temperature anomalies events that can persist for weeks or months, often causing severe ecological and socio-economic impacts. This thesis investigates the development, drivers and consequences of MHWs and MCSs in Central and Southern Chile (29°S-55°S), with a particular focus on Northern Patagonia, a region of oceanographic interest and of an economic importance due to intensive aquaculture activity. The main objective is to characterise the surface and subsurface dynamics of MHWs and MCSs, assess their spatial variability among large basins and narrow fjords, identify their drivers, and evaluate their ecological implications.

A multi-scale approach was applied, moving from offshore to coastal environments and from surface to subsurface layers. At large scale, satellite reanalysis data at low resolution (0.25°) were used to identify patterns, long-term trends and main drivers of MHWs in Central and Southern Chile, regions previously unexplored in this context. At finer scale, a novel methodology was developed, merging over three decades of in situ observations with satellite products to generate high-resolution (900 m) temperature fields for Northern Patagonia. This allowed the detection of extreme events across fjords and basins and the assessment of their spatial and temporal variability. Subsurface dynamics where also explored with a hydrodynamic model to evaluate the vertical extent of MHWs and MCS and the relation between surface and subsurface events. Additionally, the potential role of MHWs in promoting harmful algal blooms (HABs) was investigated.

This work provides the first integrated assessment of MHWs and MCSs in Central and Southern Chile, particularly in Northern Patagonia, demonstrating the importance of high-resolution approaches for capturing their complexity in coastal systems and highlighting implications for ecosystem functioning, fisheries and aquaculture management.

Radiative transfer modelling in the Black Sea: a stochastic approach coupled with a physical-biogeochemical model
Loïc Macé (MAST)

The modelling of ocean biogeochemistry faces many challenges due to the complexity of representing the living. Models must be carefully calibrated and validated thanks to increasingly available observational data, such as satellite products that provide wide spatial coverage. However, sea surface reflectance measured from space often cannot be directly compared to biogeochemical variables, leading to the use of inversion algorithms that introduce uncertainties in the comparison. The gap between biogeochemical models and satellite data can be bridged by simulating radiative transfer within models.

In this thesis, we propose to couple a radiative transfer module to NEMO-BAMHBI, a physical-biogeochemical modelling framework for the Black Sea. This module simulates radiative transfer with a three-stream scheme that accounts for the absorption and scattering properties of seawater. In particular, the simulation of the upward irradiance stream allows the simulation of sea surface reflectance that can be compared with satellite data. The coupling can be one-way or two-way, with the irradiance computed by the module being used to update the temperature and primary production in NEMO-BAMHBI in the latter. We also investigate the uncertainties associated with the coupling of the module by means of a stochastic version of the radiative transfer module. As the module relies on the optical properties of water, non-algal particles, phytoplankton, and CDOM, we introduce perturbations in the representation of their optical properties to assess their influence on the simulated variables.

We find that the radiative transfer module is able to simulate fields of irradiance and reflectance that are consistent with observations under most conditions. The stochastic model generates distributions of sea surface reflectance that match those of observations, in particular outside of blooming conditions, when the ensemble spread is consistent. During blooming conditions, biases remain in the simulation of radiometric variables. We find that CDOM appears as the main source of uncertainty in the simulated outputs, followed by phytoplankton and non-algal particles depending on bloom seasonality. In a two-way coupling configuration, perturbations significantly influence the simulation of biogeochemical variables but have little influence on the simulation of temperature.