Doctorant depuis le 03-11-2023, sous la direction de Marie-Claire Pierret et Emilie Beaulieu
email : a.saphy[at]unistra.fr
Santé des écosystèmes forestiers : Conséquences du changement climatique sur cycles biogéochimiques des nutriments et de la matière organique dans les sols forestiers
PhD subject Climate change consequences on biogeochemical cycling of nutrients and organic matter in forest soil
Approach combining field observations in the OHGE (https://ohge.unistra.fr/), laboratory experiments and modelling to better understand biogeochemical disturbances of nutrients dynamics in forest soil.
The Observatoire HydroGeochimique de l'Environnement (OHGE; Strengbach watershed) is a forested ecosystem monitored since 1986 in the Vosges mountains. Among other things, we study soil solution at four different depths under spruce plots (declining, healthy and new plots). This continuous temporal data allows us to observe drought or tree death consequences. This is also an experimental site were various sylvicultural practises are also tested such as liming. It's a place were hydrologist, geochemist, geophysicist, ecophisilogist colaborate together and develop new tools to better understand mountain forest ecosystems.
In addition, percolation experiments on soil columns, with soil coming from the same site have been designed. The objective was to decrease the complexity of natural ecosystems and
to identify and deconvolve experimentally (under controlled conditions) different biogeochemical processes. Perturbations such as drought or liming are tested on column experiments to better understand the consequences observed on the field.
Particular attention is given to the understanding of the dynamics of the exchangeable cations and its interaction with organic matter. That implies an analytical development of soil organic matter and dissolved organic matter charcaterisation with Py-GC-MS, FTIR, Fluorescence and Absorbance.
The modelling of those processes is made by coupling hydrological and geochemical modelling. The idea is to better force the model thanks to experimental data with the aim of predicting the consequences of global change.