I am fundamentaly interested in observation techniques of water storage changes, but also on assembling highly heterogeneous critical zone (CZ) datasets to reveal CZ structures and functionning.
Observing continental water storage is a challenging task, as about 99 % of the continental liquid water is stored below the ground surface: invisible water.
By measuring the changes in the Earth gravity field through time, it is possible to derive mass changes at the origin of the gravity changes: it is a new, integrative way to get access to water storage changes.
98 % of continental liquid water is stored as groundwater, that is, mostly in a saturated, porous (or fractured) medium. By measuring the saturated thickness in the aquifer (e.g. through drilling), together with the associated pore volume (here specific yield), it is possible to derive the actual groundwater storage.
Magnetic resonance soundings allow to derive both the saturated thickness and the specific yield without drilling a hole in the ground.
I have come to use highly heterogeneous datasets at local scales (elementary catchments) to understand critical zone structure and functionning (See e.g. Hector et al., 2013, 2015, Genthon et al., 2015, Mamadou et al., 2016, Wubda et al., accepted, and my PhD manuscript). These included
This helps me to build conceptual descriptive models of the subsurface, and may serve as input for the hydrological modeling of the critical zone.
I am now moving to larger scales (i.e. up to regional) to try to identify subsurface structures and clusters from remote sensing data, distributed hydrological (i.e. water table) data and other surrogates, such as geological maps, or geomorphological models.
To develop my research, I mostly rely on the AMMA-CATCH observatory and West-African partners. This hydro-meteorological observatory composed of three meso-scale sites (Mali, Niger and Benin) has the particularity to sample both an eco-climatic and a geologic gradient across West-Africa. Highly instrumented critical zone sections in each sites allow to experience new observation methods, to infer critical zone processes or to test models. Particularly, it helps to define method to regionalize parameters, state variables, or processes, based on remote sensing data trained on these local sites, for instance, or any spatial surrogate, such as geological maps.
The AMMA-CATCH observatory accross the eco-climatic N-S gradient. Zoom on the Upper Ouémé catchment in northern Benin, the meso-scale site of the humid Sudanian area, and its super-site, the Ara catchment, and the headwater Nalohou catchment. Contour lines in the regional map show average of yearly precipitation values.