Hi, I’m Boris Gailleton—a postdoctoral researcher at Géoscience Rennes (Université de Rennes, Rennes, Bretagne, France).

I am a numerical modeler and quantitative geomorphologist, dedicated to developing, sharing, and applying numerical methods to better understand tectonic, climatic, and lithologic controls on fluvial landscapes.

My work includes developing methods to extract geomorphometrics, which quantify fluvial landforms and help assess the efficiency of the fluvial processes that shape them, such as erosion, deposition, sediment transport, and flood dynamics.

I also develop stream-power-related numerical models to simulate landscapes, with a focus on those that exhibit significant heterogeneities, including lakes, lithologic boundaries, and domains governed by differing dominant processes, for example see my model CHONK developped with Luca Malatesta at GFZ (Potsdam).

Currently, I am advancing methods for modeling hydrodynamics and morphodynamics across scales, ranging from event-scale phenomena like floods and landslides to processes occurring over millions of years.

The field of landscape evolution modeling is fragmented. Short-term, risk-oriented models are highly detailed but often limited to small scales and individual events, taking hours of computation to simulate localized flood or landsclide events. On the other hand, long-term models simplify hydraulic fluxes, reducing them to drainage area flowlines with coarse time steps, making it challenging to capture flow variability below 10,000-year intervals.

My current research aims to bridge this gap by creating adaptable models that integrate the strengths of both short- and long-term modeling approaches. This is particularly important to understand and anticipate the effect of periods of climate change, characterised by sharp changes in the pattern, intensity and frequency of precipitation event, disturbing the equillibrium of landscapes, triggering thousansd of year of transience.

You can check graphflood if you are interested to see where we are at.