ESR 14 - Tracking grain-size in a sediment transport model and application to natural system


The sedimentary record contains invaluable information on past tectonic and climatic events. To decipher this information requires that we have an adequate understanding of how sediments are produced, transported and deposited. Recent work (Whittaker et al, 2011) has shown that the distribution of grain size in sedimentary deposits responds to variations in tectonic uplift and/or subsidence. However, no landscape evolution model exists that can efficiently predict the distribution of grain size in a sedimentary basin from known tectonic and climatic forcings. Davy and Lague (2009) have proposed a parameterization of sediment transport and how they affect river incision. Recently Yuan et al (subm) have developed an implementation of this parameterization in a 2D landscape evolution model that is highly efficient, i.e., implicit in time and of complexity O(n). However this model does not track grain size, nor the dependency of transport properties on grain size. Here we propose to implement into a 2D landscape evolution model the self-similar model proposed by Fedele and Paola (2007) and incorporated in a simplified 1D model of a foreland basin by Duller et al (2010). Self-similarity assumes that the distribution of grain size remains relatively constant in shape and that transport/deposition processes act only to stretch and/or translate it, implying that knowledge of the evolution of mean grain size and standard deviation are sufficient to track grain size distribution through a given sedimentary system. The first objective of this project is to incorporate this self-similar model for grain size evolution into an existing landscape evolution model while keeping its high efficiency. The efficiency is essential because the model will then be used to reproduce observed grain size distributions from a sedimentary deposit that recorded the PETM event, using a Bayesian approach that requires a large number of model simulations be performed (i.e., of the order of hundreds of thousands f simulations). In this way constraints can be obtained on how the system has reacted to a major climatic event, as well as on poorly calibrated model parameters (such as the erosional rate constant or transport coefficient, and how they vary as a function of grain size).The second objective of this project is therefore to use the model to reproduce observed grain size distributions, validate the model and calibrate the model parameters and learn how sediment production and transport are affected by climate (change in temperature and rainfall).


Expected Results:

  • A set of new algorithms that allow for the efficient tracking of grain size in a landscape evolution model;
  • the validation and calibration of the model using data collected from the literature and from WP1;
  • constraints on how climate controls the production and transport of sediments and how variations in climate/tectonic forcing are recorded in the sedimentary record.


Three publications are expected including one describing the methodological development.



  • Imperial (A. Whittaker) – Learn about the self-similar model (2 months)
  • Université de Genève (S. Castelltort) Incorporate grain size data into model (4 months)
  • TOTAL (C. Fillon) - Integration in a stratigraphic numerical model & different case studies (1 month)




S2S-FUTURE project gathers an outstanding European research and training network of 15 PhD students, hosted at world-leading academic institutions and industrial companies, whose aim is to develop the S2S paradigm as a powerful vector for understanding sedimentary accumulations as natural resources.

The project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 860383.