Jobs:Job-00114
Apply before: 1 March 2020
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Posting:
Position: PhD opportunities
Apply before: 1 March 2020
|
1. Early Stage Researcher PhD position, Innovative Training Network “S2S-FUTURE”: Modeling grain size distributions in rivers
Supervision: Jean Braun, GFZ, Alex Whittaker, Imperial College, Sebastien Castelltort, Geneva and Charlotte Fillon, Pau)
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 (2019) 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. In this project 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 of 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).
See more info, conditions and application procedure at https://gfz-potsdam.concludis.de/prj/shw/40dba662fae60cd3bcceaa76a82d2873_0/3812/Early_Stage_Researcher_PhD_position_Innovative_Training_Network_S2S-FUTURE_modeling_grain_size_distributions_in_rivers.htm?lang=en_GB
2. Early Stage Researcher PhD position, Innovative Training Network “S2S-FUTURE”: Modeling the formation of the regolith
Supervision: Jean Braun, GFZ, Emmanuelle Puceat, Universite Bourgogne Franche-Comte, Sebastien Castelltort, Geneva, and Francois Guillocheau, Rennes.
The regolith is the uppermost part of the Earth’s crust, which, because it is in contact with the hydrosphere, is composed of chemically and physically altered or weathered rocks. It is an essential part of the so-called critical zone in part because it is host to the largest fresh water reservoir on Earth, but also because it sustains most life at the Earth’s surface. The processes that control the formation and thus the present-day thickness of the regolith are, however, poorly quantified and few predictive model exists. Our estimates of regolith geometry are based on very sparse measurements and mostly rely on empirical relationships or correlations. Braun et al (2016) recently proposed a simple parameterization of regolith formation under the assumption that chemical alteration controls the rate of propagation of the weathering front through the ability of the flow of water along the interface to remove the product of the chemical dissolution, therefore keeping the system from complete saturation. Although highly simplified, the model is able to reproduce basic observations such as the control of surface erosion rate on regolith thickness and of surface slope on the distribution of regolith (in our out of phase with the surface topography). The first objective of this project is to generalize this model to 3 dimensions and to include it in an existing landscape evolution model (LEM). One of the challenges will be to find a highly efficient implementation that insures that the resulting LEM can be used in a Bayesian scheme to invert for both climate/tectonic control and model parameter values. For this we will need to improve on existing methods to compute the geometry of the water table in an arbitrary regolith geometry. The second objective is to use existing regolith estimates and data collected in WP1 by ESR5 to validate and calibrate the model and to investigate how an extreme climatic event such as the PETM affects weathering rates and is expressed and recorded in the geological archives.
See more info, conditions and application procedure at https://gfz-potsdam.concludis.de/prj/shw/a292f1c5874b2be8395ffd75f313937f_0/3826/Early_Stage_Researcher_PhD_position_Innovative_Training_Network_S2S-FUTURE_modeling_the_formation_of_the_regolith.htm?lang=en_GB
Jean Braun
Earth Surface Process Modelling
