Deep-time, planetary scale landscape evolution: Using the pre-industrial Earth as a calibration point for coupled tectonic, geomorphic and climate modelling on Earth and other planets.

crossref(2024)

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摘要
The field of Geomorphology covers the essential link between climate and geological processes such as tectonics. Because both of these processes operate on planetary scales, and over million year periods, landscape evolution models must, by necessity, do the same. With the advent of modern computing and the reduction in computational complexity of the Stream Power Law algorithm (SPL), it has become much easier to conduct investigations of landscape evolution on these scales. By doing so we can test model interactions between Earth system processes such as geodynamics, weathering, sediment flux, and erosion. In this work we aim to conduct landscape evolution modelling with the SPL algorithm on pre-industrial Earth, using high resolution climate models (CMIP – Coupled Model Intercomparison Project) and topographic maps with uplift histories as input. This model has already been used for planetary scale modelling on ancient Mars, and now we aim to use it to conduct a broad sensitivity analysis of the landscape evolution of pre-industrial Earth. We will compare the model outputs to established datasets and to other landscape evolution studies to best constrain the input parameters of the model (e.g., incision coefficient) to reproduce known water and sediment fluxes for the period. Once the model is calibrated, we aim to use it to look at periods of deep time where landscape evolution was perturbed by tectonic and climate excursions such as supercontinent assembly, and transitions to and from icehouse climate states. As with the pre-industrial study, this work would also include coupling to climate models but furthermore would be coupled to a global geodynamic model to produce topography, reducing reliance on paleo-topographical maps and allowing for comparison to previous studies that used those maps as topographic input.
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