Graph analysis for subglacial hydrology and sediment flux to the ocean

A better knowledge of sediment flux to the ocean has many implications for understanding past and future cryosphere evolution including understanding potential impacts on ice shelf stability and ocean circulation, understanding the record of past cryosphere events from sediment records and understan...

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Bibliographic Details
Main Author: Aitken, A.
Format: Conference Object
Language:English
Published: 2023
Subjects:
Online Access:https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018094
Description
Summary:A better knowledge of sediment flux to the ocean has many implications for understanding past and future cryosphere evolution including understanding potential impacts on ice shelf stability and ocean circulation, understanding the record of past cryosphere events from sediment records and understanding impacts on nutrient supply and CO 2 drawdown. Physical model studies of subglacial sediment transport are relatively few, and due to computational expense, these often do not explore the broader model space for the glacial scenario of interest. The lack of a comprehensive quantitative knowledge of the subglacial sediment transport system limits our ability to understand sedimentary records of ice sheet change, and to express the potential impacts of future cryosphere change on the ocean. I present here a graph analysis approach to enable broad exploration of model space to understand hydrological change and for quantitative estimates of water and sediment fluxes through the system and to the ocean. The analysis is based on the outputs of physical models, including an ice sheet model output and a subglacial hydrology model output. The analysis defines catchment-scale graphs of the subglacial hydrology network, from which subgraphs are defined optimised to the problem at hand. Such representations greatly reduce the model size and allow efficient development of an ensemble result. These subgraphs may be defined from prior information, or ad-hoc during run-time based on stochastic, probabilistic or adaptive algorithms. I demonstrate the approach for a synthetic example and show examples from catchment-scale model studies in Greenland and Antarctica.