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| Titel |
A multi-scalar approach for modelling river channel change in the
Anthropocene |
| VerfasserIn |
Peter Downs, Hervé Piégay, Jeremy Piffady, Laurent Valette, Lise Vaudor |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250145870
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| Publikation (Nr.) |
 EGU/EGU2017-9845.pdf |
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| Zusammenfassung |
| Adjustments in river channel morphology during the ‘Anthropocene’ arise as a cumulative
impact from the influence of numerous natural and human stressors operating at multiple
spatial and temporal scales. However, the research requirement for data on impacts at
multiple scales, and at sufficiently high spatial and temporal resolution to determine
reach-level effect, largely prevented such studies until recent improvements in digital
technologies and data availability. A meta-analysis of recent cumulative impact studies
indicates that the analytical component is still overwhelmingly interpretative, with
cause-and-effect reasoning based largely on temporal synchronicity and spatial
proximity, whereas our conceptual understanding of adjustment processes is far
more nuanced. We propose, instead, that studies of cumulative impact should be
underpinned by an analytical model of cause and effect, partly to test and enhance our
predictive capabilities and allow scenario setting, but also to learn about the relative
sensitivities involved in different parts of the model and thus to prioritize future research
endeavours. Our requirements are that the model should be inherently designed to
detect reach-level changes over Anthropocene timescales, be capable of integrating
co-existing and hierarchical human and natural pressures on fluvial systems, be able to
accommodate time-lagged effects and upstream-downstream connectivity, and be based
on an explicit conceptual model that can be refined as our process understanding
improves.
Bayesian Belief Networks (BBNs) offer some potential in this regard and are becoming
an increasingly popular option for dealing with complex, multi-scalar relationships in
ecology and other environmental sciences. BBNs consist of a conceptual model of nodes and
edges (i.e., graph theory) that qualitatively describe the structure of causal relationships
between chains of variables, and a quantitative expression of the relative strength of the
hypothesized relationships, described by probability distributions. BBNs offer the
flexibility of incorporating different variables taken at various scales from within the
catchment (thus accommodating geographical and historical differences in climate
and human occupation), can be implemented even when there is some missing
data, and can be rapidly optimised to improve data fit by modifying individual
parts of the internal probability distributions. They are particularly well-suited to
hierarchical cause and effect structuring because data uncertainties are inherently
‘internalised’ in the development of the model’s structure, thus potentially mediating
the overall error in a complex chain of relationships. We detail tests in progress to
develop models for channel width and depth changes for the main stem of the Santa
Clara River, which drains a 4,200 km2 catchment in coastal Southern California. |
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