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Title: Process-based characterization of hydromorphology at a catchment scale : approaches, modelling frameworks and links with biology
Author: Bizzi , Simone
ISNI:       0000 0004 2743 3236
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2012
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River hydromorphological characterization is an important requirement for modern river management. The process-based river characterizations which are well established in fluvial geomorphology are location-specific and highly demanding in terms of resource and expertise, and their routine application for regional or national river characterizations is not feasible at the present. Reviewing current understanding of river geomorphic processes and making use of novel datasets, such as DEM, and tools, such as GIS, this work tests the feasibility of providing characterizations of hydromorphology that require moderate resources and are potentially applicable at regional or national scale. A framework to characterize river hydromorphology from specific map derived geomorphic controls (namely channel gradient, bankfull flow, specific stream power, Strhaler stream order and degree of channel confinement) is successfully developed. These controls are shown to be important in the characterization of channel types, to be consistently inter- connected in a hierarchical framework, and to affect frequency and typologies of channel forms. Stream power emerges as a dominant driver of sediment transport and floodplain formation and as an important indicator for characterizing channel sensitivity to erosion and deposition. The proposed characterization of hydromorphology uses automatic procedures of GIS and statistical analysis and demonstrates that these are promising for regional applications. Our understanding of the links between hydromorphology and ecology are also limited. This thesis presents an application of structural equation modelling (SEM) to explore the ecology of benthic macroinvertebrate communities in riverine ecosystems using national-scale biological, water quality and physical habitat data. The statistical model identifies a nurnber of links between the degree of organic pollution, alkalinity, physical habitat conditions, and the distribution of benthic macro invertebrates. SEM develops a confirmatory approach that it requires a-priori, explicit III ---= formulations of cause-effect relationships at the model conceptualisation stage. SEM is shown to address a number of the challenges related to multicollinear datasets (common in ecology), where interpretations based on statistical association alone can be problematic. IV
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available