Sustainable management of river systems involves balancing multiple objectives. These include alleviating flood hazards and the risks they pose to people and critical assets whilst promoting good ecological status by supporting healthy biological communities and enhancing habitat diversity. In regions with high rates of coarse sediment supply to rivers, management often involves addressing the issues associated with the progressive accumulation of gravel within channels. Such sedimentation can raise riverbeds levels, resulting in reduced flood capacity which in turn may result in an increased probability of flooding and a reduction in the standards of protection associated with existing defences. One approach to manage this hazard is through the extraction of riverbed gravels to restore flood capacity, correct river alignments, prevent bank erosion and reduce the threat of catastrophic course changes. The extracted river gravels are also not without value and represent an important source of aggregate for the construction industry. So much so, that gravel-bed rivers close to urban areas are often considered ideal mines of readily available sediment. This situation can, therefore, be presented as a potential win-win game. As long as gravel extraction is balanced against the naturally occurring upstream sediment supply, an adaptive management regime can be devised to maintain flood capacity whilst generating a key commercial resource. However, it is now well-established that estimating this balance incorrectly and over-extracting gravels can lower the riverbed, steepen the channel gradient, leading to enhanced bank erosion and paradoxically reduce flood protection by destabilizing existing flood control measures. Additionally, removal of the typically coarse surface layer of riverbed gravels can alter the bed sediment composition creating a flush of fine sediment that degrades invertebrate and fish habitat. Plans to dredge rivers to enhance flood capacity, so prominently popularized by the recent events in the Somerset Levels, must therefore be based on cautious, scientifically-informed and evidence-led strategies to plan, implement and review interventions. Traditionally, sediment management plans have been based on data from sparse networks of river cross-sections. These provide a basis for monitoring trends in bed levels through periodic resurveys. The resulting data can also be used to determine a morphological gravel transport rate and estimate the background rate of sediment supply. Recent research has shown that the river level and gravel transport estimates based on section data, which is effectively blind to the river morphology between sections, can incorporate significant bias giving rise of 2-3 order of magnitude uncertainties the key data used to drive management strategies. Advances in remote sensing offer a solution to alleviate this bias by estimating channel changes through the comparison of 3D elevation models through time. Differences between these models provide reliable measures of elevation change and can be integrated to assess regional trends. Historically, the high costs of acquiring dense topographic data to create these models has prohibited their use for routine monitoring. Continuing developments, most notably in photogrammetry methods have recently and dramatically reduced the cost of these data and removed a bottleneck preventing their adoption. In this project we will work with a group of stakeholders from national and local government in the UK and NZ to develop a software tool that can support routine channel monitoring using these new streams of dense 3D topographic data. The resulting tool will facilitate simplified workflows that can be easily implemented by agency and authority staff and used to present the results within a statistical uncertainty framework that accounts for errors in the underlying topographic data.
Sustainable management of river systems involves balancing multiple objectives. These include alleviating flood hazards and the risks they pose to people and critical assets whilst promoting good ecological status by supporting healthy biological communities and enhancing habitat diversity. In regions with high rates of coarse sediment supply to rivers, management often involves addressing the issues associated with the progressive accumulation of gravel within channels. Such sedimentation can raise riverbeds levels, resulting in reduced flood capacity which in turn may result in an increased probability of flooding and a reduction in the standards of protection associated with existing defences. One approach to manage this hazard is through the extraction of riverbed gravels to restore flood capacity, correct river alignments, prevent bank erosion and reduce the threat of catastrophic course changes. The extracted river gravels are also not without value and represent an important source of aggregate for the construction industry. So much so, that gravel-bed rivers close to urban areas are often considered ideal mines of readily available sediment. This situation can, therefore, be presented as a potential win-win game. As long as gravel extraction is balanced against the naturally occurring upstream sediment supply, an adaptive management regime can be devised to maintain flood capacity whilst generating a key commercial resource. However, it is now well-established that estimating this balance incorrectly and over-extracting gravels can lower the riverbed, steepen the channel gradient, leading to enhanced bank erosion and paradoxically reduce flood protection by destabilizing existing flood control measures. Additionally, removal of the typically coarse surface layer of riverbed gravels can alter the bed sediment composition creating a flush of fine sediment that degrades invertebrate and fish habitat. Plans to dredge rivers to enhance flood capacity, so prominently popularized by the recent events in the Somerset Levels, must therefore be based on cautious, scientifically-informed and evidence-led strategies to plan, implement and review interventions. Traditionally, sediment management plans have been based on data from sparse networks of river cross-sections. These provide a basis for monitoring trends in bed levels through periodic resurveys. The resulting data can also be used to determine a morphological gravel transport rate and estimate the background rate of sediment supply. Recent research has shown that the river level and gravel transport estimates based on section data, which is effectively blind to the river morphology between sections, can incorporate significant bias giving rise of 2-3 order of magnitude uncertainties the key data used to drive management strategies. Advances in remote sensing offer a solution to alleviate this bias by estimating channel changes through the comparison of 3D elevation models through time. Differences between these models provide reliable measures of elevation change and can be integrated to assess regional trends. Historically, the high costs of acquiring dense topographic data to create these models has prohibited their use for routine monitoring. Continuing developments, most notably in photogrammetry methods have recently and dramatically reduced the cost of these data and removed a bottleneck preventing their adoption. In this project we will work with a group of stakeholders from national and local government in the UK and NZ to develop a software tool that can support routine channel monitoring using these new streams of dense 3D topographic data. The resulting tool will facilitate simplified workflows that can be easily implemented by agency and authority staff and used to present the results within a statistical uncertainty framework that accounts for errors in the underlying topographic data.

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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Name | greekSession |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Name | greekSession |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Description | Stores the user session ID for Academic Bibliometrics & Assessment Computational Utility Service. |
| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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| Provider | Academic Bibliometrics & Assessment Computational Utility Service |
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