The magnitude of river morphological changes are better analyzed through the use of quantitative approaches, wherein resolution accuracy and uncertainty assessment are treated as crucial key-factors. In this sense, th...The magnitude of river morphological changes are better analyzed through the use of quantitative approaches, wherein resolution accuracy and uncertainty assessment are treated as crucial key-factors. In this sense, the creation of precise DEMs （Digital Elevation Models） of rivers represents an affordable tool to analyze geomorphic variations and budgets, except for wetted areas, where reliable channel digitalization can normally be obtained only using expensive bathymetric surveys. The proposed work aims at improving channel surface models without having available bathymetric sensors, by deriving dry areas elevations from LiDAR data and water depth of wetted areas from aerial photos through a predictive depth-colour relationship. The methodology was applied to two different sub-reaches of the Piave River, a gravel-bed river which suffered severe flood events in 2010. Erosion and deposition patterns were identified through DEM differencing, showing a predominance of scour processes which can lead to channel instability situations. The bathymetric output was compared to other previously-derived models confirming the accuracy of the in-channel elevation estimates. Finally, a discussion on the role played by longitudinal protections during the studied flood events is proposed, focusing the attention on the incidence of two major bank erosions that removed significant volumes of stable areas.展开更多
文摘The magnitude of river morphological changes are better analyzed through the use of quantitative approaches, wherein resolution accuracy and uncertainty assessment are treated as crucial key-factors. In this sense, the creation of precise DEMs （Digital Elevation Models） of rivers represents an affordable tool to analyze geomorphic variations and budgets, except for wetted areas, where reliable channel digitalization can normally be obtained only using expensive bathymetric surveys. The proposed work aims at improving channel surface models without having available bathymetric sensors, by deriving dry areas elevations from LiDAR data and water depth of wetted areas from aerial photos through a predictive depth-colour relationship. The methodology was applied to two different sub-reaches of the Piave River, a gravel-bed river which suffered severe flood events in 2010. Erosion and deposition patterns were identified through DEM differencing, showing a predominance of scour processes which can lead to channel instability situations. The bathymetric output was compared to other previously-derived models confirming the accuracy of the in-channel elevation estimates. Finally, a discussion on the role played by longitudinal protections during the studied flood events is proposed, focusing the attention on the incidence of two major bank erosions that removed significant volumes of stable areas.
