Alluvial fans are among the most privileged settlement areas in many mountain regions. These landforms are particularly dynamic being episodically affected by distributary processes generated by extreme flood events. ...Alluvial fans are among the most privileged settlement areas in many mountain regions. These landforms are particularly dynamic being episodically affected by distributary processes generated by extreme flood events. Addressing risk assessment entails determining hazard exposure and unravelling how it might be related to process loading and to process dynamics once the flow becomes unconfined on the surface of alluvial fans. By following a ‘similarity of process concept’, rather than by attempting to scale a real-world prototype, we performed a set of 72 experimental runs on an alluvial fan model. Thereby, we considered two model layouts, one without a guiding channel and featuring a convex shape and the other one with a guiding channel, a bridge, and inclined but planar overland flow areas as to mirror an anthropic environment. Process magnitude and intensity parameters were systematically varied, and the associated biphasic distributary processes video recorded. For each experiment, the exposure was detected by mapping the exposed area in a GIS, thereby discerning between areas exposed to biphasic flows and the associated depositional phenomena or to the liquid flow phase only. Our results reveal that total event volume, sediment availability and stream power in the feeding channel, as well as depositional effects, avulsion, and channelization on the alluvial fan concur to determine the overall exposure. Stream process loading alone, even when rigorously defined in terms of its characterizing parameters, is not sufficient to exhaustively determine exposure. Hence, further developing reliable biphasic simulation models for hazard assessment on settled alluvial fans is pivotal.展开更多
The estimation of underwater features of channel bed surfaces without the use of bathymetric sensors results in very high levels of uncertainty. A revised approach enabling an automatic extraction of the wet areas to ...The estimation of underwater features of channel bed surfaces without the use of bathymetric sensors results in very high levels of uncertainty. A revised approach enabling an automatic extraction of the wet areas to create more accurate and detailed Digital Terrain Models (DTMs) is here presented. LiDAR-derived elevations of dry surfaces, water depths of wetted areas derived from aerial photos and a predictive depth-colour relationship were adopted. This methodology was applied at two different reaches of a northeastern Italian gravel-bed river (Tagliamento) before and after two flood events occurred in November and December 2010. In-channel dGPS survey points were performed taking different depth levels and different colour scales of the river bed. More than 10,473 control points were acquired, 1107 in 2010 and 9366 in 2011 respectively. A regression model that calculates channel depths using the correct intensity of three colour bands (RGB) was implemented. LiDAR and water depth points were merged and interpolated into DTMs which features an average error, for the wet areas, of ±14 cm. The different number of calibration points obtained for 2010 and 2011 showed that the bathymetric error is also sensitive to the number of acquired calibration points. The morphological evolution calculated through a difference of DTMs shows a prevalence of deposition and erosion areas into the wet areas.展开更多
基金Project FONDECYT nr.1170657 titled “The flood memory of a river system:using both experimental and field-based approaches to unravel the role of unsteady flow and antecedent flows on sediment dynamics during floods” funded by CONICYT and led by Luca MaoProject FONDECYT nr.1170413 titled “Morphological impacts in rivers affected by volcanic eruptions.Chaiten and Calbuco:similar disturbance but different fluvial evolution?(PIROFLUV)” funded by CONICYT and led by Andrés Iroumé。
文摘Alluvial fans are among the most privileged settlement areas in many mountain regions. These landforms are particularly dynamic being episodically affected by distributary processes generated by extreme flood events. Addressing risk assessment entails determining hazard exposure and unravelling how it might be related to process loading and to process dynamics once the flow becomes unconfined on the surface of alluvial fans. By following a ‘similarity of process concept’, rather than by attempting to scale a real-world prototype, we performed a set of 72 experimental runs on an alluvial fan model. Thereby, we considered two model layouts, one without a guiding channel and featuring a convex shape and the other one with a guiding channel, a bridge, and inclined but planar overland flow areas as to mirror an anthropic environment. Process magnitude and intensity parameters were systematically varied, and the associated biphasic distributary processes video recorded. For each experiment, the exposure was detected by mapping the exposed area in a GIS, thereby discerning between areas exposed to biphasic flows and the associated depositional phenomena or to the liquid flow phase only. Our results reveal that total event volume, sediment availability and stream power in the feeding channel, as well as depositional effects, avulsion, and channelization on the alluvial fan concur to determine the overall exposure. Stream process loading alone, even when rigorously defined in terms of its characterizing parameters, is not sufficient to exhaustively determine exposure. Hence, further developing reliable biphasic simulation models for hazard assessment on settled alluvial fans is pivotal.
文摘The estimation of underwater features of channel bed surfaces without the use of bathymetric sensors results in very high levels of uncertainty. A revised approach enabling an automatic extraction of the wet areas to create more accurate and detailed Digital Terrain Models (DTMs) is here presented. LiDAR-derived elevations of dry surfaces, water depths of wetted areas derived from aerial photos and a predictive depth-colour relationship were adopted. This methodology was applied at two different reaches of a northeastern Italian gravel-bed river (Tagliamento) before and after two flood events occurred in November and December 2010. In-channel dGPS survey points were performed taking different depth levels and different colour scales of the river bed. More than 10,473 control points were acquired, 1107 in 2010 and 9366 in 2011 respectively. A regression model that calculates channel depths using the correct intensity of three colour bands (RGB) was implemented. LiDAR and water depth points were merged and interpolated into DTMs which features an average error, for the wet areas, of ±14 cm. The different number of calibration points obtained for 2010 and 2011 showed that the bathymetric error is also sensitive to the number of acquired calibration points. The morphological evolution calculated through a difference of DTMs shows a prevalence of deposition and erosion areas into the wet areas.
