Author
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Canfield, Howard |
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WILSON, CATHY - LOS ALAMOS NATIONAL LAB |
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THOMAS, WILLIAM - MOBILE BOUNDARY HYDRAULIC |
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CROWELL, KELLY - LOS ALAMOS NATIONAL LAB |
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Submitted to: ASAE Annual International Meeting
Publication Type: Proceedings Publication Acceptance Date: 2/10/2003 Publication Date: 3/10/2003 Citation: Canfield, H.E., Wilson, C.J., Thomas, W.A., Crowell, K.J. 2003. On the use of field observation to improve model representation of the erosion process in channels. ASAE Annual International Meeting July 27-30, Las Vegas, NV, #032348. Interpretive Summary: Technical Abstract: Often in sediment transport modeling model parameters are adjusted so that the model better simulates field observations. A less common procedure is to use the observed data to re-evaluate the conceptual representation of the erosion process. In application of the HEC6T sediment transport model to a large post-fire flow in Pueblo Canyon at the Los Alamos National Laboratory, comparison of simulated results with observed scour caused the authors to re-evaluate the model¿s representation of scour. On its own, data from this single flow has limited value for selecting parameter values, especially given the transient nature of channel adjustment under post-fire conditions. However, these data provided valuable information on channel erosion that was incorporated into the conceptual representation of the erosion processes simulated in the HEC6T model. The model simulated scour in a scouring reach, and deposition in a depositional reach. However, it predicted scour in a reach of little change. However, model response was reasonable given the steep slopes and narrow cross-sections present in the reach. Field evaluation showed that bedrock and buried armored layers prevented scour in this otherwise sandy reach. By better accounting for these resistant materials in the model, the simulation improved. Simulation modeling and field observation must be used to support each other in producing an improved simulation, and even limited observation data can provide valuable insight that can improve model predictive capabilities. (LA-UR-02-74-93). |
