推移质
分水岭
地质学
支流
水文学(农业)
环境科学
泥沙输移
岩土工程
计算机科学
地貌学
沉积物
地理
地图学
机器学习
作者
Gilles Antoniazza,Michael Dietze,Davide Mancini,Jens M. Turowski,Dieter Rickenmann,Tobias Nicollier,Stefan Boss,Stuart N. Lane
摘要
Abstract The way Alpine rivers mobilize, convey and store coarse material during high‐magnitude events is poorly understood, notably because it is difficult to obtain measurements of bedload transport at the watershed scale. Seismic sensor data, evaluated with appropriate seismic physical models, can provide that missing link by yielding time‐varying estimates of bedload transport albeit with non‐negligible uncertainty. Low cost and ease of installation allow for networks of sensors to be deployed, providing continuous, watershed‐scale insights into bedload transport dynamics. Here, we deploy a network of 24 seismic sensors to estimate coarse material fluxes in a 13.4 km 2 Alpine watershed during a high‐magnitude transport event. First, we benchmark the seismic inversion routine with an independent time‐series of bedload transport obtained with a calibrated acoustic system. Then, we apply the procedure to the other seismic sensors across the watershed. Propagation velocities derived from cross‐correlation analysis between spatially consecutive bedload transport time‐series were too high with respect to typical bedload transport velocity suggesting that a faster‐moving water wave (re‐)mobilizes local coarse material. Spatially distributed estimates of bedload transport reveal a relative inefficiency of Alpine watersheds in evacuating coarse material, even during a relatively infrequent high‐magnitude bedload transport event. Significant inputs estimated for some tributaries were rapidly attenuated as the main river crossed less hydraulically efficient reaches. Only a small proportion of the total amount of material mobilized in the watershed was exported at the outlet. Multiple periods of competent flows are likely necessary to evacuate coarse material mobilized throughout the watershed during individual bedload transport events.
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