4.5 Article

Post-wildfire sediment cascades: A modeling framework linking debris flow generation and network-scale sediment routing

期刊

EARTH SURFACE PROCESSES AND LANDFORMS
卷 44, 期 11, 页码 2126-2140

出版社

WILEY
DOI: 10.1002/esp.4635

关键词

wildfire; debris flow; sediment transport; river network model; erosion

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Wildfires represent one of the largest disturbances in watersheds of the Intermountain West. Yet, we lack models capable of predicting post-wildfire impacts on downstream ecosystems and infrastructure. Here we present a novel modeling framework that links new and existing models to simulate the post-wildfire sediment cascade, including spatially explicit predictions of debris flows, storage of debris flow sediment within valleys, delivery of debris flow sediment to active channels, and the downstream routing of sediment through river networks. We apply the model to sediment dynamics in Clear Creek watershed following the 2010 Twitchell Canyon Fire in the Tushar Mountains of southern Utah. The debris flow generation model performed well, correctly predicting 19 out of 20 debris flows from the largest catchments, with only four false positives and two false negatives at observed rainfall intensities. In total, the model predicts the occurrence of 160 post-wildfire debris flows across the Clear Creek watershed, generating more than 650 000 m(3) of sediment. Our new storage and delivery model predicts the vast majority of this sediment is stored within valleys, and only 13% is delivered to the river network. The sediment routing model identifies numerous sediment bottlenecks within the network, which alter transport dynamics and may be hotspots for aggradation and aquatic habitat alteration. The volume of sediment exported from the watershed after seven years of simulation totals 17% of that delivered, or 2% of the total generated debris flow sediment. In the case of the Twitchell Canyon Fire, this highlights that significant post-wildfire sediment volumes can be stored in valleys (87%) and within the stream network (11%). Finally, we discuss useful insights that can be gleaned from the model framework, as well as the limitations and need for more monitoring and theory development in order to better constrain essential inputs, process rates, and morphodynamics. (c) 2019 John Wiley & Sons, Ltd.

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