Large riverbed sediment flux sustained for a decade after an earthquake
Creators
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1.
University of California, Santa Barbara
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2.
University of Southern California
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3.
California Institute of Technology
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4.
Institute of Earth Environment
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Xi'an Jiaotong University
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Chang'an University
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China University of Geosciences
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8.
Ludong University
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9.
Durham University
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10.
University of Oxford
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11.
Nanjing University
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12.
Woods Hole Research Center
Abstract
Large earthquakes induce widespread landslides that fill river channels with sediment , generating long-lasting fluvial hazards and reshaping mountain topography. However, riverine sediment fluxes after earthquakes remain poorly resolved, mostly because of a lack of data on bedload flux. Here we construct a source-to-sink sediment budget following the 2008 Mw7.9 (where Mw is the moment magnitude) Wenchuan earthquake in the eastern Tibetan mountains. We measured sediment accumulation in a man-made reservoir downstream of the earthquake-affected region. Ten years after the earthquake, the Min Jiang River had exported about 9% of the sediment mass from earthquake-triggered landslides, with around 5.7 times increase in the total riverine sediment flux sustained over that time. Bedload flux increased by 27.4^(+14.6)_(-15.6)% times compared with pre-earthquake levels, making up 65^(+12)_(-26)% of the post-earthquake sediment export—a proportion much higher than typical of most mountainous rivers. At the current pace, the river system will remove most Wenchuan landslide debris over centennial timescales. However, future sediment export rates are likely to vary because of changes on hillslopes (for example, revegetation) and in hydrology, sediment characteristics and transport processes. Our findings demonstrate a decadal bedload-dominated sediment pulse driven by earthquake-triggered landslides, suggesting that increased vulnerability to cascading hazards such as aggradation and flooding could persist for decades in populated downstream regions after a large earthquake.
Copyright and License
© The Author(s), under exclusive licence to Springer Nature Limited 2025.
Acknowledgement
This project was funded by NSF (1053504 and 1640894 to A.J.W.) and NSFC (42221003 and 41930864 to Z.J.). G.K.L. thanks support from a Hellman Family fellowship, a UCSB Regent’s junior faculty fellowship, a USC graduate college merit fellowship and a Caltech geology option postdoctoral fellowship. We thank Z. Ren for providing the DEM data from before the impoundment of Zipingpu Reservoir, and thank M. Douglas, G. Salter, D. Okaya, V. Ganti and K. Morell for their discussions. Geospatial support for this work was provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691 and 2129685. This work is dedicated to a forever-remembered colleague and friend—a scientist who has been to the mountains and oceans on this planet.
Data Availability
The derived reservoir sedimentation data shown in Fig. 1 and Extended Data Fig. 1 are available at the Hydroshare data repository (http://www.hydroshare.org/resource/210dd056c15c45208de9b5ba6a75cc61). Datasets of grain size, reservoir core sediment porosity, hydrological variability, mapped channel width, landslide volumes, high-magnitude water discharge index and suspended sediment yield are in Supplementary Tables 1–7, which are also available at the Hydroshare data repository. The DEM data used for geospatial analysis were downloaded from SRTM 90m Digital Elevation Database v.4.1 provided by the CGIAR Consortium for Spatial Information56. DEM data were analysed and visualized using ArcMap 10.8 and TopoToolbox57 in Matlab. The pre-impoundment DEM data were adopted from ref. 58. Landsat imagery was downloaded from the US Geological Survey EarthExplorer website (https://earthexplorer.usgs.gov/). SPOT imagery (SPOT 5 imagery 2008 CNES) was obtained from the SPOT imagery corporation (SICORP). Google Earth imagery was available at Google Earth Pro.
Code Availability
The central function of the automated picking algorithm was adopted from the Python package ‘obspy’, available online (https://docs.obspy.org/packages/autogen/obspy.signal.trigger.classic_sta_lta.html). Custom Matlab codes for sediment budget calculations and relevant analyses are available at the Hydroshare data repository (http://www.hydroshare.org/resource/210dd056c15c45208de9b5ba6a75cc61).
Supplemental Material
Supplementary Information: This file contains Supplementary Text, Supplementary Appendix and Supplementary Figs.
Supplementary Tables: Supplementary Tables 1–7.
Supplemental Material
Extended Data Fig. 1 Map view of the reservoir and pattern of sediment accumulation
Extended Data Fig. 4 Comparison of grain size in riverbed versus landslide sediments
Files
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Additional details
Related works
- Describes
- Journal Article: https://rdcu.be/eBfER (ReadCube)
- Is supplemented by
- Supplemental Material: https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-025-09354-8/MediaObjects/41586_2025_9354_MOESM1_ESM.pdf (URL)
- Supplemental Material: https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-025-09354-8/MediaObjects/41586_2025_9354_MOESM2_ESM.xlsx (URL)
- Dataset: http://www.hydroshare.org/resource/210dd056c15c45208de9b5ba6a75cc61 (URL)
- Software: https://docs.obspy.org/packages/autogen/obspy.signal.trigger.classic_sta_lta.html (URL)
Funding
- National Science Foundation
- EAR-1053504
- National Science Foundation
- EAR-1640894
- National Natural Science Foundation of China
- 42221003
- National Natural Science Foundation of China
- 41930864
- Hellman Foundation
- University of California, Santa Barbara
- University of Southern California
- California Institute of Technology
- National Science Foundation
- OPP-1043681
- National Science Foundation
- OPP-1559691
- National Science Foundation
- OPP-2129685
Dates
- Accepted
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2025-07-02
- Available
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2025-08-13Published