Permafrost slows Arctic riverbank erosion

Creators: Geyman, Emily C.¹; Douglas, Madison M.^{1, 2}; Avouac, Jean-Philippe¹; Lamb, Michael P.¹

1. California Institute of Technology
2. Massachusetts Institute of Technology

Abstract

The rate of river migration affects the stability of Arctic infrastructure and communities and regulates the fluxes of carbon, nutrients and sediment to the oceans. However, predicting how the pace of river migration will change in a warming Arctic has so far been stymied by conflicting observations about whether permafrost primarily acts to slow or accelerate river migration. Here we develop new computational methods that enable the detection of riverbank erosion at length scales 5–10 times smaller than the pixel size in satellite imagery, an innovation that unlocks the ability to quantify erosion at the sub-monthly timescales when rivers undergo their largest variations in water temperature and flow. We use these high-frequency observations to constrain the extent to which erosion is limited by the thermal condition of melting the pore ice that cements bank sediment, a requirement that will disappear when permafrost thaws, versus the mechanical condition of having sufficient flow to transport the sediment comprising the riverbanks, a condition experienced by all rivers. Analysis of high-resolution data from the Koyukuk River, Alaska, shows that the presence of permafrost reduces erosion rates by 47%. Using our observations, we calibrate and validate a numerical model that can be applied to diverse Arctic rivers. The model predicts that full permafrost thaw may lead to a 30–100% increase in the migration rates of Arctic rivers.

Copyright and License

Acknowledgement

We thank the Huslia Tribal Council for river and land access and S. Huffman and the Yukon River Inter-Tribal Watershed Council for field and logistical support. We also thank J. Anadu, R. Blankenship, K. Dunne, W. Fischer, Y. Ke, H. Dion-Kirschner, J. Magyar, E. Mutter, J. Nghiem, J. Reahl, R. Rugama-Montenegro, E. Seelen, I. Smith and J. West for help in the field and for fruitful discussions. Planet Labs provided the high-resolution PlanetScope imagery through their Education and Research Program. This work was supported by NSF Award 2127442, NSF Award 2031532, and Caltech’s Resnick Sustainability Institute. E.C.G. thanks the NSF Graduate Research Fellowships Program and the Fannie and John Hertz Foundation.

Contributions

E.C.G. and M.P.L. designed the study. M.M.D. and M.P.L. developed the early thermomechanical model. J.-P.A. advised the sub-pixel methodology. E.C.G. developed the sub-pixel methodology, performed the analysis and wrote the manuscript, with input from M.M.D., J.-P.A. and M.P.L.

Data Availability

The Sentinel-2 satellite images used to extract the 2016–2022 migration rates shown in Fig. 1 are freely available from the European Space Agency on data portals such as the Copernicus Open Access Hub (https://scihub.copernicus.eu/). The PlanetScope images used for the seasonal time-series analysis (Fig. 3) are available from Planet Labs (https://www.planet.com). The stream gauge data in Extended Data Fig. 2 are available from the United States Geological Survey (https://waterdata.usgs.gov/nwis). The permafrost map used in Fig. 2 and Extended Data Fig. 10 is from ref. ³² and is made available by the United States Geological Survey (https://www.sciencebase.gov/catalog/item/5602ab5ae4b03bc34f5448b4). Our spatial measurements of riverbank erosion from the Sentinel-2 and PlanetScope time-series analysis (Figs. 1–3) are packaged on the NSF Arctic Data Center⁶⁸: https://doi.org/10.18739/A2HM52M6Q. Our field observations of permafrost presence/absence (Extended Data Fig. 10) from summer 2018 and fall 2022 are published on the ESS-DIVE repository⁶⁷ (https://doi.org/10.15485/2204419).

Code Availability

Our methodology for measuring sub-pixel bank erosion, as well as our workflow for channel extraction and the measurement of channel morphometrics (width, radius of curvature, longitudinal distance and so on) (Fig. 1), is available on the NSF Arctic Data Center⁶⁸: https://doi.org/10.18739/A2HM52M6Q. The code is written in MATLAB.

Supplemental Material

Supplementary Information, including Supplementary Figs. 1–11, Supplementary Tables 1 and 2 and Supplementary References (PDF)

Peer Review File (PDF)

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