Are Arctic rivers speeding up or slowing down? Riverbank positions and migration rates of rivers in Alaska and Arctic Canada (1972-2020)
Bibliographic record
Abstract
Abstract: The pace of Arctic river migration exerts a first-order control on the mobilization timescales for the 1,700 Pg of carbon currently trapped within frozen and thawing permafrost. However, there is no consensus about whether Arctic rivers are responding to regional warming by speeding up or slowing down. Here, we reconstruct migration rates over the period 1972–2020 for Arctic and sub-Arctic rivers spanning 1,500-km of length and a variety of environments. We find that rivers in discontinuous permafrost experienced a systematic acceleration over the last 50 years, whereas rivers in continuous permafrost experienced a systematic slowdown. We identify two competing mechanisms responsible for this bifurcating behavior: a decline in the erosive intensity of river-ice breakup has supported slower migration, whereas thaw of permafrost riverbanks has caused faster migration. Other proposed mechanisms--including Arctic greening and changes in water discharge, water temperature, and riverine sediment loads--are unlikely to be driving the observed trends. Manuscript citation: Geyman, E.C. and Lamb, M.P. How fast will rivers migrate in a warmer Arctic? Insights from the last fifty years. In revision. 2025. This dataset consists of 5 parts: (1) "Arctic_river_timeseries_database.xlsx" - Summary data table listing the n = 20 analyzed rivers, their average migration rates and estimated changes in migration rate over the interval 1972-2020, and various physical and environmental parameters for each river (channel width, latitude and longitude, above-ground biomass density, greening vs. browning trend, floodplain permafrost content, mean annual temperature, etc.) (2) "Landsat_scene_IDs.xlsx" - Data table listing the scene IDs for the Landsat images used to extract riverbank positions over the period 1972-2020. (3) "Riverbank_position_shapefiles.zip" - Zip folder containing ESRI shapefiles of the left and right riverbank positions digitized from each Landsat image. The shapefiles are named following the convention: YYYY_MM_DD_lx.shp and YYYY_MM_DD_rx.shp, where YYYY is the year, MM is the month, and DD is the day, and lx and rx represent the left and right riverbanks, respectively. [Note: left and right are based on the convention of looking downstream]. (4) "Channel_belt_shapefiles.zip" - Zip folder containing ESRI shapefiles of polygons outlining the channel belts for each investigated river reach. (5) "MatlabCode.zip" - Zip folder containing Matlab code used to perform the analysis in Geyman & Lamb, 2025 (see citation above). See the README.txt document for a detailed description of the datasets and metadata. Final notes: This dataset builds on the existing dataset of Ielpi et al. (https://doi.org/10.5281/zenodo.7556050), which contains digitized left bank and right bank positions (1972-2020) for n = 10 Arctic river reaches in Alaska and Arctic Canada: the Big River, Kuparuk River, Mackenzie River, Kobuk River, Tanana River, Yukon River, Porcupine River, Kuskokwim River, Stewart River, and Slave River. Here, we build on the existing dataset by adding riverbank positions (1972-2020) for an additional n = 12 river reaches. The combined dataset (n = 22 river reaches) is included here. If using this dataset, please also credit the original authors of the Ielpi et al. dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".