Are Arctic rivers speeding up or slowing down? Riverbank positions and migration rates of rivers in Alaska and Arctic Canada (1972-2020)

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.摘要： 北极河道迁移速率对当前困于冻结及正在消融的多年冻土（permafrost）中的1700拍克（Pg）碳的活化时间尺度具有一级控制作用。然而，关于北极河道是否因区域变暖而加速或减速迁移，学界尚未达成共识。本文重建了1972—2020年间，覆盖1500千米长度与多种环境类型的北极及亚北极河流的迁移速率。研究发现，不连续多年冻土区的河流在过去50年间呈现系统性加速迁移的趋势，而连续多年冻土区的河流则表现出系统性减速特征。我们识别出两种相互竞争的机制主导了这种分叉行为：河冰解冻（river-ice breakup）的侵蚀强度下降导致河道迁移减速，而多年冻土河岸的消融则推动了迁移加速。其他已提出的机制——包括北极绿化、径流量（water discharge）变化、水温变化及河流泥沙负荷（sediment loads）变化——均不太可能驱动观测到的上述趋势。 手稿引用： 盖曼（Geyman, E.C.）与拉姆（Lamb, M.P.）. 变暖北极中的河道迁移速率将如何？近五十年的研究启示. 已修回. 2025. 本数据集包含5个部分： (1) 「Arctic_river_timeseries_database.xlsx」：汇总数据表，列有n=20条被分析河流的相关信息，包括各河流在1972—2020年间的平均迁移速率、迁移速率估算变化量，以及各项物理与环境参数（河道宽度、经纬度、地上生物量密度、绿化与褐变趋势、泛滥平原（floodplain）多年冻土占比、年平均气温等）。 (2) 「Landsat_scene_IDs.xlsx」：数据表，列有1972—2020年间用于提取河岸位置的陆地卫星（Landsat）影像场景ID。 (3) 「Riverbank_position_shapefiles.zip」：压缩文件夹，内含从各陆地卫星影像中数字化得到的左右河岸位置的ESRI形状文件（ESRI shapefiles）。该形状文件的命名遵循如下规范：YYYY_MM_DD_lx.shp与YYYY_MM_DD_rx.shp，其中YYYY为年份、MM为月份、DD为日期，lx与rx分别代表左岸与右岸。[注：左右岸的定义基于顺流观测的惯例]。 (4) 「Channel_belt_shapefiles.zip」：压缩文件夹，内含各研究河段河道带轮廓多边形的ESRI形状文件。 (5) 「MatlabCode.zip」：压缩文件夹，内含用于执行Geyman与Lamb 2025年研究中分析流程的Matlab代码（详见上文引用）。 详见README.txt文档以获取数据集与元数据的详细说明。 最终说明： 本数据集基于Ielpi等人（https://doi.org/10.5281/zenodo.7556050）的现有数据集构建，该数据集包含阿拉斯加与北极加拿大地区n=10条北极河段的河岸数字化位置（1972—2020年），分别为大河（Big River）、库帕鲁克河（Kuparuk River）、麦肯齐河（Mackenzie River）、科布克河（Kobuk River）、塔纳纳河（Tanana River）、育空河（Yukon River）、波丘派恩河（Porcupine River）、卡斯科奎姆河（Kuskokwim River）、斯图尔特河（Stewart River）以及斯莱夫河（Slave River）。本研究在现有数据集的基础上，新增了额外12条河段的1972—2020年河岸位置数据。本数据集整合后的总样本量为n=22条河段。若使用本数据集，请同时引用Ielpi等人原始数据集的作者。