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Abstract: Whether permafrost systematically alters the rate of riverbank erosion is a fundamental geomorphic question with significant importance to infrastructure, water quality, and biogeochemistry of high‐latitude watersheds. For over four decades, this question has remained unanswered due to a lack of data. Using remotely sensed imagery, we addressed this knowledge gap by quantifying riverbank erosion rates across the Arctic and subarctic. To compare these rates to non‐permafrost rivers, we assembled a global data set of published riverbank erosion rates. We found that erosion rates in rivers influenced by permafrost are on average nine times lower than non‐permafrost systems; erosion rate differences increase up to 40 times for the largest rivers. To test alternative hypotheses for the observed erosion rate difference, we examined differences in total water yield and erosional efficiency between these rivers and non‐permafrost rivers. Neither of these factors nor differences in river sediment loads provided compelling alternative explanations, leading us to conclude that permafrost limits riverbank erosion rates. This conclusion was supported by field investigations of rates and patterns of erosion along three rivers flowing through discontinuous permafrost in Alaska. Our results show that permafrost limits maximum bank erosion rates on rivers with stream powers greater than 900 Wm−1. On smaller rivers, however, hydrology rather than thaw rate may be the dominant control on bank erosion. Our findings suggest that Arctic warming and hydrological changes should increase bank erosion rates on large rivers but may reduce rates on rivers with drainage areas less than a few thousand km2.

The Braiding Index (BI), defined as the average count of intercepted channels per cross-section, is a widely used metric for characterizing multi-thread river systems. However, it does not account for the diversity of channels (e.g., in terms of water discharge) within different cross-sections, omitting important information related to system complexity. Here we present a modification of BI, the Entropic Braiding Index (eBI), which augments the information content in BI by using Shannon Entropy to encode the diversity of channels in each cross section. eBI is interpreted as the number of “effective channels” per cross-section, allowing a direct comparison with the traditional BI. We demonstrate the potential of the ratio BI/eBI to quantify channel disparity, differentiate types of multi-thread systems (braided vs. anastomosed), and assess the effect of discharge variability, such as seasonal flooding, on river cross-section stability.

Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3-60-day periods) in both GPH and SST and El Niño-Southern Oscillation (ENSO) at low frequencies (2-7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics.

The abundant lakes dotting arctic deltas are hotspots of methane emissions and biogeochemical activity, but seasonal variability in lake extents introduces uncertainty in estimates of lacustrine carbon emissions, typically performed at annual or longer time scales. To characterize variability in lake extents, we analyzed summertime lake area loss (i.e., shrinkage) on two deltas over the past 20 years, using Landsat-derived water masks. We find that monthly shrinkage rates have a pronounced structured variability around the channel network with the shrinkage rate systematically decreasing farther away from the channels. This pattern of shrinkage is predominantly attributed to a deeper active layer enhancing near-surface connectivity and storage and greater vegetation density closer to the channels leading to increased evapotranspiration rates. This shrinkage signal, easily extracted from remote sensing observations, may offer the means to constrain estimates of lacustrine methane emissions and to develop process-based estimates of depth to permafrost on arctic deltas.

Arctic deltas (ADs) are ecogeomorphologically complex systems that are at risk due to climate and human forcings, e.g. warming temperatures and permafrost thaw. This will have impacts on natural and human systems since ADs have stores of permafrost carbon on the order of 90 Pg carbon, which can be mobilized into the atmosphere and ocean. Arctic lakes are estimated to be the source of two-thirds of the natural methane emissions in the Arctic and increasing methane emissions are predicted due to permafrost thaw. The role of water, sediment, and nutrient transfer between the delta channel network (DCN) and lakes in modulating greenhouse gas emissions is not well understood and further study is needed. Specifically, the identification and quantification of the spatially explicit distribution of lake-channel connectivity via surface and subsurface pathways is crucial for understanding residence and transport times of water and nutrients across the delta top. Here, we investigate the role of the DCN in controlling lake shrinkage rates on the delta top. A spatially explicit study of remote sensing data over two ADs reveals not only the importance of structural DCN-lake connectivity on lake shrinkage rates, but the control of the DCN on subsurface connectivity between channels and lakes. The importance of resolution in capturing processes on the delta top is highlighted. Remote sensing data available at delta-wide scales over significant temporal lengths for analysis of patterns and their change is at spatial resolutions that may not fully capture structural connectivity let alone potential subsurface connectivity between the channel network and lakes. An important problem then becomes that of understanding, via multiresolution analysis and modelling, how coarse scale structure and patterns might relate to finer scale patterns such that useful inferences on process and form can be made from widely available remote sensing data over large regions. [ABSTRACT FROM AUTHOR]