The Arctic Greening Database v0.1 is an open access database created as part of the ETH+ project "Unraveling biogeochemical, microbial and vegetation feedbacks driving soil development and Arctic greening under a warming climate". The database contains data on soil, vegetation, microbial, and environmental properties from 14 active-layer tundra sites sampled in 2022 and 2023 on Svalbard. The spatially-explicit field observations, field and laboratory measurements provides an interdisciplinary collection of data from a remote and data-poor region to study linkages between vegetation, microbiome and pedogenesis in the context of Arctic Greening.
The database is structured hierarchically with four connected levels: site, plot, sample, and species. At the site level, aggregated data are provided (e.g. GHG fluxes). This is followed by plot-level data (e.g. plant functional type cover) that connects to sample-level data (soil organic matter content) and species-level data. Tables at the same level are connected via one-to-one relationships, from a broader to finer level one-to-many relationships are in place. Sampling and measurement procedures are described in Section 2 of the database description. The metadata file accompanying a specific .csv file provides further information on data creation, sample processing and units. The current version of the dataset consists of a reduced set of tables that will be updated soon with more curated data from Svalbard and Northern Norway (Finnmark). A more extensive overview of the data will be published as a data paper in the future.
This is an Arctic-delta reduced-complexity model that can reproduce the 2-m ramp feature observed in most Arctic deltas. The model is built by first reconstructing from published descriptions of the DeltaRCM-Arctic model (Lauzon et al., GRL, 2019), which is, in turn, based on DeltaRCM by Liang et al. (Esurf, 2015). All the modifications and refinements leading to this model (ArcDelRCM.jl) are detailed in a manuscript submitted to Earth Surface Dynamics journal for publication (Chan et al., 2022: esurf-2022-25). Options are retained to run this model with the "DeltaRCM-Arctic" (reconstruction) setting. The code is written purely in Julia language.
Monitoring Velocity Changes using Ambient Seismic Noise
SeisMIC (Seismological Monitoring using Interferometric Concepts) is a python software that emerged from the miic library. SeisMIC provides functionality to apply some concepts of seismic interferometry to different data of elastic waves. Its main use case is the monitoring of temporal changes in a mediums Green's Function (i.e., monitoring of temporal velocity changes).
SeisMIC will handle the whole workflow to create velocity-change time-series including: Downloading raw data, Adaptable preprocessing of the waveform data, Computating cross- and/or autocorrelation, Plotting tools for correlations, Database management of ambient seismic noise correlations, Adaptable postprocessing of correlations, Computation of velocity change (dv/v) time series, postprocessing of dv/v time series, plotting of dv/v time-series