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Advancing Permafrost Monitoring With Autonomous Electrical Resistivity Tomography (A‐ERT): Low‐Cost Instrumentation and Open‐Source Data Processing Tool
Publication . Farzamian, M.; Blanchy, G.; McLachlan, P.; Vieira, Gonçalo; Esteves, M.; de Pablo, M. A.; Triantifilis, J.; Lippmann, E.; Hauck, C.
Abstract Permafrost is a widespread phenomenon in the cold regions of the globe and is under-represented
in global monitoring networks. This study presents a novel low-cost, low-power, and robust Autonomous
Electrical Resistivity Tomography (A-ERT) monitoring system and open-source processing tools for
permafrost monitoring. The processing workflow incorporates diagnostic and filtering tools and utilizes
open-source software, ResIPy, for data inversion. The workflow facilitates quick and efficient extraction of
key information from large data sets. Field experiments conducted in Antarctica demonstrated the system's
capability to operate in harsh and remote environments and provided high-temporal-resolution imaging of
ground freezing and thawing dynamics. This data set and processing workflow allow for a detailed investigation
of how meteorological conditions impact subsurface processes. The A-ERT setup can complement existing
monitoring networks on permafrost and is suitable for continuous monitoring in polar and mountainous regions,
contributing to cryosphere research and gaining deeper insights into permafrost and active layer dynamics.
Plain Language Summary Permafrost, frozen ground in cold regions, has significant impacts
on the global environment. Monitoring of permafrost is crucial because it influences the global carbon cycle,
hydrology, contaminant movement, and ecosystem stability. However, current monitoring systems have
limitations, particularly in remote regions like Antarctica. To tackle this challenge, a new monitoring system,
Autonomous Electrical Resistivity Tomography (A-ERT), was introduced. A-ERT is a geophysical technique
that employs electrical signals to study ground freezes and thaws with high precision over time. Alongside this,
open-source processing tools were developed to process obtained A-ERT data and efficiently extract essential
information from large data sets. The developed A-ERT system is robust, low-cost, low-power, and designed to
operate in harsh conditions. Tested in Antarctica, our findings show that A-ERT data combined with processing
pipelines offers a valuable tool for examining freezing and thawing processes in extreme environments. The
proposed setup can contribute to a network of autonomous permafrost monitoring systems, important for
cryosphere research and advancing our understanding of climate change's impact on permafrost dynamics.
Ground surface temperature regimes are controlled by the topography and snow cover in the ice-free areas of Maritime Antarctica
Publication . Baptista, Joana; Vieira, Gonçalo; Lee, Hyoungseok
Ground Surface Temperature (GST) is especially relevant in permafrost regions, such as the ice-free areas of the Antarctic Peninsula, to the understanding of environmental changes, where a long-term warming trend has been detected since 1950. To better understand GST regimes and the topoclimatic controlling factors, 20 iButtons were installed at sites according to elevation, exposure, curvature, and proximity to permanent snow, recording temperatures at 3-hour intervals from March 2019 to February 2020. Multiple Factorial Analysis (MFA) was used to evaluate the influence of these factors on GST parameters and to group the sensors based on their similarities.
An analysis of daily temperatures was conducted to classify types of daily ground temperature regimes for use in a spatial model, developed using Discriminant Analysis (DA). As was predicted elevation was identified as the main controlling factor, with a negative correlation to the Mean Annual Ground Surface Temperature, ranging from 0.6 ◦C at 16 m a.s.l. to − 2 ◦C at 254 m a.s.l., and a positive correlation with Freezing Degree Days, ranging from 438 (at 16 m a.s.l.) to 1042 (at 254 m a.s.l.). Snow cover duration is the second control factor highlighted, determining the duration of the freezing season, which was prolonged where snow cover persisted longer, resulting in a more pronounced insulating effect. The diversity of conditions was reinforced with the identification of seven types of daily GST regimes (three frozen, two unfrozen, and two with freeze–thaw), leading to the categorization of four annual distribution types. These were spatialized for Barton Peninsula using the DA model (90 % accuracy). The spatialization revealed a long frost season in proximity to snow patches, moderate frost season in areas above 160 m a.s.l., a short frost season with slow warming in areas ranging from 90 to 160 m a.s. l., and a short frost season with rapid warming in areas below 90 m a.s.l.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
2022.06628.PTDC