Coincident aboveground and belowground autonomous monitoring to quantify covariability in permafrost, soil, and vegetation properties in Arctic tundra

Abstract

Coincident monitoring of the spatiotemporal distribution of and interactions between land, soil, and permafrost properties is important for advancing our understanding of ecosystem dynamics. In this study, a novel monitoring strategy was developed to quantify complex Arctic ecosystem responses to the seasonal freeze‐thaw‐growing season conditions. The strategy exploited autonomous measurements obtained through electrical resistivity tomography to monitor soil properties, pole‐mounted optical cameras to monitor vegetation dynamics, point probes to measure soil temperature, and periodic manual measurements of thaw layer thickness, snow thickness, and soil dielectric permittivity. The spatially and temporally dense monitoring data sets revealed several insights about tundra system behavior at a site located near Barrow, AK. In the active layer, the soil electrical conductivity (a proxy for soil water content) indicated an increasing positive correlation with the green chromatic coordinate (a proxy for vegetation vigor) over the growing season, with the strongest correlation (R = 0.89) near the typical peak of the growing season. Soil conductivity and green chromatic coordinate also showed significant positive correlations with thaw depth, which is influenced by soil and surface properties. In the permafrost, soil electrical conductivity revealed annual variations in solute concentration and unfrozen water content, even at temperatures well below 0°C in saline permafrost. These conditions may contribute to an acceleration of long‐term thaw in Coastal permafrost regions. Demonstration of this first aboveground and belowground geophysical monitoring approach within an Arctic ecosystem illustrates its significant potential to remotely “visualize” permafrost, soil, and vegetation ecosystem codynamics in high resolution over field relevant scales.

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

“…13) show that soil water thaws around the middle of June and freezes again around the middle of September. The thaw depth varies from 0.2 to 0.42 m. These results are compatible with our field survey data in Barrow (Dafflon et al, 2017), indicating that, although this is a synthetic study, its simulation is relatively compatible with the Arctic tundra field measurements. As for the influence of parameter uncertainties on the thaw depth estimation, we observed that the parameter uncertainties only cause thaw depth variations during the warmest period of the year (beginning of August to middle of September).…”

“…The bottom of the thaw layer at the end of the growing season is located at about 0.3 and 0.5 m depth at the center of HCP and LCP, respectively. ERT measurements were performed along the transect daily using the Wenner-Schlumberger configuration with an electrode spacing of 0.5 m. Other measurements and conditions useful for our synthetic studies -including soil temperature, soil moisture, thaw depth, snow dynamics and climate conditionswere also measured (Dafflon et al, 2017). These data have been used here to develop conceptual models and synthetic columns, while they will be used for real application of the joint inversion scheme in a subsequent study.…”

“…For inversion, ranges were provided for unknown soil and petrophysical parameters based on Hubbard et al (2013) and Dafflon et al (2017) (Table 2). To minimize non-uniqueness in the inversion procedure, we ignored the small OC content at 1 m and the small sand content at 0.1 m. For scenarios 1-7, we estimated OC content at z = 0.1, 0.3 and 0.6 m (three parameters).…”

“…Building on recent advances in the use of electrical methods in the permafrost (e.g., Minsley et al, 2015;Dafflon et al, 2017) as well as coupled hydrogeophysical inversion approaches described above, this study focuses on the development of an inverse approach that uses single or multiple datasets (soil liquid water content, soil temperature and electrical resistivity) to estimate OC content, which is a main factor that governs the subsurface hydrological-thermal dynamics. Our approach advances and couples several algorithms.…”

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High‐Resolution Remote Sensing and Surface Geophysical Data

“…Microtopography is the topographic variation on the order of submeters to several meters, which can be characterized mainly through airborne light detection and ranging (LiDAR) data. Recent studies conducted in Arctic regions—using high‐resolution geophysical and remote sensing data—have revealed that microtopography could produce significant spatial heterogeneity in soil moisture and plant community distributions at several‐meter or submeter scales (S. S. Hubbard et al, ; Dafflon et al, ; H. M. Wainwright et al, ). While the slope aspect of the hillslopes can be considered as a first‐order large‐scale control (Pelletier et al, ; Yetemen et al, ), the controlling factors within each hillslope have not yet to the authors' knowledge been investigated.…”