Quantifying and relating land-surface and subsurface variability in permafrost environments using LiDAR and surface geophysical datasets

Hubbard, Susan S.; Gangodagamage, C.; Dafflon, Baptiste; Wainwright, Haruko M.; Peterson, John; Gusmeroli, A.; Ulrich, Craig; Wu, Yuxin; Wilson, Cathy J.; Rowland, J. C.; Tweedie, C. E.; Wullschleger, Stan D.

doi:10.1007/s10040-012-0939-y

Cited by 149 publications

(210 citation statements)

References 72 publications

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“…Hauck et al (2011) developed a four-phase model of soil matrix, ice, liquid and air and used it to estimate soil liquid and ice content from combined ERT and seismic measurements in the Swiss Alps. Hubbard et al (2013) combined lidar data with multiple geophysical (ERT, GPR, electromagnetic) and point measurements to characterize active-layer thickness and permafrost variability in a large area.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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Abstract. Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-thermal processes associated with annual freeze-thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets -including soil liquid water content, temperature and electrical resistivity tomography (ERT) data -to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological-thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice-liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological-thermal-togeophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological-thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface-subsurface, deterministic-stochastic inversion approach, as well as the benefit of including multiple types of data to estima...

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Section: Introductionmentioning

confidence: 99%

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

2017

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“…Bhardwaj et al (2016) identifies Light detection and ranging (LiDAR), also referred to as laser scanning (Höfle and Rutzinger, 2011), as potentially being the best tool for increasing the performance of measuring permafrost-related processes, such as mass movements, vegetation dynamics and topographical characteristics (Bhardwaj et al, 2016). By combining point measurements with airborne laser scanning (ALS), and geophysical datasets, the study of Hubbard et al (2013) indicates a close linkage between micro-topography, active layer, and permafrost variability. This linkage of subsurface and land surface 15 variabilities emphasizes the potential of laser scanning to indirectly characterize subsurface properties in permafrost environments in a non-invasive manner (Hubbard et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…By combining point measurements with airborne laser scanning (ALS), and geophysical datasets, the study of Hubbard et al (2013) indicates a close linkage between micro-topography, active layer, and permafrost variability. This linkage of subsurface and land surface 15 variabilities emphasizes the potential of laser scanning to indirectly characterize subsurface properties in permafrost environments in a non-invasive manner (Hubbard et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Terrestrial laser scanning for quantifying small-scale vertical movements of the ground surface in Artic permafrost regions

Marx

Anders

Antonova

et al. 2017

Preprint

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Abstract. Three-dimensional data acquired by terrestrial laser scanning (TLS) provides an accurate representation of Earth's surface, which is commonly used to detect and quantify topographic changes on a small scale. However, in Arctic permafrost regions the tundra vegetation and the micro-topography have significant effects on the surface representation in the captured dataset. The resulting spatial sampling of the ground is never identical between two TLS surveys. Thus, monitoring of heave and subsidence in the context of permafrost processes are challenging. This study evaluates TLS for quantifying small-scale 15 vertical movements in an area located within the continuous permafrost zone, 50 km north-east of Inuvik, Northwest Territories, Canada. We propose a novel filter strategy, which accounts for spatial sampling effects and identifies TLS points suitable for multi-temporal deformation analyses. Further important prerequisites must be met, such as accurate co-registration of the TLS datasets. We found that if the ground surface is captured by more than one TLS scan position, plausible subsidence rates (up to mm-scale) can be derived; compared to e.g. standard raster-based DEM difference maps which contain change 20 rates strongly affected by sampling effects.

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“…The resultant cross-sections reveal ground compartments, which differ in terms of geoelectric properties. So far, electrical imaging providing this degree of detail of patterned ground structure has been presented very rarely, usually in relation to the permafrost in general and land surface dominated by polygonal features (e.g., Hubbard et al 2013). The method employed in the study is suitable for waterlogged ground, built of coarse material, where trenching would be tough and ineffective.…”

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confidence: 99%

High-resolution electrical resistivity tomography applied to patterned ground, Wedel Jarlsberg Land, south-west Spitsbergen

Kasprzak

2015

Polar Research

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Quantifying and relating land-surface and subsurface variability in permafrost environments using LiDAR and surface geophysical datasets

Cited by 149 publications

References 72 publications

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

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

Terrestrial laser scanning for quantifying small-scale vertical movements of the ground surface in Artic permafrost regions

High-resolution electrical resistivity tomography applied to patterned ground, Wedel Jarlsberg Land, south-west Spitsbergen

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