Estimating active layer thickness and volumetric water content from ground penetrating radar measurements in Barrow, Alaska

Active-layer thickness (ALT) is an important parameter for studying surface energy balance, ecosystems, and hydrologic processes in cold regions. We measured ALT along 10 routes with lengths ranging from 0.7 to 6.9 km located on the Alaska North Slope near Toolik Lake and the Happy Valley airstrip (between 68.475° and 69.150°N, and [Formula: see text] and [Formula: see text]). Using a ground-penetrating radar (GPR) system in a common-offset configuration, we measured the two-way traveltimes from the surface to the bottom of the active layer at the end of summer, when the thaw depth was greatest. We used 500 and 800 MHz antennas; the 500 MHz antenna provided suitable vertical resolution, while producing more unambiguous active-layer reflections in the presence of nonideal antenna coupling and active layer inhomogeneity. We derived ALT measurements and their uncertainties from GPR two-way traveltimes, with mechanical probing for velocity calibration. Using an empirical relationship between the wave velocity and soil volumetric water content (VWC), we found that the velocities were consistent with soil VWCs ranging from 0.46 to 0.63. In 31% of traces, the permafrost table horizon was identifiable, resulting in ALT measurements with uncertainties of generally less than 25%. The average ALT was 48.1 cm, with a standard deviation of 16.1 cm. We found distinct patterns of ALT spatial variability at different sites and different length scales. At some sites, the ALT at one point was effectively uncorrelated with ALT at other points separated by lag distances as small as tens of meters; for other sites, there was correlation at lag distances up to approximately 400 m. The ALT statistics were similar to nearby long-term in situ ALT measurements from the Circumpolar Active Layer Monitoring Network, through which yearly ALT measurements have been made since 1990.

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Using ground-penetrating radar, topography and classification of vegetation to model the sediment and active layer thickness in a periglacial lake catchment, western Greenland

Earth System Science Data ◽

10.5194/essd-8-663-2016 ◽

2016 ◽

Vol 8 (2) ◽

pp. 663-677 ◽

Cited By ~ 4

Author(s):

Johannes Petrone ◽

Gustav Sohlenius ◽

Emma Johansson ◽

Tobias Lindborg ◽

Jens-Ove Näslund ◽

...

Keyword(s):

Numerical Modeling ◽

Layer Thickness ◽

Active Layer ◽

Ground Penetrating Radar ◽

Vegetation Classification ◽

Active Layer Thickness ◽

Sediment Thickness ◽

Catchment Scale ◽

Scale Models ◽

Ground Penetrating

Abstract. The geometries of a catchment constitute the basis for distributed physically based numerical modeling of different geoscientific disciplines. In this paper results from ground-penetrating radar (GPR) measurements, in terms of a 3-D model of total sediment thickness and active layer thickness in a periglacial catchment in western Greenland, are presented. Using the topography, the thickness and distribution of sediments are calculated. Vegetation classification and GPR measurements are used to scale active layer thickness from local measurements to catchment-scale models. Annual maximum active layer thickness varies from 0.3 m in wetlands to 2.0 m in barren areas and areas of exposed bedrock. Maximum sediment thickness is estimated to be 12.3 m in the major valleys of the catchment. A method to correlate surface vegetation with active layer thickness is also presented. By using relatively simple methods, such as probing and vegetation classification, it is possible to upscale local point measurements to catchment-scale models, in areas where the upper subsurface is relatively homogeneous. The resulting spatial model of active layer thickness can be used in combination with the sediment model as a geometrical input to further studies of subsurface mass transport and hydrological flow paths in the periglacial catchment through numerical modeling. The data set is available for all users via the PANGAEA database, doi:10.1594/PANGAEA.845258.

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Estimating 3D variation in active-layer thickness beneath arctic streams using ground-penetrating radar

Journal of Hydrology ◽

10.1016/j.jhydrol.2009.05.011 ◽

2009 ◽

Vol 373 (3-4) ◽

pp. 479-486 ◽

Cited By ~ 34

Author(s):

Troy R. Brosten ◽

John H. Bradford ◽

James P. McNamara ◽

Michael N. Gooseff ◽

Jay P. Zarnetske ◽

...

Keyword(s):

Layer Thickness ◽

Active Layer ◽

Ground Penetrating Radar ◽

Active Layer Thickness ◽

Arctic Streams ◽

Ground Penetrating

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Using ground-penetrating radar, topography and classification of vegetation to model the sediment and active layer thickness in a periglacial lake catchment, Western Greenland

10.5194/essd-2016-19 ◽

2016 ◽

Cited By ~ 1

Author(s):

Johannes Petrone ◽

Gustav Sohlenius ◽

Emma Johansson ◽

Tobias Lindborg ◽

Jens-Ove Näslund ◽

...

Keyword(s):

Layer Thickness ◽

Active Layer ◽

Ground Penetrating Radar ◽

Vegetation Classification ◽

Active Layer Thickness ◽

Sediment Thickness ◽

Catchment Scale ◽

Local Point ◽

Scale Models ◽

Ground Penetrating

Abstract. The geometries of a catchment constitute the basis for distributed physically based numerical modeling of different geoscientific disciplines. In this paper results from ground-penetrating radar (GPR) measurements, in terms of a 3D model of total sediment thickness and active layer thickness in a periglacial catchment in western Greenland, are presented. Using the topography, thickness and distribution of sediments are calculated. Vegetation classification and GPR measurements are used to scale active layer thickness from local measurements to catchment scale models. Annual maximum active layer thickness varies from 0.3 m in wetlands to 2.0 m in barren areas and areas of exposed bedrock. Maximum sediment thickness is estimated to be 12.3 m in the major valleys of the catchment. A method to correlate surface vegetation with active layer thickness is also presented. By using relatively simple methods, such as probing and vegetation classification, it is possible to upscale local point measurements to catchment scale models, in areas where the upper subsurface is relatively homogenous. The resulting spatial model of active layer thickness can be used in combination with the sediment model as a geometrical input to further studies of subsurface mass-transport and hydrological flow paths in the periglacial catchment through numerical modelling. The data set is available for all users via the PANGAEA database, https://doi.pangaea.de/10.1594/PANGAEA.845258.

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Soil water content estimation using ground penetrating radar data via group intelligence optimization algorithms: An application in the Northern Shaanxi Coal Mining Area

Energy Exploration & Exploitation ◽

10.1177/0144598720973369 ◽

2020 ◽

pp. 014459872097336

Author(s):

Fan Cui ◽

Jianyu Ni ◽

Yunfei Du ◽

Yuxuan Zhao ◽

Yingqing Zhou

Keyword(s):

Dielectric Constant ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Error Rate ◽

Ground Penetrating Radar ◽

Volumetric Water Content ◽

Average Error ◽

Average Error Rate ◽

Ground Penetrating

The determination of quantitative relationship between soil dielectric constant and water content is an important basis for measuring soil water content based on ground penetrating radar (GPR) technology. The calculation of soil volumetric water content using GPR technology is usually based on the classic Topp formula. However, there are large errors between measured values and calculated values when using the formula, and it cannot be flexibly applied to different media. To solve these problems, first, a combination of GPR and shallow drilling is used to calibrate the wave velocity to obtain an accurate dielectric constant. Then, combined with experimental moisture content, the intelligent group algorithm is applied to accurately build mathematical models of the relative dielectric constant and volumetric water content, and the Topp formula is revised for sand and clay media. Compared with the classic Topp formula, the average error rate of sand is decreased by nearly 15.8%, the average error rate of clay is decreased by 31.75%. The calculation accuracy of the formula has been greatly improved. It proves that the revised model is accurate, and at the same time, it proves the rationality of the method of using GPR wave velocity calibration method to accurately calculate the volumetric water content.

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Active Layer Stratigraphy and Organic Layer Thickness at a Thermokarst Site in Arctic Alaska Identified Using Ground Penetrating Radar

Arctic Antarctic and Alpine Research ◽

10.1657/aaar00c-13-301 ◽

2015 ◽

Vol 47 (2) ◽

pp. 195-202 ◽

Cited By ~ 13

Author(s):

Alessio Gusmeroli ◽

Lin Liu ◽

Kevin Schaefer ◽

Tingjun Zhang ◽

Timothy Schaefer ◽

...

Keyword(s):

Layer Thickness ◽

Active Layer ◽

Ground Penetrating Radar ◽

Organic Layer ◽

Arctic Alaska ◽

Ground Penetrating

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Multi-offset ground-penetrating radar imaging of a lab-scale infiltration test

Hydrology and Earth System Sciences ◽

10.5194/hess-16-4009-2012 ◽

2012 ◽

Vol 16 (11) ◽

pp. 4009-4022 ◽

Cited By ~ 26

Author(s):

A. R. Mangel ◽

S. M. J. Moysey ◽

J. C. Ryan ◽

J. A. Tarbutton

Keyword(s):

Water Content ◽

Ground Penetrating Radar ◽

Time Lapse ◽

Volumetric Water Content ◽

Data Cube ◽

Sand Layer ◽

Wetting Front ◽

One Dimensional ◽

Ground Penetrating ◽

Infiltration Experiment

Abstract. A lab scale infiltration experiment was conducted in a sand tank to evaluate the use of time-lapse multi-offset ground-penetrating radar (GPR) data for monitoring dynamic hydrologic events in the vadose zone. Sets of 21 GPR traces at offsets between 0.44–0.9 m were recorded every 30 s during a 3 h infiltration experiment to produce a data cube that can be viewed as multi-offset gathers at unique times or common offset images, tracking changes in arrivals through time. Specifically, we investigated whether this data can be used to estimate changes in average soil water content during wetting and drying and to track the migration of the wetting front during an infiltration event. For the first problem we found that normal-moveout (NMO) analysis of the GPR reflection from the bottom of the sand layer provided water content estimates ranging between 0.10–0.30 volumetric water content, which underestimated the value determined by depth averaging a vertical array of six moisture probes by 0.03–0.05 volumetric water content. Relative errors in the estimated depth to the bottom of the 0.6 m thick sand layer were typically on the order of 2%, though increased as high as 25% as the wetting front approached the bottom of the tank. NMO analysis of the wetting front reflection during the infiltration event generally underestimated the depth of the front with discrepancies between GPR and moisture probe estimates approaching 0.15 m. The analysis also resulted in underestimates of water content in the wetted zone on the order of 0.06 volumetric water content and a wetting front velocity equal to about half the rate inferred from the probe measurements. In a parallel modeling effort we found that HYDRUS-1D also underestimates the observed average tank water content determined from the probes by approximately 0.01–0.03 volumetric water content, despite the fact that the model was calibrated to the probe data. This error suggests that the assumed conceptual model of laterally uniform, one-dimensional vertical flow in a homogenous material may not be fully appropriate for the experiment. Full-waveform modeling and subsequent NMO analysis of the simulated GPR response resulted in water content errors on the order of 0.01–0.03 volumetric water content, which are roughly 30–50% of the discrepancy between GPR and probe results observed in the experiment. The model shows that interference between wave arrivals affects data interpretation and the estimation of traveltimes. This is an important source of error in the NMO analysis, but it does not fully account for the discrepancies between GPR and the moisture probes observed in the experiment. The remaining discrepancy may be related to conceptual errors underlying the GPR analysis, such as the assumption of uniform one-dimensional flow, a lack of a sharply defined wetting front in the experiment, and errors in the petrophysical model used to convert dielectric constant to water content.

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