Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry

Yildiz, Semih; Akyürek, Zuhal; Binley, Andrew

doi:10.1002/hyp.14190

Cited by 8 publications

(12 citation statements)

References 46 publications

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“…A Mavic 2 Pro UAV (DJI) was used to capture the RGB images used for photogrammetry. The Mavic 2 Pro was upgraded (2016a) and used by Yildiz et al (2021), the GIS data was used to subtract a snow-free DSM produced on April 6, just after the complete thaw of the snow cover, from the DSM made using images taken over the winter. The final error of the drone photogrammetry is estimated at ±5 cm.…”

Section: Drone-based Photogrammetrymentioning

confidence: 99%

“…In dry conditions, LWC can be neglected, leaving a direct relation between ρ and the relative permittivity. In wet conditions, either assumptions are made on ρ variability from spot measurements (e.g., Webb et al (2020); Yildiz et al (2021)) or ρ maps must be produced using a separate method. In that regard, the LWC estimation from frequency-dependent attenuation of the GPR signal proposed by (Bradford et al, 2009) shows great https://doi.org/10.5194/tc-2022-42 Preprint.…”

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confidence: 99%

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Introducing drone-based GPR in snow hydrology studies

Valence

Baraër

Rosa

et al. 2022

Preprint

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Abstract. Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, for instance, the structure and properties of the different ice and snow layers dictate the quantity of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences as mild episodes and ROS events’ frequency increases. This study aims to develop a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repetitive and non-destructive way. Snowpack evolution was evaluated using a combination of drone-based GPR, photogrammetry surveys and time domain reflectometry (TDR) measurements, tested during the winter of 2020–2021 at the Sainte-Marthe experimental watershed, Quebec, Canada. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements, serve as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis are used to generate georeferenced snow depth, relative density, snow water equivalent and average liquid water content maps. In between site visits, snowpack properties are monitored using TDR probes. Despite some limitations, the results show that the approach is very promising in assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. Among others, results showed the prevalence of preferential pathways at the early stage of the ablation period, the difference in hydrological reaction to a ROS event between flat and sloped sections of the study area and the hydrological influence of solar radiation at the late stage of the ablation period.

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Section: Drone-based Photogrammetrymentioning

confidence: 99%

mentioning

confidence: 99%

Introducing drone-based GPR in snow hydrology studies

Valence

Baraër

Rosa

et al. 2022

Preprint

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“…Finally, snow depth maps were produced by subtracting a snow-free DSM produced on 6 April, just after the complete thaw of the snow cover and just before the vegetation growth, from the DSM produced in winter conditions. This was done using the Esri Geographic Information System (GIS) software ArcGIS, following the protocol presented by Bühler et al (2016a) and Yildiz et al (2021).…”

Section: Drone-based Photogrammetrymentioning

confidence: 99%

“…On the other hand, the introduction of liquid water into the snowpack cannot be accurately characterized with GPR velocity alone (Bradford and Harper, 2006). In the absence of other measurements allowing for mapping of LWC or snow density in wet conditions, either an assumption needs to be made regarding snow density variability from spot measurements (e.g., Webb et al, 2020;Yildiz et al, 2021) or an empirical relation must be parametrized by calibration (e.g. Singh et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The use of drone-based surface mapping methods such as photogrammetry or lidar in snowpack studies provides reliable snow depth maps (Bühler et al, 2016b). Lundberg et al (2016) and Yildiz et al (2021) used drone-based photogrammetry or lidar to integrate snow depth measurements into SWE calcula-tions. Combining techniques that monitor the temporal evolution of the snow permittivity, such as TDR, with GPR has been shown to be a promising approach to studying snowpack spatial variability over a given period (Godio et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Drone-based ground-penetrating radar (GPR) application to snow hydrology

Valence¹,

Baraër²,

Rosa³

et al. 2022

The Cryosphere

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Abstract. Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, the structure and properties of the different snow and ice layers dictate the quantity and timing of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences of winter melting episodes and increases in the frequency of ROS events. This study develops a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repeated and non-destructive way. Snowpack evolution during the winter of 2020–2021 was evaluated using a drone-based, ground-penetrating radar (GPR) coupled with photogrammetry surveys conducted at the Ste-Marthe experimental watershed in Quebec, Canada. Drone-based surveys were performed over a 200 m2 area with a flat and a sloped section. In addition, time domain reflectometry (TDR) measurements were used to follow water flow through the snowpack and identify drivers of the changes in snowpack conditions, as observed in the drone-based surveys. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements over the ablation period, served as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis were used to generate georeferenced snow depth, density, snow water equivalent and bulk liquid water content maps. Despite some limitations, the results show that the combination of drone-based GPR, photogrammetric surveys and TDR is very promising for assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. For instance, the tested method allowed for measuring marked differences in snow pack behaviour between the first and second weeks of the ablation period. A ROS event that occurred during the first week did not generate significant changes in snow pack density, liquid water content and water equivalent, while another one that happened in the second week of ablation generated changes in all three variables. After the second week of ablation, differences in density, liquid water content (LWC) and snow water equivalent (SWE) between the flat and the sloped sections of the study area were detected by the drone-based GPR measurements. Comparison between different events was made possible by the contact-free nature of the drone-based measurements.

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Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

Bonnell,

McGrath,

Hedrick

et al. 2023

Hydrological Processes

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Depth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

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Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry

Cited by 8 publications

References 46 publications

Introducing drone-based GPR in snow hydrology studies

Introducing drone-based GPR in snow hydrology studies

Drone-based ground-penetrating radar (GPR) application to snow hydrology

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

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