Ice Thickness Estimation from Geophysical Investigations on the Terminal Lobes of Belvedere Glacier (NW Italian Alps)

Colombero, Chiara; Comina, Cesare; Toma, Emanuele De; Franco, Diego; Godio, Alberto

doi:10.3390/rs11070805

Cited by 16 publications

(7 citation statements)

References 44 publications

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“…The ε determines the velocity of a radar wave through snow and has been used to investigate multiple regions of the cryosphere at spatial scales that range from the laboratory to multiple kilometers (e.g., [18][19][20]). Historically, ε has been used extensively in groundpenetrating radar (GPR) studies to estimate ice thickness [21], SWE [22], density [23], and snow liquid water content (LWC); [24]. Values of ε have primarily been validated for specific conditions such as polar firn and ice (e.g., [25]), with limited validation in seasonal snow.…”

Section: Introductionmentioning

confidence: 99%

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

et al. 2021

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Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

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Section: Introductionmentioning

confidence: 99%

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

et al. 2021

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“…S3M expects h G to be a spatially distributed input (included either in the so-called restart data or in the static data; see the Supplement). Spatially explicit datasets of h G could come from either in situ surveys (e.g., see Colombero et al, 2019) or from estimates based on the surface mass balance and ice-flow velocities (e.g., see Rabatel et al, 2018).…”

Section: G2: Melt-only Approach With Mass Balancementioning

confidence: 99%

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

Avanzi

Gabellani²,

Delogu³

et al. 2022

Geosci. Model Dev.

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Abstract. By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol. Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the Δh parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries.

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“…Scattering events in GPR profiles on glaciers can be attributable to three main different glaciological settings: (1) isolated debris particles or boulders within the ice generating single diffractions hyperbolas; (2) crevasses, moulins and other glaciological features producing a set of high dipping aligned scattering events, crossing most of the ice thickness; (3) a diffuse scattering facies (referred hereafter as HSZ) often imaged within GPR profiles and usually interpreted as diagnostic for liquid water (e.g., Delf et al., 2022). Indeed, such HSZ can be, therefore, related to warm ice, the temperature of which not only allows the occurrence of liquid water (e.g., Pettersson et al., 2007; Reinardy et al., 2019) but also to mixtures of ice and debris (e.g., Colombero et al., 2019; King et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

GPR modelling and inversion to quantify the debris content within ice

Santin,

Roncoroni,

Forte

et al. 2023

Near Surface Geophysics

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Scattering is often detected when ground‐penetrating radar (GPR) surveys are performed on glaciers at different latitudes and in various environments. This event is often seen as an undesirable feature on data, but it can be exploited to quantify the debris content in mountain glaciers through a dedicated scattering inversion approach. At first, we considered the possible variables affecting the scattering mechanisms, namely the dielectric properties of the scatterers, their size, shape and quantity, as well as the wavelength of the electromagnetic (EM) incident field to define the initial conditions for the inversion. Each parameter was independently evaluated with forward modelling tests to quantify its effect in the scattering mechanism. After extensive tests, we found that the dimension and the amount of scatterers are the crucial parameters. We further performed modelling randomizing the scatterer distribution and dimension, critically evaluating the stability of the approach and the complexity of the models. After the tests on synthetic data, the inversion procedure was applied to field datasets, acquired on the Eastern Gran Zebrù glacier (Central Italian Alps). The results show that even a low percentage of debris can produce high scattering. The proposed methodology is quite robust and able to provide quantitative estimates of the debris content within mountain glaciers in different conditions.

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Ice Thickness Estimation from Geophysical Investigations on the Terminal Lobes of Belvedere Glacier (NW Italian Alps)

Cited by 16 publications

References 44 publications

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

GPR modelling and inversion to quantify the debris content within ice

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