Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Rutter, Nick; Sandells, Melody; Derksen, Chris; King, Joshua; Toose, Peter; Wake, Leanne; Watts, Tom; Essery, Richard; Roy, Alexandre; Royer, A.; Marsh, Philip; Larsen, C. F.; Sturm, Matthew

doi:10.5194/tc-13-3045-2019

Cited by 37 publications

(47 citation statements)

References 45 publications

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“…Seven Areas of Interest (AOIs) within TVC were delineated in order to sample a representative range of topography and vegetation in collaboration with airborne and terrestrial radar campaigns led by the UK Meteorological Office and Environment and Climate Change Canada during the 2018 field campaign at Trail Valley Creek (Rutter et al, 2019). For each AOI, high-resolution imagery was collected using a fixed-wing UAS.…”

Section: Methodsmentioning

confidence: 99%

Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

2021

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Arctic tundra environments are characterized by a spatially heterogeneous end-of-winter snow depth resulting from wind transport and deposition. Traditional methods for measuring snow depth do not accurately capture such heterogeneity at catchment scales. In this study we address the use of high-resolution, spatially distributed, snow depth data for Arctic environments through the application of Unmanned Aerial Systems (UAS). We apply Structure-from-Motion photogrammetry to images collected using a fixed-wing UAS to produce a 1-metre resolution snow depth product across seven areas-of-interest (AOIs) within the Trail Valley Creek Research Watershed, Northwest Territories. We evaluated these snow depth products with in situ measurements of both the snow surface elevation (n=8434) and snow depth (n=7191). When all AOIs were averaged, the RMSE of the snow surface elevation models was 0.16 m (<0.01 m bias), similar to the snow depth product (UASsd) RMSE of 0.15 m (+0.04 m bias). The distribution of snow depth between in situ measurements and UASsd was similar along the transects where in situ snow depth was collected, although similarity varies by AOI. Finally, we provide a discussion around factors that may influence the accuracy of the snow depth products including vegetation, environmental conditions, and study design.

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Section: Methodsmentioning

confidence: 99%

Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

2021

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“…• Maximum density impacted by wind: Equation 2 uses the parameter ρ max set to 350 kg m −3 , which is insufficient in Arctic conditions, as shown by Domine et al (2019). In Cambridge Bay and in Trail Valley Creek, the measured surface density is generally higher than 350 kg m −3 (Derksen et al, 2014;Rutter et al, 2019;Vargel et al, 2020). We therefore relaxed this constraint from the standard version of Crocus by setting ρ max to 600 kg m −3 .…”

Section: Experimental Designmentioning

confidence: 99%

Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures

et al. 2021

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The impact of high latitude climate warming on Arctic snow cover and its insulating properties has key implications for the surface and soil energy balance. Few studies have investigated specific trends in Arctic snowpack properties because there is a lack of long-term in situ observations and current detailed snow models fail to represent the main traits of Arctic snowpacks. This results in high uncertainty in modeling snow feedbacks on ground thermal regime due to induced changes in snow insulation. To better simulate Arctic snow structure and snow thermal properties, we implemented new parameterizations of several snow physical processes—including the effect of Arctic low vegetation and wind on snowpack—in the Crocus detailed snowpack model. Significant improvements compared to standard Crocus snow simulations and ERA-Interim (ERAi) reanalysis snow outputs were observed for a large set of in-situ snow data over Siberia and North America. Arctic Crocus simulations produced improved Arctic snow density profiles over the initial Crocus version, leading to a soil surface temperature bias of −0.5 K with RMSE of 2.5 K. We performed Crocus simulations over the past 39 years (1979–2018) for circumpolar taiga (open forest) and pan-Arctic areas at a resolution of 0.5°, driven by ERAi meteorological data. Snowpack properties over that period feature significant increase in spring snow bulk density (mainly in May and June), a downward trend in snow cover duration and an upward trend in wet snow (mainly in spring and fall). The pan-Arctic maximum snow water equivalent shows a decrease of −0.33 cm dec−1. With the ERAi air temperature trend of +0.84 K dec−1 featuring Arctic winter warming, these snow property changes have led to an upward trend in soil surface temperature (Tss) at a rate of +0.41 K dec−1 in winter. We show that the implemented snowpack property changes increased the Tss trend by 36% compared to the standard simulation. Winter induced changes in Tss led to a significant increase of 16% (+4 cm dec−1) in the estimated active layer thickness (ALT) over the past 39 years. An increase in ALT could have a significant impact on permafrost evolution, Arctic erosion and hydrology.

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“…7). (Rutter et al, 2019). The measurements made at each snowpit (as described above) were repeated at 5 m intervals along the trench.…”

Section: Trail Valley Creek Canadamentioning

confidence: 99%

Airborne SnowSAR data at X- and Ku- bands over boreal forest, alpine and tundra snow cover

Lemmetyinen

Cohen

Kontu

et al. 2021

Preprint

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Abstract. The European Space Agency SnowSAR instrument is a side looking, dual polarized (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from a small aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps, and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow-covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland for a single flight mission in March 2011 and twelve missions at two sites (tundra and boreal forest) in the winter of 2011–2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones, representing a montane valley, Alpine tundra, and a glacier environment. In Canada, a total of two missions were flown in March and April 2013, over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data, as well as coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record the datasets described here are deposited in a permanent data repository (https://doi.pangaea.de/10.1594/PANGAEA.933255; Lemmetyinen et al., 2021). A temporary link to access the data without login information is provided for reviewers of this manuscript: https://www.pangaea.de/tok/e8c562c3c8a15ac34daa83d00c76fcb347330884.

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Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Cited by 37 publications

References 45 publications

Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures

Airborne SnowSAR data at X- and Ku- bands over boreal forest, alpine and tundra snow cover

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