Quantifying the Aerodynamic Roughness Length of Snow Surfaces With Time‐Lapse Structure‐From‐Motion. Issue 3 (1st February 2023)

• Record Type: Journal Article
• Authors: Liu, Junfeng; Chen, Rensheng; Ma, Shaoxiu; Guo, Shuhai; Han, Chuntan; Ding, Yongjian
• Is Part Of: Journal of geophysical research. Volume 128:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-01
• Subjects: aerodynamic roughness length -- time‐lapse SfM photogrammetry -- sensible heat flux -- latent heat flux; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2022JD037032
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The aerodynamic roughness length ( z 0m ) of snow surfaces plays an essential role in estimating turbulent fluxes of latent and sensible heat (LE and H ). Constant z 0m has been widely used in previous studies due to the difficulty in monitoring dynamic z 0m . This leads to large uncertainties in simulating LE and H fluxes. In the current study we used a time‐lapse structure‐from‐motion (SfM) photogrammetric method to study temporal variation in z 0m of snow surfaces. The study site was located at the edge of the August‐one ice cap in the Qilian Mountains of China. The study ran from August 2019 to September 2020. We measured aerodynamic roughness length over time ( z 0m_t ) using SfM‐based micro‐topographic methods and validated our results by comparing z 0m_t with z0m_prof (profile‐based) and z0m_EC (EC‐based) data. The validated dynamic z 0m_t was then used to estimate LE and H fluxes using a bulk aerodynamic method. These results were then compared with LE and H measured by eddy covariance (EC) and with manual sublimation/condensation measurements to indirectly validate z 0m_t . We found that z 0m_t overall agreed with profile‐based z0m_prof and EC‐based z0m_EC . Indeed, our estimates of bulk LE and H fluxes using dynamic z 0m_t agreed slightly better with EC and manual sublimation/condensation measurements than did static EC‐based z 0m_EC . Micro‐topographic z 0m_t can be helpful in precisely estimating varying LE and H fluxes over the course of a year. Key Points: Time‐lapse structure‐from‐motion (T‐SfM) can capture snow surface z 0m variations over time T‐SfM‐based z 0m of snow surfaces varies from 0.03 to 0.52 mm, which agrees with EC‐ and profile‐based z 0m Tracking changes in z 0m, as measured with T‐SfM, improves estimation of turbulent flux