Seasonal Dynamics of Flux Footprint for a Measuring Tower in Southern Taiga via Modeling and Experimental Data Analysis
This paper reports on the location of sources contributing to a point flux measurement in the southern taiga, Russia. The measurement tower is surrounded by a coniferous forest with a mean aerodynamically active height of 27 m ( h ). Aerodynamical parameters of the forest, such as displacement heigh...
| Published in: | Forests |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
MDPI AG
2023
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| Subjects: | |
| Online Access: | https://doi.org/10.3390/f14101968 https://doaj.org/article/e186b63ab4894fd99c5e231861bc5f34 |
| Summary: | This paper reports on the location of sources contributing to a point flux measurement in the southern taiga, Russia. The measurement tower is surrounded by a coniferous forest with a mean aerodynamically active height of 27 m ( h ). Aerodynamical parameters of the forest, such as displacement height d and aerodynamic roughness <semantics> z 0 </semantics> , derived from wind speed profile measurements for 2017–2019, were used to estimate the seasonal and daily behavior of the flux footprint. Two analytical footprint models driven by d and z 0 were used to estimate the footprint for canopy sources. The Lagrangian simulation (LS) approach driven by flow statistics from measurements and modeling was used to estimate the footprint for ground-located sources. The Flux Footprint Prediction (FFP) tool for assessing canopy flux footprint applied as the option in the EddyPro v.7 software was inspected against analytical and LS methods. For model comparisons, two parameters from estimated footprint functions were used: the upwind distance (fetch) of the peak contribution in the measured flux (X max ) and the fetch that contributed to 80% of the total flux (CF 80 ). The study shows that X max varies slightly with season but relies on wind direction and time of day. All methods yield different X max values but fall in the same range (60–130 m, around 2–5 h ); thus, they can estimate the maximum influence distance with similar confidence. The CF 80 values provided by the FFP tool are significantly lower than the CF 80 values from other methods. For instance, the FFP tool estimates a CF 80 of about 200 m (7 h ), whereas other methods estimate a range of 600–1100 m (25–40 h ). The study emphasizes that estimating the ground source footprint requires either the LS method or more complex approaches based on Computational Fluid Dynamics (CFD) techniques. These findings have essential implications in interpreting eddy-flux measurements over the quasi-homogeneous forest. |
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