Forest ecosystems are well known as powerful regulators of Earth’s climate via their impact on the fluxes of matter and energy between the land surface and the atmosphere (Hari
It was proposed that increasing concentrations of carbon dioxide (CO2) as well as other greenhouse gases (H2O, CH4, NOx) change the behaviour of radiation energy in the atmosphere (Zhong & Haigh, 2013). Forests act as terrestrial carbon sinks and their role as active modulators of the atmosphere’s radiative transfer and albedo by particle and cloud formation were reported (Kulmala, 2016; Kulmala
Gathering continuous long-term data on atmospheric and forest ecosystem relationships is important for monitoring environmental changes (Hari
The flux footprint (FFP) model concept has been used since 1972, described by Schmid (2002) in his review paper. These models are used to describe the spatial extent and position of the surface area that contributes to a turbulent flux measurement at a specific point in time, for specific atmospheric conditions and surface characteristics (Kljun
The flux tower gathers data from a certain distance that is called the source area or flux of the FFP. The FFP defines the field of view of the flux tower sensors and thus reflects the influence of the surface on the measured turbulent flux (Aubinet
The main aims of the paper are 1) to assess the yearly FFP area and changes in the forest area, the growing stock, and growth increment related to the area that is given by the cumulative FFP covering 90% of flux signals measured at the height of 30 and 70 meters; 2) to assess the changes due to wind speed and direction changes of the FFP description and; 3) to relate and grade these changes with the inclusion of knowledge on forest management and land use changes. This results in a detailed description of the FFP which gives us ample opportunity for future research.
SMEAR Estonia (58.2714°N, 27.2703°E, 36 m a.s.l.) is situated at the Järvselja Experimental Forestry Centre. The forest ecosystem within the station’s FFP is a hemiboreal forest comprising, silver birch (
All flux data were collected at SMEAR Estonia (Noe
Basic input parameters and definitions for the Kljun
Input | Description |
---|---|
zm | measurement height above ground (m) |
z0 | roughness length (m) |
d | displacement height (m) |
u_mean | mean wind speed at zm (m/s) |
L | Obukhov length (m) |
sigma_v | standard deviation of lateral velocity fluctuations after rotation (m/s) |
u* | friction velocity (m/s) |
To calculate the annual FFP climatology, we used the stand-alone Python version of the FFP model (Kljun
The workflow of the FFP (Figure 1) calculation was as follows: using the Kljun FFP model (Kljun
To compare to the annual FFP shape and area we assessed the annual heterogeneity of the horizontal wind regime. For that, we calculated the horizontal wind speed and direction density using Mathematica (Wolfram Research, Inc., Mathematica, Version 12.3.1, Champaign, IL, USA) for both measurement heights and each year of our experiment.
Basal data of forest stand parameters related with the stand height increment and volume stock are usually measured at 5-year intervals in Estonia. Remotely sensed LIDAR height measurements have a frequency of 4 years (
We therefore modelled the increase in the height of all stand elements using the models for normal forest stands as proposed by Kiviste & Kiviste (2009). The volume stock was modelled by using the national regulations according to the Forest Inventory Act (2009).
To create a forest mask, we used everything from the woody vegetation layer of the Base Map from the Estonian Topographic Data Collection (Maa-amet, 2017) and added from the wetland layer the areas with woody vegetation (column PUIS_T contains the value “Yes”). Since the forest surface layer also contains the surface areas of infrastructure elements (roads, ditches, railways, power lines, and quarter boundaries) which are usually mapped in GIS layers as so-called “line type elements” with no spatial extent we needed to determine the surfaces of such line elements. Therefore, it was first necessary to generate for each line type a fitting surface type representing the area that can then be added as a new layer into the forest mask. In this work, we selected only the layers of line elements that are passing through forests. For this purpose, we introduced a layer of roads, layer of ditches, layer of railways, layer of power lines and a layer of forest quarter boundaries. For roads, ditches, railways, and forest quarter boundaries we used again information that can be retrieved from the Base Map. For power line routes we used data from the national transmission operators Elering and Fortum. To avoid possible double accounting, we deleted areas overlapping with the surfaces of line elements from the forest layer.
The cumulated FFP covers a surrounding area of up to 600 m from the main tower if we use flux data measured at 30 m height (Figure 3). In 2015 the FFP covered 61.6 ha, in 2016 it covered 65.4 ha, in 2017 60.2 ha, in 2018 62.3 ha, in 2019 and 2020 the FFP covered areas of 61.4 ha and 58.3 ha, respectively. The average FFP area over the six-year period was 61.5 ha. The source area naturally depends on three main parameters: measurement height, wind speed and direction (Figure 3). However, the area is not only sensitive to the previous factors, but is also dependent on surface roughness and atmospheric stability (Vesala
89.4 % of the FFP area regarding the 30 m high measurement point is covered by forests (Table 2). The main tree species growing in the area are the most widespread and economically most valuable species in Estonia: Scots pine (
Land categories of the FFP area over the period of 2015–2020 at a height of 30 m.
Land type | Species | Area (ha) | Increment (m3/ha/y) | Growing stock (m3) | % of the FFP |
---|---|---|---|---|---|
Forest land | 0.1 | 0.5 | 7.6 | 0.2 | |
Forest land | 0.2 | 0.8 | 22.7 | 0.4 | |
Forest land | 4.3 | 8.9 | 368.6 | 6.9 | |
Forest land | 6.8 | 34.1 | 1179.5 | 11.1 | |
Forest land | 15.4 | 99.6 | 4234.5 | 25.0 | |
Forest land | Pinus sylvestris | 28.2 | 100.2 | 8448.0 | 45.8 |
Forest land | Unknown | 2.8 | |||
Isle in the forest | - | 0.4 | 0.6 | ||
Buildings | - | 0.0 | 0.0 | ||
Electric power lines | - | 1.4 | 2.4 | ||
Roads | - | 0.2 | 0.4 | ||
Ditches | - | 0.6 | 1.0 | ||
Clear area | Clear-cut | 1.0 | 1.6 | ||
Total | - | 61.5 | 100.0 | ||
Total forest area | - | 55.0 | 89.4 |
Forest site types in the FFP measured at a height of 30 m.
Forest site type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | Average area (ha) | % of FFP area | Average increment (m3/ha/a) | % of FFP area | Average stock (m3) | % of FFP area |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mineral dump | 3.5 | 3.9 | 3.6 | 3.9 | 3.8 | 3.5 | 3.7 | 6.0 | ||||
8.4 | 8.6 | 9.1 | 8.8 | 9.0 | 8.8 | 8.8 | 14.3 | 35.9 | 14.7 | 1560.1 | 10.9 | |
1.9 | 2.0 | 1.8 | 1.7 | 1.9 | 1.6 | 1.8 | 2.9 | 4.6 | 1.9 | 282.4 | 2.0 | |
1.5 | 3.2 | 2.6 | 2.8 | 1.9 | 2.1 | 2.3 | 3.8 | 16.0 | 6.5 | 660.0 | 4.6 | |
3.9 | 6.4 | 4.0 | 5.4 | 4.5 | 3.9 | 4.7 | 7.6 | 26.2 | 10.7 | 925.9 | 6.5 | |
22.8 | 22.9 | 22.0 | 22.3 | 22.8 | 21.2 | 22.3 | 36.3 | 99.8 | 40.9 | 6622.5 | 46.4 | |
14.2 | 13.1 | 11.7 | 12.7 | 12.4 | 12.2 | 12.7 | 20.7 | 45.9 | 18.8 | 2916.4 | 20.5 | |
5.4 | 5.4 | 5.3 | 4.8 | 5.1 | 5.0 | 5.2 | 8.4 | 15.6 | 6.4 | 1293.5 | 9.1 | |
Total | 61.6 | 65.4 | 60.2 | 62.3 | 61.4 | 58.3 | 61.5 | 100.0 | 244.0 | 100.0 | 14260.9 | 100.0 |
Growing stocks and yearly increment were calculated for each year and are listed in Table 4. Depending on the year, the figures decreased or increased. The reasons for the decrease and increase might be different: 1) thinning and clear-cutting; 2) smaller/larger area of the FFP for a particular year. If the FFP is smaller, it means that the area where fluxes are detected from is closer to the flux tower. Therefore, the forest growing on the far edges of the FFP gets out of sight of the flux tower. Given there was no thinning/clear-cutting brought about the change in the growing stock perhaps due to shifting stands with different growing stock in or out of the FFP area. Thinning was done only in 2018 and 2019 when 273 m3 and 501 m3 were cut (Table 4), thus such small amounts had no significant impact on the shape of the FFP.
Growing stock (m3/ha) and increment (m3/ha/y) changes during the six-year period.
Characteristic | Unit | Year | |||||
---|---|---|---|---|---|---|---|
2015 | 2016 | 2017 | 2018 | 2019 | 2020 | ||
Area | ha | 58.05 | 61.54 | 56.59 | 58.40 | 57.61 | 54.77 |
Stock in summer | m3 | 13988 | 14732 | 14058 | 14558 | 14430 | 13799 |
Thinning & harvesting | m3 | 0 | 0 | 0 | 273 | 501 | |
Increment | m3 | 240 | 266 | 237 | 248 | 242 | 232 |
Stock in summer | m3 | 14228 | 14998 | 14295 | 14533 | 14170 | |
Stock in summer | m3/ha | 240.98 | 239.39 | 248.40 | 249.29 | 250.45 | 251.91 |
Thinning | m3/ha | 0.00 | 0.00 | 0.00 | 4.68 | 8.70 | |
Increment | m3/ha | 4.13 | 4.32 | 4.18 | 4.24 | 4.19 | 4.24 |
Stock in next summer | m3/ha | 245.11 | 243.71 | 252.59 | 248.86 | 245.94 |
The area of the FFP measured from a height of 70 m was significantly bigger (Figure 5) and covers the surrounding area of up to 4 km from the main tower. In 2015, the FFP covered 3,288 ha, in 2016 3,317.3 ha, in 2017 3,241.8 ha, in 2018 3,332.7 ha, in 2019 and 2020 the FFP covered areas of 3,323.8 ha and 3,272.3 ha, respectively. The average FFP area over the six-year period was 3,296 ha. The main shape of the FFP remained intact, although the FFP climatology were slightly different for each year as well as for the smaller 30 m FFP. The FFP area in 2016 was 0.9% bigger than in 2015, but in 2017 it was smaller by 2.3% than in 2016. In 2018, the area was again bigger than in 2017 by 2.8%, and the FFP area was again smaller in 2019 than in the previous year by 0.3%. In 2020, it was smaller by 1.6% than in 2019. On average, the difference over the years was only 1.6%. The main growing tree species in the FFP area are Scots pine (511.2 ha on average), silver and downy birch (1,007.9 ha on average), Norway spruce (450 ha on average), common aspen (242.7 ha on average), grey and black alder 37.4 ha and 464.5 ha on average, respectively. Bogs and fens cover 86 ha of the area and 40.7 ha of them are covered by Scots pine. Overall, 2,897.3 (87.9 %) ha of the FFP area is categorized as forest land, the remaining 398.7 ha are covered with different types of land (12.1 %) (Table 5). The main forest site types for this FFP are described in Table 6.
Land categories of the FFP area over the period of 2015–2020 at a height of 70 m.
Land type | Species | Area (ha) | Increment (m3/ha/y) | Growing stock (m3) | % of FFP |
---|---|---|---|---|---|
Forest | 37.2 | 186.9 | 3251.9 | 1.1 | |
Forest | 464.5 | 1693.0 | 76381.2 | 14.1 | |
Forest | 242.7 | 786.6 | 20652.5 | 7.4 | |
Forest | 1007.9 | 3449.5 | 224027.0 | 30.6 | |
Forest | 450.0 | 2205.8 | 135347.7 | 13.7 | |
Forest | 470.5 | 1027.0 | 145716.4 | 14.3 | |
Forest | Other species | 0.1 | 0.1 | 58.0 | 0.0 |
Forest (bog) | 40.7 | 52.5 | 1004.7 | 1.2 | |
Forest | Clear-cut | 183.6 | 5.6 | ||
Bog | Without forest | 35.5 | 1.1 | ||
Fen | Without forest | 9.8 | 0.3 | ||
Agricultural land | - | 120.4 | 3.7 | ||
Clear area | - | 83.8 | 2.5 | ||
Buildings | - | 2.3 | 0.1 | ||
Standing water | - | 3.8 | 0.1 | ||
Isle in the forest | - | 30.4 | 0.9 | ||
Roads | - | 37.1 | 1.1 | ||
Watercourse | - | 40.1 | 1.2 | ||
Yard | - | 11.8 | 0.4 | ||
Other land | - | 0.3 | 0.0 | ||
Power lines | - | 23.5 | 0.7 | ||
Total forest land | - | 2897.3 | 87.9 | ||
Total area | - | 3296.0 | 100.0 |
Forest site types in the FFP measured at a height of 70 m.
Forest site type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | Average area (ha) | % of FFP | Average increment (m3/ha/a) | % of FFP | Average stock (m3) | % of FFP |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mineral dump | 418.0 | 445.5 | 397.4 | 415.5 | 429.0 | 406.6 | 418.7 | 12.703 | ||||
564.2 | 567.9 | 553.1 | 578.1 | 576.1 | 554.3 | 565.6 | 17.161 | 848.4 | 18.0 | 77460.9 | 25.4 | |
36.4 | 34.1 | 36.6 | 36.5 | 39.3 | 36.8 | 36.6 | 1.111 | 39.6 | 0.8 | 6332.8 | 2.1 | |
Raised bog | 42.9 | 45.7 | 36.2 | 25.3 | 52.6 | 18.0 | 36.8 | 1.116 | 19.5 | 0.4 | 414.8 | 0.1 |
73.2 | 75.7 | 73.3 | 74.5 | 75.4 | 73.6 | 74.3 | 2.253 | 71.9 | 1.5 | 9110.2 | 3.0 | |
Transitional bog | 87.0 | 95.0 | 85.0 | 93.0 | 89.8 | 85.3 | 89.2 | 2.706 | 63.0 | 1.3 | 10211.7 | 3.3 |
1233.1 | 1254.7 | 1205.3 | 1296.9 | 1236.7 | 1231.0 | 1242.9 | 37.711 | 2024.0 | 42.9 | 122544.0 | 40.1 | |
160.4 | 133.1 | 129.4 | 109.5 | 129.1 | 134.1 | 132.6 | 4.024 | 364.6 | 7.7 | 12762.4 | 4.2 | |
172.0 | 207.5 | 198.4 | 206.6 | 180.1 | 222.9 | 197.9 | 6.005 | 488.1 | 10.3 | 19911.9 | 6.5 | |
489.7 | 448.3 | 517.3 | 487.7 | 506.7 | 499.7 | 491.6 | 14.914 | 759.6 | 16.1 | 44217.1 | 14.5 | |
7.769 | 6.710 | 6.730 | 5.983 | 5.995 | 6.990 | 6.7 | 0.203 | 21.2 | 0.4 | 1866.7 | 0.6 | |
2.982 | 2.982 | 2.982 | 2.982 | 2.982 | 2.982 | 3.0 | 0.090 | 21.2 | 0.4 | 414.6 | 0.1 | |
0.280 | 0.000 | 0.222 | 0.000 | 0.000 | 0.000 | 0.1 | 0.003 | 0.1 | 0.0 | 21.9 | 0.0 | |
Total | 3296.0 | 100.0 | 4721.2 | 100.0 | 305268.8 | 100.0 |
Growing stocks were calculated for each year (Table 7), in some years the growing stock decreased or increased as compared to other years. The reasons behind these changes are similar to those given in the previous section.
Growing stock (m3/ha) and increment (m3/ha/y) changes during the six-year period.
Characteristic | Unit | Year | |||||
---|---|---|---|---|---|---|---|
2015 | 2016 | 2017 | 2018 | 2019 | 2020 | ||
Area | ha | 2888.86 | 2891.82 | 2864.11 | 2938.29 | 2914.90 | 2886.02 |
Stock in summer | m3 | 605713 | 603467 | 592571 | 618805 | 607847 | 610235 |
Thinning & harvesting | m3 | 8481 | 10666 | 5577 | 9394 | 5943 | |
Increment | m3 | 9187 | 9335 | 9134 | 9500 | 9551 | 9701 |
Stock in next summer | m3 | 606418 | 602136 | 596127 | 618910 | 611455 | |
Stock in summer | m3/ha | 209.67 | 208.68 | 206.90 | 210.60 | 208.53 | 211.44 |
Thinning & harvesting | m3/ha | 2.94 | 3.69 | 1.95 | 3.20 | 2.04 | |
Increment | m3/ha | 3.18 | 3.23 | 3.19 | 3.23 | 3.28 | 3.36 |
Stock in next summer | m3/ha | 209.92 | 208.22 | 208.14 | 210.64 | 209.77 |
At 70 m height, the dominant wind directions in 2015 to 2017 ranged from the southwest to south, like the 30 m anemometer measurements showed. In the more recent years, the general wind directions were also frequently from the south to southeast, but also northeast wind directions are prevalent at this height. The darker colour on the figure denotes a higher density in wind direction and speed and therefore a higher contribution from those directions to the overall FFP (Figure 6). Thus, wind speed and directions in those particular regions are more abundant which makes the input to calculate the overall FFP shape more robust.
Average relative changes in the FFP area range about ~4.9% for the measurement height of 30 m and are smaller, 1.6%, for the measurement height of 70 m. This change is not controllable by human activity and follows the annual wind patterns. Changes that are affected by human activities, e.g. reduction in the growing stock after clear-cutting or thinning were also observed. The growing stock of the FFP area from 30 m height grew for 2.8% over the period reported. Since there were no construction activities next to the station, there were no land use changes regarding buildings, ditches, etc. and these structural elements of the FFP remained constant over the time observed.
For the FFP area at 70 m height, the value was 2.2% on average. Thinning took place every year and the impact of forest management activities might have had some effect on the shape of the FFP. We calculated the growing stock and increment for each year considering the thinning that was done and thus enabled a way to compare changes in the carbon stock of the FFP. Our comparisons reveal that in both FFPs the increment and growing stock have been constant over the 6 years.
The relative change over the years was smaller for the 70 m FFP compared to the 30 m FFP. Measurements from the higher point allow us therefore a more stable assessment in terms of the changes of forest management and other elements within the FFP. The shape of both FFPs is practically the same and shows some slight differences only. This indicates that the shape is driven by the larger scale wind regime and less by the local differences.
Relating the FFP areas and setting the 70 m FFP as 100%, the 30 m FFP covers only 3.4% of the whole area. Within the bigger area are therefore more land categories. For instance, bogs and swamps in the southeast part of the 70 m FFP contributed to it every year, but the size of that area was different. In that sense, the forested area in the 70 m FFP is more variable over the years as compared to the 30 m FFP. Another example are settlements and grassland that occurs in the eastern edge of the 70 m FFP, these elements are not apparent in the 30 m FFP. On the other hand, these changes at the border areas of the FFP have a very low effect given the FFP’s transfer function’s small weights for these areas. A benefit of the 70 m FFP is that we can study the impact of different land categories and forest management regimes on the carbon exchange. The smaller, 30 m FFP, has the disadvantage that the overall variation of the FFP area and changes like clear-cutting may lead to relatively large changes within the flux signature that may be captured in one year and left out in another year and by that complicating the proper assessment of the FFP area’s carbon exchange over the years.
The application of wind data from the eddy covariance method described in this paper is an important and useful method for studying atmosphere-ecosystem relationships, fluxes of atmospheric gases and monitoring the LULUCF impacts on climate change mitigation strategies. It enables us to quantify changes in the FFP area that need consideration in assessing forest growth and linking it to climatic driven environmental changes. Utilising the SMEAR station’s data is an important step towards understanding the FFP dynamic and to what extent environmental and human drivers affect changes in the forest ecosystem and how the ecosystem responds. Our work shows that the area of the FFP varies year by year due to wind speed and direction. Additionally, the anthropogenic impact of forest management on the land use elements in the FFP like cleared areas, changes in density by thinning, changes in height lead to an increased heterogeneity in the FFP’s three-dimensional structure. This heterogeneity modifies the FFP by impacting the turbulent flow field (Aubinet
In this paper, we calculated the FFP measured at a height of 30 and 70 m in a hemiboreal mixed conifer and deciduous forest at the Järvselja Experimental Forestry Centre. The study provides a description of the dynamic changes within the FFP from 2015 to 2020. To assess the annual FFP climatology and the spatial extent of the FFP the EC method was used. Over the six-year period, the major shape of the FFP remained almost intact and all changes appeared mostly due to wind speed and direction. The wind direction was mainly from the south and southwest in 2015–2017 and from southeast in 2018–2020. Changes in the growing stock and increment were affected by forest management activities during the six-year period, however these changes were relatively small and constant over time.
Long-term measurements are crucial for understanding the relations between the forest ecosystem and the atmosphere. In this research paper we emphasize the importance of considering both natural and human factors when studying the dynamics of the FFP area, particularly in the context of climate change mitigation strategies. The use of advanced measurement methods and data from the SMEAR station is a valuable and useful tool for advancing our understanding of forest ecosystems and their response to changing environmental conditions and human activities.