The terrestrial carbon (C) cycle is one of the most important focus areas in research on global climate change (Feng
In the terrestrial C cycle, the key component is gross primary production (GPP) along with ecosystem respiration (Feng
Understanding the controlling mechanism of terrestrial GPP is very important as well as its accurate estimation process (Li
Despite its importance, there are persisting inconsistencies in GPP estimation among different models (Li
Another GPP estimation technique is the eddy covariance (EC) technique, which can estimate net CO2 exchange on the ecosystem scale and be used for GPP estimation (Wu
Process-based models generally integrate the mechanisms to simulate numerous plant functional processes, including photosynthesis, autotrophic respiration and transpiration (Feng
The Boreal Ecosystem Productivity Simulator (BEPS) model is a carbon-water coupled process model developed initially for Canadian boreal forest conditions (Liu
Observing and considering the study area’s spatial representativeness helps to understand better the annual GPP and carbon stock (Ma
The aim of this study was to evaluate the potential suitability of the BEPS model for GPP prediction in hemi-boreal forests in Estonia using the available GPP tower estimates. The following steps were taken to achieve this objective: first, the spatial heterogeneity and suitability of the Soontaga site was assessed with 30 m resolution remote sensing data at multiple spatial thresholds (0.5 km, 1 km, 2 km). Next, the BEPS model was validated by comparing GPP values with the flux tower estimates under different weather conditions (dry/wet; cold/warm) over a longer time period (2016–2019). The study period covering several years, and different weather conditions shall sufficiently provide different scenarios to assess the full performance of the BEPS model and its overall suitability for the hemi-boreal region.
The Soontaga flux tower (58°01′24 ″N, 26°04′15 ″E) is located in a dry hemi-boreal forest dominated by Scots pine (
Location and view of Soontaga flux tower.
Following Román
(A) Top-of-Atmosphere shortwave reflectance composite (ETM+ Bands 7–4–2) over Soontaga area with the derived gamma variance of the surface albedo (B).
The gamma variance estimator, was used to attain half of the average square difference between albedo values, which are within a specific distance, classes, or bins, and these are defined by the multiplication of 30 m (Román
The BEPS model consists of an advanced canopy radiation sub-model for quantifying the effects of canopy architecture on the radiation distribution and photosynthesis in the canopy (Feng
The total GPP can be calculated with the separation of sunlit and shaded leaf groups (Liu
Photosynthesis rates are calculated as:
The photosynthesis rate is calculated as the minimum of Rubisco-limited and light-limited photosynthesis rates:
The input data required by BEPS are LAI, land cover, clumping index, and daily meteorological data (Feng
MODIS product MCD15A3H.006 with a 500 m resolution and 4-day step was used to provide LAI input data. The original LAI values were interpolated linearly to daily steps. The MODIS LAI values occasionally reached the maximum value of 6, which is not realistic for the Soontaga tower area. Since LAI value of 3 was previously found to be a maximum value at a comparable Scots pine stand in Järvselja, Estonia (Pisek
The necessary meteorological data for the BEPS model include temperature, humidity, radiation, precipitation, and wind speed information (Liu
Major parameters and values used in the BEPS model or input preparation.
Input parameters | Values | Reference |
---|---|---|
Clumping Index | 0.69 | For Soontaga site according to the global clumping index map by He |
Scale factor for LAI (500 m pixel size) | 0.1 | MODIS product MCD15A3H.006 (USGS, 2021) |
CO2 concentration and wind parameters were continuously measured in the tower, at the height of 39 m (5 m above the canopy) in 10 Hz using an enclosed-path infrared CO2 gas analyzer (Li-7200, LI-COR Biosciences, Lincoln, USA) and 3D-sonic anemometer (uSonic Class A, METEK GmbH, Elmshorn, Germany). Also, environmental parameters, such as air temperature and relative humidity (HC2A-S3, Rotronic AG, Bassersdorf, Switzerland) and solar radiation (LI-190SL, LI-COR Biosciences, Lincoln, USA) were measured at the site at a sampling frequency of 1 Hz. All the readings were stored on a data logger (CR3000, Campbell Scientific, Inc., Logan, UT, USA). Tower GPP values were obtained using the nighttime-data-based flux partitioning method in REddyProcWeb online tool (Wutzler
Model parameters estimated from the nighttime data with a sliding 7-day window and daytime air temperature were then used to model daytime ER (Lloyd & Taylor, 1994). The tower footprint area was calculated using Kljun
Figure 2B presents the geostatistical assessment of the site heterogeneity assessed at four different scales (0.5 km, 1 km, 1.5 km, 2 km). The gamma variance for the 0.5 km footprint area (the nominal spatial resolution of the satellite input data) around the tower reaches a plateau at 0.0003. According to Wang
GPP trajectories for 2016 (A), 2017 (B), 2018 (C), 2019 (D).
BEPS model validation and comparison The flux tower GPPtower measurements were used to validate the BEPS result for each year. Table 2 provides the general overview of the GPPBEPS values obtained with the BEPS model and the eddy covariance data measured at the site. A very close match was observed in 2017 and 2019. There was an excellent agreement (only 0.33% difference) in the total GPPBEPS obtained with BEPS (1293) and tower measurements (1288) in 2019 with the root mean square error (RMSE) of 1.47. Similarly, only a 3.96% difference was observed in 2017, with GPP estimated by BEPS at 1214 and tower GPPtower at 1166 and RMSE of 1.51. Compared to the tower measurements, the BEPS model overestimated GPP by 14.96% in 2018 with RMSE of 2.25 and underestimated it by 23.12% with RMSE of 2.20 in 2016.
Annual total GPP from tower-based observations (with uncertainty values), BEPS model, and percentage difference.
Year | GPP Tower (g C m−2) | GPP BEPS (g C m−2) | Difference (%) |
---|---|---|---|
2016 | 1447±16 | 1175 | −23.12 |
2017 | 1166±6 | 1214 | 3.96 |
2018 | 1143±38 | 1345 | 14.96 |
2019 | 1288±2 | 1293 | 0.33 |
First, as mentioned in the Methods section, there may be a possible footprint mismatch between the tower eddy covariance measurements and scale resolution of BEPS input data from remote sensing. The footprint area of the Soontaga flux tower is 20.88 ha. The remote sensing data that are used to drive the BEPS model (LAI, clumping index) are provided at nominal 500 m resolution. However, the actual signal may come from a bigger area, and may also not always align with the footprint of the flux tower. The GPPtower measurements also have uncertainty introduced during calculation steps (Liu
The meteorological weather condition of the specific year can impact the BEPS model’s performance. Table 3 provides an overview of warm/cold and dry/wet conditions at the site during the studied period.
Local weather conditions at Soontaga study site.
Year | Average temperature of each year (°C) | Annual total precipitation (cm) | Warm/cold | Dry/wet |
---|---|---|---|---|
2016 | 7.57 | 47.40 | Cold | Wet |
2017 | 7.09 | 49.52 | Cold | Wet |
2018 | 8.18 | 32.90 | Warm | Dry |
2019 | 8.29 | 59.07 | Warm | Wet |
The expected GPPBEPS rate was higher for 2016 as it had cold, and wet weather conditions based on the meteorological data presented in Table 3. The results were less convincing compared to other years with the 23.12% underestimation of the annual sum of GPPBEPS estimates by BEPS compared to tower-based estimates (Figure 3A). While there was a close relationship with a very little difference between BEPS and tower estimates in the second half of the growing season (DOY > 210; Figure 3A), there was an unrealistic drop in BEPS GPPBEPS retrievals from the middle of June till the third week of July (DOY 180–205). Here the used meteorological data was from the field data and that had some missing values in humidity calculation and those missing humidity values caused this drop in GPPBEPS values during that period. A more precise and accurate dataset can improve the result. Additionally, clearer evidence can be seen if the daily GPPBEPS values are plotted against the tower GPPtower values with the dropped humidity input data and without the dropped humidity input data. All days of the year included in Figure 4A show a diverging relationship from the 1:1 line and the value is 0.7, while the omission of days with missing humidity inputs improves the value to 0.81 (Figure 4A). On the other hand, the GPPBEPS was over-estimated with very high values from the last week of May to the second week of June. It was caused by the apparently over-estimated MODIS LAI values at the beginning of the growing season when the LAI compared to the probable actual LAI values at the site. From the beginning of the year until mid-February, the LAI values were zero (Figure 5A), resulting in zero GPPBEPS estimates from BEPS during that period. Except for the LAI impact and the humidity input issue, the second half of the year showed BEPS GPPBEPS values to closely match the expected tower-based observations. GPPBEPS course also mirrors low temperature and high precipitation (Figure 5A) episodes well during this period.
Regressions between BEPS and tower daily GPP estimates in 2016 (A), 2017 (B), 2018 (C), 2019 (D). There are two regression lines in (A), one shows regession including GPP estimates with the missing meteorological data (empty circles; purple line), another is for the days without the missing values (full circles; red line).
Impact of precipitation, temperature and LAI inputs on BEPS GPP in 2016 (A), 2017 (B), s2018 (C), 2019 (D).
Based on the meteorological data, 2017 was relatively cold and wet (Table 3), with a much higher precipitation rate in autumn compared to other years used in this study (Estonian Weather Service, 2021). Again, there was a close relationship with very little difference between GPPBEPS and GPPtower values throughout the whole year in 2017 (Figure 3B). Compared to tower-based estimates, the BEPS model overestimated the yearly GPPBEPS sum by 4%. There were occasional small differences from the last week of May to the third week of August (Figure 3B). The GPPBEPS trajectory by BEPS closely followed the seasonal temperature profile (Figure 5C). Also, higher precipitation clearly affected higher GPPBEPS values from both approaches (Figure 5C). Similarly to the situations in 2018 and 2019, the occasional small differences in BEPS GPPBEPS from the last week of May to the third week of August were caused by the apparent LAI overestimation by the MODIS LAI product during this part of the growing season. The differences in GPPBEPS (Figure 3B) match the LAI saturation timing (Figure 5B).
The year 2018 experienced drought during July in Soontaga area (Estonian Weather Service, 2021) and the year was facing higher temperatures than usual (Table 3). According to the tower-based measurements, this caused relatively lower GPPtower production as there is less water to support the process because dry weather and high temperature caused high evaporation. The GPPBEPS matched GPPtower values very well at the beginning and end of the growing season in 2018 (Figure 3C). The BEPS model provided much higher GPPBEPS estimates than the tower GPPtower estimates for most of the growing season. The GPPBEPS course matched the radiation and temperature profiles very well (Figure 5C). Figure 5C indicates that low precipitation or dryness is behind the low GPPBEPS values. Similarly, in 2019 later on, the over-estimated LAI from the MODIS LAI product caused the over-estimation in the result of GPPBEPS from May to the third week of August (DOY 150–230). The first peak of BEPS overestimation started from 7 May (DOY 127), which matched the sudden increase in MODIS LAI values beyond the threshold LAI value of 3, which lasted with few exceptions till 17 August (DOY 229). Results from the 2018 season confirm clear, very high sensitivity BEPS to LAI input.
Based on meteorological data in Table 3, 2019 was a relatively warm and wet year at this site compared to the other years included in this analysis. The site had enough water to support the photosynthesis, but at the same time, high temperature caused a high evaporation rate. The two effects combined had a neutral effect and the GPP production was as expected. There was a good match between BEPS and tower estimates throughout most of the seasonal course in 2019 (Figure 3D). Compared to GPPtower values obtained with tower eddy covariance measurements, BEPS underestimated GPPBEPS from January to April and November to December. Figure 5A shows that the LAI input values for BEPS are mostly zero during the first few months (till the middle of April) as well as the last two months of the year. Since this is an evergreen needle-leaf site, the actual LAI values were higher and allowed the photosynthesis process to start immediately under suitable conditions, captured by the eddy covariance measurements. In contrast, close to zero input LAI values provided to BEPS did not allow to match the observed tower GPPtower values during these periods.
The GPPBEPS values were higher than the tower estimates from the middle of May to the middle of June 2019 (DOY 130–151) (Figure 3A). Compared to the beginning and towards the end of the year discussed above, input LAI values for BEPS did not suffer from underestimation during this period. The sharp increase in GPPBEPS values around 10 May (DOY 130) coincided with the moment when the maximum LAI value, as provided by the MODIS LAI product, was reached. The period from the middle of May till the end of June may point to the opposite effect compared to the start of the season. While the MODIS LAI product underestimated LAI over the site at the beginning of the season and consequently underestimated the GPP, the overestimated GPPBEPS from the middle of May till the beginning of July was caused by the apparent LAI overestimation in the MODIS LAI product during this section of the growing season. Previously, the maximum LAI for Scots pine stands was observed to be reached later in the season at the beginning of July (Heiskanen
Finally, Figure 6 is used to crosscheck the LAI impact. The model was tested with constant LAI values of 1 and 2 as input throughout 2019. A closer agreement between GPPBEPS and GPPtower estimates using tower observations is observed from the beginning of the season till the beginning of July (DOY 182), confirming the LAI overestimation during the period by the MODIS LAI product was the cause of the disagreement between the GPPBEPS and GPPtower estimates. The sudden, concurrent drops in GPP values predicted by both BEPS and tower observations are caused by cloudy conditions, limiting the incoming irradiation below optimal levels for GPP production.
Eddy covariance tower measurements and BEPS model retrievals with constant LAI input values of 1 and 2 in 2019.
This study tested the BEPS model for estimating GPP in a hemi-boreal coniferous forest in Estonia to evaluate its performance and reliability for future use in the area. Based on the analysis of BEPS GPP results for Estonian hemi-boreal conditions for different years, it may be concluded that BEPS is an efficient tool for GPP estimation in the region. From the perspective of the BEPS model tracking of the GPP changes with the season and inter-annual variation, it was found from the analysis that the BEPS model can track the changes relatively well even during extreme weather conditions, such as drought, given the model is provided with reasonable input values.
It shall be noted that BEPS is particularly sensitive to the quality and reliability of input LAI values. Feng