DOI: 10.18150/r1qitx
title: Rzecin Peatland Ecosystem: Carbon, Water & Energy Dynamics (2022-2023)

description: OverviewThis dataset contains fluxes from the Eddy Covariance (EC) system and meteorological data, collected at the PolWET peatland site in Rzecin, Poland (52°45′N, 16°18′E, 54 m a.s.l.). The PolWET site is run by Poznan University of Life Sciences (PULS). The data encompasses a range of atmospheric and surface flux variables critical for understanding the interactions between the peatland ecosystem and biophysical drivers. The dataset is particularly valuable for research on ecosystem-atmosphere interactions, including carbon sequestration, evapotranspiration, and energy balance in wetlands.Eddy Covariance (EC) dataThe Eddy Covariance (EC) dataset from the PolWET site provides high-frequency, continuous measurements of various atmospheric data and surface fluxes, collected over the period 2022-2023. The data was recorded with Infrared gas analyzer LICOR LI7200 with temporal resolution of 10Hz and were then averaged to 30 minutes resolution data points. The consecutive measurements were takin with meteorological sensors, averaged to the same temporal resolution. The following variables are comprising the EC dataset:Air Temperature (°C)CO2 flux (μmol m-2 s-1)Evapotranspiration (ET) (mm h-1)Sensible heat flux (H) (W m-2)Latent heat flux (W m-2)Relative Humidity (RH) (%)Vapor Pressure Deficit (VPD) (Pa)Wind Direction (DEG)Wind Speed (m s-1)The Eddy Covariance (EC) data includes the following files:air_temperature (C)_YYYY.xlsxET_YYYY.xlsxco2_flux_YYYY.xlsxH_YYYY.xlsxLE_YYYY.xlsxVPD_YYYY.xlsxRH_YYYY.xlsxwind_dir_YYYY.xlsxwind_speed_YYYY.xlsxShort-gap filled Eddy Covariance (EC) dataThe EC data underwent post-processing to ensure its quality. Initially, outliers were removed using a filter that compared each 30-minute value with its mean and standard deviation, eliminating inconsistent records (Bi et al. 2007; Borges et al. 2020; 2024).After this filtering step, we applied a gap-filling process for short data gaps using linear interpolation. This was applied to a maximum of 5 consecutive missing records of 30-minutes values, considering the closest available neighboring values, and therefore ensuring that only short-term data gaps were corrected (Bi et al. 2007; Lucas-Moffat et al., 2022). Note that the gaps longer than 5 consecutive data points were not filled in.Short-gap filled Eddy Covariance (EC) data includes the following files:Gapfilled_air_temperature (C)_YYYY.xlsxGapfilled_ET_YYYY.xlsxGapfilled_co2_flux_YYYY.xlsxGapfilled_H_YYYY.xlsxGapfilled_LE_YYYY.xlsxGapfilled_VPD_YYYY.xlsxGapfilled_RH_YYYY.xlsxGapfilled_wind_dir_YYYY.xlsxGapfilled_wind_speed_YYYY.xlsxEC data availability before and after short-gap fillingTo assess data completeness we quantified the percentage of EC data available before and after the filtering and short-gap filling for each variable. We initially calculated the percentage of initially available data and, after applying the filtering and linear interpolation, we recalculated this percentage.The results highlight how gap filling improves data availability, especially in months with short interruptions, ensuring the reconstruction of short-term missing records and preserving the integrity of the dataset in this context. The full analysis is available in the repository.The EC data availability includes the following files:EC_data_integrity_YYYY.txtMeteorological and environmental dataThe precipitation data (Rain; mm) were averaged at 30 minutes for 2 replicated sensors, indicated by indexes 1 and 2. Likewise, the net radiation (Rn; W m-2) was available for both sensors 1 and 2. Environmental data related to the peatland surface motion (Peat Movement; cm) are also provided.The results of the calculations of aerodynamic conductance (Ga; m s-1), canopy conductance (Gc; m s-1), and decoupling coefficient (Ω; dimensionless) are provided. The equations used for calculations of those parameters are given in Speranskaya et al (2024) and Carneiro et al. (2025).Rain Total 1 (mm)Rain Total 2 (mm)Rn 1 (W m-2)Rn 2 (W m-2)Peat Movement (cm)Ga (m s-1)Gc (m s-1)Ω (dimensionless)The Meteorological and environmental data includes the following files:Ga_YYYY.xlsxGc_YYYY.xlsxRn_1_YYYY.xlsxRn_2_YYYY.xlsxRain_Total_1_YYYY.xlsxRain_Total_2_YYYY.xlsxPeat_Movement_YYYY.xlsxOmega_YYYY.xlsxFile InformationThe data is stored in Excel format (.xlsx), except the EC data availability stored as ASCII files (.txt). Reading data in this format requires access to Microsoft Excel software, which is a proprietary software. Data can be also accessed with open software, e.g. Python, LibreOffice.ReferenceBi, X., Gao, Z., Deng, X., Wu, D., Liang, J., Zhang, H., Sparrow, M., Du, J., Li, F., Tan, H.,2007. Seasonal and diurnal variations in moisture, heat, and CO2 fluxes over grassland in the tropical monsoon region of southern China. Journal of Geophysical Research Atmospheres 112, 1–14. https://doi.org/10.1029/2006JD007889.Borges, C.K., Carneiro, R.G., Santos, C.A., Zeri, M., Poczta, P., Cunha, A.P.M.A., Stachlewska, I.S., dos Santos, C.A.C., 2024. Partitioning of water vapor and CO2 fluxes and underlying water use efficiency evaluation in a Brazilian seasonally dry tropical forest (Caatinga) using the Fluxpart model. J. S. Am. Earth Sci. 142, 104963. https://doi.org/10.1016/j.jsames.2024.104963.Borges, C.K., dos Santos, C.A.C., Carneiro, R.G., da Silva, L.L., de Oliveira, G., Mariano, D., Silva, M.T., da Silva, B.B., Bezerra, B.G., Perez-Marin, A.M., de, S., Medeiros, S., 2020. Seasonal variation of surface radiation and energy balances over two contrasting areas of the seasonally dry tropical forest (Caatinga) in the Brazilian semi-arid. Environ. Monit. Assess. 192. https://doi.org/10.1007/s10661-020- 08484-y.Carneiro, R. G., Rykowska, Z., Borges, C. K., Stachlewska, I. S., & dos Santos, C. A. C. (2025). Energy balance and Evapotranspiration response to environmental variables in the semi-arid Caatinga biome. Journal of South American Earth Sciences, 152(105319), 105319. doi:10.1016/j.jsames.2024.105319.Lucas-Moffat, A. M., Schrader, F., Herbst, M., & Brümmer, C. (2022). Multiple gap-filling for eddy covariance datasets. Agricultural and Forest Meteorology, 325(109114), 109114. doi:10.1016/j.agrformet.2022.109114.Speranskaya, L., Campbell, D. I., Lafleur, P. M., & Humphreys, E. R. (2024). Peatland evaporation across hemispheres: contrasting controls and sensitivity to climate warming driven by plant functional types. Biogeosciences, 21(5), 1173–1190. doi:10.5194/bg-21-1173-2024.AcknowledgmentsThe quality assurance and check of the published dataset was done within the National Science Centre, Poland, Weave-UNISONO programe (AEROPAN, G.A. no. 2021/03/Y/ST10/00206).We acknowledge that data curation was done within the European Commission's Horizon 2020 program (RI-URBANS G.A. No. 101036245).The PolWET site acknowledges support of Ministry of Science and Higher Education (MNiSW) under agreement no. UMOWA nr. 2024/WK/04(D-UMO25/24).ATTENTION:We offer a free access to this dataset. The user is however encouraged to share the information on the data use by sending an e-mail to rslab@fuw.edu.plIn the case this dataset is used for a scientific communication (publication, conference contribution, thesis) we would like to kindly ask for considering to acknowledge data provision by adding the following statement in Acknowledgments: "We acknowledge the data originators C.K. Borges, P. Poczta, and I.S. Stachlewska for the quality-assurance, evaluation, and provision of data sets."