Mobile Eddy-covariance tower network in the Dutch peatlands – Data-driven gap-filling creating site-specific Ecosystem Response Functions.

In the Netherlands, the peat soils degradation is estimated to contribute annually from  4.6 to 7 Mt CO2, representing around 3% of the annual national GHG emissions. Following the Paris Agreement, the Dutch government presented a national Climate agreement in 2019; reducing net CO2 emission of fen meadows by 1 Mt CO2 per year is part of the objectives for 2030. In order to comply with this, the Ministry of Agriculture, Nature and Food Quality set up a research consortium, The Dutch National Research Programme on Greenhouse Gases in Peatlands. The NOBV implemented an intensive GHG monitoring network mainly based on gas chambers, on-site and airborne Eddy-Covariance measurements. Mapping these emissions according to the diversity of peat, edaphic conditions, grassland management, and water table management is one of the challenges of this research programme.

25 measurement sites are part of the NOBV Eddy-Covariance Network and are currently investigated using Mobile EC towers; using mobile EC towers instead of permanent ones is a pragmatic solution to embrace this site diversity. These mobile station set-ups include meteorological variables measurement, these alternate between closely located sites with different characteristics, assuming the meteorological variation is weak between both. Constructing annual GHG budgets requires a robust gap-filling method, able to operate for large gaps; the traditional gap-filling algorithms require long-term measurements, while this project occurs during a limited time window. These algorithms also usually fail at predicting fluxes after abrupt changes. By combining external data sources, remote-sensing and data mining, the objective is to decrease the uncertainties introduced by these gaps in a consistent way.

More than time-series gap-filling, this approach provides site-specific data-driven Ecosystem Response Functions, it constitutes the first step to a bottom-up approach that will take into account more site-specific parameters. The interpretation of purely data-driven models is not as straightforward as process-based models, requiring the use of more ML-oriented tools, such as Shapley values. Another challenge is the partitioning of fluxes between the peat degradation-related emissions and the plant photosynthetic curves based on these data-driven models, highlighting the effect of external drivers such as soil moisture/temperature and water table depth.