Surface interpolation of environmental factors as tool for evaluation of the occurrence of high methane and nitrous oxide fluxes

Abstract

Greenhouse gas (GHG) emissions from wetlands typically exhibit extended low‐flux phases accompanied by distinct high‐flux events. Prediction and explanation of flux occurrence is hindered by various interactions of the underlying environmental predictors. Here, a novel approach is described to gain insight in patterns of environmental factors, which lead to elevated emissions. Natural neighbor interpolation was utilized to construct flux intensity maps based on two environmental predictors, the ground water level and soil temperature. Further, the effect of inhomogeneous data density was considered by filtering estimates for sufficient information content. The practical application was exemplified by CH4 and N2O flux field data of three years from two fen sites in southwest Germany. This approach considered factor interactions as well as the fluxes occurring as rare events. The application can be useful for the delineation of driving conditions for high‐flux events as well as for wetland management practice.

Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

“…We used Matlab's internal 'scatteredInterpolant class' to perform the NN interpolation. Previous studies have applied IDW and NN to (spatially) interpolate rainfall and other environmental data at multiple time scales (e.g., [60][61][62][63][64][65]).…”

The Baltic TRANSCOAST approach – investigating shallow coasts as terrestrial-marine interface of water and matter fluxes

“…The close interaction between water levels and matter cycling or gas emissions in peatlands is now well understood (e.g., Jurasinski et al 2016) and it is clear that peatlands are the most important natural methane source globally (Anderson et al 2010). However, there also seems to be a large small-scale variability within peatlands (Hendriks et al 2010, Koch et al 2014, which points to the influence of plants (Dias et al 2010) and microorganisms (Liebner et al 2015, Wen et al 2018 or substrate properties (Lengerer & Kazda 2018).…”