Inference of spatial heterogeneity in surface fluxes from eddy covariance data: a case study from a subarctic mire ecosystem

Horizontal heterogeneity causes difficulties in the eddy covariance technique for measuring surface fluxes, related to both advection and the confounding of temporal and spatial variability. Our aim here was to address this problem, using statistical modelling and footprint analysis, applied to a case study of fluxes of sensible heat and methane in a subarctic mire. We applied a new method to infer the spatial heterogeneity in fluxes of sensible heat and methane from a subarctic ecosystem in northern Sweden, where there were clear differences in surface types within the landscape. We inferred the flux from each of these surface types, using a Bayesian approach to estimate the parameters of a hierarchical model which includes coefficients for the different surface types. The approach is based on the variation in the flux observed at a single eddy covariance tower as the footprint changes over time. The method has applications wherever spatial heterogeneity is a concern in the interpretation of eddy covariance fluxes

Estimating evaporation with thermal UAV data and two-source energy balance models

Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperature-difference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST

Climate and site management as driving factors for the atmospheric greenhouse gas exchange of a restored wetland

The atmospheric greenhouse gas (GHG) budget of a restored wetland in western Denmark was established for the years 2009–2011 from eddy covariance measurements of carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) fluxes. The water table in the wetland, which was restored in 2002, was unregulated, and the vegetation height was limited through occasional grazing by cattle and grass cutting. The annual net CO<sub>2</sub> uptake varied between 195 and 983 g m<sup>−2</sup> and the annual net CH<sub>4</sub> release varied between 11 and 17 g m<sup>−2</sup>. In all three years the wetland was a carbon sink and removed between 42 and 259 g C m<sup>−2</sup> from the atmosphere. However, in terms of the full annual GHG budget (assuming that 1 g CH<sub>4</sub> is equivalent to 25 g CO<sub>2</sub> with respect to the greenhouse effect over a time horizon of 100 years) the wetland was a sink in 2009, a source in 2010 and neutral in 2011. Complementary observations of meteorological factors and management activities were used to explain the large inter-annual variations in the full atmospheric GHG budget of the wetland. The largest impact on the annual GHG fluxes, eventually defining their sign, came from site management through changes in grazing duration and animal stocking density. These changes accounted for half of the observed variability in the CO<sub>2</sub> fluxes and about two thirds of the variability in CH<sub>4</sub> fluxes. An unusually long period of snow cover in 2010 had the second largest effect on the annual CO<sub>2</sub> flux, whose interannual variability was larger than that of the CH<sub>4</sub> flux. Since integrated CO<sub>2</sub> and CH<sub>4</sub> flux data from restored wetlands are still very rare, it is concluded that more long-term flux measurements are needed to quantify the effects of ecosystem disturbance, in terms of management activities and exceptional weather patterns, on the atmospheric GHG budget more accurately