Area-Averaged Surface Fluxes Over the Litfass Region Based on Eddy-Covariance Measurements

2006 ◽  
Vol 121 (1) ◽  
pp. 33-65 ◽  
Author(s):  
Frank Beyrich ◽  
Jens-Peter Leps ◽  
Matthias Mauder ◽  
Jens Bange ◽  
Thomas Foken ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Surface Fluxes ◽  
Eddy Covariance Measurements

scholarly journals Preserving the variance in imputed eddy-covariance measurements: Alternative methods for defensible gap filling

2017 ◽  
Vol 232 ◽  
pp. 635-649 ◽  
Author(s):  
Sujit Kunwor ◽  
Gregory Starr ◽  
Henry W. Loescher ◽  
Christina L. Staudhammer
Keyword(s):  
Eddy Covariance ◽  
Alternative Methods ◽  
Gap Filling ◽  
Eddy Covariance Measurements

Application of an alternative method to derive reliable estimates of nighttime respiration from eddy covariance measurements in moderately complex topography

2008 ◽  
Vol 148 (6-7) ◽  
pp. 1174-1180 ◽  
Author(s):  
Eva van Gorsel ◽  
Ray Leuning ◽  
Helen A. Cleugh ◽  
Heather Keith ◽  
Miko U.F. Kirschbaum ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Complex Topography ◽  
Alternative Method ◽  
Eddy Covariance Measurements

Multi-year eddy covariance measurements of net ecosystem exchange in tropical dry deciduous forest of India

2021 ◽  
Vol 301-302 ◽  
pp. 108351
Author(s):  
Suraj Reddy Rodda ◽  
Kiran Chand Thumaty ◽  
MSS Praveen ◽  
Chandra Shekhar Jha ◽  
Vinay Kumar Dadhwal
Keyword(s):  
Eddy Covariance ◽  
Deciduous Forest ◽  
Net Ecosystem Exchange ◽  
Dry Deciduous Forest ◽  
Eddy Covariance Measurements

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

2016 ◽  
Vol 20 (2) ◽  
pp. 697-713 ◽  
Author(s):  
H. Hoffmann ◽  
H. Nieto ◽  
R. Jensen ◽  
R. Guzinski ◽  
P. Zarco-Tejada ◽  
...  
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
High Resolution ◽  
Eddy Covariance ◽  
Land Surface ◽  
Weather Conditions ◽  
Heat Fluxes ◽  
Thermal Camera ◽  
Model Input ◽  
Eddy Covariance Measurements

Abstract. 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.

scholarly journals Inferring <sup>222</sup>Rn soil fluxes from ambient <sup>222</sup>Rn activity and eddy covariance measurements of CO<sub>2</sub>

2016 ◽  
Vol 9 (11) ◽  
pp. 5523-5533 ◽  
Author(s):  
Sander van der Laan ◽  
Swagath Manohar ◽  
Alex Vermeulen ◽  
Fred Bosveld ◽  
Harro Meijer ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Activity Concentration ◽  
Regional Scale ◽  
Surface Flux ◽  
Surface Fluxes ◽  
Spatial And Temporal Variability ◽  
Single Pair ◽  
The South ◽  
Practical Applications ◽  
Soil Flux

Abstract. We present a new methodology, which we call Single Pair of Observations Technique with Eddy Covariance (SPOT-EC), to estimate regional-scale surface fluxes of 222Rn from tower-based observations of 222Rn activity concentration, CO2 mole fractions and direct CO2 flux measurements from eddy covariance. For specific events, the regional (222Rn) surface flux is calculated from short-term changes in ambient (222Rn) activity concentration scaled by the ratio of the mean CO2 surface flux for the specific event to the change in its observed mole fraction. The resulting 222Rn surface emissions are integrated in time (between the moment of observation and the last prior background levels) and space (i.e. over the footprint of the observations). The measurement uncertainty obtained is about ±15 % for diurnal events and about ±10 % for longer-term (e.g. seasonal or annual) means. The method does not provide continuous observations, but reliable daily averages can be obtained. We applied our method to in situ observations from two sites in the Netherlands: Cabauw station (CBW) and Lutjewad station (LUT). For LUT, which is an intensive agricultural site, we estimated a mean 222Rn surface flux of (0.29 ± 0.02) atoms cm−2 s−1 with values  > 0.5 atoms cm−2 s−1 to the south and south-east. For CBW we estimated a mean 222Rn surface flux of (0.63 ± 0.04) atoms cm−2 s−1. The highest values were observed to the south-west, where the soil type is mainly river clay. For both stations good agreement was found between our results and those from measurements with soil chambers and two recently published 222Rn soil flux maps for Europe. At both sites, large spatial and temporal variability of 222Rn surface fluxes were observed which would be impractical to measure with a soil chamber. SPOT-EC, therefore, offers an important new tool for estimating regional-scale 222Rn surface fluxes. Practical applications furthermore include calibration of process-based 222Rn soil flux models, validation of atmospheric transport models and performing regional-scale inversions, e.g. of greenhouse gases via the SPOT 222Rn-tracer method.

scholarly journals Assessing the Impact of Changes in Land Surface Conditions on WRF Predictions in Arid Regions

2020 ◽  
Vol 21 (12) ◽  
pp. 2829-2853 ◽  
Author(s):  
Marouane Temimi ◽  
Ricardo Fonseca ◽  
Narendra Nelli ◽  
Michael Weston ◽  
Mohan Thota ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Soil Texture ◽  
Land Surface ◽  
Surface Wind ◽  
Surface Fluxes ◽  
Abu Dhabi ◽  
Horizontal Wind ◽  
Cold Bias ◽  
Near Surface ◽  
The Impact

AbstractA thorough evaluation of the Weather Research and Forecasting (WRF) Model is conducted over the United Arab Emirates, for the period September 2017–August 2018. Two simulations are performed: one with the default model settings (control run), and another one (experiment) with an improved representation of soil texture and land use land cover (LULC). The model predictions are evaluated against observations at 35 weather stations, radiosonde profiles at the coastal Abu Dhabi International Airport, and surface fluxes from eddy-covariance measurements at the inland city of Al Ain. It is found that WRF’s cold temperature bias, also present in the forcing data and seen almost exclusively at night, is reduced when the surface and soil properties are updated, by as much as 3.5 K. This arises from the expansion of the urban areas, and the replacement of loamy regions with sand, which has a higher thermal inertia. However, the model continues to overestimate the strength of the near-surface wind at all stations and seasons, typically by 0.5–1.5 m s−1. It is concluded that the albedo of barren/sparsely vegetated regions in WRF (0.380) is higher than that inferred from eddy-covariance observations (0.340), which can also explain the referred cold bias. At the Abu Dhabi site, even though soil texture and LULC are not changed, there is a small but positive effect on the predicted vertical profiles of temperature, humidity, and horizontal wind speed, mostly between 950 and 750 hPa, possibly because of differences in vertical mixing.

scholarly journals Near-zero methane emission from an abandoned boreal peatland pasture based on eddy covariance measurements

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189692 ◽  
Author(s):  
Mei Wang ◽  
Jianghua Wu ◽  
Junwei Luan ◽  
Peter Lafleur ◽  
Huai Chen ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Methane Emission ◽  
Eddy Covariance Measurements
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