Abstract. The energy balance of eddy covariance (EC) flux data is normally not closed. Therefore, at least if used for modelling, EC flux data are usually post-closed, i.e. the measured turbulent fluxes are adjusted so as to close the energy balance. At the current state of knowledge, however, it is not clear how to partition the missing energy in the right way. Eddy flux data therefore contain some uncertainty due to the unknown nature of the energy balance gap, which should be considered in model evaluation and the interpretation of simulation results. We propose to construct the post-closure methods uncertainty band (PUB), which essentially designates the differences between non-adjusted flux data and flux data adjusted with the three post-closure methods (Bowen ratio, latent heat flux (LE) and sensible heat flux (H ) method). To demonstrate this approach, simulations with the NOAH-MP land surface model were evaluated based on EC measurements conducted at a winter wheat stand in southwest Germany in 2011, and the performance of the Jarvis and Ball-Berry stomatal resistance scheme was compared. The width of the PUB of the LE was up to 110 W m −2 (21 % of net radiation). Our study shows that it is crucial to account for the uncertainty in EC flux data originating from lacking energy balance closure. Working with only a single post-closing method might result in severe misinterpretations in model-data comparisons.
Abstract. The energy balance of eddy covariance (EC) flux data is typically not closed. The nature of the gap is usually not known, which hampers using EC data to parameterize and test models. In the present study we crosschecked the evapotranspiration data obtained with the EC method (ET EC ) against ET rates measured with the soil water balance method (ET WB ) at winter wheat stands in southwest Germany. During the growing seasons 2012 and 2013, we continuously measured, in a half-hourly resolution, latent heat (LE) and sensible (H ) heat fluxes using the EC technique. Measured fluxes were adjusted with either the Bowen-ratio (BR), H or LE post-closure method. ET WB was estimated based on rainfall, seepage and soil water storage measurements. The soil water storage term was determined at sixteen locations within the footprint of an EC station, by measuring the soil water content down to a soil depth of 1.5 m. In the second year, the volumetric soil water content was additionally continuously measured in 15 min resolution in 10 cm intervals down to 90 cm depth with sixteen capacitance soil moisture sensors. During the 2012 growing season, the H post-closed LE flux data (ET EC = 3.4 ± 0.6 mm day −1 ) corresponded closest with the result of the WB method (3.3 ± 0.3 mm day −1 ). ET EC adjusted by the BR (4.1 ± 0.6 mm day −1 ) or LE (4.9 ± 0.9 mm day −1 ) post-closure method were higher than the ET WB by 24 and 48 %, respectively. In 2013, ET WB was in best agreement with ET EC adjusted with the H postclosure method during the periods with low amount of rain and seepage. During these periods the BR and LE postclosure methods overestimated ET by about 46 and 70 %, respectively. During a period with high and frequent rainfalls, ET WB was in-between ET EC adjusted by H and BR post-closure methods. We conclude that, at most observation periods on our site, LE is not a major component of the energy balance gap. Our results indicate that the energy balance gap is made up by other energy fluxes and unconsidered or biased energy storage terms.
Abstract. The energy balance of eddy covariance (EC) flux data is typically not closed. The nature of the gap is usually not known, which hampers using EC data to parameterize and test models. The present study elucidates the nature of the energy gap of EC flux data from winter wheat stands in southwest Germany. During the vegetation periods 2012 and 2013, we continuously measured, in a half-hourly resolution, latent (LE) and sensible (H) heat fluxes using the EC technique. Measured fluxes were adjusted with either the Bowen-ratio (BR), H or LE post-closure method. The adjusted LE fluxes were tested against evapotranspiration data (ETWB) calculated using the soil water balance (WB) method. At sixteen locations within the footprint of an EC station, the soil water storage term was determined by measuring the soil water content down to a soil depth of 1.5 m. In the second year, the volumetric soil water content was also continuously measured in 15 min resolution in 10 cm intervals down to 90 cm depth with sixteen capacitance soil moisture sensors. During the 2012 vegetation period, the H post-closed LE flux data (ETEC = 3.4 ± 0.6 mm day−1) corresponded closest with the result of the WB method (3.3 ± 0.3 mm day−1). ETEC adjusted by the BR (4.1 ± 0.6 mm day−1) or LE (4.9 ± 0.9 mm day−1) post-closure method were higher than the ETWB by 20 and 33%, respectively. In 2013, ETWB was in best agreement with ETEC adjusted with the H post-closure method during the periods with low amount of rain and seepage. During these periods the BR and LE post-closure methods overestimated ET by about 30 and 40%, respectively. During a period with high and frequent rainfalls, ETWB was in-between ETEC adjusted by H and BR post-closure methods. We conclude that, at most vegetation periods on our site, LE is not a~major component of the energy balance gap. Our results indicate that the energy balance gap other energy fluxes and unconsidered or biased energy storage terms.
The spatial variability of topsoil water content (SWC) is often expressed through the relationship between its spatial mean 〈θ〉 and standard deviation σθ. The present study tests the concept that a reasonably performing land surface model (LSM) should be able to produce σθ–〈θ〉 data pairs that fall into a polygon, spanned by the cloud of observed data and two anchor points: σθ at the permanent wilting point σθ–〈θwp〉 and σθ at saturation σθ–〈θs〉. A state-of-the-art LSM, Noah-MP, was driven by atmospheric forcing data obtained from eddy covariance field measurements in two regions of southwestern Germany, Kraichgau (KR) and Swabian Alb (SA). KR is characterized with deep loess soils, whereas the soils in SA are shallow, clayey, and stony. The simulations series were compared with SWC data from soil moisture networks operating in the two study regions. The results demonstrate that Noah-MP matches temporal 〈θ〉 dynamics fairly well in KR, but performs poorly in SA. The best match is achieved with the van Genuchten–Mualem representation of soil hydraulic functions and site-specific rainfall, soil texture, green vegetation fraction (GVF) and leaf area index (LAI) input data. Nevertheless, most of the simulated σθ–〈θ〉 pairs are located outside the envelope of measurements and below the lower bound, which shows that the model smooths spatial SWC variability. This can be mainly attributed to missing topography and terrain information and inadequate representation of spatial variability of soil texture and hydraulic parameters, as well as the model assumption of a uniform root distribution.
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