Model-based Source Partitioning of Eddy Covariance Flux Measurements

Terrestrial ecosystems constantly exchange momentum, energy, and mass (e.g., water vapor, CO2) with the atmosphere above. This exchange is commonly measured with a micrometeorological technique, the eddy covariance (EC) method. Various components of the measured net fluxes, such as transpiration, evaporation, gross primary production, and soil respiration, cannot be depicted separately by the EC approach. Thus, so-called source partitioning approaches have to be applied to CO2 and water vapor EC data to gain a better understanding of the prevailing processes and their interrelations in terrestrial ecosystems. A large variety of partitioning procedures with diverse model approaches have been developed, including various driving variables, necessity of different input data and parameterizations. The most robust and commonly used source partitioning tools for CO2 flux components, often primarily developed to fill gaps in EC measurements, are based on the notion that during night respiration fluxes prevail. They use non-linear regressed relationships of these nighttime observations and physical drivers (e.g., temperature in the approach after Reichstein et al. 2005). Here, the challenge lies within extrapolating the nighttime relationship to daytime conditions, and analogous methods for water fluxes are lacking. In this thesis, next to the approach after Reichstein et al. (2005) various data-driven source partitioning approaches for H2O and CO2 fluxes were applied, compared, modified, and evaluated for multiple ecosystems to get a better understanding of the methods’ functionality, dependencies, uncertainties, advantages, and shortcomings. We first describe the coupling and extension of the complex terrestrial ecosystem model AgroC. Further, we conducted a comprehensive model-data fusion study to clarify the CO2 exchange in agroecosystems and estimate their annual carbon balance. For three test sites in Western Germany, AgroC was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated sufficiently in terms of hourly net ecosystem exchange (NEE) measured with the EC technique. Moreover, AgroC reproduced the flux dynamics very effectively after sudden changes in the grassland canopy due to mowing. In a second step, AgroC was optimized with the EC measurements to examine the effect of various objective functions, constraints, and data-transformations on the estimated carbon balance and to compare the results to the established gap-filling approach after Reichstein et al. (2005). It was found that modeled NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed extensively. Therefore, it is concluded that data-transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of EC measurements. Second, we applied the source partitioning approaches after Scanlon and Kustas (2010; SK10) and after Thomas et al. (2008; TH08) to high frequency EC measurements estimating transpiration, evaporation, net primary production, and soil respiration, of various ecosystems (croplands, grasslands, and forests). Both partitioning methods are based on higher-order statistics of the H2O and CO2 fluctuations, but proceed differently. SK10 had the tendency to overestimate and TH08 to underestimate soil flux components, where the partitioning of CO2 fluxes was more irregular than of H2O fluxes. Results derived with SK10 showed relatively large dependencies on estimated water use efficiency (WUE) on leaf-level, which is needed as an input. Measurements of outgoing longwave radiation used for the estimation of foliage temperature and WUE could slightly increase the quality of the partitioning results. A modification of the TH08 approach, by applying a cluster analysis for the conditional sampling of respiration/evaporation events, performed sufficiently, but did not result in significant advantages compared to the other method versions. The performance of each partitioning approach was dependent on meteorological conditions, plant development, canopy height, canopy density, and measurement height. Foremost, the performance of SK10 correlated negatively with the ratio between measurement and canopy height. The performance of TH08 was more dependent on canopy height and leaf area index. It was found, that all site characteristics which increase dissimilarities between scalars enhance partitioning performance for SK10 and TH08. Also, we conducted large eddy simulations (LES), simulating the turbulent transport of H2O and CO2. SK10 was applied to the synthetic high frequency data generated by LES, and the effects of canopy type, measurement height, given scalar sink-source-distributions, and estimated WUE input were tested regarding the partitioning performance. The LES-based analysis revealed that for a satisfying performance of SK10, a certain degree of decorrelation of the H2O and CO2 fluctuations was needed and a correct WUE estimation was favorable. Furthermore, another possible error source, which was so far not yet discussed in the literature, could be detected for the partitioning approach. In the special case of the LES experiments, validity of an essential assumption about the prevailing transport efficiencies of the scalars in the method’s derivation was found to be a crucial point for a correct application of SK10. The application of different source partitioning methods including their various involved assumptions, required input data and work effort showed that still uncertainties and unknowns prevail for the source partitioning of water vapor and CO2 fluxes. An assessment and evaluation of the partitioning results can only be conducted with additional measurements of flux components on differing spatial and temporal scales independent of the EC measurements. Further, the application of multiple partitioning methods (usage of an ensemble) to the same data can give a better idea about uncertainties in the results

A Modeling Approach to Investigate Drivers, Variability and Uncertainties in O2 Fluxes and the O2 : CO2 Exchange Ratios in a Temperate Forest

The O2 : CO2 exchange ratio (ER) between terrestrial ecosystems and the atmosphere is a key parameter for partitioning global ocean and land carbon fluxes. The long-term terrestrial ER is considered to be close to 1.10 moles of O2 consumed per mole of CO2 produced. Due to the technical challenge in measuring directly the ER of entire terrestrial ecosystems (EReco), little is known about the variations in ER at the hourly and seasonal scales as well as how different components contribute to EReco. In this modeling study, we explore the variability and drivers of EReco and evaluate the hypothetical uncertainty in determining ecosystem O2 fluxes based on current instrument precision. We adapted the one-dimensional, multi-layer atmosphere-biosphere gas exchange model, CANVEG, to simulate hourly EReco from modeled O2 and CO2 fluxes in a temperate beech forest in Germany. We found that the annual mean EReco ranged from 1.06 to 1.12 mol mol-1 within the five years&rsquo; study period. Hourly EReco showed strong variations over diel and seasonal cycles and within the vertical canopy profile. Determination of ER from O2 and CO2 mole fractions in air above and within the canopy (ERconc) varied between 1.115 and 1.15 mol mol-1. CANVEG simulations also indicated that ecosystem O2 fluxes could be derived using the flux-gradient method in combination with measurements of vertical scalar gradients and CO2, sensible heat or latent heat fluxes obtained with the eddy covariance technique. Owing to measurement uncertainties, however, the uncertainty in estimated O2 fluxes derived with the flux-gradient approach could be as high as 15 &mu;mol m-2 s-1, which represented the 90 % quantile of the uncertainty in hourly data with a high-accuracy instrument. We also demonstrated that O2 fluxes can be used to partition net CO2 exchange fluxes into their component fluxes of photosynthesis and respiration, if EReco is known. The uncertainty of the partitioned gross assimilation ranged from 1.43 to 4.88 &mu;mol m-2 s-1 assuming a measurement uncertainty of 0.1 or 2.5 &mu;mol m-2 s-1 for net ecosystem CO2 exchange and from 0.1 to 15 &mu;mol m-2 s-1 for net ecosystem O2 exchange, respectively. Our analysis suggests that O2 measurements at ecosystem scale have the potential for partitioning net CO2 fluxes into their component fluxes, but further improvement in instrument precision is needed.</p

"Off-Season" - CO2_{2}-Austausch landwirtschaftlicher Flächen

Durch zunehmende CO2-Konzentrationen und Erwärmung hat sich sowohl die Senken- (Photosynthese) als auch die Quellenfunktion (Respiration) der terrestrischen Biosphäre intensiviert. Der Nettoeffekt entspricht derzeit einer Senke, die etwa ein Drittel der CO2-Emissionen aus fossilen Brennstoffen aufgenommen hat. Allerdings stellt zugleich der Landnutzungswandel eine Nettoquelle von etwa 14% dar (5. IPCC-Sachstandsbericht, WG I, Kap. 6, S. 471, 2013).Die Klimawirksamkeit der Landwirtschaft wird von allen drei Faktoren beeinflusst – einem steigenden Senkenpotential durch den CO2-Düngeeffekt, einer steigenden (Boden)respiration durch Erwärmung, und Landnutzungsentscheidungen. Die Wechselwirkung zwischen ihnen wird im Folgenden am Beispiel der zunehmenden Klimarelevanz von Entscheidungen über die Zwischennutzung landwirtschaftlicher Flächen demonstriert.In den letzten 50 Jahren haben sich die Aussaattermine für Winterweizen in Deutschland etwa um eine, die Erntetermine um zwei Wochen nach vorne verschoben, ähnliches gilt für vergleichbare Kulturen. Die nicht für den produktiven Anbau genutzte Zeit wird sowohl länger als auch wärmer – einerseits wegen ihrer zunehmenden Verschiebung in Richtung Sommer, andererseits wegen steigender Jahresmitteltemperaturen. Die Entscheidung über die Verwendung dieser Phasen wird somit klimarelevanter: Bei vegetationsfreiem Boden ist eine stärkere respirationsbedingten Quellenfunktion, bei einer Nutzung für den produktiven Anbau oder einer Einsaat von Zwischenfrüchten eine stärkere Senkenfunktion möglich.Durch die EU-Gesetzgebung unter dem Stichwort „Greening“ erscheint eine sprunghafte Zunahme von Zwischensaaten wie z.B. Ölrettich und Gelbsenf im Winter 2015/16 wahrscheinlich. Dies bietet eine gute Gelegenheit zur Quantifizierung des möglichen Einflusses von Landnutzungsentscheidungen auf diese „Off-Season“-Klimawirksamkeit. Auf dem Poster stellen wir Ergebnisse von CO2-Austauschmessungen in Zwischensaatbeständen und Pläne zur satellitengestützten Quantifizierung ihrer Anbaufläche vor. Die Messungen des CO2-Austauschs, der Verdunstung und der Bodenrespiration mit Hilfe von Eddy-Kovarianz-Stationen und zwei verschiedenen Haubensystemen sind ein Teil des BMBF-geförderten Projektes „IDAS-GHG“, dessen Gesamtkonzept im Vorjahr vorgestellt wurde

Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics-based quantitative soil information

Agroecosystem models that simulate crop growth as a function of weather conditionsand soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field-scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics-based soil map was intersected with land use information. Soilhydraulic parameters were calculated using pedotransfer functions. Simulations of soilwater content dynamics performed with the agroecosystem model AgroC were com-pared with soil water content measured at two locations, resulting in RMSE of 0.032and of 0.056 cm3cm−3, respectively. The AgroC model was then used to simulate thegrowth of sugar beet (Beta vulgaris L.), silage maize (Zea maysL.), potato (SolanumtuberosumL.), winter wheat (Triticum aestivumL.), winter barley (Hordeum vulgareL.), and winter rapeseed (Brassica napusL.) in the 1- by 1-km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAINDVI) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics-based soilmapping was valuable for practical agricultural applications

Reconciling the Carbon Balance of Northern Sweden Through Integration of Observations and Modelling

The boreal biome plays an important role in the global carbon cycle. However, current estimates of its sink-source strength and responses to changes in climate are primarily derived from models and thus remain uncertain. A major challenge is the validation of these models at a regional scale since empirical flux estimates are typically confined to ecosystem or continental scales. The Integrated Carbon Observation System (ICOS)-Svartberget atmospheric station (SVB) provides observations including tall tower eddy covariance (EC) and atmospheric concentration measurements that can contribute to such validation in Northern Sweden. Thus, the overall aim of this study was to quantify the carbon balance in Northern Sweden region by integrating land-atmosphere fluxes and atmospheric carbon dioxide (CO2) concentrations. There were three specific objectives. First, to compare flux estimates from four models (VPRM, LPJ-GUESS, ORCHIDEE, and SiBCASA) to tall tower EC measurements at SVB during the years 2016-2018. Second to assess the fluxes' impact on atmospheric CO2 concentrations using a regional transport model. Third, to assess the impact of the drought in 2018. The comparison of estimated concentrations with ICOS observations helped the evaluation of the models' regional scale performance. Both the simulations and observations indicate there were similar reductions in the net CO2 uptake during drought. All the models (except for SiBCASA) and observations indicated the region was a net carbon sink during the 3-year study period. Our study highlights a need to improve vegetation models through comparisons with empirical data and demonstrate the ICOS network's potential utility for constraining CO2 fluxes in the region

Multi-site Calibration and Validation of a Net Ecosystem Carbon Exchange Model for Croplands

Croplands play an important role in the carbon budget of many regions. However, the estimation of their carbon balance remains difficult due to diversity and complexity of the processes involved. We report the coupling of a one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) to predict the net ecosystem exchange (NEE) of carbon. The coupled model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was sufficient with a model efficiency above 0.78 and a correlation coefficient above 0.91 for NEE. In a second step, AgroC was optimized with eddy covariance NEE measurements to examine the effect of different objective functions, constraints, and data-transformations on estimated NEE. It was found that NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. In particular, both positive and negative day‑ and nighttime fluxes were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed substantially. Therefore, it is concluded that data-transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of eddy covariance measurements

Reviews and syntheses : Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. It is difficult to partition ecosystem-scale evapotranspiration (ET) measurements into E and T, which makes it difficult to validate satellite data and land surface models. Here, we review current progress in partitioning E and T and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques create new opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. For example, many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but more analysis is necessary to determine the conditions for which this assumption holds. Another critical assumption for many partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T can exceed 95% of ET from certain ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T, yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon-water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water fluxes and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.Peer reviewe