14 results on '"Stauch, Vanessa"'
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2. Statistical properties of random CO 2 flux measurement uncertainty inferred from model residuals
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Richardson, Andrew D., Mahecha, Miguel D., Falge, Eva, Kattge, Jens, Moffat, Antje M., Papale, Dario, Reichstein, Markus, Stauch, Vanessa J., Braswell, Bobby H., Churkina, Galina, Kruijt, Bart, and Hollinger, David Y.
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- 2008
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3. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes
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Moffat, Antje M., Papale, Dario, Reichstein, Markus, Hollinger, David Y., Richardson, Andrew D., Barr, Alan G., Beckstein, Clemens, Braswell, Bobby H., Churkina, Galina, Desai, Ankur R., Falge, Eva, Gove, Jeffrey H., Heimann, Martin, Hui, Dafeng, Jarvis, Andrew J., Kattge, Jens, Noormets, Asko, and Stauch, Vanessa J.
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- 2007
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4. Datengeleitete Methoden zur Analyse und Interpretation von Turbulenz-Korrelations-Messungen
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Stauch, Vanessa Juliane
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ddc:550 ,Institut für Umweltwissenschaften und Geographie - Abstract
The terrestrial biosphere impacts considerably on the global carbon cycle. In particular, ecosystems contribute to set off anthropogenic induced fossil fuel emissions and hence decelerate the rise of the atmospheric CO₂ concentration. However, the future net sink strength of an ecosystem will heavily depend on the response of the individual processes to a changing climate. Understanding the makeup of these processes and their interaction with the environment is, therefore, of major importance to develop long-term climate mitigation strategies. Mathematical models are used to predict the fate of carbon in the soil-plant-atmosphere system under changing environmental conditions. However, the underlying processes giving rise to the net carbon balance of an ecosystem are complex and not entirely understood at the canopy level. Therefore, carbon exchange models are characterised by considerable uncertainty rendering the model-based prediction into the future prone to error. Observations of the carbon exchange at the canopy scale can help learning about the dominant processes and hence contribute to reduce the uncertainty associated with model-based predictions. For this reason, a global network of measurement sites has been established that provides long-term observations of the CO₂ exchange between a canopy and the atmosphere along with micrometeorological conditions. These time series, however, suffer from observation uncertainty that, if not characterised, limits their use in ecosystem studies. The general objective of this work is to develop a modelling methodology that synthesises physical process understanding with the information content in canopy scale data as an attempt to overcome the limitations in both carbon exchange models and observations. Similar hybrid modelling approaches have been successfully applied for signal extraction out of noisy time series in environmental engineering. Here, simple process descriptions are used to identify relationships between the carbon exchange and environmental drivers from noisy data. The functional form of these relationships are not prescribed a priori but rather determined directly from the data, ensuring the model complexity to be commensurate with the observations. Therefore, this data-led analysis results in the identification of the processes dominating carbon exchange at the ecosystem scale as reflected in the data. The description of these processes may then lead to robust carbon exchange models that contribute to a faithful prediction of the ecosystem carbon balance. This work presents a number of studies that make use of the developed data-led modelling approach for the analysis and interpretation of net canopy CO₂ flux observations. Given the limited knowledge about the underlying real system, the evaluation of the derived models with synthetic canopy exchange data is introduced as a standard procedure prior to any real data employment. The derived data-led models prove successful in several different applications. First, the data-based nature of the presented methods makes them particularly useful for replacing missing data in the observed time series. The resulting interpolated CO₂ flux observation series can then be analysed with dynamic modelling techniques, or integrated to coarser temporal resolution series for further use e.g., in model evaluation exercises. However, the noise component in these observations interferes with deterministic flux integration in particular when long time periods are considered. Therefore, a method to characterise the uncertainties in the flux observations that uses a semi-parametric stochastic model is introduced in a second study. As a result, an (uncertain) estimate of the annual net carbon exchange of the observed ecosystem can be inferred directly from a statistically consistent integration of the noisy data. For the forest measurement sites analysed, the relative uncertainty for the annual sum did not exceed 11 percent highlighting the value of the data. Based on the same models, a disaggregation of the net CO₂ flux into carbon assimilation and respiration is presented in a third study that allows for the estimation of annual ecosystem carbon uptake and release. These two components can then be further analysed for their separate response to environmental conditions. Finally, a fourth study demonstrates how the results from data-led analyses can be turned into a simple parametric model that is able to predict the carbon exchange of forest ecosystems. Given the global network of measurements available the derived model can now be tested for generality and transferability to other biomes. In summary, this work particularly highlights the potential of the presented data-led methodologies to identify and describe dominant carbon exchange processes at the canopy level contributing to a better understanding of ecosystem functioning. Der Kohlenstoffhaushalt der Erde wird maßgeblich von der bewachsenen Landoberfläche beeinflusst. Insbesondere tragen terrestrische Ökosysteme dazu bei, den Anstieg der atmosphärischen Kohlenstoffdioxid- (CO₂-) Konzentration durch anthropogen verursachte Emissionen fossiler Brennstoffe zu verlangsamen. Die Intensität der Netto-CO₂-Aufnahme wird allerdings in einem sich verändernden Klima davon abhängen, wie einzelne Prozesse auf Änderungen der sie beeinflussenden Umweltfaktoren reagieren. Fundierte Kenntnisse dieser Prozesse und das Verständnis ihrer Wechselwirkungen mit der Umwelt sind daher für eine erfolgreiche Klimaschutzpolitik von besonderer Bedeutung. Mit Hilfe von mathematischen Modellen können Vorhersagen über den Verbleib des Kohlenstoffs im System Boden-Pflanze-Atmosphäre unter zukünftigen Umweltbedingungen getroffen werden. Die verantwortlichen Prozesse und ihre Wechselwirkungen mit der Umwelt sind jedoch kompliziert und bis heute auf der Ökosystemskala nicht vollkommen verstanden. Entwickelte Modelle und deren Vorhersagen sind deshalb derzeit mit erheblichen Unsicherheiten behaftet. Messungen von CO₂-Austauschflüssen zwischen einem Ökosystem und der Atmosphäre können dabei helfen, Vorgänge besser verstehen zu lernen und die Unsicherheiten in CO₂-Austausch-Modellen zu reduzieren. Allerdings sind auch diese Beobachtungen, wie alle Umweltmessungen, von Unsicherheiten durchsetzt. Ziel dieser Arbeit ist es Methoden zu entwickeln, die physikalisches Prozessverständnis mit dem dennoch großen Informationsgehalt dieser Daten vorteilhaft zu vereinigen. Dabei soll vereinfachtes Prozessverständnis dazu genutzt werden, Zusammenhänge zwischen dem CO₂-Austausch und den umgebenden Umweltbedingungen aus den Beobachtungen abzuleiten. Das Besondere hierbei ist, dass diese Zusammenhänge direkt aus den Daten geschätzt werden, ohne vorher Annahmen über ihre funktionale Form zu machen. Die Daten als Ausgangspunkt der Modellentwicklung zu wählen gewährleistet, dass die Komplexität der Modelle dem Informationsgehalt der Messungen entspricht. Auf diese Weise lassen sich diejenigen Prozesse identifizieren, welche für den CO₂-Austausch mit der Atmosphäre dominant sind. Die gewonnenen Erkenntnisse können dann in robuste CO₂-Austauschmodelle für Ökosysteme überführt werden und zur Vorhersage von Kohlenstoffbilanzen beitragen. In der vorliegenden Arbeit werden diese entwickelten, datenbasierten Methoden zur Analyse und Interpretation von Netto-CO₂-Flüssen eingesetzt. Die erste Studie führt ein datenbasiertes Modell ein, das unvermeidliche Lücken in Messzeitreihen zuverlässig interpoliert. Dies ermöglicht erweiterte Anwendungen der Daten. In einer nächsten Studie wird ein Verfahren vorgestellt, mit dem die Unsicherheiten in den Beobachtungen charakterisiert werden können. Dies ist nötig, um jährliche Kohlenstoffbilanzen von Ökosystemen unter Berücksichtigung der Messungenauigkeiten direkt aus den Daten herzuleiten. Dabei liegt die Unsicherheit in den betrachteten Waldstandorten bei maximal 11% des Jahreswertes. In einer weiteren Studie werden dieselben Modelle genutzt, um die Netto-CO₂-Flüsse in Einzelkomponenten der CO₂-Assimilation und -Abgabe zu bestimmen. Diese Komponenten sowie die Nettobilanz sind zusammen mit ihren Ungenauigkeiten für Vorhersagen über das Kohlenstoffsenkenpotential eines Ökosystems von besonderer Bedeutung und können Abschätzungen des globalen Kohlenstoffhaushaltes maßgeblich unterstützen. Abschließend zeigt die letzte Studie ein Beispiel für die datenbasierte Entwicklung eines Modells, das die dominanten Prozesse des Kohlenstoffaustausches in Waldökosystemen beschreibt und erfolgreich vorhersagen kann. Dies unterstreicht insbesondere das Potenzial des vorgestellten Modellierungsansatzes, vorherrschende Prozesse zu identifizieren, zu beschreiben und damit zum verbesserten Verständnis des CO₂-Austauschs zwischen Ökosystem und Atmosphäre beizutragen.
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- 2007
5. Integration of large battery storage system into distribution grid with renewable generation
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Manjunatha, Aravind P., primary, Korba, Petr, additional, and Stauch, Vanessa, additional
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- 2013
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6. Use of model predictive control and weather forecasts for energy efficient building climate control
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Oldewurtel, Frauke, primary, Parisio, Alessandra, additional, Jones, Colin N., additional, Gyalistras, Dimitrios, additional, Gwerder, Markus, additional, Stauch, Vanessa, additional, Lehmann, Beat, additional, and Morari, Manfred, additional
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- 2012
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7. Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model
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Stauch, Vanessa J., primary, Jarvis, Andrew J., additional, and Schulz, Karsten, additional
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- 2008
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8. Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals
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Richardson, Andrew D., primary, Mahecha, Miguel D., additional, Falge, Eva, additional, Kattge, Jens, additional, Moffat, Antje M., additional, Papale, Dario, additional, Reichstein, Markus, additional, Stauch, Vanessa J., additional, Braswell, Bobby H., additional, Churkina, Galina, additional, Kruijt, Bart, additional, and Hollinger, David Y., additional
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- 2008
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9. A semi-parametric gap-filling model for eddy covariance CO2 flux time series data
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STAUCH, VANESSA J., primary and JARVIS, ANDREW J., additional
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- 2006
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10. The seasonal temperature dependency of photosynthesis and respiration in two deciduous forests
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Jarvis, Andrew J., primary, Stauch, Vanessa J., additional, Schulz, Karsten, additional, and Young, Peter C., additional
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- 2004
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11. Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model.
- Author
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Stauch, Vanessa J., Jarvis, Andrew J., and Schulz, Karsten
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- 2008
- Full Text
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12. A semi-parametric gap-filling model for eddy covariance CO2 flux time series data.
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Stauch, Vanessa J. and Jarvis, Andrew J.
- Subjects
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INTERPOLATION , *SPLINE theory , *ATMOSPHERIC carbon dioxide , *SOLAR radiation , *ATMOSPHERIC temperature , *SIMULATION methods & models , *PLANT canopies - Abstract
This paper introduces a method for modelling the deterministic component of eddy covariance CO2 flux time series in order to supplement missing data in these important data sets. The method is based on combining multidimensional semi-parametric spline interpolation with an assumed but unstated dependence of net CO2 flux on light, temperature and time. We test the model using a range of synthetic canopy data sets generated using several canopy simulation models realized for different micrometeorological and vegetation conditions. The method appears promising for filling large systematic gaps providing the associated missing data do not overerode critical information content in the conditioning data used for the model optimization. [ABSTRACT FROM AUTHOR]
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- 2006
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13. Use of model predictive control and weather forecasts for energy efficient building climate control
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Oldewurtel, Frauke, Parisio, Alessandra, Jones, Colin, Gyalistras, Dimitrios, Gwerder, Markus, Stauch, Vanessa, Lehmann, Beat, and Morari, Manfred
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Energy efficiency ,Building climate control ,Stochastic model predictive control ,Chance-constrained control - Abstract
This paper presents an investigation of how ModelPredictiveControl (MPC) and weatherpredictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic ModelPredictiveControl (SMPC) strategy for buildingclimatecontrol that takes into account the uncertainty due to the use of weatherpredictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weatherpredictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.
14. Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals
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Richardson, Andrew D., Mahecha, Miguel D., Falge, Eva, Kattge, Jens, Moffat, Antje M., Papale, Dario, Reichstein, Markus, Stauch, Vanessa J., Braswell, Bobby H., Churkina, Galina, Kruijt, Bart, and Hollinger, David Y.
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STATISTICAL correlation , *ANALYSIS of variance , *DISTRIBUTION (Probability theory) , *AUTOCORRELATION (Statistics) - Abstract
Abstract: Information about the uncertainties associated with eddy covariance measurements of surface–atmosphere CO2 exchange is needed for data assimilation and inverse analyses to estimate model parameters, validation of ecosystem models against flux data, as well as multi-site synthesis activities (e.g., regional to continental integration) and policy decision-making. While model residuals (mismatch between fitted model predictions and measured fluxes) can potentially be analyzed to infer data uncertainties, the resulting uncertainty estimates may be sensitive to the particular model chosen. Here we use 10 site-years of data from the CarboEurope program, and compare the statistical properties of the inferred random flux measurement error calculated first using residuals from five different models, and secondly using paired observations made under similar environmental conditions. Spectral analysis of the model predictions indicated greater persistence (i.e., autocorrelation or “memory”) compared to the measured values. Model residuals exhibited weaker temporal correlation, but were not uncorrelated white noise. Random flux measurement uncertainty, expressed as a standard deviation, was found to vary predictably in relation to the expected magnitude of the flux, in a manner that was nearly identical (for negative, but not positive, fluxes) to that reported previously for forested sites. Uncertainty estimates were generally comparable whether the uncertainty was inferred from model residuals or paired observations, although the latter approach resulted in somewhat smaller estimates. Higher order moments (e.g., skewness and kurtosis) suggested that for fluxes close to zero, the measurement error is commonly skewed and leptokurtic. Skewness could not be evaluated using the paired observation approach, because differencing of paired measurements resulted in a symmetric distribution of the inferred error. Patterns were robust and not especially sensitive to the model used, although more flexible models, which did not impose a particular functional form on relationships between environmental drivers and modeled fluxes, appeared to give the best results. We conclude that evaluation of flux measurement errors from model residuals is a viable alternative to the standard paired observation approach. [Copyright &y& Elsevier]
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- 2008
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