EGU23-7706, updated on 07 Sep 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

A statistical gap filling model for methane fluxes over an urban area in the Alps

Michael Stichaner1, Christian Lamprecht1, Martin Graus1, Ignacio Goded2, Niels Jensen2, and Thomas Karl1
Michael Stichaner et al.
  • 1Leopold-Franzens-Universität Innsbruck, Innsbruck, Austria (
  • 2Joint Research Centre (JRC) of the European Commission, Ispra, Italy

There is consensus that climate change is mostly driven by anthropogenic greenhouse gas emissions. In addition to CO2 emissions, which have been the subject of public debate for a long time, increased awareness of methane (CH4) emissions has developed in recent years. CH4 is considered the 2nd most important contributor to radiative forcing, making it the most important non-CO2 greenhouse gas.

Due to the relatively short lifetime of the gas in the  atmosphere compared to CO2, the reduction of methane emissions can lead to a climate benefit on relatively short time horizons. In order to effectively reduce emissions, the polluters must be better understood and recognized. Here, we combine methane eddy covariance observations in combination with a variety of other trace gases and meteorological parameters that have been recorded since August 2020 in an Alpine city (Innsbruck, Austria) to investigate urban methane emissions. For an accurate comparison with bottom-up emission inventories we test different gap-filling methods with the help of meteorological parameters as well as other tracer fluxes, such as NO, NO2, or CO2. In order to quantify methane emissions in urban areas as annual totals, a complete, gap-free flux dataset is desired. We have developed different statistical gap filling models which are able to predict CH4 fluxes at the study location. The method is based on a boosted regression tree model with a variety of meteorological and astronomical parameters, as well as other trace gas fluxes serving as input. Different combinations of these input parameters are tested for accuracy of their prediction. Contrasting other gap filling methods, used over uninhabited areas, adding gases like CO2 or NO can serve as important additional predictors, because sectors related to combustion processes are considered as important contributors to CH4 emissions.  In this presentation we discuss CH4 flux measurements performed during the last 2,5 years over an urban area, and highlight first results on the performance of the developed gap filling models. 

How to cite: Stichaner, M., Lamprecht, C., Graus, M., Goded, I., Jensen, N., and Karl, T.: A statistical gap filling model for methane fluxes over an urban area in the Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7706,, 2023.