A sprinkling experiment was conducted at the geodetic observatory Wettzell (Bavaria, Germany) with the intention to combine classical hydrological field observations of soil moisture with gravity data and electrical resistivity tomography (ERT). The setup consisted of 8 sprinkling units installed around a gravimeter in field enclosure. Artificial rainfall was applied for 6 hours. The sprinkling area of 15 x 15 m was equipped with 3 vertical soil moisture sensor profiles, 1 horizontal soil moisture transect, near-surface soil moisture sensors and 3 ERT profiles. The non-invasive gravity data and the ancillary monitoring data were used to infer water transport processes in the subsurface during the sprinkling experiment. To this end, the gravity data were used to identify the structure and the parameters of a subsurface flow model in an inverse modelling approach by optimizing the simulated gravity response with respect to the observations. The ancillary soil moisture and ERT data were used to evaluate the model outputs in terms of adequacy and dominant subsurface flow processes. Model data cover the following subtopics:
• virtual experiments to show the theoretical relationships between subsurface water re-distribution processes and their corresponding gravity responses
• an uncertainty analysis of the sprinkling experiment, e.g., with respect to water volumes and their spatial distribution, and the impact on the expected gravity response
• inverse modelling to identify dominant subsurface water re-distribution processes
• a synthetical model setup based on the ancillary datasets of soil moisture and ERT
Monitoring and model output data used for this investigation is provided within this data repository. A detailed description and discussion can be found in Reich et al. (2021). The inverse modelling was carried out using the R-package gravityInf (Reich, 2021).
gravityInf is a small R-package which aims at supporting the anaylsis of a sprinkling (infiltration) experiment in combination with simultaneous and continious gravity measurements, presented in the above mentioned paper. With this package you can easily walk through the necessary steps in order to set up an infiltration scenario, maybe based on your own sprinkling / irrigation experiment and carry out simple hydrological modelling of water distribution in 3D in the subsurface. An observed gravity time series is needed for the model in order to fit and thus identify the dominant infiltration process for your research area. A model functionality and limitations can be found in Reich et al. (2021), the associtated data was published by Reich et al. (2021, https://doi.org/10.5880/GFZ.4.4.2021.001).
Atmospheric methane (CH4) is the second most important well-mixed greenhouse gas in terms of radiative forcing after carbon dioxide (CO2). CH4 has a global warming potential that exceeds the one of CO2 by a factor of 23, potentially making it an even more important contributor to climate change if concentrations continued to rise over the next decades. The aim of the project is to improve the understanding of recent global atmospheric CH4 growth rate variability particularly focusing on the CH4 increase since 2007, and to quantify CH4 emissions in different regions of the world. In the context of climate change, CH4 emissions from natural wetlands and their dependency on meteorological conditions are of special importance and will be given particular weight in the project. The outcome of this study will help to improve confidence in projections of future CH4 and the potential impact on climate and atmospheric chemistry. A global particle dispersion model will be applied, combined with a simple CH4 budget taking up surface emissions from different sources and removing it by reaction with the hydroxyl radical (OH), the main sink of atmospheric CH4. CH4 surface emissions from anthropogenic activities, biomass burning, and wetlands will be prescribed to force the model toward the desired atmospheric state. OH fields will be provided by a simulation with a state-of-the-art global chemistry-climate model. Every particle transported by the global particle dispersion model will carry with it different CH4 tracers representing concentrations from different source categories additionally separated by region where required. Global monthly mean fields for each tracer will be produced by the model offering detailed insight into the contributions from different source categories and regions to the total CH4 burden. A series of multi-annual simulations will be carried out for the period 2004-2008 to improve the understanding of the roles of individual emission sources and meteorology. For this purpose, results from one reference simulation, forced by varying meteorology and emissions describing atmospheric CH4 as realistically as possible, will be compared to results from several sensitivity simulations, in which individual emission sources will be kept constant. Another important part of the study will be time dependent quantification of CH4 emissions using a mathematical optimisation procedure called inverse modelling. The inversion will provide new insights into the role of interannual and seasonal variability in emissions, in particular from wetlands and biomass burning, to the observed variability in CH4 growth rates.