Preprints
https://doi.org/10.5194/egusphere-2024-3994
https://doi.org/10.5194/egusphere-2024-3994
09 Jan 2025
 | 09 Jan 2025
Status: this preprint is open for discussion and under review for Biogeosciences (BG).

Inferring methane emissions from African livestock by fusing drone, tower, and satellite data

Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk

Abstract. Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an atmospheric dispersion model. In this study, we apply this approach to verify and quantify potential methane sources identified through radiance anomalies observed in hyperspectral satellite data. We compare different methods to estimate emissions from various ruminant livestock species in sub-Saharan Africa, including diverse herds of cattle, goats, and sheep, as well as camels, for which methane emission estimates are particularly sparse. Our estimates, derived from Bayesian inference, align with Tier 2 emission values of the Intergovernmental Panel on Climate Change. We moreover observe the hypothesized increase in methane emissions following feeding. Our findings suggest that the Bayesian inference method is more robust under non-stationary wind conditions compared to a mass balance approach using drone observations. Furthermore, the Bayesian inference method performs better in quantifying emissions from weaker sources, estimating methane emission rates as low as 100 g h-1. We find a ± 50 % uncertainty in emission rate estimates for these weaker sources, such as sheep and goat herds, which reduces to ± 12 % for stronger sources, like cattle herds emitting 1,000–1,500 g h-1. These promising results demonstrate the potential and efficacy of the Bayesian inference method for source term estimation. Future applications of drone-based Bayesian inference could extend to estimating methane emissions in Africa and other regions from various sources with complex spatiotemporal emission patterns, such as wetlands, landfills, and wastewater disposal sites. The Bayesian observing system could thereby contribute to the validation and improvement of climate models and emission inventories.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk

Status: open (until 20 Feb 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk

Data sets

Inferring methane emissions from African livestock by fusing drone, tower, and satellite data Alouette van Hove, Kristoffer Aalstad, and Norbert Pirk https://doi.org/10.5281/zenodo.14214699

Model code and software

Methane emisson rate Inference of ruminants in Kenya (MIK) Alouette van Hove https://github.com/AlouetteUiO/MIK

Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk

Viewed

Total article views: 42 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
35 4 3 42 2 0 0
  • HTML: 35
  • PDF: 4
  • XML: 3
  • Total: 42
  • Supplement: 2
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 09 Jan 2025)
Cumulative views and downloads (calculated since 09 Jan 2025)

Viewed (geographical distribution)

Total article views: 42 (including HTML, PDF, and XML) Thereof 42 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 10 Jan 2025
Download
Short summary
Research on methane emissions from African livestock is limited. We used a probabilistic method fusing drone and flux tower observations with an atmospheric model to estimate emissions from various herds. This approach proved robust under non-stationary wind conditions and effective in estimating emissions as low as 100 g h-1. We also detected herd locations using spectral anomalies in satellite data. Our approach can be used to estimate diverse sources, thereby improving emission inventories.