Tropical peatlands are among the most carbon dense ecosystems globally (Honorio-Coronado et al., 2021) but their distribution and below-ground carbon stocks remain highly uncertain, with recent estimates of the latter ranging from 70 to 288 Pg C (7 to 29 times annual fossil fuel emissions) (Ribeiro et al., 2021). South America is the greatest remaining knowledge gap in our understanding of tropical peatland distribution. We also lack large-scale models capable of representing the unique characteristics and dynamics of tropical peatlands, limiting our ability to predict their vulnerability to climate and land-use change.
The PhD will travel to Guyana (and possibly Suriname) to collect new peat depth measurements. They will use this new data along with existing data in Peru, Colombia, and Brazil to create machine learning models of peat depth and carbon distribution across tropical South America, building on the work of Dr. Hastie (Hastie et al., 2022).
The PhD will use existing data collected in Peruvian peatlands to parametrize the HPMTrop model (Kurnianto et al., 2015). HPMTrop is a 1D model driven by water table variation, which simulates mass remaining in annual peat cohorts as a balance between vegetation inputs and decomposition. The PhD will use the model to investigate the hydrological and productivity limits to peat accumulation. In other words, what are the tipping points associated with precipitation, hydrological and landcover changes, at which the peatlands are no longer accumulating carbon?
The PhD will then use a more complex land-surface model, ORCHIDEE-PEAT (Qiu et al., 2019) to integrate the spatial and temporal dynamics of tropical peatlands. They will modify and calibrate the model to be able to represent tropical peatlands, using field data collected in Guyana and Peru (as well as the HpmTrop model results, and the data-driven map of peatland distribution). This will include the ability to represent both ombrotrophic (rainwater fed) and minerotrophic (river fed) peatlands. Once calibrated, they will run the model over the historic period (last 50k years or so) to present day. Finally, the PhD will run future projections of climate change, land-use change and atmospheric CO2, to understand critical tipping points for peat/ carbon accumulation vs loss across tropical South America. They will work closely with another PhD who will apply paleo-environmental proxies on peat cores to better understand the long-term drivers of peat (& carbon) accumulation.
The successful candidate will have an MSc level degree in one of the following fields (or equivalent/ similar field): Data Science, Physical Ecology/ Geography, Earth Science, Physics, Remote sensing. They will also have experience in at least one common scientific coding language (e.g. MATLAB, R, Fortran, Python). Salary up to 28,000 CZK per month depending on performance/ activities.
Honorio-Coronado, E. N., Hastie, A., et al. Intensive field sampling increases the known extent of carbon-rich Amazonian peatland pole forests. Environ. Res. Lett. 16, 74048. https://doi.org/10.1088/1748-9326/ac0e65 (2021).
Ribeiro, K., Pacheco, FS., Ferreira, JW., de Sousa-Neto, ER., Hastie, A., Krieger, GC., Alvalá, PC., Cristina Forti, M., Ometto, JPHB. Tropical peatlands and their contribution to the global carbon cycle and climate change. Glob Change Biol; 27: 489– 505. https://doi.org/10.1111/gcb.15408, (2021).
Hastie, A., Honorio Coronado, E.N., Reyna, J. et al. Risks to carbon storage from land-use change revealed by peat thickness maps of Peru. Nat. Geosci. 15, 369–374. https://doi.org/10.1038/s41561-022-00923-4 (2022).
Kurnianto, S., Warren, M., Talbot, J., Kauffman, B., Murdiyarso, D. and Frolking, S. Carbon accumulation of tropical peatlands over millennia: a modeling approach. Glob Change Biol, 21: 431-444. https://doi.org/10.1111/gcb.12672 (2015).
Qiu, C., Zhu, D., Ciais, P., Guenet, B., Peng, S., Krinner, G., Tootchi, A., Ducharne, A., and Hastie, A.: Modelling northern peatland area and carbon dynamics since the Holocene with the ORCHIDEE-PEAT land surface model (SVN r5488), Geosci. Model Dev., 12, 2961–2982, https://doi.org/10.5194/gmd-12-2961-2019, (2019).
Kleinen, T., Brovkin, V., and Schuldt, R. J.: A dynamic model of wetland extent and peat accumulation: results for the Holocene, Biogeosciences, 9, 235–248, https://doi.org/10.5194/bg-9-235-2012 , 2012.Apply to the project