Dr Angela Gallego-Sala
Senior Lecturer In Physical Geography


Research interests

My doctoral research investigated the effects of climate change on carbon fluxes in high latitude peatlands. I am also interested in the relationship between plant root exudates and soil microorganisms responsible for trace gas cycling (CH4, CO2 and H2) in mires.

My research focuses on the effects of climate on peatland ecosystem dynamics, and in particular the effect on peatland extent and on carbon accumulation. My research at Exeter aims to assess the contribution of peatlands to the global carbon cycle over the last millennium, in order to provide a basis for projections of future peatland-climate feedbacks and to enable inclusion of these effects in climate models. To achieve this aim, we will use simulations of climate data over the last 1000 years as input in models of peat distribution, accumulation and carbon fluxes to simulate the growth and status of global peatlands over the past millennium. The results will be tested against a new global database of peat accumulation rates and hydrological change over the last millennium assembled from existing published data supplemented by carefully targeted new data acquisition in collaboration with a range of partners from around the world.

Research projects

NERC Project: Peatlands and the global Carbon cycle during the past millennium: a global assessment using observations and models

Research grants

  • 2016 NERC
    Identifying potential tipping points in the benefits derived from the UK's land ecosystems
  • 2015 South West Water
    Understanding the impact of moorland restoration on water quantity, quality and gas fluxes � Phase II
  • 2015 EU Marie Curie Training Grant
    EU2020 Marie Skłodowska-Curie Research Fellowship - Anne Quillet. PEATMOD: PEATland modelling for global carbon cycle and climate models PI with Angela Gallego-Sala. €195,455
  • 2010 NERC Std Grant
    Millipeat - Peatlands and the Global Carbon Cycle during the Past Millennium: a Global assessment using observations and models

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