Now showing 1 - 3 of 3
  • Publication
    Peatland Properties Influencing Greenhouse Gas Emissions and Removal
    A nationwide peatland survey was conducted across 50 ombrotrophic peatlands (bogs) in Ireland to ascertain a wide range of peat properties. In addition to natural (relatively intact) sites, we surveyed the most prevalent peatland land use categories (LUCs): grassland, forestry and peat extraction (both industrial and domestic), as well as management options (deep drained; shallow drained; rewetting). Furthermore, the entirety of the peat profile (down to the sub-peat mineral soil/bedrock) was sampled. Our results demonstrate that Irish bogs have been drastically altered by human activities and that the sampled peat properties reflect the nature and magnitude of the impact of the land use and management.
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  • Publication
    Insights into CO2 simulations from the Irish Blackwater peatland using ECOSSE model
    Non-degraded peatlands are known to be important carbon sink; however, if they are exposed to anthropogenic changes they can act as carbon source. This study forms a part of the larger AUGER project (http://www.ucd.ie/auger). It uses the ECOSSE process-based model to predict CO2 emissions [heterotrophic respiration (Rh)] associated with different peatland management (Smith et al., 2010). The work aims to provide preliminary insights into CO2 modelling procedures for drained and rewetted sites from Blackwater, the former Irish raised bog. After drainage in 1950’s (due to peat-extraction) and cessation of draining in 1999, the landscape developed drained ‘Bare Peat’ (BP), and rewetted ‘Reeds’ (R) and ‘Sedges’ (S) sites (Renou-Wilson et al., 2019). Modelling of CO2 from these sites was done using ECOSSE-v.6.2b model (‘site-specific’ mode) with water-table (WT) module (Smith et al., 2010), and default peatland vegetation parameters. The other model-input parameters (including soil respiration, WT and other soil parameters) were obtained from measurements reported in Renou-Wilson et al. (2019). Simulations on drained BP site were run starting from 1950 and on rewetted R and S sites starting from 1999 (which is the year of cessation of drainage). The climate data inputs (2010-2017) were obtained from ICHEC (EPA_Climate-WRF, 2019). The long-term average climate data for model spin-up were obtained from Met Éireann (2012) with potential evapotranspiration estimated by Thornthwaite (1948) method. Daily ecosystem respiration (Reco) data for May/June 2011 to Aug 2011 obtained from raw CO2 flux measurements (Renou-Wilson et al., 2019) were used. For vegetated sites Rh was estimated from Reco using method explained in Abdalla et al. (2014). Daily CO2 simulations were compared to Reco for BP site (r2 =0.20) and to Rh for R site (r2 = 0.35) and S site (r2 = 0.55). The preliminary results showed some underestimation of simulated CO2 indicating the need for further modelling refinements for satisfactory results. The results from BP site further indicated on the importance of including long-term drainage period (i.e. from 1950 on) because avoiding this step resulted in a large overestimation of predicted CO2.
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  • Publication
    Evolving Interpolating Models of Net Ecosystem CO2 Exchange Using Grammatical Evolution
    Accurate measurements of Net Ecosystem Exchange of CO2 between atmosphere and biosphere are required in order to estimate annual carbon budgets. These are typically obtained with Eddy Covariance techniques. Unfortunately, these techniques are often both noisy and incomplete, due to data loss through equipment failure and routine maintenance, and require gap-filling techniques in order to provide accurate annual budgets. In this study, a grammar-based version of Genetic Programming is employed to generate interpolating models for flux data. The evolved models are robust, and their symbolic nature provides further understanding of the environmental variables involved.
      295Scopus© Citations 7