Jobs:Job-00295
From CSDMS
Land Use/Cover Profiling & Diagnostic Modelling
Maynooth University, , Ireland
Apply before: 17 January 2021
Apply before: 17 January 2021
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Posting:
Position: (postdoctoral) research position
Apply before: 17 January 2021
Apply online:
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Terrain-AI (T-AI) is a collaborative research project coordinated by Maynooth University (http://www.mu.ie), and supported by Science Foundation Ireland’s Strategic Partnership Programme involving Teagasc (Irish Agricultural research body, Trinity College Dublin, University College Dublin, University of Limerick and Dublin City University together with primary Industry partner Microsoft. T-AI’s core R&D activity revolves around improving our knowledge and understanding of Land Use activity - as this relates to Climate Change. A critical component to the success of Terrain-AI is the development of a suite of suitable land cover and land use profiling toolsets and indices that can be integrated into model based approaches required to improve our understand of the exchanges of energy, water and gases that occur between the land surface and the atmosphere. This exciting role will focus on the development of profiling toolsets and indices and their subsequent integration into empirical/diagnostic based models (e.g. SAFYE-CO2 Pique et al., 2020) that integrate satellite, geo-spatial and other relevant datasets to estimate components of the energy, water and carbon balance (e.g. latent heat, crop yield, gross primary productivity and other relevant variables). The development of the profiling toolsets and indices will be undertaken as part of wider research, coordinated by our research partner Teagasc, who are working across a range of different land use types; while the integration of the profiling data and indices, satellite and geo-spatial into the development of the diagnostic model based approaches will contribute to the land surface modelling team, coordinated by Maynooth University. A core element of the role will focus on the integration of data from a variety of sources, including in-situ, satellite and existing geo-spatial data, along with outputs from the data platform in order to better understand and characterise different land use and cover types. The outputs from which will inform the development of suitable empirical/diagnostic and geo-spatial based approaches for estimating a range of important variables of relevance to the energy, water and carbon balance across different land use types. The diagnostic based model will be developed to derive estimates of the energy, water and carbon budget for crop and grass lands at plot scale, coupling remotely sensed (e.g. SIF, PAR, LAI, phenology, biomass etc), gridded meteorological (e.g. temperature, humidity etc), environmental data (e.g. soil) and in-situ data to semi-empirical crop models (e.g. winter wheat; grass growth). The outputs from this research will provide a basis to inform and guide more sustainable land use management and provide an evidence base for policy formulation.
Of interest for: Terrestrial Working Group Ecosystem Dynamics Focus Research Group
