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ONE Planet DTP - Developing probabilistic precipitation datasets for high mountain regions in a changing climate (OP20298)

  • Full or part time
  • Application Deadline
    Friday, January 31, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Precipitation data is fundamental to understanding hydrological processes, yet precipitation patterns and variability in mountain regions are poorly understood. Observation networks tend to be sparse, despite large climatic variability, while complex topography leads to major limitations in remote sensing products, meteorological reanalyses and climate models. The resulting uncertainty in precipitation fields is often not well quantified. These issues present huge challenges for simulations of snow, glacier and river behaviour in rapidly changing high mountain environments. The implications for water resources management in vitally important basins are vast. The aim of this project is to create a new precipitation dataset for water managers in these areas.

As measurements at high elevations are very difficult, other approaches are required to estimate mountain precipitation distributions. One promising avenue is model-based inversion. This involves using a hydrological model – one suitable for high mountain catchments in this case – to identify precipitation fields that are consistent with observed states and fluxes. These states and fluxes could include gauged river flow, point measurements of rainfall/snowfall and snow water equivalent, glacier mass balance estimates, and remote sensing of snow-covered area amongst other surface properties. There is substantial scope to adapt recent model-based inversion techniques for data-sparse mountain regions accounting for uncertainty from multiple sources.

In this project you will build on existing methods to obtain multiple realisations of plausible precipitation fields and thus probabilistic uncertainty estimates for contrasting high mountain regions (e.g. Alps/Rockies and Himalaya/Andes). You will develop skills in statistical modelling of precipitation, numerical modelling of snow, glaciers and hydrology, and analysis of local observations, remote sensing, meteorological reanalyses and climate models. You will gain advanced skills in programming, parameter estimation, optimisation algorithms and sensitivity/uncertainty analysis. Field work to make additional observations for integration with the modelling developments is encouraged. The project outputs will be highly valuable to the scientific community and water resources planners.

Funding Notes

Suitable for candidates with numerate degrees and a background in any combination of meteorology, physical geography, geoscience, maths, physics or computational sciences.

This project is part of the ONE Planet DTP. Find out more here: View Website

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