Bayesian merging of local and global hydrological forecasts

Project Description

This project is a follow up on a previous study from my team aimed at merging local and global hydrological forecasts. Global hydrological forecasts are produced by large-scale models, with a wide spatial coverage that encompass one or more countries, or even the entire world. These large-scale forecasting systems have recently gained popularity, particularly with the advent of Google in the field of hydrological forecasting. Forecasts from those systems can be extremely useful, particularly for locations where local expertise or resources are not available to set-up a local forecasting system. However, the coexistence of these large-scale systems with well-established and efficient local hydrological forecasting systems raises questions and concerns. This is potentially the case, for example, in Quebec, where the government has developed a strong expertise in hydrological forecasting over time. This series of projects is based on the central hypothesis that it is advantageous to combine forecasts from different systems (local and global) in order to take advantage of their respective strengths. In a previous project, simple combination methods have been successfully tested. This second phase, which is the subject of the proposed doctoral project, consists of testing a more sophisticated fusion method, based on Bayes' theorem. This experiment will take place across Canada, and potentially beyond.

The ideal candidate for this project must be proficient in programming (Python), in hydrology, teamwork, have good communication skills, be resourceful, respectful and creative. A master's degree in hydrological and/or meteorological modeling is essential. 

Knowledge of French is a major asset. The project could begin in September 2024 or later.

Ce projet peut accueillir un(e) ou des étudiants(es) dans les programmes suivants : 
- Doctoral thesis
- 3rd cycle research internship

Funding offered

Yes

25,000 plus performance bonus

The last update was on 18 June 2024. The University reserves the right to modify its projects without notice.