PhD defence: Skill and value of a global seasonal streamflow forecasting system

This thesis explores the apparent but yet unmet potential of global hydrological models in operational seasonal forecasting applications on a global scale. The research aims to assess the skill and value of seasonal streamflow forecasts produced by global hydrological models, as well as to investigate possible ways to improve the current skill and value.

As an initial step in assessing the prospect of using a global hydrological model for forecasting, global terrestrial hydrology is simulated with the model PCR-GLOBWB, using a climate forcing based on observations. The results are analysed adopting methods and skill scores that were developed primarily for verification of meteorological forecasts. Skill in reproducing the occurrence of past extremes in the monthly discharges of large rivers of the world is investigated.

Next, the relative contributions of meteorological forcing and initial hydrological conditions to the skill of seasonal streamflow forecasts are analysed. The impact of both sources of uncertainty is evaluated for large river basins across the globe.

The third step carries out a total skill assessment in actual forecasting mode, using seasonal climate forecasts, and quantifies the predictive skill as affected by errors in model structure and parameterization, meteorological forcing, and initial hydrological conditions.

The final step presents a study on the value of seasonal streamflow forecasts, where the interaction between skill and value is explored and possible ways to improve the value of seasonal hydrological forecasts on a global scale for water-related applications are discussed with an emphasis on flood and drought mitigation.