Statistical downscaling of climate model outputs for hydrological extremes
Chun, Kwok Pan
PublisherImperial College London
MetadataShow full item record
Changing climate poses an unprecedented challenge for hydrology. The quantification of knowledge on occurrence, circulation and distribution of the waters of the Earth becomes increasingly complex under climate projections because of uncertain effects due to anthropogenic emissions. Traditional understanding of the hydrological cycle needs to be re-examined, and new tools and frameworks for modelling hydrological series with non-stationary characteristics are required for assessing climate change impacts. The aims of this thesis are to (i) understand the relationship between climate change and hydrology at a catchment scale and (ii) develop tools to support climate change adaptation and mitigation. To achieve the aims, this thesis employs a stochastic rainfall model based on generalised linear models (GLMs) to downscale information from regional and global climate models for projecting drought conditions and annual rainfall extremes. Using a state space approach, important global circulation variables for catchment drought characteristics in the Midlands and South East of England are investigated. For annual rainfall extremes, a new approach for studying rainfall simulation series ensemble is proposed based on extreme value theory. Using a statistical modelling methodology related to GLMs, a novel potential evaporation model has been put forward and evaluated. In UK catchment scale application, the results provide insight into possible changes and implications in the shift of rainfall and drought patterns under scenarios of climate in the 2080s. The quality of potential evaporation estimation is shown to be sensitive to the interrelationship of global climate variables. For monthly maxima of potential evaporation, the projected change is high in the southern UK (~25%) but is low in the northern UK (~0%). Furthermore, 2080s streamflows have also been projected. The results show that uncertainty in streamflow projections depend on which GCMs and RCMs are used. Overall, this dissertation provides improved methods for further development in understanding our non-stationary water cycle.