Statistical Downscaling of Climate Model Outputs for Hydrological Extremes
Author(s)
Chun, Kwok Pan
Type
Thesis
Abstract
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.
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.
Date Issued
2010
Date Awarded
2011-07
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Wheater, Howard
Onof, Christian
Creator
Chun, Kwok Pan
Publisher Department
Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)