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Title: Uncertainty in hydrological scenario modelling : an investigation using the Mekong River Basin, SE Asia
Author: Robinson, Amanda Jane
ISNI:       0000 0004 7231 2644
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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This thesis investigates sources of uncertainty in hydrological scenario modelling. It quantifies the extent to which decisions made during the modelling process affect river flow projections under climate change. Sources of uncertainty explored include choice of: General Circulation Model (GCM) for generation of climate projections; hydrological model code; potential evapotranspiration (PET) method; spatial distribution of meteorological inputs within the hydrological model; and baseline precipitation dataset. The Mekong River Basin is employed as a case study site. Initially a MIKE SHE model is developed for the Mekong using, where possible, the same data as an earlier model (SLURP). Climate scenarios investigated include a set based on a 2 °C increase in global mean temperature simulated by seven GCMs. There are considerable differences in scenario discharges between GCMs, ranging from catchment-wide increases or decreases in mean discharge, to spatially varying responses. Inter-GCM differences are largely driven by differences in precipitation, rather than PET or temperature. Results from MIKE SHE, SLURP and Mac-PDM.09 (a global hydrological model) are compared. Although inter-hydrological model uncertainty is evident and sometimes considerable, its magnitude is generally smaller than GCM uncertainty. The MIKE SHE model is then recalibrated to provide five further models, each employing alternative PET methods. PET method impacts scenario changes in PET and hence scenario discharges. However, GCM-related uncertainty for change in mean discharge is on average ~3.5 times greater than PET method-related uncertainty. Additional MIKE SHE models are developed using alternative meteorological input spatial distributions and an alternative baseline precipitation dataset. These sources of uncertainty are comparable in magnitude; both are much smaller than PET- and GCM-related uncertainty. Climate impact assessment using one MIKE SHE model and an ensemble of 41 CMIP5 GCMs for the RCP4.5 scenario provides further confirmation that GCMrelated uncertainty is the dominant source of uncertainty for Mekong river flow projections.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available