| Summary: | Climate change is expected to alter the hydrological cycle in the Arctic, which would result in the increase in intensity and frequency of hydrological extreme events such as flooding. Noticeably, the changes in flooding due to climate change would severely affect human life, infrastructures, the environment, ecosystem, and socio-economic development in the impacted areas. Hydrological models are state-of-the-art tools for assessing the impact of climate change on hydrological processes. However, performing hydrological simulation/projection in the Arctic is challenging because of the complex hydrological processes and data-sparse features in the region. In consideration of those issues, this PhD research aims: (1) to assess the performances of hydrological models in the Arctic, (2) to investigate the alternative weather inputs for running the hydrological models in the Arctic region with scattered monitoring data, (3) to evaluate the effects of the models’ structure and parameterization and the spatial resolution of weather inputs on the results of hydrological simulations, and (4) to project future hydrological events under climate change impacts using the current hydrological model, and analyse the reliability/uncertainty of the projection. To fulfil the research’s objectives, several methodologies were applied. Firstly, a comprehensive review was conducted to address the current capacities and challenges of twelve well-known hydrological models, including surface hydrological models and subsurface hydrological models/groundwater models/cryo-hydrogeological models. These models have previously been applied or have the potential for application in the Arctic. Next, the physically based, semi-distributed model, SWAT (soil and water assessment tool), was selected as a suitable model, among other potential models, to assess its performance for hydrological simulations and to verify the potential application of reanalysis weather data. Moreover, the SWAT was coupled with multiple ensemble global and regional ...
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