Streamflow Prediction in Ungauged Basins: Review of Regionalization Methods
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abstract
Considering the growing population of the earth and the decreasing water resources, the need for reliable and accurate estimation and prediction of streamflow time series is increasing. Due to the climate change and anthropogenic impacts on hydrologic systems, the estimation and prediction of streamflow time series remains a challenge and it is even more difficult for regions where watersheds are ungauged in terms of streamflow. The research presented in this dissertation, was scoped to develop a reliable and accurate methodology for daily streamflow prediction/estimation in ungauged watersheds. The study area in this research encompasses Ontario natural watersheds with various areas spread in different regions.
In this research work nonlinear data-driven methods such as Artificial Neural Networks (ANN) and conventional methods such as Inverse Distance Weighted (IDW) as well as their combination are investigated for different steps in streamflow regionalization. As such, Watershed classification prior to regionalization is investigated as an independent step in regionalization. Nonlinear classification techniques such as Nonlinear Principal Component Analysis (NLPCA) and Self-Organizing Maps (SOMs) are investigated for watershed classification and finally a methodology which combines watershed classification, streamflow regionalization and hydrologic model optimization is presented for reliable streamflow prediction in ungauged basins.
The results of this research demonstrated that a multi-model approach which combines the results of proposed individual models based on their performance for the gauged similar and close watersheds to the ungauged ones can be a reliable streamflow regionalization model for all watersheds in Ontario. Physical similarity and spatial proximity of watersheds was found to play an important role in similarity between the streamflow time series, hence, it was incorporated in all individual models. It was also shown that watershed classification can significantly improve the results of streamflow regionalization. Investigated nonlinear watershed classification techniques applicable to ungauged watersheds can capture the nonlinearity in watersheds physical and hydrological attributes and classify watersheds homogeneously. It was also found that the combination of watershed classification techniques, regionalization techniques and hydrologic models can impact the results of streamflow regionalization substantially. Furthermore, to evaluate the uncertainty associated with the predictions in ungauged watersheds, an ensemble modelling framework is proposed to generate ensemble predictions based on the proposed regionalization model.
