Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting

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Adhikary, Sajal Kumar (2017) Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting. PhD thesis, Victoria University.

Abstract

Enhanced streamflow forecasting has always been an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data, especially rainfall as it constitutes the key input in transforming rainfall into runoff. This emphasizes the need for incorporating accurate rainfall input in streamflow forecasting models in order to achieve enhanced streamflow forecasting. Based on past research, it is well-known that an optimal rain gauge network is necessary to provide high quality rainfall estimates. Therefore, this study focused on the optimal design of a rain gauge network and integration of the optimal network-based rainfall input in artificial neural network (ANN) models to enhance the accuracy of streamflow forecasting. The Middle Yarra River catchment in Victoria, Australia was selected as the case study catchment, since the management of water resources in the catchment is of great importance to the majority of Victorians.

Additional Information

This thesis includes 1 published article for which access is restricted due to copyright (Chapters 3, 4 (first paper). Details of access to these papers have been inserted in the thesis, replacing the articles themselves.

Item type Thesis (PhD thesis)
URI https://vuir.vu.edu.au/id/eprint/35054
Subjects Historical > FOR Classification > 0401 Atmospheric Sciences
Historical > FOR Classification > 0406 Physical Geography and Environmental Geoscience
Current > Division/Research > College of Science and Engineering
Keywords rivers; water basins; streams; streamflow simulation; modelling; water supply; spatial interpolation; genetic programming-based ordinary kriging; thesis by publication
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