Converting daily rainfall into hourly scale using statistical tools
Rainfall is one of the most critical inputs for hydrological modeling and indicators for climate change impact studies. The rainfall data at finer timescales are generally more useful for hydrological process research, especially in extreme rainfall-event evaluation and flood risk management. Howeve...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166762 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Rainfall is one of the most critical inputs for hydrological modeling and indicators for climate change impact studies. The rainfall data at finer timescales are generally more useful for hydrological process research, especially in extreme rainfall-event evaluation and flood risk management. However, rainfall data is usually only available at timescales of daily or monthly. Hence, disaggregation methods are widely used to provide possible realization of hourly data which is aggregated up to the given daily data. This study focuses on the disaggregation reliability of HyetosMinute, which relies on the Bartlett-Lewis Rectangular Pulse Model. This study further determines if the model is suitable for future disaggregation predictions based on present parameters. It was found that the reliability of the tool is dependent on the input parameters estimated. If appropriately estimated, the model can be confidently used for future disaggregation predictions. The study outputs will be useful for helping generate high-resolution rainfall data for hydrologic studies, such as determining return periods which HyetosMinute was able to model accurately. |
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