Improving streamflow forecasting lead-time for Australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables
It is essential to develop a long-lead streamflow forecast system for providing the prior signal for possible floods. Climatic variabilities such as oceanic-atmospheric global oscillations may possess tele-connectivity with Australian rainfall-runoff. This study identifies an ocean-atmospheric regio...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
Curtin University
2021
|
| Online Access: | http://hdl.handle.net/20.500.11937/88140 |
| _version_ | 1848764971102502912 |
|---|---|
| author | Shams, Md Shamim |
| author_facet | Shams, Md Shamim |
| author_sort | Shams, Md Shamim |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | It is essential to develop a long-lead streamflow forecast system for providing the prior signal for possible floods. Climatic variabilities such as oceanic-atmospheric global oscillations may possess tele-connectivity with Australian rainfall-runoff. This study identifies an ocean-atmospheric region connected with Australian rivers streamflow. By utilizing its persistence capacity, statistical and machine learning-based forecast models are developed, predicting inter-annual streamflow forecast of Australian river flows. This outcome will be beneficial for future water planning and mitigating flood risk. |
| first_indexed | 2025-11-14T11:27:49Z |
| format | Thesis |
| id | curtin-20.500.11937-88140 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:27:49Z |
| publishDate | 2021 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-881402024-01-30T06:32:33Z Improving streamflow forecasting lead-time for Australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables Shams, Md Shamim It is essential to develop a long-lead streamflow forecast system for providing the prior signal for possible floods. Climatic variabilities such as oceanic-atmospheric global oscillations may possess tele-connectivity with Australian rainfall-runoff. This study identifies an ocean-atmospheric region connected with Australian rivers streamflow. By utilizing its persistence capacity, statistical and machine learning-based forecast models are developed, predicting inter-annual streamflow forecast of Australian river flows. This outcome will be beneficial for future water planning and mitigating flood risk. 2021 Thesis http://hdl.handle.net/20.500.11937/88140 Curtin University fulltext |
| spellingShingle | Shams, Md Shamim Improving streamflow forecasting lead-time for Australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables |
| title | Improving streamflow forecasting lead-time for Australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables |
| title_full | Improving streamflow forecasting lead-time for Australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables |
| title_fullStr | Improving streamflow forecasting lead-time for Australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables |
| title_full_unstemmed | Improving streamflow forecasting lead-time for Australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables |
| title_short | Improving streamflow forecasting lead-time for Australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables |
| title_sort | improving streamflow forecasting lead-time for australian rivers using oceanic-atmospheric oscillations and hydroclimatic variables |
| url | http://hdl.handle.net/20.500.11937/88140 |