Real Time Impact Based Flood Forecasting (IBF) for Tropical Rivers: A Case Study in Dungun River Basin
Such catastrophes may be brought on by floods to the impacted populace due to property damage, crop loss and death. Flooding caused significant damage to property and crops due to the high economic value of the property and the extent of the flood. Flood forecasts and warnings are one of the informa...
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| Main Authors: | , , , , |
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| Format: | Book chapter |
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Springer Science and Business Media Deutschland GmbH
2024
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| Institution: | Universiti Tenaga Nasional |
| Summary: | Such catastrophes may be brought on by floods to the impacted populace due to property damage, crop loss and death. Flooding caused significant damage to property and crops due to the high economic value of the property and the extent of the flood. Flood forecasts and warnings are one of the informal measures to provide warnings to affected populations. People living in flood-affected areas will be warned to evacuate their belongings before the flood arrives. This will greatly reduce the loss and damage caused by flooding, especially the loss of human life. This paper presents a comprehensive study of flood assessment and forecasting using Real-Time Flood Forecasting (RTIBFF) to assess the performance of IBF in identifying areas of potential flood risk by increasing the gap between the users and producers of timely information. Synergies are among several elements of an early warning system. Furthermore, in this paper, automated warning messages using color codes are used to initiate risk reduction measures at the local level for vulnerable groups in the Long Strait of Malaysia. RTIBFF collects information on the potential severity and likelihood of climate impacts. RTIBFF is still underutilized in Malaysia despite its extreme weather and potential catastrophe risk reduction benefits. The forecast, user understanding, and confidence are still questionable due to the forecast environment's uncertainty. To improve government-user collaboration, users should incorporate RTIBFF into popular weather forecast methodologies. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. |
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