Essay on Flood Forecasting: Disaster Risk Management Initiative

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As a non-structural measure, flood forecasting (such as discharge, water level, or flow volume) is a crucial part of flow regulation and water resources management. Worldwide, flood disasters account for about one-third of all natural disasters in terms of number and economic losses (Berz 2000). As stated by Dutta and Herath (2004), out of the total number of flood events in the world during the past 30 years, 40% occurred in Asia and Southeast Asia countries stand for the second worst region in Asia. ASEAN Disaster Risk Management Initiative (2010) reported that a catastrophic 200-year flood (0.5 percent annual probability of exceedance) would have a major impact on the economies of the Southeast Asian countries, including Myanmar, which …show more content…
As real time flood forecasting systems of Myanmar still provide river stage forecasts for 1-day lead time, provision of more lead times is an interest of this study. Myanmar is one of the tropical countries characterized by the monsoon climate and river flooding is a recurrent natural phenomenon, particularly during monsoon (Sanyal and Lu 2004). Severe floods have occurred in major rivers in Myanmar during the last decades and there seems to be a trend of frequent hydrological extreme events, leading to a high risk of flood hazards. When implementing a flood forecasting system in a developing country, special attention should be paid to the sustainability of its operation (Shamseldin 2010) and availability of hydrometric data which are commonly monitored in the region. While conceptual or physically based models are vital for the understanding of hydrological processes, there are practical situations where the main focus is to provide accurate predictions at specific locations, especially for the river basins where catchment properties are not fully monitored. Sometimes, a model is valued for its simplicity and robustness in solving the local problems. In the Myanmar context, such a strong predictive model would benefit to the key flood management actions.
In recent years, a great deal of work has been done in applying data driven models like multiple regressions and neural networks for water resources research.
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