STOCHASTIC MODELLING OF FLOOD FLOWS FOR MYANMAR RIVERS

Abstract

Flood is the most commonly occurring natural disaster in Myanmar. It occurs in Myanmar every year especially as tropical cyclones associated with surge and heavy rain during the southwest monsoon period (June-October) severely affect the lower reaches of the Ayeyarwady river. The Ayeyarwady river is the longest and the most important river among the four major rivers in Myanmar, vis: the Ayeyarwady, the Thanlwin, the Chindwin and the Sittaung. The percentages of occurrence of floods in the medium and large rivers in Myanmar are: June (6%), July (23%), August (49%), September (14%) and October (8%) respectively. The severe floods occurred in 1974, 1976, 1979, 1991, 1997, 2002, 2004, 2006, 2007 and 2008. Among them, 2008 flood due to Cyclone Nargis is the extremely dangerous event in Myanmar. The problems of floods and their computation is one of the main and most complex problems. In practicing hydrology, computation of design flood and real time flood forecasting are the two very important hydrological problems. The aim of the study is to form hydrologically homogeneous region/regions from the statistical point of view by considering flow data along with available catchment characteristics. The observed values of annual maximum flow data (AFS) is collected for 14 stations for different length of periods. Available data are tested for homogeneity using the L-moment approach of Hosking and Wallis. The analysis establishes entire Myanmar as one homogeneous region and the further analysis are taken accordingly. The flood quantiles for different return periods have been calculated for both gauged and ungauged sites of the basin. Flood forecasting, a non-structural measures of flood management, is another important aspects of applied hydrology. In this study, flood forecasting analysis has also been done to predict the daily stage at target stations using daily rainfall & stage recordings for other stations with lead period of 1 to 5 days ahead using ANN networks.