Regional Extreme Rainfall Mapping for Bangladesh Using L-Moment Technique
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 5
Abstract
Bangladesh is a flood prone country where huge damages take place every year. Therefore, to minimize flood extremes, it is important to control the flood peaks at the upstream area through suitable watershed management practices. The flood control management at the watershed scale requires good quality flood data. However, in developing countries like Bangladesh, such hydrological information is rarely available at the watershed level. Under such circumstances, it is important to use a hydrological model representing the rainfall-runoff process to arrive at the extreme flows in the rivers, which require extreme rainfall data as a major inflow to the hydrologic system. Furthermore, the density of rain gauges in Bangladesh is low and the quality of available flood data is poor. Considering this, it is important to develop regional extreme rainfall maps for the reliable estimation of flood flows in the river by using a suitable modeling approach. Therefore, in the present paper, an attempt has been made to derive the regional best fit extreme rainfall pattern for Bangladesh for the estimation of extreme rainfall quantiles. This study uses the annual maximum daily rainfall of 68 rain gauge stations. An autocorrelation test is applied to test the independency of the data. Later, considering the heterogeneity in the hydroclimatic and topographic details, entire rain gauge stations have been clustered into six hydroclimatically homogeneous regions; namely, northeast (NE), northwest (NW), southeast (SE), southwest (SW), coastal, and central regions, by using the -mean clustering technique. The stations that did not pass the discordant and heterogeneity test were discarded from the regional frequency analysis. For regional frequency analysis, the L-moment method was applied. Based on the goodness of fit test and the L-moment ratio diagram, the generalized extreme values distribution was identified as the best fit for the SE, NW, and coastal regions. However, for NE, central, SW regions, the best fit distributions were generalized logistic and generalized Pareto, respectively. Using the derived distributions, regional extreme rainfall quantiles were estimated, followed by geo-mapping in ArcGIS 9.2.
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Acknowledgments
The authors wish to thank Flood Forecasting Warning Centre (FFWC) of Bangladesh Water Development Board (BWDB) for the data used in this study.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 5 • May 2013
Pages: 603 - 615
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Oct 19, 2011
Accepted: May 23, 2012
Published online: May 26, 2012
Published in print: May 1, 2013
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