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首页> 外文期刊>Journal of irrigation engineering and rural planning >Generating A flood level probability map for Bangladesh
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Generating A flood level probability map for Bangladesh

机译:为孟加拉国生成洪水位概率图

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Flooding in Bangladesh has considerable economic and sociological impacts. For this reason a map showing consistent and easily accessible flood level information would benefit the development of flood management programs which involve flood warning, flood zoning, flood proofing, and flood protection. Flood levels in Bangladesh are affected by many factors, which give rise to considerable joint probability problems when estimating extreme events. An approach which overcomes these difficulties is to generate contours of flood levels for particular exceedance probabilities using the time series of historical annual maximum water level data. This has been performed across Bangladesh and has resulted in the preparation of a flood level probability map. Water level data have been analyzed from 266 stations, and a total of 131 out of the 266 stations examined were found to be stationary and independent. The average length of the data samples was 29 years. Stations that had less than 15 years of data were not included in the analysis. Six different probability distributions were fitted against the annual maximum water level data at selected stations for comparison purposes. The results indicated that the Generalized Extreme Value distribution was the best among the six distributions. As a result the Generalized Extreme Value distribution has been adopted to develop the flood level probability map. At most of the gauging sites the Generalized Extreme Value distribution indicated the presence of an upper bound in the extreme flood levels.
机译:孟加拉国的洪水对经济和社会产生重大影响。因此,显示一致且易于访问的洪水位信息的地图将有利于洪水管理程序的开发,其中包括洪水预警,洪水分区,防洪和防洪。孟加拉国的洪水水平受许多因素影响,在估算极端事件时会引起相当大的联合概率问题。克服这些困难的一种方法是使用历史年度最大水位数据的时间序列为特定的超出概率生成洪水位的轮廓。这项工作已在孟加拉国各地进行,并导致编制了洪水位概率图。已对266个站的水位数据进行了分析,发现266个站中的131个站是静止且独立的。数据样本的平均长度为29年。数据少于15年的台站不包括在分析中。为了进行比较,针对选定站点的年度最大水位数据拟合了六个不同的概率分布。结果表明,广义极值分布是六个分布中最好的。因此,已采用广义极值分布来开发洪水位概率图。在大多数测量地点,广义极端值分布表明极端洪水位处存在上限。

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