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Regional frequency analysis of extreme precipitation with consideration of uncertainties to update IDF curves for the city of Trondheim

机译:考虑到不确定性以更新特隆赫姆市IDF曲线的极端降水区域频率分析

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Regional frequency analysis based on the method of L-moments is performed from annual maximum series of extreme precipitation intensity to update Intensity-Duration-Frequency (IDF) curves for the city of Trondheim. The main problems addressed are (1) reduction of uncertainties of different sources for reliable estimation of quantiles: (i) testing of trend patterns and stationarity of the data series from the target site and demonstrating the dependency of results on the data used; (ii) testing regional homogeneity of extreme precipitation events for the climate regime in the study area and "pooling" of regional data for data augmentation and reduction of uncertainty due to short length of data series; and (iii) selection of distributions for extreme precipitation events of different durations to reduce the uncertainty due to choice of distributions; and (2) assessment and quantification of sampling uncertainty in terms of interval estimates (confidence bounds) of quantiles. Trend patterns and check for stationarity were demonstrated for a data from a target site based on both non-parametric Mann-Kendall and parametric regression tests. Selection of distributions was performed based on Z-statistics and L-moment ratio diagrams. Non-parametric balanced bootstrap resampling was used to quantify the sampling uncertainty. For extreme precipitation events of shorter durations (5-30. min) there are statistically significant increasing trend patterns for the data series with start years of 1992-1998 while there are no significant trend patterns for recent extremes and there are no statistically significant trend patterns for longer durations (45-180. min). The results of the analyses indicate that: (1) significance tests for trend patterns and stationarity are dependent on the data series used but the stationarity assumption is valid for the data series used from the target site. (2) the extreme precipitation events from four sites in Trondheim are homogeneous and can be "pooled" for regional analysis; (3) different types of distributions fit to extreme precipitation events of different durations which shows that thorough selection of distributions is indispensable rather than fitting a single distribution for the whole durations; (4) interval estimates from balanced bootstrap resampling indicated that there is huge sampling uncertainty in quantile estimation that needs to be addressed in any frequency analysis; and (5) large differences are observed between the IDF curves from this study and the existing IDF curves (i.e. Imetno). The IDF curves from this study are from data augmented through regional analysis, based on thorough procedures for selection of distributions and also include uncertainty bounds and hence are more reliable than the existing one. Hence, the methods and procedures followed in this study are expected to contribute to endeavors for estimating reliable IDF curves.
机译:从L矩方法进行的区域频率分析是从极端降水强度的年度最大序列进行的,以更新特隆赫姆市的强度-持续时间-频率(IDF)曲线。解决的主要问题是:(1)减少不同来源的不确定性,以可靠地估计分位数:(i)测试趋势模式和目标站点数据序列的平稳性,并证明结果对所用数据的依赖性; (ii)测试研究区域气候状况的极端降水事件的区域同质性,并“汇集”区域数据以增强数据并减少由于数据序列长度短造成的不确定性; (iii)为不同持续时间的极端降水事件选择分布,以减少由于选择分布而引起的不确定性; (2)根据分位数的区间估计(置信区间)评估和量化抽样不确定性。基于非参数Mann-Kendall和参数回归测试,对目标站点的数据显示了趋势模式和平稳性检查。分布的选择基于Z统计量和L矩比率图进行。使用非参数平衡自举重采样来量化采样不确定性。对于持续时间较短(5-30分钟)的极端降水事件,从1992-1998年开始的数据序列在统计上具有显着增加的趋势模式,而对于最近的极端情况则没有显着的趋势模式,也没有统计上显着的趋势模式持续时间更长(45-180分钟)。分析结果表明:(1)趋势模式和平稳性的显着性检验取决于所使用的数据序列,但平稳性假设对目标站点使用的数据序列有效。 (2)特隆赫姆四个地点的极端降水事件是均匀的,可以“汇集”用于区域分析; (3)不同类型的分布适合不同持续时间的极端降水事件,这表明彻底选择分布是必不可少的,而不是整个持续时间都适合单一分布; (4)平衡自举重采样的间隔估计表明,在分位数估计中存在巨大的采样不确定性,需要在任何频率分析中解决; (5)这项研究的IDF曲线与现有的IDF曲线(即Imetno)之间存在较大差异。这项研究的IDF曲线来自数据,这些数据是通过基于区域分布的全面选择程序进行区域分析而得到的,并且还包括不确定性范围,因此比现有的更加可靠。因此,预计本研究中采用的方法和程序将有助于估算可靠的IDF曲线。

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