确定极端日降水量、水库流入量和干旱期的回归水平

IF 2.6 Q2 WATER RESOURCES Frontiers in Water Pub Date : 2023-07-24 DOI:10.3389/frwa.2023.1141786
T. Milojevic, J. Blanchet, M. Lehning
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引用次数: 0

摘要

回报水平计算被广泛用于确定极端事件可能对基础设施造成的风险,包括水电站的运行。预计未来极端事件(如极端降水和干旱)的频率和强度都将增加,但各区域之间的频率和强度不一定相同。这使得局部评估对于了解特定地点的风险变化非常重要。然而,对于数据集相对较小的站点,选择一种适用的方法来计算回归水平并不是直截了当的。本研究的重点是传统的单变量极值方法(广义极值和广义帕累托)以及最近的两种方法(扩展广义帕累托和亚稳态极值分布)的应用,这两种方法特别适合于小数据集的应用。利用这些方法计算了高寒地区6个站点的极端降水回归水位和某水电站水库的高入库事件。此外,使用非参数方法确定气象干旱和低流入期(干旱期)的回归水平。每种方法使用10年、20年和40年的数据计算10年和20年的回归期的回归水平。结果表明,对于大多数方法来说,即使较短的时间序列也可以给出与较长时间序列相似的返回水平。然而,GEV对稀疏数据更敏感,对降水回归水平的估计往往较低。只有当底层分布与数据非常吻合时,MEV才会优于GPD。结果被用来组合一个10年和20年的回归水平剖面,用各种统计方法估计,极端高降水/流入和低降水/流入事件。研究结果可能有助于研究人员和从业者决定使用哪种统计方法来评估局部极端降水和个别水库的流入风险。
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Determining return levels of extreme daily precipitation, reservoir inflow, and dry spells
Return level calculations are widely used to determine the risks that extreme events may pose to infrastructure, including hydropower site operations. Extreme events (e.g., extreme precipitation and droughts) are expected to increase in frequency and intensity in the future, but not necessarily in a homogenous way across regions. This makes localized assessment important for understanding risk changes to specific sites. However, for sites with relatively small datasets, selecting an applicable method for return level calculations is not straightforward. This study focuses on the application of traditional univariate extreme value approaches (Generalized Extreme Value and Generalized Pareto) as well as two more recent approaches (extended Generalized Pareto and Metastatistical Extreme Value distributions), that are specifically suited for application to small datasets. These methods are used to calculate return levels of extreme precipitation at six Alpine stations and high reservoir inflow events for a hydropower reservoir. In addition, return levels of meteorological drought and low inflow periods (dry spells) are determined using a non-parametric approach. Return levels for return periods of 10- and 20- years were calculated using 10-, 20-, and 40- years of data for each method. The results show that even shorter timeseries can give similar return levels as longer timeseries for most methods. However, the GEV has greater sensitivity to sparse data and tended to give lower estimates for precipitation return levels. The MEV is only to be preferred over GPD if the underlying distribution fits the data well. The result is used to assemble a profile of 10- and 20-year return levels estimated with various statistical approaches, for extreme high precipitation/inflow and low precipitation/inflow events. The findings of the study may be helpful to researchers and practitioners alike in deciding which statistical approach to use to assess local extreme precipitation and inflow risks to individual reservoirs.
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来源期刊
Frontiers in Water
Frontiers in Water WATER RESOURCES-
CiteScore
4.00
自引率
6.90%
发文量
224
审稿时长
13 weeks
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