A spatial temporal downscaling approach to development of IDF relations for Perth airport region in the context of climate change
A spatial temporal downscaling approach to development of IDF relations for Perth airport region in the context of climate change
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气候变化背景下珀斯机场地区 IDF 关系发展的时空降尺度方法
DOI：
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发表时间：
2016
期刊：
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通讯作者：
V. Nguyen
中科院分区：
文献类型：
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作者：
S. Herath;P. Sarukkalige;V. Nguyen
摘要气候预测的缩减是评估气候变化在区域和地方尺度上影响的最合适的方法。这项研究利用空间和时间向下尺度方法来建立珀斯机场地区次日极端降雨量的强度-持续时间-频率(IDF)关系。使用基于多元回归的统计降尺度模型工具，利用大气环流模式(哈德利中心的大气环流模式和加拿大全球气候模式)气候变量，对日降雨量进行空间降尺度。在观测到的年最大降雨量(AM)持续30分钟至24小时的时间内，确定了一个简单的标度制度。然后，利用基于广义极值分布的不变模型对次日AM降雨量的统计特性进行了估计。估计了RMSE、Nash-Sutcliffe效率系数和百分比偏差值，以检验缩小的次日降雨量的准确性。这证明了所提出的方法在建立大尺度GCM日变量与给定位置的极端次日降雨量事件之间的联系方面的能力。最后，建立了未来时段的IDF曲线，这两个GCM在2020年代、2020年代、2020年代和2020年代的极端降雨量减少趋势相似。编辑M.C.Acreman；副编辑S.Kanae
ABSTRACT Downscaling of climate projections is the most adapted method to assess the impacts of climate change at regional and local scales. This study utilized both spatial and temporal downscaling approaches to develop intensity–duration–frequency (IDF) relations for sub-daily rainfall extremes in the Perth airport area. A multiple regression-based statistical downscaling model tool was used for spatial downscaling of daily rainfall using general circulation models (GCMs) (Hadley Centre’s GCM and Canadian Global Climate Model) climate variables. A simple scaling regime was identified for 30 minutes to 24 hours duration of observed annual maximum (AM) rainfall. Then, statistical properties of sub-daily AM rainfall were estimated by scaling an invariant model based on the generalized extreme value distribution. RMSE, Nash-Sutcliffe efficiency coefficient and percentage bias values were estimated to check the accuracy of downscaled sub-daily rainfall. This proved the capability of the proposed approach in developing a linkage between large-scale GCM daily variables and extreme sub-daily rainfall events at a given location. Finally IDF curves were developed for future periods, which show similar extreme rainfall decreasing trends for the 2020s, 2050s and 2080s for both GCMs. Editor M.C. Acreman; Associate editor S. Kanae