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Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model

机译：使用GLIMCLIM模型模拟极端降雨并预测未来变化

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摘要

In this study, the performance of the Generalized LInear Modelling of daily CLImate sequence (GLIMCLIM) statistical downscaling model was assessed to simulate extreme rainfall indices and annual maximum daily rainfall (AMDR) when downscaled daily rainfall from National Centers for Environmental Prediction (NCEP) reanalysis and Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCM) (four GCMs and two scenarios) output datasets and then their changes were estimated for the future period 2041-2060. The model was able to reproduce the monthly variations in the extreme rainfall indices reasonably well when forced by the NCEP reanalysis datasets. Frequency Adapted Quantile Mapping (FAQM) was used to remove bias in the simulated daily rainfall when forced by CMIP5 GCMs, which reduced the discrepancy between observed and simulated extreme rainfall indices. Although the observed AMDR were within the 2.5th and 97.5th percentiles of the simulated AMDR, the model consistently under-predicted the inter-annual variability of AMDR. A non-stationary model was developed using the generalized linear model for local, shape and scale to estimate the AMDR with an annual exceedance probability of 0.01. The study shows that in general, AMDR is likely to decrease in the future. The Onkaparinga catchment will also experience drier conditions due to an increase in consecutive dry days coinciding with decreases in heavy (> long term 90th percentile) rainfall days, empirical 90th quantile of rainfall and maximum 5-day consecutive total rainfall for the future period (2041-2060) compared to the base period (1961-2000).
机译：在这项研究中，通过国家环境预测中心（NCEP）重新分析每日降尺度的降雨，评估了每日CLImate序列的通用线性建模（GLIMCLIM）统计降尺度模型的性能，以模拟极端降雨指数和年度最大每日降水（AMDR）。以及耦合模型比较项目第5阶段（CMIP5）的一般流通模型（GCM）（四个GCM和两个方案）输出数据集，然后为将来的2041-2060年估算了它们的变化。在NCEP重新分析数据集的强制下，该模型能够很好地再现极端降雨指数的月变化。当由CMIP5 GCM强制使用频率自适应分位数映射（FAQM）来消除模拟日降雨中的偏差，这减少了观测到的模拟极端降雨指数之间的差异。尽管观察到的AMDR处于模拟AMDR的2.5和97.5个百分位之内，但是该模型始终低估了AMDR的年际变化。使用局部，形状和比例的广义线性模型开发了一个非平稳模型，以估计AMDR的年度超出概率为0.01。研究表明，总体而言，未来AMDR可能会下降。 Onkaparinga流域还将经历较干燥的条件，这是由于连续的干旱天数增加，这与暴雨（>长期第90个百分位数）的降雨天数的减少，经验的第90分位数的降雨以及未来一段时间内连续5天的最大总降雨量一致（2041） -2060）与基准期（1961-2000）相比。

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  • 来源
    《Theoretical and applied climatology》 |2017年第2期|453-466|共14页
  • 作者

    Rashid Md. Mamunur; Beecham Simon; Chowdhury Rezaul Kabir;

  • 作者单位

    Univ South Australia, Sch Nat & Built Environm, Nat & Built Environm Res Ctr, Mawson Lakes, SA 5095, Australia|Univ New South Wales, Water Res Ctr, Sydney, NSW 2052, Australia;

    Univ South Australia, Sch Nat & Built Environm, Nat & Built Environm Res Ctr, Mawson Lakes, SA 5095, Australia;

    Univ South Australia, Sch Nat & Built Environm, Nat & Built Environm Res Ctr, Mawson Lakes, SA 5095, Australia|United Arab Emirates Univ, Dept Civil & Environm Engn, Al Ain 15551, U Arab Emirates;

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