Generation of multi-site stochastic daily rainfall with four weather generators: a case study of Gloucester catchment in Australia
Generation of multi-site stochastic daily rainfall with four weather generators: a case study of Gloucester catchment in Australia
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使用四个天气生成器生成多地点随机日降雨量：澳大利亚格洛斯特流域的案例研究
DOI：
10.1007/s00704-017-2306-3
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发表时间：
2018
影响因子：
3.4
通讯作者：
Xiaogang Shi
中科院分区：
文献类型：
--
作者：
G. Fu;F. Chiew;Xiaogang Shi
四个天气发生器，即，R-包版本的广义线性模型的每日气候时间序列（RGLIMCLIM），随机气候库（SCL），R-包多站点降水发生器（RGENERATRPREC）和R-包多站点自回归天气发生器（RMAWGEN），在澳大利亚的一个小流域产生多站点随机日降雨量。结果表明：（1）4种模式在年、月、日降水量、日极值、多日极值和干湿历时等方面都有较好的模拟结果。但他们也模拟了大范围的变率，这不仅展示了多个天气发生器而不是单个模型的优势，而且更适合气候变化和变率影响研究。(2)每一种模型由于理论和原理的不同，都有其自身的优缺点。这增强了使用多个模型的好处。(3)这些模型可以进一步校准/改进，以便与观测相比具有“更好”的性能。然而，在本案例研究中选择不这样做，有两个原因：获得全面的气候变异性，并承认与观测数据有关的不确定性。后者是从有限的站点内插，因此具有很高的成对相关性，范围从0.69到0.99，中位数和平均值分别为0.87和0.88，日降雨量。这些结论是从澳大利亚的一个案例研究中得出的，但可以扩展到使用天气发生器进行气候变化和变率研究的一般准则。
Four weather generators, namely, R-package version of the Generalised Linear Model for daily Climate time series (RGLIMCLIM), Stochastic Climate Library (SCL), R-package multi-site precipitation generator (RGENERATRPREC) and R-package Multi-site Auto-regressive Weather GENerator (RMAWGEN), were used to generate multi-sites stochastic daily rainfall for a small catchment in Australia. The results show the following: (1) All four models produced reasonable results in terms of annual, monthly and daily rainfall occurrence and amount, as well as daily extreme, multi-day extremes and dry/wet spell length. However, they also simulated a large range of variability, which not only demonstrates the advantages of multiple weather generators rather than a single model but also is more suitable for climate change and variability impact studies. (2) Every model has its own advantages and disadvantages due to their different theories and principles. This enhances the benefits of using multiple models. (3) The models can be further calibrated/improved to have a “better” performance in comparison with observations. However, it was chosen not to do so in this case study for two reasons: to obtain a full range of climate variability and to acknowledge the uncertainties associated with observation data. The latter are interpolated from limited stations and therefore have high pairwise correlations—ranging from 0.69 to 0.99 with a median and mean value of 0.87 and 0.88, respectively, for daily rainfall. These conclusions were drawn from a case study in Australia, but could be extended to general guidelines of using weather generators for climate change and variability studies.