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首页> 外文期刊>Australian journal of water resources >Comparison of statistical downscaling techniques for multisite daily rainfall conditioned on atmospheric variables for the Sydney region
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Comparison of statistical downscaling techniques for multisite daily rainfall conditioned on atmospheric variables for the Sydney region

机译：基于悉尼地区大气变量的多站点日降水量统计降尺度技术的比较

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摘要

Predictions of rainfall spatial and temporal variability (including climate change effects) on a catchment basis are urgently required by water resource planners within Australia. Large spatial scale predictions of (typically 300 to 500 km grids) global scale climate scenarios output by General Circulation Models (GCMs) are inadequate for such use as they do not capture the large degree of spatial variability over smaller distances, which is inherent in rainfall. Multisite daily rainfall - a common requirement within many hydrological models - is a required input for modelling complex multi-catchment systems, as small scale spatial variability due to factors such as topography has a large bearing on how much rainfall falls in a given area. Statistical downscaling is a technique that can produce such fine spatial scale rainfall pattern predictions conditional on the larger scale climate scenarios output by a GCM. The GLIMCLIM (Generalised Linear Model for daily Climate time series) software package (Chandler, 2002) has been used to analyse and simulate spatial daily rainfall given natural climate variability influences in the UK, and further to predict the influence of various future climate scenarios on regional rainfall by downscaling larger spatial scale GCM simulations. This paper describes the comparison of this method to the non-parametric, non-homogeneous hidden Markov model - kernel probability density estimation (NNHMM-KDE) downscaling technique of Mehrotra & Sharma (2006), a method which has found application in Australia previously.
机译：澳大利亚的水资源规划人员迫切需要对流域的降雨时空变化（包括气候变化影响）进行预测。通用循环模型（GCM）输出的全球尺度气候情景（通常为300至500 km网格）的大型空间尺度预测不足以用于此类用途，因为它们无法捕捉到较小距离的较大空间变异性，而这是降雨固有的。多站点每日降雨量是许多水文模型中的一个共同要求，是对复杂的多集水系统进行建模的必要输入，因为地形等因素导致的小范围空间变化对给定区域的降雨量有很大影响。统计缩减是一种可​​以根据GCM输出的更大规模的气候情景来产生这种精细的空间尺度降雨模式预测的技术。 GLIMCLIM（每日气候时间序列的通用线性模型）软件包（Chandler，2002年）已用于分析和模拟在英国受到自然气候可变性影响的每日空间降雨量，并进一步预测了各种未来气候情景对英国的影响。通过缩小较大的空间尺度GCM模拟来缩小区域降雨。本文介绍了该方法与Mehrotra＆Sharma（2006）的非参数非均匀隐马尔可夫模型-核概率密度估计（NNHMM-KDE）降尺度技术的比较，该方法先前已在澳大利亚应用。

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