Bias correction of d4PDF using a moving window method and their uncertainty analysis in estimation and projection of design rainfall depth
Bias correction of d4PDF using a moving window method and their uncertainty analysis in estimation and projection of design rainfall depth
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DOI：
10.3178/hrl.14.117
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发表时间：
2020
影响因子：
1.1
通讯作者：
Satoshi Watanabe;Masafumi Yamada;Shiori Abe;Misako Hatono
中科院分区：
文献类型：
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作者：
Satoshi Watanabe;Masafumi Yamada;Shiori Abe;Misako Hatono
：设计降雨深度是河流规划中使用的基本指标，通过超集合模拟获得的降雨量进行偏差修正来估算，并预测了升温4度下的未来变化。提出对现有偏差校正方法进行修改，以解决过度拟合以及参考数据和超集成模拟数据之间大小差距的问题。分别考虑历史实验、参考数据之间的偏差以及历史和未来实验之间的变化的偏差校正方法被定义为两遍偏差校正。使用移动窗口方法进行两遍偏差校正，该方法计算时间段的移动平均值和排序统计数据。结果表明，本研究提出的方法估算的设计降雨深度与没有移动窗口的计算结果相比误差较小。移动窗口方法有效解决了过拟合问题。预测表明，海表温度（SST）模式的预测范围对于大多数流域相当于设计降雨深度的25%，对于某些特定流域相当于设计降雨深度的60%。结果表明，对于超集合模拟数据，适当的偏差校正和考虑海温图范围的重要性。
: Design rainfall depth, which is a fundamental index used in river planning, was estimated by rainfall obtained from super-ensemble simulations with bias correction, and the future change under 4 degree warming was projected. The modifications of existing bias correction methods were pro‐ posed to resolve the issue of overfitting and gap in size between reference and super-ensemble simulation data. A bias correction approach considering the bias between the historical experiment, the reference data, and the change between the historical and future experiments separately was defined as two-pass bias correction. The two-pass bias correction was performed with a moving window method that calculated moving average for time period and rank-order statistics. The result indicated that the approach pro‐ posed in this study estimates the design rainfall depth with a small error compared to that calculated without the moving window. The moving window method effectively resolves the issue of overfitting. The projection indicated that the range of projection among sea-surface temperature (SST) patterns is equivalent to 25% of the design rainfall depth for most basins and 60% for certain specific basins. The results indicate the importance of the appropriate bias correction and the consideration of range among the SST patterns for super-ensemble simulation data.