不同偏差校正法对GCM降水数据的应用效果分析
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1.中国地质大学(北京)水资源与环境学院;2.1.中国地质大学北京水资源与环境学院

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P338

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Evaluations of different bias correction methods on the GCM precipitation data
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1.School of Water Resources and Environment,China University of Geosciences,Beijing,100083;2.China

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    摘要:

    大气环流模式(GCM)是获取时空连续气候数据的主要手段之一,它模拟的气候变量能弥补气象站点稀少的不足,但由于其输出数据往往存在一定偏差,因此在使用前需要进行校正。本文以黑河流域为研究区,提取了CMIP6中的BCC-CSM2-MR模式的降水数据,采用线性标度法(LS)、经验分位数映射法(EQM)和γ分位数映射法(GQM)对1985-2014年逐月降水数据进行偏差校正,选择标准化均方根误差(NRMSE)、平均绝对误差(MAE)、皮尔逊相关系数(R)和森斜率(Sen’s slope)为评价指标,将校正后的GCM降水数据与同期实测数据进行比较,分析校正前后评价指标的变化以及不同校正方法结果的差异和有效性。结果表明:(1)LS方法校正后降水数据总量指标值有明显改进,NRMSE、MAE和均值误差明显减小,然而由于该方法没有将频率上的差异考虑进去,因此,通过LS校正后的降水数据在频率上仍存在一定偏差;(2)经过EQM和GQM方法校正后,NRMSE、MAE指标均有所改进,虽然总体效果不如LS方法,但较校正前均有很大提升;在频率指标方面,这两种方法的校正结果明显更优,校正后的降水频率空间分布与变化趋势更加符合实测数据;另外,通过分析各指标,EQM方法在本研究区的适用性更强。

    Abstract:

    General Circulation Model (GCM) is one of the main tools to obtain spatial-temporal continuous climate data which can make up for the lack of measurement data in areas where there are no stations. However, the output data of GCM often has certain bias, so it needs to be corrected before use. Taking the Heihe River Basin as the study area and extracting the precipitation data from the BCC-CSM2-MR model in CMIP6 as the source data, this paper mainly focused on evaluating the adaptabilities of different bias correction methods on the extracted monthly precipitation data from 1985-2014 in the study area. The bias correction methods selected in this study include the Linear Scale (LS) method, Empirical Quantile Mapping (EQM) method and Gamma Quantile Mapping (GQM) method, and the evaluation indicators include the Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (R) and Sen"s slope. The GCM precipitation data were compared before and after bias correction with the observed data in the same period. The results showed that: (1) The total index value of precipitation data corrected by LS method was significantly improved, and the NRMSE, MAE and mean error were significantly reduced. However, since the differences in frequency were not taken into account, there was still some errors in the frequency of precipitation data after corrected by LS method. (2) After corrected by EQM and GQM methods, NRMSE and MAE indexes were improved, although the degrees of the improvement were not as large as those from LS method, they were much higher than those before the bias correction. In terms of the frequency index, the results of these two methods were obviously better, and the spatial distributions of precipitation frequencies and variation trends were more consistent with the measured data. In addition, EQM has stronger applicability in this study area according to the indicator values in this paper.

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  • 收稿日期:2022-02-14
  • 最后修改日期:2022-02-14
  • 录用日期:2022-06-06
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