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趋势变化是水文过程变异十分重要的表征,目前研究主要侧重于趋势识别方法的改进、比较及实例运用,但针对趋势变异程度分级等研究十分缺乏.为此,提出了一种水文序列趋势变异识别与程度分级的方法.该方法通过计算水文序列和时序的相关系数值,将趋势变异程度划分为无变异、弱变异、中变异、强变异及巨变异.通过推导相关系数与趋势斜率之间的公式,说明两者的正相关关系.统计试验验证了公式的合理性,并说明了序列均值和变差系数对相关系数值的影响.将该方法应用于西江高要站不同时间尺度的平均水位序列进行趋势变异程度分级,引入权重概念分析了各尺度平均水位趋势变异之间的联系.结果表明高要站各尺度平均水位序列均呈下降趋势,但趋势变异程度存在差异;权重较大时期平均水位的趋势变异程度对整体趋势变异程度影响更大,结合物理成因分析验证了结果的合理性与所提方法的有效性,因此将有助于定量评估气候变化和人类活动对水文过程的影响.
Trend change is a very important characterization of hydrological process variation. At present, the research mainly focuses on the improvement, comparison and application of trend identification methods, but there is a lack of research on the classification of trend variation degree.Therefore, And the degree of grading.This method divides the degree of trend variation into no-variation, weak variation, medium variation, strong variation and huge variation by calculating the correlation coefficient values between the hydrological sequence and the time series.By deducing the relationship between the correlation coefficient and the trend slope Formula, indicating the positive correlation between the two.Statistical experiments verify the rationality of the formula, and illustrate the effect of sequence mean and coefficient of variation on the correlation coefficient.Application of this method to the average water level at different time scales of Gaoyao station of Xijiang station The results showed that the mean water level series at all scales of Gaoyao Station showed a downward trend, but there was a difference in the degree of trend variation; while in the period of heavier weights, the mean The degree of trend variation of water level has a greater impact on the degree of overall trend variation, combined with physical causes The analysis validates the validity of the results and the effectiveness of the proposed method and will therefore help to quantitatively assess the impact of climate change and human activities on hydrological processes.