Data Analysis in Nonstationary State
Data Analysis in Nonstationary State
复制标题
非平稳状态下的数据分析
DOI：
10.1007/s11269-018-1928-2
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发表时间：
2018
影响因子：
4.3
通讯作者：
Sadık Alashan
中科院分区：
文献类型：
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作者：
Sadık Alashan
任何水资源规划管理研究都必须考虑到气候变化的影响，因为它不允许未来的情况重复过去的情况。由于气候变化对温度、降水、蒸发、径流、流量等水文气象事件的上升或下降趋势分量起着重要作用，因此平稳性不再成立。趋势的识别可以通过众所周知的方法来表示，例如Mann-Kendall和序贯Mann-Kendall。这些方法需要限制性的假设，如数据长度，序列独立性和高斯（正态）概率分布函数（PDF）。另一方面，创新的趋势分析（ITA）的方法提出的Rewen是有助于识别，即使是视觉上的直接解释没有限制性的假设。PDF或累积分布函数（CDF）是确定风险水平的有效工具，但它不能告诉任何关于给定水文气象数据的趋势。PDF（CDF）不能提供任何关于趋势可能性的线索，因此，在任何水资源结构设计中单独使用PDF（CDF）可能会导致错误的规划研究。在这项研究中，一个给定的月度水文气象数据的非平稳性质的趋势确定程序进行检查。在应用中，使用英国牛津站的月平均日最高温度。结果表明，各月气温值均呈正趋势，前半组非平稳经验累积频率曲线与各数据组的拟合程度均优于平稳状态。
Climate change impact must be taken into account in any water resources planning management studies, because it does not allow future occurrences to be repeated as a replicate of the past. The stationarity is no longer valid, because the climate change plays significant role on ascending or descending trend components in any hydro-meteorological events such as temperature, precipitation, evaporation, runoff and discharge. The identification of trends can be represented by well-known methodologies, such as the Mann-Kendall and sequential Mann-Kendall. These methodologies require restrictive assumptions such as data length, serial independence and Gaussian (normal) probability distribution function (PDF). On the other hand, Innovative trend analysis (ITA) method proposed by Şen is helpful to identify even visually with direct interpretations without restrictive assumptions. The PDF or cumulative distribution function (CDF) is effective tool for risk level determination but it cannot tell anything about the trend in a given hydro-meteorological data. The PDF (CDF) does not yield any clue about the trend possibility, and hence, it’s alone use in any water resources structure design may lead to erroneous planning studies. In this study, nonstationary nature of a given monthly hydro-meteorological data is examined by trend determination procedures. For the application, monthly averages of maximum daily temperatures are used on Oxford station, UK. It is observed that the temperature values of each month have a positive trend and the nonstationary empirical cumulative frequency curves on first half group match better all data group than the stationary state.