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首页> 外文期刊>Hydrological Processes >Temporal trends in δ~(18)O composition of precipitation in Germany: insights from time series modelling and trend analysis
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Temporal trends in δ~(18)O composition of precipitation in Germany: insights from time series modelling and trend analysis

机译：δ〜（18）o德国降水组成的时间趋势：时间序列建模和趋势分析的见解

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摘要

Temporal and spatial variations of stable oxygen (O-18) and hydrogen (H-2) isotope measurements in precipitation act as important proxies for changing hydro-meteorological and regional and global climate patterns. Temporal trends in time series of the stable isotope composition in precipitation were rarely observed, and they are poorly understood. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here, we investigate temporal trends of O-18 in precipitation at 17 observation stations in Germany between 1978 and 2009. We test if significant trends in the isotope time series from different models can be observed. Mann-Kendall trend tests are applied on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models, which account for first and higher order serial correlations. Effects of temperature, precipitation, and geographic parameters on isotope trends are also investigated in the proposed models. To benchmark our proposed approach, the ARIMA results are compared with a trend-free pre-whitening procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we further explore whether higher order serial correlations in isotope series affects our trend results. Overall, three out of the 17 stations show significant changes when higher order autocorrelation are adjusted, and four show a significant trend when temperature and precipitation effects are considered. The significant trends in the isotope time series generally occur only at low elevation stations. Higher order autoregressive processes are shown to be important in the isotope time series analysis. Results suggest that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative. Copyright (c) 2014 John Wiley & Sons, Ltd.
机译：降水中稳定氧（O-18）和氢气（H-2）同位素测量的时间和空间变化作为改变水流气象和区域和全球气候模式的重要代理。很少观察到沉淀在沉淀中稳定同位素组合物的时间序列，它们尚未理解。这些可能是缺乏适当的趋势检测工具和探索趋势过程的努力的结果。在这里，我们在1978年至2009年间探讨了德国17个观测站的O-18在升沉中的时间趋势。我们测试是否可以观察到不同型号的同位素时间序列中的显着趋势。 Mann-Kendall趋势测试应用于Isotope系列，使用一般乘法季节性自动进球集成移动平均（Arima）模型，该模型占第一阶和更高阶的串行相关性。在拟议的模型中还研究了温度，降水和地理参数对同位素趋势的影响。为了基准，我们提出的方法，ARIMA结果与无趋势预美化程序进行了比较，最先进的方法用于去除环境趋势研究中的一级自相关的方法。此外，我们进一步探索了同位素系列中的高阶序列相关性是否会影响我们的趋势结果。总体而言，当调整高阶自相关时，17个站中的三个出现了显着的变化，并且在考虑温度和降水效果时，四个显示出显着的趋势。同位素时间序列的显着趋势通常仅在低升高站发生。高阶自回归过程显示在同位素时间序列分析中很重要。结果表明，仅使用一阶自相关调整的广泛使用的趋势分析可能无法充分考虑稳定同位素系列中的高阶自相关过程。调查的时间序列分析方法包括更高的自相关和外部气候变量调整，显示为更好的替代方案。版权所有（c）2014 John Wiley＆Sons，Ltd。

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