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Trend assessment: applications for hydrology and climate research

机译：趋势评估：水文学和气候研究的应用

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The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend.
机译：气候和水文学趋势的评估仍然是一个辩论的问题。捕获时间序列的典型属性（如趋势）与讨论全球变暖或洪水发生的潜在影响高度相关。它通过区分确定性趋势和随机变异性，为分离人为信号和自然强迫因素提供了指标。在此贡献中，通过将确定性趋势分量和随机模型部分结合到半参数方法中，分析了德国南部水域的径流数据关于其趋势行为。这样，可以明确考虑趋势与自相关结构之间的权衡。通过三个步骤介绍了显着趋势的测试：首先，将能够重现短期和长期相关性的随机分数ARIMA模型拟合到经验数据。在第二步中，假设趋势分量是小时间尺度和大时间尺度的一部分，则使用小波分析将其分开。最后，将总体变异性与限制在小规模范围内的变异性进行比较，可以检验趋势。通过小波分析提取大规模行为提供了有关趋势形状的线索。

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