What can we learn from long hydrological time-series? The case of rainfall data at Collegio Romano, Rome, Italy

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Elena Volpi, Corrado P. Mancini, Aldo Fiori
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引用次数: 0

Abstract

In this work, we explore the statistical behavior of one of the longest rainfall time-series in Italy and in the world, covering the period 1782–2017. Some standard and innovative statistical tools are applied to test the variability and change of the process across all values (in average, but also in terms of extremes) and scales (from days to years). An oscillation pattern occurs across all the time scales, from years to decades, limited by the sample length. It implies that there are no particular periods of variability, apart from seasonality, and no statistically significant trends, such that the process can be fully characterized in terms of the Hurst coefficient. Despite its exceptional length, the dataset is still insufficient to adequately capture the complex behavior of rainfall over the time scales, especially with regards to extremes, and to separate anthropogenically induced change from natural variability based on the data alone. Our findings suggest that samples of limited length do not allow robust statistical predictions, raising concerns about statistical analyses based on a limited dataset, even a relatively large one.

我们能从漫长的水文时间序列中学到什么?意大利罗马罗马学院的降雨量数据案例
在这项工作中,我们探索了意大利乃至世界上最长的降雨时间序列之一的统计行为,时间跨度为 1782-2017 年。我们应用了一些标准的和创新的统计工具,以测试所有值(平均值,也包括极端值)和尺度(从天到年)的过程的可变性和变化。受样本长度的限制,从数年到数十年的所有时间尺度上都会出现振荡模式。这意味着,除了季节性之外,不存在特定的变异期,也不存在统计意义上的显著趋势,因此可以用赫斯特系数来完全描述这一过程。尽管数据集非常长,但仍不足以充分反映降雨在时间尺度上的复杂行为,尤其是极端降雨,也无法仅凭数据将人为因素引起的变化与自然变化区分开来。我们的研究结果表明,长度有限的样本无法进行稳健的统计预测,这引起了人们对基于有限数据集(即使是相对较大的数据集)的统计分析的担忧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Hydrology X
Journal of Hydrology X Environmental Science-Water Science and Technology
CiteScore
7.00
自引率
2.50%
发文量
20
审稿时长
25 weeks
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