The hydrogen (δD) and oxygen (δO) isotopic compositions in the water molecule have been widely used as tracers for studying the global water cycle. In 1961, the Global Network of Isotopes in Precipitation (GNIP) was established to measure D and O isotopes contents in precipitation around the world. However, on the spatial scale, the long-term arithmetic and/or long-term precipitation weighted mean δD and δO have been most commonly used to interpret the GNIP isotope data for over sixty years. The spatial distributions of mean δD and δO depict well the regional moisture transport, but they vary predominantly with latitude on the global scale, especially over continental areas, obscuring the continental and circulation effects. We developed a new method of using the entropy concept to reanalyze precipitation isotopic compositions data from GNIP. Calculated entropies of isotopic compositions in precipitation at GNIP stations around the world strongly correlate in a linear fashion with a slope coefficient close to unity. The spatial distributions of both isotopic compositions entropies generally reveal oceanic sources of water vapor and main moisture transport pathways from oceans to continents globally, with different patterns between summer and winter seasons. Although these results have mostly been reported in previous studies, they provide the verification of this new analysis method. The entropy method proposed here is expected to provide a new tool for data interpretation of water isotopic compositions, with implications for tracing global hydrological processes.

中文翻译：

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基于熵的降水同位素分析方法揭示全球主要水分输送走廊

水分子中的氢 (δD) 和氧 (δO) 同位素组成已被广泛用作研究全球水循环的示踪剂。1961 年，全球降水同位素网络 (GNIP) 成立，用于测量全球降水中的 D 和 O 同位素含量。然而，在空间尺度上，60 多年来，长期算术和/或长期降水加权平均 δD 和 δO 最常用于解释 GNIP 同位素数据。平均δD和δO的空间分布很好地描绘了区域水分输送，但它们在全球范围内主要随纬度变化，特别是在大陆地区，掩盖了大陆和环流效应。我们开发了一种使用熵概念重新分析来自 GNIP 的降水同位素组成数据的新方法。全球 GNIP 台站降水中同位素组成的计算熵以线性方式与接近 1 的斜率系数密切相关。两种同位素组成熵的空间分布通常揭示了水汽的海洋来源和全球从海洋到大陆的主要水分输送途径，在夏季和冬季之间具有不同的模式。虽然这些结果大多在以前的研究中报告过，但它们为这种新的分析方法提供了验证。本文提出的熵方法有望为水同位素组成的数据解释提供一种新工具，对追踪全球水文过程具有重要意义。