Research articleAn entropy-based analysis method of precipitation isotopes revealing main moisture transport corridors globally
Introduction
The hydrogen (δD) and oxygen (δ18O) isotopic compositions in the water molecule are very sensitive to the hydrology processes. Therefore, they have been widely used as tracers for studying the global water cycle (Dansgaard, 1964; Galewsky et al., 2016; Winnick et al., 2014; Worden et al., 2007). In 1961, the International Atomic Energy Agency (IAEA), in cooperation with the World Meteorological Organization (WMO), established the Global Network of Isotopes in Precipitation (GNIP) to measure D and 18O isotopes contents in precipitation around the world (IAEA/WMO). As described by the Rayleigh model (Dansgaard, 1964), precipitation isotopic compositions depend on three climatic parameters, i.e., air temperature, precipitation amount, and air mass history, which is termed as temperature effect, amount effect, and circulation effect, respectively (Le Duy et al., 2018). The circulation effect of precipitation stable isotopes, including the origin, condensation history and mixing of the water vapor with continental air masses caused by evaporation and/or evapotranspiration, and the free troposphere vapor along transport path (Cole et al., 1999).
On the temporal scale, a number of isotope studies paid attention to monthly, seasonal, annual, and inter-annual variability using the GNIP database (Cai and Tian, 2016; Moerman et al., 2013; Vachon et al., 2010). On the spatial scale, the long-term arithmetic and long-term weighted mean δD and δ18O (weighing by the amount of precipitation) have been most commonly used to interpret the GNIP isotope data for over sixty years (Araguás-Araguás et al., 1998; Bowen and Wilkinson, 2002; Craig, 1961a, Craig, 1961b; Dansgaard, 1964; Galewsky et al., 2016; IAEA/WMO, 2019; Rozanski et al., 1993; Winnick et al., 2014; Worden et al., 2007). A few other methods have been adopted to extract information from isotope data, such as spatial precipitation isotope gradients (Liu et al., 2010). These long-term means show spatial distributions that vary predominantly with latitude, reflecting the temperature effect of isotope fractionation during vapor condensation (Dansgaard, 1964), and depicts well the moisture transport regionally (Araguás-Araguás et al., 1998; Liu et al., 2010; Rozanski et al., 1993). Unfortunately, on the global scale, the ocean-to-continent paths of moisture movement are not clear, as well as the circulation effect of precipitation stable isotopes.
Precipitation on the continents is closely linked to atmospheric fronts, monsoons, and cyclones (Catto et al., 2012; Ding et al., 2015; Wang et al., 2012; Wang et al., 2014, Wang et al., 2017a, Wang et al., 2017b). For example, the water vapor of precipitation on the Asia continent is dominated by five moist air masses, originating from the northern Pacific, the western equatorial Pacific, the Indian Ocean, the Arctic and local continental evapotranspiration, with the first three driven by the East Asian monsoon, South Asian monsoon, and Indian monsoon, respectively, and the last two together driven by cold fronts (Catto et al., 2012; Wang et al., 2016). Although these important ocean-land interaction aspects of the moisture transportation are shown in the current analysis of the GNIP isotope data based on the long-term arithmetic and/or long-term weighted mean δD and δ18O (Araguás-Araguás et al., 1998; Liu et al., 2010; Rozanski et al., 1993), they are limited to regional and continental scales. On the global scale, can the precipitation isotope data be analyzed further to reveal, directly, the origin and movement of water vapor in connection with evapotranspiration, atmospheric circulation, and condensation?
The GNIP data, based on monthly composite samples of precipitation at each station, provides episodic information about isotopic composition of atmospheric water vapor resulting from successive precipitation events, each typically with variable isotope characteristics. In addition to the long-term mean as a statistical measure of the random isotopic compositions data, we explore here how information entropy in connection to the probability distribution of measured isotopic compositions may be applied as a new proxy for further analyzing the GNIP data. Similar to the original entropy concept for a thermodynamic system, information entropy or Shannon entropy was introduced to quantify the amount of uncertainty associated with a probability distribution (e.g., shown in a time series of a random variable) and serve as a measure of the level of mixing and chaos within a system (Shannon, 1948; Singh, 1997). It has been widely applied in hydrology study (Singh, 1997; Singh et al., 2017), such as analysis of precipitation variability (Mishra et al., 2009; de Rodrigues da Silva et al., 2016), flow evaluation and forecast (Cui and Singh, 2016; Hao and Singh, 2012), characterizing drought and flood (Rajsekhar et al., 2015; Rajsekhar et al., 2013), estimation of evaporation (Wang and Bras, 2011; Xu et al., 2019), hydrometric network evaluation and design (Li et al., 2012; Wang et al., 2019), etc. Here, we applied the information entropy concept to analyze the GNIP isotope data with the aim to examine possible links between the spatial variations of isotope entropy and moisture movement on the global scale.
Section snippets
Materials and methods
On the pathway of water vapor moving from the ocean origins to continents, mixing of different air masses increases the variability of isotopes in water vapor (Fig. 1). This in turn raises the variability of isotopes in precipitation, which is also affected by fluctuations of local temperature and precipitation amount, due to the effects of both parameters on fractionation during vapor condensation. If the raw data of precipitation isotopes are filtered to have the local temperature and
δD and δ18O entropy calculation
At each selected GNIP station, monthly averaged values of δD, δ18O, near-surface air temperature, and precipitation rate were taken as the raw data from the GNIP Database. Power spectral densities (PSD) calculated from the time series of these data show similar, predominantly seasonal (over a yearly cycle) periodicity of the δD and δ18O time series to that of temperature and precipitation rate (Fig. 2, Fig. 3). The results shown in these two figures are based on the Ottawa and Hong Kong
Uncertainties
The two important effects of local temperature and precipitation amount were filtered before entropy calculation. To do so, we assumed a simple linear relation of isotopic compositions values with temperature and precipitation rate, which may cause some uncertainties. It was suggested that there existed a piecewise relationship between isotopic compositions values and temperature (Dansgaard, 1964), and a power relationship between isotopic compositions values and precipitation amount (Fischer
Concluding remarks
The global water cycle has been extensively studied based on δD and δ18O in the water molecule. However, the interpretation of GNIP isotope data on the spatial scale has been so far limited in the long-term arithmetic and/or long-term weighted mean δD and δ18O (weighing by the amount of precipitation). The spatial distributions of mean δD and δ18O show the regional moisture transport well, but on the global scale, they obscure the global ocean-to-continent paths of moisture movement. We
Declaration of Competing Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to acknowledge the support of the Fundamental Research Funds for the Central Universities (No. 2018B48814 and No. 2019B11314), the International Postdoctoral Exchange Fellowship Program from Chinese Postdoctoral Council (No. 20180071), and the National Natural Science Foundation of China (No. 41803001). All data are available from the GNIP Database and accessible at: https://nucleus.iaea.org/wiser. The data reported in this paper are tabulated in the supplementary
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