Chengcheng Xia
Giulia Zuecco
Ke Chen4,5
Jian Luo- 1School of Geography and Tourism, Chongqing Normal University, Chongqing, China
- 2Department of Land, Environment, Agriculture, and Forestry, University of Padova, Legnaro, Italy
- 3Department of Chemical Sciences, University of Padova, Padova, Italy
- 4State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China
- 5Department of Ecohydrology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
- 6College of Water Conservancy and Hydropower Engineering, Sichuan Agricultural University, Ya’an, China
- 7Inner Mongolia Key Laboratory of River and Lake Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, China
- 8Key Laboratory of Mongolian Plateau Ecology and Resource Utilization, Ministry of Education, Hohhot, China
Young water fraction (Fyw) is defined as the fraction of water in a stream with a transit time of less than 2–3 months. Fyw is a metric used to quantify the proportion of precipitation input converted into the runoff in the form of fast flow, which provides new insights for characterizing the mechanisms of water storage and release, understanding the time-scale of ecohydrological processes and indicating water-related risks. However, Fyw has been advanced for a relatively short time, and the research on its applicability conditions and main drivers is still ongoing. Studies estimating Fyw are still very few and this index has not been reported in many landscapes and climate backgrounds, limiting its further application in hydrological studies. On the basis of summarizing the progresses of Fyw in previous studies, this paper provides a preliminary analysis of the potential uncertainties in the Fyw estimation, which can be due to temporal trends in the isotopic composition of precipitation, uneven sampling interval of stream water, and complex hydrological systems. Finally, this paper provides some recommendations for the optimization of the sampling design and the methods used for the Fyw estimation.
Introduction
The rate of water transport in catchments regulates processes such as biogeochemical cycling, pollutant transport, and chemical weathering. The time water takes to travel through a catchment, from inputting as precipitation to reaching the outlet, is an important indicator to characterize the catchment hydrology and determine the sensitivity of the catchment to pollutants (Sprenger et al., 2019). Since the migration, transformation, and phase change of water are accompanied by the transport, mixing, and fractionation of hydrogen and oxygen stable isotopes (i.e., 18O and 2H), these isotopes are widely used to perform hydrological and meteorological analyses at different scales, such as the identification of water vapor sources and transport patterns, the quantification of the contribution of end members to runoff, and the evaluation of water interactions and estimation of evaporation losses (e.g., Chen et al., 2015; Eissa et al., 2016, 2018; Zuecco et al., 2019; Laonamsai et al., 2021, 2022; Wang et al., 2022). During the process of runoff generation in rainfall-dominated catchments, the isotopic signal in precipitation is usually dampened when it is transmitted to river water, due to the buffering effect of the catchment and its mixing with groundwater and soil water with relatively constant isotopic signals. Based on the regularities of isotope fluctuation decay during the conversion from precipitation into river water, mean transit time (MTT), mean residence time and damping ratio can be estimated to evaluate the water age and water transport rate (McGuire and McDonnell, 2006; Bansah and Ali, 2019; Sprenger et al., 2019; Benettin et al., 2022). Such metrics have been widely applied to analyze the catchment behaviors in many regions across the world.
Recently, Kirchner (2016) found that the distribution function of water transit time in a catchment is often not static in space, especially for those landscapes characterized by strong spatial heterogeneity, and great uncertainty is likely to exist in the estimation of MTT, because of the aggregation bias. Consequently, Kirchner (2016) developed the concept of a substitutive metric, the young water fraction (Fyw), which is defined as the runoff component that is younger than 3 months in the water flowing through the catchment outlet. Fyw is negligibly affected by the aggregation bias (with an error usually less than 2%, Kirchner (2016)), and thus it can be used to effectively investigate the hydrological processes and the response of a catchment to climate change and human activities (e.g., groundwater abstraction, contaminant transport, or land use changes), and to evaluate hydrogeological and environmental risks related to water storage and release mechanisms in catchments. The estimation of Fyw is based on the aggregate parameter model, with precipitation as the input and runoff at the outlet as the output (shown in Figure 1). The isotopic signal of the output water is considered as the convolution of the isotopic fluctuation function (set as a sine function) of the input water and the transit time distribution function (set as a Gamma function). Using the Fourier transform method, the shape and scale coefficients of the Gamma distribution function can be determined, and then Fyw can be obtained by integration. Through a numerical simulation experiment, Kirchner (2016) found that Fyw can be approximated using the isotopic amplitude ratio between stream water and precipitation obtained by sine function fitting. This simple method is known as “amplitude ratio method” and has become the most commonly used method to estimate the Fyw. In the fitting of the sine function, the precipitation amount and the discharge are commonly used as weights for the observed isotopic compositions of precipitation and stream water (e.g., von Freyberg et al., 2018; Gallart et al., 2020a). To estimate Fyw, isotopic time series of input and output water, are required to be longer than a year. Typically, higher Fyw is associated with shorter flow paths, implying that surface water and groundwater in the catchment are more susceptible to pollutant contamination, while lower Fyw is associated with longer flow paths and slower recharge rates. Therefore, this metric could benefit the understanding of water recharge mechanisms, as well as of the exchange and transfer of pollutants, and is thus of practical importance for the management of water-related risks.
Figure 1. Conceptual scheme showing the Fyw estimation based on the lumped parameter model.
Jasechko et al. (2016) collected isotopic data from more than 60,000 precipitation samples from 459 meteorological stations and 10,000 river water samples from 254 rivers worldwide, and they estimated the Fyw based on the amplitude ratio method. Jasechko et al. (2016) found that the arithmetic mean value of Fyw in rivers worldwide is 26%, the amount weighted mean value is 34% and the median value is 21%, providing a reference for studies on Fyw in different geographic regions, at different spatial scales, and under different climatic backgrounds. Since then, the Fyw estimation has been determined in different study areas. For example, Fyw was found to be positively correlated with vegetation coverage and proportion of swamp, negatively correlated with the watershed area and elevation, but not significantly correlated with average runoff coefficient in Tibetan Plateau catchments (Song et al., 2017). Higher Fyw are typically associated with wetter climates, soils with lower permeability, and higher precipitation intensity in Swiss catchments (von Freyberg et al., 2018). Wilusz et al. (2017) reported that an increase in Fyw may result from increased precipitation amount in the Plynlimon region of Wales. In the Alpine area, the increase of snowpack duration promotes the emptying of groundwater storage in winter, thus decreasing the Fyw, while the snowpack with short duration is generally associated with the increased rapid flow path, resulting in increased Fyw (Gentile et al., 2022).
In previous studies, the interference of environmental and systematic factors, such as sampling frequency, evaporation and snowmelt, on the accuracy of Fyw estimates have been preliminarily explored (e.g., Ceperley et al., 2020; Gallart et al., 2020a). However, the source of uncertainties and the extent of application of this metric have not been fully explored, which limits its development as a practical indicator for catchment properties. The goal of this paper is to summarize the potential sources of uncertainty and possible limitations of Fyw, in the aspects of both hydro-meteorological conditions and data quality. Furthermore, in this perspective paper we provide some methodological recommendations aimed to reduce the uncertainty in Fyw estimation.
Uncertainty in the complexity of isotopic composition of precipitation and stream water
The theoretical basis of the Fyw estimation is the attenuation of isotope amplitude during the mixing process between precipitation, with a clear seasonal isotope periodicity, and water sources (e.g., shallow and deep groundwater, soil water), with an isotopic composition less time variable compared to precipitation. In the Fyw estimation, the lumped parameter model is based on the premise that precipitation is the only initial water source in the hydrologic system and phase shift in the path of water migration is not considered. Under this hypothesis, the influences of precipitation input in multiple forms (e.g., snowmelt and ice melt) and natural and anthropogenic processes on the composition and periodicity of isotopes in stream water are generally overlooked. For instance, based on data from three Alpine catchments, Ceperley et al. (2020) showed the limitations of isotopic periodicity in snowmelt-dominated catchments, and proposed an optimized method for the Fyw estimation in high altitude areas, considering the influence of lag release of water caused by the seasonal snowmelt. However, the uncertainty brought by melt water should be further explored in other mountainous catchments with different size and elevations, as well as with different contributions of melt water to stream runoff. In addition, previous research (i.e., Ceperley et al., 2020; Gentile et al., 2022) showed that there is a lack of studies providing Fyw, as well as transit times estimations in glacierized catchments; in these catchments Fyw can be larger than Fyw determined for snowmelt-dominated catchments due to fast flow paths and a less permeable bedrock (Schmieder et al., 2019; Zuecco et al., 2019).
In precipitation, the isotopic variations caused by sublimation of snow and ice, water-vapor exchange and sub-cloud secondary evaporation may lead to uncertainty in the sine-wave fitting, whose impacts also need to be evaluated in the Fyw estimation. Previous studies also showed that processes such as evaporation and scheduling of reservoirs may interfere the isotopic periodicity in stream water inherited from rainfall (Reckerth et al., 2017; Xia et al., 2021).Therefore, in catchments with complex flow systems or those heavily influenced by human activities, the uncertainty of the Fyw estimation based on the current estimation methods may be large. Therefore, to estimate Fyw is necessary to develop a more flexible approach, considering the isotopic variation associated with phase behavior changes.
Effects of trends in the isotopic composition of precipitation and stream water
Previous studies have confirmed that although Fyw is dependent on catchment properties, the accuracy of its estimation results is also confounded by systematic factors such as weighting methods, sampling frequency and selection of calculation periods (von Freyberg et al., 2018; Stockinger et al., 2019; Gallart et al., 2020a). Under the influence of climate change, the global temperature increase is widespread, leading to an enrichment trend in isotopic composition in precipitation at longer time scales. Wang and Chen (2020) reported the trend variations in temperature, precipitation, and δ18O in precipitation based on data from stations of the global network of isotopes in precipitation (GNIP) with long-term records. Wang and Chen (2020) found that among 98 selected stations around the world, about 2/3 (65 stations) showed a significant upward trend of δ18O in precipitation. Such a trend may also be transmitted to the isotopes in the river water and cause the amplitudes obtained from the sinusoidal function fit to not reflect the actual seasonal fluctuations of the isotopes in the water. The commonly-used amplitude ratio method for the Fyw estimation is based on the assumption that the isotopes in the waters vary seasonally following a fixed sinusoidal curve. However, in actual catchments, the isotope signals do not vary in a constant pattern. Fitting multi-year isotope records using sine waves will oversimplify the shape of the interannual isotope signal. The interannual differences in the amplitudes of isotopic fluctuations and the effects of isotopic trends at longer time scales on the periodicity are not considered, which are possible sources for inaccurate Fyw estimates.
Impact of the sampling approach of stream water
As the Fyw estimation is based on comparison of periodicity between precipitation and stream water, the extent that the captured isotopic records can reflect the actual variability is important. Gallart et al. (2020a) found that the accuracy of estimated Fyw is greatly dependent on the sampling frequency of stream water. Weekly-resolution sampling could not reflect the isotopic variation under high flows, thus resulting in an underestimation of Fyw in their catchment. Stockinger et al. (2019) pointed out that a 1-year isotopic time series is not long enough for an accurate Fyw estimation, and the selection of the fitting period may lead to a marked uncertainty. However, as the Fyw has been proposed for a relatively short time, in many studies, the time series of isotopic data, used to estimate the Fyw, was originally collected for the investigation of hydrological processes occurring at short timescales. Furthermore, in isotope-based studies, it is common to increase the sampling frequency of stream water in the rainy season with concentrated precipitation and high flows. Previous studies have shown that flows are positively correlated with Fyw (von Freyberg et al., 2018; Gallart et al., 2020b). Therefore, isotopic data collected mainly at the event timescale (i.e., during high flows) may lead to an overestimation of Fyw, as the proportion of high-flow condition in the “isotope discharge” (i.e., the observed flow accompanied with isotope collection), which is involved in sine-wave fitting, exceeds that of the low flow records. Additionally, it is difficult to guarantee long-term and periodic stream water sampling, and the isotope datasets of river water with irregular sampling frequency and data gaps are common in many study areas, due to different research objectives and limitations in logistics conditions (e.g., Ceperley et al., 2020). In previous studies focusing on Fyw, the influence of such widespread heterogeneous datasets on the estimation uncertainty has not been determined yet.
Concluding remarks and future perspectives
Although research on Fyw based on isotopic signals has received much attention during the past few years and has been continuously promoted, there is still a lack of comparative understanding on Fyw under different landscapes and climate conditions. The knowledge about the influencing factors of this metric and the sources of uncertainty in the processes and results of its estimation is still very limited. Factors, such as temporal isotopic trends under changing climate, commonly-used irregular sampling schemes for isotope hydrology, complex recharge sources and evaporation conditions, have potential impacts on the accuracy of Fyw estimation. In future studies, more effort to explore the error sources and to develop optimization methods for Fyw estimation should be made. For example, future research should conduct Fyw estimations in areas with potential isotopic complexity (e.g., where there is a complex isotopic signal of melt waters or enhanced evaporation in the water sources), develop detrending models to reduce (or lower down) the impact of an existing trend on the isotopic periodicity, implement a sampling approach consistent with the full flow record during the observation period (i.e., able to capture all flows, without a bias toward low or high flows). To develop and test better methodological approaches for Fyw estimation, researchers should consider using long-term isotopic datasets (> 5–10 years) and datasets collected at very high temporal resolutions (e.g., von Freyberg et al., 2017). Combining Fyw estimation and transit times modeling across more catchments should help to better understand the drivers of Fyw and its sources of uncertainty, as well as to develop specific recommendations for the application of this metric. By deepening the understanding of Fyw and by reducing the uncertainty in its estimation, this metric could be applied more effectively to improve our comprehension of runoff generation and tracer transport in a variety of catchments.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
CX: formal analysis, methodology, and writing-original draft, GZ, KC, and LL: writing -review & editing and ZZ: writing-original draft. JL: formal analysis and writing -review & editing. All authors contributed to the article and approved the submitted version.
Funding
This work was financially supported by the Start-up Funding from Inner Mongolia University (21800-5223728) and the China Scholarship Council (No. 202006240192).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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References
Bansah, S., and Ali, G. (2019). Streamwater ages in nested, seasonally cold Canadian watersheds. Hydrol. Process. 33, 495–511. doi: 10.1002/hyp.13373
Benettin, P., Rodriguez, N. B., Sprenger, M., Kim, M., Klaus, J., Harman, C. J., et al. (2022). Transit time estimation in catchments: recent developments and future directions. Water Resour. Res. 58:e2022WR033096. doi: 10.1029/2022WR033096
Ceperley, N., Zuecco, G., Beria, H., Carturan, L., Michelon, A., Penna, D., et al. (2020). Seasonal snow cover decreases young water fractions in high alpine catchments. Hydrol. Process. 34, 4794–4813. doi: 10.1002/hyp.13937
Chen, F., Zhang, M., Wang, S., Ma, Q., Zhu, X., and Dong, L. (2015). Relationship between sub-cloud secondary evaporation and stable isotopes in precipitation of Lanzhou and surrounding area. Quat. Int. 380-381, 68–74. doi: 10.1016/J.QUAINT.2014.12.051
Eissa, M. A., Mahmoud, H. H., Shouakar-Stash, O., El-Shiekh, A., and Parker, B. (2016). Geophysical and geochemical studies to delineate seawater intrusion in Bagoush area, northwestern coast, Egypt. J. African Earth Sci. 121, 365–381. doi: 10.1016/j.jafrearsci.2016.05.031
Eissa, M., Shawky, H., Samy, A., Khalil, M., and El Malky, M. (2018). Geochemical and isotopic evidence of groundwater salinization processes in El Dabaa area, northwestern coast. Egypt. Geosci. 8:392. doi: 10.3390/geosciences8110392
Gallart, F., Valiente, M., Llorens, P., Cayuela, C., Sprenger, M., and Latron, J. (2020a). Investigating young water fractions in a small Mediterranean mountain catchment: both precipitation forcing and sampling frequency matter. Hydrol. Process. 34, 3618–3634. doi: 10.1002/hyp.13806
Gallart, F., Von Freyberg, J., Valiente, M., Kirchner, J. W., Llorens, P., and Latron, J. (2020b). Technical note: an improved discharge sensitivity metric for young water fractions. Hydrol. Earth Syst. Sci. 24, 1101–1107. doi: 10.5194/hess-24-1101-2020
Gentile, A., Canone, D., Ceperley, N., Gisolo, D., Previati, M., Zuecco, G., et al. (2022). What drives Fyw variations with elevation in alpine catchments? EGUsphere 2022, 1–28. doi: 10.5194/egusphere-2022-921
Jasechko, S., Kirchner, J. W., Welker, J. M., and McDonnell, J. J. (2016). Substantial proportion of global streamflow less than three months old. Nat. Geosci. 9, 126–129. doi: 10.1038/ngeo2636
Kirchner, J. W. (2016). Aggregation in environmental systems-part 1: seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments. Hydrol. Earth Syst. Sci. 20, 279–297. doi: 10.5194/hess-20-279-2016
Laonamsai, J., Ichiyanagi, K., Kamdee, K., Putthividhya, A., and Tanoue, M. (2021). Spatial and temporal distributions of stable isotopes in precipitation over Thailand. Hydrol. Process. 35:e13995. doi: 10.1002/hyp.13995
Laonamsai, J., Ichiyanagi, K., Patsinghasanee, S., Kamdee, K., and Tomun, N. (2022). Application of stable isotopic compositions of rainfall runoff for evaporation estimation in Thailand Mekong River basin. Water 14:2803. doi: 10.3390/w14182803
McGuire, K. J., and McDonnell, J. J. (2006). A review and evaluation of catchment transit time modeling. J. Hydrol. 330, 543–563. doi: 10.1016/j.jhydrol.2006.04.020
Stockinger, M. P., Reemt Bogena, H., Lücke, A., Stumpp, C., and Vereecken, H. (2019). Time variability and uncertainty in the fraction of young water in a small headwater catchment. Hydrol. Earth Syst. Sci. 23, 4333–4347. doi: 10.5194/hess-23-4333-2019
Reckerth, A., Stichler, W., Schmidt, A., and Stumpp, C. (2017). Long-term data set analysis of stable isotopic composition in German rivers. J. Hydrol. 552, 718–731. doi: 10.1016/j.jhydrol.2017.07.022
Schmieder, J., Seeger, S., Weiler, M., and Strasser, U. (2019). ‘Teflon Basin’ or not? A high-elevation catchment transit time modeling approach. Hydrology 6:92. doi: 10.3390/hydrology6040092
Song, C., Wang, G., Liu, G., Mao, T., Sun, X., and Chen, X. (2017). Stable isotope variations of precipitation and streamflow reveal the young water fraction of a permafrost watershed. Hydrol. Process. 31, 935–947. doi: 10.1002/hyp.11077
Sprenger, M., Stumpp, C., Weiler, M., Aeschbach, W., Allen, S. T., Benettin, P., et al. (2019). The demographics of water: a review of water ages in the critical zone. Rev. Geophys. 57, 800–834. doi: 10.1029/2018RG000633
von Freyberg, J., Allen, S. T., Seeger, S., Weiler, M., and Kirchner, J. W. (2018). Sensitivity of young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss catchments. Hydrol. Earth Syst. Sci. 22, 3841–3861. doi: 10.5194/hess-22-3841-2018
von Freyberg, J., Studer, B., and Kirchner, J. W. (2017). A lab in the field: high-frequency analysis of water quality and stable isotopes in stream water and precipitation. Hydrol. Earth Syst. Sci. 21, 1721–1739. doi: 10.5194/hess-21-1721-2017
Wang, T., and Chen, J. (2020). Long-term trend of precipitation stable isotopic compositions under global warming conditions. J. Radioanal. Nucl. Chem. 325, 557–565. doi: 10.1007/s10967-020-07246-x
Wang, Z., Guo, X., Kuang, Y., Chen, Q., Luo, M., and Zhou, H. (2022). Recharge sources and hydrogeochemical evolution of groundwater in a heterogeneous karst water system in Hubei Province, Central China. Appl. Geochem. 136:105165. doi: 10.1016/j.apgeochem.2021.105165
Wilusz, D. C., Harman, C. J., and Ball, W. P. (2017). Sensitivity of catchment transit times to rainfall variability under present and future climates. Water Resour. Res. 53, 10231–10256. doi: 10.1002/2017WR020894
Xia, C., Liu, G., Zhou, J., Meng, Y., Chen, K., Gu, P., et al. (2021). Revealing the impact of water conservancy projects and urbanization on hydrological cycle based on the distribution of hydrogen and oxygen isotopes in water. Environ. Sci. Pollut. Res. 28, 40160–40177. doi: 10.1007/s11356-020-11647-6
Keywords: young water fraction, isotope tracers, sources of uncertainty, water-related risks, further studies
Citation: Xia C, Zuecco G, Chen K, Liu L, Zhang Z and Luo J (2023) The estimation of young water fraction based on isotopic signals: challenges and recommendations. Front. Ecol. Evol. 11:1114259. doi: 10.3389/fevo.2023.1114259
Edited by:
Yunhui Zhang, Southwest Jiaotong University, ChinaReviewed by:
Rui Ma, Institute of Soil Science, Chinese Academy of Sciences (CAS), ChinaZejun Wang, Wuhan University, China
Copyright © 2023 Xia, Zuecco, Chen, Liu, Zhang and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jian Luo, amlhbi5sdW9AaW11LmVkdS5jbg==