# Application of Water Stable Isotopes for Hydrological Characterization of the Red River (Asia)

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{18}O_H

_{2}O data collected at the Hanoi meteo-hydrological station, Red River, in three periods; 2002–2005, 2015, and 2018–2019. The mean (min and max) values of δ

^{18}O_H

_{2}O in rainwater over the three periods are, respectively, −5.3‰ (−11.0 and −1.2‰), −5.4‰ (−10.7 and −1.4‰), and −4.5‰ (−13.9 and 1.7‰). The corresponding values in river water are −8.4‰ (−9.8 and −6.9‰), −8.5‰ (−9.1 and −7.7‰), and −8.4‰ (−9.5 and −7.2‰), respectively. The mean of Fyw calculated from the δ

^{18}O_H

_{2}O data for different periods is 22 ± 9%, 10 ± 5%, and 8 ± 3%. Mean transit time is 4.69 ± 15.57, 1.65 ± 1.53, and 2.06 ± 1.87 years. The calculated Fyw (MTT) is negatively (positively) proportional to change in reservoir volume over the three periods, which is logical, since reservoirs tend to keep more water in the catchment and slower down water flow. The strong variation of Fyw and $\overline{\tau}$, two essential variables characterizing the catchment hydrology, represents an anthropogenic impact in the Red River system.

## 1. Introduction

^{18}O or δ

^{2}H) are commonly used to investigate water storage in catchments [1,2,3]. Timescales of catchment storage are typically quantified by the mean transit time (MTT, $\overline{\tau}$), meaning the average time that elapses between parcels of water entering as precipitation and leaving again as streamflow [4]. The longer mean transit times imply greater damping of seasonal tracer cycles. Thus, the amplitudes of tracer cycles in precipitation and streamflow are commonly used to calculate catchment mean transit times. In addition to MTT, fraction of young water (Fyw), characterizing the storage capacity of a catchment, was recently proposed [5,6]. It is defined as the streamflow fraction that is roughly younger than three months after entering the catchment as meteoric water (e.g., precipitation). Estimation of Fyw is also done by comparing the amplitudes of sine waves, fitted to the seasonally varying isotope tracer signal in precipitation and streamflow. To date, this Fyw estimation method has only been applied to theoretical datasets and smaller catchments in temperate areas [5,6,7,8]. It remains to be tested if Fyw can also be estimated for a complete river system (from headwater to estuary) where large reservoirs have been built to alter water course.

## 2. Materials and Methods

#### 2.1. Red River and Its Reservoirs Built Recently

^{2}in which 86,720 km

^{2}are in Vietnam. The Red River basin consists of 3 headwater mountainous sub-basins and 1 downstream delta area [11,12]. Three upstream-mountainous sub-basins, namely, Da, Lo, and Thao, merge their water together at Viet Tri, upstream of the delta area (Figure 1).

^{3}s

^{−1}, which highly fluctuates between dry and rainy seasons. Peaks of discharge usually occur in July–August [13].

^{3}of water [9]. In the Vietnam territory, several large reservoirs such as Hoa Binh, Son, and Lai Chau have been built. These reservoirs were commissioned in the period of 1990–2016 (Table 1). In the Thao sub-basin, the Chinese side currently has about 29 reservoirs and dams including two large dams, Nanshan (commissioned around 2008) and Madushan (commissioned around 2011) [9,14] (Table 1). In Vietnam territory, no hydropower project has ever been established in this sub-basin. In the Lo sub-basin, the Chinese side has also built and operated about 8 hydropower reservoirs, of which 2 large reservoirs are Malutang (commissioned around 2018) and Baisheng (commissioned around 2010). In the Vietnam territory, a number of large reservoirs have been built, such as Thac Ba and Tuyen Quang. Thac Ba reservoir has been commissioned since 1972, and Tuyen Quang reservoir has been operated since 2010 [14] (Table 1).

#### 2.2. Sampling Site and Analysis

^{2}H and δ

^{18}O, respectively. The assigned values for the laboratory calibration standards W-39, W-34, and control W-31 were +25.4 ± 0.8‰ and +3.634 ± 0.04‰; −189.5 ± 0.9‰ and −24.778 ± 0.02‰; −61.04 ± 0.6‰ and −8.6 ± 0.09‰ for δ

^{2}H and δ

^{18}O relative to VSMOW, respectively. The control W-31 long-term (1-year running average) analytical reproducibility (±SD) was ±0.11‰ and ±0.7‰ for δ

^{18}O and δ

^{2}H, respectively. It should be noted that in this paper, only δ

^{18}O was used for the calculation of Fyw, as required by the method.

^{18}O, ‰) is defined as:

^{18}O/

^{16}O)

_{Sample}is a ratio of

^{18}O and

^{16}O abundances of sample and (

^{18}O/

^{16}O)

_{VSMOW}is a ratio of

^{18}O and

^{16}O abundances of standard—Vienna Standard Mean Ocean Water.

#### 2.3. Supplementary Data Acquisition

#### 2.4. Estimation of MTT ($\overline{\tau}$) and Fyw

^{18}O isotope signals [6,7]. These are calculated, respectively, as:

_{P}(t) is simulated precipitation and C

_{S}(t) is streamflow, δ

^{18}O isotope values of time t (decimal years), A is the amplitude (‰), φ is the phase of the seasonal cycle (in radians, with 2π rad equaling 1 year), f is the frequency (yr

^{−1}), and k (‰) is a constant describing the vertical offset of the isotope signal. After fitting these multiple regression equations, the Fyw can be calculated as:

_{p}, n, and m in Equations (2), (3), (6) and (7). The Levenberg–Marquardt algorithm was used to solve this generic curve-fitting problem.

## 3. Results and Discussion

#### 3.1. Meteo-Hydrological Seasonality

^{−1}) occurring in late summer: typical tropical monsoon climate [18,19]. The mean values of rainfall of three periods are, respectively, 148, 121, and 139 mm month

^{−1}. Water discharge at the hydrological station of Hanoi is shown in Figure 2b. Mean discharges are, respectively, 2177, 1689, and 2058 m

^{3}s

^{−1}. It can be seen that discharge in 2015 and 2019 was lower than in other periods (about more than 20%). This can be explained by lower rainfall in 2015 and 2019 (approx. 20%) than in other periods. In terms of discharge, the dry season in 2015 (about 6 months) was longer than any of the other years (3–4 months) (Figure 2b).

#### 3.2. Composition of Oxygen Isotopes—δ^{18}O

^{18}O of rainwater and river water in the study periods is shown in Figure 3. There is a clearly sinusoidal variability of rain and river water isotopic signal. The lowest value occurs during the rainy season and the highest value occurs during the dry season. This variability is agreed with a precedent study [20], which concluded that the equatorial–maritime air mass, well known for its amount effect (an inverse relationship between precipitation and the isotopic composition), dominates the precipitation regime in the region. Over the whole monitoring period, the mean (min and max) value of rainwater δ

^{18}O is −5.1‰ (−13.9 and 1.7‰). During the 2003–2005, the mean (min and max) values of rainwater and river water are −5.3‰ (−11 and −1.2‰) and 8.4‰ (−9.8 and −6.9‰), respectively. In 2015, the corresponding values are −5.4‰ (−10.7 and −1.4‰) and −8.5‰ (−9.1 and −7.7‰), respectively. During the 2018–2019, the values are −4.5‰ (−13.9 and 1.7‰) and −8.4‰ (−9.5 and −7.2‰), respectively.

#### 3.3. Fraction of Young Water (Fyw)

#### 3.4. Mean Transit Time ($\overline{\tau}$)

^{3}s

^{−1}, respectively. As a consequence, the river water isotope signal at the Hanoi Site during dry period of 2005 was peculiarly lower than of other years—the longer mixing of different water sources in the reservoirs to average out the isotopic signal (Figure 3b). Thus, calculation on this less variable river water isotope profile resulted in a low Fyw (Figure 4) and a high MTT (Figure 5). This explanation is consistent with our assumption that hydropower reservoirs and their operation are critical to the Red River hydrology. Notably, MTT calculated for 2005 should be cautiously taken into account because of its high uncertainty (high SE) (Figure 5). In term of management practices, the success of using isotopes to identify the change in reservoir operation shows the potential of using isotopes to identify changes in regulation that may impact the river system ecohydrology.

#### 3.5. Impact of Forcing Factors to the Red River Hydrology

^{3}. By 2015, the total volume of reservoirs increased to 24.5 billion m

^{3}and by 2018 was 27.0 billion m

^{3}(Table 1). Logically, in response to an increase in the number of reservoirs and the total reservoir volume is an increase in MTT and a decrease in Fyw. Thus, correlation analysis would help reveal a linkage between reservoir commission in Red River with MTT and Fyw (Table 2).

## 4. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

#### Formulation of SE Calculation for Fyw and MTT ($\overline{\tau}$)

- Calculation of ${\sigma}_{\alpha}$

_{s}/A

_{p}

_{φ}can be calculated by the expression:

- Calculation of ${\sigma}_{\beta}$

- Calculation of ${\sigma}_{\overline{\tau}}$

- Calculation of ${\sigma}_{Fyw}$

## Appendix B

#### Data Used for Correlation Calculation

**Table A1.**Annual precipitation, monthly mean discharge, reservoir volume, Fyw, and MTT over the monitoring years.

Year | Discharge (m ^{3} s^{−1}) | Rainfall (mm month ^{−1}) | Reservoir Volume (mill m ^{3}) | Fyw | MTT (years) |
---|---|---|---|---|---|

2003 | 2197 | 130 | 12,802 | 0.23 | 0.70 |

2004 | 2226 | 160 | 12,802 | 0.32 | 0.45 |

2005 | 2109 | 155 | 12,802 | 0.10 | 12.93 |

2015 | 1689 | 121 | 24,887 | 0.10 | 1.65 |

2018 | 2448 | 141 | 26,980 | 0.08 | 2.87 |

2019 | 1668 | 137 | 26,980 | 0.08 | 1.24 |

## References

- Rodriguez, N.B.; Pfister, L.; Zehe, E.; Klaus, J. Testing the truncation of travel times with StorAge Selection functions using deuterium and tritium as tracers. Hydrol. Earth Syst. Sci. Discuss.
**2019**, 1–37. [Google Scholar] [CrossRef][Green Version] - Rodriguez, N.B.; Pfister, L.; Zehe, E.; Klaus, J. A comparison of catchment travel times and storage deduced from deuterium and tritium tracers using StorAge Selection functions. Hydrol. Earth Syst. Sci.
**2021**, 25, 401–428. [Google Scholar] [CrossRef] - Zhou, J.; Liu, G.; Meng, Y.; Xia, C.; Chen, K.; Chen, Y. Using stable isotopes as tracer to investigate hydrological condition and estimate water residence time in a plain region, Chengdu, China. Sci. Rep.
**2021**, 11, 2812. [Google Scholar] [CrossRef] - Bansah, S.; Andam-Akorful, S.A.; Quaye-Ballard, J.; Wilson, M.C.; Gidigasu, S.S.; Anornu, G.K. An Evaluation of Catchment Transit Time Model Parameters: A Comparative Study between Two Stable Isotopes of Water. Geosciences
**2019**, 9, 318. [Google Scholar] [CrossRef][Green Version] - Stockinger, M.P.; Bogena, H.R.; Lücke, A.; Stumpp, C.; Vereecken, H. Time variability and uncertainty in the fraction of young water in a small headwater catchment. Hydrol. Earth Syst. Sci.
**2019**, 23, 4333–4347. [Google Scholar] [CrossRef][Green Version] - Kirchner, J.W. 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.
**2016**, 20, 279–297. [Google Scholar] [CrossRef][Green Version] - Kirchner, J.W. Aggregation in environmental systems–Part 2: Catchment mean transit times and young water fractions under hydrologic nonstationarity. Hydrol. Earth Syst. Sci.
**2016**, 20, 299–328. [Google Scholar] [CrossRef][Green Version] - Freyberg, J.V.; Allen, S.T.; Seeger, S.; Weiler, M.; Kirchner, J.W. Sensitivity of young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss catchments. Hydrol. Earth Syst. Sci.
**2018**, 22, 3841–3861. [Google Scholar] [CrossRef][Green Version] - Ha, V.K.; Vu, T.M.T. Research the effect of upstream reservoirs on China to flow regime of Da River and Thao River. J. Water Resour. Environ. Eng.
**2012**, 334, 199–214. [Google Scholar] - Strady, E.; Dang, T.H.; Dao, T.D.; Dinh, H.N.; Do, T.T.D.; Duong, T.N.; Chu, V.H. Baseline assessment of microplastic concentrations in marine and freshwater environments of a developing Southeast Asian country, Viet Nam. Mar. Pollut. Bull.
**2021**, 162, 111870. [Google Scholar] [CrossRef] [PubMed] - Le, T.P.Q.; Billen, G.; Garnier, J.; Théry, S.; Fézard, C.; Chau, V.M. Nutrient (N, P) budgets for the Red River basin (Vietnam and China). Glob. Biogeochem. Cycles
**2005**, 19, 19. [Google Scholar] - Luu, T.N.M.; Garnier, J.; Billen, G.; Orange, D.; Némery, J.; Le, T.P.Q.; Tran, H.T.; Le, L.A. Hydrological regime and water budget of the Red River Delta (Northern Vietnam). J. Asian Earth Sci.
**2010**, 37, 219–228. [Google Scholar] [CrossRef] - Le, N.D.; Le, T.P.Q.; Hoang, T.T.H.; Phung, T.X.B.; Pham, T.M.H. Heavy metals in suspended solids in the Red river system at Chuong Duong bridge (Hanoi) (in Vietnamese). J. Sci. Technol.
**2020**, 56, 114–118. [Google Scholar] - Phung, T.X.B. Study on the Effects of Upstream Reservoirs on the Conveyance of Suspended Sediment and the Binding Substances (C, N, P, and Si) in the Water in the Downstream Area of the River; Ministry of Industry and Trade: Hanoi, Vietnam, 2019.
- IAEA. Water Isotope System for Data Analysis, Visualization and Electronic Retrieval. 2016. Available online: https://nucleus.iaea.org/wiser/ (accessed on 30 April 2016).
- Wassenaar, L.; Coplen, I.; Aggarwal, P.K. Approaches for Achieving Long-Term Accuracy and Precision of δ
^{18}O and δ^{2}H for Waters Analyzed using Laser Absorption Spectrometers. Environ. Sci. Technol.**2014**, 48, 1123–1131. [Google Scholar] [CrossRef] - Coplen, T.B.; Wassenaar, L.I. LIMS for Lasers 2015 for achieving long-term accuracy and precision of δ
^{2}H, δ17O, and δ^{18}O of waters using laser absorption spectrometry. Rapid Commun. Mass Spectrom.**2015**, 29, 2122–2130. [Google Scholar] [CrossRef] [PubMed] - Vu, V.H.; Merkel, B.J. Estimating groundwater recharge for Hanoi, Vietnam. Sci. Total. Environ.
**2019**, 651, 1047–1057. [Google Scholar] - Dang, T.A.T.; Wraith, D.; Bambrick, H.; Dung, N.; Truc, T.T.; Tong, S.; Dunne, M.P. Short-term effects of temperature on hospital admissions for acute myocardial infarction: A comparison between two neighboring climate zones in Vietnam. Environ. Res.
**2019**, 175, 167–177. [Google Scholar] [CrossRef] - Trinh, A.D.; Luu, T.N.M.; Le, T.P.Q. Use of stable isotopes to understand run-off generation processes in the Red River Delta. Hydrol. Process.
**2017**, 31, 3827–3843. [Google Scholar] [CrossRef] - Ma, W.; Yamanaka, T. Factors controlling inter-catchment variation of mean transit time with consideration of temporal variability. J. Hydrol.
**2016**, 534, 193–204. [Google Scholar] [CrossRef][Green Version] - Cartwright, I.; Morgenstern, U. Using tritium to document the mean transit time and sources of water contributing to a chain-of-ponds river system: Implications for resource protection. Appl. Geochem.
**2016**, 75, 9–19. [Google Scholar] [CrossRef] - Jódar, J.; Custodio, E.; Lambán, L.J.; Martos-Rosillo, S.; Herrera-Lameli, C.; Sapriza-Azuri, G. Vertical variation in the amplitude of the seasonal isotopic content of rainfall as a tool to jointly estimate the groundwater recharge zone and transit times in the Ordesa and Monte Perdido National Park aquifer system, north-eastern Spain. Sci. Total. Environ.
**2016**, 573, 505–517. [Google Scholar] [CrossRef] [PubMed][Green Version] - Muhammad, A.; Evenson, G.R.; Unduche, F.; Stadnyk, T.A. Climate Change Impacts on Reservoir Inflow in the Prairie Pothole Region: A Watershed Model Analysis. Water
**2020**, 12, 271. [Google Scholar] [CrossRef][Green Version] - Muhammad, A.; Evenson, G.R.; Stadnyk, T.A.; Boluwade, A.; Jha, S.K.C.P. Assessing the Importance of Potholes in the Canadian Prairie Region under Future Climate Change Scenarios. Water
**2018**, 10, 1657. [Google Scholar] [CrossRef][Green Version] - Dang, N.M.; Babel, M.S.; Luong, H.T. Evaluation of food risk parameters in the day river flood diversion area, Red River delta, Vietnam. Nat. Hazards
**2011**, 56, 169–194. [Google Scholar] [CrossRef] - Nguyen, D.G. The impact of the upstream reservoir system on the variations of hydrological and hydraulic regimes and riverbeds on the downstream side. J. Water Resour. Sci. Technol.
**2016**, 32, 29–36. [Google Scholar] - McElwee, P.; Nghiem, T.; Le, H.; Vu, H. Flood vulnerability among rural households in the Red River Delta of Vietnam: Implications for future climate change risk and adaptation. Nat. Hazards
**2016**, 86, 465–492. [Google Scholar] [CrossRef] - Grill, G.; Lehner, B.; Lumsdon, A.E.; MacDonald, G.K.; Zarfl, C.; Liermann, C.R. An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams atmultiple scales. Environ. Res. Lett.
**2015**, 10, 015001. [Google Scholar] [CrossRef]

**Figure 2.**Monthly rainfall in Hanoi (

**a**) and Red River’s discharge at the Hanoi hydrological station (

**b**).

Name | Country | Volume (million m^{3}) | Commision Year |
---|---|---|---|

Thác Bà | Vietnam | 2940 | 1972 |

Hòa Bình | Vietnam | 9862 | 1989 |

Longma | China | 590 | 2007 |

Jufudu | China | 174 | 2008 |

Gelantan | China | 409 | 2008 |

Tukahe | China | 88 | 2008 |

Sinanjiang | China | 270 | 2008 |

Malutang | China | 546 | 2018 |

Sơn La | Vietnam | 9260 | 2010 |

Shimenkan | China | 197 | 2010 |

Madushan | China | 551 | 2011 |

Lai Châu | Vietnam | 1215 | 2016 |

Huổi Quảng | Vietnam | 184.2 | 2016 |

Bản Chát | Vietnam | 162.7 | 2016 |

Puxiqiao | China | 531 | 2016 |

**Table 2.**Spearman correlation coefficients (p-values) between forcing factors (annual rainfall, annual discharge, reservoir volume) and hydrological variables (Fyw and MTT); data used for correlation calculation are in Appendix B.

Annual Discharge | Annual Rainfall | Reservoir Volume | Fyw | MTT | |
---|---|---|---|---|---|

Annual discharge | 1 (--) | 0.49 (0.33) | −0.19 (0.73) | 0.43 (0.40) | −0.09 (0.87) |

Annual rainfall | 1 (--) | −0.31 (0.55) | 0.37 (0.47) | −0.03 (0.96) | |

Reservoir volume | 1 (--) | −0.93 (0.01) | 0.28 (0.59) | ||

Fyw | 1 (--) | −0.49 (0.33) | |||

MTT | 1 (--) |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Nguyen, N.L.; Do, T.N.; Trinh, A.D. Application of Water Stable Isotopes for Hydrological Characterization of the Red River (Asia). *Water* **2021**, *13*, 2051.
https://doi.org/10.3390/w13152051

**AMA Style**

Nguyen NL, Do TN, Trinh AD. Application of Water Stable Isotopes for Hydrological Characterization of the Red River (Asia). *Water*. 2021; 13(15):2051.
https://doi.org/10.3390/w13152051

**Chicago/Turabian Style**

Nguyen, Nho Lan, Thu Nga Do, and Anh Duc Trinh. 2021. "Application of Water Stable Isotopes for Hydrological Characterization of the Red River (Asia)" *Water* 13, no. 15: 2051.
https://doi.org/10.3390/w13152051