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Article

Characteristics of Hydrogen and Oxygen Isotope Composition in Precipitation, Rivers, and Lakes in Wuhan and the Ecological Environmental Effects of Lakes

by
Ao Zhang
1,
Xinwen Zhao
1,*,
Jun He
1,
Xuan Huang
2,*,
Xingyuezi Zhao
1 and
Yongbo Zhao
1
1
Wuhan Center, China Geological Survey (Geosciences Innovation Center of Central South China), Wuhan 430205, China
2
Hubei Transportation Planning Design Institute Co., Ltd., Wuhan 430050, China
*
Authors to whom correspondence should be addressed.
Water 2023, 15(16), 2996; https://doi.org/10.3390/w15162996
Submission received: 23 July 2023 / Revised: 12 August 2023 / Accepted: 17 August 2023 / Published: 19 August 2023
Browse Figures
Versions Notes

Abstract

:
Wuhan has a dense network of rivers and lakes. Due to the city’s development, the water system has been fragmented, the degradation of lakes is becoming increasingly severe, and the eco-environment has been significantly damaged. By collecting samples of the central surface water bodies in Wuhan, including Yangtze River water, Han River water, lake water, and precipitation, and by utilizing hydrogen and oxygen isotopes and multivariate statistical methods, the hydraulic connectivity and ecological environmental effects between the Yangtze River, the Han River, and the lakes were revealed. The results indicated the following: (1) The local meteoric water Line (LMWL) in the Wuhan area was δD = 7.47δ18O + 1.77. The river water line equation was approximately parallel to the atmospheric precipitation line in the Wuhan area. The intercept and slope of the lake waterline equation were significantly smaller. The enrichment degree of δ18O and δD was Yangtze River < Hanjiang River < lake water. (2) The cluster analysis showed that the lakes could be divided into two types, i.e., inner-flow degraded (IFD) lakes and outer-flow ecological (OFE) lakes. Urban expansion has resulted in fragmentation of the IFD lakes, changing the connectivity between rivers and lakes and weakening the exchange of water bodies between the Yangtze River and lakes. Simultaneously, evaporation has caused hydrogen and oxygen isotope fractionation, resulting in the relative enrichment of isotopes. The IFD lakes included the Taizi Lake, Yehu Lake, and the Shenshan Lake. The OFE lakes and the Yangtze River were active, evaporation was weak, and the hydrogen and oxygen isotopes were relatively depleted, mainly including the Huangjia Lake, the East Lake, the Tangxun Lake, etc. (3) The excessive deuterium (d-excess) parameter values in the Yangtze River and the Han River water were positive. In contrast, the d values in the lakes were mainly negative. In the case of a weakened water cycle, the effect of evaporation enrichment on lake water δ18O and δD had a significant impact. It is suggested that the water system connection project of “North Taizi Lake-South Taizi Lake-Yangtze River” and the small lakes connecting to large lakes project of “Wild Lake-Shenshan Lake-Tangxun Lake” should be implemented in time to restore the water eco-environment.

1. Introduction

Wuhan is known as the “city of lakes”, and lakes are Wuhan’s most distinctive natural resources. In recent years, due to accelerated urbanization and rapid expansion of built-up areas in Wuhan, the lake water system has been fragmented and the lake area has deteriorated, presenting the trend of “big lakes breaking into small lakes, and small lakes gradually disappearing” [1,2,3]. However, ecosystem connectivity is essential for biodiversity conservation. Fragmentation is a substantial ecological disturbance that can lead to biological extinction and environmental damage [4]. Especially due to the connectivity between rivers and lakes and the obstruction of hydraulic exchange between lakes, the flood control and storage functions of lakes have weakened, and waterlogging in Wuhan has occurred frequently, posing a significant threat to the safety of people’s lives and property [5].
Currently, research on water resources in Wuhan has mainly focused on ecological and environmental issues caused by unreasonable development and utilization of water resources. For example, starting from the mechanism of water conservation, Bai [6] revealed that changes in land cover types in the Wuhan urban area have led to a gradual weakening of its water conservation function and proposed countermeasures to prohibit the reclamation of farmland from lakes and to implement the return of farmland to lakes to improve the water conservation function. Wang [7] conducted a cluster analysis and a factor analysis on 24 water quality indicators in 70 lakes in Wuhan and found that the main factors affecting lake water quality were organic nitrogen, water bacterial species, volatiles, heavy metals, etc. Zhu [8] analyzed the current pollution characteristics of 309 lakes in Hubei Province and found that, among the 14 cities and districts in Hubei Province, Wuhan had the highest number of lakes exceeding the Class III water standard, and the number of lakes exceeding the Class III standard in each indicator also ranked first. These studies only reflect the local conditions of the water environment in Wuhan, and there is an urgent need to research the traceability of water circulation between water bodies in the region to solve the critical dilemma of urban waterlogging and to scientifically plan and develop water resources.
Stable hydrogen and oxygen isotopes in water molecules (δ18O and δD) are natural tracers that are recognized as the most potent recorder in critical water catchment processes and water balance [9,10,11,12,13]. Hydrogen and oxygen isotopes are sensitive to environmental changes and reflect isotopic fractionation during water phase transformation. They are ideal indicators for studying water cycling and they have been widely used [14,15,16,17,18]. For example, Zanazzi et al. [19] measured Utah Lake’s water balance by using stable hydrogen and oxygen isotopes and found that the groundwater recharged by snow melting was the primary source of river water in the Utah Valley; Nagavciuc et al. [20] tracked the relationship between precipitation and river water in the northern Carpathian Mountains based on water isotope data and found that temperature had a significant impact on the change of δD in the precipitation; Li et al. [21] analyzed the hydrogen and oxygen isotopes of urban rivers and believed that the comprehensive influence of climate and human factors was an essential reason for the changes in isotopic characteristics of urban rivers; Qu et al. [22] revealed the isotopic characteristics and water source of the Hemuqiao Basin in the small watershed of the lower Yangtze River by analyzing the Stable nuclide composition of precipitation.
Surface runoff is an integral part of the terrestrial water cycle, and the hydrogen and oxygen isotope values of different water sources exhibit significant spatial and temporal variations under natural conditions [23]. Precipitation is vital in the water cycle [24,25,26,27]. Affected by air masses that bring water vapor from different geographical regions, hydrogen and oxygen isotopes in precipitation reflect a combination of precipitation and recycling effects in the source area, providing the possibility of determining precipitation sources and quantifying the contribution of precipitation sources to runoff generation [28,29], as well as the dynamic processes of the watershed (such as evaporation, mixing of different sources) [30]. Numerous studies have shown that the distribution of precipitation isotopes depends on local temperature, latitude, altitude, and other climatic or geographical factors, which can be summarized as the following effects: seasonal, precipitation, temperature, continental, latitude, and sea level effects [11,31,32,33,34]. The impact of climate on hydrogen and oxygen isotopes is significant, but human influence may also alter the isotopic composition of river water [35,36]. In urban areas, research on stable hydrogen and oxygen isotopes has also been increasingly focused on the impact of human activities [37,38].
This study selected the surface water bodies of the Yangtze River, the Han River, and the major lakes in the urban area of Wuhan as the research objects. The hydrological interactive process between precipitation and the Yangtze River, the Han River, and lake water was revealed by comprehensively using hydrogen and oxygen isotopes and multivariate statistical methods. The eco-environmental effects of rivers and lakes under human activities were explored, and the results could assist with the sustainable development and utilization of water resources and the improvement of water ecology.

2. Study Area

Wuhan, commonly known as Jiangcheng, is located in the center of China’s hinterland, in the eastern part of Hubei Province, at the intersection of the Yangtze River and the Han River. The overall terrain is high in the north and low in the south, mainly characterized by undulating terrain with hills and plains alternating. The middle of the territory is low and flat, surrounded by ridges and hills in the north and south, and low mountains in the north stand tall. Located in the northern subtropical monsoon (humid) climate, Wuhan has the characteristics of abundant rainfall, sufficient heat, rainy and hot seasons, cold winters and hot summers, and four distinct seasons. The annual average temperature ranges from 15.8 to 17.5 °C, with an extreme maximum temperature of 41.3 °C (10 August 1934) and an extreme minimum temperature of −18.1 °C (30 January 1977). The annual frost-free period is generally 211–272 days, with a total annual sunshine duration of 1810–2100 h, a comprehensive annual radiation of 104–113 kcal/cm2, and an annual precipitation of 1150–1450 mm. Rainfall is concentrated from June to August yearly, accounting for about 40% of the annual rainfall.
Rivers crisscross the city, with intertwined rivers, harbors, and canals. Lakes, reservoirs, and ponds are scattered throughout the city. The Sheshui, Fuhe, Daoshui, Jushui, Jinshui, and Dongjing Rivers converge into the Yangtze River from both sides of the city, forming a vast water network with the Yangtze River as its mainstream. The total water area reaches 2217.6 km2, accounting for 26.1% of the city’s total area, and includes 140 tributaries and small rivers over 5 km, 273 reservoirs of various types, and 166 lakes. The water surface area of the lakes is about 867 km2, accounting for 41% of the city’s water area (2117.6 km2). There are 148 lakes listed in the Hubei Province Lake Protection List. The static storage capacity of groundwater is 128 × 108 m3, with a total surface water volume of 7169 × 108 m3. The domestic rainfall runoff is 43 × 108 m3, with an average annual inflow of water resources of 7122 × 108 m3 and a total outbound water volume of 7141 × 108 m3.

3. Methodology

3.1. Sampling

In this study, 75 groups of surface water and precipitation samples were collected from the Yangtze River, the Hanjiang River, lakes, and rainfall in Wuhan, including 10 groups of Hanjiang River samples, 11 groups of Yangtze River samples, 24 groups of 13 lakes in the urban area, and 20 groups of precipitation samples. The distribution of sample points is shown in Figure 1. All samples were collected by professionals. During the sampling process, the weather, temperature, geographical location, coordinates, and other information were recorded in real time. A Manta 2.0 multi-parameter water quality analyzer (Eureka company, Peoria, IL, USA) was used to measure water temperature, pH, conductivity, dissolved oxygen, and other parameters on site. The surface water samples were filtered using a cellulose acetate 0.45 μm filter membrane and put into pre-cleaned sample bottles. The precipitation samples were collected using a rain gauge and plastic funnel bottle and they were transferred to sample bottles after the precipitation event to prevent evaporation of precipitation. The sample bottles were 100 mL high-density polyethylene (HDPE) plastic bottles, sealed with waterproof tape after sampling, stored at low temperature, and sent to the laboratory for testing. In this study, we used isotopic data of atmospheric precipitation, measured data of the precipitation samples, and quoted historical data of atmospheric precipitation in Wuhan. The data were from the Global Network of Isotopes in Precipitation (GNIP), which can be downloaded from http://isohis.iaea.org (accessed on 10 March 2023), including the δ18O and δD values, precipitation, temperature, water vapor pressure, and other meteorological data [21,39]. The quoted data were the monitoring data of the Wuhan station from 1986 to 1998, and 2005 (including the missing data from 1992 to 1995), and the monitoring frequency was once a month.

3.2. Laboratory Work

The hydrogen and oxygen stable isotope tests of the samples in this study were completed by the open research laboratory of isotope geochemistry, China Geological Survey, using an element analyzer FlashEA 1112HT and a mass spectrometer MAT253 online. During the determination, the water samples were put into an AS3000 liquid automatic sampler tray, and a 0.1 µL water sample was collected by the injector and injected into the high-temperature reformer (1380 °C) of the element analyzer to generate H2 and CO through high-temperature carbon reduction. H2 and CO were separated from the carrier gas by a constant temperature chromatographic column (90 °C), and then introduced into a mass spectrometer for hydrogen and oxygen isotope determination. International standards V-SMOW and GISP, as well as industry standards GBW(E)-070016 and GBW(E)-070017, were used in the analysis process, and the quality of duplicate samples (the number of standard and duplicate samples was 30% of the total number of samples) was monitored. All results were expressed as parts per thousand relative to the Vienna Standard Mean Oceanic Water (V-SMOW) sample (Equations (1) and (2)) [40,41]. The determination results were reliable and within the allowable range of national and industrial standard errors. The measurement accuracies of δ18O and δD were ±0.15‰ and ±1.5‰, respectively.
δ D SA - V - SMOW ( 0 00 ) = [ δ D SA - ST δ D ST - V - SMOW − 1 ] × 10 3
δ 18 O SA - V - SMOW ( 0 00 ) = [ δ 18 O SA - ST δ 18 O ST - V - SMOW − 1 ] × 10 3
where δDSA-V-SMOW and δ18OSA-V-SMOW are the δ values of the sample relative to the international standard V-SMOW (‰); δDST-V-SMOW and δ18OST-V-SMOW are the δ values of the standard sample relative to the international standard V-SMOW (‰); δDSA-ST and δ18OSA-ST are the δ values of the sample relative to the working standard (‰).

3.3. Data Analysis

It is well known that δ18O and δD are linearly correlated. It was initially proposed by Craig [42] based on a study of the global meteoric water line (GMWL) and later improved by Rozanski et al. [43] that:
δD = 8.13·δ18O + 10.8
GMWL explains the relationship between δ18O and δD in precipitation on a global scale, but its slope and intercept may change locally due to differences in climatic and geographical characteristics [44,45,46]. Therefore, the use of a local meteoric water line (LMWL) is more suitable for water body characterization. The formula is as follows:
δD = a·δ18O + b
where a is the slope and b is the intercept. When the slope is greater than 8, it indicates multiple water cycles, while a value less than 8 means more significant water loss caused by evaporation.
There are many methods for calculating LMWL [46,47]. Due to the small number of samples, our study used an ordinary linear regression analysis based on the weighted average method of precipitation [48]. The calculation formulas of precipitation weighted averages of δ18O and δD in atmospheric precipitation are [49,50]:
δ 18 O = ( ∑ i = 1 n P i δ 18 O ) / ( ∑ i = 1 n P i )
δ D = ( ∑ i = 1 n P i δ D ) / ( ∑ i = 1 n P i )
where δ18O and δD are the weighted averages of the oxygen/hydrogen isotopic composition of precipitation/sampled water, Pi is the precipitation amount of the sample (i), δ18O and δD are the oxygen/hydrogen isotopic composition of the sample (i).
According to the isotope data of global atmospheric precipitation, Dansgaard [51] named the difference between δ18O and δD as deuterium excess (d-excess). The formula is as follows:
d = 8.13·δ18O − δD
The value of d-excess reflects the thermal conditions and water vapor balance conditions when seawater forms clouds during evaporation, and the natural environment and climatic conditions when precipitation is formed. This study calculates the d-excess value based on the difference between the δ18O and δD of LMWL.
In addition, Microsoft Office Excel, Origin, and statistical product and service solutions (SPSS) were used to analyze the test data statistically, and ArcGIS 10.5 was used to analyze the spatial distribution of the data. The variation and spatiotemporal distribution characteristics of hydrogen and oxygen isotopic compositions between different surface water bodies were described in detail.

4. Results and Analysis

4.1. Analysis of Hydrogen and Oxygen Isotope Characteristics in Atmospheric Precipitation

4.1.1. Seasonal Effects of δ18O and δD in Atmospheric Precipitation

Meteorological conditions restrict the composition of hydrogen and oxygen isotopes in atmospheric precipitation. According to the division method of climatic seasons in the Chinese Meteorological Industry Standard, an average temperature (the average temperature of 5 consecutive days) <10 °C is winter, ≥22 °C is summer, and 10~22 °C is the transition season of spring and autumn. The four seasons in Wuhan are spring (April–May), summer (June–September), autumn (October–November), and winter (December–March). By analyzing the obtained atmospheric precipitation data, the seasonal variation characteristics of δ18O and δD in atmospheric precipitation in the Wuhan area are shown in Table 1. We found that the seasonal variation characteristics of hydrogen and oxygen stable isotopes in atmospheric precipitation were spring > winter > autumn > summer.
Figure 2 shows that the trends of the monthly weighted averages of δD and δ18O precipitation in Wuhan are the same. In the atmospheric water cycle, the change in δ18O is significantly affected by water vapor and evaporation [52]. Therefore, δ18O is taken as an example to explore the variation characteristics of hydrogen and oxygen stable isotopes. On the whole, the δ18O of atmospheric precipitation in Wuhan shows the features of low in the summer half-year (June–November) and high in the winter half-year (December–May of the following year), with typical subtropical monsoon climate precipitation characteristics [53]. Spring is the most abundant period of isotope, and the highest value of δ18O precipitation weighted average appears in April. The summer is the most depleted, and the lowest value of δ18O precipitation weighted average appears in September. In autumn and winter, δ18O is also relatively enriched, and the variation range is relatively small. The reasons for the seasonal differences in stable isotopes in precipitation may be: (1) The study area is located in the East Asian monsoon region, and the water vapor sources of precipitation in different seasons are different. The winter half-year monsoon comes from the high-latitude Asian inland. The air is cold and dry, the rainwater is less, and the air humidity is slight, which is conducive to the fractionation of hydrogen and oxygen isotopes and the enrichment of heavy isotopes. (2) The monsoon in the summer half-year comes from the Indian Ocean (southwest monsoon) and the Pacific Ocean (southeast monsoon). The abundant rain and high air humidity are not conducive to isotope fractionation, resulting in heavy isotope depletion [54].

4.1.2. Precipitation Effect of δ18O and δD in Atmospheric Precipitation

The precipitation effect of the atmospheric precipitation isotope refers to the negative correlation between isotope composition and precipitation change. Dansgaard [51] found a good negative correlation between δ18O and annual and monthly precipitation. The greater the precipitation, the smaller the δ18O value, especially during the rainstorm period [55]. Studies have shown that under isothermal conditions, the formation of liquid water vapor from humid air obeys the Rayleigh fractionation model; therefore, the reason for the precipitation effect may be related to the evaporation effect and environmental water vapor exchange during the raindrop landing process [56]. It is generally believed that the precipitation effect is more significant in low-latitude oceans and in coastal and island areas.
At the annual scale, the δ18O and δD of all years in the Wuhan area were linearly fitted with rainfall P. The linear relationship between δ18O and rainfall in this area was obtained as δ18O = −0.0088P − 6.026, R2 = 0.0043, and the linear relationship between δD and precipitation was obtained as δD = −0.068P − 42.96, R2 = 0.0425. At the seasonal scale, the relationship equation between δ18O and precipitation in four seasons was obtained by linear fitting as follows: spring, δ18O = −0.0053P − 3.88, R2 = 0.013; summer, δ18O = −0.0093P − 6.52, R2 = 0.173; autumn, δ18O = −0.0114P − 6.795, R2 = 0.0344; winter, δ18O = −0.0029P − 5.0, R2 = 0.003. Similarly, the linear relationship between δD and precipitation in different seasons was obtained as follows: spring, δD = −0.07P − 20.9, R2 = 0.038; summer, δ = −0.056P − 51.93, R2 = 0.112; autumn, δD = −0.143P − 45.19, R2 = 0.109; winter, δD = 0.06P − 29.08, R2 = 0.022 (Figure 3).
The correlation coefficients between δ18O/δD values and precipitation are shown in Table 2. From the annual scale, the δ18O and δD values of annual rainfall in the Wuhan area negatively correlated with precipitation. Regarding seasonal performance, the δ18O and δD precipitation values in the four seasons in Wuhan were also negatively correlated with precipitation. There was a specific precipitation effect in the four seasons, only the winter was mild, and the correlation coefficient was low. Among them, δ18O strongly correlated with precipitation in summer, and δD strongly correlated with precipitation in autumn.

4.1.3. Temperature Effect of δ18O and δD in Atmospheric Precipitation

Among the many factors that affect the isotopic composition of precipitation, temperature is the most closely related [57]. Temperature affects the δ18O value by changing the isotope fractionation coefficient α in atmospheric precipitation. The lower the temperature, the greater the α, and the lower the δ18O value in precipitation. However, the temperature and δ18O show a significant positive correlation only when the δ18O content in atmospheric precipitation is basically stable [58]. The temperature effect is generally apparent in the inland areas of middle and high latitudes. The higher the latitude and the deeper the inland areas, the more pronounced the temperature effect [59].
At the annual scale, the δ18O and δD data of all years in the Wuhan area were fitted with temperature data. The linear equation of δ18O and temperature t was obtained as δ18O = −0.103t – 5.056, R2 = 0.075 and the linear equation of δD and temperature t was obtained as δD = −0.923t – 33.23, R2 = 0.099. At the seasonal scale, the relationship between δ18O and temperature t in the four seasons of the Wuhan area was linearly fitted (Figure 4). The fitting equations were obtained as follows: spring, δ18O = −0.537t + 6.75, R2 = 0.199; summer, δ18O = 0.0327t – 8.80, R2 = 0.001; autumn, δ18O = −0.032t – 6.95, R2 = 0.003; winter, δ18O = 0.142t – 6.26, R2 = 0.037. Similarly, the δD of atmospheric precipitation and the temperature t of each season were linearly fitted (Figure 4) as follows: spring, δD = −4.0t + 54.24, R2 = 0.180; summer, δD = −1.407t – 23.0, R2 = 0.025; autumn, δD = −0.264t – 49.34, R2 = 0.00; winter, δD = 0.816t – 39.16, R2 = 0.018.
The correlations between δ18O/δD and temperature are shown in Table 3. On the annual scale, δ18O and δD were negatively correlated with temperature, and the correlations were strong. The correlation between δ18O and temperature was significant (p < 0.05), and the correlation between δD and temperature was highly significant (p < 0.01). However, on the four-season scale, there were negative correlations in spring, summer, and autumn, and there was a positive correlation in winter. Only winter showed a specific temperature effect. Studies have shown that the temperature effects in the northern and northwestern regions of the Qinghai-Tibet Plateau are pronounced. In contrast, the temperature effects in the southern monsoon region and most of the eastern monsoon region are very weak [60]. It can be seen that the temperature effect of precipitation isotopes in Wuhan is closer to the southern monsoon region. The trends of the four seasons are inconsistent, and temperature effect is not obvious.

4.2. Hydrogen and Oxygen Isotope Relationships of Yangtze River Water, Hanjiang River Water, Lake Water, and Precipitation

As an essential unit in the water cycle process, atmospheric precipitation is the main recharge source of the surface water. The abundance and ratio of stable isotopes in surface water are closely related to atmospheric precipitation. According to the precipitation data collected in different parts of the world, Craig [42] first proposed the linear relationship between δ18O and δD in precipitation, in 1961, and established the GMWL. In the same year, under the joint initiative of the International Atomic Energy Commission (IAEA) and the World Meteorological Organization (WMO), the GNIP was established. Based on global station precipitation data from 1961 to 2000, the IAEA/WMO revised Craig’s GMWL [45,61]. Due to different topographies, climates, water sources, and other factors, atmospheric precipitation lines and isotope variations in different regions are quite different. Since 1980, Chinese scholars have calculated the LMWL of different areas and basins according to the data collected by the national meteorological stations and the measured rainfall data [62,63,64,65]. Based on the measured data of hydrogen and oxygen isotopes in atmospheric precipitation in Wuhan and the data from 1986 to 1998 provided by GNIP, the LMWL in Wuhan can be calculated to be δD = 7.47δ18O + 1.77. This precipitation line is also close to the research results of domestic scholars on the atmospheric precipitation lines in the Yangtze River Basin, Wuhan, and surrounding cities in recent years (Table 4).
By analyzing the hydrogen and oxygen isotope relationships of the Yangtze River water, Hanjiang River water, lake water, and rainwater water samples collected in the Wuhan area (Figure 5), it can be seen that, although the water sample data were discrete, they were evenly distributed near the atmospheric precipitation line in the Wuhan area, indicating that atmospheric precipitation is the primary source of surface water supply in this area. However, the water sample data showed an apparent grouping phenomenon, forming three grouping areas, i.e., the Yangtze River water, the Hanjiang River water, and the lake water. The enrichment degree of δ18O and δD values was Yangtze River < Hanjiang River < lake water.
(1) The water sample points of the Yangtze River and the Hanjiang River are mainly distributed in the upper left of the atmospheric precipitation line in Wuhan, and the distribution of water sample points is more concentrated. The δ18O of the Yangtze River water was mainly concentrated between −9.34 and −8.97‰, and the δD was primarily focused between −64.1 and −61.6‰. The distribution range of δ18O in the Hanjiang River water was from −7.48 to −7.32‰, and the distribution range of δD was from −48.9 to −50.6‰. The overall river water line equation was δD = 7.26δ18O + 3.80, the correlation coefficient was above 0.98, and δ18O and δD had a good correlation. The slope of the river water line equation was approximately parallel to LMWL in Wuhan, indicating that the Yangtze River and the Hanjiang River were significantly affected by atmospheric precipitation recharge and less affected by evaporation. In addition, the δ18O and δD values of the Hanjiang River water were considerably higher than those of the Yangtze River water, indicating that the Yangtze River was affected by the upstream water at the same time, and the degree of evaporation was less than that of the Hanjiang River.
(2) The lake water line equation was δD = 5.05δ18O − 10.3, and the correlation coefficient was 0.97. Compared with the LMWL in Wuhan, the slope and intercept had obvious deviations, both of which were significantly smaller, indicating that the lakes in Wuhan were local small water circulation systems, and the evaporation and enrichment of hydrogen and oxygen isotopes in the water bodies were more obvious. This phenomenon was consistent with the discovery [57] that the isotope δ18O and δD values of lake water in the middle and lower reaches of the Yangtze River were significantly higher than those of the Yangtze River water.

4.3. Hydrogen and Oxygen Isotope Characteristics of Lake Water and Lake Classification

4.3.1. The Relationship between Isotope Composition and EC in a Water Body

In water, δD and δ18O are stable isotopes of water molecules. When the hydrogen and oxygen isotope composition of a water body is significantly correlated with ion concentration and mineralization, the water body is subjected to evaporation to cause hydrogen and oxygen isotope fractionation, which makes the hydrogen and oxygen isotopes heavier, and the corresponding ion concentration and salinity also increase [72]. When the hydrogen and oxygen isotope composition has no significant correlation with the ion mass concentration and salinity, a water body is mainly affected by the recharge source [73]. Therefore, combined with the field test data of lake water, the correlation between hydrogen and oxygen isotope content and electrical conductivity (EC) in all lakes was analyzed (Figure 6).
Figure 6 showed that δ18O/δD values and EC did not show a good linear trend, and the correlation between hydrogen and oxygen isotope composition and salinity of the water body was not significant, indicating that the lake water body in the Wuhan area was mainly affected by multiple recharge sources. The recharge sources may be atmospheric precipitation, river water, groundwater, and interflow between lakes. Therefore, lakes’ isotope variation characteristics and influencing factors must be further analyzed.

4.3.2. Isotope Variation Characteristics of Lakes

The distribution range and variation range of δ18O and δD values in lake water in Wuhan City were extensive (Figure 7). The δ18O range was from −6.82 to 2.05‰, with an average value of −1.99‰. The δD range was from −42.8 to 1.1‰, with an average of −20.32‰. In all sample data, the smaller the lake water area is, the larger the isotope δ18O and δD values are (Figure 8 and Figure 9). The isotope δ18O and δD values of large lakes such as Huangjia Lake, Tangxun Lake, and Donghu Lake were the smallest, and the isotope δ18O and δD values of the smallest South Taizi Lake were the largest. The isotope δ18O and δD values of small- and medium-sized lakes such as North Taizi Lake, Shenshan Lake, and Ye Lake were medium, indicating that lake shrinkage and degradation significantly impacted the enrichment of hydrogen and oxygen isotopes.

4.3.3. Lake Classification

The medians of δ18O and δD in each lake were used as variables, and the Wald method was used for cluster analysis (Figure 10). The results showed that the lakes in Wuhan could be divided into two groups. The first group included North Taizi Lake, Ye Lake, and Shenshan Lake. The second group included South Lake, Yujia Lake, Tangxun Lake, Moshui Lake, Yezhi Lake, East Lake, Qingling Lake, Longyang Lake, South Taizi Lake, and Huangjia Lake. Combined with the field investigation of the lake water body, we concluded that the first group consisted of inner-flow degraded (IFD) lakes, mainly located in the development zone of Wuhan City. The human engineering activities were intense, and the lake area had been seriously degraded year by year. For example, lake reclamation and landscaping had changed the connectivity of rivers and lakes. The water exchange between the Yangtze River and the lake water body was weakened, and the inward river, precipitation, and groundwater mainly supplied the water source. The hydrogen and oxygen isotopes were enriched under evaporation. The second group consisted of outer-flow ecological (OFE) lakes. Most of these lakes were located in the central urban area of Wuhan, serving the ecological landscape as well as the sports and leisure of urban residents, such as Moshui Lake, Nanhu Lake, and Huangjia Lake. A few large lakes were located in the ecological conservation area near the concentration area of urban residents, such as East Lake and Tangxun Lake. The eco-environment of these lakes had been protected well, and the connectivity of the river network was good. For example, Qingling Canal was the backbone drainage channel of the Tangxun Lake water system, which connected Tangxun Lake, Huangjia Lake, Qingling Lake, and Yangtze River. The Yangtze River and lake water exchange were active, the evaporation effect was weakened, and the hydrogen and oxygen isotopes were relatively depleted.

4.4. Deuterium Excess Parameter Characteristics

The d value of deuterium excess in atmospheric precipitation can directly measure the degree of unbalanced fractionation of hydrogen and oxygen isotopes in the process of evaporation and condensation of atmospheric precipitation in this area. The d value in surface water can directly reflect the degree of evaporation and enrichment of water bodies and the recharge source between water bodies [51,74]. The characteristics of deuterium excess parameter values of surface water in the Wuhan area are shown in Figure 11 and Figure 12. The deuterium excess parameter values can be divided into three intervals, i.e., 10~0‰, 0~−10‰, and −10~−20‰. The d values of the Yangtze River and the Hanjiang River were positive, and the d values of the lake water were mainly negative. The d value of the lake water was significantly smaller than that of the river water, which was consistent with the findings of Yuan et al. [75] and Wu et al. [76] who conducted isotope investigations of lake water in the Qinghai-Tibet Plateau and Qinghai Lake. It was found that the d value in the lake water was negative. The d value of the Yangtze River water samples mainly ranged from 10.16‰ to 10.72‰, with an average of 10.58‰, and there were two abnormal values in the sample data. The d value of the Hanjiang River water samples mainly ranged from 9.08‰ to 10.24‰, with an average of 9.79‰. The d value of lake water mainly ranged from −15.3‰ to 11.76‰, with an average of −4.43‰. Lakes can be divided into two types according to the d value: (i) IFD lakes with d values ranging from −10 to −20‰. These lakes were mainly North Taizi Lake, Shenshan Lake, and Ye Lake; (ii) OFE lakes with d values ranging from 10‰ to −10‰. These lakes were mainly Huangjia Lake, East Lake, Tangxun Lake, etc., consistent with the above cluster analysis results. At the same time, there was little fluctuation in the d values of the OFE lakes, and most of them were concentrated in the range of 0~−10‰, indicating that the difference between the δ18O and δD fractionation rates of the outflow ecotype lakes in different areas was slight. It was not affected by factors such as rainfall and height and conformed to the law of the water cycle.

5. Discussion

Rivers and lakes are essential natural resources that improve the urban environment and regulate climate, flood control, and storage in a harmonious symbiotic relationship between man and nature. Due to the rapid advancement of urbanization in Wuhan, the ecological environment of rivers and lakes in Wuhan has significantly been affected by the interference of human activities. First, the rapid expansion of urban construction land, the fragmentation of urban water systems, and the gradual fragmentation of lakes have led to the apparent trend of lake shrinkage. Chen et al. [77] analyzed the evolution characteristics of lake landscape ecological security patterns in Wuhan in the past 20 years and found that the total area of the lake system in the central urban area of Wuhan was generally decreasing; among the activities, expansion of urban built-up areas, transformation of lakes from agricultural production to available development land, and real estate were the main driving forces for filling the lake system. Second, due to an increase in population density around the lakes, the construction of industrial parks, and the development of aquaculture, domestic sewage and industrial wastewater have been discharged into rivers and lakes, which has resulted in excessive nitrogen and phosphorus content in the waters; eutrophication is becoming more and more serious, and the water environment is seriously threatened [78]. According to the Wuhan Municipal Water Resources Bulletin, the overall water quality of Tangxun Lake was Class IV from 2011 to 2013. After 2014, the overall water quality of Tangxun Lake was reduced to Class V. The water quality of Yezhi Lake, Ye Lake, and North Lake is currently inferior to Class V. Third, hydraulic engineering projects formed by human activities have changed the original water ecological pattern of the basin. The construction of sluices and dams has blocked the migration channels for spawning and fattening of aquatic organisms and has significantly changed the habitat of the original waters [79].
Through the analysis of the hydrogen and oxygen isotope composition characteristics of the Yangtze River, Hanjiang River, and lake in the previous sections, two conclusions can be stated: (1) The δ18O and δD values of lake water are significantly higher than those of Yangtze River water and Hanjiang River water. (2) The smaller the lake area, the more enriched the hydrogen and oxygen isotopes. These results show that a water environment that is more closed is more susceptible to evaporation, resulting in hydrogen and oxygen isotope fractionation. In the long run, heavy isotopes are easy to enrich, and other pollutants are easy to precipitate. It can be seen that the exchange and circulation between water bodies are crucial for lakes’ eco-environments. At the same time, the cluster analysis of lake hydrogen and oxygen isotopes also points out that the North Taizi Lake, Wild Lake, and Shenshan Lake in the developing suburban area were different from the large lake water system in the central urban area, and there was a trend of blocked river–lake exchange and lake degradation. However, ecosystem connectivity is essential for biodiversity conservation, and fragmentation is a significant disturbance to ecosystem services, leading to extinction and environmental destruction [4]. Many ecological and environmental problems can occur in lakes, such as water pollution, water eutrophication, and river–lake relationship problems, and even the overall ecological service system can shrink seriously, biodiversity can decrease, and species can disappear. For example, Tangxun Lake, Huangjia Lake, Qingling Lake, and other central urban lakes have eco-environment problems of water pollution and eutrophication. North Taizi Lake and Yehu Lake have ecological diversity problems of habitat loss and a reduction in aquatic plants, birds, and fish. For lakes with rich hydrogen and oxygen isotopes, more attention should be paid to protecting and managing their eco-environments.
River–lake water system connectivity has been widely used at home and abroad as an essential means of optimal allocation of water resources, water ecological restoration and improvement, and water disaster prevention. By adjusting the hydrodynamic conditions of a lake and accelerating the renewal speed of the water cycle, the purpose of restoring the self-purification capacity of the lake, improving the water environment capacity, and improving the water quality can be achieved [80]. Therefore, the following two suggestions are put forward to enhance the eco-environments of rivers and lakes in Wuhan City: (1) The government should implement a water system reconstruction project on the north bank of the Yangtze River, connect the North Taizi Lake with the South Taizi Lake which is very close to the Yangtze River, and connect the Yangtze River through the South Prince Lake. Through the connection of the Yangtze River to the lakes, the “dead water” would be changed into “living water,” and the water eco-environment of the lake group of Taizi Lake would be restored [81]. (2) The government can optimize the existing water system connection project of the Tangxun Lake by connecting the “small lake group water system” to the “large lake group water system.” Through the new construction of ditches, the small lakes, such as the Wild Lakes and the Shenshan Lakes in the developing suburban area, would be connected to the large lakes of Tangxun Lake with mature drainage channels.

6. Conclusions

(1)
The LMWL in the Wuhan area was δD = 7.47δ18O + 1.77. Each surface water sample point was distributed near the LMWL, indicating that atmospheric precipitation was this area’s primary recharge source of surface water. However, data grouping was prominent, forming three grouping areas, i.e., the Yangtze River water, the Hanjiang River water, and the lake water. The enrichment degree of the δ18O and δD values was Yangtze River < Hanjiang River < lake water.
(2)
According to the results of the δ18O and δD cluster analysis of lake water, the lakes in Wuhan can be divided into two groups, i.e., inner-flow degraded (IFD) lakes and outer-flow ecological (OFE) lakes. The IFD lakes were affected by human engineering activities, which changed the connectivity of rivers and lakes. The exchange between the Yangtze River and the lakes was weakened, and the hydrogen and oxygen isotopes were enriched under evaporation, including North Taizi Lake, Ye Lake, and Shenshan Lake. The OFE lakes had the functions of ecological conservation and ecological landscape. The water exchange between the Yangtze River and the lakes was active, evaporation was weakened, and the hydrogen and oxygen isotopes were relatively depleted, including Huangjia Lake, East Lake, Tangxun Lake, etc.
(3)
The d values of the Yangtze River and the Hanjiang River were positive and the d values of the lakes were mainly negative, indicating that evaporation enrichment had a significant influence on the δ18O and δD of the lake water. The d values of the IFD lakes were −10~−20‰, and the d values of the OFE lakes were 10~−10‰. The d values of most OFE lakes were concentrated in the range of 0~−10‰. The fluctuation range was not extensive, indicating that the difference in the δ18O and δD fractionation rates of outflow ecotype lakes in this area was slight, which was not affected by rainfall, height, and other factors, and conformed to the law of the water cycle.
(4)
Among the many lakes in Wuhan, the eco-environmental problems of North Taizi Lake, Ye Lake, and Shenshan Lake were more serious. We suggest that the “North Taizi Lake-South Taizi Lake-Yangtze River” water system connection project and the small lakes connecting to large lakes project of “Wild Lake-Shenshan Lake-Tangxun Lake” should be implemented in time to restore the water eco-environments of lakes in Wuhan.

Author Contributions

Conceptualization, X.Z. (Xinwen Zhao); software, A.Z.; validation, A.Z.; formal analysis, A.Z.; investigation, A.Z. and Y.Z.; methodology, J.H., X.H. and X.Z. (Xingyuezi Zhao); resources, X.Z. (Xinwen Zhao), J.H. and A.Z.; data curation, J.H., X.H. and A.Z.; writing—original draft preparation, A.Z.; writing—review and editing, A.Z. and X.H.; supervision, X.Z. (Xinwen Zhao); funding acquisition, X.Z. (Xinwen Zhao), J.H. and X.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the project of the China Geological Survey (No. DD20221729 and No. DD20190291) and the Zhuhai Urban Geological Survey (including informatization) (No. MZCD-2201-008).

Data Availability Statement

The datasets presented in this study can be obtained upon request to the corresponding author.

Acknowledgments

The authors would like to thank the Open Research Laboratory of Isotope Geochemistry of China Geological Survey for its analysis of water samples.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. (a) Location of the Wuhan; (b) Location of the study area; (c) Distribution of sampling points of surface water bodies in Wuhan.
Figure 1. (a) Location of the Wuhan; (b) Location of the study area; (c) Distribution of sampling points of surface water bodies in Wuhan.
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Figure 2. Variations of weighted mean values of δ18O and δD in Wuhan atmospheric precipitation by month.
Figure 2. Variations of weighted mean values of δ18O and δD in Wuhan atmospheric precipitation by month.
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Figure 3. The relationship between δ18O/δD values and seasonal precipitation in the Wuhan area: 1—the value of δ18O/δD and precipitation in spring; 2—the value of δ18O/δD and precipitation in summer; 3—the value of δ18O/δD and precipitation in autumn; 4—the value of δ18O/δD and precipitation in winter; 5—the relationship curve between δ18O/δD and precipitation in spring; 6—the relationship curve between δ18O/δD and precipitation in summer; 7—the relationship curve between δ18O/δD and precipitation in autumn; 8—the relationship between δ18O/δD and precipitation in winter.
Figure 3. The relationship between δ18O/δD values and seasonal precipitation in the Wuhan area: 1—the value of δ18O/δD and precipitation in spring; 2—the value of δ18O/δD and precipitation in summer; 3—the value of δ18O/δD and precipitation in autumn; 4—the value of δ18O/δD and precipitation in winter; 5—the relationship curve between δ18O/δD and precipitation in spring; 6—the relationship curve between δ18O/δD and precipitation in summer; 7—the relationship curve between δ18O/δD and precipitation in autumn; 8—the relationship between δ18O/δD and precipitation in winter.
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Figure 4. The relationship between δ18O/δD values and seasonal temperature in the Wuhan area: 1—the value of δ18O/δD and temperature in spring; 2—the value of δ18O/δD and temperature in summer; 3—the value of δ18O/δD and temperature in autumn; 4—the value of δ18O/δD and temperature in winter; 5—the relationship between δ18O/δD and temperature in spring; 6—the relationship between δ18O/δD and temperature in summer; 7—the relationship between δ18O/δD and temperature in autumn; 8—the relationship between δ18O/δD and temperature in winter.
Figure 4. The relationship between δ18O/δD values and seasonal temperature in the Wuhan area: 1—the value of δ18O/δD and temperature in spring; 2—the value of δ18O/δD and temperature in summer; 3—the value of δ18O/δD and temperature in autumn; 4—the value of δ18O/δD and temperature in winter; 5—the relationship between δ18O/δD and temperature in spring; 6—the relationship between δ18O/δD and temperature in summer; 7—the relationship between δ18O/δD and temperature in autumn; 8—the relationship between δ18O/δD and temperature in winter.
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Figure 5. Relationships between δ18O and δD of the Yangtze River, Han River, and lakes in Wuhan: 1—Yangtze River water; 2—Hanjiang River Water; 3—lake water; 4—LMWL; 5—lake water line; 6—river water line.
Figure 5. Relationships between δ18O and δD of the Yangtze River, Han River, and lakes in Wuhan: 1—Yangtze River water; 2—Hanjiang River Water; 3—lake water; 4—LMWL; 5—lake water line; 6—river water line.
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Figure 6. The relationship between hydrogen and oxygen isotope composition and EC in the Wuhan area.
Figure 6. The relationship between hydrogen and oxygen isotope composition and EC in the Wuhan area.
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Figure 7. Variations of δ18O and δD values of lake water in the Wuhan area.
Figure 7. Variations of δ18O and δD values of lake water in the Wuhan area.
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Figure 8. δ18O value distribution of lake water.
Figure 8. δ18O value distribution of lake water.
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Figure 9. δD value distribution of lake water.
Figure 9. δD value distribution of lake water.
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Figure 10. Cluster analysis of lakes in Wuhan based on δ18O and δD values. The abscissa “distance” represents the distance between the two sample categories, which is dimensionless. The color of a line represents the category the sample is divided into, and different colors represent different categories.
Figure 10. Cluster analysis of lakes in Wuhan based on δ18O and δD values. The abscissa “distance” represents the distance between the two sample categories, which is dimensionless. The color of a line represents the category the sample is divided into, and different colors represent different categories.
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Figure 11. Variations in deuterium excess parameter values of surface water in the Wuhan area.
Figure 11. Variations in deuterium excess parameter values of surface water in the Wuhan area.
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Figure 12. The d value distribution of surface water.
Figure 12. The d value distribution of surface water.
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Table 1. The range of hydrogen and oxygen isotope changes in the atmospheric precipitation in the Wuhan region during different seasons.
Table 1. The range of hydrogen and oxygen isotope changes in the atmospheric precipitation in the Wuhan region during different seasons.
IsotopeSeasonMaximum/‰Minimum/‰Arithmetic Mean/‰Rainfall Arithmetic Mean/‰
δ18OSpring0.12−11.15−4.579−4.807
Summer−3.45−13.32−8.744−9.144
Autumn−4.57−10.48−7.454−7.674
Winter−2.15−9.38−5.186−5.964
δDSpring0.12−11.15−4.579−4.807
Summer−3.45−13.32−8.744−9.144
Autumn−4.57−10.48−7.454−7.674
Winter−2.15−9.38−5.186−5.964
Table 2. The correlation coefficients between δ18O/δD values and precipitation in the Wuhan area.
Table 2. The correlation coefficients between δ18O/δD values and precipitation in the Wuhan area.
IsotopeAnnualSpringSummerAutumnWinter
δ18O−0.207−0.116−0.22−0.186−0.147
δD−0.206−0.196−0.142−0.331−0.058
Table 3. The correlation coefficients between δ18O/δD values and temperature in the Wuhan area.
Table 3. The correlation coefficients between δ18O/δD values and temperature in the Wuhan area.
IsotopeAnnualSpringSummerAutumnWinter
δ18O−0.274 *−0.446−0.179−0.0580.191
δD−0.314 **−0.424−0.23−0.0690.134
Note: ** indicates a significant correlation at the 0.01 confidence level and * indicates a significant correlation at the 0.05 confidence level.
Table 4. The atmospheric precipitation line in Wuhan and its surrounding cities.
Table 4. The atmospheric precipitation line in Wuhan and its surrounding cities.
Applicable AreaLMWLData SourceSource of Literature
WuhanδD = 8.29δ18O + 7.44Precipitation events, GNIP[66]
WuhanδD = 7.85δ18O + 4.62GNIP[67]
Xiling Gorge, YichangδD = 8.45δ18O + 11.55Precipitation events[68]
YichangδD = 8.4δ18O + 15Precipitation events[69]
Xiangxi River, YichangδD = 8.17δ18O + 13.38Precipitation events[70]
Yangtze River BasinδD = 7.41δ18O + 6.04GNIP[71]
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Zhang, A.; Zhao, X.; He, J.; Huang, X.; Zhao, X.; Zhao, Y. Characteristics of Hydrogen and Oxygen Isotope Composition in Precipitation, Rivers, and Lakes in Wuhan and the Ecological Environmental Effects of Lakes. Water 2023, 15, 2996. https://doi.org/10.3390/w15162996

AMA Style

Zhang A, Zhao X, He J, Huang X, Zhao X, Zhao Y. Characteristics of Hydrogen and Oxygen Isotope Composition in Precipitation, Rivers, and Lakes in Wuhan and the Ecological Environmental Effects of Lakes. Water. 2023; 15(16):2996. https://doi.org/10.3390/w15162996

Chicago/Turabian Style

Zhang, Ao, Xinwen Zhao, Jun He, Xuan Huang, Xingyuezi Zhao, and Yongbo Zhao. 2023. "Characteristics of Hydrogen and Oxygen Isotope Composition in Precipitation, Rivers, and Lakes in Wuhan and the Ecological Environmental Effects of Lakes" Water 15, no. 16: 2996. https://doi.org/10.3390/w15162996

APA Style

Zhang, A., Zhao, X., He, J., Huang, X., Zhao, X., & Zhao, Y. (2023). Characteristics of Hydrogen and Oxygen Isotope Composition in Precipitation, Rivers, and Lakes in Wuhan and the Ecological Environmental Effects of Lakes. Water, 15(16), 2996. https://doi.org/10.3390/w15162996

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