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Article

Constraining the Water Cycle Model of an Important Karstic Catchment in Southeast Tibetan Plateau Using Isotopic Tracers (2H, 18O, 3H, 222Rn)

by 1,2,3, 1,2,3,*, 4, 1,2,3, 4, 5 and 6
1
Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2
Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China
3
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
4
Jiuzhaigou Nature Reserve Administrative Bureau, Zhangzha Town, Jiuzhaigou 623402, China
5
Aba Bureau of Hydrology and Water Resources Survey, Wenchuan 623000, China
6
China-Croatia “Belt and Road” Joint Laboratory on Biodiversity and Ecosystem Services, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3306; https://doi.org/10.3390/w12123306
Received: 6 October 2020 / Revised: 2 November 2020 / Accepted: 17 November 2020 / Published: 24 November 2020
(This article belongs to the Section Hydrology)

Abstract

Understanding the connectivity between surface water and groundwater is key to sound geo-hazard prevention and mitigation in a waterscape such as the Jiuzhaigou Natural World Heritage Site in the southeast Tibetan Plateau, China. In this study, we used environmental isotope tracers (2H, 18O 3H, and 222Rn) to constrain a water cycle model including confirming hydrological pathways, connectivity, and water source identification in the Jiuzhaigou catchments. We established the local meteoric water line (LMWL) based on the weekly precipitation isotope sampling of a precipitation station. We systematically collected water samples from various water bodies in the study area to design the local water cycle model. The regional water level and discharge changes at one month after the earthquake indicated that there was a hydraulic connection underground across the local water divide between the Rize (RZ) river in the west and Zechawa (ZCW) lake in the east by the δ18O and δ2H measurements. We employed an end-member mixing model to identify and quantify Jiuzhaigou runoff-generating sources and their contributions, and we found that the average contributions of precipitation and groundwater to the surface runoff in the catchments are about 30% and 70%, respectively. The two branches of the Shuzheng (SZ) trunk were recharged by 62 ± 19% from the ZCW lake and 38 ± 19% from the RZ river, which was consistent with the fractions calculated by the actual discharge volume. 222Rn mass balance analyses were employed to estimate the water exchange between groundwater and river, which further confirmed this estimate. 222Rn concentrations and 3H contents showed that the groundwater had a short residence time and it was moderate precipitation, thought the contribution of groundwater to the river was 70%, according to the different tracers. A three-dimensional conceptual model of the water cycle that integrated the regional hydrological and geological conditions was established for the catchments.
Keywords: hydraulic connection; end-member mixing model; 222Rn; groundwater–surface water interactions; Jiuzhaigou; Changhai Lake; water cycle model hydraulic connection; end-member mixing model; 222Rn; groundwater–surface water interactions; Jiuzhaigou; Changhai Lake; water cycle model

1. Introduction

Investigations of connectivity between surface water and groundwater are pretty common because such connectivity plays an important role in maintaining water resources and the ecosystem of river basins [1,2,3,4,5]. Therefore, a complete understanding of the water exchange direction and water flux between groundwater and surface water is good to determine water balance and groundwater discharge in the protection of the natural flow regime of rivers [6]. Investigations of the evaluation, transformation, and connectivity of two sources play critical roles in their hydrological and ecological functions. A variety of methods of investigation between surface water and groundwater interactions include hydraulic tests [7], groundwater numerical models [3], environmental tracer methods [8,9,10,11,12,13,14], groundwater residence time and dating methods [15,16,17], and natural heat tracing methods [16,18,19]. Water bidirectional exchange can be effectively determined and quantified by environmental tracers, such as the stable water isotopes (2H and 18O), 222Rn, and 3H, as well as water chemical composition.
2H and 18O isotopes are useful and mature tracers in tracing the natural water cycle and different hydrological processes, such as hydrological pathways, water connectivity, water source identification, water evaporation, water mixing, and water exchange [20]. By combining geological conceptual models with isotope data, the hydraulic connection of aquifers or groundwater and surface water has been demonstrated in several studies [21,22,23,24]. The Milk River Aquifer–Aquitard systems of Canada were used as a model to test aquifer hydraulic connection with 2H, 18O, and chemical composition, and it was shown that it was difficult for surface water to mix downward with deep groundwater due to upper impermeable layers [22]. In the Xi’an area of China, the hydraulic connection between the upper Quaternary aquifer and the lower Tertiary aquifer was confirmed based on the 2H, 18O, 3H, and 14C tracing method [24]. In the Presciano Spring System of the Gran Sasso Carbonate Aquifer of Italy, homogeneous water isotope composition was found to indicate the existence of a buried karst conduit and baseflow pathway after the 2009 L’Aquila earthquake [23]. In northeastern Kansas, USA, potential hydraulic connections of local lowland aquifers with the surface water of a nearby creek and two major rivers were proven to exist based on identical 2H and 18O isotopes [21].
A major earthquake (Mw = 6.5 of mainshock) struck the Natural World Heritage Site Jiuzhaigou national park on August 8, 2017. Plenty of geologic hazards and damages were caused in the Rize (RZ) river and Shuzheng (SZ) valley. These disasters included debris flow, landslides, collapses, dam breaks of lakes, severely damaged roads, and destroyed surface landscapes [25,26]. The waterscape was especially damaged, with the Sparking Lake gone due to dam break, Nuorilang Waterfall being cut off (accompanied by cracking and shedding), and the section of the west valley having gathered water during our sampling period (Figure 1). There have been few investigations into the water connectivity and interaction between groundwater and surface water as an important karstic catchment of Jiuzhaigou in the southeast Tibetan Plateau, especially after the Jiuzhaigou earthquake. Based on observations of the regional water level at one month after the earthquake, the lake water level of the Zechawa (ZCW) valley in the east decreased. However, the stream discharge of the RZ river in the west was found to have been increasing. The Jiuzhaigou water cycle system seemed sensitive to earthquake activities. Thus, the post-earthquake investigation of the regional hydraulic connection between the eastern and western valleys was of significance to understand the groundwater circulation process under the earthquake pulse. The water exchange between groundwater and surface water and water source identification are vital for the karst water resource management and geo-hazard prevention of waterscapes in Jiuzhaigou.
The purpose of this research was to identify regional water connectivity, investigate interactions between surface water and groundwater, and establish a groundwater circulation model in the Jiuzhaigou catchments. We carried out an isotopic geochemical and hydrochemical measurements combining the regional surface water level and streamflow changes caused by the earthquake to (1) establish the local meteoric waterline and identify the flow path with 2H and 18O, (2) quantify Jiuzhaigou river water-generating sources and their contributions with different tracing methods, (3) estimate the water exchange between groundwater and river with 222Rn, and (4) develop a conceptual three-dimensional water cycle model by integrating regional hydrological and geological conditions.

2. Study Area

Jiuzhaigou, located in a northern region of Sichuan Province, China (Figure 2), has been designated as a World Heritage Site by UNESCO (United Nations Educational, Scientific, and Cultural Organization) since 1992. It is known for karst lakes and waterfalls, shoals, streams, travertines, snow peaks, and colorful forests [27]. It is composed of three main valleys in a “Y” shaped intersection of the western RZ valley, eastern ZCW valley, and middle SZ valley, as shown in Figure 2b. There is a watershed boundary with a 4200-m mountain ridge that controls the distribution of surface flow in the RZ and ZCW valleys, as seen in Figure 2c. The RZ valley in the west is always filled with surface water, linking a series of bead-distributed high mountain lakes, whereas the ZCW valley in the east has a perennial drought, with four separate lakes. As one of the most representative lakes in ZCW, Changhai Lake is 4350 m long, 250 m wide, 80 m deep, and the largest and highest glacier-dammed lake without a visible outlet. It has a total storage capacity of 45 million m3. It is considered an important natural reservoir for water supply resources and regulation in the park, and it is located at 3075 m asl [28,29]. The Jiuzhaigou area is typical of a high plateau temperate and sub-Cambrian monsoon climate. The average annual precipitation is 880 mm, and the average annual temperature is 7.2 °C according to ZCW meteorological station (located at 2400 m asl) records. Precipitation mainly falls in the high-water season from May to October.
The strata in the Jiuzhaigou region from old to new are comprised of Devonian limestone and sandstone, Carboniferous limestone and slate, Permian limestone, Triassic limestone with dolomite, and Quaternary travertines with alluvial and glacial deposits (see Figure 2a) [30]. A set of marine carbonate rocks was developed with an exposed area of 500 km2 and a total thickness of about 4 km. The underground karstic fractured aquifers are the Devonian Yiwaigou Group formation with a thickness of 300 m and Carboniferous Minhe Group formation with a thickness of 1000 m, both deposited in the shallow and coastal marine sedimentary environment. The lower Devonian Dangduo group formation is composed of metamorphic quartzite sandstones with low porosity and is the impermeable stratum of the basement. Quaternary sediments are distributed along the riparian zones in the valleys with 20–30 m travertine deposits.
The Mw6.5 Jiuzhaigou earthquake occurred on 8 August 2017 and was located within the eastern margin of the Tibetan Plateau. Tectonically, it was the transition zone of two geomorphologic units that fall steeply from the Tibetan Plateau to the Sichuan Basin. The epicenter was located in the Jiuzhaigou catchments shown in Figure 2a. The earthquake ruptured along a hidden extension of the northern Huya Fault [31,32,33,34]. Based on the high precision relocated mainshock and aftershock sequence of the Jiuzhaigou earthquake, Fang et al. (2018) showed that the causative fault manifested SE-trending, with a sinistral strike-slip motion striking of 150°, a dip of 84°, a length of 42 km, and a depth of at least 20 km [31]. The seismogenic fault (F1) was inferred along the Jiuzhaigou Paradise-epicenter (Flowers Lake), and F2 was a secondary co-seismic fault according to a field geological survey, landslide distributions, and remote sensing interpretation (as shown in Figure 2a) [35,36]. The earthquake, as one of the most important geological tectonic activities, not only affected the fault structure at a regional scale but also the groundwater flow at an aquifer scale.
The active tectonic movement shaped the topography of Jiuzhaigou. It caused the destruction of carbonate rock strata, the formation of fractures, and the reactivation of faults, all of which played a controlling role in the underground runoff. The fractures that controlled the water system of the Jiuzhaigou area were mainly north-west thrust faults (Yingzhaodong Fault F3, Changhai Lake–Hanging Spring Fault F4, Heye Fault F7, and Zharu Fault F8) and north–south thrust faults (Jiuzhaigou Fault F5 and Zechawa Fault F6), as shown in in Figure 2a [26,30]. Plenty of NW–SE oriented folds and faults provided potential flow pathways into the neighboring ditches [29]. The F3 fault zone stretched into the Colorful Pond and Changhai Lake from Yingzhaodong in the western valley across the mountain. Its strike was 225°, and its dip angle was between 50° and 72°. It was a compression–torsion fault with a length of 18 km that cut the Carboniferous Minhe Group formation. Some localized fractured rocks in the high-permeability fault zones were found, and some sections were crushed and fractured. It intersected with the RZ valley, causing the outcrops of the RZ spring groups (S3, S4, and S5) with a steady discharge volume of 1.74 m3/s in the high-water season and 1.38 m3/s in the low-water season [30]. F4 with an attitude of NE47°∠67–72°and a length of 15 km was also a shattered cross-mountain fracture zone. It was characterized by fragmented rocks and well-developed fissures. Its south-eastern end extended into Changhai Lake of the eastern valley, and the north-western end was the Hanging Spring of the western valley. The Hanging Spring (S2) was located at the RZ valley and F4 fault intersection, the discharge of which was 0.29 m3/s. F3 and F4 acted as good conductors for karst underflow and groundwater transport [28]. Along with S–N orientation, the F5 fault had a length of 25 km and an attitude of 100°∠70–75°. It stretched along the RZ river and cut the carbonate aquifer. The F6 fault with an attitude of 280°∠70–78° and a length of 20 km originated from Changhai Lake via the lower seasonal lake, and it stretched toward the eastern slopes of the Reed Lake in the SZ valley. F5 and F6 were both tensile–shear stretching faults that were good conductors to control groundwater flow from south to north. The Jiuzhaigou lakes and rivers were mainly distributed along F5 and F6. Most discharges flowed into karst lakes and rivers via many springs along the RZ and SZ rivers. As the source of the RZ river upstream in the Primeval forest, the S1 spring fed into the RZ river with a discharge volume of 6.81 m3/s in the high-water season and 2.41 m3/s in the low-water season. The discharge of the SZ spring groups (S8 and S9) was only 51–66 L/s in the SZ river. The annual average discharge of these springs was 6.33 m3/s, accounting for about 49% of the surface runoff discharge [30]. They were important natural resources to maintain the waterscapes in the Jiuzhaigou catchments.
Figure 2. (a) Sketch map of Jiuzhaigou geological structure modified from the work of Lugli, 2017 [37]: (b) sampling sites in the (c) Jiuzhaigou section map. F1 is a seismogenic fault, and F2 is a secondary coseismic fault in the core predicted by Li and Wang [26,36]. F3–F8 are foregone thrust faults, referred to as the Yingzhaodong Fault (F3), the Changhai Lake–Hanging Spring Fault (F4), the Jiuzhaigou Fault (F5), the Zechawa Fault (F6), the Heye Fault (F7), and the Zharu Fault (F8), respectively.
Figure 2. (a) Sketch map of Jiuzhaigou geological structure modified from the work of Lugli, 2017 [37]: (b) sampling sites in the (c) Jiuzhaigou section map. F1 is a seismogenic fault, and F2 is a secondary coseismic fault in the core predicted by Li and Wang [26,36]. F3–F8 are foregone thrust faults, referred to as the Yingzhaodong Fault (F3), the Changhai Lake–Hanging Spring Fault (F4), the Jiuzhaigou Fault (F5), the Zechawa Fault (F6), the Heye Fault (F7), and the Zharu Fault (F8), respectively.
Water 12 03306 g002

3. Materials and Methods

A total of 32 water samples were collected during October 2019 along three valleys, which included surface water (river and lake water) and natural springs. Thirty-one weekly precipitation or snow samples were collected from October 2019 to June 2020 at the Changhai Lake precipitation station. Hydrological data (i.e., rainfall, stream discharge, and surface water level) were recorded and provided in the designated experimental ecological stations (four hydrologic stations and four hydrometric lakes) in the Jiuzhaigou catchments from January 2017 to December 2017 (one year in total before and after the earthquake) by Aba Prefecture Hydrographic Service in Sichuan. All sampling locations are presented in Figure 2b. We measured the water chemistry and isotopes of different water samples including the major ions, δ18O, δ2H, 3H, and 222Rn. Based on the two-component isotope (18O) hydrograph separation, we estimated the fractions of two tributaries (RZ and ZCW) in the mainstream (SZ) by combining the actual discharge volume. Additionally, the fractional uncertainty was quantified with the Gaussian error propagation technique. Based on the three-component isotope (18O and total dissolved solid TDS) hydrograph separation, we calculated the fractions of local precipitation, Changhai Lake, and groundwater in Jiuzhaigou river water. We also employed the 222Rn mass balance method to estimate the specific exchange between groundwater and river water and to calculate the groundwater proportion in river discharge.

3.1. Water Sampling and Analysis Method

Due to almost no wells or deep boreholes in the study area, 11 groundwater samples were collected from natural springs along the RZ and SZ rivers. We used different capacities of polyethylene bottles to directly collect the spring water from spring vents in order to avoid exchange with the atmosphere. The sites of S1–S7 springs were distributed mostly at the valley–fault intersections in the RZ valley. An intense-smelling H2S appeared during the sampling of S3, S4, and S5 after the earthquake. The sites of the S8–S11 springs were distributed along the RZ valley, and 17 river water samples were collected along the RZ and SZ rivers from the Primeval forest to the entrance. Four sites of lake water were distributed along the ZCW valley in Changhai Lake, Colorful Pond, and the upper and lower seasonal lakes. We collected 31 weekly precipitation or snow samples at the Changhai Lake precipitation station. Prior to sampling, the water was filtered with the 0.45 μm membrane filters and stored in the high-density polyethylene bottles for measuring δ18O and δ2H, cations, anions, and tritium. A water sample of 250 mL at a depth of 10–15 cm under the surface of the water was collected in the vacuum bottles to measure the 222Rn activity. The parameters measured and recorded in situ by using a portable multi-parameter controller (HQ40D, Hach) included pH and temperature. Before sampling, we calibrated the portable pH probes. A parameter-specific setting for pH probes, buffers set as the default modes (4.01, 7.01, and 10.01 as default), and a standard temperature value at 25 °C were firstly selected. Then, three standard solutions (pH = 4, 7, and 10) was prepared for pH calibration. The meter measured three values of the calibration solutions in order. If applicable, we chose to save the calibration. Alkalinity was titrated by a portable digital titrator (Digital Titrator 16900, Hach) in the field. The major ion analyses were determined by ion chromatography (Dionex ICS1100) (detection limit is 0.05 mg/L) in the Laboratory of Water Isotope and Water–Rock Interaction at the Institute of Geology and Geophysics, Chinese Academy of Sciences (IGG-CAS). The tritium contents were performed by electrolytic enrichment with a tritium enrichment factor of ~20 and the liquid scintillation counting (Quantulus 1220) method at the Analytical Laboratory of Beijing Research Institute of Uranium Geology. The tritium analytical precision was 0.4 TU. The δ18O and δ2H of all samples were measured by a Liquid Water Isotope Analyzer (Picarro L2130-i) in the Key Laboratory of Tibetan Environmental Changes and Land Surface Processes Chinese Academy of Sciences. The detection precisions were ±0.1‰ and ±0.4‰ for δ18O and δ2H, respectively. The 222Rn content analysis was performed in the Key Laboratory of Groundwater Resources and Environment of Jilin University by the RAD-7 portable radon instrumentation (Durridge, Billerica, MA, USA) via radioactive decay accounts after sampling within two days. The measuring error was about 41% for groundwater and about 44% for surface water with all concentrations of 222Rn.

3.2. End-Member Mixing Model

Two and three-component isotope hydrograph separation has been a powerful tool to calculate the ratio of various recharging sources in hydrologic studies [38,39,40]. It is based on the mass balance equations of water and tracer fluxes in a catchment [41]. The isotopic bivariate mixture model was used to estimate contribution in the mainstream (SZ) from tributaries (RZ and ZCW):
Q = Q1 + Q2
Qδ = Q1δ1 + Q2δ2
where Q is the discharge and δ is the concentration of tracer. Subscripts 1 and 2 refer to ZCW and RZ tributaries, respectively.
As a fraction of local precipitation, Changhai Lake versus groundwater in Jiuzhaigou river water was also calculated by the three-component mixing model. The contribution of three sources could be estimated using the following equation:
Qs = Qg + Qp + Ql
QsCs = QgCg + QpCp + QlCl
Qsδs = Qgδg + Qpδp + Qlδl
where Q is the discharge and C and δ are the concentrations of the δ18O and TDS, respectively. Subscripts s, g, p, and l refer to stream, groundwater, precipitation, and lake water, respectively. A suitable tracer choice to explain the hydrologic changes in discharge during a storm, as well as to identify dominant sources in the catchment, has become increasingly important [42]. Environmental tracers such as 18O, 2H, and excess deuterium, as well as geochemical data such as TDS, Electrical conductivity (EC), Cl, and SiO2, are effective [43,44,45,46]. TDS and δ18O were chosen as the conservative tracers in this study.
A classical Gaussian error propagation technique is useful to quantify the uncertainty of isotope hydrograph separations [47,48]. The relative error ω of the contribution of a specific runoff component is related to the uncertainty in each of the variables by the following:
ω y = ( y x 1 ω x 1 ) 2 + ( y x 2 ω x 2 ) 2 + + ( y x n ω x n ) 2
where ω represents the uncertainty in the variable specified in the subscript. Errors of all separation equation variables were considered in this study. Uncertainties were caused by tracer analysis, as well as the discharge measurement and variability of 18O.

3.3. 222Rn Mass-Balance Model

222Rn, produced by the decay of 226Ra, is an inert radioactive gas with a half-life of 3.83 days. It has great potential to identify the rapid exchange processes of groundwater and surface water that occur on time scales of hours and days. In general, groundwater has 222Rn concentration values of 2–3 times higher than surface water [49]. This difference can provide some information to estimate the percentage of groundwater inflow between two locations along a river. Based on the 222Rn mass conservation theory [50], a 222Rn mass-balance model was constructed in the Jiuzhaigou river. By taking the gas exchange and radioactive decay of 222Rn in both river and groundwater discharge into account, the model can be expressed as follows [11,12,51]:
R n d = R n g w · 1 exp ( α · L ) L · α · Q g w Q r + R n u · exp ( α · L ) · ( 1 Q g w Q r )
α = λ υ + D 0.5 υ 0.5 · h 1.5
L o g D = 980 / T + 1.59
where Rnd, Rnu, and Rngw are 222Rn concentrations measured in the downstream, upstream river, and groundwater, respectively (Bq/m3); Qr and Qgw are river and groundwater discharge, respectively (m3/s); α is the 222Rn loss coefficient caused by both radioactive decay and gas exchange for the gas exchange part; L is the length between two stream sampling sites (m); v is the average stream velocity between two stream sampling sites (m/s); h is the stream depth (m); λ is 222Rn radioactive decay coefficient (λ = 2.08 × 106 s−1); D is radon molecular diffusivity (cm2/s); −Log D = (980/T) + 1.59; and T is absolute water temperature (K). Thus, the groundwater proportion in river discharge (Qgw/Qr) could be calculated by solving the equation.
According to the hydrogeological conditions and streamflow measurements of hydrologic stations, the typical methods for quantifying groundwater fluxes and exchange with river water in the Jiuzhaigou were conducted. The mass balance method to estimate specific transformations between groundwater and river water along a detailed section at two sampling sites was employed [12].
Assuming that groundwater discharge into the river, Rnd > Rnu, Qd > Qu, where qg is the average groundwater fluxes for the entire width and per length of the river (m3/(s·m)):
R n d · Q d = 0 l q g R n g w · exp ( α · x ) d x + R n u · exp ( α · L ) · Q u
q g = R n d · Q d R n u · exp ( α · L ) · Q u R n g w α 1 exp ( α · L )
Assuming that river discharges into groundwater when Rnd < Rnu and Qd < Qu, where qr is the average river leakage for the entire width and per length of the river (m3/(s·m)):
R n d Q d = R n u · exp ( α · L ) · Q u 0 l q r R n u · exp ( α · ( L x ) ) d x
q r = R n d Q d R n u · exp ( α · L ) · Q u R n u α exp ( α · L ) 1
Assuming that two processes both occur including groundwater discharge and river leakage when Rnd < Rnu and Qd > Qu:
R n d Q d = 0 l q g R n g w · exp ( α · x ) d x + R n u · exp ( α · L ) · Q u 0 l q r ( R n u + R n d ) 2 · exp ( α · ( L x ) ) d x
Q d = Q u + q g L q r L
q r = 2 α ( R n d Q d Q u R n u · exp ( α · L ) ) ( 1 exp ( α · L ) ) ( 2 R n g w R n u R n d ) 2 R n g w ( R n d R n u ) L ( 2 R n g w R n u R n d )
q g = 2 α ( R n d Q d Q u R n u · exp ( α · L ) ) ( 1 exp ( α · L ) ) ( 2 R n g w R n u R n d ) ( R n g w + R n d ) ( R n d R n u ) L ( 2 R n g w R n u R n d )

4. Results

4.1. Regional Surface Water Level and Streamflow Change

Figure 3a shows water level variations of all the monitoring stations before and after the earthquake in a month. In the ZCW valley, the water level of Changhai Lake and Colorful Pond decreased synchronously after the earthquake despite increased precipitation amount for a month until September 2017, indicating that Colorful Pond was connected with Changhai Lake. Gradually, Changhai Lake’s water level fell by 0.8 m in a month, the seepage of which was about 870,000 cubic meters as a recharging resource due to the earthquake. This water level drop lasted for more than a month after the earthquake and eventually recovered to rise again.
In the RZ valley, Kongqi River had an incremental recovery of water level. The western river could be recharged from Changhai Lake water. The underflow groundwater flowed into the RZ river through the seepage channel from Changhai Lake, as shown in Figure 3b. On the contrary, the sharp decline of Mirror Lake’s water level as a co-seismic response reached 6 cm and returned to approximately the prestimulated value. Additionally, ground fissures had been produced on a road near Mirror Lake (as found during field observation), and the surface cracking and shedding appeared in the core landscape of Nuorilang Waterfall. It was predicted that new bedrock fractures were produced near the ruptured fault.
In the SZ valley, the Peace Bridge water level had a slight decrease due to Mirror Lake leakage or Nuorilang Waterfall cracking, both of which were upstream. Then, the Peace Bridge water level increased and sustained. A sharp decline of Sparking Lake’s water level from 2212 to 2197 m was caused by the Sparking Lake dam-break induced by the earthquake. The water levels of the entrance and Black Bridge both had abrupt increases due to 450,000 cubic meters of lake water loss and a 15-m drop of Sparking Lake. One month before the earthquake, the water level of Kongqi River, Black Bridge, and the entrance showed a falling tendency, while that of Changhai Lake slowly arose. Another month after the earthquake, the lake water level of the ZCW valley in the east decreased. However, the river water level of the RZ valley in the west increased (Figure 3b). This indicated that Changhai Lake water from the ZCW valley in the east possibly discharged into the RZ valley in the west via the seepage channel.
Figure 4 shows the streamflow variations of four hydrologic stations in the SZ and RZ rivers before and after the earthquake in 2017. The annual average discharge volumes of the entrance, Black Bridge, Peace Bridge, and Kongqi River were 11.28, 11.22, 5.14, and 4.82 m3/s, respectively. Additionally, the annual average discharge was 11.28 m3/s in the SZ trunk river and 4.82 m3/s in the RZ river. Therefore, the discharge from the ZCW valley was 6.46 m3/s, based on Equation (1). The SZ trunk was recharged by 57% from the ZCW valley, and 43% from the RZ river according to the actual discharge volume calculation.

4.2. Hydrochemical Characteristics

The water in Jiuzhaigou is divided into three categories: springs, river water in the RZ and SZ valleys, and lakes in the ZCW valley, according to the chemistry characteristic and sampling location (Table 1). A Piper plot is shown in Figure 5. The surface water (river and lake) type was found to be HCO3–Ca–Mg. In the river, the major cation is Ca and ranges in concentration from 38.7 to 59.8 mg/L, Mg concentration ranges from 10.5 to 15.2 mg/L, pH varies from 8.0 to 10.2, and TDS ranges from 160.6 to 218.6 mg/L. Lakes in the ZCW valley have the lowest concentration Ca of 34.6–37.7 mg/L, Mg of 9.6–10.1 mg/L, and TDS of 141.0–155.6 mg/L. The pH of lake water ranges from 8.31 to 8.56. The water chemistry types of springs include HCO3–Ca–Mg, HCO3–SO4–Ca–Mg, and HCO3–Ca. The concentration of Ca in the springs ranges from 32.0 to 118.1 mg/L, the concentration of Mg ranges from 7.4 to 33.7 mg/L, pH ranges from 7.1 to 8.5, and TDS ranges from 119.1 to 409.1 mg/L. The RZ spring, S5, has the highest concentration Ca2+ of 118.1 mg/L and a TDS of 409.1 mg/L, which has the water type of HCO3–Ca.

4.3. Stable Isotopic Compositions of Hydrogen (δ2H) and Oxygen (δ18O)

The stable isotopic compositions (δ18O and δ2H) of the water samples in the study area were shown in Figure 6 and Table 1 and Table 2. The local meteoric water line (LMWL) for the Jiuzhaigou catchments was established as δ2H = 8.38 × δ18O + 20, (R2 = 0.97, n = 31), based on the precipitation sampled weekly at the Changhai Lake precipitation station. The slope and intercept of the LMWL were found to be higher than the slope (8) and intercept (10) of the Global Meteoric Water Line (GMWL; δ2H = 8 × δ18O + 10) [52]. The equation for LMWL was similar to the regional meteoric water line for the Tibetan plateau (δ2H = 8.41 × δ18O + 16.72) [53].
The different water types in Jiuzhaigou showed distinct isotopic compositions, as seen in Figure 4. (i) Rainfall samples were found to have a wide range from −19.5‰ to −4.9‰ of δ18O and from −136.3‰ to −21.9‰ of δ2H. (ii) ZCW lakes (L1–L4) were found to be enriched in δ18O and δ2H isotopes with a range from −12.2‰ to −11.9‰ and −83.5‰ to −81.4‰, respectively, while RZ river water had the same altitude, with δ2H ranging from −88.9 to −85.4‰ and δ18O from −13.1 to −12.5‰. Changhai Lake (L1) was located at the highest altitude, but δ2H and δ18O in the ZCW lake were found to be the most enriched and were plotted in the top-right of the LMWL, which indicated an evaporation effect [38]. (iii) The δ2H and δ18O water levels in three valleys were found to follow the ranking of ZCW > SZ > RZ, and the average δ18O values were found to be −12.0‰, −12.3‰, and −12.8‰, respectively. Additionally, the average δ2H values were found to be about −82‰, −84‰, and −88‰, respectively. The isotope level of the SZ samples was between those of the RZ and ZCW water isotopes, indicating a mixing effect. The contributions of ZCW and RZ water in the SZ river were found to be 62 ± 19% and 38 ± 19%, respectively, based on two-component isotope hydrograph separation (Equations (1), (2), and (6)). The results were the consistent with the fractions calculated by the actual discharge volume. A mixing line (δ2H = 7.76 × δ18O + 11.46) was obtained, and its slope was lower than the 8 of the GMWL slope. This indicated surface water evaporation. Most spring samples (S2, S3, S4, and S6) fell on the mixing line, indicating that they were recharged by Changhai Lake water. Therefore, the water connectivity of ZCW and RZ existed. (iv) Our field investigation showed that the original upstream source of the RZ river was a spring; This was the S1 sample. S1 was found to fall on the LMWL with the most depleted isotope value, which was related to the higher recharging altitude from precipitation. S8 and S11 were found to fall on the LMWL with an enriched isotope level related to the lower recharging altitude. R12 and R13, which were sampled at the intersection of the RZ and ZCW valleys (Figure 6), were found to have similar isotopic compositions to that of Changhai Lake. This indicated that they directly replenished from Changhai Lake water.
Along the river stretch, the δ18O value of water was found to have an increasing trend from S1 to R17 (Figure 7). Two flow pathways could be identified. Path 1 is the main water connectivity path between Changhai Lake and the RZ upstream river. Springs (S2–S5) are the outcrops of groundwater, as Changhai Lake water discharges into the RZ valley across the surface watershed. Path 2 is different water connectivity path between Changhai Lake and the downstream RZ river. R12, R13, S8, and S11 can be considered as the underflow groundwater sites of Changhai Lake, though the ZCW valley is a dry valley on the surface. ZCW and RZ water flow into the SZ river together at the intersection of two branches.

4.4. Tritium (3H) of River, Lakes and Springs

The tritium contents of the ZCW lakes (L1, L2, and L3) were found to be 12.5, 13.7, and 13.1 TU, respectively. The average was 13.1 ± 0.6 TU, approximately representing the tritium in precipitation. In the RZ valley, the tritium contents of springs (S1, S5, and S7) were found to be 11.7, 10.2, and 9.4 TU, respectively. S7, a groundwater member, was found to have the lowest tritium content of 9.4 ± 0.4 TU, which means that it is relatively “old” site of groundwater replenishment with a lower tritium concentration injunction between the ZCW and RZ valleys as a drainage area. The average tritium content of water samples (S10 and R15) that are exposed the bottom of Sparking Lake due to the dam break in the SZ river was found to be 10.4 ± 0.3 TU. Based on Equations (1), (2), and (6), the contributions of precipitation and groundwater in the SZ river water were found to be 27 ± 12% and 73 ± 12%, respectively. Tritium, the radioactive isotope of hydrogen of mass three with a half-life of 12.43 years, has served as an important and useful tracer to determine recent groundwater recharge, movement, and residence times for surface water and groundwater in hydrological studies [54,55]. The tritium sequence in Chengdu station from Global Network of Isotopes in Precipitation (GNIP) near the Jiuzhaigou area from 1986 to 1998 was used in this study. Based on the principle of radioactive decay, the precipitation of tritium for 2019, which represented the tritium that had infiltrated the groundwater between 1986 and 1998, was calculated in Figure 8. 3H contents in Jiuzhaigou showed that groundwater had a short residence time and it was a moderate precipitation. Changhai Lake water had the highest tritium content of more than 10 TU. The higher tritium values were caused by the recycling of moisture evaporated from the lake surface without outflow. It still had a considerable amount of tritium deposited during thermonuclear tests in this region [56].

4.5. 222Rn Concentrations in River and Spring

222Rn concentration in the rivers and springs showed a wide scale that ranged from 1101 to 35,765 Bq/m3. These concentrations were found to be about 1101–35,765 and 2320–25,481 Bq/m3 for samples of the rivers and springs, respectively. Additionally, the average 222Rn concentrations were found to be about 6462 and 7770 Bq/m3 for the samples of the rivers and springs, respectively. The distribution of the 222Rn concentrations is shown in Figure 9 and Table 1.
Four sections were divided along the river stretch according to the distributions and changes of 222Rn concentration. Section 1 stretches from R1 to R6, located upstream of the RZ river. The 222Rn isotope activity of river water was found to decrease significantly from 35,760 to 3210 Bq/m3, indicating that the natural decay of 222Rn isotope in this section had become the main controlling factor. Surface water was typically transformed into groundwater, and the stream discharge of R6 was found to be more than that of R1. Section 2 was found to have an increasing 222Rn isotope activity. The S5 sample was found to have the highest 222Rn concentration in the RZ valley, indicating that it can represent groundwater characteristics from the deep aquifer. From R6 to R10 along the flow path, the 222Rn activity of the river water samples was found to generally increase from 1587 to 11,256 Bq/m3, with an average value of 5505 Bq/m3. For springs, it was found to decrease from 25,481 to 12,804 Bq/m3, with an average value of 16,626 Bq/m3. The 222Rn concentration of groundwater was found to decrease along the river stretch, while the 222Rn concentration of the river water was found to increase in this region. This indicated that the river water of this section was mainly formed by groundwater discharge. The river water got more 222Rn from groundwater. Section 3 was found to have a decreasing trend of 222Rn isotope activity from R10 to R11, while a significant increase was found from R11 to R14. This indicated that two processes of groundwater discharge and river leakage possibly occur within Section 3. In the SZ river, Section 4 was found to have the same variation trend of 222Rn concentration as Section 1. From R14 to R17, the 222Rn activity of the river water was found to decrease from 10,660 to 1400 Bq/m3.

5. Discussion

5.1. Source Identification and Quantification of River Water

A three-component mixing model was used to estimate water flux along the RZ and SZ rivers. Precipitation, groundwater, and Changhai Lake water were the end-members for the model, forming the runoff discharge. The weighted isotopes of precipitation in Jiuzhaigou were found to be −13.3‰ for δ18O and −88.9‰ for δ2H. The rain TDS was considered as 20 mg/L. According to observations and measurements on the spring discharge, the RZ spring groups (S3, S4, and S5), exposed at the valley–fault intersection, have a relatively steady discharge volume of 1.4–1.7 m3/s. S5, especially, has the highest TDS and 222Rn concentration among these springs. During the sampling of S5 after the earthquake, an intense-smelling H2S appeared. A considerable amount of H2S rose along an active fault and dissolved into the groundwater. The high TDS was related to the water–rock interactions with a relatively deep groundwater circulation. Therefore, the S5 sample was considered to be a deeper groundwater member, with an δ18O of −12.7‰ and a TDS of 409.1 mg/L. The Changhai Lake water was found to have the most enriched δ18O of −12.1‰ as a lake member, with a TDS of 141.27 mg/L. The samples of precipitation, groundwater, and Changhai Lake formed a triangle to test and verify the independence of three end-members (Figure 10). Most river samples were found to fall into the triangle. However, R12, R13, S1, and S8 lie outside of the triangle. S1, the origin spring of the RZ river with the same altitude as Changhai Lake, was not recharged by Changhai Lake water. The groundwater can be easily transported when faults trend parallel or subparallel to the hydraulic gradient [57]. Therefore, water located at a low altitude was easily recharged from Changhai Lake. S8, S11, R12, and R13, which were close to L1, could be recharged from Changhai Lake water. Other outliers possibly resulted from uncertainty in field sampling and laboratory analyses or water source area changes [58,59].
Due to the regional groundwater flow pathways, the surface runoff had three generating sources that had different hydrological characteristics and contributions. The results obtained based on Equations (3)–(5) were shown along the RZ and SZ rivers in Figure 11. In Section 1, the contribution of Changhai Lake water was found to be less than 20%. In Section 2, the fractions of Changhai Lake water in the river were found to begin to increase, as this was the main seepage area recharged by Changhai Lake. The average fractions of precipitation, groundwater, and Changhai Lake water in the RZ river were 33%, 36%, and 31%, respectively. Considering Changhai Lake’s status as an underflow groundwater member, the average contributions of precipitation and groundwater in the river were found to be about 33% and 67%, respectively. This was also consistent with the result based on 3H tracing methods. In Section 4, the contribution of Changhai Lake in the SZ river was more than 60%, which was consistent with the result of two-component isotope (18O) hydrograph separation. This was because there was water connectivity between Changhai Lake and the RZ river.

5.2. Estimation of Groundwater/River Water Interactions

A 222Rn mass-balance model was constructed in four sections with river flow monitoring data to quantitatively calculate the river leakage, groundwater discharge, and proportion of groundwater in river discharge. During calculation, D was calculated to be 8.89 × 1010 m2/s at 283.15 K (10 °C) from Equation (9), α was calculated to be 1.77 × 105 m−1 from Equation (8), Rngw was represented by the average 222Rn concentration of springs in different sections, Qu and Qd were calculated by the observed average stream discharge of hydrological stations, and V and H were measured at the river cross sections of these stations during the sampling period. Groundwater was discharged into the river water (5.5 m3/s for Qgw and 69% for Qgw/Qr from Equation (7)) in Section 2 for about 7 km during the sampling period. The flux of groundwater discharge qg was about 7.66 × 104 m3/(s·m). The contribution of groundwater in the river was 69%. All of this was consistent with the result of isotope hydrograph separation.
In Section 1, Section 3, and Section 4 along the river stretch, the upstream 222Rn concentration was more than the downstream concentration, while the upstream stream discharge was less than the downstream discharge. Two processes of groundwater discharge and river leakage occurred. The model parameters in these sections were substituted into Equations (16) and (17) (Table 3) to produce the results of groundwater discharge flux qg and river leakage flux qr: they were found to be 9.99 × 104 and 1.55 × 104 in Section 1, 11.1 × 104 and 8.39 × 104 in Section 3, and 9.19 × 104 and 1.02 × 104 m3/(s.m) in Section 4, respectively; see in Table 3. The results showed that groundwater discharge plays a dominant role in these sections. Along the river stretch, the average groundwater discharge flux was found to be 9.47 × 104 m3/(s.m), and the average river leakage flux was found to be 3.65 × 104 m3/(s.m).
It was expected that the transformation result between groundwater and surface water would be close to that obtained based on the flow balance. The 222Rn model was validated by comparing it with the result of the flow balance method. The net exchange between groundwater and river represented the difference values of downstream and upstream discharges in each section from Equation (18). If it was a positive value, the downstream discharge exceeded the upstream discharge. This indicated river water was replenished from groundwater. Otherwise, a negative value meant that river water was lost and groundwater was recharged from river water. However, the flow balance method showed no detailed process in a different section, and this could be considered a reference to validate the 222Rn model.
q = Q d Q u L
Results found with two methods were compared in Figure 12. As seen in Section 1 and Section 4, the groundwater discharge flux qg matched well with these two methods, and the calculated qg with the 222Rn method was about twice greater than that obtained with the flow balance method in Section 2 and Section 3. The differences of results were still acceptable [12]. The uncertainties of estimation on groundwater discharge to the river resulted from the analysis errors of 222Rn concentration in river water and groundwater, as well as the uncertainties from the manual measurement of stream parameters. Therefore, the water flux was validated by the 222Rn mass balance method.

5.3. Regional Groundwater Circulation Model

The conceptual model of three-dimensional water circulation was proposed in the study area (Figure 13). Jiuzhaigou valley was mainly composed of two tributaries and one trunk, including the RZ river in the west, the ZCW valley in the east, and the SZ river in the middle. The ZCW valley has perennial drought and four separate lakes. As the most important lake in the ZCW valley, Changhai Lake was recharged by precipitation and snow meltwater from surrounding high mountains. It was considered a slightly evaporated natural supply reservoir without a visible outlet. The Colorful Pond and upper and lower seasonal lakes in the ZCW valley had the same recharge altitude as Changhai Lake. They all belonged to seepage-type lakes with similar surroundings [60] that bear the same enriched isotope signatures due to evaporation. The S–N-oriented Jiuzhaigou and Zechawa Faults (F5 and F6, respectively) trended parallel or subparallel to the hydraulic gradient for water flow from south to north. They were conductive for groundwater transport. The surface water of the RZ river and ZCW valley tended to flow into the fault and out of the intersection, flowing into the SZ river together. The annual average discharges of the SZ and RZ rivers were 11.3 and 4.8 m3/s, respectively. Therefore, the discharge of the ZCW valley into the SZ river was obtained as 6.5 m3/s. Based on two-component isotope hydrograph separation, the two branches of the SZ trunk were recharged by 62 ± 19% from the ZCW valley and 38 ± 19% from the RZ river, consistent with the fractions calculated by the actual discharge volumes. The connectivity between Changhai Lake and the midstream RZ river was confirmed to exist across the local water divide according to the regional water level changes before and after the earthquake, water isotopic tracing analysis, and geological conditions. Based on the directly observed changes of regional water level for one month after the earthquake, the Changhai Lake water level decreased in the east, and the RZ river water level and discharge volume both increased in the west. Therefore, an underground hydraulic connection across the water divide was assumed between two branches. Regarding the useful confirmatory tracers, the δ18O and δ2H isotopes indicated that the RZ river water recharged from Changhai Lake water. The underground flow pathways were confirmed to exist. The cross-mountain faults (F3 and F4) may have been reactivated by the Jiuzhaigou earthquake. F3 and F4 near causative fault F1 acted as the important conduits to control the interbasin flow in the karstic catchments. Given that the high-permeability fault trended in the same direction as the hydraulic gradient, the fault normally had great effects on the groundwater flow [57]. Fault trends were good conductors with a relatively high transmissivity even for Changhai Lake water transport and discharge into the RZ river water across the water divide of a 4200-m mountain ridge. The water cycle system was sensitive to earthquake activities.
Most springs were exposed at the valley–fault intersections with relatively high and steady flow rates, maintaining the perennial abundance of RZ river water. For example, the RZ spring group had a discharge volume of 1.4–1.7 m3/s. It was exposed along the F3 fault that cut the major karst aquifer of the Carboniferous Minhe Group formation. Notably, it was found to have the highest TDS and 222Rn concentration. An intense-smelling H2S appeared in the spring vents after the earthquake. The high TDS concentration of the spring was related to the water–rock interactions with a relatively deep groundwater circulation. Therefore, the RZ spring was representative of deeper groundwater. Based on three-component isotope hydrograph separation, the RZ river was found to have been recharged by precipitation, groundwater, and Changhai Lake water. There were different fractions at the different river cross-sections, the averages of which were found to be 33%, 36%, and 31%, respectively. The source of the RZ river, a primeval forest spring, was precipitation. It was not recharged by Changhai Lake water. However, the midstream RZ river was the main discharging zone for Changhai Lake. The contribution of groundwater in the river was found to be 70% based on the 222Rn mass balance model and the 3H tracer. Most groundwater discharged into lakes and rivers via many springs from the recharge zone. Toruloid lakes along the RZ valley fed by springs were recharged by Changhai Lake. The springs were exposed at valley–fault intersections, with relatively high fracture-related permeability located in a fault damage zone. Groundwater was quickly driven and transmitted into surface water. The rapid interaction between surface water and groundwater was determined, thus allowing us to form a groundwater circulation model.

6. Conclusions

In this paper, we used multiple environmental isotope tracers (2H, 18O, 3H, and 222Rn) to discern the water cycle in the Jiuzhaigou catchments in the southeastern Tibetan Plateau. The regional water level changes over a month after the earthquake showed that the lake water level of the ZCW valley in the east had decreased and the water level of the RZ river in the west had increased. The water cycle system was found to be sensitive to earthquake activities due to the fact that there is a hydraulic connection underground across the water divide that controlled the distribution of surface flow in the two branches. A conceptual model of the water cycle was established by considering the contributions of different sources, including precipitation and groundwater, to the surface runoff. Based on isotope hydrograph separation, the surface water was found to have been recharged by precipitation, groundwater, and upper land lake (Changhai Lake) water; their fractions were 33%, 36%, and 31%, respectively, at the river outlet section. The water exchange direction and water flux between groundwater and surface water estimated using 222Rn measurements indicated that 70% of the precipitation/surface water had undergone underground circulation, which demonstrated that well-developed surface vegetation made it easy for precipitation to recharge groundwater that eventually discharged into the river channel, a mechanism that optimal for maintaining the special waterscape.
The results showed that isotope tracers were effective in gauging the fractions of different water sources. Our study also implies that the regular monitoring of hydrological processes needs to be continued and enhanced in order to better formulate geo-hazard prevention and mitigation strategies for the world’s natural heritage sites.

Supplementary Materials

The supplementary materials are available online at https://www.mdpi.com/2073-4441/12/12/3306/s1.

Author Contributions

Conceptualization, Z.P.; methodology, Z.P. and D.L.; field investigation, D.L., W.X., Y.H., J.D., G.S., and Z.P.; data handling, D.L., Y.H., and X.Y.; writing, review and editing, D.L. and Z.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC Grant 41430319) and the World Natural Heritage Conservation and Restoration Research Program.

Acknowledgments

We gratefully acknowledge F.T. Yang for analyses of Radon isotopes. Thanks also go to Y.L. Kong and J. Li for discussions and useful suggestions on an earlier version of the manuscript. Our gratitude also goes to the Jiuzhaigou Disaster Recovery Project and World Natural Heritage Conservation and Restoration Research for their financial support.

Conflicts of Interest

The authors declare no conflict of interests.

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Figure 1. Several waterscapes affected by the Jiuzhaigou earthquake: (ad) represent the Nuorilang Waterfall cutoff, the dried-up Sparking Lake, a section of road in the west valley filled with water, and the Sparking Lake dam break, respectively.
Figure 1. Several waterscapes affected by the Jiuzhaigou earthquake: (ad) represent the Nuorilang Waterfall cutoff, the dried-up Sparking Lake, a section of road in the west valley filled with water, and the Sparking Lake dam break, respectively.
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Figure 3. (a) Water levels of four hydrometric lakes and four hydrologic stations in three valleys. (b) Regional water level changes induced by the earthquake. The dashed black line represents the Mw6.5 earthquake, and the blue column refers to the amount of precipitation. The water level data are presented in Table S1 of the Electronic Supplementary Materials (ESM).
Figure 3. (a) Water levels of four hydrometric lakes and four hydrologic stations in three valleys. (b) Regional water level changes induced by the earthquake. The dashed black line represents the Mw6.5 earthquake, and the blue column refers to the amount of precipitation. The water level data are presented in Table S1 of the Electronic Supplementary Materials (ESM).
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Figure 4. Hydrographs of four hydrologic stations in the Rize (RZ) and Shuzheng (SZ) rivers in 2017. The dashed black line represents the Mw6.5 earthquake. The discharge data are presented in Table S1 of the ESM.
Figure 4. Hydrographs of four hydrologic stations in the Rize (RZ) and Shuzheng (SZ) rivers in 2017. The dashed black line represents the Mw6.5 earthquake. The discharge data are presented in Table S1 of the ESM.
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Figure 5. Piper diagram of the spring, river, and lake water samples; percentage concentrations in meq/L.
Figure 5. Piper diagram of the spring, river, and lake water samples; percentage concentrations in meq/L.
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Figure 6. Stable isotopes of rainfall, spring, lake, and river water in Jiuzhaigou. The solid circle represents the isotopic average value of three valleys. Changhai Lake (L1) water was sampled many times.
Figure 6. Stable isotopes of rainfall, spring, lake, and river water in Jiuzhaigou. The solid circle represents the isotopic average value of three valleys. Changhai Lake (L1) water was sampled many times.
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Figure 7. δ18O changes of water samples along three valleys.
Figure 7. δ18O changes of water samples along three valleys.
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Figure 8. The tritium precipitation input from 1986 to 1998 and the decayed tritium precipitation content as of 2019 (data from GNIP Chengdu station).
Figure 8. The tritium precipitation input from 1986 to 1998 and the decayed tritium precipitation content as of 2019 (data from GNIP Chengdu station).
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Figure 9. 222Rn concentration changes of river water and springs along the RZ and SZ rivers. The error bars represent the standard deviation of all 222Rn samples.
Figure 9. 222Rn concentration changes of river water and springs along the RZ and SZ rivers. The error bars represent the standard deviation of all 222Rn samples.
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Figure 10. The average values of δ18O and TDS of three end-members that form a triangle.
Figure 10. The average values of δ18O and TDS of three end-members that form a triangle.
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Figure 11. Estimated fraction of water sources as a result of hydrograph separation along the RZ river.
Figure 11. Estimated fraction of water sources as a result of hydrograph separation along the RZ river.
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Figure 12. Comparison of calculated results of water flux using different methods.
Figure 12. Comparison of calculated results of water flux using different methods.
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Figure 13. Schematic of conceptual model of three-dimensional post-earthquake water cycle. F1–6 represent faults shown in Figure 2a in detail.
Figure 13. Schematic of conceptual model of three-dimensional post-earthquake water cycle. F1–6 represent faults shown in Figure 2a in detail.
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Table 1. Field parameters, major ions, and water isotopes.
Table 1. Field parameters, major ions, and water isotopes.
TypeSample IDValleyDistance aAltitudeTpHNaKMgCaFClHCO3SO4TDSδ18Oδ2H222Rn222Rn Error
(km)(m)°Cmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/L(Bq/m3)(Bq/m3)
RiverR1RZ27.65 29806.98.0 0.0 0.3 10.5 46.6 0.3 1.7 21.6 179.9 171.0−12.8−89.635,7653813
R2RZ27.17 29427.28.4 0.0 0.3 10.5 44.5 0.4 1.7 23.6 172.2 167.0−12.8−89.572344125
R3RZ25.86 29546.88.3 0.0 0.3 10.9 44.3 0.4 1.7 23.7 172.2 167.3−13.0−88.736501800
R4RZ25.09 290015.28.4 0.0 0.3 11.0 44.6 0.4 1.8 23.6 174.8 169.0−12.8−88.343341402
R5RZ25.10 290015.28.3 0.0 0.3 8.8 46.1 0.4 1.8 23.4 167.8 164.7−12.9−88.432141235
R6RZ22.87 27608.210.2 0.0 0.3 10.9 43.9 0.4 1.6 23.5 174.0 167.6−13.0−89.51587610
R7RZ21.68 26017.18.3 0.0 0.3 10.8 43.9 0.4 1.7 23.4 170.1 165.5−12.9−88.430021578
R8RZ20.48 26457.68.3 0.3 0.4 13.1 59.8 0.5 2.3 25.6 233.1 218.6−12.8−87.336272115
R9RZ16.56 247612.58.3 0.3 0.4 12.7 50.2 0.4 1.1 24.2 200.4 189.6−12.7−86.980533576
R10RZ15.69 247711.58.2 0.6 0.5 14.2 55.7 0.6 2.4 25.8 226.1 212.8−12.6−85.911,2561100
R11RZ13.10 230610.18.2 0.7 2.2 15.2 53.1 0.6 3.7 25.7 226.0 214.1−12.6−85.71101735
R12RZ12.88 22729.88.4 0.4 0.4 13.5 44.3 0.5 1.9 24.6 178.2 174.8−12.2−83.716371093
R13RZ12.73 232298.3 0.3 0.4 12.8 38.7 0.4 1.8 23.3 165.8 160.6−12.1−81.274122441
R14SZ12.13 23149.38.1 0.5 0.5 14.5 48.4 0.4 1.0 24.7 207.9 194.0−12.3−84.610,6572046
R15SZ7.70 220711.28.3 0.6 0.5 14.7 44.8 0.5 1.1 25.4 195.1 185.1−12.4−84.637161601
R16SZ6.78 218814.38.5 0.6 0.5 14.8 44.6 0.4 0.9 29.5 195.2 188.9−12.4−84.222141350
R17SZ0.00 200411.88.5 0.7 0.5 12.1 42.9 0.4 1.0 28.5 170.1 171.1−12.2−82.91400991
SpringS1RZ27.87 29606.87.9 0.0 0.3 10.4 46.7 0.3 1.7 21.9 182.5 172.5−13.1−90.353652626
S2RZ26.76 30128.78.5 0.0 0.4 20.1 34.3 0.6 0.4 68.3 100.2 174.2−12.5−86.1693460
S3RZ21.28 27008.27.7 0.3 0.5 13.3 63.9 0.6 2.3 25.1 246.9 229.5−12.7−87.712,7025068
S4RZ21.30 26908.87.4 0.9 0.6 17.0 86.1 0.8 3.1 30.4 340.4 309.0−12.7−87.615,5164893
S5RZ21.59 267510.67.1 0.0 1.1 12.6 118.1 0.9 2.5 35.0 477.6 409.1−12.7−86.125,4814681
S6RZ16.30 254711.27.9 0.8 0.5 15.5 58.7 0.6 2.4 26.7 245.2 227.8−12.6−86.412,8043744
S7RZ15.50 24608.68.3 5.0 2.7 33.7 78.4 0.5 8.5 103.9 274.6 370.0−10.1−67.9178358
S8SZ10.30 23049.58.3 0.7 0.5 23.9 50.4 0.6 1.1 25.7 251.5 228.5−12.0−80.726101063
S9SZ8.61 226310.48.3 0.6 0.5 14.7 46.0 0.5 1.0 25.1 200.2 188.5−12.4−84.51520825
S10SZ7.85 219510.98.3 0.6 1.2 14.6 44.0 0.4 1.7 27.9 187.4 184.2 −12.4−84.428551717
S11SZ6.87 2210108.5 0.2 0.2 10.6 36.8 0.3 0.6 24.6 150.1 148.5 −12.3−82.414,4302277
LakeL1ZCW28.64 29849.68.6 0.0 0.3 9.8 34.9 0.3 1.5 24.8 134.7 139.0 −12.1−82.59143624
L2ZCW26.61 29118.18.3 0.0 0.3 10.1 34.6 0.3 0.7 25.7 135.1 139.3 −12.2−82.9--
L3ZCW26.85 29336.28.3 0.2 0.4 11.8 37.9 0.5 0.8 25.7 154.0 154.2 −12.2−83.5--
L4ZCW18.76 260410.58.6 0.1 0.4 9.6 37.7 0.4 0.8 23.6 147.0 146.1 −12.2−82.918,5392748
a The distance is the direct distance away from the location of R17.
Table 2. δ18O and δ2H isotopes of precipitation at the Changhai Lake precipitation station.
Table 2. δ18O and δ2H isotopes of precipitation at the Changhai Lake precipitation station.
TypeNoSample IDDate
(yy/mm/dd)
Amount (mm)δ18O‰δD‰
Rain1CH-012019/10/2317.5−14.0−89.8
Rain2CH-022019/10/3040−19.5−136.3
Rain3CH-032019/11/723.1−17.2−120.8
Rain4CH-042019/11/141.8−11.2−69.2
Rain5CH-052019/11/202.2−12.8−80.8
Rain6CH-062019/11/282−9.8−58.5
Rain7CH-072019/12/1912.5−12.6−78.4
Snow8CH-082019/12/261.6−10.3−58.5
Snow9CH-092020/1/91.8−14.0−97.7
Snow10CH-102020/1/172.6−13.9−97.3
Snow11CH-112020/1/234.3−14.2−97.1
Snow12CH-122020/1/312.5−14.6−98.5
Snow13CH-132020/2/65.5−14.1−87.9
Snow14CH-142020/2/132.5−12.4−75.6
Snow15CH-152020/2/202.1−14.0−84.3
Snow16CH-162020/2/260.7−8.5−49.0
Snow17CH-172020/3/511.2−12.5−80.5
Snow18CH-182020/3/1215−11.6−72.6
Rain19CH-192020/3/1917.3−10.7−65.7
Rain20CH-202020/3/2613.2−11.5−69.4
Rain21CH-212020/4/219.5−9.4−50.8
Rain22CH-222020/4/946−18.0−128.7
Rain23CH-232020/4/1610.2−7.7−38.7
Rain24CH-242020/4/2329.4−15.0−105.3
Rain25CH-252020/5/76.6−4.9−21.9
Rain26CH-262020/5/1427.4−10.5−67.8
Rain27CH-272020/5/2124.2−12.4−83.4
Rain28CH-282020/5/2831.2−12.3−83.4
Rain29CH-292020/6/421.6−12.3−83.4
Rain30CH-302020/6/1121.1−8.7−56.6
Rain31CH-312020/6/1838.6−11.6−80.6
CH: Changhai.
Table 3. Characteristics of the estimated sections and the calculated results obtained using the 222Rn mass balance method.
Table 3. Characteristics of the estimated sections and the calculated results obtained using the 222Rn mass balance method.
SectionsRnu
(Bq/L)
Rnd
(Bq/L)
Rngw
(Bq/L)
Qu
(m3/s)
Qd
(m3/s)
V
(m/s)
L
(m)
H
(m)
qr
m3/(s*m)
qg
m3/(s*m)
135.76 3.21 5.37 2.004.000.30 25491.501.55 × 1049.99 × 104
21.59 11.2616.635.858.321.07 71811.50-7.66 × 104
311.26 10.66 6.49 8.3210.10.95 35651.258.39 × 10411.1 × 104
410.66 1.40 5.35 10.118.91.11 12,1271.301.02 × 1049.19 × 104
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