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Water 2019, 11(3), 479; https://doi.org/10.3390/w11030479

Article
Recharge of River Water to Karst Aquifer Determined by Hydrogeochemistry and Stable Isotopes
1,2,3, 1,2,3,4,*, 1,2,3, 1,2,3, 5 and 5
1
Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2
Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Academician Zhaiming Guo working station, Sanya University, Hainan 572000, China
5
Shandong Provincial Water Resources Research Institute, Ji’nan 250013, China
*
Author to whom correspondence should be addressed.
Received: 23 January 2019 / Accepted: 28 February 2019 / Published: 7 March 2019

Abstract

:
The Jinan Karst Spring System in Shandong province, China, has suffered to maintain groundwater level and spring flowing for decades. Recharge of river water to karst aquifer in Jinan is important for the outflowing of four large karst springs in the city center. Field investigations were conducted for two times in May and October, 2015, respectively and water samples were collected for hydrogeochemical and isotopic measurements. Results showed that (a) the water type was predominantly Ca-HCO3-SO4 for karst groundwater, and Ca-Mg-SO4 for river water; (b) the concentration of HCO3 and NO3 in karst groundwater were higher than that in river water, in contrast, the concentration of SO42− and K+ in karst groundwater were lower than that in river water; (c) the δ2H and δ18O values with average of −51.2‰ and −6.6‰ for river water is more enriched than the values in groundwater samples (−59.1‰ and −8.3‰), in that river experienced evaporation in the upstream reservoir; (d) Based on the distribution pattern of δ18O, groundwater near river bank was found to be recharged from river water and found a preferential flow path in karst aquifer situated from Dongkema to Manzizhuang near the river bank. This study provides useful information for understanding of the hydraulic connection between river water and karst aquifer, and benefit the protection and management of water resources.
Keywords:
recharge; streambed; hydrogeochemistry; isotope; karst aquifer; Jinan

1. Introduction

The evaluation of effect of river water recharge on karst aquifers has double significances: the scientific one in terms of the relationship between river water and karst aquifers and the applied one in terms of the assessment and protection of karst water resources. However, it is not easy to identify the effect of river water infiltration through the streambed on karst groundwater due to the complex hydrogeological conditions of karst systems [1,2]. On one hand, the karst aquifer comprises a pattern of triple porosity, the matrix, fractures and conduits, which causes a high degree of heterogeneity on the hydraulic conductivity [3,4,5,6,7] and make it difficult to identify the flow paths and spatial effect areas of river water recharge in karst aquifers [8]. On the other hand, the recharge from the intermittent river is seasonally variable [9,10,11], which makes it difficult to reveal the temporal effect of river water recharge on karst groundwater [1,12].
Traditional hydrogeological investigations such as water level monitoring and river flow measurement result in uncertainty in evaluating the effect of river water recharge on karst groundwater due to the heterogeneity of karst aquifers [1,2,13,14]. The distinguishing different characteristics of hydrogeochemical and/or isotope tracers between river water and karst groundwater provide a reliable method to evaluate the river water recharge on karst groundwater [15]. Hydrogeochemistry can not only identify the recharge from river water in karst aquifer [15], but can also reveal the possible hydrogeochemical processes underlying the recharge of river water based on the kinetic study of carbonate dissolution/precipitation [13,15,16]. In addition, inverse hydrogeochemical modeling can quantitatively study the theoretical mass transformation along the flow paths [17].
δ2H and δ18O are regarded as ideal natural tracers for quantitatively identifying the interaction between river water and groundwater [9,18,19]. On one hand, the δ2H and δ18O are only altered by physical processes such as diffusion, mixing, and evaporation under low temperature conditions [14]. On the other hand, river water usually has more enriched δ2H and δ18O values due to evaporation, compared with that in groundwater [13,15,20]. Except for the stable isotope, the concentration of 222Rn in surface waters is generally less than 100 pCi/l, resulting from air degassing, while the concentration of 222Rn is about 100 to 1000 pCi/l in groundwater [21]. The different 222Rn concentration in surface water and groundwater also makes 222Rn useful for tracing river water infiltration into groundwater [9,22]. Multi-tracers analyses (major ions, stable isotopes, and 222Rn) are useful tools for delineating the flow paths of river water inflow [15].
Two of the most used multivariate statistical analyses are hierarchical cluster analysis (HCA) and principal component analysis (PCA) [2,23]. HCA enabled the classification of water samples into distinct groups based on their hydrochemical characteristics. On the other hand, PCA offers a better understanding about the factors for water quality [24,25].
The regional groundwater water level in Jinan karst system has decreased strongly, driven by exploitation, which has led to the karst springs in the city center drying occasionally in recent decades. In order to protect the karst springs, water from upstream reservoirs was drawn off artificially into the streambed of Yufu River from April to August in 2015. The river water infiltrated into the karst aquifer through the seepage section along the streambed, and formed a recharge for the Jinan karst system. This study is designed to investigate the effect of the recharge from the streambed of Yufu River on the karst groundwater in May (in the process of recharge) and October (after the recharge).
The purposes of this study include: (1) reveal the temporal and spatial effect of river water infiltration; (2) quantitate the fraction of river water in the karst aquifer; and (3) and identify the flow path of infiltrated river water and reveal the hydrogeochemical processes along the flow path. We think this study will provide an improved understanding of the hydraulic connection between river and karst aquifer, and benefit the local water resource management.

2. Study Area

Jinan city is the capital of Shandong province, China, is located between the longitude of 116°11′ to 117°44′ E and the latitude of 36°01′ to 37°32′ N, with a total area of 8177 km2 (Figure 1). In geomorphology, Jinan city is located in the north of the Mount Tai anticline, characterized by mountainous area in the southern part, inclined piedmont plain in the central part, and alluvial plain in the northern part (Figure 1). The climate is semi-arid continental monsoon climate, with cold, dry winters and hot, wet summers. Air temperature ranges from −1.4 °C in January to 27.4 °C in July, with mean temperature of 14.2 °C. The annual mean precipitation is 642 mm, of which around 75% occurs from June to September. The annual mean evaporation is 1476 mm.
The Jinan karst system is located in the central part of Jinan. Archaean metamorphic rocks are the base and outcrop in the south of the catchment, and overlaid by Cambrian and Ordovician carbonate rocks to the north. The limestone and dolomites are massive and well jointed, with a stratigraphic thickness of 1300–1400 m. The Cambrian strata are composed of thick-bedded limestone, argillaceous limestone, and dolomite limestone, and the Ordovician strata are characterized by the inter-bed of limestone and shale. They dip away from the metamorphic rocks at angles varying from 5° to 10° in the dip direction of NW 20°. Intrusive magmatic rocks (diorite and gabbro) of the Yanshan epoch in the Mesozoic are located in the top of the Ordovician strata in the north, and the intrusive rocks are buried mostly by Quaternary sediments (Figure 1).
The karst aquifers consist of Cambrian and Ordovician carbonate rocks, with the hydraulic conductivity ranges from 0.05 m/day to 120 m/day [26]. Groundwater is tending to move from the south toward the north (similar to the dip direction of strata) and is hindered by the intrusive rocks to form discharge areas. In the city of Jinan, 108 springs occurred in an area of 2.6 km2, of which Baotu Spring, Black-tiger Spring (Heihu Spring), Pearl Spring and Five-dragon Springs are the most well-known. The total discharge of springs was in the range from 3 × 105 to 4 × 105 m³/day in the 1960s, with the maximum value of 5 × 105 m³/day in 1962. Springs stopped flowing at the first time in 1973 and later on, zero flow occurred in 1982, 1989 and 2000–2002 due to the over-exploitation of groundwater. In order to restore the flow of these karst springs for environment and tourism, municipal water supply wells were switched off and river water has been drinking water as replacement. Flow of Baotu spring and Heihu spring has been restored since September 2003.
Precipitation is the main recharge source of the karst aquifer (Figure 2), with a supply module of more than 20 m3/a.km2. Spring water level responds within 5 days when the total precipitation of 10 days is more than 18 mm [27]. Yufu River is another important recharge source of the karst aquifer [28,29], which originates in the southern mountains and flows in a northerly direction for about 65 km with a watershed of approximately 1510 km2. The streambed of Yufu River is dry in recent decades, strongly driven by climate change (warmer and drier) and human activities (water withdrawal from rivers, groundwater exploitation, etc.). In the dry season, the water in Yufu River is possibly charged by water transported from upstream reservoirs, the Yellow River, and/or the Yangtze River through the South-to-North Water Diversion Project. The total volume of transported water was 4347 × 104 m³ in 2015(data obtained from the website (http://www.whssk.com)). The maximum monthly volume of transported water was 2127 × 104 m3 in August, 2015. The transported water infiltrates into the karst aquifer under the leakage section of the streambed and forms a recharge. The volume of leakage water from Yufu River was 1.02 × 108 m3 in 1963. The maximum leakage section of Yufu River occurs at the CuiMa and PanCun sections, with 4.1–4.8 m3/s.

3. Methods

In order to evaluate the effect of recharge from Yufu River on Jinan karst aquifer, two hydrogeochemical and isotopic investigations were conducted in May and October in the year of 2015, respectively. In total, 58 water samples were collected, with 28 samples (24 groundwater samples, 2 spring samples, and 2 river water samples) collected in May and 30 samples (26 groundwater samples and 4 spring samples) collected in October. River water samples were collected using the grab technique, and groundwater samples were collected from public supply wells after purging a minimum of three estimated casing volumes. The water samples were filtered through a 0.45 μm membrane filter after sampling. Samples for cation analysis were stabilized by adding 1% HNO3 immediately after filtration. The hydrogeochemical samples were collected in high density polyethylene bottles, which were pre-cleaned with 5% HNO3 and deionized water. Samples for δ18O and δ2H measurement were sealed in 50 mL glass bottles using gas-tight caps. Samples for 222Rn were sealed in 100 mL glass bottles.
Water temperature, pH, and electrical conductivity (EC) were measured in the field during sampling using WTW portable multi-parameter instrument (Multi 340i/SET), with the precision of ±0.1 °C for temperature and ±1 μs/cm for EC. The major ion (SO42−, NO3, Cl, Ca2+, Mg2+, Na+, and K+) were measured using ion chromatography (Dionex DX-120 Ion Chromatograph, Thermo Fisher Scientific, Washington, DC, USA) with analytical precision ±5%. HCO3 was analyzed by titration with 0.05 M HCl and methyl orange as an indicator. Water stable isotopes (2H/H and 18O/O) were measured using the Finnigan MAT 253 mass spectrometer (Scientific Instrument Services, Inc., Ringoes, NJ, USA). The results were reported in δ‰ referenced to VSMOW (Vienna Standard Mean Ocean Water). The measurement precision for δ2H and δ18O was ±1.0‰ and ±0.2‰, respectively. The 222Rn was measured using the RAD-7 environment radon measurement instrument (Division of Instrument Development of BRIUC, Beijing, China), with a measurement range of 0.003–100 Bq/L and a measurement precision of ±5%. All chemical and isotopic analyses were performed at the Institute of Geology and Geophysical, Chinese Academy of Sciences (IGG-CAS). The in situ parameters, together with analytical chemical parameters of the 58 water samples, are listed in Table 1, and the δ18O, δ2H, and 222Rn concentration of water sample are presented in Table 2.
The water type was calculated using AquaChem software (3.7) (waterloo hydrogeological, Kitchener, ON, Canada), the saturation index for calcite (SIc), dolomite (SId), and gypsum (SIg) were calculated by Phreeqc (version 3.3.12) (USGS, Reston, VA, USA), and Q-mode hierarchical cluster analyses (HCA) were performed on the major ions (HCO3, SO42−, NO3, Cl, Ca2+, Mg2+, Na+ and K+) and δ18O and δ2H to group the samples using SPSS software (version 21) (IBM, New York, NY, USA). Before the HCA, the above parameters were standardized by calculating their z-scores, to ensure that each variable is weighted equally. Euclidean distance together with Ward’s method for linkage were used to produce the distinctive groups. Results can be displayed as a tree diagram (dendrogram), which provides a visual summary of the clustering process by presenting a picture of the groups and their proximity.
Inverse hydrogeochemical modeling was done using the Phreeqc (version 3.3.12) to quantify the mass transfer along the water flow path. The mass transfer models are constrained by the concentrations of the dissolved constituents in initial and final waters, such as C, S, Ca, Mg, and CO2. Calcite, dolomite, and gypsum were chosen as the major mineral phases. The uncertainty (global uncertainty) for water composition was 5%; in cases where the model could not produce a result, global uncertainty was increased by integer increments up to 20%.

4. Results

4.1. Hydrogeochemistry

The temperature of groundwater samples ranged from 14.7 °C to 21.6 °C (18.2 °C on average, 1.46, standard deviation) in May and from 14.0 °C to 19.1 °C (16.5 °C on average, 1.17, standard deviation) in October, while the temperature of river water was on average 23.2 °C in May (1.00, standard deviation). HCO3, SO42− and Ca2+ constituted over 75% of the dissolved solids in groundwater. The HCO3 in the groundwater ranged from 189 mg/L to 454 mg/L in May and from 177 mg/L to 346 mg/L in October, which was obviously higher than that in river water (112.8 mg/L on average, 31.6 standard deviation). The SO42− in groundwater showed wide range, from 29 mg/L to 163 mg/L in May and from 46 mg/L to 185 mg/L in October, the SO42− in river water was averagely 134.3 mg/L (0.5, standard deviation). The Ca2+ in groundwater also showed wide range from 44 mg/L to 157 mg/L in May, and from 22 mg/L to 127 mg/L in October; the Ca2+ in river water was 51.1 mg/L (0.6 standard deviation). The NO3 in groundwater was on average 53.6 mg/L (28.7 standard deviation) with a maximum of 120 mg/L in May, and on average 41.3 mg/L (19.8 standard deviation), with the maximum value of 114 mg/L in October, obviously higher than that in river water (10.7 mg/L on averagely, 2.0 standard deviation).
The saturation index of calcite (SIc) ranged from −0.38 to 0.13 for groundwater in May and from −0.40 to 0.05 for groundwater in October (Figure 3a). According to the criterion of ±0.1, most groundwater samples were unsaturated with calcite. The saturation index of dolomite (SId) ranged from −1.26 to −0.06 for groundwater in May and from −1.34 to −0.59 for groundwater in October (Figure 3b). According to the criterion of ±0.5 [30], groundwater samples were generally unsaturated with dolomite. The saturation index of gypsum ranged from −2.15 to −1.23 for groundwater samples in May and from −1.86 to −1.33 for groundwater samples in October, indicating that the groundwater samples were unsaturated with gypsum (Figure 3c). The SIc and SId for river water were less than that of groundwater (−0.48 and −1.33, respectively), while the SIg for river water was similar to that of groundwater with a value of −1.43.
The groundwater samples were generally located between the 1:2 and 1:4 line of (Ca-SO4)/HCO3 (Figure 3d), reflecting the dissolution of calcite and dolomite. The same result was also obtained from the ratio of Mg/Ca which ranged from 0.11 to 0.73, as the Mg/Ca ratio was 0.03 and 1 for calcite and dolomite, respectively (Figure 3e). The ratio of Na/Ca in both calcite and dolomite is low, 0.005 and 0.01, respectively. Thus, the high ratio of Na/Ca in groundwater samples indicated mixing with river water, as the water from Yufu River had a higher Na/Ca, around 0.7(Figure 3f).
Based on hydrogeochemical data, the water type of most groundwater samples was Ca-HCO3-SO4, and a minority were Ca-Mg-HCO3-SO4, Ca-Na-HCO3-SO4, Ca-HCO3-Cl-SO4, Ca-HCO3-SO4-Cl, Ca-Na-Mg-HCO3, Ca -Mg-HCO3 and Ca-Na-HCO3. The water type of river water was Ca-Mg-SO4 (Figure 4).

4.2. Stable Isotopes

Among the collected water samples, river water had the most enriched δ18O and δ2H values, ranging from −6.11‰ to −5.9‰ and from −46.8‰ to −45.9‰, respectively. On the other hand, river water had the lowest d-excess (between 1.26‰ and 2.09‰). River water samples plotted below the Global Meteoric Water Line (GMWL) (δ2H = 8δ18O + 10 [31]), indicating that the river water had undergone evaporative enrichment.
For groundwater in May, the δ18O values ranged from −9.04‰ to −6.89‰ with an average of 8.3‰ and a standard deviation of 0.58. The δ2H values ranged from −62.8‰ to −46.6‰, with an average of −58.75‰ and a standard deviation of 3.93. The δ2H-δ18O relationship was δ2H = 6.39δ18O − 5.74 (R² = 0.88). The d-excess showed a range of 3.28‰ to 11.24‰.
For groundwater in October, the δ18O values ranged from −9.01‰ to −6.00‰, with an average of 8.1‰ and a standard deviation of 0.80. The δ2H values ranged from −63.4‰ to −48.5‰ with an average of −58.4‰ and a standard deviation of 4.05. The δ2H-δ18O relationship was δ2H = 4.93δ18O − 18.40 (R² = 0.96). The d-excess showed a range of −0.44‰ to 9.94‰.
Two groups of groundwater were identified based on stable isotope values (Figure 5). Samples in group 1(G1) had low stable isotopic composition and were located around the GMWL. The stable isotope signatures of these samples showed little or no evaporative enrichment, which indicated these samples were mainly recharged by local precipitation. Samples in group 2 (G2) had elevated stable isotopic values, and were located below the GMWL with deviation direction toward to the river water samples. These signatures suggested that these samples were mixed with infiltrated river water, because the precipitation recharge did not show evaporative enrichment prior to recharging the aquifer. The sample collected in Longdong (JN20) was the only sample located above the global meteoric water line GMWL, reflecting the heterogeneous water flow in karst aquifer.

4.3. 222Rn

The average 222Rn concentration in river water was 1.22 Bq/L, while the 222Rn concentration in groundwater showed considerable variability, from 0.01 to 40.26 Bq/L in May and from 0 to 40.11 Bq/L in October. The groundwater samples were divided into three groups based on the relationship between 222Rn and TDS, indicating different flow paths with different dynamic conditions. The groundwater samples in group 1 had a lower 222Rn concentration, which was positively correlated with TDS. The groundwater samples in group 2 had higher 222Rn concentration, which was also positively correlated with TDS. The two groundwater samples in group 3 had the highest 222Rn (Figure 6).

4.4. Hierarchical Cluster Analysis

Three groups (C1, C2, and C3) were obtained by hierarchical cluster analysis (HCA) for groundwater samples in May using a Euclidean distance of 15 (Figure 7). Table 3 presents the average values of chemical and isotope data in each group. The group C1 mainly consisted of the groundwater samples from the water source area (JN21/JN22/JN23) and karst springs (JN24/JN25/JN26), with lowest average TDS values of 431 mg/L. The groundwater samples from group C2 had the lowest HCO3, highest SO42−, and most enriched stable isotope. The groundwater samples in group C3 had the highest TDS of 610 mg/L with highest concentration of HCO3, SO42−, Ca2+, and 222Rn, and depleted stable isotope compositions.
Similarly, three groups (G1, G2, and G3) were obtained for groundwater samples in October using a Euclidean distance of 10. Table 4 presents the average values of chemical and isotope data in each group. The groundwater samples in group G1 had the lowest average TDS value of 365 mg/L. The groundwater samples in group G2 had the lowest HCO3, NO3, highest SO42−, Na+, least SIc, SId and Sig, and most enriched stable isotope. The groundwater samples in group G3 had the highest average TDS of 508 mg/L.

5. Discussion

5.1. The Temporal Effect of the Recharge

32 groundwater samples from 16 sites were collected in both May and October. Based on the hydrogeochemical, stable isotope, and 222Rn characteristics of these samples, there were four kinds of temporal evaluation mechanisms for karst groundwater (Figure 8).
Type I: 3 sites located far away the streambed of Yufu River had similar TDS in May and October, did not show seasonal variation. Sample 20 was collected in Longdong, and had obviously enriched δ18O and δ2H in May, which may be due to the dependent water flow in May caused by the heterogeneity of the karst aquifer.
Type II: 9 sites from the reginal scope had higher TDS in May than in October, and similar δ18O and δ2H composition without showing seasonal variation.
Type III: 3 sites located very close to the streambed of Yufu River also had higher TDS in May than in October, and the seasonal differences were more obvious than that in type II. In addition, these sites had higher δ18O and δ2H values than other sites, and the stable isotope values were more enriched in October than in May. The TDS and stable isotope signatures proved the temporal variations were mainly caused by the recharge from the streambed of Yufu River. The effect of river water recharge was more obvious in October than in May for karst groundwater in this study; this was consistent with the flow conditions of the fracture karst aquifers in North China.
Type IV: 1 site from the east of the study area had the highest TDS in May, which may be from mixing with other water bodies, such as pollution water.
The 222Rn did not show regular variation, which may due to the complex seasonal dynamic changes of the water flow environment. The hydrogeochemistry and stable isotope signatures reveal that the river water recharge changes the temporal variation of karst groundwater. The temporal variation revealed there was lag time between river water recharge event and the responses from karst groundwater.

5.2. The Spatial Effect of the Recharge

The spatial effect of the recharge from the streambed could be revealed by the distribution of the groundwater samples. Figure 9 shows the distribution of groundwater samples based on the results from the hierarchical cluster analysis (HCA). According to Figure 9, the Jinan karst system can be divided into four zones.
Zone I is located close to the streambed of Yufu River. Samples in this zone are recharged by river water with lower HCO3, Sic and SId, higher SO4, and enriched δ18O values compared with the groundwater in other zones.
Zone II is located in the middle area between the Yufu River and the karst springs in city center. Groundwater in this zone has both the highest TDS (groundwater samples in C3/G3) and the lowest TDS (groundwater samples in C1/G1). The different TDS concentration reflects the different recharge area, the higher one represents the water from the indirect recharge zone with a longer flow path, eand the lower one represents the water from the direct recharge with a short flow path.
Zone Ⅲ is located in the discharge area. Karst springs in this zone respond to the river water recharge immediately in the aspect of water level dynamic [27]. The average flow rate of karst groundwater is 100 m/day in Jinan by the tracer test. The distance between the springs in city center and the permeable section of the Yufu River is around 20 km; the shortest response time is 200 day, without consideration of the tortuosity factor. Therefore, the immediate responses of spring water level to river water infiltration are the results of pressure waves, showing the hydraulic connection between the streambed and karst springs. The responses of hydrogeochemistry and isotope are the responses to the actual recharge with time tag [32]. Therefore, in respect to hydrogeochemistry and isotope, these springs belong to C1 in May, indicating they do not receive the river water recharge, of mixing with river water, and belong to G2 in October, reflecting the recharge from river water.
Zone IV is located in the northwest of Jinan city with several water resource sites. Groundwater in this zone has better quality with less TDS.

5.3. Hydrogeochemical Processes Along the Flow Paths

Based on the signatures of groundwater samples in different spatial zones, the main effect area of the river water recharge is around the streambed. The potential flow path of the river water recharge was identified by the counter line of δ18O in groundwater, which was from Dongkema to Manzi to Shaoer, through Shanyao toward springs in city center (JN4 JN10 JN9 JN5 JN28/JN29 in May, and from JN35 JN30 JN42 JN33 JN53/JN54 in October). The hydrogeochemical processes along the flow path of river water recharge were complex and different in different zones (Figure 10).
At the beginning of the flow path (zone I), from Dongkema to Manzi, the SIc, Sid, and SIg decreased in May (JN4 to JN10), while it increased in October (JN35 to JN30). In addition, Cl and Na+ decreased and NO3 increased in both May and October. The variations were mainly the results of mixing with the less mineral river water.
At the middle of the flow path (zone II), from Manzi to Shanyao, the SIc and SId increased in May and October. The Na+ concentration did not show obvious variation, while the Cl and NO3 increased obviously. The variations of hydrogeochemistry were controlled by both river water mixing and natural water–rock interaction.
At the end of the flow path (zone III), from Shanyao to Baotu Spring, the SIc, Sid, and SIg decreased in both May and October (JN5 to JN28 in May and JN33 to JN53); NO3, Cl, and Na+ also decreased, reflecting the fact that the water in discharge area is a mixture of water from different recharge area.
The quantitative variations of mineral phases along the flow paths were calculated by inverse hydrogeochemical modeling. For the streambed of Yufu River to springs in city center, the hydrogeochemical processes were complex and different in May and October (Table 5). In May, calcite dissolved first and then precipitated along the flow path, gypsum precipitated first and then dissolved along the flow path, and dolomite generally precipitated along the flow path. In October, calcite and dolomite dissolved first and then precipitated along the flow path, and gypsum was generally in the condition of precipitation along the flow path.

5.4. Quantify the Proportion of River Water in Groundwater

A two component mixing model was used to estimate the proportion of river water ( ω ) in groundwater, defined as:
ω = c g c g c g c r × 100 %
where c g , c g , and c r are the δ18O values in the groundwater, groundwater without mixing with river water, and river water, respectively.
The δ18O value of river water endmember was −6.0‰, which was the average value of the δ18O values in river water samples. The groundwater samples located around the GMWL were the groundwater endmember, with an average δ18O value of −8.6‰ both for May and October. Substituting the δ18O values of the two endmembers into the above equation, the results show that the river water proportion in groundwater ranged from 26.5% to 66.2% in May, and from 39.1% to 100% in October (Table 6).
Among these sites, the Longdong (JN20), Baiyunguan (JN18), and Kuangcun (JN51) sites could not receive the recharge from Yufu River based on the topographic and hydrogeological conditions. The enrichment of stable isotopes of these sites may be caused by other recharge sources or different flow paths. Dongkema (JN36) had a river water mixing ratio of 100% in October, which indicates that the streambed of Yufu River in Dongkema section had a strong infiltration capacity. In addition, from the river water proportion, the recharge effect was more obvious in October than in May, which could be proven by: (1) the groundwater samples from four sites, Dongkema (JN4/JN39), Zhongqi (JN6/JN31), Luoer (JN7/8/JN32), and Manzi (JN10/JN30), had higher river water proportions in October than that in May; (2) the Shigu (JN3/37) site did not show mixing with river water in May, while it was recharged by river water in October.
Chloride is typically considered to be conservative, and was used to calculate the mixing proportion of river water in groundwater [6]. However, the similar concentrations in river water (38.4 mg/L) and groundwater (averagely 49.6 mg/L in May and 36.2 mg/L in October) precluded its effectiveness as a tracer to quantify the mixing between river water and groundwater in the Jinan karst system. The efficiency of tracers in quantifying the interaction between river water and groundwater is dependent on the differences in endmember values [4]. The exception was the stable isotope 222Rn, which showed obvious differences in river water and groundwater. However, it was not effective in determining the mixing fractions of river water and groundwater in this study, because 222Rn does not necessarily move conservatively with water.

6. Conclusions

The effects of recharge from streambed of Yufu River on Jinan karst system are evaluated using hydrogeochemistry and stable isotope. The infiltration recharge from streambed changes the chemical and isotopic characteristics of karst groundwater with enrichment of δ18O value, decrease of HCO3 concentration and NO3 concentration, increase of SO42− concentration. Jinan karst system could be divided into four zone based on the hierarchical cluster analysis. The effect area of river water recharge is mainly around the streambed of Yufu River. The proportion of river water in groundwater samples around the streambed is calculated by binary mixing model based on δ18O. The flow path of river water recharge is delineated from the isoline of δ18O. Along the flow path, the quantitative variations of mineral phases are obtained from inverse chemical modeling.

Author Contributions

Y.G., D.Q., J.S. and L.L. did the preliminary studies, designed and performed the experiments, conducted field studies, analyzed the data, and wrote the paper. F.L and J.H help the yield investigations.

Funding

This research was funded by National Natural Sciences Foundation of China, grant No. 41172215.

Acknowledgments

This study was funded by the National Natural Sciences Foundation (41172215), the project of pilot technology support scheme on aquatic ecological civilization of Water Resources Department of Shandong Province and Shandon Province Financial Bureau.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location, sampling sites and hydrogeological map of Jinan karst system. (a) Location of the study area, (b) Topographic map of the study area. (c) Hydrogeological map of the Jinan karst system. (1, 11: Archaean metamorphic rocks; 2, 12: Cambrian limestone; 3, 13: Ordovician limestone; 4, 14: Quaternary sediments; 5, 15: magmatic rocks; 6: the range of study area; 7: fault; 8. river; 9, 16: spring; 10: sampling sites. A-A’ in Figure c: geological cross section).
Figure 1. Location, sampling sites and hydrogeological map of Jinan karst system. (a) Location of the study area, (b) Topographic map of the study area. (c) Hydrogeological map of the Jinan karst system. (1, 11: Archaean metamorphic rocks; 2, 12: Cambrian limestone; 3, 13: Ordovician limestone; 4, 14: Quaternary sediments; 5, 15: magmatic rocks; 6: the range of study area; 7: fault; 8. river; 9, 16: spring; 10: sampling sites. A-A’ in Figure c: geological cross section).
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Figure 2. The plot of daily water levels of Baotu Spring and Heihu Spring, monthly precipitation, and monthly water flow of Yufu River in 2015.
Figure 2. The plot of daily water levels of Baotu Spring and Heihu Spring, monthly precipitation, and monthly water flow of Yufu River in 2015.
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Figure 3. Relationship between TDS(Total dissolved solids ) and saturation index of calcite (SIc) (a), TDS and saturation index of dolomite (SId) (b), TDS and saturation index of gypsum (SIg) (c), HCO3 and (Ca-SO4) (d), HCO3 and Mg/Ca (e), and HCO3 and Na/Ca (f) in groundwater and river water. (In this figure the following figures, the GW-5 represents the groundwater collected in May and GW-10 represents the groundwater collected in October 2015.)
Figure 3. Relationship between TDS(Total dissolved solids ) and saturation index of calcite (SIc) (a), TDS and saturation index of dolomite (SId) (b), TDS and saturation index of gypsum (SIg) (c), HCO3 and (Ca-SO4) (d), HCO3 and Mg/Ca (e), and HCO3 and Na/Ca (f) in groundwater and river water. (In this figure the following figures, the GW-5 represents the groundwater collected in May and GW-10 represents the groundwater collected in October 2015.)
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Figure 4. Piper diagram of water samples in Jinan.
Figure 4. Piper diagram of water samples in Jinan.
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Figure 5. Plot δ18O versus δ2H for river water and karst groundwater in the study area (GMWL = global meteoric water line; LMWL = local meteoric water line).
Figure 5. Plot δ18O versus δ2H for river water and karst groundwater in the study area (GMWL = global meteoric water line; LMWL = local meteoric water line).
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Figure 6. Relationship between 222Rn with TDS for groundwater in May (left) and October (right).
Figure 6. Relationship between 222Rn with TDS for groundwater in May (left) and October (right).
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Figure 7. The HCA dendrogram of groundwater samples in May (left) and in October (right).
Figure 7. The HCA dendrogram of groundwater samples in May (left) and in October (right).
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Figure 8. The relationships between TDS (a), δ2H (b), δ18O (c), and 222Rn (d) for groundwater between May and October.
Figure 8. The relationships between TDS (a), δ2H (b), δ18O (c), and 222Rn (d) for groundwater between May and October.
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Figure 9. Distribution of groundwater samples based on the cluster analysis in May (left) and October (right) (black line is the isoline based on the δ18O values of groundwater samples; the C1, C2, and C2 in May and G1, G2, and G3 in October are the clusters from the hierarchical cluster analysis).
Figure 9. Distribution of groundwater samples based on the cluster analysis in May (left) and October (right) (black line is the isoline based on the δ18O values of groundwater samples; the C1, C2, and C2 in May and G1, G2, and G3 in October are the clusters from the hierarchical cluster analysis).
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Figure 10. The variation of SIc, SId, SIg, NO3, Cl, and Na along the flow path of river water recharge (zone I, zone II, and zone III were obtained from the cluster analysis from Figure 9). (a) the variation of SIc, SId, SIg along the flow path in May, (b) the variation of SIc, SId, SIg along the flow path in October, (c) the variation of NO3, Cl, and Na along the flow path in May, (d) the variation of NO3, Cl, and Na along the flow path in October.
Figure 10. The variation of SIc, SId, SIg, NO3, Cl, and Na along the flow path of river water recharge (zone I, zone II, and zone III were obtained from the cluster analysis from Figure 9). (a) the variation of SIc, SId, SIg along the flow path in May, (b) the variation of SIc, SId, SIg along the flow path in October, (c) the variation of NO3, Cl, and Na along the flow path in May, (d) the variation of NO3, Cl, and Na along the flow path in October.
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Table 1. List of the field and analytical data as well as the results of some hydrochemical calculations (saturation indexes and water type).
Table 1. List of the field and analytical data as well as the results of some hydrochemical calculations (saturation indexes and water type).
SamplecodeMonth TemppHHCO3SO4NO3ClCaMgNaKSIcSIdSIgWater Type
°Cmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/Lmg/L
JN1May18.1 311.3111.281.732.597.19.211.71.5−0.07−0.91−1.48Ca-HCO3-SO4
JN29October15.37.71346.190.271.527.4126.78.38.50.70.05−0.88−1.47Ca-HCO3-SO4
JN2May17.4 301.293.379.437.8126.48.710.80.40.02−0.89−1.46Ca-HCO3-SO4
JN38October16.87.36300.378.270.633118.29.2110.4−0.01−0.90−1.55Ca-HCO3-SO4
JN3May19.1 255.7110.653.626.856.314.417.63.2−0.36−1.04−1.67Ca-HCO3-SO4
JN37October16.37.45250.49243.121.889.312.713.41.3−0.20−1.03−1.56Ca-HCO3-SO4
JN4May19.4 208.3162.719.667.487.614.351.83.6−0.27−1.06−1.36Ca-Na-HCO3-SO4
JN35October14.97.7190.4136.79.24877.814.228.12.1−0.40−1.34−1.44Ca-HCO3-SO4
JN36October16.38.02197.6120.19.44374.513.425.52−0.38−1.28−1.51Ca-HCO3-SO4
JN5May19.2 326.8107.382.779.5103.417.921.20.4−0.02−0.53−1.50Ca-HCO3-Cl-SO4
JN33October15.97.62322.784.766.560.9117.216.819.50.4−0.01−0.64−1.54Ca-HCO3
JN6May17.8 248.5130.138.934.1102.815.515.20.9−0.14−0.86−1.39Ca-HCO3-SO4
JN31October17.67.66242.699.93224.78313.813.11.1−0.23−0.99−1.56Ca-HCO3-SO4
JN7May17.9 221.8130.325.863.999.115.331.92.3−0.21−0.97−1.4Ca-HCO3-SO4-Cl
JN8May18.4 213.4132.427.172.595.715.1401.4−0.23−1.01−1.41Ca-Na-HCO3-SO4
JN32October17.77.59226.1101.323.637.681.513.819.21.3−0.26−1.05−1.55Ca-HCO3-SO4
JN9May18.4 267.591.741.837.5106.713.816.31.3−0.08−0.79−1.52Ca-HCO3-SO4
JN42October16.17.73278.780.933.828.592.413.312.80.4−0.14−0.91−1.61Ca-HCO3-SO4
JN10May14.7 251.1146.848.629.169.112.218.32.7−0.35−1.26−1.47Ca-HCO3-SO4
JN30October16.47.48253.7107.847.323.292.911.714.42−0.19−1.05−1.49Ca-HCO3-SO4
JN11May19.1 188.814081.449.7138.519.719.71.3−0.14−0.85−1.28Ca-HCO3-SO4
JN39October16.57.62287.7118.481.649.4120.61916.40.5−0.05−0.67−1.40Ca-HCO3-SO4
JN12May17.4 310.7149.966.152.715115.127.31.20.08−0.06−1.23Ca-HCO3-SO4
JN13May17.1 345.1136.1103.258.215614.924.41.30.13−0.53−1.27Ca-HCO3-SO4
JN14May17.6 303.8138.379.7140.4157.221.728.21.40.08−0.45−1.27Ca-HCO3-Cl-SO4
JN40October15.87.42299.2102.266.940.3118.313.618.70.3−0.04−0.80−1.45Ca-HCO3-SO4
JN15May18.8 295.262.841.63199.314.814.91.4−0.05−0.67−1.70Ca-HCO3
JN41October19.17.48294.556.736.524.787.114.611.60.5−0.10−0.70−1.78Ca-HCO3
JN19May18.1 454.384.2113.7118.5154.517.5281.70.25−0.19−1.49Ca-HCO3-Cl
JN51October147.62277106.517.217.3105.76.99.10.5−0.12−1.24−1.42Ca-HCO3-SO4
JN20May21.6 274.1136.835.424.697.819.54.50.5−0.07−0.54−1.40Ca-Mg-HCO3-SO4
JN47October147.92327.4122.23214.7103.325.15.20.5−0.08−0.59−1.42Ca-Mg-HCO3-SO4
JN16May17 342.390.255.537.744.427.980.73.5−0.38−0.73−1.90Na-Mg-Ca-HCO3-SO4
JN17May20.7 322.6109.240.152.6113.62125.61.50.04−0.36−1.45Ca-HCO3-SO4
JN18May14.8 294.4116.33530.6111.16.114.51.4−0.08−1.21−1.39Ca-HCO3-SO4
JN21May16.6 252.029.01.024.472.731.370.01.8−0.27−0.67−2.15Ca-Na-Mg-HCO3
JN22May18.8 221.533.816.418.962.611.712.61.5−0.33−1.12−2.08Ca-HCO3
JN23May18.8 234.033.215.519.6150.930.839.61.10.02−0.38−1.85Ca-Na-Mg-HCO3
JN27October16.77.67262.157.631.11568.912.76.80.4−0.27−1.03−1.84Ca-HCO3-SO4
JN28October16.57.44234.4128.572.148.1114.424.816.10.4−0.16−0.75−1.38Ca-Mg-HCO3-SO4
JN34October15.47.54339.145.716.824.586.412.7180.3−0.09−0.79−1.86Ca-HCO3
JN43October16.27.62212.4116.31936.581.914.119.42−0.31−1.16−1.49Ca-HCO3-SO4
JN44October15.17.8259.47949.77.797.56.77.80.9−0.16−1.28−1.58Ca-HCO3-SO4
JN45October16.37.83204.7149.421.62996.610.514.60.8−0.27−1.28−1.33Ca-HCO3-SO4
JN46October17.77.59231.69420.839.179.714.718.71.3−0.26−1.01−1.59Ca-HCO3-SO4
JN48October16.57.78280.151.82712.275.515.25.50.5−0.20−0.87−1.85Ca-Mg-HCO3
JN49October17.17.9126851.525.812.87614.85.10.5−0.21−0.89−1.85Ca-Mg-HCO3
JN50October17.17.53265.175.745.38.990.112.15.20.5−0.16−0.95−1.64Ca-HCO3-SO4
JN52October17.97.6270.576.830.914.881.817.170.5−0.18−0.78−1.67Ca-HCO3-SO4
JN25May18.6 273.98642.744.6101.315.619.30.8−0.09−0.73−1.57Ca-HCO3-SO4
JN53October17.57.48271.172.734.535.778.413.316.30.9−0.20−0.93−1.71Ca-Na-HCO3-SO4
JN26May18.5 297.2107.750.457.5114.117.327.91.8−0.02−0.60−1.45Ca-HCO3-SO4
JN55October17.57.4528584.239.745.989.21521.70.9−0.14−0.80−162Ca-HCO3-SO4
JN24May19.2 279.368.435.547.277.121.217.62.6−0.18−0.64−1.76Ca-Mg-HCO3
JN54October18.17.56333.272.72238.860.813.223.81.1−0.21−0.83−1.81Ca-HCO3-SO4
JN56October187.64242.365.825.53266.913.719.91.2−0.30−1.03−1.80Ca-HCO3-SO4
JN57May22.2 144.4133.710.733.351.715.119.63.2−0.58−1.39−1.60Ca-Mg-SO4-HCO3
JN58May24.2 81.3134.86.643.5150.520.9312.3−0.37−1.26−1.25Ca-Mg-SO4
Table 2. Isotope data of the water samples in Jinan.
Table 2. Isotope data of the water samples in Jinan.
SiteCodeMonth Rnδ18O δ2Hd-Excess
Bq/L
Groundwater
JN1May7.55−8.93−62.189.26
JN29October10.78−8.73−61.428.42
JN2May29.87−8.97−62.639.13
JN38October26.85−8.69−61.647.88
JN3May6.54−8.13−58.246.8
JN37October20.53−7.48−55.774.07
JN4May12−6.89−51.843.28
JN35October31.53−6.29−50.040.28
JN36October13.69−6.01−48.52−0.44
JN5May12.97−8.79−61.68.72
JN33October14.9−8.48−61.436.41
JN6May27.44−7.91−57.096.19
JN31October19.38−7.59−55.914.81
JN7May7.33−7.68−56.095.35
JN8May6.71−7.57−55.195.37
JN32October11.65−7.35−53.824.98
JN9May7.24−8.49−60.357.57
JN42October12.01−8.41−60.067.22
JN10May6.63−7.38−53.016.03
JN30October9.29−7.21−53.194.49
JN11May10.29−9.04−62.349.98
JN39October12.9−8.68−61.298.15
JN12May34.3−8.82−61.39.26
JN13May16.85−8.79−61.139.19
JN14May24.42−8.46−60.427.26
JN40October0−8.77−61.738.43
JN15May21.31−8.48−60.577.27
JN41October13.95−8.55−61.177.23
JN19October20.87−8.35−57.519.29
JN51May22.77−7.57−55.075.49
JN20May1.34−7.23−46.611.24
JN47May3.31−8.17−56.748.62
JN16May33.86−8.39−59.337.79
JN17October26.98−8.18−57.987.46
JN18May40.26−7.62−53.767.2
JN21May0.01−8.73−62.567.28
JN22May29.83−8.63−61.847.2
JN23May17.9−8.79−62.757.57
JN27October11.97−8.89−61.99.22
JN28October18.79−8.58−59.968.68
JN34October40.11−8.73−63.46.44
JN43October19.46−6.83−51.752.89
JN44October12.17−8.72−60.39.46
JN45October28.1−7.23−53.644.2
JN46October17.1−7.31−54.484
JN48October11.91−9.01−62.779.31
JN49October11.33−8.99−62.998.93
JN50October23.72−8.82−60.629.94
JN52October38.33−8.82−61.569
JN25May10.45−8.3−59.456.95
JN53October6.85−8.48−60.687.16
JN26May5.25−8.33−59.237.41
JN55October4.3−8.35−59.77.1
JN24May2.51−8.89−62.58.62
JN54October5.25−8.11−59.255.63
JN56October2.65−8.2−59.625.98
River water
JN57May1.46−6.11−46.792.09
JN58May0.98−5.9−45.941.26
Table 3. The statistical results of clusters for groundwater samples from May.
Table 3. The statistical results of clusters for groundwater samples from May.
HCO3SO4NO3ClCaMgNaKTDSSIcSIdSIgRnOH
mg/L Bq/L
C12797134379421322431−0.1−0.60−1.6015.53−8.52−60.66
C224613336449014242466−0.16−0.82−1.5313.53−7.55−53.98
C3318120867113616211610−0.15−0.92−1.5019.64−8.77−61.14
Table 4. The statistical results of clusters for groundwater samples from October.
Table 4. The statistical results of clusters for groundwater samples from October.
HCO3SO4NO3ClCaMgNaKTDSSIcSIdSIgRnOH
mg/L Bq/L
G12856532198213101365−0.2−0.9−1.718.1−8.7−61.4
G223710426338413181399−2.6−1.1−13.915.9−7.4−54.8
G33031036639117171405080.0−0.7−1.512.5−8.6−60.6
Table 5. The simulation results for the inverse hydrogeochemical model.
Table 5. The simulation results for the inverse hydrogeochemical model.
No.Flow PathTransferring Molar Concentration of Mineral Phase (mmol/L)
CalciteDolomiteGypsumHaliteCO2
MayJN4 JN100.24−0.09−0.53−1.331.08
JN10 JN90.57−0.03−0.120.03−0.20
JN9 JN50.100.19−0.060.530.93
JN5 JN28−0.20−0.110.00−0.28−0.83
JN5 JN29−0.15−0.040.230.110.23
OctoberJN35 JN300.590.07−0.07−0.480.99
JN30 JN420.070.07−0.28−0.010.14
JN42 JN330.060.140.040.460.64
JN33 JN55−0.24−0.09−0.28−0.31−0.55
JN33 JN56−0.15−0.07−0.05−0.07−0.34
Notes: Positive means dissolution and negative indicates precipitation.
Table 6. The proportion of river water in groundwater samples in May and October.
Table 6. The proportion of river water in groundwater samples in May and October.
MayOctober
CodeSiteδ18O (‰) ω   ( % ) CodeSiteδ18O (‰) ω   ( % )
JN4Dongkema−6.8966.2JN36Dongkema−6.01100
JN10Manzi−7.3847.2JN30Manzi−7.2153.8
JN7/JN8Luoer−7.6237.8JN32Luoer−7.3548.4
JN6Zhongqi−7.9126.5JN31Zhongqi−7.5939.1
JN20Longdong−7.2353.0----
JN18Baiyunguan−7.6238.0----
----JN43CuiMa−6.8368.3
----JN45Xikema−7.2353.2
----JN46Yinjialin−7.3150.1
JN3Shigu−8.130JN37Shigu−7.4843.6
----JN51Kuangcu−7.5739.9

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