Contrastive mechanisms of lacustrine groundwater discharge and associated pollutant fluxes into two typical inland lakes in Inner Mongolia, Northwest China.

Abstract

Highlights

Mechanisms of LGD in two inland lakes were compared using multiple tracers.
LGD and associated with pollutant fluxes were estimated using ²²²Rn models.
LGD was controlled by hydrogeology, lakebed permeability and human activities.
Results provide an insight for water resources management in Yellow River Basin.

Lacustrine groundwater discharge (LGD) plays an important role in water resources management. Previous studies have focused on LGD process in a single lake, but the differences in LGD process within the same region have not been thoroughly investigated. In this study, multiple tracers (hydrochemistry, 8D, 8¹⁸O and ²²²Rn) were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongolia, Northwest China. The hydrochemical types showed a trend from groundwater to lake water, indicating a hydraulic connection between them. In addition, the 8D and 8¹⁸O values of sediment pore water were between the groundwater and lake water, indicating the LGD processes. The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake, which were 2.79 mm/day and 3.02 mm/day, respectively. The total nitrogen (TN), total phosphorus (TP), and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52 %, 96.59 %, and 95.84 % of the total inputs, respectively. In contrast, TN, TP and fluoride inputs in Ulansuhai Lake were 53.56 %, 40.98 %, and 36.25 %, respectively. This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake. By comparison, the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions, lakebed permeability and human activities. This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.

Graphical abstract

1. Introduction

Groundwater plays a crucial role in maintaining lake water balance and ecosystem sustainability around the world, especially in arid and semi-arid regions (Javadzadeh et al., 2020; Olid et al., 2022; Su et al., 2023). For this reason, lakes are often thought of as a confluence of groundwater. Lacustrine groundwater discharge (LGD) is a vital land-lake interaction and hydrological cycle process. It controls the hydrological characteristics of the lake (Kazmierczak et al., 2020; Sun et al., 2022). LGD is closely related to the change in lake water quantity and water quality because it affects the water budget of lakes on both temporal and spatial scales (Meinikmann et al., 2015). Therefore, it is important to understand LGD for the management of water resources and the protection of the eco-environment.

Groundwater is mainly discharged to lakes by crossing lakebed (Petermann., 2018; Werner et al., 2020). For effective evaluation of LGD, several methods have been developed: (i) Piezometer direct measurement method (Kazmierczak et al., 2016; Solomon et al., 2020) is mainly suitable for measuring vertical seepage at specific points. However, it does not apply to large-scale areas with high heterogeneity in aquifers. (ii) Thermal tracing (Liao et al., 2022; Meinikmann et al., 2013) utilizes the high specific heat capacity of water to monitor localized groundwater discharge, though it may suffer from temperature signals lagging behind groundwater movement. Therefore, it has limitations in capturing real-time dynamics of groundwater discharge. (iii) Numerical simulation (Lu et al., 2021; Xu et al., 2021) provides efficient and accurate numerical solutions for different research needs. However, this method requires high precision in aquifer parameters and simulation processes, as well as extensive field data to support the accuracy of the simulation results. (1x) Environmental element tracing (Sun et al., 2021) reveals the temporal and spatial sources of surface water components by comparing specific elements in groundwater and surface water. This method is commonly used to study the hydraulic connection and material transfer between surface water and groundwater, including water chemistry (Yang et al., 2021), isotopes (Liao et al., 2020a; Qu et al., 2023; Ren et al., 2024), and basic physicochemical indicators. Among them, hydrogen (H) and oxygen (O) isotopes were inherent tracers of water molecules and transport in aquifers. They were associated with climate differences, water mixing, and evaporation (Balagizi et al., 2022; Mao et al., 2021). Therefore, hydrogen and oxygen isotopes are regarded as effective tracer tools for determining LGD (Vystavna et al., 2021). (v) Water (Salt) balance method (Liao et al., 2020b) is a quantitative analysis method that estimates and analyzes the groundwater and surface water exchange by measuring and evaluating the water (salt) input and output flow rates within a specific period, constructing corresponding water (salt) balance equations. In addition, the radioactive ²²²Rn isotope has been extensively used in the study of LGD (Luo et al., 2018; Pall et al., 2023). Radon (T_1/₂ = 3.82 days) is a radioactive isotope produced by radium decay. It is highly enriched in groundwater and exhibits chemical and biological inertness, making it resistant to adsorption or chemical reactions with suspended matter, dissolved substances, or aquifer rocks. These properties make it an ideal geochemical tracer (Kluge et al., 2007). Radium-bearing minerals in aquifers continuously generate and accumulate radon. However, in surface water, low radium concentrations suppress radon production, and further reduction occurs due to radon decay. Additionally, radon losses in surface water are primarily driven by atmospheric diffusion, a process known as radon atmospheric escape. As a result, surface water radon concentrations are often significantly lower than those in groundwater, typically differing by a factor of 100 to 1000. This stark contrast enables the identification of groundwater discharge zones in surface water systems (Jiang et al., 2023). Notably, the ²²²Rn mass balance model effectively calculates LGD fluxes (Liao et al., 2022; Su et al., 2023; Sun et al., 2022). Overall, combining multiple methods provides a robust approach to identifying and quantifying LGD.

LGD is influenced by multiple factors, including climate change, hydrogeological conditions, and anthropogenic activities (Hare et al., 2017; Jiang et al., 2023). Among them, hydrogeological conditions such as aquifer water richness, hydraulic gradient and lake sediment lithology are key factors of LGD (Wang et al., 2022). Higher water-rich aquifers and larger hydraulic gradients contribute to LGD (Ren et al., 2024). The water richness and hydraulic gradient of aquifers are related to precipitation, evaporation and land use type of catchment area (Ammar et al., 2017; Jesiya et al., 2021). Meanwhile, the larger the sediment porosity, the better the permeability of the lakebed. It can provide favorable conditions for groundwater discharge (Wang et al., 2022). In addition, lakebed permeability is controlled by preferential channels. For example, cracks caused by plant root growth and other factors in viscous soil can become preferential channels for LGD (Hare et al., 2017). As a result, even lakes within the same region or watershed may have differences in LGD. Most previous studies on LGD have focused on single lakes (Saleem et al., 2017; Shaw et al., 2013; Sun et al., 2021). However, comparisons of LGD in different lakes within the same basin are ignored, resulting in an incomplete understanding of the factors influencing LGD.

In this study, the Daihai and Ulansuhai Lake in Inner Mongolia Reaches of the Yellow River Basin (IMR-YRB) were considered as the research objects. Ulansuhai Lake is a shallow opened lake (oxbow Lake; Hou et al., 2021), while Daihai Lake is a shallow closed lake (tectonic lake; Ren et al., 2022a). Although both belong to the same river basin and groundwater is an important source of lake water, there are significant differences in hydrogeology, habitat and human activities between them (Zhang, 2020). These provide an ideal condition for studying the LGD in different types of lakes within the same basin. The main objectives of this study are to: (1) reveal the spatial distribution characteristics of hydrochemistry, hydrogen and oxygen isotopes and radon isotopes of different water bodies in Ulansuhai and Daihai Lake; (2) identify and quantify LGD and associated pollutant fluxes; and (3) investigate controlling factors and mechanism of LGD.

2. Materials and methods

2.1. Sampling and testing

Water sample points and sediment pore water sample points were established in the Daihai Lake (DL) basin and Ulansuhai Lake (UL) basin, taking into account the hydrological conditions of the study area, the representativeness and typicality of sampling points, water flow directions, and lake morphology (Appendix A Text S1). In this study, a total of 28 sampling sites were set up in the DL basin, including 13 groundwater sites around the lake, 6 lake water sites, 6 sediment pore water sites and 3 river water sites (Appendix A Fig. S1). A total of 45 sampling sites were set up in the UL basin, including 20 groundwater sites around the lake, 11 lake water sites, 11 sediment pore water sites and 3 river water sites (Appendix A Fig. S1). Water samples were collected in the April 2023. At each sampling point, the sampling bottle was repeatedly rinsed three times before sampling. 500 mL and 30 mL water samples were collected with a 1.5 L plexiglass sampler, which was respectively packed into polyethylene plastic bottles and brown glass bottles, sealed to avoid bubbles and shipped to the laboratory for analysis. The sampling depth of the river/canal water sample was 0.2 m below the water surface and the sampling depth of the lake water sample was 0.5 m below the water surface. In addition, 6 and 11 sites were respectively set up in DL and UL to take sediment columnar cores, which were collected by a gravity mud sampler and plexiglass tubes. The porosity of sediment (n) was estimated using the bulk density and particle density of dried sediment samples (Anovitz and Cole, 2015). The calculation formula for total soil porosity (%) was: n (%) = 100 × (1 – Bulk Density/Particle Density). A Rhizon soil solution collector with a pore size of 0.1 um and a filter membrane was used to collect the pore water.

The pH, TDS and salinity (Sal) were in-situ measured by a portable water quality meter (Multi 3630, Wissenschaftlich-Technische Werkstätten, Germany). The concentrations of Ca²⁺, Mg²⁺, Na⁺ and K⁺ were determined by inductively coupled plasma emission spectrometry. HCO₃^– was measured by the titration method; and Cl^–, SO₄^2–, and F^– were measured by ion chromatography. Total nitrogen (TN) and total phosphorus (TP) were determined by spectrophotometry (UV-2600 PC, Shimadzu, Japan). The hydrogen and oxygen isotopes (8D and 8¹⁸O) were determined by a liquid water isotope analyzer (TIWA-45EP, Asea Brown Boveri, Canada). The testing results of 8D and 8¹⁸O were based on the Vienna Mean Seawater (VSMOW) standard and the testing errors of 8D and 8¹⁸O were ±1.0 ‰ and ±0.2 ‰, respectively. Radon concentration (²²²Rn) was measured in situ using portable radon instrumentation (RAD-7, Durridge, America) with a precision of 10 %-25 %.

2.2. ²²²Rn mass balance model

Radon (²²²Rn) is a colorless, odorless, and radioactive inert gas and has a half-life of 3.83 days. ²²²Rn is produced by the natural radioisotope ²²⁶Ra and continues to decay to ²¹⁸Po. In nature, various radium-bearing minerals continuously produce ²²²Rn. Therefore, ²²²Rn is often enriched in groundwater. In contrast, ²²²Rn in surface water could easily escape into the air. Thus, radon concentrations in surface water are 2–3 orders of magnitude lower than those in groundwater. On this basis, the ²²²Rn mass balance model is as follows (Luo et al., 2016; Sun et al., 2021):(1) $\begin{matrix} \frac{\partial I_{Rn}}{\partial t} = C_{gw} \times J + F_{diff} + C_{Ra} \times λ \times V - F_{atm} - C_{w} \times V \times λ \end{matrix}$

where, I_Rn (Bq/day) is the ²²²Rn storage in the lake water; C_w and C_gw (Bq/m³) are ²²²Rn concentrations in lake water and groundwater, respectively; C_Ra (Bq/m³) is ²²⁶Ra concentration in lake water; J (m³/day) is the groundwater discharge flux to the lake; F_at_m and F_diff (Bq/day) are atmospheric loss and sediment diffusion of ²²²Rn, respectively; V (m³) is lake volume; ? (0.181 day^-1) is the decay constant of ²²²Rn.

The source terms of ²²²Rn in DL include groundwater discharge, sediment diffusion, ²²⁶Ra attenuation and river inflow. The sink terms include atmospheric escape and lake self-decay. The source terms of ²²²Rn in UL include groundwater discharge, sediment diffusion, ²²⁶Ra attenuation and river inflow. The sink terms include atmospheric escape, self-decay and lake outflow. The mass balance models are represented as follows:(2) $\begin{matrix} DL : \frac{\partial I_{Rn}}{\partial t} & = & C_{gw} \times J + F_{diff} + C_{Ra} \times λ \times V + C_{riv_i} \times V_{riv_i} \times λ - F_{atm} \\ - C_{w} \times V \times λ \end{matrix}$

$\begin{matrix} DL : \frac{\partial I_{Rn}}{\partial t} & = & C_{gw} \times J + F_{diff} + C_{Ra} \times λ \times V + C_{riv_i} \times V_{riv_i} \times λ - F_{atm} \\ - C_{w} \times V \times λ \end{matrix}$ (3) $\begin{matrix} UL : \frac{\partial I_{Rn}}{\partial t} & = & C_{gw} \times J + F_{diff} + C_{Ra} \times λ \times V + C_{riv_i} \times V_{riv_i} \times λ - F_{atm} \\ - C_{w} \times V \times λ - C_{riv_o} \times V_{riv_o} \times λ \end{matrix}$ $\begin{matrix} UL : \frac{\partial I_{Rn}}{\partial t} & = & C_{gw} \times J + F_{diff} + C_{Ra} \times λ \times V + C_{riv_i} \times V_{riv_i} \times λ - F_{atm} \\ - C_{w} \times V \times λ - C_{riv_o} \times V_{riv_o} \times λ \end{matrix}$

where, C_{riv_i} (Bq/m³) and C_{riv_o} (Bq/m³) are the concentration of ²²²Rn in and out of lake rivers. V_{riv_i} (m³/day) and V_{riv_o} (m³/day) are the discharge rates of rivers entering and exiting the lake. The specific calculation process for each source and sink term is described in Appendix A Text S2.

The equation of groundwater discharge rate is used as follows (Luo et al., 2016):(4) $ν = \frac{J}{M}$

where, v (m/day) is the groundwater discharge rate and M (m²) is the area of lake.

3. Results

3.1. Hydrochemical characteristics in water bodies

The statistical results of hydrochemical parameters were illustrated in Appendix A Table S1. The lake water samples (average pH: 9.3) in the DL basin were alkaline, while the groundwater samples (pH: 7.9) and river water samples (pH: 8.5) were weakly alkaline. Previous studies have found that internal pollution sources, such as the metabolism of pollutants in DL, had a promoting effect on the pH of the lake water (Ren et al., 2022b). The decline in lake water level due to the over-extraction of surrounding groundwater has resulted in the pH of the lake water being higher than that of the surrounding groundwater and river water (Sun et al., 2006). The average value of pH in groundwater, lake water, and river water in the UL basin was 8.3, 8.8 and 8.6, respectively, showing that the water environment of UL was weakly alkaline.

Freshwater is defined as having a TDS (total dissolved solids) concentration of <1000 mg/L, whereas saline water is defined as having a TDS concentration greater than 3000 mg/L (Liu et al., 2019). The lake water in DL had high TDS (26,750 mg/L) and salinity (Sal: 16.2 g/L) values, which were defined as saline water. In the DL basin, Ca²⁺ was the primary cation in groundwater and river water. Na⁺ was the dominant cation in lake water (Appendix A Table S1). Cl^– was the dominant anion in lake water, while HCO₃^– was the main anion in groundwater and river water. In the UL basin, Na⁺ was the dominant cation of the three water types (Appendix A Table S1). Cl^– was the primary anion in the lake water and river water. HCO₃^– and Cl^–were the dominant anions in the groundwater. As shown in Fig. 1, the hydrochemical type of groundwater and river water in DL basin was HCO₃-Ca·Mg type and that of lake water was SO₄·Cl-Na type. Although the hydrochemical type of lake water was different from that of groundwater and river water, there might be potential hydraulic connection, which were related to the lake bottom sediments (Wang et al., 2017; Zhu, 2014a). In contrast, the hydrochemical types of river water and lake water samples in UL basin were dominant by SO₄·Cl-Na type. The groundwater samples were characterized by multiple types, with overlap with river water and lake water samples, indicating a good hydraulic connection between surface water and groundwater in UL.

3.2. Hydrogen and oxygen isotopic characteristics

The relationship between 8D and 8¹⁸O can be used to determine the origin and hydraulic connection of water bodies (Li et al., 2022). Due to the lack of precipitation isotope data in the study area, the meteoritic water lines of adjacent Liangcheng (8D = 7.43 × 8 ¹⁸O + 2.9; Zhang, 2020) and Baotou (8 D= 6.36 × 8 ¹⁸O – 5.21; Zhu, 2014b) cities were served as references for DL and UL basins, respectively.

As shown in Fig 2a, the 8¹⁸O (-10.1 ‰) and 8 D (-75.5 ‰) values of groundwater in DL basin were closer to those of precipitation (8¹⁸O: –12.58 ‰, 8D: 90.57 ‰; Zhang, 2020), showing that the groundwater received recharge from precipitation. The 8¹⁸O and 8D values of groundwater in DL basin were more depleted relative to the river water (8¹⁸O: -8.1 ‰, 8D: -64.1 ‰; Appendix A Table S1) and lake water (8¹⁸O: 2.7 ‰, 8D: -5.8 ‰; Appendix A Table S1). In addition to being related to the intense evaporation of DL (annual average evaporation exceeding 1000 mm, annual average precipitation of <500 mm; Li et al., 2023), previous studies have shown that there was an exchange of infiltration between the groundwater and river water in DL Basin (Ren et al., 2024; Zhang et al., 2015), which further enriched the 8D and 8¹⁸O values of the groundwater.

Fig 2

Fig. 2. Relationship between 8D and 8¹⁸O values of different water bodies in Daihai Lake (a) and Ulansuhai Lake (b) (GMWL: global meteoritic water line; LMWL: local meteoritic water line; LEL: local evaporation line).

As shown in Fig. 2b, the 8¹⁸O (-5.2 ‰) and 8D (-73.9 ‰) values of the groundwater in UL basin deviated from global meteoric water line (GMWL: 8D = 8 × 8¹⁸O + 10; Craig, 1961) and local meteoric water line (LMWL), indicating that groundwater samples in UL were weakly affected by atmospheric precipitation recharge supply. The river waters in Hetao irrigation (located in UL Basin) produced a close water exchange with the groundwater (Wang et al., 2023), It could be inferred that the 8¹⁸O and 8D values of groundwater were recharged by river water (8¹⁸O: -3.6 ‰, 8D: -58.7 ‰; Appendix A Table S1), further affecting the LGD process.

As shown in Fig. 2a and 2b, groundwater samples were located near the GMWL and LMWL, indicating that the groundwater originated from the atmospheric precipitation. The surface water samples of DL basin and UL basin deviated from GMWL and LMWL, indicating that surface water was affected by evaporation (Li et al., 2022). The dotted red lines were used to represent the local evaporation line (LEL), formed by fitting the isotopic values of lake water and river water samples. The LEL was well fitted with R² values of 0.99 for DL (Fig. 2a) and 0.97 for UL (Fig. 2b), respectively, indicating the water bodies were affected by the same evaporation trend and climate characteristics (Yan et al., 2016).

3.3. Radon concentrations in water bodies

²²²Rn is an ideal tracer that can effectively identify the LGD process through the evolution trend of its concentration (Gleeson et al., 2009; Su et al., 2023; Sun et al., 2021). As shown in Fig. 3, the concentration of ²²²Rn in the surface water samples in DL (lake water: 36.9 Bq/m³, river water: 581 Bq/m³) and UL (lake water: 49.21 Bq/m³, river water: 152.05 Bq/m³) were lower than that of groundwater samples in DL (13,001.54 Bq/m³) and UL (5208.5 Bq/m³), with a difference of 1–2 orders of magnitude. The ²²²Rn concentration of sediment pore water in DL ranged from 1010 Bq/m³ to 5470 Bq/m³, with an average value of 2631.7 Bq/m³. The ²²²Rn concentration of sediment pore water in UL ranged from 608 to 7510 Bq/m³, with an average value of 3956 Bq/m³. The concentration of ²²²Rn in sediment pore water was between that of the lake water and that of the groundwater indicating the LGD process in two lakes.

Fig 3

Fig. 3. ²²²Rn concentration in Daihai Lake and Ulansuhai Lake.

4. Discussions

4.1. Tracing LGD processes

4.1.1. Indication of hydrochemistry

The hydrochemical component is the result of the interaction between the water cycle process and the surrounding environment (Saleem et al., 2017). Thus, the hydrochemical changes significantly associated with the exchange of water quantity between groundwater and lake water (Yang et al., 2021). On this basis, changes in the hydrochemistry can be used to trace LGD process. Although there were differences in the hydrochemical characteristics of groundwater and lake water in DL basin, the hydrochemical type showed a trend from HCO₃-Ca in groundwater to Cl-Na in lake water (Fig. 1). In addition, the overall TDS gradually increased from groundwater to lake water along the flow direction of groundwater (Fig. 4), indicating a slow LGD process. In contrast, the hydrochemical characteristics of lake water and groundwater in UL basin were similar, indicating a more significant LGD process than DL basin. In this case, TDS and Sal of groundwater in the eastern part of UL were significantly lower than that of the lake water in the western part, indicating that the LGD process was obvious in the eastern part of UL (Fig. 5).

4.1.2. Indications of 8D and 8¹⁸O

As shown in Fig. 2, the sediment pore water samples of the two lakes were distributed between groundwater and lake water samples, indicating that groundwater was mainly discharged to the lakes through sediments (Simmons, 1992). Therefore, groundwater – sediment pore water – lake water was the vital path of LGD in two lakes (Costelloe et al., 2009). In addition, groundwater was more depleted in hydrogen and oxygen isotopes than lake water (Fig. 4, Fig. 5). This indicates that groundwater is subjected to significant evaporation during LGD process, resulting in a gradual enrichment of the isotopic content (Li et al., 2017). By comparison, it could be found that the isotopic enrichment in the LGD process in DL was more significant than that in UL. This indicates that groundwater discharge into the lake took a longer time and experienced stronger evaporation. Therefore, the LGD process was slower in DL than in UL. This is consistent with the above analysis. In addition, the sediment pore water and groundwater samples of the two lakes were located on the LEL, indicating a good hydraulic connection between surface water and groundwater (Ren et al., 2024).

4.1.3. Indication of ²²²Rn

As shown in Fig. 4, Fig. 5, the concentration of ²²²Rn in the groundwater of DL and UL basins was 1–2 orders of magnitude higher than that of the lake water. This was attributed to ²²²Rn atmospheric loss resulting in significantly smaller ²²²Rn concentrations in surface water than in groundwater. Thus, groundwater is subjected to ²²²Rn atmospheric loss during LGD process, resulting in a gradual decline of the ²²²Rn concentration (Luo et al., 2016). By comparison, it could be found that the isotopic enrichment in the LGD process in DL was more significant than that in UL. Generally, the longer time the water body stays, the more it is subjected to evaporation, which separates the light stable isotopes from the water and causes a continuous enrichment of the heavy isotopes in the remaining water (Ren et al., 2024). Accordingly, smaller LGD rates can result in groundwater discharge into the lake over a longer period, which leads to the water with groundwater mixed with lake water experiencing stronger evaporation and having more enriched isotopes. Thus, the more enriched isotopes of lake water in DL prove that its LGD process was slower than that of UL.

4.2. Comparative analysis of LGD

4.2.1. The quantification of LGD

After a comprehensive analysis of the Rn sources and sinks of DL and UL (Appendix A Text S3), the LGD fluxes and rates of DL and UL were calculated according to formula (2–3). As shown in Appendix A Table S2, the LGD flux and rate in DL was 1.54 × 10⁵ m³/day and 2.79 mm/day, respectively. The LGD flux and rate in UL was 8.86 × 10⁵ m³/day and 3.02 mm/day, respectively. In addition, the estimated results of DL and UL were compared with other lakes in the world (Appendix A Table S3). Among them, the smallest area was Willersinnweiher Lake in Germany, where LGD rate was the smallest (0.2 mm/day-4.6 mm/day; Kluge et al., 2007). However, the largest LGD rate was Dongting Lake with an area of 688.62 km² (73.94 mm/day; Sun et al., 2021) rather than Poyang Lake with an area of 924 km² (24.18 ± 6.8 mm/day; Liao et al., 2018). Therefore, LGD is controlled by the hydrogeological conditions, lakebed permeability, climate and human activities. On the one hand, the LGD rates of DL and UL were within the ranges of different lakes, which proved the reliability of the calculated results. On the other hand, there was a significant difference in LGD rate between DL and UL, indicating the influence of natural and human factors.

4.2.2. Material flux associated with LGD

In the lake ecosystem, the materials in the lake water are generally related to the inflow of rivers and groundwater (Liao et al., 2018). Based on the LGD results of the ²²²Rn mass balance model, the fluxes (calculated from formula (S8) in Appendix A Text S2) and contribution rates of typical contaminants from groundwater to lake were estimated (Appendix A Table S4). Due to the low precipitation during the study period, the contribution of precipitation to lake water was ignored in this study.

Nitrogen and phosphorus are important components to maintain nutrient balance in lakes. However, excessive nitrogen and phosphorus can lead to eutrophication of lake water (Ren et al., 2022a). As shown in Appendix A Table S4, the TN (total nitrogen) and TP (total phosphorus) fluxes associated with LGD in UL (TN: 6.42 × 10⁶ g/day; TP: 7.97 × 10⁴ g/day) were higher than that in DL (TN: 8.97 × 10⁵ g/day; TP: 1.23 × 10⁴ g/day), but the contribution rates in UL (TN: 53.56 %; TP: 40.98 %) were smaller than that in DL (TN: 97.52 %; TP: 96.59 %). The surface runoff in the DL basin was less and mainly relied on LGD to recharge the lake water. Therefore, the contribution rate of groundwater discharge to material flux was high. In contrast, the western side of UL was a large agricultural irrigation area (Hetao Irrigation District), where a large amount of nitrogen and phosphorus nutrients infiltrated into groundwater through irrigation activities and then entered the lake through LGD process. Meanwhile, multiple irrigation canals formed by farmland drainage directly discharged into the UL (Han et al., 2021). Frequent irrigation activities in UL basin brought a large amount of nutrients to the groundwater, making the material flux associated with LGD significant.

Furthermore, the widespread farmland drainage canals reduced the contribution rate of LGD to the material flux of lake water.

Fluoride is another contaminant that seriously affected the eco-environment of these two lakes. As shown in Appendix A Table S4, the fluoride flux associated with LGD in UL (1.14 × 10⁶ g/day) was higher than that in DL (2.13 × 10⁵ g/day) The contribution rate in UL (36.25 %) was smaller than that in DL (95.84 %). This was similar to the nitrogen and phosphorus inputs, suggesting a large flux input from the LGD process in UL and a large lake impact from the LGD process in DL. Fluoride concentrations in groundwater in UL (1.39 mg/L) and DL (1.29 mg/L) were twice as high as those in river water in UL (0.64 mg/L) and DL (0.35 mg/L). Groundwater had a slower flow velocity than river water, which promoted water-rock interaction and F^– release (Kim and Jeong, 2005). In this case, fluoride in groundwater could be continuously transported into the lake associated with LGD process.

In summary, material flux associated with LGD played an important role in the ecological balance of DL and UL basins even though LGD flux was significantly smaller than river inflow. Notably, TN, TP, and fluoride concentrations in groundwater would continue to increase along with water-rock interaction or human activities, resulting in an increase of material flux of LGD. Therefore, LGD is one of the vital factors of lake water quality degradation and eutrophication.

To ensure sustainable water use, groundwater management must be optimized to control its discharge into lakes. Adjusting well placement and promoting water-saving technologies, such as drip irrigation, can reduce the impact of groundwater extraction on lakes. Additionally, utilizing surface water resources, like rainwater collection and floodwater storage, alleviates pressure on groundwater. In terms of water quality protection, regular dredging and enhanced water quality monitoring are essential, along with controlling agricultural non-point source pollution to prevent contaminants from entering lakes through groundwater. By delineating protection zones and managing groundwater discharge, pollution spread can be minimized, ensuring water quality.

4.2.3. Controlling factors of LGD in Daihai and Ulansuhai Lake

As mentioned above, there was a significant difference in LGD process between DL and UL. As shown in Appendix A Table S2, the average LGD flux and rate in UL were significantly higher than that in DL. Generally, the difference in LGD was related to the hydrogeological conditions, climate, lakebed permeability and human activities (Ren et al., 2024). On this basis, the conceptual model of LGD process and its controlling factors in DL and UL is shown in Fig. 6. According to a previous investigation, the average hydraulic conductivity of the aquifers at the bottom of the DL and UL were 7.14 m/day and 26 m/day, respectively (Hou et al., 2021; Wang, 2021). In addition, the average hydraulic gradient of DL and UL was 0.67 ‰ and 1.08 ‰, respectively. Thus, larger hydraulic conductivity and gradients could promote LGD, resulting a stronger LGD process in UL than in DL.

Fig 6

Fig. 6. Hydrological conceptual model in Daihai Lake and Ulansuhai Lake.

In addition, the larger the porosity of sediments, the better the lakebed permeability. The porosity of sediment layers in DL and UL was 28.35 % and 53.34 %, respectively (Appendix A Table S2). This was consistent with the regularity of hydraulic conductivity. In addition, lakebed permeability was also affected by local preferential channels. For example, cracks caused by plant root growth and other factors in the viscous soil may become preferential channels for groundwater discharge (Hare et al., 2017). There was less aquatic vegetation in DL and the vegetation coverage was 4.1 %, which was difficult to form groundwater channel. In contrast, aquatic plants were widely distributed in UL and the vegetation coverage was 56.46 %, including emergent plants and submerged plants (Li, 2021). In this case, the preferential channels formed by vegetation roots in UL promoted LGD. Therefore, habitat conditions of lake could affect LGD by affecting the permeability of the lakebed.

Since LGD is controlled by the hydrogeological conditions of the lake basin, and the water yield property of the aquifer and hydraulic gradient respond closely to climate variations, LGD could also be controlled by the meteorological conditions of the lake basin, especially DL, where the water balance is mainly determined by precipitation and evaporation (Ren et al., 2024). On the one hand, since groundwater ultimately comes from precipitation, an increase in precipitation can provide sufficient water to the aquifer and increase the LGD intensity. On the other hand, lake water is more susceptible to evaporation, and continuous evaporation can further increase the difference in water levels between groundwater and lake water, which can provide sufficient dynamic for LGD and increase its intensity. Therefore, climate can alter the water volume and dynamical conditions of LGD, which has an important effect on the LGD.

However, human activities, such as the development and utilization of water resources, could affect surface water-groundwater interaction (Meinikmann et al., 2013). Groundwater in DL and UL basins was mainly used for drinking, farmland irrigation and industrial production (Wang et al., 2021). However, the overexploitation of groundwater could form a cone of depression, resulting in decreased groundwater discharge. The DL basin mainly depended on well irrigation to extract groundwater, while the UL basin mainly depended on canal irrigation to divert the Yellow River. Thus, the surface water irrigation in the UL basin could recharge groundwater, causing the groundwater level to rise. In this case, the LGD process was further enhanced and vice versa. Therefore, the influence of human activities was one of the primary reasons for the difference in LGD between the DL and UL basins.

5. Conclusions

In this study, multiple tracers (hydrochemistry, 8D and 8¹⁸O) and ²²²Rn mass balance model were used to contrast LGD process and its controlling factors in Daihai and Ulansuhai Lake basins. The hydrochemical types of the two lakes showed a trend from groundwater to lake water, indicating a hydraulic connection between lake water and groundwater. In addition, the 8D and 8¹⁸O values of sediment pore water were between the groundwater and lake water, indicating the LGD processes.

The average groundwater discharge fluxes in Daihai and Ulansuhai Lake estimating by radon mass balance model were 1.54 × 10⁵ and 8.86 × 10⁵ m³/day, respectively. The average discharge rates in Daihai and Ulansuhai Lake were 2.79 mm/day and 3.02 mm/day, respectively. On this basis, the TN, TP and F^– inputs associated with LGD in Daihai Lake accounted for 97.52 %, 96.59 %, and 95.84 % of the total inputs and those in Ulansuhai Lake accounted for 53.56 %, 40.98 %, and 36.25 %, respectively. By comparison, the differences in LGD process and associated pollutant flux between the two lakes were mainly related to hydrogeological conditions, lakebed permeability, and human activities.

Overall, this study not only provided a new insight in contrastive mechanisms of different LGD process within the same river basin, but also contributed to the sustainable development of water resources in the Yellow River Basin, China. In future studies, the number of long-term monitoring sites will be increased to minimize uncertainty in the identification and quantification. In addition, more attention needs to be paid to the seasonal variation and controlling factors of LGD processes in different lake basins. Future research should further consider the impact of precipitation and evaporation on water balance, particularly the variations under different seasonal and climatic conditions. This aspect of research still holds significant potential for development.

CRediT authorship contribution statement

Yuanzhen Zhao: Writing – original draft, Investigation, Formal analysis, Data curation. Xiaohui Ren: Methodology, Formal analysis. Shen Qu: Writing – review & editing, Methodology, Funding acquisition, Conceptualization. Fu Liao: Writing – review & editing, Methodology, Conceptualization. Keyi Zhang: Investigation. Muhan Li: Investigation. Juliang Wang: Investigation. Ruihong Yu: Writing – review & editing, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the Natural Science Foundation of Inner Mongolia Autonomous Region of China (No. 2023QN04011), the National Natural Science Foundation of China (Nos. 42307092 and 52279067), Ordos Science and Technology Major Project (No. ZD20232303) and Project of Key Laboratory of River and Lake in Inner Mongolia Autonomous Region (No. 2022QZBZ0003). We thank Yuan Li, Mingzhe Du, Yiwei Zhang, Quanzhi Liu and Yiming Zhao for their assistance in field works.

Appendix. Supplementary materials

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Contrastive mechanisms of lacustrine groundwater discharge and associated pollutant fluxes into two typical inland lakes in Inner Mongolia, Northwest China.

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