Abstract
Challenged by rapidly changing climate in combination with an increase in anthropogenic pressures, karst groundwater resources in the Old Town of Lijiang (OTLJ), SW China, are diminishing. Higher frequency and longer duration of dried-up periods have been observed at the Heilongtan Park (HP) Springs in recent years. Thus, there is an urgent need for an artificial recharge scheme, aimed at replenishing groundwater in the aquifer and increasing the outflow of the springs to ensure effective water resources management. Evaluation of the scheme feasibility, prior to its implementation, is important. In this study, tracer tests were conducted between the recharge area and receiving springs in order to gain insight into the transport mechanisms of karst groundwater and the structural characteristics of the aquifer. Multiple underground flow paths, exhibiting high conductivity between the recharge area and HP Springs, were revealed by the interpretation of tracer breakthrough curves. Three springs considered as the leakages of the scheme were identified. Moreover, the outflow of springs at HP and OTLJ were predicted to be increased by the artificially recharged water after 9.2 and 12.5 days, respectively. Quantitative analysis of tracer recoveries demonstrates that the springs to be recharged and the springs considered as leakages, respectively, share 45 and 55% of the increased outflow. The feasibility of the scheme has been confirmed by the tracer tests. This report provides references for the evaluation of artificial groundwater recharge and protection strategies, particularly in large and poorly investigated karst spring fields.
Résumé
Les ressources en eaux souterraines karstiques de la Vieille Ville de Lijiang (VVLJ), dans le sud-ouest de la Chine, diminuent en raison du changement rapide du climat et de l’augmentation des pressions anthropiques. Une fréquence plus élevée et une durée plus longue des périodes d’assèchement ont été observées aux sources du parc Heilongtan (HP) ces dernières années. Il est donc urgent de mettre en place un programme de recharge artificielle, visant à reconstituer les eaux souterraines de l’aquifère et à augmenter le débit des sources pour assurer une gestion efficace des ressources en eau. L’évaluation de la faisabilité du projet, avant sa mise en œuvre, est importante. Dans cette étude, des essais de traçage ont été réalisés entre la zone de recharge et les sources réceptrices afin de mieux comprendre les mécanismes de transport des eaux souterraines karstiques et les caractéristiques structurelles de l’aquifère. De multiples voies d’écoulement souterraines, présentant une conductivité élevée entre la zone de recharge et les sources de HP, ont été révélées par l’interprétation des courbes de restitution des traceurs. Trois sources considérées comme des fuites du système ont été identifiées. De plus, on prédit que le débit des sources à HP et VVLJ serait augmenté par l’eau rechargée artificiellement après 9.2 et 12.5 jours, respectivement. L’analyse quantitative des taux de restitution des traceurs démontre que les sources à recharger et les sources considérées comme fuites se partagent respectivement 45 et 55% de l’augmentation du débit sortant. La faisabilité du système de recharge artificielle a été confirmée par les essais de traçage. Ce rapport fournit des références pour l’évaluation des stratégies de protection et de recharge artificielle des eaux souterraines, en particulier dans les grands systèmes de sources karstiques peu étudiés.
Resumen
Los recursos de agua subterránea kárstica de la Old Town of Lijiang (OTLJ), en el suroeste de China, están disminuyendo debido al cambio climático y al aumento de las presiones antropogénicas. En los últimos años se ha observado una mayor frecuencia y duración de los períodos de desecación en los manantiales del Parque Heilongtan (HP). Por lo tanto, existe una necesidad urgente de un plan de recarga artificial, destinado a reponer las aguas subterráneas en el acuífero y aumentar el flujo de salida de los manantiales para garantizar una gestión adecuada de los recursos hídricos. Es importante evaluar la viabilidad del plan antes de su aplicación. En este estudio, se realizaron pruebas de trazadores entre la zona de recarga y los manantiales receptores para conocer los mecanismos de transporte del agua subterránea kárstica y las características estructurales del acuífero. La interpretación de las curvas de ruptura del trazador puso de manifiesto la existencia de múltiples trayectorias de flujo subterráneo que presentaban una alta conductividad entre la zona de recarga y los manantiales HP. Se identificaron tres manantiales considerados como las filtraciones del esquema. Además, se predijo que el flujo de salida de los manantiales en HP y OTLJ sería aumentado por el agua recargada artificialmente después de 9.2 y 12.5 días, respectivamente. El análisis cuantitativo de las recuperaciones del trazador demuestra que los manantiales que se van a recargar y los considerados como filtraciones, respectivamente, comparten el 45 y el 55% del aumento del caudal. La viabilidad del esquema ha sido confirmada por las pruebas de trazadores. Este informe proporciona referencias para la evaluación de las estrategias de recarga y protección artificial de las aguas subterráneas, en particular en campos de manantiales kársticos de gran tamaño y poco investigados.
摘要
由于快速变化的气候和人为压力增加的影响, 中国西南部丽江古城(OTLJ)的岩溶地下水资源正在减少。近年来, 在黑龙潭公园(HP)温泉中观察到更高频次和更长的干涸期。因此, 迫切需要一种人工补给方案, 旨在补充含水层中的地下水并增加泉水溢出量, 以确保有效的水资源管理。在实施之前对计划的可行性进行评估很重要。在这项研究中, 为了深入了解岩溶地下水的输送机制和含水层的结构特征, 在补给区和排泄泉之间进行了示踪试验。通过对示踪穿透曲线的解释, 揭示了在补给区和 HP泉之间表现出高电导率的多个地下流动路径。考虑将三个泉作为方案的泄露点。此外, 预计在 9.2 天和 12.5 天后, 人工补给水将增加 HP 和 OTLJ 的泉水溢出量。示踪剂回收的定量分析表明, 待再补给的泉和被视为泄漏的泉分别分享增加的流出量的 45 和 55%。示踪试验证实了该方案的可行性。本报告为评价人工地下水补给和保护策略为大型和研究不充分的喀斯特泉群研究提供了参考。
Resumo
Desafiados pelas rápidas mudanças climáticas em combinação com um aumento das pressões antropogênicas, os recursos hídricos subterrâneos cársticos na Cidade Velha de Lijiang (CVL), SO China, estão diminuindo. Maior frequência e maior duração dos períodos de seca têm sido observados nas nascentes do Parque Heilongtan (PH) nos últimos anos. Assim, há uma necessidade urgente de um esquema de recarga artificial, visando a reposição de águas subterrâneas no aquífero e o aumento do escoamento das nascentes para garantir uma gestão efetiva dos recursos hídricos. É importante uma avaliação da viabilidade do regime, antes de sua implementação. Neste estudo, foram realizados testes de traçadores entre a área de recarga e as nascentes recebedoras, a fim de obter uma visão dos mecanismos de transporte das águas subterrâneas cársticas e as características estruturais do aquífero. Múltiplos caminhos de fluxo subterrâneo, exibindo alta condutividade entre a área de recarga e as nascentes do PH, foram revelados pela interpretação das curvas de avanço do traçador. Foram identificadas três nascentes consideradas como vazamentos do esquema. Além disso, as vazões de saída das nascentes do PH e da CVL foram previstas para ser aumentados pela água artificialmente recarregada após 9.2 e 12.5 dias, respectivamente. A análise quantitativa da recuperação dos traçadores demonstra que as nascentes a serem recarregadas e as nascentes consideradas como vazamentos, respectivamente, compartilham 45 e 55% da vazão de saída. A viabilidade do esquema foi confirmada pelos testes do traçador. Este relatório fornece referências para a avaliação de estratégias de recarga artificial e proteção de águas subterrâneas, particularmente no campo de nascentes amplamente e insuficientemente investigadas.
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Introduction
Karst regions cover 7–12% of the global land area, and almost a quarter of the world population is wholly or partially dependent upon drinking water derived from karst aquifers (Hartmann et al. (2014). Due to relatively small watershed size, the absence of a continuous supply of surface water, and increasing water consumption caused by population growth, karst groundwater is likely to become more prone to the adverse impacts of climate change (Klaas et al. 2020; Ostad-Ali-Askari et al. 2020; Sivelle et al. 2021). The projected reduction in water resources will challenge the manufacturing industries, agriculture and fisheries, as well as people’s daily lives all over the world (Goel and Kumar 2005; Nerantzaki and Nikolaidis 2020; Sun et al. 2017; Yin et al. 2020). Moreover, karst groundwater is associated with valuable landscape resources, and water scarcity and droughts can seriously damage the scenery associated with tourist attractions (Jiang et al. 2020; Luo et al. 2020; Ostad-Ali-Askari et al. 2017).
The Old Town of Lijiang (OTLJ) in Yunnan province, SW China, is listed as a World Heritage (Cultural) site by UNESCO for its incredible historical and cultural values. Located in the north of OTLJ, Heilongtan Park (HP) is known for its springs and pools, and it also serves as the water source of the OTLJ. However, due to insufficient precipitation and increasing extraction of groundwater in OTLJ, springs in HP have suffered from frequent drying up since 1960 (Han et al. 2019; Kang et al. 2013; Zeng et al. 2013). Between Jan. 2012 and Sept. 2014, HP Springs ceased flowing for approximately 956 days. Besides this, the frequency of drying up has increased from once every 20 years to once every 5 years since 1960. Consequently, the outflow of the springs in OTLJ has been decreasing in recent years; therefore, a series of actions are needed to preserve springs in HP and OTLJ.
Due to the rapidly changing climate and overexploitation of karst groundwater, the occurrences of attenuation and drying-up of karst springs are increasingly recorded globally (Hao et al. 2009; Schrader et al. 2014; Sivelle et al. 2021). To deal with these predicaments, regulation of groundwater extraction has been proposed and conducted (Al-Assa’d and Abdulla 2010; Kang et al. 2011); however, it is hard to meet expectations particularly in the areas exhibiting decreasing precipitation. Accordingly, managed aquifer recharge is considered as a promising sustainable technique for maintaining perennial outflow of karst springs and for enabling significant storage within the aquifers (Daher et al. 2011; Kang et al. 2011). This technique involves transferring multisource water (stormwater, surface water, etc.) into the karst aquifer by well injection and natural infiltration (Al-Assa’d and Abdulla 2010; Dar et al. 2017; Stevanović 2015; Xanke et al. 2016; Zhang and Wang 2021). Although a few operations have been conducted, the structure of the karst aquifer and transportation processes of water during the transition from supplied water to the outflow springs often remain unrevealed. Moreover, evaluation methods for the feasibility of these schemes are still theoretical and numerically based, while the field experiments are expected to deliver more valuable information. In this study, an artificial recharge scheme aiming at improving groundwater storage in a karstic aquifer and thus increasing the discharge of springs is introduced. To evaluate the feasibility of this scheme, field tracer tests have been conducted for further understanding of the karst aquifer.
Karst aquifers are characterized by strong heterogeneity and anisotropy, leading to complex system behavior (Knoll and Scheytt 2018). Artificial tracer tests have been widely utilized for determining the structures which cannot be explored directly by researchers in karst systems (Dewaide et al. 2018; Goldscheider 2008; Knoll and Scheytt 2018). The flow path geometry and number of major karst conduits have been successfully deduced from tracer tests (Long et al. 2008; Luhmann et al. 2012). The primary results of quantitative tracer tests are breakthrough curves (BTCs), i.e. the discrete or continuous series of concentration-time data (Goldscheider et al. 2008). Transport modeling of the BTCs is not only allowing considerable insight into groundwater flow and drainage networks, but also making it possible to predict travel times and transport characterization in the system (Morales et al. 2007; Ostad-Ali-Askari et al. 2019). BTCs are also of great value for the calibration of physically based groundwater flow models (Schilling et al. 2019). Oehlmann et al. (2015) utilized peak arrival times of BTCs for calibration of a karst system and therefore strongly reduced the ambiguity of karst aquifer models. Accordingly, transport modeling of the BTCs is considered as the key to deciphering the structural properties and flow mechanism of the karst aquifer in this case.
In the following sections of this report, a short description of the study area and dried-up springs are first given, and then the artificial recharge scheme is introduced. To evaluate this scheme, artificial tracer tests are performed and the experimental BTCs are analyzed. Moreover, tracer recoveries, the conceptual model of karst conduits, and karst aquifer parameters are discussed. Finally, the article discusses the implication of tracer tests for the evaluation of an artificial recharge scheme in a karst spring field.
Methods and materials
Drying-up of the Heilongtan Park Springs and the artificial recharge scheme
According to the local historical records, the earliest record of the drying-up of HP Springs can be dated back to the Qing Dynasty (1736) (Anonymous, Lijiang Naxi Autonomous County Annals, 1997). Since then, the drying-up of the springs has been repeatedly mentioned in the historical materials. The dried-up periods of HP Springs have been monitored by the Lijiang Hydrological Bureau since 1990 and the records are listed in Table 1. In addition, average monthly precipitation from four rain-gauge stations within the study area, discharge of the HP springs, and groundwater level measurements from the observation well are collected and shown in Fig. 1. The locations of the four rain-gauge stations and the observation well are shown in Fig. 2a.
Strong seasonal variation and significant correlation among precipitation, discharge, and groundwater level measurements can be observed in Fig. 1. In the period with abundant rainfall (1990–1991 and 1998–2002), discharge of HP Springs and groundwater level are high, ranging from 1 to 4 m3/s and 2,407 to 2,413 m respectively. While in the years with insufficient precipitation (1992–1997 and 2003–2011), groundwater level drops by 2–5 m, and discharge of HP Springs decreases by 1–2 m3/s, leading to frequent drying up of HP Springs, indicated by the red circles in Fig. 1.
According to the survey of water consumption in Lijiang City, the amount of exploitation of karst groundwater was very low before 2005, yet two periods of drying up at HP Springs were recorded, in 1993 and 1995. The level of exploitation has increased significantly due to the development of local industry and tourism since 2006, and has led to a higher frequency and longer duration of drying up from 2006 to 2011, although the precipitation during this period was almost the same as that recorded before 1998. Thus, it can be deduced that the drying-up of HP Springs is fundamentally driven by insufficient precipitation and, at the same time, greatly influenced by overexploitation of the groundwater resource. This finding is consistent with the previous studies conducted by Kang et al. (2013) and Zeng et al. (2013). By Oct. 2018, measurements from observation wells show that the groundwater is close to being restored to the level before 2005, owing to efforts that have been undertaken to regulate the overexploitation since 2017. However, a managed aquifer recharge scheme for restoring and preserving the groundwater resources of HP and OTLJ is considered as the optimal solution. Taking surface water, ecology, and especially the geological and hydrogeological conditions into account, an artificial recharge scheme is proposed. This scheme consists of constructing a reservoir in a karst depression 17 km north of HP, transferring water from nearby rivers into the reservoir through ditches, and finally recharging springs in HP and OTLJ by replenishing the karst aquifer. In this context, field tracer tests were chosen to characterize the karst aquifer and evaluate the feasibility of this artificial recharge scheme. It should be noted that the construction and water-transfer processes are not in the scope of this report.
Study area
Located in the northwest of Yunnan Province, SW China, the Old Town of Lijiang is listed as a World Heritage (Cultural) site by UNESCO for its incredible historical and cultural values, see Fig. 2a. With a population of more than 1.2 million, the study area is characterized by a monsoon climate with typical wet and dry seasons. Annual precipitation in the catchment averages 957.2 mm, predominantly occurring from June to September (Zeng et al. 2015). The annual average temperature is 16.3 °C in Lijiang (Qi et al. 2018).
Surrounded by a variety of outcropping strata including Triassic limestone (T), Permian basalt (P), Eocene calcibreccia (E) and Quaternary sediments (Q), the study area shows elevation ranging from 2,396 to 3,500 m above sea level (asl). Along the eastern foothills, an impermeable fault (F1) striking approximately N–S presents a hydrogeological boundary and it also separates the Triassic limestone and Permian basalt. Triassic carbonate aquifers are found predominantly cropping out in the mountainous areas, offering favorable paths for karst groundwater. The aquifers are covered by Eocene calcibreccia and Quaternary sediments in the south and Jiuzihai karst depression, respectively (Fig. 2a).
As suggested by Fig. 2b, being the main aquifers, the middle Triassic limestone strata presents valuable landscape and supplies domestic water for HP and OTLJ. With an area of more than 8.2 km2 as well as favorable precipitation confluence conditions composed by sinkholes and the surrounding higher elevations, Jiuzihai karst depression forms the main recharge area of this spring field. Aquifers are rapidly recharged by surface water coming from precipitation (Pu et al. 2013). Groundwater is generally flowing from northeast to southwest and discharging through springs situated at the F1 fault zones as well as contact between carbonate exposures and impervious formations including the Permian basalt and Quaternary sediments.
Tracer tests
In 2014, multi-tracer tests were conducted from November to December. Uranine (3 kg) and potassium iodide (KI) powder (400 kg) were dissolved in 300 m3 water and injected quasi-instantaneously (within 2 h) into the sinkhole of Jiuzihai karst depression (Fig. 2). A large volume of water (1 × 106 m3) was subsequently introduced for the next 24 h in order to flush the tracers into the aquifer. Groundwater samples were taken at nine springs. In addition to the three springs in HP (S1, S2, and S3) and three springs in OTLJ (S5, S6, and S7), Clear Spring (S4) and Fault Springs (S8 and S9) which were serving as the main outlets of the karst system, were also selected as the receiving points for determining the potential leakage of the scheme (Fig. 2a). Before the tests, standard samples from every receiving point were collected and measured for use in calibration. At every receiving point, water samples were first taken once a day and then the sampling interval gradually decreased to every 2 h with the increase of concentration. The frequent sampling lasted until the peaks passed, and then the interval gradually increased for the next few days. Sampling campaigns were conducted for 1 month, with a total of 934 water samples collected. Samples were analyzed in the field, using a DR6000 UV spectrometer. During the experiment, the springs’ discharge was recorded along with the sampling, which suggested an almost constant rate at each receiving point. There were no rainfall events in the study area according to the field observations and meteorological data provided by the regional authorities.
Curve fitting
All observed breakthrough curves were fitted with the multidispersion model (MDM) and the two-region nonequilibrium (2RNE) model using the software CXTFIT (Toride 1995). Theoretically, MDM could be interpreted as a superposition of the individual flow paths characterized by the conventional advection-dispersion model (ADM; Field and Leij 2012):
where C is the aqueous concentration at time t [M/L3], D is the longitudinal dispersion [L2/T], vm is the mean flow velocity [L/T] and x is the length of the flow path [L]. The basic assumption of MDM is that each flow path is characterized by different flow velocities and dispersivities (or dispersion parameters). It is assumed (1) that the mass of tracer injected is divided into several flow paths proportional to the volumetric flow rates along those paths and (2) that there are no interactions between the flow paths (Leibundgut et al. 2009). Therefore, the outlet of the karst system, in this case the spring, can be regarded as a place where the tracer distributed in each flow path finally converges. The MDM was initially developed for tracer transport in heterogeneous multilayered porous media but can also be applied to karst aquifers with multiple flow paths (Kübeck et al. 2013; Małoszewski et al. 2006; Maloszewski and Zuber 1992).
In ADM, the mean flow velocity (vm) and longitudinal dispersion coefficient (D) were firstly estimated by fitting the modeled BTCs to observed values. Advection can be expressed as mean vm. Dispersion can be described by the D or the longitudinal dispersivity α = D/v (Goeppert and Goldscheider 2019). The general advection–dispersion equation (Eq. 1) is solved analytically by assuming homogeneous profiles, a uniform and unidirectional flow field that is constant in time and space, and constant flow parameters (van Genuchten et al. 2012). A stepwise determination of the transport parameters needs to be realized for modeling multiple peaks in CXTFIT (Goeppert and Goldscheider 2019). The earliest peak of the BTC should be fitted firstly for the determination of vm and D, then subtracted. The residual concentrations are used for further fitting until the last peak is completed.
For relatively simple right-skewed curves, the 2RNE model has been confirmed to be effective by numerous studies (Barberá et al. 2017; Field and Pinsky 2000; Lauber et al. 2014). By accounting for mobile and immobile fluid phases, this approach assumes that the persistent skewness of curves is attributed to immobile fluid regions. Since the 2RNE model further accounts for the two regions and the exchange process, the advection-dispersion equation has been extended by two parameters, a portioning coefficient β, and a mass transfer coefficient ω. Thus, a total of four parameters (vm, D, β, and ω) need to be fitted simultaneously (Lauber et al. 2014).
Results and discussion
General results of tracer tests
A set of 12 parameters (see abbreviations and descriptions in Table 2) were considered for the analysis of the BTCs. These variables include tracer mass, time of travel, time difference, tracer velocity, tracer concentration, geographical distance, and spring discharge. The tracer recovery rates (R) were calculated using the discharge (Q):
where m0 is the injected tracer mass and C(t) is the aqueous concentration at time t (Field and Nash 1997).
As shown in Figs. 4 and 5, I− and uranine were detected at all receiving points, confirming the connectivity between Jiuzihai karst depression and all springs. The observed BTCs of the two tracers received at each spring show similar morphology. The curves observed at HP Springs (S1, S2 and S3) and Clear Spring (S4) are triple-peaked and double-peaked respectively (Fig. 4), while the rest of the curves show a single peak (Fig. 5). Almost identical first detection times for two tracers were observed at S5 (222 h), S8 (144 h), and S9 (132 h), while I− was generally first detected 20–30 h earlier than uranine at the remaining springs. For the HP Springs, the first peaks of two tracers were reached after 220 h, while the second peaks acquired from I− were detected about 45 h earlier than that from uranine (Fig. 3; Table 3). The first peak of I− was observed 192 h after injection at S4, which was 25 h earlier than uranine. Regarding the single-peaked curves obtained at OTLJ Springs (S5, S6, and S7), maximum concentrations of two tracers were measured after 300, 320, and 270 h, respectively. The average tp1 is about 180 h at Fault Springs (S8 and S9).
The concentrations of uranine were significantly lower than that of I− at every receiving point due to the distinctive injected mass (Table 3). Among all springs, the lowest first peak concentrations (Cp1) of I− were observed at HP Springs, with concentrations of 16.86, 25.2 and 30.75 μg/L. While the highest Cp1 (236.6 μg/L) of I− was measured at Clear Spring. The Cp1 of 5.08, 4.26, 9.58 and 16.76 μg/L were recorded for uranine at those springs respectively. Relatively medium Cp1 values were found at the OTLJ Springs, for which variations ranged from 105 to 140.3 μg/L and 10.26 to 13.21 μg/L for I− and uranine, respectively. Generally higher Cp1 of 176.2 to 211.5 μg/L (I−) and 13.07 to 25.32 μg/L (uranine) were found at Fault Springs.
The flow velocity considering the time of the first peak (vp1) at HP Springs was measured as approximately 68 m/h for both tracers (Fig. 3; Table 3). The highest vp1 (81.25 m/h) was calculated with I− at S4, and the velocity of 72.22 m/h was derived from uranine. vp1 values of between 48.8 and 56.67 m/h were calculated with both tracers at OTLJ Springs. Regarding the Fault Springs, vp1 of S8 (69.35 and 71.67 m/h for I− and uranine respectively) were noticeably higher than that of S9 (48.96 and 54.02 m/h respectively). Overall, the highest vp1 was observed at S4, followed by HP Springs, S8, OTLJ Springs and S9.
Multi-peaked BTCs and transport parameters of the MDM
Triple-peaked and double-peaked curves were acquired at HP Springs and Clear Spring, respectively (Fig. 4). Multi-peaked BTCs have always been considered as emblematic of anastomoses in the karst aquifer (Field and Leij 2012; Goldscheider 2008; Perrin and Luetscher 2008). Although other factors including precipitation events and injection mode of tracers may be responsible for multi-peaked BTCs as well (Brouyere et al. 2005), there were no precipitation events over the experiment period and the tracers were injected into the sinkhole within 2 h, which can be regarded as instantaneous release compared with the total test time of 600 h. Therefore, it can be inferred that (1) three major flow paths (FP) exist between Jiuzihai karst depression and HP Springs and, (2) two major flow paths exist between the depression and Clear Spring.
Regarding the HP Springs, almost identical mean flow velocities (vm) derived from two tracers are recognized for every FP (Fig. 4a–f). It should be noted that the flow velocities derived from FP2 and FP3 are 1.5–2.5 times smaller than that from FP1. The highest vm value (80.33 m/h) among all springs was modeled from the FP1 at Clear Spring, which is consistent with the vp1 (81.25 m/h) calculated from observation values. Generally, longitudinal dispersion (D) differs for two tracers (Fig. 6a,b): maximum and minimum values generated from I− are 5,236 m2/h (S4) and 471.7 m2/h (S3), while those obtained from uranine are 1,562 m2/h (S4) and 221 m2/h (S3). It can be noted that Clear Spring exhibits significantly higher values of D than HP Springs, owing to the higher flow velocities. In addition, it can be inferred from Fig. 4 and Table 4 that the data acquired from I− fits better with the MDM, and this is suggested by higher R2 ranging from 0.951 to 0.975 compared with 0.88 to 0.974 derived from uranine.
Single-peaked BTCs and transport parameters of 2RNE model
All the BTCs resulting from the OTLJ Springs show a single and nearly symmetric peak (Fig. 5a–c), while asymmetric curves with persistent skewness are obtained from Fault Springs (Fig. 5d,e). The persistent skewness is commonly attributed to immobile-flow regions that have been confirmed by numerous studies (Field and Leij 2012; Field and Pinsky 2000). In practical terms, dead-end passages, fissured matrix, storage of tracer in large cavities, as well as cave breakdown (roof collapse) are expected to have a major effect on solute transport, creating no-flow regions (Field and Pinsky 2000; Geyer et al. 2007; Goldscheider 2008; Li and Field 2014). Thus, it can be inferred that (1) there is only one major flow path that exists between Jiuzihai karst depression and S5–S9, and (2) no-flow regions exist along the flow path between the depression and Fault Springs (S8 and S9).
The mean flow velocity (vm) of 52.9 m/h was calculated at OTLJ Springs (Table 5). Although there is no noteworthy difference in vm among the simulated results from the springs, the average dispersion (4,008 m2/h) of S5 is significantly higher than that of S6 (1,285 m2/h) and S7 (1,191.5 m2/h; Fig. 6a,b). Regarding the Fault Springs, two parameters of S8 (vm = 55.53 m/h, D = 2885.5 m2/h) were markedly higher than those of S9 (vm = 36.79 m/h, D = 1,472.5 m2/h). The BTCs acquired from the OTLJ Springs deliver higher partition coefficients (0.8–0.95) than those from the Fault Springs (0.71–0.8), suggesting the higher contribution of the mobile-flow regions during transporting (Fig. 6c). In contrast, OTLJ Springs (0.005–0.09) yield lower transfer coefficients than the Fault Springs (0.4–1.23), implying less-intense solute exchange (Fig. 6d). The coefficients of determination (R2) ranged from 0.933 to 0.989 suggesting a quite good fit; moreover, the coefficients fitted for I− (0.979–0.989) are generally higher than those for uranine (0.933–0.979). The rising and receding limbs of BTCs are both quite well fitted for the relatively symmetric curves obtained from OTLJ Springs, while the rising limbs are fitted much better than the receding limbs for the curves exhibiting persistent skewness obtained from Fault Springs (Fig. 5).
Tracer recoveries
In the field tests, salt (I−) and fluorescence (uranine) were used as artificial tracers. Despite direct injection into the sinkhole, total tracer recoveries only reached 0.164 and 12.1% for I− and uranine, respectively (Table 3). This finding can be related to the following causes. Firstly, long tracing distance (up to 16.4 km without considering tortuosity) and large study area (approximately 150 km2) may lead to high retention of tracers in the karst system. Secondly, considering the possibility of seasonal karst springs as well as the limitation of field investigation, it is very likely that some springs were not found and then missed monitoring during the tests. Finally, tracer may enter deep flow paths and the regional flow system according to the local and regional flow pattern in mountainous areas, as summarized by Tóth (1963).
It can be further noted that total recoveries of I− are almost two orders of magnitude lower than that of uranine. This difference in recoveries can be attributed to the different properties of tracers. Uranine is an ideal tracer with conservative properties, while care should be exercised about the potential conversion of iodine species under different geochemical conditions (Hu and Moran 2005; Leibundgut et al. 2009). Hu and Moran (2005) concluded that the conversion of iodide to iodate or elemental iodine under oxidizing conditions will confound the interpretation of a tracer study’s results. Furthermore, iodate is more reactive and exhibits retarded transport due to its interactions with clays and organic matter (Couture and Seitz 1983; Yoshida and Uchida 1992). According to the field survey, fractures filled with red clay are widely developed in the main aquifer (T2b). The red clay provides favorable conditions for the oxidation of iodide anion and the reaction of iodate.
Conceptual model of karst conduit and karst aquifer parameters
According to the tracer tests, Jiuzihai karst depression and HP Springs are deduced to be connected by three major flow paths (Fig. 7). In addition, almost identical breakthrough curves were obtained at three springs in HP, suggesting these springs are individual outlets of the same drainage system. Different tracer recoveries are attributable to varying discharges, which occur possibly due to different pressure conditions and sizes of the outlets. The fast-flow component is associated with larger velocities and dispersions, indicating high advective and turbulent flow in the conduits, while the lower-flow component occurs due to the flow through well-drained fractures and fissures. There are two main branches of drainage between the depression and Clear Spring (Fig. 7). Several highest-value parameters and modeling results, including tracer concentrations, velocities, recoveries, discharge and dispersion coefficients, were acquired at Clear Spring (Tables 3 and 4). The highest value for dispersion, determined by I− at S4, is 5,236 m2/h for the first peak, which can be attributed to the effect of rapids in the drainage system. It should be noted that the velocities and dispersion coefficients derived from S4 are noticeably higher than that from S1–S3 despite the identical value of hydraulic gradient being obtained. This implies that the underground pathway leading to Clear Spring is characterized by higher permeability than that to HP Springs. According to the relations between flow conditions and maximum tracer concentrations in the karst aquifer described by several studies (Goppert and Goldscheider 2008; Lauber and Goldscheider 2014; Pronk et al. 2007, 2009), it can be deduced that the highest maximum concentrations acquired at Clear Spring resulted from higher flow velocities. In addition, the highest discharge and concentrations among all observed results delivered the maximal tracer recoveries at Clear Spring.
There is only one major drainage from Jiuzihai karst depression to the OTLJ Springs (Fig. 7). Although the values of radical tracing distance (x) and hydraulic gradient (I) obtained from OTLJ Springs and HP Springs are similar, velocities calculated at S5–S7 were found to be about 20 m/h smaller than those at S1–S3. This indicates that the flow path between the depression and OTLJ Springs is featured with lower permeability compared with that between the depression and HP Springs. Results with the 2RNE model show a high percentage of karst water transferring within the mobile fluid region (80–95%), indicating highly heterogeneous and anisotropic flow paths. In contrast, a limited rate of exchange between the mobile-fluid and immobile (no flow) regions can be deduced according to the low mass transfer coefficients (0.005–0.09), suggesting underdevelopment of immobile regions, e.g., dead-end passages along the main flow channel. The average elevation of the OTLJ Springs is about 5 m lower than that of the groundwater level in the observation well according to the measurements from Mar. 1992 to Dec. 2011. Thus, it appears that approaching the springs, conduits are mostly below the level of the regional groundwater level and thus fully saturated. These fully saturated conduits are accounted for in that the outflow of S5–S7 only showed a decreasing trend during the dried-up periods for S1–S3. In the previous tracer test conducted in 2012 (Southwest Nonferrous Kunming Exploration Surveying and Designing (Institute) Inc., unpublished report, 2012), ammonium molybdate was used as a tracer and injected into the pool of HP to find out whether the S5 and S6 springs are supplied by the HP Springs. In that study, single-peaked BTCs were obtained, which confirms the hydraulic connection between OTLJ and HP. However, in this study, in contrast to S1–S3, higher concentrations and fewer peaks were obtained from curves received at S5–S7 (Fig. 4a–f and Fig. 5a–c). Thus, it can be deduced that regional groundwater is the predominant recharge source of the OTLJ Springs and streamflow seepage from HP only accounts for a very limited proportion.
Only one major flow path was determined from Jiuzihai karst depression to each spring located in the fault zones (S8 and S9; Fig. 7). In contrast to S5–S7, the 2RNE model delivered the lower partitioning coefficient (71–80%) and the higher mass transfer coefficient (0.48–1.23) for S8–S9, indicating higher percentage of the immobile fluid region and more extensive exchange between mobile and immobile regions along the flow paths. In practical terms, fracture zones generated by tectonic activity provide favorable conditions for the emergence of immobile fluid regions. In addition, compared with S8, S9 produced significantly lower velocities, likely due to the lower permeability of the flow path.
Implications of tracer tests for the evaluation of an artificial recharge scheme
Tracer tests were firstly employed for the evaluation of an artificial recharge scheme in a karstic area. By injecting tracers into the recharge area of the scheme, it was possible to acquire more intuitionistic and realistic information related to the practical engineering. According to the tracer tests, Jiuzihai karst depression is confirmed to be the main concentrated recharge area of the whole karst aquifer system. This system is characterized by multiple conduits in which only the conduits connecting Jiuzihai with the springs in HP and OTLJ can be regarded as the available recharge pathways for this scheme. Highly conductive conduits and triple-conduit structures provide favorable conditions for the artificial recharge scheme. In 2018, a similar tracer experiment had been conducted in the study area. According to Han et al. (2019), 500 kg of potassium iodide powder were dissolved and injected into the sinkhole of Jiuzihai karst depression. Results show that this depression is significantly connected with the springs in HP and OTLJ through deep-buried karst conduits. It was also confirmed by Han et al. (2019) that the dried-up situation can be effectively repaired by recharging in the depression.
Tracer tests also suggest that the shortest recharge time from Jiuzihai to HP Springs is about 190 h (tle). Previous study concluded that the time of first tracer detection and maximum concentration (or peak time) yield an estimate of maximum flow velocity (vle) and dominant velocity (vp1), respectively (Goldscheider et al. 2008). Thus, it can be deduced that the discharge of HP Springs will increase after 220 h (tp1) of the implementation of artificial recharge in Jiuzihai. Similarly, it will take about 300 h for OTLJ Springs to receive the recharge. Overall, time lags of the artificial concentrated recharge are 9.2 and 12.5 days for springs in HP and OTLJ respectively, while the time lag of natural recharge by regional precipitation is about 2–3 months according to Li (2016). This intensive and rapid recharging process forms one of the advantages of the scheme; however, it should be noticed that the tracers were forced into the aquifer with pressure by flushing water. This artificial pressure-bearing injection differs from the natural infiltration in practical engineering. Consequently, it will take a little more time in the implementation of the scheme to receive the recharged water.
Clear Spring and Fault Springs are identified as the leakages in this scheme. The pathway between Jiuzihai karst depression and Clear Spring characterize high permeability and exhibit two main branches of drainage. Owing to the shortest transporting distance, recharged water is predicted to be firstly received at the Fault Springs. Besides, as the main fault zones with considerable extension length, there might be other undiscovered leakages along the F1 due to the limitation of field investigation.
Since the fully dissolved and mixed tracers were injected into the aquifer and monitored in the same period under the condition of no precipitation, the tracer recoveries calculated at each spring can offer references for the distribution proportion of recharged water. Considering the possibility of interference with the recoveries obtained from I− as discussed in section ‘Tracer recoveries’, only the recoveries from uranine are used for the following analysis. As shown in Fig. 8, tracer mass calculated at the HP Springs and OTLJ Springs accounts for 24 and 21% of the total recovered tracer mass, respectively, compared with 40 and 15% at Clear Spring and Fault Springs. This indicates that the springs to be recharged (HP Springs and OTLJ Springs) and the springs considered as leakages (Clear Spring and Fault Springs) respectively share 45 and 55% of the increased outflow provided by recharged water.
Conclusion
By conducting the multitracer tests, it was possible to determine the hydrogeological conditions from Jiuzihai karst depression to the water receiving springs, and therefore to provide references for the evaluation of an artificial recharge scheme. Interpretation and modeling of the BTCs provide insights into the transporting mechanisms of karst groundwater and the structural characteristics of the aquifer. The drainages between Jiuzihai karst depression and each spring are respectively characterized by a triple conduit (HP Springs), double conduit (Clear Spring) and single conduit (OTLJ Springs and Fault Springs). With higher flow velocities and multiple conduits, favorable recharge conditions can be recognized from the recharge area to HP springs. According to the temporal variation of tracer concentrations, it is predicted that the springs in HP and OTLJ will be effectively recharged by the water supplied at Jiuzihai karst depression after 9.2 and 12.5 days, respectively. By setting additional springs as receiving points during the tracer tests, it is possible to identify the leakages of the recharge scheme, which included Clear Spring and Fault Springs. Based on the calculation of the uranine recoveries, it can be further deduced that the springs to be recharged (HP Springs and OTLJ Springs) and the springs considered as leakages, respectively, share 45 and 55% of the increased outflow. Parameters and spatially resolved information obtained from tracer tests allow for a better understanding of the karst aquifer and karst groundwater and therefore may help to restore and preserve karst water resources in the Old Town of Lijiang. Moreover, this study broadens the possibility of applying combined tracer tests to practical engineering projects.
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Acknowledgements
We are grateful to Dr. Junjie Zhang, Dr. Leilei Guo, Yuxin Li, Wei Gao, Anbang Huang, Wanting Zhang, Chengwen Wang, Ruiwen Ling, Qingzhong Ding and Leilei Yu for participating of the field tracer work. We also gratefully acknowledge the very useful technical edit by Sue Duncan. Special thanks are given to the editors and anonymous reviewers for their valuable comments.
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This article was funded by the State Key Laboratory for Geo-hazard Prevention and Geo-environment Protection (Chengdu University of Technology; grant No. SKLGP2020K002).
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Cen, X., Xu, M., Qi, J. et al. Characterization of karst conduits by tracer tests for an artificial recharge scheme. Hydrogeol J 29, 2381–2396 (2021). https://doi.org/10.1007/s10040-021-02398-w
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DOI: https://doi.org/10.1007/s10040-021-02398-w