Karst water resources are valuable freshwater sources for around 10 % of the world population. Nonetheless, anthropogenic factors and global changes have been seriously deteriorating the karst water quality and dependent ecosystems. Solute transport models are powerful tools to monitor, control, and manage the water quality and dependent ecosystem functioning. By representing and predicting the spatiotemporal behavior of solute migration in karst systems, the transport models enhance our understanding about the karst transport processes, thus enabling us to explore contamination risks and potential outcomes. This paper reviews the current state of knowledge on the modelling of solute transport processes in karst aquifers, thereby unveiling the fundamental challenges underlying a successful karst transport modelling. We discuss to what extent and in what ways we can handle these challenges and derive the key challenges and directions for reliable modelling of transport processes in karst systems in the present context of global changes.

Karst groundwater resources play a crucial role in global water supply. But due to their intrinsic heterogeneity, karst aquifers are especially vulnerable to contamination and environmental changes. To sustain karst water quality, assessing karst aquifer vulnerability to contamination and revealing the influence of changing environmental conditions on karst vulnerability are essential. This study unveils the impact of climate and land-use changes on karst aquifer vulnerability by proposing a novel process-based approach that uses the relationship between karst transport parameters and karst flow and site characteristics. Karst transport parameters are derived from tracer tests previously conducted within the study area by applying a simple transport model to observed breakthrough curves (BTCs). By coupling this transport model with a karst flow model we can predict the BTCs under projected changing environmental conditions. We apply our approach to the karstic aquifer system of the Unica springs that largely contribute to drinking water supply in Southwestern Slovenia. Our findings indicate that karst aquifer vulnerability can vary depending on the specific hydrogeological setting, prevalent flow conditions, as well as the current and future climate, and land use. More specifically, we find that impacts of climate change on karst aquifer vulnerability exceed the impacts of land-use changes. For our study site, we find that both higher and lower karst vulnerabilities can occur in future projections. While seasonally changing patterns of precipitation and temperature can lead to a decreased vulnerability in the summer months, they can lead to an increased vulnerability in the spring and winter months. Our study demonstrates that the proposed approach can be used as a tool for vulnerability assessments in karst aquifers, especially for revealing the impacts of future changes on karst water resources. We emphasize the need for continuous improvements in this field to ensure a safe management of karst water resources in the future.

Susuz karst aquifer is a mountainous and highly karstified aquifer located at the Central Taurus karst belt, Seydis,ehir, T & uuml;rkiye. P & imath;narbas,& imath; karst spring is a major water resource of the Susuz karst aquifer which drains approximately 15 million m3 of water annually, mostly between January and July. As the P & imath;narbas,& imath; spring dries up for the rest of the year, local water needs frequently emerge during the dry periods, especially for animal livestock and domestic usage. However, one major problem in the karst region is the decreasing trend in the spring discharge rate and increasing length of the dry period due to the impact of climate change. For this reason, to reduce the climate impact on the karst aquifer it is essential to explore alternative engineering solutions where they are applicable. This study proposes the construction of a cave dam in the Susuz karst system to retain and store groundwater during the dry period. Based on the accumulated hydrogeological knowledge and experiences in the karst region, we first conceptualized the cave dam construction and then indicated the positive influence of the stored groundwater under changing climate considering two main climate scenarios (SSP245 and SSP585). Our findings indicate that the (proposed) cave dam potentially stores 4 million m3 of water, which represents nearly 35 % of the mean annual spring discharge under current climate conditions. This amount is expected to rise over 50 % of the total discharge in future climate conditions.

Modelling solute mixing and transport processes is one of the key steps to effectively managing karstic groundwater resources, particularly under the threat of climate change and risk of contamination. For that reason, a considerable body of literature has been devoted to understanding and describing solute mixing and transport processes in karst aquifers. However, due to the strong multiscale heterogeneity (from microscale to aquifer scale), modelling solute mixing and transport processes in karst aquifers remains a challenging task. This presentation critically reviews the current state of knowledge and fundamental challenges in the modelling of solute mixing and transport processes in karst aquifers, thereby collocating and synthesizing the existing body of knowledge in the literature. To provide a holistic and objective picture of the state-of-the-art of the solute mixing and transport modelling, we performed a bibliometric analysis on the relevant literature for karst groundwater studies (over 2800 scientific papers). Further, with a meta-analysis of scientific papers focusing on the quantitative tracer tests, we evaluated the field-based transport parameters that are typically served for the solute mixing and transport models.The review unveils the fundamental modelling hinges underlying a successful modelling practice for the solute mixing and transport processes in karst, thereby discussing to what extent and in what ways we are dealing with these challenges. The major modelling challenges are defined as follows: (i) Model conceptualization based on data collection and system understanding (e.g., How well is the problem of interest defined? To what extent is the domain of interest described?), ii) Model selection considering the choice of a dominant physicochemical process (e.g., How well is the process of interest represented by a set of governing equations over the problem domain?), iii) Time-variability of solute mixing and transport processes (e.g., To what extent do the parameters represent the process of interest under the different time-scales?), iv) Model parametrization considering the parameter non-uniqueness and transferability (e.g., How realistic are the model parameters? To which extent are they transferable over the same aquifer?), v) Uncertainty quantification in model results (e.g., How robust are the model results? How much are we (un)certain about our model?). Finally, we address potential research directions and knowledge gaps by encouraging the community for building a protocol for solute transport modelling in karst aquifers, as well as providing more transparent and reproducible results.

Introducing additional information sources, such as hydrochemical signatures and water isotopes, into the model calibration has shown to be useful to enhance model robustness by increasing parameter identifiability and maintaining simulation reliability. Our study explores the added value of discharge young water fractions (Fyw, derived from the volume-weighted delta 18O concentrations) on the model reliability as a calibration constraint. For this, we coupled a karst hydrological model (VarKarst) with a catchment-scale transport model (StorAge Selection (SAS) function approach) to simulate discharge delta 18O concentration (delta 18OQ) and corresponding Fyw. We performed a multi-variable calibration scheme by simultaneously constraining a large model ensemble (1x106 realizations) with respect to the model performance on discharge and Fyw. By searching a model output space in which the model performance on discharge and model performance on Fyw provides an optimal trade-off, we extracted hydrologically more informative model realizations. We tested our calibration approach at the Wasseralm spring, which supplies drinking water to the city of Vienna, Austria. The contribution of the information content of Fyw to the model robustness was assessed by the degree of reduction in the parameter and simulation uncertainties. Our results indicate that the inclusion of Fyw notably reduced the uncertainty in model parameters (6 parameters out of 8), simulations (14 % vs. 10 % by delta 18OQ), and water balance components for the model internal states (around 40 %). Our findings reveal that Fyw confirms the model reliability (KGE: 0.71 +/- 0.01 in validation) in that it mainly reduces the parameter equifinality by providing physically more plausible and identifiable parameter sets. Therefore, Fyw is a potentially useful metric to better constrain the model outputs, thereby limiting the model uncertainty.

The robustness of a hydrological model is mainly dependent on how well the simulations resemble a quantity of interest, typically discharge, which is mainly achieved by successful model calibration. Performance metrics are used to quantify to what degree the system of interest is represented by a model. However, as there is not a general procedure and a rule for the selection of a performance metric, it is ultimately the modeller’s subjective decision. This study explores the contribution of discharge young water fractions—derived from stable water isotopes—to the selection of a hydrologically appropriate performance metric for the model calibration. For our analysis, we examined different metric combinations by a multi-variable calibration scheme on the Pareto-optimal frontier. By searching for a trade-off between the model performance on discharge and the model performance on discharge young water age, we not only satisfy the minimization of model simulation bias but also bring the process-based discharge-age information content into the model parametrization. To test our hypothesis, we applied our approach to one of the karstic water sources in Austria. Our finding indicates that the information content of young water age supports the proper choice of a performance metric for the model calibration scheme while reducing the modeller’s subjectivity on performance metric selection, thereby ensuring physically more plausible parameter sets.

Snow recharge is an important dominant hydrological process in the high altitude mountainous karstic aquifer systems. In general, widely used karst hydrological models (e.g., KarstMod, Varkarst) do not include a snow routine in the model structure to avoid increasing the number of model parameters while representing the complex hydrological process. As a result, recharge process is not represented well, which questions the optimality of the results that can be obtained under available datasets. This study presents a novel pre-processing method –called SCA routine– to compensate for the missing snow routine in karst models. The proposed pre-processing method is driven by the temperature, precipitation, and satellite-based snow observation datasets while classifying the precipitation input into three physical phases (rain, snow, and mixed) based on the temperature datasets to distribute each phase over the catchment using satellite-driven Snow-Covered Area (SCA) products. By the proposed method, the spring discharge simulation result is regulated well in time and magnitude. To examine the added utility of the SCA routine, the SCA-included simulation results are compared to the model performances with no routine and the classical Degree-Day method as a benchmark. To test the efficiency of our proposed method we use a karst hydrological model (KarstMod) to simulate the karst spring discharge in a well-observed semi-arid snow-dominated karstic aquifer (Central Taurus, Turkey). Our results confirm that the KarstMod model coupled by SCA routine ensures better model performance with a value of NSE = 0.784 than those of the classical Degree-day method (NSE = 0.760) and the model with no routine (NSE = 0.306) while providing a physically more realistic parameter set.Key Words: MODIS, Degree-Day, Hydrological model, Snowmelt, Mountainous karst

Recession Curve Analysis is a common method to characterize karstic aquifers and their discharge dynamics. Although this technique provides crucial information on quantifying system hydrodynamic properties, the manually selected recession curves analysis is neither a practical technique to cover all candidate recession curves, nor it allows extracting the entire hydrological diversity of the recession behavior. This study aimed to comparatively evaluate the applicability of automated recession selection procedures to the late-time recession analysis of karst spring hydrograph. For the comparative evaluation of the three automated recession extraction methods (Vogel Method, Brutsaert Method, and Aksoy and Wittenberg Method), we quantified the late-time recession parameters of spring hydrographs by combining three extraction methods with four recession analysis methods (Maillet, 1905; Boussinesq, 1904; Coutagne, 1948; and Wittenberg, 1999). By applying our experimental design into the five karst springs located in Austria, we identified the possible weaknesses of the automated recession extraction procedures for the late-time recession analysis for spring hydrographs. To explore the value of the karst spring’s physicochemical data (electrical conductivity and water temperature) as a completion data for the recession curve analysis, we carried out the hydro-chemograph analysis to examine the recession time and its duration. The research provides a research direction as to how the automated recession extraction procedures for the karst spring hydrographs could be improved by the physicochemical signatures of karst springs.

In this study, we examined the potential impact of climate change on the depletion of groundwater levels and storage. To achieve so, we simulated the groundwater flow using the HİDROTÜRK hydrogeological model under the climate change projections considering the RCP4.5 and RCP8.5 scenarios. To estimate the model forcing input (recharge and evapotranspiration) for the hydrogeological model, we used precipitation and temperature outputs from two Global Circulation Models, namely HadGEM2-ES and MPI-ESM-MR. To assess the changes in groundwater level and storage, we applied our experimental design in the Şuhut alluvial aquifer in Akarçay Basin (Turkey). The study revealed that there is not necessarily a substantial difference tracked over the estimated groundwater levels between the RCP4.5 and RCP8.5 scenarios until the end of 2050s. Yet, a significant reduction in the hydraulic head (approximately 114 m) and storage change (-17.25 %) – particularly in the western part of the aquifer – is expected in 2100, according to RCP8.5. This study confirmed that the selected climate model not only leads to the different predictions in the groundwater depletion, yet also results in a different degree of confidence in the model simulations.

Model assessment is a crucial part of hydrological modelling studies. The traditional intuitive approach is to judge model performance to explore its effectiveness and informativeness about the system reality. However, this approach does not necessarily guarantee that one captures system hydrological functioning from the model output. Here, we proposed a novel model assessment strategy that provides a direction to constrain model output space with the concept of model functionality. In the study, we used StorAge Selection (SAS) function approach as a process diagnostic tool to explore the model output space which is simultaneously informative on the system hydrological behaviour and functioning. To do that, the SAS model was fed by a karst-dedicated hydrological model (VarKarst) simulations to model the karst aquifer d18O transport and young water fraction of system discharge, Fyw. Model functionality was assessed by a new model verification metric, named Exceedance Probability Ranked Score, EPRS. The eligible clusters from the model output were then served in order to examine the model parameter space. Our findings provide direction to indicate that using young water fraction, Fyw as a process-diagnostic metric leads to an improvement in model realism while carrying a physically realistic model parameter set throughout the model output space.