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This study aims to analyze spatio-temporal piezometric data and integrate them with geological, geotechnical, and geophysical data to enhance their interpretation. The research focuses on a site located in the Meuse and Haute-Marne departments of France, which has been under investigation since 1994 as part of the surface facilities characterization for the Meuse-Haute-Marne underground laboratory and the CIGEO (Centre Industriel de stockage Géologique) Andra project. These investigations span different spatial and temporal scales. We observed the contribution of water masses associated with external forcing and identified two types of aquifer responses: a rapid response to rainfall events when fractures are well-connected, and minor reactivity at the matrix level. Additionally, we demonstrated that the matrix compartment can be finely characterized through a combined interpretation of piezometric response analysis, fracture analysis, and surface nuclear magnetic resonance (SNMR) soundings. The methodology developed in this project offers an improved understanding of karst piezometry and/or unsaturated zone extension, which is essential for comprehend ding flow dynamics and better constraining the functioning of karst aquifers. Furthermore, this site serves as an ideal workshop for studying flow in fractured media, providing valuable insights into hydrodynamic behavior in complex subsurface environments. [ABSTRACT FROM AUTHOR]

Performance criteria play a key role in the calibration and evaluation of hydrological models and have been extensively developed and studied, but some of the most used criteria still have unknown pitfalls. This study set out to examine counterbalancing errors, which are inherent to the Kling–Gupta efficiency (KGE) and its variants. A total of nine performance criteria – including the KGE and its variants, as well as the Nash–Sutcliffe efficiency (NSE) and the modified index of agreement (d1) – were analysed using synthetic time series and a real case study. Results showed that, when assessing a simulation, the score of the KGE and some of its variants can be increased by concurrent overestimation and underestimation of discharge. These counterbalancing errors may favour bias and variability parameters, therefore preserving an overall high score of the performance criteria. As bias and variability parameters generally account for two-thirds of the weight in the equation of performance criteria such as the KGE, this can lead to an overall higher criterion score without being associated with an increase in model relevance. We recommend using (i) performance criteria that are not or less prone to counterbalancing errors (d1 , modified KGE, non-parametric KGE, diagnostic efficiency) and/or (ii) scaling factors in the equation to reduce the influence of relative parameters. [ABSTRACT FROM AUTHOR]

Hydrological models are widely used to characterize, understand and manage hydrosystems. Lumped parameter models are of particular interest in karst environments given the complexity and heterogeneity of these systems. There is a multitude of lumped parameter modelling approaches, which can make it difficult for a manager or researcher to choose. We therefore conducted a comparison of two lumped parameter modelling approaches: artificial neural networks (ANNs) and reservoir models. We investigate five karst systems in the Mediterranean and Alpine regions with different characteristics in terms of climatic conditions, hydrogeological properties and data availability. We compare the results of ANN and reservoir modelling approaches using several performance criteria over different hydrological periods. The results show that both ANNs and reservoir models can accurately simulate karst spring discharge but also that they have different advantages and drawbacks: (i) ANN models are very flexible regarding the format and amount of input data, (ii) reservoir models can provide good results even with a few years of relevant discharge in the calibration period and (iii) ANN models seem robust for reproducing high-flow conditions, while reservoir models are superior in reproducing low-flow conditions. However, both modelling approaches struggle to reproduce extreme events (droughts, floods), which is a known problem in hydrological modelling. For research purposes, ANN models have been shown to be useful for identifying recharge areas and delineating catchments, based on insights into the input data. Reservoir models are adapted to understand the hydrological functioning of a system by studying model structure and parameters. [ABSTRACT FROM AUTHOR]

Karst spring discharge time series analyses are often used to gain preliminary insights into the hydrological functioning of a karst system. KarstID is an R Shiny application that facilitates the completion of such analyses and allows the identification of karst system hydrological functioning. The application permits (i) to perform statistical, recession curves, classified discharges and signal (simple correlational and spectral) analyses; (ii) to calculate relevant indicators representative of distinct hydrological characteristics of karst systems, (iii) to classify karst systems hydrological functioning; and (iv) to compare the results to a database of 78 karst systems. The KarstID software is free, open source, and actively developed on a developer community platform. The user-friendly installation and launch make it especially accessible even for non-programmers; therefore, KarstID can be used for both research and educational purposes. The application and its user manual are both available on the French SNO KARST website (https://sokarst.org/en/softwares-en/karstid-en/). [ABSTRACT FROM AUTHOR]

We propose an updated version of KarstMod, an adjustable platform dedicated to lumped parameter rainfall-discharge modeling of karst aquifers. KarstMod provides a modular, user-friendly modeling environment for educational, research and operational purposes. It also includes numerical tools for time series analysis, model evaluation and sensitivity analysis. The modularity of the platform facilitates common operations related to lumped parameter rainfall-discharge modeling, such as (i) set up and parameter estimation of a relevant model structure, and (ii) evaluation of internal consistency, parameter sensitivity and hydrograph characteristics. The updated version now includes (i) external routines to better consider the input data and their related uncertainties, i.e. evapotranspiration and solid precipitation, (ii) enlargement of multi-objective calibration possibilities, allowing more flexibility in terms of objective functions as well as observation type and (iii) additional tools for model performance evaluation including further performance criteria and tools for model errors representation. [ABSTRACT FROM AUTHOR]

Transit time can be estimated thanks to natural tracers, but few of them are usable in the 0–6-month range. The main purpose of this work is to analyze the potential of the ratio of heavy- to light-weight organic compounds (the humification index (HIX); Ohno, 2002; Zsolnay et al., 1999) as a natural tracer of short transit time (Blondel et al., 2012). Critical analysis of former studies shows that although the link between HIX and transit time seems consistent, the whole methodological approach needs to be consolidated. Natural organic matter fluorescence from 289 groundwater samples from four springs and 10 flow points located in the unsaturated zone of the Vaucluse karst system is characterized by parallel factor analysis (PARAFAC) thanks to the excitation–emission matrix (EEM), thus (i) allowing for the identification of main fluorescent compounds of sampled groundwater and (ii) evidencing the inadequacy of HIX 2D emission windows to characterize groundwater organic matter. We then propose a new humification index called the Transit Time index (TTi) based on the Ohno (2002) formula but using PARAFAC components of heavy and light organic matter from our samples instead of 2D windows. Finally, we evaluate TTi relevance as a transit time tracer by (i) performing a detailed analysis of its dynamics on a selected spring (Millet) and (ii) comparing its mean value over karst springs of the Vaucluse karst system. Principal component analysis (PCA) of TTi and other hydrochemical parameters monitored at Millet spring put in relief the different ranges of transit time associated with the different organic matter compounds. PCA results also provide evidence that TTi can detect a small proportion of fast infiltration water within a mix, while other natural tracers of transit time provide no or less sensitive information. TTi distributions at monitored karst springs are consistent with relative transit times expected for the small-scale, short average transit time systems. TTi thus appears as a relevant qualitative tracer of transit time in the 0–6-month range where existing tracers fail and may remain applicable, even in the case of anthropic contamination thanks to PARAFAC modeling. Transforming it into quantitative information is a challenging task which may be possible thanks to intensive studies of organic matter degradation kinetics in natural waters with the help of radiogenic isotope usage or an artificial tracer test. [ABSTRACT FROM AUTHOR]

Performance criteria play a key role in the calibration and evaluation of hydrological models and have been extensively developed and studied, but some of the most used criteria still have unknown pitfalls. This study set out to examine counterbalancing errors, which are inherent to the Kling-Gupta Efficiency (KGE) and its variants. A total of nine performance criteria – including the KGE and its variants, as well as the Nash-Sutcliffe Efficiency (NSE) and the refined version of the Willmott’s index of agreement (dr) – were analysed using synthetic time series and a real case study. Results showed that, assessing a simulation, the score of the KGE and some of its variants can be increased by concurrent over- and underestimation of discharge. These counterbalancing errors may favour bias and variability parameters, therefore preserving an overall high score of the performance criteria. As bias and variability parameters generally account for 2/3 of the weight in the equation of performance criteria such as the KGE, this can lead to an overall higher criterion score without being associated to an increase in model relevance. We recommend using (i) performance criteria that are not or less prone to counterbalancing errors (NSE, dr, modified KGE, non-parametric KGE, Diagnostic Efficiency) in a multi-criteria framework, and/or (ii) scaling factors in the equation to reduce the influence of relative parameters. [ABSTRACT FROM AUTHOR]

Hydrological models are widely used to characterise, understand and manage hydrosystems. Data-driven models are of particular interest in karst environments given the complexity and heterogeneity of these systems. There is a multitude of data-driven modelling approaches, which can make it difficult for a manager or researcher to choose. We therefore conducted a comparison of two data-driven modelling approaches: artificial neural networks (ANN) and reservoir models. We investigate five karst systems in the Mediterranean and Alpine regions with different characteristics in terms of climatic conditions, hydrogeological properties and data availability. We compare the results of ANN and reservoir modelling approaches using several performance criteria over different hydrological periods. The results show that both ANN and reservoir models can accurately simulate karst spring discharge, but also that they have different advantages and drawbacks: (i) ANN models are very flexible regarding the format and amount of input data, (ii) reservoir models can provide good results even with short calibration periods, and (iii) ANN models seem robust for reproducing high-flow conditions while reservoir models are superior for reproducing low-flow conditions. However, both modelling approaches struggle to reproduce extreme events (droughts, floods), which is a known problem in hydrological modelling. For research purposes, ANN models have shown to be useful to identify recharge areas and delineate catchment, based on insights into the input data. Reservoir models are adapted to understand the hydrological functioning of a system, by studying model structure and parameters. [ABSTRACT FROM AUTHOR]

Despite many existing approaches, modeling karst water resources remains challenging as conventional approaches usually heavily rely on distinct system knowledge. Artificial neural networks (ANNs), however, require only little prior knowledge to automatically establish an input–output relationship. For ANN modeling in karst, the temporal and spatial data availability is often an important constraint, as usually no or few climate stations are located within or near karst spring catchments. Hence, spatial coverage is often not satisfactory and can result in substantial uncertainties about the true conditions in the catchment, leading to lower model performance. To overcome these problems, we apply convolutional neural networks (CNNs) to simulate karst spring discharge and to directly learn from spatially distributed climate input data (combined 2D–1D CNNs). We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorological–hydrological characteristics and hydrodynamic system properties. We compare the proposed approach both to existing modeling studies in these regions and to our own 1D CNN models that are conventionally trained with climate station input data. Our results show that all the models are excellently suited to modeling karst spring discharge (NSE: 0.73–0.87, KGE: 0.63–0.86) and can compete with the simulation results of existing approaches in the respective areas. The 2D models show a better fit than the 1D models in two of three cases and automatically learn to focus on the relevant areas of the input domain. By performing a spatial input sensitivity analysis, we can further show their usefulness in localizing the position of karst catchments. [ABSTRACT FROM AUTHOR]

Karst systems consist of heterogeneous aquifers characterized by non-linear hydrogeological behavior. This is intrinsically linked to the coexistence of saturated versus unsaturated, open-channel versus closed-conduit flow and laminar versus turbulent flows within these aquifers. These multiple dualities together with a lack of knowledge of their internal structure lead to increasing difficulties in the management of groundwater resources related to karst aquifers. However, karst aquifers constitute strategic fresh water resources and many stakeholders carry out withdrawals sometimes continuously or centered on the period of low water level as for the irrigation. These withdrawals generate discharge decreases that can constitute sources of conflicts between upstream and downstream users of the resource. In this study, we propose a methodology to assess the impact of withdrawals on the spring discharge of a karst aquifer based on a conceptual non-linear reservoirs model. This methodology is applied to the second largest karst system in France: The Touvre karst system (La Rochefoucauld). The simulated influence of the total withdrawals on runoff deficit is larger than the total withdrawals in relationship with the non-linearity of the conceptual models. Globally, this impact is about one and a half of the total withdrawal depending of the year under consideration. Moreover, we show that it also implies a decrease from 10% to 20% of the low-flow annual discharge. The next step will be to also address the issues raised by the climate change projections. [ABSTRACT FROM AUTHOR]

Transit time can be estimated thanks to natural tracers but few of them are usable in the 0-6 months range. The main purpose of this work is to analyze the potential of the ratio of heavy to light-weight organic compounds (HIX, Ohno, 2002; Zsolnay et al., 1999) as a natural tracer of short transit time (Blondel et al., 2012). Critical analysis of former studies shows that although the link between HIX and transit time seems consistent, the whole methodological approach needs to be consolidated. Natural organic matter fluorescence from 289 water samples from 4 springs and flow points located in the unsaturated zone of the Vaucluse karst system is characterized by parallel factor analysis (PARAFAC) thanks to excitation- emission matrix (EEM), thus (i) allowing the identification of main fluorescent compounds of sampled groundwater; and (ii) evidencing the inadequacy of HIX 2D emission windows to characterize groundwater organic matter. We then propose a new humification index called Transit Time index (TTi) based on Ohno (2002) formula but using PARAFAC components of heavy and light organic matter from our samples instead of 2D windows. Finally, we evaluate TTi relevance as a transit time tracer by: (i) performing a detailed analysis of its dynamics on a selected spring (Millet) and (ii) comparing its mean value over karst springs of Fontaine de Vaucluse system. Principal component analysis (PCA) of TTi and other hydrochemical parameters monitored at Millet put in relief the different ranges of transit time associated with the different organic matter compounds. PCA results also provide evidence that TTi can detect a small proportion of fast infiltration water within a mix, while other natural tracers of transit time provide no or less sensitive information. TTi distributions at monitored karst springs are consistent with relative transit times expected for the small-scale, short average transit times systems. TTi thus appears as a relevant tracer of transit time in the 0-6 months range where existing tracers fail, even if the information on transit time is only qualitative at this stage. [ABSTRACT FROM AUTHOR]

Despite many existing approaches, modeling karst water resources remains challenging and often requires solid system knowledge. Artificial Neural Network approaches offer a convenient solution by establishing a simple input-output relationship on their own. However, in this context, temporal and especially spatial data availability is often an important constraint, as usually no or few climate stations within a karst spring catchment are available. Hence spatial coverage is often unsatisfying and can introduce severe uncertainties. To avoid these problems, we use 2D-Convolutional Neural Networks (CNN) to directly process gridded meteorological data followed by a 1D-CNN to perform karst spring discharge simulation. We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorologic-hydrological characteristics and hydrodynamic system properties. We compare our 2D-models both to existing modeling studies in these regions and to 1D-models, which use climate station data, as it is common practice. Our results show that our models are ) excellently suited to model karst spring discharge and rival the simulation results of existing approaches in the respective areas. The 2D-models learn relevant parts of the input data and by performing a spatial input sensitivity analysis we can further show their potential for karst catchment localization and delineation. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article focuses on a proposed Quantum Geographic Information System (QGIS) plugin based on the PaPRIKa method used for karst aquifer vulnerability mapping. It states that the PaPRIKa method is based on an analysis of hydrological and geological aquifer properties. It comments that the QGIS plugin is a fast and secure method of creating thematic maps and vulnerability karst maps and allows for sharing of data and decision rules.

The magnitude of epikarstic water storage variation is evaluated in various karst settings using a relative spring gravimeter. Gravity measurements are performed over a 1.5-year period at the surface and inside caves at different depths on three karst hydro-systems in southern France: two limestone karst systems and one dolomite karst system. We find that significant water storage variations occur in the first 10m of karst unsaturated zone. The subsurface water storage is also evidenced by complementary magnetic resonance sounding. The comparison between sites of the depth gravity measurements with respect to net water inflow suggests that seasonal water storage depends on the lithology. The transmissive function of the epikarst on the seasonal scale has been deduced from the water storage change estimation. Long (> 6 months) and short (< 6 months) transfer times are revealed in the dolomite and in the limestone respectively. [ABSTRACT FROM AUTHOR]

Recharge processes of karst aquifers are difficult to assess given their strong heterogeneity and the poorly known effect of vadose zone on infiltration. However, recharge assessment is crucial for the evaluation of groundwater resources. Moreover, the vulnerability of karst aquifers depends on vadose zone behaviour because it is the place where most contamination takes place. In this work, an in situ experimental approach was performed to identify and quantify flow and storage processes occurring in karst vadose zone. Cave percolation monitoring and dye tracing were used to investigate unsaturated zone hydrological processes. Two flow components (diffuse and quick) were identified and, respectively, account for 66% and 34% of the recharge. Quickflow was found to be the result of bypass phenomenon in vadose zone related to water saturation. We identify the role of epikarst as a shunting area, most of the storage in the vadose zone occurring via the diffuse flow component in low permeability zones. Relationship between rainfall intensity and transit velocity was demonstrated, with 5 times higher velocities for the quick recharge mode than the diffuse mode. Modelling approach with KarstMod software allowed to simulate the hybrid recharge through vadose zone and shows promising chances to properly assess the recharge processes in karst aquifer based on simple physical models. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work examines the relevance of the inclusion of ground-based gravity data in the calibration process of a global rainfall-discharge reservoir model. The analysis is performed for the Durzon karst system (Larzac, France). The first part of the study focuses on the hydrological interpretation of the ground-based gravity measurements. The second part of the study investigates further the information content of the gravity data with respect to water storage dynamics modelling. The gravity-derived information is found unable to either reduce equifinality of the single-objective, discharge-based model calibration process or enhance model performance through assimilation. [ABSTRACT FROM AUTHOR]

The main outlets of karst systems are springs, the hydrographs of which are largely affected by flow processes in the unsaturated zone. These processes differ between the epikarst and transmission zone on the one hand and the matrix and conduit on the other hand. However, numerical models rarely consider the unsaturated zone, let alone distinguishing its subsystems. Likewise, few models represent conduits through a second medium, and even fewer do this explicitly with discrete features. This paper focuses on the interest of hybrid models that take into account both unsaturated subsystems and discrete conduits to simulate the reservoir-scale response, especially the outlet hydrograph. In a synthetic karst aquifer model, we performed simulations for several parameter sets and showed the ability of hybrid models to simulate the overall response of complex karst aquifers. Varying parameters affect the pathway distribution and transit times, which results in a large variety of hydrograph shapes. We propose a classification of hydrographs and selected characteristics, which proves useful for analysing the results. The relationships between model parameters and hydrograph characteristics are not all linear; some of them have local extrema or threshold limits. The numerous simulations help to assess the sensitivity of hydrograph characteristics to the different parameters and, conversely, to identify the key parameters which can be manipulated to enhance the modelling of field cases. [ABSTRACT FROM AUTHOR]

Usual conceptual models of karst hydrodynamics highlight the important role of unsaturated subsystems in recharge repartition. However, few of them have been compared with scarce suitable physically-based numerical models. Hybrid models that couple single continuum medium with discrete features promise an improved consideration of karst specificities. Here we evaluate their capability to properly reproduce interactions between a vertical conduit and the surrounding unsaturated matrix. We simulate the response of such a configuration to a single recharge event for various sets of parameters. We show the ability of hybrid models to reproduce the most significant behaviors described in the literature, i.e., transient storage and distribution of recharge, flow concentration towards conduits in the epikarst, and matrix-conduit exchanges varying in time and space. In addition to the explicit conduits, simulating variably saturated flows with the Richards equation and distinguishing the epikarst and the transmission zone are key elements to reproduce most processes. The contrasts between subsystems necessary to observe desired behaviors have been quantified. They are reinforced by the varying matrix saturation that causes realistic competition between matrix and explicit conduits. The study also highlights the need to deepen knowledge of the scaled medium properties we need to know to apply such models to actual cases. [ABSTRACT FROM AUTHOR]

Understanding karst unsaturated zone (UZ) recharge dynamics is crucial for achieving sustainable management of karst hydrosystems. In this paper, we provide the first report of the application of surface nuclear magnetic resonance (SNMR) monitoring of a karst UZ during a typical Mediterranean rain event. This 79 days' SNMR monitoring is a part of a more than 2 years of SNMR monitoring at the Low Noise Underground Laboratory (LSBB) experimental site located within the Fontaine de Vaucluse karst hydrosystem (southeastern France). We present eight SNMR soundings conducted before and after the rain event that accumulated 168 mm in 5 days. The obtained results demonstrate the applicability and the efficiency of SNMR for investigating infiltration dynamics in karst UZs at the time scale of a few days. We present the SNMR amplitudes that highlight strong signal variations related to water dynamics in the karst UZ. Infiltrated water cause increased SNMR signal during 5 days after the rain event. A significant draining process of the medium starts 15 days after the main event. Finally, after 42 days, the SNMR signal returns close to the initial state. [ABSTRACT FROM AUTHOR]

The focus of this paper is to investigate the ability to assess the flow exchanges between the matrix and the conduits in two karstified watersheds (Aliou and Baget, Ariège, France) using the KarstMod modeling platform. The modeling is applied using hourly and daily time series. First, the flow dynamics between the conduit and the surrounding matrix are described on a rainfall event scale (i.e., a few days). The model allows us to describe a physical reality concerning the flow reversal between matrix and conduit when there is a significant rainfall event. Then, the long-term trends (i.e., inter-annual) in the matrix water level are evidenced using the moving average over shifting horizon method (MASH). The mean water level in the matrix dropped about 10% to 15% since the late 1960s. Also, the matrix recharge has been delayed from February in the late 1960s to April since the 1990s. Moreover, the contribution of the matrix in the total spring flow is estimated though mass balance. It is estimated that the annual matrix contribution in the total spring flow is about 3% and it can increase to up to 25% during periods with low rainfall. [ABSTRACT FROM AUTHOR]