A methodology for the assessment of groundwater resource variability in karst catchments with sparse temporal measurements

Sivelle, Vianney; Jourde, Hervé

doi:10.1007/s10040-020-02239-2

Cited by 6 publications

(6 citation statements)

References 67 publications

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“…There are two stable isotopes of hydrogen: 1 H and 2 H. The lighter hydrogen isotope accounts for ≈ 99.985 % of total stable hydrogen, with the heavier isotope 2 H accounting for the remaining ≈0.015 %. There are three stable forms of oxygen: 16 O, 17 O, and 18 O. The lightest one, is the most abundant, while 17 O and 18 O are less common in nature.…”

Section: Methodsmentioning

confidence: 99%

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The use of 2H and 18O isotopes in the study of coastal karstic aquifers

Mance

Lenac

Radišić

et al. 2022

Ninth International Symposium “Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques”

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Complexity of karst groundwater networks requires implementation of standard hydrogeological monitoring and unconventional methods of investigation. We present the results of isotope composition analysis for three karst springs and rain water collected in their hinterland. During two years, spring water samples were collected on a weekly and rain samples on a monthly basis. The main findings are: winter precipitation of Mediterranean origin dominates springs' recharge, a dual porosity model is a fit for the studied systems, and hinterlands of the springs have different retention capabilities.

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Section: Methodsmentioning

confidence: 99%

“…There are three stable forms of oxygen: 16 O, 17 O, and 18 O. The lightest one, is the most abundant, while 17 O and 18 O are less common in nature.…”

Section: Methodsmentioning

confidence: 99%

The use of 2H and 18O isotopes in the study of coastal karstic aquifers

Mance

Lenac

Radišić

et al. 2022

Ninth International Symposium “Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques”

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“…Indeed, considering piezometric head variations in lumped parameters rainfall-discharge models may lead to better model performance (Mazzilli et al, 2011;Cousquer and Jourde, 2022). Nonetheless, the information content of the piezometric head time series (directly measured, or derived from ground-based gravity measurements) for lumped parameters rainfall-discharge models calibration purpose can be disputable when the available data is not adequate to characterize the whole catchment due to the important heterogeneity in karst aquifers (Sivelle and Jourde, 2020;Mazzilli et al, 2013). Also, Cousquer and Jourde (2022) account for the surface runoff in a lumped parameters rainfall-discharge model calibration procedure allowing to reduce the parametric uncertainties.…”

Section: Challenges In Lumped Parameters Modeling In Karst Hydrologymentioning

confidence: 99%

“…In order to address these issues, different modeling approaches have been developed (Jeannin et al, 2021) such as, among others, fully-distributed models (Chen and Goldscheider, 2014), semi-distributed models (Doummar et al, 2012;Dubois et al, 2020;Ollivier et al, 2020), and lumped parameter models (Mazzilli et al, 2019) including semi-distributed recharge (Bittner et al, 2018;Sivelle et al, 2022a). Among these, lumped parameter models are recognized as major tools to explore the ability of conceptual representations to explain observations in karst systems (Poulain et al, 2018;Sivelle et al, 2019;Duran et al, 2020;Frank et al, 2021) and for managing karst groundwater resources (Sivelle and Jourde, 2020;Sivelle et al, 2021;Labat et al, 2022;Cousquer and Jourde, 2022).…”

Section: Introductionmentioning

confidence: 99%

Improvement of the KarstMod modeling platform for a better assessment of karst groundwater resources

Sivelle

Cinkus

Mazzilli

et al. 2023

Preprint

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Abstract. 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.

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“…After extraction, we applied Mangin's (1975) recession analysis model which has been widely used for estimating drainage characteristics and aquifer dynamics in fractured non-homogeneous media (Schuler et al, 2020;Sivelle and Jourde, 2021;Fleury et al, 2007;Liu et al, 2010;Xu et al, 2018). To analyze the extracted recessions, we used this method which considers a two-component recession curve by distinguishing between quick-flow (mostly through karstic conduits) and slow-flow (mostly through the fissure matrix of the carbonate rock) recessions (Fig.…”

Section: Mangin Modelmentioning

confidence: 99%

Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components

Olarinoye

Gleeson²,

Hartmann³

2022

Hydrol. Earth Syst. Sci.

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Abstract. Analysis of karst spring recession hydrographs is essential for determining hydraulic parameters, geometric characteristics, and transfer mechanisms that describe the dynamic nature of karst aquifer systems. The extraction and separation of different fast- and slow-flow components constituting a karst spring recession hydrograph typically involve manual and subjective procedures. This subjectivity introduces a bias that exists, while manual procedures can introduce errors into the derived parameters representing the system. To provide an alternative recession extraction procedure that is automated, fully objective, and easy to apply, we modified traditional streamflow extraction methods to identify components relevant for karst spring recession analysis. Mangin's karst-specific recession analysis model was fitted to individual extracted recession segments to determine matrix and conduit recession parameters. We introduced different parameter optimization approaches into Mangin's model to increase the degree of freedom, thereby allowing for more parameter interaction. The modified recession extraction and parameter optimization approaches were tested on three karst springs under different climate conditions. Our results showed that the modified extraction methods are capable of distinguishing different recession components and derived parameters that reasonably represent the analyzed karst systems. We recorded an average Kling–Gupta efficiency KGE > 0.85 among all recession events simulated by the recession parameters derived from all combinations of recession extraction methods and parameter optimization approaches. While there are variabilities among parameters estimated by different combinations of extraction methods, optimization approaches, and seasons, we found much higher variability among individual recession events. We provided suggestions to reduce the uncertainty among individual recession events and raised questions about how to improve confidence in the system's attributes derived from recession parameters.

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A methodology for the assessment of groundwater resource variability in karst catchments with sparse temporal measurements

Cited by 6 publications

References 67 publications

The use of 2H and 18O isotopes in the study of coastal karstic aquifers

The use of 2H and 18O isotopes in the study of coastal karstic aquifers

Improvement of the KarstMod modeling platform for a better assessment of karst groundwater resources

Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components

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