Articles | Volume 26, issue 19
https://doi.org/10.5194/hess-26-5035-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5035-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Transit Time index (TTi) as an adaptation of the humification index to illustrate transit time differences in karst hydrosystems: application to the karst springs of the Fontaine de Vaucluse system (southeastern France)
Leïla Serène
CORRESPONDING AUTHOR
HSM, Univ. Montpellier, CNRS, IMT, IRD, Montpellier, France
Christelle Batiot-Guilhe
HSM, Univ. Montpellier, CNRS, IMT, IRD, Montpellier, France
Naomi Mazzilli
UMR 1114 EMMAH (AU-INRAE), Université d'Avignon, 84000 Avignon,
France
Christophe Emblanch
UMR 1114 EMMAH (AU-INRAE), Université d'Avignon, 84000 Avignon,
France
Milanka Babic
UMR 1114 EMMAH (AU-INRAE), Université d'Avignon, 84000 Avignon,
France
Julien Dupont
UMR 1114 EMMAH (AU-INRAE), Université d'Avignon, 84000 Avignon,
France
Roland Simler
UMR 1114 EMMAH (AU-INRAE), Université d'Avignon, 84000 Avignon,
France
Matthieu Blanc
Independent Researcher, Montpellier, France
Gérard Massonnat
Total Energies, CSTJF, Avenue Larribau, CEDEX 64018 Pau, France
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The Kling–Gupta Efficiency (KGE) is a performance criterion extensively used to evaluate hydrological models. We conduct a critical study on the KGE and its variant to examine counterbalancing errors. Results show that, when assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without an associated increase in model relevance. We suggest that one carefully choose performance criteria and use scaling factors.
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Numerous modelling approaches can be used for studying karst water resources, which can make it difficult for a stakeholder or researcher to choose the appropriate method. We conduct a comparison of two widely used karst modelling approaches: artificial neural networks (ANNs) and reservoir models. Results show that ANN models are very flexible and seem great for reproducing high flows. Reservoir models can work with relatively short time series and seem to accurately reproduce low flows.
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Modeling complex karst water resources is difficult enough, but often there are no or too few climate stations available within or close to the catchment to deliver input data for modeling purposes. We apply image recognition algorithms to time-distributed, spatially gridded meteorological data to simulate karst spring discharge. Our models can also learn the approximate catchment location of a spring independently.
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Short summary
This work aims to develop the Transit Time index (TTi) as a natural tracer of karst groundwater transit time, usable in the 0–6-month range. Based on the fluorescence of organic matter, TTi shows its relevance to detect a small proportion of fast infiltration water within a mix, while other natural transit time tracers provide no or less sensitive information. Comparison of the average TTi of different karst springs also provides consistent results with the expected relative transit times.
This work aims to develop the Transit Time index (TTi) as a natural tracer of karst groundwater...