Abstract
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/).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
KarstID is a free R Shiny application embedded into an R package (4.6 Mo), developed by Guillaume Cinkus (guillaume.cinkus@umontpellier.fr) and first made available in 2021. The software requires R version 4.0.0 or later, can be run on any web browser, and is licensed under Creative Commons Attribution 4.0 International. The code repository is available on GitHub: https://github.com/busemorose/KarstID.
References
Allaire J, Xie Y, McPherson J, et al (2021) Rmarkdown: dynamic documents for r. https://cran.r-project.org/package=rmarkdown. Last accessed: 01 Oct 2022
Arciniega-Esparza S, Breña-Naranjo JA, Pedrozo-Acuña A, Appendini CM (2017) HYDRORECESSION: a Matlab toolbox for streamflow recession analysis. Comput Geosci 98:87–92. https://doi.org/10.1016/j.cageo.2016.10.005
Attali D (2020) Shinyjs: easily improve the user experience of your shiny apps in seconds. R package version 2.0.0. https://CRAN.R-project.org/package=shinyjs
Bakalowicz M (2005) Karst groundwater: a challenge for new resources. Hydrogeol J 13:148–160. https://doi.org/10.1007/s10040-004-0402-9
Bakalowicz M (2011) Management of Karst groundwater resources. In: van Beynen PE (ed) Karst management. Springer, Netherlands, Dordrecht, pp 263–282
Banque Hydro (2021) French ministry of ecology, energy, sustainable development, archive of hydrological data. Available online: http://hydro.eaufrance.fr/. Last accessed: 01 Oct 2022
Barnes BS (1939) The structure of discharge-recession curves. Trans AGU 20:5. https://doi.org/10.1029/TR020i004p00721
Bonacci O (1993) Karst springs hydrographs as indicators of karst aquifers. Hydrol Sci J 38:51–62. https://doi.org/10.1080/02626669309492639
Boussinesq J (1903) Sur un mode simple d’écoulement des nappes d’eau d’infiltration à lit horizontal, avec rebord vertical tout autour lorsqu’une partie de ce rebord est enlevée depuis la surface jusqu’au fond. C R Acad Sci 137:5–11
Box GEP, Jenkins GM (1976) Time Series Analysis: Forecasting and Control, Revised Edition. Holden Day, San Franscisco and Düsseldrof and Johannesburg etc.
BRGM (2022) XLKarst : une application Excel pour la caractérisation hydrodynamique des systèmes karstiques. https://www.brgm.fr/en/software/xlkarst-excel-application-hydrodynamic-characterisation-karst-systems. Last accessed: 2022–10–01
Brillinger D (1975) The identification of point process systems. Ann Probab 3:909–924. https://doi.org/10.1214/aop/1176996218
Carlotto T, Chaffe PLB (2019) Master recession curve parameterization tool (MRCPtool): different approaches to recession curve analysis. Comput Geosci 132:1–8. https://doi.org/10.1016/j.cageo.2019.06.016
Carrière SD, Chalikakis K, Danquigny C et al (2016) The role of porous matrix in water flow regulation within a karst unsaturated zone: an integrated hydrogeophysical approach. Hydrogeol J 24:1905–1918. https://doi.org/10.1007/s10040-016-1425-8
Chang W, Cheng J, Allaire J, et al (2021) Shiny: web application framework for R. R package version 1.6.0. https://CRAN.R-project.org/package=shiny. Last accessed: 01 Oct 2022
Cinkus G, Mazzilli N, Jourde H (2021) Identification of relevant indicators for the assessment of karst systems hydrological functioning: proposal of a new classification. J Hydrol 603:127006. https://doi.org/10.1016/j.jhydrol.2021.127006
Coene J (2021) Waiter: loading screen for ’shiny’. R package version 0.2.3. https://CRAN.R-project.org/package=waiter. Last accessed: 01 Oct 2022
Coutagne A (1948) Étude générale des variations de débit en fonction des facteurs qui les conditionnent. La Houille Blanche 134–146. https://doi.org/10.1051/lhb/1949025
Dewandel B, Lachassagne P, Bakalowicz M et al (2003) Evaluation of aquifer thickness by analysing recession hydrographs. Application to the Oman ophiolite hard-rock aquifer. J Hydrol 274:248–269. https://doi.org/10.1016/S0022-1694(02)00418-3
Dowle M, Srinivasan A (2021) Data.table: extension of ‘data.frame‘. R package version 1.14.0. https://CRAN.R-project.org/package=data.table. Last accessed: 01 Oct 2022
Drogue C (1972) Analyse statistique des hydrogrammes de decrues des sources karstiques statistical analysis of hydrographs of karstic springs. J Hydrol 15:49–68. https://doi.org/10.1016/0022-1694(72)90075-3
Elzhov TV, Mullen KM, Spiess A-N, Bolker B (2016) Minpack.lm: R Interface to the Levenberg-Marquardt nonlinear least-squares algorithm found in MINPACK, plus support for bounds. R package version 1.2-1. https://CRAN.R-project.org/package=minpack.lm. Accessed 01 Oct 2022
Fiorillo F (2014) The recession of spring hydrographs, focused on karst aquifers. Water Resour Manage 28:1781–1805. https://doi.org/10.1007/s11269-014-0597-z
Flora SP (2004) Hydrogeological characterization and discharge variability of Springs in the Middle Verde River Watershed, Central Arizona. PhD thesis, Northern Arizona University
Ford D, Williams P (2007) Karst hydrogeology. In: Karst Hydrogeology and Geomorphology. John Wiley & Sons, Ltd, pp 103–144
Forkasiewicz MJ, Paloc H (1967) Régime de tarissement de la foux-de-la-vis (Gard) étude préliminaire. La Houille Blanche 29–36. https://doi.org/10.1051/lhb/1967002
Gárfias-Soliz J, Llanos-Acebo H, Martel R (2010) Time series and stochastic analyses to study the hydrodynamic characteristics of karstic aquifers. Hydrol Process 24:300–316. https://doi.org/10.1002/hyp.7487
Gregor M, Malík P (2016) User manual for HydroOffice RC 4.0 tool. Online only, 35 pp. http://hydrooffice.org. Last accessed: 01 Oct 2022
Guo Y, Wang F, Qin D et al (2021) Hydrodynamic characteristics of a typical karst spring system based on time series analysis in northern China. China Geol 4:433–445. https://doi.org/10.31035/cg2021049
Hakoun V, Bailly-Comte V, Charlier J-B, et al (2022) Definition of new indicators for the characterization and classification of karst aquifers using discharge time series. In: Eurokarst 2022
Horton RE (1933) The Role of infiltration in the hydrologic cycle. Trans AGU 14:446. https://doi.org/10.1029/TR014i001p00446
Jeannin P-Y, Sauter M (1998) Analysis of karst hydrodynamic behaviour using global approaches: a review. Bull Hydrogeol 16:31–48
Jenkins GM, Watts DG (1968) Spectral analysis and its applications. Louvain Econ Rev 36:554. https://doi.org/10.1017/S0770451800043062
Jourde H, Massei N, Mazzilli N et al (2018) SNO KARST: a french network of observatories for the multidisciplinary study of critical zone processes in karst watersheds and aquifers. Vadose Zone J 17:180094. https://doi.org/10.2136/vzj2018.04.0094
Kovács A (2003) Geometry and hydraulic parameters of karst aquifers: a hydrodynamic modeling approach. PhD thesis, Neuchâtel University
Kovács A (2021) Quantitative classification of carbonate aquifers based on hydrodynamic behaviour. Hydrogeol J 29:33–52. https://doi.org/10.1007/s10040-020-02285-w
Krešić N (2007) Hydrogeology and groundwater modeling, 2e edition. CRC Press, Boca Raton (Fla.) and London and New york
Kullman E (2000) Nové metodické prístupy k riešeniu ochrany a ochranných pásiem zdrojov podzemných vôd v horninových prostrediach s krasovopuklinovou a puklinovou priepustnosťou. Podzemná voda ISSN 1335–1052:31–41
Larocque M, Mangin A, Razack M, Banton O (1998) Contribution of correlation and spectral analyses to the regional study of a large karst aquifer (Charente, France). J Hydrol 205:217–231. https://doi.org/10.1016/S0022-1694(97)00155-8
Lorette G, Lastennet R, Peyraube N, Denis A (2018) Groundwater-flow characterization in a multilayered karst aquifer on the edge of a sedimentary basin in western France. J Hydrol 566:137–149. https://doi.org/10.1016/j.jhydrol.2018.09.017
Maillet ET (1905) Essais d’hydraulique souterraine et fluviale. A. Hermann, Paris
Malík P (2006) Assessment of regional karstification degree and groundwater sensitivity to pollution using hydrograph analysis in the Velka Fatra Mountains, Slovakia. Environ Geol 51:707–711. https://doi.org/10.1007/s00254-006-0384-0
Malík P (2015) Evaluating discharge regimes of karst Aquifer. In: Stevanović Z (ed) Karst aquifers and engineering. Springer International Publishing, Cham, pp 205–249
Malík P, Vojtková S (2012) Use of recession-curve analysis for estimation of karstification degree and its application in assessing overflow/underflow conditions in closely spaced karstic springs. Environ Earth Sci 65:2245–2257. https://doi.org/10.1007/s12665-012-1596-0
Malík P, Švasta J, Bajtoš P, Gregor M (2021) Discharge recession patterns of karstic springs as observed in Triassic carbonate aquifers of Slovakia. Hydrogeol J 29:397–427. https://doi.org/10.1007/s10040-020-02276-x
Mangin A (1971) Etude des débits classés d’exutoires karstiques portant sur un cycle hydrologique. Ann Spéléol 26:283–329
Mangin A (1975) Contribution à l’étude hydrodynamique des aquifères karstiques. PhD thesis, Université de Dijon
Mangin A (1984) Pour une meilleure connaissance des systèmes hydrologiques à partir des analyses corrélatoire et spectrale. J Hydrol 67:25–43. https://doi.org/10.1016/0022-1694(84)90230-0
Marsaud B (1997) Structure et fonctionnement de la zone noyée des karsts à partir des résultats expérimentaux. PhD thesis, Université Paris XI Orsay
Mason-Thom C (2019) Shinyhelper: Easily Add Markdown Help Files to ’shiny’ App Elements. R package version 0.3.2. https://CRAN.R-project.org/package=shinyhelper. Last accessed: 01 Oct 2022
Massei N, Dupont JP, Mahler BJ et al (2006) Investigating transport properties and turbidity dynamics of a karst aquifer using correlation, spectral, and wavelet analyses. J Hydrol 329:244–257. https://doi.org/10.1016/j.jhydrol.2006.02.021
Merlino A, Howard P (2020) shinyFeedback: display user feedback in shiny apps. R package version 0.3.0. https://CRAN.R-project.org/package=shinyFeedback. Last accessed: 01 Oct 2022
Netopil R (1971) Ke Klasifikaci pramenu podle variability vydatnasti (The classification of water springs based on the basis of the variability of yields). Sbornik-Hydrological Conference, Papers 22:145–150
Nurkholis A, Adji TN, Haryono E, et al (2019) Time series analysis application for karst aquifer characterisation in Pindul Cave karst system, Indonesia. Acta Carsologica. https://doi.org/10.3986/ac.v48i1.6745
Olarinoye T, Gleeson T, Marx V et al (2020) Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater. Sci Data 7:59. https://doi.org/10.1038/s41597-019-0346-5
Ollivier C, Danquigny C, Mazzilli N, Barbel-Perineau A (2015) Contribution of hydrogeological time series statistical analysis to the study of karst unsaturated zone (Rustrel, France). In: Andreo B, Carrasco F, Durán JJ et al (eds) Hydrogeological and environmental investigations in karst systems. Springer, Berlin, Heidelberg, pp 27–33
Ollivier C, Chalikakis K, Mazzilli N et al (2019) Challenges and Limitations of Karst Aquifer Vulnerability Mapping Based on the PaPRIKa Method to a Large European Karst Aquifer (Fontaine de Vaucluse, France). Environments 6:39. https://doi.org/10.3390/environments6030039
Padilla A, Pulido-Bosch A, Mangin A (1994) Relative Importance of Baseflow and Quickflow from Hydrographs of Karst Spring. Ground Water 32:267–277. https://doi.org/10.1111/j.1745-6584.1994.tb00641.x
Posavec K, Giacopetti M, Materazzi M, Birk S (2017) Method and excel VBA algorithm for modeling master recession curve using trigonometry approach. Groundwater 55:891–898. https://doi.org/10.1111/gwat.12549
R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Last accessed: 01 Oct 2022
Rashed KA (2012) Assessing degree of karstification: a new method of classifying karst aquifers. Sixteenth International Water Technology Conference (IWTC)
Sağır Ç, Kurtuluş B, Razack M (2020) Hydrodynamic characterization of Mugla Karst aquifer using correlation and spectral analyses on the rainfall and springs water-level time series. Water 12:85. https://doi.org/10.3390/w12010085
Sievert C (2020) Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020. https://cran.r-project.org/package=plotly. Last accessed: 01 Oct 2022
Soulios G (1991) Contribution à l’étude des courbes de récession des sources karstiques: Exemples du pays Hellénique. J Hydrol 124:29–42. https://doi.org/10.1016/0022-1694(91)90004-2
Springer AE, Stevens LE, Anderson DE et al (2008) A comprehensive springs classification system: Integrating geomorphic, hydrogeochemical, and ecological criteria. In: Stevens LE, Meretsky VJ (eds) Aridland springs in North America: ecology and conservation. University of Arizona Press, Tucson, AZ, pp 49–75
Stevanović Z (ed) (2015) Karst aquifers and engineering. Springer International Publishing, Cham
Stevanović Z (2019) Karst waters in potable water supply: a global scale overview. Environ Earth Sci 78:662. https://doi.org/10.1007/s12665-019-8670-9
Thoen E (2021) Padr: quickly get datetime data ready for analysis. https://CRAN.R-project.org/package=padr. Last accessed: 01 Oct 2022
Toebes C, Strang DD (1964) On recession curves—recession equations. J Hydrol 3:2–15
Vrsalović A, Andrić I, Buzjak N, Bonacci O (2022) Karst Lake’s dynamics analysis as a tool for aquifer characterisation at field scale. Example of Cryptodepression in Croatia. Water 14:830. https://doi.org/10.3390/w14050830
Wickham H (2011) Testthat: get started with testing. R J 3:5–10
Wickham H, Bryan J (2021) Usethis: automate package and project setup. R package version 2.0.1. https://CRAN.R-project.org/package=usethis. Last accessed: 01 Oct 2022
Wickham H, Averick M, Bryan J et al (2019) Welcome to the tidyverse. J Open Source Softw 4:1686. https://doi.org/10.21105/joss.01686
Wickham H, Hester J, Chang W (2021) Devtools: tools to make developing r packages easier. R package version 2.4.2. https://CRAN.R-project.org/package=devtools. Last accessed: 01 Oct 2022
Xie Y (2021) Knitr: a general-purpose package for dynamic report generation in R. R package version 1.33. https://cran.r-project.org/package=knitr. Last accessed: 01 Oct 2022
Xie Y, Allaire JJ, Grolemund G (2018) R markdown: the definitive guide. Chapman and Hall/CRC, Boca Raton, Florida
Xie Y, Dervieux C, Riederer E (2020) R markdown cookbook. Chapman and Hall/CRC, Boca Raton, Florida
Xie Y, Cheng J, Tan X (2021) DT: A Wrapper of the JavaScript Library ’DataTables’. R package version 0.19. https://CRAN.R-project.org/package=DT. Last accessed: 01 Oct 2022
Zeileis A, Grothendieck G (2005) Zoo: S3 infrastructure for regular and irregular time series. J Stat Softw 14:1–27. https://doi.org/10.18637/jss.v014.i06
Zerouali B, Chettih M, Alwetaishi M et al (2021) Evaluation of karst spring discharge response using time-scale-based methods for a Mediterranean basin of Northern Algeria. Water 13:2946. https://doi.org/10.3390/w13212946
Zhang R, Chen X, Zhang Z, Soulsby C (2020) Using hysteretic behaviour and hydrograph classification to identify hydrological function across the “hillslopedepressionstream” continuum in a karst catchment. Hydrol Process 34:3464–3480. https://doi.org/10.1002/hyp.13793
Acknowledgements
The French Ministry of Higher Education and Research is thanked for the thesis scholarship of G. Cinkus. This application is developed within the framework of (i) the KARST observatory network (www.sokarst.org) initiated by the INSU/CNRS, which aims to strengthen knowledge-sharing and promote cross-disciplinary research on karst systems; and (ii) the KARMA project (Karst Aquifer Resources availability and quality in the Mediterranean Area, http://karma-project.org/). The DREAL Provence Alpes-Côtes d’Azur (PACA) is also acknowledged for providing Fontaine de Vaucluse’s data. The manuscript was written with the Rmarkdown framework (Allaire et al., 2021; Xie et al., 2020, 2018), using R (R Core Team, 2021) and knitr (Xie, 2021). KarstID software uses functions from the following packages: data.table (Dowle and Srinivasan 2021), DT (Xie et al., 2021), minpack.lm (Elzhov et al., 2016), padr (Thoen, 2021), plotly (Sievert, 2020), shiny (Chang et al., 2021), shinyFeedback (Merlino and Howard, 2020), shinyhelper (Mason-Thom, 2019), shinyjs (Attali, 2020), waiter (Coene, 2021), zoo (Zeileis and Grothendieck, 2005), and readxl, readr, dplyr, stringr, magrittr, ggplot2, lubridate (Wickham et al., 2019). The KarstID package was developed and structured using devtools (Wickham et al., 2021), usethis (Wickham and Bryan, 2021) and testthat (Wickham, 2011) packages.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
GC, NM and HJ worked on the methodology and underlying sciences; GC programmed, coded and developed the application; GC wrote the original draft; GC, NM and HJ reviewed and edited the original draft.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix A
Appendix A
A. Comparison of indicators values from the correlational and spectral analyses between the original publication of Mangin (1984) and recent work of Cinkus et al. (2021)—calculated with KarstID. The exact time series used by Mangin (1984) are unavailable so different—and more recent—time series were used by Cinkus et al. (2021).
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Cinkus, G., Mazzilli, N. & Jourde, H. KarstID: an R Shiny application for the analysis of karst spring discharge time series and the classification of karst system hydrological functioning. Environ Earth Sci 82, 136 (2023). https://doi.org/10.1007/s12665-023-10830-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-023-10830-5