KarstID is a free, open-source and user-friendly software dedicated to the analysis of karst spring discharge time series.

KarstID was developed in the framework of the French SNO Karst and the KARMA Project. It can be launch with the R software.

KarstID provides the user a toolbox to analyse karst spring discharge time series and characterize karst systems hydrological functioning. The software supports statistical, recession curves, classified discharges and signal (simple correlational and spectral analyses). The completion of the analyses are facilitated with the graphical interface. The classification is based on the proposal of Cinkus et al. (2021), which use three indicators of functioning derived from Mangin’s recession model (Mangin, 1975).

KarstID is build with the R Shiny framework (Chang et al., 2021) and is embedded into an R package (R Core Team, 2021), which make the installation and launch easy even for non-programmers. It is also free, open-source and actively developed on a developer community platform.

KarstID is available from R (version 4.0.0 or later)

Please quote us as follows

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 systems hydrological functioning. Under review for Environmental Modelling & Software.

To install the software

if (!require("devtools")) {



More details can be found in the user guide (https://github.com/busemorose/KarstID).


  • Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., Borges, B., 2021. shiny: Web Application Framework for R. R package version 1.6.0. https://CRAN.R-project.org/package=shiny.
  • Cinkus, G., Mazzilli, N., Jourde, H., 2021. Identification of relevant indicators for the assessment of karst systems hydrological functioning: Proposal of a new classification. Journal of Hydrology 603, 127006. https://doi.org/10.1016/j.jhydrol.2021.127006
  • Mangin, A., 1975. Contribution à l’étude hydrodynamique des aquifères karstiques (PhD). Université de Dijon, France.
  • 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/.